Differentiating Individuals with and without Alcohol Use Disorder Using Resting-State fMRI Functional Connectivity of Reward Network, Neuropsychological Performance, and Impulsivity Measures

Chella Kamarajan; Babak A. Ardekani; Ashwini K. Pandey; Sivan Kinreich; Gayathri Pandey; David B. Chorlian; Jacquelyn L. Meyers; Jian Zhang; Elaine Bermudez; Weipeng Kuang; Arthur T. Stimus; Bernice Porjesz

doi:10.3390/bs12050128

Differentiating Individuals with and without Alcohol Use Disorder Using Resting-State fMRI Functional Connectivity of Reward Network, Neuropsychological Performance, and Impulsivity Measures

Kamarajan, Chella;Ardekani, Babak A.;Pandey, Ashwini K.;Kinreich, Sivan;Pandey, Gayathri;Chorlian, David B.;Meyers, Jacquelyn L.;Zhang, Jian;Bermudez, Elaine;Kuang, Weipeng;Stimus, Arthur T.;Porjesz, Bernice 2022-04-28 00:00:00 behavioral sciences Article Differentiating Individuals with and without Alcohol Use Disorder Using Resting-State fMRI Functional Connectivity of Reward Network, Neuropsychological Performance, and Impulsivity Measures 1 , 2 , 3 1 1 1 Chella Kamarajan * , Babak A. Ardekani , Ashwini K. Pandey , Sivan Kinreich , Gayathri Pandey , 1 1 1 3 1 1 David B. Chorlian , Jacquelyn L. Meyers , Jian Zhang , Elaine Bermudez , Weipeng Kuang , Arthur T. Stimus and Bernice Porjesz Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; ashwini.pandey@downstate.edu (A.K.P.); sivan.kinreich@downstate.edu (S.K.); gayathri.pandey@downstate.edu (G.P.); david.chorlian@downstate.edu (D.B.C.); jacquelyn.meyers@downstate.edu (J.L.M.); jian.zhang@downstate.edu (J.Z.); weipeng.kuang@downstate.edu (W.K.); arthur.stimus@downstate.edu (A.T.S.); bernice.porjesz@downstate.edu (B.P.) Center for Advanced Brain Imaging, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA; babak.ardekani@nki.rfmh.org Department of Psychiatry, NYU School of Medicine, New York, NY 10016, USA; bermue01@nyu.edu * Correspondence: chella.kamarajan@downstate.edu Citation: Kamarajan, C.; Ardekani, Abstract: Individuals with alcohol use disorder (AUD) may manifest an array of neural and behav- B.A.; Pandey, A.K.; Kinreich, S.; ioral abnormalities, including altered brain networks, impaired neurocognitive functioning, and Pandey, G.; Chorlian, D.B.; Meyers, J.L.; Zhang, J.; Bermudez, E.; Kuang, heightened impulsivity. Using multidomain measures, the current study aimed to identify speciﬁc W.; et al. Differentiating Individuals features that can differentiate individuals with AUD from healthy controls (CTL), utilizing a random with and without Alcohol Use forests (RF) classiﬁcation model. Features included fMRI-based resting-state functional connectivity Disorder Using Resting-State fMRI (rsFC) across the reward network, neuropsychological task performance, and behavioral impulsivity Functional Connectivity of Reward scores, collected from thirty abstinent adult males with prior history of AUD and thirty CTL individ- Network, Neuropsychological uals without a history of AUD. It was found that the RF model achieved a classiﬁcation accuracy of Performance, and Impulsivity 86.67% (AUC = 93%) and identiﬁed key features of FC and impulsivity that signiﬁcantly contributed Measures. Behav. Sci. 2022, 12, 128. to classifying AUD from CTL individuals. Impulsivity scores were the topmost predictors, followed https://doi.org/10.3390/bs12050128 by twelve rsFC features involving seventeen key reward regions in the brain, such as the ventral Academic Editor: Scott D. Lane tegmental area, nucleus accumbens, anterior insula, anterior cingulate cortex, and other cortical and subcortical structures. Individuals with AUD manifested signiﬁcant differences in impulsivity and Received: 15 March 2022 alterations in functional connectivity relative to controls. Speciﬁcally, AUD showed heightened im- Accepted: 27 April 2022 pulsivity and hypoconnectivity in nine connections across 13 regions and hyperconnectivity in three Published: 28 April 2022 connections involving six regions. Relative to controls, visuo-spatial short-term working memory was Publisher’s Note: MDPI stays neutral also found to be impaired in AUD. In conclusion, speciﬁc multidomain features of brain connectivity, with regard to jurisdictional claims in impulsivity, and neuropsychological performance can be used in a machine learning framework to published maps and institutional afﬁl- effectively classify AUD individuals from healthy controls. iations. Keywords: alcohol use disorder (AUD); random forests (RF); resting-state functional connectivity (FC); reward network (RN); functional MRI (fMRI); neuropsychological tests; impulsivity Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and 1. Introduction conditions of the Creative Commons The drugs of abuse, including alcohol, exert and maintain their reinforcing effects through Attribution (CC BY) license (https:// the reward circuitry of the brain [1,2]. Neuroimaging studies have documented the disruption creativecommons.org/licenses/by/ of reward processing in addiction [3] and implicated brain reward circuitry in different 4.0/). Behav. Sci. 2022, 12, 128. https://doi.org/10.3390/bs12050128 https://www.mdpi.com/journal/behavsci Behav. Sci. 2022, 12, 128 2 of 26 stages of alcohol use disorder (AUD) [4]. The reward network, in the context of addiction, primarily includes structures from the midbrain (i.e., ventral tegmental area, VTA), basal ganglia (i.e., nucleus accumbens, NAc; caudate nucleus; and putamen), limbic system (i.e., amygdala, hippocampus, and thalamus), and cerebral cortical regions (i.e., prefrontal cortices, insula, cingular cortices, and superior and inferior parietal lobules) [1,2,5–7]. Deficient reward processing due to abnormalities in the reward network in individuals with chronic AUD has been widely reported for both monetary and drug-related rewards [8–16]. For instance, an fMRI finding reported by Wrase et al. [10] showed that the ventral striatum (VS), a key reward region, showed increased activation for alcohol-related cues, but decreased activation for monetary gain in detoxified alcoholics, relative to healthy controls, suggesting differential activation of VS in response to the type of reward. Both animal and human studies have revealed that chronic administration of addictive substances results in neuroadaptations in reward structures, especially in the midbrain dopaminergic areas (e.g., VTA, substantia nigra), as well as the structures to which they project (e.g., NAc) [17,18]. Nevertheless, brain network interactions between mesocorticolimbic regions and other subcortical and cortical structures, especially in human participants with chronic AUD, have not been well-understood [19]. Historically, dysregulation in brain dopaminergic system has been postulated as the primary mechanism underlying substance use disor- ders (SUD) [20,21], and, therefore, studies have attempted to elucidate alterations in the reward network in human patients with SUD (cf. Sutherland et al. [19]). The integrity and communication strength of neural networks have been measured using functional connectivity [22], which is deﬁned as the temporal dependency of neuronal activation patterns of anatomically separated brain regions [23]. Resting-state functional connectivity (rsFC) provides a reliable approach for studying various networks underlying ongoing cognitive and emotional processes [24]. Since the phenomenon was ﬁrst reported by Biswal et al. [25], a growing number of studies have examined rsFC and elucidated brain func- tioning, in terms of neural communication in both healthy subjects and patients with various neurological and psychiatric [24,26,27] and substance use disorders (SUD) [28,29], including AUD [30–36]. However, to our knowledge, there have been no studies exploring both cortical and subcortical reward networks during resting state in abstinent individuals with past history of AUD, and who manifest a variety of neurocognitive deﬁcits [37]. Since studying detoxiﬁed and abstinent AUD individuals relative to healthy controls without past AUD has an advantage in determining the brain impacts due to chronic past drinking without the confounding effects of current drinking [8,38,39], the current study provides an important opportunity to investigate the resting-state functional connectivity of the reward network in abstinent individuals with past AUD. Studies have also shown that abstinent individuals with AUD often manifest poor neu- ropsychological performance [40–45] and heightened impulsivity [46–48], and, therefore, it is important to include these domains in the models exploring concomitants and/or deter- minants of alcohol-related outcomes. In our previous study, we reported that a random forest algorithm was highly useful for classifying AUD individuals from controls using multi-modal measures, including fMRI functional connectivity of the default mode net- work, neuropsychological performance, and impulsivity. Since AUD and other addiction traits are shown to be associated with reward networks [49,50] and as substance induced neuroadaptation primarily involves reward structures [51], the current study aimed to examine the functional connectivity across the reward network, along with measures of neuropsychological performance and impulsivity, to differentiate abstinent AUD individ- uals from healthy controls. Similar to our previous study, we computed the predictive power of these multi-domain measures in terms of classiﬁcation accuracy in a machine learning framework and evaluated the utility of these phenotypic features, especially the reward network connectivity measures. Since recent studies have proposed that resting state fMRI connectivity can potentially serve as one of the key neuroimaging biomarkers for quantitative clinical evaluation of AUD [36,52,53], the ﬁndings from the current study may elucidate speciﬁc connectivity patterns across reward regions of the brain in abstinent Behav. Sci. 2022, 12, x FOR PEER REVIEW 3 of 28 biomarkers for quantitative clinical evaluation of AUD [36,52,53], the findings from the current study may elucidate specific connectivity patterns across reward regions of the Behav. Sci. 2022, 12, 128 3 of 26 brain in abstinent AUD individuals, along with key neuropsychological and impulsivity features, which are distinctly different from healthy controls. AUD individuals, along with key neuropsychological and impulsivity features, which are 2. Materials and M distinctly ethodsdif ferent from healthy controls. The study protocol is illustrated in Figure 1. The sample consisted of 30 abstinent 2. Materials and Methods individuals with past diagnosis of AUD and 30 healthy volunteers (CTL) (see Section 2.1 The study protocol is illustrated in Figure 1. The sample consisted of 30 abstinent for details). The analytic measures included rs-fMRI (Sections 2.4–2.6), self-rated impul- individuals with past diagnosis of AUD and 30 healthy volunteers (CTL) (see Section 2.1 for sivity scores (Section 2.3), and neuropsychological test scores (Section 2.2). Major analyses details). The analytic measures included rs-fMRI (Sections 2.4–2.6), self-rated impulsivity employed in the study were (i) feature selection to extract relevant features that will be scores (Section 2.3), and neuropsychological test scores (Section 2.2). Major analyses used in classificatemployed ion analyin sis the (Sec study tion 2. wer7) e (i) , (ifeatur i) rando e selection m fores to t met extract hod to id relevantent featur ify the ke es that will y be used in classiﬁcation analysis (Section 2.7), (ii) random forest method to identify the key features features that significantly contribute to classifying AUD from the CTL participants (Sec- that signiﬁcantly contribute to classifying AUD from the CTL participants (Section 2.8), tion 2.8), (iii) zero-order correlations were used to compute associations of the key varia- (iii) zero-order correlations were used to compute associations of the key variables identiﬁed bles identified by the random forest classification model, (a) among themselves (Section by the random forest classiﬁcation model, (a) among themselves (Section 3.2) and (b) with 3.2) and (b) with age in each group (Section 3.3). Partial correlations were employed to age in each group (Section 3.3). Partial correlations were employed to identify associations identify associations between age and the key variables of classification by controlling the between age and the key variables of classiﬁcation by controlling the group effects in the group effects in the total sample (Section 3.3). total sample (Section 3.3). Figure 1. The study protocol listing the sample, measures, and analytic techniques. The sample con- Figure 1. The study protocol listing the sample, measures, and analytic techniques. The sample con- sisted of two groups of 30 individuals each, viz., AUD and CTL. The measures used in the prediction sisted of two groups of 30 individuals each, viz., AUD and CTL. The measures used in the prediction model included rs-fMRI functional connectivity (reward network), impulsivity assessed with Bar- model included rs-fMRI functional connectivity (reward network), impulsivity assessed with Barratt ratt impulsiveness scale (BIS), and neuropsychological performance scores. Major analyses were impulsiveness scale (BIS), and neuropsychological performance scores. Major analyses were features features selection for selecting FC variables, random forest classification method, and correlational selection for selecting FC variables, random forest classiﬁcation method, and correlational analyses, analyses, including zero-order and partial correlations. including zero-order and partial correlations. 2.1. Sample 2.1. Sample The sample characteristics are presented in Table 1, and a detailed description is also The sample characteristics are presented in Table 1, and a detailed description is also available in Pandey et al. [54]. All participants in the current study were drawn from the available in Pandey et al. [54]. All participants in the current study were drawn from the sample of a larger study on brain dysfunction in chronic alcoholism conducted at the SUNY sample of a larger study on brain dysfunction in chronic alcoholism conducted at the Downstate Health Sciences University, Brooklyn, NY, USA. Thirty currently abstinent adult SUNY Downstatemales Health with Sciences past AUD Univ (mean ersity, age Brookl (SD)y= n, 41.42 NY, U (7.31) SA. years) Thirtyand curren thirty tly unaf abstfected i- male controls (mean age (SD) = 27.44 (4.74) years), who had undergone multimodal assessments, nent adult males with past AUD (mean age (SD) = 41.42 (7.31) years) and thirty unaffected including structural and functional MRI and neuropsychological tests, were selected for male controls (mean age (SD) = 27.44 (4.74) years), who had undergone multimodal as- the present study. The “race/ethnic” distribution of the sample was: African Ancestry = 25; sessments, including structural and functional MRI and neuropsychological tests, were European Ancestry = 9; Asian = 21; American Indian = 1; More than one race = 2; and selected for the present study. The “race/ethnic” distribution of the sample was: African Unknown = 2. Participants with AUD were recruited from alcohol treatment centers in and Ancestry = 25; European Ancestry = 9; Asian = 21; American Indian = 1; More than one around New York City after they had been detoxiﬁed and abstinent for at least 30 days prior race = 2; and Unknown = 2. Participants with AUD were recruited from alcohol treatment to testing. As shown in Table 1, some of the participants from the AUD group had consumed centers in and around New York City after they had been detoxified and abstinent for at tobacco (N = 20) and/or marijuana (N = 10) during the last 6 months (but not at least 5 days least 30 days prior to befor te e st testing). ing. As None shown of the in Table participants 1, some of th were in withdrawal e participant for s from alcohol the A or any UD other drugs group had consumed tobacco (N = 20) and/or marijuana (N = 10) during the last 6 months Behav. Sci. 2022, 12, 128 4 of 26 (including nicotine) at the time of testing. Individuals for the control group (CTL) were recruited through advertisements and screened to exclude any personal or family history of major medical, psychiatric, or substance-related disorders. The CTL participants did not have any past or present history of substance dependence or abuse (DSM-IV), although some of them (N = 12) were light/regular drinkers and had used alcohol in the last 6 months (N = 18) (see Table 1 for details). All participants were asked to abstain from alcohol and other drugs for 5 days prior to MRI scans. Clinical information regarding substance use, psychiatric disorder, and family history were assessed using a modiﬁed version of the semi-structured assessment of genetics of alcoholism (SSAGA) [55]. The majority of subjects were right-handed, with only a few who were either left-handed (5 in AUD and 2 in CTL group) or bi-dexterous (2 in AUD and 1 in CTL group). Clinical and psychometric data were collected at the SUNY Downstate Health Sciences University, while the fMRI data were acquired at the Nathan Kline Institute (NKI) for Psychiatric Research. Standard MRI safety protocols and exclusion criteria (implants, tattoos, cosmetics, claustrophobia, etc.) were followed to ensure subjects’ safety and data quality. Individuals with hearing/visual impairment, a history of head injury, or moderate and severe cognitive deﬁcits (<21) on mini-mental state examination (MMSE) [56] were also excluded from the study. All participants provided informed consent and the Institutional Review Boards of both centers approved the research protocols (IRB approval ID: SUNY–266893; NKI–212263). Table 1. Demographic and clinical characteristics of the sample. AUD CTL Variable N * Mean SD N * Mean SD Age (in years) 30 41.42 7.31 30 27.44 4.74 Education (in Years) 30 11.93 2.35 30 15.77 1.87 Age of onset (regular alcohol use) 30 15.77 2.58 12 20.50 3.80 Alcohol: Drinks/day 30 12.08 10.02 12 2.88 1.93 (heavy alcohol use period) Alcohol: Days/month 30 20.30 9.01 12 3.35 3.64 (heavy alcohol use period) Alcohol: Drinks (last 6 months) 30 2.68 6.61 18 2.61 1.98 Alcohol: Days (last 6 months) 30 3.97 8.02 18 2.94 3.62 Length of Abstinence (in months) 30 22.43 28.16 18 1.9 4.99 Tobacco: Times/day (last 6 months) 20 9.90 5.80 6 2.33 1.63 Tobacco: Days/month (last 6 months) 20 28.35 4.83 6 14.17 13.82 Marijuana: Times in last 6 months 10 98.80 91.38 4 18.75 27.61 * N refers to the number of subjects included in these mean and SD calculations for each variable. Individuals who did not consume alcohol or drugs were not included in the respective calculations. 2.2. Neuropsychological Assessment Participants were administered two computerized tests from the Colorado assessment tests for cognitive and neuropsychological assessment [57], namely, the Tower of London Test (TOLT) [58], and the visual span test (VST) [59,60]. 2.2.1. Tower of London Test (TOLT) The TOLT assesses planning and problem-solving ability of the executive functions. In this test, participants were asked to solve a set of puzzles with graded difﬁculty levels by arranging a number of colored beads one at a time from a starting position to the desired goal position in as few moves as possible. The test consisted of 3 puzzle types, with 3, 4, and 5 colored beads placed on the same number of pegs, with 7 problems/trials per type and a total of 21 trials. The following performance measures from the sum total Behav. Sci. 2022, 12, 128 5 of 26 of all puzzle types were used in the analysis: (i) excess moves, which is the additional moves beyond the minimum moves required to solve the puzzle (“ExcMovMade_All”); (ii) average pickup time, which is the initial thinking/planning time spent until picking up the ﬁrst bead to solve the puzzle (“AvgPicTime_All”); (iii) average total time, which is the total thinking/planning time to solve the problem in each puzzle type (“AvgTotTime_All”); (iv) total trial time, which is the total performance/execution time spent on all trials within each puzzle type (“TotTrlTime_All”); and (v) average trial time, which is the mean performance/execution time across trials per puzzle type (“AvgTrlTime_All”). 2.2.2. Visual Span Test (VST) The VST measured visuospatial memory span from the forward condition and work- ing memory from the backward condition. In this test, a set of randomly arranged squares, ranging from 2 to 8 squares per trial, ﬂashed in a predetermined sequence depending on the span level being assessed. Each span level was administered twice, with a total of 14 trials in each condition. During the forward condition, subjects were required to repeat the sequence in the same order by clicking on the squares using a computer mouse. In the backward condition, subjects were required to repeat the sequence in the reverse order (starting from the last square). The following performance measures were collected during forward and backward conditions: (i) total number of correctly performed trials (“Tot- Cor_Fw” and “TotCor_Bw”); (ii) maximum span or sequence-length achieved (“Span_Fw” and “Span_Bw”); (iii) total average time, which is the sum of mean time-taken across all trials performed (“TotAvgTime_Fw” and “TotAvgTime_Bw”); and (iv) total correct average time, which is the sum of mean time-taken across all trials correctly performed (“TotCorAvgTime_Fw” and “TotCorAvgTime_Bw”). 2.3. Impulsivity Scores Barratt Impulsiveness Scale—Version 11 (BIS-11) [61], a 30-item self-administered tool with excellent psychometric properties [62], was used to assess aspects of impulsivity. All three subscales, viz., motor impulsivity (BIS_MI), non-planning (BIS_NP), and attentional impulsivity (BIS_AI), were included in the random forest model. 2.4. MRI Data Acquisition The details of fMRI data acquisition on the same sample have been described previ- ously by our group [63]. Brieﬂy, MRI scanning was performed at the Nathan Kline Institute using a 3.0 Tesla Siemens Tim Trio scanner (Erlangen, Germany). The resting-state fMRI data, used in the current study, were collected during eyes closed alert state for 6.11 s. A series of T2*-weighted gradient echo single-shot echo-planar imaging (EPI) volumes with the following sequence parameters was acquired: TR = 2750 ms; TE = 30 ms; ﬂip angle = 80 ; voxel size = 2.5 2.5 3.5 mm ; matrix size = 96 96; number of slices = 50; number of volumes = 130; FOV = 240 mm; and Grappa acceleration factor = 3. The sequence was carefully optimized to minimize the effects of magnetic susceptibility inhomogeneities (such as distortions and signal dropouts), as well as the effects of mechanical vibrations, which elevate Nyquist ghosting levels. In addition, a magnetization-prepared rapid gradient-echo (MPRAGE) high-resolution three-dimensional T1-weighted structural image served as an anatomical reference for the fMRI data, as well as for the non-linear registration of imaging data be- tween subjects. The sequence parameters for the MPRAGE were: TR = 2500 ms; TE = 3.5 ms; TI = 1200 ms; ﬂip angle = 8 ; voxel size = 1 1 1 mm ; matrix size = 256 256 192; FOV = 256 mm; and number of averages = 1. 2.5. Image Processing The details of image processing for the resting state fMRI are available in our previous study [63]. Brieﬂy, the intra-subject inter-modality linear registration module [64] of the automatic registration toolbox (ART; www.nitrc.org/projects/art (accessed on 20 December 2019) was used to register the structural (MPRAGE) and functional MRI volumes. The Behav. Sci. 2022, 12, 128 6 of 26 brainwash program within the ART toolbox was used for skull-stripping the MPRAGE volumes. Motion detection and correction were performed using the 3dvolreg module of the AFNI software package [65]. Furthermore, the non-linear registration module of the ART [66] was used to correct for the geometric distortions of the fMRI images due to magnetic susceptibility differences in the head, particularly at brain/air interfaces. The skull-stripped MPRAGE images from all subjects were non-linearly registered to a study- speciﬁc population template using ART’s non-linear registration algorithm, which is one of the most accurate inter-subject registration methods available [67]. The population template was formed using an iterative method [68], and the motion-corrected fMRI time-series were detrended using PCA [69]. Finally, fMRI images from all subjects were normalized to a standard space. 2.6. Reward Network Seed Regions and rsFC Calculations The regions of interest (ROIs) for the reward network were identiﬁed from the pub- lished literature of review and meta-analyses of reward processing, e.g., [6,70,71]. These included 34 ROIs from 17 bilateral structures involving nine bilateral subcortical structures and eight bilateral cortical regions (Table 2 and Figure 2). The ROIs were marked using ITK-SNAP, an image processing application [72]. The diameters of subcortical and corti- cal ROIs were 7 mm and 11 mm, respectively, from the MNI coordinates (Table 2). The ROI-to-ROI connectivity [73], the most commonly used method to derive rsFC across brain regions [74], was computed using Pearson correlation coefﬁcients between all unique pairs (N = 561) of BOLD time series data of all 34 ROIs listed in Table 2. The resulting correlation coefﬁcients were Fisher Z-transformed for further statistical analyses. All 561 connections derived from unique combinations of the ROIs were used in the feature selection process (see Section 2.7), which provided the subset of features to be used in the random forest model (see Section 2.8). Table 2. Brain regions of interest (ROI) analyzed for reward network functional connectivity. MNI Coordinates ROI Brain Region Notation Location X Y Z 1 L. Ventral Tegmental Area L.VTA Subcortical 4 16 14 2 R. Ventral Tegmental Area R.VTA Subcortical 4 16 14 3 L. Nucleus Accumbens L.NAc Subcortical 8 10 10 4 R. Nucleus Accumbens R.NAc Subcortical 8 10 10 5 L. Amygdala L.Amg Subcortical 24 2 14 6 R. Amygdala R.Amg Subcortical 24 2 14 7 L. Hippocampus L.Hip Subcortical 28 10 22 8 R. Hippocampus R.Hip Subcortical 28 10 22 9 L. Caudate L.Cdt Subcortical 13 15 9 10 R. Caudate R.Cdt Subcortical 13 15 9 11 L. Pallidum L.Pal Subcortical 18 2 4 12 R. Pallidum R.Pal Subcortical 18 2 4 13 L. Thalamus L.Tha Subcortical 8 12 6 14 R. Thalamus R.Tha Subcortical 8 12 6 15 L. Insula (anterior) L.Ins Subcortical 30 17 15 16 R. Insula (anterior) R.Ins Subcortical 30 17 15 17 L. Parahippocampal Gyrus L.PHG Cortical 24 39 12 Behav. Sci. 2022, 12, 128 7 of 26 Table 2. Cont. MNI Coordinates ROI Brain Region Notation Location X Y Z 18 R. Parahippocampal Gyrus R.PHG Cortical 27 39 12 19 L. Anterior Cingulate Cortex L.ACC Cortical 6 44 10 20 R. Anterior Cingulate Cortex R.ACC Cortical 6 44 10 21 L. Mid-Cingulate Cortex L.MCC Cortical 6 2 40 22 R. Mid-Cingulate Cortex R.MCC Cortical 6 2 40 Behav. Sci. 2022, 12, x FOR PEER REVIEW 7 of 28 23 L. Posterior Cingulate Cortex L.PCC Cortical 6 46 30 24 R. Posterior Cingulate Cortex R.PCC Cortical 6 46 30 25 L. Orbitofrontal Cortex L.OFC Cortical 32 42 16 22 R. Mid-Cingulate Cortex R.MCC Cortical 6 2 40 23 L. Posterior Cingulate Cortex L.PCC Cortical −6 −46 30 26 R. Orbitofrontal Cortex R.OFC Cortical 32 42 16 24 R. Posterior Cingulate Cortex R.PCC Cortical 6 −46 30 27 L. Angular Gyrus L.Ang Cortical 45 58 30 25 L. Orbitofrontal Cortex L.OFC Cortical −32 42 −16 28 R. Angular Gyrus L.Ang Cortical 45 58 30 26 R. Orbitofrontal Cortex R.OFC Cortical 32 42 −16 29 L. Superior Parietal Lobule L.SPL Cortical 24 68 56 27 L. Angular Gyrus L.Ang Cortical −45 −58 30 28 30 R. Ang R. Superior ular Gyrus Parietal Lobule L.Ang R.SPL Cortical Cortical 45 −58 24 30 68 56 29 L. Superior Parietal Lobule L.SPL Cortical −24 −68 56 31 L. Dorsolateral PFC L.DLP Cortical 44 38 19 30 R. Superior Parietal Lobule R.SPL Cortical 24 −68 56 32 R. Dorsolateral PFC R.DLP Cortical 44 38 19 31 L. Dorsolateral PFC L.DLP Cortical −44 38 19 33 L. Putamen L.Ptm Subcortical 27 5 6 32 R. Dorsolateral PFC R.DLP Cortical 44 38 19 33 L. 34 P R.utamen Putamen L.Ptm R.Ptm Subcortical Subcortical −27 5 27−6 5 6 34 R. Putamen R.Ptm Subcortical 27 5 −6 Figure 2. Brain reward network regions of interest comprising 16 cortical areas (top panels, C1–C3) Figure 2. Brain reward network regions of interest comprising 16 cortical areas (top panels, C1–C3) and 18 subcortical areas (bottom panels, S1–S3), as listed in Table 1. Bilateral cortical regions: Insula a (Ins), Parahippocampal Gyrus (PH nd 18 subcortical areas (botG to ),m Ante parior nels Cingulate Cortex (ACC), , S1–S3), as listed in Mid-Cingulate Cortex Table 1. Bilateral cortical regions: Insula (MCC), Mid-Cingulate Cortex (MCC), Posterior Cingulate Cortex (PCC), Orbitofrontal Cortex (Ins), Parahippocampal Gyrus (PHG), Anterior Cingulate Cortex (ACC), Mid-Cingulate Cortex (MCC), (OFC), Angular Gyrus (Ang), Superior Parietal Lobule (SPL), Dorsolateral (PFC), and Dorsolateral Mid-Cingulate Cortex (MCC), Posterior Cingulate Cortex (PCC), Orbitofrontal Cortex (OFC), Angular Prefrontal Cortex (DLP). Bilateral subcortical regions: Ventral Tegmental Area (V), Nucleus Accum- bens (N), Amygdala (A), Hippocampus (Hi), Caudate (Cd), Pallidum (Pa), Thalamus (T), and Puta- Gyrus (Ang), Superior Parietal Lobule (SPL), Dorsolateral (PFC), and Dorsolateral Prefrontal Cortex men (Pt). Colors: Blue–Left hemispheric regions; Red–Right hemispheric regions. Views: C1/S1–Axial (DLP). Bilateral subcortical regions: Ventral Tegmental Area (V), Nucleus Accumbens (N), Amygdala (A), top view; C2/S2–Coronal front view; C3/S3–Sagittal left view. Directions: A–Anterior; P–Posterior; S–Superior; I–Inferior; L–Left; R–Right. Hippocampus (Hi), Caudate (Cd), Pallidum (Pa), Thalamus (T), and Putamen (Pt). Colors: Blue—Left hemispheric regions; Red—Right hemispheric regions. Views: C1/S1—Axial top view; C2/S2—Coronal 2.7. Feature Selection of FC Variables front view; C3/S3—Sagittal left view. Directions: A—Anterior; P—Posterior; S—Superior; I—Inferior; Recent approaches dealing with machine learning analyses have used a two-stage L—Left; R—Right. approach, consisting of feature selection followed by a predictive algorithm using a Behav. Sci. 2022, 12, 128 8 of 26 2.7. Feature Selection of FC Variables Recent approaches dealing with machine learning analyses have used a two-stage approach, consisting of feature selection followed by a predictive algorithm using a selected sets of variables [75–79]. Feature selection methods are used as the ﬁrst stage to reduce irrelevant and redundant variables, which may otherwise add noise to the predictive mod- els [75–77]. The advantages of feature selection include a better understanding of the data, reducing computation requirements, mitigating the effect of the curse of dimensionality, and also improving the predictor performance [76]. We applied binomial lasso regres- sion [80–82] as a feature selection method [83,84], as implemented in R-package “glmnet”, to extract a subset of fMRI FC variables (N = 561) that held signiﬁcant predictive value to discriminate AUD from the CTL group. Feature selection was implemented only for the rsFC variables, due to a high number of connections, most of which were deemed not relevant for our purpose of AUD classiﬁcation. The method adapted in the current analysis is based on the Lasso method, as implemented in Fonti and Belitser [84]. The maximum number of output features “pmax” was set to 10% (i.e., 56 of the total 561 variables). A 10-fold cross-validation and lambda thresholding with 1 SE ( ) were set to extract the 1se ﬁnal set of key variables. The area under the curve (AUC) was plotted to determine the classiﬁcation performance of the selected features. The ﬁnal subset resulting from the feature selection process included 21 rsFC variables (see Table A1, Appendix A). 2.8. Random Forest Classiﬁcation The random forest classiﬁcation model, as used in the current study, has been described in our previous work on rsFC of the default mode network (DMN) [63]. The predictor variables included in the model were 21 reward network connections identiﬁed by feature selection (Table A1), 13 neuropsychological scores consisting of 5 TOLT scores and 8 VST scores (see Section 2.2), and 3 BIS scores (see Section 2.3), while the group status (AUD and CTL) served as the outcome variable (see Table A1, Appendix A). Although age was signiﬁcantly different between the groups, we did not include age as an input variable in the classiﬁcation model for the following reasons: (i) as done in our previous publications on the same sample of subjects [63,85], we performed post hoc correlational analysis of age with the signiﬁcant features of the random forest model, to see if any of the top variables had associations with age in the individual groups or in total sample (see Section 3.4); and (ii) since the age difference between the groups was highly signiﬁcant, including age as a feature in the classiﬁcation model would likely artiﬁcially increase the accuracy of classiﬁcation, which would not be desirable. To compute the classiﬁcation accuracy of the random forest model, we used an out-of-bag (OOB) error estimate. According to Breiman and Cutler [86], in random forests, owing to the inbuilt OOB feature in the model, additional cross-validation is not a requirement to obtain an unbiased estimate of the test sample error, since it is estimated internally in the OOB algorithm. The random forest algorithm constructs each of the decision trees using separate bootstrap subsamples from the training data, and about one-third of the observations from the training data are left out during each bootstrap, called the OOB sample, which are used as a form of test data only, to estimate the prediction accuracy of the RF model. While classiﬁcation trees are grown for each bootstrap sample (which is approximately two-thirds of the training data), the OOB error rate is calculated for each classiﬁcation tree built. According to Breiman [87], there are two reasons for using bagging: (i) to enhance the accuracy when random features are used; and (ii) to give ongoing estimates of the generalization error of the combined ensemble of trees, as well as estimates for the strength and correlation. The aggregate of OOB scores on all “ntree” trees (which is the maximum number of trees pre-set in the model calculation) provides the ensemble OOB error rate. Thus, the OOB score provides validation for the RF model. Therefore, unlike in other machine learning algorithms, random forests method does not require separate training data and test data while specifying the model term. In the current study, the maximum number of trees “ntree” was set at 1000. The optimal number of features analyzed at each node (“Mtry”) in the model was estimated to be 8 Behav. Sci. 2022, 12, 128 9 of 26 Behav. Sci. 2022, 12, x FOR PEER REVIEW 9 of 28 (using the “tuneRF” function). The ﬁnal list of variables that signiﬁcantly contributed to the classiﬁcation was tabulated and sorted based on their importance to classiﬁcation. For across ROIs was mapped onto a 3-dimensional ICBM atlas [88] using custom Matlab the top signiﬁcant FC variables, the brain connectivity across ROIs was mapped onto a scripts. 3-dimensional ICBM atlas [88] using custom Matlab scripts. 3. Results 3. Results 3.1. Random Forests Classification 3.1. Random Forests Classiﬁcation 3.1.1. Classification Accuracy and Top Significant Variables 3.1.1. Classiﬁcation Accuracy and Top Signiﬁcant Variables The RF algorithm correctly identified group membership of 26 out of 30 individuals The RF algorithm correctly identiﬁed group membership of 26 out of 30 individuals in in each group, in classifying them into either AUD or CTL group, with an accuracy rate each group, in classifying them into either AUD or CTL group, with an accuracy rate of of 86.67% and the area under the curve of 93% (Figure 3). The OOB error or the misclassi- 86.67% and the area under the curve of 93% (Figure 3). The OOB error or the misclassiﬁca- fication rate was 13.33% (for each group). The model also identified 12 rsFC connections tion rate was 13.33% (for each group). The model also identiﬁed 12 rsFC connections and and two impulsivity scores (motor and non-planning) as significantly (p < 0.05) contrib- two impulsivity scores (motor and non-planning) as signiﬁcantly (p < 0.05) contributing uting to the classification (Table 3). Relative to the CTL individuals, AUD subjects showed to the classiﬁcation (Table 3). Relative to the CTL individuals, AUD subjects showed a a predominant pattern of hypoconnectivity (i.e., decreased rsFC in 9 out of 12 connections) predominant pattern of hypoconnectivity (i.e., decreased rsFC in 9 out of 12 connections) across the major cortical and subcortical nodes of the reward network, in addition to three across the major cortical and subcortical nodes of the reward network, in addition to three connections with hyperconnectivity in specific nodes (i.e., left nucleus accumbens–left connections with hyperconnectivity in speciﬁc nodes (i.e., left nucleus accumbens–left posterior cingulate cortex (PCC), right pallidum–right PCC, and right hippocampus–left posterior cingulate cortex (PCC), right pallidum–right PCC, and right hippocampus–left dorsolateral dorsolateral prefrontal prefrontal cortex). cortex). AUD AUD indivi individuals duals al also so showed showed in incr creas eased ed impu impulsivity lsivity in in motor motor and and no non-planning n-planning c categories. ategories. Ho However wever, , none none of the of the neur neurop opsychological sychological vari variables ables were significant based on the p-value criterion. were signiﬁcant based on the p-value criterion. Figure 3. Receiver operating characteristic (ROC) curve for the RF model for different true positive Figure 3. Receiver operating characteristic (ROC) curve for the RF model for different true positive rate (sensitivity) and false positive rate (1-specificity) are shown. The area under the ROC curve was rate (sensitivity) and false positive rate (1-speciﬁcity) are shown. The area under the ROC curve was 93%. (The further away the curve from the diagonal dashed line and the closer the swelling to the 93%. (The further away the curve from the diagonal dashed line and the closer the swelling to the top top left corner, the better the classification.). left corner, the better the classiﬁcation). Table 3. Random forest importance parameters mean minimal depth, number of nodes, accuracy decrease, Gini decrease, number of trees, times a root, and p-value), and the direction of significance for the top significant variables (p < 0.05) are shown. Two of the impulsivity scores (motor and non- planning) and 12 rsFC connections were identified as important features to classify individuals into either AUD or CTL group. The variables are sorted based on the p-values. Behav. Sci. 2022, 12, 128 10 of 26 Table 3. Random forest importance parameters mean minimal depth, number of nodes, accuracy decrease, Gini decrease, number of trees, times a root, and p-value), and the direction of signiﬁcance for the top signiﬁcant variables (p < 0.05) are shown. Two of the impulsivity scores (motor and non-planning) and 12 rsFC connections were identiﬁed as important features to classify individuals into either AUD or CTL group. The variables are sorted based on the p-values. Feature Mean Minimum Depth No. of Nodes Accuracy Decrease Gini Decrease No. of Trees Time a Root p-Value Direction BIS Motor 1.3824 348 0.0170 2.0202 319 94 1.87E-19 A > C Impulsivity FC_3_23 1.9354 330 0.0130 1.6903 295 74 1.82E-15 A > C (L.NAc–L.PCC) FC_16_20 2.0062 326 0.0149 1.5132 291 57 1.23E-14 C > A (R.Ins–R.ACC) BIS 1.7619 319 0.0187 1.6830 294 74 3.05E-13 A > C Non-planning FC_2_19 2.0561 313 0.0040 1.4706 274 58 4.23E-12 C > A (R.VTA–L.ACC) FC_20_26 2.2513 299 0.0101 1.3203 272 45 1.24E-09 C > A (R.ACC–R.OFC) FC_6_7 2.3798 275 0.0063 1.1669 258 41 4.40E-06 C > A (R.Amg–L.Hip) FC_9_12 2.5018 268 0.0039 0.9725 255 29 3.26E-05 C > A (L.Cdt–R.Pal) FC_20_24 2.4732 266 0.0054 1.0002 249 28 5.58E-05 C > A (R.ACC–R.PCC) FC_13_14 2.5681 252 0.0023 0.9843 233 40 0.0016 C > A (L.Tha–R.Tha) FC_12_24 2.8735 249 0.0022 0.8844 223 20 0.0030 A > C (R.Pal–R.PCC) FC_9_13 2.7035 249 0.0056 0.9541 228 34 0.0030 C > A (L.Cdt–L.Tha) FC_13_34 2.7980 247 0.0053 0.9212 234 24 0.0044 C > A (L.Tha–R.Ptm) FC_8_31 2.9451 238 0.0047 0.8020 217 20 0.0220 A > C (R.Hip–L.DLP) Acronym: BIS—Barratt Impulsiveness Scale, FC—Functional Connectivity, R—Right, L—Left, ACC—Anterior Cingu- late Cortex, Amg—Amygdala, Ang—Angular Gyrus, Cdt—Caudate, DLP—Dorsolateral PFC, Hip—Hippocampus, Ins—Insula (anterior), NAc—Nucleus Accumbens, OFC—Orbitofrontal Cortex, Pal—Pallidum, PCC—Posterior Cingulate Cortex, PHG—Parahippocampal Gyrus, Ptm—Putamen, Tha—Thalamus, VTA—Ventral Tegmental Area. Key: A > C = AUD > CTL; C > A = CTL > AUD. 3.1.2. Distribution of Minimal Depth The distribution of minimal depth among the decision trees of the forests for the top signiﬁcant variables is shown in Figure 4. Minimal depth of a variable represents the depth of the node that splits on that variable and is the closest to the root of the decision tree. The lower mean minimal depth of a variable represents a higher number of observations (participants) categorized in a speciﬁc group based on that variable. The ranking based on the minimal depth parameter shows that two of the impulsivity variables are a the top of the importance list, followed by several reward network connections and a neuropsychological feature (total correct score in the forward trials of the visual span test). 3.1.3. Multi-Way Importance The top signiﬁcant variables can also be shown by a multi-way importance plot based on any of the three RF importance measures. Figure 5 illustrates the important features that contributed to group classiﬁcation based on Gini decrease, number of trees, and p-value. These features include: (i) 12 FC variables (representing connections across cortical and subcortical regions) (see Table 2 for details), (ii) one neuropsychological variable (i.e., total number of correct trials in forward span), and (iii) two impulsivity scores (motor and non-planning categories). Behav. Sci. 2022, 12, x FOR PEER REVIEW 11 of 28 Behav. Sci. 2022, 12, 128 11 of 26 Figure 4. The distribution of minimal depth among the trees of the forest for the significant variables Figure 4. The distribution of minimal depth among the trees of the forest for the signiﬁcant variables is shown in different colors for each level of minimal depth. The mean minimal depth in the distri- is shown in different colors for each level of minimal depth. The mean minimal depth in the bution for each variable is marked by a vertical black bar overlapped by a value label inside a box. distribution for each variable is marked by a vertical black bar overlapped by a value label inside a Based on the mean minimal depth values, the importance list comprised 2 BIS scores, 13 FC, and 1 box. Based on the mean minimal depth values, the importance list comprised 2 BIS scores, 13 FC, and 1 neuropsychological score, which contributed to the RF classiﬁcation of AUD and CTL individuals. neuropsychological score, which contributed to the RF classification of AUD and CTL individuals. The lower mean minimal depth of a feature represents a higher number of observations (participants) The lower mean minimal depth of a feature represents a higher number of observations (partici- categorized in a speciﬁc group based on the feature. The number of trees for a feature represents the pants) categorized in a specific group based on the feature. The number of trees for a feature repre- total number of decision trees in which a split occurs on the feature (see Table 2 for details about the sents the total number of decision trees in which a split occurs on the feature (see Table 2 for details ROI numbers (1–34) represented in the FC variables). about the ROI numbers (1–34) represented in the FC variables). 3.1.3. Multi-Way Importance The top significant variables can also be shown by a multi-way importance plot based on any of the three RF importance measures. Figure 5 illustrates the important features that contributed to group classification based on Gini decrease, number of trees, and p-value. These features include: (i) 12 FC variables (representing connections across cortical and subcortical regions) (see Table 2 for details), (ii) one neuropsychological variable (i.e., total number of correct trials in forward span), and (iii) two impulsivity scores (motor and non- planning categories). Behav. Sci. 2022, 12, x FOR PEER REVIEW 12 of 28 Behav. Sci. 2022, 12, 128 12 of 26 Figure 5. Multi-way importance Figure 5. Multi-way plot showin importance g the top plot showing significant the top features (label signiﬁcant featur ed and es (labeled marked with and marked with black circles) that contributed to the classiﬁcation of alcohol use disorder from control individuals black circles) that contributed to the classification of alcohol use disorder from control individuals based on the measures Gini decrease (mean decrease in node impurity or classiﬁcation error), number based on the measures Gini decrease (mean decrease in node impurity or classification error), number of trees (total number of decision trees in which a split occurred), and p-value (probability of node of trees (total number of decision trees in which a split occurred), and p-value (probability of node splits). The top variables of importance included 2 impulsivity scores, 12 rsFC connections, and splits). The top variables of importance included 2 impulsivity scores, 12 rsFC connections, and 1 1 neuropsychological variable (see the circled and labeled dots). Notations in the variable labels: neuropsychological variable (see the circled and labeled dots). Notations in the variable labels: BIS– BIS–Barratt Impulsivity Scale; MI–Motor impulsivity; NP–Non-planning; TotCor_Fw–Total correct Barratt Impulsivity Scale; MI–Motor impulsivity; NP–Non-planning; TotCor_Fw–Total correct for- forward. ROIs of FC variables: Refer to Table 2 for details about the ROI numbers (1-34) that are ward. ROIs of FC variables: Refer to Table 2 for details about the ROI numbers (1-34) that are rep- represented in the rsFC variable names. resented in the rsFC variable names. 3.1.4. Correlations among Rankings of RF Parameters The correlations among the rankings of features based on different RF parameters 3.1.4. Correlations among Rankings of RF Parameters (Figure 6) were very high and signiﬁcant (r > 0.9), suggesting that each of the RF parameters The correlations among the rankings of features based on different RF parameters would rank the features in a very similar order while classifying the individuals into either (Figure 6) were very high and significant (r > 0.9), suggesting that each of the RF parame- the AUD or CTL group. High correlations among these parameters also suggest that each parameter is very valuable and reliable in terms of its classiﬁcation performance, ters would rank the features in a very similar order while classifying the individuals into lending further support to the utility of the RF technique as a powerful tool for classifying either the AUD or CTL group. High correlations among these parameters also suggest individuals using a set of multi-domain features. that each parameter is very valuable and reliable in terms of its classification performance, lending further support to the utility of the RF technique as a powerful tool for classifying individuals using a set of multi-domain features. Behav. Sci. 2022, 12, 128 13 of 26 Behav. Sci. 2022, 12, x FOR PEER REVIEW 13 of 28 Figure 6. Illustration of rankings of features based on correlation between any two random forest Figure 6. Illustration of rankings of features based on correlation between any two random forest (RF) parameters. The panels in the lower triangle of the grid show the distribution of rankings of all (RF) parameters. The panels in the lower triangle of the grid show the distribution of rankings of predictor variables with black dots along a blue trend line. The panels in the upper triangle of the all predictor variables with black dots along a blue trend line. The panels in the upper triangle of grid show correlation coefficients across rankings of any two parameters. It is shown that all RF the grid show correlation coefﬁcients across rankings of any two parameters. It is shown that all RF parameters of importance were found to have very high correlations among each other, suggesting parameters the high reliab of importance ility of each of wer te hese parameter found to havesvery in rank high ing corr the im elations portance among of featu eachrother es for , suggesting classifica- tion. The asterisks (***) represents that the correlations were highly significant (p < 0.001). the high reliability of each of these parameters in ranking the importance of features for classiﬁcation. The asterisks (***) represents that the correlations were highly signiﬁcant (p < 0.001). 3.1.5. Connectivity Mapping of Significant rsFC Connections 3.1.5. Connectivity Mapping of Signiﬁcant rsFC Connections Significant reward network connections are illustrated in Figure 7. Among the 12 sig- Signiﬁcant reward network connections are illustrated in Figure 7. Among the nificant connections, nine were hypoconnected and three were hyperconnected in AUD 12 signiﬁcant connections, nine were hypoconnected and three were hyperconnected in individuals, involving 17 regions (7 from the left and 10 from the right hemisphere) of the AUD individuals, involving 17 regions (7 from the left and 10 from the right hemisphere) 34 reward network ROIs. While the majority of these nodes (12 of 17) were of solo paths, of the 34 reward network ROIs. While the majority of these nodes (12 of 17) were of solo connecting to another single node (ROI # 2, 3, 6, 7, 8, 14, 16, 19, 23, 26, 31, 34), two of them paths, connecting to another single node (ROI # 2, 3, 6, 7, 8, 14, 16, 19, 23, 26, 31, 34), (ROI # 13, 20) were linked with three connections each, and three of them (ROI # 9, 12, 24) two of them (ROI # 13, 20) were linked with three connections each, and three of them had two connections each. Out of nine hypoconnected FC variables, three were cortico- (ROI # 9, 12, 24) had two connections each. Out of nine hypoconnected FC variables, cortical connections (R.Ins–R.ACC, R.ACC–R.OFC, and R.ACC–R.PCC), involving only three were cortico-cortical connections (R.Ins–R.ACC, R.ACC–R.OFC, and R.ACC–R.PCC), the right hemisphere, and five were subcortical–subcortical connections (R.Amg–L.Hip, involving only the right hemisphere, and ﬁve were subcortical–subcortical connections L.Cdt–R.Pal, L.Tha–R.Tha, L.Cdt–L.Tha, and L.Tha–R.Ptm) involving both hemispheres, (R.Amg–L.Hip, L.Cdt–R.Pal, L.Tha–R.Tha, L.Cdt–L.Tha, and L.Tha–R.Ptm) involving both and one inter-hemispheric subcortical–cortical connection (R.VTA–L.ACC). Interestingly, hemispheres, and one inter-hemispheric subcortical–cortical connection (R.VTA–L.ACC). all three hyperconnected FC variables were subcortical–cortical connections, involving a Interestingly, all three hyperconnected FC variables were subcortical–cortical connections, left-hemispheric connection (L.NAc–L.PCC), a right-hemispheric connection (R.Pal– involving a left-hemispheric connection (L.NAc–L.PCC), a right-hemispheric connection R.PCC), and an inter-hemispheric connection (R.Hip–L.DLP). (R.Pal–R.PCC), and an inter-hemispheric connection (R.Hip–L.DLP). Behav. Sci. 2022, 12, x FOR PEER REVIEW 14 of 28 Behav. Sci. 2022, 12, 128 14 of 26 Figure 7. Significant reward network connections that contribute to the random forest classification Figure 7. Signiﬁcant reward network connections that contribute to the random forest classiﬁcation of alcohol use disorder (AUD) from control (CTL), as listed in Table 3. (A): Hypoconnectivity manifested of alcohol use disorder (AUD) from control (CTL), as listed in Table 3. (A): Hypoconnectivity man- by the AUD group (cyan lines) across cortical and subcortical ROI regions of the RN, predominantly ifested by the AUD group (cyan lines) across cortical and subcortical ROI regions of the RN, pre- involving right hemisphere structures; (B): Hyperconnectivity in three connections manifested by the dominantly involving right hemisphere structures; (B): Hyperconnectivity in three connections AUD group (orange lines). Images within each panel: Left: axial (top) view; Middle: coronal (front) manifested by the AUD group (orange lines). Images within each panel: Left: axial (top) view; Mid- view; Right: sagittal (left) view. dle: coronal (front) view; Right: sagittal (left) view. 3.2. Correlations among the Top Signiﬁcant Variables Exploratory (descriptive) analysis using zero-order correlations among the top signiﬁ- 3.2. Correlations among the Top Significant Variables cant variables is shown in Figure 8. Only ﬁve positive correlations survived Bonferroni cor- Exploratory (descriptive) analysis using zero-order correlations among the top sig- rection for multiple comparisons (as represented by the sign +++), which include: (i) three positive correlations between FC features (L.Cdt–R.Pal with L.Cdt–L.Tha; L.Tha–R.Tha with nificant variables is shown in Figure 8. Only five positive correlations survived Bonferroni L.Tha–R.Ptm; and R.Ins–R.ACC with R.ACC–R.PCC), (ii) a negative correlation between correction for multiple comparisons (as represented by the sign +++), which include: (i) memory recall and motor impulsivity, and (iii) a positive correlation between non-planning three positive correlations between FC features (L.Cdt–R.Pal with L.Cdt–L.Tha; L.Tha– and motor impulsivity. Each of the signiﬁcant FC correlations had a common node, i.e., L.Cdt, L.Tha, and R.ACC, respectively. R.Tha with L.Tha–R.Ptm; and R.Ins–R.ACC with R.ACC–R.PCC), (ii) a negative correla- tion between memory recall and motor impulsivity, and (iii) a positive correlation be- tween non-planning and motor impulsivity. Each of the significant FC correlations had a common node, i.e., L.Cdt, L.Tha, and R.ACC, respectively. Behav. Sci. 2022, 12, x FOR PEER REVIEW 15 of 28 Behav. Sci. 2022, 12, 128 15 of 26 Figure 8. Correlation matrix showing associations among the top significant variables based on ex- Figure 8. Correlation matrix showing associations among the top signiﬁcant variables based on explorative (descriptive) correlational analysis for the interpretative purpose. Values within each cell plorative (descriptive) correlational analysis for the interpretative purpose. Values within each cell represent a bivariate Pearson correlation between the variable on its vertical axis and the variable represent a bivariate Pearson correlation between the variable on its vertical axis and the variable on its horizontal axis. Correlation coefﬁcients are color-coded (red/pink shades represent negative on its horizontal axis. Correlation coefficients are color-coded (red/pink shades represent negative r-values, blue/cyan shades indicate positive r-values, darker color represent higher magnitude) and r-values, blue/cyan shades indicate positive r-values, darker color represent higher magnitude) and signiﬁcant correlations (before Bonferroni correction) have been marked with asterisks in black font significant correlations (before Bonferroni correction) have been marked with asterisks in black font (* p < 0.05; ** p < 0.01; *** p < 0.001). The signiﬁcant correlations that survived Bonferroni correction (* p < 0.05; ** p < 0.01; *** p < 0.001). The significant correlations that survived Bonferroni correction have been marked with +++ sign in white font (+++ Signiﬁcant after Bonferroni correction). Refer to Table 2 for details about the ROI numbers (1–34) that are represented in the rsFC variable names. have been marked with +++ sign in white font (+++ Significant after Bonferroni correction). Refer to Acronyms: FC–Functional connectivity, TotCor_Fw–total number of correctly performed forward Table 2 for details about the ROI numbers (1–34) that are represented in the rsFC variable names. trials, BIS–Barratt Impulsiveness Scale, NP–Non-planning, MI–Motor Impulsivity. Acronyms: FC–Functional connectivity, TotCor_Fw–total number of correctly performed forward trials, BIS–Barratt Impu 3.3. lsiv Correness elations Sca between le, NP– Signiﬁcant Non-planning, MI–Motor Variables and Age Impulsivity. Since the age difference across the groups was statistically signiﬁcant (p < 0.001), the association of age with signiﬁcant predictor variables from the RF analysis was calculated 3.3. Correlations between Significant Variables and Age within each group using bivariate Pearson correlation and in the total sample using partial Since the age difference correlation adjusted across forthe g the grr oup oup effect s wa (Table s stat 4) is astic ana exploratory lly signif(descriptive) icant (p < 0. analysis. 001), the It was found that there was no signiﬁcant association of age with the top signiﬁcant variables association of age with significant predictor variables from the RF analysis was calculated in any of the groups or in the total sample, after correcting for multiple comparisons. within each group using bivariate Pearson correlation and in the total sample using partial However, a single FC variable (FC_6_7) representing the connectivity between the right correlation adjusted fo amygdala r the and group left hippocampus effect (Tab was le signiﬁcant 4) as an exp (r = 0.38; lora ptor = 0.0374), y (descrip but it tive did not ) asurvive nalysis. multiple testing correction. It was found that there was no significant association of age with the top significant vari- ables in any of the groups or in the total sample, after correcting for multiple comparisons. However, a single FC variable (FC_6_7) representing the connectivity between the right amygdala and left hippocampus was significant (r = 0.38; p = 0.0374), but it did not survive multiple testing correction. Table 4. Pearson bivariate correlations between the age of the participant and the top significant variables of the RF model. Correlation coefficient (r) and p-values (before Bonferroni correction) are provided for alcohol use disorder (AUD), control (CTL) group, and the total sample (ALL). None of the variables survived Bonferroni correction for multiple comparisons. Zero-order correlations were used for each group separately (N = 30) and partial correlations controlling for group effects were used for the all sample (N = 60). AUD (N = 30) CTL (N = 30) ALL (N = 60) Feature r p r p r p FC_2_19 (R.VTA–L.ACC) −0.08 0.6744 0.22 0.2449 0.03 0.8131 FC_3_23 (L.NAc–L.PCC) 0.16 0.3949 −0.21 0.2693 0.02 0.8956 FC_6_7 (R.Amg–L.Hip) −0.02 0.8993 0.38 0.0374 * 0.11 0.4225 FC_8_31 (R.Hip–L.DLP) 0.16 0.3977 0.27 0.1489 0.20 0.1276 Behav. Sci. 2022, 12, 128 16 of 26 Table 4. Pearson bivariate correlations between the age of the participant and the top signiﬁcant variables of the RF model. Correlation coefﬁcient (r) and p-values (before Bonferroni correction) are provided for alcohol use disorder (AUD), control (CTL) group, and the total sample (ALL). None of the variables survived Bonferroni correction for multiple comparisons. Zero-order correlations were used for each group separately (N = 30) and partial correlations controlling for group effects were used for the all sample (N = 60). AUD (N = 30) CTL (N = 30) ALL (N = 60) Feature r p r p r p FC_2_19 (R.VTA–L.ACC) 0.08 0.6744 0.22 0.2449 0.03 0.8131 FC_3_23 (L.NAc–L.PCC) 0.16 0.3949 0.21 0.2693 0.02 0.8956 FC_6_7 (R.Amg–L.Hip) 0.02 0.8993 0.38 0.0374 * 0.11 0.4225 FC_8_31 (R.Hip–L.DLP) 0.16 0.3977 0.27 0.1489 0.20 0.1276 FC_9_12 (L.Cdt–R.Pal) 0.16 0.3964 0.01 0.9583 0.09 0.5079 FC_9_13 (L.Cdt–L.Tha) 0.15 0.4430 0.05 0.7812 0.06 0.6349 FC_12_24 (R.Pal–R.PCC) 0.01 0.9579 0.09 0.6402 0.03 0.8298 FC_13_14 (L.Tha–R.Tha) 0.26 0.1616 0.02 0.9168 0.15 0.2588 FC_13_34 (L.Tha–R.Ptm) 0.07 0.7127 0.12 0.5196 0.09 0.5002 FC_16_20 (R.Ins–R.ACC) 0.19 0.3149 0.09 0.6203 0.07 0.6062 FC_20_24 (R.ACC–R.PCC) 0.05 0.7849 0.01 0.9574 0.03 0.8195 FC_20_26 (R.ACC–R.OFC) 0.19 0.3182 0.11 0.5460 0.16 0.2110 BIS_NP (Non-planning) 0.03 0.8936 0.21 0.2644 0.09 0.4815 BIS_MI (Motor Impulsivity) 0.23 0.2121 0.12 0.5432 0.20 0.1268 # § * p < 0.05 (before Bonferroni correction); Not signiﬁcant after Bonferroni correction; Based on partial correlation adjusted for group effect. Refer to Table 2 for the details of the ROIs in the FC variable. 3.4. Neuropsychological Scores between the Groups Since the rankings of neuropsychological variables varied widely across different parameters of the random forest classiﬁcation, these features were statistically compared across the participant groups using one-way analysis of variance (ANOVA) to determine the level of signiﬁcance (see Table 5). Only two variables from the visual span test (i.e., TotCor_Fw and Span_Fw) were signiﬁcant after Bonferroni corrections. The score “Tot- Cor_Fw” (total number of correctly performed forward trials), which showed the highest signiﬁcance level, was also identiﬁed by some of the parameters of the random forest as a variable contributing to group classiﬁcation. Table 5. Comparison of neuropsychological variables between AUD or CTL group using one- way ANOVA. AUD CTL Mean SD Mean SD ExcMovMade_All 15.04 17.02 7.83 6.66 4.43 0.0402 * AvgPicTime_All 3.09 1.09 2.81 0.96 1.02 0.3167 AvgTotTime_All 5.16 1.58 4.72 1.64 1.01 0.3199 TotTrlTime_All 482.60 178.18 404.24 139.05 3.29 0.0755 AvgTrlTime_All 22.98 8.48 19.25 6.62 3.29 0.0755 TotCor_Fw 7.00 2.58 10.21 2.78 19.06 0.0001 ++ TotCor_Bw 6.31 3.02 8.31 1.87 8.95 0.0042 * Span_Fw 5.44 1.33 6.83 1.36 14.25 0.0004 ++ Span_Bw 4.65 1.44 5.52 0.95 7.02 0.0106 * TotAvgTime_Fw 26.72 9.13 28.31 10.53 0.35 0.5591 TotAvgTime_Bw 17.72 9.39 17.79 10.01 0.00 0.9791 TotCorAvgTime_Fw 38.15 12.39 32.48 8.07 4.06 0.0490 * TotCorAvgTime_Bw 28.93 13.92 27.16 10.66 0.28 0.5963 * Signiﬁcant before Bonferroni correction; ++ Signiﬁcant after Bonferroni correction. Behav. Sci. 2022, 12, 128 17 of 26 4. Discussion The goal of the present study was to identify speciﬁc features from a set of multi- domain measures, including functional connectivity in the reward network, neuropsy- chological performance, and impulsivity, to classify individuals with AUD from healthy controls. The results showed that the random forest algorithm was highly successful in identifying the key features that contributed signiﬁcantly to differentiating AUD from CTL individuals. Relative to controls, AUD individuals manifested (i) alterations in functional connectivity across reward network regions (including ventral tegmental area, nucleus accumbens, anterior insula, anterior cingulate cortex, and other cortical and subcorti- cal structures), showing hypoconnectivity in nine connections and hyperconnectivity in three connections, (ii) increased impulsivity in motor and non-planning categories, and (iii) poorer neuropsychological performance, in terms of total number of correct trials in the forward sequence of the visual-spatial memory span test. In summary, relative to healthy controls, AUD individuals manifested aberrant functional connectivity in the reward network, increased impulsivity, and poor neuropsychological performance in visual–spatial memory. 4.1. Altered Functional Connectivity across Reward Network in AUD Individuals Addiction to drugs and alcohol involves a cascade of neuroadaptive processes, causing changes in the brain circuitries at different stages of addiction [18,89,90]. Our ﬁndings on resting state FC in the reward network indicate that AUD subjects manifested alterations in connectivity patterns, in terms of hypoconnectivity in nine connections and hyperconnec- tivity in three connections, involving 17 key reward structures [see Figure 7]. In particular, out of the nine reward network functional connections that showed hypoconnectivity, three were cortico–cortical connections (R.Ins–R.ACC, R.ACC–R.OFC, and R.ACC–R.PCC) in the right hemisphere, and ﬁve were subcortical–subcortical connections (R.Amg–L.Hip, L.Cdt–R.Pal, L.Tha–R.Tha, L.Cdt–L.Tha, and L.Tha–R.Ptm) involving both hemispheres, and a single inter-hemispheric subcortical-cortical connection (R.VTA–L.ACC). The three subnetworks that were hyperconnected in AUD individuals were subcortical–cortical con- nections, involving a left-hemispheric connection (L.NAc–L.PCC), a right-hemispheric connection (R.Pal–R.PCC), and an inter-hemispheric connection (R.Hip–L.DLP). These ﬁndings of altered brain connectivity in AUD individuals may be suggestive of neuroad- aptation in the hub regions of the reward network, caused by chronic alcohol intake. In general, previous fMRI studies have reported such aberrations in resting state connectivity underlying multiple brain networks in individuals with SUD [19,28,91], including those with AUD [30–33,63,92–94]. Our previous study on the same sample of participants as in the current study reported that AUD individuals manifested altered default mode net- work (DMN) connectivity compared to controls [63]. It is clear from the ﬁndings of our past and current rsFC studies that AUD individuals manifest brain connectivity changes across neural structures involved in spontaneous, self-referential thoughts, as elicited by the DMN [95], as well as the commonly reported reward processing deﬁcits, as elicited by the RN [10]. It is also remarkable to note that both studies showed hypoconnectivity between the ACC and PCC nodes in AUD subjects, conﬁrming the notion of abnormal self-referential processing in these individuals [92]. Furthermore, hypoconnectivity across reward structures during resting state, as found in the current study, may also indicate a vulnerability to relapse in individuals with a history of SUD [96]. Taken together, these ﬁndings lend support to the ﬁndings of the current study, that individuals with a long history of alcohol use continue to manifest network abnormalities across the reward regions, in addition to the previously reported aberrations in DMN, even after prolonged abstinence from alcohol consumption. The predominant pattern observed in AUD individuals was hypoconnectivity (9 out of 12 connections) across subcortical reward regions (5 connections) followed by cortical (3 connections) and cortical–subcortical (1 connection) subnetworks. Speciﬁcally, these hypoconnected nodes include key reward regions such as the VTA, amygdala, hippocam- Behav. Sci. 2022, 12, 128 18 of 26 pus, thalamus, pallidum, putamen, insula, ACC, PCC, and OFC, possibly indicating lower or less efﬁcient neural communication across these subnetworks [6]. Since addiction has been characterized as a reward deﬁciency syndrome [97], hypoconnectivity across these reward structures in abstinent AUD subjects may indicate reduced responsiveness to re- warding stimuli during resting state, possibly due to decreased dopamine function in these individuals [50,98]. Although elevated levels of dopamine in the dorsal striatum are associated with motivation to seek and consume alcohol and drugs, long-term substance use is associated with decreased dopaminergic function, as evidenced by reductions in D2 dopamine receptors and dopamine release in the striatum in addicted subjects [50], which can also lead to reduced activity in other cortical reward regions such as the orbitofrontal cortex and cingulate gyrus, resulting in loss of control and compulsive substance use. It may be worth noting that the majority of the hypoconnected regions in AUD (9 out of 13 regions) involved the right hemisphere, implicating hemispheric asymmetry in connec- tivity in AUD and, hence, their functional attributes [99]. For instance, laterality studies on motivation and emotions reported that the right hemisphere responds more to unpredicted, urgent, and novel environmental events, while the left hemisphere engages with routine and habitual behaviors [100]. Therefore, it is possible that the alterations in resting state brain connectivity across reward network seen in AUD individuals have more impact on right hemispheric function, including novelty seeking and impulsivity [101]. The other FC ﬁnding was that AUD individuals manifested hyperconnectivity in three connections across ﬁve brain regions, i.e., nucleus accumbens, pallidum, hippocam- pus, posterior cingulate, and dorsolateral prefrontal cortex, possibly suggesting excessive and/or less focused communication during resting state among these structures. While each of these key regions is associated with distinct and shared neurocognitive processes, hyperconnectivity across these nodes during resting state can be generally interpreted as excessive rumination about reward and preoccupation with reward-related imagery or inherent reward-seeking tendencies, such as craving. Similar to our ﬁnding, a higher FC between nucleus accumbens and posterior cingulate gyrus was observed in relapsers compared to abstainers of stimulant use [96]. In addition, akin to our ﬁnding, higher hippocampal–prefrontal connectivity has also been observed in internet gaming addic- tion [102]. Furthermore, recent evidence implicating the pallidum as an important structure in mesocorticolimbic reward processing [103], and, therefore, the increased connectivity between pallidum and PCC, may indicate reward related tendencies (e.g., drug seeking) in the resting state. However, despite the growing number of fMRI brain connectivity studies on AUD and other SUDs during resting state and task conditions [19,28,63,94,102,104], more studies are needed to understand the exact role of speciﬁc connectivity patterns across the reward network. 4.2. Heightened Impulsivity in AUD Individuals The ﬁndings of the current study also showed that motor and non-planning impul- sivity components were the topmost features contributing to the classiﬁcation of AUD individuals from controls. This ﬁnding reinforces the long-held notion that AUD and other externalizing traits are part of the externalizing spectrum disorders [105–109]. It is also known that AUD is associated with making impulsive choices during decision-making [16]. It is possible that the increased impulsivity manifested by AUD individuals may be due to altered FC across the frontal nodes [63] and/or relatively lower brain volumes of the frontal regions, as reported in our previous study [54]. Evidence from the imaging lit- erature strongly suggest that both structural and functional aspects of the frontal lobes contribute to increased impulsivity in AUD patients [110–113]. Furthermore, recent studies have also reported associations between impulsivity and resting state measures of EEG power [114], EEG-based FC [115], and fMRI-based FC [36], suggesting that speciﬁc brain networks may mediate aspects of impulsivity in AUD, as well as other externalizing dis- orders. Therefore, identifying and quantifying behavioral impulsivity may contribute to improving prevention and intervention programs related to alcohol and other substance Behav. Sci. 2022, 12, 128 19 of 26 use problems [116,117]. Although attentional impulsivity was not found to signiﬁcantly contribute to the classiﬁcation, our previous studies [63,85] found contributions from all three components of impulsivity, while motor and non-planning aspects were top of the key features of classiﬁcation, suggesting their relative importance in AUD pathology. 4.3. Poorer Memory Span in AUD Individuals In the current study, the neuropsychological score “TotCor_Fw” (i.e., total number of correctly performed forward trials) from the visual span test was also identiﬁed as one of the key variables contributing to the classiﬁcation of AUD individuals from controls by the RF model. On the other hand, parametric group comparison of neuropsychological variables (Table 5) revealed that the AUD group performed poorly in both “TotCor_Fw” and “Span_Fw” (i.e., span of recalled items during forward trials), compared to controls. Interestingly, these two variables tapping short-term memory performance were also found to be signiﬁcant in our previous classiﬁcation studies with the same sample of participants [63,85]. Furthermore, there is a strong literature support for memory deﬁcits in individuals with chronic AUD [118–121], and some of the deﬁcits linger even after prolonged abstinence [37]. It is also worth noting that in our previous structural MRI study on the same groups of subjects, we found that the AUD group showed lower volumes in prefrontal cortex and left hippocampus, which were associated with poorer visuospatial memory performance [54]. Furthermore, in another study on the same sample, we found that AUD subjects manifested hyperconnectivity across the parahippocampal hub regions [63], adding support to the current ﬁnding related to memory deﬁcits. On the other hand, it is surprising that none of the scores related to executive functioning in the TOLT were signiﬁcantly different from controls, possibly suggesting a partial or complete recovery of these functions in the AUD group due to abstinence. It is also possible that the current study failed to capture deﬁcits in additional domains, as the data involved in the current study were limited to only two tests and the sample size was only modest. Future studies may employ a comprehensive and sensitive battery of neuropsychological tests in a larger sample of abstinent AUD individuals, to map neuropsychological performance in multiple domains. 4.4. Correlations of Signiﬁcant Variables among Themselves and with Age Correlations among the signiﬁcant variables that were identiﬁed by the RF classiﬁca- tion model revealed three highly signiﬁcant positive associations among the FC features (L.Cdt–R.Pal with L.Cdt–L.Tha; L.Tha–R.Tha with L.Tha–R.Ptm; and R.Ins–R.ACC with R.ACC–R.PCC), and each of these signiﬁcant pairs had a common node, i.e., L.Cdt, L.Tha, and R.ACC, respectively. While it is expected that the pairs with a common node would correlate with each other, they are also known to be structurally connected. For instance, while the caudate nucleus connects the pallidum with radial ﬁbers [122], these basal ganglia structures have reciprocal connections with the thalamus and cortical regions and, thus, mediate cognitive and motor functions [123]. Similarly, while the ACC has both structural and functional connectivity with the PCC as part of the DMN [124], reciprocal interaction between the ACC and anterior insula serves as a major connection with the salience and reward network [125]. Interestingly, the negative correlation between memory performance (working memory) and impulsivity observed in our study was also previously reported by other studies on individuals with substance use disorders [126,127]. Lastly, as expected, non-planning and motor impulsivity were positively correlated with each other [128], and both of these dimensions were shown to be associated with craving and relapse among alcohol-dependent males [129]. On the other hand, none of the correlations between the top predictor variables and age sustained a statistical signiﬁcance after multiple testing corrections, suggesting that age did not impact the classiﬁcation of groups based on these top predictors. However, it is suggested that future studies may conﬁrm these preliminary ﬁndings using a large sample of subjects involving both males and females matched on multiple characteristics, such as education, ethnicity, premorbid IQ, etc. Behav. Sci. 2022, 12, 128 20 of 26 4.5. Limitations of the Current Study The current study has several limitations. (i) The sample consisted of only males, and the ﬁndings may not be generalizable to females. In general, ﬁndings from prediction-based studies will be more useful when they are generalizable to different strata of the population from which the sample is drawn. Therefore, we suggest that future predictive studies aim for samples from both genders. (ii) The outcome groups (AUD and CTL) were not matched for age, as the age difference was statistically signiﬁcant. Although age was not signiﬁcantly associated with the key features, either in each group or in the total sample, the results would have been more credible if the groups were matched for age. Therefore, future studies should also aim to apply predictive models on age-matched groups, to avoid confounding the results. (iii) Finally, the sample size for a predictive model was rather small, although the random forest algorithm is known to handle such situations more effectively than other machine learning models. Therefore, similar studies with larger sample sizes are needed to conﬁrm the ﬁndings of the present study. A larger sample is also warranted to explore associations among the features from multiple domains, since the current study did not identify potential associations among the features, possibly due to a lack of statistical power. Given these limitations, it is to be noted that the obtained results are only preliminary, while the ﬁndings of the present study might help to design future studies avoiding or mitigating these limitations. 5. Summary and Conclusions The ﬁndings of the current study suggest that multidomain features drawn from the measures of brain connectivity, impulsivity, and neuropsychological tests can be success- fully used in a machine learning framework to classify AUD individuals from healthy controls. In summary, our study revealed that the abstinent individuals with past AUD showed impaired brain connectivity across speciﬁc reward regions and also manifested relatively increased impulsivity and poor memory function. Evidence from the litera- ture suggests that these anomalies may have been caused by neuroadaptation due to chronic drinking. Since treatment interventions intended to reverse these neuroadaptations show promise as potential therapeutic approaches for addiction [1], the ﬁndings of the current study may have important clinical implications. We suggest that future studies should further characterize these neural and behavioral abnormalities at multiple levels and groups, such as gender, race/ethnicity, educational attainment, socio-economic sta- tus, genomic liability, and drinking patterns, so that the ﬁndings may contribute towards personalized medicine. Supplementary Materials: The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/bs12050128/s1. Author Contributions: Conceptualization: C.K., B.P., B.A.A., A.K.P.; Methodology: C.K., B.P., B.A.A., A.K.P., S.K., D.B.C., A.T.S.; Data Collection: A.T.S., B.A.A., E.B.; Data Curation: B.A.A., C.K.; Formal Analysis: C.K., B.A.A., J.Z.; Manuscript Preparation: C.K., B.A.A., B.P.; Review and Editing: B.P., B.A.A., A.K.P., S.K., G.P., J.L.M., D.B.C., W.K., A.T.S.; Funding Acquisition: B.P. All authors have read and agreed to the published version of the manuscript. Funding: This research work was funded by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) of the National Institutes of Health (NIH) through the grants (R01 AA002686 to Bernice Porjesz (PI) and R01 AA028848 to Bernice Porjesz (PI), Chella Kamarajan (MPI), Jacquelyn Meyers (MPI), Ashwini Pandey (MPI)). Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Boards of SUNY Downstate Health Sciences University and Nathan Kline Institute (IRB approval ID: SUNY–266893; NKI–212263). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data presented in this study are available in Supplementary Material. Behav. Sci. 2022, 12, 128 21 of 26 Acknowledgments: In memory of Henri Begleiter, founder and longtime mentor of the Neurody- namics Laboratory, we acknowledge with great admiration his seminal scientiﬁc contributions to the ﬁeld. We are sincerely indebted to his charismatic leadership and luminous guidance, truly inspired by his scientiﬁc mission and vision, and highly motivated to carry forward the work he fondly cherished. We are grateful for the valuable technical assistance of Carlene Haynes, Joyce Alonzia, Chamion Thomas, Alec Musial, and Kristina Horne. Conﬂicts of Interest: The authors declare no conﬂict of interest. Appendix A Table A1. List of features (F) included in the random forest classiﬁcation model: (i) 21 reward network connections identiﬁed by the feature selection process (F1–F21), 13 neuropsychological scores involving 5 TOLT scores (F22–F26) and 8 VST scores (F27–F34), and 3 BIS scores (F35–F37). F# Feature Detail 1. FC_2_19 (R.VTA–L.ACC) FC between R. Ventral Tegmental Area and L. Anterior Cingulate Cortex 2. FC_3_23 (L.NAc–L.PCC) FC between L. Nucleus Accumbens and L. Posterior Cingulate Cortex 3. FC_6_11 (R.Amg–L.Pal) FC between R. Amygdala and L. Pallidum 4. FC_6_7 (R.Amg–L.Hip) FC between R. Amygdala and L. Hippocampus 5. FC_8_31 (R.Hip–L.DLP) FC between R. Hippocampus and L. Dorsolateral Prefrontal Cortex 6. FC_9_12 (L.Cdt–R.Pal) FC between L. Caudate and R. Pallidum 7. FC_9_13 (L.Cdt–L.Tha) FC between L. Caudate and L. Thalamus 8. FC_9_18 (L.Cdt–R.PHG) FC between L. Caudate and R. Parahippocampal Gyrus 9. FC_9_23 (L.Cdt–L.PCC) FC between L. Caudate and L. Posterior Cingulate Cortex 10. FC_9_27 (L.Cdt–L.Ang) FC between L. Caudate and L. Angular Gyrus 11. FC_12_24 (R.Pal–R.PCC) FC between R. Pallidum and R. Posterior Cingulate Cortex 12. FC_13_14 (L.Tha–R.Tha) FC between L. Thalamus and R. Thalamus 13. FC_13_34 (L.Tha–R.Ptm) FC between L. Thalamus and R. Putamen 14. FC_16_20 (R.Ins–R.ACC) FC between R. Insula and R. Anterior Cingulate Cortex 15. FC_17_31 (L.PHG–L.DLP) FC between L. Parahippocampal Gyrus and L. Dorsolateral Prefrontal Cortex 16. FC_20_24 (R.ACC–R.PCC) FC between R. Anterior Cingulate Cortex and R. Posterior Cingulate Cortex 17. FC_20_26 (R.ACC–R.OFC) FC between R. Anterior Cingulate Cortex and R. Orbitofrontal Cortex 18. FC_20_31 (R.ACC–L.DLP) FC between R. Anterior Cingulate Cortex and L. Dorsolateral Prefrontal Cortex 19. FC_21_24 (L.MCC–R.PCC) FC between L. Middle Cingulate Cortex and R. Posterior Cingulate Cortex 20. FC_22_33 (R.MCC–L.Ptm) FC between R. Middle Cingulate Cortex and L. Putamen 21. FC_28_29 (L.Ang–L.SPL) FC between L. Angular Gyrus and L. Superior Parietal Lobule 22. ExcMovMade_All Overall excess moves beyond the minimum moves required to solve the puzzle 23. AvgPicTime_All Overall average pickup time to solve the puzzle 24. AvgTotTime_All Overall average total time to solve the puzzle 25. TotTrlTime_All Overall total trial time within each puzzle type 26. AvgTrlTime_All Overall average trial time across trials per puzzle type 27. TotCor_Fw Total number of correctly performed trials in forward sequence 28. TotCor_Bw Total number of correctly performed trials in backward sequence 29. Span_Fw Maximum span or sequence-length achieved in forward sequence 30. Span_Bw Maximum span or sequence-length achieved in backward sequence Behav. Sci. 2022, 12, 128 22 of 26 Table A1. Cont. F# Feature Detail 31. TotAvgTime_Fw Total average time taken across all trials performed in forward sequence 32. TotAvgTime_Bw Total average time taken across all trials performed in backward sequence 33. TotCorAvgTime_Fw Total correct average time taken across all correct trials in forward sequence 34. TotCorAvgTime_Bw Total correct average time taken across all correct trials in backward sequence 35. BIS_AI Barratt Impulsiveness Scale, Attentional Impulsivity Score 36. BIS_MI Barratt Impulsiveness Scale, Motor Impulsivity Score 37. BIS_NP Barratt Impulsiveness Scale, Non-planning Impulsivity Score References 1. Volkow, N.D.; Michaelides, M.; Baler, R. The Neuroscience of Drug Reward and Addiction. Physiol. Rev. 2019, 99, 2115–2140. [CrossRef] [PubMed] 2. Volkow, N.D.; Morales, M. The Brain on Drugs: From Reward to Addiction. Cell 2015, 162, 712–725. [CrossRef] [PubMed] 3. Luijten, M.; Schellekens, A.F.; Kuhn, S.; Machielse, M.W.; Sescousse, G. 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DOI: 10.3390/bs12050128
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Abstract

behavioral sciences Article Differentiating Individuals with and without Alcohol Use Disorder Using Resting-State fMRI Functional Connectivity of Reward Network, Neuropsychological Performance, and Impulsivity Measures 1 , 2 , 3 1 1 1 Chella Kamarajan * , Babak A. Ardekani , Ashwini K. Pandey , Sivan Kinreich , Gayathri Pandey , 1 1 1 3 1 1 David B. Chorlian , Jacquelyn L. Meyers , Jian Zhang , Elaine Bermudez , Weipeng Kuang , Arthur T. Stimus and Bernice Porjesz Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA; ashwini.pandey@downstate.edu (A.K.P.); sivan.kinreich@downstate.edu (S.K.); gayathri.pandey@downstate.edu (G.P.); david.chorlian@downstate.edu (D.B.C.); jacquelyn.meyers@downstate.edu (J.L.M.); jian.zhang@downstate.edu (J.Z.); weipeng.kuang@downstate.edu (W.K.); arthur.stimus@downstate.edu (A.T.S.); bernice.porjesz@downstate.edu (B.P.) Center for Advanced Brain Imaging, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA; babak.ardekani@nki.rfmh.org Department of Psychiatry, NYU School of Medicine, New York, NY 10016, USA; bermue01@nyu.edu * Correspondence: chella.kamarajan@downstate.edu Citation: Kamarajan, C.; Ardekani, Abstract: Individuals with alcohol use disorder (AUD) may manifest an array of neural and behav- B.A.; Pandey, A.K.; Kinreich, S.; ioral abnormalities, including altered brain networks, impaired neurocognitive functioning, and Pandey, G.; Chorlian, D.B.; Meyers, J.L.; Zhang, J.; Bermudez, E.; Kuang, heightened impulsivity. Using multidomain measures, the current study aimed to identify speciﬁc W.; et al. Differentiating Individuals features that can differentiate individuals with AUD from healthy controls (CTL), utilizing a random with and without Alcohol Use forests (RF) classiﬁcation model. Features included fMRI-based resting-state functional connectivity Disorder Using Resting-State fMRI (rsFC) across the reward network, neuropsychological task performance, and behavioral impulsivity Functional Connectivity of Reward scores, collected from thirty abstinent adult males with prior history of AUD and thirty CTL individ- Network, Neuropsychological uals without a history of AUD. It was found that the RF model achieved a classiﬁcation accuracy of Performance, and Impulsivity 86.67% (AUC = 93%) and identiﬁed key features of FC and impulsivity that signiﬁcantly contributed Measures. Behav. Sci. 2022, 12, 128. to classifying AUD from CTL individuals. Impulsivity scores were the topmost predictors, followed https://doi.org/10.3390/bs12050128 by twelve rsFC features involving seventeen key reward regions in the brain, such as the ventral Academic Editor: Scott D. Lane tegmental area, nucleus accumbens, anterior insula, anterior cingulate cortex, and other cortical and subcortical structures. Individuals with AUD manifested signiﬁcant differences in impulsivity and Received: 15 March 2022 alterations in functional connectivity relative to controls. Speciﬁcally, AUD showed heightened im- Accepted: 27 April 2022 pulsivity and hypoconnectivity in nine connections across 13 regions and hyperconnectivity in three Published: 28 April 2022 connections involving six regions. Relative to controls, visuo-spatial short-term working memory was Publisher’s Note: MDPI stays neutral also found to be impaired in AUD. In conclusion, speciﬁc multidomain features of brain connectivity, with regard to jurisdictional claims in impulsivity, and neuropsychological performance can be used in a machine learning framework to published maps and institutional afﬁl- effectively classify AUD individuals from healthy controls. iations. Keywords: alcohol use disorder (AUD); random forests (RF); resting-state functional connectivity (FC); reward network (RN); functional MRI (fMRI); neuropsychological tests; impulsivity Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and 1. Introduction conditions of the Creative Commons The drugs of abuse, including alcohol, exert and maintain their reinforcing effects through Attribution (CC BY) license (https:// the reward circuitry of the brain [1,2]. Neuroimaging studies have documented the disruption creativecommons.org/licenses/by/ of reward processing in addiction [3] and implicated brain reward circuitry in different 4.0/). Behav. Sci. 2022, 12, 128. https://doi.org/10.3390/bs12050128 https://www.mdpi.com/journal/behavsci Behav. Sci. 2022, 12, 128 2 of 26 stages of alcohol use disorder (AUD) [4]. The reward network, in the context of addiction, primarily includes structures from the midbrain (i.e., ventral tegmental area, VTA), basal ganglia (i.e., nucleus accumbens, NAc; caudate nucleus; and putamen), limbic system (i.e., amygdala, hippocampus, and thalamus), and cerebral cortical regions (i.e., prefrontal cortices, insula, cingular cortices, and superior and inferior parietal lobules) [1,2,5–7]. Deficient reward processing due to abnormalities in the reward network in individuals with chronic AUD has been widely reported for both monetary and drug-related rewards [8–16]. For instance, an fMRI finding reported by Wrase et al. [10] showed that the ventral striatum (VS), a key reward region, showed increased activation for alcohol-related cues, but decreased activation for monetary gain in detoxified alcoholics, relative to healthy controls, suggesting differential activation of VS in response to the type of reward. Both animal and human studies have revealed that chronic administration of addictive substances results in neuroadaptations in reward structures, especially in the midbrain dopaminergic areas (e.g., VTA, substantia nigra), as well as the structures to which they project (e.g., NAc) [17,18]. Nevertheless, brain network interactions between mesocorticolimbic regions and other subcortical and cortical structures, especially in human participants with chronic AUD, have not been well-understood [19]. Historically, dysregulation in brain dopaminergic system has been postulated as the primary mechanism underlying substance use disor- ders (SUD) [20,21], and, therefore, studies have attempted to elucidate alterations in the reward network in human patients with SUD (cf. Sutherland et al. [19]). The integrity and communication strength of neural networks have been measured using functional connectivity [22], which is deﬁned as the temporal dependency of neuronal activation patterns of anatomically separated brain regions [23]. Resting-state functional connectivity (rsFC) provides a reliable approach for studying various networks underlying ongoing cognitive and emotional processes [24]. Since the phenomenon was ﬁrst reported by Biswal et al. [25], a growing number of studies have examined rsFC and elucidated brain func- tioning, in terms of neural communication in both healthy subjects and patients with various neurological and psychiatric [24,26,27] and substance use disorders (SUD) [28,29], including AUD [30–36]. However, to our knowledge, there have been no studies exploring both cortical and subcortical reward networks during resting state in abstinent individuals with past history of AUD, and who manifest a variety of neurocognitive deﬁcits [37]. Since studying detoxiﬁed and abstinent AUD individuals relative to healthy controls without past AUD has an advantage in determining the brain impacts due to chronic past drinking without the confounding effects of current drinking [8,38,39], the current study provides an important opportunity to investigate the resting-state functional connectivity of the reward network in abstinent individuals with past AUD. Studies have also shown that abstinent individuals with AUD often manifest poor neu- ropsychological performance [40–45] and heightened impulsivity [46–48], and, therefore, it is important to include these domains in the models exploring concomitants and/or deter- minants of alcohol-related outcomes. In our previous study, we reported that a random forest algorithm was highly useful for classifying AUD individuals from controls using multi-modal measures, including fMRI functional connectivity of the default mode net- work, neuropsychological performance, and impulsivity. Since AUD and other addiction traits are shown to be associated with reward networks [49,50] and as substance induced neuroadaptation primarily involves reward structures [51], the current study aimed to examine the functional connectivity across the reward network, along with measures of neuropsychological performance and impulsivity, to differentiate abstinent AUD individ- uals from healthy controls. Similar to our previous study, we computed the predictive power of these multi-domain measures in terms of classiﬁcation accuracy in a machine learning framework and evaluated the utility of these phenotypic features, especially the reward network connectivity measures. Since recent studies have proposed that resting state fMRI connectivity can potentially serve as one of the key neuroimaging biomarkers for quantitative clinical evaluation of AUD [36,52,53], the ﬁndings from the current study may elucidate speciﬁc connectivity patterns across reward regions of the brain in abstinent Behav. Sci. 2022, 12, x FOR PEER REVIEW 3 of 28 biomarkers for quantitative clinical evaluation of AUD [36,52,53], the findings from the current study may elucidate specific connectivity patterns across reward regions of the Behav. Sci. 2022, 12, 128 3 of 26 brain in abstinent AUD individuals, along with key neuropsychological and impulsivity features, which are distinctly different from healthy controls. AUD individuals, along with key neuropsychological and impulsivity features, which are 2. Materials and M distinctly ethodsdif ferent from healthy controls. The study protocol is illustrated in Figure 1. The sample consisted of 30 abstinent 2. Materials and Methods individuals with past diagnosis of AUD and 30 healthy volunteers (CTL) (see Section 2.1 The study protocol is illustrated in Figure 1. The sample consisted of 30 abstinent for details). The analytic measures included rs-fMRI (Sections 2.4–2.6), self-rated impul- individuals with past diagnosis of AUD and 30 healthy volunteers (CTL) (see Section 2.1 for sivity scores (Section 2.3), and neuropsychological test scores (Section 2.2). Major analyses details). The analytic measures included rs-fMRI (Sections 2.4–2.6), self-rated impulsivity employed in the study were (i) feature selection to extract relevant features that will be scores (Section 2.3), and neuropsychological test scores (Section 2.2). Major analyses used in classificatemployed ion analyin sis the (Sec study tion 2. wer7) e (i) , (ifeatur i) rando e selection m fores to t met extract hod to id relevantent featur ify the ke es that will y be used in classiﬁcation analysis (Section 2.7), (ii) random forest method to identify the key features features that significantly contribute to classifying AUD from the CTL participants (Sec- that signiﬁcantly contribute to classifying AUD from the CTL participants (Section 2.8), tion 2.8), (iii) zero-order correlations were used to compute associations of the key varia- (iii) zero-order correlations were used to compute associations of the key variables identiﬁed bles identified by the random forest classification model, (a) among themselves (Section by the random forest classiﬁcation model, (a) among themselves (Section 3.2) and (b) with 3.2) and (b) with age in each group (Section 3.3). Partial correlations were employed to age in each group (Section 3.3). Partial correlations were employed to identify associations identify associations between age and the key variables of classification by controlling the between age and the key variables of classiﬁcation by controlling the group effects in the group effects in the total sample (Section 3.3). total sample (Section 3.3). Figure 1. The study protocol listing the sample, measures, and analytic techniques. The sample con- Figure 1. The study protocol listing the sample, measures, and analytic techniques. The sample con- sisted of two groups of 30 individuals each, viz., AUD and CTL. The measures used in the prediction sisted of two groups of 30 individuals each, viz., AUD and CTL. The measures used in the prediction model included rs-fMRI functional connectivity (reward network), impulsivity assessed with Bar- model included rs-fMRI functional connectivity (reward network), impulsivity assessed with Barratt ratt impulsiveness scale (BIS), and neuropsychological performance scores. Major analyses were impulsiveness scale (BIS), and neuropsychological performance scores. Major analyses were features features selection for selecting FC variables, random forest classification method, and correlational selection for selecting FC variables, random forest classiﬁcation method, and correlational analyses, analyses, including zero-order and partial correlations. including zero-order and partial correlations. 2.1. Sample 2.1. Sample The sample characteristics are presented in Table 1, and a detailed description is also The sample characteristics are presented in Table 1, and a detailed description is also available in Pandey et al. [54]. All participants in the current study were drawn from the available in Pandey et al. [54]. All participants in the current study were drawn from the sample of a larger study on brain dysfunction in chronic alcoholism conducted at the SUNY sample of a larger study on brain dysfunction in chronic alcoholism conducted at the Downstate Health Sciences University, Brooklyn, NY, USA. Thirty currently abstinent adult SUNY Downstatemales Health with Sciences past AUD Univ (mean ersity, age Brookl (SD)y= n, 41.42 NY, U (7.31) SA. years) Thirtyand curren thirty tly unaf abstfected i- male controls (mean age (SD) = 27.44 (4.74) years), who had undergone multimodal assessments, nent adult males with past AUD (mean age (SD) = 41.42 (7.31) years) and thirty unaffected including structural and functional MRI and neuropsychological tests, were selected for male controls (mean age (SD) = 27.44 (4.74) years), who had undergone multimodal as- the present study. The “race/ethnic” distribution of the sample was: African Ancestry = 25; sessments, including structural and functional MRI and neuropsychological tests, were European Ancestry = 9; Asian = 21; American Indian = 1; More than one race = 2; and selected for the present study. The “race/ethnic” distribution of the sample was: African Unknown = 2. Participants with AUD were recruited from alcohol treatment centers in and Ancestry = 25; European Ancestry = 9; Asian = 21; American Indian = 1; More than one around New York City after they had been detoxiﬁed and abstinent for at least 30 days prior race = 2; and Unknown = 2. Participants with AUD were recruited from alcohol treatment to testing. As shown in Table 1, some of the participants from the AUD group had consumed centers in and around New York City after they had been detoxified and abstinent for at tobacco (N = 20) and/or marijuana (N = 10) during the last 6 months (but not at least 5 days least 30 days prior to befor te e st testing). ing. As None shown of the in Table participants 1, some of th were in withdrawal e participant for s from alcohol the A or any UD other drugs group had consumed tobacco (N = 20) and/or marijuana (N = 10) during the last 6 months Behav. Sci. 2022, 12, 128 4 of 26 (including nicotine) at the time of testing. Individuals for the control group (CTL) were recruited through advertisements and screened to exclude any personal or family history of major medical, psychiatric, or substance-related disorders. The CTL participants did not have any past or present history of substance dependence or abuse (DSM-IV), although some of them (N = 12) were light/regular drinkers and had used alcohol in the last 6 months (N = 18) (see Table 1 for details). All participants were asked to abstain from alcohol and other drugs for 5 days prior to MRI scans. Clinical information regarding substance use, psychiatric disorder, and family history were assessed using a modiﬁed version of the semi-structured assessment of genetics of alcoholism (SSAGA) [55]. The majority of subjects were right-handed, with only a few who were either left-handed (5 in AUD and 2 in CTL group) or bi-dexterous (2 in AUD and 1 in CTL group). Clinical and psychometric data were collected at the SUNY Downstate Health Sciences University, while the fMRI data were acquired at the Nathan Kline Institute (NKI) for Psychiatric Research. Standard MRI safety protocols and exclusion criteria (implants, tattoos, cosmetics, claustrophobia, etc.) were followed to ensure subjects’ safety and data quality. Individuals with hearing/visual impairment, a history of head injury, or moderate and severe cognitive deﬁcits (<21) on mini-mental state examination (MMSE) [56] were also excluded from the study. All participants provided informed consent and the Institutional Review Boards of both centers approved the research protocols (IRB approval ID: SUNY–266893; NKI–212263). Table 1. Demographic and clinical characteristics of the sample. AUD CTL Variable N * Mean SD N * Mean SD Age (in years) 30 41.42 7.31 30 27.44 4.74 Education (in Years) 30 11.93 2.35 30 15.77 1.87 Age of onset (regular alcohol use) 30 15.77 2.58 12 20.50 3.80 Alcohol: Drinks/day 30 12.08 10.02 12 2.88 1.93 (heavy alcohol use period) Alcohol: Days/month 30 20.30 9.01 12 3.35 3.64 (heavy alcohol use period) Alcohol: Drinks (last 6 months) 30 2.68 6.61 18 2.61 1.98 Alcohol: Days (last 6 months) 30 3.97 8.02 18 2.94 3.62 Length of Abstinence (in months) 30 22.43 28.16 18 1.9 4.99 Tobacco: Times/day (last 6 months) 20 9.90 5.80 6 2.33 1.63 Tobacco: Days/month (last 6 months) 20 28.35 4.83 6 14.17 13.82 Marijuana: Times in last 6 months 10 98.80 91.38 4 18.75 27.61 * N refers to the number of subjects included in these mean and SD calculations for each variable. Individuals who did not consume alcohol or drugs were not included in the respective calculations. 2.2. Neuropsychological Assessment Participants were administered two computerized tests from the Colorado assessment tests for cognitive and neuropsychological assessment [57], namely, the Tower of London Test (TOLT) [58], and the visual span test (VST) [59,60]. 2.2.1. Tower of London Test (TOLT) The TOLT assesses planning and problem-solving ability of the executive functions. In this test, participants were asked to solve a set of puzzles with graded difﬁculty levels by arranging a number of colored beads one at a time from a starting position to the desired goal position in as few moves as possible. The test consisted of 3 puzzle types, with 3, 4, and 5 colored beads placed on the same number of pegs, with 7 problems/trials per type and a total of 21 trials. The following performance measures from the sum total Behav. Sci. 2022, 12, 128 5 of 26 of all puzzle types were used in the analysis: (i) excess moves, which is the additional moves beyond the minimum moves required to solve the puzzle (“ExcMovMade_All”); (ii) average pickup time, which is the initial thinking/planning time spent until picking up the ﬁrst bead to solve the puzzle (“AvgPicTime_All”); (iii) average total time, which is the total thinking/planning time to solve the problem in each puzzle type (“AvgTotTime_All”); (iv) total trial time, which is the total performance/execution time spent on all trials within each puzzle type (“TotTrlTime_All”); and (v) average trial time, which is the mean performance/execution time across trials per puzzle type (“AvgTrlTime_All”). 2.2.2. Visual Span Test (VST) The VST measured visuospatial memory span from the forward condition and work- ing memory from the backward condition. In this test, a set of randomly arranged squares, ranging from 2 to 8 squares per trial, ﬂashed in a predetermined sequence depending on the span level being assessed. Each span level was administered twice, with a total of 14 trials in each condition. During the forward condition, subjects were required to repeat the sequence in the same order by clicking on the squares using a computer mouse. In the backward condition, subjects were required to repeat the sequence in the reverse order (starting from the last square). The following performance measures were collected during forward and backward conditions: (i) total number of correctly performed trials (“Tot- Cor_Fw” and “TotCor_Bw”); (ii) maximum span or sequence-length achieved (“Span_Fw” and “Span_Bw”); (iii) total average time, which is the sum of mean time-taken across all trials performed (“TotAvgTime_Fw” and “TotAvgTime_Bw”); and (iv) total correct average time, which is the sum of mean time-taken across all trials correctly performed (“TotCorAvgTime_Fw” and “TotCorAvgTime_Bw”). 2.3. Impulsivity Scores Barratt Impulsiveness Scale—Version 11 (BIS-11) [61], a 30-item self-administered tool with excellent psychometric properties [62], was used to assess aspects of impulsivity. All three subscales, viz., motor impulsivity (BIS_MI), non-planning (BIS_NP), and attentional impulsivity (BIS_AI), were included in the random forest model. 2.4. MRI Data Acquisition The details of fMRI data acquisition on the same sample have been described previ- ously by our group [63]. Brieﬂy, MRI scanning was performed at the Nathan Kline Institute using a 3.0 Tesla Siemens Tim Trio scanner (Erlangen, Germany). The resting-state fMRI data, used in the current study, were collected during eyes closed alert state for 6.11 s. A series of T2*-weighted gradient echo single-shot echo-planar imaging (EPI) volumes with the following sequence parameters was acquired: TR = 2750 ms; TE = 30 ms; ﬂip angle = 80 ; voxel size = 2.5 2.5 3.5 mm ; matrix size = 96 96; number of slices = 50; number of volumes = 130; FOV = 240 mm; and Grappa acceleration factor = 3. The sequence was carefully optimized to minimize the effects of magnetic susceptibility inhomogeneities (such as distortions and signal dropouts), as well as the effects of mechanical vibrations, which elevate Nyquist ghosting levels. In addition, a magnetization-prepared rapid gradient-echo (MPRAGE) high-resolution three-dimensional T1-weighted structural image served as an anatomical reference for the fMRI data, as well as for the non-linear registration of imaging data be- tween subjects. The sequence parameters for the MPRAGE were: TR = 2500 ms; TE = 3.5 ms; TI = 1200 ms; ﬂip angle = 8 ; voxel size = 1 1 1 mm ; matrix size = 256 256 192; FOV = 256 mm; and number of averages = 1. 2.5. Image Processing The details of image processing for the resting state fMRI are available in our previous study [63]. Brieﬂy, the intra-subject inter-modality linear registration module [64] of the automatic registration toolbox (ART; www.nitrc.org/projects/art (accessed on 20 December 2019) was used to register the structural (MPRAGE) and functional MRI volumes. The Behav. Sci. 2022, 12, 128 6 of 26 brainwash program within the ART toolbox was used for skull-stripping the MPRAGE volumes. Motion detection and correction were performed using the 3dvolreg module of the AFNI software package [65]. Furthermore, the non-linear registration module of the ART [66] was used to correct for the geometric distortions of the fMRI images due to magnetic susceptibility differences in the head, particularly at brain/air interfaces. The skull-stripped MPRAGE images from all subjects were non-linearly registered to a study- speciﬁc population template using ART’s non-linear registration algorithm, which is one of the most accurate inter-subject registration methods available [67]. The population template was formed using an iterative method [68], and the motion-corrected fMRI time-series were detrended using PCA [69]. Finally, fMRI images from all subjects were normalized to a standard space. 2.6. Reward Network Seed Regions and rsFC Calculations The regions of interest (ROIs) for the reward network were identiﬁed from the pub- lished literature of review and meta-analyses of reward processing, e.g., [6,70,71]. These included 34 ROIs from 17 bilateral structures involving nine bilateral subcortical structures and eight bilateral cortical regions (Table 2 and Figure 2). The ROIs were marked using ITK-SNAP, an image processing application [72]. The diameters of subcortical and corti- cal ROIs were 7 mm and 11 mm, respectively, from the MNI coordinates (Table 2). The ROI-to-ROI connectivity [73], the most commonly used method to derive rsFC across brain regions [74], was computed using Pearson correlation coefﬁcients between all unique pairs (N = 561) of BOLD time series data of all 34 ROIs listed in Table 2. The resulting correlation coefﬁcients were Fisher Z-transformed for further statistical analyses. All 561 connections derived from unique combinations of the ROIs were used in the feature selection process (see Section 2.7), which provided the subset of features to be used in the random forest model (see Section 2.8). Table 2. Brain regions of interest (ROI) analyzed for reward network functional connectivity. MNI Coordinates ROI Brain Region Notation Location X Y Z 1 L. Ventral Tegmental Area L.VTA Subcortical 4 16 14 2 R. Ventral Tegmental Area R.VTA Subcortical 4 16 14 3 L. Nucleus Accumbens L.NAc Subcortical 8 10 10 4 R. Nucleus Accumbens R.NAc Subcortical 8 10 10 5 L. Amygdala L.Amg Subcortical 24 2 14 6 R. Amygdala R.Amg Subcortical 24 2 14 7 L. Hippocampus L.Hip Subcortical 28 10 22 8 R. Hippocampus R.Hip Subcortical 28 10 22 9 L. Caudate L.Cdt Subcortical 13 15 9 10 R. Caudate R.Cdt Subcortical 13 15 9 11 L. Pallidum L.Pal Subcortical 18 2 4 12 R. Pallidum R.Pal Subcortical 18 2 4 13 L. Thalamus L.Tha Subcortical 8 12 6 14 R. Thalamus R.Tha Subcortical 8 12 6 15 L. Insula (anterior) L.Ins Subcortical 30 17 15 16 R. Insula (anterior) R.Ins Subcortical 30 17 15 17 L. Parahippocampal Gyrus L.PHG Cortical 24 39 12 Behav. Sci. 2022, 12, 128 7 of 26 Table 2. Cont. MNI Coordinates ROI Brain Region Notation Location X Y Z 18 R. Parahippocampal Gyrus R.PHG Cortical 27 39 12 19 L. Anterior Cingulate Cortex L.ACC Cortical 6 44 10 20 R. Anterior Cingulate Cortex R.ACC Cortical 6 44 10 21 L. Mid-Cingulate Cortex L.MCC Cortical 6 2 40 22 R. Mid-Cingulate Cortex R.MCC Cortical 6 2 40 Behav. Sci. 2022, 12, x FOR PEER REVIEW 7 of 28 23 L. Posterior Cingulate Cortex L.PCC Cortical 6 46 30 24 R. Posterior Cingulate Cortex R.PCC Cortical 6 46 30 25 L. Orbitofrontal Cortex L.OFC Cortical 32 42 16 22 R. Mid-Cingulate Cortex R.MCC Cortical 6 2 40 23 L. Posterior Cingulate Cortex L.PCC Cortical −6 −46 30 26 R. Orbitofrontal Cortex R.OFC Cortical 32 42 16 24 R. Posterior Cingulate Cortex R.PCC Cortical 6 −46 30 27 L. Angular Gyrus L.Ang Cortical 45 58 30 25 L. Orbitofrontal Cortex L.OFC Cortical −32 42 −16 28 R. Angular Gyrus L.Ang Cortical 45 58 30 26 R. Orbitofrontal Cortex R.OFC Cortical 32 42 −16 29 L. Superior Parietal Lobule L.SPL Cortical 24 68 56 27 L. Angular Gyrus L.Ang Cortical −45 −58 30 28 30 R. Ang R. Superior ular Gyrus Parietal Lobule L.Ang R.SPL Cortical Cortical 45 −58 24 30 68 56 29 L. Superior Parietal Lobule L.SPL Cortical −24 −68 56 31 L. Dorsolateral PFC L.DLP Cortical 44 38 19 30 R. Superior Parietal Lobule R.SPL Cortical 24 −68 56 32 R. Dorsolateral PFC R.DLP Cortical 44 38 19 31 L. Dorsolateral PFC L.DLP Cortical −44 38 19 33 L. Putamen L.Ptm Subcortical 27 5 6 32 R. Dorsolateral PFC R.DLP Cortical 44 38 19 33 L. 34 P R.utamen Putamen L.Ptm R.Ptm Subcortical Subcortical −27 5 27−6 5 6 34 R. Putamen R.Ptm Subcortical 27 5 −6 Figure 2. Brain reward network regions of interest comprising 16 cortical areas (top panels, C1–C3) Figure 2. Brain reward network regions of interest comprising 16 cortical areas (top panels, C1–C3) and 18 subcortical areas (bottom panels, S1–S3), as listed in Table 1. Bilateral cortical regions: Insula a (Ins), Parahippocampal Gyrus (PH nd 18 subcortical areas (botG to ),m Ante parior nels Cingulate Cortex (ACC), , S1–S3), as listed in Mid-Cingulate Cortex Table 1. Bilateral cortical regions: Insula (MCC), Mid-Cingulate Cortex (MCC), Posterior Cingulate Cortex (PCC), Orbitofrontal Cortex (Ins), Parahippocampal Gyrus (PHG), Anterior Cingulate Cortex (ACC), Mid-Cingulate Cortex (MCC), (OFC), Angular Gyrus (Ang), Superior Parietal Lobule (SPL), Dorsolateral (PFC), and Dorsolateral Mid-Cingulate Cortex (MCC), Posterior Cingulate Cortex (PCC), Orbitofrontal Cortex (OFC), Angular Prefrontal Cortex (DLP). Bilateral subcortical regions: Ventral Tegmental Area (V), Nucleus Accum- bens (N), Amygdala (A), Hippocampus (Hi), Caudate (Cd), Pallidum (Pa), Thalamus (T), and Puta- Gyrus (Ang), Superior Parietal Lobule (SPL), Dorsolateral (PFC), and Dorsolateral Prefrontal Cortex men (Pt). Colors: Blue–Left hemispheric regions; Red–Right hemispheric regions. Views: C1/S1–Axial (DLP). Bilateral subcortical regions: Ventral Tegmental Area (V), Nucleus Accumbens (N), Amygdala (A), top view; C2/S2–Coronal front view; C3/S3–Sagittal left view. Directions: A–Anterior; P–Posterior; S–Superior; I–Inferior; L–Left; R–Right. Hippocampus (Hi), Caudate (Cd), Pallidum (Pa), Thalamus (T), and Putamen (Pt). Colors: Blue—Left hemispheric regions; Red—Right hemispheric regions. Views: C1/S1—Axial top view; C2/S2—Coronal 2.7. Feature Selection of FC Variables front view; C3/S3—Sagittal left view. Directions: A—Anterior; P—Posterior; S—Superior; I—Inferior; Recent approaches dealing with machine learning analyses have used a two-stage L—Left; R—Right. approach, consisting of feature selection followed by a predictive algorithm using a Behav. Sci. 2022, 12, 128 8 of 26 2.7. Feature Selection of FC Variables Recent approaches dealing with machine learning analyses have used a two-stage approach, consisting of feature selection followed by a predictive algorithm using a selected sets of variables [75–79]. Feature selection methods are used as the ﬁrst stage to reduce irrelevant and redundant variables, which may otherwise add noise to the predictive mod- els [75–77]. The advantages of feature selection include a better understanding of the data, reducing computation requirements, mitigating the effect of the curse of dimensionality, and also improving the predictor performance [76]. We applied binomial lasso regres- sion [80–82] as a feature selection method [83,84], as implemented in R-package “glmnet”, to extract a subset of fMRI FC variables (N = 561) that held signiﬁcant predictive value to discriminate AUD from the CTL group. Feature selection was implemented only for the rsFC variables, due to a high number of connections, most of which were deemed not relevant for our purpose of AUD classiﬁcation. The method adapted in the current analysis is based on the Lasso method, as implemented in Fonti and Belitser [84]. The maximum number of output features “pmax” was set to 10% (i.e., 56 of the total 561 variables). A 10-fold cross-validation and lambda thresholding with 1 SE ( ) were set to extract the 1se ﬁnal set of key variables. The area under the curve (AUC) was plotted to determine the classiﬁcation performance of the selected features. The ﬁnal subset resulting from the feature selection process included 21 rsFC variables (see Table A1, Appendix A). 2.8. Random Forest Classiﬁcation The random forest classiﬁcation model, as used in the current study, has been described in our previous work on rsFC of the default mode network (DMN) [63]. The predictor variables included in the model were 21 reward network connections identiﬁed by feature selection (Table A1), 13 neuropsychological scores consisting of 5 TOLT scores and 8 VST scores (see Section 2.2), and 3 BIS scores (see Section 2.3), while the group status (AUD and CTL) served as the outcome variable (see Table A1, Appendix A). Although age was signiﬁcantly different between the groups, we did not include age as an input variable in the classiﬁcation model for the following reasons: (i) as done in our previous publications on the same sample of subjects [63,85], we performed post hoc correlational analysis of age with the signiﬁcant features of the random forest model, to see if any of the top variables had associations with age in the individual groups or in total sample (see Section 3.4); and (ii) since the age difference between the groups was highly signiﬁcant, including age as a feature in the classiﬁcation model would likely artiﬁcially increase the accuracy of classiﬁcation, which would not be desirable. To compute the classiﬁcation accuracy of the random forest model, we used an out-of-bag (OOB) error estimate. According to Breiman and Cutler [86], in random forests, owing to the inbuilt OOB feature in the model, additional cross-validation is not a requirement to obtain an unbiased estimate of the test sample error, since it is estimated internally in the OOB algorithm. The random forest algorithm constructs each of the decision trees using separate bootstrap subsamples from the training data, and about one-third of the observations from the training data are left out during each bootstrap, called the OOB sample, which are used as a form of test data only, to estimate the prediction accuracy of the RF model. While classiﬁcation trees are grown for each bootstrap sample (which is approximately two-thirds of the training data), the OOB error rate is calculated for each classiﬁcation tree built. According to Breiman [87], there are two reasons for using bagging: (i) to enhance the accuracy when random features are used; and (ii) to give ongoing estimates of the generalization error of the combined ensemble of trees, as well as estimates for the strength and correlation. The aggregate of OOB scores on all “ntree” trees (which is the maximum number of trees pre-set in the model calculation) provides the ensemble OOB error rate. Thus, the OOB score provides validation for the RF model. Therefore, unlike in other machine learning algorithms, random forests method does not require separate training data and test data while specifying the model term. In the current study, the maximum number of trees “ntree” was set at 1000. The optimal number of features analyzed at each node (“Mtry”) in the model was estimated to be 8 Behav. Sci. 2022, 12, 128 9 of 26 Behav. Sci. 2022, 12, x FOR PEER REVIEW 9 of 28 (using the “tuneRF” function). The ﬁnal list of variables that signiﬁcantly contributed to the classiﬁcation was tabulated and sorted based on their importance to classiﬁcation. For across ROIs was mapped onto a 3-dimensional ICBM atlas [88] using custom Matlab the top signiﬁcant FC variables, the brain connectivity across ROIs was mapped onto a scripts. 3-dimensional ICBM atlas [88] using custom Matlab scripts. 3. Results 3. Results 3.1. Random Forests Classification 3.1. Random Forests Classiﬁcation 3.1.1. Classification Accuracy and Top Significant Variables 3.1.1. Classiﬁcation Accuracy and Top Signiﬁcant Variables The RF algorithm correctly identified group membership of 26 out of 30 individuals The RF algorithm correctly identiﬁed group membership of 26 out of 30 individuals in in each group, in classifying them into either AUD or CTL group, with an accuracy rate each group, in classifying them into either AUD or CTL group, with an accuracy rate of of 86.67% and the area under the curve of 93% (Figure 3). The OOB error or the misclassi- 86.67% and the area under the curve of 93% (Figure 3). The OOB error or the misclassiﬁca- fication rate was 13.33% (for each group). The model also identified 12 rsFC connections tion rate was 13.33% (for each group). The model also identiﬁed 12 rsFC connections and and two impulsivity scores (motor and non-planning) as significantly (p < 0.05) contrib- two impulsivity scores (motor and non-planning) as signiﬁcantly (p < 0.05) contributing uting to the classification (Table 3). Relative to the CTL individuals, AUD subjects showed to the classiﬁcation (Table 3). Relative to the CTL individuals, AUD subjects showed a a predominant pattern of hypoconnectivity (i.e., decreased rsFC in 9 out of 12 connections) predominant pattern of hypoconnectivity (i.e., decreased rsFC in 9 out of 12 connections) across the major cortical and subcortical nodes of the reward network, in addition to three across the major cortical and subcortical nodes of the reward network, in addition to three connections with hyperconnectivity in specific nodes (i.e., left nucleus accumbens–left connections with hyperconnectivity in speciﬁc nodes (i.e., left nucleus accumbens–left posterior cingulate cortex (PCC), right pallidum–right PCC, and right hippocampus–left posterior cingulate cortex (PCC), right pallidum–right PCC, and right hippocampus–left dorsolateral dorsolateral prefrontal prefrontal cortex). cortex). AUD AUD indivi individuals duals al also so showed showed in incr creas eased ed impu impulsivity lsivity in in motor motor and and no non-planning n-planning c categories. ategories. Ho However wever, , none none of the of the neur neurop opsychological sychological vari variables ables were significant based on the p-value criterion. were signiﬁcant based on the p-value criterion. Figure 3. Receiver operating characteristic (ROC) curve for the RF model for different true positive Figure 3. Receiver operating characteristic (ROC) curve for the RF model for different true positive rate (sensitivity) and false positive rate (1-specificity) are shown. The area under the ROC curve was rate (sensitivity) and false positive rate (1-speciﬁcity) are shown. The area under the ROC curve was 93%. (The further away the curve from the diagonal dashed line and the closer the swelling to the 93%. (The further away the curve from the diagonal dashed line and the closer the swelling to the top top left corner, the better the classification.). left corner, the better the classiﬁcation). Table 3. Random forest importance parameters mean minimal depth, number of nodes, accuracy decrease, Gini decrease, number of trees, times a root, and p-value), and the direction of significance for the top significant variables (p < 0.05) are shown. Two of the impulsivity scores (motor and non- planning) and 12 rsFC connections were identified as important features to classify individuals into either AUD or CTL group. The variables are sorted based on the p-values. Behav. Sci. 2022, 12, 128 10 of 26 Table 3. Random forest importance parameters mean minimal depth, number of nodes, accuracy decrease, Gini decrease, number of trees, times a root, and p-value), and the direction of signiﬁcance for the top signiﬁcant variables (p < 0.05) are shown. Two of the impulsivity scores (motor and non-planning) and 12 rsFC connections were identiﬁed as important features to classify individuals into either AUD or CTL group. The variables are sorted based on the p-values. Feature Mean Minimum Depth No. of Nodes Accuracy Decrease Gini Decrease No. of Trees Time a Root p-Value Direction BIS Motor 1.3824 348 0.0170 2.0202 319 94 1.87E-19 A > C Impulsivity FC_3_23 1.9354 330 0.0130 1.6903 295 74 1.82E-15 A > C (L.NAc–L.PCC) FC_16_20 2.0062 326 0.0149 1.5132 291 57 1.23E-14 C > A (R.Ins–R.ACC) BIS 1.7619 319 0.0187 1.6830 294 74 3.05E-13 A > C Non-planning FC_2_19 2.0561 313 0.0040 1.4706 274 58 4.23E-12 C > A (R.VTA–L.ACC) FC_20_26 2.2513 299 0.0101 1.3203 272 45 1.24E-09 C > A (R.ACC–R.OFC) FC_6_7 2.3798 275 0.0063 1.1669 258 41 4.40E-06 C > A (R.Amg–L.Hip) FC_9_12 2.5018 268 0.0039 0.9725 255 29 3.26E-05 C > A (L.Cdt–R.Pal) FC_20_24 2.4732 266 0.0054 1.0002 249 28 5.58E-05 C > A (R.ACC–R.PCC) FC_13_14 2.5681 252 0.0023 0.9843 233 40 0.0016 C > A (L.Tha–R.Tha) FC_12_24 2.8735 249 0.0022 0.8844 223 20 0.0030 A > C (R.Pal–R.PCC) FC_9_13 2.7035 249 0.0056 0.9541 228 34 0.0030 C > A (L.Cdt–L.Tha) FC_13_34 2.7980 247 0.0053 0.9212 234 24 0.0044 C > A (L.Tha–R.Ptm) FC_8_31 2.9451 238 0.0047 0.8020 217 20 0.0220 A > C (R.Hip–L.DLP) Acronym: BIS—Barratt Impulsiveness Scale, FC—Functional Connectivity, R—Right, L—Left, ACC—Anterior Cingu- late Cortex, Amg—Amygdala, Ang—Angular Gyrus, Cdt—Caudate, DLP—Dorsolateral PFC, Hip—Hippocampus, Ins—Insula (anterior), NAc—Nucleus Accumbens, OFC—Orbitofrontal Cortex, Pal—Pallidum, PCC—Posterior Cingulate Cortex, PHG—Parahippocampal Gyrus, Ptm—Putamen, Tha—Thalamus, VTA—Ventral Tegmental Area. Key: A > C = AUD > CTL; C > A = CTL > AUD. 3.1.2. Distribution of Minimal Depth The distribution of minimal depth among the decision trees of the forests for the top signiﬁcant variables is shown in Figure 4. Minimal depth of a variable represents the depth of the node that splits on that variable and is the closest to the root of the decision tree. The lower mean minimal depth of a variable represents a higher number of observations (participants) categorized in a speciﬁc group based on that variable. The ranking based on the minimal depth parameter shows that two of the impulsivity variables are a the top of the importance list, followed by several reward network connections and a neuropsychological feature (total correct score in the forward trials of the visual span test). 3.1.3. Multi-Way Importance The top signiﬁcant variables can also be shown by a multi-way importance plot based on any of the three RF importance measures. Figure 5 illustrates the important features that contributed to group classiﬁcation based on Gini decrease, number of trees, and p-value. These features include: (i) 12 FC variables (representing connections across cortical and subcortical regions) (see Table 2 for details), (ii) one neuropsychological variable (i.e., total number of correct trials in forward span), and (iii) two impulsivity scores (motor and non-planning categories). Behav. Sci. 2022, 12, x FOR PEER REVIEW 11 of 28 Behav. Sci. 2022, 12, 128 11 of 26 Figure 4. The distribution of minimal depth among the trees of the forest for the significant variables Figure 4. The distribution of minimal depth among the trees of the forest for the signiﬁcant variables is shown in different colors for each level of minimal depth. The mean minimal depth in the distri- is shown in different colors for each level of minimal depth. The mean minimal depth in the bution for each variable is marked by a vertical black bar overlapped by a value label inside a box. distribution for each variable is marked by a vertical black bar overlapped by a value label inside a Based on the mean minimal depth values, the importance list comprised 2 BIS scores, 13 FC, and 1 box. Based on the mean minimal depth values, the importance list comprised 2 BIS scores, 13 FC, and 1 neuropsychological score, which contributed to the RF classiﬁcation of AUD and CTL individuals. neuropsychological score, which contributed to the RF classification of AUD and CTL individuals. The lower mean minimal depth of a feature represents a higher number of observations (participants) The lower mean minimal depth of a feature represents a higher number of observations (partici- categorized in a speciﬁc group based on the feature. The number of trees for a feature represents the pants) categorized in a specific group based on the feature. The number of trees for a feature repre- total number of decision trees in which a split occurs on the feature (see Table 2 for details about the sents the total number of decision trees in which a split occurs on the feature (see Table 2 for details ROI numbers (1–34) represented in the FC variables). about the ROI numbers (1–34) represented in the FC variables). 3.1.3. Multi-Way Importance The top significant variables can also be shown by a multi-way importance plot based on any of the three RF importance measures. Figure 5 illustrates the important features that contributed to group classification based on Gini decrease, number of trees, and p-value. These features include: (i) 12 FC variables (representing connections across cortical and subcortical regions) (see Table 2 for details), (ii) one neuropsychological variable (i.e., total number of correct trials in forward span), and (iii) two impulsivity scores (motor and non- planning categories). Behav. Sci. 2022, 12, x FOR PEER REVIEW 12 of 28 Behav. Sci. 2022, 12, 128 12 of 26 Figure 5. Multi-way importance Figure 5. Multi-way plot showin importance g the top plot showing significant the top features (label signiﬁcant featur ed and es (labeled marked with and marked with black circles) that contributed to the classiﬁcation of alcohol use disorder from control individuals black circles) that contributed to the classification of alcohol use disorder from control individuals based on the measures Gini decrease (mean decrease in node impurity or classiﬁcation error), number based on the measures Gini decrease (mean decrease in node impurity or classification error), number of trees (total number of decision trees in which a split occurred), and p-value (probability of node of trees (total number of decision trees in which a split occurred), and p-value (probability of node splits). The top variables of importance included 2 impulsivity scores, 12 rsFC connections, and splits). The top variables of importance included 2 impulsivity scores, 12 rsFC connections, and 1 1 neuropsychological variable (see the circled and labeled dots). Notations in the variable labels: neuropsychological variable (see the circled and labeled dots). Notations in the variable labels: BIS– BIS–Barratt Impulsivity Scale; MI–Motor impulsivity; NP–Non-planning; TotCor_Fw–Total correct Barratt Impulsivity Scale; MI–Motor impulsivity; NP–Non-planning; TotCor_Fw–Total correct for- forward. ROIs of FC variables: Refer to Table 2 for details about the ROI numbers (1-34) that are ward. ROIs of FC variables: Refer to Table 2 for details about the ROI numbers (1-34) that are rep- represented in the rsFC variable names. resented in the rsFC variable names. 3.1.4. Correlations among Rankings of RF Parameters The correlations among the rankings of features based on different RF parameters 3.1.4. Correlations among Rankings of RF Parameters (Figure 6) were very high and signiﬁcant (r > 0.9), suggesting that each of the RF parameters The correlations among the rankings of features based on different RF parameters would rank the features in a very similar order while classifying the individuals into either (Figure 6) were very high and significant (r > 0.9), suggesting that each of the RF parame- the AUD or CTL group. High correlations among these parameters also suggest that each parameter is very valuable and reliable in terms of its classiﬁcation performance, ters would rank the features in a very similar order while classifying the individuals into lending further support to the utility of the RF technique as a powerful tool for classifying either the AUD or CTL group. High correlations among these parameters also suggest individuals using a set of multi-domain features. that each parameter is very valuable and reliable in terms of its classification performance, lending further support to the utility of the RF technique as a powerful tool for classifying individuals using a set of multi-domain features. Behav. Sci. 2022, 12, 128 13 of 26 Behav. Sci. 2022, 12, x FOR PEER REVIEW 13 of 28 Figure 6. Illustration of rankings of features based on correlation between any two random forest Figure 6. Illustration of rankings of features based on correlation between any two random forest (RF) parameters. The panels in the lower triangle of the grid show the distribution of rankings of all (RF) parameters. The panels in the lower triangle of the grid show the distribution of rankings of predictor variables with black dots along a blue trend line. The panels in the upper triangle of the all predictor variables with black dots along a blue trend line. The panels in the upper triangle of grid show correlation coefficients across rankings of any two parameters. It is shown that all RF the grid show correlation coefﬁcients across rankings of any two parameters. It is shown that all RF parameters of importance were found to have very high correlations among each other, suggesting parameters the high reliab of importance ility of each of wer te hese parameter found to havesvery in rank high ing corr the im elations portance among of featu eachrother es for , suggesting classifica- tion. The asterisks (***) represents that the correlations were highly significant (p < 0.001). the high reliability of each of these parameters in ranking the importance of features for classiﬁcation. The asterisks (***) represents that the correlations were highly signiﬁcant (p < 0.001). 3.1.5. Connectivity Mapping of Significant rsFC Connections 3.1.5. Connectivity Mapping of Signiﬁcant rsFC Connections Significant reward network connections are illustrated in Figure 7. Among the 12 sig- Signiﬁcant reward network connections are illustrated in Figure 7. Among the nificant connections, nine were hypoconnected and three were hyperconnected in AUD 12 signiﬁcant connections, nine were hypoconnected and three were hyperconnected in individuals, involving 17 regions (7 from the left and 10 from the right hemisphere) of the AUD individuals, involving 17 regions (7 from the left and 10 from the right hemisphere) 34 reward network ROIs. While the majority of these nodes (12 of 17) were of solo paths, of the 34 reward network ROIs. While the majority of these nodes (12 of 17) were of solo connecting to another single node (ROI # 2, 3, 6, 7, 8, 14, 16, 19, 23, 26, 31, 34), two of them paths, connecting to another single node (ROI # 2, 3, 6, 7, 8, 14, 16, 19, 23, 26, 31, 34), (ROI # 13, 20) were linked with three connections each, and three of them (ROI # 9, 12, 24) two of them (ROI # 13, 20) were linked with three connections each, and three of them had two connections each. Out of nine hypoconnected FC variables, three were cortico- (ROI # 9, 12, 24) had two connections each. Out of nine hypoconnected FC variables, cortical connections (R.Ins–R.ACC, R.ACC–R.OFC, and R.ACC–R.PCC), involving only three were cortico-cortical connections (R.Ins–R.ACC, R.ACC–R.OFC, and R.ACC–R.PCC), the right hemisphere, and five were subcortical–subcortical connections (R.Amg–L.Hip, involving only the right hemisphere, and ﬁve were subcortical–subcortical connections L.Cdt–R.Pal, L.Tha–R.Tha, L.Cdt–L.Tha, and L.Tha–R.Ptm) involving both hemispheres, (R.Amg–L.Hip, L.Cdt–R.Pal, L.Tha–R.Tha, L.Cdt–L.Tha, and L.Tha–R.Ptm) involving both and one inter-hemispheric subcortical–cortical connection (R.VTA–L.ACC). Interestingly, hemispheres, and one inter-hemispheric subcortical–cortical connection (R.VTA–L.ACC). all three hyperconnected FC variables were subcortical–cortical connections, involving a Interestingly, all three hyperconnected FC variables were subcortical–cortical connections, left-hemispheric connection (L.NAc–L.PCC), a right-hemispheric connection (R.Pal– involving a left-hemispheric connection (L.NAc–L.PCC), a right-hemispheric connection R.PCC), and an inter-hemispheric connection (R.Hip–L.DLP). (R.Pal–R.PCC), and an inter-hemispheric connection (R.Hip–L.DLP). Behav. Sci. 2022, 12, x FOR PEER REVIEW 14 of 28 Behav. Sci. 2022, 12, 128 14 of 26 Figure 7. Significant reward network connections that contribute to the random forest classification Figure 7. Signiﬁcant reward network connections that contribute to the random forest classiﬁcation of alcohol use disorder (AUD) from control (CTL), as listed in Table 3. (A): Hypoconnectivity manifested of alcohol use disorder (AUD) from control (CTL), as listed in Table 3. (A): Hypoconnectivity man- by the AUD group (cyan lines) across cortical and subcortical ROI regions of the RN, predominantly ifested by the AUD group (cyan lines) across cortical and subcortical ROI regions of the RN, pre- involving right hemisphere structures; (B): Hyperconnectivity in three connections manifested by the dominantly involving right hemisphere structures; (B): Hyperconnectivity in three connections AUD group (orange lines). Images within each panel: Left: axial (top) view; Middle: coronal (front) manifested by the AUD group (orange lines). Images within each panel: Left: axial (top) view; Mid- view; Right: sagittal (left) view. dle: coronal (front) view; Right: sagittal (left) view. 3.2. Correlations among the Top Signiﬁcant Variables Exploratory (descriptive) analysis using zero-order correlations among the top signiﬁ- 3.2. Correlations among the Top Significant Variables cant variables is shown in Figure 8. Only ﬁve positive correlations survived Bonferroni cor- Exploratory (descriptive) analysis using zero-order correlations among the top sig- rection for multiple comparisons (as represented by the sign +++), which include: (i) three positive correlations between FC features (L.Cdt–R.Pal with L.Cdt–L.Tha; L.Tha–R.Tha with nificant variables is shown in Figure 8. Only five positive correlations survived Bonferroni L.Tha–R.Ptm; and R.Ins–R.ACC with R.ACC–R.PCC), (ii) a negative correlation between correction for multiple comparisons (as represented by the sign +++), which include: (i) memory recall and motor impulsivity, and (iii) a positive correlation between non-planning three positive correlations between FC features (L.Cdt–R.Pal with L.Cdt–L.Tha; L.Tha– and motor impulsivity. Each of the signiﬁcant FC correlations had a common node, i.e., L.Cdt, L.Tha, and R.ACC, respectively. R.Tha with L.Tha–R.Ptm; and R.Ins–R.ACC with R.ACC–R.PCC), (ii) a negative correla- tion between memory recall and motor impulsivity, and (iii) a positive correlation be- tween non-planning and motor impulsivity. Each of the significant FC correlations had a common node, i.e., L.Cdt, L.Tha, and R.ACC, respectively. Behav. Sci. 2022, 12, x FOR PEER REVIEW 15 of 28 Behav. Sci. 2022, 12, 128 15 of 26 Figure 8. Correlation matrix showing associations among the top significant variables based on ex- Figure 8. Correlation matrix showing associations among the top signiﬁcant variables based on explorative (descriptive) correlational analysis for the interpretative purpose. Values within each cell plorative (descriptive) correlational analysis for the interpretative purpose. Values within each cell represent a bivariate Pearson correlation between the variable on its vertical axis and the variable represent a bivariate Pearson correlation between the variable on its vertical axis and the variable on its horizontal axis. Correlation coefﬁcients are color-coded (red/pink shades represent negative on its horizontal axis. Correlation coefficients are color-coded (red/pink shades represent negative r-values, blue/cyan shades indicate positive r-values, darker color represent higher magnitude) and r-values, blue/cyan shades indicate positive r-values, darker color represent higher magnitude) and signiﬁcant correlations (before Bonferroni correction) have been marked with asterisks in black font significant correlations (before Bonferroni correction) have been marked with asterisks in black font (* p < 0.05; ** p < 0.01; *** p < 0.001). The signiﬁcant correlations that survived Bonferroni correction (* p < 0.05; ** p < 0.01; *** p < 0.001). The significant correlations that survived Bonferroni correction have been marked with +++ sign in white font (+++ Signiﬁcant after Bonferroni correction). Refer to Table 2 for details about the ROI numbers (1–34) that are represented in the rsFC variable names. have been marked with +++ sign in white font (+++ Significant after Bonferroni correction). Refer to Acronyms: FC–Functional connectivity, TotCor_Fw–total number of correctly performed forward Table 2 for details about the ROI numbers (1–34) that are represented in the rsFC variable names. trials, BIS–Barratt Impulsiveness Scale, NP–Non-planning, MI–Motor Impulsivity. Acronyms: FC–Functional connectivity, TotCor_Fw–total number of correctly performed forward trials, BIS–Barratt Impu 3.3. lsiv Correness elations Sca between le, NP– Signiﬁcant Non-planning, MI–Motor Variables and Age Impulsivity. Since the age difference across the groups was statistically signiﬁcant (p < 0.001), the association of age with signiﬁcant predictor variables from the RF analysis was calculated 3.3. Correlations between Significant Variables and Age within each group using bivariate Pearson correlation and in the total sample using partial Since the age difference correlation adjusted across forthe g the grr oup oup effect s wa (Table s stat 4) is astic ana exploratory lly signif(descriptive) icant (p < 0. analysis. 001), the It was found that there was no signiﬁcant association of age with the top signiﬁcant variables association of age with significant predictor variables from the RF analysis was calculated in any of the groups or in the total sample, after correcting for multiple comparisons. within each group using bivariate Pearson correlation and in the total sample using partial However, a single FC variable (FC_6_7) representing the connectivity between the right correlation adjusted fo amygdala r the and group left hippocampus effect (Tab was le signiﬁcant 4) as an exp (r = 0.38; lora ptor = 0.0374), y (descrip but it tive did not ) asurvive nalysis. multiple testing correction. It was found that there was no significant association of age with the top significant vari- ables in any of the groups or in the total sample, after correcting for multiple comparisons. However, a single FC variable (FC_6_7) representing the connectivity between the right amygdala and left hippocampus was significant (r = 0.38; p = 0.0374), but it did not survive multiple testing correction. Table 4. Pearson bivariate correlations between the age of the participant and the top significant variables of the RF model. Correlation coefficient (r) and p-values (before Bonferroni correction) are provided for alcohol use disorder (AUD), control (CTL) group, and the total sample (ALL). None of the variables survived Bonferroni correction for multiple comparisons. Zero-order correlations were used for each group separately (N = 30) and partial correlations controlling for group effects were used for the all sample (N = 60). AUD (N = 30) CTL (N = 30) ALL (N = 60) Feature r p r p r p FC_2_19 (R.VTA–L.ACC) −0.08 0.6744 0.22 0.2449 0.03 0.8131 FC_3_23 (L.NAc–L.PCC) 0.16 0.3949 −0.21 0.2693 0.02 0.8956 FC_6_7 (R.Amg–L.Hip) −0.02 0.8993 0.38 0.0374 * 0.11 0.4225 FC_8_31 (R.Hip–L.DLP) 0.16 0.3977 0.27 0.1489 0.20 0.1276 Behav. Sci. 2022, 12, 128 16 of 26 Table 4. Pearson bivariate correlations between the age of the participant and the top signiﬁcant variables of the RF model. Correlation coefﬁcient (r) and p-values (before Bonferroni correction) are provided for alcohol use disorder (AUD), control (CTL) group, and the total sample (ALL). None of the variables survived Bonferroni correction for multiple comparisons. Zero-order correlations were used for each group separately (N = 30) and partial correlations controlling for group effects were used for the all sample (N = 60). AUD (N = 30) CTL (N = 30) ALL (N = 60) Feature r p r p r p FC_2_19 (R.VTA–L.ACC) 0.08 0.6744 0.22 0.2449 0.03 0.8131 FC_3_23 (L.NAc–L.PCC) 0.16 0.3949 0.21 0.2693 0.02 0.8956 FC_6_7 (R.Amg–L.Hip) 0.02 0.8993 0.38 0.0374 * 0.11 0.4225 FC_8_31 (R.Hip–L.DLP) 0.16 0.3977 0.27 0.1489 0.20 0.1276 FC_9_12 (L.Cdt–R.Pal) 0.16 0.3964 0.01 0.9583 0.09 0.5079 FC_9_13 (L.Cdt–L.Tha) 0.15 0.4430 0.05 0.7812 0.06 0.6349 FC_12_24 (R.Pal–R.PCC) 0.01 0.9579 0.09 0.6402 0.03 0.8298 FC_13_14 (L.Tha–R.Tha) 0.26 0.1616 0.02 0.9168 0.15 0.2588 FC_13_34 (L.Tha–R.Ptm) 0.07 0.7127 0.12 0.5196 0.09 0.5002 FC_16_20 (R.Ins–R.ACC) 0.19 0.3149 0.09 0.6203 0.07 0.6062 FC_20_24 (R.ACC–R.PCC) 0.05 0.7849 0.01 0.9574 0.03 0.8195 FC_20_26 (R.ACC–R.OFC) 0.19 0.3182 0.11 0.5460 0.16 0.2110 BIS_NP (Non-planning) 0.03 0.8936 0.21 0.2644 0.09 0.4815 BIS_MI (Motor Impulsivity) 0.23 0.2121 0.12 0.5432 0.20 0.1268 # § * p < 0.05 (before Bonferroni correction); Not signiﬁcant after Bonferroni correction; Based on partial correlation adjusted for group effect. Refer to Table 2 for the details of the ROIs in the FC variable. 3.4. Neuropsychological Scores between the Groups Since the rankings of neuropsychological variables varied widely across different parameters of the random forest classiﬁcation, these features were statistically compared across the participant groups using one-way analysis of variance (ANOVA) to determine the level of signiﬁcance (see Table 5). Only two variables from the visual span test (i.e., TotCor_Fw and Span_Fw) were signiﬁcant after Bonferroni corrections. The score “Tot- Cor_Fw” (total number of correctly performed forward trials), which showed the highest signiﬁcance level, was also identiﬁed by some of the parameters of the random forest as a variable contributing to group classiﬁcation. Table 5. Comparison of neuropsychological variables between AUD or CTL group using one- way ANOVA. AUD CTL Mean SD Mean SD ExcMovMade_All 15.04 17.02 7.83 6.66 4.43 0.0402 * AvgPicTime_All 3.09 1.09 2.81 0.96 1.02 0.3167 AvgTotTime_All 5.16 1.58 4.72 1.64 1.01 0.3199 TotTrlTime_All 482.60 178.18 404.24 139.05 3.29 0.0755 AvgTrlTime_All 22.98 8.48 19.25 6.62 3.29 0.0755 TotCor_Fw 7.00 2.58 10.21 2.78 19.06 0.0001 ++ TotCor_Bw 6.31 3.02 8.31 1.87 8.95 0.0042 * Span_Fw 5.44 1.33 6.83 1.36 14.25 0.0004 ++ Span_Bw 4.65 1.44 5.52 0.95 7.02 0.0106 * TotAvgTime_Fw 26.72 9.13 28.31 10.53 0.35 0.5591 TotAvgTime_Bw 17.72 9.39 17.79 10.01 0.00 0.9791 TotCorAvgTime_Fw 38.15 12.39 32.48 8.07 4.06 0.0490 * TotCorAvgTime_Bw 28.93 13.92 27.16 10.66 0.28 0.5963 * Signiﬁcant before Bonferroni correction; ++ Signiﬁcant after Bonferroni correction. Behav. Sci. 2022, 12, 128 17 of 26 4. Discussion The goal of the present study was to identify speciﬁc features from a set of multi- domain measures, including functional connectivity in the reward network, neuropsy- chological performance, and impulsivity, to classify individuals with AUD from healthy controls. The results showed that the random forest algorithm was highly successful in identifying the key features that contributed signiﬁcantly to differentiating AUD from CTL individuals. Relative to controls, AUD individuals manifested (i) alterations in functional connectivity across reward network regions (including ventral tegmental area, nucleus accumbens, anterior insula, anterior cingulate cortex, and other cortical and subcorti- cal structures), showing hypoconnectivity in nine connections and hyperconnectivity in three connections, (ii) increased impulsivity in motor and non-planning categories, and (iii) poorer neuropsychological performance, in terms of total number of correct trials in the forward sequence of the visual-spatial memory span test. In summary, relative to healthy controls, AUD individuals manifested aberrant functional connectivity in the reward network, increased impulsivity, and poor neuropsychological performance in visual–spatial memory. 4.1. Altered Functional Connectivity across Reward Network in AUD Individuals Addiction to drugs and alcohol involves a cascade of neuroadaptive processes, causing changes in the brain circuitries at different stages of addiction [18,89,90]. Our ﬁndings on resting state FC in the reward network indicate that AUD subjects manifested alterations in connectivity patterns, in terms of hypoconnectivity in nine connections and hyperconnec- tivity in three connections, involving 17 key reward structures [see Figure 7]. In particular, out of the nine reward network functional connections that showed hypoconnectivity, three were cortico–cortical connections (R.Ins–R.ACC, R.ACC–R.OFC, and R.ACC–R.PCC) in the right hemisphere, and ﬁve were subcortical–subcortical connections (R.Amg–L.Hip, L.Cdt–R.Pal, L.Tha–R.Tha, L.Cdt–L.Tha, and L.Tha–R.Ptm) involving both hemispheres, and a single inter-hemispheric subcortical-cortical connection (R.VTA–L.ACC). The three subnetworks that were hyperconnected in AUD individuals were subcortical–cortical con- nections, involving a left-hemispheric connection (L.NAc–L.PCC), a right-hemispheric connection (R.Pal–R.PCC), and an inter-hemispheric connection (R.Hip–L.DLP). These ﬁndings of altered brain connectivity in AUD individuals may be suggestive of neuroad- aptation in the hub regions of the reward network, caused by chronic alcohol intake. In general, previous fMRI studies have reported such aberrations in resting state connectivity underlying multiple brain networks in individuals with SUD [19,28,91], including those with AUD [30–33,63,92–94]. Our previous study on the same sample of participants as in the current study reported that AUD individuals manifested altered default mode net- work (DMN) connectivity compared to controls [63]. It is clear from the ﬁndings of our past and current rsFC studies that AUD individuals manifest brain connectivity changes across neural structures involved in spontaneous, self-referential thoughts, as elicited by the DMN [95], as well as the commonly reported reward processing deﬁcits, as elicited by the RN [10]. It is also remarkable to note that both studies showed hypoconnectivity between the ACC and PCC nodes in AUD subjects, conﬁrming the notion of abnormal self-referential processing in these individuals [92]. Furthermore, hypoconnectivity across reward structures during resting state, as found in the current study, may also indicate a vulnerability to relapse in individuals with a history of SUD [96]. Taken together, these ﬁndings lend support to the ﬁndings of the current study, that individuals with a long history of alcohol use continue to manifest network abnormalities across the reward regions, in addition to the previously reported aberrations in DMN, even after prolonged abstinence from alcohol consumption. The predominant pattern observed in AUD individuals was hypoconnectivity (9 out of 12 connections) across subcortical reward regions (5 connections) followed by cortical (3 connections) and cortical–subcortical (1 connection) subnetworks. Speciﬁcally, these hypoconnected nodes include key reward regions such as the VTA, amygdala, hippocam- Behav. Sci. 2022, 12, 128 18 of 26 pus, thalamus, pallidum, putamen, insula, ACC, PCC, and OFC, possibly indicating lower or less efﬁcient neural communication across these subnetworks [6]. Since addiction has been characterized as a reward deﬁciency syndrome [97], hypoconnectivity across these reward structures in abstinent AUD subjects may indicate reduced responsiveness to re- warding stimuli during resting state, possibly due to decreased dopamine function in these individuals [50,98]. Although elevated levels of dopamine in the dorsal striatum are associated with motivation to seek and consume alcohol and drugs, long-term substance use is associated with decreased dopaminergic function, as evidenced by reductions in D2 dopamine receptors and dopamine release in the striatum in addicted subjects [50], which can also lead to reduced activity in other cortical reward regions such as the orbitofrontal cortex and cingulate gyrus, resulting in loss of control and compulsive substance use. It may be worth noting that the majority of the hypoconnected regions in AUD (9 out of 13 regions) involved the right hemisphere, implicating hemispheric asymmetry in connec- tivity in AUD and, hence, their functional attributes [99]. For instance, laterality studies on motivation and emotions reported that the right hemisphere responds more to unpredicted, urgent, and novel environmental events, while the left hemisphere engages with routine and habitual behaviors [100]. Therefore, it is possible that the alterations in resting state brain connectivity across reward network seen in AUD individuals have more impact on right hemispheric function, including novelty seeking and impulsivity [101]. The other FC ﬁnding was that AUD individuals manifested hyperconnectivity in three connections across ﬁve brain regions, i.e., nucleus accumbens, pallidum, hippocam- pus, posterior cingulate, and dorsolateral prefrontal cortex, possibly suggesting excessive and/or less focused communication during resting state among these structures. While each of these key regions is associated with distinct and shared neurocognitive processes, hyperconnectivity across these nodes during resting state can be generally interpreted as excessive rumination about reward and preoccupation with reward-related imagery or inherent reward-seeking tendencies, such as craving. Similar to our ﬁnding, a higher FC between nucleus accumbens and posterior cingulate gyrus was observed in relapsers compared to abstainers of stimulant use [96]. In addition, akin to our ﬁnding, higher hippocampal–prefrontal connectivity has also been observed in internet gaming addic- tion [102]. Furthermore, recent evidence implicating the pallidum as an important structure in mesocorticolimbic reward processing [103], and, therefore, the increased connectivity between pallidum and PCC, may indicate reward related tendencies (e.g., drug seeking) in the resting state. However, despite the growing number of fMRI brain connectivity studies on AUD and other SUDs during resting state and task conditions [19,28,63,94,102,104], more studies are needed to understand the exact role of speciﬁc connectivity patterns across the reward network. 4.2. Heightened Impulsivity in AUD Individuals The ﬁndings of the current study also showed that motor and non-planning impul- sivity components were the topmost features contributing to the classiﬁcation of AUD individuals from controls. This ﬁnding reinforces the long-held notion that AUD and other externalizing traits are part of the externalizing spectrum disorders [105–109]. It is also known that AUD is associated with making impulsive choices during decision-making [16]. It is possible that the increased impulsivity manifested by AUD individuals may be due to altered FC across the frontal nodes [63] and/or relatively lower brain volumes of the frontal regions, as reported in our previous study [54]. Evidence from the imaging lit- erature strongly suggest that both structural and functional aspects of the frontal lobes contribute to increased impulsivity in AUD patients [110–113]. Furthermore, recent studies have also reported associations between impulsivity and resting state measures of EEG power [114], EEG-based FC [115], and fMRI-based FC [36], suggesting that speciﬁc brain networks may mediate aspects of impulsivity in AUD, as well as other externalizing dis- orders. Therefore, identifying and quantifying behavioral impulsivity may contribute to improving prevention and intervention programs related to alcohol and other substance Behav. Sci. 2022, 12, 128 19 of 26 use problems [116,117]. Although attentional impulsivity was not found to signiﬁcantly contribute to the classiﬁcation, our previous studies [63,85] found contributions from all three components of impulsivity, while motor and non-planning aspects were top of the key features of classiﬁcation, suggesting their relative importance in AUD pathology. 4.3. Poorer Memory Span in AUD Individuals In the current study, the neuropsychological score “TotCor_Fw” (i.e., total number of correctly performed forward trials) from the visual span test was also identiﬁed as one of the key variables contributing to the classiﬁcation of AUD individuals from controls by the RF model. On the other hand, parametric group comparison of neuropsychological variables (Table 5) revealed that the AUD group performed poorly in both “TotCor_Fw” and “Span_Fw” (i.e., span of recalled items during forward trials), compared to controls. Interestingly, these two variables tapping short-term memory performance were also found to be signiﬁcant in our previous classiﬁcation studies with the same sample of participants [63,85]. Furthermore, there is a strong literature support for memory deﬁcits in individuals with chronic AUD [118–121], and some of the deﬁcits linger even after prolonged abstinence [37]. It is also worth noting that in our previous structural MRI study on the same groups of subjects, we found that the AUD group showed lower volumes in prefrontal cortex and left hippocampus, which were associated with poorer visuospatial memory performance [54]. Furthermore, in another study on the same sample, we found that AUD subjects manifested hyperconnectivity across the parahippocampal hub regions [63], adding support to the current ﬁnding related to memory deﬁcits. On the other hand, it is surprising that none of the scores related to executive functioning in the TOLT were signiﬁcantly different from controls, possibly suggesting a partial or complete recovery of these functions in the AUD group due to abstinence. It is also possible that the current study failed to capture deﬁcits in additional domains, as the data involved in the current study were limited to only two tests and the sample size was only modest. Future studies may employ a comprehensive and sensitive battery of neuropsychological tests in a larger sample of abstinent AUD individuals, to map neuropsychological performance in multiple domains. 4.4. Correlations of Signiﬁcant Variables among Themselves and with Age Correlations among the signiﬁcant variables that were identiﬁed by the RF classiﬁca- tion model revealed three highly signiﬁcant positive associations among the FC features (L.Cdt–R.Pal with L.Cdt–L.Tha; L.Tha–R.Tha with L.Tha–R.Ptm; and R.Ins–R.ACC with R.ACC–R.PCC), and each of these signiﬁcant pairs had a common node, i.e., L.Cdt, L.Tha, and R.ACC, respectively. While it is expected that the pairs with a common node would correlate with each other, they are also known to be structurally connected. For instance, while the caudate nucleus connects the pallidum with radial ﬁbers [122], these basal ganglia structures have reciprocal connections with the thalamus and cortical regions and, thus, mediate cognitive and motor functions [123]. Similarly, while the ACC has both structural and functional connectivity with the PCC as part of the DMN [124], reciprocal interaction between the ACC and anterior insula serves as a major connection with the salience and reward network [125]. Interestingly, the negative correlation between memory performance (working memory) and impulsivity observed in our study was also previously reported by other studies on individuals with substance use disorders [126,127]. Lastly, as expected, non-planning and motor impulsivity were positively correlated with each other [128], and both of these dimensions were shown to be associated with craving and relapse among alcohol-dependent males [129]. On the other hand, none of the correlations between the top predictor variables and age sustained a statistical signiﬁcance after multiple testing corrections, suggesting that age did not impact the classiﬁcation of groups based on these top predictors. However, it is suggested that future studies may conﬁrm these preliminary ﬁndings using a large sample of subjects involving both males and females matched on multiple characteristics, such as education, ethnicity, premorbid IQ, etc. Behav. Sci. 2022, 12, 128 20 of 26 4.5. Limitations of the Current Study The current study has several limitations. (i) The sample consisted of only males, and the ﬁndings may not be generalizable to females. In general, ﬁndings from prediction-based studies will be more useful when they are generalizable to different strata of the population from which the sample is drawn. Therefore, we suggest that future predictive studies aim for samples from both genders. (ii) The outcome groups (AUD and CTL) were not matched for age, as the age difference was statistically signiﬁcant. Although age was not signiﬁcantly associated with the key features, either in each group or in the total sample, the results would have been more credible if the groups were matched for age. Therefore, future studies should also aim to apply predictive models on age-matched groups, to avoid confounding the results. (iii) Finally, the sample size for a predictive model was rather small, although the random forest algorithm is known to handle such situations more effectively than other machine learning models. Therefore, similar studies with larger sample sizes are needed to conﬁrm the ﬁndings of the present study. A larger sample is also warranted to explore associations among the features from multiple domains, since the current study did not identify potential associations among the features, possibly due to a lack of statistical power. Given these limitations, it is to be noted that the obtained results are only preliminary, while the ﬁndings of the present study might help to design future studies avoiding or mitigating these limitations. 5. Summary and Conclusions The ﬁndings of the current study suggest that multidomain features drawn from the measures of brain connectivity, impulsivity, and neuropsychological tests can be success- fully used in a machine learning framework to classify AUD individuals from healthy controls. In summary, our study revealed that the abstinent individuals with past AUD showed impaired brain connectivity across speciﬁc reward regions and also manifested relatively increased impulsivity and poor memory function. Evidence from the litera- ture suggests that these anomalies may have been caused by neuroadaptation due to chronic drinking. Since treatment interventions intended to reverse these neuroadaptations show promise as potential therapeutic approaches for addiction [1], the ﬁndings of the current study may have important clinical implications. We suggest that future studies should further characterize these neural and behavioral abnormalities at multiple levels and groups, such as gender, race/ethnicity, educational attainment, socio-economic sta- tus, genomic liability, and drinking patterns, so that the ﬁndings may contribute towards personalized medicine. Supplementary Materials: The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/bs12050128/s1. Author Contributions: Conceptualization: C.K., B.P., B.A.A., A.K.P.; Methodology: C.K., B.P., B.A.A., A.K.P., S.K., D.B.C., A.T.S.; Data Collection: A.T.S., B.A.A., E.B.; Data Curation: B.A.A., C.K.; Formal Analysis: C.K., B.A.A., J.Z.; Manuscript Preparation: C.K., B.A.A., B.P.; Review and Editing: B.P., B.A.A., A.K.P., S.K., G.P., J.L.M., D.B.C., W.K., A.T.S.; Funding Acquisition: B.P. All authors have read and agreed to the published version of the manuscript. Funding: This research work was funded by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) of the National Institutes of Health (NIH) through the grants (R01 AA002686 to Bernice Porjesz (PI) and R01 AA028848 to Bernice Porjesz (PI), Chella Kamarajan (MPI), Jacquelyn Meyers (MPI), Ashwini Pandey (MPI)). Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Boards of SUNY Downstate Health Sciences University and Nathan Kline Institute (IRB approval ID: SUNY–266893; NKI–212263). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data presented in this study are available in Supplementary Material. Behav. Sci. 2022, 12, 128 21 of 26 Acknowledgments: In memory of Henri Begleiter, founder and longtime mentor of the Neurody- namics Laboratory, we acknowledge with great admiration his seminal scientiﬁc contributions to the ﬁeld. We are sincerely indebted to his charismatic leadership and luminous guidance, truly inspired by his scientiﬁc mission and vision, and highly motivated to carry forward the work he fondly cherished. We are grateful for the valuable technical assistance of Carlene Haynes, Joyce Alonzia, Chamion Thomas, Alec Musial, and Kristina Horne. Conﬂicts of Interest: The authors declare no conﬂict of interest. Appendix A Table A1. List of features (F) included in the random forest classiﬁcation model: (i) 21 reward network connections identiﬁed by the feature selection process (F1–F21), 13 neuropsychological scores involving 5 TOLT scores (F22–F26) and 8 VST scores (F27–F34), and 3 BIS scores (F35–F37). F# Feature Detail 1. FC_2_19 (R.VTA–L.ACC) FC between R. Ventral Tegmental Area and L. Anterior Cingulate Cortex 2. FC_3_23 (L.NAc–L.PCC) FC between L. Nucleus Accumbens and L. Posterior Cingulate Cortex 3. FC_6_11 (R.Amg–L.Pal) FC between R. Amygdala and L. Pallidum 4. FC_6_7 (R.Amg–L.Hip) FC between R. Amygdala and L. Hippocampus 5. FC_8_31 (R.Hip–L.DLP) FC between R. Hippocampus and L. Dorsolateral Prefrontal Cortex 6. FC_9_12 (L.Cdt–R.Pal) FC between L. Caudate and R. Pallidum 7. FC_9_13 (L.Cdt–L.Tha) FC between L. Caudate and L. Thalamus 8. FC_9_18 (L.Cdt–R.PHG) FC between L. Caudate and R. Parahippocampal Gyrus 9. FC_9_23 (L.Cdt–L.PCC) FC between L. Caudate and L. Posterior Cingulate Cortex 10. FC_9_27 (L.Cdt–L.Ang) FC between L. Caudate and L. Angular Gyrus 11. FC_12_24 (R.Pal–R.PCC) FC between R. Pallidum and R. Posterior Cingulate Cortex 12. FC_13_14 (L.Tha–R.Tha) FC between L. Thalamus and R. Thalamus 13. FC_13_34 (L.Tha–R.Ptm) FC between L. Thalamus and R. Putamen 14. FC_16_20 (R.Ins–R.ACC) FC between R. Insula and R. Anterior Cingulate Cortex 15. FC_17_31 (L.PHG–L.DLP) FC between L. Parahippocampal Gyrus and L. Dorsolateral Prefrontal Cortex 16. FC_20_24 (R.ACC–R.PCC) FC between R. Anterior Cingulate Cortex and R. Posterior Cingulate Cortex 17. FC_20_26 (R.ACC–R.OFC) FC between R. Anterior Cingulate Cortex and R. Orbitofrontal Cortex 18. FC_20_31 (R.ACC–L.DLP) FC between R. Anterior Cingulate Cortex and L. Dorsolateral Prefrontal Cortex 19. FC_21_24 (L.MCC–R.PCC) FC between L. Middle Cingulate Cortex and R. Posterior Cingulate Cortex 20. FC_22_33 (R.MCC–L.Ptm) FC between R. Middle Cingulate Cortex and L. Putamen 21. FC_28_29 (L.Ang–L.SPL) FC between L. Angular Gyrus and L. Superior Parietal Lobule 22. ExcMovMade_All Overall excess moves beyond the minimum moves required to solve the puzzle 23. AvgPicTime_All Overall average pickup time to solve the puzzle 24. AvgTotTime_All Overall average total time to solve the puzzle 25. TotTrlTime_All Overall total trial time within each puzzle type 26. AvgTrlTime_All Overall average trial time across trials per puzzle type 27. TotCor_Fw Total number of correctly performed trials in forward sequence 28. TotCor_Bw Total number of correctly performed trials in backward sequence 29. Span_Fw Maximum span or sequence-length achieved in forward sequence 30. Span_Bw Maximum span or sequence-length achieved in backward sequence Behav. Sci. 2022, 12, 128 22 of 26 Table A1. Cont. F# Feature Detail 31. TotAvgTime_Fw Total average time taken across all trials performed in forward sequence 32. TotAvgTime_Bw Total average time taken across all trials performed in backward sequence 33. TotCorAvgTime_Fw Total correct average time taken across all correct trials in forward sequence 34. TotCorAvgTime_Bw Total correct average time taken across all correct trials in backward sequence 35. BIS_AI Barratt Impulsiveness Scale, Attentional Impulsivity Score 36. BIS_MI Barratt Impulsiveness Scale, Motor Impulsivity Score 37. BIS_NP Barratt Impulsiveness Scale, Non-planning Impulsivity Score References 1. Volkow, N.D.; Michaelides, M.; Baler, R. The Neuroscience of Drug Reward and Addiction. Physiol. Rev. 2019, 99, 2115–2140. [CrossRef] [PubMed] 2. Volkow, N.D.; Morales, M. The Brain on Drugs: From Reward to Addiction. Cell 2015, 162, 712–725. [CrossRef] [PubMed] 3. Luijten, M.; Schellekens, A.F.; Kuhn, S.; Machielse, M.W.; Sescousse, G. 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Journal

Behavioral Sciences – Multidisciplinary Digital Publishing Institute

Published: Apr 28, 2022

Keywords: alcohol use disorder (AUD); random forests (RF); resting-state functional connectivity (FC); reward network (RN); functional MRI (fMRI); neuropsychological tests; impulsivity

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Differentiating Individuals with and without Alcohol Use Disorder Using Resting-State fMRI Functional Connectivity of Reward Network, Neuropsychological Performance, and Impulsivity Measures

Differentiating Individuals with and without Alcohol Use Disorder Using Resting-State fMRI Functional Connectivity of Reward Network, Neuropsychological Performance, and Impulsivity Measures

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Differentiating Individuals with and without Alcohol Use Disorder Using Resting-State fMRI Functional Connectivity of Reward Network, Neuropsychological Performance, and Impulsivity Measures

Differentiating Individuals with and without Alcohol Use Disorder Using Resting-State fMRI Functional Connectivity of Reward Network, Neuropsychological Performance, and Impulsivity Measures

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