國家衛生研究院 NHRI:Item 3990099045/8460
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    Please use this identifier to cite or link to this item: http://ir.nhri.org.tw/handle/3990099045/8460


    Title: A multimodal approach to investigate the pathophysiology of first-episode psychosis in Taiwan
    Authors: Lan, TH;Lin, CP;Chou, PH;Hung, CC;Chan, CH
    Contributors: Center for Neuropsychiatric Research
    Abstract: Objectives: Schizophrenia is a chronic mental illness with high burden disease. However, little is known in the underlying brain pathology because of limitations in each neuroimaging measurement. Multimodal approaches, hence, are required to elucidate the pathophysiology of schizophrenia, and to develop predictors easy to use in clinical settings. The aim of our project is to investigate the feasibility of the multimodal approach (clinical assessment, neurocognitive tests, fNIRS, and MRI) in Taiwan using a neuro-fuzzy model (NFM) to predict clinical and functional outcomes in patients with fi rst-episode schizophrenia spectrum disorder (FES). Materials and Methods: Starting from 2013/11/1, we continuously recruit FES patients and their brain functions, and structures will be measured by using multimodal approach at baseline and 0.5, 1, and 2 years and longer. Acquired data were analysed by not only existing methodologies but also using a machine learning model (NFM) we previously used successfully in predicting body weight gain in patients with schizophrenia. Results: By the end of April, 2014, we ’ ve recruited 18 patients with first episode schizophrenia spectrum disorder, including 11 patients with schizophrenia, 4 patients with schizophreniform disorder, and 3 patients with psychotic disorder, NOS) and had their brain functions measured. We will keep conducting this study and hope that through this research project, we could clarify the pathophysiological features around the onset of psychosis.
    Date: 2014-11
    Relation: Early Intervention in Psychiatry. 2014 Nov;8(Suppl. 1):61.
    Link to: http://dx.doi.org/10.1111/eip.12186
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=1751-7885&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:000344785700226
    Appears in Collections:[Others] Conference Papers/Meeting Abstract

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