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Deranged Dimensionality of Vestibular Re-Weighting in Multiple Chemical Sensitivity

Deranged Dimensionality of Vestibular Re-Weighting in Multiple Chemical Sensitivity applied sciences Article Deranged Dimensionality of Vestibular Re-Weighting in Multiple Chemical Sensitivity 1 , 2 , 1 3 4 Alessandro Micarelli *, Andrea Viziano , Elisa Micarelli , Giuseppe Genovesi , 1 1 Ernesto Bruno and Marco Alessandrini Department of Clinical Sciences and Translational Medicine, University of Rome Tor Vergata, Rome 00133, Italy; andrea.viziano@gmail.com (A.V.); bruno.ernesto@libero.it (E.B.); malessandrini63@gmail.com (M.A.) Neuroscience Unit, Department of Systems Medicine, University of Rome Tor Vergata, Rome 00133, Italy Department of Biology, University of Rome Tor Vergata, Rome 00133, Italy; elisamicarelli@yahoo.it Department of Experimental Medicine, “La Sapienza” University, Rome 00185, Italy; mcs.genovesi@libero.it * Correspondence: alessandromicarelli@yahoo.it; Tel.: +39-06-2090-2925; Fax: +39-06-2090-2930 Academic Editor: Yang Kuang Received: 22 August 2016; Accepted: 25 October 2016; Published: 2 November 2016 Abstract: Background: Multiple chemical sensitivity (MCS) is a chronic multisystem condition characterized by low levels of multiple chemical susceptibility inducing a spectrum of central nervous system symptoms, including dizziness. Thus, considering (i) the overlapping psychogenic and organic burdens shared in MCS development and in vestibular disorders; (ii) the number of previous studies describing central processing impairment related to inner ear inflow in this syndrome; and (iii) the lack of literature with respect to clinical evidence of the presentation of MCS dizziness, the purpose of the present study was to highlight the possible hidden aspects of vestibular impairment by applying the recent contribution of implemented otoneurological testing, inferential statistic and principal component (PC) analysis in 18 MCS and 20 healthy subjects (HC); Methods: Both groups filled in a dizziness and environment exposure inventory and underwent the Rod and Disc and Rod and Frame Test, video Head Impulse Test (vHIT) and Static Posturography Test (SPT) with fast Fourier Transform (FFT). Between-group analysis of variance and PC analysis implemented on otoneurological variables were performed; Results: Defective vestibular processing was identified in 18 MCS patients (11 female and 7 male; mean age 49.5  9.3 years) by finding a significant increase in SPT and FFT parameters and in Visual Dependency (VD) behaviour and a decrease in vHIT scores. Component correlation analysis in MCS showed a positive correlation of FFT parameters in PC1 and SPT parameters in PC2 with a negative correlation of vHIT and VD values in PC2. HC subjects demonstrated a positive correlation of VD and SPT parameters in PC1 and FFT parameters in PC2. Conclusion: Inferential and PC analysis provided the opportunity to disclose such possible hidden phenomena to (i) support that MCS physiopathological cascades could lead to a vestibular decay; and (ii) suggest rearrangement of the dimension of the variables as an aspect of near-optimal re-weighting, possibly underpinning the dizzy symptoms complained of by MCS patients. Keywords: multiple chemical sensitivity; vestibular diseases; visual dependency; component analysis; fourier transform 1. Introduction Multiple chemical sensitivity (MCS) is a chronic polysymptomatic, multisystem condition characterized by subjective susceptibility to low levels of a wide spectrum of environmental compounds (such as petrol, perfume or pesticides) [1,2]. It constitutes a relatively common clinical diagnosis in western populations [2], considering that the prevalence of self-reported chemical sensitivity symptoms in population-based studies has been found to range from 9% to 33% [2]. Relapsing and remitting Appl. Sci. 2016, 6, 330; doi:10.3390/app6110330 www.mdpi.com/journal/applsci Appl. Sci. 2016, 6, 330 2 of 15 central nervous system (CNS) manifestations including difficulty concentrating, memory problems, fatigue, depression, daytime sleepiness, “spaciness” (derealisation), tension, irritability and dizziness are commonly referred to by the patients [2,3]. Major criteria to define the clinical spectrum of MCS were introduced by Cullen [4]; however, the discussion on definition and nomenclature reflects the fact that the aetiology of MCS is still unclear and a matter of debate [2,5]. The most frequently discussed aetiologies include neurogenic inflammation [6], classical conditioning [7] and biochemical disruptions [8]. According to the neural sensitization theory, MCS is attributed to a pathological hyper-reactivity of neurons in olfactory and limbic areas of the brain [9]. However, the altered CNS responses are hypothesized to be paralleled by increased anxiety, avoidance, anticipatory stress [9] and attention bias [6] to chemical exposure. According to these theories, several studies in the last few years have shown cerebral blood flow distribution abnormalities in patients with MCS, especially while processing odorous substances [10–12]. In particular, MCS sufferers have been seen to react peculiarly to sensory stimuli, with activation of brain areas connected with motivational, emotional and non-conscious processing of information, such as the amygdala and the hippocampus, encompassed in the limbic system [10–14], whose pathways are known to mediate CNS sensitization and their dysregulation contributes to abnormalities of behaviour and mood, endocrine, autonomic and immune function [15]. In turn, the same brain circuits seem to be pivotal in vestibular processing, given the fact that the limbic system—previously thought to be engaged in the physiopathological underpinning of this disorder [1]—receives neural input from the vestibular cortex, and therefore it could be involved in vestibular stream processing also below the level of awareness [16,17]. To screen populations for MCS correctly, Miller and Prihoda developed the environment exposure sensitivity inventory (EESI) and its quick form (QEESI), which are considered the most reliable tools for research on patients complaining of chemical sensitivity [18]. For instance, researchers have investigated the association between chemical sensitivity and other kinds of environmental intolerances, including noise and olfactory sensitivity [19]. This has been carried out principally by means of questionnaire surveys in clusters of healthy and affected subjects (i.e., teenage students, twins) and, although overlapping phenomena involving physiopathological aspects of MCS and vestibular processing are possible, any research on intolerance to chemicals and vestibular disorder had been devised previously. Thus, due to (i) the overlapping psychogenic and organic burdens that, below the level of consciousness, are shared not only in MCS development but also in the vestibular disorders [1,10–13,15–17,20]; (ii) the number of previous studies describing central processing impairment related to inner ear inflow in this syndrome [21,22]; and (iii) the lack of literature on clinical evidence of the presentation of dizziness [2,3], the purpose of the present exploratory study was to highlight the possible MCS hidden clinical/subclinical aspects of vestibular impairment by inferential and principal component (PC) analysis applied on the contribution of recently implemented otoneurological testings. This, with the aim of disclosing possible implementations in diagnosing and monitoring undiscovered aspects underpinning the natural history of MCS, will possibly assist in suggesting future treatment choices in a clinical setting. 2. Experimental Section 2.1. Participants And Study Design Twenty-three consecutive MCS subjects were enrolled in the study. Diagnosis of MCS was achieved according to the US Consensus Criteria for MCS [23] and the revisions proposed by Lacour et al. [24]. We also enrolled 20 gender- and age-matched healthy subjects serving as the control group (HC). By means of a thorough medical history, collected by experienced specialized medical personnel, both eligible MCS and HC subjects were required to report a negative anamnesis for malignancy, Appl. Sci. 2016, 6, 330 3 of 15 head trauma, neuropsychiatric disorders, metabolic, cardiovascular, endocrine, infectious and otoneurological diseases. The peripheral blood of MCS and HC was tested for the usual parameters and neurological as well as neuropsychiatric disturbances were excluded with the Mini Mental State Examination, Magnetic Resonance Imaging and DSM IV-TR General Help-Seeking Questionnaire. Finally, no patient was pregnant or breastfeeding and all subjects taking drugs possibly affecting vestibular functions were excluded. The study adhered to the principles of the Declaration of Helsinki and all the participants provided written informed consent after receiving a detailed explanation of the study. 2.2. Otoneurological Testing After a thorough visual, oculomotor, locomotor and otoneurological clinical examination (the latter including pure tone audiometry and impedance, binocular electrooculography analysis with positional manoeuvres, Head Shaking Test, clinical Head Impulse Test [cHIT] as well as limb coordination, gait observation and Romberg stance Test) performed by an experienced neurologist and otoneurologist, all MCS and HC subjects underwent: - Rod and Disc Test (RDT): According to Cousins et al. [25] visual dependency (VD) was evaluated by means of the RDT software (http://www.imperial.ac.uk/medicine/dizzinessandvertigo) on a laptop computer in order to study visual vertical under kinetic visual stimulation [26,27]. Subjects were seated in front of the computer in a darkened room with their heads held against a viewing cone that blocked extraneous visual orientation cues. The diameter of the cone at the subjects’ eyes was 15 cm with a depth of field of 30 cm, subtending a viewing angle of 39 . The visual stimulus consisted of a luminous white 6 cm rod on a black background. The rod rotated 360 in either direction about its midpoint in the central 11 of the visual filed. Outside this central zone, the viewing screen was filled with a collage of 220 off-white dots, each 8 mm (1.5 of visual field) in diameter, randomly distributed on a black background. Subjects controlled the orientation of the rod with a roller mouse. They were instructed to align the rod to their perceived vertical (the subjective visual vertical) under three conditions. In the first test the collage of dots was stationary (N) while in the second and third the collage rotated at 30 /s clockwise (CW) or counter-clockwise (CCW), respectively. Subjects were tested four times per each condition, presenting the latter two conditions randomly after condition 1. During each trial the rod was initially set randomly at 40 from vertical and the rod tilt per trial was recorded as the degrees’ difference between true vertical and the subjects’ final rod placement. - Rod and Frame Test (RFT): According to Witkin and Asch [28], in order to study the subjects’ visual vertical tilts under static visual stimulation, a standard RFT square frame device described by Fiori et al. [29] was used. The frame was tilted 33 CW, CCW, or not tilted (0 , N); the rod 11 or 22 , CW or CCW. Thus, 12 randomly presented conditions were achieved, each containing three trials [30]. Errors were calculated as deviation from the gravitational vertical position of the rod. - Video Head Impulse Testing (vHIT): For vestibulo-ocular reflex (VOR) gain measurements, TM the reliability of vHIT [31] (EyeSeeCam System [32]) and the technique proposed by Blodow et al. [33] were used to test the lateral semicircular canal. Thus, anomalies in vHIT outcomes were assessed in case of (1) abnormal gain with respect to the calculated normative data; and (2) the presence of refixation saccades. Amplitudes ( /s) and latencies (ms) of both pinpointed refixation and physiological saccades were computed at peak eye velocity ( /s). According to TM the manufacturer of the software (OtoAccess ), both side gain mean and median (med) values recorded at 60 ms as well as regression slope (reslo) were calculated and extracted onto an .xls file. Artefacts possibly related to mask slippage and positioning, calibration, handedness of the operator, patients’ neck stiffness and setting of the laboratory, were controlled by means of a technique proposed by McGarvie et al. [34]. According to the technique proposed by the same group [34] to reach adequate numbers of head values for the lateral semi-circular canal, Appl. Sci. 2016, 6, 330 4 of 15 the operator delivered a range of velocities in a random order and direction to achieve at least 10 artefact-free impulses. By means of this technique, 50–60 impulses were delivered in each canal direction to ensure that the desired number of artefact-free responses was achieved. - Static Posturography Testing (SPT): The previously instructed subjects maintained an upright position on a standardized platform for static posturography (EDM Euroclinic ) during a 60 s recording period per condition, defined as eyes closed (CE) or opened (OE) while standing on either the stiff platform (SP) or a 6 cm heightened AIREX Balance-pad plus foam carpet (FC, AIREX AG, 25% compression resistance, 20 kPa; apparent density 55 kg/m ; tensile strength 260 kPa). The sampling frequency was set at 25 Hz [35,36] and the centre of pressure (CoP) was supervised during the test. Classic posturographic parameter evaluations involving the trace length (length), the surface of the ellipse of confidence (surf), and the mean velocity (avspe) of body sways with relative SD (stdspe) were performed, as well as the fast Fourier transform (FFT) elaboration of oscillations on both the X (right-left) and Y (forward-backwards) planes [35,36]. Time-domain oscillation signals (X and Y) were extracted by the original software manufacturer into .txt format and the FFT elaborations were gained through Matlab software. In order to obtain the FFT of X and Y oscillations, a core function (Appendix A) was implemented using as input the.txt file provided by the software manufacturer. A main script was written to acquire the FFT for each patient, related to four different conditions, by accessing to the above mentioned Matlab function. For each condition the script returned a file containing the normalized value of the FFT for X and Y oscillations. Spectral values (power spectra, PS) of body oscillations were quantified on an .xls file, for every frequency from 0.01 to 5.00 Hz [35,36]. According to previous experiences [35,36], the frequency spectrum was clustered into three groups: 0.01–0.70 Hz (low frequency interval); 0.70–1.00 Hz (middle frequency interval); 1.00–5.00 Hz (high frequency interval). Within each group, the spectral intensity was determined by adding the relative PS and the group mean PS ( SD) [35,36]. 2.3. Validated Questionnaires (VQ) In order to fulfil the exploratory approach of the study all MCS and HC subjects filled in the following forms before instrumental measurement: (1) a modified version of the QEESI Symptom Severity scale [37] in which subjects scored from 1 (low) to 3 (severe) the intensity of head-related (HEAD), cognitive-related (COG), affective-related (AFF), neuromuscular-related (NM), musculoskeletal-related (MS), skin-related (SKIN), genitourinary-related (GU), gastrointestinal-related (GI), heart/chest-related (COR), airway or mucous membrane-related (AIR/MM) symptoms. (2) the Italian Dizziness Handicap Inventory (DHI), including 25 items aimed at assessing patients’ functional (nine questions), emotional (nine questions) and physical (seven questions) limitations [38] by means a multiple choice scheme: “yes” (4 points), “sometimes” (2 points) and “no” (0 points). 2.4. Data Handling and Statistical Analysis In order to fulfil the exploratory approach of the study, in which a large number of variables were studied, the protocol data handling was conceived in a double-folded inferential and multivariate statistical analysis: - Inferential Statistical Analysis Mean and standard deviations (SDs) for all recorded variables and for QEESI and DHI subscale scores were calculated in both HC and MCS. In order to assess that data were of Gaussian distribution, the D’Agostino K squared normality test was applied (where the null hypothesis is that the data are normally distributed). Appl. Sci. 2016, 6, 330 5 of 15 A ‘between-groups’ analysis of variance (ANOVA) was performed for each VQ and otoneurological variable. Age and gender were handled as continuous and categorical predictors, respectively. This study tested the null hypothesis (H0) that the two group measurements would not show any statistically significant difference. The significant cut-off level ( ) was set at a p value of 0.01 and the Bonferroni correction for multiple comparisons was performed with post hoc tests of significant main effects. Thus, p levels associated with the difference between the two groups were considered as statistically significant if lower than 0.01. Then, Spearman’s rank correlation was implemented between QEESI and DHI sub-items scores and significant variables and a Bonferroni correction for multiple comparisons was applied in order to avoid family-wise error, (STATISTICA 7 package for Windows). - Principal Component Analysis Regarding the multivariate analysis step of the study, Principal Component Analysis (PCA) with varimax rotation was performed to detect—with statistically significant otoneurological variables in both groups—hidden phenomena mainly through reducing the variables to find the most important ones, finding patterns in the data and classifying any combination of variables. The number of factors in principle was determined by a combination of the Kaiser/Guttman criterion (only factors with eigenvalues larger than 1 are incorporated in the model) and interpretability. Also by using scree plots, the eigenvalue was plotted against the component number, to evaluate if a factor was relevant or not. Finally, a component coefficient analysis was performed between variables and factors. 3. Results 3.1. Subjects Of 23 MCS patients, 3 were using psychiatric medications, 1 referred positive anamnesis for alcohol addiction, and 1 received diagnosis of hypothyroidism, and they were excluded. Thus, 18 right-handed MCS patients (11 female and 7 male; mean age 49.5  9.3 years) were included in the study. In turn, the HC group consisted of 20 right-handed healthy subjects (12 female and 8 male; mean age 48.6  11.4 years). Socio-demographic and medical details of MCS included in the study after the clinical work-up are presented in Table 1. Table 1. Socio-demographic and medical details of total and included MCS patients after clinical work-up. MCS, Multiple Chemical Sensitivity. Socio-demographical Total MCS Reason of Exclusion, Number, Gender Included MCS Variables Number 23 See below 18 Gender 14 female, 9 male See below 11 female, 7 male Age 50.3  8.9 years - 49.5  9.3 years Cosmetologist (n = 4) psychiatric medications (n = 1, female) Cosmetologist (n = 3) Hairdresser (n = 5) - Hairdresser (n = 5) Teacher (n = 3) hypothyroidism (n = 1, female) Teacher (n = 2) Nurse (n = 3) psychiatric medications (n = 1, female) Nurse (n = 2) Employment Ex-soldier (n = 3) alcohol addiction (n = 1, male) Ex-soldier (n = 2) Obstetrician (n = 2) - Obstetrician (n = 2) Taxi driver (n = 2) psychiatric medications (n = 1, male) Taxi driver (n = 1) University Professor (n = 1) - University Professor (n = 1) “-“ = none. 3.2. Validated Questionnaires Significant differences in results (p < 0.01, corrected) in DHI and QEESI scores in MCS and HC subjects are shown in Figure 1A,B, respectively. Appl. Sci. 2016, 6, 330  6 of 16  Appl. Sci. 2016, 6, 330 6 of 15 Appl. Sci. 2016, 6, 330  6 of 16  Figure 1. In (A) histograms highlighting dizziness handicap inventory (DHI) total scores in Multiple  Chemical Sensitivity (MCS) and healthy subjects (HC); In (B) star diagram showing MCS and HC  quick environment exposure sensitivity inventory (QEESI) sub‐items results in head‐related (HEAD),  Figure 1. In (A) histograms highlighting dizziness handicap inventory (DHI) total scores in Multiple  Figure 1. In (A) histograms highlighting dizziness handicap inventory (DHI) total scores in Multiple cognitive‐related  (COG),  affective‐related  (AFF),  neuromuscular‐related  (NM),  Chemical Sensitivity (MCS) and healthy subjects (HC); In (B) star diagram showing MCS and HC  Chemical Sensitivity (MCS) and healthy subjects (HC); In (B) star diagram showing MCS and HC musculoskeletal‐related  (MS),  skin‐related  (SKIN),  genitourinary‐related  (GU),  quick environment exposure sensitivity inventory (QEESI) sub‐items results in head‐related (HEAD),  quick environment exposure sensitivity inventory (QEESI) sub-items results in head-related (HEAD), gastrointestinal‐related  (GI),  heart/chest‐related  (COR),  airway  or  mucous  membrane‐related  cog cognitive-r nitive‐related elated (COG), (COG), affective-r   affect elatedive (AFF), ‐relate neur d  omuscular (AFF),  -related neurom (NM), uscmusculoskeletal-r ular‐related  (NM) elated,  (AIR/MM) symptoms.  m (MS), uscu skin-r loskelet elatal ed‐related (SKIN),  genitourinary-r (MS),  skelated in‐related (GU),  gast (SrK ointestinal-r IN),  genito elated urinary (GI), heart/chest-r ‐related  elated (GU),  g (COR), astrointestina airwaylor‐related mucous   (GI), membrane-r   heart/chest elated ‐related (AIR/MM)   (COR), symptoms.   airway  or  mucous  membrane‐related  3.3. Otoneurological Data  (AIR/MM) symptoms.  3.3. Otoneurological Data MCS  and  HC  subjects  did  not  show  any  clinical  otoneurological  abnormalities  during  3.3. Otoneurological Data  examination.  MCS and HC subjects did not show any clinical otoneurological abnormalities MCS  and  HC  subjects  did  not  show  any  clinical  otoneurological  abnormalities  during  during examination. 3.4. Inferential Statistical Analysis  examination.  3.4. Inferential Statistical Analysis When  comparing  HC  and  MCS  subjects,  a  significant  ‘between‐groups’  effect  (p  <  0.01,  3.4. Inferential Statistical Analysis  corrected) When was comparing  found, HC high and light MCS ing  subjects, statistically a significant  significan‘between-gr t higher vaoups’ lues in ef MCS fect ( ppa <ti 0.01, entsc in orr: ected) (i) PS  was recorded found, When on  highlighting comparin  both X pla g  n HC statistically e (X)   an an d dMCS  Ysignificant  pla  subj ne (Y ects, ) higher within   a  sign values low ificant  (L) in  an ‘between MCS d middle patients ‐gro (M ups’ in: ) frequency  (i) effect PS r ecor (p in  < ded t erval 0.0 on 1s,   corrected) was found, highlighting statistically significant higher values in MCS patients in: (i) PS  both recorded X plane  on (X) SP and and Ydurin planeg (Y) CEwithin  and OE low condit (L) and ionmiddle s (Figur (M) e 2A, frequency  Table 2) intervals ; (ii) clasrsi ecor c posturogra ded on SP phy and  recorded during parameters CE on and   main both OEly X conditions   in pla CE ne  condit (X)(Figur  anion d Y e  an 2pla A, d n T on e able  (Y foam ) 2within ); (ii) carpet classic  low (Tab  (L postur )le an 2)d;ography   middle and (iii )parameters (M  RD ) Tfrequency  measureme mainly  intn ein rval ts,CE  in s   recorded on SP and during CE and OE conditions (Figure 2A, Table 2); (ii) classic posturography  condition both CW  and and on +40° foam  andcarpet  CCW(T an able d −240 );°and  condit (iii)ions RDT (F measur igure 2A, ements,  Table in 2) both . Conver CW and sely,+40  MCS and  subjects CCW  pa and were rameters  found 40 conditions   main to bely sign  in (Figur if CE ic acondit nt ely 2 A, impai ion Table  an red d 2  ).in on Conversely   VO foam R ga carpet in ,and MCS  (Tab  reslo subjects le  2) values, ; anwer d  bi (ieiilat )found  RD erallTy measureme to (Figu be significantly re 2B,n Table ts, in   both CW and +40° and CCW and −40° conditions (Figure 2A, Table 2). Conversely, MCS subjects  impair 2).  Mean ed,in  SD VOR s  and gain   both and  srignif esloic values, ant  and bilaterally   not  sign(Figur ificant e  effects 2B, Table   for 2 ).alMean, l  variaSDs bles  and measur bothemen significant ts  are  were and shown not  found  in significant  Tab tol ebe 2.  sign effects ificafor ntly all impai variables red in measur  VOR ga ements in and ar re e slo shown  values, in T able bilate 2r .ally (Figure 2B, Table  2).  Mean,  SDs  and  both  significant  and  not  significant  effects  for  all  variables  measurements  are  shown in Table 2.  Figure 2. This represents all statistical significant differences in Visual Dependency scores, and spectral Figure  2.  This  represents  all  statistical  significant  differences  in  Visual  Dependency  scores,  and  values of body oscillations (A) and video Head Impulse Test measurements (B) when comparing spectral  values  of  body  oscillations  (A)  and  video  Head  Impulse  Test  measurements  (B)  when  Multiple Chemical Sensitivity (MCS) and healthy subjects (HC). Acronyms represent tags for Rod and Figure comparing   2.  This  Mu ltiple represents  Chemic   alal l  stati Senssitiv tical ity  sig  (MnCS) ificant  and  differe  healthnces y subjects   in  Visual  (HC) .Dependency  Acronyms rep   scores resent,  and tags   Disc test, RDT (counter-clockwise, CCW; clockwise, CW), spectral values of body oscillations on X spectral for  Rod  values and  Disc   of  body test,  RDT oscill  (counter ations  (A‐clock )  andw ise, video   CCW;   Hea dcl ock Impu wise, lse   Te CW), st  measurement spectral  values s  ( Bof )  when body   plane (X) and Y plane (Y) within low (L) and middle (M) frequency intervals recorded on stiff platform comparing oscillations Mu   onltiple   X  plane  Chemic   (X) aland  Sens  Yitiv   plane ity (M   (YCS) )  within  and health   low y(L su )  bjects and  mi (Hdd C)le.  Ac (M) ronyms   frequency  represent   intervals  tags   and during closed (CE) and opened eyes (OE) conditions and for vestibulo-ocular reflex (VOR) gain for recorded   Rod  and   on   Disc stiff   platform test,  RDT   and (counter   during ‐clock   clowsed ise,  (CE) CCW;   and   cl ock opened wise,   CW), eyes  (OE) spec tral conditions   values  of and   body   for   and regression slope, reslo; L, left; R, right; med, median. osci vestibulo llations‐oc  on ular  X reflex   plane (VOR)   (X)  and  gai nY  and plane  reg  (Y ression )  within  slope   low , reslo;   (L)  L, and left  mi ; R, dd rig le h(M) t; med   frequency , median.  intervals     recorded  on  stiff  platform  and  during  closed  (CE)  and  opened  eyes  (OE)  conditions  and  for  vestibulo‐ocular reflex (VOR) gain and regression slope, reslo; L, left; R, right; med, median.  Appl. Sci. 2016, 6, 330 7 of 15 Table 2. Otoneurological scores in MCS and HC subjects. MCS HC MCS HC Variables Variables Mean SD Mean SD p Value Mean SD Mean SD p Value Rod and Disc Test Fast Fourier Transform RDT/N/+40 2.39 2.05 1.63 1.82 p > 0.01 Low Freqeuncy Interval RDT/CCW/40 8.18 2.29 3.11 1.95 p < 0.01 CEXL 10.17 6.8 4.68 2.77 p < 0.01 RDT/CW/+40 8.09 2.43 2.83 1.24 p < 0.01 CEYL 6.24 4.02 2.89 1.3 p < 0.01 RDT/N/40 5.3 7.67 2.05 1.89 p > 0.01 OEXL 8.23 3.41 5.35 2.65 p < 0.01 RDT/CCW/+40 4.06 2.4 3.76 4.04 p > 0.01 OEYL 6 2.56 4.08 2.06 p < 0.01 RDT/CW/40 4.6 1.93 3.58 2.42 p > 0.01 CEXL-FC 8.28 2.18 9.67 1.9 p > 0.01 Rod and Frame Test CEYL-FC 8.24 2.55 7.51 1.79 p > 0.01 RFT/CW/+11 1.608333 0.793216 1.6125 0.43766 p > 0.01 OEXL-FC 7.21 3.99 6.87 2.54 p > 0.01 RFT/CCW/+11 2.238889 0.777166 2.13 0.706437 p > 0.01 OEYL-FC 6.14 2.83 5.13 2.22 p > 0.01 RFT/CW/11 1.758333 0.895947 1.58 0.88055 p > 0.01 Middle Frequency Interval RFT/CCW/11 2.183333 0.56983 2.1475 0.609459 p > 0.01 CEXM 0.67 0.37 0.28 0.07 p < 0.01 RFT/CW/+22 1.536111 0.875954 1.4925 0.739537 p > 0.01 CEYM 0.39 0.19 0.17 0.05 p < 0.01 RFT/CCW/+22 2.319444 0.518238 2.2225 0.713309 p > 0.01 OEXM 0.34 0.03 0.3 0.09 p > 0.01 RFT/CW/22 2.019444 0.500335 2.1125 0.783695 p > 0.01 OEYM 0.23 0.05 0.2 0.06 p > 0.01 RFT/CCW/22 1.580556 0.75909 1.435 0.675336 p > 0.01 CEXM-FC 0.84 0.29 1.02 0.5 p > 0.01 RFT/N/+11 1.883333 0.582338 1.9175 0.681962 p > 0.01 CEYM-FC 0.71 0.38 0.6 0.26 p > 0.01 RFT/N/11 2.125 0.53667 2.15 0.585797 p > 0.01 OEXM-FC 0.01 0 0.01 0 p > 0.01 RFT/N/+22 1.827778 0.68368 1.905 0.657927 p > 0.01 OEYM-FC 0.27 0.09 0.29 0.09 p > 0.01 RFT/N/22 2.119444 0.839142 2.0255 0.608324 p > 0.01 High Frequency Interval Classic Posturography Parameters CEXH 1.24 0.17 1.09 0.28 p > 0.01 RI 241.29 77.61 168.73 87.85 p < 0.01 CEYH 1.04 0.29 1.05 0.26 p > 0.01 RI-FC 348.46 101.2 251.55 135.66 p < 0.01 OEXH 0.96 0.32 1.01 0.28 p > 0.01 surf OE 407.25 124.89 414.06 212.59 p > 0.01 OEYH 0.72 0.13 0.77 0.13 p > 0.01 surf CE 939.71 312.53 656.25 369.4 p < 0.01 CEXH-FC 1.65 0.19 1.61 0.25 p > 0.01 surf OE-FC 831.72 243.34 740 452.75 p > 0.01 CEYH-FC 1.57 0.25 1.63 0.2 p > 0.01 surf CE-FC 2769.06 750.03 1565.61 782.82 p < 0.01 OEXH-FC 0 0 0 0 p > 0.01 length OE 552.94 103.76 512.85 98.4 p > 0.01 OEYH-FC 0.75 0.26 0.72 0.17 p > 0.01 length CE 986.93 310.77 739.17 150.98 p < 0.01 Video Head Impulse Test length 822.41 285.98 722 182.44 p > 0.01 gain med L 0.72 0.3 1.07 0.3 p < 0.01 OE-FC length 2237.64 675.93 1694.96 642.48 p < 0.01 gain mean L 0.72 0.31 1.08 0.3 p < 0.01 CE-FC avspe OE 9.11 1.15 8.55 1.63 p > 0.01 gain med R 0.72 0.3 0.97 0.22 p < 0.01 avspe CE 18.68 4 12.07 2.73 p < 0.01 gain mean R 0.72 0.31 0.97 0.23 p < 0.01 avspe 13.04 2.94 12.7 5.56 p > 0.01 reslo mean L 0.89 0.35 1.35 0.63 p < 0.01 OE-FC avspe 38.12 13.63 27.71 11.28 p < 0.01 reslo med L 0.87 0.34 1.34 0.64 p < 0.01 CE-FC stdspe OE 5.47 0.96 5.03 1.14 p > 0.01 reslo mean R 0.84 0.34 1.33 0.72 p < 0.01 stdspe CE 10.68 3.24 7.61 2.09 p < 0.01 reslo med R 0.84 0.35 1.32 0.72 p < 0.01 stdspe 9.92 3.23 8.06 3.24 p > 0.01 OE-FC stdspe 25.35 6.83 18.18 7.46 p < 0.01 CE-FC RDT, Rod and Disc test; RFT, Rod and Frame Test; CCW, counter-clockwise; CW, clockwise; N, neutral condition; CE, closed eyes; OE, opened eyes; X, spectral values of body oscillations on X plane; Y, spectral values of body oscillations on Y plane; L, low frequency interval; M, middle frequency interval; H, high frequency interval; surf, surface of the ellipse of confidence; avspe, mean velocity of body sways; stdspe, standard deviation of velocity of body sways; RI, Romberg index; FC, foam carpet; med, median; reslo, regression slope; L, left; R, right; SD, standard deviation; MCS, multiple chemical sensitivity; HC, healthy subjects. Significant results are reported in bold character. Appl. Sci. 2016, 6, 330 8 of 15 Appl. Sci. 2016, 6, x FOR PEER REVIEW  9 of 16  Within the vHIT results, analysed using the 36 ears of the 18 MCS patients, fifteen ears (41.6%) Appl. Sci. 2016, 6, x FOR PEER REVIEW  9 of 16  Within the vHIT results, analysed using the 36 ears of the 18 MCS patients, fifteen ears (41.6%)  had both covert and overt saccade components, and only one overt or covert saccade was observed in had both covert and overt saccade components, and only one overt or covert saccade was observed  the remaining Within twelve  the vH(33.3%) IT results, and  ana nine lysed(25%)  using ears, the 36r espectively ears of the 18 .  Head MCS pa peak tients, velocity  fifteen ear of ten s (41extracte .6%)  d in  the  remaining  twelve  (33.3%)  and  nine  (25%)  ears,  respectively.  Head  peak  velocity  of  ten  had both covert and overt saccade components, and only one overt or covert saccade was observed  measures per subject was 162 /s  11. extracted measures per subject was 162°/s ± 11.  in  the  remaining  twelve  (33.3%)  and  nine  (25%)  ears,  respectively.  Head  peak  velocity  of  ten  For illustrativ For illustraetipurposes, ve purposes, Figur  Figuere 3 3depicts  depicts an an ex example ample of of a asing single le MCS MCS  and and  HCHC  subject’s subject’s  patient patient   extracted measures per subject was 162°/s ± 11.  changes in postural and VOR parameters.  changes in postural and VOR parameters. For illustrative purposes, Figure 3 depicts an example of a single MCS and HC subject’s patient  changes in postural and VOR parameters.  Figure 3. An example of a single MCS and HC subject respectively represented in (A) and (D) for  Figure 3. An example of a single MCS and HC subject respectively represented in (A) and (D) video Head Impulse Test trace and results; in (B) and (E) for fast Fourier transform results; in (C) and  for video Head Impulse Test trace and results; in (B) and (E) for fast Fourier transform results; Figure 3. An example of a single MCS and HC subject respectively represented in (A) and (D) for  (F) for X‐Y plane plot. CE, closed eyes; OE, open eyes; X, spectral values of body oscillations on X  in (C) and (F) for X-Y plane plot. CE, closed eyes; OE, open eyes; X, spectral values of body video Head Impulse Test trace and results; in (B) and (E) for fast Fourier transform results; in (C) and  plane;  Y,  spectral  values  of  body  oscillations  on  Y  plane;  L,  low  frequency  interval;  M,  middle  oscillations on X plane; Y, spectral values of body oscillations on Y plane; L, low frequency interval; (F) for X‐Y plane plot. CE, closed eyes; OE, open eyes; X, spectral values of body oscillations on X  frequency interval; H, high frequency interval; FC, foam carpet; MCS, multiple chemical sensitivity;  M, middle frequency interval; H, high frequency interval; FC, foam carpet; MCS, multiple chemical plane;  Y,  spectral  values  of  body  oscillations  on  Y  plane;  L,  low  frequency  interval;  M,  middle  HC, healthy subject.  sensitivity; HC, healthy subject. frequency interval; H, high frequency interval; FC, foam carpet; MCS, multiple chemical sensitivity;  HC, healthy subject.  3.5. Correlation Analysis  3.5. Correlation Analysis A  negative  correlation  was  found  between  QEESI  total  score  and  RI  (r  = −0.50,  p  <  0.01,  3.5. Correlation Analysis  corrected).  A negative correlation was found between QEESI total score and RI (r = 0.50, p < 0.01, corrected). A  negative  correlation  was  found  between  QEESI  total  score  and  RI  (r  = −0.50,  p  <  0.01,  corrected).  3.6. PCA  3.6. PCA From  the  analysis  of  the  values  which  measure  the  amount  of  variance  due  to  each  main  3.6. PCA  From the analysis of the values which measure the amount of variance due to each main component,  17  PCs  were  identified  in  MCS  and  19  in  HC  (Figure  4).  In  order  to  simplify  the  component, 17 PCs were identified in MCS and 19 in HC (Figure 4). In order to simplify the From  the  analysis  of  the  values  which  measure  the  amount  of  variance  due  to  each  main  interpretation of the PCs we used the varimax method and Kaiser/Guttman criterion. This indicated  interpretation of the PCs we used the varimax method and Kaiser/Guttman criterion. This indicated component,  17  PCs  were  identified  in  MCS  and  19  in  HC  (Figure     4).  In  order  to  simplify  the  as significant the components with a coefficient higher than 1 [39], thus leaving only seven PCs for  as significant the components with a coefficient higher than 1 [39], thus leaving only seven PCs for interpretation of the PCs we used the varimax method and Kaiser/Guttman criterion. This indicated  analysis in both groups. The optimal factorial solution is that with two extracted factors because they  as significant the components with a coefficient higher than 1 [39], thus leaving only seven PCs for  analysis in both groups. The optimal factorial solution is that with two extracted factors because they explained a maximal amount of variance. Proportion of variance expressed by the first factor in MCS  analysis in both groups. The optimal factorial solution is that with two extracted factors because they  explained a maximal amount of variance. Proportion of variance expressed by the first factor in MCS and HC was 31.47% and 25.44%, respectively, and the second factor also expressed 20.01% in MCS  explained a maximal amount of variance. Proportion of variance expressed by the first factor in MCS  and 18.5% HC.  and HC was 31.47% and 25.44%, respectively, and the second factor also expressed 20.01% in MCS and and HC was 31.47% and 25.44%, respectively, and the second factor also expressed 20.01% in MCS  18.5% HC. and 18.5% HC.  Figure 4. Scree plot depicting HC (on the left) and MCS (on the right) eigenvalues, percentage of total  variance,  cumulative  eigenvalues  and  cumulative  percentage  of  total  variance.  Vertical  black  rod  Figure Figure 4. Scr  4. ee Scre plot e plot depicting  depicting HC HC (on (on the the leleft) ft) and and  MCS MCS  (on the (on right) the right)  eigenveigenvalues, alues, percentaper ge of centage  total  of variance,  cumulative  eigenvalues  and  cumulative  percentage  of  total  variance.  Vertical  black  rod  total variance, cumulative eigenvalues and cumulative percentage of total variance. Vertical black rod indicates numbers of principal components resulting from Kaiser/Guttman criterion and vertical red rod indicates number of principal components needed to overcome 50% of total variance. Appl. Sci. 2016, 6, x FOR PEER REVIEW  10 of 16  Appl. Sci. 2016, 6, 330 9 of 15 indicates numbers of principal components resulting from Kaiser/Guttman criterion and vertical red  rod indicates number of principal components needed to overcome 50% of total variance.  Figure 5A showed PCA score plot of the first two components (PC1 vs. PC2), which explained Figure 5A showed PCA score plot of the first two components (PC1 vs. PC2), which explained  43.95% of the total variance in HC and Figure 4B showed PCA score plot of the first two components 43.95% of the total variance in HC and Figure 4B showed PCA score plot of the first two components  (PC1 vs. PC2), which explained 51.49% of the total variance in MCS. (PC1 vs. PC2), which explained 51.49% of the total variance in MCS.  Figure 5. Component coefficient analysis showing variables projection on the factor plane Figure 5. Component coefficient analysis showing variables projection on the factor plane (Factor 1 ×  (Factor 1  Factor 2) in HC (A) and MCS (B) subjects and contribution of variables (C) in both groups Factor 2) in HC (A) and MCS (B) subjects and contribution of variables (C) in both groups subjects  subjects within PC1 and PC2. RDT, Rod and Disc test; CCW, counter-clockwise; CW, clockwise, CE, within PC1 and PC2. RDT, Rod and Disc test; CCW, counter‐clockwise; CW, clockwise, CE, closed  closed eyes; X, spectral values of body oscillations on X plane; Y, spectral values of body oscillations eyes; X, spectral values of body oscillations on X plane; Y, spectral values of body oscillations on Y  on Y plane; L, low frequency interval, M, middle frequency interval; surf, surface of the ellipse of plane;  L,  low  frequency  interval,  M,  middle  frequency  interval;  surf,  surface  of  the  ellipse  of  confidence; avspe, mean velocity of body sways; stdspe, standard deviation of velocity of body sways; confidence;  avspe,  mean  velocity  of  body  sways;  stdspe,  standard  deviation  of  velocity  of  body  RI, Romberg index; FC, foam carpet; med, median; reslo, regression slope; L, left; R, right. In the centre sways; RI, Romberg index; FC, foam carpet; med, median; reslo, regression slope; L, left; R, right. In  of the figure, (A) and (B) legend colours; in bottom-left, legend colour of (C). the centre of the figure, (A) and (B) legend colours; in bottom‐left, legend colour of (C).  Loading function (component correlation analysis) and PCA scree plots (Figure 5A–C) highlighted Loading  function  (component  correlation  analysis)  and  PCA  scree  plots  (Figure  5A–C)  different dimensions and contributions of the variables when comparing MCS and HC groups. highlighted different dimensions and contributions of the variables when comparing MCS and HC  In particular, despite a negative correlation of vHIT parameters among both groups PC1 and groups.  MCS PC2, within MCS and HC subjects PC1 was found to positively correlate with FFT and RDT and In particular, despite a negative correlation of vHIT parameters among both groups PC1 and  classical posturography parameters, respectively. In turn, classical posturography parameters and RDT MCS PC2, within MCS and HC subjects PC1 was found to positively correlate with FFT and RDT  parameters were found respectively to positively and negatively correlate with PC2 in MCS patients. Appl. Sci. 2016, 6, 330 10 of 15 FFT and classical posturography parameters were found respectively to positively and negatively correlate with the same component in HC (component coefficients shown in scree plots in Figure 5). 4. Discussion Although the otoneurological examination was found to be negative, the first interesting finding in this study was the significant decrease in vHIT scores in MCS subjects (Figure 2B) in whom DHI highlighted a prevalence of dizziness-related symptoms (Figure 1A). In turn, a significant increase in classical posturography and FFT parameters as well as in VD behaviour was found in the same group (Figure 2A). This syndrome has been classified by many authors as part of a wide group of emerging diseases—labelled idiopathic environmental intolerances (IEIs)—which are characterized by symptoms attributed to environmental agents [40]. For instance, many studies have tried to find an unifying pathophysiology for all IEIs, focusing on CNS reactions to environmental stimuli, with activation changes of the brain areas related to sensory processing and emotional responses [1,5–15,41,42]. Since environmental noise has been recognised as an important health quality stressor [43], relevant literature up to now has focused mainly on auditory symptoms [21,22] without highlighting possible vestibular and postural consequences. In particular, no study protocol has found an impaired VOR function in MCS patients with vHIT. By providing a new dimension in vestibular evaluations [33], in this study the test permitted a quantitative assessment of the lateral semi-circular canal function and highlighted side and receptor specific information about the VOR [31–33], resulting in a bilateral reduction of both MCS gain and reslo (Figure 2B, Table 2). These aspects suggested that MCS patients, when compared with HC normative data, have a vestibular impairment (although not clinically evident by means of a cHIT). In line, FFT PS analysis and classical posturography parameters were demonstrated to be congruent with the above-mentioned vestibular damage in MCS. In fact, literature demonstrated body sways at the low frequency interval to be mainly under vestibular control [35,36,44] and patients with vestibular deficits (i.e., vestibular neuritis, Meniere’s disease) have consequently increased body sways at the low frequency interval as compared with normal subjects [35,44]. Furthermore, increased body sways within the middle interval and in classical posturography parameters especially during closed eye condition tend to confirm similar findings in previous studies, and result in a decreased ability to adapt to balance perturbations under complex conditions in certain disorders affected by a lack of vigilance [45–49]. It has previously been noted that the integration of information from the visual, vestibular and somatosensory receptors and motor coordination are processes requiring attention and vigilance [48], especially when information from other sensory systems is not reliable [50]. Considering the closeness between MCS and other related idiopathic disorders affected by functional postural impairment and attention and vigilance decrease (i.e., fibromyalgia, chronic fatigue syndrome) [45,51] it could be postulated that such phenomena may lead to slower or inappropriate sensory integration [48]. In relation to this, negative correlation found between RI values and QEESI total scores further strengthen these aspects, highlighting possible negative influences between higher values on the questionnaire tool used in the study and this kind of body sway parameter. Moreover, MCS patients demonstrated an increase in VD parameters (Figure 2A, Table 2), the concept of which has been postulated by previous authors [28,52] observing that, in conflicting circumstances between sensory modalities involved in spatial orientation, some subjects depend mainly on visual input. These visual-dependent subjects are more likely to be bewildered by tilted or moving visual surroundings [28,52]. From a clinical perspective it has been noted that vestibular patients whose dizzy symptoms are unleashed by certain visual scenarios (i.e., visual vertigo) also demonstrate increased VD when evaluated with conventional approaches [27]. In agreement with this notion, an impaired vestibular modality in MCS patients would have led to an enhanced reliance on visual input, inducing subjects to upregulate this process and to increase VD by a sensory re-weighting [27,53,54]. Appl. Sci. 2016, 6, 330 11 of 15 To this end, the ability to adapt sensory–motor control to challenging circumstances by selection and weighting of alternative reference frames was postulated as one of the main aspects of postural control [55,56]. Moreover, theoretical models of multisensory combination based on neurophysiological [57], perceptual [58] and behavioural evidence [59] proposed that adaptation to environmental changes would rely on sensory re-weighting to optimize both the relevance of individual sensory inputs and the reliability of estimates [48]. This kind of phenomenon is thought to be determined by maximum likelihood estimation so that the conscious and non-conscious brain processing appears as a near-optimal Bayesian estimator of object properties [56,60]. Furthermore, recent findings have demonstrated disorders of vestibular function as characterized by complex perceptual, sensorimotor and behavioural deficits that exceed basic perceptions of head acceleration or motor responses, such as VOR or vestibulo-spinal reflexes [61,62]. In order to thoroughly study these aspects of perceptual complexity, PCA was usefully performed to reduce the information dimensionality from the vast data arrays and to minimize the loss of information, relying on the fact that most of the descriptors are inter-correlated and these correlations in some instances are high [63,64]. In accordance with these aspects, a rearrangement of the dimensions, clusters and contributions of the variables within PC1 and PC2 in MCS was found to be a conceivable model of re-weighting along vestibular sensory processing. In particular, in Figure 5A–C varimax rotation highlighted that although vHIT and VD parameters were almost equally clustered in both groups, MCS patients showed a positive correlation of FFT parameters in PC1 and classical posturography parameters in PC2. Moreover, together with vHIT parameters, VD parameters were found to be negatively correlated within PC2. In turn, HC subjects demonstrated a positive correlation of VD and classical posturography parameters in PC1, FFT PS in PC2 and vHIT values in PC3. Possible explanations for the rearranged model along vestibular processing and symptoms cohort referred to by MCS patients could be considered to reside within pathophysiological cascades, such as those involving limbic structures in the natural history of MCS [1,4–15]. According to these notions, repeated exposure to MCS inducing agents were found to provide in MCS subjects both time-dependent sensitization and kindling, especially within hippocampal and limbic areas [65]. In turn, recent advances have postulated that additional “higher” vestibular dysfunctional aspects could really result from an impaired integration of the vestibular network within these areas [62]. This integration comprises both the internal representation of the body scheme and the internal model of the surrounding space as well as multisensory motion perception, attention, spatial memory, and navigation [62,66]. In conclusion, although disorders of vestibular function integration remain to be completely elucidated, the present data would consider the MCS re-weighting process along bottom-up and top-down vestibular streams as possibly (i) related to MCS physiopathological cascades involving vestibular network at multiple levels; and (ii) underpinning the symptoms of dizziness referred to by the patients. Thus, findings showed in this work could be considered as a first attempt at uncovering the literature gap among otoneurological consequences related to CNS hypersensitisation to environmental compounds. Considering the emerging prevalence of the disorder and the incidence of inner ear symptoms referred to by the patients, evaluation in otoneurological setting should increase in the future. In conclusion, this pilot study could be relevant in correctly screening, monitoring and clustering further clinical history data with the purpose of uncovering future therapeutic strategies useful in the improvement of otoneurological cohort symptoms. 5. Strengths and Limitations of the Study Many uncertainties in the literature involving MCS otoneurological pathways could clearly be explained by different criteria for patient enrolment, different kinds of questionnaires employed in Appl. Sci. 2016, 6, 330 12 of 15 order to investigate symptom spectrums, distortion related to the general incidence of personality traits in control subjects, and many nuisance variables biasing the general research in MCS [22]. As in previous studies, we tried to reduce artefacts that could affect symptom spectrum outcomes by enrolling, clustering and studying—by means of validated questionnaires (such as QEESI and DHI)—only MCS patients with commonly accepted criteria who were regularly followed by the local centre for the diagnosis, treatment and prevention of MCS [22]. Moreover, we performed an ANOVA model aimed at studying outcomes by using a powerful between effect. Thus, we tried to reduce biases related to sex and age ratios, misdiagnosis of MCS, and possible outcome distortions related to the general incidence of personality and social disturbances in the general population [22]. On the other hand, a possible limitation of the study could be represented by the Bonferroni multiple correction applied as post hoc test to both ANOVA and Spearman’s correlation, possibly increasing the likelihood of type II statistical errors. Author Contributions: Marco Alessandrini and Alessandro Micarelli conceived and designed the experiment; Alessandro Micarelli, Andrea Viziano and Marco Alessandrini performed the experiment; Alessandro Micarelli, Elisa Micarelli and Marco Alessandrini analysed the data; Ernesto Bruno and Giuseppe Genovesi contributed clinical data/materials/analysis tools; Alessandro Micarelli, Andrea Viziano and Marco Alessandrini wrote the paper. Conflicts of Interest: The authors declare no conflict of interest. Appendix A Core function implemented in Matlab in order to obtain fast Fourier transform of X and Y oscillations. The symbol ‘%’ and ‘s’ represent a Matlab comment and X or Y oscillations, respectively: L = length(s) % s vector of signal values Fs = 25; % Sampling frequency NFFT = 2ˆnextpow2(L); % Next power of 2 from length of s f = Fs/2*linspace(0,1,NFFT/2 + 1); S = fft(s,NFFT)/L; % FFT of a signal s modS = 2*abs(S(1:NFFT/2 + 1)); ms = max(modS); S_norm = modS/ms; References 1. Bell, I.R. 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This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Sciences Multidisciplinary Digital Publishing Institute

Deranged Dimensionality of Vestibular Re-Weighting in Multiple Chemical Sensitivity

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applied sciences Article Deranged Dimensionality of Vestibular Re-Weighting in Multiple Chemical Sensitivity 1 , 2 , 1 3 4 Alessandro Micarelli *, Andrea Viziano , Elisa Micarelli , Giuseppe Genovesi , 1 1 Ernesto Bruno and Marco Alessandrini Department of Clinical Sciences and Translational Medicine, University of Rome Tor Vergata, Rome 00133, Italy; andrea.viziano@gmail.com (A.V.); bruno.ernesto@libero.it (E.B.); malessandrini63@gmail.com (M.A.) Neuroscience Unit, Department of Systems Medicine, University of Rome Tor Vergata, Rome 00133, Italy Department of Biology, University of Rome Tor Vergata, Rome 00133, Italy; elisamicarelli@yahoo.it Department of Experimental Medicine, “La Sapienza” University, Rome 00185, Italy; mcs.genovesi@libero.it * Correspondence: alessandromicarelli@yahoo.it; Tel.: +39-06-2090-2925; Fax: +39-06-2090-2930 Academic Editor: Yang Kuang Received: 22 August 2016; Accepted: 25 October 2016; Published: 2 November 2016 Abstract: Background: Multiple chemical sensitivity (MCS) is a chronic multisystem condition characterized by low levels of multiple chemical susceptibility inducing a spectrum of central nervous system symptoms, including dizziness. Thus, considering (i) the overlapping psychogenic and organic burdens shared in MCS development and in vestibular disorders; (ii) the number of previous studies describing central processing impairment related to inner ear inflow in this syndrome; and (iii) the lack of literature with respect to clinical evidence of the presentation of MCS dizziness, the purpose of the present study was to highlight the possible hidden aspects of vestibular impairment by applying the recent contribution of implemented otoneurological testing, inferential statistic and principal component (PC) analysis in 18 MCS and 20 healthy subjects (HC); Methods: Both groups filled in a dizziness and environment exposure inventory and underwent the Rod and Disc and Rod and Frame Test, video Head Impulse Test (vHIT) and Static Posturography Test (SPT) with fast Fourier Transform (FFT). Between-group analysis of variance and PC analysis implemented on otoneurological variables were performed; Results: Defective vestibular processing was identified in 18 MCS patients (11 female and 7 male; mean age 49.5  9.3 years) by finding a significant increase in SPT and FFT parameters and in Visual Dependency (VD) behaviour and a decrease in vHIT scores. Component correlation analysis in MCS showed a positive correlation of FFT parameters in PC1 and SPT parameters in PC2 with a negative correlation of vHIT and VD values in PC2. HC subjects demonstrated a positive correlation of VD and SPT parameters in PC1 and FFT parameters in PC2. Conclusion: Inferential and PC analysis provided the opportunity to disclose such possible hidden phenomena to (i) support that MCS physiopathological cascades could lead to a vestibular decay; and (ii) suggest rearrangement of the dimension of the variables as an aspect of near-optimal re-weighting, possibly underpinning the dizzy symptoms complained of by MCS patients. Keywords: multiple chemical sensitivity; vestibular diseases; visual dependency; component analysis; fourier transform 1. Introduction Multiple chemical sensitivity (MCS) is a chronic polysymptomatic, multisystem condition characterized by subjective susceptibility to low levels of a wide spectrum of environmental compounds (such as petrol, perfume or pesticides) [1,2]. It constitutes a relatively common clinical diagnosis in western populations [2], considering that the prevalence of self-reported chemical sensitivity symptoms in population-based studies has been found to range from 9% to 33% [2]. Relapsing and remitting Appl. Sci. 2016, 6, 330; doi:10.3390/app6110330 www.mdpi.com/journal/applsci Appl. Sci. 2016, 6, 330 2 of 15 central nervous system (CNS) manifestations including difficulty concentrating, memory problems, fatigue, depression, daytime sleepiness, “spaciness” (derealisation), tension, irritability and dizziness are commonly referred to by the patients [2,3]. Major criteria to define the clinical spectrum of MCS were introduced by Cullen [4]; however, the discussion on definition and nomenclature reflects the fact that the aetiology of MCS is still unclear and a matter of debate [2,5]. The most frequently discussed aetiologies include neurogenic inflammation [6], classical conditioning [7] and biochemical disruptions [8]. According to the neural sensitization theory, MCS is attributed to a pathological hyper-reactivity of neurons in olfactory and limbic areas of the brain [9]. However, the altered CNS responses are hypothesized to be paralleled by increased anxiety, avoidance, anticipatory stress [9] and attention bias [6] to chemical exposure. According to these theories, several studies in the last few years have shown cerebral blood flow distribution abnormalities in patients with MCS, especially while processing odorous substances [10–12]. In particular, MCS sufferers have been seen to react peculiarly to sensory stimuli, with activation of brain areas connected with motivational, emotional and non-conscious processing of information, such as the amygdala and the hippocampus, encompassed in the limbic system [10–14], whose pathways are known to mediate CNS sensitization and their dysregulation contributes to abnormalities of behaviour and mood, endocrine, autonomic and immune function [15]. In turn, the same brain circuits seem to be pivotal in vestibular processing, given the fact that the limbic system—previously thought to be engaged in the physiopathological underpinning of this disorder [1]—receives neural input from the vestibular cortex, and therefore it could be involved in vestibular stream processing also below the level of awareness [16,17]. To screen populations for MCS correctly, Miller and Prihoda developed the environment exposure sensitivity inventory (EESI) and its quick form (QEESI), which are considered the most reliable tools for research on patients complaining of chemical sensitivity [18]. For instance, researchers have investigated the association between chemical sensitivity and other kinds of environmental intolerances, including noise and olfactory sensitivity [19]. This has been carried out principally by means of questionnaire surveys in clusters of healthy and affected subjects (i.e., teenage students, twins) and, although overlapping phenomena involving physiopathological aspects of MCS and vestibular processing are possible, any research on intolerance to chemicals and vestibular disorder had been devised previously. Thus, due to (i) the overlapping psychogenic and organic burdens that, below the level of consciousness, are shared not only in MCS development but also in the vestibular disorders [1,10–13,15–17,20]; (ii) the number of previous studies describing central processing impairment related to inner ear inflow in this syndrome [21,22]; and (iii) the lack of literature on clinical evidence of the presentation of dizziness [2,3], the purpose of the present exploratory study was to highlight the possible MCS hidden clinical/subclinical aspects of vestibular impairment by inferential and principal component (PC) analysis applied on the contribution of recently implemented otoneurological testings. This, with the aim of disclosing possible implementations in diagnosing and monitoring undiscovered aspects underpinning the natural history of MCS, will possibly assist in suggesting future treatment choices in a clinical setting. 2. Experimental Section 2.1. Participants And Study Design Twenty-three consecutive MCS subjects were enrolled in the study. Diagnosis of MCS was achieved according to the US Consensus Criteria for MCS [23] and the revisions proposed by Lacour et al. [24]. We also enrolled 20 gender- and age-matched healthy subjects serving as the control group (HC). By means of a thorough medical history, collected by experienced specialized medical personnel, both eligible MCS and HC subjects were required to report a negative anamnesis for malignancy, Appl. Sci. 2016, 6, 330 3 of 15 head trauma, neuropsychiatric disorders, metabolic, cardiovascular, endocrine, infectious and otoneurological diseases. The peripheral blood of MCS and HC was tested for the usual parameters and neurological as well as neuropsychiatric disturbances were excluded with the Mini Mental State Examination, Magnetic Resonance Imaging and DSM IV-TR General Help-Seeking Questionnaire. Finally, no patient was pregnant or breastfeeding and all subjects taking drugs possibly affecting vestibular functions were excluded. The study adhered to the principles of the Declaration of Helsinki and all the participants provided written informed consent after receiving a detailed explanation of the study. 2.2. Otoneurological Testing After a thorough visual, oculomotor, locomotor and otoneurological clinical examination (the latter including pure tone audiometry and impedance, binocular electrooculography analysis with positional manoeuvres, Head Shaking Test, clinical Head Impulse Test [cHIT] as well as limb coordination, gait observation and Romberg stance Test) performed by an experienced neurologist and otoneurologist, all MCS and HC subjects underwent: - Rod and Disc Test (RDT): According to Cousins et al. [25] visual dependency (VD) was evaluated by means of the RDT software (http://www.imperial.ac.uk/medicine/dizzinessandvertigo) on a laptop computer in order to study visual vertical under kinetic visual stimulation [26,27]. Subjects were seated in front of the computer in a darkened room with their heads held against a viewing cone that blocked extraneous visual orientation cues. The diameter of the cone at the subjects’ eyes was 15 cm with a depth of field of 30 cm, subtending a viewing angle of 39 . The visual stimulus consisted of a luminous white 6 cm rod on a black background. The rod rotated 360 in either direction about its midpoint in the central 11 of the visual filed. Outside this central zone, the viewing screen was filled with a collage of 220 off-white dots, each 8 mm (1.5 of visual field) in diameter, randomly distributed on a black background. Subjects controlled the orientation of the rod with a roller mouse. They were instructed to align the rod to their perceived vertical (the subjective visual vertical) under three conditions. In the first test the collage of dots was stationary (N) while in the second and third the collage rotated at 30 /s clockwise (CW) or counter-clockwise (CCW), respectively. Subjects were tested four times per each condition, presenting the latter two conditions randomly after condition 1. During each trial the rod was initially set randomly at 40 from vertical and the rod tilt per trial was recorded as the degrees’ difference between true vertical and the subjects’ final rod placement. - Rod and Frame Test (RFT): According to Witkin and Asch [28], in order to study the subjects’ visual vertical tilts under static visual stimulation, a standard RFT square frame device described by Fiori et al. [29] was used. The frame was tilted 33 CW, CCW, or not tilted (0 , N); the rod 11 or 22 , CW or CCW. Thus, 12 randomly presented conditions were achieved, each containing three trials [30]. Errors were calculated as deviation from the gravitational vertical position of the rod. - Video Head Impulse Testing (vHIT): For vestibulo-ocular reflex (VOR) gain measurements, TM the reliability of vHIT [31] (EyeSeeCam System [32]) and the technique proposed by Blodow et al. [33] were used to test the lateral semicircular canal. Thus, anomalies in vHIT outcomes were assessed in case of (1) abnormal gain with respect to the calculated normative data; and (2) the presence of refixation saccades. Amplitudes ( /s) and latencies (ms) of both pinpointed refixation and physiological saccades were computed at peak eye velocity ( /s). According to TM the manufacturer of the software (OtoAccess ), both side gain mean and median (med) values recorded at 60 ms as well as regression slope (reslo) were calculated and extracted onto an .xls file. Artefacts possibly related to mask slippage and positioning, calibration, handedness of the operator, patients’ neck stiffness and setting of the laboratory, were controlled by means of a technique proposed by McGarvie et al. [34]. According to the technique proposed by the same group [34] to reach adequate numbers of head values for the lateral semi-circular canal, Appl. Sci. 2016, 6, 330 4 of 15 the operator delivered a range of velocities in a random order and direction to achieve at least 10 artefact-free impulses. By means of this technique, 50–60 impulses were delivered in each canal direction to ensure that the desired number of artefact-free responses was achieved. - Static Posturography Testing (SPT): The previously instructed subjects maintained an upright position on a standardized platform for static posturography (EDM Euroclinic ) during a 60 s recording period per condition, defined as eyes closed (CE) or opened (OE) while standing on either the stiff platform (SP) or a 6 cm heightened AIREX Balance-pad plus foam carpet (FC, AIREX AG, 25% compression resistance, 20 kPa; apparent density 55 kg/m ; tensile strength 260 kPa). The sampling frequency was set at 25 Hz [35,36] and the centre of pressure (CoP) was supervised during the test. Classic posturographic parameter evaluations involving the trace length (length), the surface of the ellipse of confidence (surf), and the mean velocity (avspe) of body sways with relative SD (stdspe) were performed, as well as the fast Fourier transform (FFT) elaboration of oscillations on both the X (right-left) and Y (forward-backwards) planes [35,36]. Time-domain oscillation signals (X and Y) were extracted by the original software manufacturer into .txt format and the FFT elaborations were gained through Matlab software. In order to obtain the FFT of X and Y oscillations, a core function (Appendix A) was implemented using as input the.txt file provided by the software manufacturer. A main script was written to acquire the FFT for each patient, related to four different conditions, by accessing to the above mentioned Matlab function. For each condition the script returned a file containing the normalized value of the FFT for X and Y oscillations. Spectral values (power spectra, PS) of body oscillations were quantified on an .xls file, for every frequency from 0.01 to 5.00 Hz [35,36]. According to previous experiences [35,36], the frequency spectrum was clustered into three groups: 0.01–0.70 Hz (low frequency interval); 0.70–1.00 Hz (middle frequency interval); 1.00–5.00 Hz (high frequency interval). Within each group, the spectral intensity was determined by adding the relative PS and the group mean PS ( SD) [35,36]. 2.3. Validated Questionnaires (VQ) In order to fulfil the exploratory approach of the study all MCS and HC subjects filled in the following forms before instrumental measurement: (1) a modified version of the QEESI Symptom Severity scale [37] in which subjects scored from 1 (low) to 3 (severe) the intensity of head-related (HEAD), cognitive-related (COG), affective-related (AFF), neuromuscular-related (NM), musculoskeletal-related (MS), skin-related (SKIN), genitourinary-related (GU), gastrointestinal-related (GI), heart/chest-related (COR), airway or mucous membrane-related (AIR/MM) symptoms. (2) the Italian Dizziness Handicap Inventory (DHI), including 25 items aimed at assessing patients’ functional (nine questions), emotional (nine questions) and physical (seven questions) limitations [38] by means a multiple choice scheme: “yes” (4 points), “sometimes” (2 points) and “no” (0 points). 2.4. Data Handling and Statistical Analysis In order to fulfil the exploratory approach of the study, in which a large number of variables were studied, the protocol data handling was conceived in a double-folded inferential and multivariate statistical analysis: - Inferential Statistical Analysis Mean and standard deviations (SDs) for all recorded variables and for QEESI and DHI subscale scores were calculated in both HC and MCS. In order to assess that data were of Gaussian distribution, the D’Agostino K squared normality test was applied (where the null hypothesis is that the data are normally distributed). Appl. Sci. 2016, 6, 330 5 of 15 A ‘between-groups’ analysis of variance (ANOVA) was performed for each VQ and otoneurological variable. Age and gender were handled as continuous and categorical predictors, respectively. This study tested the null hypothesis (H0) that the two group measurements would not show any statistically significant difference. The significant cut-off level ( ) was set at a p value of 0.01 and the Bonferroni correction for multiple comparisons was performed with post hoc tests of significant main effects. Thus, p levels associated with the difference between the two groups were considered as statistically significant if lower than 0.01. Then, Spearman’s rank correlation was implemented between QEESI and DHI sub-items scores and significant variables and a Bonferroni correction for multiple comparisons was applied in order to avoid family-wise error, (STATISTICA 7 package for Windows). - Principal Component Analysis Regarding the multivariate analysis step of the study, Principal Component Analysis (PCA) with varimax rotation was performed to detect—with statistically significant otoneurological variables in both groups—hidden phenomena mainly through reducing the variables to find the most important ones, finding patterns in the data and classifying any combination of variables. The number of factors in principle was determined by a combination of the Kaiser/Guttman criterion (only factors with eigenvalues larger than 1 are incorporated in the model) and interpretability. Also by using scree plots, the eigenvalue was plotted against the component number, to evaluate if a factor was relevant or not. Finally, a component coefficient analysis was performed between variables and factors. 3. Results 3.1. Subjects Of 23 MCS patients, 3 were using psychiatric medications, 1 referred positive anamnesis for alcohol addiction, and 1 received diagnosis of hypothyroidism, and they were excluded. Thus, 18 right-handed MCS patients (11 female and 7 male; mean age 49.5  9.3 years) were included in the study. In turn, the HC group consisted of 20 right-handed healthy subjects (12 female and 8 male; mean age 48.6  11.4 years). Socio-demographic and medical details of MCS included in the study after the clinical work-up are presented in Table 1. Table 1. Socio-demographic and medical details of total and included MCS patients after clinical work-up. MCS, Multiple Chemical Sensitivity. Socio-demographical Total MCS Reason of Exclusion, Number, Gender Included MCS Variables Number 23 See below 18 Gender 14 female, 9 male See below 11 female, 7 male Age 50.3  8.9 years - 49.5  9.3 years Cosmetologist (n = 4) psychiatric medications (n = 1, female) Cosmetologist (n = 3) Hairdresser (n = 5) - Hairdresser (n = 5) Teacher (n = 3) hypothyroidism (n = 1, female) Teacher (n = 2) Nurse (n = 3) psychiatric medications (n = 1, female) Nurse (n = 2) Employment Ex-soldier (n = 3) alcohol addiction (n = 1, male) Ex-soldier (n = 2) Obstetrician (n = 2) - Obstetrician (n = 2) Taxi driver (n = 2) psychiatric medications (n = 1, male) Taxi driver (n = 1) University Professor (n = 1) - University Professor (n = 1) “-“ = none. 3.2. Validated Questionnaires Significant differences in results (p < 0.01, corrected) in DHI and QEESI scores in MCS and HC subjects are shown in Figure 1A,B, respectively. Appl. Sci. 2016, 6, 330  6 of 16  Appl. Sci. 2016, 6, 330 6 of 15 Appl. Sci. 2016, 6, 330  6 of 16  Figure 1. In (A) histograms highlighting dizziness handicap inventory (DHI) total scores in Multiple  Chemical Sensitivity (MCS) and healthy subjects (HC); In (B) star diagram showing MCS and HC  quick environment exposure sensitivity inventory (QEESI) sub‐items results in head‐related (HEAD),  Figure 1. In (A) histograms highlighting dizziness handicap inventory (DHI) total scores in Multiple  Figure 1. In (A) histograms highlighting dizziness handicap inventory (DHI) total scores in Multiple cognitive‐related  (COG),  affective‐related  (AFF),  neuromuscular‐related  (NM),  Chemical Sensitivity (MCS) and healthy subjects (HC); In (B) star diagram showing MCS and HC  Chemical Sensitivity (MCS) and healthy subjects (HC); In (B) star diagram showing MCS and HC musculoskeletal‐related  (MS),  skin‐related  (SKIN),  genitourinary‐related  (GU),  quick environment exposure sensitivity inventory (QEESI) sub‐items results in head‐related (HEAD),  quick environment exposure sensitivity inventory (QEESI) sub-items results in head-related (HEAD), gastrointestinal‐related  (GI),  heart/chest‐related  (COR),  airway  or  mucous  membrane‐related  cog cognitive-r nitive‐related elated (COG), (COG), affective-r   affect elatedive (AFF), ‐relate neur d  omuscular (AFF),  -related neurom (NM), uscmusculoskeletal-r ular‐related  (NM) elated,  (AIR/MM) symptoms.  m (MS), uscu skin-r loskelet elatal ed‐related (SKIN),  genitourinary-r (MS),  skelated in‐related (GU),  gast (SrK ointestinal-r IN),  genito elated urinary (GI), heart/chest-r ‐related  elated (GU),  g (COR), astrointestina airwaylor‐related mucous   (GI), membrane-r   heart/chest elated ‐related (AIR/MM)   (COR), symptoms.   airway  or  mucous  membrane‐related  3.3. Otoneurological Data  (AIR/MM) symptoms.  3.3. Otoneurological Data MCS  and  HC  subjects  did  not  show  any  clinical  otoneurological  abnormalities  during  3.3. Otoneurological Data  examination.  MCS and HC subjects did not show any clinical otoneurological abnormalities MCS  and  HC  subjects  did  not  show  any  clinical  otoneurological  abnormalities  during  during examination. 3.4. Inferential Statistical Analysis  examination.  3.4. Inferential Statistical Analysis When  comparing  HC  and  MCS  subjects,  a  significant  ‘between‐groups’  effect  (p  <  0.01,  3.4. Inferential Statistical Analysis  corrected) When was comparing  found, HC high and light MCS ing  subjects, statistically a significant  significan‘between-gr t higher vaoups’ lues in ef MCS fect ( ppa <ti 0.01, entsc in orr: ected) (i) PS  was recorded found, When on  highlighting comparin  both X pla g  n HC statistically e (X)   an an d dMCS  Ysignificant  pla  subj ne (Y ects, ) higher within   a  sign values low ificant  (L) in  an ‘between MCS d middle patients ‐gro (M ups’ in: ) frequency  (i) effect PS r ecor (p in  < ded t erval 0.0 on 1s,   corrected) was found, highlighting statistically significant higher values in MCS patients in: (i) PS  both recorded X plane  on (X) SP and and Ydurin planeg (Y) CEwithin  and OE low condit (L) and ionmiddle s (Figur (M) e 2A, frequency  Table 2) intervals ; (ii) clasrsi ecor c posturogra ded on SP phy and  recorded during parameters CE on and   main both OEly X conditions   in pla CE ne  condit (X)(Figur  anion d Y e  an 2pla A, d n T on e able  (Y foam ) 2within ); (ii) carpet classic  low (Tab  (L postur )le an 2)d;ography   middle and (iii )parameters (M  RD ) Tfrequency  measureme mainly  intn ein rval ts,CE  in s   recorded on SP and during CE and OE conditions (Figure 2A, Table 2); (ii) classic posturography  condition both CW  and and on +40° foam  andcarpet  CCW(T an able d −240 );°and  condit (iii)ions RDT (F measur igure 2A, ements,  Table in 2) both . Conver CW and sely,+40  MCS and  subjects CCW  pa and were rameters  found 40 conditions   main to bely sign  in (Figur if CE ic acondit nt ely 2 A, impai ion Table  an red d 2  ).in on Conversely   VO foam R ga carpet in ,and MCS  (Tab  reslo subjects le  2) values, ; anwer d  bi (ieiilat )found  RD erallTy measureme to (Figu be significantly re 2B,n Table ts, in   both CW and +40° and CCW and −40° conditions (Figure 2A, Table 2). Conversely, MCS subjects  impair 2).  Mean ed,in  SD VOR s  and gain   both and  srignif esloic values, ant  and bilaterally   not  sign(Figur ificant e  effects 2B, Table   for 2 ).alMean, l  variaSDs bles  and measur bothemen significant ts  are  were and shown not  found  in significant  Tab tol ebe 2.  sign effects ificafor ntly all impai variables red in measur  VOR ga ements in and ar re e slo shown  values, in T able bilate 2r .ally (Figure 2B, Table  2).  Mean,  SDs  and  both  significant  and  not  significant  effects  for  all  variables  measurements  are  shown in Table 2.  Figure 2. This represents all statistical significant differences in Visual Dependency scores, and spectral Figure  2.  This  represents  all  statistical  significant  differences  in  Visual  Dependency  scores,  and  values of body oscillations (A) and video Head Impulse Test measurements (B) when comparing spectral  values  of  body  oscillations  (A)  and  video  Head  Impulse  Test  measurements  (B)  when  Multiple Chemical Sensitivity (MCS) and healthy subjects (HC). Acronyms represent tags for Rod and Figure comparing   2.  This  Mu ltiple represents  Chemic   alal l  stati Senssitiv tical ity  sig  (MnCS) ificant  and  differe  healthnces y subjects   in  Visual  (HC) .Dependency  Acronyms rep   scores resent,  and tags   Disc test, RDT (counter-clockwise, CCW; clockwise, CW), spectral values of body oscillations on X spectral for  Rod  values and  Disc   of  body test,  RDT oscill  (counter ations  (A‐clock )  andw ise, video   CCW;   Hea dcl ock Impu wise, lse   Te CW), st  measurement spectral  values s  ( Bof )  when body   plane (X) and Y plane (Y) within low (L) and middle (M) frequency intervals recorded on stiff platform comparing oscillations Mu   onltiple   X  plane  Chemic   (X) aland  Sens  Yitiv   plane ity (M   (YCS) )  within  and health   low y(L su )  bjects and  mi (Hdd C)le.  Ac (M) ronyms   frequency  represent   intervals  tags   and during closed (CE) and opened eyes (OE) conditions and for vestibulo-ocular reflex (VOR) gain for recorded   Rod  and   on   Disc stiff   platform test,  RDT   and (counter   during ‐clock   clowsed ise,  (CE) CCW;   and   cl ock opened wise,   CW), eyes  (OE) spec tral conditions   values  of and   body   for   and regression slope, reslo; L, left; R, right; med, median. osci vestibulo llations‐oc  on ular  X reflex   plane (VOR)   (X)  and  gai nY  and plane  reg  (Y ression )  within  slope   low , reslo;   (L)  L, and left  mi ; R, dd rig le h(M) t; med   frequency , median.  intervals     recorded  on  stiff  platform  and  during  closed  (CE)  and  opened  eyes  (OE)  conditions  and  for  vestibulo‐ocular reflex (VOR) gain and regression slope, reslo; L, left; R, right; med, median.  Appl. Sci. 2016, 6, 330 7 of 15 Table 2. Otoneurological scores in MCS and HC subjects. MCS HC MCS HC Variables Variables Mean SD Mean SD p Value Mean SD Mean SD p Value Rod and Disc Test Fast Fourier Transform RDT/N/+40 2.39 2.05 1.63 1.82 p > 0.01 Low Freqeuncy Interval RDT/CCW/40 8.18 2.29 3.11 1.95 p < 0.01 CEXL 10.17 6.8 4.68 2.77 p < 0.01 RDT/CW/+40 8.09 2.43 2.83 1.24 p < 0.01 CEYL 6.24 4.02 2.89 1.3 p < 0.01 RDT/N/40 5.3 7.67 2.05 1.89 p > 0.01 OEXL 8.23 3.41 5.35 2.65 p < 0.01 RDT/CCW/+40 4.06 2.4 3.76 4.04 p > 0.01 OEYL 6 2.56 4.08 2.06 p < 0.01 RDT/CW/40 4.6 1.93 3.58 2.42 p > 0.01 CEXL-FC 8.28 2.18 9.67 1.9 p > 0.01 Rod and Frame Test CEYL-FC 8.24 2.55 7.51 1.79 p > 0.01 RFT/CW/+11 1.608333 0.793216 1.6125 0.43766 p > 0.01 OEXL-FC 7.21 3.99 6.87 2.54 p > 0.01 RFT/CCW/+11 2.238889 0.777166 2.13 0.706437 p > 0.01 OEYL-FC 6.14 2.83 5.13 2.22 p > 0.01 RFT/CW/11 1.758333 0.895947 1.58 0.88055 p > 0.01 Middle Frequency Interval RFT/CCW/11 2.183333 0.56983 2.1475 0.609459 p > 0.01 CEXM 0.67 0.37 0.28 0.07 p < 0.01 RFT/CW/+22 1.536111 0.875954 1.4925 0.739537 p > 0.01 CEYM 0.39 0.19 0.17 0.05 p < 0.01 RFT/CCW/+22 2.319444 0.518238 2.2225 0.713309 p > 0.01 OEXM 0.34 0.03 0.3 0.09 p > 0.01 RFT/CW/22 2.019444 0.500335 2.1125 0.783695 p > 0.01 OEYM 0.23 0.05 0.2 0.06 p > 0.01 RFT/CCW/22 1.580556 0.75909 1.435 0.675336 p > 0.01 CEXM-FC 0.84 0.29 1.02 0.5 p > 0.01 RFT/N/+11 1.883333 0.582338 1.9175 0.681962 p > 0.01 CEYM-FC 0.71 0.38 0.6 0.26 p > 0.01 RFT/N/11 2.125 0.53667 2.15 0.585797 p > 0.01 OEXM-FC 0.01 0 0.01 0 p > 0.01 RFT/N/+22 1.827778 0.68368 1.905 0.657927 p > 0.01 OEYM-FC 0.27 0.09 0.29 0.09 p > 0.01 RFT/N/22 2.119444 0.839142 2.0255 0.608324 p > 0.01 High Frequency Interval Classic Posturography Parameters CEXH 1.24 0.17 1.09 0.28 p > 0.01 RI 241.29 77.61 168.73 87.85 p < 0.01 CEYH 1.04 0.29 1.05 0.26 p > 0.01 RI-FC 348.46 101.2 251.55 135.66 p < 0.01 OEXH 0.96 0.32 1.01 0.28 p > 0.01 surf OE 407.25 124.89 414.06 212.59 p > 0.01 OEYH 0.72 0.13 0.77 0.13 p > 0.01 surf CE 939.71 312.53 656.25 369.4 p < 0.01 CEXH-FC 1.65 0.19 1.61 0.25 p > 0.01 surf OE-FC 831.72 243.34 740 452.75 p > 0.01 CEYH-FC 1.57 0.25 1.63 0.2 p > 0.01 surf CE-FC 2769.06 750.03 1565.61 782.82 p < 0.01 OEXH-FC 0 0 0 0 p > 0.01 length OE 552.94 103.76 512.85 98.4 p > 0.01 OEYH-FC 0.75 0.26 0.72 0.17 p > 0.01 length CE 986.93 310.77 739.17 150.98 p < 0.01 Video Head Impulse Test length 822.41 285.98 722 182.44 p > 0.01 gain med L 0.72 0.3 1.07 0.3 p < 0.01 OE-FC length 2237.64 675.93 1694.96 642.48 p < 0.01 gain mean L 0.72 0.31 1.08 0.3 p < 0.01 CE-FC avspe OE 9.11 1.15 8.55 1.63 p > 0.01 gain med R 0.72 0.3 0.97 0.22 p < 0.01 avspe CE 18.68 4 12.07 2.73 p < 0.01 gain mean R 0.72 0.31 0.97 0.23 p < 0.01 avspe 13.04 2.94 12.7 5.56 p > 0.01 reslo mean L 0.89 0.35 1.35 0.63 p < 0.01 OE-FC avspe 38.12 13.63 27.71 11.28 p < 0.01 reslo med L 0.87 0.34 1.34 0.64 p < 0.01 CE-FC stdspe OE 5.47 0.96 5.03 1.14 p > 0.01 reslo mean R 0.84 0.34 1.33 0.72 p < 0.01 stdspe CE 10.68 3.24 7.61 2.09 p < 0.01 reslo med R 0.84 0.35 1.32 0.72 p < 0.01 stdspe 9.92 3.23 8.06 3.24 p > 0.01 OE-FC stdspe 25.35 6.83 18.18 7.46 p < 0.01 CE-FC RDT, Rod and Disc test; RFT, Rod and Frame Test; CCW, counter-clockwise; CW, clockwise; N, neutral condition; CE, closed eyes; OE, opened eyes; X, spectral values of body oscillations on X plane; Y, spectral values of body oscillations on Y plane; L, low frequency interval; M, middle frequency interval; H, high frequency interval; surf, surface of the ellipse of confidence; avspe, mean velocity of body sways; stdspe, standard deviation of velocity of body sways; RI, Romberg index; FC, foam carpet; med, median; reslo, regression slope; L, left; R, right; SD, standard deviation; MCS, multiple chemical sensitivity; HC, healthy subjects. Significant results are reported in bold character. Appl. Sci. 2016, 6, 330 8 of 15 Appl. Sci. 2016, 6, x FOR PEER REVIEW  9 of 16  Within the vHIT results, analysed using the 36 ears of the 18 MCS patients, fifteen ears (41.6%) Appl. Sci. 2016, 6, x FOR PEER REVIEW  9 of 16  Within the vHIT results, analysed using the 36 ears of the 18 MCS patients, fifteen ears (41.6%)  had both covert and overt saccade components, and only one overt or covert saccade was observed in had both covert and overt saccade components, and only one overt or covert saccade was observed  the remaining Within twelve  the vH(33.3%) IT results, and  ana nine lysed(25%)  using ears, the 36r espectively ears of the 18 .  Head MCS pa peak tients, velocity  fifteen ear of ten s (41extracte .6%)  d in  the  remaining  twelve  (33.3%)  and  nine  (25%)  ears,  respectively.  Head  peak  velocity  of  ten  had both covert and overt saccade components, and only one overt or covert saccade was observed  measures per subject was 162 /s  11. extracted measures per subject was 162°/s ± 11.  in  the  remaining  twelve  (33.3%)  and  nine  (25%)  ears,  respectively.  Head  peak  velocity  of  ten  For illustrativ For illustraetipurposes, ve purposes, Figur  Figuere 3 3depicts  depicts an an ex example ample of of a asing single le MCS MCS  and and  HCHC  subject’s subject’s  patient patient   extracted measures per subject was 162°/s ± 11.  changes in postural and VOR parameters.  changes in postural and VOR parameters. For illustrative purposes, Figure 3 depicts an example of a single MCS and HC subject’s patient  changes in postural and VOR parameters.  Figure 3. An example of a single MCS and HC subject respectively represented in (A) and (D) for  Figure 3. An example of a single MCS and HC subject respectively represented in (A) and (D) video Head Impulse Test trace and results; in (B) and (E) for fast Fourier transform results; in (C) and  for video Head Impulse Test trace and results; in (B) and (E) for fast Fourier transform results; Figure 3. An example of a single MCS and HC subject respectively represented in (A) and (D) for  (F) for X‐Y plane plot. CE, closed eyes; OE, open eyes; X, spectral values of body oscillations on X  in (C) and (F) for X-Y plane plot. CE, closed eyes; OE, open eyes; X, spectral values of body video Head Impulse Test trace and results; in (B) and (E) for fast Fourier transform results; in (C) and  plane;  Y,  spectral  values  of  body  oscillations  on  Y  plane;  L,  low  frequency  interval;  M,  middle  oscillations on X plane; Y, spectral values of body oscillations on Y plane; L, low frequency interval; (F) for X‐Y plane plot. CE, closed eyes; OE, open eyes; X, spectral values of body oscillations on X  frequency interval; H, high frequency interval; FC, foam carpet; MCS, multiple chemical sensitivity;  M, middle frequency interval; H, high frequency interval; FC, foam carpet; MCS, multiple chemical plane;  Y,  spectral  values  of  body  oscillations  on  Y  plane;  L,  low  frequency  interval;  M,  middle  HC, healthy subject.  sensitivity; HC, healthy subject. frequency interval; H, high frequency interval; FC, foam carpet; MCS, multiple chemical sensitivity;  HC, healthy subject.  3.5. Correlation Analysis  3.5. Correlation Analysis A  negative  correlation  was  found  between  QEESI  total  score  and  RI  (r  = −0.50,  p  <  0.01,  3.5. Correlation Analysis  corrected).  A negative correlation was found between QEESI total score and RI (r = 0.50, p < 0.01, corrected). A  negative  correlation  was  found  between  QEESI  total  score  and  RI  (r  = −0.50,  p  <  0.01,  corrected).  3.6. PCA  3.6. PCA From  the  analysis  of  the  values  which  measure  the  amount  of  variance  due  to  each  main  3.6. PCA  From the analysis of the values which measure the amount of variance due to each main component,  17  PCs  were  identified  in  MCS  and  19  in  HC  (Figure  4).  In  order  to  simplify  the  component, 17 PCs were identified in MCS and 19 in HC (Figure 4). In order to simplify the From  the  analysis  of  the  values  which  measure  the  amount  of  variance  due  to  each  main  interpretation of the PCs we used the varimax method and Kaiser/Guttman criterion. This indicated  interpretation of the PCs we used the varimax method and Kaiser/Guttman criterion. This indicated component,  17  PCs  were  identified  in  MCS  and  19  in  HC  (Figure     4).  In  order  to  simplify  the  as significant the components with a coefficient higher than 1 [39], thus leaving only seven PCs for  as significant the components with a coefficient higher than 1 [39], thus leaving only seven PCs for interpretation of the PCs we used the varimax method and Kaiser/Guttman criterion. This indicated  analysis in both groups. The optimal factorial solution is that with two extracted factors because they  as significant the components with a coefficient higher than 1 [39], thus leaving only seven PCs for  analysis in both groups. The optimal factorial solution is that with two extracted factors because they explained a maximal amount of variance. Proportion of variance expressed by the first factor in MCS  analysis in both groups. The optimal factorial solution is that with two extracted factors because they  explained a maximal amount of variance. Proportion of variance expressed by the first factor in MCS and HC was 31.47% and 25.44%, respectively, and the second factor also expressed 20.01% in MCS  explained a maximal amount of variance. Proportion of variance expressed by the first factor in MCS  and 18.5% HC.  and HC was 31.47% and 25.44%, respectively, and the second factor also expressed 20.01% in MCS and and HC was 31.47% and 25.44%, respectively, and the second factor also expressed 20.01% in MCS  18.5% HC. and 18.5% HC.  Figure 4. Scree plot depicting HC (on the left) and MCS (on the right) eigenvalues, percentage of total  variance,  cumulative  eigenvalues  and  cumulative  percentage  of  total  variance.  Vertical  black  rod  Figure Figure 4. Scr  4. ee Scre plot e plot depicting  depicting HC HC (on (on the the leleft) ft) and and  MCS MCS  (on the (on right) the right)  eigenveigenvalues, alues, percentaper ge of centage  total  of variance,  cumulative  eigenvalues  and  cumulative  percentage  of  total  variance.  Vertical  black  rod  total variance, cumulative eigenvalues and cumulative percentage of total variance. Vertical black rod indicates numbers of principal components resulting from Kaiser/Guttman criterion and vertical red rod indicates number of principal components needed to overcome 50% of total variance. Appl. Sci. 2016, 6, x FOR PEER REVIEW  10 of 16  Appl. Sci. 2016, 6, 330 9 of 15 indicates numbers of principal components resulting from Kaiser/Guttman criterion and vertical red  rod indicates number of principal components needed to overcome 50% of total variance.  Figure 5A showed PCA score plot of the first two components (PC1 vs. PC2), which explained Figure 5A showed PCA score plot of the first two components (PC1 vs. PC2), which explained  43.95% of the total variance in HC and Figure 4B showed PCA score plot of the first two components 43.95% of the total variance in HC and Figure 4B showed PCA score plot of the first two components  (PC1 vs. PC2), which explained 51.49% of the total variance in MCS. (PC1 vs. PC2), which explained 51.49% of the total variance in MCS.  Figure 5. Component coefficient analysis showing variables projection on the factor plane Figure 5. Component coefficient analysis showing variables projection on the factor plane (Factor 1 ×  (Factor 1  Factor 2) in HC (A) and MCS (B) subjects and contribution of variables (C) in both groups Factor 2) in HC (A) and MCS (B) subjects and contribution of variables (C) in both groups subjects  subjects within PC1 and PC2. RDT, Rod and Disc test; CCW, counter-clockwise; CW, clockwise, CE, within PC1 and PC2. RDT, Rod and Disc test; CCW, counter‐clockwise; CW, clockwise, CE, closed  closed eyes; X, spectral values of body oscillations on X plane; Y, spectral values of body oscillations eyes; X, spectral values of body oscillations on X plane; Y, spectral values of body oscillations on Y  on Y plane; L, low frequency interval, M, middle frequency interval; surf, surface of the ellipse of plane;  L,  low  frequency  interval,  M,  middle  frequency  interval;  surf,  surface  of  the  ellipse  of  confidence; avspe, mean velocity of body sways; stdspe, standard deviation of velocity of body sways; confidence;  avspe,  mean  velocity  of  body  sways;  stdspe,  standard  deviation  of  velocity  of  body  RI, Romberg index; FC, foam carpet; med, median; reslo, regression slope; L, left; R, right. In the centre sways; RI, Romberg index; FC, foam carpet; med, median; reslo, regression slope; L, left; R, right. In  of the figure, (A) and (B) legend colours; in bottom-left, legend colour of (C). the centre of the figure, (A) and (B) legend colours; in bottom‐left, legend colour of (C).  Loading function (component correlation analysis) and PCA scree plots (Figure 5A–C) highlighted Loading  function  (component  correlation  analysis)  and  PCA  scree  plots  (Figure  5A–C)  different dimensions and contributions of the variables when comparing MCS and HC groups. highlighted different dimensions and contributions of the variables when comparing MCS and HC  In particular, despite a negative correlation of vHIT parameters among both groups PC1 and groups.  MCS PC2, within MCS and HC subjects PC1 was found to positively correlate with FFT and RDT and In particular, despite a negative correlation of vHIT parameters among both groups PC1 and  classical posturography parameters, respectively. In turn, classical posturography parameters and RDT MCS PC2, within MCS and HC subjects PC1 was found to positively correlate with FFT and RDT  parameters were found respectively to positively and negatively correlate with PC2 in MCS patients. Appl. Sci. 2016, 6, 330 10 of 15 FFT and classical posturography parameters were found respectively to positively and negatively correlate with the same component in HC (component coefficients shown in scree plots in Figure 5). 4. Discussion Although the otoneurological examination was found to be negative, the first interesting finding in this study was the significant decrease in vHIT scores in MCS subjects (Figure 2B) in whom DHI highlighted a prevalence of dizziness-related symptoms (Figure 1A). In turn, a significant increase in classical posturography and FFT parameters as well as in VD behaviour was found in the same group (Figure 2A). This syndrome has been classified by many authors as part of a wide group of emerging diseases—labelled idiopathic environmental intolerances (IEIs)—which are characterized by symptoms attributed to environmental agents [40]. For instance, many studies have tried to find an unifying pathophysiology for all IEIs, focusing on CNS reactions to environmental stimuli, with activation changes of the brain areas related to sensory processing and emotional responses [1,5–15,41,42]. Since environmental noise has been recognised as an important health quality stressor [43], relevant literature up to now has focused mainly on auditory symptoms [21,22] without highlighting possible vestibular and postural consequences. In particular, no study protocol has found an impaired VOR function in MCS patients with vHIT. By providing a new dimension in vestibular evaluations [33], in this study the test permitted a quantitative assessment of the lateral semi-circular canal function and highlighted side and receptor specific information about the VOR [31–33], resulting in a bilateral reduction of both MCS gain and reslo (Figure 2B, Table 2). These aspects suggested that MCS patients, when compared with HC normative data, have a vestibular impairment (although not clinically evident by means of a cHIT). In line, FFT PS analysis and classical posturography parameters were demonstrated to be congruent with the above-mentioned vestibular damage in MCS. In fact, literature demonstrated body sways at the low frequency interval to be mainly under vestibular control [35,36,44] and patients with vestibular deficits (i.e., vestibular neuritis, Meniere’s disease) have consequently increased body sways at the low frequency interval as compared with normal subjects [35,44]. Furthermore, increased body sways within the middle interval and in classical posturography parameters especially during closed eye condition tend to confirm similar findings in previous studies, and result in a decreased ability to adapt to balance perturbations under complex conditions in certain disorders affected by a lack of vigilance [45–49]. It has previously been noted that the integration of information from the visual, vestibular and somatosensory receptors and motor coordination are processes requiring attention and vigilance [48], especially when information from other sensory systems is not reliable [50]. Considering the closeness between MCS and other related idiopathic disorders affected by functional postural impairment and attention and vigilance decrease (i.e., fibromyalgia, chronic fatigue syndrome) [45,51] it could be postulated that such phenomena may lead to slower or inappropriate sensory integration [48]. In relation to this, negative correlation found between RI values and QEESI total scores further strengthen these aspects, highlighting possible negative influences between higher values on the questionnaire tool used in the study and this kind of body sway parameter. Moreover, MCS patients demonstrated an increase in VD parameters (Figure 2A, Table 2), the concept of which has been postulated by previous authors [28,52] observing that, in conflicting circumstances between sensory modalities involved in spatial orientation, some subjects depend mainly on visual input. These visual-dependent subjects are more likely to be bewildered by tilted or moving visual surroundings [28,52]. From a clinical perspective it has been noted that vestibular patients whose dizzy symptoms are unleashed by certain visual scenarios (i.e., visual vertigo) also demonstrate increased VD when evaluated with conventional approaches [27]. In agreement with this notion, an impaired vestibular modality in MCS patients would have led to an enhanced reliance on visual input, inducing subjects to upregulate this process and to increase VD by a sensory re-weighting [27,53,54]. Appl. Sci. 2016, 6, 330 11 of 15 To this end, the ability to adapt sensory–motor control to challenging circumstances by selection and weighting of alternative reference frames was postulated as one of the main aspects of postural control [55,56]. Moreover, theoretical models of multisensory combination based on neurophysiological [57], perceptual [58] and behavioural evidence [59] proposed that adaptation to environmental changes would rely on sensory re-weighting to optimize both the relevance of individual sensory inputs and the reliability of estimates [48]. This kind of phenomenon is thought to be determined by maximum likelihood estimation so that the conscious and non-conscious brain processing appears as a near-optimal Bayesian estimator of object properties [56,60]. Furthermore, recent findings have demonstrated disorders of vestibular function as characterized by complex perceptual, sensorimotor and behavioural deficits that exceed basic perceptions of head acceleration or motor responses, such as VOR or vestibulo-spinal reflexes [61,62]. In order to thoroughly study these aspects of perceptual complexity, PCA was usefully performed to reduce the information dimensionality from the vast data arrays and to minimize the loss of information, relying on the fact that most of the descriptors are inter-correlated and these correlations in some instances are high [63,64]. In accordance with these aspects, a rearrangement of the dimensions, clusters and contributions of the variables within PC1 and PC2 in MCS was found to be a conceivable model of re-weighting along vestibular sensory processing. In particular, in Figure 5A–C varimax rotation highlighted that although vHIT and VD parameters were almost equally clustered in both groups, MCS patients showed a positive correlation of FFT parameters in PC1 and classical posturography parameters in PC2. Moreover, together with vHIT parameters, VD parameters were found to be negatively correlated within PC2. In turn, HC subjects demonstrated a positive correlation of VD and classical posturography parameters in PC1, FFT PS in PC2 and vHIT values in PC3. Possible explanations for the rearranged model along vestibular processing and symptoms cohort referred to by MCS patients could be considered to reside within pathophysiological cascades, such as those involving limbic structures in the natural history of MCS [1,4–15]. According to these notions, repeated exposure to MCS inducing agents were found to provide in MCS subjects both time-dependent sensitization and kindling, especially within hippocampal and limbic areas [65]. In turn, recent advances have postulated that additional “higher” vestibular dysfunctional aspects could really result from an impaired integration of the vestibular network within these areas [62]. This integration comprises both the internal representation of the body scheme and the internal model of the surrounding space as well as multisensory motion perception, attention, spatial memory, and navigation [62,66]. In conclusion, although disorders of vestibular function integration remain to be completely elucidated, the present data would consider the MCS re-weighting process along bottom-up and top-down vestibular streams as possibly (i) related to MCS physiopathological cascades involving vestibular network at multiple levels; and (ii) underpinning the symptoms of dizziness referred to by the patients. Thus, findings showed in this work could be considered as a first attempt at uncovering the literature gap among otoneurological consequences related to CNS hypersensitisation to environmental compounds. Considering the emerging prevalence of the disorder and the incidence of inner ear symptoms referred to by the patients, evaluation in otoneurological setting should increase in the future. In conclusion, this pilot study could be relevant in correctly screening, monitoring and clustering further clinical history data with the purpose of uncovering future therapeutic strategies useful in the improvement of otoneurological cohort symptoms. 5. Strengths and Limitations of the Study Many uncertainties in the literature involving MCS otoneurological pathways could clearly be explained by different criteria for patient enrolment, different kinds of questionnaires employed in Appl. Sci. 2016, 6, 330 12 of 15 order to investigate symptom spectrums, distortion related to the general incidence of personality traits in control subjects, and many nuisance variables biasing the general research in MCS [22]. As in previous studies, we tried to reduce artefacts that could affect symptom spectrum outcomes by enrolling, clustering and studying—by means of validated questionnaires (such as QEESI and DHI)—only MCS patients with commonly accepted criteria who were regularly followed by the local centre for the diagnosis, treatment and prevention of MCS [22]. Moreover, we performed an ANOVA model aimed at studying outcomes by using a powerful between effect. Thus, we tried to reduce biases related to sex and age ratios, misdiagnosis of MCS, and possible outcome distortions related to the general incidence of personality and social disturbances in the general population [22]. On the other hand, a possible limitation of the study could be represented by the Bonferroni multiple correction applied as post hoc test to both ANOVA and Spearman’s correlation, possibly increasing the likelihood of type II statistical errors. Author Contributions: Marco Alessandrini and Alessandro Micarelli conceived and designed the experiment; Alessandro Micarelli, Andrea Viziano and Marco Alessandrini performed the experiment; Alessandro Micarelli, Elisa Micarelli and Marco Alessandrini analysed the data; Ernesto Bruno and Giuseppe Genovesi contributed clinical data/materials/analysis tools; Alessandro Micarelli, Andrea Viziano and Marco Alessandrini wrote the paper. Conflicts of Interest: The authors declare no conflict of interest. Appendix A Core function implemented in Matlab in order to obtain fast Fourier transform of X and Y oscillations. The symbol ‘%’ and ‘s’ represent a Matlab comment and X or Y oscillations, respectively: L = length(s) % s vector of signal values Fs = 25; % Sampling frequency NFFT = 2ˆnextpow2(L); % Next power of 2 from length of s f = Fs/2*linspace(0,1,NFFT/2 + 1); S = fft(s,NFFT)/L; % FFT of a signal s modS = 2*abs(S(1:NFFT/2 + 1)); ms = max(modS); S_norm = modS/ms; References 1. Bell, I.R. 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Applied SciencesMultidisciplinary Digital Publishing Institute

Published: Nov 2, 2016

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