Defining data-driven subgroups of obsessive-compulsive disorder with different treatment responses based on resting-state functional connectivity

Resting-state functional connectivity of amygdala subregions across different symptom subtypes of obsessive-compulsive disorder patients

AIM

Obsessive-compulsive disorder (OCD) is a heterogeneous condition characterized by distinct symptom subtypes, each with varying pathophysiologies and treatment responses. Recent research has highlighted the role of the amygdala, a brain region that is central to emotion processing, in these variations. However, the role of amygdala subregions with distinct functions has not yet been fully elucidated. In this study, we aimed to clarify the biological mechanisms underlying OCD subtype heterogeneity by investigating the functional connectivity (FC) of amygdala subregions across distinct OCD symptom subtypes.

METHODS

Resting-state functional magnetic resonance images were obtained from 107 medication-free OCD patients and 110 healthy controls (HCs). Using centromedial, basolateral, and superficial subregions of the bilateral amygdala as seed regions, whole-brain FC was compared between OCD patients and HCs and among patients with different OCD symptom subtypes, which included contamination fear and washing, obsessive (i.e., harm due to injury, aggression, sexual, and religious), and compulsive (i.e., symmetry, ordering, counting, and checking) subtypes.

RESULTS

Compared to HCs, compulsive-type OCD patients exhibited hypoconnectivity between the left centromedial amygdala (CMA) and bilateral superior frontal gyri. Compared with patients with contamination fear and washing OCD subtypes, patients with compulsive-type OCD showed hypoconnectivity between the left CMA and left frontal cortex.

CONCLUSIONS

CMA-frontal cortex hypoconnectivity may contribute to the compulsive presentation of OCD through impaired control of behavioral responses to negative emotions. Our findings underscored the potential significance of the distinct neural underpinnings of different OCD manifestations, which could pave the way for more targeted treatment strategies in the future.

Abnormal functional connectivity density involvement in freezing of gait and its application for subtyping Parkinson's disease

The pathophysiological mechanisms at work in Parkinson's disease (PD) patients with freezing of gait (FOG) remain poorly understood. Functional connectivity density (FCD) could provide an unbiased way to analyse connectivity across the brain. In this study, a total of 23 PD patients with FOG (PD FOG + patients), 26 PD patients without FOG (PD FOG- patients), and 22 healthy controls (HCs) were recruited, and their resting-state functional magnetic resonance imaging (rs-fMRI) images were collected. FCD mapping was first performed to identify differences between groups. Pearson correlation analysis was used to explore relationships between FCD values and the severity of FOG. Then, a machine learning model was employed to classify each pair of groups. PD FOG + patients showed significantly increased short-range FCD in the precuneus, cingulate gyrus, and fusiform gyrus and decreased long-range FCD in the frontal gyrus, temporal gyrus, and cingulate gyrus. Short-range FCD values in the middle temporal gyrus and inferior temporal gyrus were positively correlated with FOG questionnaire (FOGQ) scores, and long-range FCD values in the middle frontal gyrus were negatively correlated with FOGQ scores. Using FCD in abnormal regions as input, a support vector machine (SVM) classifier can achieve classification with good performance. The mean accuracy values were 0.895 (PD FOG + vs. HC), 0.966 (PD FOG- vs. HC), and 0.897 (PD FOG + vs. PD FOG-). This study demonstrates that PD FOG + patients showed altered short- and long-range FCD in several brain regions involved in action planning and control, motion processing, emotion, cognition, and object recognition.

A Scalable Approach to Independent Vector Analysis by Shared Subspace Separation for Multi-Subject fMRI Analysis

Joint blind source separation (JBSS) has wide applications in modeling latent structures across multiple related datasets. However, JBSS is computationally prohibitive with high-dimensional data, limiting the number of datasets that can be included in a tractable analysis. Furthermore, JBSS may not be effective if the data's true latent dimensionality is not adequately modeled, where severe overparameterization may lead to poor separation and time performance. In this paper, we propose a scalable JBSS method by modeling and separating the "shared" subspace from the data. The shared subspace is defined as the subset of latent sources that exists across all datasets, represented by groups of sources that collectively form a low-rank structure. Our method first provides the efficient initialization of the independent vector analysis (IVA) with a multivariate Gaussian source prior (IVA-G) specifically designed to estimate the shared sources. Estimated sources are then evaluated regarding whether they are shared, upon which further JBSS is applied separately to the shared and non-shared sources. This provides an effective means to reduce the dimensionality of the problem, improving analyses with larger numbers of datasets. We apply our method to resting-state fMRI datasets, demonstrating that our method can achieve an excellent estimation performance with significantly reduced computational costs.

Current Approaches in Computational Psychiatry for the Data-Driven Identification of Brain-Based Subtypes

The ability of our current psychiatric nosology to accurately delineate clinical populations and inform effective treatment plans has reached a critical point with only moderately successful interventions and high relapse rates. These challenges continue to motivate the search for approaches to better stratify clinical populations into more homogeneous delineations, to better inform diagnosis and disease evaluation, and prescribe and develop more precise treatment plans. The promise of brain-based subtyping based on neuroimaging data is that finding subgroups of individuals with a common biological signature will facilitate the development of biologically grounded, targeted treatments. This review provides a snapshot of the current state of the field in empirical brain-based subtyping studies in child, adolescent, and adult psychiatric populations published between 2019 and March 2022. We found that there is vast methodological exploration and a surprising number of new methods being created for the specific purpose of brain-based subtyping. However, this methodological exploration and advancement is not being met with rigorous validation approaches that assess both reproducibility and clinical utility of the discovered brain-based subtypes. We also found evidence for a collaboration crisis, in which methodological exploration and advancements are not clearly grounded in clinical goals. We propose several steps that we believe are crucial to address these shortcomings in the field. We conclude, and agree with the authors of the reviewed studies, that the discovery of biologically grounded subtypes would be a significant advancement for treatment development in psychiatry.

Robust sex differences in functional brain connectivity are present in utero

Sex-based differences in brain structure and function are observable throughout development and are thought to contribute to differences in behavior, cognition, and the presentation of neurodevelopmental disorders. Using multiple support vector machine (SVM) models as a data-driven approach to assess sex differences, we sought to identify regions exhibiting sex-dependent differences in functional connectivity and determine whether they were robust and sufficiently reliable to classify sex even prior to birth. To accomplish this, we used a sample of 110 human fetal resting state fMRI scans from 95 fetuses, performed between 19 and 40 gestational weeks. Functional brain connectivity patterns classified fetal sex with 73% accuracy. Across SVM models, we identified features (functional connections) that reliably differentiated fetal sex. Highly consistent predictors included connections in the somatomotor and frontal areas alongside the hippocampus, cerebellum, and basal ganglia. Moreover, high consistency features also implicated a greater magnitude of cross-region connections in females, while male weighted features were predominately within anatomically bounded regions. Our findings indicate that these differences, which have been observed later in childhood, are present and reliably detectable even before birth. These results show that sex differences arise before birth in a manner that is consistent and reliable enough to be highly identifiable.

Functional connectivity abnormalities of brain networks in obsessive–compulsive disorder: a systematic review

Obsessive-compulsive disorder (OCD) is characterized by cognitive abnormalities encompassing several executive processes. Neuroimaging studies highlight functional abnormalities of executive fronto-parietal network (FPN) and default-mode network (DMN) in OCD patients, as well as of the prefrontal cortex (PFC) more specifically. We aim at assessing the presence of functional connectivity (FC) abnormalities of intrinsic brain networks and PFC in OCD, possibly underlying specific computational impairments and clinical manifestations. A systematic review of resting-state fMRI studies investigating FC was conducted in unmedicated OCD patients by querying three scientific databases (PubMed, Scopus, PsycInfo) up to July 2022 (search terms: “obsessive–compulsive disorder” AND “resting state” AND “fMRI” AND “function* *connect*” AND “task-positive” OR “executive” OR “central executive” OR “executive control” OR “executive-control” OR “cognitive control” OR “attenti*” OR “dorsal attention” OR “ventral attention” OR “frontoparietal” OR “fronto-parietal” OR “default mode” AND “network*” OR “system*”). Collectively, 20 studies were included. A predominantly reduced FC of DMN – often related to increased symptom severity – emerged. Additionally, intra-network FC of FPN was predominantly increased and often positively related to clinical scores. Concerning PFC, a predominant hyper-connectivity of right-sided prefrontal links emerged. Finally, FC of lateral prefrontal areas correlated with specific symptom dimensions. Several sources of heterogeneity in methodology might have affected results in unpredictable ways and were discussed. Such findings might represent endophenotypes of OCD manifestations, possibly reflecting computational impairments and difficulties in engaging in self-referential processes or in disengaging from cognitive control and monitoring processes.

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.

Defining brain-based OCD patient profiles using task-based fMRI and unsupervised machine learning

While much research has highlighted phenotypic heterogeneity in obsessive compulsive disorder (OCD), less work has focused on heterogeneity in neural activity. Conventional neuroimaging approaches rely on group averages that assume homogenous patient populations. If subgroups are present, these approaches can increase variability and can lead to discrepancies in the literature. They can also obscure differences between various subgroups. To address this issue, we used unsupervised machine learning to identify subgroup clusters of patients with OCD who were assessed by task-based fMRI. We predominantly focused on activation of cognitive control and performance monitoring neurocircuits, including three large-scale brain networks that have been implicated in OCD (the frontoparietal network, cingulo-opercular network, and default mode network). Participants were patients with OCD (n = 128) that included both adults (ages 24-45) and adolescents (ages 12-17), as well as unaffected controls (n = 64). Neural assessments included tests of cognitive interference and error processing. We found three patient clusters, reflecting a "normative" cluster that shared a brain activation pattern with unaffected controls (65.9% of clinical participants), as well as an "interference hyperactivity" cluster (15.2% of clinical participants) and an "error hyperactivity" cluster (18.9% of clinical participants). We also related these clusters to demographic and clinical correlates. After post-hoc correction for false discovery rates, the interference hyperactivity cluster showed significantly longer reaction times than the other patient clusters, but no other between-cluster differences in covariates were detected. These findings increase precision in patient characterization, reframe prior neurobehavioral research in OCD, and provide a starting point for neuroimaging-guided treatment selection.

Triple-Network Dysconnectivity in Patients With First-Episode Psychosis and Individuals at Clinical High Risk for Psychosis

OBJECTIVE

In the triple-network model, the salience network (SN) plays a crucial role in switching between the default-mode network (DMN) and the central executive network (CEN). Aberrant patterns of triple-network connectivity have been reported in schizophrenia patients, while findings have been less consistent for patients in the early stages of psychotic disorders. Thus, the present study examined the connectivity among the SN, DMN, and CEN in first-episode psychosis (FEP) patients and individuals at clinical high risk (CHR) for psychosis.

METHODS

Thirty-nine patients with FEP, 78 patients with CHR for psychosis, and 110 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging. We compared the SN, DMN, and CEN connectivity patterns of the three groups. The role of the SN in networks with significant connectivity differences was examined by mediation analysis.

RESULTS

FEP patients showed lower SN-DMN and SN-CEN (cluster-level F=5.83, false discovery rate [FDR] corrected-p=0.001) connectivity than HCs. There was lower SN-DMN connectivity (cluster-level F=3.06, FDR corrected-p=0.053) at a trend level in CHR subjects compared to HCs. Between HCs and FEP patients, mediation analysis showed that SN-DMN connectivity was a mediator between group and SN-CEN connectivity. Additionally, SN-CEN connectivity functioned as a mediator between group and SN-DMN connectivity.

CONCLUSION

Aberrant connectivity between the SN and DMN/CEN suggests disrupted network switching in FEP patients, although CHR subjects showed trend-level SN-DMN dysconnectivity. Our findings suggest that dysfunctional triple-network dynamics centered on the SN can appear in patients in the early stages of psychotic disorders.

Unbalanced fronto-pallidal neurocircuit underlying set shifting in obsessive-compulsive disorder

Maladaptive habitual behaviours of obsessive-compulsive disorder are characterized by cognitive inflexibility, which hypothetically arises from dysfunctions of a certain cortico-basal ganglia-thalamo-cortical circuit including the ventrolateral prefrontal region. Inside this neurocircuit, an imbalance between distinct striatal projections to basal ganglia output nuclei, either directly or indirectly via the external globus pallidus, is suggested to be relevant for impaired arbitration between facilitation and inhibition of cortically initiated activity. However, current evidence of individually altered cortico-striatal or thalamo-cortical connectivities is insufficient to understand how cortical dysconnections are linked to the imbalanced basal ganglia system in patients. In this study, we aimed to identify aberrant ventrolateral prefronto-basal ganglia-thalamic subnetworks representing direct-indirect imbalance and its association with cognitive inflexibility in patients. To increase network detection sensitivity, we constructed a cortico-basal ganglia-thalamo-cortical network model incorporating striatal, pallidal and thalamic subregions defined by unsupervised clustering in 105 medication-free patients with obsessive-compulsive disorder (age = 25.05 ± 6.55 years, male/female = 70/35) and 99 healthy controls (age = 23.93 ± 5.80 years, male/female = 64/35). By using the network-based statistic method, we analysed group differences in subnetworks formed by suprathreshold dysconnectivities. Using linear regression models, we tested subnetwork dysconnectivity effects on symptom severity and set-shifting performance assessed by well-validated clinical and cognitive tests. Compared with the healthy controls, patients were slower to track the Part B sequence of the Trail Making Test when the effects of psychomotor and visuospatial functions were adjusted (t = 3.89, P < 0.001) and made more extradimensional shift errors (t = 4.09, P < 0.001). In addition to reduced fronto-striatal and striato-external pallidal connectivities and hypoconnected striato-thalamic subnetwork [P = 0.001, family-wise error rate (FWER) corrected], patients had hyperconnected fronto-external pallidal (P = 0.012, FWER corrected) and intra-thalamic (P = 0.015, FWER corrected) subnetworks compared with the healthy controls. Among the patients, the fronto-pallidal subnetwork alteration, especially ventrolateral prefronto-external globus pallidal hyperconnectivity, was associated with relatively fewer extradimensional shifting errors (β = -0.30, P = 0.001). Our findings suggest that the hyperconnected fronto-external pallidal subnetwork may have an opposite effect to the imbalance caused by the reduced indirect pathway (fronto-striato-external pallidal) connectivities in patients. This ventrolateral prefrontal hyperconnectivity may help the external globus pallidus disinhibit basal ganglia output nuclei, which results in behavioural inhibition, so as to compensate for the impaired set shifting. We suggest the ventrolateral prefrontal and external globus pallidus as neuromodulatory targets for inflexible habitual behaviours in obsessive-compulsive disorder.

Eye movement as a biomarker of impaired organizational strategies during visual memory encoding in obsessive-compulsive disorder

The symptoms of obsessive–compulsive disorder (OCD) are largely related to impaired executive functioning due to frontostriatal dysfunction. To better treat OCD, the development of biomarkers to bridge the gap between the symptomatic-cognitive phenotype and brain abnormalities is warranted. Therefore, we aimed to identify biomarkers of impaired organizational strategies during visual encoding processes in OCD patients by developing an eye tracking-based Rey–Osterrieth complex figure test (RCFT). In 104 OCD patients and 114 healthy controls (HCs), eye movements were recorded during memorization of the RCFT figure, and organizational scores were evaluated. Kullback–Leibler divergence (KLD) scores were calculated to evaluate the distance between a participant’s eye gaze distribution and a hypothetical uniform distribution within the RCFT figure. Narrower gaze distributions within the RCFT figure, which yielded higher KLD scores, indicated that the participant was more obsessed with detail and had less organizational strategy. The OCD patients showed lower organizational scores than the HCs. Although no group differences in KLD scores were noted, KLD scores were significantly associated with organization T scores in the OCD group. The current study findings suggest that eye tracking biomarkers of visual memory encoding provide a rapidly determined index of executive functioning, such as organizational strategies, in OCD patients.

Identifying Internet Addiction and Evaluating the Efficacy of Treatment Based on Functional Connectivity Density: A Machine Learning Study

Although mounting neuroimaging studies have greatly improved our understanding of the neurobiological mechanism underlying internet addiction (IA), the results based on traditional group-level comparisons are insufficient in guiding individual clinical practice directly. Specific neuroimaging biomarkers are urgently needed for IA diagnosis and the evaluation of therapy efficacy. Therefore, this study aimed to develop support vector machine (SVM) models to identify IA and assess the efficacy of cognitive behavior therapy (CBT) based on unbiased functional connectivity density (FCD). Resting-state fMRI data were acquired from 27 individuals with IA before and after 8-week CBT sessions and 30 demographically matched healthy controls (HCs). The discriminative FCDs were computed as the features of the support vector classification (SVC) model to identify individuals with IA from HCs, and the changes in these discriminative FCDs after treatment were further used as features of the support vector regression (SVR) model to evaluate the efficacy of CBT. Based on the informative FCDs, our SVC model successfully differentiated individuals with IA from HCs with an accuracy of 82.5% and an area under the curve (AUC) of 0.91. Our SVR model successfully evaluated the efficacy of CBT using the FCD change ratio with a correlation efficient of 0.59. The brain regions contributing to IA classification and CBT efficacy assessment were the left inferior frontal cortex (IFC), middle frontal cortex (MFC) and angular gyrus (AG), the right premotor cortex (PMC) and middle cingulate cortex (MCC), and the bilateral cerebellum, orbitofrontal cortex (OFC) and superior frontal cortex (SFC). These findings confirmed the FCDs of hyperactive impulsive habit system, hypoactive reflecting system and sensitive interoceptive reward awareness system as potential neuroimaging biomarkers for IA, which might provide objective indexes for the diagnosis and efficacy evaluation of IA.

Abnormal brain functional network dynamics in obsessive-compulsive disorder patients and their unaffected first-degree relatives

We utilized dynamic functional network connectivity (dFNC) analysis to compare participants with obsessive–compulsive disorder (OCD) with their unaffected first‐degree relative (UFDR) and healthy controls (HC). Resting state fMRI was performed on 46 OCD, 24 UFDR, and 49 HCs, along with clinical assessments. dFNC analyses revealed two distinct connectivity states: a less frequent, integrated state characterized by the predominance of between‐network connections (State I), and a more frequent, segregated state with strong within‐network connections (State II). OCD patients spent more time in State II and less time in State I than HC, as measured by fractional windows and mean dwell time. Time in each state for the UFDR were intermediate between OCD patients and HC. Within the OCD group, fractional windows of time spent in State I was positively correlated with OCD symptoms (as measured by the obsessive compulsive inventory‐revised [OCI‐R], r = .343, p<.05, FDR correction) and time in State II was negatively correlated with symptoms (r = −.343, p<.05, FDR correction). Within each state we also examined connectivity within and between established intrinsic connectivity networks, and found that UFDR were similar to the OCD group in State I, but more similar to the HC groups in State II. The similarities between OCD and UFDR groups in temporal properties and State I connectivity indicate that these features may reflect the endophenotype for OCD. These results indicate that the temporal dynamics of functional connectivity could be a useful biomarker to identify those at risk.