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Supekar K, de Los Angeles C, Ryali S, Kushan L, Schleifer C, Repetto G, Crossley NA, Simon T, Bearden CE, Menon V. Robust and replicable functional brain signatures of 22q11.2 deletion syndrome and associated psychosis: a deep neural network-based multi-cohort study. Mol Psychiatry 2024:10.1038/s41380-024-02495-8. [PMID: 38605171 DOI: 10.1038/s41380-024-02495-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 02/22/2024] [Accepted: 02/26/2024] [Indexed: 04/13/2024]
Abstract
A major genetic risk factor for psychosis is 22q11.2 deletion (22q11.2DS). However, robust and replicable functional brain signatures of 22q11.2DS and 22q11.2DS-associated psychosis remain elusive due to small sample sizes and a focus on small single-site cohorts. Here, we identify functional brain signatures of 22q11.2DS and 22q11.2DS-associated psychosis, and their links with idiopathic early psychosis, using one of the largest multi-cohort data to date. We obtained multi-cohort clinical phenotypic and task-free fMRI data from 856 participants (101 22q11.2DS, 120 idiopathic early psychosis, 101 idiopathic autism, 123 idiopathic ADHD, and 411 healthy controls) in a case-control design. A novel spatiotemporal deep neural network (stDNN)-based analysis was applied to the multi-cohort data to identify functional brain signatures of 22q11.2DS and 22q11.2DS-associated psychosis. Next, stDNN was used to test the hypothesis that the functional brain signatures of 22q11.2DS-associated psychosis overlap with idiopathic early psychosis but not with autism and ADHD. stDNN-derived brain signatures distinguished 22q11.2DS from controls, and 22q11.2DS-associated psychosis with very high accuracies (86-94%) in the primary cohort and two fully independent cohorts without additional training. Robust distinguishing features of 22q11.2DS-associated psychosis emerged in the anterior insula node of the salience network and the striatum node of the dopaminergic reward pathway. These features also distinguished individuals with idiopathic early psychosis from controls, but not idiopathic autism or ADHD. Our results reveal that individuals with 22q11.2DS exhibit a highly distinct functional brain organization compared to controls. Additionally, the brain signatures of 22q11.2DS-associated psychosis overlap with those of idiopathic early psychosis in the salience network and dopaminergic reward pathway, providing substantial empirical support for the theoretical aberrant salience-based model of psychosis. Collectively, our findings, replicated across multiple independent cohorts, advance the understanding of 22q11.2DS and associated psychosis, underscoring the value of 22q11.2DS as a genetic model for probing the neurobiological underpinnings of psychosis and its progression.
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Affiliation(s)
- Kaustubh Supekar
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.
- Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, CA, USA.
| | - Carlo de Los Angeles
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Srikanth Ryali
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Leila Kushan
- Department of Psychiatry and Behavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Charlie Schleifer
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Gabriela Repetto
- Center for Genetics and Genomics, Facultad de Medicina, Clinica Alemana Universidad del Desarrollo, Santiago, Chile
| | - Nicolas A Crossley
- Department of Psychiatry, Pontificia Universidad Católica de Chile, Santiago, Chile
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Tony Simon
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, Sacramento, CA, USA
- MIND Institute, University of California, Davis, Sacramento, CA, USA
| | - Carrie E Bearden
- Department of Psychiatry and Behavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Vinod Menon
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.
- Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, CA, USA.
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Dai P, Shi Y, Lu D, Zhou Y, Luo J, He Z, Chen Z, Zou B, Tang H, Huang Z, Liao S. Classification of recurrent major depressive disorder using a residual denoising autoencoder framework: Insights from large-scale multisite fMRI data. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 247:108114. [PMID: 38447315 DOI: 10.1016/j.cmpb.2024.108114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 02/14/2024] [Accepted: 03/01/2024] [Indexed: 03/08/2024]
Abstract
BACKGROUND AND OBJECTIVE Recurrent major depressive disorder (rMDD) has a high recurrence rate, and symptoms often worsen with each episode. Classifying rMDD using functional magnetic resonance imaging (fMRI) can enhance understanding of brain activity and aid diagnosis and treatment of this disorder. METHODS We developed a Residual Denoising Autoencoder (Res-DAE) framework for the classification of rMDD. The functional connectivity (FC) was extracted from fMRI data as features. The framework addresses site heterogeneity by employing the Combat method to harmonize feature distribution differences. A feature selection method based on Fisher scores was used to reduce redundant information in the features. A data augmentation strategy using a Synthetic Minority Over-sampling Technique algorithm based on Extended Frobenius Norm measure was incorporated to increase the sample size. Furthermore, a residual module was integrated into the autoencoder network to preserve important features and improve the classification accuracy. RESULTS We tested our framework on a large-scale, multisite fMRI dataset, which includes 189 rMDD patients and 427 healthy controls. The Res-DAE achieved an average accuracy of 75.1 % (sensitivity = 69 %, specificity = 77.8 %) in cross-validation, thereby outperforming comparison methods. In a larger dataset that also includes first-episode depression (comprising 832 MDD patients and 779 healthy controls), the accuracy reached 70 %. CONCLUSIONS We proposed a deep learning framework that can effectively classify rMDD and 33 identify the altered FC associated with rMDD. Our study may reveal changes in brain function 34 associated with rMDD and provide assistance for the diagnosis and treatment of rMDD.
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Affiliation(s)
- Peishan Dai
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China.
| | - Yun Shi
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Da Lu
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Ying Zhou
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Jialin Luo
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Zhuang He
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Zailiang Chen
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Beiji Zou
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Hui Tang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410083, China
| | - Zhongchao Huang
- Department of Biomedical Engineering, School of Basic Medical Science, Central South University, Changsha, Hunan 410083, China
| | - Shenghui Liao
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
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Itahashi T, Yamashita A, Takahara Y, Yahata N, Aoki YY, Fujino J, Yoshihara Y, Nakamura M, Aoki R, Ohta H, Sakai Y, Takamura M, Ichikawa N, Okada G, Okada N, Kasai K, Tanaka SC, Imamizu H, Kato N, Okamoto Y, Takahashi H, Kawato M, Yamashita O, Hashimoto RI. Generalizable neuromarker for autism spectrum disorder across imaging sites and developmental stages: A multi-site study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.26.534053. [PMID: 37034620 PMCID: PMC10081283 DOI: 10.1101/2023.03.26.534053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Autism spectrum disorder (ASD) is a lifelong condition, and its underlying biological mechanisms remain elusive. The complexity of various factors, including inter-site and development-related differences, makes it challenging to develop generalizable neuroimaging-based biomarkers for ASD. This study used a large-scale, multi-site dataset of 730 Japanese adults to develop a generalizable neuromarker for ASD across independent sites (U.S., Belgium, and Japan) and different developmental stages (children and adolescents). Our adult ASD neuromarker achieved successful generalization for the US and Belgium adults (area under the curve [AUC] = 0.70) and Japanese adults (AUC = 0.81). The neuromarker demonstrated significant generalization for children (AUC = 0.66) and adolescents (AUC = 0.71; all P < 0.05 , family-wise-error corrected). We identified 141 functional connections (FCs) important for discriminating individuals with ASD from TDCs. These FCs largely centered on social brain regions such as the amygdala, hippocampus, dorsomedial and ventromedial prefrontal cortices, and temporal cortices. Finally, we mapped schizophrenia (SCZ) and major depressive disorder (MDD) onto the biological axis defined by the neuromarker and explored the biological continuity of ASD with SCZ and MDD. We observed that SCZ, but not MDD, was located proximate to ASD on the biological dimension defined by the ASD neuromarker. The successful generalization in multifarious datasets and the observed relations of ASD with SCZ on the biological dimensions provide new insights for a deeper understanding of ASD.
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Affiliation(s)
- Takashi Itahashi
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Ayumu Yamashita
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
- Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan
| | - Yuji Takahara
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
- Laboratory for Drug Discovery and Disease Research, SHIONOGI & CO., LTD, Osaka, Japan
| | - Noriaki Yahata
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
- Institute for Quantum Life Science, National Institutes for Quantum Science and Technology, Chiba, Japan
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yuta Y. Aoki
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
- Department of Psychiatry, Aoki Clinic, Tokyo, Japan
| | - Junya Fujino
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
- Department of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yujiro Yoshihara
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Motoaki Nakamura
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Ryuta Aoki
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
- Department of Language Sciences, Tokyo Metropolitan University, Tokyo, Japan
| | - Haruhisa Ohta
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Yuki Sakai
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
| | - Masahiro Takamura
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
- Department of Neurology, Shimane University, Shimane, Japan
| | - Naho Ichikawa
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Go Okada
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Naohiro Okada
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- The International Research Center for Neurointelligence (WPI-IRCN) at The University of Tokyo Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan
| | - Kiyoto Kasai
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- The International Research Center for Neurointelligence (WPI-IRCN) at The University of Tokyo Institutes for Advanced Study (UTIAS), The University of Tokyo, Tokyo, Japan
- UTokyo Institute for Diversity and Adaptation of Human Mind (UTIDAHM), The University of Tokyo, Tokyo, Japan
| | - Saori C. Tanaka
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
- Division of Information Science, Nara Institute of Science and Technology, Nara, Japan
| | - Hiroshi Imamizu
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
- Department of Psychology, Graduate School of Humanities and Sociology, The University of Tokyo, Tokyo, Japan
| | - Nobumasa Kato
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Yasumasa Okamoto
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Hidehiko Takahashi
- Department of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Center for Brain Integration Research, Tokyo Medical and Dental University, Tokyo, Japan
| | - Mitsuo Kawato
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
- XNef Incorporation, Kyoto, Japan
| | - Okito Yamashita
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
- RIKEN, Center for Advanced Intelligence Project, Tokyo, Japan
| | - Ryu-ichiro Hashimoto
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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Joyce DW, Kormilitzin A, Smith KA, Cipriani A. Explainable artificial intelligence for mental health through transparency and interpretability for understandability. NPJ Digit Med 2023; 6:6. [PMID: 36653524 PMCID: PMC9849399 DOI: 10.1038/s41746-023-00751-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Accepted: 01/10/2023] [Indexed: 01/19/2023] Open
Abstract
The literature on artificial intelligence (AI) or machine learning (ML) in mental health and psychiatry lacks consensus on what "explainability" means. In the more general XAI (eXplainable AI) literature, there has been some convergence on explainability meaning model-agnostic techniques that augment a complex model (with internal mechanics intractable for human understanding) with a simpler model argued to deliver results that humans can comprehend. Given the differing usage and intended meaning of the term "explainability" in AI and ML, we propose instead to approximate model/algorithm explainability by understandability defined as a function of transparency and interpretability. These concepts are easier to articulate, to "ground" in our understanding of how algorithms and models operate and are used more consistently in the literature. We describe the TIFU (Transparency and Interpretability For Understandability) framework and examine how this applies to the landscape of AI/ML in mental health research. We argue that the need for understandablity is heightened in psychiatry because data describing the syndromes, outcomes, disorders and signs/symptoms possess probabilistic relationships to each other-as do the tentative aetiologies and multifactorial social- and psychological-determinants of disorders. If we develop and deploy AI/ML models, ensuring human understandability of the inputs, processes and outputs of these models is essential to develop trustworthy systems fit for deployment.
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Affiliation(s)
- Dan W. Joyce
- grid.416938.10000 0004 0641 5119University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX UK ,grid.10025.360000 0004 1936 8470Institute of Population Health, Department of Primary Care and Mental Health, University of Liverpool, Liverpool, L69 3GF UK
| | - Andrey Kormilitzin
- grid.416938.10000 0004 0641 5119University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX UK
| | - Katharine A. Smith
- grid.416938.10000 0004 0641 5119University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX UK ,grid.416938.10000 0004 0641 5119Oxford Precision Psychiatry Lab, NIHR Oxford Health Biomedical Research Centre, Warneford Hospital, Oxford, OX3 7JX UK ,grid.416938.10000 0004 0641 5119Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, OX3 7JX UK
| | - Andrea Cipriani
- grid.416938.10000 0004 0641 5119University of Oxford, Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX UK ,grid.416938.10000 0004 0641 5119Oxford Precision Psychiatry Lab, NIHR Oxford Health Biomedical Research Centre, Warneford Hospital, Oxford, OX3 7JX UK ,grid.416938.10000 0004 0641 5119Oxford Health NHS Foundation Trust, Warneford Hospital, Oxford, OX3 7JX UK
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