Jing Sui spends much of his time researching Schizophrenia, Neuroscience, Artificial intelligence, Neuroimaging and Resting state fMRI. His studies deal with areas such as Developmental psychology, Cognitive psychology, Functional magnetic resonance imaging and Cognition as well as Schizophrenia. His Fractional anisotropy research extends to Neuroscience, which is thematically connected.
The Artificial intelligence study which covers Machine learning that intersects with Schizophrenia and Data mining. Jing Sui interconnects Deep learning, Superior temporal gyrus, Disease and Big data in the investigation of issues within Neuroimaging. As a member of one scientific family, Jing Sui mostly works in the field of Resting state fMRI, focusing on Functional networks and, on occasion, Functional neuroimaging, Major depressive disorder and Clinical psychology.
His primary areas of study are Artificial intelligence, Neuroscience, Neuroimaging, Schizophrenia and Pattern recognition. His research in Artificial intelligence intersects with topics in Schizophrenia and Machine learning. As part of his studies on Neuroscience, he often connects relevant subjects like Fractional anisotropy.
Jing Sui combines subjects such as Cognition, Modality, Discriminative model, Feature selection and Brain mapping with his study of Neuroimaging. Jing Sui works mostly in the field of Schizophrenia, limiting it down to topics relating to Bipolar disorder and, in certain cases, Major depressive disorder, Clinical psychology and Mood disorders, as a part of the same area of interest. His research integrates issues of Psychosis and Voxel in his study of Functional magnetic resonance imaging.
Jing Sui mostly deals with Neuroscience, Neuroimaging, Cognition, Artificial intelligence and Major depressive disorder. In his research on the topic of Neuroscience, Underlying disease and Functional networks is strongly related with Autism. His Neuroimaging research incorporates themes from Schizophrenia, Electroencephalography, Bipolar disorder, Connectome and Clinical psychology.
In the field of Schizophrenia, his study on Positive and Negative Syndrome Scale overlaps with subjects such as In patient. His biological study spans a wide range of topics, including Cognitive psychology, Multimodal imaging and Mediation. His studies in Artificial intelligence integrate themes in fields like Regression analysis, Brain mapping and Pattern recognition.
His primary scientific interests are in Neuroscience, Neuroimaging, Cognition, Functional magnetic resonance imaging and Default mode network. He connects Neuroscience with Mechanism in his research. His work deals with themes such as Connectome and Artificial intelligence, which intersect with Neuroimaging.
His research investigates the connection between Cognition and topics such as Cognitive psychology that intersect with issues in Neuropsychology. His Functional magnetic resonance imaging research integrates issues from Schizophrenia, Fractional anisotropy, Diffusion MRI, Major depressive disorder and Pattern recognition. His study in Default mode network is interdisciplinary in nature, drawing from both Clinical psychology and Attention deficit hyperactivity disorder.
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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.
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)
Altered topological properties of functional network connectivity in schizophrenia during resting state: a small-world brain network study.
Qingbao Yu;Jing Sui;Srinivas Rachakonda;Hao He;Hao He.
PLOS ONE (2011)
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)
Profile was last updated on December 6th, 2021.
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