Grey and White Matter alterations in Obsessive-Compulsive Personality disorder: a Data Fusion Machine Learning approach

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Marrero, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5721098/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Obsessive-Compulsive Personality Disorder (OCPD) is a complex mental condition marked by excessive perfectionism, orderliness, and rigidity, often starting in adolescence or early adulthood; it affects 1.9–7.8% of the population. The disorder differs from Obsessive-Compulsive Disorder (OCD) for an apparent compromise of personality, distorted self-representation and altered perception of others. Although the two disorders present evident differences, unlike OCD, the neural bases of OCPD are understudied. The few studies conducted so far have identified grey matter alterations in brain regions such as the striatum and prefrontal cortex, but a comprehensive model of its neurobiology, and the eventual contribution of white matter abnormalities, are still unclear. One intriguing hypothesis is that regions ascribed to the Default Mode Network are involved in OCPD, similar to what has been shown for OCD and other anxiety disorders. To test this hypothesis, the grey and white matter images of 30 individuals diagnosed with OCPD (73% female, mean age = 29.300), and 34 healthy matched controls (82% female, mean age = 25.599) were analyzed with a data fusion unsupervised machine learning method known as Parallel Independent Component Analysis (pICA) to detect the joint contribution of these modalities to the OCPD diagnosis. Results indicated that two gray matter networks (GM5 and GM23) and one white matter network (WM25) differed between the OCPD and the control group. GM5 included brain regions belonging to the Default Mode Network and Salience Network, and was significantly correlated with anxiety; GM-23 included portions of the cerebellum, the precuneus, and the fusiform gyrus; WM-25 included white matter portions adjacent to Default Mode Network regions. These findings shed new light on the grey and white matter contributions to OCPD and may pave the way to developing objective markers of this disorder. Obsessive-compulsive personality disorder personality disorder affective neuroscience machine learning data fusion Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Obsessive-Compulsive Personality Disorder (OCPD) is a complex mental disorder characterized by a pervasive pattern of perfectionism, excessive orderliness, and rigid conformity to rules and procedures at the expense of psychological flexibility and efficiency, with an onset in adolescence or early adulthood (APA, 2013; Marincowitz et al., 2022). People with OCPD are characterized by preoccupation with details that lead to missing the major point of the activity, extreme perfectionism that is often the cause of delay, with tasks not being completed, and excessive rigidity that causes leisure of time and friendship (APA, 2022; Pinto et al., 2022). All these characteristics lead people with OCPD to distress and reduced quality of life. This disorder affects approximately 1.9–7.8% of the general population (Grant et al., 2004; Nestadt et al., 1991), with individuals affected by this condition frequently presenting for treatment in mental health and primary care services (Bender et al., 2001; Sansone et al., 2003). Yet, the disorder is still understudied ( Diedrich & Voderholzer, 2015; Pinto et al., 2022), especially if we compare it to Obsessive-Compulsive disorder (OCD), which is instead vastly studied (Maia et al., 2008; Mataix-Cols & van den Heuvel, 2006; Nakao et al., 2014; Robbins et al., 2024; Stein et al., 2019). Studies on OCD have generally highlighted the role of the Orbitofrontal cortex, the cingulate cortex, a hub of the Default Mode Network (DMN), and the head of the caudate nucleus, which hyperactivation seems to have a role in the compulsions typical of OCD (De Casale et al., 2015; Maia et al., 2007; Rasgon et al., 2017; Stein et al., 2019). Moreover, OCD typically shows reduced functionality and morphometric properties in regions associated with cognitive control, such as the dorsomedial prefrontal cortex (De Wit et al., 2014; Pujol et al., 2004; Radua & Mataix-Cols, 2009; Rasgon et al., 2017), and a hub of the Central Executive Network (CEN). Other areas that are altered in terms of gray matter volume in OCD are the insulo-opercular region, which appears to be more extended in patients with OCD, anterior cerebellum, and bilateral ventral putamen, that present instead a reduction for OCD subjects (Pujol et al., 2004). A study conducted by Geffen and colleagues (2021) reported alterations in brain connectivity of OCD patients confronted with healthy controls. In particular, the patients displayed hyperconnectivity between the lateral parietal lobe and a cluster, including the precuneus, and extended into the superior lateral occipital cortex. Considering these results, one might expect OCPD to exhibit neural alterations similar to those observed in OCD. Although this is the case for some areas, the two disorders present their specific pattern of altered areas. OCPD and OCD indeed share some psychological and neural alterations, but the two disorders also present some differences: unlike OCD, OCPD does not necessarily involve compulsions. At the same time, OCD, in contrast to OCPD, does not typically entail an apparent compromise of personality, such as distorted self-representation or altered perceptions of others. The few existing neuroimaging studies support both common and distinct mechanisms in these two disorders (Marincowitz et al., 2022). For example, a resting state functional magnetic resonance (fMRI) study found increased functional connectivity in the precuneus, the posterior hub of the DMN, of individuals with OCPD when compared to healthy controls (Coutinho et al., 2016). Another study by Lei and colleagues (2020), found increased amplitudes of low-frequency fluctuation (ALFF) inside the caudate, the precuneus, the insula, and the medial superior frontal gyrus in individuals with OCPD, while decreased ALFF was detected inside the fusiform and lingual gyri. Also, other studies have found significantly reduced hippocampus and amygdala volumes in people with OCPD compared to healthy controls (Atmaca et al., 2019; Gurok et al., 2019). Of these regions, alterations in the insula, the caudate, and the amygdala are commonly found also in OCD, whereas precuneus and hippocampus alterations seem more specific to OCPD (Marincowitz et al., 2022). Alterations of the caudate tail, ventral striatum, and prefrontal cortex have been reported to probably being associated to the disorder, but the main focus of the study that support this hypothesis is on Cluster C personality disorders in general (Payer et al., 2015). More specificity is needed to clearly define the exact contribution of these areas to OCPD. The primary aim of this study is to detect GM abnormalities in OCPD compared to controls to specifically test the hypothesis the regions ascribed to the DMN are affected. In sum, the evidence reviewed so far indicates that both common and distinct mechanisms are present in OCPD and OCD, but a reliable and comprehensive model of the neurobiology of OCPD is still lacking in literature (Marincowitz et al., 2021). Moreover, the contribution of the white matter to OCPD has been only partially studied so far. To the best of our knowledge, two studies explored the white matter contribution to OCPD. One study failed to report significant white matter alterations (Atmaca et al., 2019), and another found abnormalities in white matter connection to be associated with specific obsessive compulsive symptoms (Grazioplene et al., 2022). Specifically, negative thinking, repetition/checking and behavioral symmetry were associated with white matter abnormalities in posterior and dorsal corpus callosum, dorsal parietal lobe and posterior parietal and occipital lobe. Moreover, Laricchiuta and colleagues (2014) found that cerebellar white matter increment were associated with novelty seeking, while a reduction was associated with harm avoidance, but again, no evidence specific to OCPD were reported. In sum, the evidence relative to white matter contributions to OCPD is limited. Thus, the second aim of the study is to detect white matter abnormalities in OCPD compared to healthy controls. It is worth noting that the previous studies on OCPD have some methodological limitations. One for all, the use of massive univariate methods such as Voxel-based methodology or a priori selected regions of interest (ROIs) (Colic et al., 2018; Gurok et al., 2019; Lei et al., 2020; Payer et al., 2015), which limit our understanding of whole brain anomalies in OCPD. Also, univariate approaches neglect the relationship between voxels (Aguilar-Ortiz et al., 2018; Dadomo et al., 2022), they cannot exploit the joint mutual information across multiple neuroimaging modalities. Finally, these approaches do not test for the generalization of the results. In other words, they do not have an estimation of how those results can be used to predict new unobserved cases. In order to overcome the limitations of traditional univariate methods, a new class of multivariate statistical techniques, such as multivoxel pattern analysis (MVPA) or machine learning (ML) methods, has been increasingly utilized in neuroscience (Scarano et al., 2024). The main benefit of such methods are greater accuracy and sensitivity in detecting complex latent patterns in brain signals. Moreover, such are sensitive to spatially distributed effects (Norman et al., 2006). Data fusion approaches, are a specific class of machine learning algorithm that can also exploit the joint information across different neuroimaging modalities, identifying naturally grouped circuits that include information from both gray matter and white matter features (Grecucci et al., 2016; Lapomarda et al., 2021a,b; Saviola et al., 2020; Sorella et al., 2019). The joint contribution of GM-WM alterations may return a more complete picture of the neural alterations behind OCPD. These approaches are becoming largely used in the context of neuroscience and psychiatry to understand other personality disorders, such as borderline personality disorder (Caria & Grecucci, 2022; Dadomo et al., 2022; Grecucci et al., 2022; 2023), narcissistic personality disorder (Jornkokgoud et al., 2023; Jornkokgoud et al., 2024), and antisocial personality disorder (Sorella et al., 2022). To the best of our knowledge, no previous study has utilized a data fusion (GM-WM) machine learning approach to investigate OCPD. Therefore, our study aims to offer novel and compelling insights into the neural bases of OCPD through data fusion and unsupervised machine learning techniques. The machine learning method employed in this study is a specialized form of Independent Component Analysis (ICA; Lee, 1998) known as Parallel ICA (pICA; Liu et al., 2007; Liu et al., 2009). This approach enables simultaneous analysis of two modalities (e.g., gray matter and white matter), assessing their interrelationships while decomposing brain data into naturally grouped networks with reduced dimensionality (Yang et al., 2019). This method optimizes the extraction of relevant information for analysis. Data fusion, in particular, allows for the integrated analysis of gray and white matter without losing the connections between the two modalities. This is especially important given the likelihood that these modalities are influenced by shared genetic factors and that both may play a role in the development of OCPD. We predict alterations in regions ascribed to the Default Mode Network (DMN), such as the precuneus and medial frontal gyrus. These regions may be linked to the abnormal self and other representations observed in OCPD, as well as anxious rumination. The DMN is a network of brain areas including the retrosplenial cortex, inferior parietal cortex, dorsolateral frontal cortex, inferior frontal cortex, left inferior temporal gyrus, medial frontal regions, and amygdala (Alves et al., 2019). known to support internally oriented cognitive functions (Yeshurun et al., 2021), self-referential processing, and pain processing. These functions are often disrupted in personality disorders (Kluetsch et al., 2012). We also predict alterations in regions belonging to the Salience Network, such as the cingulate gyrus. These changes may underlie the heightened processing of anxiety-laden stimuli (Grahn et al., 2008; Uddin et al., 2017) that is characteristic of OCPD. The hypothesis of DMN involvement in OCD has already been investigated by Gonçalves and colleagues, who found no patterns of deactivation of DMN in subjects with OCD for the presentation of unpleasant stimuli condition, finding instead a minor deactivation (Gonçalves et al., 2017) of DMN in subjects with OCD for the presentation of pleasant stimuli. In the study, patients with OCD, relative to healthy controls, presented difficulties in DMN deactivation. Given this evidence and the functional similarities between OCD and OCPD, predicting a DMN involvement in that disorder seems reasonable and a hypothesis worth investigating. Material and methods Participants Participants included in the sample were 64 right-handed adults (22% male and 78% female, aged 18 to 56 years, average = 28.37 Sd = 10.716) resident in Tenerife (Canary Island, Spain). Neither sex nor age were normally distributed, according to the Shapiro-Wilk test for normality (Sex: w = 0.790, p-value < 0.001 Age: w = 0.510, p-value < 0.001). The participants were originally divided into two groups, according to the diagnosis of small animal phobia, but for the present study, after assessing via a t-test that it is not correlated with OCPD diagnosis (t = 1.506, p = 0.137), this classification has been ignored. The participants were, instead, divided into two groups according to the OCPD diagnosis, defined using the International Personality Disorder Examination (IPDE, Loranger et al., 1994), and divided as follows: 34 healthy controls (HC, M age =27.559, SD age =10.810) and 30 Participants with OCPD (OCPD, M age =29.300, SD age =10.710), matching for sex (t=-0.720, p = 0.477) and age (t=-0.613,p = 0.545). See Table 1 . This study adhered to the ethical standards of the Declaration of Helsinki and was approved by the Ethics Committee for Research and Animal Welfare of the University of La Laguna, Spain (CEIBA2013-0086). Table 1 Participants demographics OCPD CTRL p-value N. 30 34 Sex 8 M, 22 F 6 M, 28 F 0.477 Age 29.3 (± 10.7) 25.5 (± 10.8) 0.545 Inclusion criteria kept in consideration the following characteristics: no impediment to undergoing magnetic resonance imaging, that is to say, no metallic implants, braces, non-removable piercings, no tattoos, no pregnancy, no claustrophobia, no tinnitus and no surgical operation in the last three months; part of the group was recruited on the basis of a diagnosis of small animal phobia, according to specific questionnaires and psychological evaluation, that had to be the primary psychological disorder and that could not be explained by another health condition; the control group had the same criteria of phobic group but their scores in the psychological assessment of phobia was lower. Questionnaires The participants were administered questionnaires to assess some of their psychological and physiological characteristics. The Hamilton Anxiety Rating Scale (HARS; Hamilton, 1959; Bruss et al., 1994) is a 14-item clinician-administered tool used to assess the severity of anxiety symptoms. Each item is rated on a 5-point Likert scale ranging from 0 (not present) to 4 (very severe). The scale demonstrates good inter-rater reliability, with intraclass correlation coefficients ranging from 0.74 to 0.96 (Bruss et al., 1994; Thompson, 2015). The S–R (Situation–Response) Inventory of Anxiousness (Endler et al., 1962) was administered to participants in both groups (with and without phobia). This 14-item inventory uses a 5-point Likert scale to evaluate the most common physiological, cognitive, and behavioral symptoms associated with responses to anxiogenic stimuli, such as cockroaches, spiders, lizards, or mice. It has demonstrated high internal consistency (Cronbach’s alpha = 0.95) and adequate convergent validity (Kameoka & Tanaka-Matsumi, 1981). The Edinburgh Handedness Inventory (Oldfield, 1971) was used to confirm that all participants were right-handed. This 10-item inventory employs a forced-choice format, with scores above 0 indicating a right-hand preference and scores below 0 indicating a left-hand preference. The Beck Anxiety Inventory (BAI, Beck et al., 1988) is a 21-item self-report questionnaire used to discriminate anxiety disorders from depressive disorders in psychiatric patients. The items are 4-point scales that consist of the perceived rate of how much the patient has been bothered by each symptom over the past week. The BAI showed high internal consistency (alpha = 0.92) and test-retest reliability over 1 week, r( 81 ) = 0.75. The Hospital Anxiety and depression scale (HADS, Zigmond & Snaith, 1983) is a scale used to evaluate the presence of anxiety and depression caseness in the subjects. This 14-item inventory consists of 4-point items ranging from 1 to 4. The scale has good internal consistency (mean Cronbach alpha = 0.83 for Depression and 0.82 for Anxiety, Bjelland et al., 2002). Finally, the International Personality Disorder Examination (IPDE, Loranger et al., 1997) is an inventory designed to assess the nine personality disorders using a true/false response format. In this study, 8 of the 20 items related to cluster C (avoidant, dependent, and obsessive–compulsive personality disorders) correspond to obsessive-compulsive personality disorder (OCPD). These items were administered, with a score of 3 or more positive responses being used to consider the vulnerability to OCPD. Data acquisition High resolution three-dimensional T1-weighted whole-brain resting state structural MRI images were acquired on a 3.0 T MR scanner with a 12-channel head coil (GE 3.0T Sigma Excite HD). The subject was instructed to keep their eyes closed, relax, and lie as still as possible. Repetition time (TR)/echo time (TE) = 8852 ms/1756 ms, flip angle = 10°, 172 sagittal slices, slice thickness = 1 mm, field of view (FOV) = 256 × 256 mm2, data matrix = 256 × 256 × 172, the voxel size was 1 × 1 × 1 mm and TI = 650 ms. Preprocessing After a quality check conducted by an experienced neuroradiologist to rule out visible movement artifacts and gross structural abnormalities, all data were pre-processed using the segmentation routines provided by the Computational Anatomy Toolbox (CAT12, http://www.neuro.uni-jena.de/cat/ ), a toolbox available for the Statistical Parametric Mapping software (SPM12, https://www.fil.ion.ucl.ac.uk/spm/software ), for the MATLAB environment. The segmentation was registered using Diffeomorphic Anatomical Registration through Exponential Lie algebra tools (DARTEL) (Ashburner, 2007), a potential alternative to SPM’s traditional registration approaches that operates using a whole-brain approach, (Grecucci et al., 2016; Pappaianni et al., 2018; Yassa and Stark, 2009). Also, surface and Thickness were estimated. Finally, Dartel files were normalized to MNI space using a Spatial Gaussian Smoothing of eight. Data fusion unsupervised machine learning Independent Component Analysis is a blind source separation method that allows finding latent and independent components in a data set. ICA, in its forms of joint ICA and parallel ICA, is also used to provide a multivariate data fusion approach, capable of maintaining the latent association within data. Parallel ICA is particularly useful when data are assumed to be mixed in similar patterns (Moustafa et al., 2014), making the method particularly suitable for correlation among similar but independent components and across different modalities. For example, it is recognized that gray and white matter, even if different modalities, are probably correlated and share some genetic and biological common origins (Spalletta et al., 2018). We opted to respect this correlation between two different modalities and use a parallel ICA approach for the dimensional decomposition of brain data. The parallel ICA was applied to GM and WM data using the Fusion ICA Toolbox (FIT, http://mialab.mrn.org/software/fit ) (Calhoun, Adali, Pearlson, & Kihel, 2006) in MATLAB 2018a environment ( https://it.mathworks.com/products/matlab.html ). ICA works by converting every structural image into a one-dimensional vector, then using these vectors to build a data matrix. This matrix is then decomposed into a mixing matrix, which indicates the relationship between subjects and the degree to which each component contributes to a given subject, as well as a source matrix. Then, the scores are used to correlate each network with the psychological variable of interest. For more information, see the work of Xu and colleagues, 2009. Parallel ICA conducts an individual ICA for each modality, maximizing independence within each modality and optimizing the correlation between the same modality. The decomposition of the brain in different components has been conducted in steps: In the first step, an estimation of the number of components for both modalities was conducted with information theoretic criteria (Wax & Kailath, 1985). Then, the ICA was performed using the ICASSO algorithm (Himberg et al., 2003; 2004) to assess the consistency of each modality. To ensure the reliability of our findings and mitigate the risk of false discoveries due to overfitting, we employed a leave-one-out assessment (Liu, Pearlson, et al., 2009). With these settings, the ICASSO algorithm was conducted 100 times with identical parameter settings, excluding one subject each run, allowing us to assess consistency by examining the results from the 100 repetitions. The software was then used to convert the components into Talairach coordinates, a process necessary to define the brain areas included in each component. The data were then considered in their positive values and plotted in Surf Ice for visualization ( https://www.nitrc.org/projects/surfice/ , Rorden). Statistical analysis A Backward stepwise regression model was run for white and gray matter separately to determine the components with the highest association with OCPD. The decision to run one analysis for each feature was determined to avoid redundancy in the model, given the assumption of correlation between GM and WM (an assumption we could make given the peculiarity of the data fusion approach). We used the loading coefficients from each independent network derived through p-ICA as covariates, keeping the OCPD assessment as the dependent variable. Results Network decomposition The information-theoretic criteria estimated 28 independent within-subjects covarying grey (GM) and 28 white (WM) matter networks (see Fig. 1 ), but after a visual inspection of the components, the GM03 component was rejected because it appeared to be an artifact or at least too noisy to find some patterns. WM and GM densities were weighted, and each component's results were included in a Tailarach table. The positive values indicated increased GM or WM density, while negative values indicated decreased density. The p-ICA allowed us to compute a correlation within gray matter and white matter components; the correlation coefficients between the maximally correlated components of GM and WM are listed in Table 2 . Table 2 Correlations between the linked components. Component number is displayed for each feature and the linked correlation is reported GM WM Correlation T-value P-value 22 6 -0.95726 -26.061 4.159e-35 4 5 -0.91634 -18.02 2.4892e-26 1 4 0.88822 15.223 1.2916e-22 3 1 -0.75384 -9.0339 6.5017e-13 10 13 -0.7109 -7.9591 4.6591e-11 12 3 -0.67979 -7.2984 6.5372e-10 8 26 0.6512 6.7566 5.6632e-09 11 14 -0.62404 -6.2884 3.6025e-08 13 11 0.59347 5.806 2.3627e-07 27 19 0.58998 5.7535 2.8936e-07 9 25 0.57793 5.5761 5.7171e-07 26 18 -0.53573 -4.9957 5.073e-06 17 16 -0.5079 -4.6426 1.8352e-05 21 7 -0.49643 -4.5029 3.0198e-05 5 17 0.48894 4.4134 4.1416e-05 7 24 0.46849 4.1754 9.4553e-05 18 23 -0.44604 -3.9241 0.00022078 20 12 -0.44297 -3.8905 0.00024685 25 18 0.44065 3.8652 0.00026834 6 9 -0.43831 -3.8397 0.00029179 16 21 0.43322 3.7847 0.00034933 14 20 -0.40744 -3.513 0.00083311 23 1 -0.39909 -3.4272 0.0010881 15 1 0.37687 3.2037 0.0021439 2 17 0.34769 2.9198 0.0048767 24 23 -0.33679 -2.8164 0.0065042 28 12 0.32745 2.7288 0.0082604 19 8 -0.31226 -2.5881 0.012005 Stepwise regression on loading coefficients A Spearman Rank correlation analysis was conducted to examine the impact age and gender have on the construct of OCPD. The findings revealed that neither age (rho = 0.121, p = 0.342) nor gender (rho=-0.109, p = 0.392) exhibited any significant correlation with OCPD. In order to detect the components associated with OCPD, a stepwise backward regression was conducted on the loading coefficients found by the ICA. A significant winning model was found for each of the two modalities (GM: R = 0.517, R 2 = 0.267, Adjusted R 2 = 0.243, RMSE = 0.437; WM: R = 0.386, R 2 = 0.149, Adjusted R 2 = 0.121, RMSE = 0.472). The components resulting significantly associated with OCPD were two gray matter components (GM-05: t = 3.584, p < 0.001 and GM-23: t=-3.091, p = 0,003) and one white matter component (WM-25: t = 2.816, p = 0.007). In order to assess the specificity for OCPD correlation of the significant components, we conducted a t-test between the significant components and the other two cluster C personality disorder (avoidant personality disorder and dependent personality disorder) diagnosis. no significant component appeared to be correlated with any of the two From further analysis, WM25 did not correlate with any of the gray matter components. Interestingly, GM05 correlated significantly with both the anxiety scales used in the study. In particular, it correlated with BAI (t = 3.079; p = 0.003) and with the Anxiety subscale of HAD (t = 3.369, 0.001), but not the depression one. The two scales do not correlate significantly with any of the other components. GM05 component mainly included medial frontal gyrus, superior frontal gyrus and anterior cingulate, lingual gyrus, supramarginal gyrus, insula and precuneus (see Table 3 and Fig. 2 ). GM23 component consisted, among the others, of portions of precuneus, fusiform gyrus, medial frontal gyrus, cingulate areas and temporal gyrus (see Table 3 and Fig. 3 ). Finally, WM25 partially covered cingulate, post central and precentral gyrus, inferior parietal lobule, fusiform gyrus, cuneus and precuneus, middle frontal gyrus (see Table 3 and Fig. 4 ). Table 3 Talairach tables of the components significantly correlated with OCPD Area Broadmann area Left/right Volume (cc) Random effects max value (x, y, z) GM-05 Medial Frontal Gyrus 6, 8, 9, 10 1.5/1.8 6.4 (-3, 62, 16)/6.1 ( 3 , 57 , 5 ) Sub-Gyral 6 0.8/1.2 4.1 (-30, -35, 38)/6.3 ( 37 , 20 , 20 ) Superior Frontal Gyrus 8, 9, 10 0.9/1.0 4.9 (-3, 54, 25)/5.3 ( 3 , 48 , 31 ) Superior Temporal Gyrus 22, 38, 42 0.9/0.6 4.6 (-67, -40, 16)/5.3 ( 49 , 17 , – 7 ) Middle Frontal Gyrus 8, 9, 10 0.5/0.6 4.3 (-21, 34, 38)/5.0 ( 36 , 53 , 21 ) Anterior Cingulate 10, 32 0.4/0.4 4.6 (-3, 47, 3)/4.5 ( 3 , 47 , 6 ) Inferior Frontal Gyrus 45, 47 0.2/0.9 3.4 (-42, 15, -11)/4.5 ( 43 , 15 , – 13 ) Postcentral Gyrus 40 0.1/0.0 3.2 (-65, -22, 18)/-999.0 (0, 0, 0) Middle Temporal Gyrus 21, 39 0.1/0.1 3.1 (-65, -31, -4)/3.1 ( 36 , – 61 , 27 ) Supramarginal Gyrus * 0.0/0.1 -999.0 (0, 0, 0)/3.0 ( 50 , – 36 , 34 ) GM-23 Culmen * 4.5/6.7 5.6 (-1, -45, -4)/8.3 ( 10 , – 36 , – 15 ) Fourth Ventricle * 0.2/0.2 4.0 (-1, -40, -22)/6.4 ( 1 , – 40 , – 19 ) Cerebellar Lingual * 0.3/0.6 5.2 (0, -44, -8)/6.1 ( 6 , – 44 , – 8 ) Sub-Gyral * 1.0/1.9 4.7 (-27, -46, 33)/5.7 ( 31 , – 36 , 35 ) Declive * 0.3/2.0 3.5 (-9, -57, -11)/4.9 ( 9 , – 56 , – 11 ) Culmen of Vermis * 0.2/0.0 4.6 (0, -64, -7)/-999.0 (0, 0, 0) Precentral Gyrus * 0.1/0.1 4.1 (-34, 16, 35)/3.7 ( 37 , 1 , 28 ) Fastigium * 0.0/0.2 -999.0 (0, 0, 0)/4.0 ( 10 , – 49 , – 19 ) Precuneus 7 0.3/0.0 4.0 (-33, -64, 36)/-999.0 (0, 0, 0) Inferior Occipital Gyrus * 0.1/0.2 3.3 (-30, -82, -5)/4.0 ( 36 , – 76 , – 5 ) Supramarginal Gyrus * 0.0/0.1 -999.0 (0, 0, 0)/3.8 ( 50 , – 43 , 34 ) Medial Frontal Gyrus * 0.1/0.0 3.8 (-19, 39, 19)/-999.0 (0, 0, 0) Fusiform Gyrus 37 0.1/0.3 3.7 (-36, -52, -11)/3.5 ( 40 , – 43 , – 13 ) Cerebellar Tonsil * 0.1/0.1 3.7 (0, -51, -35)/3.4 ( 6 , – 51 , – 37 ) Nodule * 0.0/0.1 -999.0 (0, 0, 0)/3.5 ( 3 , – 48 , – 29 ) Superior Frontal Gyrus 8 0.2/0.1 3.4 (-22, 42, 19)/3.4 ( 30 , 30 , 51 ) Inferior Parietal Lobule * 0.1/0.1 3.3 (-34, -52, 39)/3.4 ( 49 , – 29 , 22 ) Middle Occipital Gyrus * 0.1/0.0 3.4 (-37, -77, 9)/-999.0 (0, 0, 0) Cingulate Gyrus * 0.1/0.1 3.0 (-12, -44, 28)/3.3 ( 15 , – 39 , 31 ) Lingual Gyrus * 0.0/0.1 -999.0 (0, 0, 0)/3.3 ( 10 , – 85 , – 2 ) Middle Frontal Gyrus 6 0.4/0.0 3.3 (-31, 48, 2)/-999.0 (0, 0, 0) Uvula * 0.0/0.1 -999.0 (0, 0, 0)/3.1 ( 25 , – 80 , – 23 ) Insula * 0.0/0.1 -999.0 (0, 0, 0)/3.1 ( 46 , – 31 , 20 ) Inferior Frontal Gyrus * 0.0/0.1 -999.0 (0, 0, 0)/3.1 ( 37 , 3 , 32 ) Inferior Parietal Lobule 40 0.2/0.0 4.4 (-45, -39, 39)/-999.0 (0, 0, 0) Angular Gyrus * 0.1/0.0 4.3 (-40, -66, 30)/-999.0 (0, 0, 0) Precentral Gyrus * 0.0/0.1 -999.0 (0, 0, 0)/4.2 ( 40 , 19 , 35 ) Postcentral Gyrus 5 0.0/0.2 -999.0 (0, 0, 0)/4.0 ( 39 , – 42 , 60 ) Inferior Frontal Gyrus * 0.0/0.1 -999.0 (0, 0, 0)/4.0 ( 43 , 37 , 4 ) Declive * 0.2/0.1 3.7 (-45, -68, -21)/3.9 ( 45 , – 67 , – 22 ) Pyramis * 0.1/0.0 3.7 (-40, -66, -33)/-999.0 (0, 0, 0) Inferior Temporal Gyrus * 0.1/0.0 3.6 (-59, -12, -16)/-999.0 (0, 0, 0) Anterior Cingulate 32 0.1/0.0 3.5 (-9, 32, -7)/-999.0 (0, 0, 0) Superior Parietal Lobule 7 0.0/0.1 -999.0 (0, 0, 0)/3.5 ( 30 , – 64 , 54 ) WM-25 Middle Temporal Gyrus 19, 20, 21, 37, 39 1.7/3.3 8.2 (-58, -34, -12)/10.4 ( 56 , – 33 , – 14 ) Inferior Temporal Gyrus 20, 21 0.9/1.2 6.7 (-58, -31, -15)/7.4 ( 56 , – 30 , – 16 ) Precuneus 7 0.2/1.0 4.9 (-22, -57, 50)/6.2 ( 25 , – 54 , 52 ) Sub-Gyral * 0.2/1.2 3.8 (-21, -57, 54)/6.1 ( 52 , – 33 , – 10 ) Superior Parietal Lobule 7 0.4/1.0 4.2 (-24, -60, 53)/5.9 ( 28 , – 55 , 44 ) Middle Frontal Gyrus 8, 9, 10 0.7/1.1 5.6 (-42, 50, 3)/5.5 ( 25 , 41 , 37 ) Medial Frontal Gyrus 6, 10 0.3/0.6 4.9 (-7, -12, 60)/4.7 ( 13 , 57 , – 5 ) Inferior Parietal Lobule 40 0.3/0.4 3.7 (-58, -38, 36)/4.9 ( 39 , – 48 , 52 ) Superior Frontal Gyrus 6, 8, 9 0.3/1.3 3.7 (-10, 54, 26)/4.8 ( 10 , 24 , 51 ) Superior Temporal Gyrus 39 0.5/0.6 4.2 (-50, -32, 8)/4.8 ( 46 , – 57 , 29 ) Inferior Frontal Gyrus 9, 46 0.3/0.2 4.2 (-45, 50, 0)/3.5 ( 52 , 12 , 30 ) Fusiform Gyrus * 0.0/0.1 -999.0 (0, 0, 0)/4.2 ( 56 , – 33 , – 19 ) Middle Occipital Gyrus 18 0.2/0.0 4.1 (-13, -88, 15)/-999.0 (0, 0, 0) Postcentral Gyrus 3 0.1/0.1 3.9 (-59, -20, 37)/3.3 ( 52 , – 29 , 40 ) Parahippocampal Gyrus * 0.0/0.1 -999.0 (0, 0, 0)/3.9 ( 36 , – 16 , – 23 ) Precentral Gyrus * 0.0/0.3 -999.0 (0, 0, 0)/3.7 ( 53 , – 11 , 36 ) Cuneus 18, 19 0.3/0.1 3.7 (-10, -88, 18)/3.4 ( 22 , – 81 , 33 ) Supramarginal Gyrus 40 0.3/0.0 3.5 (-56, -42, 30)/-999.0 (0, 0, 0) Paracentral Lobule 5 0.1/0.0 3.4 (-9, -35, 54)/-999.0 (0, 0, 0) Cingulate Gyrus 32 0.1/0.0 3.3 (-12, 25, 32)/-999.0 (0, 0, 0) Angular Gyrus * 0.0/0.1 -999.0 (0, 0, 0)/3.2 ( 46 , – 60 , 32 ) Inferior Semi-Lunar Lobule * 0.1/0.0 3.1 (-15, -69, -37)/-999.0 (0, 0, 0) Please note that WM nomenclature is identified in terms of adjacency to GM regions Discussion The primary goal of our study was to detect joint GM - WM differences between individuals diagnosed with OCPD and matched healthy controls. To achieve these objectives, we initially employed a data fusion unsupervised ML algorithm to analyze the structural MRI images of 64 individuals (30 OCPD). This approach facilitated the decomposition of the brain into independent networks characterized by the covariation of GM and WM. The ICA-based approach was specifically used to find brain networks that are known to approximate resting state macro-networks. This was necessary to explore the specific role of the DMN but preserving the normal individual differences in how this network may be expressed in each individual, without over imposing a predetermined mask (Scarano et al., in press; Sorella et al., in press). Subsequently, we utilized stepwise regression to identify the GM-WM network associated with OCPD diagnosis. The data fusion revealed twenty-eight covarying GM-WM brain networks, but only three components (GM5, GM23 and WM25) were significantly associated with OCPD. GM5 and WM25 were more expressed (increased matter concentration) in OCPD subjects compared to controls, while GM23 showed the opposite trend. GM05 includes portions of medial frontal gyrus, sub-gyral, superior frontal gyrus, superior temporal gyrus, middle frontal gyrus, anterior cingulate and inferior frontal gyrus, post central gyrus, middle temporal gyrus and supramarginal gyrus. These areas are all expressed with higher density in the OCPD compared to control ones. Previous studies evidenced different types of alterations in some of the areas that we found in component GM05 for OCPD: both superior and medial frontal gyrus were found to have increased activity fluctuation in individuals with OCPD (Lei et al., 2019), while other studies underline the role of cingulate cortex alterations in explaining some OCPD’s typical traits, such as harm avoidance and anxiety-related traits (Tuominen et al., 2012; Colic et al., 2018). Moreover, from the analysis emerges a correlation between GM-05 and two of the three subscales for anxiety. This correlation can be interpreted as evidence of an effect of alterations to the areas GM05 is composed of on anxious traits that characterize OCPD specifically; we can support this hypothesis since GM05 didn’t present any significant correlation with the diagnosis of the other two cluster C personality disorders we measured. GM23 comprises cerebellar regions (such as Culmen, cerebellar Tonsil, Nodule, uvula, declive, pyramis) the sub-gyral, the precentral gyrus, precuneus, occipital gyrus, supramarginal gyrus, fusiform gyrus, superior frontal gyrus, inferior parietal lobule, middle occipital gyrus, cingulate gyrus, lingual gyrus, middle frontal gyrus, insula, inferior frontal gyrus, inferior parietal lobule, angular gyrus, precentral and postcentral gyrus, inferior temporal gyrus, anterior cingulate cortex, superior parietal lobule. Particularly interesting is the presence of precuneus in this component; such area is in fact related to important traits of OCPD, such as perfectionism and rumination (Coutinho et al., 2016); alteration of gray matter density in culmen, cerebellar lingual, cerebellar tonsil and declive suggest an involvement in cerebellum for subjects with OCPD; this hypothesis is supported by previous studies (Laricchiuta et al., 2014; Petrosini et al., 2017) that link cerebellar volumes to construct important for OCPD, such as novelty seeking and harm avoidance. In particular, novelty seeking is positively associated with cerebellar volumes, while harm avoidance is negatively correlated with the same measure. The component includes portions of insula, an area associated with characteristics altered in OCD and OCPD, such as empathy, cognitive flexibility (Gogolla, 2017) and that has been found to be altered in precedent studies investigating OCPD (Lei et al., 2019). Most importantly, including portions of insula and anterior cingulate cortex, the component appears to be related to Salience Network (Seeley, 2019). The salience network is an important brain network that functions as a dynamic switch between concentration on self (mediated by DMN) and task related and directed attention (Schimmelpfennig et al., 2023). Its involvement in OCPD could imply difficulty in this dynamic switching process in the individuals with the condition. Lastly, WM-25 positive component is composed of WM regions adjacent to the middle temporal gyrus, inferior temporal gyrus, precuneus, sub-gyral, superior parietal lobule, middle frontal gyrus, medial frontal gyrus, inferior parietal lobule, fusiform gyrus, middle occipital gyrus, postcentral and precentral gyrus, parahippocampal gyrus, cuneus, supramarginal gyrus, paracentral lobule, cingulate gyrus, angular gyrus and inferior Semi-lunar lobule. The increased density of white matter circuits in the precuneus has already been observed in a previous resting state functional MRI study (Coutinho et al., 2016). In that particular study, the increase was found to be related to self-reflection, as well as accessing past events for problem-solving and future planning. Although our study does not specifically measure any of these constructs, our results further support the already known involvement of precuneus connectivity in OCPD. Interestingly, the WM25 network also includes increased WM volume in frontal areas, such as medial and middle frontal gyrus, coherently with what we observed for GM-05. The presence of parahippocampal gyrus suggests a limbic alteration in subjects with OCPD, supporting the results of previous studies that link this system to OCPD (Kyeong et al., 2014, Millon & Davis, 1996). As expected, the resulting networks overlap with parts of the Default Mode Network (DMN). The Default mode Network is the name given to a network of distributed and interconnected brain areas that are typically suppressed when an individual is focused on external stimuli and are, instead, mainly activated for internally focused thought processes (Menon, 2023), for example during self-referential processing, future planning, cues evaluation and emotional regulation (Raichle, 2015). The network includes medial prefrontal cortex, posterior cingulate cortex with the adjacent precuneus, the bilateral inferior parietal cortex and medial temporal cortex (Buckner et al., 2008; Fox et al., 2007; Raichle, 2015). The link between DMN and personality disorders is well documented in literature (see, for example Coutinho et al., 2016, for OCPD, Yang et al., 2016 for borderline personality disorder and Zhang et al., 2014 for Schizotypal personality disorder). Thus, a result that includes it was expected. In particular, for what concerns OCPD, DMN is responsible for several psychological processes that may be relevant in its pathophysiology (Coutinho et al., 2016), for example reflective self awareness processes and introspection (Buckner et al., 2008), retrospective memories, prospective thoughts (Delamillieure et al., 2010); propensity for self-referent thoughts, rethinking about recent past, and future planning; these are all processes that depend by the DMN and that are altered in OCPD. The involvement of DMN in similar symptoms and in the development of anxious states had been previously observed for Obsessive-Compulsive Disorder (Gonçalves and colleagues, 2017). Our study further extend the previous evidences supporting the hypothesis of a DMN involvement in OCPD; out of the areas that present an increment of volume in OCPD patients, GM5 includes medial frontal gyrus, superior temporal gyrus, anterior cingulate and inferior frontal gyrus; medial frontal gyrus and superior temporal gyrus have been respectively correlated and overlapped to Default Mode Network (Menon, 2023; Uddin et al., 2008); the Anterior Cingulate is not only considered part of default mode network (Buckner and DiNicola, 2019) but is also recognized to be part of Salience Network (Seeley et al., 2007), an important network that is involved in DMN suppression. The presence of the insula and Anterior cingulate cortex in GM23 further supports the involvement of the salience network. Finally, inferior frontal gyrus seems to be directly involved in the network (Menon, 2023; Uddin et al. 2008). GM23 includes the precuneus, an area that is considered a part of the DMN; WM25 confirm a white matter involvement adjacent to the precuneus, the medial frontal gyrus and inferior temporal gyrus, already discussed for the gray matter component were included, but also includes cingulate gyrus and inferior parietal lobule, two areas that are part of DMN (Buckner and DiNicola, 2019). In conclusion, GM5 and WM25 are two components that include portions of DMN associated with OCPD. In contrast, GM23 includes mainly areas (except for Precuneus) that have never been reported to be related to DMN. This result can be interpreted in the direction of an increase of Default Mode Network volume for patients with OCPD, while GM23, which is little if not associated with DMN, is more expressed in controls; thus, a volume decrement in areas included in GM23 is associated with OCPD, while for what concerns GM5 and WM25 (the circuits that include areas associated with DMN) is instead an increment in volume to be related to OCPD. Of note, GM5 and GM23 don’t correlate with WM-25. This means that although OCPD is characterized by alterations in both modalities (GM and WM), these networks are not joint. Despite the merits, our study does not come without limitations. First, while the sample size in our study is in line or even more significant than in some previous research, it remains relatively modest. Larger samples would improve the robustness of the findings and enable more nuanced subgroup analyses. Second, our study exclusively focused on structural MRI data, specifically examining gray and white matter concentrations. Although this approach provided valuable insights into the structural abnormalities associated with OCPD, it does not address the functional aspects of brain activity. Incorporating functional neuroimaging data, such as resting-state or task-based fMRI, could offer a more comprehensive understanding of OCPD neural mechanisms by functional connectivity patterns and dynamic brain activity related to its typical behaviors. While we identified specific brain regions and networks associated with OCPD, it remains unclear to whether these findings are specific to this personality disorder or extend to other personalities especially the cluster C. Comparative studies involving different types of personalities could help clarify the specificity and shared neural mechanisms underlying these disorders. Lastly, although the approach we proposed in this study is based on Parallel ICA, other data fusion approaches could be implemented (in the form, for example, of Joint ICA or tIVA). Conclusion This study aimed to get a better understanding of the GM and WM alterations in individuals diagnosed with OCPD. In particular we tested the hypothesis that regions ascribed to the DMN were altered in OCPD. We used Parallel ICA, a data fusion machine learning method to extract GM and WM networks that approximate macro-resting state networks. Results indicate a contribution of many brain regions overlapping with the DMN. As such these results may pave the way for future OCPD biomarkers and neurostimulation methods to target dysfunctional brain regions. Disruptions in the DMN can affect how emotions are processed and regulated, potentially interfering with the therapeutic mechanisms of interventions like Cognitive-Behavior Therapy. For instance, when the DMN is hyperactive or dysregulated, it may impair the cognitive flexibility necessary for effective emotion regulation, which could undermine the success of interventions focused on emotional processing. These findings not only highlight the importance of the DMN in emotional regulation, but also have significant implications for the development of biomarkers for disorders such as OCPD. Furthermore, they suggest potential pathways for neurostimulation approaches aimed at modulating dysfunctional brain regions. By targeting these disrupted areas, it may be possible to enhance the effectiveness of existing therapies and open new avenues for treating psychological conditions associated with maladaptive brain network activity Declarations Funding This research was funded by the Ministry of Science, Innovation, and Universities of Spain, projects PSI2013-42912-R and PSI2017-83222-R, and by the European Commission, project E-MOTION 1808341802 Erasmus project. Conflicts of interest/Competing interests Authors declare no conflict of interest Ethics approval This study adhered to the ethical standards of the Declaration of Helsinki and was approved by the Ethics Committee for Research and Animal Welfare of the University of La Laguna, Spain (CEIBA2013-0086). Consent to participate Participants provide a signed informed consent to participate to the study Consent for publication Not applicable Availability of data and materials Data will be available upon reasonable request Code availability Not applicable Author contributions: Lorenzo Arena: methodology, data curation, formal analysis, and writing—original draft preparation, Writing – review & editing. Alessandro Grecucci: methodology, data curation, formal analysis, and writing—original draft preparation, Writing – review & editing. Francisco Rivero: Conceptualization, methodology, data curation, investigation, Writing – review & editing. Rosario J. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5721098","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":399102847,"identity":"875172b1-d08e-425f-b5c7-33ae5b9f7b41","order_by":0,"name":"Lorenzo Arena","email":"","orcid":"","institution":"University of Trento","correspondingAuthor":false,"prefix":"","firstName":"Lorenzo","middleName":"","lastName":"Arena","suffix":""},{"id":399102848,"identity":"d545a6d0-fff9-434f-921d-994c803b67ee","order_by":1,"name":"Wenceslao Peñate","email":"","orcid":"","institution":"Universidad de La Laguna","correspondingAuthor":false,"prefix":"","firstName":"Wenceslao","middleName":"","lastName":"Peñate","suffix":""},{"id":399102849,"identity":"1ac72de5-4df3-4bc7-8146-7e1771068c68","order_by":2,"name":"Francisco Rivero","email":"","orcid":"","institution":"Universidad de La Laguna","correspondingAuthor":false,"prefix":"","firstName":"Francisco","middleName":"","lastName":"Rivero","suffix":""},{"id":399102850,"identity":"ed85922d-8a97-4558-9011-3ec3b706e33a","order_by":3,"name":"Rosario J. 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Hot colors represent areas where the density is higher than expected.\u003c/p\u003e\n\u003cp\u003eWM and GM densities were weighted, and each component's results were included in a Tailarach table. The positive values indicated increased GM or WM density, while negative values indicated decreased density. The p-ICA allowed us to compute a correlation within gray matter and white matter components; the correlation coefficients between the maximally correlated components of GM and WM are listed in Table 2.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5721098/v1/dec48d0db078c5f2778cbe1a.png"},{"id":73516052,"identity":"3e433bf2-4a4c-4913-8b23-b8e0889be398","added_by":"auto","created_at":"2025-01-10 17:46:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":229968,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGM-05\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5721098/v1/f6091585f89d04ce462de921.png"},{"id":73516054,"identity":"ed94be20-b08a-4c92-ab52-9a068a0cedbc","added_by":"auto","created_at":"2025-01-10 17:46:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":260997,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGM-23\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5721098/v1/1b3fa09f91ee97b3d6be5c30.png"},{"id":73516055,"identity":"bdb1b6be-8a6e-484d-a169-d10664d17f7b","added_by":"auto","created_at":"2025-01-10 17:46:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":253790,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWM-25\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5721098/v1/9cc4aa6ed86d52c4f91110a1.png"},{"id":73518349,"identity":"fe73a209-6373-4ac5-aeed-06a85f4d8e6b","added_by":"auto","created_at":"2025-01-10 18:02:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2097033,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5721098/v1/38cfb10f-82a2-42fd-8e53-fe08d7c4e27b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Grey and White Matter alterations in Obsessive-Compulsive Personality disorder: a Data Fusion Machine Learning approach","fulltext":[{"header":"Introduction","content":"\u003cp\u003eObsessive-Compulsive Personality Disorder (OCPD) is a complex mental disorder characterized by a pervasive pattern of perfectionism, excessive orderliness, and rigid conformity to rules and procedures at the expense of psychological flexibility and efficiency, with an onset in adolescence or early adulthood (APA, 2013; Marincowitz et al., 2022). People with OCPD are characterized by preoccupation with details that lead to missing the major point of the activity, extreme perfectionism that is often the cause of delay, with tasks not being completed, and excessive rigidity that causes leisure of time and friendship (APA, 2022; Pinto et al., 2022). All these characteristics lead people with OCPD to distress and reduced quality of life. This disorder affects approximately 1.9\u0026ndash;7.8% of the general population (Grant et al., 2004; Nestadt et al., 1991), with individuals affected by this condition frequently presenting for treatment in mental health and primary care services (Bender et al., 2001; Sansone et al., 2003). Yet, the disorder is still understudied ( Diedrich \u0026amp; Voderholzer, 2015; Pinto et al., 2022), especially if we compare it to Obsessive-Compulsive disorder (OCD), which is instead vastly studied (Maia et al., 2008; Mataix-Cols \u0026amp; van den Heuvel, 2006; Nakao et al., 2014; Robbins et al., 2024; Stein et al., 2019). Studies on OCD have generally highlighted the role of the Orbitofrontal cortex, the cingulate cortex, a hub of the Default Mode Network (DMN), and the head of the caudate nucleus, which hyperactivation seems to have a role in the compulsions typical of OCD (De Casale et al., 2015; Maia et al., 2007; Rasgon et al., 2017; Stein et al., 2019). Moreover, OCD typically shows reduced functionality and morphometric properties in regions associated with cognitive control, such as the dorsomedial prefrontal cortex (De Wit et al., 2014; Pujol et al., 2004; Radua \u0026amp; Mataix-Cols, 2009; Rasgon et al., 2017), and a hub of the Central Executive Network (CEN). Other areas that are altered in terms of gray matter volume in OCD are the insulo-opercular region, which appears to be more extended in patients with OCD, anterior cerebellum, and bilateral ventral putamen, that present instead a reduction for OCD subjects (Pujol et al., 2004). A study conducted by Geffen and colleagues (2021) reported alterations in brain connectivity of OCD patients confronted with healthy controls. In particular, the patients displayed hyperconnectivity between the lateral parietal lobe and a cluster, including the precuneus, and extended into the superior lateral occipital cortex.\u003c/p\u003e \u003cp\u003eConsidering these results, one might expect OCPD to exhibit neural alterations similar to those observed in OCD. Although this is the case for some areas, the two disorders present their specific pattern of altered areas. OCPD and OCD indeed share some psychological and neural alterations, but the two disorders also present some differences: unlike OCD, OCPD does not necessarily involve compulsions. At the same time, OCD, in contrast to OCPD, does not typically entail an apparent compromise of personality, such as distorted self-representation or altered perceptions of others. The few existing neuroimaging studies support both common and distinct mechanisms in these two disorders (Marincowitz et al., 2022).\u003c/p\u003e \u003cp\u003eFor example, a resting state functional magnetic resonance (fMRI) study found increased functional connectivity in the precuneus, the posterior hub of the DMN, of individuals with OCPD when compared to healthy controls (Coutinho et al., 2016). Another study by Lei and colleagues (2020), found increased amplitudes of low-frequency fluctuation (ALFF) inside the caudate, the precuneus, the insula, and the medial superior frontal gyrus in individuals with OCPD, while decreased ALFF was detected inside the fusiform and lingual gyri. Also, other studies have found significantly reduced hippocampus and amygdala volumes in people with OCPD compared to healthy controls (Atmaca et al., 2019; Gurok et al., 2019). Of these regions, alterations in the insula, the caudate, and the amygdala are commonly found also in OCD, whereas precuneus and hippocampus alterations seem more specific to OCPD (Marincowitz et al., 2022). Alterations of the caudate tail, ventral striatum, and prefrontal cortex have been reported to probably being associated to the disorder, but the main focus of the study that support this hypothesis is on Cluster C personality disorders in general (Payer et al., 2015). More specificity is needed to clearly define the exact contribution of these areas to OCPD. The primary aim of this study is to detect GM abnormalities in OCPD compared to controls to specifically test the hypothesis the regions ascribed to the DMN are affected.\u003c/p\u003e \u003cp\u003eIn sum, the evidence reviewed so far indicates that both common and distinct mechanisms are present in OCPD and OCD, but a reliable and comprehensive model of the neurobiology of OCPD is still lacking in literature (Marincowitz et al., 2021). Moreover, the contribution of the white matter to OCPD has been only partially studied so far. To the best of our knowledge, two studies explored the white matter contribution to OCPD. One study failed to report significant white matter alterations (Atmaca et al., 2019), and another found abnormalities in white matter connection to be associated with specific obsessive compulsive symptoms (Grazioplene et al., 2022). Specifically, negative thinking, repetition/checking and behavioral symmetry were associated with white matter abnormalities in posterior and dorsal corpus callosum, dorsal parietal lobe and posterior parietal and occipital lobe. Moreover, Laricchiuta and colleagues (2014) found that cerebellar white matter increment were associated with novelty seeking, while a reduction was associated with harm avoidance, but again, no evidence specific to OCPD were reported. In sum, the evidence relative to white matter contributions to OCPD is limited. Thus, the second aim of the study is to detect white matter abnormalities in OCPD compared to healthy controls.\u003c/p\u003e \u003cp\u003eIt is worth noting that the previous studies on OCPD have some methodological limitations. One for all, the use of massive univariate methods such as Voxel-based methodology or a priori selected regions of interest (ROIs) (Colic et al., 2018; Gurok et al., 2019; Lei et al., 2020; Payer et al., 2015), which limit our understanding of whole brain anomalies in OCPD. Also, univariate approaches neglect the relationship between voxels (Aguilar-Ortiz et al., 2018; Dadomo et al., 2022), they cannot exploit the joint mutual information across multiple neuroimaging modalities. Finally, these approaches do not test for the generalization of the results. In other words, they do not have an estimation of how those results can be used to predict new unobserved cases. In order to overcome the limitations of traditional univariate methods, a new class of multivariate statistical techniques, such as multivoxel pattern analysis (MVPA) or machine learning (ML) methods, has been increasingly utilized in neuroscience (Scarano et al., 2024). The main benefit of such methods are greater accuracy and sensitivity in detecting complex latent patterns in brain signals. Moreover, such are sensitive to spatially distributed effects (Norman et al., 2006). Data fusion approaches, are a specific class of machine learning algorithm that can also exploit the joint information across different neuroimaging modalities, identifying naturally grouped circuits that include information from both gray matter and white matter features (Grecucci et al., 2016; Lapomarda et al., 2021a,b; Saviola et al., 2020; Sorella et al., 2019). The joint contribution of GM-WM alterations may return a more complete picture of the neural alterations behind OCPD.\u003c/p\u003e \u003cp\u003eThese approaches are becoming largely used in the context of neuroscience and psychiatry to understand other personality disorders, such as borderline personality disorder (Caria \u0026amp; Grecucci, 2022; Dadomo et al., 2022; Grecucci et al., 2022; 2023), narcissistic personality disorder (Jornkokgoud et al., 2023; Jornkokgoud et al., 2024), and antisocial personality disorder (Sorella et al., 2022).\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, no previous study has utilized a data fusion (GM-WM) machine learning approach to investigate OCPD. Therefore, our study aims to offer novel and compelling insights into the neural bases of OCPD through data fusion and unsupervised machine learning techniques. The machine learning method employed in this study is a specialized form of Independent Component Analysis (ICA; Lee, 1998) known as Parallel ICA (pICA; Liu et al., 2007; Liu et al., 2009). This approach enables simultaneous analysis of two modalities (e.g., gray matter and white matter), assessing their interrelationships while decomposing brain data into naturally grouped networks with reduced dimensionality (Yang et al., 2019). This method optimizes the extraction of relevant information for analysis. Data fusion, in particular, allows for the integrated analysis of gray and white matter without losing the connections between the two modalities. This is especially important given the likelihood that these modalities are influenced by shared genetic factors and that both may play a role in the development of OCPD.\u003c/p\u003e \u003cp\u003eWe predict alterations in regions ascribed to the Default Mode Network (DMN), such as the precuneus and medial frontal gyrus. These regions may be linked to the abnormal self and other representations observed in OCPD, as well as anxious rumination. The DMN is a network of brain areas including the retrosplenial cortex, inferior parietal cortex, dorsolateral frontal cortex, inferior frontal cortex, left inferior temporal gyrus, medial frontal regions, and amygdala (Alves et al., 2019). known to support internally oriented cognitive functions (Yeshurun et al., 2021), self-referential processing, and pain processing. These functions are often disrupted in personality disorders (Kluetsch et al., 2012). We also predict alterations in regions belonging to the Salience Network, such as the cingulate gyrus. These changes may underlie the heightened processing of anxiety-laden stimuli (Grahn et al., 2008; Uddin et al., 2017) that is characteristic of OCPD. The hypothesis of DMN involvement in OCD has already been investigated by Gon\u0026ccedil;alves and colleagues, who found no patterns of deactivation of DMN in subjects with OCD for the presentation of unpleasant stimuli condition, finding instead a minor deactivation (Gon\u0026ccedil;alves et al., 2017) of DMN in subjects with OCD for the presentation of pleasant stimuli. In the study, patients with OCD, relative to healthy controls, presented difficulties in DMN deactivation. Given this evidence and the functional similarities between OCD and OCPD, predicting a DMN involvement in that disorder seems reasonable and a hypothesis worth investigating.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eParticipants included in the sample were 64 right-handed adults (22% male and 78% female, aged 18 to 56 years, average\u0026thinsp;=\u0026thinsp;28.37 Sd\u0026thinsp;=\u0026thinsp;10.716) resident in Tenerife (Canary Island, Spain). Neither sex nor age were normally distributed, according to the Shapiro-Wilk test for normality (Sex: w\u0026thinsp;=\u0026thinsp;0.790, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001 Age: w\u0026thinsp;=\u0026thinsp;0.510, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The participants were originally divided into two groups, according to the diagnosis of small animal phobia, but for the present study, after assessing via a t-test that it is not correlated with OCPD diagnosis (t\u0026thinsp;=\u0026thinsp;1.506, p\u0026thinsp;=\u0026thinsp;0.137), this classification has been ignored. The participants were, instead, divided into two groups according to the OCPD diagnosis, defined using the International Personality Disorder Examination (IPDE, Loranger et al., 1994), and divided as follows: 34 healthy controls (HC, \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eage\u003c/em\u003e\u003c/sub\u003e =27.559, \u003cem\u003eSD\u003c/em\u003e\u003csub\u003e\u003cem\u003eage\u003c/em\u003e\u003c/sub\u003e =10.810) and 30 Participants with OCPD (OCPD, \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eage\u003c/em\u003e\u003c/sub\u003e =29.300, \u003cem\u003eSD\u003c/em\u003e\u003csub\u003e\u003cem\u003eage\u003c/em\u003e\u003c/sub\u003e =10.710), matching for sex (t=-0.720, p\u0026thinsp;=\u0026thinsp;0.477) and age (t=-0.613,p\u0026thinsp;=\u0026thinsp;0.545). See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This study adhered to the ethical standards of the Declaration of Helsinki and was approved by the Ethics Committee for Research and Animal Welfare of the University of La Laguna, Spain (CEIBA2013-0086).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipants demographics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOCPD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTRL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 M, 22 F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 M, 28 F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.3 (\u0026plusmn;\u0026thinsp;10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.5 (\u0026plusmn;\u0026thinsp;10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eInclusion criteria kept in consideration the following characteristics: no impediment to undergoing magnetic resonance imaging, that is to say, no metallic implants, braces, non-removable piercings, no tattoos, no pregnancy, no claustrophobia, no tinnitus and no surgical operation in the last three months; part of the group was recruited on the basis of a diagnosis of small animal phobia, according to specific questionnaires and psychological evaluation, that had to be the primary psychological disorder and that could not be explained by another health condition; the control group had the same criteria of phobic group but their scores in the psychological assessment of phobia was lower.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eQuestionnaires\u003c/h3\u003e\n\u003cp\u003eThe participants were administered questionnaires to assess some of their psychological and physiological characteristics. The Hamilton Anxiety Rating Scale (HARS; Hamilton, 1959; Bruss et al., 1994) is a 14-item clinician-administered tool used to assess the severity of anxiety symptoms. Each item is rated on a 5-point Likert scale ranging from 0 (not present) to 4 (very severe). The scale demonstrates good inter-rater reliability, with intraclass correlation coefficients ranging from 0.74 to 0.96 (Bruss et al., 1994; Thompson, 2015). The S\u0026ndash;R (Situation\u0026ndash;Response) Inventory of Anxiousness (Endler et al., 1962) was administered to participants in both groups (with and without phobia). This 14-item inventory uses a 5-point Likert scale to evaluate the most common physiological, cognitive, and behavioral symptoms associated with responses to anxiogenic stimuli, such as cockroaches, spiders, lizards, or mice. It has demonstrated high internal consistency (Cronbach\u0026rsquo;s alpha\u0026thinsp;=\u0026thinsp;0.95) and adequate convergent validity (Kameoka \u0026amp; Tanaka-Matsumi, 1981). The Edinburgh Handedness Inventory (Oldfield, 1971) was used to confirm that all participants were right-handed. This 10-item inventory employs a forced-choice format, with scores above 0 indicating a right-hand preference and scores below 0 indicating a left-hand preference. The Beck Anxiety Inventory (BAI, Beck et al., 1988) is a 21-item self-report questionnaire used to discriminate anxiety disorders from depressive disorders in psychiatric patients. The items are 4-point scales that consist of the perceived rate of how much the patient has been bothered by each symptom over the past week. The BAI showed high internal consistency (alpha\u0026thinsp;=\u0026thinsp;0.92) and test-retest reliability over 1 week, r(\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e)\u0026thinsp;=\u0026thinsp;0.75. The Hospital Anxiety and depression scale (HADS, Zigmond \u0026amp; Snaith, 1983) is a scale used to evaluate the presence of anxiety and depression caseness in the subjects. This 14-item inventory consists of 4-point items ranging from 1 to 4. The scale has good internal consistency (mean Cronbach alpha\u0026thinsp;=\u0026thinsp;0.83 for Depression and 0.82 for Anxiety, Bjelland et al., 2002). Finally, the International Personality Disorder Examination (IPDE, Loranger et al., 1997) is an inventory designed to assess the nine personality disorders using a true/false response format. In this study, 8 of the 20 items related to cluster C (avoidant, dependent, and obsessive\u0026ndash;compulsive personality disorders) correspond to obsessive-compulsive personality disorder (OCPD). These items were administered, with a score of 3 or more positive responses being used to consider the vulnerability to OCPD.\u003c/p\u003e\n\u003ch3\u003eData acquisition\u003c/h3\u003e\n\u003cp\u003eHigh resolution three-dimensional T1-weighted whole-brain resting state structural MRI images were acquired on a 3.0 T MR scanner with a 12-channel head coil (GE 3.0T Sigma Excite HD). The subject was instructed to keep their eyes closed, relax, and lie as still as possible. Repetition time (TR)/echo time (TE)\u0026thinsp;=\u0026thinsp;8852 ms/1756 ms, flip angle\u0026thinsp;=\u0026thinsp;10\u0026deg;, 172 sagittal slices, slice thickness\u0026thinsp;=\u0026thinsp;1 mm, field of view (FOV)\u0026thinsp;=\u0026thinsp;256 \u0026times; 256 mm2, data matrix\u0026thinsp;=\u0026thinsp;256 \u0026times; 256 \u0026times; 172, the voxel size was 1 \u0026times; 1 \u0026times; 1 mm and TI\u0026thinsp;=\u0026thinsp;650 ms.\u003c/p\u003e\n\u003ch3\u003ePreprocessing\u003c/h3\u003e\n\u003cp\u003eAfter a quality check conducted by an experienced neuroradiologist to rule out visible movement artifacts and gross structural abnormalities, all data were pre-processed using the segmentation routines provided by the Computational Anatomy Toolbox (CAT12, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.neuro.uni-jena.de/cat/\u003c/span\u003e\u003cspan address=\"http://www.neuro.uni-jena.de/cat/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a toolbox available for the Statistical Parametric Mapping software (SPM12, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fil.ion.ucl.ac.uk/spm/software\u003c/span\u003e\u003cspan address=\"https://www.fil.ion.ucl.ac.uk/spm/software\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), for the MATLAB environment. The segmentation was registered using Diffeomorphic Anatomical Registration through Exponential Lie algebra tools (DARTEL) (Ashburner, 2007), a potential alternative to SPM\u0026rsquo;s traditional registration approaches that operates using a whole-brain approach, (Grecucci et al., 2016; Pappaianni et al., 2018; Yassa and Stark, 2009). Also, surface and Thickness were estimated. Finally, Dartel files were normalized to MNI space using a Spatial Gaussian Smoothing of eight.\u003c/p\u003e\n\u003ch3\u003eData fusion unsupervised machine learning\u003c/h3\u003e\n\u003cp\u003eIndependent Component Analysis is a blind source separation method that allows finding latent and independent components in a data set. ICA, in its forms of joint ICA and parallel ICA, is also used to provide a multivariate data fusion approach, capable of maintaining the latent association within data. Parallel ICA is particularly useful when data are assumed to be mixed in similar patterns (Moustafa et al., 2014), making the method particularly suitable for correlation among similar but independent components and across different modalities. For example, it is recognized that gray and white matter, even if different modalities, are probably correlated and share some genetic and biological common origins (Spalletta et al., 2018). We opted to respect this correlation between two different modalities and use a parallel ICA approach for the dimensional decomposition of brain data. The parallel ICA was applied to GM and WM data using the Fusion ICA Toolbox (FIT, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mialab.mrn.org/software/fit\u003c/span\u003e\u003cspan address=\"http://mialab.mrn.org/software/fit\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e (Calhoun, Adali, Pearlson, \u0026amp; Kihel, 2006) in MATLAB 2018a environment (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://it.mathworks.com/products/matlab.html\u003c/span\u003e\u003cspan address=\"https://it.mathworks.com/products/matlab.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e ICA works by converting every structural image into a one-dimensional vector, then using these vectors to build a data matrix. This matrix is then decomposed into a mixing matrix, which indicates the relationship between subjects and the degree to which each component contributes to a given subject, as well as a source matrix. Then, the scores are used to correlate each network with the psychological variable of interest. For more information, see the work of Xu and colleagues, 2009. Parallel ICA conducts an individual ICA for each modality, maximizing independence within each modality and optimizing the correlation between the same modality. The decomposition of the brain in different components has been conducted in steps: In the first step, an estimation of the number of components for both modalities was conducted with information theoretic criteria (Wax \u0026amp; Kailath, 1985). Then, the ICA was performed using the ICASSO algorithm (Himberg et al., 2003; 2004) to assess the consistency of each modality. To ensure the reliability of our findings and mitigate the risk of false discoveries due to overfitting, we employed a leave-one-out assessment (Liu, Pearlson, et al., 2009). With these settings, the ICASSO algorithm was conducted 100 times with identical parameter settings, excluding one subject each run, allowing us to assess consistency by examining the results from the 100 repetitions. The software was then used to convert the components into Talairach coordinates, a process necessary to define the brain areas included in each component. The data were then considered in their positive values and plotted in Surf Ice for visualization (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nitrc.org/projects/surfice/\u003c/span\u003e\u003cspan address=\"https://www.nitrc.org/projects/surfice/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, Rorden).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eA Backward stepwise regression model was run for white and gray matter separately to determine the components with the highest association with OCPD. The decision to run one analysis for each feature was determined to avoid redundancy in the model, given the assumption of correlation between GM and WM (an assumption we could make given the peculiarity of the data fusion approach). We used the loading coefficients from each independent network derived through p-ICA as covariates, keeping the OCPD assessment as the dependent variable.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eNetwork decomposition\u003c/h2\u003e \u003cp\u003eThe information-theoretic criteria estimated 28 independent within-subjects covarying grey (GM) and 28 white (WM) matter networks (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), but after a visual inspection of the components, the GM03 component was rejected because it appeared to be an artifact or at least too noisy to find some patterns.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWM and GM densities were weighted, and each component's results were included in a Tailarach table. The positive values indicated increased GM or WM density, while negative values indicated decreased density. The p-ICA allowed us to compute a correlation within gray matter and white matter components; the correlation coefficients between the maximally correlated components of GM and WM are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelations between the linked components. Component number is displayed for each feature and the linked correlation is reported\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCorrelation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.95726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-26.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.159e-35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.91634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-18.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.4892e-26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.88822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.2916e-22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.75384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-9.0339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.5017e-13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.7109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-7.9591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.6591e-11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.67979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-7.2984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.5372e-10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.7566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.6632e-09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.62404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.2884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.6025e-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.59347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.3627e-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e27\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.7535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.8936e-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.57793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.5761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.7171e-07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.53573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.9957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.073e-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.5079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.6426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.8352e-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.49643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.5029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.0198e-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.48894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.4134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.1416e-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.46849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.1754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.4553e-05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.44604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.9241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00022078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.44297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.8905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00024685\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.8652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00026834\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.43831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.8397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00029179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.7847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00034933\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.40744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00083311\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.39909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.4272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0010881\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.37687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.2037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0021439\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.34769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.9198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0048767\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.33679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.8164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0065042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e28\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.32745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.7288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0082604\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.31226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.5881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.012005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStepwise regression on loading coefficients\u003c/h2\u003e \u003cp\u003eA Spearman Rank correlation analysis was conducted to examine the impact age and gender have on the construct of OCPD. The findings revealed that neither age (rho\u0026thinsp;=\u0026thinsp;0.121, p\u0026thinsp;=\u0026thinsp;0.342) nor gender (rho=-0.109, p\u0026thinsp;=\u0026thinsp;0.392) exhibited any significant correlation with OCPD. In order to detect the components associated with OCPD, a stepwise backward regression was conducted on the loading coefficients found by the ICA. A significant winning model was found for each of the two modalities (GM: R\u0026thinsp;=\u0026thinsp;0.517, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.267, Adjusted R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.243, RMSE\u0026thinsp;=\u0026thinsp;0.437; WM: R\u0026thinsp;=\u0026thinsp;0.386, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.149, Adjusted R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.121, RMSE\u0026thinsp;=\u0026thinsp;0.472). The components resulting significantly associated with OCPD were two gray matter components (GM-05: t\u0026thinsp;=\u0026thinsp;3.584, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and GM-23: t=-3.091, p\u0026thinsp;=\u0026thinsp;0,003) and one white matter component (WM-25: t\u0026thinsp;=\u0026thinsp;2.816, p\u0026thinsp;=\u0026thinsp;0.007). In order to assess the specificity for OCPD correlation of the significant components, we conducted a t-test between the significant components and the other two cluster C personality disorder (avoidant personality disorder and dependent personality disorder) diagnosis. no significant component appeared to be correlated with any of the two From further analysis, WM25 did not correlate with any of the gray matter components. Interestingly, GM05 correlated significantly with both the anxiety scales used in the study. In particular, it correlated with BAI (t\u0026thinsp;=\u0026thinsp;3.079; p\u0026thinsp;=\u0026thinsp;0.003) and with the Anxiety subscale of HAD (t\u0026thinsp;=\u0026thinsp;3.369, 0.001), but not the depression one. The two scales do not correlate significantly with any of the other components. GM05 component mainly included medial frontal gyrus, superior frontal gyrus and anterior cingulate, lingual gyrus, supramarginal gyrus, insula and precuneus (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). GM23 component consisted, among the others, of portions of precuneus, fusiform gyrus, medial frontal gyrus, cingulate areas and temporal gyrus (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Finally, WM25 partially covered cingulate, post central and precentral gyrus, inferior parietal lobule, fusiform gyrus, cuneus and precuneus, middle frontal gyrus (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTalairach tables of the components significantly correlated with OCPD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eBroadmann\u003c/p\u003e \u003cp\u003earea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eLeft/right\u003c/p\u003e \u003cp\u003eVolume (cc)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRandom effects\u003c/p\u003e \u003cp\u003emax value (x, y, z)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eGM-05\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedial Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e6, 8, 9, 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.5/1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e6.4 (-3, 62, 16)/6.1 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-Gyral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.8/1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4.1 (-30, -35, 38)/6.3 (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuperior Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e8, 9, 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.9/1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4.9 (-3, 54, 25)/5.3 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuperior Temporal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e22, 38, 42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.9/0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4.6 (-67, -40, 16)/5.3 (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e8, 9, 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.5/0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4.3 (-21, 34, 38)/5.0 (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnterior Cingulate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e10, 32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.4/0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4.6 (-3, 47, 3)/4.5 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInferior Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e45, 47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.2/0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.4 (-42, 15, -11)/4.5 (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostcentral Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.1/0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.2 (-65, -22, 18)/-999.0 (0, 0, 0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle Temporal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e21, 39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.1/0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.1 (-65, -31, -4)/3.1 (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupramarginal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.0/0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-999.0 (0, 0, 0)/3.0 (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGM-23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCulmen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e4.5/6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e5.6 (-1, -45, -4)/8.3 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFourth Ventricle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.2/0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4.0 (-1, -40, -22)/6.4 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebellar Lingual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.3/0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e5.2 (0, -44, -8)/6.1 (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-Gyral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.0/1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4.7 (-27, -46, 33)/5.7 (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeclive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.3/2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.5 (-9, -57, -11)/4.9 (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCulmen of Vermis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.2/0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4.6 (0, -64, -7)/-999.0 (0, 0, 0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecentral Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.1/0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4.1 (-34, 16, 35)/3.7 (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFastigium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.0/0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-999.0 (0, 0, 0)/4.0 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecuneus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.3/0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4.0 (-33, -64, 36)/-999.0 (0, 0, 0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInferior Occipital Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.1/0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.3 (-30, -82, -5)/4.0 (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupramarginal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.0/0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-999.0 (0, 0, 0)/3.8 (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedial Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.1/0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.8 (-19, 39, 19)/-999.0 (0, 0, 0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFusiform Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.1/0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.7 (-36, -52, -11)/3.5 (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCerebellar Tonsil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.1/0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.7 (0, -51, -35)/3.4 (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNodule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.0/0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-999.0 (0, 0, 0)/3.5 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuperior Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.2/0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.4 (-22, 42, 19)/3.4 (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInferior Parietal Lobule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.1/0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.3 (-34, -52, 39)/3.4 (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle Occipital Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.1/0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.4 (-37, -77, 9)/-999.0 (0, 0, 0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCingulate Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.1/0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.0 (-12, -44, 28)/3.3 (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLingual Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.0/0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-999.0 (0, 0, 0)/3.3 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.4/0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.3 (-31, 48, 2)/-999.0 (0, 0, 0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUvula\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.0/0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-999.0 (0, 0, 0)/3.1 (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsula\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.0/0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-999.0 (0, 0, 0)/3.1 (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInferior Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.0/0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-999.0 (0, 0, 0)/3.1 (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInferior Parietal Lobule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.2/0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4.4 (-45, -39, 39)/-999.0 (0, 0, 0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAngular Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.1/0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4.3 (-40, -66, 30)/-999.0 (0, 0, 0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecentral Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.0/0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-999.0 (0, 0, 0)/4.2 (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostcentral Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.0/0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-999.0 (0, 0, 0)/4.0 (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInferior Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.0/0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-999.0 (0, 0, 0)/4.0 (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeclive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.2/0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.7 (-45, -68, -21)/3.9 (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePyramis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.1/0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.7 (-40, -66, -33)/-999.0 (0, 0, 0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInferior Temporal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.1/0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.6 (-59, -12, -16)/-999.0 (0, 0, 0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnterior Cingulate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.1/0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.5 (-9, 32, -7)/-999.0 (0, 0, 0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuperior Parietal Lobule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.0/0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-999.0 (0, 0, 0)/3.5 (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWM-25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle Temporal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e19, 20, 21, 37, 39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.7/3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e8.2 (-58, -34, -12)/10.4 (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInferior Temporal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e20, 21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.9/1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e6.7 (-58, -31, -15)/7.4 (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecuneus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.2/1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4.9 (-22, -57, 50)/6.2 (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-Gyral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.2/1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.8 (-21, -57, 54)/6.1 (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuperior Parietal Lobule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.4/1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4.2 (-24, -60, 53)/5.9 (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e8, 9, 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.7/1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e5.6 (-42, 50, 3)/5.5 (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedial Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e6, 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.3/0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4.9 (-7, -12, 60)/4.7 (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInferior Parietal Lobule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.3/0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.7 (-58, -38, 36)/4.9 (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuperior Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e6, 8, 9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.3/1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.7 (-10, 54, 26)/4.8 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuperior Temporal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.5/0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4.2 (-50, -32, 8)/4.8 (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInferior Frontal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e9, 46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.3/0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4.2 (-45, 50, 0)/3.5 (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFusiform Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.0/0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-999.0 (0, 0, 0)/4.2 (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle Occipital Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.2/0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e4.1 (-13, -88, 15)/-999.0 (0, 0, 0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostcentral Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.1/0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.9 (-59, -20, 37)/3.3 (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParahippocampal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.0/0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-999.0 (0, 0, 0)/3.9 (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecentral Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.0/0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-999.0 (0, 0, 0)/3.7 (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCuneus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e18, 19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.3/0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.7 (-10, -88, 18)/3.4 (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupramarginal Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.3/0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.5 (-56, -42, 30)/-999.0 (0, 0, 0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParacentral Lobule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.1/0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.4 (-9, -35, 54)/-999.0 (0, 0, 0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCingulate Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.1/0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.3 (-12, 25, 32)/-999.0 (0, 0, 0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAngular Gyrus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.0/0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e-999.0 (0, 0, 0)/3.2 (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInferior Semi-Lunar Lobule\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.1/0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e3.1 (-15, -69, -37)/-999.0 (0, 0, 0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003ePlease note that WM nomenclature is identified in terms of adjacency to GM regions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe primary goal of our study was to detect joint GM - WM differences between individuals diagnosed with OCPD and matched healthy controls. To achieve these objectives, we initially employed a data fusion unsupervised ML algorithm to analyze the structural MRI images of 64 individuals (30 OCPD). This approach facilitated the decomposition of the brain into independent networks characterized by the covariation of GM and WM. The ICA-based approach was specifically used to find brain networks that are known to approximate resting state macro-networks. This was necessary to explore the specific role of the DMN but preserving the normal individual differences in how this network may be expressed in each individual, without over imposing a predetermined mask (Scarano et al., in press; Sorella et al., in press). Subsequently, we utilized stepwise regression to identify the GM-WM network associated with OCPD diagnosis. The data fusion revealed twenty-eight covarying GM-WM brain networks, but only three components (GM5, GM23 and WM25) were significantly associated with OCPD. GM5 and WM25 were more expressed (increased matter concentration) in OCPD subjects compared to controls, while GM23 showed the opposite trend.\u003c/p\u003e \u003cp\u003eGM05 includes portions of medial frontal gyrus, sub-gyral, superior frontal gyrus, superior temporal gyrus, middle frontal gyrus, anterior cingulate and inferior frontal gyrus, post central gyrus, middle temporal gyrus and supramarginal gyrus. These areas are all expressed with higher density in the OCPD compared to control ones. Previous studies evidenced different types of alterations in some of the areas that we found in component GM05 for OCPD: both superior and medial frontal gyrus were found to have increased activity fluctuation in individuals with OCPD (Lei et al., 2019), while other studies underline the role of cingulate cortex alterations in explaining some OCPD\u0026rsquo;s typical traits, such as harm avoidance and anxiety-related traits (Tuominen et al., 2012; Colic et al., 2018). Moreover, from the analysis emerges a correlation between GM-05 and two of the three subscales for anxiety. This correlation can be interpreted as evidence of an effect of alterations to the areas GM05 is composed of on anxious traits that characterize OCPD specifically; we can support this hypothesis since GM05 didn\u0026rsquo;t present any significant correlation with the diagnosis of the other two cluster C personality disorders we measured.\u003c/p\u003e \u003cp\u003eGM23 comprises cerebellar regions (such as Culmen, cerebellar Tonsil, Nodule, uvula, declive, pyramis) the sub-gyral, the precentral gyrus, precuneus, occipital gyrus, supramarginal gyrus, fusiform gyrus, superior frontal gyrus, inferior parietal lobule, middle occipital gyrus, cingulate gyrus, lingual gyrus, middle frontal gyrus, insula, inferior frontal gyrus, inferior parietal lobule, angular gyrus, precentral and postcentral gyrus, inferior temporal gyrus, anterior cingulate cortex, superior parietal lobule. Particularly interesting is the presence of precuneus in this component; such area is in fact related to important traits of OCPD, such as perfectionism and rumination (Coutinho et al., 2016); alteration of gray matter density in culmen, cerebellar lingual, cerebellar tonsil and declive suggest an involvement in cerebellum for subjects with OCPD; this hypothesis is supported by previous studies (Laricchiuta et al., 2014; Petrosini et al., 2017) that link cerebellar volumes to construct important for OCPD, such as novelty seeking and harm avoidance. In particular, novelty seeking is positively associated with cerebellar volumes, while harm avoidance is negatively correlated with the same measure. The component includes portions of insula, an area associated with characteristics altered in OCD and OCPD, such as empathy, cognitive flexibility (Gogolla, 2017) and that has been found to be altered in precedent studies investigating OCPD (Lei et al., 2019). Most importantly, including portions of insula and anterior cingulate cortex, the component appears to be related to Salience Network (Seeley, 2019). The salience network is an important brain network that functions as a dynamic switch between concentration on self (mediated by DMN) and task related and directed attention (Schimmelpfennig et al., 2023). Its involvement in OCPD could imply difficulty in this dynamic switching process in the individuals with the condition.\u003c/p\u003e \u003cp\u003eLastly, WM-25 positive component is composed of WM regions adjacent to the middle temporal gyrus, inferior temporal gyrus, precuneus, sub-gyral, superior parietal lobule, middle frontal gyrus, medial frontal gyrus, inferior parietal lobule, fusiform gyrus, middle occipital gyrus, postcentral and precentral gyrus, parahippocampal gyrus, cuneus, supramarginal gyrus, paracentral lobule, cingulate gyrus, angular gyrus and inferior Semi-lunar lobule. The increased density of white matter circuits in the precuneus has already been observed in a previous resting state functional MRI study (Coutinho et al., 2016). In that particular study, the increase was found to be related to self-reflection, as well as accessing past events for problem-solving and future planning. Although our study does not specifically measure any of these constructs, our results further support the already known involvement of precuneus connectivity in OCPD. Interestingly, the WM25 network also includes increased WM volume in frontal areas, such as medial and middle frontal gyrus, coherently with what we observed for GM-05. The presence of parahippocampal gyrus suggests a limbic alteration in subjects with OCPD, supporting the results of previous studies that link this system to OCPD (Kyeong et al., 2014, Millon \u0026amp; Davis, 1996).\u003c/p\u003e \u003cp\u003eAs expected, the resulting networks overlap with parts of the Default Mode Network (DMN). The Default mode Network is the name given to a network of distributed and interconnected brain areas that are typically suppressed when an individual is focused on external stimuli and are, instead, mainly activated for internally focused thought processes (Menon, 2023), for example during self-referential processing, future planning, cues evaluation and emotional regulation (Raichle, 2015). The network includes medial prefrontal cortex, posterior cingulate cortex with the adjacent precuneus, the bilateral inferior parietal cortex and medial temporal cortex (Buckner et al., 2008; Fox et al., 2007; Raichle, 2015). The link between DMN and personality disorders is well documented in literature (see, for example Coutinho et al., 2016, for OCPD, Yang et al., 2016 for borderline personality disorder and Zhang et al., 2014 for Schizotypal personality disorder). Thus, a result that includes it was expected. In particular, for what concerns OCPD, DMN is responsible for several psychological processes that may be relevant in its pathophysiology (Coutinho et al., 2016), for example reflective self awareness processes and introspection (Buckner et al., 2008), retrospective memories, prospective thoughts (Delamillieure et al., 2010); propensity for self-referent thoughts, rethinking about recent past, and future planning; these are all processes that depend by the DMN and that are altered in OCPD.\u003c/p\u003e \u003cp\u003eThe involvement of DMN in similar symptoms and in the development of anxious states had been previously observed for Obsessive-Compulsive Disorder (Gon\u0026ccedil;alves and colleagues, 2017). Our study further extend the previous evidences supporting the hypothesis of a DMN involvement in OCPD; out of the areas that present an increment of volume in OCPD patients, GM5 includes medial frontal gyrus, superior temporal gyrus, anterior cingulate and inferior frontal gyrus; medial frontal gyrus and superior temporal gyrus have been respectively correlated and overlapped to Default Mode Network (Menon, 2023; Uddin et al., 2008); the Anterior Cingulate is not only considered part of default mode network (Buckner and DiNicola, 2019) but is also recognized to be part of Salience Network (Seeley et al., 2007), an important network that is involved in DMN suppression. The presence of the insula and Anterior cingulate cortex in GM23 further supports the involvement of the salience network.\u003c/p\u003e \u003cp\u003eFinally, inferior frontal gyrus seems to be directly involved in the network (Menon, 2023; Uddin et al. 2008). GM23 includes the precuneus, an area that is considered a part of the DMN; WM25 confirm a white matter involvement adjacent to the precuneus, the medial frontal gyrus and inferior temporal gyrus, already discussed for the gray matter component were included, but also includes cingulate gyrus and inferior parietal lobule, two areas that are part of DMN (Buckner and DiNicola, 2019). In conclusion, GM5 and WM25 are two components that include portions of DMN associated with OCPD. In contrast, GM23 includes mainly areas (except for Precuneus) that have never been reported to be related to DMN. This result can be interpreted in the direction of an increase of Default Mode Network volume for patients with OCPD, while GM23, which is little if not associated with DMN, is more expressed in controls; thus, a volume decrement in areas included in GM23 is associated with OCPD, while for what concerns GM5 and WM25 (the circuits that include areas associated with DMN) is instead an increment in volume to be related to OCPD.\u003c/p\u003e \u003cp\u003eOf note, GM5 and GM23 don\u0026rsquo;t correlate with WM-25. This means that although OCPD is characterized by alterations in both modalities (GM and WM), these networks are not joint.\u003c/p\u003e \u003cp\u003eDespite the merits, our study does not come without limitations. First, while the sample size in our study is in line or even more significant than in some previous research, it remains relatively modest. Larger samples would improve the robustness of the findings and enable more nuanced subgroup analyses. Second, our study exclusively focused on structural MRI data, specifically examining gray and white matter concentrations. Although this approach provided valuable insights into the structural abnormalities associated with OCPD, it does not address the functional aspects of brain activity. Incorporating functional neuroimaging data, such as resting-state or task-based fMRI, could offer a more comprehensive understanding of OCPD neural mechanisms by functional connectivity patterns and dynamic brain activity related to its typical behaviors. While we identified specific brain regions and networks associated with OCPD, it remains unclear to whether these findings are specific to this personality disorder or extend to other personalities especially the cluster C. Comparative studies involving different types of personalities could help clarify the specificity and shared neural mechanisms underlying these disorders. Lastly, although the approach we proposed in this study is based on Parallel ICA, other data fusion approaches could be implemented (in the form, for example, of Joint ICA or tIVA).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study aimed to get a better understanding of the GM and WM alterations in individuals diagnosed with OCPD. In particular we tested the hypothesis that regions ascribed to the DMN were altered in OCPD. We used Parallel ICA, a data fusion machine learning method to extract GM and WM networks that approximate macro-resting state networks. Results indicate a contribution of many brain regions overlapping with the DMN. As such these results may pave the way for future OCPD biomarkers and neurostimulation methods to target dysfunctional brain regions.\u003c/p\u003e \u003cp\u003eDisruptions in the DMN can affect how emotions are processed and regulated, potentially interfering with the therapeutic mechanisms of interventions like Cognitive-Behavior Therapy. For instance, when the DMN is hyperactive or dysregulated, it may impair the cognitive flexibility necessary for effective emotion regulation, which could undermine the success of interventions focused on emotional processing. These findings not only highlight the importance of the DMN in emotional regulation, but also have significant implications for the development of biomarkers for disorders such as OCPD. Furthermore, they suggest potential pathways for neurostimulation approaches aimed at modulating dysfunctional brain regions. By targeting these disrupted areas, it may be possible to enhance the effectiveness of existing therapies and open new avenues for treating psychological conditions associated with maladaptive brain network activity\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Ministry of Science, Innovation, and Universities of Spain, projects PSI2013-42912-R and PSI2017-83222-R, and by the European Commission, project E-MOTION 1808341802 Erasmus project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest/Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare no conflict of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study adhered to the ethical standards of the Declaration of Helsinki and was approved by the Ethics Committee for Research and Animal Welfare of the University of La Laguna, Spain (CEIBA2013-0086).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants provide a signed informed consent to participate to the study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be available upon reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003eLorenzo Arena: methodology, data curation, formal analysis, and writing\u0026mdash;original draft preparation, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eAlessandro Grecucci: methodology, data curation, formal analysis, and writing\u0026mdash;original draft preparation, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eFrancisco Rivero: Conceptualization, methodology, data curation, investigation, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eRosario J. Marrero: Conceptualization, methodology, data curation, investigation, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eTeresa Olivares: Conceptualization, methodology, data curation, investigation, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eYolanda \u0026Aacute;lvarez-P\u0026eacute;rez: Conceptualization, methodology, data curation, investigation, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\u003cp\u003eJuan M. Bethencourt: Conceptualization, methodology, data curation, investigation, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eAscensi\u0026oacute;n Fumero: Conceptualization, methodology, data curation, investigation, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\u003cp\u003eAlessandro Scarano: Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eWenceslao Pe\u0026ntilde;ate: Conceptualization, methodology, data curation, investigation, Project Administration, Resources and Supervision, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAguilar-Ortiz, S., Salgado-Pineda, P., Marco-Pallar\u0026eacute;s, J., Pascual, J. C., Vega, D., Soler, J., et al. (2018). Abnormalities in gray matter Vol. in patients with borderline personality disorder and their relation to lifetime depression: a VBM study. 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Altered default mode network functional connectivity in schizotypal personality disorder. Schizophrenia Research, 160(1), 51\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.schres.2014.10.013\u003c/span\u003e\u003cspan address=\"10.1016/j.schres.2014.10.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Obsessive-compulsive personality disorder, personality disorder, affective neuroscience, machine learning, data fusion","lastPublishedDoi":"10.21203/rs.3.rs-5721098/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5721098/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObsessive-Compulsive Personality Disorder (OCPD) is a complex mental condition marked by excessive perfectionism, orderliness, and rigidity, often starting in adolescence or early adulthood; it affects 1.9\u0026ndash;7.8% of the population. The disorder differs from Obsessive-Compulsive Disorder (OCD) for an apparent compromise of personality, distorted self-representation and altered perception of others. Although the two disorders present evident differences, unlike OCD, the neural bases of OCPD are understudied. The few studies conducted so far have identified grey matter alterations in brain regions such as the striatum and prefrontal cortex, but a comprehensive model of its neurobiology, and the eventual contribution of white matter abnormalities, are still unclear. One intriguing hypothesis is that regions ascribed to the Default Mode Network are involved in OCPD, similar to what has been shown for OCD and other anxiety disorders. To test this hypothesis, the grey and white matter images of 30 individuals diagnosed with OCPD (73% female, mean age\u0026thinsp;=\u0026thinsp;29.300), and 34 healthy matched controls (82% female, mean age\u0026thinsp;=\u0026thinsp;25.599) were analyzed with a data fusion unsupervised machine learning method known as Parallel Independent Component Analysis (pICA) to detect the joint contribution of these modalities to the OCPD diagnosis. Results indicated that two gray matter networks (GM5 and GM23) and one white matter network (WM25) differed between the OCPD and the control group. GM5 included brain regions belonging to the Default Mode Network and Salience Network, and was significantly correlated with anxiety; GM-23 included portions of the cerebellum, the precuneus, and the fusiform gyrus; WM-25 included white matter portions adjacent to Default Mode Network regions. These findings shed new light on the grey and white matter contributions to OCPD and may pave the way to developing objective markers of this disorder.\u003c/p\u003e","manuscriptTitle":"Grey and White Matter alterations in Obsessive-Compulsive Personality disorder: a Data Fusion Machine Learning approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-10 17:46:01","doi":"10.21203/rs.3.rs-5721098/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0a0b7c21-a25d-48ac-b214-e5aab707c220","owner":[],"postedDate":"January 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-10T17:46:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-10 17:46:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5721098","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5721098","identity":"rs-5721098","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}