Multivariate resting-state functional connectivity predicts response to cognitive behavioral therapy in obsessive-compulsive disorder

Comparative analysis of resting-state EEG functional connectivity in depression and obsessive-compulsive disorder

Major depressive disorder (MDD) and obsessive-compulsive disorder (OCD) are psychiatric disorders that often co-occur. We aimed to investigate whether their high comorbidity could be traced not only by clinical manifestations, but also at the level of functional brain activity. In this paper, we examined the differences in functional connectivity (FC) at the whole-brain level and within the default mode network (DMN). Resting-state EEG was obtained from 43 controls, 26 OCD patients, and 34 MDD patients. FC was analyzed between 68 cortical sources, and between-group differences in the 4-30 Hz range were assessed via the Network Based Statistic method. The strength of DMN intra-connectivity was compared between groups in the theta, alpha and beta frequency bands. A cluster of 67 connections distinguished the OCD, MDD and control groups. The majority of the connections, 8 of which correlated with depressive symptom severity, were found to be weaker in the clinical groups. Only 3 connections differed between the clinical groups, and one of them correlated with OCD severity. The DMN strength was reduced in the clinical groups in the alpha and beta bands. It can be concluded that the high comorbidity of OCD and MDD can be traced at the level of FC.

Lack of evidence for predictive utility from resting state fMRI data for individual exposure-based cognitive behavioral therapy outcomes: A machine learning study in two large multi-site samples in anxiety disorders

Data-based predictions of individual Cognitive Behavioral Therapy (CBT) treatment response are a fundamental step towards precision medicine. Past studies demonstrated only moderate prediction accuracy (i.e. ability to discriminate between responders and non-responders of a given treatment) when using clinical routine data such as demographic and questionnaire data, while neuroimaging data achieved superior prediction accuracy. However, these studies may be considerably biased due to very limited sample sizes and bias-prone methodology. Adequately powered and cross-validated samples are a prerequisite to evaluate predictive performance and to identify the most promising predictors. We therefore analyzed resting state functional magnet resonance imaging (rs-fMRI) data from two large clinical trials to test whether functional neuroimaging data continues to provide good prediction accuracy in much larger samples. Data came from two distinct German multicenter studies on exposure-based CBT for anxiety disorders, the Protect-AD and SpiderVR studies. We separately and independently preprocessed baseline rs-fMRI data from n = 220 patients (Protect-AD) and n = 190 patients (SpiderVR) and extracted a variety of features, including ROI-to-ROI and edge-functional connectivity, sliding-windows, and graph measures. Including these features in sophisticated machine learning pipelines, we found that predictions of individual outcomes never significantly differed from chance level, even when conducting a range of exploratory post-hoc analyses. Moreover, resting state data never provided prediction accuracy beyond the sociodemographic and clinical data. The analyses were independent of each other in terms of selecting methods to process resting state data for prediction input as well as in the used parameters of the machine learning pipelines, corroborating the external validity of the results. These similar findings in two independent studies, analyzed separately, urge caution regarding the interpretation of promising prediction results based on neuroimaging data from small samples and emphasizes that some of the prediction accuracies from previous studies may result from overestimation due to homogeneous data and weak cross-validation schemes. The promise of resting-state neuroimaging data to play an important role in the prediction of CBT treatment outcomes in patients with anxiety disorders remains yet to be delivered.

Predicting treatment outcome based on resting-state functional connectivity in internalizing mental disorders: A systematic review and meta-analysis

Predicting treatment outcome in internalizing mental disorders prior to treatment initiation is pivotal for precision mental healthcare. In this regard, resting-state functional connectivity (rs-FC) and machine learning have often shown promising prediction accuracies. This systematic review and meta-analysis evaluates these studies, considering their risk of bias through the Prediction Model Study Risk of Bias Assessment Tool (PROBAST). We examined the predictive performance of features derived from rs-FC, identified features with the highest predictive value, and assessed the employed machine learning pipelines. We searched the electronic databases Scopus, PubMed and PsycINFO on the 12th of December 2022, which resulted in 13 included studies. The mean balanced accuracy for predicting treatment outcome was 77% (95% CI: [72%- 83%]). rs-FC of the dorsolateral prefrontal cortex had high predictive value in most studies. However, a high risk of bias was identified in all studies, compromising interpretability. Methodological recommendations are provided based on a comprehensive exploration of the studies' machine learning pipelines, and potential fruitful developments are discussed.

Nuclei-specific hypothalamus networks predict a dimensional marker of stress in humans

The hypothalamus is part of the hypothalamic-pituitary-adrenal axis which activates stress responses through release of cortisol. It is a small but heterogeneous structure comprising multiple nuclei. In vivo human neuroimaging has rarely succeeded in recording signals from individual hypothalamus nuclei. Here we use human resting-state fMRI (n = 498) with high spatial resolution to examine relationships between the functional connectivity of specific hypothalamic nuclei and a dimensional marker of prolonged stress. First, we demonstrate that we can parcellate the human hypothalamus into seven nuclei in vivo. Using the functional connectivity between these nuclei and other subcortical structures including the amygdala, we significantly predict stress scores out-of-sample. Predictions use 0.0015% of all possible brain edges, are specific to stress, and improve when using nucleus-specific compared to whole-hypothalamus connectivity. Thus, stress relates to connectivity changes in precise and functionally meaningful subcortical networks, which may be exploited in future studies using interventions in stress disorders.

Self-focused brain predictors of cognitive behavioral therapy response in a transdiagnostic sample

BACKGROUND

Effective biomarkers of cognitive behavioral therapy (CBT) response provide information beyond available behavioral or self-report measures and may optimize treatment selection for patients based on likelihood of benefit. No single biomarker reliably predicts CBT response. In this study, we evaluated patterns of brain connectivity associated with self-focused attention (SFA) as biomarkers of CBT response for anxiety and obsessive-compulsive disorders. We hypothesized that pre-treatment as well as pre-to post-treatment changes in functional connectivity would be associated with improvement during CBT in a transdiagnostic sample.

METHODS

Twenty-seven patients with primary social anxiety disorder (n = 14) and primary body dysmorphic disorder (n = 13) were scanned before and after 12 sessions of CBT targeting their primary disorder. Eligibility was based on elevated trait SFA scores on the Public Self-Consciousness Scale. Seed-based resting state functional connectivity associated with symptom improvement was computed using a seed in the posterior cingulate cortex of the default mode network.

RESULTS

At pre-treatment, stronger positive connectivity of the seed with the cerebellum, and stronger negative connectivity with the putamen, were associated with greater clinical improvement. Between pre-to post-treatment, greater anticorrelation between the seed and postcentral gyrus, extending into the inferior parietal lobule and precuneus/superior parietal lobule was associated with clinical improvement, although this did not survive thresholding.

CONCLUSIONS

Pre-treatment functional connectivity with the default mode network was associated with CBT response. Behavioral and self-report measures of SFA did not contribute to predictions, thus highlighting the value of neuroimaging-based measures of SFA.

CLINICAL TRIALS REGISTRATION

ClinicalTrials.gov Identifier: NCT02808702 https://clinicaltrials.gov/ct2/show/NCT02808702.

On the variability of dynamic functional connectivity assessment methods

BACKGROUND

Dynamic functional connectivity (dFC) has become an important measure for understanding brain function and as a potential biomarker. However, various methodologies have been developed for assessing dFC, and it is unclear how the choice of method affects the results. In this work, we aimed to study the results variability of commonly used dFC methods.

METHODS

We implemented 7 dFC assessment methods in Python and used them to analyze the functional magnetic resonance imaging data of 395 subjects from the Human Connectome Project. We measured the similarity of dFC results yielded by different methods using several metrics to quantify overall, temporal, spatial, and intersubject similarity.

RESULTS

Our results showed a range of weak to strong similarity between the results of different methods, indicating considerable overall variability. Somewhat surprisingly, the observed variability in dFC estimates was found to be comparable to the expected functional connectivity variation over time, emphasizing the impact of methodological choices on the final results. Our findings revealed 3 distinct groups of methods with significant intergroup variability, each exhibiting distinct assumptions and advantages.

CONCLUSIONS

Overall, our findings shed light on the impact of dFC assessment analytical flexibility and highlight the need for multianalysis approaches and careful method selection to capture the full range of dFC variation. They also emphasize the importance of distinguishing neural-driven dFC variations from physiological confounds and developing validation frameworks under a known ground truth. To facilitate such investigations, we provide an open-source Python toolbox, PydFC, which facilitates multianalysis dFC assessment, with the goal of enhancing the reliability and interpretability of dFC studies.

Hyperconnecitivity between dorsal attention and frontoparietal networks predicts treatment response in obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) presented with repetitive obsessions and/or compulsions were associated with disrupted resting-state functional connectivity (rs-FC). To investigate the pharmacological treatment effect on rs-FC changes in OCD patients we conducted the seed-to-voxel FC analyses using dorsal attention network (DAN), default mode network (DMN), salience network (SN) and frontoparietal network (FPN) and basal ganglia seeds. Twenty-two healthy subjects and twenty-four unmedicated OCD patients underwent resting-state functional magnetic resonance imaging. Patients were rescanned after 12 weeks of escitalopram treatment. We found increased FC both within the DAN and between the DAN and the FPN which was ameliorated after medication and correlated significantly with the clinical improvement in obsession scores. We also observed an anticorrelation between the left caudate and the supplementary motor area in unmedicated OCD patients which also normalized with treatment. Results further showed treatment related normalization of orbitofrontal cortex hyperconnectivity with DMN and hypoconnectivity with DAN whereas aberrant FC between the SN and visual areas appears to be a medication effect. We suggest that DAN to FPN hyperconnectivity which is positively correlated with clinical improvement in obsession scores at pre-treatment stage in present study has a potential for being a neuroimaging marker to predict the treatment response in OCD.

Altered interhemispheric functional connectivity in patients with obsessive-compulsive disorder and its potential in therapeutic response prediction

The trajectory of voxel-mirrored homotopic connectivity (VMHC) after medical treatment in obsessive-compulsive disorder (OCD) and its value in prediction of treatment response remains unclear. This study aimed to investigate the pathophysiological mechanism of OCD, as well as biomarkers for prediction of pharmacological efficacy. Medication-free patients with OCD and healthy controls (HCs) underwent magnetic resonance imaging. The patients were scanned again after a 4-week treatment with paroxetine. The acquired data were subjected to VMHC, support vector regression (SVR), and correlation analyses. Compared with HCs (36 subjects), patients with OCD (34 subjects after excluding two subjects with excessive head movement) exhibited significantly lower VMHC in the bilateral superior parietal lobule (SPL), postcentral gyrus, and calcarine cortex, and VMHC in the postcentral gyrus was positively correlated with cognitive function. After treatment, the patients showed increased VMHC in the bilateral posterior cingulate cortex/precuneus (PCC/PCu) with the improvement of symptoms. SVR results showed that VMHC in the postcentral gyrus at baseline could aid to predict a change in the scores of OCD scales. This study revealed that SPL, postcentral gyrus, and calcarine cortex participate in the pathophysiological mechanism of OCD while PCC/PCu participate in the pharmacological mechanism. VMHC in the postcentral gyrus is a potential predictive biomarker of the treatment effects in OCD.

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

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

Self-Focused Brain Predictors of Cognitive Behavioral Therapy Response in a Transdiagnostic Sample

Background

Effective biomarkers of cognitive behavioral therapy (CBT) response provide information beyond available behavioral or self-report measures and may optimize treatment selection for patients based on likelihood of benefit. No single biomarker reliably predicts CBT response. In this study, we evaluated patterns of brain connectivity associated with self-focused attention (SFA) as biomarkers of CBT response for anxiety and obsessive-compulsive disorders. We hypothesized that pre-treatment as well as pre- to post-treatment changes in functional connectivity would be associated with improvement during CBT in a transdiagnostic sample.

Methods

Twenty-seven patients with primary social anxiety disorder (n=14) and primary body dysmorphic disorder (n=13) were scanned before and after 12 sessions of CBT targeting their primary disorder. Eligibility was based on elevated trait SFA scores on the Public Self-Consciousness Scale. Seed-based resting state functional connectivity associated with symptom improvement was computed using a seed in the posterior cingulate cortex/precuneus that delineated a self-other functional network.

Results

At pre-treatment, stronger positive connectivity of the seed with the cerebellum, insula, middle occipital gyrus, postcentral gyrus, and precuneus/superior parietal lobule, and stronger negative connectivity with the putamen, were associated with greater clinical improvement. Between pre- to post-treatment, greater anticorrelation between the seed and precuneus/superior parietal lobule was associated with clinical improvement, although this did not survive thresholding.

Conclusions

Pre-treatment functional connectivity between regions involved in attentional salience, self-generated thoughts, and external attention predicted greater CBT response. Behavioral and self-report measures of SFA did not contribute to predictions, thus highlighting the value of neuroimaging-based measures of SFA.

Clinical Trials Registration

ClinicalTrials.gov Identifier: NCT02808702 https://clinicaltrials.gov/ct2/show/NCT02808702.

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.

Cortical glutamate and GABA are related to compulsive behaviour in individuals with obsessive compulsive disorder and healthy controls

There has been little analysis of neurochemical correlates of compulsive behaviour to illuminate its underlying neural mechanisms. We use 7-Tesla proton magnetic resonance spectroscopy (¹H-MRS) to assess the balance of excitatory and inhibitory neurotransmission by measuring glutamate and GABA levels in anterior cingulate cortex (ACC) and supplementary motor area (SMA) of healthy volunteers and participants with Obsessive-Compulsive Disorder (OCD). Within the SMA, trait and clinical measures of compulsive behaviour are related to glutamate levels, whereas a behavioural index of habitual control correlates with the glutamate:GABA ratio. Participants with OCD also show the latter relationship in the ACC while exhibiting elevated glutamate and lower GABA levels in that region. This study highlights SMA mechanisms of habitual control relevant to compulsive behaviour, common to the healthy sub-clinical and OCD populations. The results also demonstrate additional involvement of anterior cingulate in the balance between goal-directed and habitual responding in OCD.

Canalization and plasticity in psychopathology

This theoretical article revives a classical bridging construct, canalization, to describe a new model of a general factor of psychopathology. To achieve this, we have distinguished between two types of plasticity, an early one that we call 'TEMP' for 'Temperature or Entropy Mediated Plasticity', and another, we call 'canalization', which is close to Hebbian plasticity. These two forms of plasticity can be most easily distinguished by their relationship to 'precision' or inverse variance; TEMP relates to increased model variance or decreased precision, whereas the opposite is true for canalization. TEMP also subsumes increased learning rate, (Ising) temperature and entropy. Dictionary definitions of 'plasticity' describe it as the property of being easily shaped or molded; TEMP is the better match for this. Importantly, we propose that 'pathological' phenotypes develop via mechanisms of canalization or increased model precision, as a defensive response to adversity and associated distress or dysphoria. Our model states that canalization entrenches in psychopathology, narrowing the phenotypic state-space as the agent develops expertise in their pathology. We suggest that TEMP - combined with gently guiding psychological support - can counter canalization. We address questions of whether and when canalization is adaptive versus maladaptive, furnish our model with references to basic and human neuroscience, and offer concrete experiments and measures to test its main hypotheses and implications. This article is part of the Special Issue on "National Institutes of Health Psilocybin Research Speaker Series".

Predicting responses to cognitive behavioral therapy in obsessive-compulsive disorder based on multilevel indices of rs-fMRI

OBJECTIVE

This study aimed to identify neuroimaging predictors to predict the response of cognitive behavioral therapy (CBT) in patients with obsessive-compulsive disorder (OCD) based on indices of resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS

Fifty patients with OCD were enrolled and allocated to either high or low responder groups after CBT using a 50 % response rate as the delineator. The pre-treatment amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), and degree centrality (DC) in each cerebrum region, defined by automated anatomical labeling atlas, were extracted. Least absolute shrinkage and selection operator and logistic regression were used to select features and establish models.

RESULTS

The combination of multilevel rs-fMRI indices achieved the best performance, with a cross-validation area under the receiver operating characteristic curve (AUC) of 0.900. In this combined model, an increase of interquartile range (IQR) in fALFF of right inferior orbital frontal gyrus (IOFG), and ReHo of left hippocampus and superior occipital gyrus (SOG) corresponded to a 26.52 %, 38.67 % and 24.38 % increase in the possibility to be high responders of CBT, respectively. ALFF of left thalamus and ReHo of left putamen were negatively associated with the response to CBT, with a 14.30 % and 19.91 % decrease per IQR increase of the index value.

CONCLUSION

The combination of ALFF, fALFF and ReHo achieved a better predictive performance than separate index. Pre-treatment ALFF of the left thalamus, fALFF of the right IOFG, ReHo of the left hippocampus, SOG and putamen can be used as predictors of CBT response.

Prediction of Chinese clients' satisfaction with psychotherapy by machine learning

Background

Effective psychotherapy should satisfy the client, but that satisfaction depends on many factors. We do not fully understand the factors that affect client satisfaction with psychotherapy and how these factors synergistically affect a client's psychotherapy experience.

Aims

This study aims to use machine learning to predict Chinese clients' satisfaction with psychotherapy and analyze potential outcome contributors.

Methods

In this cross-sectional investigation, a self-compiled online questionnaire was delivered through the WeChat app. The information of 791 participants who had received psychotherapy was used in the study. A series of features, for example, the participants' demographic features and psychotherapy-related features, were chosen to distinguish between participants satisfied and dissatisfied with the psychotherapy they received. With our dataset, we trained seven supervised machine-learning-based algorithms to implement prediction models.

Results

Among the 791 participants, 619 (78.3%) reported being satisfied with the psychotherapy sessions that they received. The occupation of the clients, the location of psychotherapy, and the form of access to psychotherapy are the three most recognizable features that determined whether clients are satisfied with psychotherapy. The machine-learning model based on the CatBoost achieved the highest prediction performance in classifying satisfied and psychotherapy clients with an F1 score of 0.758.

Conclusion

This study clarified the factors related to clients' satisfaction with psychotherapy, and the machine-learning-based classifier accurately distinguished clients who were satisfied or unsatisfied with psychotherapy. These results will help provide better psychotherapy strategies for specific clients, so they may achieve better therapeutic outcomes.

Resting-State Connectivity and Response to Psychotherapy Treatment in Adolescents and Adults With OCD: A Randomized Clinical Trial

OBJECTIVE

Cortical-subcortical hyperconnectivity related to affective-behavioral integration and cortical network hypoconnectivity related to cognitive control have been demonstrated in obsessive-compulsive disorder (OCD); the study objective was to examine whether these connectivity patterns predict treatment response.

METHODS

Adolescents (ages 12-17) and adults (ages 24-45) were randomly assigned to 12 sessions of exposure and response prevention (ERP) or stress management therapy (SMT), an active control. Before treatment, resting-state connectivity of ventromedial prefrontal cortical (vmPFC), cingulo-opercular, frontoparietal, and subcortical regions was assessed with functional MRI. OCD severity was assessed with the Yale-Brown Obsessive Compulsive Scale before, during, and after treatment. Usable fMRI and longitudinal symptom data were obtained from 116 patients (68 female; 54 adolescents; 60 medicated).

RESULTS

ERP produced greater decreases in symptom scores than SMT. ERP was selectively associated with less vmPFC-subcortical (caudate and thalamus) connectivity in both age groups and primarily in unmedicated participants. Greater symptom improvement with both ERP and SMT was associated with greater cognitive-control (cingulo-opercular and frontoparietal) and subcortical (putamen) connectivity across age groups. Developmental specificity was observed across ERP and SMT treatments, such that greater improvements with ERP than SMT were associated with greater frontoparietal-subcortical (nucleus accumbens) connectivity in adolescents but greater connectivity between frontoparietal regions in adults. Comparison of response-predictive connections revealed no significant differences compared with a matched healthy control group.

CONCLUSIONS

The results suggest that less vmPFC-subcortical connectivity related to affect-influenced behavior may be important for ERP engagement, whereas greater cognitive-control and motor circuit connectivity may generally facilitate response to psychotherapy. Finally, neural predictors of treatment response may differ by age.

Resting-state connectivity subtype of comorbid PTSD and alcohol use disorder moderates improvement from integrated prolonged exposure therapy in Veterans

BACKGROUND

Posttraumatic stress disorder (PTSD) and alcohol use disorder (AUD) are highly comorbid and are associated with significant functional impairment and inconsistent treatment outcomes. Data-driven subtyping of this clinically heterogeneous patient population and the associated underlying neural mechanisms are highly needed to identify who will benefit from psychotherapy.

METHODS

In 53 comorbid PTSD/AUD patients, resting-state functional magnetic resonance imaging was collected prior to undergoing individual psychotherapy. We used a data-driven approach to subgroup patients based on directed connectivity profiles. Connectivity subgroups were compared on clinical measures of PTSD severity and heavy alcohol use collected at pre- and post-treatment.

RESULTS

We identified a subgroup of patients associated with improvement in PTSD symptoms from integrated-prolonged exposure therapy. This subgroup was characterized by lower insula to inferior parietal cortex (IPC) connectivity, higher pregenual anterior cingulate cortex (pgACC) to posterior midcingulate cortex connectivity and a unique pgACC to IPC path. We did not observe any connectivity subgroup that uniquely benefited from integrated-coping skills or subgroups associated with change in alcohol consumption.

CONCLUSIONS

Data-driven approaches to characterize PTSD/AUD subtypes have the potential to identify brain network profiles that are implicated in the benefit from psychological interventions - setting the stage for future research that targets these brain circuit communication patterns to boost treatment efficacy.

Psychotherapies and digital interventions for OCD in adults: What do we know, what do we need still to explore?

BACKGROUND

Despite significant advances in the understanding and treatment of obsessive compulsive disorder (OCD), current treatment options are limited in terms of efficacy for symptom remission. Thus, assessing the potential role of iterative or alternate psychotherapies is important. Also, the potential role of digital technologies to enhance the accessibility of these therapies, should not be underestimated. We also need to embrace the idea of a more personalized treatment choice, being cognisant of clinical, genetic and neuroimaging predictors of treatment response.

PROCEDURES

Non-systematic review of current literature on emerging psychological and digital therapies for OCD, as well as of potential biomarkers of treatment response.

FINDINGS

A number of 'third wave' therapies (e.g., Acceptance and Commitment Therapy, Mindfulness-Based Cognitive Therapy) have an emerging and encouraging evidence base in OCD. Other approaches entail employment of elements of other psychotherapies such as Dialectical Behaviour Therapy; or trauma-focussed therapies such as Eye Movement Desensitisation and Reprocessing, and Imagery Rescripting and Narrative Therapy. Further strategies include Danger Ideation Reduction Therapy and Habit Reversal. For these latter approaches, large-scale randomised controlled trials are largely lacking, and the precise role of these therapies in treating people with OCD, remains to be clarified. A concentrated 4-day program (the Bergen program) has shown promising short- and long-term results. Exercise, music, and art therapy have not been adequately tested in people with OCD, but may have an adjunctive role. Digital technologies are being actively investigated for enhancing reach and efficacy of psychological therapies for OCD. Biomarkers, including genetic and neuroimaging, are starting to point to a future with more 'personalised medicine informed' treatment strategizing for OCD.

CONCLUSIONS

There are a number of potential psychological options for the treatment of people with OCD who do not respond adequately to exposure/response prevention or cognitive behaviour therapy. Adjunctive exercise, music, and art therapy might be useful, albeit the evidence base for these is very small. Consideration should be given to different ways of delivering such interventions, including group-based, concentrated, inpatient, or with outreach, where appropriate. Digital technologies are an emerging field with a number of potential applications for aiding the treatment of OCD. Biomarkers for treatment response determination have much potential capacity and deserve further empirical testing.

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 brain in 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.

Single-dose psilocybin for treatment-resistant obsessive-compulsive disorder: A case report

Classic psychedelics, such as psilocybin, act on the brain's serotonin system and produce striking psychological effects. Early work in the 1950s and 1960s and more recent controlled studies suggest benefit from psychedelic treatment in a number of conditions. A few case reports in recreational users and a single experimental study suggest benefit in patients with obsessive-compulsive disorder (OCD), but careful clinical data and long-term follow-up have been lacking. Here we describe a case of a patient with refractory OCD treated with psilocybin and followed prospectively for a year, with marked symptomatic improvement. We provide qualitative and quantitative detail of his experience during and after treatment. Improvement in OCD symptoms (YBOCS declined from 24 to 0-2) was accompanied by broader changes in his relationship to his emotions, social and work function, and quality of life. This individual was an early participant in an ongoing controlled study of psilocybin in the treatment of OCD (NCT03356483). These results are preliminary but promising, motivating ongoing investigations of the therapeutic potential of appropriately monitored and supported psychedelic treatment in the treatment of patients with obsessions and compulsions.

Relationship between nuclei-specific amygdala connectivity and mental health dimensions in humans

There has been increasing interest in using neuroimaging measures to predict psychiatric disorders. However, predictions usually rely on large brain networks and large disorder heterogeneity. Thus, they lack both anatomical and behavioural specificity, preventing the advancement of targeted interventions. Here we address both challenges. First, using resting-state functional magnetic resonance imaging, we parcellated the amygdala, a region implicated in mood disorders, into seven nuclei. Next, a questionnaire factor analysis provided subclinical mental health dimensions frequently altered in anxious-depressive individuals, such as negative emotions and sleep problems. Finally, for each behavioural dimension, we identified the most predictive resting-state functional connectivity between individual amygdala nuclei and highly specific regions of interest, such as the dorsal raphe nucleus in the brainstem or medial frontal cortical regions. Connectivity in circumscribed amygdala networks predicted behaviours in an independent dataset. Our results reveal specific relations between mental health dimensions and connectivity in precise subcortical networks.

Modern views of machine learning for precision psychiatry

In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of ML methodologies and applications by combining neuroimaging, neuromodulation, and advanced mobile technologies in psychiatry practice. We further review the role of ML in molecular phenotyping and cross-species biomarker identification in precision psychiatry. We also discuss explainable AI (XAI) and neuromodulation in a closed human-in-the-loop manner and highlight the ML potential in multi-media information extraction and multi-modal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.

Error-related activity of the sensorimotor network contributes to the prediction of response to cognitive-behavioral therapy in obsessive-compulsive disorder

BACKGROUND

Although cognitive behavioral therapy is a highly effective treatment for obsessive-compulsive disorder (OCD), yielding large symptom reductions on the group level, individual treatment response varies considerably. Identification of treatment response predictors may provide important information for maximizing individual treatment response and thus achieving efficient treatment resource allocation. Here, we investigated the predictive value of previously identified biomarkers of OCD, namely the error-related activity of the supplementary motor area (SMA) and the sensorimotor network (SMN, postcentral gyrus/precuneus).

METHODS

Seventy-two participants with a primary diagnosis of OCD underwent functional magnetic resonance imaging (fMRI) scanning while performing a flanker task prior to receiving routine-care CBT.

RESULTS

Error-related BOLD response of the SMN significantly contributed to the prediction of treatment response beyond the variance accounted for by clinical and sociodemographic variables. Stronger error-related SMN activity at baseline was associated with a higher likelihood of treatment response.

CONCLUSIONS

The present results illustrate that the inclusion of error-related SMN activity can significantly increase treatment response prediction quality in OCD. Stronger error-related activity of the SMN may reflect the ability to activate symptom-relevant processing networks and may thus facilitate response to exposure-based CBT interventions.

Functional network alterations in young brain tumor patients with radiotherapy-induced memory impairments and vascular injury

Background

Cognitive impairment and cerebral microbleeds (CMBs) are long-term side-effects of cranial radiation therapy (RT). Previously we showed that memory function is disrupted in young patients and that the rate of cognitive decline correlates with CMB development. However, vascular injury alone cannot explain RT-induced cognitive decline. Here we use resting-state functional MRI (rsfMRI) to further investigate the complex mechanisms underlying memory impairment after RT.

Methods

Nineteen young patients previously treated with or without focal or whole-brain RT for a brain tumor underwent cognitive testing followed by 7T rsfMRI and susceptibility-weighted imaging for CMB detection. Global brain modularity and efficiency, and rsfMRI signal variability within the dorsal attention, salience, and frontoparietal networks were computed. We evaluated whether MR metrics could distinguish age- and sex-matched controls (N = 19) from patients and differentiate patients based on RT exposure and aggressiveness. We also related MR metrics with memory performance, CMB burden, and risk factors for cognitive decline after RT.

Results

Compared to controls, patients exhibited widespread hyperconnectivity, similar modularity, and significantly increased efficiency (p < 0.001) and network variability (p < 0.001). The most abnormal values were detected in patients treated with high dose whole-brain RT, having supratentorial tumors, and who did not undergo RT but had hydrocephalus. MR metrics and memory performance were correlated (R = 0.34–0.53), though MR metrics were more strongly related to risk factors for cognitive worsening and CMB burden with evidence of functional recovery.

Conclusions

MR metrics describing brain connectivity and variability represent promising candidate imaging biomarkers for monitoring of long-term cognitive side-effects after RT.

The functional connectivity predictor of therapeutic effect of continuous theta burst stimulation on obsessive-compulsive disorder: A preliminary study

OBJECTIVE

To evaluate the efficacy of continuous theta burst stimulation (cTBS) on the bilateral supplementary motor area (SMA) among patients with obsessive-compulsive disorder (OCD) and to explore the potential predictors of cTBS outcome based on neuroimaging.

METHODS

29 OCD patients and 29 healthy controls (HCs) were enrolled in this pilot study. Twenty consecutive cTBS intervention targeting at bilateral SMA was applied. MRI scan was carried out before cTBS and 15 regions in the executive control and sensorimotor network were chosen and analyzed using MATLAB, DPABI, and SPM12.

RESULTS

11 out of 29 patients responded to cTBS (37.93%), and the clinical symptom of OCD patients was significantly relieved after receiving regular cTBS. Also, the FC between Cerebelum_Crus2_L and Frontal_Inf_Tri_L of OCD patients showed positive prognosis for the efficacy of cTBS, with the area under the curve (AUC) of 0.85 (95% confidence interval: 0.718-0.989, p = 0.002). None of the patients had any serious adverse event.

CONCLUSION

cTBS intervention on bilateral SMA can significantly improve the symptoms of medicated OCD patients with moderate severity. And the pretherapy FC could be a valuable potential predictor of the cTBS treatment outcome among OCD patients.

Predicting acupuncture efficacy for functional dyspepsia based on functional brain network features: a machine learning study

Acupuncture is effective in treating functional dyspepsia (FD), while its efficacy varies significantly from different patients. Predicting the responsiveness of different patients to acupuncture treatment based on the objective biomarkers would assist physicians to identify the candidates for acupuncture therapy. One hundred FD patients were enrolled, and their clinical characteristics and functional brain MRI data were collected before and after treatment. Taking the pre-treatment functional brain network as features, we constructed the support vector machine models to predict the responsiveness of FD patients to acupuncture treatment. These features contributing critically to the accurate prediction were identified, and the longitudinal analyses of these features were performed on acupuncture responders and non-responders. Results demonstrated that prediction models achieved an accuracy of 0.76 ± 0.03 in predicting acupuncture responders and non-responders, and a R2 of 0.24 ± 0.02 in predicting dyspeptic symptoms relief. Thirty-eight functional brain network features associated with the orbitofrontal cortex, caudate, hippocampus, and anterior insula were identified as the critical predictive features. Changes in these predictive features were more pronounced in responders than in non-responders. In conclusion, this study provided a promising approach to predicting acupuncture efficacy for FD patients and is expected to facilitate the optimization of personalized acupuncture treatment plans for FD.

Abnormal spontaneous neural activity in hippocampal-cortical system of patients with obsessive-compulsive disorder and its potential for diagnosis and prediction of early treatment response

Early brain functional changes induced by pharmacotherapy in patients with obsessive-compulsive disorder (OCD) in relation to drugs per se or because of the impact of such drugs on the improvement of OCD remain unclear. Moreover, no neuroimaging biomarkers are available for diagnosis of OCD and prediction of early treatment response. We performed a longitudinal study involving 34 patients with OCD and 36 healthy controls (HCs). Patients with OCD received 5-week treatment with paroxetine (40 mg/d). Resting-state functional magnetic resonance imaging (fMRI), regional homogeneity (ReHo), support vector machine (SVM), and support vector regression (SVR) were applied to acquire and analyze the imaging data. Compared with HCs, patients with OCD had higher ReHo values in the right superior temporal gyrus and bilateral hippocampus/parahippocampus/fusiform gyrus/cerebellum at baseline. ReHo values in the left hippocampus and parahippocampus decreased significantly after treatment. The reduction rate (RR) of ReHo values was positively correlated with the RRs of the scores of Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) and obsession. Abnormal ReHo values at baseline could serve as potential neuroimaging biomarkers for OCD diagnosis and prediction of early therapeutic response. This study highlighted the important role of the hippocampal-cortical system in the neuropsychological mechanism underlying OCD, pharmacological mechanism underlying OCD treatment, and the possibility of building models for diagnosis and prediction of early treatment response based on spontaneous activity in the hippocampal-cortical system.

Reducing severe fatigue in patients with diffuse glioma: a study protocol for an RCT on the effect of blended cognitive behavioural therapy

Background

Fatigue is the most frequent and burdensome symptom of patients with diffuse glioma. It is closely linked to decreased health-related quality of life and symptoms such as depression and sleep disturbances. Currently, there is no evidence-based treatment that targets severe fatigue in patients with brain tumours. Cognitive behavioural therapy is aimed at fatigue-maintaining beliefs and behaviour. This therapy has been proven effective in reducing severe fatigue in cancer survivors and patients with multiple sclerosis. A blended therapy program combines sessions with a therapist with therapist-guided web-based therapy modules. The aim of this randomized controlled trial is to determine the efficacy of blended cognitive behavioural therapy in treating severe fatigue in patients with diffuse glioma.

Methods

We will include a maximum of 100 patients with diffuse glioma with clinically and radiologically stable disease and severe fatigue (i.e. Checklist Individual Strength, subscale fatigue severity ≥ 35). Patients will be randomized to blended cognitive behavioural therapy or a waiting list condition. The 12-week intervention GRIP on fatigue consists of five patient-therapist sessions and five to eight individualized web-based therapy modules supported by email contact. The primary outcome measure is fatigue severity. Secondary outcome measures include sleep quality, health-related quality of life, depression, anxiety, functional impairment and subjective and objective cognitive functioning. Primary and secondary outcome measures will be assessed at baseline and after 14 and 24 weeks. Magnetoencephalography and MRI will be used to evaluate potential biomarkers for intervention success. This trial has a Bayesian design: we will conduct multiple interim analyses to test for efficacy or futility of the trial. This is the first trial within the GRIP trial platform: a platform developing four to five different interventions for the most common symptoms in patients with diffuse glioma.

Discussion

The results of the GRIP on fatigue trial will provide information about the efficacy of this intervention on fatigue in patients with diffuse glioma. Multiple other outcomes and possible predictors of treatment success will also be explored.

Trial registration

Netherlands Trial Register NL8711. Registered on 14 June 2020.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13063-022-06485-5.

Interpreting Brain Biomarkers: Challenges and solutions in interpreting machine learning-based predictive neuroimaging

Predictive modeling of neuroimaging data (predictive neuroimaging) for evaluating individual differences in various behavioral phenotypes and clinical outcomes is of growing interest. However, the field is experiencing challenges regarding the interpretability of the results. Approaches to defining the specific contribution of functional connections, regions, or networks in prediction models are urgently needed, which may help explore the underlying mechanisms. In this article, we systematically review the methods and applications for interpreting brain signatures derived from predictive neuroimaging based on a survey of 326 research articles. Strengths, limitations, and the suitable conditions for major interpretation strategies are also deliberated. In-depth discussion of common issues in existing literature and the corresponding recommendations to address these pitfalls are provided. We highly recommend exhaustive validation on the reliability and interpretability of the biomarkers across multiple datasets and contexts, which thereby could translate technical advances in neuroimaging into concrete improvements in precision medicine.

A Machine Learning Approach for Predicting Non-Suicidal Self-Injury in Young Adults

Artificial intelligence techniques were explored to assess the ability to anticipate self-harming behaviour in the mental health context using a database collected by an app previously designed to record the emotional states and activities of a group of subjects exhibiting self-harm. Specifically, the Leave-One-Subject-Out technique was used to train classification trees with a maximum of five splits. The results show an accuracy of 84.78%, a sensitivity of 64.64% and a specificity of 85.53%. In addition, positive and negative predictive values were also obtained, with results of 14.48% and 98.47%, respectively. These results are in line with those reported in previous work using a multilevel mixed-effect regression analysis. The combination of apps and AI techniques is a powerful way to improve the tools to accompany and support the care and treatment of patients with this type of behaviour. These studies also guide the improvement of apps on the user side, simplifying and collecting more meaningful data, and on the therapist side, progressing in pathology treatments. Traditional therapy involves observing and reconstructing what had happened before episodes once they have occurred. This new generation of tools will make it possible to monitor the pathology more closely and to act preventively.

Brain Mechanism of Acupuncture Treatment of Chronic Pain: An Individual-Level Positron Emission Tomography Study

Objective

Acupuncture has been shown to be effective in the treatment of chronic pain. However, their neural mechanism underlying the effective acupuncture response to chronic pain is still unclear. We investigated whether metabolic patterns in the pain matrix network might predict acupuncture therapy responses in patients with primary dysmenorrhea (PDM) using a machine-learning-based multivariate pattern analysis (MVPA) on positron emission tomography data (PET).

Methods

Forty-two patients with PDM were selected and randomized into two groups: real acupuncture and sham acupuncture (three menstrual cycles). Brain metabolic data from the three special brain networks (the sensorimotor network (SMN), default mode network (DMN), and salience network (SN)) were extracted at the individual level by using PETSurfer in fluorine-18 fluorodeoxyglucose positron emission tomography (18F-FDG-PET) data. MVPA analysis based on metabolic network features was employed to predict the pain relief after treatment in the pooled group and real acupuncture treatment, separately.

Results

Paired t-tests revealed significant alterations in pain intensity after real but not sham acupuncture treatment. Traditional mass-univariate correlations between brain metabolic and alterations in pain intensity were not significant. The MVPA results showed that the brain metabolic pattern in the DMN and SMN did predict the pain relief in the pooled group of patients with PDM (R2 = 0.25, p = 0.005). In addition, the metabolic pattern in the DMN could predict the pain relief after treatment in the real acupuncture treatment group (R2 = 0.40, p = 0.01).

Conclusion

This study indicates that the individual-level metabolic patterns in DMN is associated with real acupuncture treatment response in chronic pain. The present findings advanced the knowledge of the brain mechanism of the acupuncture treatment in chronic pain.

Neurobiological outcomes of cognitive behavioral therapy for obsessive-compulsive disorder: A systematic review

INTRODUCTION

Obsessive-compulsive disorder (OCD) is characterized by recurrent distressing thoughts and repetitive behaviors, or mental rituals performed to reduce anxiety. Recent neurobiological techniques have been particularly convincing in suggesting that cortico-striatal-thalamic-cortico (CSTC) circuits, including orbitofrontal cortex (OFC) and striatum regions (caudate nucleus and putamen), are responsible for mediation of OCD symptoms. However, it is still unclear how these regions are affected by OCD treatments in adult patients. To address this yet open question, we conducted a systematic review of all studies examining neurobiological changes before and after first-line psychological OCD treatment, i.e., cognitive-behavioral therapy (CBT).

METHODS

Studies were included if they were conducted in adults with OCD and they assessed the neurobiological effects of CBT before and after treatment. Two databases were searched: PsycINFO and PubMed for the time frame up to May 2022.

RESULTS

We obtained 26 pre-post CBT treatment studies performed using different neurobiological techniques, namely functional magnetic resonance imaging (fMRI), Positron emission tomography (PET), regional cerebral blood flow (rCBF), 5-HT concentration, magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS), Electroencephalography (EEG). Neurobiological data show the following after CBT intervention: (i) reduced activations in OFC across fMRI, EEG, and rCBF; (ii) decreased activity in striatum regions across fMRI, rCBF, PET, and MRI; (iii) increased activations in cerebellum (CER) across fMRI and MRI; (iv) enhanced neurochemical concentrations in MRS studies in OFC, anterior cingulate cortex (ACC) and striatum regions. Most of these neurobiological changes are also accompanied by an improvement in symptom severity as assessed by a reduction in the Y-BOCS scores.

CONCLUSION

Cognitive-behavioral therapy seems to be able to restructure, modify, and transform the neurobiological component of OCD, in addition to the clinical symptoms. Nevertheless, further studies are necessary to frame the OCD spectrum in a dimensional way.

Prediction of illness remission in patients with Obsessive-Compulsive Disorder with supervised machine learning

INTRODUCTION

The course of OCD differs widely among OCD patients, varying from chronic symptoms to full remission. No tools for individual prediction of OCD remission are currently available. This study aimed to develop a machine learning algorithm to predict OCD remission after two years, using solely predictors easily accessible in the daily clinical routine.

METHODS

Subjects were recruited in a longitudinal multi-center study (NOCDA). Gradient boosted decision trees were used as supervised machine learning technique. The training of the algorithm was performed with 227 predictors and 213 observations collected in a single clinical center. Hyper-parameter optimization was performed with cross-validation and a Bayesian optimization strategy. The predictive performance of the algorithm was subsequently tested in an independent sample of 215 observations collected in five different centers. Between-center differences were investigated with a bootstrap resampling approach.

RESULTS

The average predictive performance of the algorithm in the test centers resulted in an AUROC of 0.7820, a sensitivity of 73.42%, and a specificity of 71.45%. Results also showed a significant between-center variation in the predictive performance. The most important predictors resulted related to OCD severity, OCD chronic course, use of psychotropic medications, and better global functioning.

LIMITATIONS

All recruiting centers followed the same assessment protocol and are in The Netherlands. Moreover, the sample of the data recruited in some of the test centers was limited in size.

DISCUSSION

The algorithm demonstrated a moderate average predictive performance, and future studies will focus on increasing the stability of the predictive performance across clinical settings.

An overview of the first 5 years of the ENIGMA obsessive-compulsive disorder working group: The power of worldwide collaboration

Neuroimaging has played an important part in advancing our understanding of the neurobiology of obsessive-compulsive disorder (OCD). At the same time, neuroimaging studies of OCD have had notable limitations, including reliance on relatively small samples. International collaborative efforts to increase statistical power by combining samples from across sites have been bolstered by the ENIGMA consortium; this provides specific technical expertise for conducting multi-site analyses, as well as access to a collaborative community of neuroimaging scientists. In this article, we outline the background to, development of, and initial findings from ENIGMA's OCD working group, which currently consists of 47 samples from 34 institutes in 15 countries on 5 continents, with a total sample of 2,323 OCD patients and 2,325 healthy controls. Initial work has focused on studies of cortical thickness and subcortical volumes, structural connectivity, and brain lateralization in children, adolescents and adults with OCD, also including the study on the commonalities and distinctions across different neurodevelopment disorders. Additional work is ongoing, employing machine learning techniques. Findings to date have contributed to the development of neurobiological models of OCD, have provided an important model of global scientific collaboration, and have had a number of clinical implications. Importantly, our work has shed new light on questions about whether structural and functional alterations found in OCD reflect neurodevelopmental changes, effects of the disease process, or medication impacts. We conclude with a summary of ongoing work by ENIGMA-OCD, and a consideration of future directions for neuroimaging research on OCD within and beyond ENIGMA.

Transcranial direct current stimulation targeting the medial prefrontal cortex modulates functional connectivity and enhances safety learning in obsessive-compulsive disorder: Results from two pilot studies

BACKGROUND

Exposed-based psychotherapy is a mainstay of treatment for obsessive-compulsive disorder (OCD) and anxious psychopathology. The medial prefrontal cortex (mPFC) and the default mode network (DMN), which is anchored by the mPFC, promote safety learning. Neuromodulation targeting the mPFC might augment therapeutic safety learning and enhance response to exposure-based therapies.

METHODS

To characterize the effects of mPFC neuromodulation on functional connectivity, 17 community volunteers completed resting-state functional magnetic resonance imaging scans before and after 20 min of frontopolar anodal multifocal transcranial direct current stimulation (tDCS). To examine the effects of tDCS on therapeutic safety learning, 24 patients with OCD completed a pilot randomized clinical trial; they were randomly assigned (double-blind, 50:50) to receive active or sham frontopolar tDCS before completing an in vivo exposure and response prevention (ERP) challenge. Changes in subjective emotional distress during the ERP challenge were used to index therapeutic safety learning.

RESULTS

In community volunteers, frontal pole functional connectivity with the middle and superior frontal gyri increased, while connectivity with the anterior insula and basal ganglia decreased (ps < .001, corrected) after tDCS; functional connectivity between DMN and salience network also decreased after tDCS (ps < .001, corrected). OCD patients who received active tDCS exhibited more rapid therapeutic safety learning (ps < .05) during the ERP challenge than patients who received sham tDCS.

CONCLUSIONS

Frontopolar tDCS may modulate mPFC and DMN functional connectivity and can accelerate therapeutic safety learning. Though limited by small samples, these findings motivate further exploration of the effects of frontopolar tDCS on neural and behavioral targets associated with exposure-based psychotherapies.

Functional connectivity alterations between default mode network and occipital cortex in patients with obsessive-compulsive disorder (OCD)

Altered brain network connectivity is a potential biomarker for obsessive-compulsive disorder (OCD). A meta-analysis of resting-state MRI studies by Gürsel et al. (2018) described altered functional connectivity in OCD patients within and between the default mode network (DMN), the salience network (SN), and the frontoparietal network (FPN), as well as evidence for aberrant fronto-striatal circuitry. Here, we tested the replicability of these meta-analytic rsfMRI findings by measuring functional connectivity during resting-state fMRI in a new sample of OCD patients (n = 24) and matched controls (n = 33). We performed seed-to-voxel analyses using 30 seed regions from the prior meta-analysis. OCD patients showed reduced functional connectivity between the SN and the DMN compared to controls, replicating previous findings. We did not observe significant group differences of functional connectivity within the DMN, SN, nor FPN. Additionally, we observed reduced connectivity between the visual network to both the DMN and SN in OCD patients, in particular reduced functional connectivity between lateral parietal seeds and the left inferior lateral occipital pole. Furthermore, the right lateral parietal seed (associated with the DMN) was more strongly correlated with a cluster in the right lateral occipital cortex and precuneus (a region partly overlapping with the Dorsal Attentional Network (DAN)) in patients. Importantly, this latter finding was positively correlated to OCD symptom severity. Overall, our study partly replicated prior meta-analytic findings, highlighting hypoconnectivity between SN and DMN as a potential biomarker for OCD. Furthermore, we identified changes between the SN and the DMN with the visual network. This suggests that abnormal connectivity between cortex regions associated with abstract functions (transmodal regions such as the DMN), and cortex regions associated with constrained neural processing (unimodal regions such as the visual cortex), may be important in OCD.

Resting-state functional connectivity of the amygdala subregions in unmedicated patients with obsessive-compulsive disorder before and after cognitive behavioural therapy

BACKGROUND

Cognitive behavioural therapy (CBT) is considered an effective first-line treatment for obsessive-compulsive disorder (OCD). However, the neural basis of CBT for OCD has not yet been elucidated. The role of the amygdala in OCD and its functional coupling with the cerebral cortex have received increasing attention, and may provide new understanding of the neural basis of CBT for OCD.

METHODS

We acquired baseline resting-state functional MRI (fMRI) scans from 45 unmedicated patients with OCD and 40 healthy controls; we then acquired another wave of resting-state fMRI scans from the patients with OCD after 12 weeks of CBT. We performed seed-based resting-state functional connectivity analyses of the amygdala subregions to examine changes in patients with OCD as a result of CBT.

RESULTS

Compared to healthy controls, patients with OCD showed significantly increased resting-state functional connectivity at baseline between the left basolateral amygdala and the right middle frontal gyrus, and between the superficial amygdala and the right cuneus. In patients with OCD who responded to CBT, we found decreased resting-state functional connectivity after CBT between the amygdala subregions and the visual association cortices and increased resting-state functional connectivity between the amygdala subregions and the right inferior parietal lobe. Furthermore, these changes in resting-state functional connectivity were positively associated with changes in scores on the compulsion or obsession subscales of the Yale-Brown Obsessive-Compulsive Scale.

LIMITATIONS

Because of the lack of a second scan for healthy controls after 12 weeks, our results may have been confounded by other variables.

CONCLUSION

Our findings yield insights into the pathophysiology of OCD; they also reveal the potential neural changes elicited by CBT, and thus have implications for guiding effective treatment strategies with CBT for OCD.

Decreased left amygdala functional connectivity by cognitive-coping therapy in obsessive-compulsive disorder

It is of great clinical importance to explore more efficacious treatments for OCD. Recently, cognitive-coping therapy (CCT), mainly focusing on recognizing and coping with a fear of negative events, has been reported as an efficacious psychotherapy. However, the underlying neurophysiological mechanism remains unknown. This study of 79 OCD patients collected Yale-Brown Obsessive Compulsive Scale (Y-BOCS) and resting-state functional magnetic resonance imaging (rs-fMRI) scans before and after four weeks of CCT, pharmacotherapy plus CCT (pCCT), or pharmacotherapy. Amygdala seed-based functional connectivity (FC) analysis was performed. Compared post- to pretreatment, pCCT-treated patients showed decreased left amygdala (LA) FC with the right anterior cingulate gyrus (cluster 1) and with the left paracentral lobule/the parietal lobe (cluster 2), while CCT-treated patients showed decreased LA-FC with the left middle occipital gyrus/the left superior parietal/left inferior parietal (cluster 3). The z-values of LA-FC with the three clusters were significantly lower after pCCT or CCT than pretreatment in comparisons of covert vs. overt and of non-remission vs. remission patients, except the z-value of cluster 2 in covert OCD. CCT and pCCT significantly reduced the Y-BOCS score. The reduction in the Y-BOCS score was positively correlated with the z-value of cluster 1. Our findings demonstrate that both pCCT and CCT with large effect sizes lowered LA-FC, indicating that FCs were involved in OCD. Additionally, decreased LA-FC with the anterior cingulate cortex (ACC) or paracentral/parietal cortex may be a marker for pCCT response or a marker for distinguishing OCD subtypes. Decreased LA-FC with the parietal region may be a common pathway of pCCT and CCT. Trial registration: ChiCTR-IPC-15005969.

Resting-state functional connectivity predictors of treatment response in schizophrenia - A systematic review and meta-analysis

We aimed to systematically synthesize and quantify the utility of pre-treatment resting-state functional magnetic resonance imaging (rs-fMRI) in predicting antipsychotic response in schizophrenia. We searched the PubMed/MEDLINE database for studies that examined the magnitude of association between baseline rs-fMRI assessment and subsequent response to antipsychotic treatment in persons with schizophrenia. We also performed meta-analyses for quantifying the magnitude and accuracy of predicting response defined continuously and categorically. Data from 22 datasets examining 1280 individuals identified striatal and default mode network functional segregation and integration metrics as consistent determinants of treatment response. The pooled correlation coefficient for predicting improvement in total symptoms measured continuously was ~0.47 (12 datasets; 95% CI: 0.35 to 0.59). The pooled odds ratio of predicting categorically defined treatment response was 12.66 (nine datasets; 95% CI: 7.91-20.29), with 81% sensitivity and 76% specificity. rs-fMRI holds promise as a predictive biomarker of antipsychotic treatment response in schizophrenia. Future efforts need to focus on refining feature characterization to improve prediction accuracy, validate prediction models, and evaluate their implementation in clinical practice.

Brain parcellation selection: An overlooked decision point with meaningful effects on individual differences in resting-state functional connectivity

Over the past decade extensive research has examined the segregation of the human brain into large-scale functional networks. The resulting network maps, i.e. parcellations, are now commonly used for the a priori identification of functional networks. However, the use of these parcellations, particularly in developmental and clinical samples, hinges on four fundamental assumptions: (1) the various parcellations are equally able to recover the networks of interest; (2) adult-derived parcellations well represent the networks in children’s brains; (3) network properties, such as within-network connectivity, are reliably measured across parcellations; and (4) parcellation selection does not impact the results with regard to individual differences in given network properties. In the present study we examined these assumptions using eight common parcellation schemes in two independent developmental samples. We found that the parcellations are equally able to capture networks of interest in both children and adults. However, networks bearing the same name across parcellations (e.g., default network) do not produce reliable within-network measures of functional connectivity. Critically, parcellation selection significantly impacted the magnitude of associations of functional connectivity with age, poverty, and cognitive ability, producing meaningful differences in interpretation of individual differences in functional connectivity based on parcellation choice. Our findings suggest that work employing parcellations may benefit from the use of multiple schemes to confirm the robustness and generalizability of results. Furthermore, researchers looking to gain insight into functional networks may benefit from employing more nuanced network identification approaches such as using densely-sampled data to produce individual-derived network parcellations. A transition towards precision neuroscience will provide new avenues in the characterization of functional brain organization across development and within clinical populations.

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.

Artificial intelligence applications in psychoradiology

One important challenge in psychiatric research is to translate findings from brain imaging research studies that identified brain alterations in patient groups into an accurate diagnosis at an early stage of illness, prediction of prognosis before treatment, and guidance for selection of effective treatments that target patient-relevant pathophysiological features. This is the primary aim of the field of Psychoradiology. Using databases collected from large samples at multiple centers, sophisticated artificial intelligence (AI) algorithms may be used to develop clinically useful image analysis pipelines that can help physicians diagnose, predict, and make treatment decisions. In this review, we selectively summarize psychoradiological research using magnetic resonance imaging of the brain to explore the neural mechanism of psychiatric disorders, and outline progress and the path forward for the combination of psychoradiology and AI for complementing clinical examinations in patients with psychiatric disorders, as well as limitations in the application of AI that should be considered in future translational research.

Pre-supplementary motor network connectivity and clinical outcome of magnetic stimulation in obsessive-compulsive disorder

A large proportion of patients with obsessive–compulsive disorder (OCD) respond unsatisfactorily to pharmacological and psychological treatments. An alternative novel treatment for these patients is repetitive transcranial magnetic stimulation (rTMS). This study aimed to investigate the underlying neural mechanism of rTMS treatment in OCD patients. A total of 37 patients with OCD were randomized to receive real or sham 1‐Hz rTMS (14 days, 30 min/day) over the right pre‐supplementary motor area (preSMA). Resting‐state functional magnetic resonance imaging data were collected before and after rTMS treatment. The individualized target was defined by a personalized functional connectivity map of the subthalamic nucleus. After treatment, patients in the real group showed a better improvement in the Yale–Brown Obsessive Compulsive Scale than the sham group (F_1,35 = 6.0, p = .019). To show the neural mechanism involved, we identified an “ideal target connectivity” before treatment. Leave‐one‐out cross‐validation indicated that this connectivity pattern can significantly predict patients' symptom improvements (r = .60, p = .009). After real treatment, the average connectivity strength of the target network significantly decreased in the real but not in the sham group. This network‐level change was cross‐validated in three independent datasets. Altogether, these findings suggest that personalized magnetic stimulation on preSMA may alleviate obsessive–compulsive symptoms by decreasing the connectivity strength of the target network.

Effects of short-term cognitive-coping therapy on resting-state brain function in obsessive-compulsive disorder

BACKGROUND

Obsessive-compulsive disorder (OCD) tends to be treatment refractory. Recently, cognitive-coping therapy (CCT) for OCD is reported to be an efficacious psychotherapy. However, the underlying neurophysiological mechanism remains unknown. Here, the effects of CCT on OCD and the resting-state brain function were investigated.

METHODS

Fifty-nine OCD patients underwent CCT, pharmacotherapy plus CCT (pCCT), or pharmacotherapy. Before and after a 4-week treatment, Yale-Brown obsessive-compulsive scale (Y-BOCS) was evaluated and resting-state functional magnetic resonance imaging (rs-fMRI) was scanned.

RESULTS

Compared with the baseline, significant reduction of Y-BOCS scores was found after four-week treatment (p < .001) in groups of CCT and pCCT, not in pharmacotherapy. Post-treatment Y-BOCS scores of CCT group and pCCT group were not different, but significantly lower than that of pharmacotherapy group (p < .001). Compared with pretreatment, two clusters of brain regions with significant change in amplitude of low-frequency fluctuation (ALFF) were obtained in those who treated with CCT and pCCT, but not in those who received pharmacotherapy. The ALFF in cluster 1 (insula, putamen, and postcentral gyrus in left cerebrum) was decreased, while the ALFF in cluster 2 (occipital medial gyrus, occipital inferior gyrus, and lingual gyrus in right hemisphere) was increased after treatment (corrected p < .05). The changes of ALFF were correlated with the reduction of Y-BOCS score and were greater in remission than in nonremission. The reduction of the fear of negative events was correlated to the changes of ALFF of clusters and the reduction of Y-BOCS score.

CONCLUSIONS

The effectiveness of CCT for OCD was related to the alteration of resting-state brain function-the brain plasticity.

TRIAL REGISTRATION

ChiCTR-IPC-15005969.

Identification of psychiatric disorder subtypes from functional connectivity patterns in resting-state electroencephalography

The understanding and treatment of psychiatric disorders, which are known to be neurobiologically and clinically heterogeneous, could benefit from the data-driven identification of disease subtypes. Here, we report the identification of two clinically relevant subtypes of post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) on the basis of robust and distinct functional connectivity patterns, prominently within the frontoparietal control network and the default mode network. We identified the disease subtypes by analysing, via unsupervised and supervised machine learning, the power-envelope-based connectivity of signals reconstructed from high-density resting-state electroencephalography in four datasets of patients with PTSD and MDD, and show that the subtypes are transferable across independent datasets recorded under different conditions. The subtype whose functional connectivity differed most from those of healthy controls was less responsive to psychotherapy treatment for PTSD and failed to respond to an antidepressant medication for MDD. By contrast, both subtypes responded equally well to two different forms of repetitive transcranial magnetic stimulation therapy for MDD. Our data-driven approach may constitute a generalizable solution for connectome-based diagnosis.

Network-based functional connectivity predicts response to exposure therapy in unmedicated adults with obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) is associated with alterations in cortico-striato-thalamo-cortical brain networks, but some resting-state functional magnetic resonance imaging studies report more diffuse alterations in brain connectivity. Few studies have assessed functional connectivity within or between networks across the whole brain in unmedicated OCD patients or how patterns of connectivity predict response to exposure and ritual prevention (EX/RP) therapy, a first-line treatment for OCD. Herein, multiband resting-state functional MRI scans were collected from unmedicated, adult patients with OCD (n = 41) and healthy participants (n = 36); OCD patients were then offered twice weekly EX/RP (17 sessions). A whole-brain-network-based statistic approach was used to identify group differences in resting-state connectivity. We detected altered pre-treatment functional connectivity between task-positive regions in the temporal gyri (middle and superior) and regions of the cingulo-opercular and default networks in individuals with OCD. Signal extraction was performed using a reconstruction independent components analysis and isolated two independent subcomponents (IC1 and IC2) within this altered connectivity. In the OCD group, linear mixed-effects models tested whether IC1 or IC2 values predicted the slope of change in Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) scores across EX/RP treatment. Lower (more different from controls) IC2 score significantly predicted greater symptom reduction with EX/RP (Bonferroni-corrected p = 0.002). Collectively, these findings suggest that an altered balance between task-positive and task-negative regions centered around temporal gyri may contribute to difficulty controlling intrusive thoughts or urges to perform ritualistic behaviors.

The effects of cognitive behavioral therapy on the whole brain structural connectome in unmedicated patients with obsessive-compulsive disorder

Cognitive behavioral therapy (CBT) is considered a first-line treatment for patients with obsessive-compulsive disorder (OCD), and it possesses advantages over pharmacological treatments in stronger tolerance to distress, lower rates of drop out and relapse, and no physical "side-effects". Previous studies have reported CBT-related alterations in focal brain regions and connections. However, the effects of CBT on whole-brain structural networks have not yet been elucidated. Here, we collected diffusion MRI data from 34 unmedicated OCD patients before and after 12 weeks of CBT. Fifty healthy controls (HCs) were also scanned twice at matched intervals. We constructed individual brain white matter connectome and performed a graph-theoretical network analysis to investigate the effects of CBT on whole-brain structural topology. We observed significant group-by-time interactions on the global network clustering coefficient and the nodal clustering of the left lingual gyrus, the left middle temporal gyrus, the left precuneus, and the left fusiform gyrus of 26 CBT responders in OCD patients. Further analysis revealed that these CBT responders showed prominently higher global and nodal clustering compared to HCs at baseline and reduced to normal levels after CBT. Such significant changes in the nodal clustering of the left lingual gyrus were also found in 8 CBT non-responders. The pre-to-post decreases in nodal clustering of the left lingual gyrus and the left fusiform gyrus positively correlated with the improvements in obsessive-compulsive symptoms in the CBT-responding patients. These findings indicated that the network segregation of the whole-brain white matter network in OCD patients was abnormally higher and might recover to normal after CBT, which provides mechanistic insights into the CBT response in OCD and potential imaging biomarkers for clinical practice.