Publications by authors named "Weizheng Yan"

Article Synopsis
  • Sleep deprivation (SD) negatively impacts brain dynamics, leading to reduced dwell time and transition probabilities in specific brain states.
  • Researchers used functional magnetic resonance imaging and positron emission tomography to assess how SD affects brain activity and dopamine D receptor availability in individuals after one night of sleep deprivation.
  • The study concluded that SD alters brain state occurrence and increases the energy required for brain transitions, linking these changes to the distribution of dopamine receptors in the brain.
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Background: Environmental exposures play a crucial role in shaping children's behavioral development. However, the mechanisms by which these exposures interact with brain functional connectivity and influence behavior remain unexplored.

Methods: We investigated the comprehensive environment-brain-behavior triple interactions through rigorous association, prediction, and mediation analyses, while adjusting for multiple confounders.

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Article Synopsis
  • The study investigates how the amplitude of low-frequency fluctuations (ALFF) and global functional connectivity density (gFCD) are affected by variations in dopamine levels after administering methylphenidate (MP).
  • The research used simultaneous PET-fMRI scanning on participants receiving either a placebo, intravenous (IV), or oral methylphenidate, revealing that IV-MP significantly altered brain activity metrics compared to oral administration.
  • Results showed that gFCD relates to both the increase in dopamine levels and the rate at which it rises, while ALFF is only sensitive to the overall level of dopamine, indicating that these measures reflect different aspects of brain activation in response to stimulants.
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Objective: Multi-site collaboration is essential for overcoming small-sample problems when exploring reproducible biomarkers in MRI studies. However, various scanner-specific factors dramatically reduce the cross-scanner replicability. Moreover, existing harmony methods mostly could not guarantee the improved performance of downstream tasks.

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Substance use disorder (SUD) is a chronic relapsing disorder with long-lasting changes in brain intrinsic networks. While most research to date has focused on static functional connectivity, less is known about the effect of chronic drug use on dynamics of brain networks. Here we investigated brain state dynamics in individuals with opioid use (OUD) and alcohol use disorder (AUD) and assessed how concomitant nicotine use, which is frequent among individuals with OUD and AUD, affects brain dynamics.

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Background: Accurate psychiatric risk assessment requires biomarkers that are both stable and adaptable to development. Functional network connectivity (FNC), which steadily reconfigures over time, potentially contains abundant information to assess psychiatric risks. However, the absence of suitable analytical methodologies has constrained this area of investigation.

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Background: Accurate classification based on affordable objective neuroimaging biomarkers are important steps toward designing individualized treatment.

Methods: In this work, we investigated a deep learning classification model, multi-scale convolutional recurrent neural network (MCRNN), to explore psychiatric disorder-related biomarkers by leveraging the spatiotemporal information of resting-state EEG (rsEEG) using a multiple psychiatric disorder database containing 327 individuals diagnosed with schizophrenia, bipolar, major depressive disorders, and healthy controls. All subjects were mapped to a shared low-dimensional subspace for intuitively interpreting the inter-relationship and separation of psychiatric disorders.

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Multi-site collaboration, which gathers together samples from multiple sites, is a powerful way to overcome the small-sample problem in the neuroimaging field and has the potential to discover more robust and reproducible biomarkers. However, confounds among the datasets caused by various site-specific factors may dramatically reduce the cross-site reproducibility performance. To properly remove confounds while improving cross-site task performances, we propose a maximum classifier discrepancy generative adversarial network (MCD-GAN) that combines the advantages of generative models and maximum discrepancy theory.

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The COVID-19 pandemic has caused massive effects on the situation of public mental health. A fast online questionnaire for screening and evaluating mental symptoms is urgent. In this work, we developed a new 19-item self-assessment Fast Screen Questionnaire for Mental Illness Symptoms (FSQ-MIS) to quickly identify mental illness symptoms.

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Functional magnetic resonance imaging (fMRI) as a promising tool to investigate psychotic disorders can be decomposed into useful imaging features such as time courses (TCs) of independent components (ICs) and functional network connectivity (FNC) calculated by TC cross-correlation. TCs reflect the temporal dynamics of brain activity and the FNC characterizes temporal coherence across intrinsic brain networks. Both features have been used as input to deep learning approaches with decent results.

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Habituation is considered to have protective and filtering mechanisms. The present study is aim to find the casual relationship and mechanisms of excitatory-inhibitory (E/I) dysfunctions in schizophrenia (SCZ) via habituation. A dichotic listening paradigm was performed with simultaneous EEG recording on 22 schizophrenia patients and 22 gender- and age-matched healthy controls.

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Deep learning has shown great potential to adaptively learn hidden patterns from high dimensional neuroimaging data, so as to extract subtle group differences. Motivated by the convolutional neural networks and prototype learning, we developed a brain-network-based convolutional prototype learning model (BNCPL), which can learn representations that simultaneously maximize inter-class separation while minimize within-class distance. When applying BNCPL to distinguish 208 depressive disorders from 210 healthy controls using resting-state functional connectivity (FC), we achieved an accuracy of 71.

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Autism spectrum disorder (ASD) is a neurodevelopmental condition with early childhood onset and high heterogeneity. As the pathogenesis is still elusive, ASD diagnosis is comprised of a constellation of behavioral symptoms. Non-invasive brain imaging techniques, such as magnetic resonance imaging (MRI), provide a valuable objective measurement of the brain.

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Background: Psychiatric disorders are categorized using self-report and observational information rather than biological data. There is also considerable symptomatic overlap between different types of psychiatric disorders, which makes diagnostic categorization and multi-class classification challenging.

Methods: In this work, we propose a unified framework for supervised classification and unsupervised clustering of psychotic disorders using brain imaging data.

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As a popular deep learning method, generative adversarial networks (GAN) have achieved outstanding performance in multiple classifications and segmentation tasks. However, the application of GANs to fMRI data is relatively rare. In this work, we proposed a functional network connectivity (FNC) based GAN for classifying psychotic disorders from healthy controls (HCs), in which FNC matrices were calculated by correlation of time courses derived from non-artefactual fMRI independent components (ICs).

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Background: Current fMRI-based classification approaches mostly use functional connectivity or spatial maps as input, instead of exploring the dynamic time courses directly, which does not leverage the full temporal information.

Methods: Motivated by the ability of recurrent neural networks (RNN) in capturing dynamic information of time sequences, we propose a multi-scale RNN model, which enables classification between 558 schizophrenia and 542 healthy controls by using time courses of fMRI independent components (ICs) directly. To increase interpretability, we also propose a leave-one-IC-out looping strategy for estimating the top contributing ICs.

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Brain functional connectivity (FC) extracted from resting-state fMRI (RS-fMRI) has become a popular approach for disease diagnosis, where discriminating subjects with mild cognitive impairment (MCI) from normal controls (NC) is still one of the most challenging problems. Dynamic functional connectivity (dFC), consisting of time-varying spatiotemporal dynamics, may characterize "chronnectome" diagnostic information for improving MCI classification. However, most of the current dFC studies are based on detecting discrete major "brain status" via spatial clustering, which ignores rich spatiotemporal dynamics contained in such chronnectome.

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Emerging and re-emerging zoonotic diseases caused by pathogens such as Middle East respiratory syndrome coronavirus (MERS-CoV), West Nile virus (WNV) or Chikungunya virus (CHIKV) pose considerable threats to public health worldwide. Research on the mechanism of cross-species infection and transmission for animal-origin emerging and re-emerging zoonosis (2016YFD0500300) has won support by the National Key Research and Development Program of China. Professor Wenjie Tan (National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention) was the primary principal investigator of this research group.

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A large number of researches focused on glycoproteins E1 and E2 of hepatitis C virus (HCV) aimed at the development of anti-HCV vaccines and inhibitors. Enhancement of E1/E2 expression and secretion is critical for the characterization of these glycoproteins and thus for subunit vaccine development. In this study, we designed and synthesized three signal peptide sequences based on online programs SignalP, TargetP, and PSORT, then removed and replaced the signal peptide preceding E1/E2 by overlapping the polymerase chain reaction method.

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To study IgG antibody persistence and temporal change in SARS coronavirus (SARS-CoV) infected patients, 22 patients recovered from SARS in Beijing were recruited and followed-up from 2004 to 2008, serum samples from patients were collected every year. We checked and analyzed the SARS-CoV IgG antibody (Ab) for five consecutive years using the commercial ELISA test kit. The results showed that: all of the serum were SARS-IgG antibody-positive the first year after recovery, the titer of most serum remained at high levels at the 2ed and 3rd year post infection.

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