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Fan Y, Tao Y, Wang J, Gao Y, Wei W, Zheng C, Zhang X, Song XM, Northoff G. Irregularity of visual motion perception and negative symptoms in schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2024; 10:82. [PMID: 39349502 PMCID: PMC11443095 DOI: 10.1038/s41537-024-00496-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Accepted: 08/20/2024] [Indexed: 10/02/2024]
Abstract
Schizophrenia (SZ) is a severe psychiatric disorder characterized by perceptual, emotional, and behavioral abnormalities, with cognitive impairment being a prominent feature of the disorder. Recent studies demonstrate irregularity in SZ with increased variability on the neural level. Is there also irregularity on the psychophysics level like in visual perception? Here, we introduce a methodology to analyze the irregularity in a trial-by-trial way to compare the SZ and healthy control (HC) subjects. In addition, we use an unsupervised clustering algorithm K-means + + to identify SZ subgroups in the sample, followed by validation of the subgroups based on intraindividual visual perception variability and clinical symptomatology. The K-means + + method divided SZ patients into two subgroups by measuring durations across trials in the motion discrimination task, i.e., high, and low irregularity of SZ patients (HSZ, LSZ). We found that HSZ and LSZ subgroups are associated with more negative and positive symptoms respectively. Applying a mediation model in the HSZ subgroup, the enhanced irregularity mediates the relationship between visual perception and negative symptoms. Together, we demonstrate increased irregularity in visual perception of a HSZ subgroup, including its association with negative symptoms. This may serve as a promising marker for identifying and distinguishing SZ subgroups.
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Affiliation(s)
- Yi Fan
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Biomedical Engineering of Ministry of Education, Qiushi Academy for Advanced Studies, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Yunhai Tao
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jue Wang
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yuan Gao
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China
| | - Wei Wei
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China
| | - Chanying Zheng
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China
- Key Laboratory of Biomedical Engineering of Ministry of Education, Qiushi Academy for Advanced Studies, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Xiaotong Zhang
- MOE Frontier Science Center for Brain Science & Brain-Machine Integration, Zhejiang University, Hangzhou, China
- College of Electrical Engineering, Zhejiang University, Hangzhou, China
| | - Xue Mei Song
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China.
- Key Laboratory of Biomedical Engineering of Ministry of Education, Qiushi Academy for Advanced Studies, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China.
| | - Georg Northoff
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Interdisciplinary Institute of Neuroscience and Technology, School of Medicine, Zhejiang University, Hangzhou, China.
- University of Ottawa Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada.
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Anbarasi J, Kumari R, Ganesh M, Agrawal R. Translational Connectomics: overview of machine learning in macroscale Connectomics for clinical insights. BMC Neurol 2024; 24:364. [PMID: 39342171 PMCID: PMC11438080 DOI: 10.1186/s12883-024-03864-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Accepted: 09/16/2024] [Indexed: 10/01/2024] Open
Abstract
Connectomics is a neuroscience paradigm focused on noninvasively mapping highly intricate and organized networks of neurons. The advent of neuroimaging has led to extensive mapping of the brain functional and structural connectome on a macroscale level through modalities such as functional and diffusion MRI. In parallel, the healthcare field has witnessed a surge in the application of machine learning and artificial intelligence for diagnostics, especially in imaging. While reviews covering machine learn ing and macroscale connectomics exist for specific disorders, none provide an overview that captures their evolving role, especially through the lens of clinical application and translation. The applications include understanding disorders, classification, identifying neuroimaging biomarkers, assessing severity, predicting outcomes and intervention response, identifying potential targets for brain stimulation, and evaluating the effects of stimulation intervention on the brain and connectome mapping in patients before neurosurgery. The covered studies span neurodegenerative, neurodevelopmental, neuropsychiatric, and neurological disorders. Along with applications, the review provides a brief of common ML methods to set context. Conjointly, limitations in ML studies within connectomics and strategies to mitigate them have been covered.
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Affiliation(s)
- Janova Anbarasi
- BrainSightAI, No. 677, 1st Floor, 27th Main, 13th Cross, HSR Layout, Sector 1, Adugodi, Bengaluru, Karnataka, 560102, India
| | - Radha Kumari
- BrainSightAI, No. 677, 1st Floor, 27th Main, 13th Cross, HSR Layout, Sector 1, Adugodi, Bengaluru, Karnataka, 560102, India
| | - Malvika Ganesh
- BrainSightAI, No. 677, 1st Floor, 27th Main, 13th Cross, HSR Layout, Sector 1, Adugodi, Bengaluru, Karnataka, 560102, India
| | - Rimjhim Agrawal
- BrainSightAI, No. 677, 1st Floor, 27th Main, 13th Cross, HSR Layout, Sector 1, Adugodi, Bengaluru, Karnataka, 560102, India.
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Scott I, Nelson B. Are Psychiatric Nosologies Limiting the Success of Clinical Prediction Models? JAMA Psychiatry 2024:2823667. [PMID: 39292481 DOI: 10.1001/jamapsychiatry.2024.2662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/19/2024]
Abstract
This Viewpoint discusses the limitations of clinical prediction models in psychiatric research.
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Affiliation(s)
- Isabelle Scott
- Centre for Youth Mental Health, The University of Melbourne, Parkville, Melbourne, Victoria, Australia
- Orygen, Parkville, Melbourne, Victoria, Australia
| | - Barnaby Nelson
- Centre for Youth Mental Health, The University of Melbourne, Parkville, Melbourne, Victoria, Australia
- Orygen, Parkville, Melbourne, Victoria, Australia
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Sundermann B, Pfleiderer B, McLeod A, Mathys C. Seeing more than the Tip of the Iceberg: Approaches to Subthreshold Effects in Functional Magnetic Resonance Imaging of the Brain. Clin Neuroradiol 2024; 34:531-539. [PMID: 38842737 PMCID: PMC11339104 DOI: 10.1007/s00062-024-01422-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 05/05/2024] [Indexed: 06/07/2024]
Abstract
Many functional magnetic resonance imaging (fMRI) studies and presurgical mapping applications rely on mass-univariate inference with subsequent multiple comparison correction. Statistical results are frequently visualized as thresholded statistical maps. This approach has inherent limitations including the risk of drawing overly-selective conclusions based only on selective results passing such thresholds. This article gives an overview of both established and newly emerging scientific approaches to supplement such conventional analyses by incorporating information about subthreshold effects with the aim to improve interpretation of findings or leverage a wider array of information. Topics covered include neuroimaging data visualization, p-value histogram analysis and the related Higher Criticism approach for detecting rare and weak effects. Further examples from multivariate analyses and dedicated Bayesian approaches are provided.
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Affiliation(s)
- Benedikt Sundermann
- Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus Oldenburg, Universitätsmedizin Oldenburg, Steinweg 13-17, 26122, Oldenburg, Germany.
- Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany.
- Clinic of Radiology, Medical Faculty, University of Münster, Münster, Germany.
| | - Bettina Pfleiderer
- Clinic of Radiology, Medical Faculty, University of Münster, Münster, Germany
| | - Anke McLeod
- Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus Oldenburg, Universitätsmedizin Oldenburg, Steinweg 13-17, 26122, Oldenburg, Germany
| | - Christian Mathys
- Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus Oldenburg, Universitätsmedizin Oldenburg, Steinweg 13-17, 26122, Oldenburg, Germany
- Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany
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Chen H, Lei Y, Li R, Xia X, Cui N, Chen X, Liu J, Tang H, Zhou J, Huang Y, Tian Y, Wang X, Zhou J. Resting-state EEG dynamic functional connectivity distinguishes non-psychotic major depression, psychotic major depression and schizophrenia. Mol Psychiatry 2024; 29:1088-1098. [PMID: 38267620 DOI: 10.1038/s41380-023-02395-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 12/17/2023] [Accepted: 12/21/2023] [Indexed: 01/26/2024]
Abstract
This study aims to identify dynamic patterns within the spatiotemporal feature space that are specific to nonpsychotic major depression (NPMD), psychotic major depression (PMD), and schizophrenia (SCZ). The study also evaluates the effectiveness of machine learning algorithms based on these network manifestations in differentiating individuals with NPMD, PMD, and SCZ. A total of 579 participants were recruited, including 152 patients with NPMD, 45 patients with PMD, 185 patients with SCZ, and 197 healthy controls (HCs). A dynamic functional connectivity (DFC) approach was employed to estimate the principal FC states within each diagnostic group. Incremental proportions of data (ranging from 10% to 100%) within each diagnostic group were used for variability testing. DFC metrics, such as proportion, mean duration, and transition number, were examined among the four diagnostic groups to identify disease-related neural activity patterns. These patterns were then used to train a two-layer classifier for the four groups (HC, NPMD, PMD, and SCZ). The four principal brain states (i.e., states 1,2,3, and 4) identified by the DFC approach were highly representative within and across diagnostic groups. Between-group comparisons revealed significant differences in network metrics of state 2 and state 3, within delta, theta, and gamma frequency bands, between healthy individuals and patients in each diagnostic group (p < 0.01, FDR corrected). Moreover, the identified key dynamic network metrics achieved an accuracy of 73.1 ± 2.8% in the four-way classification of HC, NPMD, PMD, and SCZ, outperforming the static functional connectivity (SFC) approach (p < 0.001). These findings suggest that the proposed DFC approach can identify dynamic network biomarkers at the single-subject level. These biomarkers have the potential to accurately differentiate individual subjects among various diagnostic groups of psychiatric disorders or healthy controls. This work may contribute to the development of a valuable EEG-based diagnostic tool with enhanced accuracy and assistive capabilities.
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Affiliation(s)
- Hui Chen
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Yanqin Lei
- TeleBrain Medical Technology Co., Beijing, 100000, China
| | - Rihui Li
- Center for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Macau S.A.R., 999078, China
- Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau S.A.R., 999078, China
| | - Xinxin Xia
- TeleBrain Medical Technology Co., Beijing, 100000, China
| | - Nanyi Cui
- TeleBrain Medical Technology Co., Beijing, 100000, China
| | - Xianliang Chen
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Jiali Liu
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Huajia Tang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Jiawei Zhou
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Ying Huang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Yusheng Tian
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China
| | - Xiaoping Wang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China.
| | - Jiansong Zhou
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, and National Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, 410011, Hunan, China.
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Sandoval-Araujo LE, Cusack CE, Ralph-Nearman C, Glatt S, Han Y, Bryan J, Hooper MA, Karem A, Levinson CA. Differentiation between atypical anorexia nervosa and anorexia nervosa using machine learning. Int J Eat Disord 2024; 57:937-950. [PMID: 38352982 PMCID: PMC11091846 DOI: 10.1002/eat.24160] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 01/21/2024] [Accepted: 01/22/2024] [Indexed: 04/16/2024]
Abstract
OBJECTIVE Body mass index (BMI) is the primary criterion differentiating anorexia nervosa (AN) and atypical anorexia nervosa despite prior literature indicating few differences between disorders. Machine learning (ML) classification provides us an efficient means of accurately distinguishing between two meaningful classes given any number of features. The aim of the present study was to determine if ML algorithms can accurately distinguish AN and atypical AN given an ensemble of features excluding BMI, and if not, if the inclusion of BMI enables ML to accurately classify between the two. METHODS Using an aggregate sample from seven studies consisting of individuals with AN and atypical AN who completed baseline questionnaires (N = 448), we used logistic regression, decision tree, and random forest ML classification models each trained on two datasets, one containing demographic, eating disorder, and comorbid features without BMI, and one retaining all features and BMI. RESULTS Model performance for all algorithms trained with BMI as a feature was deemed acceptable (mean accuracy = 74.98%, mean area under the receiving operating characteristics curve [AUC] = 74.75%), whereas model performance diminished without BMI (mean accuracy = 59.37%, mean AUC = 59.98%). DISCUSSION Model performance was acceptable, but not strong, if BMI was included as a feature; no other features meaningfully improved classification. When BMI was excluded, ML algorithms performed poorly at classifying cases of AN and atypical AN when considering other demographic and clinical characteristics. Results suggest a reconceptualization of atypical AN should be considered. PUBLIC SIGNIFICANCE There is a growing debate about the differences between anorexia nervosa and atypical anorexia nervosa as their diagnostic differentiation relies on BMI despite being similar otherwise. We aimed to see if machine learning could distinguish between the two disorders and found accurate classification only if BMI was used as a feature. This finding calls into question the need to differentiate between the two disorders.
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Affiliation(s)
| | - Claire E. Cusack
- Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY
| | | | - Sofie Glatt
- Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY
| | - Yuchen Han
- Department of Biostatistics & Bioinformatics, University of Louisville, Louisville, KY
| | - Jeffrey Bryan
- Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY
| | | | - Andrew Karem
- Department of Computer Science & Engineering, University of Louisville, Louisville, KY
| | - Cheri A. Levinson
- Department of Psychological & Brain Sciences, University of Louisville, Louisville, KY
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J SG, P D, P E. Enhancing drug discovery in schizophrenia: a deep learning approach for accurate drug-target interaction prediction - DrugSchizoNet. Comput Methods Biomech Biomed Engin 2024:1-18. [PMID: 38375638 DOI: 10.1080/10255842.2023.2282951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 10/17/2023] [Indexed: 02/21/2024]
Abstract
Drug discovery relies on the precise prognosis of drug-target interactions (DTI). Due to their ability to learn from raw data, deep learning (DL) methods have displayed outstanding performance over traditional approaches. However, challenges such as imbalanced data, noise, poor generalization, high cost, and time-consuming processes hinder progress in this field. To overcome the above challenges, we propose a DL-based model termed DrugSchizoNet for drug interaction (DI) prediction of Schizophrenia. Our model leverages drug-related data from the DrugBank and repoDB databases, employing three key preprocessing techniques. First, data cleaning eliminates duplicate or incomplete entries to ensure data integrity. Next, normalization is performed to enhance security and reduce costs associated with data acquisition. Finally, feature extraction is applied to improve the quality of input data. The three layers of the DrugSchizoNet model are the input, hidden and output layers. In the hidden layer, we employ dropout regularization to mitigate overfitting and improve generalization. The fully connected (FC) layer extracts relevant features, while the LSTM layer captures the sequential nature of DIs. In the output layer, our model provides confidence scores for potential DIs. To optimize the prediction accuracy, we utilize hyperparameter tuning through OB-MOA optimization. Experimental results demonstrate that DrugSchizoNet achieves a superior accuracy of 98.70%. The existing models, including CNN-RNN, DANN, CKA-MKL, DGAN, and CNN, across various evaluation metrics such as accuracy, recall, specificity, precision, F1 score, AUPR, and AUROC are compared with the proposed model. By effectively addressing the challenges of imbalanced data, noise, poor generalization, high cost and time-consuming processes, DrugSchizoNet offers a promising approach for accurate DTI prediction in Schizophrenia. Its superior performance demonstrates the potential of DL in advancing drug discovery and development processes.
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Affiliation(s)
- Sherine Glory J
- Department of Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, India
| | - Durgadevi P
- Department of Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, India
| | - Ezhumalai P
- Department of Computer Science and Engineering, R.M.D. Engineering College, Kavaraipettai, India
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Chai Y, Sheline YI, Oathes DJ, Balderston NL, Rao H, Yu M. Functional connectomics in depression: insights into therapies. Trends Cogn Sci 2023; 27:814-832. [PMID: 37286432 PMCID: PMC10476530 DOI: 10.1016/j.tics.2023.05.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 05/09/2023] [Accepted: 05/09/2023] [Indexed: 06/09/2023]
Abstract
Depression is a common mental disorder characterized by heterogeneous cognitive and behavioral symptoms. The emerging research paradigm of functional connectomics has provided a quantitative theoretical framework and analytic tools for parsing variations in the organization and function of brain networks in depression. In this review, we first discuss recent progress in depression-associated functional connectome variations. We then discuss treatment-specific brain network outcomes in depression and propose a hypothetical model highlighting the advantages and uniqueness of each treatment in relation to the modulation of specific brain network connectivity and symptoms of depression. Finally, we look to the future promise of combining multiple treatment types in clinical practice, using multisite datasets and multimodal neuroimaging approaches, and identifying biological depression subtypes.
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Affiliation(s)
- Ya Chai
- Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China; Center for Functional Neuroimaging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yvette I Sheline
- Center for Neuromodulation in Depression and Stress (CNDS), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Desmond J Oathes
- Center for Neuromodulation in Depression and Stress (CNDS), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn Brain Science, Translation, Innovation and Modulation Center (brainSTIM), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Nicholas L Balderston
- Center for Neuromodulation in Depression and Stress (CNDS), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Hengyi Rao
- Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China; Center for Functional Neuroimaging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Meichen Yu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana University Network Science Institute, Bloomington, IN, USA.
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Starke G, D’Imperio A, Ienca M. Out of their minds? Externalist challenges for using AI in forensic psychiatry. Front Psychiatry 2023; 14:1209862. [PMID: 37692304 PMCID: PMC10483237 DOI: 10.3389/fpsyt.2023.1209862] [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: 04/21/2023] [Accepted: 08/07/2023] [Indexed: 09/12/2023] Open
Abstract
Harnessing the power of machine learning (ML) and other Artificial Intelligence (AI) techniques promises substantial improvements across forensic psychiatry, supposedly offering more objective evaluations and predictions. However, AI-based predictions about future violent behaviour and criminal recidivism pose ethical challenges that require careful deliberation due to their social and legal significance. In this paper, we shed light on these challenges by considering externalist accounts of psychiatric disorders which stress that the presentation and development of psychiatric disorders is intricately entangled with their outward environment and social circumstances. We argue that any use of predictive AI in forensic psychiatry should not be limited to neurobiology alone but must also consider social and environmental factors. This thesis has practical implications for the design of predictive AI systems, especially regarding the collection and processing of training data, the selection of ML methods, and the determination of their explainability requirements.
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Affiliation(s)
- Georg Starke
- Faculty of Medicine, Institute for History and Ethics of Medicine, Technical University of Munich, Munich, Germany
- École Polytechnique Fédérale de Lausanne, College of Humanities, Lausanne, Switzerland
- Munich School of Philosophy, Munich, Germany
| | - Ambra D’Imperio
- Faculty of Medicine, Institute for History and Ethics of Medicine, Technical University of Munich, Munich, Germany
- Department of Psychiatry, Hôpitaux Universitaires de Genève, Geneva, Switzerland
- Service of Forensic Psychiatry CURML, Geneva University Hospitals, Geneva, Switzerland
| | - Marcello Ienca
- Faculty of Medicine, Institute for History and Ethics of Medicine, Technical University of Munich, Munich, Germany
- École Polytechnique Fédérale de Lausanne, College of Humanities, Lausanne, Switzerland
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10
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Chen Z, Hu B, Liu X, Becker B, Eickhoff SB, Miao K, Gu X, Tang Y, Dai X, Li C, Leonov A, Xiao Z, Feng Z, Chen J, Chuan-Peng H. Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry. BMC Med 2023; 21:241. [PMID: 37400814 DOI: 10.1186/s12916-023-02941-4] [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: 02/10/2023] [Accepted: 06/13/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. METHODS Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. RESULTS A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β = - 2.75, p < .001, R2adj = 0.40; r = - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. CONCLUSIONS Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.
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Affiliation(s)
- Zhiyi Chen
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China.
- Faculty of Psychology, Southwest University, Chongqing, China.
| | - Bowen Hu
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Xuerong Liu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, Chengdu, China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kuan Miao
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Xingmei Gu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Yancheng Tang
- School of Business and Management, Shanghai International Studies University, Shanghai, China
| | - Xin Dai
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Chao Li
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangdong, China
| | - Artemiy Leonov
- School of Psychology, Clark University, Worcester, MA, USA
| | - Zhibing Xiao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zhengzhi Feng
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China.
- Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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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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Pan Y, Wen Y, Wang Y, Schilbach L, Chen J. Interpersonal coordination in schizophrenia: a concise update on paradigms, computations, and neuroimaging findings. PSYCHORADIOLOGY 2023; 3:kkad002. [PMID: 38666124 PMCID: PMC10917372 DOI: 10.1093/psyrad/kkad002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 01/31/2023] [Accepted: 02/06/2023] [Indexed: 04/28/2024]
Affiliation(s)
- Yafeng Pan
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Yalan Wen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Yajie Wang
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Leonhard Schilbach
- Department of General Psychiatry 2 and Neuroimaging Section, LVR-Klinikum Düsseldorf, Düsseldorf 40629, Germany
- Medical Faculty, Ludwig-Maximilians University, Munich 80539, Germany
| | - Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
- Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang 322000, China
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13
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Broadening the Use of Machine Learning in Psychiatry. Biol Psychiatry 2023; 93:4-5. [PMID: 36456077 DOI: 10.1016/j.biopsych.2022.10.006] [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: 10/18/2022] [Accepted: 10/20/2022] [Indexed: 11/29/2022]
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