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

Regional brain activity and connectivity associated with childhood trauma in drug-naive patients with obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) is characterized by intrusive thoughts and repetitive, compulsive behaviors, with childhood trauma recognized as a contributing factor to its pathophysiology. This study aimed to delineate brain functional aberrations in OCD patients and explore the association between these abnormalities and childhood trauma, to gain insights into the neural underpinnings of OCD. Forty-eight drug-naive OCD patients and forty-two healthy controls (HC) underwent resting-state functional magnetic resonance imaging and clinical assessments, including the Yale-Brown Obsessive Compulsive Scale (Y-BOCS) and Childhood Trauma Questionnaire-Short Form (CTQ-SF). Compared to HCs, OCD patients exhibited significantly decreased amplitude of low-frequency fluctuations (ALFF) in the right cerebellum, decreased regional homogeneity (ReHo) in the right cerebellum and right superior occipital lobes (FWE-corrected p < 0.05), which negatively correlated with Y-BOCS scores (p < 0.05). Furthermore, cerebellar ALFF negatively correlated with the CTQ emotional abuse subscale (r = - 0.514, p < 0.01). Mediation analysis revealed that cerebellar ALFF mediated the relationship between CTQ-emotional abuse and Y-BOCS (good model fit: R2 = 0.231, MSE = 14.311, F = 5.721, p < 0.01; direct effect, c' = 0.153, indirect effect, a*b = 0.191). Findings indicated abnormal spontaneous and regional cerebellar activity in OCD, suggesting childhood trauma impacts OCD symptoms through cerebellar neural remodeling, highlighting its importance for clinical treatment selection.

Convergent functional change of frontoparietal network in obsessive-compulsive disorder: a voxel-based meta-analysis

Background

Obsessive-compulsive disorder (OCD) is a chronic psychiatric illness with complex clinical manifestations. Cognitive dysfunction may underlie OC symptoms. The frontoparietal network (FPN) is a key region involved in cognitive control. However, the findings of impaired FPN regions have been inconsistent. We employed meta-analysis to identify the fMRI-specific abnormalities of the FPN in OCD.

Methods

PubMed, Web of Science, Scopus, and EBSCOhost were searched to screen resting-state functional magnetic resonance imaging (rs-fMRI) studies exploring dysfunction in the FPN of OCD patients using three indicators: the amplitude of low-frequency fluctuation/fractional amplitude of low-frequency fluctuation (ALFF/fALFF), regional homogeneity (ReHo) and functional connectivity (FC). We compared all patients with OCD and control group in a primary analysis, and divided the studies by medication in secondary meta-analyses with the activation likelihood estimation (ALE) algorithm.

Results

A total of 31 eligible studies with 1359 OCD patients (756 men) and 1360 healthy controls (733 men) were included in the primary meta-analysis. We concluded specific changes in brain regions of FPN, mainly in the left dorsolateral prefrontal cortex (DLPFC, BA9), left inferior frontal gyrus (IFG, BA47), left superior temporal gyrus (STG, BA38), right posterior cingulate cortex (PCC, BA29), right inferior parietal lobule (IPL, BA40) and bilateral caudate. Additionally, altered connectivity within- and between-FPN were observed in the bilateral DLPFC, right cingulate gyrus and right thalamus. The secondary analyses showed improved convergence relative to the primary analysis.

Conclusion

OCD patients showed dysfunction FPN, including impaired local important nodal brain regions and hypoconnectivity within the FPN (mainly in the bilateral DLPFC), during the resting state. Moreover, FPN appears to interact with the salience network (SN) and default mode network (DMN) through pivotal brain regions. Consistent with the hypothesis of fronto-striatal circuit dysfunction, especially in the dorsal cognitive circuit, these findings provide strong evidence for integrating two pathophysiological models of OCD.

The association of childhood trauma with suicidality in adult psychiatric patients: The mediating role of NSSI and the moderating role of self-esteem

BACKGROUND

The contribution of specific childhood trauma subtypes to suicidal thoughts and the associated mechanisms remains unclear, particularly in psychiatric patients.

METHODS

Face-to-face interviews were conducted with 449 psychiatric patients aged 18-73. Childhood trauma, self-esteem, nonsuicidal self-injury (NSSI), and suicidality were assessed retrospectively. Regression and moderated mediation model were employed to examine these relationships.

RESULTS

Emotional and sexual abuse were independently associated with suicidality. Female patients reported higher levels of emotional and sexual abuse, lower self-esteem, and a heightened risk of suicide. Self-esteem moderated the links between childhood trauma and NSSI, as well as between NSSI and suicidality. NSSI served as a mediator between childhood trauma and suicidality.

CONCLUSIONS

Suicide prevention in mentally ill patients should involve targeted programs addressing specific childhood trauma. Additionally, psychological interventions to enhance self-esteem and assist individuals engaging in NSSI behavior are crucial.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

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.

Alterations of regional spontaneous brain activity in obsessive-compulsive disorders: A meta-analysis

BACKGROUND

Recent studies using resting-state functional magnetic resonance imaging (rs-fMRI) demonstrate that there is aberrant regional spontaneous brain activity in obsessive-compulsive disorders (OCD). Nevertheless, the results of previous studies are contradictory, especially in the abnormal brain regions and the directions of their activities. It is necessary to perform a meta-analysis to identify the common pattern of altered regional spontaneous brain activity in patients with OCD.

METHODS

The present study conducted a systematic search for studies in English published up to May 2023 in PubMed, Web of Science, and Embase. These studies measured differences in regional spontaneous brain activity at the whole brain level using regional homogeneity (ReHo), the amplitude of low-frequency fluctuations (ALFF) and the fractional amplitude of low-frequency fluctuations (fALFF). Then the Anisotropic effect-size version of seed-based d mapping (AES-SDM) was used to investigate the consistent abnormality of regional spontaneous brain activity in patients with OCD.

RESULTS

27 studies (33 datasets) were included with 1256 OCD patients (650 males, 606 females) and 1176 healthy controls (HCs) (588 males, 588 females). Compared to HCs, patients with OCD showed increased spontaneous brain activity in the right inferior parietal gyrus (Brodmann Area 39), left median cingulate and paracingulate gyri (Brodmann Area 24), bilateral inferior cerebellum, right middle frontal gyrus (Brodmann Area 46), left inferior frontal gyrus in triangular part (Brodmann Area 45) and left middle frontal gyrus in orbital part (Brodmann Area 11). Meanwhile, decreased spontaneous brain activity was identified in the right precentral gyrus (Brodmann Area 4), right insula (Brodmann Area 48), left postcentral gyrus (Brodmann Area 43), bilateral superior cerebellum and left caudate (Brodmann Area 25).

CONCLUSIONS

This meta-analysis provided a quantitative review of spontaneous brain activity in OCD. The results demonstrated that the brain regions in the frontal lobe, sensorimotor cortex, cerebellum, caudate and insula are crucially involved in the pathophysiology of OCD. This research contributes to the understanding of the pathophysiologic mechanism underlying OCD and could provide a new perspective on future diagnosis and treatment of OCD.

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.

Decreased gray matter volume and dynamic functional alterations in medicine-free obsessive-compulsive disorder

BACKGROUND

Previous studies discovered the presence of abnormal structures and functions in the brain regions of patients with obsessive-compulsive disorder (OCD). Nevertheless, whether structural changes in brain regions are coupled with alterations in dynamic functional connectivity (dFC) at rest in medicine-free patients with OCD remains vague.

METHODS

Three-dimensional T₁-weighed magnetic resonance imaging (MRI) and resting-state functional MRI were performed on 50 medicine-free OCD and 50 healthy controls (HCs). Firstly, the differences in gray matter volume (GMV) between OCD and HCs were compared. Then, brain regions with aberrant GMV were used as seeds for dFC analysis. The relationship of altered GMV and dFC with clinical parameters in OCD was explored using partial correlation analysis. Finally, support vector machine was applied to examine whether altered multimodal imaging data might be adopted to distinguish OCD from HCs.

RESULTS

Our findings indicated that GMV in the left superior temporal gyrus (STG) and right supplementary motor area (SMA) was reduced in OCD, and the dFC between the left STG and the left cerebellum Crus I and left thalamus, and between the right SMA and right dorsolateral prefrontal cortex (DLPFC) and left precuneus was decreased at rest in OCD. The brain regions both with altered GMV and dFC values could discriminate OCD from HCs with the accuracy of 0.85, sensitivity of 0.90 and specificity of 0.80.

CONCLUSION

The decreased gray matter structure coupling with dynamic function in the left STG and right SMA at rest may be crucial in the pathophysiology of OCD.

TRIAL REGISTRATION

Study on the mechanism of brain network in obsessive-compulsive disorder with multi-model magnetic resonance imaging (registration date: 08/11/2017; registration number: ChiCTR-COC-17,013,301).

Neuroimaging biomarkers for detecting schizophrenia: A resting-state functional MRI-based radiomics analysis

Schizophrenia (SZ) is a common psychiatric disorder that is difficult to accurately diagnose in clinical practice. Quantifiable biomarkers are urgently required to explore the potential physiological mechanism of SZ and improve its diagnostic accuracy. Thus, this study aimed to identify biomarkers that classify SZ patients and healthy control subjects and investigate the potential neural mechanisms of SZ using degree centrality (DC)- and voxel-mirrored homotopic connectivity (VMHC)-based radiomics. Radiomics features were extracted from DC and VMHC metrics generated via resting-state functional magnetic resonance imaging, and significant features were selected and dimensionality was reduced using t-tests and least absolute shrinkage and selection operator. Subsequently, we built our model using a support vector machine classifier. We observed that our method obtained great classification performance (area under the curve, 0.808; accuracy, 74.02%), and it could be generalized to different brain atlases. The regions that we identified as discriminative features mainly included bilateral dorsal caudate and front-parietal, somatomotor, limbic, and default mode networks. Our findings showed that the radiomics-based machine learning method could facilitate us to understand the potential pathological mechanism of SZ more comprehensively and contribute to the accurate diagnosis of patients with SZ.

Abnormal spontaneous neural activity as a potential predictor of early treatment response in patients with obsessive-compulsive disorder

BACKGROUND

We aimed to explore the value of early improvement in obsessive-compulsive disorder (OCD) along with potential imaging changes after treatment with paroxetine in building diagnostic models and predicting treatment response.

METHODS

The clinical symptoms of patients with OCD were assessed at baseline and post-treatment (four weeks). Resting-state functional magnetic resonance imaging, fractional amplitudes of low-frequency fluctuations (fALFF) indicator, support vector machine (SVM), support vector regression (SVR), and correlation analysis were performed to acquire and analyze the data.

RESULTS

In comparison with healthy controls, OCD patients at baseline had abnormal fALFF in several brain regions. The abnormal fALFF in the left precuneus/ posterior cingulate cortex (PCC) (r = -0.526, p = 0.001) and right middle cingulate cortex (MCC) (r = -0.588, p < 0.001) were negatively correlated with the severity of compulsions. Patients with OCD showed significantly clinical improvement along with significantly decreased fALFF in the left precuneus after treatment. The SVM analysis showed that the classifier had an accuracy of 90.00% based on the fALFF in the right precentral gyrus and right MCC at baseline. The SVR analysis showed that the actual remission of OCD was positively correlated with the predicted remission based on the fALFF in the left precuneus/PCC and right MCC at baseline.

LIMITATIONS

This monocentric study with the relatively small sample size might restrict the generalizability of the results to other centers.

CONCLUSIONS

Abnormal spontaneous neural activities in patients with OCD could serve as potential neuroimaging biomarkers for diagnosis and prediction of early treatment response.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

A resting-state fMRI study of temporal lobe epilepsy using multivariate pattern analysis and Granger causality analysis

BACKGROUND AND PURPOSE

Understanding the pathogenesis of temporal lobe epilepsy (TLE) is essential for its diagnosis and treatment. The study aimed to explore regional homogeneity (ReHo) and changes in effective connectivity (EC) between brain regions in TLE patients, hoping to discover potential abnormalities in certain brain regions in TLE patients.

METHODS

Resting-state functional magnetic resonance data were collected from 23 TLE patients and 32 normal controls (NC). ReHo was used as a feature of multivariate pattern analysis (MVPA) to explore the ability of its alterations in identifying TLE. Based on the results of the MVPA, certain brain regions were selected as seed points to further explore alterations in EC between brain regions using Granger causality analysis.

RESULTS

MVPA results showed that the classification accuracy for the TLE and NC groups was 87.27%, and the right posterior cerebellum lobe, right lingual gyrus (LING_R), right cuneus (CUN_R), and left superior temporal gyrus (STG_L) provided significant contributions. Moreover, the EC from STG_L to right fusiform gyrus (FFG_R) and LING_R and the EC from CUN_R to the right occipital superior gyrus (SOG_R) and right occipital middle gyrus (MOG_R) were altered compared to the NC group.

CONCLUSION

The MVPA results indicated that ReHo abnormalities in brain regions may be an important feature in the identification of TLE. The enhanced EC from STG_L to FFG_R and LING_R indicates a shift in language processing to the right hemisphere, and the weakened EC from SOG_R and MOG_R to CUN_R may reveal an underlying mechanism of TLE.

Altered Brain Morphometry in Cerebral Small Vessel Disease With Cerebral Microbleeds: An Investigation Combining Univariate and Multivariate Pattern Analyses

Purpose

The objective of this study was to evaluate whether altered gray matter volume (GMV) and white matter volume (WMV) are associated with the presence of cerebral microbleeds (CMBs) in cerebral small vessel disease (CSVD).

Materials and Methods

In this study, we included 26 CSVD patients with CMBs (CSVD-c), 43 CSVD patients without CMBs (CSVD-n) and 39 healthy controls. All participants underwent cognitive assessment testing. Both univariate analysis and multivariate pattern analysis (MVPA) approaches were applied to investigate differences in brain morphometry among groups.

Results

In univariate analysis, GMV and WMV differences were compared among groups using voxel-based morphometry (VBM) with diffeomorphic anatomical registration through exponentiated lie algebra (DARTEL). Compared to healthy controls, the CSVD-c group and CSVD-n group showed significantly lower GMV than the control group in similar brain clusters, mainly including the right superior frontal gyrus (medial orbital), left anterior cingulate gyrus, right inferior frontal gyrus (triangular part) and left superior frontal gyrus (medial), while the CSVD-n group also showed significantly lower WMV in the cluster of the left superior frontal gyrus (medial). No significant GMV or WMV differences were found between the CSVD-c group and the CSVD-n group. Specifically, we applied the multiple kernel learning (MKL) technique in MVPA to combine GMV and WMV features, yielding an average of >80% accuracy for three binary classification problems, which was a considerable improvement over the individual modality approach. Consistent with the univariate analysis, the MKL weight maps revealed default mode network and subcortical region damage associated with CSVD compared to controls. On the other hand, when classifying the CSVD-c group and CSVD-n group in the MVPA analysis, we found that some WMVs were highly weighted regions (left olfactory cortex and right middle frontal gyrus), which hinted at the presence of different white matter alterations in the CSVD-c group.

Conclusion

Our findings not only suggested that the localized alterations in GMV and WMV appeared to be associated with the pathophysiology of CSVD but also indicated that altered brain morphometry could be a potential discriminative pattern to detect CSVD at the individual level.

Neural classification of internet gaming disorder and prediction of treatment response using a cue-reactivity fMRI task in young men

BACKGROUND

Neural mechanisms underlying internet gaming disorder (IGD) are important for diagnostic considerations and treatment development. However, neurobiological underpinnings of IGD remain relatively poorly understood.

METHODS

We employed multi-voxel pattern analysis (MVPA), a machine-learning approach, to examine the potential of neural features to statistically predict IGD status and treatment outcome (percentage change in weekly gaming time) for IGD. Cue-reactivity fMRI-task data were collected from 40 male IGD subjects and 19 male healthy control (HC) subjects. 23 IGD subjects received 6 weeks of craving behavioral intervention (CBI) treatment. MVPA was applied to classify IGD subjects from HCs and statistically predict clinical outcomes.

RESULTS

MVPA displayed a high (92.37%) accuracy (sensitivity of 90.00% and specificity of 94.74%) in the classification of IGD and HC subjects. The most discriminative brain regions that contribute to classification were the bilateral middle frontal gyrus, precuneus, and posterior lobe of the right cerebellum. MVPA statistically predicted clinical outcomes in the craving behavioral intervention (CBI) group (r = 0.48, p = 0.0032). The most strongly implicated brain regions in the prediction model were the right middle frontal gyrus, superior frontal gyrus, supramarginal gyrus, anterior/posterior lobes of the cerebellum and left postcentral gyrus.

CONCLUSIONS

The findings about cue-reactivity neural correlates could help identify IGD subjects and predict CBI-related treatment outcomes provide mechanistic insight into IGD and its treatment and may help promote treatment development efforts.

Abnormalities of Localized Connectivity in Obsessive-Compulsive Disorder: A Voxel-Wise Meta-Analysis

Background: A large amount of resting-state functional magnetic resonance imaging (rs-fMRI) studies have revealed abnormalities of regional homogeneity (ReHo, an index of localized intraregional connectivity) in the obsessive-compulsive disorder (OCD) in the past few decades, However, the findings of these ReHo studies have remained inconsistent. Hence, we performed a meta-analysis to investigate the concurrence across ReHo studies for clarifying the most consistent localized connectivity underpinning this disorder. Methods: A systematic review of online databases was conducted for whole-brain rs-fMRI studies comparing ReHo between OCD patients and healthy control subjects (HCS). Anisotropic effect size version of the seed-based d mapping, a voxel-wise meta-analytic approach, was adopted to explore regions of abnormal ReHo alterations in OCD patients relative to HCS. Additionally, meta-regression analyses were conducted to explore the potential effects of clinical features on the reported ReHo abnormalities. Results: Ten datasets comprising 359 OCD patients and 361 HCS were included. Compared with HCS, patients with OCD showed higher ReHo in the bilateral inferior frontal gyri and orbitofrontal cortex (OFC). Meanwhile, lower ReHo was identified in the supplementary motor area (SMA) and bilateral cerebellum in OCD patients. Meta-regression analysis demonstrated that the ReHo in the OFC was negatively correlated with illness duration in OCD patients. Conclusions: Our meta-analysis gave a quantitative overview of ReHo findings in OCD and demonstrated that the most consistent localized connectivity abnormalities in individuals with OCD are in the prefrontal cortex. Meanwhile, our findings provided evidence that the hypo-activation of SMA and cerebellum might be associated with the pathophysiology of OCD.

Temporal variability of regional intrinsic neural activity in drug-naïve patients with obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) displays alterations in regional brain activity represented by the amplitude of low-frequency fluctuation (ALFF), but the time-varying characteristics of this local neural activity remain to be clarified. We aimed to investigate the dynamic changes of intrinsic brain activity in a relatively large sample of drug-naïve OCD patients using univariate and multivariate analyses. We applied a sliding-window approach to calculate the dynamic ALFF (dALFF) and compared the difference between 73 OCD patients and age- and sex-matched healthy controls (HCs). We also utilized multivariate pattern analysis to determine whether dALFF could differentiate OCD patients from HCs at the individual level. Compared with HCs, OCD patients exhibited increased dALFF mainly within regions of the cortical-striatal-thalamic-cortical (CSTC) circuit, including the bilateral dorsal anterior cingulate cortex, medial prefrontal cortex and striatum, and right dorsolateral prefrontal cortex (dlPFC). Decreased dALFF was identified in the bilateral inferior parietal lobule (IPL), posterior cingulate cortex, insula, fusiform gyrus, and cerebellum. Moreover, we found negative correlations between illness duration and dALFF values in the right IPL and between dALFF values in the left cerebellum and Hamilton Depression Scale scores. Furthermore, dALFF can distinguish OCD patients from HCs with the most discriminative regions located in the IPL, dlPFC, middle occipital gyrus, and cuneus. Taken together, in the current study, we demonstrated a characteristic pattern of higher variability of regional brain activity within the CSTC circuits and lower variability in regions outside the CSTC circuits in drug-naïve OCD patients.

Altered Functional Brain Network in Systemic Lupus Erythematosus Patients Without Overt Neuropsychiatric Symptoms Based on Resting-State Functional Magnetic Resonance Imaging and Multivariate Pattern Analysis

Objective: This study aims to investigate the alterations in functional brain network in systemic lupus erythematosus patients without overt neuropsychiatric symptoms [neuropsychiatric systemic lupus erythematosus (non-NPSLE)] from the perspective of degree centrality (DC) and functional connectivity (FC) using resting-state functional magnetic resonance imaging (MRI) and multivariate pattern analysis (MVPA) approach.

Methods: DC analysis was performed based on the resting-state functional MRI data derived from 47 non-NPSLE patients and 47 healthy controls (HCs). Nodes with abnormal DC were utilized as seeds for further FC analysis. The correlation between MRI variables and clinical or neuropsychological data was analyzed using Pearson correlation analysis. Finally, MVPA classification based on DC was performed.

Results: When compared with the HCs, the non-NPSLE patients exhibited remarkably higher DC in the bilateral hippocampus (HIP), right insula (INS), and lower DC in the left superior parietal gyrus. Furthermore, the patients displayed significantly higher FC between the left HIP and the left INS/left dorsolateral middle frontal gyrus/left supramarginal gyrus and higher FC between the right HIP and the right middle temporal gyrus/right dorsolateral middle frontal gyrus/right dorsolateral inferior frontal gyrus/right supramarginal gyrus (all imaging variables mentioned earlier underwent cluster-level false discovery rate corrections, the voxel threshold was p < 0.001, cluster threshold was p < 0.05). Correlation analysis revealed significantly negative correlations between DC values of the right INS and disease activity and the DC values of the right HIP and the Montreal Cognitive Assessment scores. The accuracy, sensitivity, and specificity of MVPA classification based on DC were 72.34, 63.83, and 80.85%, respectively. The most discriminative power brain regions were chiefly located within the temporal, parietal, and frontal regions.

Conclusion: Patients with non-NPSLE exhibited abnormal DC and FC in the brain network. MVPA based on DC possessed commendable classification ability. Our study may provide a novel perspective on the neuropathological mechanisms underlying subclinical brain damage in non-NPSLE.

Altered interhemispheric functional homotopy and connectivity in temporal lobe epilepsy based on fMRI and multivariate pattern analysis

PURPOSE

This study aimed to investigate how the functional homotopy and further functional connectivity (FC) of whole brain changed in temporal lobe epilepsy (TLE). We also evaluated which brain regions played a decisive role in classification by using functional magnetic resonance imaging (fMRI).

METHODS

Patients with TLE and matched healthy controls were included to collect the fMRI data and perform the voxel-mirrored homotopic connectivity (VMHC) and FC analyses. The correlation between the changed functional homotopy and neuropsychology tests was examined. Based on VMHC, the weight of each region in the classification was obtained using multivariate pattern analysis (MVPA).

RESULTS

The patients exhibited decreased functional coordination in the bilateral inferior temporal gyrus (ITG) and increased functional homotopy in the bilateral lingual gyrus compared with the control group in the VMHC analysis. Compared with healthy controls, the Montreal Cognitive Assessment score was lower, and the scores of Hamilton Anxiety (HAMA) and Hamilton Depression Scales were higher. The score of the HAMA Scale was positively correlated with the altered bilateral ITG. The FC analysis revealed increased connections between the right lingual gyrus and the left superior temporal gyrus/left insula. The MVPA showed that the accuracy, sensitivity, and specificity of classification were 68.49, 66.67 and 70.27%, respectively, and it confirmed that the temporal lobe, cerebellum, and parietal lobe provided significant contributions.

CONCLUSION

These findings demonstrated that the VMHC and FC changed in TLE, and the alterations were correlated with the anxiety state. The MVPA indicated that the abnormal VMHC was a crucial fMRI feature.

Comparing Effectiveness Between a Mobile App Program and Traditional Cognitive Behavior Therapy in Obsessive-Compulsive Disorder: Evaluation Study

BACKGROUND

This study proposes a digital program for the treatment of mental illness that could increase motivation and improve learning outcomes for patients. Several studies have already applied this method by using an exposure and response prevention-inspired serious game to treat patients with obsessive-compulsive disorder (OCD).

OBJECTIVE

We hypothesized that a mobile cognitive behavior therapy (CBT) program would be as effective in treating OCD as traditional offline CBT. In addition, the treatment efficacy in response to mobile CBT for OCD might be associated with increased brain activity within the cortico-striato-thalamo-cortical (CSTC) tract.

METHODS

The digital CBT treatment program for OCD, OCfree, consists of 6 education sessions, 10 quests, and 7 casual games. Information was gathered from 27 patients with OCD (15 offline CBT and 12 OCfree CBT). During the 6-week intervention period, changes in clinical symptoms and brain function activity were analyzed.

RESULTS

There was no significant difference in the change in OCD symptoms and depressive symptoms between the two groups. However, the OCfree group showed greater improvement in anxiety symptoms compared to the offline CBT group. Both offline CBT and OCfree CBT increased the functional connectivity within the CSTC tract in all patients with OCD. However, CBT using OCfree showed greater changes in brain connectivity within the thalamus and insula, compared to offline CBT.

CONCLUSIONS

OCfree, an OCD treatment app program, was effective in the treatment of drug-naïve patients with OCD. The treatment effects of OCfree are associated with increased brain connectivity within the CSTC tract. Multisensory stimulation by education, quests, and games in OCfree increases the activity within the thalamus and insula in patients with OCD.

Decreased effective connection from the parahippocampal gyrus to the prefrontal cortex in Internet gaming disorder: A MVPA and spDCM study

OBJECTIVES

Understanding the neural mechanisms underlying Internet gaming disorder (IGD) is essential for the condition's diagnosis and treatment. Nevertheless, the pathological mechanisms of IGD remain elusive at present. Hence, we employed multi-voxel pattern analysis (MVPA) and spectral dynamic causal modeling (spDCM) to explore this issue.

METHODS

Resting-state fMRI data were collected from 103 IGD subjects (male = 57) and 99 well-matched recreational game users (RGUs, male = 51). Regional homogeneity was calculated as the feature for MVPA based on the support vector machine (SVM) with leave-one- out cross-validation. Mean time series data extracted from the brain regions in accordance with the MVPA results were used for further spDCM analysis.

RESULTS

Results display a high accuracy of 82.67% (sensitivity of 83.50% and specificity of 81.82%) in the classification of the two groups. The most discriminative brain regions that contributed to the classification were the bilateral parahippocampal gyrus (PG), right anterior cingulate cortex (ACC), and middle frontal gyrus (MFG). Significant correlations were found between addiction severity (IAT and DSM scores) and the ReHo values of the brain regions that contributed to the classification. Moreover, the results of spDCM showed that compared with RGU, IGD showed decreased effective connectivity from the left PG to the right MFG and from the right PG to the ACC and decreased self-connection in the right PG.

CONCLUSIONS

These results show that the weakening of the PG and its connection with the prefrontal cortex, including the ACC and MFG, may be an underlying mechanism of IGD.

Functional connectivity within the salience network differentiates autogenous- from reactive-type obsessive-compulsive disorder

BACKGROUND

Obsessive-compulsive disorder (OCD) is a clinically heterogeneous condition. To better understand and treat patients, symptomatology of OCD has been categorized into more homogenous symptom dimensions. The autogenous-reactive classification model has proven helpful in the elucidation of the neurobiological substrates for clinical heterogeneity in OCD. The purpose of the current study was to systematically compare regional and network functional alterations between OCD subtypes based on the autogenous-reactive model.

METHODS

Autogenous-type OCD patients (OCD-AO, n = 40), reactive-type patients (OCD-RO, n = 42), and healthy controls (HC, n = 70) underwent functional magnetic resonance imaging (fMRI) scans. The amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) were compared among subjects. Areas of abnormal local spontaneous brain activity that differentiated OCD-AO and OCD-RO patients were identified and entered as seeds in functional connectivity (FC) analysis.

RESULTS

Compared to OCD-RO patients and HC participants, OCD-AO patients showed increased ALFF in the left anterior insula (AI), increased ReHo in the right AI, and hyperconnectivity between bilateral AI and anterior cingulate cortex (ACC). Both OCD-AO and OCD-RO patients shared regional function deficits in several areas within the prefrontal cortex, and stronger FC between bilateral AI and major nodes of the default mode network (DMN) compared to healthy controls.

CONCLUSION

The current results suggest that aberrant functional interaction between the salience network (SN) and the DMN may represent a common substrate in the pathophysiology of OCD, while impaired functional coupling within the SN is distinct to autogenous-type OCD patients. These findings provide further neurobiological evidence to support the autogenous-reactive classification model and contribute to the understanding of the neurobiological basis for clinical heterogeneity in OCD.