Structural neuroimaging biomarkers for obsessive-compulsive disorder in the ENIGMA-OCD consortium: medication matters

Genome-wide analyses identify 30 loci associated with obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) affects ~1% of children and adults and is partly caused by genetic factors. We conducted a genome-wide association study (GWAS) meta-analysis combining 53,660 OCD cases and 2,044,417 controls and identified 30 independent genome-wide significant loci. Gene-based approaches identified 249 potential effector genes for OCD, with 25 of these classified as the most likely causal candidates, including WDR6, DALRD3 and CTNND1 and multiple genes in the major histocompatibility complex (MHC) region. We estimated that ~11,500 genetic variants explained 90% of OCD genetic heritability. OCD genetic risk was associated with excitatory neurons in the hippocampus and the cortex, along with D1 and D2 type dopamine receptor-containing medium spiny neurons. OCD genetic risk was shared with 65 of 112 additional phenotypes, including all the psychiatric disorders we examined. In particular, OCD shared genetic risk with anxiety, depression, anorexia nervosa and Tourette syndrome and was negatively associated with inflammatory bowel diseases, educational attainment and body mass index.

Brain Network Abnormalities in Obsessive-Compulsive Disorder: Insights from Edge Functional Connectivity Analysis

Functional differences in key brain networks, including the dorsal attention network (DAN), control network (CN), and default mode network (DMN), have been identified in individuals with obsessive-compulsive disorder (OCD). However, the precise nature of these differences remains unclear. In this study, we further explored these differences and validated previous findings using a novel edge functional connectivity (eFC) approach, which enables a more refined analysis of brain network interaction. By employing this advanced method, we sought to gain deeper insights into FC alterations that may underlie the pathology of OCD. We collected data during movie watching from 44 patients with OCD and 33 healthy controls (HCs). The two-sample t test was used to assess differences in entropy between the DAN, CN, and DMN between groups. The analysis was performed with control for potentially confounding variables to ensure the robustness of the findings. Significant differences in network entropy were found between the OCD and HC groups. Relative to HCs, patients with OCD showed significantly reduced entropy in the DAN and increased entropy in the CN and DMN. The decreased entropy in the DAN and increased entropy in the CN and DMN observed in this study may be related to the core symptoms of OCD, such as attention deficit, impaired cognitive control, and self-referential thinking. These results provide valuable insights into the neurobiological mechanisms of OCD and highlight the potential of network entropy as a biomarker for the disorder. Future research should further explore the relationship between these network changes and the severity of OCD symptoms, as well as assess their implications for the development of treatment strategies.

Adaptive Whole-Brain Dynamics Predictive Method: Relevancy to Mental Disorders

The Hopf whole-brain model, based on structural connectivity, overcomes limitations of traditional structural or functional connectivity-focused methods by incorporating heterogeneity parameters, quantifying dynamic brain characteristics in healthy and diseased states. Traditional parameter fitting techniques lack precision, restricting broader use. To address this, we validated parameter fitting methods using simulated networks and synthetic models, introducing improvements such as individual-specific initialization and optimized gradient descent, which reduced individual data loss. We also developed an approximate loss function and gradient adjustment mechanism, enhancing parameter fitting accuracy and stability. Applying this refined method to datasets for major depressive disorder (MDD) and autism spectrum disorder (ASD), we identified differences in brain regions between patients and healthy controls, explaining related anomalies. This rigorous validation is crucial for clinical application, paving the way for precise neuropathological identification and novel treatments in neuropsychiatric research, demonstrating substantial potential in clinical neurology.

A systematic review of structural neuroimaging markers of psychotherapeutic and pharmacological treatment for obsessive-compulsive disorder

Identifying individual difference factors associated with treatment response and putative mechanisms of therapeutic change may improve treatment for Obsessive Compulsive Disorder (OCD). Our systematic review of structural neuroimaging markers (i.e., morphometry, structural connectivity) of psychotherapy and medication treatment response for OCD identified 26 eligible publications from 20 studies (average study total n=54 ± 41.6 [range: 11-175]; OCD group n=29 ± 19) in child, adolescent, and adult samples evaluating baseline brain structure correlates of treatment response as well as treatment-related changes in brain structure. Findings were inconsistent across studies; significant associations within the anterior cingulate cortex (3/5 regional, 2/8 whole brain studies) and orbitofrontal cortex (5/10 regional, 2/7 whole brain studies) were most common, but laterality and directionality were not always consistent. Structural neuroimaging markers of treatment response do not currently hold clinical utility. Given increasing evidence that associations between complex behavior and brain structure are characterized by small, but potentially meaningful, effects, much larger samples are likely needed. Multivariate approaches (e.g., machine learning) may also improve the clinical predictive utility of neuroimaging data.

Decoding Obsessive-Compulsive Disorder: The Regional Vulnerability Index and Its Association With Clinical Symptoms

BACKGROUND

Patients with obsessive-compulsive disorder (OCD) exhibit notable alterations in brain structure, which are likely to be of clinical relevance. Recently, in schizophrenia, the regional vulnerability index (RVI) was introduced to translate findings from ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) studies to the individual level. Building on this framework, in the current study, we sought to investigate whether the RVI might also serve as a vulnerability index for OCD.

METHODS

Toward this aim, we assessed subcortical volume and cortical thickness in a sample of 250 participants (140 patients with OCD, 110 healthy volunteers) and calculated the RVI by leveraging ENIGMA-derived deficits as the "ground truth" for expected regional brain alterations.

RESULTS

Subcortical volume and cortical thickness RVI values were significantly different in patients compared with healthy control participants. In addition, RVI values based on subcortical volume were significantly correlated with the severity of clinical symptoms. Moreover, RVI values for both subcortical volume and cortical thickness were significantly different in medicated subgroups while there was no significant difference in unmedicated patients.

CONCLUSIONS

The current results suggest that the RVI may represent an individual characteristic that reflects the degree of correspondence between individual patterns of structural alterations and disease-characteristic patterns of structural alterations. However, our findings also indicate that relatively large effect sizes in the meta-analytic ground truth are a prerequisite for obtaining a meaningful RVI parameter that can also be related to clinical severity. Therefore, the current findings require further validation through additional research to confirm the RVI's robustness and determine its predictive value.

Altered eotaxin-1 and interleukin-34 levels in obsessive-compulsive disorder: a case-control observational study in Bangladesh

BACKGROUND

Obsessive-compulsive disorder (OCD) is a prevalent mental health condition that impacts daily life. It is thought to be associated with genetic, biological, and structural brain changes, serotonergic abnormalities, altered neuromodulation, and environmental factors. Limited observational studies have examined cytokines in Bangladeshi patients with OCD. This study aimed to assess the levels of eotaxin-1 and interleukin (IL)-34 in individuals with this disorder.

METHODS

This case-control observational study included 58 patients with OCD and 30 healthy controls (HCs) matched for age, sex, and body mass index. The severity of OCD was assessed using the Yale-Brown obsessive-compulsive scale (Y-BOCS). Psychiatrists evaluated participants according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. Serum levels of eotaxin-1 and IL-34 were measured using enzyme-linked immunosorbent assay kits.

RESULTS

Patients with OCD exhibited significantly higher serum eotaxin-1 levels (121.13±7.84 pg/mL) than HCs (85.52±9.42 pg/mL). Conversely, IL-34 levels were considerably lower in patients than in HCs (119.02±14.53 pg/mL vs. 179.96±27.88 pg/mL). The Cohen d values for eotaxin-1 and IL-34 were 0.55 and -0.48, respectively. Among patients with OCD, a significant positive correlation was found between serum eotaxin-1 level and Y-BOCS score, along with a negative correlation between serum eotaxin-1 and IL-34 levels.

CONCLUSION

The findings suggest that altered eotaxin-1 and IL-34 levels may be associated with OCD. These chemokines and cytokines could serve as primary tools for assessing the risk of OCD, warranting further clinical investigation. This could potentially support more extensive research and the development of diagnostic and therapeutic strategies targeting these pathways.

Unraveling Gender Differences in Obsessive-Compulsive Disorder: A Focus on Key Micronutrients

Introduction Obsessive-compulsive disorder (OCD) is a persistent psychiatric condition that causes significant clinical and functional impairments. Recent research suggests a link between deficiencies in micronutrients, particularly vitamin B12, folic acid, and elevated homocysteine, and the development of OCD. This study investigates the blood levels of these micronutrients and their relationship to OCD severity, with an emphasis on potential gender differences. Methods This cross-sectional study included 300 drug-free OCD patients. Serum levels of vitamin B12, folic acid, and homocysteine were measured using established biochemical methods. The Yale-Brown Obsessive Compulsive Scale (Y-BOCS) was used to assess clinical severity. Data were examined to determine relationships between micronutrient levels and OCD severity and differences between male and female patients. Results This study found that women had higher levels of vitamin B12 (405.3 ± 15.1 vs. 360.4 ± 14.3) and folic acid (7.18 ± 0.27 vs. 5.76 ± 0.25) but lower levels of homocysteine (9.35 ± 0.64 vs. 14.4 ± 0.60) compared to men. Higher folic acid levels were linked to study participants having higher levels of education (at a college or university, where subjects are studied at an advanced level) compared to those with primary-level education. Lower vitamin B12 levels were linked to family mental health history and noncommunicable diseases. Women exhibited lower levels of substance use but higher rates of self-harm and suicide attempts. Elevated homocysteine levels were linked to longer illness duration and more severe OCD symptoms. Conclusion These findings suggest that imbalances in micronutrients, particularly vitamin B12, folic acid, and homocysteine, may contribute to OCD severity and treatment resistance. Gender-specific variations in micronutrient levels could provide valuable insights into personalized OCD therapy choices. Further longitudinal studies are needed to understand these relationships and their potential as therapeutic targets.

Ketogenic Diet as a Nutritional Metabolic Intervention for Obsessive-Compulsive Disorder: A Narrative Review

The substantial evidence supporting the ketogenic diet (KD) in epilepsy management has spurred research into its effects on other neurological and psychiatric conditions. Despite differences in characteristics, symptoms, and underlying mechanisms, these conditions share common pathways that the KD may influence. The KD reverses metabolic dysfunction. Moreover, it has been shown to support neuroprotection through mechanisms such as neuronal energy support, inflammation reduction, amelioration of oxidative stress, and reversing mitochondrial dysfunction. The adequate intake of dietary nutrients is essential for maintaining normal brain functions, and strong evidence supports the role of nutrition in the treatment and prevention of many psychiatric and neurological disorders. Obsessive-compulsive disorder (OCD) is a neuropsychiatric condition marked by persistent, distressing thoughts or impulses (obsessions) and repetitive behaviors performed in response to these obsessions (compulsions). Recent studies have increasingly examined the role of nutrition and metabolic disorders in OCD. This narrative review examines current evidence on the potential role of the KD in the treatment of OCD. We explore research on the KD's effects on psychiatric disorders to assess its potential relevance for OCD treatment. Additionally, we identify key gaps in the preclinical and clinical research that warrant further study in applying the KD as a metabolic therapy for OCD.

A coordinate-based meta-analysis of grey matter volume differences between adults with obsessive-compulsive disorder (OCD) and healthy controls

According to the cortico-striato-thalamo-cortical (CSTC) model of obsessive-compulsive disorder (OCD), the striatum plays a primary role in its neuropathophysiology. Hypothesising that volumetric alterations are more pronounced in subcortical areas of patients within the CSTC circuit compared to healthy controls (HCs), we conducted a coordinate-based meta-analysis of magnetic resonance imaging (MRI) studies. We included 26 whole-brain MRI studies, comprising 3,010 subjects: 1,508 patients (788 men, 720 women; mean age: 30.26 years, SD = 8.16) and 1,502 HCs (801 men, 701 women; mean age: 29.47 years, SD = 7.88). This meta-analysis demonstrated significant grey matter volume increases in the bilateral putamen, lateral globus pallidus, left parietal cortex, right pulvinar, and left cerebellum in adults with OCD, alongside decreases in the right hippocampus/caudate, bilateral medial frontal gyri, and other cortical regions. Volume increases were predominantly observed in subcortical areas, with the exception of the left parietal cortex and cerebellar dentate, while volume decreases were primarily cortical, aside from the right hippocampus/caudate. Further exploration of these neuropathophysiological correlates could inform specific prevention and treatment strategies, advancing precision mental health in clinical applications.

Current best practices and future opportunities for reproducible findings using large-scale neuroimaging in psychiatry

Research into the brain basis of psychopathology is challenging due to the heterogeneity of psychiatric disorders, extensive comorbidities, underdiagnosis or overdiagnosis, multifaceted interactions with genetics and life experiences, and the highly multivariate nature of neural correlates. Therefore, increasingly larger datasets that measure more variables in larger cohorts are needed to gain insights. In this review, we present current "best practice" approaches for using existing databases, collecting and sharing new repositories for big data analyses, and future directions for big data in neuroimaging and psychiatry with an emphasis on contributing to collaborative efforts and the challenges of multi-study data analysis.

Application of Hybrid DeepLearning Architectures for Identification of Individuals with Obsessive Compulsive Disorder Based on EEG Data

Objective: Obsessive-compulsive disorder (OCD) is a highly common psychiatric disorder. The symptoms of this condition overlap and co-occur with those of other psychiatric illnesses, making diagnosis difficult. The availability of biomarkers could be useful for aiding in diagnosis, although prior neuroimaging studies were unable to provide such biomarkers. Method: In this study, patients with OCD were classified from healthy controls using 2 different hybrid deep learning models: one-dimensional convolutional neural networks (1DCNN) together with long-short term memory (LSTM) and gradient recurrent units (GRU), respectively. Results: Both models exhibited exceptional classification accuracies in cross-validation and external validation phases. The mean classification accuracies in the cross-validation stage were 90.88% and 85.91% for the 1DCNN-LSTM and 1DCNN-GRU models, respectively. The inferior frontal, temporal, and occipital electrodes were predominant in providing discriminative features. Conclusion: Our findings underscore the potential of hybrid deep learning architectures utilizing EEG data to effectively differentiate patients with OCD from healthy controls. This promising approach holds implications for advancing clinical decision-making by offering valuable insights into diagnostic markers for OCD.

Dataset factors associated with age-related changes in brain structure and function in neurodevelopmental conditions

With brain structure and function undergoing complex changes throughout childhood and adolescence, age is a critical consideration in neuroimaging studies, particularly for those of individuals with neurodevelopmental conditions. However, despite the increasing use of large, consortium-based datasets to examine brain structure and function in neurotypical and neurodivergent populations, it is unclear whether age-related changes are consistent between datasets and whether inconsistencies related to differences in sample characteristics, such as demographics and phenotypic features, exist. To address this, we built models of age-related changes of brain structure (regional cortical thickness and regional surface area; N = 1218) and function (resting-state functional connectivity strength; N = 1254) in two neurodiverse datasets: the Province of Ontario Neurodevelopmental Network and the Healthy Brain Network. We examined whether deviations from these models differed between the datasets, and explored whether these deviations were associated with demographic and clinical variables. We found significant differences between the two datasets for measures of cortical surface area and functional connectivity strength throughout the brain. For regional measures of cortical surface area, the patterns of differences were associated with race/ethnicity, while for functional connectivity strength, positive associations were observed with head motion. Our findings highlight that patterns of age-related changes in the brain may be influenced by demographic and phenotypic characteristics, and thus future studies should consider these when examining or controlling for age effects in analyses.

Atypical neurocognitive functioning in children and adolescents with obsessive-compulsive disorder (OCD)

Atypical neurocognitive functioning has been found in adult patients with obsessive-compulsive disorder (OCD). However, little work has been done in children and adolescents with OCD. In this study, we investigated neurocognitive functioning in a large and representative sample of newly diagnosed children and adolescents with OCD compared to non-psychiatric controls. Children and adolescents with OCD (n = 119) and non-psychiatric controls (n = 90) underwent psychopathological assessment, intelligence testing, and a neurocognitive test battery spanning cognitive flexibility, planning and decision-making, working memory, fluency, and processing speed. The MANOVA main effect revealed that children and adolescents with OCD performed significantly worse than the control group (p < .001, η p 2 = 0.256). Atypical patient performance was particularly found for indices of cognitive flexibility, decision-making, working memory, and processing speed. We found no evidence of differences in planning or fluency. Moreover, we found no significant associations between neurocognitive performance and OCD symptom severity or comorbidity status. Our results indicate that children and adolescents with OCD show selective atypical neurocognitive functioning. These difficulties do not appear to drive their OCD symptoms. However, they may contribute to lifespan difficulties and interfere with treatment efficacy, an objective of our research currently.

Increases in functional connectivity between the default mode network and sensorimotor network correlate with symptomatic improvement after transcranial direct current stimulation for obsessive-compulsive disorder

BACKGROUND

Non-invasive neuromodulation is a promising intervention for obsessive-compulsive disorder (OCD), although its neurobiological mechanisms of action are still poorly understood. Recent evidence suggests that abnormalities in the connectivity of the default mode network (DMN) and the supplementary motor area (SMA) with other brain regions and networks are involved in OCD pathophysiology. We examined if transcranial direct current stimulation (tDCS) alters these connectivity patterns and if they correlate with symptom improvement in treatment-resistant OCD.

METHODS

In 23 patients from a larger clinical trial (comparing active tDCS to sham) who underwent pre- and post-treatment MRI scans, we assessed resting-state functional MRI (rs-fMRI) data. The treatment involved 30-minute daily tDCS sessions for four weeks (weekdays only), with the cathode over the SMA and the anode over the left deltoid. We conducted whole-brain connectivity analysis comparing active tDCS-treated to sham-treated patients.

RESULTS

We found that active tDCS, but not sham, led to connectivity increasing between the DMN and the bilateral pre/postcentral gyri (p = 0.004, FDR corrected) and temporal-auditory areas plus the SMA (p = 0.028, FDR corrected). Also, symptom improvement was directly associated with connectivity increasing between the left lateral sensorimotor network and the left precuneus (r = 0.589, p = 0.034).

LIMITATIONS

Limited sample size (23 participants with resting-state neuroimaging), inability to analyze specific OCD symptom dimensions (e.g., harm, sexual/religious, symmetry/checking, cleaning/contamination).

CONCLUSIONS

These data offer novel information concerning functional connectivity changes associated with non-invasive neuromodulation interventions in OCD and can guide new brain stimulation interventions in the framework of personalized interventions.

White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group

White matter pathways, typically studied with diffusion tensor imaging (DTI), have been implicated in the neurobiology of obsessive-compulsive disorder (OCD). However, due to limited sample sizes and the predominance of single-site studies, the generalizability of OCD classification based on diffusion white matter estimates remains unclear. Here, we tested classification accuracy using the largest OCD DTI dataset to date, involving 1336 adult participants (690 OCD patients and 646 healthy controls) and 317 pediatric participants (175 OCD patients and 142 healthy controls) from 18 international sites within the ENIGMA OCD Working Group. We used an automatic machine learning pipeline (with feature engineering and selection, and model optimization) and examined the cross-site generalizability of the OCD classification models using leave-one-site-out cross-validation. Our models showed low-to-moderate accuracy in classifying (1) "OCD vs. healthy controls" (Adults, receiver operator characteristic-area under the curve = 57.19 ± 3.47 in the replication set; Children, 59.8 ± 7.39), (2) "unmedicated OCD vs. healthy controls" (Adults, 62.67 ± 3.84; Children, 48.51 ± 10.14), and (3) "medicated OCD vs. unmedicated OCD" (Adults, 76.72 ± 3.97; Children, 72.45 ± 8.87). There was significant site variability in model performance (cross-validated ROC AUC ranges 51.6-79.1 in adults; 35.9-63.2 in children). Machine learning interpretation showed that diffusivity measures of the corpus callosum, internal capsule, and posterior thalamic radiation contributed to the classification of OCD from HC. The classification performance appeared greater than the model trained on grey matter morphometry in the prior ENIGMA OCD study (our study includes subsamples from the morphometry study). Taken together, this study points to the meaningful multivariate patterns of white matter features relevant to the neurobiology of OCD, but with low-to-moderate classification accuracy. The OCD classification performance may be constrained by site variability and medication effects on the white matter integrity, indicating room for improvement for future research.

Genome-wide association study identifies 30 obsessive-compulsive disorder associated loci

Obsessive-compulsive disorder (OCD) affects ~1% of the population and exhibits a high SNP-heritability, yet previous genome-wide association studies (GWAS) have provided limited information on the genetic etiology and underlying biological mechanisms of the disorder. We conducted a GWAS meta-analysis combining 53,660 OCD cases and 2,044,417 controls from 28 European-ancestry cohorts revealing 30 independent genome-wide significant SNPs and a SNP-based heritability of 6.7%. Separate GWAS for clinical, biobank, comorbid, and self-report sub-groups found no evidence of sample ascertainment impacting our results. Functional and positional QTL gene-based approaches identified 249 significant candidate risk genes for OCD, of which 25 were identified as putatively causal, highlighting WDR6, DALRD3, CTNND1 and genes in the MHC region. Tissue and single-cell enrichment analyses highlighted hippocampal and cortical excitatory neurons, along with D1- and D2-type dopamine receptor-containing medium spiny neurons, as playing a role in OCD risk. OCD displayed significant genetic correlations with 65 out of 112 examined phenotypes. Notably, it showed positive genetic correlations with all included psychiatric phenotypes, in particular anxiety, depression, anorexia nervosa, and Tourette syndrome, and negative correlations with a subset of the included autoimmune disorders, educational attainment, and body mass index.. This study marks a significant step toward unraveling its genetic landscape and advances understanding of OCD genetics, providing a foundation for future interventions to address this debilitating disorder.

Intrusive Traumatic Re-Experiencing Domain: Functional Connectivity Feature Classification by the ENIGMA PTSD Consortium

Background

Intrusive traumatic re-experiencing domain (ITRED) was recently introduced as a novel perspective on posttraumatic psychopathology, proposing to focus research of posttraumatic stress disorder (PTSD) on the unique symptoms of intrusive and involuntary re-experiencing of the trauma, namely, intrusive memories, nightmares, and flashbacks. The aim of the present study was to explore ITRED from a neural network connectivity perspective.

Methods

Data were collected from 9 sites taking part in the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) PTSD Consortium (n= 584) and included itemized PTSD symptom scores and resting-state functional connectivity (rsFC) data. We assessed the utility of rsFC in classifying PTSD, ITRED-only (no PTSD diagnosis), and trauma-exposed (TE)-only (no PTSD or ITRED) groups using a machine learning approach, examining well-known networks implicated in PTSD. A random forest classification model was built on a training set using cross-validation, and the averaged cross-validation model performance for classification was evaluated using the area under the curve. The model was tested using a fully independent portion of the data (test dataset), and the test area under the curve was evaluated.

Results

rsFC signatures differentiated TE-only participants from PTSD and ITRED-only participants at about 60% accuracy. Conversely, rsFC signatures did not differentiate PTSD from ITRED-only individuals (45% accuracy). Common features differentiating TE-only participants from PTSD and ITRED-only participants mainly involved default mode network-related pathways. Some unique features, such as connectivity within the frontoparietal network, differentiated TE-only participants from one group (PTSD or ITRED-only) but to a lesser extent from the other group.

Conclusions

Neural network connectivity supports ITRED as a novel neurobiologically based approach to classifying posttrauma psychopathology.

Brain activation during fear extinction recall in unmedicated patients with obsessive-compulsive disorder

Specific brain activation patterns during fear conditioning and the recall of previously extinguished fear responses have been associated with obsessive-compulsive disorder (OCD). However, further replication studies are necessary. We measured skin-conductance response and blood oxygenation level-dependent responses in unmedicated adult patients with OCD (n = 27) and healthy participants (n = 22) submitted to a two-day fear-conditioning experiment comprising fear conditioning, extinction (day 1) and extinction recall (day 2). During conditioning, groups differed regarding the skin conductance reactivity to the aversive stimulus (shock) and regarding the activation of the right opercular cortex, insular cortex, putamen, and lingual gyrus in response to conditioned stimuli. During extinction recall, patients with OCD had higher responses to stimuli and smaller differences between responses to conditioned and neutral stimuli. For the entire sample, the higher the response delta between conditioned and neutral stimuli, the greater the dACC activation for the same contrast during early extinction recall. While activation of the dACC predicted the average difference between responses to stimuli for the entire sample, groups did not differ regarding the activation of the dACC during extinction recall. Larger unmedicated samples might be necessary to replicate the previous findings reported in patients with OCD.

Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium

BACKGROUND

Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group.

METHODS

We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality.

RESULTS

We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance.

CONCLUSION

These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.

Association of granulocyte macrophage colony-stimulating factor and interleukin-17 levels with obsessive-compulsive disorder: a case-control study findings

Obsessive-compulsive disorder (OCD) is a mental condition that affects many people and is characterized by recurring obsessions and compulsions. It significantly impacts individuals' ability to function ordinarily daily, affecting people of all ages. This study aimed to investigate whether or not the cytokines granulocyte macrophage colony-stimulating factor (GM-CSF) and interleukin-17 (IL-17) are involved in the pathophysiology of OCD. A case-control study with 50 OCD patients and 38 healthy volunteers served as the controls for this investigation. The levels of GM-CSF and IL-17 in the serum of both groups were measured with enzyme-linked immunosorbent assay (ELISA) kits. In addition, the sociodemographic characteristics of the population under study were studied. Based on the findings of this study, OCD patients had significantly elevated levels of IL-17 than the controls, it appears that there may be a function for IL-17 in the pathophysiology of OCD. It was also discovered that the severity of OCD and IL-17 levels had a significant positive correlation. On the other hand, when comparing the levels of GM-CSF, there was no significant difference between the patients and the controls. This study provides evidence supporting the involvement of cytokine IL-17 in the pathophysiology of OCD. This study suggests IL-17 as a diagnostic biomarker for OCD and adds to our knowledge of the function that the immune system plays in this condition.

Pathological Networking of Gray Matter Dendritic Density With Classic Brain Morphometries in OCD

Importance

The pathogenesis of obsessive-compulsive disorder (OCD) may involve altered dendritic morphology, but in vivo imaging of neurite morphology in OCD remains limited. Such changes must be interpreted functionally within the context of the multimodal neuroimaging approach to OCD.

Objective

To examine whether dendritic morphology is altered in patients with OCD compared with healthy controls (HCs) and whether such alterations are associated with other brain structural metrics in pathological networks.

Design, Setting, and Participants

This case-control study used cross-sectional data, including multimodal brain images and clinical symptom assessments, from 108 patients with OCD and 108 HCs from 2014 to 2017. Patients with OCD were recruited from Shanghai Mental Health Center, Shanghai, China, and HCs were recruited via advertisements. The OCD group comprised unmedicated adults with a Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition) (DSM-IV) diagnosis of OCD, while the HCs were adults without any DSM-IV diagnosis, matched for age, sex, and education level. Data were analyzed from September 2019 to April 2023.

Exposure

DSM-IV diagnosis of OCD.

Main Outcomes and Measures

Multimodal brain imaging was used to compare neurite microstructure and classic morphometries between patients with OCD and HCs. The whole brain was searched to identify regions exhibiting altered morphology in patients with OCD and explore the interplay between the brain metrics representing these alterations. Brain-symptom correlations were analyzed, and the performance of different brain metric configurations were evaluated in distinguishing patients with OCD from HCs.

Results

Among 108 HCs (median [IQR] age, 26 [23-31] years; 50 [46%] female) and 108 patients with OCD (median [IQR] age, 26 [24-31] years; 46 [43%] female), patients with OCD exhibited deficient neurite density in the right lateral occipitoparietal regions (peak t = 3.821; P ≤ .04). Classic morphometries also revealed widely-distributed alterations in the brain (peak t = 4.852; maximum P = .04), including the prefrontal, medial parietal, cingulate, and fusiform cortices. These brain metrics were interconnected into a pathological brain network associated with OCD symptoms (global strength: HCs, 0.253; patients with OCD, 0.941; P = .046; structural difference, 0.572; P < .001). Additionally, the neurite density index exhibited high discriminatory power in distinguishing patients with OCD from HCs (accuracy, ≤76.85%), and the entire pathological brain network also exhibited excellent discriminative classification properties (accuracy, ≤82.87%).

Conclusions and Relevance

The findings of this case-control study underscore the utility of in vivo imaging of gray matter dendritic density in future OCD research and the development of neuroimaging-based biomarkers. They also endorse the concept of connectopathy, providing a potential framework for interpreting the associations among various OCD symptom-related morphological anomalies.

The functional connectome in obsessive-compulsive disorder: resting-state mega-analysis and machine learning classification for the ENIGMA-OCD consortium

Current knowledge about functional connectivity in obsessive-compulsive disorder (OCD) is based on small-scale studies, limiting the generalizability of results. Moreover, the majority of studies have focused only on predefined regions or functional networks rather than connectivity throughout the entire brain. Here, we investigated differences in resting-state functional connectivity between OCD patients and healthy controls (HC) using mega-analysis of data from 1024 OCD patients and 1028 HC from 28 independent samples of the ENIGMA-OCD consortium. We assessed group differences in whole-brain functional connectivity at both the regional and network level, and investigated whether functional connectivity could serve as biomarker to identify patient status at the individual level using machine learning analysis. The mega-analyses revealed widespread abnormalities in functional connectivity in OCD, with global hypo-connectivity (Cohen's d: -0.27 to -0.13) and few hyper-connections, mainly with the thalamus (Cohen's d: 0.19 to 0.22). Most hypo-connections were located within the sensorimotor network and no fronto-striatal abnormalities were found. Overall, classification performances were poor, with area-under-the-receiver-operating-characteristic curve (AUC) scores ranging between 0.567 and 0.673, with better classification for medicated (AUC = 0.702) than unmedicated (AUC = 0.608) patients versus healthy controls. These findings provide partial support for existing pathophysiological models of OCD and highlight the important role of the sensorimotor network in OCD. However, resting-state connectivity does not so far provide an accurate biomarker for identifying patients at the individual level.

Posterior cingulate cortex spontaneous activity associated with motor response inhibition in patients with obsessive-compulsive disorder: A resting-state fMRI study

Recent evidence suggests that broad brain regions, not limited to the fronto-striato-thalamo-cortical circuit, play an important role in motor response inhibition. However, it is still unclear which specific key brain region is responsible for impaired motor response inhibition observed in obsessive-compulsive disorder (OCD). We calculated the fractional amplitude of low-frequency fluctuations (fALFF) and measured response inhibition ability using the stop-signal task in 41 medication-free patients with OCD and 49 healthy control (HC) participants. We explored the brain region that shows different association between the fALFF and the ability of motor response inhibition. Significant differences in fALFF associated with the ability of motor response inhibition were identified in dorsal posterior cingulate cortex (PCC). There was a positive correlation between increased fALFF in the dorsal PCC and impaired motor response inhibition in OCD. In the HC group, there was a negative correlation between the two variables. Our results suggest that the magnitude of resting-state blood oxygen level-dependent oscillation of the dorsal PCC is a key brain region for the underlying mechanisms of impaired motor response inhibition in OCD. Future studies should examine whether this characteristic of dorsal PCC affects other large-scale networks responsible for motor response inhibition of OCD.

A study of serum brain-derived neurotrophic factor level in individuals with obsessive compulsive disorder and their first-degree relatives as compared to the healthy population

Background

The nosological tradition in psychiatry defines diagnostic criteria for disorders based on expert consensus than objective biological markers reflecting underlying neurobiological correlates. Endophenotypes have been researched as heritable biological markers that can be quantified and defined to represent intermediate measures of a psychiatric illness. In obsessive-compulsive disorder (OCD), various putative biomarkers such as neuropsychological, neurophysiological, neuroradiological, brain-derived neurotrophic factor (BDNF), etc., have been explored.

Aim

The study aimed to compare levels of serum BDNF in individuals with OCD and their unaffected first-degree relatives (FDR) with healthy controls (HC).

Methods

This cross-sectional study compared serum BDNF levels in medication-free/naive individuals with OCD (n = 30) to their FDR (n = 30) and age-sex matched HC (n = 30). Intergroup comparison was done using analysis of variance (ANOVA) and post-hoc Tukey's test. Correlation analysis was conducted to find the relationship of sociodemographic and clinical correlates to serum BDNF as well as dimensional subtypes of OCD.

Results

No significant difference in BDNF levels was observed between OCD and HC (P = 0.13) but a significantly higher level was found in the FDR group compared to age-sex matched HC (P = 0.02).

Conclusion

BDNF levels may have a complex interplay influencing the genetic inheritance and clinical manifestations of OCD. Further research is required before considering it a viable biomarker.

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.

Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies

The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN), and performance was evaluated using 5-fold cross-validation. Features were visualized using GCN-Explainer, an ablation study and univariate t-testing. The results showed a mean classification accuracy of 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Sex classification accuracy was substantially better across datasets (73-81%). Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes. These results suggest that whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity as further supported by the higher accuracy for sex classification using the same methods. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies.

In search of environmental risk factors for obsessive-compulsive disorder: study protocol for the OCDTWIN project

BACKGROUND

The causes of obsessive-compulsive disorder (OCD) remain unknown. Gene-searching efforts are well underway, but the identification of environmental risk factors is at least as important and should be a priority because some of them may be amenable to prevention or early intervention strategies. Genetically informative studies, particularly those employing the discordant monozygotic (MZ) twin design, are ideally suited to study environmental risk factors. This protocol paper describes the study rationale, aims, and methods of OCDTWIN, an open cohort of MZ twin pairs who are discordant for the diagnosis of OCD.

METHODS

OCDTWIN has two broad aims. In Aim 1, we are recruiting MZ twin pairs from across Sweden, conducting thorough clinical assessments, and building a biobank of biological specimens, including blood, saliva, urine, stool, hair, nails, and multimodal brain imaging. A wealth of early life exposures (e.g., perinatal variables, health-related information, psychosocial stressors) are available through linkage with the nationwide registers and the Swedish Twin Registry. Blood spots stored in the Swedish phenylketonuria (PKU) biobank will be available to extract DNA, proteins, and metabolites, providing an invaluable source of biomaterial taken at birth. In Aim 2, we will perform within-pair comparisons of discordant MZ twins, which will allow us to isolate unique environmental risk factors that are in the causal pathway to OCD, while strictly controlling for genetic and early shared environmental influences. To date (May 2023), 43 pairs of twins (21 discordant for OCD) have been recruited.

DISCUSSION

OCDTWIN hopes to generate unique insights into environmental risk factors that are in the causal pathway to OCD, some of which have the potential of being actionable targets.

In search of environmental risk factors for obsessive-compulsive disorder: Study protocol for the OCDTWIN project

Background The causes of obsessive-compulsive disorder (OCD) remain unknown. Gene-searching efforts are well underway, but the identification of environmental risk factors is at least as important and should be a priority because some of them may be amenable to prevention or early intervention strategies. Genetically informative studies, particularly those employing the discordant monozygotic (MZ) twin design, are ideally suited to study environmental risk factors. This protocol paper describes the study rationale, aims, and methods of OCDTWIN, an open cohort of MZ twin pairs who are discordant for the diagnosis of OCD. Methods OCDTWIN has two broad aims. In Aim 1, we are recruiting MZ twin pairs from across Sweden, conducting thorough clinical assessments, and building a biobank of biological specimens, including blood, saliva, urine, stool, hair, nails, and multimodal brain imaging. A wealth of early life exposures (e.g., perinatal variables, health-related information, psychosocial stressors) are available through linkage with the nationwide registers and the Swedish Twin Registry. Blood spots stored in the Swedish phenylketonuria (PKU) biobank will be available to extract DNA, proteins, and metabolites, providing an invaluable source of biomaterial taken at birth. In Aim 2, we will perform within-pair comparisons of discordant MZ twins, which will allow us to isolate unique environmental risk factors that are in the causal pathway to OCD, while strictly controlling for genetic and early shared environmental influences. To date (May 2023), 43 pairs of twins (21 discordant for OCD) have been recruited. Discussion OCDTWIN hopes to generate unique insights into environmental risk factors that are in the causal pathway to OCD, some of which have the potential of being actionable targets.

Mapping the neuroanatomical heterogeneity of OCD using a framework integrating normative model and non-negative matrix factorization

Obsessive-compulsive disorder (OCD) is a spectrum disorder with high interindividual heterogeneity. We propose a comprehensible framework integrating normative model and non-negative matrix factorization (NMF) to quantitatively estimate the neuroanatomical heterogeneity of OCD from a dimensional perspective. T1-weighted magnetic resonance images of 98 first-episode untreated patients with OCD and matched healthy controls (HCs, n = 130) were acquired. We derived individualized differences in gray matter morphometry using normative model and parsed them into latent disease factors using NMF. Four robust disease factors were identified. Each patient expressed multiple factors and exhibited a unique factor composition. Factor compositions of patients were significantly correlated with severity of symptom, age of onset, illness duration, and exhibited sex differences, capturing sources of clinical heterogeneity. In addition, the group-level morphological differences obtained with two-sample t test could be quantitatively derived from the identified disease factors, reconciling the group-level and subject-level findings in neuroimaging studies. Finally, we uncovered two distinct subtypes with opposite morphological differences compared with HCs from factor compositions. Our findings suggest that morphological differences of individuals with OCD are the unique combination of distinct neuroanatomical patterns. The proposed framework quantitatively estimating neuroanatomical heterogeneity paves the way for precision medicine in OCD.

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.

Preliminary Observations of Resting-State Magnetoencephalography in Nonmedicated Children with Obsessive-Compulsive Disorder

Background: Cortico-striato-thalamo-cortical (CSTC) network alterations are hypothesized to contribute to symptoms of obsessive-compulsive disorder (OCD). To date, very few studies have examined whether CSTC network alterations are present in children with OCD, who are medication naive. Medication-naive pediatric imaging samples may be optimal to study neural correlates of illness and identify brain-based markers, given the proximity to illness onset. Methods: Magnetoencephalography (MEG) data were analyzed at rest, in 18 medication-naive children with OCD (M = 12.1 years ±2.0 standard deviation [SD]; 10 M/8 F) and 13 typically developing children (M = 12.3 years ±2.2 SD; 6 M/7 F). Whole-brain MEG-derived resting-state functional connectivity (rs-fc), for alpha- and gamma-band frequencies were compared between OCD and typically developing (control) groups. Results: Increased MEG-derived rs-fc across alpha- and gamma-band frequencies was found in the OCD group compared to the control group. Increased MEG-derived rs-fc at alpha-band frequencies was evident across a number of regions within the CSTC circuitry and beyond, including the cerebellum and limbic regions. Increased MEG-derived rs-fc at gamma-band frequencies was restricted to the frontal and temporal cortices. Conclusions: This MEG study provides preliminary evidence of altered alpha and gamma networks, at rest, in medication-naive children with OCD. These results support prior findings pointing to the relevance of CSTC circuitry in pediatric OCD and further support accumulating evidence of altered connectivity between regions that extend beyond this network, including the cerebellum and limbic regions. Given the substantial portion of children and youth whose OCD symptoms do not respond to conventional treatments, our findings have implications for future treatment innovation research aiming to target and track whether brain patterns associated with having OCD may change with treatment and/or predict treatment response.

Associations of medication with subcortical morphology across the lifespan in OCD: Results from the international ENIGMA Consortium

BACKGROUND

Widely used psychotropic medications for obsessive-compulsive disorder (OCD) may change the volumes of subcortical brain structures, and differently in children vs. adults. We measured subcortical volumes cross-sectionally in patients finely stratified for age taking various common classes of OCD drugs.

METHODS

The ENIGMA-OCD consortium sample (1081 medicated/1159 unmedicated OCD patients and 2057 healthy controls aged 6-65) was divided into six successive 6-10-year age-groups. Individual structural MRIs were parcellated automatically using FreeSurfer into 8 regions-of-interest (ROIs). ROI volumes were compared between unmedicated and medicated patients and controls, and between patients taking serotonin reuptake inhibitors (SRIs), tricyclics (TCs), antipsychotics (APs), or benzodiazepines (BZs) and unmedicated patients.

RESULTS

Compared to unmedicated patients, volumes of accumbens, caudate, and/or putamen were lower in children aged 6-13 and adults aged 50-65 with OCD taking SRIs (Cohen's d = -0.24 to -0.74). Volumes of putamen, pallidum (d = 0.18-0.40), and ventricles (d = 0.31-0.66) were greater in patients aged 20-29 receiving APs. Hippocampal volumes were smaller in patients aged 20 and older taking TCs and/or BZs (d = -0.27 to -1.31).

CONCLUSIONS

Results suggest that TCs and BZs could potentially aggravate hippocampal atrophy of normal aging in older adults with OCD, whereas SRIs may reduce striatal volumes in young children and older adults. Similar to patients with psychotic disorders, OCD patients aged 20-29 may experience subcortical nuclear and ventricular hypertrophy in relation to APs. Although cross-sectional, present results suggest that commonly prescribed agents exert macroscopic effects on subcortical nuclei of unknown relation to therapeutic response.

Recent advances in psychoradiology

Psychiatry, as a field, lacks objective markers for diagnosis, progression, treatment planning, and prognosis, in part due to difficulties studying the brainin vivo, and diagnoses are based on self-reported symptoms and observation of patient behavior and cognition. Rapid advances in brain imaging techniques allow clinical investigators to noninvasively quantify brain features at the structural, functional, and molecular levels. Psychoradiology is an emerging discipline at the intersection of psychiatry and radiology. Psychoradiology applies medical imaging technologies to psychiatry and promises not only to improve insight into structural and functional brain abnormalities in patients with psychiatric disorders but also to have potential clinical utility. We searched for representative studies related to recent advances in psychoradiology through May 1, 2022, and conducted a selective review of 165 references, including 75 research articles. We summarize the novel dynamic imaging processing methods to model brain networks and present imaging genetics studies that reveal the relationship between various neuroimaging endophenotypes and genetic markers in psychiatric disorders. Furthermore, we survey recent advances in psychoradiology, with a focus on future psychiatric diagnostic approaches with dimensional analysis and a shift from group-level to individualized analysis. Finally, we examine the application of machine learning in psychoradiology studies and the potential of a novel option for brain stimulation treatment based on psychoradiological findings in precision medicine. Here, we provide a summary of recent advances in psychoradiology research, and we hope this review will help guide the practice of psychoradiology in the scientific and clinical fields.

Disorganized functional architecture of amygdala subregional networks in obsessive-compulsive disorder

A precise understanding of amygdala-centered subtle networks may help refine neurocircuitry models of obsessive-compulsive disorder (OCD). We applied connectivity-based parcellation methodology to segment the amygdala based on resting-state fMRI data of 92 medication-free OCD patients without comorbidity and 90 matched healthy controls (HC). The amygdala was parcellated into two subregions corresponding to basolateral amygdala (BLA) and centromedial amygdala (CMA). Amygdala subregional functional connectivity (FC) maps were generated and group differences were evaluated with diagnosis-by-subregion flexible factorial ANOVA. We found significant diagnosis × subregion FC interactions in insula, supplementary motor area (SMA), midcingulate cortex (MCC), superior temporal gyrus (STG) and postcentral gyrus (PCG). In HC, the BLA demonstrated stronger connectivity with above regions compared to CMA, whereas in OCD, the connectivity pattern reversed to stronger CMA connectivity comparing to BLA. Relative to HC, OCD patients exhibited hypoconnectivity between left BLA and left insula, and hyperconnectivity between right CMA and SMA, MCC, insula, STG, and PCG. Moreover, OCD patients showed reduced volume of left BLA and right CMA compared to HC. Our findings characterized disorganized functional architecture of amygdala subregional networks in accordance with structural defects, providing direct evidence regarding the specific role of amygdala subregions in the neurocircuitry models of OCD.

Shape analysis of subcortical structures in obsessive-compulsive disorder and the relationship with comorbid anxiety, depression, and medication use: A meta-analysis by the OCD Brain Imaging Consortium

OBJECTIVE

Neuroimaging studies of obsessive-compulsive disorder (OCD) patients have highlighted the important role of deep gray matter structures. Less work has however focused on subcortical shape in OCD patients.

METHODS

Here we pooled brain MRI scans from 412 OCD patients and 368 controls to perform a meta-analysis utilizing the ENIGMA-Shape protocol. In addition, we investigated modulating effects of medication status, comorbid anxiety or depression, and disease duration on subcortical shape.

RESULTS

There was no significant difference in shape thickness or surface area between OCD patients and healthy controls. For the subgroup analyses, OCD patients with comorbid depression or anxiety had lower thickness of the hippocampus and caudate nucleus and higher thickness of the putamen and pallidum compared to controls. OCD patients with comorbid depression had lower shape surface area in the thalamus, caudate nucleus, putamen, hippocampus, and nucleus accumbens and higher shape surface area in the pallidum. OCD patients with comorbid anxiety had lower shape surface area in the putamen and the left caudate nucleus and higher shape surface area in the pallidum and the right caudate nucleus. Further, OCD patients on medication had lower shape thickness of the putamen, thalamus, and hippocampus and higher thickness of the pallidum and caudate nucleus, as well as lower shape surface area in the hippocampus and amygdala and higher surface area in the putamen, pallidum, and caudate nucleus compared to controls. There were no significant differences between OCD patients without co-morbid anxiety and/or depression and healthy controls on shape measures. In addition, there were also no significant differences between OCD patients not using medication and healthy controls.

CONCLUSIONS

The findings here are partly consistent with prior work on brain volumes in OCD, insofar as they emphasize that alterations in subcortical brain morphology are associated with comorbidity and medication status. Further work is needed to understand the biological processes contributing to subcortical shape.

Functional Connectivity within the Frontal–Striatal Network Differentiates Checkers from Washers of Obsessive-Compulsive Disorder

Background: Obsessive-compulsive disorder (OCD) is a psychiatric disorder with high clinical heterogeneity manifested by the presence of obsessions and/or compulsions. The classification of the symptom dimensional subtypes is helpful for further exploration of the pathophysiological mechanisms underlying the clinical heterogeneity of OCD. Washing and checking symptoms are the two major symptom subtypes in OCD, but the neural mechanisms of the different types of symptoms are not yet clearly understood. The purpose of this study was to compare regional and network functional alterations between washing and checking OCD based on resting-state functional magnetic resonance imaging (rs-fMRI). Methods: In total, 90 subjects were included, including 15 patients in the washing group, 30 patients in the checking group, and 45 healthy controls (HCs). Regional homogeneity (ReHo) was used to compare the differences in regional spontaneous neural activity among the three groups, and local indicators were analyzed by receiver operating characteristic (ROC) curves as imaging markers for the prediction of the clinical subtypes of OCD. Furthermore, differently activated local brain areas, as regions of interest (ROIs), were used to explore differences in altered brain functioning between washing and checking OCD symptoms based on a functional connectivity (FC) analysis. Results: Extensive abnormalities in spontaneous brain activity involving frontal, temporal, and occipital regions were observed in the patients compared to the HCs. The differences in local brain functioning between checking and washing OCD were mainly concentrated in the bilateral middle frontal gyrus, right supramarginal gyrus, right angular gyrus, and right inferior occipital gyrus. The ROC curve analysis revealed that the hyperactivation right middle frontal gyrus had a better discriminatory value for checking and washing OCD. Furthermore, the seed-based FC analysis revealed higher FC between the left medial superior frontal gyrus and right caudate nucleus compared to that in the healthy controls. Conclusions: These findings suggest that extensive local differences exist in intrinsic spontaneous activity among the checking group, washing group, and HCs. The neural basis of checking OCD may be related to dysfunction in the frontal–striatal network, which distinguishes OCD from washing OCD.

Changes in Volume of Subregions Within Basal Ganglia in Obsessive-Compulsive Disorder: A Study With Atlas-Based and VBM Methods

BACKGROUND

The role of basal ganglia in the pathogenesis of obsessive-compulsive disorder (OCD) remains unclear. The studies on volume changes of basal ganglia in OCD commonly use the VBM method; however, the Atlas-based method used in such research has not been reported. Atlas-based method has a lower false positive rate compared with VBM method, thus having advantages partly.

OBJECTIVES

The current study aimed to detect the volume changes of subregions within basal ganglia in OCD using Atlas-based method to further delineate the precise neural circuitry of OCD. What is more, we explored the influence of software used in Atlas-based method on the volumetric analysis of basal ganglia and compared the results of Atlas-based method and regularly used VBM method.

METHODS

We analyzed the brain structure images of 37 patients with OCD and 41 healthy controls (HCs) using the VBM method, Atlas-based method based on SPM software, or Freesurfer software to find the areas with significant volumetric variation between the two groups, and calculated the effects size of these areas.

RESULTS

VBM analysis revealed a significantly increased volume of bilateral lenticular nucleus in patients compared to HCs. In contrast, Atlas-based method based on Freesurfer revealed significantly increased volume of left globus pallidus in patients, and the largest effect size of volumetric variation was revealed by Freesurfer analysis.

CONCLUSIONS

This study showed that the volume of bilateral lenticular nucleus significantly increased in patients compared to HCs, especially left globus pallidus, which was in accordance with the previous findings. In addition, Freesurfer is better than SPM and a good choice for Atlas-based volumetric analysis of basal ganglia.

Subcortical shape in pediatric and adult obsessive-compulsive disorder

BACKGROUND

Obsessive-compulsive disorder (OCD) implicates alterations in cortico-striato-thalamo-cortical and fronto-limbic circuits. Building on prior structural findings, this is the largest study to date examining subcortical surface morphometry in OCD.

METHODS

Structural magnetic resonance imaging data were collected from 200 participants across development (5-55 years): 28 youth and 75 adults with OCD and 27 psychiatrically healthy youth and 70 adults. General linear models were used to assess group differences and group-by-age interactions on subcortical shape (FSL FIRST).

RESULTS

Compared to healthy participants, those with OCD exhibited surface expansions on the right nucleus accumbens and inward left amygdala deformations, which were associated with greater OCD symptom severity ([Children's] Yale-Brown Obsessive-Compulsive Scale). Group-by-age interactions indicated that accumbens group differences were driven by younger participants and that right pallidum shape was associated inversely with age in healthy participants, but not in participants with OCD. No differences in the shape of other subcortical regions or in volumes (FreeSurfer) were detected in supplementary analyses.

CONCLUSIONS

This study is the largest to date examining subcortical shape in OCD and the first to do so across the developmental spectrum. NAcc and amygdala shape deformation builds on extant neuroimaging findings and suggests subtle, subregional alterations beyond volumetric findings. Results shed light on morphometric alterations in OCD, informing current pathophysiological models.

Two distinct subtypes of obsessive compulsive disorder revealed by heterogeneity through discriminative analysis

Neurobiological heterogeneity in obsessive compulsive disorder (OCD) is understudied leading to conflicting neuroimaging findings. Therefore, we investigated objective neuroanatomical subtypes of OCD by adopting a newly proposed method based on gray matter volumes (GMVs). GMVs were derived from T1‐weighted anatomical images of patients with OCD (n = 100) and matched healthy controls (HCs; n = 106). We first inquired whether patients with OCD presented higher interindividual variability HCs in terms of GMVs. Then, we identified distinct subtypes of OCD by adopting heterogeneity through discriminative analysis (HYDRA), where regional GMVs were treated as features. Patients with OCD presented higher interindividual variability than HCs, suggesting a high structural heterogeneity of OCD. HYDRA identified two distinct robust subtypes of OCD presenting opposite neuroanatomical aberrances compared with HCs, while sharing indistinguishable clinical and demographic features. Specifically, Subtype 1 exhibited widespread increased GMVs in cortical and subcortical regions, including the orbitofrontal gyrus, right anterior insula, bilateral hippocampus, and bilateral parahippocampus and cerebellum. Subtype 2 demonstrated overall decreased GMVs in regions such as the orbitofrontal gyrus, right anterior insula, and precuneus. When mixed together, none of patients presented significant differences compared with HCs. In addition, the total intracranial volume of Subtype 2 was significantly correlated with the total score of the Yale–Brown Obsessive Compulsive Scale while that of Subtype 1 was not. These results identified two distinct neuroanatomical subtypes, providing a possible explanation for conflicting neuroimaging findings, and proposed a potential objective taxonomy contributing to precise clinical diagnosis and treatment in OCD.

Distinct alterations of amygdala subregional functional connectivity in early- and late-onset obsessive-compulsive disorder

BACKGROUND

Age of onset may be an important feature associated with distinct subtypes of obsessive-compulsive disorder (OCD). The amygdala joined neurocircuitry models of OCD for its role in mediating fear and regulating anxiety. The present study aims to identify the underlying pathophysiological specifics in OCD with different onset times by assessing amygdala subregional functional connectivity (FC) alterations in early-onset OCD (EO-OCD) and late-onset OCD (LO-OCD).

METHODS

Resting-state functional magnetic resonance imaging data were acquired from 88 medication-free OCD patients (including 30 EO-OCD and 58 LO-OCD) and age- and sex-matched healthy controls (HC) for each patient group. Onset-by-diagnosis interactions were examined and comparisons between each OCD group and the corresponding HC group were performed regarding the FC of amygdala subregions including the basolateral amygdala (BLA), centromedial amygdala (CMA), superficial amygdala (SFA) and amygdalostriatal transition area (Astr).

RESULTS

Significant onset-by-diagnosis interactions were found in FC between bilateral SFA, right CMA, left Astr and the cerebellum. EO-OCD patients showed abnormally increased BLA/SFA-cerebellum, BLA-precuneus and BLA/SFA-fusiform connectivity in addition to decreased BLA/SFA-orbitofrontal cortex connectivity. In contrast, LO-OCD patients exhibited increased CMA/Astr-precentral/postcentral gyrus and CMA-cuneus connectivity as well as decreased CMA/Astr-cerebellum and BLA-striatum connectivity.

LIMITATIONS

The exclusion of comorbidity may reduce the generalizability of our results.

CONCLUSIONS

These findings emphasized the different patterns of amygdala subregional connectivity alterations associated with EO-OCD and LO-OCD patients. These results provide unique insights into constructing evidence-based distinct OCD subtypes based on brain intrinsic connectivity and point to the need of specified management for EO-OCD and LO-OCD in clinical setting.

Classification of Obsessive-Compulsive Disorder Using Distance Correlation on Resting-State Functional MRI Images

Both the Pearson correlation and partial correlation methods have been widely used in the resting-state functional MRI (rs-fMRI) studies. However, they can only measure linear relationship, although partial correlation excludes some indirect effects. Recent distance correlation can discover both the linear and non-linear dependencies. Our goal was to use the multivariate pattern analysis to compare the ability of such three correlation methods to distinguish between the patients with obsessive-compulsive disorder (OCD) and healthy control subjects (HCSs), so as to find optimal correlation method. The main process includes four steps. First, the regions of interest are defined by automated anatomical labeling (AAL). Second, functional connectivity (FC) matrices are constructed by the three correlation methods. Third, the best discriminative features are selected by support vector machine recursive feature elimination (SVM-RFE) with a stratified N-fold cross-validation strategy. Finally, these discriminative features are used to train a classifier. We had a total of 128 subjects out of which 61 subjects had OCD and 67 subjects were normal. All the three correlation methods with SVM have achieved good results, among which distance correlation is the best [accuracy = 93.01%, specificity = 89.71%, sensitivity = 95.08%, and area under the receiver-operating characteristic curve (AUC) = 0.94], followed by Pearson correlation and partial correlation is the last. The most discriminative regions of the brain for distance correlation are right dorsolateral superior frontal gyrus, orbital part of left superior frontal gyrus, orbital part of right middle frontal gyrus, right anterior cingulate and paracingulate gyri, left the supplementary motor area, and right precuneus, which are the promising biomarkers of OCD.

Genomics of Obsessive-Compulsive Disorder-Toward Personalized Medicine in the Era of Big Data

Pathogenesis of obsessive-compulsive disorder (OCD) mainly involves dysregulation of serotonergic neurotransmission, but a number of other factors are involved. Genetic underprints of OCD fall under the category of "common disease common variant hypothesis," that suggests that if a disease that is heritable is common in the population (a prevalence >1-5%), then the genetic contributors-specific variations in the genetic code-will also be common in the population. Therefore, the genetic contribution in OCD is believed to come from multiple genes simultaneously and it is considered a polygenic disorder. Genomics offers a number of advanced tools to determine causal relationship between the exposure and the outcome of interest. Particularly, methods such as polygenic risk score (PRS) or Mendelian Randomization (MR) enable investigation of new pathways involved in OCD pathogenesis. This premise is also facilitated by the existence of publicly available databases that include vast study samples. Examples include population-based studies such as UK Biobank, China Kadoorie Biobank, Qatar Biobank, All of US Program sponsored by National Institute of Health or Generations launched by Yale University, as well as disease-specific databases, that include patients with OCD and co-existing pathologies, with the following examples: Psychiatric Genomics Consortium (PGC), ENIGMA OCD, The International OCD Foundation Genetics Collaborative (IOCDF-GC) or OCD Collaborative Genetic Association Study. The aim of this review is to present a comprehensive overview of the available Big Data resources for the study of OCD pathogenesis in the context of genomics and demonstrate that OCD should be considered a disorder which requires the approaches offered by personalized medicine.

The Behavior of Patients with Obsessive-Compulsive Disorder in Dental Clinics

Objectives

This review documents published obsessive-compulsive disorder (OCD) cases with dental and oral conditions with potential impact on the dental procedure. The research question was, what are the psychiatric and behavioral features of people with OCD that might affect dental sessions?

Methods

This review followed the PRISMA guidelines (PROSPERO registration No. CRD42020212371). Six databases (PubMed, Scopus, Web of Science, LILACS, Cochrane Library, and PsycINFO) were screened for published clinical studies that report dental patients with obsessions or compulsions behaviors as identified by National Institute of Mental Health (NIMH). Inclusion of the studies was performed according to the eligibility criteria. The quality evaluation was carried out using the Joanna Briggs Institute's (JBI) Critical Appraisal Checklist. The results were qualitatively assessed for synthesis.

Results

After elimination of duplication, 530 articles were screened, and 35 articles were evaluated for eligibility. 17 studies met the inclusion criteria (8 case reports, 5 cross-sectional studies, 1 longitudinal cohort study, and 3 case-control studies) and were included in the review. All case reports demonstrated symptoms of obsessions or compulsions such as fear of germs and contamination, aggressive thoughts, having things symmetric in perfect order, excessive cleaning or handwashing, repeatedly checking things, and compulsive counting. OCD-related behavior was assessed in the included clinical investigations using standardized protocols such as Florida Obsessive-Compulsive Inventory, Symptom Checklist-90-Revised, 4-item Corah Dental Anxiety Scale, Diagnostic and Statistical Manual of Mental Disorders, and the Crown Crisp Experimental Index. Quality assessment of the 17 included articles revealed 14 articles with low risk of bias and 3 articles with moderate risk of bias.

Conclusion

The reported OCD symptoms may implement psychological difficulties during dental procedures without affecting the outcome. Although there was no contraindication for planning or performing dental treatments for a patient with OCD, dental-related procedures and protocols might be modified for successful dental appointments.

Stopping a Response When You Really Care about the Action: Considerations from a Clinical Perspective

Response inhibition, whether reactive or proactive, is mostly investigated in a narrow cognitive framework. We argue that it be viewed within a broader frame than the action being inhibited, i.e., in the context of emotion and motivation of the individual at large. This is particularly important in the clinical domain, where the motivational strength of an action can be driven by threat avoidance or reward seeking. The cognitive response inhibition literature has focused on stopping reactively with responses in anticipation of clearly delineated external signals, or proactively in limited contexts, largely independent of clinical phenomena. Moreover, the focus has often been on stopping efficiency and its correlates rather than on inhibition failures. Currently, the cognitive and clinical perspectives are incommensurable. A broader context may explain the apparent paradox where individuals with disorders characterised by maladaptive action control have difficulty inhibiting their actions only in specific circumstances. Using Obsessive Compulsive Disorder as a case study, clinical theorising has focused largely on compulsions as failures of inhibition in relation to specific internal or external triggers. We propose that the concept of action tendencies may constitute a useful common denominator bridging research into motor, emotional, motivational, and contextual aspects of action control failure. The success of action control may depend on the interaction between the strength of action tendencies, the ability to withhold urges, and contextual factors.

Current directions in biomarkers and endophenotypes for anorexia nervosa: A scoping review

There are currently no validated biomarkers for anorexia nervosa (AN), though recent literature suggests an increased research interest in this area. Biomarkers are objective, measurable indicators of illness that can be used to assist with diagnosis, risk assessment, and tracking of illness state. Related to biomarkers are endophenotypes, which are quantifiable phenomena that are distinct from symptoms and which link genes to manifest illness. In this scoping review, we sought to provide a summary of recent research conducted in the pursuit of biomarkers and endophenotypes for AN. The findings indicate that a number of possible biomarkers which can assess the presence or severity of AN independently of weight status, including psychophysical (e.g., eye-tracking) and biological (e.g., immune, endocrine, metabolomic, neurobiological) markers, are currently under investigation. However, this research is still in early phases and lacking in replication studies. Endophenotype research has largely been confined to the study of several neurocognitive features, with mixed evidence to support their classification as possible endophenotypes for the disorder. The study of biomarkers and endophenotypes in AN involves significant challenges due to confounding factors of illness-related sequalae, such as starvation. Future research in these areas must prioritise direct evaluation of the sensitivity, specificity and test-retest reliability of proposed biomarkers and enhanced control of confounding physical consequences of AN in the study of biomarkers and endophenotypes.

Cellular and Extracellular White Matter Abnormalities in Obsessive-Compulsive Disorder: A Diffusion Magnetic Resonance Imaging Study

BACKGROUND

While previous studies have implicated white matter (WM) as a core pathology of obsessive-compulsive disorder (OCD), the underlying neurobiological processes remain elusive. This study used free-water (FW) imaging derived from diffusion magnetic resonance imaging to identify cellular and extracellular WM abnormalities in patients with OCD compared with control subjects. Next, we investigated the association between diffusion measures and clinical variables in patients.

METHODS

We collected diffusion-weighted magnetic resonance imaging and clinical data from 83 patients with OCD (56 women/27 men, age 37.7 ± 10.6 years) and 52 control subjects (27 women/25 men, age 32.8 ± 11.5 years). Fractional anisotropy (FA), FA of cellular tissue, and extracellular FW maps were extracted and compared between patients and control subjects using tract-based spatial statistics and voxelwise comparison in FSL Randomise. Next, we correlated these WM measures with clinical variables (age of onset and symptom severity) and compared them between patients with and without comorbidities and patients with and without psychiatric medication.

RESULTS

Patients with OCD demonstrated lower FA (43.4% of the WM skeleton), lower FA of cellular tissue (31% of the WM skeleton), and higher FW (22.5% of the WM skeleton) compared with control subjects. We did not observe significant correlations between diffusion measures and clinical variables. Comorbidities and medication status did not influence diffusion measures.

CONCLUSIONS

Our findings of widespread FA, FA of cellular tissue, and FW abnormalities suggest that OCD is associated with microstructural cellular and extracellular abnormalities beyond the corticostriatothalamocortical circuits. Future multimodal longitudinal studies are needed to understand better the influence of essential clinical variables across the illness trajectory.

Deep brain stimulation response in obsessive-compulsive disorder is associated with preoperative nucleus accumbens volume

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Preoperative MRI was associated with 12-months DBS treatment outcome in OCD patients.

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Larger nucleus accumbens volume was associated with larger clinical improvement.

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Machine learning analysis was not successful in predicting clinical improvement.

Background

Deep brain stimulation (DBS) is a new treatment option for patients with therapy-resistant obsessive–compulsive disorder (OCD). Approximately 60% of patients benefit from DBS, which might be improved if a biomarker could identify patients who are likely to respond. Therefore, we evaluated the use of preoperative structural magnetic resonance imaging (MRI) in predicting treatment outcome for OCD patients on the group- and individual-level.

Methods

In this retrospective study, we analyzed preoperative MRI data of a large cohort of patients who received DBS for OCD (n = 57). We used voxel-based morphometry to investigate whether grey matter (GM) or white matter (WM) volume surrounding the DBS electrode (nucleus accumbens (NAc), anterior thalamic radiation), and whole-brain GM/WM volume were associated with OCD severity and response status at 12-month follow-up. In addition, we performed machine learning analyses to predict treatment outcome at an individual-level and evaluated its performance using cross-validation.

Results

Larger preoperative left NAc volume was associated with lower OCD severity at 12-month follow-up (p_FWE < 0.05). None of the individual-level regression/classification analyses exceeded chance-level performance.

Conclusions

These results provide evidence that patients with larger NAc volumes show a better response to DBS, indicating that DBS success is partly determined by individual differences in brain anatomy. However, the results also indicate that structural MRI data alone does not provide sufficient information to guide clinical decision making at an individual level yet.

Harmonizing the Neurobiology and Treatment of Obsessive-Compulsive Disorder

Obsessive-compulsive disorder (OCD) is a common, chronic, and oftentimes disabling disorder. The only established first-line treatments for OCD are exposure and response prevention, and serotonin reuptake inhibitor medications (SRIs). However, a subset of patients fails to respond to either modality, and few experience complete remission. Beyond SRI monotherapy, antipsychotic augmentation is the only medication approach for OCD with substantial empirical support. Our incomplete understanding of the neurobiology of OCD has hampered efforts to develop new treatments or enhance extant interventions. This review focuses on several promising areas of research that may help elucidate the pathophysiology of OCD and advance treatment. Multiple studies support a significant genetic contribution to OCD, but pinpointing the specific genetic determinants requires additional investigation. The preferential efficacy of SRIs in OCD has neither led to discovery of serotonergic abnormalities in OCD nor to development of new serotonergic medications for OCD. Several lines of preclinical and clinical evidence suggest dysfunction of the glutamatergic system in OCD, prompting testing of several promising glutamate modulating agents. Functional imaging studies in OCD show consistent evidence for increased activity in brain regions that form a cortico-striato-thalamo-cortical (CSTC) loop. Neuromodulation treatments with either noninvasive devices (e.g., transcranial magnetic stimulation) or invasive procedures (e.g., deep brain stimulation) provide further support for the CSTC model of OCD. A common substrate for various interventions (whether drug, behavioral, or device) may be modulation (at different nodes or connections) of the CSTC circuit that mediates the symptoms of OCD.