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fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases

fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases Significance: Clinical use of fNIRS-derived features has always suffered low sensitivity and specificity due to signal contamination from background systemic physiological fluctuations. We provide an algorithm to extract cognition-related features by eliminating the effect of back- ground signal contamination, hence improving the classification accuracy. Aim: The aim in this study is to investigate the classification accuracy of an fNIRS-derived biomarker based on global efficiency (GE). To this end, fNIRS data were collected during a computerized Stroop task from healthy controls and patients with migraine, obsessive compul- sive disorder, and schizophrenia. Approach: Functional connectivity (FC) maps were computed from [HbO] time series data for neutral (N), congruent (C), and incongruent (I) stimuli using the partial correlation approach. Reconstruction of FC matrices with optimal choice of principal components yielded two inde- pendent networks: cognitive mode network (CM) and default mode network (DM). Results: GE values computed for each FC matrix after applying principal component analysis (PCA) yielded strong statistical significance leading to a higher specificity and accuracy. A new index, neurocognitive ratio (NCR), was computed by multiplying the cognitive quotients (CQ) and ratio of GE of CM to GE of DM. When mean values of NCR (NCR) over all stimuli were computed, they showed high sensitivity (100%), specificity (95.5%), and accuracy (96.3%) for all subjects groups. Conclusions: NCR can reliable be used as a biomarker to improve the classification of healthy to neuropsychiatric patients. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original pub- lication, including its DOI. [DOI: 10.1117/1.NPh.8.3.035008] Keywords: fNIRS; neuropsychiatric diseases; functional connectivity; global efficiency; cog- nitive quotient; neurocognitive ratio. Paper 21020R received May 10, 2021; accepted for publication Sep. 16, 2021; published online Sep. 30, 2021. 1 Introduction Although fNIRS has been around over 30 years now, its clinical efficacy and role are still being questioned due to its low specificity and sensitivity, especially in the area of neuropsychiatric diseases. Many researchers have been trying to improve its efficacy, sensitivity, and specificity in clinical settings by either improving its technology or the post processing analysis methods. Over the last 20 years, the richness of fNIRS data due to its ease and speed of data collection, non- invasiveness and access to local activity have become even more attractive to cognitive neuro- scientists in testing multitude of data processing and neuroscientific hypotheses. Strangely 1–4 though, the brain does not work locally. fNIRSians have long been in search of a killer application that would secure the place of fNIRS in clinical settings. To this end, fNIRS have been applied to subjects of all ages and health 5–8 conditions. Discussions regarding the limitations and ways to overcome these are a few yet *Address all correspondence to Ata Akn, ata.akin@acibadem.edu.tr Neurophotonics 035008-1 Jul–Sep 2021 Vol. 8(3) Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases they all have helped us redirect our efforts in proposing ever more innovative solutions. There are 5,7,9–14 good reviews on the promise of fNIRS in neuropsychiatry. So far, fNIRS researchers have focused more on the data analytics side than developing of novel technologies. We have enjoyed the availability of various fNIRS systems but at the cost of standardization of probe designs, data collection methodologies, and even more on the data analytics. The lack of standardization on these issues has made it increasingly more difficult to compare the findings from different studies. Moreover, physics of photon migration through the layers of the head limits the specificity of the fNIRS device to cortical layers. Collected data become an amalgam of physiological activity from each layer the photon interacts with. Hence the data are known to be contaminated with background systemic physiological fluctuations that 16,17 are undoubtedly correlated with the cognitive activity. The low specificity of the CW-fNIRS devices can be overcome with time resolved systems albeit at a greater cost and complications of data collection. Many novel data analysis methods have been proposed to extract the brain 18–20 originated, task related data from the collected data. Still there is no consensus on how to approach the fNIRS data, leading to the unsettling yet quite accurate prediction of Drs. Quaresima and Ferrari: “The prediction of the future directions of fNIRS for assessing brain function during human behavior in natural and social situations is not easy.” It is, hence, only logical to propose an analysis method (a pipeline of data analysis) that would avoid the pitfalls of the standardization issues faced in fNIRS signal processing field. The following Table 1 is a list of minimum hardware, data collection, and analysis recommendations for fNIRS-based cogni- tive research that are derived from experience and literature: This study proposes an improved post-processing approach to data obtained from fNIRS recordings over our previous paper. The sole aim was to converge on a data analysis pipeline that will be accepted and adapted easily by fellow fNIRSians. The proposed algorithm should have a common denominator, a base for anyone to build upon. The algorithm aims to improve the statistical significance of the fNIRS findings, and hence, the trust on the system. The major aim is to boost the statistical significance of the GE values; hence, the accu- racy of classification of fNIRS findings in a set clinical data obtained in our group’s previous studies. Table 1 Recommendations for fNIRS based research. Hardware requirements � Number of channels ≥8 � Collect data from both hemispheres � Sampling rate ≥0.5 Hz per channel � Number of wavelengths ≥2 Task requirements � Duration of collected data ≥10 min � Block stimulus with block duration ≥20 s � Stimulus type ≥2 � Resting data ≈30 s Analysis requirements � Prefer [HbO] data � Avoid single channel data analysis � Prefer a dimensionless analysis (i.e., normalization of data) � Prefer a multichannel analysis (i.e., FC) � Prefer a metric that summarizes and captures the behavior of multichannel data (i.e., FC strength such as GE.) Neurophotonics 035008-2 Jul–Sep 2021 Vol. 8(3) Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases 2 Methods and Materials 2.1 Subjects and Experimental Procedure About 13 healthy subjects (six female) at an average age of 26, 20 patients with migraine without aura (12 female) at an average age of 27, 26 patients with obsessive compulsive disorder (11 female) at an average age of 29, and 21 schizophrenia patients (10 female) at an average age of 28 participated in this study. The study protocol was approved by the Ethics Committee of Pamukkale University in 2008. Parts of these data were published by our group and 22–30 coworkers. Consents were obtained from all subjects and they were all informed about the study before the experiment. Subjects were seated in a dimly illuminated insulated room and they were told to look at a computer screen placed in front of them. Subjects responded to the computerized color word matching Stroop task that involved three sets of stimuli: neutral (N), congruent (C), and incongruent (I) stimuli. The task involved 15 N, 15 C, and 15 I stimuli presented in blocks of five sequential stimuli. The inter stimulus interval was 4 s. The rest between each block was 20 s. The stimuli blocks were randomized for each subject. The subject was asked to respond with left or right mouse click depending on whether the stimulus was a match or not. The task started with a 30 s of rest and ended with a 30 s 24,31 of rest. 2.2 fNIRS Equipment The fNIRS system (NIROXCOPE 301) was developed at the Neuro-Optical Imaging Laboratory 23,30 of Bogazici University. NIROXCOPE 301 has a sampling frequency of 1.77 Hz, and it con- sists of a data acquisition unit, a data collecting computer, and a flexible probe to place on the forehead of the subjects. The probe has a rectangular design housing four dual wavelength light- emitting diodes (LED) emitting at 730 and 850 nm. Each LED (L , i ¼ 1::: 4) is surrounded by four detectors (D , i ¼ 1::: 10) placed 2.5 cm away from the center of an LED as seen in Fig. 1. A channel is a pair of LED and detector that surrounds that LED. Since several of the in-between detectors are shared, there are 16 channels (C i ¼ 1::: 16). Since the received light intensity is inversely proportional to the square of the distance between a source and a detector, data from long range channel pairs were not collected (i.e., between L and D , D ,or D ). 1 5 7 9 The validity of this probe design and its ability to detect brain tissue were discussed in our 18 21,24–26,30,32 previous study as well as its efficacy in providing cognition-related signals. 2.3 Analysis of the fNIRS Data fNIRS data are known to be contaminated with systemic background fluctuations. So before attempting to generate connectivity matrices from pair-wise correlations, one should try to minimize the effect of this background fluctuation so that the effect of this dominant signal is eliminated. Assuming that any correlation between two channels will be dominated by this Fig. 1 Rectangular probe geometry of the fNIRS NIROXCOPE 301. L are the LEDs; D , the i i detectors; and C , the channels. Neurophotonics 035008-3 Jul–Sep 2021 Vol. 8(3) Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases Fig. 2 Block diagram of the NCR algorithm (abbreviations are given in the text). common background signal, our previous study aimed to show that a partial correlation (PC)-based analysis will yield a less biased insight into the underlying connectivity due to task. In that paper, an outline of the signal processing steps were given in detail. As a quick sum- mary, a signal processing pipeline was developed to compute the FC matrices (FC) from [HbO] signals by using a PC method, rather than the conventional pearson correlation analysis. Then these matrices were used to compute the GE values. In this paper, an additional step in between the FC matrices and GE computation is proposed by employing the principal component analy- sis (PCA). As a last step, a new biomarker as a function of behavioral and fNIRS deriven features: neurocognitive ratio (NCR). The details of the derivation and computation of this biomarker is explained in Sec. 2.4 and the block diagram of the algorithm is shown in Fig. 2. This paper will present the results of this PCA-based FC analysis, hence called the FC-(PC)2: FC analysis via PCA based PC. 2.3.1 Preparation of fNIRS data for FC analysis FC is a method where correlations from time series data are used to create a matrix called the functional connectivity matrix (FC). The matrix is a N × N matrix, where N is the number of channels for fNIRS (number of voxels for fMRI). In this study, N ¼ 16. Since the study protocol involved the Stroop task with three types of stimuli, it was reasonable to create 3FCs. fNIRS data that will be fed into FC calculations were prepared in the following steps to generate these matrices: 1. Locate the stimuli blocks in fNIRS time signal for each subject [i.e., Fig. 3(a)] 2. Create three concatenated stimulus time series for each channel [i.e., Figs. 3(b) and 3(d)] 3. Generate the regressor to be used in PC calculations as explained in Sec. 2.3.2, [i.e., Fig. 3(e)]. 2.3.2 Functional connectivity via PC PC provides a cleaner (or less biased) relationship between two variables after removing a common effect present in both of the variables. The PC coefficient (r ) between any two chan- i;jjk nels ði; jÞ in the presence of a common influencer (k) is computed as follows: r − r r i;j i;k j;k EQ-TARGET;temp:intralink-;e001;116;190r ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiqffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi . (1) i;jjk 2 2 ð1 − r Þ ð1 − r Þ i;k j;k [HbO] data from each channel are passed through a high pass filter (butterworth, eigth order, cutoff at f ¼ 0.09 Hz, stop-band at f ¼ 0.1 Hz) to obtain the HBO . The regressor used in c s H PC-based FC analysis is obtained by averaging this signal over all the channels. Hence HBO ¼ HBO is used to regress out the systemic physiological affects from the correlation of the i H unprocessed [HbO] signals from two channels. Once the regressor HBO is computed, indi- vidual regressors for N, C, and I stimuli are generated similar to the concatenation explained Neurophotonics 035008-4 Jul–Sep 2021 Vol. 8(3) Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases Sample Data from Sub=1, ch=12 Data N C I I1 N1 C1 C2 I2 I3 C3 N2 I4 N3 C4 I5 C5 N4 N5 200 400 600 800 1000 1200 (a) 50 100 150 200 (b) 1.5 0.5 50 100 150 200 (c) -1 50 100 150 200 (d) 0.5 -0.5 200 400 600 800 1000 1200 Sample Point Fig. 3 Preparation steps shown on a sample [HbO] data. (a) Raw and unprocessed [HbO] data of subject 3 (control), from channel 12, where the patched regions designate the stimuli blocks. (b) Concatenated [HbO] data for N stimulus where segments of N data (N , i ¼ 1::: 5) from the original data are concatenated sequentially to create the [HbO] (N) time series data, (c) Concatenated [HbO] (C) time series data for C stimulus, (d) Concatenated [HbO] (I) time series data for I stimulus, and (e) the regressor computed as explained in Sec. 2.3.2. in Fig. 3. Then these regressors are used in the computations of the PC coefficients as entries to the FC. This analysis is performed for each subject. At the end of this part of this analysis, 3FCs are generated from each subject’s fNIRS. The FC matrices computed for stimulus based time series are thus termed FC , FC , and FC . N C I 2.3.3 Functional connectivity via PCA based PC In a review by Du et al., it is claimed that statistical significance of the FC derived features (i.e., GE) can be improved by adding PCA after the FCs are calculated. Once an FC was generated, PCA was applied to the it. Since the matrices were 16 × 16, there were 16 PCs. The assumption in applying PCA to FC is the following: CM DM EQ-TARGET;temp:intralink-;e002;116;206FC ¼ FC þ FC ;i ¼ N; C; I; (2) i i DM CM where FC is the FC matrix of the default mode network, and FC is the matrix for the i i cognitive mode network. Since these two matrices can be assumed to be linearly independent, PCA can be applied to separate them into their independent parts. The choice of PCs turned out CM to affect the statistical significance of the GE values computed after new FCs were recon- structed from the chosen PCs. The expectation is the convergence to a subset of PCs that will CM yield the strongest significance for GE (as explained in Sec. 2.3.4) while no significance for 35,36 DM the GE . A combinatorial search analysis was performed to find the best principal com- CM ponents to reconstruct the new FC . The choice of how many principal components to be used was based mostly on the strongest PCA eigenvalues. However, components with lower Neurophotonics 035008-5 Jul–Sep 2021 Vol. 8(3) HBO (R) HBO (C) HBO (I) HBO (N) HBO (raw) Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases strengths were also included in some cases. Once the best PC subset was found, this subset was CM DM used to resonstruct the FC . Remaining PCs were then used to reconstruct the FC , i i DM i ¼ N; C; I. The expectation from these FC matrices is such that the t-statistics of the DM GE , i ¼ N; C; I will be low (no statistical significance). So the algorithm works as an opti- mization approach where the goal was to maximize the t-statistics (minimize the p value) of the CM GE , i ¼ N; C; I. 2.3.4 Global efficiency Global efficiency (GE) is one of the many metrics of graph-based network analysis and has been 21,37–39 used in brain connectivity studies. This approach is intrinsic to cognitive neuroscience 40–42 where the aim is to investigate the neural correlates of cognition. Several groups, including ours have reported that GE can be reliably used as a metric to quantify the information sharing CM;DM efficiency of the FC s. In this analysis, channels can be considered as a set of vertices V and the PC coefficients as assigned weights on the set of edges E, between vertices to construct 43–45 an undirected complete weighted graph G ¼ðV; EÞ. GE can be evaluated for a wide range of networks, including weighted graphs. Maximal possible GE occurs when all edges are present in the network. The GE value was computed by using the formulation of Latora and Marchiori’s: 1 1 EQ-TARGET;temp:intralink-;e003;116;503GE ¼ ; (3) NðN − 1Þ d ij i≠j∈G where d is defined as the smallest sum of the physical distances throughout all the possible ij paths in the graph from i to j. This equation requires the use of binary matrix entries. So, since there was always some sort of a connection between channels (the entries were never 0) a thresh- old had to be used to eliminate very low connections. So choosing an appropriate threshold value was necessary to convert the FCs to binary matrices: 1if jFCði; jÞj > Θ EQ-TARGET;temp:intralink-;e004;116;395FCði; jÞ¼ ; (4) 0 otherwise where FC is a binarized matrix after a hard thresholding at the value Θ is applied to the jFCj. Here, Θ is not an actual correlation value, rather the number of highest correlation values to be kept in the matrix. It is worth noting that absolute values of the FC were used in this equation. This threshold value determines the number of non-zero nodes to be kept in the binarized matrix, which in turn effects the computation of the GE values. In this analysis, two specific Θ node values were determined iteratively for each subject CM group (see Table 2 for group dependent Θ values), one for CM (Θ ) and one for DM DM (Θ ). A review on the choice of such a threshold (Θ) yielded a value of the highest 10% to 20% of all the entries in the jFCjs. A sweep of the best group-wise Θ that provided the highest CM statistical significance (lowest p value as shown in Table 2) for GE s for three types of stimuli CM DM yielded specific Θ values for different subject groups. In contrast, Θ was chosen for the CM Table 2 PCA components for subject groups that yielded the best p value for GE . CM DM Group CM DM Θ Θ p value Controls ½1 ··· 9 Remaining 43 32 0.033 Migraine [2, 3, 6, 7, 9, 16] Remaining 40 28 0.0017 OCD ½1 ··· 12;14; 16 Remaining 38 48 0.02 Schizo ½1 ··· 5;6 ··· 9;10; 12 ··· 16 Remaining 27 41 0.048 Neurophotonics 035008-6 Jul–Sep 2021 Vol. 8(3) Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases highest p value for three stimuli types. The computation of the GE values by Eq. (3) was per- formed by the efficiency.m code from the Brain Connectivity Toolbox. 2.4 Neurocognitive Ratio Cognitive quotient (CQ) can be considered as a measure of level of cognitive effort exerted to fulfill a task. There are several indices, quotients, and metrics proposed to assess this effort. 47–49 Usually these are in the form of combinations of various neuropsychological task scores and sometimes in the form of physiological parameters. Cognitive load is a similar concept and many physiological measures have also been proposed to quantify this effort. The assumption for using physiological measures to quantify cognitive load is that brain, just like a muscle, has to execute some sort of a physiological activity (preferably measurable) for a specific cognitive task. So, the holy grail of neuroscience is to find this link between neuro- physiological activity and psychological activity, also called the neurobiological basis of 12,53 behavior. Hence, researchers have defined a new concept, “neural efficiency,” to quantify the level of efficiency of collaborative effort of the brain in solving a difficult cognitive task (for a complete review see Ref. 54). Neural efficiency can be computed from physiological parameters such as heart rate variability, EEG measurements, and fMRI recordings, and recently 55–57 from fNIRS findings. Researchers preferred to find a relationship between the neuropsycho- logical data and neurophysiological data mostly in terms of correlation coefficients or regression analysis. The equation eventually obtained transforms one finding to another one, consequently assumes a causal relationship. Borrowing an idea from neurophilosophy, one can impose the duality principle in brain’s operations where an independent relation between the brain and mind can be the source of cog- nition. Hence, one can simply propose a metric (an index) that combines these two so-called independent measures; namely, the behavioral findings with physiological findings in assessing the neurocognitive effort. Here, a new combined metric is proposed, by which NCR is defined as follows: EQ-TARGET;temp:intralink-;e005;116;401CQ ¼ ACC ∕RT ; (5) i i i CM DM EQ-TARGET;temp:intralink-;e006;116;359R ¼ GE ∕GE ; (6) i i EQ-TARGET;temp:intralink-;e007;116;336NCR ¼ CQ × R ; (7) i i i where i is the stimulus type, ACC is the accuracy in percentage, and RT is the reaction (response) time in seconds. CQ is defined for each stimulus type. NCR , as calculated from Eq. (7), can be assumed to be a biomarker specific for each subject group (i.e., healthy controls, patients with migraine, OCD, or schizophrenia disorder). The underlying assumption in proposing this index as a biomarker is that the GE of a default mode network should be different than the GE com- CM DM puted during a cognitive task (GE ≠ GE ) and that the ratio of the two [R calculated as in Eq. (6)] can be considered as an objective indicator of attention and inhibition control. A rea- sonable expectation would be that R ≥ 1 for healthy subjects. In fact, one can even hypothesize that an increased demand for inhibitory control can be associated with restructuring of the global network into a configuration that must be more optimized for specialized processing (functional segregation), more efficient at communicating the output of such processing across the network (functional integration), and more resilient to potential interruption (resilience). Thus, investi- gation of graph theoretical metrics under varying levels of inhibitory control can provide 53,58,59 clinicians with a quantitative and objective metric in their clinical decision processes. 3 Results 3.1 Behavioral Results The reaction times and accuracy rates for the subjects for all stimuli types are given in Figs. 4(a) and 4(b). Reaction times are calculated by averaging the response times to all the responses, not just the correct answers. Neurophotonics 035008-7 Jul–Sep 2021 Vol. 8(3) Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases Reaction times wrt stimuli, p=2.8509e-22 Accuracy rates wrt stimuli, p=0.00014314 2.5 Neutral 8.2e-05 Congruent 0.03 0.082 0.00076 6.6e-06 Incongruent 0.006 Mean 0.075 1.5 0.038 Neutral Congruent Incongruent Mean 0.5 0 0 Controls Migraine OCD Schizo Controls Migraine OCD Schizo (a) (b) Stroop interference effect CQ values wrt stimuli, p=6.7782e-23 0.7 Neutra Controls 120 0.015 Congruent Migraine 0.6 Incongruent OCD Mean Schizo 100 0.034 0.5 0.0024 80 5.5e-06 0.4 0.051 0.0066 0.3 0.2 0.1 0 0 I-N I-C Controls Migraine OCD Schizo (c) (d) Fig. 4 (a) RT values at the columns of Table 3 for all subjects across stimuli. p values are pre- sented on top of bars. (b) ACC values at the columns of Table 4 for all subjects across stimuli. p values are presented on top of bars; (c) Stroop interference effect in reaction times; (d) CQ values computed by Eq. (5). Error bars represent the standard deviations. All values in these graphs are given in Tables 3–5. Two-way ANOVA for RT values yielded significant values for subject comparison −6 −6 (p ≪ 10 ), and stimulus type (p ≪ 10 ) and no interaction for SUB⋆STIM (p ¼ 0.749). Accuracy was calculated by taking the ratio of total number correct answers to total number of questions. Two-way ANOVA for the ACC yielded significant values for subject comparison −6 −6 (p ≪ 10 ), and stimulus type (p ≪ 10 ) and no interaction for SUB⋆STIM (p ¼ 0.1588). CQ with respect to subjects and stimuli can be seen in Fig. 4(d). Two-way ANOVA for CQ −6 −6 yielded significant values for subject comparison (p ≪ 10 ), and stimulus type (p ≪ 10 ) and no interaction for SUB⋆STIM (p ¼ 0.9958). CQ can also be considered as a metric of cognitive load. In several studies, such scores from different tests are linearly combined (sometimes with weights) to provide a stronger metric. 3.2 fNIRS Data Analysis 3.2.1 GE analysis CM;DM CM;DM The computation of GE from the FC matrices yielded quite interesting dynamics as CM shown in Figs. 5(a) and 5(b). First, PCA and Θ optimized GE values showed a strong sta- tistically significant difference within subjects [see the p values displayed on top of the bars of Fig. 5(a)] with the optimal choice of principal components and Θ values given in Table 2. This is expected since the optimization for the PCA components and Θ is supposed to lead to Neurophotonics 035008-8 Jul–Sep 2021 Vol. 8(3) RT [sec] RT [sec] CQ Accuracy [%] Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases GE values for CMN and DMN wrt Stimuli Mean GE values for CM and DM wrt Groups 0.45 0.4 CM CM CM DM 0.033 0.4 CM 0.35 2.3 DM DM 0.35 0.0017 0.02 C 0.3 1.2 0.96 DM 0.3 0.25 0.81 0.25 0.048 0.2 0.2 0.15 0.15 0.1 0.1 0.05 0.05 0 0 Controls Migraine OCD Schizo Controls Migraine OCD Schizo (a) (b) CM;DM Fig. 5 (a) GE values for all subjects across three types of stimuli. p values are presented on top of bars, the error bars are the standard deviations and are also given in Tables 6 and 7. CM;DM (b) Mean of GE values for all stimuli with respect to subject group. The numbers above CM DM the bars are the ratios (R ¼ GE ∕GE ) as explained in Sec. 2.4. The error bars are the within group standard deviations. CM CM statistically significant different GE values for each subject group. Group averaged GE values were higher for controls than for the patients [see Fig. 5(b)] although different optimized parameters were used for each subject group, whereas no significant difference was observed for DM any of the subject-wise GE values (p> 0.05). CM DM Second, both the GE and GE values were observed to be different between subject CM groups [as more evident from the means graph in Fig. 5(b)]. As the GE value decreases from DM healthy controls to schizophrenics, the GE increases. This is somewhat expected since the CM threshold node value for CM (Θ ) that yielded strong significance ended up being lower in DM patients. Vice versa, the threshold node value for DM (Θ ) were higher for patients than con- CM trols in most of the cases. Higher value of GE in controls over patients could mean that a healthy brain recruits a wider brain circuitry with more efficiency during a cognitive task, DM whereas diseased brain cannot. In contrast, higher values of GE for diseased population might be due to a domination of the DM leading to a lesser space for CM; hence, the poor performance CM DM on cognition related activities. Figure 5(b) shows the ratio of mean of GE ∕GE for each subject group. The ratio is in favor of healthy controls and significantly lower in diseased groups. CM;DM A note of caution is that these values of GE depend heavily on the choice of optimal CM;DM principal components and threshold values when computing the FC matrices. Hence, many iterations and heuristic reasoning were employed to find the best GE values that would yield the highest statistical significance for GE. An iterative approach in search of the best PCA components yielded the results in Table 2 that led to the highest statistical significance for CM the GE , i ¼ N; C; I for a specific group (i.e., controls, migraine, OCD, or schizophrenia subjects). Similarly, the convergence to the optimal threshold values required an extensive search. The number of highest connectivity values had to be found specifically for each subject group that CM would lead to the most statistically significant p-value for the GE . Table 2 reports the best CM combinations for PCA components to be used in the computation of the new FC matrices and CM the threshold values that would give the highest significance in the calculation of the GE and DM GE values. CM Figure 6 provides global representations for mean of FC maps, with the GE values printed on top of each connectivity map. These maps were obtained by averaging the PC-based CM FCðN; C; IÞ for subject groups and then reconstructing the FC ðN; C; IÞ with the PCA com- CM CM ponents given in Table 2 to find an averaged representative FC ðN; C; IÞ. GE ðN; C; IÞ were computed by thresholding for the highest number of entries given Table 2. Neurophotonics 035008-9 Jul–Sep 2021 Vol. 8(3) GE GE Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases Binarized FC Maps Control, GE(N) = 0.329 Control, GE(C) = 0.347 Control, GE(I) = 0.345 5 5 5 10 10 10 15 15 15 510 15 5 10 15 510 15 Migraine, GE(N) = 0.194 Migraine, GE(C) = 0.183 Migraine, GE(I) = 0.199 5 5 5 10 10 10 15 15 15 510 15 5 10 15 510 15 OCD, GE(N) = 0.205 OCD, GE(C) = 0.208 OCD, GE(I) = 0.208 5 5 5 10 10 10 15 15 15 510 15 5 10 15 510 15 Schizo, GE(N) = 0.14 Schizo, GE(C) = 0.133 Schizo, GE(I) = 0.164 5 5 5 10 10 10 15 15 15 510 15 5 10 15 510 15 Fig. 6 Averaged FC binary maps of subject groups (rows) with respect to stimulus types (columns) CM where the GE values are presented at the titles. A group-wise representation of such binary matrices can be seen in Fig. 6. These represen- CM CM tative binary FC matrices (FC ) were computed by first subject-wise averaging of FC CM ( FC where s is the subject number within a subject group) and then applying the thresh- olding approach as in Eq. 4 with the parameters in Table 2. 3.2.2 NCR analysis Both the R and NCR values computed by Eqs. (6) and (7) elucidated strong statistical significance between healthy controls and rest of the diseased groups as seen in Figs. 7(a) and 7(b). −6 Two-way ANOVA for NCR yielded significant values for subject comparison (p ≪ 10 ), and stimulus type (p ¼ 0.001) and no interaction for SUB⋆STIM (p ¼ 0.3575). As expected NCR values are highest for the healthy controls since both the CQ and R values are higher in controls. 3.3 Receiver Operating Characteristic Analysis Receiver operating characteristics (ROCs) provide a comparison of the accuracy of classification between the healthy controls and the rest of the cases (2-case comparison). Figure 8(a) shows Neurophotonics 035008-10 Jul–Sep 2021 Vol. 8(3) Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases Ratio Values, p=1.2263e-33 NCR Values, p=1.4311e-47 4 400 Neutral Neutral 0.86 0.34 Congruent Congruent 3.5 350 Incongruent Incongruent Mean Mean 3 300 2.5 250 2 200 0.049 1.5 0.056 150 0.098 0.25 0.017 1 100 8.9e-05 0.5 50 0 0 Controls Migraine OCD Schizo Controls Migraine OCD Schizo (a) (b) Fig. 7 (a) R values as computed by Eq. (6) for all stimuli across subjects and (b) NCR values as computed by Eq. (7) for all subjects across stimuli all presented with their standard deviations as error bars. Within group p, values are presented on top of bars, whereas inter group p values are given at the title of the graphs. ROC Improvement ROC Between Group Comparisons 100 100 ACCUR AUC 90 90 80 80 70 70 60 60 50 50 40 40 30 30 20 20 10 10 0 0 Sensitivity Specificity Accuracy AUC C-M C-O C-S M-O M-S O-S (a) (b) CM Fig. 8 (a) ROC values computed for mean values across stimulus types of CQ, GE , and NCR between healthy controls and the rest of the patients. SENS: sensitivity, SPEC: specificity, ACCUR: accuracy, AUC: area under the curve. (b) ROC values between pairs of subject groups. C: controls, M: migraineurs, O: OCD patients, S: schizophrenia patients. the results of performance of classification by using means across three stimulus types of the various different parameters obtained in this paper. As can be observed from Table 8, the accuracy of classification with respect to mean of CQ (CQ ¼ 1∕3 CQ , i ¼ N; C; I) values is 81.2% while the accuracy with respect to mean value of NCR (NCR) peaks at 96.3% with a very high AUC score of 99.33%. As promised, the ROC values show a dramatic increase once the features derived from fNIRS findings are included alongside the behavioral findings. ROC values computed from the NCR holds a great promise. Except the sensitivity, there are remarkable increases in the other ROC parameters between CQ and NCR. Specificity increased by 17.9%, accuracy by 15.1%, and AUC by 10% as seen in Table 8. One might wonder how the classification performance of NCR behaves between non-healthy subjects. That ROC analysis is given in Fig. 8(b). AUC values for controls versus diseased patients are very high; hence, the sensitivity and specificity of the NCR values are very promising in separating healthy from non-healthy brain. The clas- sification accuracy of NCR between controls and OCD patients is 100% but it drops to 74.47% between OCD and schizophrenia patients. The accuracy is high at 86.95% between migraine and schizophrenia patients. Neurophotonics 035008-11 Jul–Sep 2021 Vol. 8(3) ROC Values (%) Ratios ROC Values (%) NCR Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases 4 Discussions 4.1 Behavioral Findings The Stroop task is one of the most favorable neuropsychological tests to investigate the cognitive 31,60–62 impairments in attention and inhibition control. The behavioral results in this paper con- firm the claim that both the reaction times and accuracy rates are statistically different between healthy controls and patients with neuropsychiatric diseases as seen in Tables 3 and 4 and in Figs. 4(a)–4(d). The reaction rates for a specific stimulus type increase, accuracy rates decrease (error rates increase) as the severity of the attention and inhibition controls are impaired. This phenomenon has been also observed in this study as seen in Figs. 4(a)–4(d). In a review by Foti et al., several studies reported an impairment in executive functions observed in migraine patients as measured by different neuropsychological tasks, including the Stroop task. The Stroop interference effect, as measured by the difference in the reaction times, provides an insight to inhibition (I-N) and facilitation (I-C) controls. The results are in parallel with most of the findings in literature where an impairment in executive functions in neuropsychiatric patients was observed for many tasks including the Stroop task. There are several methods to further quantify the behavioral results of the stroop test. These are usually in form of combi- nations of error (accuracy) rates and reaction times. This paper used a simpler metric: CQ, where the difference in executive functions of these four groups were emphasized better than any one parameter alone. In fact the classification accuracy of this metric between healthy controls and diseased subjects was 76.25% as seen in Fig. 8(a). In a study by Erdodi et al., classification accuracy of inverted Stroop test metrics between healthy controls and patients that were clin- ically referred for neuropsychological assessment were found to be less sensitive (14% to 25%), but comparably specific (85% to 90%) while the findings in this study were contradictory with very high sensitivity (100%) but less specificity (77.6%) for this metric. Certainly there are many differences especially in the choice of subject groups, the Stroop test employed and the param- eters used in the analysis of that study and this one, but it is evident that the behavioral param- eters alone cannot yield high accuracies in classification for neuropsychological assessment. Table 3 Reaction times in milliseconds (mean ± standard deviation). Number of subjects are given in parentheses. Group NS CS ICS p-value Controls 975.7  134.7 1041.8  190.8 1170.6  227.60.0375 Migraineurs 1233.0  394.6 1352.2  345.9 1560.7  576.8 0.0748 OCD 1593.1  453.3 1631.5  400.7 2019.7  512.70.006 Schizo 1750.9  458.1 1815.5  323.4 2202.2  346.4 <0.0001 −7 −9 −9 p-value <10 <10 <10 0 Table 4 Accuracy rates in percentages (mean ± standard deviation). Group NS CS ICS p-value Controls (13) 98.2  2.297.2  4.792.8  7.40.0297 Migraineurs 92.0  18.295.2  8.482.3  24.9 0.0815 OCD 98.4  2.596.8  4.486.3  17.5 <0.001 Schizo 91.4  13.990.4  13.769.6  21.4 <0.00001 −7 −9 −8 p-value <10 <10 <10 0 Neurophotonics 035008-12 Jul–Sep 2021 Vol. 8(3) Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases 4.2 fNIRS Findings GE has been proposed and shown to be a robust and reliable biomarker in many fNIRS 21,64–69 studies. In most cases resting fNIRS data were used to calculate the FC matrices. GE values were always computed after a threshold value was adapted. This threshold value was an actual correlation coefficient value. Since a fixed correlation coefficient threshold would yield different number of non-zero entires in the binarized FC matrices, the GE values would be incomparable (For a good review on the appropriate choice of a threshold value, we can refer to Ref. 70). Regardless of the choice of PCA components and threshold values, almost all of the studies concluded that as the cognitive impairment increases, global network efficiency 64,71 decreases compared to healthy controls. GE is also shown to be affected by age. The GE based findings in this paper are in alignment with the literature, where a decline in GE was observed for neuropsychiatric patients. On average across all stimulus types, there was a 17% CM reduction in the GE of migraineurs, 20% of OCD, and 48% of schizophrenics from controls as can be inferred from Table 6. 4.3 On the Classification Accuracy of NCR There have been several studies that investigated the classifier accuracy for schizophrenia 72–78 patients. Yet, the same cannot be said for migraine and OCD patients. Table 9 is a selection of such studies where search words: {fNIRS, classification, schizophrenia, and accuracy} were used. Most of the classification studies including schizophrenia patients reported a classification accuracy in the range of 76% to 89.7% as seen in Table 9. This study achieves at a 100% accu- racy score for schizophrenia patients as seen in Fig. 8(a). The strength of this value is inherent to the computation of the NCR where behavioral and physiologic data are fused. The accuracy is lower between neuropsychiatric patients as seen in Fig. 8(b) columns M-O, M-S and O-S with M-S being the highest at 89.81%. This is an indicator that cognitive impairments in migraine patients might not be as severe as the OCD and schizophrenics, which are close to dissociative disorder diseases. 4.4 Proposal This study is yet another one that proposes an algorithmic approach to the data analysis pipeline of fNIRS studies. The aim is to improve the clinical significance of the features extracted from fNIRS recordings so as to pledge an everlasting position of fNIRS in clinical settings. Only a 85–89 handful studies investigated the differential diagnostic accuracy of fNIRS features. To start- off, here is a checklist of specific expectations of any fNIRS-based algorithmic approach for a clinical study: E1: Provide clinically relevant information regarding brain physiology E2: Provide strong specificity for clinical data E3: Provide a better statistics than behavioral data alone E4: Provide an easy and applicable/adoptable algorithm fNIRS has been one of the few instruments that can provide insight to brain neurophysiology non-invasively and rapidly. Yet, these two offerings should match with the expectations listed above. Since fNIRS provides information regarding the cerebrovascular reactivity to cognitive or physiological stimuli, we expect that any measurement from patients with brain disorders should provide insight to neurobiology of the disease. To address E1, fNIRS is famous for bestowing local hemodynamic activity. Moreover, GE extracted from the dynamic changes of such a local data elucidate the level of collaborative effort exerted during a cognitive task. So with a number such as GE one can capture the hemodynamics of cognition. To address E2, several groups reported medium to high accuracies for classification of fNIRS 29,34,73,74,79,81 signals. These studies mostly included two groups: healthy controls and a diseased Neurophotonics 035008-13 Jul–Sep 2021 Vol. 8(3) Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases group. No multi-group comparison has been attempted with fNIRS, unlike fMRI. NCR derived from CQ and ratio of GEs are shown to be highly specific for diseases. To address E3, so far the statistical significance of behavioral data have been praised in many studies in classifying patient groups. fNIRS is expected to improve the statistics and this is what NCR offers. It gives a higher accuracy in separating healthy from diseased brain, much like a blood pressure monitor. To address E4, a valid critique is that the proposed approach is easy and applicable. It is more of an iterative approach than a theoretical one. Yet, the FC-ðPCÞ seems to do the trick in sepa- rating the FC matrices. Interestingly there are not many studies of other psychiatric diseases. Still there are pioneer- 14 10 ing works on autism spectrum disorder, obsessive compulsive disorder, depression, and 23 11,12 migraine. There is always those who have not lost faith in fNIRS. A very hopeful study by Ehlis et al. points us to the right direction: future studies should also focus on the usefulness of fNIRS as a supportive tool for choosing the most promising treatment approach for a specific patient. Using fNIRS, neurophysiological markers that might predict treatment outcomes (and may thus be relevant for personalized medicine) could be easily identified. Several studies actually achieved this ambitious goal set by Ehlis et al. For a good review of use of fNIRS in psychiatry, please see Refs. 9, 90, and 91, specifically in autism, and its role in neurofeed- 92 93 94 back, in pain, and in neurology. Only a handful of them are listed in Table 9. This study is the first in several aspects: (1) to show a high specificity of fNIRS for various types of neuro- psychiatric diseases (more than 2); (2) in providing an fNIRS-derived biomarker (namely the NCR) with very high accuracy that is also clinically relevant; and (3) in that it does not attempt to find a correlation between behavioral data and physiological data, rather it combines them since behavior cannot be produced without physiological activation. As observed by James, “A sci- ence of the relations of mind and brain must show how the elementary ingredients of the former correspond to the elementary functions of the latter.” 5 Conclusion This study is an extension of a previous work, which concluded that a PC-based approach should be preferred when generating the FC matrices. Separating the FC into a CM and DM network led to the ratio of the GE values calculated from these two matrices. This ratio was then multiplied with the CQ, which is a direct measure of cognitive load. Therefore, a new biomarker, NCR, was generated and proposed. The mean NCR across all stimuli for four subject groups in Table 10 (NCRðControlÞ¼ 210  66, NCRðMigraineÞ¼ 89  34, NCRðOCDÞ¼ 57  20, −10 and NCRðSchizoÞ¼ 38  15, p < 10 ) gives the best classification accuracy with respect to ROC between healthy controls and diseased subjects (ACCUR ¼ 96.25%,and AUC ¼ 99.31%), much better than the accuracies obtained from only CQ, behavioral parameter (CQðControlÞ¼ 94  16, CQðMigraineÞ¼ 73  24, CQðOCDÞ¼ 59  18,and −8 CQðSchizoÞ¼ 48  15, p < 10 ) where (ACCUR ¼ 81.2%, and AUC ¼ 89.3%). The results are all in favor of this biomarker. So we might conclude that fNIRS-derived NCR is a strong candidate as a biomarker for neuropsychiatric diseases. It can safely be used in diagnosis and prognosis of neuropsychological assessments of at least a group of neuropsychiatric disorders. 6 Appendix The following tables present the values of the figures in the manuscript. 6.1 Behavioral Data The following Table 5 is formed from Tables 3 and 4. Neurophotonics 035008-14 Jul–Sep 2021 Vol. 8(3) Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases Table 5 CQ values (mean ± standard deviation). Group NS CS ICS p-value Controls 102.42  14.15 96.00  16.87 82.44  19.19 0.0147 Migraineurs 82.80  27.50 75.04  19.68 61.41  28.62 0.0341 OCD 66.85  19.49 63.12  16.49 47.18  19.44 0.0024 Schizo 56.99  19.77 52.05  14.62 33.53  15.19 <0.00001 −7 −9 −8 −8 p-value <10 <10 <10 <10 CM CM Table 6 Optimized GE values computed from FC s (mean ± standard deviation). Group NS CS ICS p-value Controls 0.2951  0.0482 0.3478  0.0543 0.3207  0.0634 0.0326 Migraineurs 0.2821  0.0275 0.2342  0.0321 0.2784  0.0287 0.0017 OCD 0.2350  0.0662 0.2768  0.0549 0.2548  0.0549 0.0197 Schizo 0.1793  0.0527 0.1587  0.0701 0.1624  0.0635 0.0482 −10 −10 −10 −10 p-value <10 <10 <10 <10 DM DM Table 7 Optimized GE values computed from FC s (mean ± standard deviation). Group NS CS ICS p-value Controls 0.1554  0.0482 0.1621  0.0543 0.1710  0.0634 0.78 Migraineurs 0.2246  0.0275 0.2210  0.0321 0.2255  0.0287 0.82 OCD 0.2901  0.0891 0.2728  0.0662 0.2766  0.0549 0.71 Schizo 0.2153  0.0527 0.2183  0.0701 0.2292  0.0635 0.70 −4 −4 −4 −10 p-value <10 <10 <10 <10 6.2 GE Values Table 8 ROC analysis of mean values of features. SENS: sensitivity, SPEC: specificity, ACCUR: accuracy, AUC: area under the curve. Feature SENS SPEC ACCUR AUC CQ 100 77.6 81.2 89.3 GE 92.3 89.6 90 95.1 NCR 100 95.5 96.3 99.3 Neurophotonics 035008-15 Jul–Sep 2021 Vol. 8(3) Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases Table 9 Classification performances of fNIRS studies. Study Diseased group Features Classifier Accuracy Chen et al. Schizophrenia GLM based SVM 85% Dadgostar et al. Schizophrenia Time series SVM 87% Einolou et al. Schizophrenia Energy SVM 84% Eken et al. SSD FC based SVM 82% Hahn et al. Schizophrenia Time series LOOCV 76% Ji et al. Schizophrenia FC based SVM 89.7% Shoushtarian et al. Chronic tinnitus FC based Naïve Bayes 78.3% Xu et al. ASD Time series CNN & LSTM 93.3% Yang et al. Schizophrenia FC based SVM 84.67% Yang et al. MCI Time series CNN 98.61% Yang et al. MCI FC based CNN 95.81% Yoo et al. MCI Time series SVM 79.49% 6.3 NCR Values Table 10 NCR values computed from Table 11 (mean ± standard deviation). Group NCR Controls 209.89  65.67 Migraine 88.50  33.97 OCD 57.36  20.28 Schizo 38.22  14.52 −10 p-value <10 Table 11 NCR values computed from Eq. (7) (mean ± standard deviation). Group NS CS ICS p-value Controls 225.97  125.93 226.84  80.88 176.85  80.35 0.342 Migraine 104.00  40.53 81.09  28.04 80.41  45.20 0.098 OCD 57.68  23.92 68.54  27.39 45.88  22.78 0.017 −5 Schizo 49.48  25.05 39.68  15.37 25.48  14.43 <10 −10 −10 −10 −10 p-value <10 <10 <10 <10 Neurophotonics 035008-16 Jul–Sep 2021 Vol. 8(3) Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases Disclosures The author does not declare any biomedical financial interests or potential conflicts of interest. The author would like to thank the reviewers for their insightful and constructive comments. Acknowledgments This project was sponsored by a grant from TUBITAK (Project No: 108S101) and Bogazici University Research Fund (Project No: 5106). The author wishes to thank Deniz Nevsehirli for his contributions to fNIRS optode development; Dr. Nermin Topaloĝu and Dr. Sinem Burcu Erdoĝan for data collection. References 1. B. J. 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Ata Akın received his PhD in biomedical engineering from Drexel University in 1998, his MS degree in biomedical engineering, and BS degree in electronics and telecommunication engi- neering both from Istanbul Technical University in 1995 and 1993, respectively. He worked at Drexel University, Bogaziçi ˘ University, Bilgi University, and recently joined Acibadem University as the Dean of the Faculty of Engineering. His research interests are primarily in functional brain imaging and design of medical instrumentation. Neurophotonics 035008-21 Jul–Sep 2021 Vol. 8(3) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neurophotonics SPIE

fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases

Akın, Ata
Neurophotonics , Volume 8 (3) – Jul 1, 2021
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Abstract

Significance: Clinical use of fNIRS-derived features has always suffered low sensitivity and specificity due to signal contamination from background systemic physiological fluctuations. We provide an algorithm to extract cognition-related features by eliminating the effect of back- ground signal contamination, hence improving the classification accuracy. Aim: The aim in this study is to investigate the classification accuracy of an fNIRS-derived biomarker based on global efficiency (GE). To this end, fNIRS data were collected during a computerized Stroop task from healthy controls and patients with migraine, obsessive compul- sive disorder, and schizophrenia. Approach: Functional connectivity (FC) maps were computed from [HbO] time series data for neutral (N), congruent (C), and incongruent (I) stimuli using the partial correlation approach. Reconstruction of FC matrices with optimal choice of principal components yielded two inde- pendent networks: cognitive mode network (CM) and default mode network (DM). Results: GE values computed for each FC matrix after applying principal component analysis (PCA) yielded strong statistical significance leading to a higher specificity and accuracy. A new index, neurocognitive ratio (NCR), was computed by multiplying the cognitive quotients (CQ) and ratio of GE of CM to GE of DM. When mean values of NCR (NCR) over all stimuli were computed, they showed high sensitivity (100%), specificity (95.5%), and accuracy (96.3%) for all subjects groups. Conclusions: NCR can reliable be used as a biomarker to improve the classification of healthy to neuropsychiatric patients. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original pub- lication, including its DOI. [DOI: 10.1117/1.NPh.8.3.035008] Keywords: fNIRS; neuropsychiatric diseases; functional connectivity; global efficiency; cog- nitive quotient; neurocognitive ratio. Paper 21020R received May 10, 2021; accepted for publication Sep. 16, 2021; published online Sep. 30, 2021. 1 Introduction Although fNIRS has been around over 30 years now, its clinical efficacy and role are still being questioned due to its low specificity and sensitivity, especially in the area of neuropsychiatric diseases. Many researchers have been trying to improve its efficacy, sensitivity, and specificity in clinical settings by either improving its technology or the post processing analysis methods. Over the last 20 years, the richness of fNIRS data due to its ease and speed of data collection, non- invasiveness and access to local activity have become even more attractive to cognitive neuro- scientists in testing multitude of data processing and neuroscientific hypotheses. Strangely 1–4 though, the brain does not work locally. fNIRSians have long been in search of a killer application that would secure the place of fNIRS in clinical settings. To this end, fNIRS have been applied to subjects of all ages and health 5–8 conditions. Discussions regarding the limitations and ways to overcome these are a few yet *Address all correspondence to Ata Akn, ata.akin@acibadem.edu.tr Neurophotonics 035008-1 Jul–Sep 2021 Vol. 8(3) Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases they all have helped us redirect our efforts in proposing ever more innovative solutions. There are 5,7,9–14 good reviews on the promise of fNIRS in neuropsychiatry. So far, fNIRS researchers have focused more on the data analytics side than developing of novel technologies. We have enjoyed the availability of various fNIRS systems but at the cost of standardization of probe designs, data collection methodologies, and even more on the data analytics. The lack of standardization on these issues has made it increasingly more difficult to compare the findings from different studies. Moreover, physics of photon migration through the layers of the head limits the specificity of the fNIRS device to cortical layers. Collected data become an amalgam of physiological activity from each layer the photon interacts with. Hence the data are known to be contaminated with background systemic physiological fluctuations that 16,17 are undoubtedly correlated with the cognitive activity. The low specificity of the CW-fNIRS devices can be overcome with time resolved systems albeit at a greater cost and complications of data collection. Many novel data analysis methods have been proposed to extract the brain 18–20 originated, task related data from the collected data. Still there is no consensus on how to approach the fNIRS data, leading to the unsettling yet quite accurate prediction of Drs. Quaresima and Ferrari: “The prediction of the future directions of fNIRS for assessing brain function during human behavior in natural and social situations is not easy.” It is, hence, only logical to propose an analysis method (a pipeline of data analysis) that would avoid the pitfalls of the standardization issues faced in fNIRS signal processing field. The following Table 1 is a list of minimum hardware, data collection, and analysis recommendations for fNIRS-based cogni- tive research that are derived from experience and literature: This study proposes an improved post-processing approach to data obtained from fNIRS recordings over our previous paper. The sole aim was to converge on a data analysis pipeline that will be accepted and adapted easily by fellow fNIRSians. The proposed algorithm should have a common denominator, a base for anyone to build upon. The algorithm aims to improve the statistical significance of the fNIRS findings, and hence, the trust on the system. The major aim is to boost the statistical significance of the GE values; hence, the accu- racy of classification of fNIRS findings in a set clinical data obtained in our group’s previous studies. Table 1 Recommendations for fNIRS based research. Hardware requirements � Number of channels ≥8 � Collect data from both hemispheres � Sampling rate ≥0.5 Hz per channel � Number of wavelengths ≥2 Task requirements � Duration of collected data ≥10 min � Block stimulus with block duration ≥20 s � Stimulus type ≥2 � Resting data ≈30 s Analysis requirements � Prefer [HbO] data � Avoid single channel data analysis � Prefer a dimensionless analysis (i.e., normalization of data) � Prefer a multichannel analysis (i.e., FC) � Prefer a metric that summarizes and captures the behavior of multichannel data (i.e., FC strength such as GE.) Neurophotonics 035008-2 Jul–Sep 2021 Vol. 8(3) Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases 2 Methods and Materials 2.1 Subjects and Experimental Procedure About 13 healthy subjects (six female) at an average age of 26, 20 patients with migraine without aura (12 female) at an average age of 27, 26 patients with obsessive compulsive disorder (11 female) at an average age of 29, and 21 schizophrenia patients (10 female) at an average age of 28 participated in this study. The study protocol was approved by the Ethics Committee of Pamukkale University in 2008. Parts of these data were published by our group and 22–30 coworkers. Consents were obtained from all subjects and they were all informed about the study before the experiment. Subjects were seated in a dimly illuminated insulated room and they were told to look at a computer screen placed in front of them. Subjects responded to the computerized color word matching Stroop task that involved three sets of stimuli: neutral (N), congruent (C), and incongruent (I) stimuli. The task involved 15 N, 15 C, and 15 I stimuli presented in blocks of five sequential stimuli. The inter stimulus interval was 4 s. The rest between each block was 20 s. The stimuli blocks were randomized for each subject. The subject was asked to respond with left or right mouse click depending on whether the stimulus was a match or not. The task started with a 30 s of rest and ended with a 30 s 24,31 of rest. 2.2 fNIRS Equipment The fNIRS system (NIROXCOPE 301) was developed at the Neuro-Optical Imaging Laboratory 23,30 of Bogazici University. NIROXCOPE 301 has a sampling frequency of 1.77 Hz, and it con- sists of a data acquisition unit, a data collecting computer, and a flexible probe to place on the forehead of the subjects. The probe has a rectangular design housing four dual wavelength light- emitting diodes (LED) emitting at 730 and 850 nm. Each LED (L , i ¼ 1::: 4) is surrounded by four detectors (D , i ¼ 1::: 10) placed 2.5 cm away from the center of an LED as seen in Fig. 1. A channel is a pair of LED and detector that surrounds that LED. Since several of the in-between detectors are shared, there are 16 channels (C i ¼ 1::: 16). Since the received light intensity is inversely proportional to the square of the distance between a source and a detector, data from long range channel pairs were not collected (i.e., between L and D , D ,or D ). 1 5 7 9 The validity of this probe design and its ability to detect brain tissue were discussed in our 18 21,24–26,30,32 previous study as well as its efficacy in providing cognition-related signals. 2.3 Analysis of the fNIRS Data fNIRS data are known to be contaminated with systemic background fluctuations. So before attempting to generate connectivity matrices from pair-wise correlations, one should try to minimize the effect of this background fluctuation so that the effect of this dominant signal is eliminated. Assuming that any correlation between two channels will be dominated by this Fig. 1 Rectangular probe geometry of the fNIRS NIROXCOPE 301. L are the LEDs; D , the i i detectors; and C , the channels. Neurophotonics 035008-3 Jul–Sep 2021 Vol. 8(3) Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases Fig. 2 Block diagram of the NCR algorithm (abbreviations are given in the text). common background signal, our previous study aimed to show that a partial correlation (PC)-based analysis will yield a less biased insight into the underlying connectivity due to task. In that paper, an outline of the signal processing steps were given in detail. As a quick sum- mary, a signal processing pipeline was developed to compute the FC matrices (FC) from [HbO] signals by using a PC method, rather than the conventional pearson correlation analysis. Then these matrices were used to compute the GE values. In this paper, an additional step in between the FC matrices and GE computation is proposed by employing the principal component analy- sis (PCA). As a last step, a new biomarker as a function of behavioral and fNIRS deriven features: neurocognitive ratio (NCR). The details of the derivation and computation of this biomarker is explained in Sec. 2.4 and the block diagram of the algorithm is shown in Fig. 2. This paper will present the results of this PCA-based FC analysis, hence called the FC-(PC)2: FC analysis via PCA based PC. 2.3.1 Preparation of fNIRS data for FC analysis FC is a method where correlations from time series data are used to create a matrix called the functional connectivity matrix (FC). The matrix is a N × N matrix, where N is the number of channels for fNIRS (number of voxels for fMRI). In this study, N ¼ 16. Since the study protocol involved the Stroop task with three types of stimuli, it was reasonable to create 3FCs. fNIRS data that will be fed into FC calculations were prepared in the following steps to generate these matrices: 1. Locate the stimuli blocks in fNIRS time signal for each subject [i.e., Fig. 3(a)] 2. Create three concatenated stimulus time series for each channel [i.e., Figs. 3(b) and 3(d)] 3. Generate the regressor to be used in PC calculations as explained in Sec. 2.3.2, [i.e., Fig. 3(e)]. 2.3.2 Functional connectivity via PC PC provides a cleaner (or less biased) relationship between two variables after removing a common effect present in both of the variables. The PC coefficient (r ) between any two chan- i;jjk nels ði; jÞ in the presence of a common influencer (k) is computed as follows: r − r r i;j i;k j;k EQ-TARGET;temp:intralink-;e001;116;190r ¼ qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiqffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi . (1) i;jjk 2 2 ð1 − r Þ ð1 − r Þ i;k j;k [HbO] data from each channel are passed through a high pass filter (butterworth, eigth order, cutoff at f ¼ 0.09 Hz, stop-band at f ¼ 0.1 Hz) to obtain the HBO . The regressor used in c s H PC-based FC analysis is obtained by averaging this signal over all the channels. Hence HBO ¼ HBO is used to regress out the systemic physiological affects from the correlation of the i H unprocessed [HbO] signals from two channels. Once the regressor HBO is computed, indi- vidual regressors for N, C, and I stimuli are generated similar to the concatenation explained Neurophotonics 035008-4 Jul–Sep 2021 Vol. 8(3) Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases Sample Data from Sub=1, ch=12 Data N C I I1 N1 C1 C2 I2 I3 C3 N2 I4 N3 C4 I5 C5 N4 N5 200 400 600 800 1000 1200 (a) 50 100 150 200 (b) 1.5 0.5 50 100 150 200 (c) -1 50 100 150 200 (d) 0.5 -0.5 200 400 600 800 1000 1200 Sample Point Fig. 3 Preparation steps shown on a sample [HbO] data. (a) Raw and unprocessed [HbO] data of subject 3 (control), from channel 12, where the patched regions designate the stimuli blocks. (b) Concatenated [HbO] data for N stimulus where segments of N data (N , i ¼ 1::: 5) from the original data are concatenated sequentially to create the [HbO] (N) time series data, (c) Concatenated [HbO] (C) time series data for C stimulus, (d) Concatenated [HbO] (I) time series data for I stimulus, and (e) the regressor computed as explained in Sec. 2.3.2. in Fig. 3. Then these regressors are used in the computations of the PC coefficients as entries to the FC. This analysis is performed for each subject. At the end of this part of this analysis, 3FCs are generated from each subject’s fNIRS. The FC matrices computed for stimulus based time series are thus termed FC , FC , and FC . N C I 2.3.3 Functional connectivity via PCA based PC In a review by Du et al., it is claimed that statistical significance of the FC derived features (i.e., GE) can be improved by adding PCA after the FCs are calculated. Once an FC was generated, PCA was applied to the it. Since the matrices were 16 × 16, there were 16 PCs. The assumption in applying PCA to FC is the following: CM DM EQ-TARGET;temp:intralink-;e002;116;206FC ¼ FC þ FC ;i ¼ N; C; I; (2) i i DM CM where FC is the FC matrix of the default mode network, and FC is the matrix for the i i cognitive mode network. Since these two matrices can be assumed to be linearly independent, PCA can be applied to separate them into their independent parts. The choice of PCs turned out CM to affect the statistical significance of the GE values computed after new FCs were recon- structed from the chosen PCs. The expectation is the convergence to a subset of PCs that will CM yield the strongest significance for GE (as explained in Sec. 2.3.4) while no significance for 35,36 DM the GE . A combinatorial search analysis was performed to find the best principal com- CM ponents to reconstruct the new FC . The choice of how many principal components to be used was based mostly on the strongest PCA eigenvalues. However, components with lower Neurophotonics 035008-5 Jul–Sep 2021 Vol. 8(3) HBO (R) HBO (C) HBO (I) HBO (N) HBO (raw) Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases strengths were also included in some cases. Once the best PC subset was found, this subset was CM DM used to resonstruct the FC . Remaining PCs were then used to reconstruct the FC , i i DM i ¼ N; C; I. The expectation from these FC matrices is such that the t-statistics of the DM GE , i ¼ N; C; I will be low (no statistical significance). So the algorithm works as an opti- mization approach where the goal was to maximize the t-statistics (minimize the p value) of the CM GE , i ¼ N; C; I. 2.3.4 Global efficiency Global efficiency (GE) is one of the many metrics of graph-based network analysis and has been 21,37–39 used in brain connectivity studies. This approach is intrinsic to cognitive neuroscience 40–42 where the aim is to investigate the neural correlates of cognition. Several groups, including ours have reported that GE can be reliably used as a metric to quantify the information sharing CM;DM efficiency of the FC s. In this analysis, channels can be considered as a set of vertices V and the PC coefficients as assigned weights on the set of edges E, between vertices to construct 43–45 an undirected complete weighted graph G ¼ðV; EÞ. GE can be evaluated for a wide range of networks, including weighted graphs. Maximal possible GE occurs when all edges are present in the network. The GE value was computed by using the formulation of Latora and Marchiori’s: 1 1 EQ-TARGET;temp:intralink-;e003;116;503GE ¼ ; (3) NðN − 1Þ d ij i≠j∈G where d is defined as the smallest sum of the physical distances throughout all the possible ij paths in the graph from i to j. This equation requires the use of binary matrix entries. So, since there was always some sort of a connection between channels (the entries were never 0) a thresh- old had to be used to eliminate very low connections. So choosing an appropriate threshold value was necessary to convert the FCs to binary matrices: 1if jFCði; jÞj > Θ EQ-TARGET;temp:intralink-;e004;116;395FCði; jÞ¼ ; (4) 0 otherwise where FC is a binarized matrix after a hard thresholding at the value Θ is applied to the jFCj. Here, Θ is not an actual correlation value, rather the number of highest correlation values to be kept in the matrix. It is worth noting that absolute values of the FC were used in this equation. This threshold value determines the number of non-zero nodes to be kept in the binarized matrix, which in turn effects the computation of the GE values. In this analysis, two specific Θ node values were determined iteratively for each subject CM group (see Table 2 for group dependent Θ values), one for CM (Θ ) and one for DM DM (Θ ). A review on the choice of such a threshold (Θ) yielded a value of the highest 10% to 20% of all the entries in the jFCjs. A sweep of the best group-wise Θ that provided the highest CM statistical significance (lowest p value as shown in Table 2) for GE s for three types of stimuli CM DM yielded specific Θ values for different subject groups. In contrast, Θ was chosen for the CM Table 2 PCA components for subject groups that yielded the best p value for GE . CM DM Group CM DM Θ Θ p value Controls ½1 ··· 9 Remaining 43 32 0.033 Migraine [2, 3, 6, 7, 9, 16] Remaining 40 28 0.0017 OCD ½1 ··· 12;14; 16 Remaining 38 48 0.02 Schizo ½1 ··· 5;6 ··· 9;10; 12 ··· 16 Remaining 27 41 0.048 Neurophotonics 035008-6 Jul–Sep 2021 Vol. 8(3) Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases highest p value for three stimuli types. The computation of the GE values by Eq. (3) was per- formed by the efficiency.m code from the Brain Connectivity Toolbox. 2.4 Neurocognitive Ratio Cognitive quotient (CQ) can be considered as a measure of level of cognitive effort exerted to fulfill a task. There are several indices, quotients, and metrics proposed to assess this effort. 47–49 Usually these are in the form of combinations of various neuropsychological task scores and sometimes in the form of physiological parameters. Cognitive load is a similar concept and many physiological measures have also been proposed to quantify this effort. The assumption for using physiological measures to quantify cognitive load is that brain, just like a muscle, has to execute some sort of a physiological activity (preferably measurable) for a specific cognitive task. So, the holy grail of neuroscience is to find this link between neuro- physiological activity and psychological activity, also called the neurobiological basis of 12,53 behavior. Hence, researchers have defined a new concept, “neural efficiency,” to quantify the level of efficiency of collaborative effort of the brain in solving a difficult cognitive task (for a complete review see Ref. 54). Neural efficiency can be computed from physiological parameters such as heart rate variability, EEG measurements, and fMRI recordings, and recently 55–57 from fNIRS findings. Researchers preferred to find a relationship between the neuropsycho- logical data and neurophysiological data mostly in terms of correlation coefficients or regression analysis. The equation eventually obtained transforms one finding to another one, consequently assumes a causal relationship. Borrowing an idea from neurophilosophy, one can impose the duality principle in brain’s operations where an independent relation between the brain and mind can be the source of cog- nition. Hence, one can simply propose a metric (an index) that combines these two so-called independent measures; namely, the behavioral findings with physiological findings in assessing the neurocognitive effort. Here, a new combined metric is proposed, by which NCR is defined as follows: EQ-TARGET;temp:intralink-;e005;116;401CQ ¼ ACC ∕RT ; (5) i i i CM DM EQ-TARGET;temp:intralink-;e006;116;359R ¼ GE ∕GE ; (6) i i EQ-TARGET;temp:intralink-;e007;116;336NCR ¼ CQ × R ; (7) i i i where i is the stimulus type, ACC is the accuracy in percentage, and RT is the reaction (response) time in seconds. CQ is defined for each stimulus type. NCR , as calculated from Eq. (7), can be assumed to be a biomarker specific for each subject group (i.e., healthy controls, patients with migraine, OCD, or schizophrenia disorder). The underlying assumption in proposing this index as a biomarker is that the GE of a default mode network should be different than the GE com- CM DM puted during a cognitive task (GE ≠ GE ) and that the ratio of the two [R calculated as in Eq. (6)] can be considered as an objective indicator of attention and inhibition control. A rea- sonable expectation would be that R ≥ 1 for healthy subjects. In fact, one can even hypothesize that an increased demand for inhibitory control can be associated with restructuring of the global network into a configuration that must be more optimized for specialized processing (functional segregation), more efficient at communicating the output of such processing across the network (functional integration), and more resilient to potential interruption (resilience). Thus, investi- gation of graph theoretical metrics under varying levels of inhibitory control can provide 53,58,59 clinicians with a quantitative and objective metric in their clinical decision processes. 3 Results 3.1 Behavioral Results The reaction times and accuracy rates for the subjects for all stimuli types are given in Figs. 4(a) and 4(b). Reaction times are calculated by averaging the response times to all the responses, not just the correct answers. Neurophotonics 035008-7 Jul–Sep 2021 Vol. 8(3) Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases Reaction times wrt stimuli, p=2.8509e-22 Accuracy rates wrt stimuli, p=0.00014314 2.5 Neutral 8.2e-05 Congruent 0.03 0.082 0.00076 6.6e-06 Incongruent 0.006 Mean 0.075 1.5 0.038 Neutral Congruent Incongruent Mean 0.5 0 0 Controls Migraine OCD Schizo Controls Migraine OCD Schizo (a) (b) Stroop interference effect CQ values wrt stimuli, p=6.7782e-23 0.7 Neutra Controls 120 0.015 Congruent Migraine 0.6 Incongruent OCD Mean Schizo 100 0.034 0.5 0.0024 80 5.5e-06 0.4 0.051 0.0066 0.3 0.2 0.1 0 0 I-N I-C Controls Migraine OCD Schizo (c) (d) Fig. 4 (a) RT values at the columns of Table 3 for all subjects across stimuli. p values are pre- sented on top of bars. (b) ACC values at the columns of Table 4 for all subjects across stimuli. p values are presented on top of bars; (c) Stroop interference effect in reaction times; (d) CQ values computed by Eq. (5). Error bars represent the standard deviations. All values in these graphs are given in Tables 3–5. Two-way ANOVA for RT values yielded significant values for subject comparison −6 −6 (p ≪ 10 ), and stimulus type (p ≪ 10 ) and no interaction for SUB⋆STIM (p ¼ 0.749). Accuracy was calculated by taking the ratio of total number correct answers to total number of questions. Two-way ANOVA for the ACC yielded significant values for subject comparison −6 −6 (p ≪ 10 ), and stimulus type (p ≪ 10 ) and no interaction for SUB⋆STIM (p ¼ 0.1588). CQ with respect to subjects and stimuli can be seen in Fig. 4(d). Two-way ANOVA for CQ −6 −6 yielded significant values for subject comparison (p ≪ 10 ), and stimulus type (p ≪ 10 ) and no interaction for SUB⋆STIM (p ¼ 0.9958). CQ can also be considered as a metric of cognitive load. In several studies, such scores from different tests are linearly combined (sometimes with weights) to provide a stronger metric. 3.2 fNIRS Data Analysis 3.2.1 GE analysis CM;DM CM;DM The computation of GE from the FC matrices yielded quite interesting dynamics as CM shown in Figs. 5(a) and 5(b). First, PCA and Θ optimized GE values showed a strong sta- tistically significant difference within subjects [see the p values displayed on top of the bars of Fig. 5(a)] with the optimal choice of principal components and Θ values given in Table 2. This is expected since the optimization for the PCA components and Θ is supposed to lead to Neurophotonics 035008-8 Jul–Sep 2021 Vol. 8(3) RT [sec] RT [sec] CQ Accuracy [%] Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases GE values for CMN and DMN wrt Stimuli Mean GE values for CM and DM wrt Groups 0.45 0.4 CM CM CM DM 0.033 0.4 CM 0.35 2.3 DM DM 0.35 0.0017 0.02 C 0.3 1.2 0.96 DM 0.3 0.25 0.81 0.25 0.048 0.2 0.2 0.15 0.15 0.1 0.1 0.05 0.05 0 0 Controls Migraine OCD Schizo Controls Migraine OCD Schizo (a) (b) CM;DM Fig. 5 (a) GE values for all subjects across three types of stimuli. p values are presented on top of bars, the error bars are the standard deviations and are also given in Tables 6 and 7. CM;DM (b) Mean of GE values for all stimuli with respect to subject group. The numbers above CM DM the bars are the ratios (R ¼ GE ∕GE ) as explained in Sec. 2.4. The error bars are the within group standard deviations. CM CM statistically significant different GE values for each subject group. Group averaged GE values were higher for controls than for the patients [see Fig. 5(b)] although different optimized parameters were used for each subject group, whereas no significant difference was observed for DM any of the subject-wise GE values (p> 0.05). CM DM Second, both the GE and GE values were observed to be different between subject CM groups [as more evident from the means graph in Fig. 5(b)]. As the GE value decreases from DM healthy controls to schizophrenics, the GE increases. This is somewhat expected since the CM threshold node value for CM (Θ ) that yielded strong significance ended up being lower in DM patients. Vice versa, the threshold node value for DM (Θ ) were higher for patients than con- CM trols in most of the cases. Higher value of GE in controls over patients could mean that a healthy brain recruits a wider brain circuitry with more efficiency during a cognitive task, DM whereas diseased brain cannot. In contrast, higher values of GE for diseased population might be due to a domination of the DM leading to a lesser space for CM; hence, the poor performance CM DM on cognition related activities. Figure 5(b) shows the ratio of mean of GE ∕GE for each subject group. The ratio is in favor of healthy controls and significantly lower in diseased groups. CM;DM A note of caution is that these values of GE depend heavily on the choice of optimal CM;DM principal components and threshold values when computing the FC matrices. Hence, many iterations and heuristic reasoning were employed to find the best GE values that would yield the highest statistical significance for GE. An iterative approach in search of the best PCA components yielded the results in Table 2 that led to the highest statistical significance for CM the GE , i ¼ N; C; I for a specific group (i.e., controls, migraine, OCD, or schizophrenia subjects). Similarly, the convergence to the optimal threshold values required an extensive search. The number of highest connectivity values had to be found specifically for each subject group that CM would lead to the most statistically significant p-value for the GE . Table 2 reports the best CM combinations for PCA components to be used in the computation of the new FC matrices and CM the threshold values that would give the highest significance in the calculation of the GE and DM GE values. CM Figure 6 provides global representations for mean of FC maps, with the GE values printed on top of each connectivity map. These maps were obtained by averaging the PC-based CM FCðN; C; IÞ for subject groups and then reconstructing the FC ðN; C; IÞ with the PCA com- CM CM ponents given in Table 2 to find an averaged representative FC ðN; C; IÞ. GE ðN; C; IÞ were computed by thresholding for the highest number of entries given Table 2. Neurophotonics 035008-9 Jul–Sep 2021 Vol. 8(3) GE GE Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases Binarized FC Maps Control, GE(N) = 0.329 Control, GE(C) = 0.347 Control, GE(I) = 0.345 5 5 5 10 10 10 15 15 15 510 15 5 10 15 510 15 Migraine, GE(N) = 0.194 Migraine, GE(C) = 0.183 Migraine, GE(I) = 0.199 5 5 5 10 10 10 15 15 15 510 15 5 10 15 510 15 OCD, GE(N) = 0.205 OCD, GE(C) = 0.208 OCD, GE(I) = 0.208 5 5 5 10 10 10 15 15 15 510 15 5 10 15 510 15 Schizo, GE(N) = 0.14 Schizo, GE(C) = 0.133 Schizo, GE(I) = 0.164 5 5 5 10 10 10 15 15 15 510 15 5 10 15 510 15 Fig. 6 Averaged FC binary maps of subject groups (rows) with respect to stimulus types (columns) CM where the GE values are presented at the titles. A group-wise representation of such binary matrices can be seen in Fig. 6. These represen- CM CM tative binary FC matrices (FC ) were computed by first subject-wise averaging of FC CM ( FC where s is the subject number within a subject group) and then applying the thresh- olding approach as in Eq. 4 with the parameters in Table 2. 3.2.2 NCR analysis Both the R and NCR values computed by Eqs. (6) and (7) elucidated strong statistical significance between healthy controls and rest of the diseased groups as seen in Figs. 7(a) and 7(b). −6 Two-way ANOVA for NCR yielded significant values for subject comparison (p ≪ 10 ), and stimulus type (p ¼ 0.001) and no interaction for SUB⋆STIM (p ¼ 0.3575). As expected NCR values are highest for the healthy controls since both the CQ and R values are higher in controls. 3.3 Receiver Operating Characteristic Analysis Receiver operating characteristics (ROCs) provide a comparison of the accuracy of classification between the healthy controls and the rest of the cases (2-case comparison). Figure 8(a) shows Neurophotonics 035008-10 Jul–Sep 2021 Vol. 8(3) Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases Ratio Values, p=1.2263e-33 NCR Values, p=1.4311e-47 4 400 Neutral Neutral 0.86 0.34 Congruent Congruent 3.5 350 Incongruent Incongruent Mean Mean 3 300 2.5 250 2 200 0.049 1.5 0.056 150 0.098 0.25 0.017 1 100 8.9e-05 0.5 50 0 0 Controls Migraine OCD Schizo Controls Migraine OCD Schizo (a) (b) Fig. 7 (a) R values as computed by Eq. (6) for all stimuli across subjects and (b) NCR values as computed by Eq. (7) for all subjects across stimuli all presented with their standard deviations as error bars. Within group p, values are presented on top of bars, whereas inter group p values are given at the title of the graphs. ROC Improvement ROC Between Group Comparisons 100 100 ACCUR AUC 90 90 80 80 70 70 60 60 50 50 40 40 30 30 20 20 10 10 0 0 Sensitivity Specificity Accuracy AUC C-M C-O C-S M-O M-S O-S (a) (b) CM Fig. 8 (a) ROC values computed for mean values across stimulus types of CQ, GE , and NCR between healthy controls and the rest of the patients. SENS: sensitivity, SPEC: specificity, ACCUR: accuracy, AUC: area under the curve. (b) ROC values between pairs of subject groups. C: controls, M: migraineurs, O: OCD patients, S: schizophrenia patients. the results of performance of classification by using means across three stimulus types of the various different parameters obtained in this paper. As can be observed from Table 8, the accuracy of classification with respect to mean of CQ (CQ ¼ 1∕3 CQ , i ¼ N; C; I) values is 81.2% while the accuracy with respect to mean value of NCR (NCR) peaks at 96.3% with a very high AUC score of 99.33%. As promised, the ROC values show a dramatic increase once the features derived from fNIRS findings are included alongside the behavioral findings. ROC values computed from the NCR holds a great promise. Except the sensitivity, there are remarkable increases in the other ROC parameters between CQ and NCR. Specificity increased by 17.9%, accuracy by 15.1%, and AUC by 10% as seen in Table 8. One might wonder how the classification performance of NCR behaves between non-healthy subjects. That ROC analysis is given in Fig. 8(b). AUC values for controls versus diseased patients are very high; hence, the sensitivity and specificity of the NCR values are very promising in separating healthy from non-healthy brain. The clas- sification accuracy of NCR between controls and OCD patients is 100% but it drops to 74.47% between OCD and schizophrenia patients. The accuracy is high at 86.95% between migraine and schizophrenia patients. Neurophotonics 035008-11 Jul–Sep 2021 Vol. 8(3) ROC Values (%) Ratios ROC Values (%) NCR Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases 4 Discussions 4.1 Behavioral Findings The Stroop task is one of the most favorable neuropsychological tests to investigate the cognitive 31,60–62 impairments in attention and inhibition control. The behavioral results in this paper con- firm the claim that both the reaction times and accuracy rates are statistically different between healthy controls and patients with neuropsychiatric diseases as seen in Tables 3 and 4 and in Figs. 4(a)–4(d). The reaction rates for a specific stimulus type increase, accuracy rates decrease (error rates increase) as the severity of the attention and inhibition controls are impaired. This phenomenon has been also observed in this study as seen in Figs. 4(a)–4(d). In a review by Foti et al., several studies reported an impairment in executive functions observed in migraine patients as measured by different neuropsychological tasks, including the Stroop task. The Stroop interference effect, as measured by the difference in the reaction times, provides an insight to inhibition (I-N) and facilitation (I-C) controls. The results are in parallel with most of the findings in literature where an impairment in executive functions in neuropsychiatric patients was observed for many tasks including the Stroop task. There are several methods to further quantify the behavioral results of the stroop test. These are usually in form of combi- nations of error (accuracy) rates and reaction times. This paper used a simpler metric: CQ, where the difference in executive functions of these four groups were emphasized better than any one parameter alone. In fact the classification accuracy of this metric between healthy controls and diseased subjects was 76.25% as seen in Fig. 8(a). In a study by Erdodi et al., classification accuracy of inverted Stroop test metrics between healthy controls and patients that were clin- ically referred for neuropsychological assessment were found to be less sensitive (14% to 25%), but comparably specific (85% to 90%) while the findings in this study were contradictory with very high sensitivity (100%) but less specificity (77.6%) for this metric. Certainly there are many differences especially in the choice of subject groups, the Stroop test employed and the param- eters used in the analysis of that study and this one, but it is evident that the behavioral param- eters alone cannot yield high accuracies in classification for neuropsychological assessment. Table 3 Reaction times in milliseconds (mean ± standard deviation). Number of subjects are given in parentheses. Group NS CS ICS p-value Controls 975.7  134.7 1041.8  190.8 1170.6  227.60.0375 Migraineurs 1233.0  394.6 1352.2  345.9 1560.7  576.8 0.0748 OCD 1593.1  453.3 1631.5  400.7 2019.7  512.70.006 Schizo 1750.9  458.1 1815.5  323.4 2202.2  346.4 <0.0001 −7 −9 −9 p-value <10 <10 <10 0 Table 4 Accuracy rates in percentages (mean ± standard deviation). Group NS CS ICS p-value Controls (13) 98.2  2.297.2  4.792.8  7.40.0297 Migraineurs 92.0  18.295.2  8.482.3  24.9 0.0815 OCD 98.4  2.596.8  4.486.3  17.5 <0.001 Schizo 91.4  13.990.4  13.769.6  21.4 <0.00001 −7 −9 −8 p-value <10 <10 <10 0 Neurophotonics 035008-12 Jul–Sep 2021 Vol. 8(3) Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases 4.2 fNIRS Findings GE has been proposed and shown to be a robust and reliable biomarker in many fNIRS 21,64–69 studies. In most cases resting fNIRS data were used to calculate the FC matrices. GE values were always computed after a threshold value was adapted. This threshold value was an actual correlation coefficient value. Since a fixed correlation coefficient threshold would yield different number of non-zero entires in the binarized FC matrices, the GE values would be incomparable (For a good review on the appropriate choice of a threshold value, we can refer to Ref. 70). Regardless of the choice of PCA components and threshold values, almost all of the studies concluded that as the cognitive impairment increases, global network efficiency 64,71 decreases compared to healthy controls. GE is also shown to be affected by age. The GE based findings in this paper are in alignment with the literature, where a decline in GE was observed for neuropsychiatric patients. On average across all stimulus types, there was a 17% CM reduction in the GE of migraineurs, 20% of OCD, and 48% of schizophrenics from controls as can be inferred from Table 6. 4.3 On the Classification Accuracy of NCR There have been several studies that investigated the classifier accuracy for schizophrenia 72–78 patients. Yet, the same cannot be said for migraine and OCD patients. Table 9 is a selection of such studies where search words: {fNIRS, classification, schizophrenia, and accuracy} were used. Most of the classification studies including schizophrenia patients reported a classification accuracy in the range of 76% to 89.7% as seen in Table 9. This study achieves at a 100% accu- racy score for schizophrenia patients as seen in Fig. 8(a). The strength of this value is inherent to the computation of the NCR where behavioral and physiologic data are fused. The accuracy is lower between neuropsychiatric patients as seen in Fig. 8(b) columns M-O, M-S and O-S with M-S being the highest at 89.81%. This is an indicator that cognitive impairments in migraine patients might not be as severe as the OCD and schizophrenics, which are close to dissociative disorder diseases. 4.4 Proposal This study is yet another one that proposes an algorithmic approach to the data analysis pipeline of fNIRS studies. The aim is to improve the clinical significance of the features extracted from fNIRS recordings so as to pledge an everlasting position of fNIRS in clinical settings. Only a 85–89 handful studies investigated the differential diagnostic accuracy of fNIRS features. To start- off, here is a checklist of specific expectations of any fNIRS-based algorithmic approach for a clinical study: E1: Provide clinically relevant information regarding brain physiology E2: Provide strong specificity for clinical data E3: Provide a better statistics than behavioral data alone E4: Provide an easy and applicable/adoptable algorithm fNIRS has been one of the few instruments that can provide insight to brain neurophysiology non-invasively and rapidly. Yet, these two offerings should match with the expectations listed above. Since fNIRS provides information regarding the cerebrovascular reactivity to cognitive or physiological stimuli, we expect that any measurement from patients with brain disorders should provide insight to neurobiology of the disease. To address E1, fNIRS is famous for bestowing local hemodynamic activity. Moreover, GE extracted from the dynamic changes of such a local data elucidate the level of collaborative effort exerted during a cognitive task. So with a number such as GE one can capture the hemodynamics of cognition. To address E2, several groups reported medium to high accuracies for classification of fNIRS 29,34,73,74,79,81 signals. These studies mostly included two groups: healthy controls and a diseased Neurophotonics 035008-13 Jul–Sep 2021 Vol. 8(3) Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases group. No multi-group comparison has been attempted with fNIRS, unlike fMRI. NCR derived from CQ and ratio of GEs are shown to be highly specific for diseases. To address E3, so far the statistical significance of behavioral data have been praised in many studies in classifying patient groups. fNIRS is expected to improve the statistics and this is what NCR offers. It gives a higher accuracy in separating healthy from diseased brain, much like a blood pressure monitor. To address E4, a valid critique is that the proposed approach is easy and applicable. It is more of an iterative approach than a theoretical one. Yet, the FC-ðPCÞ seems to do the trick in sepa- rating the FC matrices. Interestingly there are not many studies of other psychiatric diseases. Still there are pioneer- 14 10 ing works on autism spectrum disorder, obsessive compulsive disorder, depression, and 23 11,12 migraine. There is always those who have not lost faith in fNIRS. A very hopeful study by Ehlis et al. points us to the right direction: future studies should also focus on the usefulness of fNIRS as a supportive tool for choosing the most promising treatment approach for a specific patient. Using fNIRS, neurophysiological markers that might predict treatment outcomes (and may thus be relevant for personalized medicine) could be easily identified. Several studies actually achieved this ambitious goal set by Ehlis et al. For a good review of use of fNIRS in psychiatry, please see Refs. 9, 90, and 91, specifically in autism, and its role in neurofeed- 92 93 94 back, in pain, and in neurology. Only a handful of them are listed in Table 9. This study is the first in several aspects: (1) to show a high specificity of fNIRS for various types of neuro- psychiatric diseases (more than 2); (2) in providing an fNIRS-derived biomarker (namely the NCR) with very high accuracy that is also clinically relevant; and (3) in that it does not attempt to find a correlation between behavioral data and physiological data, rather it combines them since behavior cannot be produced without physiological activation. As observed by James, “A sci- ence of the relations of mind and brain must show how the elementary ingredients of the former correspond to the elementary functions of the latter.” 5 Conclusion This study is an extension of a previous work, which concluded that a PC-based approach should be preferred when generating the FC matrices. Separating the FC into a CM and DM network led to the ratio of the GE values calculated from these two matrices. This ratio was then multiplied with the CQ, which is a direct measure of cognitive load. Therefore, a new biomarker, NCR, was generated and proposed. The mean NCR across all stimuli for four subject groups in Table 10 (NCRðControlÞ¼ 210  66, NCRðMigraineÞ¼ 89  34, NCRðOCDÞ¼ 57  20, −10 and NCRðSchizoÞ¼ 38  15, p < 10 ) gives the best classification accuracy with respect to ROC between healthy controls and diseased subjects (ACCUR ¼ 96.25%,and AUC ¼ 99.31%), much better than the accuracies obtained from only CQ, behavioral parameter (CQðControlÞ¼ 94  16, CQðMigraineÞ¼ 73  24, CQðOCDÞ¼ 59  18,and −8 CQðSchizoÞ¼ 48  15, p < 10 ) where (ACCUR ¼ 81.2%, and AUC ¼ 89.3%). The results are all in favor of this biomarker. So we might conclude that fNIRS-derived NCR is a strong candidate as a biomarker for neuropsychiatric diseases. It can safely be used in diagnosis and prognosis of neuropsychological assessments of at least a group of neuropsychiatric disorders. 6 Appendix The following tables present the values of the figures in the manuscript. 6.1 Behavioral Data The following Table 5 is formed from Tables 3 and 4. Neurophotonics 035008-14 Jul–Sep 2021 Vol. 8(3) Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases Table 5 CQ values (mean ± standard deviation). Group NS CS ICS p-value Controls 102.42  14.15 96.00  16.87 82.44  19.19 0.0147 Migraineurs 82.80  27.50 75.04  19.68 61.41  28.62 0.0341 OCD 66.85  19.49 63.12  16.49 47.18  19.44 0.0024 Schizo 56.99  19.77 52.05  14.62 33.53  15.19 <0.00001 −7 −9 −8 −8 p-value <10 <10 <10 <10 CM CM Table 6 Optimized GE values computed from FC s (mean ± standard deviation). Group NS CS ICS p-value Controls 0.2951  0.0482 0.3478  0.0543 0.3207  0.0634 0.0326 Migraineurs 0.2821  0.0275 0.2342  0.0321 0.2784  0.0287 0.0017 OCD 0.2350  0.0662 0.2768  0.0549 0.2548  0.0549 0.0197 Schizo 0.1793  0.0527 0.1587  0.0701 0.1624  0.0635 0.0482 −10 −10 −10 −10 p-value <10 <10 <10 <10 DM DM Table 7 Optimized GE values computed from FC s (mean ± standard deviation). Group NS CS ICS p-value Controls 0.1554  0.0482 0.1621  0.0543 0.1710  0.0634 0.78 Migraineurs 0.2246  0.0275 0.2210  0.0321 0.2255  0.0287 0.82 OCD 0.2901  0.0891 0.2728  0.0662 0.2766  0.0549 0.71 Schizo 0.2153  0.0527 0.2183  0.0701 0.2292  0.0635 0.70 −4 −4 −4 −10 p-value <10 <10 <10 <10 6.2 GE Values Table 8 ROC analysis of mean values of features. SENS: sensitivity, SPEC: specificity, ACCUR: accuracy, AUC: area under the curve. Feature SENS SPEC ACCUR AUC CQ 100 77.6 81.2 89.3 GE 92.3 89.6 90 95.1 NCR 100 95.5 96.3 99.3 Neurophotonics 035008-15 Jul–Sep 2021 Vol. 8(3) Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases Table 9 Classification performances of fNIRS studies. Study Diseased group Features Classifier Accuracy Chen et al. Schizophrenia GLM based SVM 85% Dadgostar et al. Schizophrenia Time series SVM 87% Einolou et al. Schizophrenia Energy SVM 84% Eken et al. SSD FC based SVM 82% Hahn et al. Schizophrenia Time series LOOCV 76% Ji et al. Schizophrenia FC based SVM 89.7% Shoushtarian et al. Chronic tinnitus FC based Naïve Bayes 78.3% Xu et al. ASD Time series CNN & LSTM 93.3% Yang et al. Schizophrenia FC based SVM 84.67% Yang et al. MCI Time series CNN 98.61% Yang et al. MCI FC based CNN 95.81% Yoo et al. MCI Time series SVM 79.49% 6.3 NCR Values Table 10 NCR values computed from Table 11 (mean ± standard deviation). Group NCR Controls 209.89  65.67 Migraine 88.50  33.97 OCD 57.36  20.28 Schizo 38.22  14.52 −10 p-value <10 Table 11 NCR values computed from Eq. (7) (mean ± standard deviation). Group NS CS ICS p-value Controls 225.97  125.93 226.84  80.88 176.85  80.35 0.342 Migraine 104.00  40.53 81.09  28.04 80.41  45.20 0.098 OCD 57.68  23.92 68.54  27.39 45.88  22.78 0.017 −5 Schizo 49.48  25.05 39.68  15.37 25.48  14.43 <10 −10 −10 −10 −10 p-value <10 <10 <10 <10 Neurophotonics 035008-16 Jul–Sep 2021 Vol. 8(3) Akın: fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases Disclosures The author does not declare any biomedical financial interests or potential conflicts of interest. The author would like to thank the reviewers for their insightful and constructive comments. Acknowledgments This project was sponsored by a grant from TUBITAK (Project No: 108S101) and Bogazici University Research Fund (Project No: 5106). The author wishes to thank Deniz Nevsehirli for his contributions to fNIRS optode development; Dr. Nermin Topaloĝu and Dr. Sinem Burcu Erdoĝan for data collection. References 1. B. J. 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Ata Akın received his PhD in biomedical engineering from Drexel University in 1998, his MS degree in biomedical engineering, and BS degree in electronics and telecommunication engi- neering both from Istanbul Technical University in 1995 and 1993, respectively. He worked at Drexel University, Bogaziçi ˘ University, Bilgi University, and recently joined Acibadem University as the Dean of the Faculty of Engineering. His research interests are primarily in functional brain imaging and design of medical instrumentation. Neurophotonics 035008-21 Jul–Sep 2021 Vol. 8(3)

Journal

Neurophotonics – SPIE

Published: Jul 1, 2021

Keywords: fNIRS; neuropsychiatric diseases; functional connectivity; global efficiency; cognitive quotient; neurocognitive ratio

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