Multivariate pattern analysis of obsessive-compulsive disorder using structural neuroanatomy

Abnormal structure and function of white matter in obsessive-compulsive disorder

BACKGROUND

Abnormal structure and function of gray matter (GM) have been discovered in the cortico-striatal-thalamic-cortical (CSTC) circuit in obsessive-compulsive disorder (OCD). The GM structure and function may be influenced by the structure and function of the white matter (WM). Therefore, it is crucial to explore the characteristics of WM in OCD.

METHODS

Diffusion tensor imaging and resting-state functional magnetic resonance imaging data of 52 patients with OCD and 39 healthy controls (HCs) were collected. The tract-based spatial statistics, amplitude of low-frequency fluctuations (ALFF), and structural-functional coupling approaches were utilized to explore the WM structure and function. Furthermore, the relationship between the abnormal WM structure and function and clinical symptoms of OCD was investigated using Pearson's correlation. Support vector machine was performed to evaluate whether patients with OCD could be identified with the changed WM structure and function.

RESULTS

Compared to HCs, the lower fractional anisotropy (FA) values of four clusters including the superior corona radiata, anterior corona radiata, right superior longitudinal fasciculus, corpus callosum, left posterior corona radiata, fornix, and the right anterior limb of internal capsule, reduced ALFF/FA ratio in the left anterior thalamic radiation (ATR), and the decreased functional connectivity between the left ATR and the left dorsal lateral prefrontal cortex within CSTC circuit at rest were observed in OCD. The decreased ALFF/FA ratio in the left ATR negatively correlated with Yale-Brown Obsessive-Compulsive Scale obsessive thinking scores and Hamilton Anxiety Rating Scale scores in OCD. Furthermore, the features that combined the abnormal WM structure and function performed best in distinguishing OCD from HCs with the appropriate accuracy (0.80), sensitivity (0.82), as well as specificity (0.80).

CONCLUSION

Current research discovered changed WM structure and function in OCD. Furthermore, abnormal WM structural-functional coupling may lead to aberrant GM connectivity within the CSTC circuit at rest in OCD.

TRIAL REGISTRATION

Study on the mechanism of brain network in obsessive-compulsive disorder with multi-model magnetic resonance imaging (ChiCTR-COC-17013301).

A multimodal meta-analysis of regional functional and structural brain abnormalities in obsessive-compulsive disorder

Numerous neuroimaging studies of resting-state functional imaging and voxel-based morphometry (VBM) have revealed abnormalities in specific brain regions in obsessive-compulsive disorder (OCD), but results have been inconsistent. We conducted a whole-brain voxel-wise meta-analysis on resting-state functional imaging and VBM studies that investigated differences of functional activity and gray matter volume (GMV) between patients with OCD and healthy controls (HCs) using seed-based d mapping (SDM) software. A total of 41 independent studies (51 datasets) for resting-state functional imaging and 42 studies (46 datasets) for VBM were included by a systematic literature search. Overall, patients with OCD displayed increased spontaneous functional activity in the bilateral inferior frontal gyrus (IFG) (extending to the bilateral insula) and bilateral medial prefrontal cortex/anterior cingulate cortex (mPFC/ACC), as well as decreased spontaneous functional activity in the bilateral paracentral lobule, bilateral cerebellum, left caudate nucleus, left inferior parietal gyri, and right precuneus cortex. For the VBM meta-analysis, patients with OCD displayed increased GMV in the bilateral thalamus (extending to the bilateral cerebellum), right striatum, and decreased GMV in the bilateral mPFC/ACC and left IFG (extending to the left insula). The conjunction analyses found that the bilateral mPFC/ACC, left IFG (extending to the left insula) showed decreased GMV with increased intrinsic function in OCD patients compared to HCs. This meta-analysis demonstrated that OCD exhibits abnormalities in both function and structure in the bilateral mPFC/ACC, insula, and IFG. A few regions exhibited only functional or only structural abnormalities in OCD, such as the default mode network, striatum, sensorimotor areas, and cerebellum. It may provide useful insights for understanding the underlying pathophysiology of OCD and developing more targeted and efficacious treatment and intervention strategies.

A comprehensive review for machine learning on neuroimaging in obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) is a common mental disease, which can exist as a separate disease or become one of the symptoms of other mental diseases. With the development of society, statistically, the incidence rate of obsessive-compulsive disorder has been increasing year by year. At present, in the diagnosis and treatment of OCD, The clinical performance of patients measured by scales is no longer the only quantitative indicator. Clinical workers and researchers are committed to using neuroimaging to explore the relationship between changes in patient neurological function and obsessive-compulsive disorder. Through machine learning and artificial learning, medical information in neuroimaging can be better displayed. In this article, we discuss recent advancements in artificial intelligence related to neuroimaging in the context of Obsessive-Compulsive Disorder.

Functional and structural MRI based obsessive-compulsive disorder diagnosis using machine learning methods

BACKGROUND

The success of neuroimaging in revealing neural correlates of obsessive-compulsive disorder (OCD) has raised hopes of using magnetic resonance imaging (MRI) indices to discriminate patients with OCD and the healthy. The aim of this study was to explore MRI based OCD diagnosis using machine learning methods.

METHODS

Fifty patients with OCD and fifty healthy subjects were allocated into training and testing set by eight to two. Functional MRI (fMRI) indices, including amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), degree of centrality (DC), and structural MRI (sMRI) indices, including volume of gray matter, cortical thickness and sulcal depth, were extracted in each brain region as features. The features were reduced using least absolute shrinkage and selection operator regression on training set. Diagnosis models based on single MRI index / combined MRI indices were established on training set using support vector machine (SVM), logistic regression and random forest, and validated on testing set.

RESULTS

SVM model based on combined fMRI indices, including ALFF, fALFF, ReHo and DC, achieved the optimal performance, with a cross-validation accuracy of 94%; on testing set, the area under the receiver operating characteristic curve was 0.90 and the validation accuracy was 85%. The selected features were located both within and outside the cortico-striato-thalamo-cortical (CSTC) circuit of OCD. Models based on single MRI index / combined fMRI and sMRI indices underperformed on the classification, with a largest validation accuracy of 75% from SVM model of ALFF on testing set.

CONCLUSION

SVM model of combined fMRI indices has the greatest potential to discriminate patients with OCD and the healthy, suggesting a complementary effect of fMRI indices on the classification; the features were located within and outside the CSTC circuit, indicating an importance of including various brain regions in the model.

Dissecting Psychiatric Heterogeneity and Comorbidity with Core Region-Based Machine Learning

Machine learning approaches are increasingly being applied to neuroimaging data from patients with psychiatric disorders to extract brain-based features for diagnosis and prognosis. The goal of this review is to discuss recent practices for evaluating machine learning applications to obsessive-compulsive and related disorders and to advance a novel strategy of building machine learning models based on a set of core brain regions for better performance, interpretability, and generalizability. Specifically, we argue that a core set of co-altered brain regions (namely 'core regions') comprising areas central to the underlying psychopathology enables the efficient construction of a predictive model to identify distinct symptom dimensions/clusters in individual patients. Hypothesis-driven and data-driven approaches are further introduced showing how core regions are identified from the entire brain. We demonstrate a broadly applicable roadmap for leveraging this core set-based strategy to accelerate the pursuit of neuroimaging-based markers for diagnosis and prognosis in a variety of psychiatric disorders.

Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry

BACKGROUND

The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation.

METHODS

Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses.

RESULTS

A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β =  - 2.75, p < .001, R²_adj = 0.40; r =  - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF₁₀ > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance.

CONCLUSIONS

Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review

IMPORTANCE

Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated.

OBJECTIVE

To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis.

EVIDENCE REVIEW

PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality.

FINDINGS

A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%).

CONCLUSIONS AND RELEVANCE

This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.

Anatomical distance affects functional connectivity at rest in medicine-free obsessive-compulsive disorder

BACKGROUND

Brain functional abnormalities at rest have been observed in obsessive-compulsive disorder (OCD). However, whether and how anatomical distance influences functional connectivity (FC) at rest is ambiguous in OCD.

METHODS

Using resting-state functional magnetic resonance imaging data, we calculated the FC of each voxel in the whole-brain and divided FC into short- and long-range FCs in 40 medicine-free patients with OCD and 40 healthy controls (HCs). A support vector machine (SVM) was used to determine whether the altered short- and long-range FCs could be utilized to distinguish OCD from HCs.

RESULTS

Patients had lower short-range positive FC (spFC) and long-range positive FC (lpFC) in the left precentral/postcentral gyrus (t = -5.57 and -5.43; P < 0.05, GRF corrected) and higher lpFC in the right thalamus/caudate, left thalamus, left inferior parietal lobule (IPL) and left cerebellum CrusI/VI (t = 4.59, 4.61, 4.41, and 5.93; P < 0.05, GRF corrected). Furthermore, lower spFC in the left precentral/postcentral gyrus might be used to distinguish OCD from HCs with an accuracy of 80.77%, a specificity of 81.58%, and a sensitivity of 80.00%.

CONCLUSION

These findings highlight that anatomical distance has an effect on the whole-brain FC patterns at rest in OCD. Meanwhile, lower spFC in the left precentral/postcentral gyrus might be applied in distinguishing OCD from HCs.

Distinct functional brain abnormalities in insomnia disorder and obstructive sleep apnea

Insomnia disorder (ID) and obstructive sleep apnea (OSA) are the two most prevalent sleep disorders worldwide, but the pathological mechanism has not been fully understood. Functional neuroimaging findings indicated regional abnormal neural activities existed in both diseases, but the results were inconsistent. This meta-analysis aimed to explore concordant regional functional brain changes in ID and OSA, respectively. We conducted a coordinate-based meta-analysis (CBMA) of resting-state functional magnetic resonance imaging (rs-fMRI) studies using the anisotropic effect-size seed-based d mapping (AES-SDM) approach. Studies that applied regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF) or fractional ALFF (fALFF) to analyze regional spontaneous brain activities in ID or OSA were included. Meta-regressions were then applied to investigate potential associations between demographic variables and regional neural activity alterations. Significantly increased brain activities in the left superior temporal gyrus (STG.L) and right superior longitudinal fasciculus (SLF.R), as well as decreased brain activities in several right cerebral hemisphere areas were identified in ID patients. As for OSA patients, more distinct and complicated functional activation alterations were identified. Several neuroimaging alterations were functionally correlated with mean age, duration or illness severity in two patients groups revealed by meta-regressions. These functionally altered areas could be served as potential targets for non-invasive brain stimulation methods. This present meta-analysis distinguished distinct brain function changes in ID and OSA, improving our knowledge of the neuropathological mechanism of these two most common sleep disturbances, and also provided potential orientations for future clinical applications.Registration number: CRD42022301938.

Multi-modality connectome-based predictive modeling of individualized compulsions in obsessive-compulsive disorder

BACKGROUND

While previous neuroimaging studies are mainly focused on dichotomous classification of obsessive-compulsive disorder (OCD) from controls, predicting continuous severity of specific symptom is also pivotal to clinical diagnosis and treatment.

METHODS

We applied a machine-learning approach, connectome-based predictive modeling, on functional and structural brain networks constructed from resting-state functional magnetic resonance imaging and diffusion tensor imaging data to decode compulsions and obsessions of fifty-four patients with OCD.

RESULTS

We successfully predicted individualized compulsions with a positive model of structural brain network and with a negative model of functional brain network. The structural predictive brain network comprises the motor cortex, cerebellum and limbic lobe, which are involved in basic motor control, motor execution and emotion processing, respectively. The functional predictive brain network is composed by the prefrontal and limbic systems which are related to cognitive and affective control. Computational lesion analysis shows that functional connectivity among the salience network (SN), the frontal parietal network and the default mode network, as well as structural connectivity within the SN are vital in the individualized prediction of compulsions in OCD.

LIMITATIONS

There was no external validation of large samples to test the robustness of our predictive model.

CONCLUSIONS

These findings provide the first evidence for the predictive role of the triple network model in individualized compulsions and have important implications in diagnosis, prognosis and treatment of patients with OCD.

Prediction of Obsessive-Compulsive Disorder: Importance of Neurobiology-Aided Feature Design and Cross-Diagnosis Transfer Learning

BACKGROUND

Machine learning applications using neuroimaging provide a multidimensional, data-driven approach that captures the level of complexity necessary for objectively aiding diagnosis and prognosis in psychiatry. However, models learned from small training samples often have limited generalizability, which continues to be a problem with automated diagnosis of mental illnesses such as obsessive-compulsive disorder (OCD). Earlier studies have shown that features incorporating prior neurobiological knowledge of brain function and combining brain parcellations from various sources can potentially improve the overall prediction. However, it is unknown whether such knowledge-driven methods can provide a performance that is comparable to state-of-the-art approaches based on neural networks.

METHODS

In this study, we apply a transparent and explainable multiparcellation ensemble learning framework EMPaSchiz (Ensemble algorithm with Multiple Parcellations for Schizophrenia prediction) to the task of predicting OCD, based on a resting-state functional magnetic resonance imaging dataset of 350 subjects. Furthermore, we apply transfer learning using the features found effective for schizophrenia to OCD to leverage the commonality in brain alterations across these psychiatric diagnoses.

RESULTS

We show that our knowledge-based approach leads to a prediction performance of 80.3% accuracy for OCD diagnosis that is better than domain-agnostic and automated feature design using neural networks. Furthermore, we show that a selection of reduced feature sets can be transferred from schizophrenia to the OCD prediction model without significant loss in prediction performance.

CONCLUSIONS

This study presents a machine learning framework for OCD prediction with neurobiology-aided feature design using resting-state functional magnetic resonance imaging that is generalizable and reasonably interpretable.

Abnormal Spontaneous Brain Activity and Cognitive Impairment in Obstructive Sleep Apnea

PURPOSE

This study aimed to explore the alterations in spontaneous brain activity in obstructive sleep apnea (OSA) using percent amplitude of fluctuation (PerAF) and investigate the relationship between abnormal spontaneous brain activity and cognitive impairment in OSA.

PATIENTS AND METHODS

Overall, 52 patients with moderate to severe OSA and 61 healthy controls (HCs) were eventually enrolled in this study. All participants underwent resting-state functional magnetic resonance (rs-fMRI) and T1-weighted imaging. The PerAF was calculated and compared between patients with OSA and HCs, with voxel level P < 0.001 and cluster level P < 0.05 corrected with Gaussian Random Field was be considered statistically different. A partial correlation analysis was used to assess the relationship between altered PerAF and clinical assessments in patients with OSA.

RESULTS

Compared to HCs, patients with OSA had significantly lower PerAF values in the right rectal gyrus and left superior frontal gyrus, but higher PerAF values in the right cerebellum posterior lobe and left middle frontal gyrus. The PerAF values of some specific regions in patients with OSA correlated with sleep efficiency and Montreal Cognitive Assessment scores. Additionally, support vector machine analysis showed that PerAF values in all differential brain regions could differentiate patients with OSA from HCs with good accuracy.

CONCLUSION

Specific brain areas in OSA patients may exhibit aberrant neuronal activity, and these anomalies may be linked to decreased cognitive performance. This discovery offers fresh perspectives on these patients' neurocognition.

Altered Functional Connectivity Strength at Rest in Medication-Free Obsessive-Compulsive Disorder

Background

Previous studies explored the whole-brain functional connectome using the degree approach in patients with obsessive-compulsive disorder (OCD). However, whether the altered degree values can be used to discriminate OCD from healthy controls (HCs) remains unclear.

Methods

A total of 40 medication-free patients with OCD and 38 HCs underwent a resting-state functional magnetic resonance imaging (rs-fMRI) scan. Data were analyzed with the degree approach and a support vector machine (SVM) classifier.

Results

Patients with OCD showed increased degree values in the left thalamus and left cerebellum Crus I and decreased degree values in the left dorsolateral prefrontal cortex, right precuneus, and left postcentral gyrus. SVM classification analysis indicated that the increased degree value in the left thalamus is a marker of OCD, with an acceptable accuracy of 88.46%, sensitivity of 87.50%, and specificity of 89.47%.

Conclusion

Altered degree values within and outside the cortical-striatal-thalamic-cortical (CSTC) circuit may cocontribute to the pathophysiology of OCD. Increased degree values of the left thalamus can be used as a future marker for OCD understanding-classification.

Decreased Nucleus Accumbens Connectivity at Rest in Medication-Free Patients with Obsessive-Compulsive Disorder

Background

Patients with obsessive-compulsive disorder (OCD) experience deficiencies in reward processing. The investigation of the reward circuit and its essential connectivity may further clarify the pathogenesis of OCD.

Methods

The current research was designed to analyze the nucleus accumbens (NAc) functional connectivity at rest in medicine-free patients with OCD. Forty medication-free patients and 38 gender-, education-, and age-matched healthy controls (HCs) were recruited for resting-state functional magnetic resonance imaging. Seed-based functional connectivity (FC) was used to analyze the data. LIBSVM (library for support vector machines) was designed to identify whether altered FC could be applied to differentiate OCD.

Results

Patients with OCD showed remarkably decreased FC values between the left NAc and the bilateral orbitofrontal cortex (OFC) and bilateral medial prefrontal cortex (MPFC) and between the right NAc and the left OFC at rest in the reward circuit. Moreover, decreased left NAc-bilateral MPFC connectivity can be deemed as a potential biomarker to differentiate OCD from HCs with a sensitivity of 80.00% and a specificity of 76.32%.

Conclusion

The current results emphasize the importance of the reward circuit in the pathogenesis of OCD.

Translational application of neuroimaging in major depressive disorder: a review of psychoradiological studies

Major depressive disorder (MDD) causes great decrements in health and quality of life with increments in healthcare costs, but the causes and pathogenesis of depression remain largely unknown, which greatly prevent its early detection and effective treatment. With the advancement of neuroimaging approaches, numerous functional and structural alterations in the brain have been detected in MDD and more recently attempts have been made to apply these findings to clinical practice. In this review, we provide an updated summary of the progress in translational application of psychoradiological findings in MDD with a specified focus on potential clinical usage. The foreseeable clinical applications for different MRI modalities were introduced according to their role in disorder classification, subtyping, and prediction. While evidence of cerebral structural and functional changes associated with MDD classification and subtyping was heterogeneous and/or sparse, the ACC and hippocampus have been consistently suggested to be important biomarkers in predicting treatment selection and treatment response. These findings underlined the potential utility of brain biomarkers for clinical practice.

Microstructural white matter abnormalities in obsessive-compulsive disorder: A coordinate-based meta-analysis of diffusion tensor imaging studies

BACKGROUND

There are no conclusive diffusion tensor imaging (DTI) findings on obsessive-compulsive disorder (OCD) for now. We conducted a comprehensive meta-analysis of DTI studies to identify white matter (WM) microarchitecture changes in OCD, and also to compare the results differences between the two most frequently used methods (voxel-based analysis, VBA versus tract-based spatial statistics, TBSS) for DTI data.

METHODS

A systematic search was performed on relevant studies that reported fractional anisotropy (FA) alterations between patients with OCD and healthy controls (HC). Seed-based d mapping (SDM) was applied to analyze microstructural WM abnormalities in OCD patients. Subgroup meta-analysis was subsequently performed to explore methodological differences between VBA and TBSS approaches.

RESULTS

A total of 30 studies (with 31 datasets) that comprised 855 patients and 875 HC were identified. OCD patients exhibited significantly decreased FA in the right cerebellar hemispheric lobule, corpus callosum (CC), left superior frontal gyrus (orbital part), right gyrus rectus, left superior longitudinal fasciculus and right lenticular nucleus in the pooled meta-analysis. The VBA subgroup showed lower FA in several brain regions while the TBSS subgroup only exhibited significant FA reductions in the CC.

CONCLUSION

According to the pooled meta-analysis, OCD patients presented microstructural abnormalities in distributed WM tracts. However, heterogeneous results were found between VBA and TBSS studies.

Structural Imaging in OCD

Anatomical imaging in OCD using magnetic resonance imaging (MRI) has been performed since the late 1980s. MRI research was further stimulated with the advent of automated image processing techniques such as voxel-based morphometry (VBM) and surface-based methods (e.g., FreeSurfer) which allow for detailed whole-brain data analyses. Early studies suggesting involvement of corticostriatal circuitry (particularly orbitofrontal cortex and ventral striatum) have been complemented by meta-analyses and pooled analyses indicating additional involvement of posterior brain regions, in particular parietal cortex. Recent large-scale meta-analyses from the ENIGMA consortium have revealed greater pallidum and smaller hippocampus volume in adult OCD, coupled with parietal cortical thinning. Frontal cortical thinning was only observed in medicated patients. Previous reports of symptom dimension-specific alterations were not confirmed. In paediatric OCD, thalamus enlargement has been a consistent finding. Studies investigating white matter volume (VBM) or integrity (using diffusion tensor imaging (DTI)) have shown mixed results, with recent DTI meta-analyses mainly showing involvement of posterior cortical-subcortical tracts in addition to subcortical-prefrontal connections. To which extent these abnormalities are unique to OCD or common to other psychiatric disorders is unclear, as few comparative studies have been performed. Overall, neuroanatomical alterations in OCD appear to be subtle and may vary with time, stressing the need for adequately powered longitudinal studies. Although multivariate approaches using machine learning methodologies have so far been disappointing in distinguishing individual OCD patients from healthy controls, including multimodal data in such analyses may aid in further establishing a neurobiological profile of OCD.

Towards a brain-based predictome of mental illness

Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utilized features from a variety of neuroimaging modalities, including structural, functional, and diffusion magnetic resonance imaging data, as well as jointly estimated features from multiple modalities, to assess patients with heterogeneous mental disorders, such as schizophrenia and autism. We use the term "predictome" to describe the use of multivariate brain network features from one or more neuroimaging modalities to predict mental illness. In the predictome, multiple brain network-based features (either from the same modality or multiple modalities) are incorporated into a predictive model to jointly estimate features that are unique to a disorder and predict subjects accordingly. To date, more than 650 studies have been published on subject-level prediction focusing on psychiatric disorders. We have surveyed about 250 studies including schizophrenia, major depression, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, obsessive-compulsive disorder, social anxiety disorder, posttraumatic stress disorder, and substance dependence. In this review, we present a comprehensive review of recent neuroimaging-based predictomic approaches, current trends, and common shortcomings and share our vision for future directions.

White matter disruption in obsessive-compulsive disorder revealed by meta-analysis of tract-based spatial statistics

BACKGROUND

Exploring white matter (WM) microstructural alterations is a momentous step for gaining insights about underlying mechanisms of obsessive-compulsive disorder (OCD) and improving the efficacy of therapies for this condition. Many tract-based spatial statistics (TBSS) studies have revealed abnormalities of fractional anisotropy (FA; an index of WM integrity) in OCD. However, research works have not drawn robust conclusions. Therefore, we integrated the findings of TBSS studies to identify the most consistent FA changes in OCD using meta-analytical approach.

METHODS

Online databases were systematically searched for all TBSS studies comparing FA between patients with OCD and controls. A coordinate-based meta-analysis was performed using anisotropic effect size version of the seed-based d mapping software. Meanwhile, meta-regression was used to explore the potential association of clinical characteristics with regional FA abnormalities.

RESULTS

Our meta-analysis included 488 OCD patients and 519 controls across 17 datasets. FA reductions were identified in the genu of the corpus callosum and the left orbitofrontal WM in OCD patients relative to controls. Metaregression analyses showed that the FA in the left orbitofrontal WM was negatively and independently correlated with symptom severity and illness duration in patients with OCD.

CONCLUSIONS

The current study provides a quantitative overview of TBSS findings in OCD and demonstrates the most prominent and replicable WM abnormalities in OCD are in the anterior part of the brain including interhemispheric connection and orbitofrontal region. Additionally, our findings suggest that FA reduction in the orbitofrontal WM might be a potential biomarker in predicting disease severity and progression in patients with OCD.

Altered network connectivity predicts response to cognitive-behavioral therapy in pediatric obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) is commonly associated with alterations in cortico-striato-thalamo-cortical brain networks. Yet, recent investigations of large-scale brain networks suggest that more diffuse alterations in brain connectivity may underlie its pathophysiology. Few studies have assessed functional connectivity within or between networks across the whole brain in pediatric OCD or how patterns of connectivity associate with treatment response. Resting-state functional magnetic resonance imaging scans were acquired from 25 unmedicated, treatment-naive children and adolescents with OCD (12.8 ± 2.9 years) and 23 matched healthy control (HC) participants (11.0 ± 3.3 years) before participants with OCD completed a course of cognitive-behavioral therapy (CBT). Participants were re-scanned after 12-16 weeks. Whole-brain connectomic analyses were conducted to assess baseline group differences and group-by-time interactions, corrected for multiple comparisons. Relationships between functional connectivity and OCD symptoms pre- and post-CBT were examined using longitudinal cross-lagged panel modeling. Reduced connectivity in OCD relative to HC participants was detected between default mode and task-positive network regions. Greater (less altered) connectivity between left angular gyrus and left frontal pole predicted better response to CBT in the OCD group. Altered connectivity between task-positive and task-negative networks in pediatric OCD may contribute to the impaired control over intrusive thoughts early in the illness. This is the first study to show that altered connectivity between large-scale network regions may predict response to CBT in pediatric OCD, highlighting the clinical relevance of these networks as potential circuit-based targets for the development of novel treatments.

Decreased Resting-State Interhemispheric Functional Connectivity in Medication-Free Obsessive-Compulsive Disorder

OBJECTIVE

Decreased homotopic connectivity of brain networks such as the cortico-striato-thalamo-cortical (CSTC) circuits may contribute to the pathophysiology of obsessive-compulsive disorder (OCD). However, little is known about interhemispheric functional connectivity (FC) at rest in OCD. In this study, the voxel-mirrored homotopic connectivity (VMHC) method was applied to explore interhemispheric coordination at rest in OCD.

METHODS

Forty medication-free patients with OCD and 38 sex-, age-, and education level-matched healthy controls (HCs) underwent a resting-state functional magnetic resonance imaging. The VMHC and support vector machine (SVM) methods were used to analyze the data.

RESULTS

Patients with OCD had remarkably decreased VMHC values in the orbitofrontal cortex, thalamus, middle occipital gyrus, and precentral and postcentral gyri compared with HCs. A combination of the VMHC values in the thalamus and postcentral gyrus could optimally distinguish patients with OCD from HCs.

CONCLUSIONS

Our findings highlight the contribution of decreased interhemispheric FC within and outside the CSTC circuits in OCD and provide evidence to the pathophysiology of OCD.

How New Technologies Can Improve Prediction, Assessment, and Intervention in Obsessive-Compulsive Disorder (e-OCD): Review

BACKGROUND

New technologies are set to profoundly change the way we understand and manage psychiatric disorders, including obsessive-compulsive disorder (OCD). Developments in imaging and biomarkers, along with medical informatics, may well allow for better assessments and interventions in the future. Recent advances in the concept of digital phenotype, which involves using computerized measurement tools to capture the characteristics of a given psychiatric disorder, is one paradigmatic example.

OBJECTIVE

The impact of new technologies on health professionals' practice in OCD care remains to be determined. Recent developments could disrupt not just their clinical practices, but also their beliefs, ethics, and representations, even going so far as to question their professional culture. This study aimed to conduct an extensive review of new technologies in OCD.

METHODS

We conducted the review by looking for titles in the PubMed database up to December 2017 that contained the following terms: [Obsessive] AND [Smartphone] OR [phone] OR [Internet] OR [Device] OR [Wearable] OR [Mobile] OR [Machine learning] OR [Artificial] OR [Biofeedback] OR [Neurofeedback] OR [Momentary] OR [Computerized] OR [Heart rate variability] OR [actigraphy] OR [actimetry] OR [digital] OR [virtual reality] OR [Tele] OR [video].

RESULTS

We analyzed 364 articles, of which 62 were included. Our review was divided into 3 parts: prediction, assessment (including diagnosis, screening, and monitoring), and intervention.

CONCLUSIONS

The review showed that the place of connected objects, machine learning, and remote monitoring has yet to be defined in OCD. Smartphone assessment apps and the Web Screening Questionnaire demonstrated good sensitivity and adequate specificity for detecting OCD symptoms when compared with a full-length structured clinical interview. The ecological momentary assessment procedure may also represent a worthy addition to the current suite of assessment tools. In the field of intervention, CBT supported by smartphone, internet, or computer may not be more effective than that delivered by a qualified practitioner, but it is easy to use, well accepted by patients, reproducible, and cost-effective. Finally, new technologies are enabling the development of new therapies, including biofeedback and virtual reality, which focus on the learning of coping skills. For them to be used, these tools must be properly explained and tailored to individual physician and patient profiles.

Neuroanatomical features and its usefulness in classification of patients with PANDAS

OBJECTIVE

An obsessive-compulsive disorder (OCD) subtype has been associated with streptococcal infections and is called pediatric autoimmune neuropsychiatric disorders associated with streptococci (PANDAS). The neuroanatomical characterization of subjects with this disorder is crucial for the better understanding of its pathophysiology; also, evaluation of these features as classifiers between patients and controls is relevant to determine potential biomarkers and useful in clinical diagnosis. This was the first multivariate pattern analysis (MVPA) study on an early-onset OCD subtype.

METHODS

Fourteen pediatric patients with PANDAS were paired with 14 healthy subjects and were scanned to obtain structural magnetic resonance images (MRI). We identified neuroanatomical differences between subjects with PANDAS and healthy controls using voxel-based morphometry, diffusion tensor imaging (DTI), and surface analysis. We investigated the usefulness of these neuroanatomical differences to classify patients with PANDAS using MVPA.

RESULTS

The pattern for the gray and white matter was significantly different between subjects with PANDAS and controls. Alterations emerged in the cortex, subcortex, and cerebellum. There were no significant group differences in DTI measures (fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity) or cortical features (thickness, sulci, volume, curvature, and gyrification). The overall accuracy of 75% was achieved using the gray matter features to classify patients with PANDAS and healthy controls.

CONCLUSION

The results of this integrative study allow a better understanding of the neural substrates in this OCD subtype, suggesting that the anatomical gray matter characteristics could have an immune origin that might be helpful in patient classification.

Progress in psychoradiology, the clinical application of psychiatric neuroimaging

Psychoradiology is an emerging field that applies radiological imaging technologies to psychiatric conditions. In the past three decades, brain imaging techniques have rapidly advanced understanding of illness and treatment effects in psychiatry. Based on these advances, radiologists have become increasingly interested in applying these advances for differential diagnosis and individualized patient care selection for common psychiatric illnesses. This shift from research to clinical practice represents the beginning evolution of psychoradiology. In this review, we provide a summary of recent progress relevant to this field based on their clinical functions, namely the (1) classification and subtyping; (2) prediction and monitoring of treatment outcomes; and (3) treatment selection. In addition, we provide guidelines for the practice of psychoradiology in clinical settings and suggestions for future research to validate broader clinical applications. Given the high prevalence of psychiatric disorders and the importance of increased participation of radiologists in this field, a guide regarding advances in this field and a description of relevant clinical work flow patterns help radiologists contribute to this fast-evolving field.

Multivariate classification of drug-naive obsessive-compulsive disorder patients and healthy controls by applying an SVM to resting-state functional MRI data

BACKGROUND

Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies have revealed intrinsic regional activity alterations in obsessive-compulsive disorder (OCD), but those results were based on group analyses, which limits their applicability to clinical diagnosis and treatment at the level of the individual.

METHODS

We examined fractional amplitude low-frequency fluctuation (fALFF) and applied support vector machine (SVM) to discriminate OCD patients from healthy controls on the basis of rs-fMRI data. Values of fALFF, calculated from 68 drug-naive OCD patients and 68 demographically matched healthy controls, served as input features for the classification procedure.

RESULTS

The classifier achieved 72% accuracy (p ≤ 0.001). This discrimination was based on regions that included the left superior temporal gyrus, the right middle temporal gyrus, the left supramarginal gyrus and the superior parietal lobule.

CONCLUSIONS

These results indicate that OCD-related abnormalities in temporal and parietal lobe activation have predictive power for group membership; furthermore, the findings suggest that machine learning techniques can be used to aid in the identification of individuals with OCD in clinical diagnosis.

Localized Connectivity in Obsessive-Compulsive Disorder: An Investigation Combining Univariate and Multivariate Pattern Analyses

Recent developments in psychoradiological researches have highlighted the disrupted organization of large-scale functional brain networks in obsessive-compulsive disorder (OCD). However, whether abnormal activation of localized brain areas would affect network dysfunction remains to be fully characterized. We applied both univariate analysis and multivariate pattern analysis (MVPA) approaches to investigate the abnormalities of regional homogeneity (ReHo), an index to measure the localized connectivity, in 88 medication-free patients with OCD and 88 healthy control subjects (HCS). Resting-state functional magnetic resonance imaging (RS-fMRI) data of all the participants were acquired in a 3.0-T scanner. First, we adopted a traditional univariate analysis to explore ReHo alterations between the patient group and the control group. Subsequently, we utilized a support vector machine (SVM) to examine whether ReHo could be further used to differentiate patients with OCD from HCS at the individual level. Relative to HCS, OCD patients showed lower ReHo in the bilateral cerebellum and higher ReHo in the bilateral superior frontal gyri (SFG), right inferior parietal gyrus (IPG), and precuneus [P < 0.05, family-wise error (FWE) correction]. ReHo value in the left SFG positively correlated with Yale-Brown Obsessive Compulsive Scale (Y-BOCS) total score (r = 0 0.241, P = 0.024) and obsessive subscale (r = 0.224, P = 0.036). The SVM classification regarding ReHo yielded an accuracy of 78.98% (sensitivity = 78.41%, specificity = 79.55%) with P < 0.001 after permutation testing. The most discriminative regions contributing to the SVM classification were mainly located in the frontal, temporal, and parietal regions as well as in the cerebellum while the right orbital frontal cortex was identified with the highest discriminative power. Our findings not only suggested that the localized activation disequilibrium between the prefrontal cortex (PFC) and the cerebellum appeared to be associated with the pathophysiology of OCD but also indicated the translational role of the localized connectivity as a potential discriminative pattern to detect OCD at the individual level.

Diagnostic neuroimaging markers of obsessive-compulsive disorder: Initial evidence from structural and functional MRI studies

As of yet, no diagnostic biomarkers are available for obsessive-compulsive disorder (OCD), and its diagnosis relies entirely upon the recognition of behavioural features assessed through clinical interview. Neuroimaging studies have shown that various brain structures are abnormal in OCD patients compared to healthy controls. However, the majority of these results are based on average differences between groups, which limits diagnostic usage in clinical practice. In recent years, a growing number of studies have applied multivariate pattern analysis (MVPA) techniques on neuroimaging data to extract patterns of altered brain structure, function and connectivity typical for OCD. MVPA techniques can be used to develop predictive models that extract regularities in data to classify individual subjects based on their diagnosis. In the present paper, we reviewed the literature of MVPA studies using data from different imaging modalities to distinguish OCD patients from controls. A systematic search retrieved twelve articles that fulfilled the inclusion and exclusion criteria. Reviewed studies have been able to classify OCD diagnosis with accuracies ranging from 66% up to 100%. Features important for classification were different across imaging modalities and widespread throughout the brain. Although studies have shown promising results, sample sizes used are typically small which can lead to high variance of the estimated model accuracy, cohort-specific solutions and lack of generalizability of findings. Some of the challenges are discussed that need to be overcome in order to move forward toward clinical applications.

Investigating the predictive value of different resting-state functional MRI parameters in obsessive-compulsive disorder

Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies of obsessive-compulsive disorder (OCD) have facilitated our understanding of OCD pathophysiology based on its intrinsic activity. However, whether the group difference derived from univariate analysis could be useful for informing the diagnosis of individual OCD patients remains unclear. We aimed to apply multivariate pattern analysis of different rs-fMRI parameters to distinguish drug-naive patients with OCD from healthy control subjects (HCS). Fifty-four drug-naive OCD patients and 54 well-matched HCS were recruited. Four different rs-fMRI parameter maps, including the amplitude of low-frequency fluctuations (ALFF), fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo) and functional connectivity strength (FCS), were calculated. Training of a support vector machine (SVM) classifier using rs-fMRI maps produced voxelwise discrimination maps. Overall, the classification accuracies were acceptable for the four rs-fMRI parameters. Excellent performance was achieved when ALFF maps were employed (accuracy, 95.37%, p < 0.01), good performance was achieved by using ReHo maps, weaker performance was achieved by using fALFF maps, and fair performance was achieved by using FCS maps. The brain regions showing the greatest discriminative power included the prefrontal cortex, anterior cingulate cortex, precentral gyrus, and occipital lobes. The application of SVM to rs-fMRI features may provide potential power for OCD classification.

Decoding moral emotions in obsessive-compulsive disorder

Background

Patients with obsessive-compulsive disorder (OCD) exhibit abnormal neural responses when they experience particular emotions or when they evaluate stimuli with emotional value. Whether these brain responses are sufficiently distinctive to discriminate between OCD patients and healthy controls is unknown. The present study is the first to investigate the discriminative power of multivariate pattern analysis of regional fMRI responses to moral and non-moral emotions.

Method

To accomplish this goal, we performed a searchlight-based multivariate pattern analysis to unveil brain regions that could discriminate 18 OCD patients from 18 matched healthy controls during provoked guilt, disgust, compassion, and anger. We also investigated the existence of distinctive neural patterns while combining those four emotions (herein termed multiemotion analysis).

Results

We found that different frontostriatal regions discriminated OCD patients from controls based on individual emotional experiences. Most notably, the left nucleus accumbens (NAcc) discriminated OCD patients from controls during both disgust and the multiemotion analysis. Among other regions, the angular gyrus responses to anger and the lingual and the middle temporal gyri in the multi-emotion analysis were highly discriminant between samples. Additional BOLD analyses supported the directionality of these findings.

Conclusions

In line with previous studies, differential activity in regions beyond the frontostriatal circuitry differentiates OCD from healthy volunteers. The finding that the response of the left NAcc to different basic and moral emotions is highly discriminative for a diagnosis of OCD confirms current pathophysiological models and points to new venues of research.

Support Vector Machine Classification of Obsessive-Compulsive Disorder Based on Whole-Brain Volumetry and Diffusion Tensor Imaging

Magnetic resonance imaging (MRI) methods have been used to detect cerebral anatomical distinction between obsessive-compulsive disorder (OCD) patients and healthy controls (HC). Machine learning approach allows for the possibility of discriminating patients on the individual level. However, few studies have used this automatic technique based on multiple modalities to identify potential biomarkers of OCD. High-resolution structural MRI and diffusion tensor imaging (DTI) data were acquired from 48 OCD patients and 45 well-matched HC. Gray matter volume (GMV), white matter volume (WMV), fractional anisotropy (FA), and mean diffusivity (MD) were extracted as four features were examined using support vector machine (SVM). Ten brain regions of each feature contributed most to the classification were also estimated. Using different algorithms, the classifier achieved accuracies of 72.08, 61.29, 80.65, and 77.42% for GMV, WMV, FA, and MD, respectively. The most discriminative gray matter regions that contributed to the classification were mainly distributed in the orbitofronto-striatal "affective" circuit, the dorsolateral, prefronto-striatal "executive" circuit and the cerebellum. For WMV feature and the two feature sets of DTI, the shared regions contributed the most to the discrimination mainly included the uncinate fasciculus, the cingulum in the hippocampus, corticospinal tract, as well as cerebellar peduncle. Based on whole-brain volumetry and DTI images, SVM algorithm revealed high accuracies for distinguishing OCD patients from healthy subjects at the individual level. Computer-aided method is capable of providing accurate diagnostic information and might provide a new perspective for clinical diagnosis of OCD.

The relevance of feature selection methods to the classification of obsessive-compulsive disorder based on volumetric measures

BACKGROUND

Magnetic resonance images (MRI) show detectable anatomical and functional differences between individuals with obsessive-compulsive disorder (OCD) and healthy subjects. Moreover, machine learning techniques have been proposed as tools to identify potential biomarkers and, ultimately, to support clinical diagnosis. However, few studies to date have investigated feature selection (FS) influences in OCD MRI-based classification.

METHODS

Volumes of cortical and subcortical structures, from MRI data of 38 OCD patients (split into two groups according symptoms severity) and 36 controls, were submitted to seven feature selection algorithms. FS aims to select the most relevant and less redundant features which discriminate between two classes. Then, a classification step was applied, from which the classification performances before and after different FS were compared. For the performance evaluation, leave-one-subject-out accuracies of Support Vector Machine classifiers were considered.

RESULTS

Using different FS algorithms, performance improvement was achieved for Controls vs. All OCD discrimination (19.08% of improvement reducing by 80% the amount of features), Controls vs. Low OCD (20.10%, 75%), Controls vs. High OCD (17.32%, 85%) and Low OCD vs. High OCD (10.53%, 75%). Furthermore, all algorithms pointed out classical cortico-striato-thalamo-cortical circuitry structures as relevant features for OCD classification.

LIMITATIONS

Limitations include the sample size and using only filter approaches for FS.

CONCLUSIONS

Our results suggest that FS positively impacts OCD classification using machine-learning techniques. Complementarily, FS algorithms were able to select biologically plausible features automatically.

A Neural Marker of Obsessive-Compulsive Disorder from Whole-Brain Functional Connectivity

Obsessive-compulsive disorder (OCD) is a common psychiatric disorder with a lifetime prevalence of 2–3%. Recently, brain activity in the resting state is gathering attention for exploring altered functional connectivity in psychiatric disorders. Although previous resting-state functional magnetic resonance imaging studies investigated the neurobiological abnormalities of patients with OCD, there are concerns that should be addressed. One concern is the validity of the hypothesis employed. Most studies used seed-based analysis of the fronto-striatal circuit, despite the potential for abnormalities in other regions. A hypothesis-free study is a promising approach in such a case, while it requires researchers to handle a dataset with large dimensions. Another concern is the reliability of biomarkers derived from a single dataset, which may be influenced by cohort-specific features. Here, our machine learning algorithm identified an OCD biomarker that achieves high accuracy for an internal dataset (AUC = 0.81; N = 108) and demonstrates generalizability to an external dataset (AUC = 0.70; N = 28). Our biomarker was unaffected by medication status, and the functional networks contributing to the biomarker were distributed widely, including the frontoparietal and default mode networks. Our biomarker has the potential to deepen our understanding of OCD and to be applied clinically.

Identifying the white matter impairments among ART-naïve HIV patients: a multivariate pattern analysis of DTI data

OBJECTIVE

To identify the white matter (WM) impairments of the antiretroviral therapy (ART)-naïve HIV patients by conducting a multivariate pattern analysis (MVPA) of Diffusion Tensor Imaging (DTI) data METHODS: We enrolled 33 ART-naïve HIV patients and 32 Normal controls in the current study. Firstly, the DTI metrics in whole brain WM tracts were extracted for each subject and feed into the Least Absolute Shrinkage and Selection Operators procedure (LASSO)-Logistic regression model to identify the impaired WM tracts. Then, Support Vector Machines (SVM) model was constructed based on the DTI metrics in the impaired WM tracts to make HIV-control group classification. Pearson correlations between the WM impairments and HIV clinical statics were also investigated.

RESULTS

Extensive HIV-related impairments were observed in the WM tracts associated with motor function, the corpus callosum (CC) and the frontal WM. With leave-one-out cross validation, accuracy of 83.08% (P=0.002) and the area under the Receiver Operating Characteristic curve of 0.9110 were obtained in the SVM classification model. The impairments of the CC were significantly correlated with the HIV clinic statics.

CONCLUSION

The MVPA was sensitive to detect the HIV-related WM changes. Our findings indicated that the MVPA had considerable potential in exploring the HIV-related WM impairments.

KEY POINTS

• WM impairments along motor pathway were detected among the ART-naïve HIV patients • Prominent HIV-related WM impairments were observed in CC and frontal WM • The impairments of CC were significantly related to the HIV clinic statics • The CC might be susceptible to immune dysfunction and HIV replication • Multivariate pattern analysis had potential for studying the HIV-related white matter impairments.

Can Neuroimaging Provide Reliable Biomarkers for Obsessive-Compulsive Disorder? A Narrative Review

In this integrative review, we discuss findings supporting the use neuroimaging biomarkers in the diagnosis and treatment of obsessive-compulsive disorder (OCD). To do so, we have selected the most recent studies that attempted to identify the underlying pathogenic process associated with OCD and whether they provide useful information to predict clinical features, natural history or treatment responses. Studies using functional magnetic resonance (fMRI), voxel-based morphometry (VBM), diffusion tensor imaging (DTI) and proton magnetic resonance spectroscopy (1H MRS) in OCD patients are generally supportive of an expanded version of the earlier cortico-striatal-thalamus-cortical (CSTC) model of OCD. Although it is still unclear whether this information will be incorporated into the daily clinical practice (due to current conceptual approaches to mental illness), statistical techniques, such as pattern recognition methods, appear promising in identifying OCD patients and predicting their outcomes.

Cognitive and emotional impairments in obsessive-compulsive disorder: Evidence from functional brain alterations

There is a common agreement on the existence of dysfunctional cortico-striatal-thalamus-cortical pathways in OCD. Despite this consensus, recent studies showed that brain regions other than the CSTC loops are needed to understand the complexity and diversity of cognitive and emotional deficits in OCD. This review presents examples of research using functional neuroimaging, reporting abnormal brain processes in OCD that may underlie specific cognitive/executive (inhibitory control, cognitive flexibility, working memory), and emotional impairments (fear/defensive, disgust, guilt, shame). Studies during resting state conditions show that OCD patients have alterations in connectivity not only within the CSTC pathways but also in more extended resting state networks, particularly the default mode network and the fronto-parietal network. Additionally, abnormalities in brain functioning have been found in several cognitive and emotionally task conditions, namely: inhibitory control (e.g., CSTC loops, fronto-parietal networks, anterior cingulate); cognitive flexibility (e.g., CSTC loops, extended temporal, parietal, and occipital regions); working memory (e.g., CSTC loops, frontal parietal networks, dorsal anterior cingulate); fear/defensive (e.g., amygdala, additional brain regions associated with perceptual - parietal, occipital - and higher level cognitive processing - prefrontal, temporal); disgust (e.g., insula); shame (e.g., decrease activity in middle frontal gyrus and increase in frontal, limbic, temporal regions); and guilt (e.g., decrease activity anterior cingulate and increase in frontal, limbic, temporal regions). These findings may contribute to the understanding of OCD as both an emotional (i.e., anxiety) and cognitive (i.e., executive control) disorder.