1
|
Guo J, He C, Song H, Gao H, Yao S, Dong SS, Yang TL. Unveiling Promising Neuroimaging Biomarkers for Schizophrenia Through Clinical and Genetic Perspectives. Neurosci Bull 2024; 40:1333-1352. [PMID: 38703276 DOI: 10.1007/s12264-024-01214-1] [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: 07/14/2023] [Accepted: 01/08/2024] [Indexed: 05/06/2024] Open
Abstract
Schizophrenia is a complex and serious brain disorder. Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes (IDPs) to investigate the etiology of psychiatric disorders. IDPs capture valuable clinical advantages and hold biological significance in identifying brain abnormalities. In this review, we aim to discuss current and prospective approaches to identify potential biomarkers for schizophrenia using clinical multimodal neuroimaging and imaging genetics. We first described IDPs through their phenotypic classification and neuroimaging genomics. Secondly, we discussed the applications of multimodal neuroimaging by clinical evidence in observational studies and randomized controlled trials. Thirdly, considering the genetic evidence of IDPs, we discussed how can utilize neuroimaging data as an intermediate phenotype to make association inferences by polygenic risk scores and Mendelian randomization. Finally, we discussed machine learning as an optimum approach for validating biomarkers. Together, future research efforts focused on neuroimaging biomarkers aim to enhance our understanding of schizophrenia.
Collapse
Affiliation(s)
- Jing Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Changyi He
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Huimiao Song
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Huiwu Gao
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Shi Yao
- Guangdong Key Laboratory of Age-Related Cardiac and Cerebral Diseases, Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524000, China
| | - Shan-Shan Dong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China.
| | - Tie-Lin Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China.
| |
Collapse
|
2
|
Zhang R, Ren J, Lei X, Wang Y, Chen X, Fu L, Li Q, Guo C, Teng X, Wu Z, Yu L, Wang D, Chen Y, Zhang C. Aberrant patterns of spontaneous brain activity in schizophrenia: A resting-state fMRI study and classification analysis. Prog Neuropsychopharmacol Biol Psychiatry 2024; 134:111066. [PMID: 38901758 DOI: 10.1016/j.pnpbp.2024.111066] [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: 01/17/2024] [Accepted: 06/17/2024] [Indexed: 06/22/2024]
Abstract
BACKGROUND Schizophrenia is a prevalent mental disorder, leading to severe disability. Currently, the absence of objective biomarkers hinders effective diagnosis. This study was conducted to explore the aberrant spontaneous brain activity and investigate the potential of abnormal brain indices as diagnostic biomarkers employing machine learning methods. METHODS A total of sixty-one schizophrenia patients and seventy demographically matched healthy controls were enrolled in this study. The static indices of resting-state functional magnetic resonance imaging (rs-fMRI) including amplitude of low frequency fluctuations (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), and degree centrality (DC) were calculated to evaluate spontaneous brain activity. Subsequently, a sliding-window method was then used to conduct temporal dynamic analysis. The comparison of static and dynamic rs-fMRI indices between the patient and control groups was conducted using a two-sample t-test. Finally, the machine learning analysis was applied to estimate the diagnostic value of abnormal indices of brain activity. RESULTS Schizophrenia patients exhibited a significant increase ALFF value in inferior frontal gyrus, alongside significant decreases in fALFF values observed in left postcentral gyrus and right cerebellum posterior lobe. Pervasive aberrations in ReHo indices were observed among schizophrenia patients, particularly in frontal lobe and cerebellum. A noteworthy reduction in voxel-wise concordance of dynamic indices was observed across gray matter regions encompassing the bilateral frontal, parietal, occipital, temporal, and insular cortices. The classification analysis achieved the highest values for area under curve at 0.87 and accuracy at 81.28% when applying linear support vector machine and leveraging a combination of abnormal static and dynamic indices in the specified brain regions as features. CONCLUSIONS The static and dynamic indices of brain activity exhibited as potential neuroimaging biomarkers for the diagnosis of schizophrenia.
Collapse
Affiliation(s)
- Rong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Juanjuan Ren
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaoxia Lei
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yewei Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaochang Chen
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lirong Fu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qingyi Li
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chaoyue Guo
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xinyue Teng
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zenan Wu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lingfang Yu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Dandan Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yan Chen
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Chen Zhang
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| |
Collapse
|
3
|
Breuer F, Meyhöfer I, Lencer R, Sprenger A, Roesmann K, Schag K, Dannlowski U, Leehr EJ. Aberrant inhibitory control as a transdiagnostic dimension of mental disorders - A meta-analysis of the antisaccade task in different psychiatric populations. Neurosci Biobehav Rev 2024; 165:105840. [PMID: 39103067 DOI: 10.1016/j.neubiorev.2024.105840] [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: 03/19/2024] [Revised: 06/29/2024] [Accepted: 08/01/2024] [Indexed: 08/07/2024]
Abstract
This meta-analysis examined inhibitory control performance in the antisaccade task across mental disorders. Following PRISMA guidelines, we analyzed data from k = 146 studies (n = 13,807 participants) on antisaccade performance. Effect sizes were estimated using random-effects models and restricted maximum-likelihood estimation, with robustness tests for study heterogeneity and publication bias. Most disorders displayed elevated error rates, with schizophrenia showing the greatest impairments, followed by autism spectrum disorder, bipolar disorder and attention deficit hyperactivity disorder. Small to medium impairments were also found in eating disorders, major depressive disorder, obsessive-compulsive disorder and substance use disorder. Results were robust against corrections for publication bias and largely unaffected by confounding variables. Prolonged latencies were observed in schizophrenia, attention deficit hyperactivity disorder, bipolar disorder and obsessive compulsive disorder, with smaller and less robust effect sizes. Results indicate inhibitory control deficits in the antisaccade task across mental disorders, especially evident for error rates. While present in most disorders, results imply varying degrees of impairments, ranging from small to large in effect sizes, with largest impairments in schizophrenia.
Collapse
Affiliation(s)
- Fabian Breuer
- Institute for Translational Psychiatry, University of Münster, Germany
| | - Inga Meyhöfer
- Institute for Translational Psychiatry, University of Münster, Germany; Otto-Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Germany
| | - Rebekka Lencer
- Institute for Translational Psychiatry, University of Münster, Germany; Otto-Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, Germany; Department of Psychiatry and Psychotherapy, University of Lübeck, Germany
| | - Andreas Sprenger
- Department of Psychiatry and Psychotherapy, University of Lübeck, Germany; Institute of Psychology II, University of Lübeck, Germany
| | - Kati Roesmann
- Institute for Psychology, Unit for Clinical Psychology and Psychotherapy in Childhood and Adolescence. University of Osnabrück, Germany; Institute for Biomagnetism and Biosignalanalysis, University of Münster, Germany
| | - Kathrin Schag
- Medical University Hospital Tübingen, Department of Psychosomatic Medicine and Psychotherapy, Tübingen, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Germany
| | | |
Collapse
|
4
|
Dagnew TM, Tseng CEJ, Yoo CH, Makary MM, Goodheart AE, Striar R, Meyer TN, Rattray AK, Kang L, Wolf KA, Fiedler SA, Tocci D, Shapiro H, Provost S, Sultana E, Liu Y, Ding W, Chen P, Kubicki M, Shen S, Catana C, Zürcher NR, Wey HY, Hooker JM, Weiss RD, Wang C. Toward AI-driven neuroepigenetic imaging biomarker for alcohol use disorder: A proof-of-concept study. iScience 2024; 27:110159. [PMID: 39021792 PMCID: PMC11253155 DOI: 10.1016/j.isci.2024.110159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 04/13/2024] [Accepted: 05/29/2024] [Indexed: 07/20/2024] Open
Abstract
Alcohol use disorder (AUD) is a disorder of clinical and public health significance requiring novel and improved therapeutic solutions. Both environmental and genetic factors play a significant role in its pathophysiology. However, the underlying epigenetic molecular mechanisms that link the gene-environment interaction in AUD remain largely unknown. In this proof-of-concept study, we showed, for the first time, the neuroepigenetic biomarker capability of non-invasive imaging of class I histone deacetylase (HDAC) epigenetic enzymes in the in vivo brain for classifying AUD patients from healthy controls using a machine learning approach in the context of precision diagnosis. Eleven AUD patients and 16 age- and sex-matched healthy controls completed a simultaneous positron emission tomography-magnetic resonance (PET/MR) scan with the HDAC-binding radiotracer [11C]Martinostat. Our results showed lower HDAC expression in the anterior cingulate region in AUD. Furthermore, by applying a genetic algorithm feature selection, we identified five particular brain regions whose combined [11C]Martinostat relative standard uptake value (SUVR) features could reliably classify AUD vs. controls. We validate their promising classification reliability using a support vector machine classifier. These findings inform the potential of in vivo HDAC imaging biomarkers coupled with machine learning tools in the objective diagnosis and molecular translation of AUD that could complement the current diagnostic and statistical manual of mental disorders (DSM)-based intervention to propel precision medicine forward.
Collapse
Affiliation(s)
- Tewodros Mulugeta Dagnew
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Chieh-En J. Tseng
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Chi-Hyeon Yoo
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Meena M. Makary
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Systems and Biomedical Engineering Department, Cairo University, Giza, Egypt
| | - Anna E. Goodheart
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Robin Striar
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Tyler N. Meyer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Anna K. Rattray
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Leyi Kang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Kendall A. Wolf
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Stephanie A. Fiedler
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Darcy Tocci
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hannah Shapiro
- Division of Alcohol, Drugs, and Addiction, McLean Hospital, Belmont, MA, USA
| | - Scott Provost
- Division of Alcohol, Drugs, and Addiction, McLean Hospital, Belmont, MA, USA
| | - Eleanor Sultana
- Division of Alcohol, Drugs, and Addiction, McLean Hospital, Belmont, MA, USA
| | - Yan Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Wei Ding
- Department of Computer Science, University of Massachusetts Boston, Boston, MA, USA
| | - Ping Chen
- Department of Engineering, University of Massachusetts Boston, Boston, MA, USA
| | - Marek Kubicki
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Psychiatry Neuroimaging Laboratory, Departments of Psychiatry and Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Shiqian Shen
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ciprian Catana
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Nicole R. Zürcher
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hsiao-Ying Wey
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jacob M. Hooker
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Roger D. Weiss
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Division of Alcohol, Drugs, and Addiction, McLean Hospital, Belmont, MA, USA
| | - Changning Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| |
Collapse
|
5
|
Yu T, Pei WZ, Xu CY, Deng CC, Zhang XL. Identification of male schizophrenia patients using brain morphology based on machine learning algorithms. World J Psychiatry 2024; 14:804-811. [PMID: 38984327 PMCID: PMC11230103 DOI: 10.5498/wjp.v14.i6.804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 05/01/2024] [Accepted: 05/21/2024] [Indexed: 06/19/2024] Open
Abstract
BACKGROUND Schizophrenia is a severe psychiatric disease, and its prevalence is higher. However, diagnosis of early-stage schizophrenia is still considered a challenging task. AIM To employ brain morphological features and machine learning method to differentiate male individuals with schizophrenia from healthy controls. METHODS The least absolute shrinkage and selection operator and t tests were applied to select important features from structural magnetic resonance images as input features for classification. Four commonly used machine learning algorithms, the general linear model, random forest (RF), k-nearest neighbors, and support vector machine algorithms, were used to develop the classification models. The performance of the classification models was evaluated according to the area under the receiver operating characteristic curve (AUC). RESULTS A total of 8 important features with significant differences between groups were considered as input features for the establishment of classification models based on the four machine learning algorithms. Compared to other machine learning algorithms, RF yielded better performance in the discrimination of male schizophrenic individuals from healthy controls, with an AUC of 0.886. CONCLUSION Our research suggests that brain morphological features can be used to improve the early diagnosis of schizophrenia in male patients.
Collapse
Affiliation(s)
- Tao Yu
- Department of Clinical Nutrition, Hefei Fourth People’s Hospital, Hefei 230032, Anhui Province, China
| | - Wen-Zhi Pei
- Department of Psychiatry, Hefei Fourth People’s Hospital, Hefei 230032, Anhui Province, China
| | - Chun-Yuan Xu
- Department of Clinical Nutrition, Hefei Fourth People’s Hospital, Hefei 230032, Anhui Province, China
| | - Chen-Chen Deng
- Department of Gynaecology, Anhui Maternal and Child Health Hospital, Hefei 230032, Anhui Province, China
| | - Xu-Lai Zhang
- Department of Psychiatry, Hefei Fourth People’s Hospital, Hefei 230032, Anhui Province, China
| |
Collapse
|
6
|
Bryant AG, Aquino K, Parkes L, Fornito A, Fulcher BD. Extracting interpretable signatures of whole-brain dynamics through systematic comparison. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.10.573372. [PMID: 38915560 PMCID: PMC11195072 DOI: 10.1101/2024.01.10.573372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
The brain's complex distributed dynamics are typically quantified using a limited set of manually selected statistical properties, leaving the possibility that alternative dynamical properties may outperform those reported for a given application. Here, we address this limitation by systematically comparing diverse, interpretable features of both intra-regional activity and inter-regional functional coupling from resting-state functional magnetic resonance imaging (rs-fMRI) data, demonstrating our method using case-control comparisons of four neuropsychiatric disorders. Our findings generally support the use of linear time-series analysis techniques for rs-fMRI case-control analyses, while also identifying new ways to quantify informative dynamical fMRI structures. While simple statistical representations of fMRI dynamics performed surprisingly well (e.g., properties within a single brain region), combining intra-regional properties with inter-regional coupling generally improved performance, underscoring the distributed, multifaceted changes to fMRI dynamics in neuropsychiatric disorders. The comprehensive, data-driven method introduced here enables systematic identification and interpretation of quantitative dynamical signatures of multivariate time-series data, with applicability beyond neuroimaging to diverse scientific problems involving complex time-varying systems.
Collapse
Affiliation(s)
- Annie G. Bryant
- School of Physics, The University of Sydney, Camperdown, NSW, Australia
| | - Kevin Aquino
- School of Physics, The University of Sydney, Camperdown, NSW, Australia
- Brain Key Incorporated, San Francisco, CA, USA
| | - Linden Parkes
- Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, USA
- Turner Institute for Brain & Mental Health, Monash University, VIC, Australia
| | - Alex Fornito
- Turner Institute for Brain & Mental Health, Monash University, VIC, Australia
| | - Ben D. Fulcher
- School of Physics, The University of Sydney, Camperdown, NSW, Australia
| |
Collapse
|
7
|
Zhu Y, Maikusa N, Radua J, Sämann PG, Fusar-Poli P, Agartz I, Andreassen OA, Bachman P, Baeza I, Chen X, Choi S, Corcoran CM, Ebdrup BH, Fortea A, Garani RR, Glenthøj BY, Glenthøj LB, Haas SS, Hamilton HK, Hayes RA, He Y, Heekeren K, Kasai K, Katagiri N, Kim M, Kristensen TD, Kwon JS, Lawrie SM, Lebedeva I, Lee J, Loewy RL, Mathalon DH, McGuire P, Mizrahi R, Mizuno M, Møller P, Nemoto T, Nordholm D, Omelchenko MA, Raghava JM, Røssberg JI, Rössler W, Salisbury DF, Sasabayashi D, Smigielski L, Sugranyes G, Takahashi T, Tamnes CK, Tang J, Theodoridou A, Tomyshev AS, Uhlhaas PJ, Værnes TG, van Amelsvoort TAMJ, Waltz JA, Westlye LT, Zhou JH, Thompson PM, Hernaus D, Jalbrzikowski M, Koike S. Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk. Mol Psychiatry 2024; 29:1465-1477. [PMID: 38332374 PMCID: PMC11189817 DOI: 10.1038/s41380-024-02426-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 12/22/2023] [Accepted: 01/08/2024] [Indexed: 02/10/2024]
Abstract
Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK). T1-weighted structural brain MRI scans from 1165 individuals at CHR (CHR-PS+, n = 144; CHR-PS-, n = 793; and CHR-UNK, n = 228), and 1029 HCs, were obtained from 21 sites. We used ComBat to harmonize measures of subcortical volume, cortical thickness and surface area data and corrected for non-linear effects of age and sex using a general additive model. CHR-PS+ (n = 120) and HC (n = 799) data from 20 sites served as a training dataset, which we used to build a classifier. The remaining samples were used external validation datasets to evaluate classifier performance (test, independent confirmatory, and independent group [CHR-PS- and CHR-UNK] datasets). The accuracy of the classifier on the training and independent confirmatory datasets was 85% and 73% respectively. Regional cortical surface area measures-including those from the right superior frontal, right superior temporal, and bilateral insular cortices strongly contributed to classifying CHR-PS+ from HC. CHR-PS- and CHR-UNK individuals were more likely to be classified as HC compared to CHR-PS+ (classification rate to HC: CHR-PS+, 30%; CHR-PS-, 73%; CHR-UNK, 80%). We used multisite sMRI to train a classifier to predict psychosis onset in CHR individuals, and it showed promise predicting CHR-PS+ in an independent sample. The results suggest that when considering adolescent brain development, baseline MRI scans for CHR individuals may be helpful to identify their prognosis. Future prospective studies are required about whether the classifier could be actually helpful in the clinical settings.
Collapse
Affiliation(s)
- Yinghan Zhu
- Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan
| | - Norihide Maikusa
- Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan
| | - Joaquim Radua
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), CIBERSAM, Instituto de Salud Carlos III, Universitat de Barcelona, Barcelona, Spain
| | | | - Paolo Fusar-Poli
- Early Psychosis: Interventions and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Ingrid Agartz
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
- KG Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- KG Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Peter Bachman
- Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, MA, USA
| | - Inmaculada Baeza
- Department of Child and Adolescent Psychiatry and Psychology, Institute of Neuroscience, 2017SGR-881, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Universitat de Barcelona, Barcelona, Spain
| | - Xiaogang Chen
- National Clinical Research Center for Mental Disorders and Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, Hunan, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Sunah Choi
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, South Korea
| | - Cheryl M Corcoran
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Mental Illness Research, Education, and Clinical Center, James J Peters VA Medical Center, New York City, NY, USA
| | - Bjørn H Ebdrup
- Centre for Neuropsychiatric Schizophrenia Research (CNSR), Mental Health Centre Glostrup, Copenhagen University Hospital, Glostrup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Adriana Fortea
- Department of Child and Adolescent Psychiatry and Psychology, Institute of Neuroscience, Hospital Clinic Barcelona, Fundació Clínic Recerca Biomèdica, Universitat de Barcelona, Barcelona, Spain
| | - Ranjini Rg Garani
- Douglas Research Center; Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada
| | - Birte Yding Glenthøj
- Centre for Neuropsychiatric Schizophrenia Research (CNSR), Mental Health Centre Glostrup, Copenhagen University Hospital, Glostrup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Louise Birkedal Glenthøj
- Copenhagen Research Center for Mental Health, Mental Health Center Copenhagen, University of Copenhagen Copenhagen, Copenhagen, Denmark
| | - Shalaila S Haas
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Holly K Hamilton
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
- San Francisco Veterans Affairs Health Care System, San Francisco, CA, USA
| | - Rebecca A Hayes
- Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, MA, USA
| | - Ying He
- National Clinical Research Center for Mental Disorders and Department of Psychiatry, the Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Karsten Heekeren
- Department of Psychiatry and Psychotherapy I, LVR-Hospital Cologne, Cologne, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Kiyoto Kasai
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- The University of Tokyo Institute for Diversity and Adaptation of Human Mind, The University of Tokyo, Tokyo, Japan
- The International Research Center for Neurointelligence at The University of Tokyo Institutes for Advanced Study (WPI-IRCN), The University of Tokyo, Tokyo, Japan
| | - Naoyuki Katagiri
- Department of Neuropsychiatry, Toho University School of Medicine, Tokyok, Japan
| | - Minah Kim
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea
| | - Tina D Kristensen
- Centre for Neuropsychiatric Schizophrenia Research (CNSR), Mental Health Centre Glostrup, Copenhagen University Hospital, Glostrup, Denmark
| | - Jun Soo Kwon
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, South Korea
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, South Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea
| | | | - Irina Lebedeva
- Laboratory of Neuroimaging and Multimodal Analysis, Mental Health Research Center, Moscow, Russian Federation
| | - Jimmy Lee
- Department of Psychosis, Institute of Mental Health, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Rachel L Loewy
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Daniel H Mathalon
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
- San Francisco Veterans Affairs Health Care System, San Francisco, CA, USA
| | - Philip McGuire
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Romina Mizrahi
- Douglas Research Center; Department of Psychiatry, McGill University, Montreal, QC, Canada
| | | | - Paul Møller
- Department for Mental Health Research and Development, Division of Mental Health and Addiction, Vestre Viken Hospital Trust, Drammen, Norway
| | - Takahiro Nemoto
- Department of Neuropsychiatry, Toho University School of Medicine, Tokyok, Japan
| | - Dorte Nordholm
- Copenhagen Research Center for Mental Health, Mental Health Center Copenhagen, University of Copenhagen Copenhagen, Copenhagen, Denmark
| | - Maria A Omelchenko
- Department of Youth Psychiatry, Mental Health Research Center, Moscow, Russian Federation
| | - Jayachandra M Raghava
- Centre for Neuropsychiatric Schizophrenia Research (CNSR), Mental Health Centre Glostrup, Copenhagen University Hospital, Glostrup, Denmark
- Department of Clinical Physiology, Nuclear Medicine and PET, Functional Imaging, University of Copenhagen Copenhagen, Copenhagen, Denmark
| | - Jan I Røssberg
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Wulf Rössler
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Dean F Salisbury
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Daiki Sasabayashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan
- Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Lukasz Smigielski
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Child and Adolescent Psychiatry, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Gisela Sugranyes
- Department of Child and Adolescent Psychiatry and Psychology, Institute of Neuroscience, 2017SGR-881, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Universitat de Barcelona, Barcelona, Spain
| | - Tsutomu Takahashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan
- Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Christian K Tamnes
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Jinsong Tang
- Department of Psychiatry, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Zhejiang, China
- Key Laboratory of Medical Neurobiology of Zhejiang Province, School of Medicine, Zhejiang University, Zhejiang, China
| | - Anastasia Theodoridou
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Alexander S Tomyshev
- Laboratory of Neuroimaging and Multimodal Analysis, Mental Health Research Center, Moscow, Russian Federation
| | - Peter J Uhlhaas
- Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin Berlin, Berlin, Germany
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, UK
| | - Tor G Værnes
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Early Intervention in Psychosis Advisory Unit for South-East Norway, TIPS Sør-Øst, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Therese A M J van Amelsvoort
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Faculty of Health Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - James A Waltz
- Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore County, Baltimore, MD, USA
| | - Lars T Westlye
- KG Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- Norwegian Centre for Mental Disorders Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Juan H Zhou
- Centre for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Dennis Hernaus
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Faculty of Health Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Maria Jalbrzikowski
- Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Cambridge, MA, USA
| | - Shinsuke Koike
- Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan.
- The University of Tokyo Institute for Diversity and Adaptation of Human Mind, The University of Tokyo, Tokyo, Japan.
| |
Collapse
|
8
|
Chen M, Xia X, Kang Z, Li Z, Dai J, Wu J, Chen C, Qiu Y, Liu T, Liu Y, Zhang Z, Shen Q, Tao S, Deng Z, Lin Y, Wei Q. Distinguishing schizophrenia and bipolar disorder through a Multiclass Classification model based on multimodal neuroimaging data. J Psychiatr Res 2024; 172:119-128. [PMID: 38377667 DOI: 10.1016/j.jpsychires.2024.02.024] [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: 11/15/2023] [Revised: 02/05/2024] [Accepted: 02/07/2024] [Indexed: 02/22/2024]
Abstract
This study aimed to identify neural biomarkers for schizophrenia (SZ) and bipolar disorder (BP) by analyzing multimodal neuroimaging. Utilizing data from structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and resting-state functional magnetic resonance imaging (rs-fMRI), multiclass classification models were created for SZ, BP, and healthy controls (HC). A total of 113 participants (BP: 31, SZ: 39, and HC: 43) were recruited under strict enrollment control, from which 272, 200, and 1875 features were extracted from sMRI, DTI, and rs-fMRI data, respectively. A support vector machine (SVM) with recursive feature elimination (RFE) was employed to build the models using a one-against-one approach and leave-one-out cross-validation, achieving a classification accuracy of 70.8%. The most discriminative features were primarily from rs-fMRI, along with significant findings in sMRI and DTI. Key biomarkers identified included the increased thickness of the left cuneus cortex and decreased regional functional connectivity strength (rFCS) in the left supramarginal gyrus as shared indicators for BP and SZ. Additionally, decreased fractional anisotropy in the left superior fronto-occipital fasciculus was suggested as specific to BP, while decreased rFCS in the left inferior parietal area might serve as a specific biomarker for SZ. These findings underscore the potential of multimodal neuroimaging in distinguishing between BP and SZ and contribute to the understanding of their neural underpinnings.
Collapse
Affiliation(s)
- Ming Chen
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Guangdong Mental Health Institute, Guangdong ProvincialPeople's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Xiaowei Xia
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zhuang Kang
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zhinan Li
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jiamin Dai
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Junyan Wu
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Cai Chen
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Yong Qiu
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Department of Psychiatry, Mindfront Caring Medical, Guangzhou, China
| | - Tong Liu
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Department of Psychiatry, The First Affiliated Hospital of Xi'an Jiaotong University, Shaanxi, China
| | - Yanxi Liu
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Ziyi Zhang
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; Department of Medical Division, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Qingni Shen
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Sichu Tao
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zixin Deng
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Ying Lin
- Department of Psychology, Sun Yat-sen University, Guangzhou, China.
| | - Qinling Wei
- Department of Psychiatry, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
| |
Collapse
|
9
|
Wang X, Yan C, Yang PY, Xia Z, Cai XL, Wang Y, Kwok SC, Chan RCK. Unveiling the potential of machine learning in schizophrenia diagnosis: A meta-analytic study of task-based neuroimaging data. Psychiatry Clin Neurosci 2024; 78:157-168. [PMID: 38013639 DOI: 10.1111/pcn.13625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 11/01/2023] [Accepted: 11/24/2023] [Indexed: 11/29/2023]
Abstract
The emergence of machine learning (ML) techniques has opened up new avenues for identifying biomarkers associated with schizophrenia (SCZ) using task-related fMRI (t-fMRI) designs. To evaluate the effectiveness of this approach, we conducted a comprehensive meta-analysis of 31 t-fMRI studies using a bivariate model. Our findings revealed a high overall sensitivity of 0.83 and specificity of 0.82 for t-fMRI studies. Notably, neuropsychological domains modulated the classification performance, with selective attention demonstrating a significantly higher specificity than working memory (β = 0.98, z = 2.11, P = 0.04). Studies involving older, chronic patients with SCZ reported higher sensitivity (P <0.015) and specificity (P <0.001) than those involving younger, first-episode patients or high-risk individuals for psychosis. Additionally, we found that the severity of negative symptoms was positively associated with the specificity of the classification model (β = 7.19, z = 2.20, P = 0.03). Taken together, these results support the potential of using task-based fMRI data in combination with machine learning techniques to identify biomarkers related to symptom outcomes in SCZ, providing a promising avenue for improving diagnostic accuracy and treatment efficacy. Future attempts to deploy ML classification should consider the factors of algorithm choice, data quality and quantity, as well as issues related to generalization.
Collapse
Affiliation(s)
- Xuan Wang
- Key Laboratory of Brain Functional Genomics (MOE&STCSM), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
- Shanghai Changning Mental Health Center, Shanghai, China
- Neuropsychology and Applied Cognitive Neuroscience Laboratory; CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Chao Yan
- Key Laboratory of Brain Functional Genomics (MOE&STCSM), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
- Shanghai Changning Mental Health Center, Shanghai, China
| | | | - Zheng Xia
- Key Laboratory of Brain Functional Genomics (MOE&STCSM), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Xin-Lu Cai
- Institute of Brain Science and Department of Physiology, School of Basic Medical Sciences, Hangzhou Normal University, Hangzhou, China
| | - Yi Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory; CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Sze Chai Kwok
- Key Laboratory of Brain Functional Genomics (MOE&STCSM), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
- Shanghai Changning Mental Health Center, Shanghai, China
- Phylo-Cognition Laboratory, Division of Natural and Applied Sciences, Data Science Research Center, Duke Kunshan University, Kunshan, China
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Raymond C K Chan
- Neuropsychology and Applied Cognitive Neuroscience Laboratory; CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| |
Collapse
|
10
|
Voineskos AN, Hawco C, Neufeld NH, Turner JA, Ameis SH, Anticevic A, Buchanan RW, Cadenhead K, Dazzan P, Dickie EW, Gallucci J, Lahti AC, Malhotra AK, Öngür D, Lencz T, Sarpal DK, Oliver LD. Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions. World Psychiatry 2024; 23:26-51. [PMID: 38214624 PMCID: PMC10786022 DOI: 10.1002/wps.21159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/13/2024] Open
Abstract
Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case-control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data-driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case-control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.
Collapse
Affiliation(s)
- Aristotle N Voineskos
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Colin Hawco
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Nicholas H Neufeld
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Jessica A Turner
- Department of Psychiatry and Behavioral Health, Wexner Medical Center, Ohio State University, Columbus, OH, USA
| | - Stephanie H Ameis
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Cundill Centre for Child and Youth Depression and McCain Centre for Child, Youth and Family Mental Health, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Alan Anticevic
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Robert W Buchanan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Kristin Cadenhead
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Paola Dazzan
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Erin W Dickie
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Julia Gallucci
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Adrienne C Lahti
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Anil K Malhotra
- Institute for Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Psychiatry, Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, USA
| | - Dost Öngür
- McLean Hospital/Harvard Medical School, Belmont, MA, USA
| | - Todd Lencz
- Institute for Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Department of Psychiatry, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Department of Psychiatry, Zucker Hillside Hospital Division of Northwell Health, Glen Oaks, NY, USA
| | - Deepak K Sarpal
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lindsay D Oliver
- Campbell Family Mental Health Research Institute and Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
| |
Collapse
|
11
|
Quinn TP, Hess JL, Marshe VS, Barnett MM, Hauschild AC, Maciukiewicz M, Elsheikh SSM, Men X, Schwarz E, Trakadis YJ, Breen MS, Barnett EJ, Zhang-James Y, Ahsen ME, Cao H, Chen J, Hou J, Salekin A, Lin PI, Nicodemus KK, Meyer-Lindenberg A, Bichindaritz I, Faraone SV, Cairns MJ, Pandey G, Müller DJ, Glatt SJ. A primer on the use of machine learning to distil knowledge from data in biological psychiatry. Mol Psychiatry 2024; 29:387-401. [PMID: 38177352 PMCID: PMC11228968 DOI: 10.1038/s41380-023-02334-2] [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: 07/12/2022] [Revised: 09/21/2023] [Accepted: 11/17/2023] [Indexed: 01/06/2024]
Abstract
Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers.
Collapse
Affiliation(s)
- Thomas P Quinn
- Applied Artificial Intelligence Institute (A2I2), Burwood, VIC, 3125, Australia
| | - Jonathan L Hess
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Victoria S Marshe
- Institute of Medical Science, University of Toronto, Toronto, ON, M5S 1A1, Canada
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
| | - Michelle M Barnett
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, 2308, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, 2308, Australia
| | - Anne-Christin Hauschild
- Department of Medical Informatics, Medical University Center Göttingen, Göttingen, Lower Saxony, 37075, Germany
| | - Malgorzata Maciukiewicz
- Hospital Zurich, University of Zurich, Zurich, 8091, Switzerland
- Department of Rheumatology and Immunology, University Hospital Bern, Bern, 3010, Switzerland
- Department for Biomedical Research (DBMR), University of Bern, Bern, 3010, Switzerland
| | - Samar S M Elsheikh
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
| | - Xiaoyu Men
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, M5S 1A1, Canada
| | - Emanuel Schwarz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Yannis J Trakadis
- Department Human Genetics, McGill University Health Centre, Montreal, QC, H4A 3J1, Canada
| | - Michael S Breen
- Psychiatry, Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Eric J Barnett
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Yanli Zhang-James
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Mehmet Eren Ahsen
- Department of Business Administration, Gies College of Business, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA
- Department of Biomedical and Translational Sciences, Carle-Illinois School of Medicine, University of Illinois at Urbana-Champaign, Champaign, IL, 61820, USA
| | - Han Cao
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Junfang Chen
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Jiahui Hou
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Asif Salekin
- Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, 13244, USA
| | - Ping-I Lin
- Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, NSW, 2052, Australia
- Mental Health Research Unit, South Western Sydney Local Health District, Liverpool, NSW, 2170, Australia
| | | | - Andreas Meyer-Lindenberg
- Clinical Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Mannheim, Baden-Württemberg, J5 68159, Germany
| | - Isabelle Bichindaritz
- Biomedical and Health Informatics/Computer Science Department, State University of New York at Oswego, Oswego, NY, 13126, USA
- Intelligent Bio Systems Lab, State University of New York at Oswego, Oswego, NY, 13126, USA
| | - Stephen V Faraone
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, 2308, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, 2308, Australia
| | - Gaurav Pandey
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Daniel J Müller
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, M5S 1A1, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, M5S 1A1, Canada
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Würzburg, Würzburg, 97080, Germany
| | - Stephen J Glatt
- Department of Psychiatry and Behavioral Sciences, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
- Department of Neuroscience and Physiology, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
- Department of Public Health and Preventive Medicine, Norton College of Medicine at SUNY Upstate Medical University, Syracuse, NY, 13210, USA.
| |
Collapse
|
12
|
Shen S, Wei R, Gao Y, Yang X, Zhang G, Yan B, Xiao Z, Li J. Cortical atrophy in early-stage patients with anti-NMDA receptor encephalitis: a machine-learning MRI study with various feature extraction. Cereb Cortex 2024; 34:bhad499. [PMID: 38185983 DOI: 10.1093/cercor/bhad499] [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: 08/10/2023] [Revised: 11/29/2023] [Accepted: 11/30/2023] [Indexed: 01/09/2024] Open
Abstract
Conventional brain magnetic resonance imaging (MRI) of anti-N-methyl-D-aspartate-receptor encephalitis (NMDARE) is non-specific, thus showing little differential diagnostic value, especially for MRI-negative patients. To characterize patterns of structural alterations and facilitate the diagnosis of MRI-negative NMDARE patients, we build two support vector machine models (NMDARE versus healthy controls [HC] model and NMDARE versus viral encephalitis [VE] model) based on radiomics features extracted from brain MRI. A total of 109 MRI-negative NMDARE patients in the acute phase, 108 HCs and 84 acute MRI-negative VE cases were included for training. Another 29 NMDARE patients, 28 HCs and 26 VE cases were included for validation. Eighty features discriminated NMDARE patients from HCs, with area under the receiver operating characteristic curve (AUC) of 0.963 in validation set. NMDARE patients presented with significantly lower thickness, area, and volume and higher mean curvature than HCs. Potential atrophy predominately presented in the frontal lobe (cumulative weight = 4.3725, contribution rate of 29.86%), and temporal lobe (cumulative weight = 2.573, contribution rate of 17.57%). The NMDARE versus VE model achieved certain diagnostic power, with AUC of 0.879 in validation set. Our research shows potential atrophy across the entire cerebral cortex in acute NMDARE patients, and MRI machine learning model has a potential to facilitate the diagnosis MRI-negative NMDARE.
Collapse
Affiliation(s)
- Sisi Shen
- Department of Neurology, West China Hospital of Sichuan University, 37 GuoXue Alley, Chengdu 610041, China
| | - Ran Wei
- School of Information and Communication Engineering, University of Electronic Science and Technology of China No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731| Chengdu, Sichuan, P.R. China
| | - Yu Gao
- School of Information and Communication Engineering, University of Electronic Science and Technology of China No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731| Chengdu, Sichuan, P.R. China
| | - Xinyuan Yang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731| Chengdu, Sichuan, P.R. China
| | - Guoning Zhang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731| Chengdu, Sichuan, P.R. China
| | - Bo Yan
- School of Information and Communication Engineering, University of Electronic Science and Technology of China No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731| Chengdu, Sichuan, P.R. China
| | - Zhuoling Xiao
- School of Information and Communication Engineering, University of Electronic Science and Technology of China No.2006, Xiyuan Ave, West Hi-Tech Zone, 611731| Chengdu, Sichuan, P.R. China
| | - Jinmei Li
- Department of Neurology, West China Hospital of Sichuan University, 37 GuoXue Alley, Chengdu 610041, China
| |
Collapse
|
13
|
Lenge M, Balestrini S, Napolitano A, Mei D, Conti V, Baldassarri G, Trivisano M, Pellacani S, Macconi L, Longo D, Rossi Espagnet MC, Cappelletti S, D'Incerti L, Barba C, Specchio N, Guerrini R. Morphometric network-based abnormalities correlate with psychiatric comorbidities and gene expression in PCDH19-related developmental and epileptic encephalopathy. Transl Psychiatry 2024; 14:35. [PMID: 38238304 PMCID: PMC10796344 DOI: 10.1038/s41398-024-02753-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 01/03/2024] [Accepted: 01/08/2024] [Indexed: 01/22/2024] Open
Abstract
Protocadherin-19 (PCDH19) developmental and epileptic encephalopathy causes an early-onset epilepsy syndrome with limbic seizures, typically occurring in clusters and variably associated with intellectual disability and a range of psychiatric disorders including hyperactive, obsessive-compulsive and autistic features. Previous quantitative neuroimaging studies revealed abnormal cortical areas in the limbic formation (parahippocampal and fusiform gyri) and underlying white-matter fibers. In this study, we adopted morphometric, network-based and multivariate statistical methods to examine the cortex and substructure of the hippocampus and amygdala in a cohort of 20 PCDH19-mutated patients and evaluated the relation between structural patterns and clinical variables at individual level. We also correlated morphometric alterations with known patterns of PCDH19 expression levels. We found patients to exhibit high-significant reductions of cortical surface area at a whole-brain level (left/right pvalue = 0.045/0.084), and particularly in the regions of the limbic network (left/right parahippocampal gyri pvalue = 0.230/0.016; left/right entorhinal gyri pvalue = 0.002/0.327), and bilateral atrophy of several subunits of the amygdala and hippocampus, particularly in the CA regions (head of the left CA3 pvalue = 0.002; body of the right CA3 pvalue = 0.004), and differences in the shape of hippocampal structures. More severe psychiatric comorbidities correlated with more significant altered patterns, with the entorhinal gyrus (pvalue = 0.013) and body of hippocampus (pvalue = 0.048) being more severely affected. Morphometric alterations correlated significantly with the known expression patterns of PCDH19 (rvalue = -0.26, pspin = 0.092). PCDH19 encephalopathy represents a model of genetically determined neural network based neuropsychiatric disease in which quantitative MRI-based findings correlate with the severity of clinical manifestations and had have a potential predictive value if analyzed early.
Collapse
Affiliation(s)
- Matteo Lenge
- Child Neurology Unit and Laboratories, Neuroscience Department, Meyer Children's Hospital IRCCS, 50139, Florence, Italy
| | - Simona Balestrini
- Child Neurology Unit and Laboratories, Neuroscience Department, Meyer Children's Hospital IRCCS, 50139, Florence, Italy
| | - Antonio Napolitano
- Medical Physics Department, Bambino Gesù Children's Hospital, IRCCS, 00100, Rome, Italy
| | - Davide Mei
- Child Neurology Unit and Laboratories, Neuroscience Department, Meyer Children's Hospital IRCCS, 50139, Florence, Italy
| | - Valerio Conti
- Child Neurology Unit and Laboratories, Neuroscience Department, Meyer Children's Hospital IRCCS, 50139, Florence, Italy
| | - Giulia Baldassarri
- Medical Physics Department, Bambino Gesù Children's Hospital, IRCCS, 00100, Rome, Italy
| | - Marina Trivisano
- Neurology, Epilepsy and Movement Disorders, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, 00165, Rome, Italy
| | - Simona Pellacani
- Child Neurology Unit and Laboratories, Neuroscience Department, Meyer Children's Hospital IRCCS, 50139, Florence, Italy
| | - Letizia Macconi
- Pediatric Radiology Unit, Meyer Children's Hospital IRCCS, 50139, Florence, Italy
| | - Daniela Longo
- Functional and Interventional Neuroimaging Unit, Bambino Gesù Children's Hospital, IRCCS, 00165, Rome, Italy
| | | | - Simona Cappelletti
- Neurology, Epilepsy and Movement Disorders, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, 00165, Rome, Italy
| | - Ludovico D'Incerti
- Pediatric Radiology Unit, Meyer Children's Hospital IRCCS, 50139, Florence, Italy
| | - Carmen Barba
- Child Neurology Unit and Laboratories, Neuroscience Department, Meyer Children's Hospital IRCCS, 50139, Florence, Italy
| | - Nicola Specchio
- Neurology, Epilepsy and Movement Disorders, Bambino Gesù Children's Hospital, IRCCS, Full Member of European Reference Network EpiCARE, 00165, Rome, Italy
| | - Renzo Guerrini
- Child Neurology Unit and Laboratories, Neuroscience Department, Meyer Children's Hospital IRCCS, 50139, Florence, Italy.
| |
Collapse
|
14
|
Zhu T, Kou R, Hu Y, Yuan M, Yuan C, Luo L, Zhang W. Dissecting clinical and biological heterogeneity in clinical states of bipolar disorder: a 10-year retrospective study from China. Front Psychiatry 2023; 14:1128862. [PMID: 38179244 PMCID: PMC10764613 DOI: 10.3389/fpsyt.2023.1128862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 12/01/2023] [Indexed: 01/06/2024] Open
Abstract
Objectives To dissect clinical and biological heterogeneity in clinical states of bipolar disorder (BD), and investigate if neuropsychological symptomatology, comorbidity, vital signs, and blood laboratory indicators are predictors of distinct BD states. Methods A retrospective BD cohort was established with data extracted from a Chinese hospital's electronic medical records (EMR) between 2009 and 2018. Subjects were inpatients with a main discharge diagnosis of BD and were assessed for clinical state at hospitalization. We categorized all subjects into manic state, depressive state, and mixed state. Four machine learning classifiers were utilized to classify the subjects. A Shapley additive explanations (SHAP) algorithm was applied to the classifiers to aid in quantifying and visualizing the contributions of each feature that drive patient-specific classifications. Results A sample of 3,085 records was included (38.54% as manic, 56.69% as depressive, and 4.77% as mixed state). Mixed state showed more severe suicidal ideation and psychomotor abnormalities, while depressive state showed more common anxiety, sleep, and somatic-related symptoms and more comorbid conditions. Higher levels of body temperature, pulse, and systolic and diastolic blood pressures were present during manic episodes. Xgboost achieved the best AUC of 88.54% in manic/depressive states classification; Logistic regression and Random forest achieved the best AUCs of 75.5 and 75% in manic/mixed states and depressive/mixed states classifications, respectively. Myocardial enzymes and the non-enzymatic antioxidant uric acid and bilirubin contributed significantly to distinguish BD clinical states. Conclusion The observed novel biological associations with BD clinical states confirm that biological heterogeneity contributes to clinical heterogeneity of BD.
Collapse
Affiliation(s)
- Ting Zhu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Ran Kou
- Business School, Sichuan University, Chengdu, China
| | - Yao Hu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Minlan Yuan
- Mental Health Center of West China Hospital, Sichuan University, Chengdu, China
| | - Cui Yuan
- Sichuan Provincial Center for Mental Health, The Center of Psychosomatic Medicine of Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Li Luo
- Business School, Sichuan University, Chengdu, China
| | - Wei Zhang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
- Mental Health Center of West China Hospital, Sichuan University, Chengdu, China
| |
Collapse
|
15
|
Manduca G, Zeni V, Moccia S, Milano BA, Canale A, Benelli G, Stefanini C, Romano D. Learning algorithms estimate pose and detect motor anomalies in flies exposed to minimal doses of a toxicant. iScience 2023; 26:108349. [PMID: 38058310 PMCID: PMC10696104 DOI: 10.1016/j.isci.2023.108349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 10/04/2023] [Accepted: 10/24/2023] [Indexed: 12/08/2023] Open
Abstract
Pesticide exposure, even at low doses, can have detrimental effects on ecosystems. This study aimed at validating the use of machine learning for recognizing motor anomalies, produced by minimal insecticide exposure on a model insect species. The Mediterranean fruit fly, Ceratitis capitata (Diptera: Tephritidae), was exposed to food contaminated with low concentrations of Carlina acaulis essential oil (EO). A deep learning approach enabled fly pose estimation on video recordings in a custom-built arena. Five machine learning algorithms were trained on handcrafted features, extracted from the predicted pose, to distinguish treated individuals. Random Forest and K-Nearest Neighbor algorithms best performed, with an area under the receiver operating characteristic (ROC) curve of 0.75 and 0.73, respectively. Both algorithms achieved an accuracy of 0.71. Results show the machine learning potential for detecting sublethal effects arising from insecticide exposure on fly motor behavior, which could also affect other organisms and environmental health.
Collapse
Affiliation(s)
- Gianluca Manduca
- The BioRobotics Institute, Sant'Anna School of Advanced Studies, Viale Rinaldo Piaggio 34, 56025, Pontedera, Pisa, Italy
- Department of Excellence in Robotics and AI, Sant'Anna School of Advanced Studies, Piazza Martiri della Libertà 33, 56127, Pisa, Italy
| | - Valeria Zeni
- Department of Agriculture, Food and Environment, University of Pisa, Via del Borghetto 80, 56124, Pisa, Italy
| | - Sara Moccia
- The BioRobotics Institute, Sant'Anna School of Advanced Studies, Viale Rinaldo Piaggio 34, 56025, Pontedera, Pisa, Italy
- Department of Excellence in Robotics and AI, Sant'Anna School of Advanced Studies, Piazza Martiri della Libertà 33, 56127, Pisa, Italy
| | - Beatrice A. Milano
- Institute of Life Sciences, Sant'Anna School of Advanced Studies, Piazza Martiri della Libertà 33, 56127, Pisa, Italy
- Faculty of Medicine and Surgery, University of Pisa, Via Roma 55/Building 57, 56126, Pisa, Italy
| | - Angelo Canale
- Department of Agriculture, Food and Environment, University of Pisa, Via del Borghetto 80, 56124, Pisa, Italy
| | - Giovanni Benelli
- Department of Agriculture, Food and Environment, University of Pisa, Via del Borghetto 80, 56124, Pisa, Italy
| | - Cesare Stefanini
- The BioRobotics Institute, Sant'Anna School of Advanced Studies, Viale Rinaldo Piaggio 34, 56025, Pontedera, Pisa, Italy
- Department of Excellence in Robotics and AI, Sant'Anna School of Advanced Studies, Piazza Martiri della Libertà 33, 56127, Pisa, Italy
| | - Donato Romano
- The BioRobotics Institute, Sant'Anna School of Advanced Studies, Viale Rinaldo Piaggio 34, 56025, Pontedera, Pisa, Italy
- Department of Excellence in Robotics and AI, Sant'Anna School of Advanced Studies, Piazza Martiri della Libertà 33, 56127, Pisa, Italy
| |
Collapse
|
16
|
Bayar Kapici O, Kapici Y, Tekın A, Şırık M. A novel diagnosis method for schizophrenia based on globus pallidus data. Psychiatry Res Neuroimaging 2023; 336:111732. [PMID: 37922672 DOI: 10.1016/j.pscychresns.2023.111732] [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: 05/09/2023] [Revised: 08/25/2023] [Accepted: 10/09/2023] [Indexed: 11/07/2023]
Abstract
This research aims to diagnose schizophrenia with machine learning-based algorithms. Bayesian neural network, logistic regression, decision tree, k-nearest neighbor, and gaussian kernel classification techniques are investigated to diagnose schizophrenia with data from 125 persons. This study showed that left lateral ventricles and left globus pallidus volumes and their percentages in the brain were significantly lower than HCs in FEP patients. Using brain volumes, we were able to diagnose FEP with an accuracy of 73.6 % via logistic regression and with an accuracy of 86.4 % using the SVM kernel classifier method. Therefore, brain volumes can be used to diagnose FEP with the SVM kernel classifier method.
Collapse
Affiliation(s)
- Olga Bayar Kapici
- Department of Radiology, Adıyaman Training and Research Hospital, Adıyaman, Turkey
| | - Yaşar Kapici
- Department of Psychiatry, Kahta State Hospital, Adıyaman, Turkey.
| | - Atilla Tekın
- Department of Psychiatry, Adıyaman University Faculty of Medicine, Adıyaman, Turkey
| | - Mehmet Şırık
- Department of Radiology, Adıyaman University Faculty of Medicine, Adıyaman, Turkey
| |
Collapse
|
17
|
Yassin W, Hoftman GD, Bergen SE, del Re EC. Editorial: Diagnostic and prognostic brain-based biomarkers in psychosis spectrum. Front Psychiatry 2023; 14:1332447. [PMID: 38076681 PMCID: PMC10703481 DOI: 10.3389/fpsyt.2023.1332447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 11/06/2023] [Indexed: 02/12/2024] Open
Affiliation(s)
- Walid Yassin
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
| | - Gil D. Hoftman
- Department of Psychiatry, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States
| | - Sarah E. Bergen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | | |
Collapse
|
18
|
Ali MT, Gebreil A, ElNakieb Y, Elnakib A, Shalaby A, Mahmoud A, Sleman A, Giridharan GA, Barnes G, Elbaz AS. A personalized classification of behavioral severity of autism spectrum disorder using a comprehensive machine learning framework. Sci Rep 2023; 13:17048. [PMID: 37813914 PMCID: PMC10562430 DOI: 10.1038/s41598-023-43478-z] [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: 03/09/2023] [Accepted: 09/25/2023] [Indexed: 10/11/2023] Open
Abstract
Autism Spectrum Disorder (ASD) is characterized as a neurodevelopmental disorder with a heterogeneous nature, influenced by genetics and exhibiting diverse clinical presentations. In this study, we dissect Autism Spectrum Disorder (ASD) into its behavioral components, mirroring the diagnostic process used in clinical settings. Morphological features are extracted from magnetic resonance imaging (MRI) scans, found in the publicly available dataset ABIDE II, identifying the most discriminative features that differentiate ASD within various behavioral domains. Then, each subject is categorized as having severe, moderate, or mild ASD, or typical neurodevelopment (TD), based on the behavioral domains of the Social Responsiveness Scale (SRS). Through this study, multiple artificial intelligence (AI) models are utilized for feature selection and classifying each ASD severity and behavioural group. A multivariate feature selection algorithm, investigating four different classifiers with linear and non-linear hypotheses, is applied iteratively while shuffling the training-validation subjects to find the set of cortical regions with statistically significant association with ASD. A set of six classifiers are optimized and trained on the selected set of features using 5-fold cross-validation for the purpose of severity classification for each behavioural group. Our AI-based model achieved an average accuracy of 96%, computed as the mean accuracy across the top-performing AI models for feature selection and severity classification across the different behavioral groups. The proposed AI model has the ability to accurately differentiate between the functionalities of specific brain regions, such as the left and right caudal middle frontal regions. We propose an AI-based model that dissects ASD into behavioral components. For each behavioral component, the AI-based model is capable of identifying the brain regions which are associated with ASD as well as utilizing those regions for diagnosis. The proposed system can increase the speed and accuracy of the diagnostic process and result in improved outcomes for individuals with ASD, highlighting the potential of AI in this area.
Collapse
Affiliation(s)
- Mohamed T Ali
- Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA
- UT Southwestern Medical Center, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Ahmad Gebreil
- Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA
| | - Yaser ElNakieb
- Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA
- UT Southwestern Medical Center, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Ahmed Elnakib
- Electrical and Computer Engineering, Penn State Erie-The Behrend College, Erie, PA, 16563, USA
| | - Ahmed Shalaby
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Ali Mahmoud
- Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA
| | - Ahmed Sleman
- Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA
| | | | - Gregory Barnes
- Department of Neurology and Pediatric Research Institute, University of Louisville, Louisville, KY, 40202, USA
| | - Ayman S Elbaz
- Bioengineering Department, University of Louisville, Louisville, KY, 40292, USA.
| |
Collapse
|
19
|
Zhu T, Liu X, Wang J, Kou R, Hu Y, Yuan M, Yuan C, Luo L, Zhang W. Explainable machine-learning algorithms to differentiate bipolar disorder from major depressive disorder using self-reported symptoms, vital signs, and blood-based markers. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107723. [PMID: 37480646 DOI: 10.1016/j.cmpb.2023.107723] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 06/26/2023] [Accepted: 07/15/2023] [Indexed: 07/24/2023]
Abstract
BACKGROUND AND OBJECTIVE Caused by shared genetic risk factors and similar neuropsychological symptoms, bipolar disorder (BD) and major depressive disorder (MDD) are at high risk of misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. We aimed to develop a machine learning (ML)-based diagnostic system, based on electronic medical records (EMR) data, to mimic the clinical reasoning of human physicians to differentiate MDD and BD (especially BD depressive episodes) patients about to be admitted to a hospital and, hence, reduce the misdiagnosis of BD as MDD on admission. In addition, we examined to what extent our ML model could be made interpretable by quantifying and visualizing the features that drive the predictions. METHODS By identifying 16,311 patients admitted to a hospital located in western China between 2009 and 2018 with a recorded main diagnosis of MDD or BD, we established three sub-cohorts with different combinations of features for both the MDD-BD cohort and the MDD-BD depressive episodes cohort, respectively. Four different ML algorithms (logistic regression, extreme gradient boosting (XGBoost), random forest, and support vector machine) and four train-test splits were used to train and validate diagnostic models, and explainable methods (SHAP and Break Down) were utilized to analyze the contribution of each of the features at both population-level and individual-level, including feature importance, feature interaction, and feature effect on prediction decision for a specific subject. RESULTS The XGBoost algorithm provided the best test performance (AUC: 0.838 (0.810-0.867), PPV: 0.810 and NPV: 0.834) for separating patients with BD from those with MDD. Core predictors included symptoms (mood-up, exciting, bad sleep, loss of interest, talking, mood-down, provoke), along with age, job, myocardial enzyme markers (creatine kinase, hydroxybutyrate dehydrogenase), diabetes-associated marker (glucose), bone function marker (alkaline phosphatase), non-enzymatic antioxidant (uric acid), markers of immune/inflammation (white blood cell count, lymphocyte count, basophil percentage, monocyte count), cardiovascular function marker (low density lipoprotein), renal marker (total protein), liver biochemistry marker (indirect bilirubin), and vital signs like pulse. For separating patients with BD depressive episodes from those with MDD, the test AUC was 0.777 (0.732-0.822), with PPV 0.576 and NPV 0.899. Additional validation in models built with self-reported symptoms removed from the feature set, showed test AUC of 0.701 (0.666-0.736) for differentiating BD and MDD, and AUC of 0.564 (0.515-0.614) for detecting patients in BD depressive episodes from MDD patients. Validation in the datasets without removing the patients with comorbidity showed an AUC of 0.826 (0.806-0.846). CONCLUSION The diagnostic system accurately identified patients with BD in various clinical scenarios, and differences in patterns of peripheral markers between BD and MDD could enrich our understanding of potential underlying pathophysiological mechanisms of them.
Collapse
Affiliation(s)
- Ting Zhu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China; Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Xiaofei Liu
- Business School, Sichuan University, Chengdu, China
| | - Junren Wang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China; Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Ran Kou
- Business School, Sichuan University, Chengdu, China
| | - Yao Hu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China; Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Minlan Yuan
- Mental Health Center of West China Hospital, Sichuan University, Chengdu, China
| | - Cui Yuan
- Sichuan Provincial Center for Mental Health, The Center of Psychosomatic Medicine of Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Li Luo
- Business School, Sichuan University, Chengdu, China
| | - Wei Zhang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China; Med-X Center for Informatics, Sichuan University, Chengdu, China; Mental Health Center of West China Hospital, Sichuan University, Chengdu, China.
| |
Collapse
|
20
|
Saglam Y, Oz A, Yildiz G, Ermis C, Kargin OA, Arslan S, Karacetin G. Can diffusion tensor imaging have a diagnostic utility to differentiate early-onset forms of bipolar disorder and schizophrenia: A neuroimaging study with explainable machine learning algorithms. Psychiatry Res Neuroimaging 2023; 335:111696. [PMID: 37595386 DOI: 10.1016/j.pscychresns.2023.111696] [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: 02/27/2023] [Revised: 06/11/2023] [Accepted: 07/26/2023] [Indexed: 08/20/2023]
Abstract
BACKGROUND/AIM Accurate diagnosis of early-onset psychotic disorders is crucial to improve clinical outcomes. This study aimed to differentiate patients with early-onset schizophrenia (EOS) from early-onset bipolar disorder (EBD) with machine learning (ML) algorithms using white matter tracts (WMT). METHOD Diffusion tensor imaging was obtained from adolescents with either EOS (n = 43) or EBD (n = 32). Global probabilistic tractography using an automated tract-based TRACULA software was performed to analyze the fractional anisotropy (FA) of forty-two WMT. The nested cross-validation was performed in feature selection and model construction. EXtreme Gradient Boosting (XGBoost) was applied to select the features that can give the best performance in the ML model. The interpretability of the model was explored with the SHApley Additive exPlanations (SHAP). FINDINGS The XGBoost algorithm identified nine out of the 42 major WMTs with significant predictive power. Among ML models, Support Vector Machine-Linear showed the best performance. Higher SHAP values of left acoustic radiation, bilateral anterior thalamic radiation, and the corpus callosum were associated with a higher likelihood of EOS. CONCLUSIONS Our findings suggested that ML models based on the FA values of major WMT reconstructed by global probabilistic tractography can unveil hidden microstructural aberrations to distinguish EOS from EBD.
Collapse
Affiliation(s)
- Yesim Saglam
- Department of Child and Adolescent Psychiatry, University of Health Sciences, Bakirkoy Prof Dr Mazhar Osman Research and Training Hospital for Psychiatry, Neurology and Neurosurgery, Istanbul, Turkey.
| | - Ahmet Oz
- Department of Radiology, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Gokcen Yildiz
- Department of Radiology, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Cagatay Ermis
- Queen Silvia Children's Hospital, Department of Child Psychiatry, Gothenburg, Sweden
| | - Osman Aykan Kargin
- Department of Radiology, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Serdar Arslan
- Division of Neuroradiology, Department of Radiology, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Gul Karacetin
- Department of Child and Adolescent Psychiatry, University of Health Sciences, Bakirkoy Prof Dr Mazhar Osman Research and Training Hospital for Psychiatry, Neurology and Neurosurgery, Istanbul, Turkey
| |
Collapse
|
21
|
Kozyrev EA, Ermakov EA, Boiko AS, Mednova IA, Kornetova EG, Bokhan NA, Ivanova SA. Building Predictive Models for Schizophrenia Diagnosis with Peripheral Inflammatory Biomarkers. Biomedicines 2023; 11:1990. [PMID: 37509629 PMCID: PMC10377576 DOI: 10.3390/biomedicines11071990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 07/10/2023] [Accepted: 07/12/2023] [Indexed: 07/30/2023] Open
Abstract
Machine learning and artificial intelligence technologies are known to be a convenient tool for analyzing multi-domain data in precision psychiatry. In the case of schizophrenia, the most commonly used data sources for such purposes are neuroimaging, voice and language patterns, and mobile phone data. Data on peripheral markers can also be useful for building predictive models. Here, we have developed five predictive models for the binary classification of schizophrenia patients and healthy individuals. Data on serum concentrations of cytokines, chemokines, growth factors, and age were among 38 parameters used to build these models. The sample consisted of 217 schizophrenia patients and 90 healthy individuals. The models architecture was involved logistic regression, deep neural networks, decision trees, support vector machine, and k-nearest neighbors algorithms. It was shown that the algorithm based on a deep neural network (consisting of five layers) showed a slightly higher sensitivity (0.87 ± 0.04) and specificity (0.52 ± 0.06) than other algorithms. Combining all variables into a single classifier showed a cumulative effect that exceeded the effectiveness of individual variables, indicating the need to use multiple biomarkers to diagnose schizophrenia. Thus, the data obtained showed the promise of using data on peripheral biomarkers and machine learning methods for diagnosing schizophrenia.
Collapse
Affiliation(s)
- Evgeny A Kozyrev
- Budker Institute of Nuclear Physics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
| | - Evgeny A Ermakov
- Institute of Chemical Biology and Fundamental Medicine, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia
| | - Anastasiia S Boiko
- Mental Health Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, 634014 Tomsk, Russia
| | - Irina A Mednova
- Mental Health Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, 634014 Tomsk, Russia
| | - Elena G Kornetova
- Mental Health Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, 634014 Tomsk, Russia
- University Hospital, Siberian State Medical University, 634050 Tomsk, Russia
| | - Nikolay A Bokhan
- Mental Health Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, 634014 Tomsk, Russia
- Psychiatry, Addiction Psychiatry and Psychotherapy Department, Siberian State Medical University, 634050 Tomsk, Russia
| | - Svetlana A Ivanova
- Mental Health Research Institute, Tomsk National Research Medical Center of the Russian Academy of Sciences, 634014 Tomsk, Russia
- Psychiatry, Addiction Psychiatry and Psychotherapy Department, Siberian State Medical University, 634050 Tomsk, Russia
| |
Collapse
|
22
|
Tilwani D, Bradshaw J, Sheth A, O'Reilly C. ECG Recordings as Predictors of Very Early Autism Likelihood: A Machine Learning Approach. Bioengineering (Basel) 2023; 10:827. [PMID: 37508854 PMCID: PMC10376813 DOI: 10.3390/bioengineering10070827] [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: 05/07/2023] [Revised: 06/22/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023] Open
Abstract
In recent years, there has been a rise in the prevalence of autism spectrum disorder (ASD). The diagnosis of ASD requires behavioral observation and standardized testing completed by highly trained experts. Early intervention for ASD can begin as early as 1-2 years of age, but ASD diagnoses are not typically made until ages 2-5 years, thus delaying the start of intervention. There is an urgent need for non-invasive biomarkers to detect ASD in infancy. While previous research using physiological recordings has focused on brain-based biomarkers of ASD, this study investigated the potential of electrocardiogram (ECG) recordings as an ASD biomarker in 3-6-month-old infants. We recorded the heart activity of infants at typical and elevated familial likelihood for ASD during naturalistic interactions with objects and caregivers. After obtaining the ECG signals, features such as heart rate variability (HRV) and sympathetic and parasympathetic activities were extracted. Then we evaluated the effectiveness of multiple machine learning classifiers for classifying ASD likelihood. Our findings support our hypothesis that infant ECG signals contain important information about ASD familial likelihood. Amongthe various machine learning algorithms tested, KNN performed best according to sensitivity (0.70 ± 0.117), F1-score (0.689 ± 0.124), precision (0.717 ± 0.128), accuracy (0.70 ± 0.117, p-value = 0.02), and ROC (0.686 ± 0.122, p-value = 0.06). These results suggest that ECG signals contain relevant information about the likelihood of an infant developing ASD. Future studies should consider the potential of information contained in ECG, and other indices of autonomic control, for the development of biomarkers of ASD in infancy.
Collapse
Affiliation(s)
- Deepa Tilwani
- Artificial Intelligence Institute, University of South Carolina, Columbia, SC 29208, USA
- Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA
- Carolina Autism and Neurodevelopment Research Center, University of South Carolina, Columbia, SC 29208, USA
- Institute for Mind and Brain, University of South Carolina, Columbia, SC 29208, USA
| | - Jessica Bradshaw
- Carolina Autism and Neurodevelopment Research Center, University of South Carolina, Columbia, SC 29208, USA
- Institute for Mind and Brain, University of South Carolina, Columbia, SC 29208, USA
- Department of Psychology, University of South Carolina, Columbia, SC 29208, USA
| | - Amit Sheth
- Artificial Intelligence Institute, University of South Carolina, Columbia, SC 29208, USA
- Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA
| | - Christian O'Reilly
- Artificial Intelligence Institute, University of South Carolina, Columbia, SC 29208, USA
- Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA
- Carolina Autism and Neurodevelopment Research Center, University of South Carolina, Columbia, SC 29208, USA
- Institute for Mind and Brain, University of South Carolina, Columbia, SC 29208, USA
| |
Collapse
|
23
|
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.
Collapse
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
| |
Collapse
|
24
|
Squires M, Tao X, Elangovan S, Gururajan R, Zhou X, Acharya UR, Li Y. Deep learning and machine learning in psychiatry: a survey of current progress in depression detection, diagnosis and treatment. Brain Inform 2023; 10:10. [PMID: 37093301 PMCID: PMC10123592 DOI: 10.1186/s40708-023-00188-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 03/08/2023] [Indexed: 04/25/2023] Open
Abstract
Informatics paradigms for brain and mental health research have seen significant advances in recent years. These developments can largely be attributed to the emergence of new technologies such as machine learning, deep learning, and artificial intelligence. Data-driven methods have the potential to support mental health care by providing more precise and personalised approaches to detection, diagnosis, and treatment of depression. In particular, precision psychiatry is an emerging field that utilises advanced computational techniques to achieve a more individualised approach to mental health care. This survey provides an overview of the ways in which artificial intelligence is currently being used to support precision psychiatry. Advanced algorithms are being used to support all phases of the treatment cycle. These systems have the potential to identify individuals suffering from mental health conditions, allowing them to receive the care they need and tailor treatments to individual patients who are mostly to benefit. Additionally, unsupervised learning techniques are breaking down existing discrete diagnostic categories and highlighting the vast disease heterogeneity observed within depression diagnoses. Artificial intelligence also provides the opportunity to shift towards evidence-based treatment prescription, moving away from existing methods based on group averages. However, our analysis suggests there are several limitations currently inhibiting the progress of data-driven paradigms in care. Significantly, none of the surveyed articles demonstrate empirically improved patient outcomes over existing methods. Furthermore, greater consideration needs to be given to uncertainty quantification, model validation, constructing interdisciplinary teams of researchers, improved access to diverse data and standardised definitions within the field. Empirical validation of computer algorithms via randomised control trials which demonstrate measurable improvement to patient outcomes are the next step in progressing models to clinical implementation.
Collapse
Affiliation(s)
- Matthew Squires
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD, Australia.
| | - Xiaohui Tao
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD, Australia
| | | | - Raj Gururajan
- School of Business, University of Southern Queensland, Springfield, QLD, Australia
| | - Xujuan Zhou
- School of Business, University of Southern Queensland, Springfield, QLD, Australia
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD, Australia
| | - Yuefeng Li
- School of Computer Science, Queensland University of Technology, Brisbane, QLD, Australia
| |
Collapse
|
25
|
He M, Kolesar TA, Goertzen AL, Ng MC, Ko JH. Do Epilepsy Patients with Cognitive Impairment Have Alzheimer's Disease-like Brain Metabolism? Biomedicines 2023; 11:biomedicines11041108. [PMID: 37189726 DOI: 10.3390/biomedicines11041108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 03/31/2023] [Accepted: 04/04/2023] [Indexed: 05/17/2023] Open
Abstract
Although not classically considered together, there is emerging evidence that Alzheimer's disease (AD) and epilepsy share a number of features and that each disease predisposes patients to developing the other. Using machine learning, we have previously developed an automated fluorodeoxyglucose positron emission tomography (FDG-PET) reading program (i.e., MAD), and demonstrated good sensitivity (84%) and specificity (95%) for differentiating AD patients versus healthy controls. In this retrospective chart review study, we investigated if epilepsy patients with/without mild cognitive symptoms also show AD-like metabolic patterns determined by the MAD algorithm. Scans from a total of 20 patients with epilepsy were included in this study. Because AD diagnoses are made late in life, only patients aged ≥40 years were considered. For the cognitively impaired patients, four of six were identified as MAD+ (i.e., the FDG-PET image is classified as AD-like by the MAD algorithm), while none of the five cognitively normal patients was identified as MAD+ (χ2 = 8.148, p = 0.017). These results potentially suggest the usability of FDG-PET in prognosticating later dementia development in non-demented epilepsy patients, especially when combined with machine learning algorithms. A longitudinal follow-up study is warranted to assess the effectiveness of this approach.
Collapse
Affiliation(s)
- Michael He
- Undergraduate Medical Education, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0T6, Canada
| | - Tiffany A Kolesar
- Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0J9, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Health Sciences Centre, Winnipeg, MB R3E 3J7, Canada
| | - Andrew L Goertzen
- Section of Nuclear Medicine, Department of Radiology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
- Graduate Program in Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
| | - Marcus C Ng
- Graduate Program in Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
- Section of Neurology, Department of Internal Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0W2, Canada
| | - Ji Hyun Ko
- Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB R3E 0J9, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Health Sciences Centre, Winnipeg, MB R3E 3J7, Canada
- Graduate Program in Biomedical Engineering, Price Faculty of Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
| |
Collapse
|
26
|
Ma X, Yang WFZ, Zheng W, Li Z, Tang J, Yuan L, Ouyang L, Wang Y, Li C, Jin K, Wang L, Bearden CE, He Y, Chen X. Neuronal dysfunction in individuals at early stage of schizophrenia, A resting-state fMRI study. Psychiatry Res 2023; 322:115123. [PMID: 36827856 DOI: 10.1016/j.psychres.2023.115123] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 02/14/2023] [Accepted: 02/18/2023] [Indexed: 02/22/2023]
Abstract
Schizophrenia has been associated with abnormal intrinsic brain activity, involving various cognitive impairments. Qualitatively similar abnormalities are seen in individuals at ultra-high risk (UHR) for psychosis. In this study, resting-state fMRI (rs-fMRI) data were collected from 44 drug-naïve first-episode schizophrenia (Dn-FES) patients, 48 UHR individuals, and 40 healthy controls (HCs). The fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity (ReHo), and functional connectivity (FC), were performed to evaluate resting brain function. A support vector machine (SVM) was applied for classification analysis. Compared to HCs, both clinical groups showed increased fALFF in the central executive network (CEN), decreased ReHo in the ventral visual pathway (VVP) and decreased FC in temporal-sensorimotor regions. Excellent performance was achieved by using fALFF value in distinguishing both FES (sensitivity=83.21%, specificity=80.58%, accuracy=81.37%, p=0.009) and UHR (sensitivity=75.88%, specificity=85.72%, accuracy=80.72%, p<0.001) from HC group. Moreover, the study highlighted the importance of frontal and temporal alteration in the pathogenesis of schizophrenia. However, no fMRI features were observed that could well distinguish Dn-FES from UHR group. To conclude, fALFF in the CEN may provide potential power for identifying individuals at the early stage of schizophrenia and the alteration in the frontal and temporal lobe may be important to these individuals.
Collapse
Affiliation(s)
- Xiaoqian Ma
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, No.139, Renmin Rd, Second Xiangya Hospital, Changsha, Hunan, China; Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California at Los Angeles, Los Angeles, United States
| | - Winson Fu Zun Yang
- Department of Psychological Sciences, Texas Tech University, Lubbock, United States
| | - Wenxiao Zheng
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, No.139, Renmin Rd, Second Xiangya Hospital, Changsha, Hunan, China; Department of Clinical Medicine, Third Xiangya Hospital, Central South University, Changsha, China
| | - Zongchang Li
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, No.139, Renmin Rd, Second Xiangya Hospital, Changsha, Hunan, China
| | - Jinsong Tang
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, No.139, Renmin Rd, Second Xiangya Hospital, Changsha, Hunan, China
| | - Liu Yuan
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, No.139, Renmin Rd, Second Xiangya Hospital, Changsha, Hunan, China
| | - Lijun Ouyang
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, No.139, Renmin Rd, Second Xiangya Hospital, Changsha, Hunan, China
| | - Yujue Wang
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, No.139, Renmin Rd, Second Xiangya Hospital, Changsha, Hunan, China
| | - Chunwang Li
- Department of Radiology, Hunan Children's Hospital, Changsha, China
| | - Ke Jin
- Department of Radiology, Hunan Children's Hospital, Changsha, China
| | - Lingyan Wang
- Department of Deratology&Traditional Chinese Medicine, Changsha Hospital of Traditional Chinese Medicine (Changsha Eighth Hospital)
| | - Carrie E Bearden
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California at Los Angeles, Los Angeles, United States
| | - Ying He
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, No.139, Renmin Rd, Second Xiangya Hospital, Changsha, Hunan, China; Mental Health Institute of Central South University, Changsha, Hunan, China; National Clinical Research Center for Mental Disorders, Changsha, Hunan, China; National Technology Institute of Mental Disorders, Changsha, Hunan, China; Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan, China; Hunan Medical Center for Mental Health, Changsha, Hunan, China.
| | - Xiaogang Chen
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, No.139, Renmin Rd, Second Xiangya Hospital, Changsha, Hunan, China; Mental Health Institute of Central South University, Changsha, Hunan, China; National Clinical Research Center for Mental Disorders, Changsha, Hunan, China; National Technology Institute of Mental Disorders, Changsha, Hunan, China; Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan, China; Hunan Medical Center for Mental Health, Changsha, Hunan, China.
| |
Collapse
|
27
|
Shah SJH, Albishri A, Kang SS, Lee Y, Sponheim SR, Shim M. ETSNet: A deep neural network for EEG-based temporal-spatial pattern recognition in psychiatric disorder and emotional distress classification. Comput Biol Med 2023; 158:106857. [PMID: 37044046 DOI: 10.1016/j.compbiomed.2023.106857] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 03/06/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023]
Abstract
The use of EEG for evaluating and diagnosing neurological abnormalities related to psychiatric diseases and identifying human emotions has been improved by deep learning advancements. This research aims to categorize individuals with schizophrenia (SZ), their biological relatives (REL), and healthy controls (HC) using resting EEG brain source signal data defined by regions of interest (ROIs). The proposed solution is a deep neural network for the cortical source signals of the ROIs, incorporating a Squeeze-and-Excitation Block and multiple CNNs designed for eyes-open and closed resting states. The model, called EEG Temporal Spatial Network (ETSNet), has two variants: ETSNets and ETSNetf. Two evaluations were conducted to show the effectiveness of the proposed model. The average accuracy for the classification of SZ, REL, and HC using EEG resting data was 99.57% (ETSNetf), and the average accuracy for the classification of eyes-open (EO) and eyes-closed (EC) resting states was 93.15% (ETSNets). An ablation study was also conducted using two public datasets for intellectual and developmental disorders and emotional states, showing improved classification accuracy compared to advanced EEG classification algorithms when using ETSNets.
Collapse
|
28
|
Chen Z, Liu X, Yang Q, Wang YJ, Miao K, Gong Z, Yu Y, Leonov A, Liu C, Feng Z, Chuan-Peng H. Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review. JAMA Netw Open 2023; 6:e231671. [PMID: 36877519 PMCID: PMC9989906 DOI: 10.1001/jamanetworkopen.2023.1671] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2023] Open
Abstract
IMPORTANCE Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated. OBJECTIVE To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis. EVIDENCE REVIEW PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality. FINDINGS A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%). CONCLUSIONS AND RELEVANCE This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.
Collapse
Affiliation(s)
- Zhiyi Chen
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Xuerong Liu
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Qingwu Yang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Yan-Jiang Wang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Kuan Miao
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Zheng Gong
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Yang Yu
- School of Psychology, Third Military Medical University, Chongqing, China
| | - Artemiy Leonov
- Department of Psychology, Clark University, Worcester, Massachusetts
| | - Chunlei Liu
- School of Psychology, Qufu Normal University, Qufu, China
| | - Zhengzhi Feng
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
| |
Collapse
|
29
|
Bajaj S, Blair KS, Dobbertin M, Patil KR, Tyler PM, Ringle JL, Bashford-Largo J, Mathur A, Elowsky J, Dominguez A, Schmaal L, Blair RJR. Machine learning based identification of structural brain alterations underlying suicide risk in adolescents. DISCOVER MENTAL HEALTH 2023; 3:6. [PMID: 37861863 PMCID: PMC10501026 DOI: 10.1007/s44192-023-00033-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 02/09/2023] [Indexed: 10/21/2023]
Abstract
Suicide is the third leading cause of death for individuals between 15 and 19 years of age. The high suicide mortality rate and limited prior success in identifying neuroimaging biomarkers indicate that it is crucial to improve the accuracy of clinical neural signatures underlying suicide risk. The current study implements machine-learning (ML) algorithms to examine structural brain alterations in adolescents that can discriminate individuals with suicide risk from typically developing (TD) adolescents at the individual level. Structural MRI data were collected from 79 adolescents who demonstrated clinical levels of suicide risk and 79 demographically matched TD adolescents. Region-specific cortical/subcortical volume (CV/SCV) was evaluated following whole-brain parcellation into 1000 cortical and 12 subcortical regions. CV/SCV parameters were used as inputs for feature selection and three ML algorithms (i.e., support vector machine [SVM], K-nearest neighbors, and ensemble) to classify adolescents at suicide risk from TD adolescents. The highest classification accuracy of 74.79% (with sensitivity = 75.90%, specificity = 74.07%, and area under the receiver operating characteristic curve = 87.18%) was obtained for CV/SCV data using the SVM classifier. Identified bilateral regions that contributed to the classification mainly included reduced CV within the frontal and temporal cortices but increased volume within the cuneus/precuneus for adolescents at suicide risk relative to TD adolescents. The current data demonstrate an unbiased region-specific ML framework to effectively assess the structural biomarkers of suicide risk. Future studies with larger sample sizes and the inclusion of clinical controls and independent validation data sets are needed to confirm our findings.
Collapse
Affiliation(s)
- Sahil Bajaj
- Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, 14015 Flanagan Blvd. Suite #102, Boys Town, NE, USA.
| | - Karina S Blair
- Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, 14015 Flanagan Blvd. Suite #102, Boys Town, NE, USA
| | - Matthew Dobbertin
- Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, 14015 Flanagan Blvd. Suite #102, Boys Town, NE, USA
- Child and Adolescent Psychiatric Inpatient Center, Boys Town National Research Hospital, Boys Town, NE, USA
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Patrick M Tyler
- Child and Family Translational Research Center, Boys Town National Research Hospital, Boys Town, NE, USA
| | - Jay L Ringle
- Child and Family Translational Research Center, Boys Town National Research Hospital, Boys Town, NE, USA
| | - Johannah Bashford-Largo
- Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, 14015 Flanagan Blvd. Suite #102, Boys Town, NE, USA
- Center for Brain, Biology, and Behavior, University of Nebraska-Lincoln, Lincoln, NE, USA
| | - Avantika Mathur
- Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, 14015 Flanagan Blvd. Suite #102, Boys Town, NE, USA
| | - Jaimie Elowsky
- Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, 14015 Flanagan Blvd. Suite #102, Boys Town, NE, USA
| | - Ahria Dominguez
- Multimodal Clinical Neuroimaging Laboratory (MCNL), Center for Neurobehavioral Research, Boys Town National Research Hospital, 14015 Flanagan Blvd. Suite #102, Boys Town, NE, USA
| | - Lianne Schmaal
- Center for Youth Mental Health, University of Melbourne, Melbourne, VIC, Australia
- Orygen, Parkville, Australia
| | - R James R Blair
- Child and Adolescent Mental Health Centre, Mental Health Services, Capital Region of Denmark, Copenhagen, Denmark
| |
Collapse
|
30
|
Cîrstian R, Pilmeyer J, Bernas A, Jansen JFA, Breeuwer M, Aldenkamp AP, Zinger S. Objective biomarkers of depression: A study of Granger causality and wavelet coherence in resting-state fMRI. J Neuroimaging 2023; 33:404-414. [PMID: 36710075 DOI: 10.1111/jon.13085] [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: 10/01/2022] [Revised: 01/09/2023] [Accepted: 01/10/2023] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND AND PURPOSE The lack of a robust diagnostic biomarker makes understanding depression from a neurobiological standpoint an important goal, especially in the context of brain imaging. METHODS In this study, we aim to create novel image-based features for objective diagnosis of depression. Resting-state network time series are used to investigate neurodynamics with the help of wavelet coherence and Granger causality (G-causality). Three new features are introduced: total wavelet coherence, wavelet lead coherence, and wavelet coherence blob analysis. The fourth feature, pair-wise conditional G-causality, is used to establish the causality between resting-state networks. We use the proposed features to classify depression in adult subjects. RESULTS We obtained an accuracy of 86% in the wavelet lead coherence, 80% in Granger causality, and 86% in wavelet coherence blob analysis. Subjects with depression showed hyperconnectivity between the dorsal attention network and the auditory network as well as between the posterior default mode network and the dorsal attention network. Hypoconnectivity was found between the anterior default mode network and the auditory network as well as the right frontoparietal network and the lateral visual network. An abnormal co-activation pattern was found between cerebellum and the lateral motor network according to the wavelet coherence blob analysis. CONCLUSION Based on abnormal functional dynamics between brain networks, we were able to identify subjects with depression with high accuracy. The findings of this study contribute to the understanding of the impaired emotional and attention processing associated with depression, as well as decreased motor activity.
Collapse
Affiliation(s)
- Ramona Cîrstian
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Jesper Pilmeyer
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Research and Development, Epilepsy Center Kempenhaeghe, Heeze, The Netherlands
| | - Antoine Bernas
- Department of Biophysics, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Jacobus F A Jansen
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Radiology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Marcel Breeuwer
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Albert P Aldenkamp
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Neurology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Svitlana Zinger
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.,Department of Research and Development, Epilepsy Center Kempenhaeghe, Heeze, The Netherlands
| |
Collapse
|
31
|
Ruiz de Miras J, Ibáñez-Molina A, Soriano M, Iglesias-Parro S. Schizophrenia classification using machine learning on resting state EEG signal. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
|
32
|
Moridian P, Ghassemi N, Jafari M, Salloum-Asfar S, Sadeghi D, Khodatars M, Shoeibi A, Khosravi A, Ling SH, Subasi A, Alizadehsani R, Gorriz JM, Abdulla SA, Acharya UR. Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review. Front Mol Neurosci 2022; 15:999605. [PMID: 36267703 PMCID: PMC9577321 DOI: 10.3389/fnmol.2022.999605] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 08/09/2022] [Indexed: 12/04/2022] Open
Abstract
Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging.
Collapse
Affiliation(s)
- Parisa Moridian
- Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Navid Ghassemi
- Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Mahboobeh Jafari
- Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran
| | - Salam Salloum-Asfar
- Neurological Disorders Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Delaram Sadeghi
- Department of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Marjane Khodatars
- Department of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Afshin Shoeibi
- Data Science and Computational Intelligence Institute, University of Granada, Granada, Spain
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC, Australia
| | - Sai Ho Ling
- Faculty of Engineering and IT, University of Technology Sydney (UTS), Ultimo, NSW, Australia
| | - Abdulhamit Subasi
- Faculty of Medicine, Institute of Biomedicine, University of Turku, Turku, Finland
- Department of Computer Science, College of Engineering, Effat University, Jeddah, Saudi Arabia
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC, Australia
| | - Juan M. Gorriz
- Data Science and Computational Intelligence Institute, University of Granada, Granada, Spain
| | - Sara A. Abdulla
- Neurological Disorders Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - U. Rajendra Acharya
- Ngee Ann Polytechnic, Singapore, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore
| |
Collapse
|
33
|
Huang H, Ma X, Yue X, Kang S, Rao Y, Long W, Liang Y, Li Y, Chen Y, Lyu W, Wu J, Tan X, Qiu S. Cortical gray matter microstructural alterations in patients with type 2 diabetes mellitus. Brain Behav 2022; 12:e2746. [PMID: 36059152 PMCID: PMC9575596 DOI: 10.1002/brb3.2746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 08/02/2022] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND AND PURPOSE Neurodegenerative processes are widespread in the brains of type 2 diabetes mellitus (T2DM) patients; gaps remain to exist in the current knowledge of the associated gray matter (GM) microstructural alterations. METHODS A cross-sectional study was conducted to investigate alterations in GM microarchitecture in T2DM patients by diffusion tensor imaging and neurite orientation dispersion and density imaging (NODDI). Seventy-eight T2DM patients and seventy-four age-, sex-, and education level-matched healthy controls (HCs) without cognitive impairment were recruited. Cortical macrostructure and GM microstructure were assessed by surface-based analysis and GM-based spatial statistics (GBSS), respectively. Machine learning models were trained to evaluate the diagnostic values of cortical intracellular volume fraction (ICVF) for the classification of T2DM versus HCs. RESULTS There were no differences in cortical thickness or area between the groups. GBSS analysis revealed similar GM microstructural patterns of significantly decreased fractional anisotropy, increased mean diffusivity and radial diffusivity in T2DM patients involving the frontal and parietal lobes, and significantly lower ICVF values were observed in nearly all brain regions of T2DM patients. A support vector machine model with a linear kernel was trained to realize the T2DM versus HC classification and exhibited the highest performance among the trained models, achieving an accuracy of 74% and an area under the curve of 83%. CONCLUSIONS NODDI may help to probe the widespread GM neuritic density loss in T2DM patients occurs before measurable macrostructural alterations. The cortical ICVF values may provide valuable diagnostic information regarding the early GM microstructural alterations in T2DM.
Collapse
Affiliation(s)
- Haoming Huang
- The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, P.R. China.,Department of Radiology, The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, P.R. China
| | - Xiaomeng Ma
- The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, P.R. China.,Department of Radiology, The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, P.R. China
| | - Xiaomei Yue
- The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, P.R. China.,Department of Radiology, The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, P.R. China
| | - Shangyu Kang
- The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, P.R. China.,Department of Radiology, The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, P.R. China
| | - Yawen Rao
- The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, P.R. China.,Department of Radiology, The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, P.R. China
| | - Wenjie Long
- Department of Geriatrics, The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, P.R. China
| | - Yi Liang
- Department of Radiology, The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, P.R. China
| | - Yifan Li
- The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, P.R. China.,Department of Radiology, The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, P.R. China
| | - Yuna Chen
- The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, P.R. China.,Department of Radiology, The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, P.R. China
| | - Wenjiao Lyu
- The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, P.R. China.,Department of Radiology, The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, P.R. China
| | - Jinjian Wu
- The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, P.R. China.,Department of Radiology, The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, P.R. China
| | - Xin Tan
- Department of Radiology, The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, P.R. China
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, P.R. China
| |
Collapse
|
34
|
Bahathiq RA, Banjar H, Bamaga AK, Jarraya SK. Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging. Front Neuroinform 2022; 16:949926. [PMID: 36246393 PMCID: PMC9554556 DOI: 10.3389/fninf.2022.949926] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 08/10/2022] [Indexed: 11/13/2022] Open
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects approximately 1% of the population and causes significant burdens. ASD's pathogenesis remains elusive; hence, diagnosis is based on a constellation of behaviors. Structural magnetic resonance imaging (sMRI) studies have shown several abnormalities in volumetric and geometric features of the autistic brain. However, inconsistent findings prevented most contributions from being translated into clinical practice. Establishing reliable biomarkers for ASD using sMRI is crucial for the correct diagnosis and treatment. In recent years, machine learning (ML) and specifically deep learning (DL) have quickly extended to almost every sector, notably in disease diagnosis. Thus, this has led to a shift and improvement in ASD diagnostic methods, fulfilling most clinical diagnostic requirements. However, ASD discovery remains difficult. This review examines the ML-based ASD diagnosis literature over the past 5 years. A literature-based taxonomy of the research landscape has been mapped, and the major aspects of this topic have been covered. First, we provide an overview of ML's general classification pipeline and the features of sMRI. Next, representative studies are highlighted and discussed in detail with respect to methods, and biomarkers. Finally, we highlight many common challenges and make recommendations for future directions. In short, the limited sample size was the main obstacle; Thus, comprehensive data sets and rigorous methods are necessary to check the generalizability of the results. ML technologies are expected to advance significantly in the coming years, contributing to the diagnosis of ASD and helping clinicians soon.
Collapse
Affiliation(s)
- Reem Ahmed Bahathiq
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Haneen Banjar
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
- Center of Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ahmed K. Bamaga
- Neuromuscular Medicine Unit, Department of Pediatric, Faculty of Medicine and King Abdulaziz University Hospital, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Salma Kammoun Jarraya
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| |
Collapse
|
35
|
Rahaman MA, Chen J, Fu Z, Lewis N, Iraji A, van Erp TGM, Calhoun VD. Deep multimodal predictome for studying mental disorders. Hum Brain Mapp 2022; 44:509-522. [PMID: 36574598 PMCID: PMC9842924 DOI: 10.1002/hbm.26077] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 07/29/2022] [Accepted: 08/16/2022] [Indexed: 01/25/2023] Open
Abstract
Characterizing neuropsychiatric disorders is challenging due to heterogeneity in the population. We propose combining structural and functional neuroimaging and genomic data in a multimodal classification framework to leverage their complementary information. Our objectives are two-fold (i) to improve the classification of disorders and (ii) to introspect the concepts learned to explore underlying neural and biological mechanisms linked to mental disorders. Previous multimodal studies have focused on naïve neural networks, mostly perceptron, to learn modality-wise features and often assume equal contribution from each modality. Our focus is on the development of neural networks for feature learning and implementing an adaptive control unit for the fusion phase. Our mid fusion with attention model includes a multilayer feed-forward network, an autoencoder, a bi-directional long short-term memory unit with attention as the features extractor, and a linear attention module for controlling modality-specific influence. The proposed model acquired 92% (p < .0001) accuracy in schizophrenia prediction, outperforming several other state-of-the-art models applied to unimodal or multimodal data. Post hoc feature analyses uncovered critical neural features and genes/biological pathways associated with schizophrenia. The proposed model effectively combines multimodal neuroimaging and genomics data for predicting mental disorders. Interpreting salient features identified by the model may advance our understanding of their underlying etiological mechanisms.
Collapse
Affiliation(s)
- Md Abdur Rahaman
- Department of Computational Science and EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA,Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| | - Jiayu Chen
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| | - Zening Fu
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| | - Noah Lewis
- Department of Computational Science and EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA,Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| | - Armin Iraji
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| | - Theo G. M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human BehaviorUniversity of California IrvineIrvineCaliforniaUSA,Center for the Neurobiology of Learning and MemoryUniversity of California IrvineIrvineCaliforniaUSA
| | - Vince D. Calhoun
- Department of Computational Science and EngineeringGeorgia Institute of TechnologyAtlantaGeorgiaUSA,Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| |
Collapse
|
36
|
Qin C, Zhu X, Ye L, Peng L, Li L, Wang J, Ma J, Liu T. Autism detection based on multiple time scale model. J Neural Eng 2022; 19. [PMID: 35985297 DOI: 10.1088/1741-2552/ac8b39] [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: 03/22/2022] [Accepted: 08/19/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Current autism clinical detection relies on doctor observation and filling of clinical scales, which is subjective and easily misdetection. Existing autism research of functional magnetic resonance imaging (fMRI) over-compresses the time-scale information and has poor generalization ability. This study extracts multiple time scale brain features of fMRI, providing objective detection. APPROACH We first use least absolute shrinkage and selection operator (LASSO) to build a sparse network and extract features with a time scale of 1. Then, we use hidden markov model (HMM) to extract features that describe the dynamic changes of the brain, with a time scale of 2. Additionally, to analyze the features of the potential network activity of autism from a higher time scale, we use long short-term memory (LSTM) to construct an auto-encoder to re-encode the original data and extract the features of the at a higher time scale, with a time scale of T, and T is the time length of fMRI. We use Recursive Feature Elimination (RFE) for feature selection for three different time scale features, merge them into multiple time scale features, and finally use one-dimensional convolution neural network (1DCNN) for classification. MAIN RESULTS Compared with well-established models, our method has achieved better results. The accuracy of our method is 76.0%, and the area under the roc curve is 0.83, tested on the completely independent data, so our method has better generalization ability. SIGNIFICANCE This research analyzes fMRI sequences from multiple time scale to detect autism, and it also provides a new framework and research ideas for subsequent fMRI analysis.
Collapse
Affiliation(s)
- Chi Qin
- Xi'an Jiaotong University, School of Life Science and Technology, Xi'an, 710049, CHINA
| | - Xiaofei Zhu
- Tangdu Hospital Fourth Military Medical University, Department of Radiology, Xi'an, Shaanxi, 710038, CHINA
| | - Lin Ye
- Xi'an Jiaotong University, School of Life Science and Technology, Xi'an, 710049, CHINA
| | - Li Peng
- Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology, Department of Radiology, Wuhan, Hubei, 430030, CHINA
| | - Long Li
- Xi'an Jiaotong University, School of Life Science and Technology, Xi'an, 710049, CHINA
| | - Jue Wang
- Xi'an Jiaotong University, School of Life Science and Technology, Xi'an, 710049, CHINA
| | - Jin Ma
- Air Force Medical University, School of Aerospace Medicine, Xi'an, 710032, CHINA
| | - Tian Liu
- Xi'an Jiaotong University, School of Life Science and Technology, Xi'an, 710049, CHINA
| |
Collapse
|
37
|
Du Y, He X, Kochunov P, Pearlson G, Hong LE, van Erp TGM, Belger A, Calhoun VD. A new multimodality fusion classification approach to explore the uniqueness of schizophrenia and autism spectrum disorder. Hum Brain Mapp 2022; 43:3887-3903. [PMID: 35484969 PMCID: PMC9294304 DOI: 10.1002/hbm.25890] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 03/24/2022] [Accepted: 04/08/2022] [Indexed: 11/13/2022] Open
Abstract
Schizophrenia (SZ) and autism spectrum disorder (ASD) sharing overlapping symptoms have a long history of diagnostic confusion. It is unclear what their differences at a brain level are. Here, we propose a multimodality fusion classification approach to investigate their divergence in brain function and structure. Using brain functional network connectivity (FNC) calculated from resting-state fMRI data and gray matter volume (GMV) estimated from sMRI data, we classify the two disorders using the main data (335 SZ and 380 ASD patients) via an unbiased 10-fold cross-validation pipeline, and also validate the classification generalization ability on an independent cohort (120 SZ and 349 ASD patients). The classification accuracy reached up to 83.08% for the testing data and 72.10% for the independent data, significantly better than the results from using the single-modality features. The discriminative FNCs that were automatically selected primarily involved the sub-cortical, default mode, and visual domains. Interestingly, all discriminative FNCs relating to the default mode network showed an intermediate strength in healthy controls (HCs) between SZ and ASD patients. Their GMV differences were mainly driven by the frontal gyrus, temporal gyrus, and insula. Regarding these regions, the mean GMV of HC fell intermediate between that of SZ and ASD, and ASD showed the highest GMV. The middle frontal gyrus was associated with both functional and structural differences. In summary, our work reveals the unique neuroimaging characteristics of SZ and ASD that can achieve high and generalizable classification accuracy, supporting their potential as disorder-specific neural substrates of the two entwined disorders.
Collapse
Affiliation(s)
- Yuhui Du
- School of Computer and Information TechnologyShanxi UniversityTaiyuanShanxiChina
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data ScienceGeorgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| | - Xingyu He
- School of Computer and Information TechnologyShanxi UniversityTaiyuanShanxiChina
| | - Peter Kochunov
- Center for Brain Imaging ResearchUniversity of MarylandBaltimoreMarylandUSA
| | | | - L. Elliot Hong
- Center for Brain Imaging ResearchUniversity of MarylandBaltimoreMarylandUSA
| | - Theo G. M. van Erp
- Department of Psychiatry and Human BehaviorUniversity of CaliforniaIrvineCaliforniaUSA
- Center for the Neurobiology of Learning and MemoryUniversity of CaliforniaIrvineCaliforniaUSA
| | - Aysenil Belger
- Department of PsychiatryUniversity of North CarolinaChapel HillNorth CarolinaUSA
| | - Vince D. Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data ScienceGeorgia State University, Georgia Institute of Technology, Emory UniversityAtlantaGeorgiaUSA
| |
Collapse
|
38
|
Levman J, Jennings M, Rouse E, Berger D, Kabaria P, Nangaku M, Gondra I, Takahashi E. A morphological study of schizophrenia with magnetic resonance imaging, advanced analytics, and machine learning. Front Neurosci 2022; 16:926426. [PMID: 36046472 PMCID: PMC9420897 DOI: 10.3389/fnins.2022.926426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 07/05/2022] [Indexed: 11/16/2022] Open
Abstract
We have performed a morphological analysis of patients with schizophrenia and compared them with healthy controls. Our analysis includes the use of publicly available automated extraction tools to assess regional cortical thickness (inclusive of within region cortical thickness variability) from structural magnetic resonance imaging (MRI), to characterize group-wise abnormalities associated with schizophrenia based on a publicly available dataset. We have also performed a correlation analysis between the automatically extracted biomarkers and a variety of patient clinical variables available. Finally, we also present the results of a machine learning analysis. Results demonstrate regional cortical thickness abnormalities in schizophrenia. We observed a correlation (rho = 0.474) between patients’ depression and the average cortical thickness of the right medial orbitofrontal cortex. Our leading machine learning technology evaluated was the support vector machine with stepwise feature selection, yielding a sensitivity of 92% and a specificity of 74%, based on regional brain measurements, including from the insula, superior frontal, caudate, calcarine sulcus, gyrus rectus, and rostral middle frontal regions. These results imply that advanced analytic techniques combining MRI with automated biomarker extraction can be helpful in characterizing patients with schizophrenia.
Collapse
Affiliation(s)
- Jacob Levman
- Department of Computer Science, St. Francis Xavier University, Antigonish, NS, Canada
- Center for Clinical Research, Nova Scotia Health Authority - Research, Innovation and Discovery, Halifax, NS, Canada
- Department of Radiology, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts Institute of Technology, Boston, MA, United States
- *Correspondence: Jacob Levman,
| | - Maxwell Jennings
- Department of Computer Science, St. Francis Xavier University, Antigonish, NS, Canada
- Department of Mathematics and Statistics, St. Francis Xavier University, Antigonish, NS, Canada
| | - Ethan Rouse
- Department of Computer Science, St. Francis Xavier University, Antigonish, NS, Canada
| | - Derek Berger
- Department of Computer Science, St. Francis Xavier University, Antigonish, NS, Canada
| | - Priya Kabaria
- Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Masahito Nangaku
- Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Iker Gondra
- Department of Computer Science, St. Francis Xavier University, Antigonish, NS, Canada
| | - Emi Takahashi
- Department of Radiology, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Massachusetts Institute of Technology, Boston, MA, United States
- Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
| |
Collapse
|
39
|
Erdoğan SB, Yükselen G. Four-Class Classification of Neuropsychiatric Disorders by Use of Functional Near-Infrared Spectroscopy Derived Biomarkers. SENSORS (BASEL, SWITZERLAND) 2022; 22:5407. [PMID: 35891088 PMCID: PMC9322944 DOI: 10.3390/s22145407] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 06/20/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
Diagnosis of most neuropsychiatric disorders relies on subjective measures, which makes the reliability of final clinical decisions questionable. The aim of this study was to propose a machine learning-based classification approach for objective diagnosis of three disorders of neuropsychiatric or neurological origin with functional near-infrared spectroscopy (fNIRS) derived biomarkers. Thirteen healthy adolescents and sixty-seven patients who were clinically diagnosed with migraine, obsessive compulsive disorder, or schizophrenia performed a Stroop task, while prefrontal cortex hemodynamics were monitored with fNIRS. Hemodynamic and cognitive features were extracted for training three supervised learning algorithms (naïve bayes (NB), linear discriminant analysis (LDA), and support vector machines (SVM)). The performance of each algorithm in correctly predicting the class of each participant across the four classes was tested with ten runs of a ten-fold cross-validation procedure. All algorithms achieved four-class classification performances with accuracies above 81% and specificities above 94%. SVM had the highest performance in terms of accuracy (85.1 ± 1.77%), sensitivity (84 ± 1.7%), specificity (95 ± 0.5%), precision (86 ± 1.6%), and F1-score (85 ± 1.7%). fNIRS-derived features have no subjective report bias when used for automated classification purposes. The presented methodology might have significant potential for assisting in the objective diagnosis of neuropsychiatric disorders associated with frontal lobe dysfunction.
Collapse
|
40
|
Benkarim O, Paquola C, Park BY, Kebets V, Hong SJ, Vos de Wael R, Zhang S, Yeo BTT, Eickenberg M, Ge T, Poline JB, Bernhardt BC, Bzdok D. Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging. PLoS Biol 2022; 20:e3001627. [PMID: 35486643 PMCID: PMC9094526 DOI: 10.1371/journal.pbio.3001627] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 05/11/2022] [Accepted: 04/11/2022] [Indexed: 12/18/2022] Open
Abstract
Brain imaging research enjoys increasing adoption of supervised machine learning for single-participant disease classification. Yet, the success of these algorithms likely depends on population diversity, including demographic differences and other factors that may be outside of primary scientific interest. Here, we capitalize on propensity scores as a composite confound index to quantify diversity due to major sources of population variation. We delineate the impact of population heterogeneity on the predictive accuracy and pattern stability in 2 separate clinical cohorts: the Autism Brain Imaging Data Exchange (ABIDE, n = 297) and the Healthy Brain Network (HBN, n = 551). Across various analysis scenarios, our results uncover the extent to which cross-validated prediction performances are interlocked with diversity. The instability of extracted brain patterns attributable to diversity is located preferentially in regions part of the default mode network. Collectively, our findings highlight the limitations of prevailing deconfounding practices in mitigating the full consequences of population diversity.
Collapse
Affiliation(s)
- Oualid Benkarim
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
| | - Casey Paquola
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany
| | - Bo-yong Park
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
- Department of Data Science, Inha University, Incheon, South Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University, Suwon, South Korea
| | - Valeria Kebets
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
| | - Seok-Jun Hong
- Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University, Suwon, South Korea
- Center for the Developing Brain, Child Mind Institute, New York, New York, United States of America
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Reinder Vos de Wael
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
| | - Shaoshi Zhang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | - B. T. Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | | | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
| | - Jean-Baptiste Poline
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
| | - Boris C. Bernhardt
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
| | - Danilo Bzdok
- McConnell Brain Imaging Centre (BIC), Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montreal, Canada
- Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, Canada
- School of Computer Science, McGill University, Montreal, Canada
- Mila—Quebec Artificial Intelligence Institute, Montreal, Canada
| |
Collapse
|
41
|
Dwyer D, Koutsouleris N. Annual Research Review: Translational machine learning for child and adolescent psychiatry. J Child Psychol Psychiatry 2022; 63:421-443. [PMID: 35040130 DOI: 10.1111/jcpp.13545] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/06/2021] [Indexed: 12/14/2022]
Abstract
Children and adolescents could benefit from the use of predictive tools that facilitate personalized diagnoses, prognoses, and treatment selection. Such tools have not yet been deployed using traditional statistical methods, potentially due to the limitations of the paradigm and the need to leverage large amounts of digital data. This review will suggest that a machine learning approach could address these challenges and is designed to introduce new readers to the background, methods, and results in the field. A rationale is first introduced followed by an outline of fundamental elements of machine learning approaches. To provide an overview of the use of the techniques in child and adolescent literature, a scoping review of broad trends is then presented. Selected studies are also highlighted in order to draw attention to research areas that are closest to translation and studies that exhibit a high degree of experimental innovation. Limitations to the research, and machine learning approaches generally, are outlined in the penultimate section highlighting issues related to sample sizes, validation, clinical utility, and ethical challenges. Finally, future directions are discussed that could enhance the possibility of clinical implementation and address specific questions relevant to the child and adolescent psychiatry. The review gives a broad overview of the machine learning paradigm in order to highlight the benefits of a shift in perspective towards practically oriented statistical solutions that aim to improve clinical care of children and adolescents.
Collapse
Affiliation(s)
- Dominic Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Orygen, The National Centre of Excellence in Youth Mental Health, Melbourne, Australia.,Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany.,Max-Planck Institute of Psychiatry, Munich, Germany.,Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| |
Collapse
|
42
|
Zhu Y, Nakatani H, Yassin W, Maikusa N, Okada N, Kunimatsu A, Abe O, Kuwabara H, Yamasue H, Kasai K, Okanoya K, Koike S. Application of a Machine Learning Algorithm for Structural Brain Images in Chronic Schizophrenia to Earlier Clinical Stages of Psychosis and Autism Spectrum Disorder: A Multiprotocol Imaging Dataset Study. Schizophr Bull 2022; 48:563-574. [PMID: 35352811 PMCID: PMC9077435 DOI: 10.1093/schbul/sbac030] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
BACKGROUND AND HYPOTHESIS Machine learning approaches using structural magnetic resonance imaging (MRI) can be informative for disease classification; however, their applicability to earlier clinical stages of psychosis and other disease spectra is unknown. We evaluated whether a model differentiating patients with chronic schizophrenia (ChSZ) from healthy controls (HCs) could be applied to earlier clinical stages such as first-episode psychosis (FEP), ultra-high risk for psychosis (UHR), and autism spectrum disorders (ASDs). STUDY DESIGN Total 359 T1-weighted MRI scans, including 154 individuals with schizophrenia spectrum (UHR, n = 37; FEP, n = 24; and ChSZ, n = 93), 64 with ASD, and 141 HCs, were obtained using three acquisition protocols. Of these, data regarding ChSZ (n = 75) and HC (n = 101) from two protocols were used to build a classifier (training dataset). The remainder was used to evaluate the classifier (test, independent confirmatory, and independent group datasets). Scanner and protocol effects were diminished using ComBat. STUDY RESULTS The accuracy of the classifier for the test and independent confirmatory datasets were 75% and 76%, respectively. The bilateral pallidum and inferior frontal gyrus pars triangularis strongly contributed to classifying ChSZ. Schizophrenia spectrum individuals were more likely to be classified as ChSZ compared to ASD (classification rate to ChSZ: UHR, 41%; FEP, 54%; ChSZ, 70%; ASD, 19%; HC, 21%). CONCLUSION We built a classifier from multiple protocol structural brain images applicable to independent samples from different clinical stages and spectra. The predictive information of the classifier could be useful for applying neuroimaging techniques to clinical differential diagnosis and predicting disease onset earlier.
Collapse
Affiliation(s)
- Yinghan Zhu
- Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
| | - Hironori Nakatani
- Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
- Department of Information Media Technology, School of Information and Telecommunication Engineering, Tokai University, 2-3-23, Takanawa, Minato-ku, Tokyo 108-8619, Japan
| | - Walid Yassin
- Department of Child Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Norihide Maikusa
- Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
| | - Naohiro Okada
- The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Akira Kunimatsu
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
- Department of Radiology, IMSUT Hospital, The Institute of Medical Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Hitoshi Kuwabara
- Department of Psychiatry, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu City, Shizuoka 431-3192, Japan
| | - Hidenori Yamasue
- Department of Psychiatry, Hamamatsu University School of Medicine, 1-20-1 Handayama, Higashi-ku, Hamamatsu City, Shizuoka 431-3192, Japan
| | - Kiyoto Kasai
- The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
- University of Tokyo Center for Integrative Science of Human Behavior (CiSHuB), 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
| | - Kazuo Okanoya
- Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
- The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
- University of Tokyo Center for Integrative Science of Human Behavior (CiSHuB), 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan
| | - Shinsuke Koike
- To whom correspondence should be addressed; Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan; tel: +81-3-5454-4327, fax: +81-3-5454-4327, e-mail:
| |
Collapse
|
43
|
Jutla A, Foss-Feig J, Veenstra-VanderWeele J. Autism spectrum disorder and schizophrenia: An updated conceptual review. Autism Res 2022; 15:384-412. [PMID: 34967130 PMCID: PMC8931527 DOI: 10.1002/aur.2659] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 11/08/2021] [Accepted: 12/12/2021] [Indexed: 12/19/2022]
Abstract
Autism spectrum disorder (ASD) and schizophrenia (SCZ) are separate disorders, with distinct clinical profiles and natural histories. ASD, typically diagnosed in childhood, is characterized by restricted or repetitive interests or behaviors and impaired social communication, and it tends to have a stable course. SCZ, typically diagnosed in adolescence or adulthood, is characterized by hallucinations and delusions, and tends to be associated with declining function. However, youth with ASD are three to six times more likely to develop SCZ than their neurotypical counterparts, and increasingly, research has shown that ASD and SCZ converge at several levels. We conducted a systematic review of studies since 2013 relevant to understanding this convergence, and present here a narrative synthesis of key findings, which we have organized into four broad categories: symptoms and behavior, perception and cognition, biomarkers, and genetic and environmental risk. We then discuss opportunities for future research into the phenomenology and neurobiology of overlap between ASD and SCZ. Understanding this overlap will allow for researchers, and eventually clinicians, to understand the factors that may make a child with ASD vulnerable to developing SCZ. LAY SUMMARY: Autism spectrum disorder and schizophrenia are distinct diagnoses, but people with autism and people with schizophrena share several characteristics. We review recent studies that have examined these areas of overlap, and discuss the kinds of studies we will need to better understand how these disorders are related. Understanding this will be important to help us identify which autistic children are at risk of developing schizophrenia.
Collapse
Affiliation(s)
- Amandeep Jutla
- Columbia University Vagelos College of Physicians and
Surgeons, 630 W 168th St, New York, NY 10032, United States
- New York State Psychiatric Institute, 1051 Riverside
Drive, Mail Unit 78, New York, NY 10032, United States
| | - Jennifer Foss-Feig
- Seaver Autism Center for Research and Treatment, Icahn
School of Medicine at Mount Sinai, Department of Psychiatry, 1 Gustave L. Levy
Place, Box 1230, New York, NY 10029, United States
| | - Jeremy Veenstra-VanderWeele
- Columbia University Vagelos College of Physicians and
Surgeons, 630 W 168th St, New York, NY 10032, United States
- New York State Psychiatric Institute, 1051 Riverside
Drive, Mail Unit 78, New York, NY 10032, United States
- Center for Autism and the Developing Brain, New
York-Presbyterian Westchester Behavioral Health Center, 21 Bloomingdale Road, White
Plains, NY 10605, United States
| |
Collapse
|
44
|
Chilla GS, Yeow LY, Chew QH, Sim K, Prakash KNB. Machine learning classification of schizophrenia patients and healthy controls using diverse neuroanatomical markers and Ensemble methods. Sci Rep 2022; 12:2755. [PMID: 35177708 PMCID: PMC8854385 DOI: 10.1038/s41598-022-06651-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 02/03/2022] [Indexed: 12/12/2022] Open
Abstract
Schizophrenia is a major psychiatric disorder that imposes enormous clinical burden on patients and their caregivers. Determining classification biomarkers can complement clinical measures and improve understanding of the neural basis underlying schizophrenia. Using neuroanatomical features, several machine learning based investigations have attempted to classify schizophrenia from healthy controls but the range of neuroanatomical measures employed have been limited in range to date. In this study, we sought to classify schizophrenia and healthy control cohorts using a diverse set of neuroanatomical measures (cortical and subcortical volumes, cortical areas and thickness, cortical mean curvature) and adopted Ensemble methods for better performance. Additionally, we correlated such neuroanatomical features with Quality of Life (QoL) assessment scores within the schizophrenia cohort. With Ensemble methods and diverse neuroanatomical measures, we achieved classification accuracies ranging from 83 to 87%, sensitivities and specificities varying between 90-98% and 65-70% respectively. In addition to lower QoL scores within schizophrenia cohort, significant correlations were found between specific neuroanatomical measures and psychological health, social relationship subscale domains of QoL. Our results suggest the utility of inclusion of subcortical and cortical measures and Ensemble methods to achieve better classification performance and their potential impact of parsing out neurobiological correlates of quality of life in schizophrenia.
Collapse
Affiliation(s)
- Geetha Soujanya Chilla
- Institute of Bioengineering and Bioimaging, Agency for Science, Technology and Research, Singapore, Singapore, 138667.
| | - Ling Yun Yeow
- Institute of Bioengineering and Bioimaging, Agency for Science, Technology and Research, Singapore, Singapore, 138667
| | - Qian Hui Chew
- Institute of Mental Health, Singapore, Singapore, 539747
| | - Kang Sim
- Institute of Mental Health, Singapore, Singapore, 539747
| | - K N Bhanu Prakash
- Institute of Bioengineering and Bioimaging, Agency for Science, Technology and Research, Singapore, Singapore, 138667.
| |
Collapse
|
45
|
Ali MT, ElNakieb Y, Elnakib A, Shalaby A, Mahmoud A, Ghazal M, Yousaf J, Abu Khalifeh H, Casanova M, Barnes G, El-Baz A. The Role of Structure MRI in Diagnosing Autism. Diagnostics (Basel) 2022; 12:165. [PMID: 35054330 PMCID: PMC8774643 DOI: 10.3390/diagnostics12010165] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 12/30/2021] [Accepted: 01/05/2022] [Indexed: 12/30/2022] Open
Abstract
This study proposes a Computer-Aided Diagnostic (CAD) system to diagnose subjects with autism spectrum disorder (ASD). The CAD system identifies morphological anomalies within the brain regions of ASD subjects. Cortical features are scored according to their contribution in diagnosing a subject to be ASD or typically developed (TD) based on a trained machine-learning (ML) model. This approach opens the hope for developing a new CAD system for early personalized diagnosis of ASD. We propose a framework to extract the cerebral cortex from structural MRI as well as identifying the altered areas in the cerebral cortex. This framework consists of the following five main steps: (i) extraction of cerebral cortex from structural MRI; (ii) cortical parcellation to a standard atlas; (iii) identifying ASD associated cortical markers; (iv) adjusting feature values according to sex and age; (v) building tailored neuro-atlases to identify ASD; and (vi) artificial neural networks (NN) are trained to classify ASD. The system is tested on the Autism Brain Imaging Data Exchange (ABIDE I) sites achieving an average balanced accuracy score of 97±2%. This paper demonstrates the ability to develop an objective CAD system using structure MRI and tailored neuro-atlases describing specific developmental patterns of the brain in autism.
Collapse
Affiliation(s)
- Mohamed T. Ali
- Bioengineering Department, University of Louisville, Louisville, KY 40208, USA; (M.T.A.); (Y.E.); (A.E.); (A.S.); (A.M.)
| | - Yaser ElNakieb
- Bioengineering Department, University of Louisville, Louisville, KY 40208, USA; (M.T.A.); (Y.E.); (A.E.); (A.S.); (A.M.)
| | - Ahmed Elnakib
- Bioengineering Department, University of Louisville, Louisville, KY 40208, USA; (M.T.A.); (Y.E.); (A.E.); (A.S.); (A.M.)
| | - Ahmed Shalaby
- Bioengineering Department, University of Louisville, Louisville, KY 40208, USA; (M.T.A.); (Y.E.); (A.E.); (A.S.); (A.M.)
| | - Ali Mahmoud
- Bioengineering Department, University of Louisville, Louisville, KY 40208, USA; (M.T.A.); (Y.E.); (A.E.); (A.S.); (A.M.)
| | - Mohammed Ghazal
- Department of Electrical and Computer Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (M.G.); (J.Y.); (H.A.K.)
| | - Jawad Yousaf
- Department of Electrical and Computer Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (M.G.); (J.Y.); (H.A.K.)
| | - Hadil Abu Khalifeh
- Department of Electrical and Computer Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates; (M.G.); (J.Y.); (H.A.K.)
| | - Manuel Casanova
- Department of Biomedical Sciences, School of Medicine Greenville, University of South Carolina, Greenville, SC 29425, USA;
| | - Gregory Barnes
- Department of Neurology, Norton Children’s Autism Center, University of Louisville, Louisville, KY 40208, USA;
| | - Ayman El-Baz
- Bioengineering Department, University of Louisville, Louisville, KY 40208, USA; (M.T.A.); (Y.E.); (A.E.); (A.S.); (A.M.)
| |
Collapse
|
46
|
Guo X, Wang W, Kang L, Shu C, Bai H, Tu N, Bu L, Gao Y, Wang G, Liu Z. Abnormal degree centrality in first-episode medication-free adolescent depression at rest: A functional magnetic resonance imaging study and support vector machine analysis. Front Psychiatry 2022; 13:926292. [PMID: 36245889 PMCID: PMC9556654 DOI: 10.3389/fpsyt.2022.926292] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 07/28/2022] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Depression in adolescents is more heterogeneous and less often diagnosed than depression in adults. At present, reliable approaches to differentiating between adolescents who are and are not affected by depression are lacking. This study was designed to assess voxel-level whole-brain functional connectivity changes associated with adolescent depression in an effort to define an imaging-based biomarker associated with this condition. MATERIALS AND METHODS In total, 71 adolescents affected by major depressive disorder (MDD) and 71 age-, sex-, and education level-matched healthy controls were subjected to resting-state functional magnetic resonance imaging (rs-fMRI) based analyses of brain voxel-wise degree centrality (DC), with a support vector machine (SVM) being used for pattern classification analyses. RESULTS DC patterns derived from 16-min rs-fMRI analyses were able to effectively differentiate between adolescent MDD patients and healthy controls with 95.1% accuracy (136/143), and with respective sensitivity and specificity values of 92.1% (70/76) and 98.5% (66/67) based upon DC abnormalities detected in the right cerebellum. Specifically, increased DC was evident in the bilateral insula and left lingual area of MDD patients, together with reductions in the DC values in the right cerebellum and bilateral superior parietal lobe. DC values were not significantly correlated with disease severity or duration in these patients following correction for multiple comparisons. CONCLUSION These results suggest that whole-brain network centrality abnormalities may be present in many brain regions in adolescent depression patients. Accordingly, these DC maps may hold value as candidate neuroimaging biomarkers capable of differentiating between adolescents who are and are not affected by MDD, although further validation of these results will be critical.
Collapse
Affiliation(s)
- Xin Guo
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.,Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College of London, London, United Kingdom
| | - Wei Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Lijun Kang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Chang Shu
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Hanpin Bai
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Ning Tu
- PET/CT/MRI and Molecular Imaging Center, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Lihong Bu
- PET/CT/MRI and Molecular Imaging Center, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Yujun Gao
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Gaohua Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Zhongchun Liu
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| |
Collapse
|
47
|
Solanes A, Radua J. Advances in Using MRI to Estimate the Risk of Future Outcomes in Mental Health - Are We Getting There? Front Psychiatry 2022; 13:fpsyt-13-826111. [PMID: 35492715 PMCID: PMC9039205 DOI: 10.3389/fpsyt.2022.826111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 03/16/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Aleix Solanes
- Imaging of Mood- and Anxiety-Related Disorders (IMARD) Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.,Department of Psychiatry and Forensic Medicine, School of Medicine, Autonomous University of Barcelona, Barcelona, Spain
| | - Joaquim Radua
- Imaging of Mood- and Anxiety-Related Disorders (IMARD) Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.,Early Psychosis: Interventions and Clinical-detection Lab, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Department of Clinical Neuroscience, Stockholm Health Care Services, Stockholm County Council, Karolinska Institutet, Stockholm, Sweden
| |
Collapse
|
48
|
Gou N, Xiang Y, Zhou J, Zhang S, Zhong S, Lu J, Liang X, Liu J, Wang X. Identification of violent patients with schizophrenia using a hybrid machine learning approach at the individual level. Psychiatry Res 2021; 306:114294. [PMID: 34823086 DOI: 10.1016/j.psychres.2021.114294] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/24/2021] [Accepted: 11/14/2021] [Indexed: 12/14/2022]
Abstract
Despite numerous risk factors associated with violence in patients with schizophrenia, predicting and preventing violent behavior is still a challenge. At present, machine learning (ML) has become a promising strategy for guiding individualized assessment. To build an effective model to predict the risk of violence in patients with schizophrenia, we proposed a hybrid ML method to improve the prediction capability in 42 violent offenders with schizophrenia and 33 non-violent patients with schizophrenia. The results revealed that the final model, which combined multimodal data, achieved the highest prediction performance with an accuracy of 90.67%. Specifically, the model, which fused three modalities of neuroimaging data, achieved a better accuracy than other fused models. In addition, the msot discriminative neuroimaging features involved in the prefrontal-temporal cognitive circuit and striatum reward system, indicating that dysfunction in cortical-subcortical circuits might be associated with high risk of violence in patients with schizophrenia. This study provides the first evidence supporting that the combination of specific multimodal neuroimaging and clinical data in ML analysis can effectively identify violent patients with schizophrenia. Furthermore, this work is crucial for the development of neuro-prediction models that could facilitate individualized treatment and interventions for violent behaviors in patients with schizophrenia.
Collapse
Affiliation(s)
- Ningzhi Gou
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital of Central South University, 139 Middle Renmin Road, Changsha, Hunan 410011, China
| | - Yizhen Xiang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Jiansong Zhou
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital of Central South University, 139 Middle Renmin Road, Changsha, Hunan 410011, China
| | - Simei Zhang
- Shenzhen Mental Health Center, Shenzhen Kangning Hospital, Shenzhen 518020, China
| | - Shaoling Zhong
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital of Central South University, 139 Middle Renmin Road, Changsha, Hunan 410011, China
| | - Juntao Lu
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital of Central South University, 139 Middle Renmin Road, Changsha, Hunan 410011, China
| | - Xiaoxi Liang
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital of Central South University, 139 Middle Renmin Road, Changsha, Hunan 410011, China
| | - Jin Liu
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China.
| | - Xiaoping Wang
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital of Central South University, 139 Middle Renmin Road, Changsha, Hunan 410011, China.
| |
Collapse
|
49
|
Surface area in the insula was associated with 28-month functional outcome in first-episode psychosis. NPJ SCHIZOPHRENIA 2021; 7:56. [PMID: 34845247 PMCID: PMC8630202 DOI: 10.1038/s41537-021-00186-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 11/03/2021] [Indexed: 11/28/2022]
Abstract
Many studies have tested the relationship between demographic, clinical, and psychobiological measurements and clinical outcomes in ultra-high risk for psychosis (UHR) and first-episode psychosis (FEP). However, no study has investigated the relationship between multi-modal measurements and long-term outcomes for >2 years. Thirty-eight individuals with UHR and 29 patients with FEP were measured using one or more modalities (cognitive battery, electrophysiological response, structural magnetic resonance imaging, and functional near-infrared spectroscopy). We explored the characteristics associated with 13- and 28-month clinical outcomes. In UHR, the cortical surface area in the left orbital part of the inferior frontal gyrus was negatively associated with 13-month disorganized symptoms. In FEP, the cortical surface area in the left insula was positively associated with 28-month global social function. The left inferior frontal gyrus and insula are well-known structural brain characteristics in schizophrenia, and future studies on the pathological mechanism of structural alteration would provide a clearer understanding of the disease.
Collapse
|
50
|
Rahaman MA, Chen J, Fu Z, Lewis N, Iraji A, Calhoun VD. Multi-modal deep learning of functional and structural neuroimaging and genomic data to predict mental illness. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3267-3272. [PMID: 34891938 DOI: 10.1109/embc46164.2021.9630693] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Neuropsychiatric disorders such as schizophrenia are very heterogeneous in nature and typically diagnosed using self-reported symptoms. This makes it difficult to pose a confident prediction on the cases and does not provide insight into the underlying neural and biological mechanisms of these disorders. Combining neuroimaging and genomic data with a multi-modal 'predictome' paves the way for biologically informed markers and may improve prediction reliability. With that, we develop a multi-modal deep learning framework by fusing data from different modalities to capture the interaction between the latent features and evaluate their complementary information in characterizing schizophrenia. Our deep model uses structural MRI, functional MRI, and genome-wide polymorphism data to perform the classification task. It includes a multi-layer feed-forward network, an encoder, and a long short-term memory (LSTM) unit with attention to learn the latent features and adopt a joint training scheme capturing synergies between the modalities. The hybrid network also uses different regularizers for addressing the inherent overfitting and modality-specific bias in the multi-modal setup. Next, we run the network through a saliency model to analyze the learned features. Integrating modalities enhances the performance of the classifier, and our framework acquired 88% (P < 0.0001) accuracy on a dataset of 437 subjects. The trimodal accuracy is comparable to the state-of-the-art performance on a data collection of this size and outperforms the unimodal and bimodal baselines we compared. Model introspection was used to expose the salient neural features and genes/biological pathways associated with schizophrenia. To our best knowledge, this is the first approach that fuses genomic information with structural and functional MRI biomarkers for predicting schizophrenia. We believe this type of modality blending can better explain the disorder's dynamics by adding cross-modal prospects.Clinical Relevance- This study combinedly learns imaging and genomic features for the classification of schizophrenia. The data fusion scheme extracts modality interactions, and the saliency experiments report multiple functional and structural networks closely connected to the disorder.
Collapse
|