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In Vivo Hippocampal Subfield Volumes in Schizophrenia and Bipolar Disorder
2015
Biological Psychiatry
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Patients with bipolar disorder were classified as having psychotic or nonpsychotic bipolar disorder based on the presence of either a current psychotic episode (defined as a score of $4 on any one of the ...
We found smaller subiculum volume to be related to poorer immediate and delayed verbal recall in patients with bipolar disorder and healthy control subjects. ...
doi:10.1016/j.biopsych.2014.06.020
pmid:25127742
fatcat:chdtpmuljfaoxhcvzcvkyhpu3e
Distinguishing between Unipolar Depression and Bipolar Depression: Current and Future Clinical and Neuroimaging Perspectives
2013
Biological Psychiatry
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Differentiating bipolar disorder (BD) from recurrent unipolar depression (UD) is a major clinical challenge. ...
, and differential patterns of functional abnormalities in emotion regulation and attentional control neural circuitry in the two depression types. ...
Phillips reports having support from R01 MH076971-01, RO1 MH073953 (LAMS), 2RO1 MH060952-11 (BIOS) and U01MH092221-01 (EMBARC). ...
doi:10.1016/j.biopsych.2012.06.010
pmid:22784485
pmcid:PMC3494754
fatcat:iipn3ugegbaw3jq7qk4l2j6hxi
Neuroimaging biomarkers in bipolar disorder
2012
Frontiers in Bioscience (Elite Edition)
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The diagnosis of bipolar disorder is currently based entirely on clinical evaluation, without any possibility of confirming the diagnosis by laboratory tests. ...
These WMH are located in the deep white matter and in the periventricular areas, and have been identified in BD patients during their first episode (8) . ...
doi:10.2741/e402
fatcat:idokvr2ajfburpmvoji3mq2lie
Deep Neural Network to Differentiate Brain Activity Between Patients With First-Episode Schizophrenia and Healthy Individuals: A Multi-Channel Near Infrared Spectroscopy Study
2021
Frontiers in Psychiatry
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This study aimed to differentiate between patients with FES and healthy controls (HCs) on basis of the frontotemporal activity measured by NIRS with a support vector machine (SVM) and deep neural network ...
Backgrounds: Reduced brain cortical activity over the frontotemporal regions measured by near infrared spectroscopy (NIRS) has been reported in patients with first-episode schizophrenia (FES). ...
To the best of our knowledge, this is the first study using deep learning to automatically differentiate FES from HC based on brain cortical activity features. ...
doi:10.3389/fpsyt.2021.655292
pmid:33935840
pmcid:PMC8081971
fatcat:5ys235d3yffhnlz3fd6x3y5bvy
Brain gray matter phenotypes across the psychosis dimension
2012
Psychiatry Research : Neuroimaging
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), 17 with psychotic bipolar I disorder (BD-P) and 10 healthy controls (HC). ...
This study sought to examine whole brain and regional gray matter (GM) phenotypes across the schizophrenia (SZ)-bipolar disorder psychosis dimension using voxel-based morphometry (VBM 8.0 with DARTEL segmentation ...
Funding This work was supported by National Institute of Mental Health (MH077851-01A1, and MH 78113, Bipolar & Schizophrenia Consortium for Parsing Endophenotypes). ...
doi:10.1016/j.pscychresns.2012.05.001
pmid:23177922
pmcid:PMC3589584
fatcat:lwxdse4urvhktdfcu6bb6xyh4a
Machine Learning in Detecting Schizophrenia: An Overview
2021
Intelligent Automation and Soft Computing
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Reviewing a large number of studies shows that a support vector machine, deep neural network, and random forest predict SZ with a high accuracy of 70%-90%. ...
Recently, scientists applied machine learning (ML) and artificial intelligence for the detection, monitoring, and prognosis of a range of diseases, including SZ, because these techniques show a high performance ...
Another study [38] used NLP for predicting psychosis based on 40 interview transcripts with the first episode of psychosis. ...
doi:10.32604/iasc.2021.015049
fatcat:q2jyzc4on5btrlnhafqis3kxt4
Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging
2018
Frontiers in Neuroscience
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In terms of model building, we include traditional classifiers as well as more recently applied deep learning methods. ...
Moreover, we review representative applications with remarkable classification accuracy for psychosis and mood disorders, neurodevelopmental disorder, and neurological disorders using fMRI data. ...
Science Foundation of China (Grant No. 61703253, to YD) and Natural Science Foundation of Shanxi Province (Grant No. 2016021077, to YD). ...
doi:10.3389/fnins.2018.00525
pmid:30127711
pmcid:PMC6088208
fatcat:b7hdtmrz4vehlhjw4fo2bewhwq
Recent advances of deep learning in psychiatric disorders
2020
Precision Clinical Medicine
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Deep learning (DL) is a recently proposed subset of machine learning methods that has gained extensive attention in the academic world, breaking benchmark records in areas such as visual recognition and ...
Here, we provide a brief review of recent advances and associated challenges in neuroimaging studies of DL applied to psychiatric disorders. ...
Acknowledgements This work was supported by National Natural Science Foundation of China (Grant No. 91859203) and Young Elite Scientists Sponsorship Program by CAST (YESS20160060). ...
doi:10.1093/pcmedi/pbaa029
pmid:35694413
pmcid:PMC8982596
fatcat:46k3vpw65ndnzcslne4a5fznem
Integrated Neurobiology of Bipolar Disorder
2014
Frontiers in Psychiatry
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Lateral ventricles were significantly larger in patients with multiple episodes than in the first episode or healthy subjects. ...
CHANGES IN VENTRICULAR SIZE AND CEREBRAL GRAY MATTER VOLUME Strakowski et al. utilized MRI to compare cerebral ventricle volumes in healthy controls vs. patients suffering their first bipolar episode or ...
doi:10.3389/fpsyt.2014.00098
pmid:25202283
pmcid:PMC4142322
fatcat:eemet5r5arfelecpwfxl5uf4r4
Diffusion tensor imaging in first degree relatives of schizophrenia and bipolar disorder patients
2015
Schizophrenia Research
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In this review, we evaluate published diffusion tensor imaging (DTI) studies comparing first degree relatives of SZ and BD patients and healthy control subjects. ...
Objectives-White matter (WM) abnormalities are one of the most widely and consistently reported findings in schizophrenia (SZ) and bipolar disorder (BD). ...
Acknowledgments This work was supported by R01MH094594 (to DO) and the Shervert Frazier Research Institute at McLean Hospital (to BMC) Role of funding source: The funding source had no influence on the ...
doi:10.1016/j.schres.2014.12.008
pmid:25542860
pmcid:PMC4308443
fatcat:xybsjxjg5zhtheubs7myz6aufi
A Comprehensive Review of Computer-Aided Diagnosis of Major Mental and Neurological Disorders and Suicide: A Biostatistical Perspective on Data Mining
2021
Diagnostics
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Depressive disorders, schizophrenia, bipolar disorder, Alzheimer's disease, and other dementias account for 1.84%, 0.60%, 0.33%, and 1.00% of total Disability Adjusted Life Years (DALYs). ...
The World Health Organization (WHO) suggests that mental disorders, neurological disorders, and suicide are growing causes of morbidity. ...
At least one period of mania is necessary for a specific diagnosis of bipolar disorder I (BD-I), while one hypomania and major depressive episode without a manic episode is essential for bipolar II (BD-II ...
doi:10.3390/diagnostics11030393
pmid:33669114
pmcid:PMC7996506
fatcat:zynohu6szjc2rd4kwnd4jc4nje
High-Precise Bipolar Disorder Detection by Using Radial Basis Functions Based Neural Network
2022
Electronics
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The results show that the proposed method is an effective approach for discrimination of two kinds of classes, i.e., bipolar disorder patients and healthy persons. ...
Thus, the modelling, characterization, classification, diagnosis, and analysis of such mental disorders bears great significance in medical research. ...
This study was based on the use of deep learning techniques. ...
doi:10.3390/electronics11030343
fatcat:fvoa2cvs4bes3gh3pnq6fcakqu
Psychiatric Neural Networks and Precision Therapeutics by Machine Learning
2021
Biomedicines
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Learning and environmental adaptation increase the likelihood of survival and improve the quality of life. ...
In this review, decision-making in real life and psychiatric disorders and the applications of machine learning in brain imaging studies on psychiatric disorders are summarized, and considerations for ...
Azad and Wei-hsuan Yu for reviewing this manuscript.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/biomedicines9040403
pmid:33917863
fatcat:qnptg73avbc5bdurwcjvf472p4
Artificial intelligence applications in psychoradiology
2021
Psychoradiology
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In this review, we selectively summarize psychoradiological research using magnetic resonance imaging of the brain to explore the neural mechanism of psychiatric disorders, and outline progress and the ...
path forward for the combination of psychoradiology and AI for complementing clinical examinations in patients with psychiatric disorders, as well as limitations in the application of AI that should be ...
Wenjing Zhang, Lekai Luo, Wanfang You, and Yuxia Wang, who are from Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, for performing aspects of the work ...
doi:10.1093/psyrad/kkab009
fatcat:7z3shbc4vzfg7pwmfyumqsgtia
7th European Conference on Schizophrenia Research: Time for precision medicine?
2019
European Archives of Psychiatry and Clinical Neuroscience
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The costs for publication were fully covered by delegates' payments of registration fees. ...
controls, 42 healthy students scoring either high or low in schizotypal
traits, and 22 patients with first episodes of psychosis (FEP). ...
Methods: Twenty-three drug-naïve, first-episode psychosis patients and twenty-six matched healthy controls completed the study. ...
doi:10.1007/s00406-019-01045-6
fatcat:4tbfk4p4yngqto7uxhrdczle54
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