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Tandon R. Computational psychiatry and the Asian Journal of Psychiatry. Asian J Psychiatr 2024; 95:104055. [PMID: 38679536 DOI: 10.1016/j.ajp.2024.104055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/01/2024]
Affiliation(s)
- Rajiv Tandon
- Department of Psychiatry, WMU Homer Stryker School of Medicine, Kalamazoo, MI, United States.
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2
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Papazova I, Wunderlich S, Papazov B, Vogelmann U, Keeser D, Karali T, Falkai P, Rospleszcz S, Maurus I, Schmitt A, Hasan A, Malchow B, Stöcklein S. Characterizing cognitive subtypes in schizophrenia using cortical curvature. J Psychiatr Res 2024; 173:131-138. [PMID: 38531143 DOI: 10.1016/j.jpsychires.2024.03.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 03/11/2024] [Accepted: 03/19/2024] [Indexed: 03/28/2024]
Abstract
Cognitive deficits are a core symptom of schizophrenia, but research on their neural underpinnings has been challenged by the heterogeneity in deficits' severity among patients. Here, we address this issue by combining logistic regression and random forest to classify two neuropsychological profiles of patients with high (HighCog) and low (LowCog) cognitive performance in two independent samples. We based our analysis on the cortical features grey matter volume (VOL), cortical thickness (CT), and mean curvature (MC) of N = 57 patients (discovery sample) and validated the classification in an independent sample (N = 52). We investigated which cortical feature would yield the best classification results and expected that the 10 most important features would include frontal and temporal brain regions. The model based on MC had the best performance with area under the curve (AUC) values of 76% and 73%, and identified fronto-temporal and occipital brain regions as the most important features for the classification. Moreover, subsequent comparison analyses could reveal significant differences in MC of single brain regions between the two cognitive profiles. The present study suggests MC as a promising neuroanatomical parameter for characterizing schizophrenia cognitive subtypes.
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Affiliation(s)
- Irina Papazova
- Psychiatry and Psychotherapy, Faculty of Medicine, University of Augsburg, Geschwister-Schönert-Straße 1, 86156, Augsburg, Germany; Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany; DZPG (German Center for Mental Health), partner site München, Augsburg, Germany.
| | - Stephan Wunderlich
- Department of Radiology, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany; Department of Interventional Radiology, Technical University of Munich, Munich, Germany
| | - Boris Papazov
- Department of Radiology, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Ulrike Vogelmann
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Daniel Keeser
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany; Department of Radiology, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Temmuz Karali
- Department of Radiology, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany; Max Planck Institute of Psychiatry, Munich, Germany
| | - Susanne Rospleszcz
- Institute of Epidemiology, Helmholtz Zentrum Munich, German Research Center for Environmental Health, Munich, Germany; Department of Epidemiology, Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Isabel Maurus
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Andrea Schmitt
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany; Laboratory of Neuroscience (LIM27), Institute of Psychiatry, University of São Paulo (USP), São Paulo, Brazil
| | - Alkomiet Hasan
- Psychiatry and Psychotherapy, Faculty of Medicine, University of Augsburg, Geschwister-Schönert-Straße 1, 86156, Augsburg, Germany; DZPG (German Center for Mental Health), partner site München, Augsburg, Germany
| | - Berend Malchow
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Göttingen, Germany
| | - Sophia Stöcklein
- Department of Radiology, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany
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3
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Lin E, Lin CH, Lane HY. Inference of social cognition in schizophrenia patients with neurocognitive domains and neurocognitive tests using automated machine learning. Asian J Psychiatr 2024; 91:103866. [PMID: 38128351 DOI: 10.1016/j.ajp.2023.103866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 12/07/2023] [Accepted: 12/09/2023] [Indexed: 12/23/2023]
Abstract
AIM It has been suggested that single neurocognitive domain or neurocognitive test can be used to determine the overall cognitive function in schizophrenia using machine learning algorithms. It is unknown whether social cognition in schizophrenia patients can be estimated with machine learning based on neurocognitive domains or neurocognitive tests. METHODS To predict social cognition in schizophrenia, we applied an automated machine learning (AutoML) framework resulting from the analysis of predictive factors such as six neurocognitive domain scores and nine neurocognitive test scores of 380 schizophrenia patients in the Taiwanese population. Four clinical parameters (i.e., age, gender, subgroup, and education) were also used as predictive factors. We utilized an AutoML framework called Tree-based Pipeline Optimization Tool (TPOT) to generate predictive pipelines automatically. RESULTS The analysis revealed that all neurocognitive domains and tests except the reasoning and problem solving domain/test showed significant associations with social cognition. In addition, a TPOT-generated pipeline can best predict social cognition in schizophrenia using seven predictive factors, including five neurocognitive domains (i.e., speed of processing, sustained attention, working memory, verbal learning and memory, and visual learning and memory) and two clinical parameters (i.e., age and gender). This predictive pipeline consists of machine learning algorithms such as function transformers, an approximate feature map, independent component analysis, and linear regression. CONCLUSION The study indicates that an AutoML framework such as TPOT may provide a promising way to produce truly effective machine learning pipelines for predicting social cognition in schizophrenia using neurocognitive domains and/or neurocognitive tests.
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Affiliation(s)
- Eugene Lin
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA; Department of Electrical & Computer Engineering, University of Washington, Seattle, WA 98195, USA; Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan
| | - Chieh-Hsin Lin
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan; Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan; School of Medicine, Chang Gung University, Taoyuan, Taiwan.
| | - Hsien-Yuan Lane
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan; Department of Psychiatry, China Medical University Hospital, Taichung, Taiwan; Brain Disease Research Center, China Medical University Hospital, Taichung, Taiwan; Department of Psychology, College of Medical and Health Sciences, Asia University, Taichung, Taiwan.
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4
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Sun J, Dong QX, Wang SW, Zheng YB, Liu XX, Lu TS, Yuan K, Shi J, Hu B, Lu L, Han Y. Artificial intelligence in psychiatry research, diagnosis, and therapy. Asian J Psychiatr 2023; 87:103705. [PMID: 37506575 DOI: 10.1016/j.ajp.2023.103705] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 07/16/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023]
Abstract
Psychiatric disorders are now responsible for the largest proportion of the global burden of disease, and even more challenges have been seen during the COVID-19 pandemic. Artificial intelligence (AI) is commonly used to facilitate the early detection of disease, understand disease progression, and discover new treatments in the fields of both physical and mental health. The present review provides a broad overview of AI methodology and its applications in data acquisition and processing, feature extraction and characterization, psychiatric disorder classification, potential biomarker detection, real-time monitoring, and interventions in psychiatric disorders. We also comprehensively summarize AI applications with regard to the early warning, diagnosis, prognosis, and treatment of specific psychiatric disorders, including depression, schizophrenia, autism spectrum disorder, attention-deficit/hyperactivity disorder, addiction, sleep disorders, and Alzheimer's disease. The advantages and disadvantages of AI in psychiatry are clarified. We foresee a new wave of research opportunities to facilitate and improve AI technology and its long-term implications in psychiatry during and after the COVID-19 era.
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Affiliation(s)
- Jie Sun
- Pain Medicine Center, Peking University Third Hospital, Beijing 100191, China; Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Qun-Xi Dong
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - San-Wang Wang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China; Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Yong-Bo Zheng
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China; Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Xiao-Xing Liu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Tang-Sheng Lu
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence Research, Peking University, Beijing 100191, China
| | - Kai Yuan
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Jie Shi
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence Research, Peking University, Beijing 100191, China
| | - Bin Hu
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China.
| | - Lin Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China; Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China.
| | - Ying Han
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence Research, Peking University, Beijing 100191, China.
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Tandon R. Application of computational methods to the study of schizophrenia an exciting but treacherous frontier. Asian J Psychiatr 2023; 87:103752. [PMID: 37643481 DOI: 10.1016/j.ajp.2023.103752] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Affiliation(s)
- Rajiv Tandon
- Department of Psychiatry, WMU Homer Stryker School of Medicine, Kalamazoo, MI, United States.
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6
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Malik K, Widyarini IGAA, Kaligis F, Kusumawardhani A, Yusuf PA, Krisnadhi AA, Riandi O, Pujitresnani A. Differences in syntactic and semantic analysis based on machine learning algorithms in prodromal psychosis and normal adolescents. Asian J Psychiatr 2023; 85:103633. [PMID: 37243985 DOI: 10.1016/j.ajp.2023.103633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 05/17/2023] [Accepted: 05/17/2023] [Indexed: 05/29/2023]
Abstract
Schizophrenia has the main symptom of psychosis which is characterized by speech incoherence due to thought process disturbance. Before schizophrenia, there is a prodromal phase of psychosis in adolescence. Early recognition of this phase is important to prevent the development of symptoms into a severe mental disorder. Machine learning technology can be used to predict thought process disturbance through syntactic and semantic analysis of speech. This study aims to describe the differences in syntactic and semantic analysis in prodromal psychosis and normal adolescents. The research subjects consisted of 70 adolescents aged 14-19 years which were divided into 2 groups. Based on the results of the Prodromal Questionnaire-Brief (PQ-B) Indonesian version, the subjects were split into two groups: prodromal and normal. All participants were voice-recorded during interviews using an open-ended qualitative questionnaire. Syntactic and semantic analysis was carried out on all data which amounted to 1017 phrase segments and classified by machine learning. This is the first study in Indonesia to compare the analysis of syntactic and semantic aspects in prodromal psychosis and normal adolescent populations. There were significant differences in syntactic and semantic analysis between groups of adolescents with prodromal psychosis and normal adolescents at the minimum value of coherence and frequency of use of nouns, personal pronouns, subordinate conjunctions, adjectives, prepositions, and proper nouns.
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Affiliation(s)
- Khamelia Malik
- Department of Psychiatry, Neuroscience and Brain Development Cluster Indonesia Medical Education and Research Institute (IMERI), Faculty of Medicine Universitas Indonesia, Cipto Mangunkusumo National Hospital, Jakarta, Indonesia
| | - I Gusti Agung Ayu Widyarini
- Department of Psychiatry, Faculty of Medicine Universitas Indonesia, Cipto Mangunkusumo National Hospital, Jakarta, Indonesia.
| | - Fransiska Kaligis
- Department of Psychiatry, Faculty of Medicine Universitas Indonesia, Cipto Mangunkusumo National Hospital, Jakarta, Indonesia
| | - Aaaa Kusumawardhani
- Department of Psychiatry, Faculty of Medicine Universitas Indonesia, Cipto Mangunkusumo National Hospital, Jakarta, Indonesia
| | - Prasandhya Astagiri Yusuf
- Department of Medical Physiology and Biophysics, Medical Technology Cluster Indonesia Medical Education and Research Institute (IMERI), Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia
| | - Adila Alfa Krisnadhi
- Department of Computer Science and Information System, Universitas Indonesia, West Java, Indonesia
| | | | - Arierta Pujitresnani
- Department of Medical Physiology and Biophysics, Medical Technology Cluster Indonesia Medical Education and Research Institute (IMERI), Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia
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Liu XQ, Ji XY, Weng X, Zhang YF. Artificial intelligence ecosystem for computational psychiatry: Ideas to practice. World J Meta-Anal 2023; 11:79-91. [DOI: 10.13105/wjma.v11.i4.79] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 03/18/2023] [Accepted: 04/04/2023] [Indexed: 04/14/2023] Open
Abstract
Computational psychiatry is an emerging field that not only explores the biological basis of mental illness but also considers the diagnoses and identifies the underlying mechanisms. One of the key strengths of computational psychiatry is that it may identify patterns in large datasets that are not easily identifiable. This may help researchers develop more effective treatments and interventions for mental health problems. This paper is a narrative review that reviews the literature and produces an artificial intelligence ecosystem for computational psychiatry. The artificial intelligence ecosystem for computational psychiatry includes data acquisition, preparation, modeling, application, and evaluation. This approach allows researchers to integrate data from a variety of sources, such as brain imaging, genetics, and behavioral experiments, to obtain a more complete understanding of mental health conditions. Through the process of data preprocessing, training, and testing, the data that are required for model building can be prepared. By using machine learning, neural networks, artificial intelligence, and other methods, researchers have been able to develop diagnostic tools that can accurately identify mental health conditions based on a patient’s symptoms and other factors. Despite the continuous development and breakthrough of computational psychiatry, it has not yet influenced routine clinical practice and still faces many challenges, such as data availability and quality, biological risks, equity, and data protection. As we move progress in this field, it is vital to ensure that computational psychiatry remains accessible and inclusive so that all researchers may contribute to this significant and exciting field.
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Affiliation(s)
- Xin-Qiao Liu
- School of Education, Tianjin University, Tianjin 300350, China
| | - Xin-Yu Ji
- School of Education, Tianjin University, Tianjin 300350, China
| | - Xing Weng
- Huzhou Educational Science & Research Center, Huzhou 313000, Zhejiang Province, China
| | - Yi-Fan Zhang
- School of Education, Tianjin University, Tianjin 300350, China
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8
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Tandon R. Computational psychiatry and the psychopathology of psychosis: Promising leads and blind alleys. Schizophr Res 2023; 254:143-145. [PMID: 36889180 DOI: 10.1016/j.schres.2023.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 02/03/2023] [Accepted: 02/04/2023] [Indexed: 03/10/2023]
Affiliation(s)
- Rajiv Tandon
- Department of Psychiatry, WMU Homer Stryker School of Medicine, Kalamazoo, MI, United States of America.
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9
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Investigation of social and cognitive predictors in non-transition ultra-high-risk' individuals for psychosis using spiking neural networks. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:10. [PMID: 36792634 PMCID: PMC9931713 DOI: 10.1038/s41537-023-00335-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 01/26/2023] [Indexed: 02/17/2023]
Abstract
Finding predictors of social and cognitive impairment in non-transition Ultra-High-Risk individuals (UHR) is critical in prognosis and planning of potential personalised intervention strategies. Social and cognitive functioning observed in youth at UHR for psychosis may be protective against transition to clinically relevant illness. The current study used a computational method known as Spiking Neural Network (SNN) to identify the cognitive and social predictors of transitioning outcome. Participants (90 UHR, 81 Healthy Control (HC)) completed batteries of neuropsychological tests in the domains of verbal memory, working memory, processing speed, attention, executive function along with social skills-based performance at baseline and 4 × 6-month follow-up intervals. The UHR status was recorded as Remitters, Converters or Maintained. SNN were used to model interactions between variables across groups over time and classify UHR status. The performance of SNN was examined relative to other machine learning methods. Higher interaction between social and cognitive variables was seen for the Maintained, than Remitter subgroup. Findings identified the most important cognitive and social variables (particularly verbal memory, processing speed, attention, affect and interpersonal social functioning) that showed discriminative patterns in the SNN models of HC vs UHR subgroups, with accuracies up to 80%; outperforming other machine learning models (56-64% based on 18 months data). This finding is indicative of a promising direction for early detection of social and cognitive impairment in UHR individuals that may not anticipate transition to psychosis and implicate early initiated interventions to stem the impact of clinical symptoms of psychosis.
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10
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Mukku L, Thomas J. A machine learning model to predict suicidal tendencies in students. Asian J Psychiatr 2023; 79:103363. [PMID: 36481568 DOI: 10.1016/j.ajp.2022.103363] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 10/20/2022] [Accepted: 11/10/2022] [Indexed: 11/27/2022]
Affiliation(s)
- Lalasa Mukku
- CHRIST (Deemed to be University), Kengeri Campus, Bengaluru 560074, India.
| | - Jyothi Thomas
- CHRIST (Deemed to be University), Kengeri Campus, Bengaluru 560074, India
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11
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SupriyaPatro P, Goel T, VaraPrasad SA, Tanveer M, Murugan R. Lightweight 3D Convolutional Neural Network for Schizophrenia Diagnosis Using MRI Images and Ensemble Bagging Classifier. Cognit Comput 2022. [DOI: 10.1007/s12559-022-10093-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
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12
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Tandon R, Greden JF. Reconstructing schizophrenia: Lessons from major mood disorders. Schizophr Res 2022; 242:42-44. [PMID: 35168835 DOI: 10.1016/j.schres.2022.02.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 02/01/2022] [Accepted: 02/02/2022] [Indexed: 11/17/2022]
Affiliation(s)
- Rajiv Tandon
- Department of Psychiatry, WMU Homer Stryker School of Medicine, Kalamazoo, MI, United States of America.
| | - John F Greden
- Department of Psychiatry, University of Michigan Medical Center, Ann Arbor, MI, United States of America
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13
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Tandon R, Keshavan M, Nasrallah H. Reinventing schizophrenia. Updating the construct. Schizophr Res 2022; 242:1-3. [PMID: 35241314 DOI: 10.1016/j.schres.2022.02.024] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 02/17/2022] [Indexed: 02/04/2023]
Affiliation(s)
- Rajiv Tandon
- Department of Psychiatry, WMU Homer Stryker School of Medicine, Kalamazoo, MI, United States of America.
| | - Matcheri Keshavan
- Department of Psychiatry, BIDMC, Harvard Medical School, Boston, MA, United States of America
| | - Henry Nasrallah
- Department of Psychiatry, University of Cincinnati Medical School, Cincinnati, OH, United States of America
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14
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Tandon R. Agreement on the contours of schizophrenia: The first order of business. Schizophr Res 2022; 242:135-137. [PMID: 35067457 DOI: 10.1016/j.schres.2022.01.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Accepted: 01/02/2022] [Indexed: 11/25/2022]
Affiliation(s)
- Rajiv Tandon
- Department of Psychiatry, WMU Homer Stryker School of Medicine, Kalamazoo, MI, United States of America.
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Wiese W, Friston KJ. AI ethics in computational psychiatry: From the neuroscience of consciousness to the ethics of consciousness. Behav Brain Res 2021; 420:113704. [PMID: 34871706 PMCID: PMC9125160 DOI: 10.1016/j.bbr.2021.113704] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 11/25/2021] [Accepted: 11/29/2021] [Indexed: 12/11/2022]
Abstract
Methods used in artificial intelligence (AI) overlap with methods used in computational psychiatry (CP). Hence, considerations from AI ethics are also relevant to ethical discussions of CP. Ethical issues include, among others, fairness and data ownership and protection. Apart from this, morally relevant issues also include potential transformative effects of applications of AI—for instance, with respect to how we conceive of autonomy and privacy. Similarly, successful applications of CP may have transformative effects on how we categorise and classify mental disorders and mental health. Since many mental disorders go along with disturbed conscious experiences, it is desirable that successful applications of CP improve our understanding of disorders involving disruptions in conscious experience. Here, we discuss prospects and pitfalls of transformative effects that CP may have on our understanding of mental disorders. In particular, we examine the concern that even successful applications of CP may fail to take all aspects of disordered conscious experiences into account. Considerations from AI ethics are also relevant to the ethics of computational psychiatry. Ethical issues include, among others, fairness and data ownership and protection. They also include potential transformative effects. Computational psychiatry may transform conceptions of mental disorders and health. Disordered conscious experiences may pose a particular challenge.
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Affiliation(s)
- Wanja Wiese
- Institute of Philosophy II, Ruhr University Bochum, Universitätsstraße 150, 44780 Bochum, Germany.
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, University College London, 12 Queen Square, London WC1N 3AR, UK
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Nomura J, Mardo M, Takumi T. Molecular signatures from multi-omics of autism spectrum disorders and schizophrenia. J Neurochem 2021; 159:647-659. [PMID: 34537986 DOI: 10.1111/jnc.15514] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 08/11/2021] [Accepted: 09/07/2021] [Indexed: 01/25/2023]
Abstract
The genetic and phenotypic heterogeneity of autism spectrum disorder (ASD) impedes the unification of multiple biological hypotheses in an attempt to explain the complex features of ASD, such as impaired social communication, social interaction deficits, and restricted and repetitive patterns of behavior. However, recent psychiatric genetic studies have identified numerous risk genes and chromosome loci (copy number variation: CNV) which enable us to analyze at the single gene level and utilize system-level approaches. In this review, we focus on ASD as a major neurodevelopmental disorder and review recent findings mainly from the bioinformatics of omics studies. Additionally, by comparing these data with other major psychiatric disorders, including schizophrenia (SCZ), we identify unique characteristics of both diseases from multiple enrichment, pathway, and protein-protein interaction networks (PPIs) analyses using susceptible genes found in recent large-scale genetic studies. These unified, systematic approaches highlight unique characteristics of both disorders from multiple aspects and demonstrate how convergent pathways can contribute to an understanding of the complex etiology of such neurodevelopmental and neuropsychiatric disorders.
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Affiliation(s)
- Jun Nomura
- Department of Physiology and Cell Biology, Kobe University School of Medicine, Kobe, Japan
| | - Matthew Mardo
- Neuroscience concentration, Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts, USA
| | - Toru Takumi
- Department of Physiology and Cell Biology, Kobe University School of Medicine, Kobe, Japan
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17
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Rebouças DB, Sartori JM, Librenza-Garcia D, Rabelo-da-Ponte FD, Massuda R, Czepielewski LS, Passos IC, Gama CS. Accelerated aging signatures in subjects with schizophrenia and their unaffected siblings. J Psychiatr Res 2021; 139:30-37. [PMID: 34022473 DOI: 10.1016/j.jpsychires.2021.04.029] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 04/10/2021] [Accepted: 04/25/2021] [Indexed: 01/19/2023]
Abstract
Schizophrenia (SZ) is a chronic debilitating disease. Subjects with SZ have significant shorter life expectancy. Growing evidence suggests that a process of pathological accelerated aging occurs in SZ, leading to early development of severe clinical diseases and worse morbimortality. Furthermore, unaffected relatives can share certain endophenotypes with subjects with SZ. We aim to characterize accelerated aging as a possible endophenotype of schizophrenia by using a machine learning (ML) model of peripheral biomarkers to accurately differentiate subjects with SZ (n = 35), their unaffected siblings (SB, n = 36) and healthy controls (HC, n = 47). We used a random forest algorithm that included biomarkers related to aging: eotaxins CCL-11 and CCL-24; the oxidative stress markers thiobarbituric acid-reactive substances (TBARS), protein carbonyl content (PCC), glutathione peroxidase (GPx); and telomere length (TL). The ML algorithm of biomarkers was able to distinguish individuals with SZ from HC with prediction accuracy of 79.7%, SZ from SB with 62.5% accuracy and SB from HC with 75.5% accuracy. These results support the hypothesis that a pathological accelerated aging might occur in SZ, and this pathological aging could be an endophenotype of the disease, once this profile was also observed in SB, suggesting that SB might suffer from an accelerated aging in some level.
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Affiliation(s)
- Diego Barreto Rebouças
- Laboratório de Psiquiatria Molecular, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Programa de Pós-Graduação em Psiquiatria e Ciências do Comportamento, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Juliana Mastella Sartori
- Laboratório de Psiquiatria Molecular, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Programa de Pós-Graduação em Psiquiatria e Ciências do Comportamento, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Diego Librenza-Garcia
- Laboratório de Psiquiatria Molecular, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Francisco Diego Rabelo-da-Ponte
- Laboratório de Psiquiatria Molecular, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Programa de Pós-Graduação em Psiquiatria e Ciências do Comportamento, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Raffael Massuda
- Laboratório de Psiquiatria Molecular, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Departamento de Psiquiatria, Universidade Federal do Paraná, Curitiba, Brazil
| | - Leticia Sanguinetti Czepielewski
- Laboratório de Psiquiatria Molecular, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Programa de Pós- Graduação em Psicologia, Departamento de Psicologia do Desenvolvimento e da Personalidade, Instituto de Psicologia, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Ives Cavalcante Passos
- Laboratório de Psiquiatria Molecular, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Programa de Pós-Graduação em Psiquiatria e Ciências do Comportamento, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Clarissa Severino Gama
- Laboratório de Psiquiatria Molecular, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil; Programa de Pós-Graduação em Psiquiatria e Ciências do Comportamento, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil.
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18
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Trajkovic J, Di Gregorio F, Ferri F, Marzi C, Diciotti S, Romei V. Resting state alpha oscillatory activity is a valid and reliable marker of schizotypy. Sci Rep 2021; 11:10379. [PMID: 34001914 PMCID: PMC8129121 DOI: 10.1038/s41598-021-89690-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 04/30/2021] [Indexed: 12/20/2022] Open
Abstract
Schizophrenia is among the most debilitating neuropsychiatric disorders. However, clear neurophysiological markers that would identify at-risk individuals represent still an unknown. The aim of this study was to investigate possible alterations in the resting alpha oscillatory activity in normal population high on schizotypy trait, a physiological condition known to be severely altered in patients with schizophrenia. Direct comparison of resting-state EEG oscillatory activity between Low and High Schizotypy Group (LSG and HSG) has revealed a clear right hemisphere alteration in alpha activity of the HSG. Specifically, HSG shows a significant slowing down of right hemisphere posterior alpha frequency and an altered distribution of its amplitude, with a tendency towards a reduction in the right hemisphere in comparison to LSG. Furthermore, altered and reduced connectivity in the right fronto-parietal network within the alpha range was found in the HSG. Crucially, a trained pattern classifier based on these indices of alpha activity was able to successfully differentiate HSG from LSG on tested participants further confirming the specific importance of right hemispheric alpha activity and intrahemispheric functional connectivity. By combining alpha activity and connectivity measures with a machine learning predictive model optimized in a nested stratified cross-validation loop, current research offers a promising clinical tool able to identify individuals at-risk of developing psychosis (i.e., high schizotypy individuals).
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Affiliation(s)
- Jelena Trajkovic
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum - Università di Bologna, Campus di Cesena, 47521, Cesena, Italy
| | - Francesco Di Gregorio
- UO Medicina Riabilitativa e Neuroriabilitazione, Azienda Unità Sanitaria Locale, 40139, Bologna, Italy
| | - Francesca Ferri
- Department of Neuroscience, Imaging and Clinical Sciences, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy.,Institute for Advanced Biomedical Technologies, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Chiara Marzi
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena, Italy
| | - Stefano Diciotti
- Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Cesena, Italy.,Alma Mater Research Institute for Human-Centered Artificial Intelligence, University of Bologna, Bologna, Italy
| | - Vincenzo Romei
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum - Università di Bologna, Campus di Cesena, 47521, Cesena, Italy. .,IRCCS Fondazione Santa Lucia, 00179, Rome, Italy.
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19
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Vatansever S, Schlessinger A, Wacker D, Kaniskan HÜ, Jin J, Zhou M, Zhang B. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Med Res Rev 2021; 41:1427-1473. [PMID: 33295676 PMCID: PMC8043990 DOI: 10.1002/med.21764] [Citation(s) in RCA: 95] [Impact Index Per Article: 31.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 10/30/2020] [Accepted: 11/20/2020] [Indexed: 01/11/2023]
Abstract
Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.
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Affiliation(s)
- Sezen Vatansever
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Avner Schlessinger
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Daniel Wacker
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of NeuroscienceIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - H. Ümit Kaniskan
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Jian Jin
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Ming‐Ming Zhou
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Bin Zhang
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
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20
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Barros C, Silva CA, Pinheiro AP. Advanced EEG-based learning approaches to predict schizophrenia: Promises and pitfalls. Artif Intell Med 2021; 114:102039. [PMID: 33875158 DOI: 10.1016/j.artmed.2021.102039] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 12/11/2020] [Accepted: 02/16/2021] [Indexed: 01/10/2023]
Abstract
The complexity and heterogeneity of schizophrenia symptoms challenge an objective diagnosis, which is typically based on behavioral and clinical manifestations. Moreover, the boundaries of schizophrenia are not precisely demarcated from other nosologic categories, such as bipolar disorder. The early detection of schizophrenia can lead to a more effective treatment, improving patients' quality of life. Over the last decades, hundreds of studies aimed at specifying the neurobiological mechanisms that underpin clinical manifestations of schizophrenia, using techniques such as electroencephalography (EEG). Changes in event-related potentials of the EEG have been associated with sensory and cognitive deficits and proposed as biomarkers of schizophrenia. Besides contributing to a more effective diagnosis, biomarkers can be crucial to schizophrenia onset prediction and prognosis. However, any proposed biomarker requires substantial clinical research to prove its validity and cost-effectiveness. Fueled by developments in computational neuroscience, automatic classification of schizophrenia at different stages (prodromal, first episode, chronic) has been attempted, using brain imaging pattern recognition methods to capture differences in functional brain activity. Advanced learning techniques have been studied for this purpose, with promising results. This review provides an overview of recent machine learning-based methods for schizophrenia classification using EEG data, discussing their potentialities and limitations. This review is intended to serve as a starting point for future developments of effective EEG-based models that might predict the onset of schizophrenia, identify subjects at high-risk of psychosis conversion or differentiate schizophrenia from other disorders, promoting more effective early interventions.
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Affiliation(s)
- Carla Barros
- Center for Research in Psychology (CIPsi), School of Psychology, University of Minho, Braga, Portugal
| | - Carlos A Silva
- Center for Microelectromechanical Systems (CMEMS), School of Engineering, University of Minho, Guimarães, Portugal
| | - Ana P Pinheiro
- Center for Research in Psychology (CIPsi), School of Psychology, University of Minho, Braga, Portugal; CICPSI, Faculdade de Psicologia, Universidade de Lisboa, Lisboa, Portugal.
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21
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Person-based machine learning: Accounting for patient experience to explain the nature of schizophrenia. Schizophr Res 2021; 228:619-620. [PMID: 33229226 DOI: 10.1016/j.schres.2020.11.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 07/26/2020] [Accepted: 11/16/2020] [Indexed: 11/21/2022]
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22
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Cui LB, Fu YF, Liu L, Wu XS, Xi YB, Wang HN, Qin W, Yin H. Baseline structural and functional magnetic resonance imaging predicts early treatment response in schizophrenia with radiomics strategy. Eur J Neurosci 2020; 53:1961-1975. [PMID: 33206423 DOI: 10.1111/ejn.15046] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 10/27/2020] [Accepted: 11/11/2020] [Indexed: 02/07/2023]
Abstract
Multimodal neuroimaging features provide opportunities for accurate classification and personalized treatment options in the psychiatric domain. This study aimed to investigate whether brain features predict responses to the overall treatment of schizophrenia at the end of the first or a single hospitalization. Structural and functional magnetic resonance imaging (MRI) data from two independent samples (N = 85 and 63, separately) of schizophrenia patients at baseline were included. After treatment, patients were classified as responders and non-responders. Radiomics features of gray matter morphology and functional connectivity were extracted using Least Absolute Shrinkage and Selection Operator. Support vector machine was used to explore the predictive performance. Prediction models were based on structural features (cortical thickness, surface area, gray matter regional volume, mean curvature, metric distortion, and sulcal depth), functional features (functional connectivity), and combined features. There were 12 features after dimensionality reduction. The structural features involved the right precuneus, cuneus, and inferior parietal lobule. The functional features predominately included inter-hemispheric connectivity. We observed a prediction accuracy of 80.38% (sensitivity: 87.28%; specificity 82.47%) for the model using functional features, and 69.68% (sensitivity: 83.96%; specificity: 72.41%) for the one using structural features. Our model combining both structural and functional features achieved a higher accuracy of 85.03%, with 92.04% responder and 80.23% non-responders to the overall treatment to be correctly predicted. These results highlight the power of structural and functional MRI-derived radiomics features to predict early response to treatment in schizophrenia. Prediction models of the very early treatment response in schizophrenia could augment effective therapeutic strategies.
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Affiliation(s)
- Long-Biao Cui
- Department of Clinical Psychology, School of Medical Psychology, Fourth Military Medical University, Xi'an, China.,Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, China.,Department of Radiology, The Second Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Yu-Fei Fu
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Lin Liu
- School of Life Sciences and Technology, Xidian University, Xi'an, China.,Sixth Hospital/Institute of Mental Health and Key Laboratory of Mental Health, Peking University, Beijing, China
| | - Xu-Sha Wu
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Yi-Bin Xi
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Hua-Ning Wang
- Department of Psychiatry, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Wei Qin
- School of Life Sciences and Technology, Xidian University, Xi'an, China
| | - Hong Yin
- Department of Radiology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
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23
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Development of Machine Learning Model to Predict the 5-Year Risk of Starting Biologic Agents in Patients with Inflammatory Bowel Disease (IBD): K-CDM Network Study. J Clin Med 2020; 9:jcm9113427. [PMID: 33114505 PMCID: PMC7693158 DOI: 10.3390/jcm9113427] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 10/14/2020] [Accepted: 10/21/2020] [Indexed: 12/17/2022] Open
Abstract
Background: The incidence and global burden of inflammatory bowel disease (IBD) have steadily increased in the past few decades. Improved methods to stratify risk and predict disease-related outcomes are required for IBD. Aim: The aim of this study was to develop and validate a machine learning (ML) model to predict the 5-year risk of starting biologic agents in IBD patients. Method: We applied an ML method to the database of the Korean common data model (K-CDM) network, a data sharing consortium of tertiary centers in Korea, to develop a model to predict the 5-year risk of starting biologic agents in IBD patients. The records analyzed were those of patients diagnosed with IBD between January 2006 and June 2017 at Gil Medical Center (GMC; n = 1299) or present in the K-CDM network (n = 3286). The ML algorithm was developed to predict 5- year risk of starting biologic agents in IBD patients using data from GMC and externally validated with the K-CDM network database. Result: The ML model for prediction of IBD-related outcomes at 5 years after diagnosis yielded an area under the curve (AUC) of 0.86 (95% CI: 0.82–0.92), in an internal validation study carried out at GMC. The model performed consistently across a range of other datasets, including that of the K-CDM network (AUC = 0.81; 95% CI: 0.80–0.85), in an external validation study. Conclusion: The ML-based prediction model can be used to identify IBD-related outcomes in patients at risk, enabling physicians to perform close follow-up based on the patient’s risk level, estimated through the ML algorithm.
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24
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Tikka SK, Singh BK, Nizamie SH, Garg S, Mandal S, Thakur K, Singh LK. Artificial intelligence-based classification of schizophrenia: A high density electroencephalographic and support vector machine study. Indian J Psychiatry 2020; 62:273-282. [PMID: 32773870 PMCID: PMC7368447 DOI: 10.4103/psychiatry.indianjpsychiatry_91_20] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 03/31/2020] [Accepted: 04/04/2020] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Interview-based schizophrenia (SCZ) diagnostic methods are not completely valid. Moreover, SCZ-the disease entity is very heterogeneous. Supervised-Machine-Learning (sML) application of Artificial-Intelligence holds a tremendous promise in solving these issues. AIMS To sML-based discriminating validity of resting-state electroencephalographic (EEG) quantitative features in classifying SCZ from healthy and, positive (PS) and negative symptom (NS) subgroups, using a high-density recording. SETTINGS AND DESIGN Data collected at a tertiary care mental-health institute using a cross-sectional study design and analyzed at a premier Engineering Institute. MATERIALS AND METHODS Data of 38-SCZ patients and 20-healthy controls were retrieved. The positive-negative subgroup classification was done using Positive and Negative Syndrome Scale operational-criteria. EEG was recorded using 256-channel high-density equipment. Eight priori regions-of-interest were selected. Six-level wavelet decomposition and Kernel-Support Vector Machine (SVM) method were used for feature extraction and data classification. STATISTICAL ANALYSIS Mann-Whitney test was used for comparison of machine learning-features. Accuracy, sensitivity, specificity, and area under receiver operating characteristics-curve were measured as discriminatory indices of classifications. RESULTS Accuracy of classifying SCZ from healthy and PS from NS SCZ, were 78.95% and 89.29%, respectively. While beta and gamma frequency related features most accurately classified SCZ from healthy controls, delta and theta frequency related features most accurately classified positive from negative SCZ. Inferior frontal gyrus features most accurately contributed to both the classificatory instances. CONCLUSIONS SVM-based classification and sub-classification of SCZ using EEG data is optimal and might help in improving the "validity" and reducing the "heterogeneity" in the diagnosis of SCZ. These results might only be generalized to acute and moderately ill male SCZ patients.
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Affiliation(s)
- Sai Krishna Tikka
- Department of Psychiatry, All India Institute of Medical Sciences , Raipur, Chhattisgarh, India
| | - Bikesh Kumar Singh
- Department of Bio-Medical Engineering, National Institute of Technology , Raipur, Chhattisgarh, India
| | - S Haque Nizamie
- Department of Psychiatry, Central Institute of Psychiatry, Ranchi, Jharkhand, India
| | - Shobit Garg
- Department of Psychiatry, Shri Guru Ram Rai Institute of Medical and Health Sciences, Dehradun, Uttarakhand, India
| | - Sunandan Mandal
- School of Studies in Electronics and Photonics, Pt. Ravishankar Shukla University, Raipur, Chhattisgarh, India
| | - Kavita Thakur
- School of Studies in Electronics and Photonics, Pt. Ravishankar Shukla University, Raipur, Chhattisgarh, India
| | - Lokesh Kumar Singh
- Department of Psychiatry, All India Institute of Medical Sciences , Raipur, Chhattisgarh, India
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25
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Perera IR, Hettiarachchige L, Liyanage S. Machine Learning Applications for optimized mental health outcomes in Asia: Translating Hype to Hope. Asian J Psychiatr 2020; 49:101977. [PMID: 32120297 DOI: 10.1016/j.ajp.2020.101977] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 01/26/2020] [Accepted: 02/21/2020] [Indexed: 12/27/2022]
Affiliation(s)
| | | | - Sidath Liyanage
- Department of Software Engineering, Faculty of Computing & Technology, University of Kelaniya, Sri Lanka
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26
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Chakravarty MM. Guest editorial: Special issue on machine learning in schizophrenia. Schizophr Res 2019; 214:1-2. [PMID: 31711732 DOI: 10.1016/j.schres.2019.10.044] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 10/20/2019] [Indexed: 11/16/2022]
Affiliation(s)
- M Mallar Chakravarty
- Computational Brain Anatomy (CoBrA Laboratory), Douglas Institute, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada.
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27
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Affiliation(s)
- Neeraj Tandon
- Department of Psychiatry, WMU Homer Stryker School of Medicine, Kalamazoo, MI, United States
| | - Rajiv Tandon
- Department of Psychiatry, WMU Homer Stryker School of Medicine, Kalamazoo, MI, United States.
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