Jing Sui focuses on Schizophrenia, Artificial intelligence, Neuroscience, Neuroimaging and Resting state fMRI. The various areas that she examines in her Schizophrenia study include Developmental psychology, Cognitive psychology and Dynamic functional connectivity. Within one scientific family, Jing Sui focuses on topics pertaining to Machine learning under Artificial intelligence, and may sometimes address concerns connected to Schizophrenia and Data mining.
Her research in Neuroscience focuses on subjects like Fractional anisotropy, which are connected to Visual cortex, Positive and Negative Syndrome Scale and Frontal lobe. Her Neuroimaging research is multidisciplinary, relying on both Big data, Deep learning, Disease and Autism. Her Resting state fMRI study combines topics from a wide range of disciplines, such as Brain network, Functional networks and Prefrontal cortex.
Jing Sui mostly deals with Neuroscience, Artificial intelligence, Neuroimaging, Schizophrenia and Functional magnetic resonance imaging. Her study looks at the intersection of Neuroscience and topics like Fractional anisotropy with Corpus callosum. Her Artificial intelligence study combines topics in areas such as Schizophrenia, Machine learning and Pattern recognition.
Her research in Neuroimaging intersects with topics in Deep learning, Disease, Autism spectrum disorder, Discriminative model and Feature selection. The Schizophrenia study combines topics in areas such as Bipolar disorder, Temporal lobe and Human brain. Her Functional magnetic resonance imaging research includes themes of Psychosis and Audiology.
Her primary areas of investigation include Neuroscience, Neuroimaging, Cognition, Artificial intelligence and Major depressive disorder. Her Neuroscience research integrates issues from Fractional anisotropy and Autism. She works mostly in the field of Neuroimaging, limiting it down to concerns involving Schizophrenia and, occasionally, Human brain.
Her Cognition study integrates concerns from other disciplines, such as Cognitive psychology and Mediation. Her studies in Artificial intelligence integrate themes in fields like Machine learning and Pattern recognition. Her work deals with themes such as Audiology, Functional magnetic resonance imaging, Dynamic functional connectivity, Default mode network and Electroconvulsive therapy, which intersect with Major depressive disorder.
Neuroimaging, Neuroscience, Cognition, Default mode network and Functional magnetic resonance imaging are her primary areas of study. As a part of the same scientific study, Jing Sui usually deals with the Neuroimaging, concentrating on Autism spectrum disorder and frequently concerns with Major depressive disorder, Bipolar disorder, Inferior temporal gyrus and Anterior cingulate cortex. Her Major depressive disorder course of study focuses on Schizophrenia and Fractional anisotropy.
The Temporal cortex, Insula and Electroconvulsive therapy research Jing Sui does as part of her general Neuroscience study is frequently linked to other disciplines of science, such as Mechanism, therefore creating a link between diverse domains of science. She combines subjects such as Cognitive psychology and Connectome with her study of Cognition. Her Functional magnetic resonance imaging research incorporates themes from Diffusion MRI, Artificial intelligence and Pattern recognition.
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Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.
Mohammad R. Arbabshirani;Sergey M. Plis;Jing Sui;Vince D. Calhoun.
A review of multivariate methods for multimodal fusion of brain imaging data
Jing Sui;Tülay Adali;Qingbao Yu;Jiayu Chen.
Journal of Neuroscience Methods (2012)
Multimodal Fusion of Brain Imaging Data: A Key to Finding the Missing Link(s) in Complex Mental Illness
Vince D. Calhoun;Jing Sui.
Biological Psychiatry: Cognitive Neuroscience and Neuroimaging (2016)
Discriminating schizophrenia and bipolar disorder by fusing fMRI and DTI in a multimodal CCA+ joint ICA model.
Jing Sui;Godfrey D. Pearlson;Arvind Caprihan;Tülay Adali.
Exploring the Psychosis Functional Connectome: Aberrant Intrinsic Networks in Schizophrenia and Bipolar Disorder
Vince D Calhoun;Vince D Calhoun;Jing Sui;Kent Kiehl;Kent Kiehl;Jessica A Turner.
Frontiers in Psychiatry (2012)
A Hybrid Machine Learning Method for Fusing fMRI and Genetic Data: Combining both Improves Classification of Schizophrenia
Honghui Yang;Honghui Yang;Jingyu Liu;Jingyu Liu;Jing Sui;Jing Sui;Godfrey Pearlson.
Frontiers in Human Neuroscience (2010)
Assessing dynamic brain graphs of time-varying connectivity in fMRI data: application to healthy controls and patients with schizophrenia.
Qingbao Yu;Erik B. Erhardt;Jing Sui;Yuhui Du.
Interaction among subsystems within default mode network diminished in schizophrenia patients: A dynamic connectivity approach.
Yuhui Du;Yuhui Du;Godfrey D. Pearlson;Qingbao Yu;Hao He.
Schizophrenia Research (2016)
Synthesis of Polyaniline with a Hollow, Octahedral Morphology by Using a Cuprous Oxide Template
Zhiming Zhang;Jing Sui;Lijuan Zhang;Meixiang Wan.
Advanced Materials (2005)
In search of multimodal neuroimaging biomarkers of cognitive deficits in schizophrenia.
Jing Sui;Jing Sui;Godfrey D. Pearlson;Yuhui Du;Qingbao Yu.
Biological Psychiatry (2015)
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