Local structural and functional MRI markers of compulsive behaviors and obsessive-compulsive disorder diagnosis within striatum-based circuits.

Background: Obsessive-compulsive disorder (OCD) is a classic disorder on the compulsivity spectrum, with diverse comorbidities. In the current study, we sought to understand OCD from a dimensional perspective by identifying multimodal neuroimaging patterns correlated with multiple phenotypic characteristics within the striatum-based circuits known to be affected by OCD.

Methods: Neuroimaging measurements of local functional and structural features and clinical information were collected from 110 subjects, including 51 patients with OCD and 59 healthy control subjects.

Impairment of arbitration between model-based and model-free reinforcement learning in obsessive-compulsive disorder.

Introduction: Obsessive-compulsive disorder (OCD) is characterized by an imbalance between goal-directed and habitual learning systems in behavioral control, but it is unclear whether these impairments are due to a single system abnormality of the goal-directed system or due to an impairment in a separate arbitration mechanism that selects which system controls behavior at each point in time.

Methods: A total of 30 OCD patients and 120 healthy controls performed a 2-choice, 3-stage Markov decision-making paradigm. Reinforcement learning models were used to estimate goal-directed learning (as model-based reinforcement learning) and habitual learning (as model-free reinforcement learning).

Dynamic multi-site graph convolutional network for autism spectrum disorder identification.

Multi-site learning has attracted increasing interests in autism spectrum disorder (ASD) identification tasks by its efficacy on capturing data heterogeneity of neuroimaging taken from different medical sites. However, existing multi-site graph convolutional network (MSGCN) often ignores the correlations between different sites, and may obtain suboptimal identification results. Moreover, current feature extraction methods characterizing temporal variations of functional magnetic resonance imaging (fMRI) signals require the time series to be of the same length and cannot be directly applied to multi-site fMRI datasets.

A network perspective on cognitive function and obsessive-compulsive related symptoms.

Background: The relationship between cognitive function and psychopathological symptoms has been an important research field in recent years. Previous studies have typically applied case-control designs to explore differences in certain cognitive variables. Multivariate analyses are needed to deepen our understanding of the intercorrelations among cognitive and symptom phenotypes in OCD.

Obsessive-compulsive disorder is characterized by decreased Pavlovian influence on instrumental behavior.

Obsessive-compulsive disorder (OCD) is characterized by uncontrollable repetitive actions thought to rely on abnormalities within fundamental instrumental learning systems. We investigated cognitive and computational mechanisms underlying Pavlovian biases on instrumental behavior in both clinical OCD patients and healthy controls using a Pavlovian-Instrumental Transfer (PIT) task. PIT is typically evidenced by increased responding in the presence of a positive (previously rewarded) Pavlovian cue, and reduced responding in the presence of a negative cue.

Imbalance between the caudate and putamen connectivity in obsessive-compulsive disorder.

Background: Compulsive behaviors in obsessive-compulsive disorder (OCD) have been suggested to result from an imbalance in cortico-striatal connectivity. However, the nature of this impairment, the relative involvement of different striatal areas, their imbalance in genetically related but unimpaired individuals, and their relationship with cognitive dysfunction in OCD patients, remain unknown.

Methods: In the current study, striatal (i.

Connectome-based predictive modeling of compulsion in obsessive-compulsive disorder.

Compulsion is one of core symptoms of obsessive-compulsive disorder (OCD). Although many studies have investigated the neural mechanism of compulsion, no study has used brain-based measures to predict compulsion. Here, we used connectome-based predictive modeling (CPM) to identify networks that could predict the levels of compulsion based on whole-brain functional connectivity in 57 OCD patients.

Imbalance in functional and structural connectivity underlying goal-directed and habitual learning systems in obsessive-compulsive disorder.

An imbalance between the goal-directed and habitual learning systems has been proposed to underlie compulsivity in obsessive-compulsive disorder (OCD). In addition, the overall balance between these systems may be influenced by stress hormones. We examined the multimodal networks underlying these dual learning systems.

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

Both the Pearson correlation and partial correlation methods have been widely used in the resting-state functional MRI (rs-fMRI) studies. However, they can only measure linear relationship, although partial correlation excludes some indirect effects. Recent distance correlation can discover both the linear and non-linear dependencies.

Diagnosis of obsessive-compulsive disorder via spatial similarity-aware learning and fused deep polynomial network.

Obsessive-compulsive disorder (OCD) is a type of hereditary mental illness, which seriously affect the normal life of the patients. Sparse learning has been widely used in detecting brain diseases objectively by removing redundant information and retaining monitor valuable biological characteristics from the brain functional connectivity network (BFCN). However, most existing methods ignore the relationship between brain regions in each subject.

Multi-Band Brain Network Analysis for Functional Neuroimaging Biomarker Identification.

The functional connectomic profile is one of the non-invasive imaging biomarkers in the computer-assisted diagnostic system for many neuro-diseases. However, the diagnostic power of functional connectivity is challenged by mixed frequency-specific neuronal oscillations in the brain, which makes the single Functional Connectivity Network (FCN) often underpowered to capture the disease-related functional patterns. To address this challenge, we propose a novel functional connectivity analysis framework to conduct joint feature learning and personalized disease diagnosis, in a semi-supervised manner, aiming at focusing on putative multi-band functional connectivity biomarkers from functional neuroimaging data.

Joint hub identification for brain networks by multivariate graph inference.

Recent developments in neuroimaging allow us to investigate the structural and functional connectivity between brain regions in vivo. Mounting evidence suggests that hub nodes play a central role in brain communication and neural integration. Such high centrality, however, makes hub nodes particularly susceptible to pathological network alterations and the identification of hub nodes from brain networks has attracted much attention in neuroimaging.

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).

Brain functional connectivity analysis based on multi-graph fusion.

In this paper, we propose a framework for functional connectivity network (FCN) analysis, which conducts the brain disease diagnosis on the resting state functional magnetic resonance imaging (rs-fMRI) data, aiming at reducing the influence of the noise, the inter-subject variability, and the heterogeneity across subjects. To this end, our proposed framework investigates a multi-graph fusion method to explore both the common and the complementary information between two FCNs, i.e.

Dysfunction of the Auditory Brainstem as a Neurophysiology Subtype of Autism Spectrum Disorder.

Autism spectrum disorder (ASD) is very heterogeneous, particularly in language. Studies have suggested that language impairment is linked to auditory-brainstem dysfunction in ASD. However, not all ASD children have these deficits, which suggests potential subtypes of ASD.

White Matter Alterations of the Goal-Directed System in Patients With Obsessive-Compulsive Disorder and Their Unaffected First-Degree Relatives.

Background: It has been postulated that the neurobiological mechanism responsible for the onset of symptoms of obsessive-compulsive disorder (OCD), especially compulsive behavior, is related to alterations of the goal-directed and habitual learning systems. However, little is known about whether changes in these learning systems co-occur with changes in the white matter structure of patients with OCD and their unaffected first-degree relatives (UFDRs).

Methods: Diffusion tensor imaging data were acquired from 32 patients with OCD (21 male), 32 UFDRs (16 male), and 32 healthy control subjects (16 male).

Abnormal Anhedonia as a Potential Endophenotype in Obsessive-Compulsive Disorder.

Background: Obsessive-compulsive disorder (OCD) is often accompanied by cognitive, particularly executive function, impairments. Recently, anhedonia has emerged as an apparently important symptom of OCD reflecting altered emotion regulation. These two aspects are often comorbid in OCD.

Modeling essential connections in obsessive-compulsive disorder patients using functional MRI.

Object: Obsessive-compulsive disorder (OCD) is a mental disease in which people experience uncontrollable and repetitive thoughts or behaviors. Clinical diagnosis of OCD is achieved by using neuropsychological assessment metrics, which are often subjectively affected by psychologists and patients. In this study, we propose a classification model for OCD diagnosis using functional MR images.