Publications by authors named "Ziwen Peng"

Article Synopsis
  • The study investigates the relationship between compulsivity and the balance of goal-directed versus habitual learning systems, focusing on whether the issue lies within one system or in the arbitration mechanism that manages which system to use.
  • Nineteen alcohol use disorder patients, 30 patients with obsessive-compulsive disorder (OCD), and 20 patients with major depressive disorder participated in a decision-making task, supported by reinforcement learning models for analysis.
  • Findings revealed that both alcohol use disorder and OCD patients favored less effective strategies compared to healthy controls, while OCD patients showed increased control switching; major depressive disorder patients did not show significant differences.
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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.

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Article Synopsis
  • - Evidence indicates a strong link between brain structure changes and symptoms in individuals with autism spectrum disorder (ASD), with social visual preference being affected by specific brain areas and also related to symptom severity.
  • - The study involved 43 children with ASD and 26 typically developing children, revealing notable differences in social visual preference and brain structure measurements between the two groups.
  • - Results showed that less attention to social images correlated with certain brain structure thickness and symptom severity, suggesting that irregularities in brain anatomy might both directly and indirectly influence ASD symptoms through how children visually engage with social stimuli.
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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).

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Article Synopsis
  • Recent studies indicate that the rich club organization in the brain, which helps with communication and information integration, may be abnormally increased in individuals with obsessive-compulsive disorder (OCD).
  • The study involved 32 OCD patients, 30 unaffected first-degree relatives (FDR), and 32 healthy controls (HC), using diffusion tensor imaging (DTI) and functional MRI (fMRI) to analyze brain structures and functions.
  • Results showed that OCD patients had lower brain connection strength compared to HC, with FDR exhibiting intermediate levels, suggesting that changes in rich club organization might serve as a vulnerability marker for OCD, potentially buffered by structural and functional connections in FDR.
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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.

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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.

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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).

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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.

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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.

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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).

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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.

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Article Synopsis
  • The study focuses on how functional connectivity (FC) of the brain influences cognition and behavior, highlighting the need to investigate temporal changes in FC for deeper insights into brain networks.
  • A new spatio-temporal hub identification method is proposed to analyze hub nodes in functional brain networks, allowing for the exploration of changes over time within these networks.
  • The method was tested on fMRI data from an OCD study, showing improved accuracy and consistency compared to existing methods that do not incorporate functional dynamics.
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Article Synopsis
  • The social brain hypothesis helps explain social cognition, particularly in autism spectrum disorder (ASD), but the link between social deficits in ASD and the social brain is still unclear.
  • Research is focusing on genetically at-risk family members of individuals with ASD, as they offer important insights into neuroimaging studies.
  • A meta-analysis of nine studies found that both individuals with ASD and their unaffected relatives showed hyperactivation in specific brain regions (the inferior frontal gyrus and superior temporal gyrus), with ASD individuals showing more hyperactivation in the amygdala compared to unaffected family members and typical development controls.
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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.

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