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Bailey NW, Fulcher BD, Caldwell B, Hill AT, Fitzgibbon B, van Dijk H, Fitzgerald PB. Uncovering a stability signature of brain dynamics associated with meditation experience using massive time-series feature extraction. Neural Netw 2024; 171:171-185. [PMID: 38091761 DOI: 10.1016/j.neunet.2023.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 11/02/2023] [Accepted: 12/04/2023] [Indexed: 01/29/2024]
Abstract
Previous research has examined resting electroencephalographic (EEG) data to explore brain activity related to meditation. However, previous research has mostly examined power in different frequency bands. The practical objective of this study was to comprehensively test whether other types of time-series analysis methods are better suited to characterize brain activity related to meditation. To achieve this, we compared >7000 time-series features of the EEG signal to comprehensively characterize brain activity differences in meditators, using many measures that are novel in meditation research. Eyes-closed resting-state EEG data from 49 meditators and 46 non-meditators was decomposed into the top eight principal components (PCs). We extracted 7381 time-series features from each PC and each participant and used them to train classification algorithms to identify meditators. Highly differentiating individual features from successful classifiers were analysed in detail. Only the third PC (which had a central-parietal maximum) showed above-chance classification accuracy (67 %, pFDR = 0.007), for which 405 features significantly distinguished meditators (all pFDR < 0.05). Top-performing features indicated that meditators exhibited more consistent statistical properties across shorter subsegments of their EEG time-series (higher stationarity) and displayed an altered distributional shape of values about the mean. By contrast, classifiers trained with traditional band-power measures did not distinguish the groups (pFDR > 0.05). Our novel analysis approach suggests the key signatures of meditators' brain activity are higher temporal stability and a distribution of time-series values suggestive of longer, larger, or more frequent non-outlying voltage deviations from the mean within the third PC of their EEG data. The higher temporal stability observed in this EEG component might underpin the higher attentional stability associated with meditation. The novel time-series properties identified here have considerable potential for future exploration in meditation research and the analysis of neural dynamics more broadly.
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Affiliation(s)
- Neil W Bailey
- Monarch Research Institute, Monarch Mental Health Group, Sydney, NSW, Australia; School of Medicine and Psychology, The Australian National University, Canberra, ACT, Australia; Central Clinical School, Department of Psychiatry, Monash University, Victoria, Australia.
| | - Ben D Fulcher
- School of Physics, University of Sydney, Camperdown, NSW, Australia
| | - Bridget Caldwell
- Monarch Research Institute, Monarch Mental Health Group, Sydney, NSW, Australia
| | - Aron T Hill
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Melbourne, Victoria, Australia
| | - Bernadette Fitzgibbon
- Monarch Research Institute, Monarch Mental Health Group, Sydney, NSW, Australia; School of Medicine and Psychology, The Australian National University, Canberra, ACT, Australia; Central Clinical School, Department of Psychiatry, Monash University, Victoria, Australia
| | - Hanneke van Dijk
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Kingdom of the Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, University Maastricht, Maastricht, the Kingdom of the Netherlands
| | - Paul B Fitzgerald
- Monarch Research Institute, Monarch Mental Health Group, Sydney, NSW, Australia; School of Medicine and Psychology, The Australian National University, Canberra, ACT, Australia
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Nogay HS, Adeli H. Multiple Classification of Brain MRI Autism Spectrum Disorder by Age and Gender Using Deep Learning. J Med Syst 2024; 48:15. [PMID: 38252192 PMCID: PMC10803393 DOI: 10.1007/s10916-023-02032-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 12/31/2023] [Indexed: 01/23/2024]
Abstract
The fact that the rapid and definitive diagnosis of autism cannot be made today and that autism cannot be treated provides an impetus to look into novel technological solutions. To contribute to the resolution of this problem through multiple classifications by considering age and gender factors, in this study, two quadruple and one octal classifications were performed using a deep learning (DL) approach. Gender in one of the four classifications and age groups in the other were considered. In the octal classification, classes were created considering gender and age groups. In addition to the diagnosis of ASD (Autism Spectrum Disorders), another goal of this study is to find out the contribution of gender and age factors to the diagnosis of ASD by making multiple classifications based on age and gender for the first time. Brain structural MRI (sMRI) scans of participators with ASD and TD (Typical Development) were pre-processed in the system originally designed for this purpose. Using the Canny Edge Detection (CED) algorithm, the sMRI image data was cropped in the data pre-processing stage, and the data set was enlarged five times with the data augmentation (DA) techniques. The most optimal convolutional neural network (CNN) models were developed using the grid search optimization (GSO) algorism. The proposed DL prediction system was tested with the five-fold cross-validation technique. Three CNN models were designed to be used in the system. The first of these models is the quadruple classification model created by taking gender into account (model 1), the second is the quadruple classification model created by taking into account age (model 2), and the third is the eightfold classification model created by taking into account both gender and age (model 3). ). The accuracy rates obtained for all three designed models are 80.94, 85.42 and 67.94, respectively. These obtained accuracy rates were compared with pre-trained models by using the transfer learning approach. As a result, it was revealed that age and gender factors were effective in the diagnosis of ASD with the system developed for ASD multiple classifications, and higher accuracy rates were achieved compared to pre-trained models.
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Affiliation(s)
- Hidir Selcuk Nogay
- Electrical and Energy Department, Bursa Uludag University, Bursa, Turkey
| | - Hojjat Adeli
- Departments of Biomedical Informatics and Neuroscience, College of Medicine, The Ohio State University Neurology, 370 W. 9th Avenue, Columbus, OH, 43210, USA.
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Stefano GB. Quantum Computing and the Future of Neurodegeneration and Mental Health Research. Brain Sci 2024; 14:93. [PMID: 38248308 PMCID: PMC10813156 DOI: 10.3390/brainsci14010093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 12/25/2023] [Indexed: 01/23/2024] Open
Abstract
Quantum computing and supercomputing are two distinct approaches that can be used to solve complex computational problems [...].
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Affiliation(s)
- George B Stefano
- Department of Psychiatry, First Faculty of Medicine, Charles University and General University Hospital in Prague, Ke Karlovu 11, 120 00 Prague, Czech Republic
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Okada N, Fukunaga M, Miura K, Nemoto K, Matsumoto J, Hashimoto N, Kiyota M, Morita K, Koshiyama D, Ohi K, Takahashi T, Koeda M, Yamamori H, Fujimoto M, Yasuda Y, Hasegawa N, Narita H, Yokoyama S, Mishima R, Kawashima T, Kobayashi Y, Sasabayashi D, Harada K, Yamamoto M, Hirano Y, Itahashi T, Nakataki M, Hashimoto RI, Tha KK, Koike S, Matsubara T, Okada G, van Erp TGM, Jahanshad N, Yoshimura R, Abe O, Onitsuka T, Watanabe Y, Matsuo K, Yamasue H, Okamoto Y, Suzuki M, Turner JA, Thompson PM, Ozaki N, Kasai K, Hashimoto R. Subcortical volumetric alterations in four major psychiatric disorders: a mega-analysis study of 5604 subjects and a volumetric data-driven approach for classification. Mol Psychiatry 2023; 28:5206-5216. [PMID: 37537281 DOI: 10.1038/s41380-023-02141-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 05/18/2023] [Accepted: 06/16/2023] [Indexed: 08/05/2023]
Abstract
Differential diagnosis is sometimes difficult in practical psychiatric settings, in terms of using the current diagnostic system based on presenting symptoms and signs. The creation of a novel diagnostic system using objective biomarkers is expected to take place. Neuroimaging studies and others reported that subcortical brain structures are the hubs for various psycho-behavioral functions, while there are so far no neuroimaging data-driven clinical criteria overcoming limitations of the current diagnostic system, which would reflect cognitive/social functioning. Prior to the main analysis, we conducted a large-scale multisite study of subcortical volumetric and lateralization alterations in schizophrenia, bipolar disorder, major depressive disorder, and autism spectrum disorder using T1-weighted images of 5604 subjects (3078 controls and 2526 patients). We demonstrated larger lateral ventricles volume in schizophrenia, bipolar disorder, and major depressive disorder, smaller hippocampus volume in schizophrenia and bipolar disorder, and schizophrenia-specific smaller amygdala, thalamus, and accumbens volumes and larger caudate, putamen, and pallidum volumes. In addition, we observed a leftward alteration of lateralization for pallidum volume specifically in schizophrenia. Moreover, as our main objective, we clustered the 5,604 subjects based on subcortical volumes, and explored whether data-driven clustering results can explain cognitive/social functioning in the subcohorts. We showed a four-biotype classification, namely extremely (Brain Biotype [BB] 1) and moderately smaller limbic regions (BB2), larger basal ganglia (BB3), and normal volumes (BB4), being associated with cognitive/social functioning. Specifically, BB1 and BB2-3 were associated with severe and mild cognitive/social impairment, respectively, while BB4 was characterized by normal cognitive/social functioning. Our results may lead to the future creation of novel biological data-driven psychiatric diagnostic criteria, which may be expected to be useful for prediction or therapeutic selection.
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Affiliation(s)
- Naohiro Okada
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- The International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, Japan
| | - Masaki Fukunaga
- Division of Cerebral Integration, National Institute for Physiological Sciences, Aichi, Japan
| | - Kenichiro Miura
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Kiyotaka Nemoto
- Department of Psychiatry, Institute of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Junya Matsumoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Naoki Hashimoto
- Department of Psychiatry, Hokkaido University Graduate School of Medicine, Hokkaido, Japan
| | - Masahiro Kiyota
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Kentaro Morita
- Department of Rehabilitation, University of Tokyo Hospital, Tokyo, Japan
| | - Daisuke Koshiyama
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Kazutaka Ohi
- Department of Psychiatry, Gifu University Graduate School of Medicine, Gifu, Japan
- Department of General Internal Medicine, Kanazawa Medical University, Ishikawa, Japan
| | - 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
| | - Michihiko Koeda
- Department of Neuropsychiatry, Graduate School of Medicine, Nippon Medical School, Tokyo, Japan
| | - Hidenaga Yamamori
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
- Japan Community Health Care Organization Osaka Hospital, Osaka, Japan
| | - Michiko Fujimoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Yuka Yasuda
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
- Life Grow Brilliant Mental Clinic, Medical Corporation Foster, Osaka, Japan
| | - Naomi Hasegawa
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Hisashi Narita
- Department of Psychiatry, Hokkaido University Graduate School of Medicine, Hokkaido, Japan
| | - Satoshi Yokoyama
- Department of Psychiatry and Neuroscience, Hiroshima University, Hiroshima, Japan
| | - Ryo Mishima
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Takahiko Kawashima
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yuko Kobayashi
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - 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
| | - Kenichiro Harada
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Yamaguchi, Japan
| | - Maeri Yamamoto
- Department of Psychiatry, Graduate School of Medicine, Nagoya University, Aichi, Japan
| | - Yoji Hirano
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
- Department of Psychiatry, Division of Clinical Neuroscience, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan
| | - Takashi Itahashi
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Masahito Nakataki
- Department of Psychiatry, Graduate School of Biomedical Sciences, Tokushima University, Tokushima, Japan
| | - Ryu-Ichiro Hashimoto
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
- Department of Language Sciences, Graduate School of Humanities, Tokyo Metropolitan University, Tokyo, Japan
| | - Khin K Tha
- Department of Diagnostic Imaging, Hokkaido University Faculty of Medicine, Hokkaido, Japan
- Global Center for Biomedical Science and Engineering, Hokkaido University Faculty of Medicine, Hokkaido, Japan
| | - Shinsuke Koike
- The International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, Japan
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), Tokyo, Japan
- Center for Evolutionary Cognitive Sciences, Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, Japan
| | - Toshio Matsubara
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Yamaguchi, Japan
| | - Go Okada
- Department of Psychiatry and Neuroscience, Hiroshima University, Hiroshima, Japan
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Reiji Yoshimura
- Department of Psychiatry, University of Occupational and Environmental Health, Fukuoka, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | | | - Yoshiyuki Watanabe
- Department of Radiology, Shiga University of Medical Science, Shiga, Japan
| | - Koji Matsuo
- Department of Psychiatry, Faculty of Medicine, Saitama Medical University, Saitama, Japan
| | - Hidenori Yamasue
- Department of Psychiatry, Hamamatsu University School of Medicine, Shizuoka, Japan
| | - Yasumasa Okamoto
- Department of Psychiatry and Neuroscience, Hiroshima University, Hiroshima, Japan
| | - Michio Suzuki
- 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
| | - Jessica A Turner
- Department of Psychiatry and Behavioral Health, Wexner Medical Center, The Ohio State University, Columbus, OH, USA
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Norio Ozaki
- Department of Psychiatry, Graduate School of Medicine, Nagoya University, Aichi, Japan
- Pathophysiology of Mental Disorders, Graduate School of Medicine, Nagoya University, Aichi, Japan
| | - Kiyoto Kasai
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- The International Research Center for Neurointelligence (WPI-IRCN), The University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, Japan
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), Tokyo, Japan
| | - Ryota Hashimoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan.
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan.
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He M, Zhou J, Li X, Wang R. Investigating the causal effects of smoking, sleep, and BMI on major depressive disorder and bipolar disorder: a univariable and multivariable two-sample Mendelian randomization study. Front Psychiatry 2023; 14:1206657. [PMID: 37900287 PMCID: PMC10602671 DOI: 10.3389/fpsyt.2023.1206657] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 09/19/2023] [Indexed: 10/31/2023] Open
Abstract
Background Mental disorders, characterized as products of biopsychosocial interactions, have emerged as a leading contributor to the worldwide rise in overall morbidity and disability rates. Life's essentials can affect nearly every aspect of our lives, from physical to mental health. In this study, we try to identify the associations between life's essentials and mental disorders. Method Three assumptions of Mendelian randomization (MR) were applied to obtain the genetic instruments associated with smoking, sleep, and body mass index (BMI) in genome-wide association studies. Then, we conducted univariable MR (UVMR) and multivariable MR (MVMR) two-sample analyses to estimate the causal effects of these life's essentials on two mental disorders namely, major depressive disorder (MDD) and bipolar disorder (BD). Additionally, multiple sensitivity analyses were performed to evaluate the reliability and stability of the study results. Results In the MR analysis of the association of smoking, sleep, and BMI with MDD, we obtained 78, 39, and 302 genetic instruments, respectively. Smoking [odds ratio (OR), 1.03; 95% confidence interval (CI), 1.01-1.06; p = 0.004], sleep (OR, 1.04; 95% CI, 1.02-1.06; p < 0.001), and BMI (OR, 1.01; 95% CI, 1.01-1.02; p < 0.001) were all considered as risk factors for MDD and were independent of each other (smoking: OR, 1.03, 95% CI, 1.01-1.06, p = 0.008; sleep: OR, 1.03, 95% CI, 1.01-1.05, p = 0.001; and BMI: OR, 1.01, 95% CI, 1.01-1.02, p < 0.001). Additionally, 78, 38, and 297 genetic instruments were obtained in the MR analysis of smoking, sleep, and BMI with BD, respectively. Causal associations were observed between smoking (OR, 2.46; 95% CI, 1.17-5.15; p = 0.017), sleep (OR, 2.73; 95% CI, 1.52-4.92; p < 0.001), and BD, and smoking (OR, 2.43; 95% CI, 1.69-3.16; p = 0.018) might be a mediator in the causal effects of sleep on BD. Finally, there was no inconsistency between sensitivity and causality analysis, proving that our results are convincing. Conclusion The study results provide strong evidence that smoking, sleep, and BMI are causally related to MDD and BD, which need further research to clarify the underlying mechanism.
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Affiliation(s)
| | | | - Xuehan Li
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, China
| | - Rurong Wang
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, China
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Vásquez V, Orozco J. Detection of COVID-19-related biomarkers by electrochemical biosensors and potential for diagnosis, prognosis, and prediction of the course of the disease in the context of personalized medicine. Anal Bioanal Chem 2023; 415:1003-1031. [PMID: 35970970 PMCID: PMC9378265 DOI: 10.1007/s00216-022-04237-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 06/30/2022] [Accepted: 07/18/2022] [Indexed: 02/07/2023]
Abstract
As a more efficient and effective way to address disease diagnosis and intervention, cutting-edge technologies, devices, therapeutic approaches, and practices have emerged within the personalized medicine concept depending on the particular patient's biology and the molecular basis of the disease. Personalized medicine is expected to play a pivotal role in assessing disease risk or predicting response to treatment, understanding a person's health status, and, therefore, health care decision-making. This work discusses electrochemical biosensors for monitoring multiparametric biomarkers at different molecular levels and their potential to elucidate the health status of an individual in a personalized manner. In particular, and as an illustration, we discuss several aspects of the infection produced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as a current health care concern worldwide. This includes SARS-CoV-2 structure, mechanism of infection, biomarkers, and electrochemical biosensors most commonly explored for diagnostics, prognostics, and potentially assessing the risk of complications in patients in the context of personalized medicine. Finally, some concluding remarks and perspectives hint at the use of electrochemical biosensors in the frame of other cutting-edge converging/emerging technologies toward the inauguration of a new paradigm of personalized medicine.
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Affiliation(s)
- Viviana Vásquez
- Max Planck Tandem Group in Nanobioengineering, Institute of Chemistry, Faculty of Natural and Exact Sciences, University of Antioquia, Complejo Ruta N, Calle 67 N° 52-20, Medellín, 050010, Colombia
| | - Jahir Orozco
- Max Planck Tandem Group in Nanobioengineering, Institute of Chemistry, Faculty of Natural and Exact Sciences, University of Antioquia, Complejo Ruta N, Calle 67 N° 52-20, Medellín, 050010, Colombia.
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Selcuk Nogay H, Adeli H. Diagnostic of autism spectrum disorder based on structural brain MRI images using, grid search optimization, and convolutional neural networks. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104234] [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]
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Ippolito G, Bertaccini R, Tarasi L, Di Gregorio F, Trajkovic J, Battaglia S, Romei V. The Role of Alpha Oscillations among the Main Neuropsychiatric Disorders in the Adult and Developing Human Brain: Evidence from the Last 10 Years of Research. Biomedicines 2022; 10:biomedicines10123189. [PMID: 36551945 PMCID: PMC9775381 DOI: 10.3390/biomedicines10123189] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/03/2022] [Accepted: 12/05/2022] [Indexed: 12/14/2022] Open
Abstract
Alpha oscillations (7-13 Hz) are the dominant rhythm in both the resting and active brain. Accordingly, translational research has provided evidence for the involvement of aberrant alpha activity in the onset of symptomatological features underlying syndromes such as autism, schizophrenia, major depression, and Attention Deficit and Hyperactivity Disorder (ADHD). However, findings on the matter are difficult to reconcile due to the variety of paradigms, analyses, and clinical phenotypes at play, not to mention recent technical and methodological advances in this domain. Herein, we seek to address this issue by reviewing the literature gathered on this topic over the last ten years. For each neuropsychiatric disorder, a dedicated section will be provided, containing a concise account of the current models proposing characteristic alterations of alpha rhythms as a core mechanism to trigger the associated symptomatology, as well as a summary of the most relevant studies and scientific contributions issued throughout the last decade. We conclude with some advice and recommendations that might improve future inquiries within this field.
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Affiliation(s)
- Giuseppe Ippolito
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum—Università di Bologna, 47521 Cesena, Italy
| | - Riccardo Bertaccini
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum—Università di Bologna, 47521 Cesena, Italy
| | - Luca Tarasi
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum—Università di Bologna, 47521 Cesena, Italy
| | - Francesco Di Gregorio
- UO Medicina Riabilitativa e Neuroriabilitazione, Azienda Unità Sanitaria Locale, 40133 Bologna, Italy
| | - Jelena Trajkovic
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum—Università di Bologna, 47521 Cesena, Italy
| | - Simone Battaglia
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum—Università di Bologna, 47521 Cesena, Italy
- Dipartimento di Psicologia, Università di Torino, 10124 Torino, Italy
| | - Vincenzo Romei
- Centro Studi e Ricerche in Neuroscienze Cognitive, Dipartimento di Psicologia, Alma Mater Studiorum—Università di Bologna, 47521 Cesena, Italy
- Correspondence:
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Yang B, Huang Y, Li Z, Hu X. Management of Post-stroke Depression (PSD) by Electroencephalography for Effective Rehabilitation. ENGINEERED REGENERATION 2022. [DOI: 10.1016/j.engreg.2022.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
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10
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Aoki R, Saito T, Ninomiya K, Shimasaki A, Ashizawa T, Ito K, Ikeda M, Iwata N. Shared genetic components between metabolic syndrome and schizophrenia: Genetic correlation using multipopulation data sets. Psychiatry Clin Neurosci 2022; 76:361-366. [PMID: 35536160 PMCID: PMC9546074 DOI: 10.1111/pcn.13372] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/24/2022] [Accepted: 04/21/2022] [Indexed: 11/30/2022]
Abstract
AIM The genetic relationship between schizophrenia (SCZ) and other nonpsychiatric disorders remains largely unknown. We examined the shared genetic components between these disorders based on multipopulation data sets. METHODS We used two data sets for East Asian (EAS) and European (EUR) samples. SCZ data was based on the Psychiatric Genomics Consortium Asia with our own genome-wide association study for EAS and Psychiatric Genomics Consortium for EUR. Nonpsychiatric data (20 binary traits [mainly nonpsychiatric complex disorders] and 34 quantitative traits [mainly laboratory examinations and physical characteristics]) were obtained from Biobank Japan and UK Biobank for EAS and EUR samples, respectively. To evaluate genetic correlation, linkage disequilibrium score regression analysis was utilized with further meta-analysis for each result from EAS and EUR samples to obtain robust evidence. Subsequent mendelian randomization analysis was also included to examine the causal effect. RESULTS A significant genetic correlation between SCZ and several metabolic syndrome (MetS) traits was detected in the combined samples (meta-analysis between EAS and EUR data) (body mass index [rg = -0.10, q-value = 1.0 × 10-9 ], high-density-lipoprotein cholesterol [rg = 0.072, q-value = 2.9 × 10-3 ], blood sugar [rg = -0.068, q-value = 1.4 × 10-2 ], triglycerides [rg = -0.052, q-value = 2.4 × 10-2 ], systolic blood pressure [rg = -0.054, q-value = 3.5 × 10-2 ], and C-reactive protein [rg = -0.076, q-value = 7.8 × 10-5 ]. However, no causal relationship on SCZ susceptibility was detected for these traits based on the mendelian randomization analysis. CONCLUSION Our results indicate shared genetic components between SCZ and MetS traits and C-reactive protein. Specifically, we found it interesting that the correlation between MetS traits and SCZ was the opposite of that expected from clinical studies: this genetic study suggests that SCZ susceptibility was associated with reduced MetS. This implied that MetS in patients with SCZ was not associated with genetic components but with environmental factors, including antipsychotics, lifestyle changes, poor diet, lack of exercise, and living conditions.
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Affiliation(s)
- Rei Aoki
- Department of Psychiatry, Fujita Health University School of Medicine, Toyoake, Japan
| | - Takeo Saito
- Department of Psychiatry, Fujita Health University School of Medicine, Toyoake, Japan
| | - Kohei Ninomiya
- Department of Psychiatry, Fujita Health University School of Medicine, Toyoake, Japan
| | - Ayu Shimasaki
- Department of Psychiatry, Fujita Health University School of Medicine, Toyoake, Japan
| | - Takuma Ashizawa
- Department of Psychiatry, Fujita Health University School of Medicine, Toyoake, Japan
| | - Kenta Ito
- Department of Psychiatry, Fujita Health University School of Medicine, Toyoake, Japan
| | - Masashi Ikeda
- Department of Psychiatry, Fujita Health University School of Medicine, Toyoake, Japan
| | - Nakao Iwata
- Department of Psychiatry, Fujita Health University School of Medicine, Toyoake, Japan
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11
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Zhang Q, Li B, Jin S, Liu W, Liu J, Xie S, Zhang L, Kang Y, Ding Y, Zhang X, Cheng W, Yang Z. Comparing the Effectiveness of Brain Structural Imaging, Resting-state fMRI, and Naturalistic fMRI in Recognizing Social Anxiety Disorder in Children and Adolescents. Psychiatry Res Neuroimaging 2022; 323:111485. [PMID: 35567906 DOI: 10.1016/j.pscychresns.2022.111485] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 04/07/2022] [Accepted: 04/16/2022] [Indexed: 01/11/2023]
Abstract
Social anxiety disorder (SAD) is a common anxiety disorder in childhood and adolescence. Studies on SAD in adults have reported both structural and functional aberrancies of the brain at the group level. However, evidence has shown differences in anxiety-related brain abnormalities between adolescents and adults. Since children and adolescents can afford limited scan time, optimizing the scan tasks is essential for SAD research in children and adolescents. Thus, we need to address whether brain structure, resting-state fMRI, and naturalistic imaging enable individualized identification of SAD in children and adolescents, which measurement is more effective, and whether pooling multi-modal features can improve the identification of SAD. We comprehensively addressed these questions by building machine learning models based on parcel-wise brain features. We found that naturalistic fMRI yielded higher classification accuracy (69.17%) than the other modalities and the classification performance showed dependence on the contents of the movie. The classification models also identified contributing brain regions, some of which exhibited correlations with the symptoms scores of SAD. However, pooling brain features from the three modalities did not help enhance the classification accuracy. These results support the application of carefully designed naturalistic imaging in recognizing children and adolescents at risk of SAD.
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Affiliation(s)
- Qinjian Zhang
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Baobin Li
- School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Shuyu Jin
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wenjing Liu
- Department of Child and Adolescent Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jingjing Liu
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shuqi Xie
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lei Zhang
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yinzhi Kang
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yue Ding
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaochen Zhang
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wenhong Cheng
- Department of Child and Adolescent Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Zhi Yang
- Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Institute of Psychological and Behavioral Science, Shanghai Jiao Tong University, Shanghai, China.
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12
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Bilal AM, Fransson E, Bränn E, Eriksson A, Zhong M, Gidén K, Elofsson U, Axfors C, Skalkidou A, Papadopoulos FC. Predicting perinatal health outcomes using smartphone-based digital phenotyping and machine learning in a prospective Swedish cohort (Mom2B): study protocol. BMJ Open 2022; 12:e059033. [PMID: 35477874 PMCID: PMC9047888 DOI: 10.1136/bmjopen-2021-059033] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 04/12/2022] [Indexed: 11/24/2022] Open
Abstract
INTRODUCTION Perinatal complications, such as perinatal depression and preterm birth, are major causes of morbidity and mortality for the mother and the child. Prediction of high risk can allow for early delivery of existing interventions for prevention. This ongoing study aims to use digital phenotyping data from the Mom2B smartphone application to develop models to predict women at high risk for mental and somatic complications. METHODS AND ANALYSIS All Swedish-speaking women over 18 years, who are either pregnant or within 3 months postpartum are eligible to participate by downloading the Mom2B smartphone app. We aim to recruit at least 5000 participants with completed outcome measures. Throughout the pregnancy and within the first year postpartum, both active and passive data are collected via the app in an effort to establish a participant's digital phenotype. Active data collection consists of surveys related to participant background information, mental and physical health, lifestyle, and social circumstances, as well as voice recordings. Participants' general smartphone activity, geographical movement patterns, social media activity and cognitive patterns can be estimated through passive data collection from smartphone sensors and activity logs. The outcomes will be measured using surveys, such as the Edinburgh Postnatal Depression Scale, and through linkage to national registers, from where information on registered clinical diagnoses and received care, including prescribed medication, can be obtained. Advanced machine learning and deep learning techniques will be applied to these multimodal data in order to develop accurate algorithms for the prediction of perinatal depression and preterm birth. In this way, earlier intervention may be possible. ETHICS AND DISSEMINATION Ethical approval has been obtained from the Swedish Ethical Review Authority (dnr: 2019/01170, with amendments), and the project fully fulfils the General Data Protection Regulation (GDPR) requirements. All participants provide consent to participate and can withdraw their participation at any time. Results from this project will be disseminated in international peer-reviewed journals and presented in relevant conferences.
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Affiliation(s)
- Ayesha M Bilal
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
- Centre for Women's Mental Health during the Reproductive Lifespan (Womher), Uppsala University, Uppsala, Sweden
| | - Emma Fransson
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
- Centre for Translational Microbiome Research, Department of Microbiology, Tumor and Cell Biology, Karolinska Institute, Stockholm, Sweden
| | - Emma Bränn
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
| | - Allison Eriksson
- Centre for Women's Mental Health during the Reproductive Lifespan (Womher), Uppsala University, Uppsala, Sweden
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
| | - Mengyu Zhong
- Centre for Women's Mental Health during the Reproductive Lifespan (Womher), Uppsala University, Uppsala, Sweden
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Karin Gidén
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
| | - Ulf Elofsson
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
| | - Cathrine Axfors
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
| | - Alkistis Skalkidou
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
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13
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Čukić M, López V. Progress in Objective Detection of Depression and Online Monitoring of Patients Based on Physiological Complexity. Front Psychiatry 2022; 13:828773. [PMID: 35418885 PMCID: PMC8995561 DOI: 10.3389/fpsyt.2022.828773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 03/02/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Milena Čukić
- Institute for Technology of Knowledge, Complutense University, Madrid, Spain
- 3EGA B.V., Amsterdam, Netherlands
- General Physiology and Biophysics Department, Belgrade University, Belgrade, Serbia
| | - Victoria López
- Quantitative Methods Department, Cunef University, Madrid, Spain
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14
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Khaleghi A, Mohammadi MR, Shahi K, Nasrabadi AM. Computational Neuroscience Approach to Psychiatry: A Review on Theory-driven Approaches. CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE 2022; 20:26-36. [PMID: 35078946 PMCID: PMC8813324 DOI: 10.9758/cpn.2022.20.1.26] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 06/09/2021] [Accepted: 06/14/2021] [Indexed: 11/21/2022]
Abstract
Translating progress in neuroscience into clinical benefits for patients with psychiatric disorders is challenging because it involves the brain as the most complex organ and its interaction with a complex environment and condition. Dealing with such complexity requires powerful techniques. Computational neuroscience approach to psychiatry integrates multiple levels and types of simulation, analysis and computation according to the different types of computational models to enhance comprehending, prediction and treatment of psychiatric disorder. This approach comprises two approaches: theory-driven and data-driven. In this review, we focus on recent advances in theory-driven approaches that mathematically and mechanistically examine the relationships between disorder-related changes and behavior at different level of brain organization. We discuss recent progresses in computational neuroscience models that relate to psychiatry and show how principles of neural computational modeling can be employed to explain psychopathology.
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Affiliation(s)
- Ali Khaleghi
- Psychiatry and Psychology Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Mohammadi
- Psychiatry and Psychology Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Kian Shahi
- Psychiatry and Psychology Research Center, Tehran University of Medical Sciences, Tehran, Iran
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15
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Taylor JE, Yamada T, Kawashima T, Kobayashi Y, Yoshihara Y, Miyata J, Murai T, Kawato M, Motegi T. Depressive symptoms reduce when dorsolateral prefrontal cortex-precuneus connectivity normalizes after functional connectivity neurofeedback. Sci Rep 2022; 12:2581. [PMID: 35173179 PMCID: PMC8850610 DOI: 10.1038/s41598-022-05860-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 01/18/2022] [Indexed: 11/09/2022] Open
Abstract
Depressive disorders contribute heavily to global disease burden; This is possibly because patients are often treated homogeneously, despite having heterogeneous symptoms with differing underlying neural mechanisms. A novel treatment that can directly influence the neural circuit relevant to an individual patient's subset of symptoms might more precisely and thus effectively aid in the alleviation of their specific symptoms. We tested this hypothesis in a proof-of-concept study using fMRI functional connectivity neurofeedback. We targeted connectivity between the left dorsolateral prefrontal cortex/middle frontal gyrus and the left precuneus/posterior cingulate cortex, because this connection has been well-established as relating to a specific subset of depressive symptoms. Specifically, this connectivity has been shown in a data-driven manner to be less anticorrelated in patients with melancholic depression than in healthy controls. Furthermore, a posterior cingulate dominant state-which results in a loss of this anticorrelation-is expected to specifically relate to an increase in rumination symptoms such as brooding. In line with predictions, we found that, with neurofeedback training, the more a participant normalized this connectivity (restored the anticorrelation), the more related (depressive and brooding symptoms), but not unrelated (trait anxiety), symptoms were reduced. Because these results look promising, this paradigm next needs to be examined with a greater sample size and with better controls. Nonetheless, here we provide preliminary evidence for a correlation between the normalization of a neural network and a reduction in related symptoms. Showing their reproducibility, these results were found in two experiments that took place several years apart by different experimenters. Indicative of its potential clinical utility, effects of this treatment remained one-two months later.Clinical trial registration: Both experiments reported here were registered clinical trials (UMIN000015249, jRCTs052180169).
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Affiliation(s)
- Jessica Elizabeth Taylor
- Department of Decoded Neurofeedback (DecNef), Computational Neuroscience Laboratories, Advanced Telecommunications Research Institute International (ATR), Hikaridai 2-2-2. Seika-cho, Soraku, Kyoto, 619-0237, Japan
| | - Takashi Yamada
- Department of Decoded Neurofeedback (DecNef), Computational Neuroscience Laboratories, Advanced Telecommunications Research Institute International (ATR), Hikaridai 2-2-2. Seika-cho, Soraku, Kyoto, 619-0237, Japan.,Department of Cognitive, Linguistic and Psychological Sciences, Brown University, Providence, USA.,Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Takahiko Kawashima
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yuko Kobayashi
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yujiro Yoshihara
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Jun Miyata
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Toshiya Murai
- Department of Psychiatry, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Mitsuo Kawato
- Department of Decoded Neurofeedback (DecNef), Computational Neuroscience Laboratories, Advanced Telecommunications Research Institute International (ATR), Hikaridai 2-2-2. Seika-cho, Soraku, Kyoto, 619-0237, Japan
| | - Tomokazu Motegi
- Department of Decoded Neurofeedback (DecNef), Computational Neuroscience Laboratories, Advanced Telecommunications Research Institute International (ATR), Hikaridai 2-2-2. Seika-cho, Soraku, Kyoto, 619-0237, Japan. .,Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Gunma, Japan.
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16
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Application of Mass Multivariate Analysis on Neuroimaging Data Sets for Precision Diagnostics of Depression. Diagnostics (Basel) 2022; 12:diagnostics12020469. [PMID: 35204560 PMCID: PMC8871050 DOI: 10.3390/diagnostics12020469] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/18/2022] [Accepted: 02/08/2022] [Indexed: 01/29/2023] Open
Abstract
We used the Mass Multivariate Method on structural, resting-state, and task-related fMRI data from two groups of patients with schizophrenia and depression in order to define several regions of significant relevance to the differential diagnosis of those conditions. The regions included the left planum polare (PP), the left opercular part of the inferior frontal gyrus (OpIFG), the medial orbital gyrus (MOrG), the posterior insula (PIns), and the parahippocampal gyrus (PHG). This study delivered evidence that a multimodal neuroimaging approach can potentially enhance the validity of psychiatric diagnoses. Structural, resting-state, or task-related functional MRI modalities cannot provide independent biomarkers. Further studies need to consider and implement a model of incremental validity combining clinical measures with different neuroimaging modalities to discriminate depressive disorders from schizophrenia. Biological signatures of disease on the level of neuroimaging are more likely to underpin broader nosological entities in psychiatry.
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17
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Liu Y, Pu C, Xia S, Deng D, Wang X, Li M. Machine learning approaches for diagnosing depression using EEG: A review. Transl Neurosci 2022; 13:224-235. [PMID: 36045698 PMCID: PMC9375981 DOI: 10.1515/tnsci-2022-0234] [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: 04/13/2022] [Revised: 06/18/2022] [Accepted: 07/04/2022] [Indexed: 12/02/2022] Open
Abstract
Depression has become one of the most crucial public health issues, threatening the quality of life of over 300 million people throughout the world. Nevertheless, the clinical diagnosis of depression is now still hampered by behavioral diagnostic methods. Due to the lack of objective laboratory diagnostic criteria, accurate identification and diagnosis of depression remained elusive. With the rise of computational psychiatry, a growing number of studies have combined resting-state electroencephalography with machine learning (ML) to alleviate diagnosis of depression in recent years. Despite the exciting results, these were worrisome of these studies. As a result, ML prediction models should be continuously improved to better screen and diagnose depression. Finally, this technique would be used for the diagnosis of other psychiatric disorders in the future.
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Affiliation(s)
- Yuan Liu
- Department of Psychosomatic Medicine, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang 330006, Jiangxi Province, China
| | - Changqin Pu
- Queen Mary College, Nanchang University, Nanchang 330031, Jiangxi Province, China
| | - Shan Xia
- Department of Psychosomatic Medicine, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang 330006, Jiangxi Province, China
| | - Dingyu Deng
- Department of Internal Neurology, The First Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi Province, China
| | - Xing Wang
- School of Life Sciences, Nanchang University, No.999 Xuefu Avenue, Honggutan District, Nanchang 330036, Jiangxi Province, China.,Clinical Diagnostics Laboratory, Clinical Medical Experiment Center, Nanchang University, Nanchang 330036, China
| | - Mengqian Li
- Department of Psychosomatic Medicine, The First Affiliated Hospital of Nanchang University, No.17 Yongwaizheng Street, Donghu District, Nanchang 330006, Jiangxi Province, China
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18
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Automated diagnosis of depression from EEG signals using traditional and deep learning approaches: A comparative analysis. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2021.12.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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19
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Ji S, Liu B, Li Y, Chen N, Fu Y, Shi J, Zhao Z, Yao Z, Hu B. Trait and state alterations in excitatory connectivity between subgenual anterior cingulate cortex and cerebellum in patients with current and remitted depression. Psychiatry Res Neuroimaging 2021; 317:111356. [PMID: 34509806 DOI: 10.1016/j.pscychresns.2021.111356] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 07/06/2021] [Accepted: 08/03/2021] [Indexed: 11/27/2022]
Abstract
Neuroimaging studies have indicated that the altered functional connectivity (FC) of the subgenual anterior cingulate cortex (sgACC) might be potential pathophysiology of major depressive disorder (MDD). However, directed connectivity is proven to be more closely to neurophysiological processes underlying brain activity than FC. This study aimed to identify the alterations underlying directed connectivity of the sgACC in patients with current and remitted MDD. We conducted a cross-sectional neuroimaging study by recruiting 36 patients with current MDD, 20 patients with remitted MDD, and 36 matched healthy controls. Multiple linear regression was employed to estimate bidirectional connectivity between bilateral sgACC and 115 brain regions over 230 time points. Besides, graph theory was applied to further investigate the information transfer across bilateral sgACC and abnormal brain regions. We found that both patients with current and remitted MDD showed a similar abnormality in bidirectional excitatory connectivity between the left sgACC and the right cerebellum. Patients with current MDD exhibited an increase in excitatory connectivity from the left cerebellum to the right sgACC, which was positively correlated with the HAMD score. Meanwhile, significantly decreased betweenness of the left sgACC was detected in all depressive patients. Our findings suggest that the changed bidirectional excitatory connectivity between the left sgACC and the right cerebellum might be a trait alteration and the abnormal increased excitatory connectivity from the left cerebellum to the right sgACC might be a state alteration of MDD. This work may provide a valuable contribution to identify trait and state alterations in the brain for depression.
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Affiliation(s)
- Shanling Ji
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, PR China
| | - Bangshan Liu
- Department of Psychiatry, the Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, PR China; Mental Health Institute of Central South University, China National Clinical Research Center on Mental Disorders (Xiangya), China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan 410011, PR China
| | - Yongchao Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, PR China
| | - Nan Chen
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, PR China
| | - Yu Fu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, PR China
| | - Jie Shi
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, PR China
| | - Ziyang Zhao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, PR China
| | - Zhijun Yao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, PR China.
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, PR China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, PR China.
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20
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Al-Hiyali MI, Yahya N, Faye I, Hussein AF. Identification of Autism Subtypes Based on Wavelet Coherence of BOLD FMRI Signals Using Convolutional Neural Network. SENSORS 2021; 21:s21165256. [PMID: 34450699 PMCID: PMC8398492 DOI: 10.3390/s21165256] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Revised: 07/16/2021] [Accepted: 07/20/2021] [Indexed: 11/25/2022]
Abstract
The functional connectivity (FC) patterns of resting-state functional magnetic resonance imaging (rs-fMRI) play an essential role in the development of autism spectrum disorders (ASD) classification models. There are available methods in literature that have used FC patterns as inputs for binary classification models, but the results barely reach an accuracy of 80%. Additionally, the generalizability across multiple sites of the models has not been investigated. Due to the lack of ASD subtypes identification model, the multi-class classification is proposed in the present study. This study aims to develop automated identification of autism spectrum disorder (ASD) subtypes using convolutional neural networks (CNN) using dynamic FC as its inputs. The rs-fMRI dataset used in this study consists of 144 individuals from 8 independent sites, labeled based on three ASD subtypes, namely autistic disorder (ASD), Asperger’s disorder (APD), and pervasive developmental disorder not otherwise specified (PDD-NOS). The blood-oxygen-level-dependent (BOLD) signals from 116 brain nodes of automated anatomical labeling (AAL) atlas are used, where the top-ranked node is determined based on one-way analysis of variance (ANOVA) of the power spectral density (PSD) values. Based on the statistical analysis of the PSD values of 3-level ASD and normal control (NC), putamen_R is obtained as the top-ranked node and used for the wavelet coherence computation. With good resolution in time and frequency domain, scalograms of wavelet coherence between the top-ranked node and the rest of the nodes are used as dynamic FC feature input to the convolutional neural networks (CNN). The dynamic FC patterns of wavelet coherence scalogram represent phase synchronization between the pairs of BOLD signals. Classification algorithms are developed using CNN and the wavelet coherence scalograms for binary and multi-class identification were trained and tested using cross-validation and leave-one-out techniques. Results of binary classification (ASD vs. NC) and multi-class classification (ASD vs. APD vs. PDD-NOS vs. NC) yielded, respectively, 89.8% accuracy and 82.1% macro-average accuracy, respectively. Findings from this study have illustrated the good potential of wavelet coherence technique in representing dynamic FC between brain nodes and open possibilities for its application in computer aided diagnosis of other neuropsychiatric disorders, such as depression or schizophrenia.
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Affiliation(s)
- Mohammed Isam Al-Hiyali
- Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia; (M.I.A.-H.); (I.F.)
| | - Norashikin Yahya
- Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia; (M.I.A.-H.); (I.F.)
- Correspondence: ; Tel.: +605-3687861
| | - Ibrahima Faye
- Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia; (M.I.A.-H.); (I.F.)
| | - Ahmed Faeq Hussein
- Biomedical Engineering Department, Faculty of Engineering, Al-Nahrain University, Baghdad 10072, Iraq;
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21
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Svaldi DO, Goñi J, Abbas K, Amico E, Clark DG, Muralidharan C, Dzemidzic M, West JD, Risacher SL, Saykin AJ, Apostolova LG. Optimizing differential identifiability improves connectome predictive modeling of cognitive deficits from functional connectivity in Alzheimer's disease. Hum Brain Mapp 2021; 42:3500-3516. [PMID: 33949732 PMCID: PMC8249900 DOI: 10.1002/hbm.25448] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 03/07/2021] [Accepted: 04/06/2021] [Indexed: 12/29/2022] Open
Abstract
Functional connectivity, as estimated using resting state functional MRI, has shown potential in bridging the gap between pathophysiology and cognition. However, clinical use of functional connectivity biomarkers is impeded by unreliable estimates of individual functional connectomes and lack of generalizability of models predicting cognitive outcomes from connectivity. To address these issues, we combine the frameworks of connectome predictive modeling and differential identifiability. Using the combined framework, we show that enhancing the individual fingerprint of resting state functional connectomes leads to robust identification of functional networks associated to cognitive outcomes and also improves prediction of cognitive outcomes from functional connectomes. Using a comprehensive spectrum of cognitive outcomes associated to Alzheimer's disease (AD), we identify and characterize functional networks associated to specific cognitive deficits exhibited in AD. This combined framework is an important step in making individual level predictions of cognition from resting state functional connectomes and in understanding the relationship between cognition and connectivity.
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Affiliation(s)
| | - Joaquín Goñi
- School of Industrial EngineeringPurdue UniversityWest LafayetteIndianaUSA
- Purdue Institute for Integrative Neuroscience, Purdue UniversityWest LafayetteIndianaUSA
- Weldon School of Biomedical EngineeringPurdue UniversityWest LafayetteIndianaUSA
| | - Kausar Abbas
- School of Industrial EngineeringPurdue UniversityWest LafayetteIndianaUSA
- Purdue Institute for Integrative Neuroscience, Purdue UniversityWest LafayetteIndianaUSA
| | - Enrico Amico
- School of Industrial EngineeringPurdue UniversityWest LafayetteIndianaUSA
- Purdue Institute for Integrative Neuroscience, Purdue UniversityWest LafayetteIndianaUSA
| | - David G. Clark
- Indiana University School of MedicineIndianapolisIndianaUSA
| | | | | | - John D. West
- Indiana University School of MedicineIndianapolisIndianaUSA
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22
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Abstract
"Precision medicine" and "personalized medicine" constitute goals of research since antiquity and this was intensified with the arrival of the "evidence-based medicine." precision and personalized psychiatry (3P) when achieved will constitute a radical shift in our paradigm and it will be even more transformative than in other fields of medicine. The biggest problems so far are the problematic definition of mental disorder, available treatments seem to concern broad categories rather than specific disorders and finally clinical predictors of treatment response or side effects and biological markers do not exist. Precision and personalized psychiatry like all precision medicine will be a laborious and costly task; thus the partnership of scientists with industry and the commercialization of new methods and technologies will be an important element for success. The development of an appropriate legal framework which will both support development and progress but also will protect the rights and the privacy of patients and their families is essential.
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23
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Huang ZA, Zhu Z, Yau CH, Tan KC. Identifying Autism Spectrum Disorder From Resting-State fMRI Using Deep Belief Network. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021; 32:2847-2861. [PMID: 32692687 DOI: 10.1109/tnnls.2020.3007943] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
With the increasing prevalence of autism spectrum disorder (ASD), it is important to identify ASD patients for effective treatment and intervention, especially in early childhood. Neuroimaging techniques have been used to characterize the complex biomarkers based on the functional connectivity anomalies in the ASD. However, the diagnosis of ASD still adopts the symptom-based criteria by clinical observation. The existing computational models tend to achieve unreliable diagnostic classification on the large-scale aggregated data sets. In this work, we propose a novel graph-based classification model using the deep belief network (DBN) and the Autism Brain Imaging Data Exchange (ABIDE) database, which is a worldwide multisite functional and structural brain imaging data aggregation. The remarkable connectivity features are selected through a graph extension of K -nearest neighbors and then refined by a restricted path-based depth-first search algorithm. Thanks to the feature reduction, lower computational complexity could contribute to the shortening of the training time. The automatic hyperparameter-tuning technique is introduced to optimize the hyperparameters of the DBN by exploring the potential parameter space. The simulation experiments demonstrate the superior performance of our model, which is 6.4% higher than the best result reported on the ABIDE database. We also propose to use the data augmentation and the oversampling technique to identify further the possible subtypes within the ASD. The interpretability of our model enables the identification of the most remarkable autistic neural correlation patterns from the data-driven outcomes.
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24
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Wolf A, Ueda K. Contribution of Eye-Tracking to Study Cognitive Impairments Among Clinical Populations. Front Psychol 2021; 12:590986. [PMID: 34163391 PMCID: PMC8215550 DOI: 10.3389/fpsyg.2021.590986] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 05/07/2021] [Indexed: 11/29/2022] Open
Abstract
In the field of psychology, the merge of decision-theory and neuroscientific methods produces an array of scientifically recognized paradigms. For example, by exploring consumer’s eye-movement behavior, researchers aim to deepen the understanding of how patterns of retinal activation are being meaningfully transformed into visual experiences and connected with specific reactions (e.g., purchase). Notably, eye-movements provide knowledge of one’s homeostatic balance and gatekeep information that shape decisions. Hence, vision science investigates the quality of observed environments determined under various experimental conditions. Moreover, it answers questions on how human process visual stimuli and use gained information for a successful strategy to achieve certain goals. While capturing cognitive states with the support of the eye-trackers progresses at a relatively fast pace in decision-making research, measuring the visual performance of real-life tasks, which require complex cognitive skills, is tentatively translated into clinical experiments. Nevertheless, the potential of the human eye as a highly valuable source of biomarkers has been underlined. In this article, we aim to draw readers attention to decision-making experimental paradigms supported with eye-tracking technology among clinical populations. Such interdisciplinary approach may become an important component that will (i) help in objectively illustrating patient’s models of beliefs and values, (ii) support clinical interventions, and (iii) contribute to health services. It is possible that shortly, eye-movement data from decision-making experiments will grant the scientific community a greater understanding of mechanisms underlining mental states and consumption practices that medical professionals consider as obsessions, disorders or addiction.
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Affiliation(s)
- Alexandra Wolf
- JSPS International Research Fellow, Research Center for Applied Perceptual Science, Kyushu University, Fukuoka, Japan
| | - Kazuo Ueda
- Unit of Perceptual Psychology, Dept. Human Science, Research Center for Applied Perceptual Science, Division of Auditory and Visual Perception Research, Research and Development Center for Five-Sense Devices, Kyushu University, Fukuoka, Japan
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25
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Paek AY, Brantley JA, Evans BJ, Contreras-Vidal JL. Concerns in the Blurred Divisions between Medical and Consumer Neurotechnology. IEEE SYSTEMS JOURNAL 2021; 15:3069-3080. [PMID: 35126800 PMCID: PMC8813044 DOI: 10.1109/jsyst.2020.3032609] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Neurotechnology has traditionally been central to the diagnosis and treatment of neurological disorders. While these devices have initially been utilized in clinical and research settings, recent advancements in neurotechnology have yielded devices that are more portable, user-friendly, and less expensive. These improvements allow laypeople to monitor their brain waves and interface their brains with external devices. Such improvements have led to the rise of wearable neurotechnology that is marketed to the consumer. While many of the consumer devices are marketed for innocuous applications, such as use in video games, there is potential for them to be repurposed for medical use. How do we manage neurotechnologies that skirt the line between medical and consumer applications and what can be done to ensure consumer safety? Here, we characterize neurotechnology based on medical and consumer applications and summarize currently marketed uses of consumer-grade wearable headsets. We lay out concerns that may arise due to the similar claims associated with both medical and consumer devices, the possibility of consumer devices being repurposed for medical uses, and the potential for medical uses of neurotechnology to influence commercial markets related to employment and self-enhancement.
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Affiliation(s)
- Andrew Y Paek
- Department of Electrical & Computer Engineering and the IUCRC BRAIN Center at the University of Houston, Houston, TX, USA
| | - Justin A Brantley
- Department of Electrical & Computer Engineering and the IUCRC BRAIN Center at the University of Houston. He is now with the Department of Bioengineering at the University of Pennsylvania, Philadelphia, PA, USA
| | - Barbara J Evans
- Law Center and IUCRC BRAIN Center at the University of Houston. University of Houston, Houston, TX. She is now with the Wertheim College of Engineering and Levin College of Law at the University of Florida, Gainesville, FL, USA
| | - Jose L Contreras-Vidal
- Department of Electrical & Computer Engineering and the IUCRC BRAIN Center at the University of Houston, Houston, TX, USA
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26
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Mill RD, Winfield EC, Cole MW, Ray S. Structural MRI and functional connectivity features predict current clinical status and persistence behavior in prescription opioid users. NEUROIMAGE-CLINICAL 2021; 30:102663. [PMID: 33866300 PMCID: PMC8060550 DOI: 10.1016/j.nicl.2021.102663] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 03/24/2021] [Accepted: 04/02/2021] [Indexed: 01/10/2023]
Abstract
Prescription opioid use disorder (POUD) has reached epidemic proportions in the United States, raising an urgent need for diagnostic biological tools that can improve predictions of disease characteristics. The use of neuroimaging methods to develop such biomarkers have yielded promising results when applied to neurodegenerative and psychiatric disorders, yet have not been extended to prescription opioid addiction. With this long-term goal in mind, we conducted a preliminary study in this understudied clinical group. Univariate and multivariate approaches to distinguishing between POUD (n = 26) and healthy controls (n = 21) were investigated, on the basis of structural MRI (sMRI) and resting-state functional connectivity (restFC) features. Univariate approaches revealed reduced structural integrity in the subcortical extent of a previously reported addiction-related network in POUD subjects. No reliable univariate between-group differences in cortical structure or edgewise restFC were observed. Contrasting these mixed univariate results, multivariate machine learning classification approaches recovered more statistically reliable group differences, especially when sMRI and restFC features were combined in a multi-modal model (classification accuracy = 66.7%, p < .001). The same multivariate multi-modal approach also yielded reliable prediction of individual differences in a clinically relevant behavioral measure (persistence behavior; predicted-to-actual overlap r = 0.42, p = .009). Our findings suggest that sMRI and restFC measures can be used to reliably distinguish the neural effects of long-term opioid use, and that this endeavor numerically benefits from multivariate predictive approaches and multi-modal feature sets. This can serve as theoretical proof-of-concept for future longitudinal modeling of prognostic POUD characteristics from neuroimaging features, which would have clearer clinical utility.
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Affiliation(s)
- Ravi D Mill
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA
| | - Emily C Winfield
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA
| | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA
| | - Suchismita Ray
- Department of Health Informatics, School of Health Professions, Rutgers Biomedical and Health Sciences, Newark, NJ 07103, USA.
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27
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Magnetic resonance imaging for individual prediction of treatment response in major depressive disorder: a systematic review and meta-analysis. Transl Psychiatry 2021; 11:168. [PMID: 33723229 PMCID: PMC7960732 DOI: 10.1038/s41398-021-01286-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 02/05/2021] [Accepted: 02/18/2021] [Indexed: 02/06/2023] Open
Abstract
No tools are currently available to predict whether a patient suffering from major depressive disorder (MDD) will respond to a certain treatment. Machine learning analysis of magnetic resonance imaging (MRI) data has shown potential in predicting response for individual patients, which may enable personalized treatment decisions and increase treatment efficacy. Here, we evaluated the accuracy of MRI-guided response prediction in MDD. We conducted a systematic review and meta-analysis of all studies using MRI to predict single-subject response to antidepressant treatment in patients with MDD. Classification performance was calculated using a bivariate model and expressed as area under the curve, sensitivity, and specificity. In addition, we analyzed differences in classification performance between different interventions and MRI modalities. Meta-analysis of 22 samples including 957 patients showed an overall area under the bivariate summary receiver operating curve of 0.84 (95% CI 0.81-0.87), sensitivity of 77% (95% CI 71-82), and specificity of 79% (95% CI 73-84). Although classification performance was higher for electroconvulsive therapy outcome prediction (n = 285, 80% sensitivity, 83% specificity) than medication outcome prediction (n = 283, 75% sensitivity, 72% specificity), there was no significant difference in classification performance between treatments or MRI modalities. Prediction of treatment response using machine learning analysis of MRI data is promising but should not yet be implemented into clinical practice. Future studies with more generalizable samples and external validation are needed to establish the potential of MRI to realize individualized patient care in MDD.
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28
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Guimarães RSQ, Bandeira ID, Barretto BL, Barretto TL, Wanke T, Alves COC, de Carvalho CF, Lucena PH, Rodrigues-Silva L, Lucena R. The effects of transcranial direct current stimulation on attention and inhibitory control of children and adolescents with attention-deficit/hyperactivity disorder (ADHD): Study protocol for a randomized, sham-controlled, triple-blind, cross-over trial. Medicine (Baltimore) 2021; 100:e24283. [PMID: 33663047 PMCID: PMC7909171 DOI: 10.1097/md.0000000000024283] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 12/21/2020] [Indexed: 11/25/2022] Open
Abstract
ABSTRACT Attention-deficit/hyperactivity disorder (ADHD) is characterized by a persistent pattern of inattention and hyperactivity/impulsivity. Despite the proven efficacy of pharmacological treatment, many individuals continue to suffer socially and academically and some experience significant side effects that negate the use psychotropic drugs. Transcranial direct current stimulation (tDCS) is a cortical neuromodulation feature that has shown positive results in the treatment of various neuropsychiatric conditions. OBJECTIVES To investigate the effect of tDCS on the performance of children and adolescents with ADHD in the neuropsychological tests of visual attention, verbal, and inhibitory control. METHODOLOGY Triple blind, randomized, sham-controlled, cross-over trial involving tDCS in children and adolescents with ADHD. Initial screening will be performed using Swanson, Nolan, and Pelham - IVand Wechsler intelligence scale for children fourth edition vocabulary and cube subtests. Individuals will be evaluated pre-tDCS and post-tDCS with the Wechsler intelligence scale for children fourth edition Digitus subtest, neuropsychological assessment battery second edition inhibiting responses subtest, Corsi cubes, and visual attention test-4.
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Affiliation(s)
| | - Igor D. Bandeira
- Postgraduate Program in Medicine and Health, Medical School of Bahia
- Laboratory of Neuropsychopharmacology, Psychiatry Division of the Professor Edgard Santos University Hospital
| | | | | | - Thamires Wanke
- Institute of Psychology, Federal University of Bahia, Salvador
| | | | | | | | | | - Rita Lucena
- Department of Neuroscience and Mental Health, Medical School of Bahia, Federal University of Bahia, Salvador, Brazil
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29
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Liloia D, Mancuso L, Uddin LQ, Costa T, Nani A, Keller R, Manuello J, Duca S, Cauda F. Gray matter abnormalities follow non-random patterns of co-alteration in autism: Meta-connectomic evidence. Neuroimage Clin 2021; 30:102583. [PMID: 33618237 PMCID: PMC7903137 DOI: 10.1016/j.nicl.2021.102583] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 12/15/2020] [Accepted: 01/30/2021] [Indexed: 02/06/2023]
Abstract
BACKGROUND Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by atypical brain anatomy and connectivity. Graph-theoretical methods have mainly been applied to detect altered patterns of white matter tracts and functional brain activation in individuals with ASD. The network topology of gray matter (GM) abnormalities in ASD remains relatively unexplored. METHODS An innovative meta-connectomic analysis on voxel-based morphometry data (45 experiments, 1,786 subjects with ASD) was performed in order to investigate whether GM variations can develop in a distinct pattern of co-alteration across the brain. This pattern was then compared with normative profiles of structural and genetic co-expression maps. Graph measures of centrality and clustering were also applied to identify brain areas with the highest topological hierarchy and core sub-graph components within the co-alteration network observed in ASD. RESULTS Individuals with ASD exhibit a distinctive and topologically defined pattern of GM co-alteration that moderately follows the structural connectivity constraints. This was not observed with respect to the pattern of genetic co-expression. Hub regions of the co-alteration network were mainly left-lateralized, encompassing the precuneus, ventral anterior cingulate, and middle occipital gyrus. Regions of the default mode network appear to be central in the topology of co-alterations. CONCLUSION These findings shed new light on the pathobiology of ASD, suggesting a network-level dysfunction among spatially distributed GM regions. At the same time, this study supports pathoconnectomics as an insightful approach to better understand neuropsychiatric disorders.
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Affiliation(s)
- Donato Liloia
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Lorenzo Mancuso
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, FL, USA; Neuroscience Program, University of Miami Miller School of Medicine, Miami, FL, USA.
| | - Tommaso Costa
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin (NIT), Turin, Italy.
| | - Andrea Nani
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Roberto Keller
- Adult Autism Center, DSM Local Health Unit, ASL TO, Turin, Italy.
| | - Jordi Manuello
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Sergio Duca
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy.
| | - Franco Cauda
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy; Functional Neuroimaging and Complex Neural Systems (FOCUS) Laboratory, Department of Psychology, University of Turin, Turin, Italy; Neuroscience Institute of Turin (NIT), Turin, Italy.
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30
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Todeva-Radneva A, Paunova R, Kandilarova S, St Stoyanov D. The Value of Neuroimaging Techniques in the Translation and Transdiagnostic Validation of Psychiatric Diagnoses - Selective Review. Curr Top Med Chem 2021; 20:540-553. [PMID: 32003690 DOI: 10.2174/1568026620666200131095328] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 12/09/2019] [Accepted: 12/12/2019] [Indexed: 01/05/2023]
Abstract
Psychiatric diagnosis has long been perceived as more of an art than a science since its foundations lie within the observation, and the self-report of the patients themselves and objective diagnostic biomarkers are lacking. Furthermore, the diagnostic tools in use not only stray away from the conventional medical framework but also remain invalidated with evidence-based concepts. However, neuroscience, as a source of valid objective knowledge has initiated the process of a paradigm shift underlined by the main concept of psychiatric disorders being "brain disorders". It is also a bridge closing the explanatory gap among the different fields of medicine via the translation of the knowledge within a multidisciplinary framework. The contemporary neuroimaging methods, such as fMRI provide researchers with an entirely new set of tools to reform the current status quo by creating an opportunity to define and validate objective biomarkers that can be translated into clinical practice. Combining multiple neuroimaging techniques with the knowledge of the role of genetic factors, neurochemical imbalance and neuroinflammatory processes in the etiopathophysiology of psychiatric disorders is a step towards a comprehensive biological explanation of psychiatric disorders and a final differentiation of psychiatry as a well-founded medical science. In addition, the neuroscientific knowledge gained thus far suggests a necessity for directional change to exploring multidisciplinary concepts, such as multiple causality and dimensionality of psychiatric symptoms and disorders. A concomitant viewpoint transition of the notion of validity in psychiatry with a focus on an integrative validatory approach may facilitate the building of a collaborative bridge above the wall existing between the scientific fields analyzing the mind and those studying the brain.
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Affiliation(s)
- Anna Todeva-Radneva
- Department of Psychiatry and Medical Psychology and Scientific Research Institute, The Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Rositsa Paunova
- Department of Psychiatry and Medical Psychology and Scientific Research Institute, The Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Sevdalina Kandilarova
- Department of Psychiatry and Medical Psychology and Scientific Research Institute, The Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Drozdstoy St Stoyanov
- Department of Psychiatry and Medical Psychology and Scientific Research Institute, The Medical University of Plovdiv, Plovdiv, Bulgaria
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31
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Yamashita A, Sakai Y, Yamada T, Yahata N, Kunimatsu A, Okada N, Itahashi T, Hashimoto R, Mizuta H, Ichikawa N, Takamura M, Okada G, Yamagata H, Harada K, Matsuo K, Tanaka SC, Kawato M, Kasai K, Kato N, Takahashi H, Okamoto Y, Yamashita O, Imamizu H. Common Brain Networks Between Major Depressive-Disorder Diagnosis and Symptoms of Depression That Are Validated for Independent Cohorts. Front Psychiatry 2021; 12:667881. [PMID: 34177657 PMCID: PMC8224760 DOI: 10.3389/fpsyt.2021.667881] [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: 02/16/2021] [Accepted: 05/12/2021] [Indexed: 12/02/2022] Open
Abstract
Large-scale neuroimaging data acquired and shared by multiple institutions are essential to advance neuroscientific understanding of pathophysiological mechanisms in psychiatric disorders, such as major depressive disorder (MDD). About 75% of studies that have applied machine learning technique to neuroimaging have been based on diagnoses by clinicians. However, an increasing number of studies have highlighted the difficulty in finding a clear association between existing clinical diagnostic categories and neurobiological abnormalities. Here, using resting-state functional magnetic resonance imaging, we determined and validated resting-state functional connectivity related to depression symptoms that were thought to be directly related to neurobiological abnormalities. We then compared the resting-state functional connectivity related to depression symptoms with that related to depression diagnosis that we recently identified. In particular, for the discovery dataset with 477 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a brain network prediction model of depression symptoms (Beck Depression Inventory-II [BDI] score). The prediction model significantly predicted BDI score for an independent validation dataset with 439 participants from 4 different imaging sites. Finally, we found 3 common functional connections between those related to depression symptoms and those related to MDD diagnosis. These findings contribute to a deeper understanding of the neural circuitry of depressive symptoms in MDD, a hetero-symptomatic population, revealing the neural basis of MDD.
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Affiliation(s)
- Ayumu Yamashita
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
| | - Yuki Sakai
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
| | - Takashi Yamada
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan.,Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Noriaki Yahata
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan.,Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.,Quantum Life Informatics Group, Institute for Quantum Life Science, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan.,Department of Molecular Imaging and Theranostics, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan
| | - Akira Kunimatsu
- Department of Radiology, The Institute of Medical Science The University of Tokyo (IMSUT) Hospital, Institute of Medical Science, The University of Tokyo, Tokyo, Japan.,Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Naohiro Okada
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.,The International Research Center for Neurointelligence (WPI-IRCN) at the University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, Japan
| | - Takashi Itahashi
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Ryuichiro Hashimoto
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan.,Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan.,Department of Language Sciences, Tokyo Metropolitan University, Tokyo, Japan
| | - Hiroto Mizuta
- Department of Psychiatry, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Naho Ichikawa
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Masahiro Takamura
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Go Okada
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Hirotaka Yamagata
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Yamaguchi, Japan
| | - Kenichiro Harada
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Yamaguchi, Japan
| | - Koji Matsuo
- Department of Psychiatry, Faculty of Medicine, Saitama Medical University, Saitama, Japan
| | - Saori C Tanaka
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
| | - Mitsuo Kawato
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan.,Center for Advanced Intelligence Project, Institute of Physical and Chemical Research (RIKEN), Tokyo, Japan
| | - Kiyoto Kasai
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan.,Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.,The International Research Center for Neurointelligence (WPI-IRCN) at the University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, Japan
| | - Nobumasa Kato
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan.,Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Hidehiko Takahashi
- Department of Psychiatry, Kyoto University Graduate School of Medicine, Kyoto, Japan.,Department of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yasumasa Okamoto
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Okito Yamashita
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan.,Center for Advanced Intelligence Project, Institute of Physical and Chemical Research (RIKEN), Tokyo, Japan
| | - Hiroshi Imamizu
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan.,Department of Psychology, Graduate School of Humanities and Sociology, The University of Tokyo, Tokyo, Japan
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32
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Yamashita A, Sakai Y, Yamada T, Yahata N, Kunimatsu A, Okada N, Itahashi T, Hashimoto R, Mizuta H, Ichikawa N, Takamura M, Okada G, Yamagata H, Harada K, Matsuo K, Tanaka SC, Kawato M, Kasai K, Kato N, Takahashi H, Okamoto Y, Yamashita O, Imamizu H. Generalizable brain network markers of major depressive disorder across multiple imaging sites. PLoS Biol 2020; 18:e3000966. [PMID: 33284797 PMCID: PMC7721148 DOI: 10.1371/journal.pbio.3000966] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 11/02/2020] [Indexed: 12/19/2022] Open
Abstract
Many studies have highlighted the difficulty inherent to the clinical application of fundamental neuroscience knowledge based on machine learning techniques. It is difficult to generalize machine learning brain markers to the data acquired from independent imaging sites, mainly due to large site differences in functional magnetic resonance imaging. We address the difficulty of finding a generalizable marker of major depressive disorder (MDD) that would distinguish patients from healthy controls based on resting-state functional connectivity patterns. For the discovery dataset with 713 participants from 4 imaging sites, we removed site differences using our recently developed harmonization method and developed a machine learning MDD classifier. The classifier achieved an approximately 70% generalization accuracy for an independent validation dataset with 521 participants from 5 different imaging sites. The successful generalization to a perfectly independent dataset acquired from multiple imaging sites is novel and ensures scientific reproducibility and clinical applicability.
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Affiliation(s)
- Ayumu Yamashita
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
| | - Yuki Sakai
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
| | - Takashi Yamada
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Noriaki Yahata
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Institute for Quantum Life Science, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan
- Department of Molecular Imaging and Theranostics, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan
| | - Akira Kunimatsu
- Department of Radiology, IMSUT Hospital, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Naohiro Okada
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- The International Research Center for Neurointelligence (WPI-IRCN) at the University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, Japan
| | - Takashi Itahashi
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Ryuichiro Hashimoto
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
- Department of Language Sciences, Tokyo Metropolitan University, Tokyo, Japan
| | - Hiroto Mizuta
- Department of Psychiatry, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Naho Ichikawa
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Masahiro Takamura
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Go Okada
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Hirotaka Yamagata
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Yamaguchi, Japan
| | - Kenichiro Harada
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Yamaguchi, Japan
| | - Koji Matsuo
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Yamaguchi, Japan
- Department of Psychiatry, Faculty of Medicine, Saitama Medical University, Saitama, Japan
| | - Saori C. Tanaka
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
| | - Mitsuo Kawato
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
| | - Kiyoto Kasai
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- The International Research Center for Neurointelligence (WPI-IRCN) at the University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, Japan
| | - Nobumasa Kato
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Hidehiko Takahashi
- Department of Psychiatry, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Department of Psychiatry and Behavioral Sciences, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yasumasa Okamoto
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Okito Yamashita
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
| | - Hiroshi Imamizu
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Department of Psychology, Graduate School of Humanities and Sociology, The University of Tokyo, Tokyo, Japan
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33
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Popovic D, Ruef A, Dwyer DB, Antonucci LA, Eder J, Sanfelici R, Kambeitz-Ilankovic L, Oztuerk OF, Dong MS, Paul R, Paolini M, Hedderich D, Haidl T, Kambeitz J, Ruhrmann S, Chisholm K, Schultze-Lutter F, Falkai P, Pergola G, Blasi G, Bertolino A, Lencer R, Dannlowski U, Upthegrove R, Salokangas RKR, Pantelis C, Meisenzahl E, Wood SJ, Brambilla P, Borgwardt S, Koutsouleris N. Traces of Trauma: A Multivariate Pattern Analysis of Childhood Trauma, Brain Structure, and Clinical Phenotypes. Biol Psychiatry 2020; 88:829-842. [PMID: 32782139 DOI: 10.1016/j.biopsych.2020.05.020] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Revised: 05/02/2020] [Accepted: 05/04/2020] [Indexed: 12/22/2022]
Abstract
BACKGROUND Childhood trauma (CT) is a major yet elusive psychiatric risk factor, whose multidimensional conceptualization and heterogeneous effects on brain morphology might demand advanced mathematical modeling. Therefore, we present an unsupervised machine learning approach to characterize the clinical and neuroanatomical complexity of CT in a larger, transdiagnostic context. METHODS We used a multicenter European cohort of 1076 female and male individuals (discovery: n = 649; replication: n = 427) comprising young, minimally medicated patients with clinical high-risk states for psychosis; patients with recent-onset depression or psychosis; and healthy volunteers. We employed multivariate sparse partial least squares analysis to detect parsimonious associations between combinations of items from the Childhood Trauma Questionnaire and gray matter volume and tested their generalizability via nested cross-validation as well as via external validation. We investigated the associations of these CT signatures with state (functioning, depressivity, quality of life), trait (personality), and sociodemographic levels. RESULTS We discovered signatures of age-dependent sexual abuse and sex-dependent physical and sexual abuse, as well as emotional trauma, which projected onto gray matter volume patterns in prefronto-cerebellar, limbic, and sensory networks. These signatures were associated with predominantly impaired clinical state- and trait-level phenotypes, while pointing toward an interaction between sexual abuse, age, urbanicity, and education. We validated the clinical profiles for all three CT signatures in the replication sample. CONCLUSIONS Our results suggest distinct multilayered associations between partially age- and sex-dependent patterns of CT, distributed neuroanatomical networks, and clinical profiles. Hence, our study highlights how machine learning approaches can shape future, more fine-grained CT research.
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Affiliation(s)
- David Popovic
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University of Munich, Munich, Germany; International Max Planck Research School for Translational Psychiatry, Max Planck Society, Munich, Germany
| | - Anne Ruef
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University of Munich, Munich, Germany
| | - Dominic B Dwyer
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University of Munich, Munich, Germany
| | - Linda A Antonucci
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University of Munich, Munich, Germany; Department of Education, Psychology and Communication, University of Bari Aldo Moro, Bari, Italy
| | - Julia Eder
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University of Munich, Munich, Germany
| | - Rachele Sanfelici
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University of Munich, Munich, Germany; Max Planck School of Cognition, Max Planck Schools, Leipzig, Germany
| | - Lana Kambeitz-Ilankovic
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University of Munich, Munich, Germany; Department of Psychiatry and Psychotherapy, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Omer Faruk Oztuerk
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University of Munich, Munich, Germany; International Max Planck Research School for Translational Psychiatry, Max Planck Society, Munich, Germany
| | - Mark S Dong
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University of Munich, Munich, Germany
| | - Riya Paul
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University of Munich, Munich, Germany; Max Planck Institute of Psychiatry, Max Planck Schools, Munich, Germany
| | - Marco Paolini
- Department of Radiology, Ludwig Maximilian University of Munich, Munich, Germany
| | - Dennis Hedderich
- Department of Diagnostic and Interventional Neuroradiology, Technical University of Munich, Munich, Germany
| | - Theresa Haidl
- Department of Psychiatry and Psychotherapy, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Joseph Kambeitz
- Department of Psychiatry and Psychotherapy, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Stephan Ruhrmann
- Department of Psychiatry and Psychotherapy, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Katharine Chisholm
- School of Psychology, University of Birmingham, Birmingham, United Kingdom; Department of Psychology, School of Life and Health Sciences, Aston University, Birmingham, United Kingdom
| | - Frauke Schultze-Lutter
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University of Munich, Munich, Germany
| | - Giulio Pergola
- Group of Psychiatric Neuroscience, Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Giuseppe Blasi
- Group of Psychiatric Neuroscience, Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Alessandro Bertolino
- Group of Psychiatric Neuroscience, Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Rebekka Lencer
- Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany
| | - Udo Dannlowski
- Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany
| | - Rachel Upthegrove
- School of Psychology, University of Birmingham, Birmingham, United Kingdom; Institute for Mental Health, University of Birmingham, Birmingham, United Kingdom
| | | | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Australia; Melbourne Health, Carlton South, Victoria, Australia
| | - Eva Meisenzahl
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Stephen J Wood
- School of Psychology, University of Birmingham, Birmingham, United Kingdom; Centre for Youth Mental Health, University of Melbourne, Melbourne, Australia; Orygen, the National Centre of Excellence for Youth Mental Health, Melbourne, Victoria, Australia
| | - Paolo Brambilla
- Department of Neurosciences and Mental Health, Fondazione IRCCS Cà Granda Ospedale Maggiore Policlinico, University of Milan, Milan, Italy
| | - Stefan Borgwardt
- Neuropsychiatry and Brain Imaging Group, Department of Psychiatry, University of Basel, Basel, Switzerland
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University of Munich, Munich, Germany; International Max Planck Research School for Translational Psychiatry, Max Planck Society, Munich, Germany.
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Bučková B, Brunovský M, Bareš M, Hlinka J. Predicting Sex From EEG: Validity and Generalizability of Deep-Learning-Based Interpretable Classifier. Front Neurosci 2020; 14:589303. [PMID: 33192274 PMCID: PMC7652844 DOI: 10.3389/fnins.2020.589303] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 09/17/2020] [Indexed: 11/13/2022] Open
Abstract
Explainable artificial intelligence holds a great promise for neuroscience and plays an important role in the hypothesis generation process. We follow-up a recent machine learning-oriented study that constructed a deep convolutional neural network to automatically identify biological sex from EEG recordings in healthy individuals and highlighted the discriminative role of beta-band power. If generalizing, this finding would be relevant not only theoretically by pointing to some specific neurobiological sexual dimorphisms, but potentially also as a relevant confound in quantitative EEG diagnostic practice. To put this finding to test, we assess whether the automatic identification of biological sex generalizes to another dataset, particularly in the presence of a psychiatric disease, by testing the hypothesis of higher beta power in women compared to men on 134 patients suffering from Major Depressive Disorder. Moreover, we construct ROC curves and compare the performance of the classifiers in determining sex both before and after the antidepressant treatment. We replicate the observation of a significant difference in beta-band power between men and women, providing classification accuracy of nearly 77%. The difference was consistent across the majority of electrodes, however multivariate classification models did not generally improve the performance. Similar results were observed also after the antidepressant treatment (classification accuracy above 70%), further supporting the robustness of the initial finding.
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Affiliation(s)
- Barbora Bučková
- Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czechia.,Department of Complex Systems, Institute of Computer Science of the Czech Academy of Sciences, Prague, Czechia
| | - Martin Brunovský
- National Institute of Mental Health, Klecany, Czechia.,Third Faculty of Medicine, Charles University, Prague, Czechia
| | - Martin Bareš
- National Institute of Mental Health, Klecany, Czechia.,Third Faculty of Medicine, Charles University, Prague, Czechia
| | - Jaroslav Hlinka
- Department of Complex Systems, Institute of Computer Science of the Czech Academy of Sciences, Prague, Czechia.,National Institute of Mental Health, Klecany, Czechia
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35
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Čukić M, López V, Pavón J. Classification of Depression Through Resting-State Electroencephalogram as a Novel Practice in Psychiatry: Review. J Med Internet Res 2020; 22:e19548. [PMID: 33141088 PMCID: PMC7671839 DOI: 10.2196/19548] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 07/19/2020] [Accepted: 09/04/2020] [Indexed: 12/28/2022] Open
Abstract
Background Machine learning applications in health care have increased considerably in the recent past, and this review focuses on an important application in psychiatry related to the detection of depression. Since the advent of computational psychiatry, research based on functional magnetic resonance imaging has yielded remarkable results, but these tools tend to be too expensive for everyday clinical use. Objective This review focuses on an affordable data-driven approach based on electroencephalographic recordings. Web-based applications via public or private cloud-based platforms would be a logical next step. We aim to compare several different approaches to the detection of depression from electroencephalographic recordings using various features and machine learning models. Methods To detect depression, we reviewed published detection studies based on resting-state electroencephalogram with final machine learning, and to predict therapy outcomes, we reviewed a set of interventional studies using some form of stimulation in their methodology. Results We reviewed 14 detection studies and 12 interventional studies published between 2008 and 2019. As direct comparison was not possible due to the large diversity of theoretical approaches and methods used, we compared them based on the steps in analysis and accuracies yielded. In addition, we compared possible drawbacks in terms of sample size, feature extraction, feature selection, classification, internal and external validation, and possible unwarranted optimism and reproducibility. In addition, we suggested desirable practices to avoid misinterpretation of results and optimism. Conclusions This review shows the need for larger data sets and more systematic procedures to improve the use of the solution for clinical diagnostics. Therefore, regulation of the pipeline and standard requirements for methodology used should become mandatory to increase the reliability and accuracy of the complete methodology for it to be translated to modern psychiatry.
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Affiliation(s)
- Milena Čukić
- HealthInc 3EGA, Amsterdam Health and Technology Institute, Amsterdam, Netherlands
| | - Victoria López
- Instituto de Tecnología del Conocimiento, Institute of Knowledge Technology, Universidad Complutense Madrid, Ciudad Universitaria s/n, 28040, Madrid, Spain
| | - Juan Pavón
- Instituto de Tecnología del Conocimiento, Institute of Knowledge Technology, Universidad Complutense Madrid, Ciudad Universitaria s/n, 28040, Madrid, Spain
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Spronk M, Keane BP, Ito T, Kulkarni K, Ji JL, Anticevic A, Cole MW. A Whole-Brain and Cross-Diagnostic Perspective on Functional Brain Network Dysfunction. Cereb Cortex 2020; 31:547-561. [PMID: 32909037 DOI: 10.1093/cercor/bhaa242] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Revised: 07/07/2020] [Accepted: 08/01/2020] [Indexed: 12/13/2022] Open
Abstract
A wide variety of mental disorders have been associated with resting-state functional network alterations, which are thought to contribute to the cognitive changes underlying mental illness. These observations appear to support theories postulating large-scale disruptions of brain systems in mental illness. However, existing approaches isolate differences in network organization without putting those differences in a broad, whole-brain perspective. Using a graph distance approach-connectome-wide similarity-we found that whole-brain resting-state functional network organization is highly similar across groups of individuals with and without a variety of mental diseases. This similarity was observed across autism spectrum disorder, attention-deficit hyperactivity disorder, and schizophrenia. Nonetheless, subtle differences in network graph distance were predictive of diagnosis, suggesting that while functional connectomes differ little across health and disease, those differences are informative. These results suggest a need to reevaluate neurocognitive theories of mental illness, with a role for subtle functional brain network changes in the production of an array of mental diseases. Such small network alterations suggest the possibility that small, well-targeted alterations to brain network organization may provide meaningful improvements for a variety of mental disorders.
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Affiliation(s)
- Marjolein Spronk
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA
| | - Brian P Keane
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA.,Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA
| | - Takuya Ito
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA
| | - Kaustubh Kulkarni
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA
| | - Jie Lisa Ji
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA
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37
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Giordano GM, Pezzella P, Perrottelli A, Galderisi S. Die "Präzisionspsychiatrie" muss Teil der "personalisierten Psychiatrie" werden. FORTSCHRITTE DER NEUROLOGIE-PSYCHIATRIE 2020; 88:767-772. [PMID: 32869236 DOI: 10.1055/a-1211-2826] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
'Precision medicine' is defined as 'an emerging approach for treatment and prevention that takes into account each person's variability in genes, environment, and lifestyle'. Sometimes the term 'personalized medicine' is also used, either as a synonym or in a broader sense. In psychiatry, the term 'personalized' applies to different levels of health-care provision, such as the service organization and the choice of treatment plans based on the characterization of the individual patient. This approach is already feasible but, currently, it is often hampered by the shortage of human and financial resources. Recently, the terminology of 'precision medicine' has been extended to psychiatry: the term 'precision psychiatry' refers to the full exploitation of recent scientific and technological advances to achieve a close match between individual biosignature and prevention / treatment strategies. This article provides an overview of recent advances in neuroimaging, multi-omics and computational neuroscience, which have contributed to foster our understanding of the neurobiology of major mental disorders, and led to the implementation of a precision medicine-oriented approach in psychiatry.We argue that, while 'precision psychiatry' represents an important step to further advance the effectiveness of the 'personalized psychiatry', the distinction between the two terms is important to avoid dangerous neglect of the current potential of personalized care in psychiatry and to underscore the need for disseminating good existing practices aimed at organizing mental health services and providing care according to person's psychopathological characteristics, illness trajectory, needs, environment and preferences.In conclusion, 'precision psychiatry' will contribute to advance 'personalized psychiatry', but for the time being keeping the distinction between the two terms will contribute to fully exploit the current potential of personalized care.
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38
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Nogay HS, Adeli H. Machine learning (ML) for the diagnosis of autism spectrum disorder (ASD) using brain imaging. Rev Neurosci 2020; 31:/j/revneuro.ahead-of-print/revneuro-2020-0043/revneuro-2020-0043.xml. [PMID: 32866134 DOI: 10.1515/revneuro-2020-0043] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Accepted: 07/25/2020] [Indexed: 02/24/2024]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental incurable disorder with a long diagnostic period encountered in the early years of life. If diagnosed early, the negative effects of this disease can be reduced by starting special education early. Machine learning (ML), an increasingly ubiquitous technology, can be applied for the early diagnosis of ASD. The aim of this study is to examine and provide a comprehensive state-of-the-art review of ML research for the diagnosis of ASD based on (a) structural magnetic resonance image (MRI), (b) functional MRI and (c) hybrid imaging techniques over the past decade. The accuracy of the studies with a large number of participants is in general lower than those with fewer participants leading to the conclusion that further large-scale studies are needed. An examination of the age of the participants shows that the accuracy of the automated diagnosis of ASD is higher at a younger age range. ML technology is expected to contribute significantly to the early and rapid diagnosis of ASD in the coming years and become available to clinicians in the near future. This review is aimed to facilitate that.
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Affiliation(s)
- Hidir Selcuk Nogay
- Department of Electrical and Energy, Kayseri University, Kayseri, Turkey
- The Ohio State University, Mathematical Bioscience Institute, Columbus, OH, USA
| | - Hojjat Adeli
- Departments of Biomedical Informatics and Neuroscience, The Ohio State University, Columbus, US
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Hirano S, Spencer KM, Onitsuka T, Hirano Y. Language-Related Neurophysiological Deficits in Schizophrenia. Clin EEG Neurosci 2020; 51:222-233. [PMID: 31741393 DOI: 10.1177/1550059419886686] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Schizophrenia is a severe psychiatric disorder that affects all aspects of one's life with several cognitive and social dysfunctions. However, there is still no objective and universal index for diagnosis and treatment of this disease. Many researchers have studied language processing in schizophrenia since most of the patients show symptoms related to language processing, such as thought disorder, auditory verbal hallucinations, or delusions. Electroencephalography (EEG) and magnetoencephalography (MEG) with millisecond order high temporal resolution, have been applied to reveal the abnormalities in language processing in schizophrenia. The aims of this review are (a) to provide an overview of recent findings in language processing in schizophrenia with EEG and MEG using neurophysiological indices, providing insights into underlying language related pathophysiological deficits in this disease and (b) to emphasize the advantage of EEG and MEG in research on language processing in schizophrenia.
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Affiliation(s)
- Shogo Hirano
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Higashiku, Fukuoka, Japan.,Neural Dynamics Laboratory, Research Service, VA Boston Healthcare System, and Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Kevin M Spencer
- Neural Dynamics Laboratory, Research Service, VA Boston Healthcare System, and Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Toshiaki Onitsuka
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Higashiku, Fukuoka, Japan
| | - Yoji Hirano
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Higashiku, Fukuoka, Japan.,Neural Dynamics Laboratory, Research Service, VA Boston Healthcare System, and Department of Psychiatry, Harvard Medical School, Boston, MA, USA
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40
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Brückl TM, Spoormaker VI, Sämann PG, Brem AK, Henco L, Czamara D, Elbau I, Grandi NC, Jollans L, Kühnel A, Leuchs L, Pöhlchen D, Schneider M, Tontsch A, Keck ME, Schilbach L, Czisch M, Lucae S, Erhardt A, Binder EB. The biological classification of mental disorders (BeCOME) study: a protocol for an observational deep-phenotyping study for the identification of biological subtypes. BMC Psychiatry 2020; 20:213. [PMID: 32393358 PMCID: PMC7216390 DOI: 10.1186/s12888-020-02541-z] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Accepted: 03/10/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND A major research finding in the field of Biological Psychiatry is that symptom-based categories of mental disorders map poorly onto dysfunctions in brain circuits or neurobiological pathways. Many of the identified (neuro) biological dysfunctions are "transdiagnostic", meaning that they do not reflect diagnostic boundaries but are shared by different ICD/DSM diagnoses. The compromised biological validity of the current classification system for mental disorders impedes rather than supports the development of treatments that not only target symptoms but also the underlying pathophysiological mechanisms. The Biological Classification of Mental Disorders (BeCOME) study aims to identify biology-based classes of mental disorders that improve the translation of novel biomedical findings into tailored clinical applications. METHODS BeCOME intends to include at least 1000 individuals with a broad spectrum of affective, anxiety and stress-related mental disorders as well as 500 individuals unaffected by mental disorders. After a screening visit, all participants undergo in-depth phenotyping procedures and omics assessments on two consecutive days. Several validated paradigms (e.g., fear conditioning, reward anticipation, imaging stress test, social reward learning task) are applied to stimulate a response in a basic system of human functioning (e.g., acute threat response, reward processing, stress response or social reward learning) that plays a key role in the development of affective, anxiety and stress-related mental disorders. The response to this stimulation is then read out across multiple levels. Assessments comprise genetic, molecular, cellular, physiological, neuroimaging, neurocognitive, psychophysiological and psychometric measurements. The multilevel information collected in BeCOME will be used to identify data-driven biologically-informed categories of mental disorders using cluster analytical techniques. DISCUSSION The novelty of BeCOME lies in the dynamic in-depth phenotyping and omics characterization of individuals with mental disorders from the depression and anxiety spectrum of varying severity. We believe that such biology-based subclasses of mental disorders will serve as better treatment targets than purely symptom-based disease entities, and help in tailoring the right treatment to the individual patient suffering from a mental disorder. BeCOME has the potential to contribute to a novel taxonomy of mental disorders that integrates the underlying pathomechanisms into diagnoses. TRIAL REGISTRATION Retrospectively registered on June 12, 2019 on ClinicalTrials.gov (TRN: NCT03984084).
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Affiliation(s)
- Tanja M. Brückl
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany
| | - Victor I. Spoormaker
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany
| | - Philipp G. Sämann
- grid.419548.50000 0000 9497 5095Max Planck Institute of Psychiatry, Munich, Germany
| | - Anna-Katharine Brem
- grid.419548.50000 0000 9497 5095Max Planck Institute of Psychiatry, Munich, Germany ,grid.38142.3c000000041936754XBerenson-Allen Center for Noninvasive Brain Stimulation and Division for Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA USA
| | - Lara Henco
- grid.419548.50000 0000 9497 5095Independent Max Planck Research Group for Social Neuroscience, Max Planck Institute of Psychiatry, Munich, Germany
| | - Darina Czamara
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany
| | - Immanuel Elbau
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany
| | - Norma C. Grandi
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany
| | - Lee Jollans
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany
| | - Anne Kühnel
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany ,grid.419548.50000 0000 9497 5095International Max Planck Research School – Translational Psychiatry (IMPRS-TP), Max Planck Institute of Psychiatry, Munich, Germany
| | - Laura Leuchs
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany
| | - Dorothee Pöhlchen
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany ,grid.419548.50000 0000 9497 5095International Max Planck Research School – Translational Psychiatry (IMPRS-TP), Max Planck Institute of Psychiatry, Munich, Germany
| | - Maximilian Schneider
- grid.419548.50000 0000 9497 5095Max Planck Institute of Psychiatry, Munich, Germany
| | - Alina Tontsch
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany
| | - Martin E. Keck
- grid.419548.50000 0000 9497 5095Max Planck Institute of Psychiatry, Munich, Germany
| | - Leonhard Schilbach
- grid.419548.50000 0000 9497 5095Independent Max Planck Research Group for Social Neuroscience, Max Planck Institute of Psychiatry, Munich, Germany
| | - Michael Czisch
- grid.419548.50000 0000 9497 5095Max Planck Institute of Psychiatry, Munich, Germany
| | - Susanne Lucae
- grid.419548.50000 0000 9497 5095Max Planck Institute of Psychiatry, Munich, Germany
| | - Angelika Erhardt
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany
| | - Elisabeth B. Binder
- grid.419548.50000 0000 9497 5095Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstr. 2-10, 80804 Munich, Germany ,grid.189967.80000 0001 0941 6502Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, USA
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Gilson M, Zamora-López G, Pallarés V, Adhikari MH, Senden M, Campo AT, Mantini D, Corbetta M, Deco G, Insabato A. Model-based whole-brain effective connectivity to study distributed cognition in health and disease. Netw Neurosci 2020; 4:338-373. [PMID: 32537531 PMCID: PMC7286310 DOI: 10.1162/netn_a_00117] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 12/02/2019] [Indexed: 12/12/2022] Open
Abstract
Neuroimaging techniques are now widely used to study human cognition. The functional associations between brain areas have become a standard proxy to describe how cognitive processes are distributed across the brain network. Among the many analysis tools available, dynamic models of brain activity have been developed to overcome the limitations of original connectivity measures such as functional connectivity. This goes in line with the many efforts devoted to the assessment of directional interactions between brain areas from the observed neuroimaging activity. This opinion article provides an overview of our model-based whole-brain effective connectivity to analyze fMRI data, while discussing the pros and cons of our approach with respect to other established approaches. Our framework relies on the multivariate Ornstein-Uhlenbeck (MOU) process and is thus referred to as MOU-EC. Once tuned, the model provides a directed connectivity estimate that reflects the dynamical state of BOLD activity, which can be used to explore cognition. We illustrate this approach using two applications on task-evoked fMRI data. First, as a connectivity measure, MOU-EC can be used to extract biomarkers for task-specific brain coordination, understood as the patterns of areas exchanging information. The multivariate nature of connectivity measures raises several challenges for whole-brain analysis, for which machine-learning tools present some advantages over statistical testing. Second, we show how to interpret changes in MOU-EC connections in a collective and model-based manner, bridging with network analysis. Our framework provides a comprehensive set of tools that open exciting perspectives to study distributed cognition, as well as neuropathologies.
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Affiliation(s)
- Matthieu Gilson
- Center for Brain and Cognition and Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Gorka Zamora-López
- Center for Brain and Cognition and Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Vicente Pallarés
- Center for Brain and Cognition and Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Mohit H. Adhikari
- Center for Brain and Cognition and Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Mario Senden
- Department of Cognitive Neuroscience, University of Maastricht, Maastricht, The Netherlands
| | | | - Dante Mantini
- Neuroplasticity and Motor Control Research Group, KU Leuven, Leuven, Belgium
- Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy
| | - Maurizio Corbetta
- Department of Neuroscience, Venetian Institute of Molecular Medicine (VIMM) and Padova Neuroscience Center (PNC), University of Padua, Italy
- Department of Neurology, Radiology, and Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
| | - Gustavo Deco
- Center for Brain and Cognition and Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Andrea Insabato
- Institut de Neurosciences de la Timone, CNRS, Marseille, France
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Chattun MR, Zhang S, Chen Y, Wang Q, Amdanee N, Tian S, Lu Q, Yao Z. Caudothalamic dysfunction in drug-free suicidally depressed patients: an MEG study. Eur Arch Psychiatry Clin Neurosci 2020; 270:217-227. [PMID: 30552507 DOI: 10.1007/s00406-018-0968-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2018] [Accepted: 12/06/2018] [Indexed: 12/16/2022]
Abstract
Major depressive disorder (MDD), characterized by low mood or anhedonia, is commonly associated with a greater suicidal susceptibility. There are numerous suicide-related findings pertaining to the dorsolateral prefrontal cortex (DLPFC), caudate nucleus and thalamus, which form a cortico-striato-thalamo-cortical (CSTC) circuit responsible for executive function and working memory. An aberrant CSTC circuitry is hypothesized to be implicated in depressed patients with a high suicidal risk. 27 MDD patients were assessed with the Nurses Global Assessment of Suicide Risk (NGASR), following which 14 patients were classified into a high suicide risk group (NGASR ≥ 12) and 13 patients were assigned to a low suicide risk group (NGASR < 6). All 27 patients were enrolled with 25 healthy controls for resting-state magnetoencephalography (MEG). Cross-frequency coupling (CFC) measured the phase of alpha-band (8-13 Hz) as it modulated to cortical gamma-band (30-48 Hz). There was a significantly lower alpha-to-gamma phase-amplitude coupling (PAC) between the right caudate and left thalamus in high-risk suicide group compared to both the low-risk suicide group and healthy controls. The presence of a weaker coupling between the right caudate and left thalamus is indicative of a caudothalamic abnormality in suicidally depressed patients. This implies that a disruption of CSTC loop could result in executive dysfunction and working memory impairment, leading to an increased suicidal risk in MDD patients. In the future, this preliminary study has the possibility of being replicated on a larger scale, and hence validates caudothalamic dysfunction as a reliable neuroimaging biomarker for suicide in depression.
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Affiliation(s)
- Mohammad Ridwan Chattun
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, No. 264 Guangzhou Road, Nanjing, 210029, China
| | - Siqi Zhang
- School of Biological Sciences and Medical Engineering, Southeast University, No. 2 Sipailou, Nanjing, 210096, China.,Key Laboratory of Child Development and Learning Science, Southeast University, Nanjing, 210096, China
| | - Yu Chen
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, No. 264 Guangzhou Road, Nanjing, 210029, China
| | - Qiang Wang
- Medical School of Nanjing University, Nanjing Brain Hospital, 22 Hankou Road, Nanjing, 210093, China
| | - Nousayhah Amdanee
- Department of Geriatrics, Jiangsu Province Hospital Affiliated to Nanjing Medical University, Nanjing, 210029, China
| | - Shui Tian
- School of Biological Sciences and Medical Engineering, Southeast University, No. 2 Sipailou, Nanjing, 210096, China.,Key Laboratory of Child Development and Learning Science, Southeast University, Nanjing, 210096, China
| | - Qing Lu
- School of Biological Sciences and Medical Engineering, Southeast University, No. 2 Sipailou, Nanjing, 210096, China. .,Key Laboratory of Child Development and Learning Science, Southeast University, Nanjing, 210096, China.
| | - Zhijian Yao
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, No. 264 Guangzhou Road, Nanjing, 210029, China. .,Medical School of Nanjing University, Nanjing Brain Hospital, 22 Hankou Road, Nanjing, 210093, China.
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43
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Recommendations and future directions for supervised machine learning in psychiatry. Transl Psychiatry 2019; 9:271. [PMID: 31641106 PMCID: PMC6805872 DOI: 10.1038/s41398-019-0607-2] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Revised: 07/05/2019] [Accepted: 07/30/2019] [Indexed: 12/22/2022] Open
Abstract
Machine learning methods hold promise for personalized care in psychiatry, demonstrating the potential to tailor treatment decisions and stratify patients into clinically meaningful taxonomies. Subsequently, publication counts applying machine learning methods have risen, with different data modalities, mathematically distinct models, and samples of varying size being used to train and test models with the promise of clinical translation. Consequently, and in part due to the preliminary nature of such works, many studies have reported largely varying degrees of accuracy, raising concerns over systematic overestimation and methodological inconsistencies. Furthermore, a lack of procedural evaluation guidelines for non-expert medical professionals and funding bodies leaves many in the field with no means to systematically evaluate the claims, maturity, and clinical readiness of a project. Given the potential of machine learning methods to transform patient care, albeit, contingent on the rigor of employed methods and their dissemination, we deem it necessary to provide a review of current methods, recommendations, and future directions for applied machine learning in psychiatry. In this review we will cover issues of best practice for model training and evaluation, sources of systematic error and overestimation, model explainability vs. trust, the clinical implementation of AI systems, and finally, future directions for our field.
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Clinical-learning versus machine-learning for transdiagnostic prediction of psychosis onset in individuals at-risk. Transl Psychiatry 2019; 9:259. [PMID: 31624229 PMCID: PMC6797779 DOI: 10.1038/s41398-019-0600-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2019] [Revised: 05/03/2019] [Accepted: 05/31/2019] [Indexed: 02/08/2023] Open
Abstract
Predicting the onset of psychosis in individuals at-risk is based on robust prognostic model building methods including a priori clinical knowledge (also termed clinical-learning) to preselect predictors or machine-learning methods to select predictors automatically. To date, there is no empirical research comparing the prognostic accuracy of these two methods for the prediction of psychosis onset. In a first experiment, no improved performance was observed when machine-learning methods (LASSO and RIDGE) were applied-using the same predictors-to an individualised, transdiagnostic, clinically based, risk calculator previously developed on the basis of clinical-learning (predictors: age, gender, age by gender, ethnicity, ICD-10 diagnostic spectrum), and externally validated twice. In a second experiment, two refined versions of the published model which expanded the granularity of the ICD-10 diagnosis were introduced: ICD-10 diagnostic categories and ICD-10 diagnostic subdivisions. Although these refined versions showed an increase in apparent performance, their external performance was similar to the original model. In a third experiment, the three refined models were analysed under machine-learning and clinical-learning with a variable event per variable ratio (EPV). The best performing model under low EPVs was obtained through machine-learning approaches. The development of prognostic models on the basis of a priori clinical knowledge, large samples and adequate events per variable is a robust clinical prediction method to forecast psychosis onset in patients at-risk, and is comparable to machine-learning methods, which are more difficult to interpret and implement. Machine-learning methods should be preferred for high dimensional data when no a priori knowledge is available.
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45
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Chiba T, Kanazawa T, Koizumi A, Ide K, Taschereau-Dumouchel V, Boku S, Hishimoto A, Shirakawa M, Sora I, Lau H, Yoneda H, Kawato M. Current Status of Neurofeedback for Post-traumatic Stress Disorder: A Systematic Review and the Possibility of Decoded Neurofeedback. Front Hum Neurosci 2019; 13:233. [PMID: 31379538 PMCID: PMC6650780 DOI: 10.3389/fnhum.2019.00233] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 06/25/2019] [Indexed: 11/13/2022] Open
Abstract
Background: Post-traumatic stress disorder (PTSD) is a neuropsychiatric affective disorder that can develop after traumatic life-events. Exposure-based therapy is currently one of the most effective treatments for PTSD. However, exposure to traumatic stimuli is so aversive that a significant number of patients drop-out of therapy during the course of treatment. Among various attempts to develop novel therapies that bypass such aversiveness, neurofeedback appears promising. With neurofeedback, patients can unconsciously self-regulate brain activity via real-time monitoring and feedback of the EEG or fMRI signals. With conventional neurofeedback methods, however, it is difficult to induce neural representation related to specific trauma because the feedback is based on the neural signals averaged within specific brain areas. To overcome this difficulty, novel neurofeedback approaches such as Decoded Neurofeedback (DecNef) might prove helpful. Instead of the average BOLD signals, DecNef allows patients to implicitly regulate multivariate voxel patterns of the BOLD signals related with feared stimuli. As such, DecNef effects are postulated to derive either from exposure or counter-conditioning, or some combination of both. Although the exact mechanism is not yet fully understood. DecNef has been successfully applied to reduce fear responses induced either by fear-conditioned or phobic stimuli among non-clinical participants. Methods: Follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic review was conducted to compare DecNef effect with those of conventional EEG/fMRI-based neurofeedback on PTSD amelioration. To elucidate the possible mechanisms of DecNef on fear reduction, we mathematically modeled the effects of exposure-based and counter conditioning separately and applied it to the data obtained from past DecNef studies. Finally, we conducted DecNef on four PTSD patients. Here, we review recent advances in application of neurofeedback to PTSD treatments, including the DecNef. This review is intended to be informative for neuroscientists in general as well as practitioners planning to use neurofeedback as a therapeutic strategy for PTSD. Results: Our mathematical model suggested that exposure is the key component for DecNef effects in the past studies. Following DecNef a significant reduction of PTSD severity was observed. This effect was comparable to those reported for conventional neurofeedback approach. Conclusions: Although a much larger number of participants will be needed in future, DecNef could be a promising therapy that bypasses the unpleasantness of conscious exposure associated with conventional therapies for fear related disorders, including PTSD.
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Affiliation(s)
- Toshinori Chiba
- Computational Neuroscience Laboratories, Department of Decoded Neurofeedback, Advanced Telecommunications Research Institute International, Kyoto, Japan.,Department of Psychiatry, Graduate School of Medicine, Kobe University, Kobe, Japan
| | - Tetsufumi Kanazawa
- Department of Neuropsychiatry, Osaka Medical College, Osaka, Japan.,The Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia
| | - Ai Koizumi
- Sony Computer Science Laboratories, Inc., Tokyo, Japan
| | - Kentarou Ide
- Computational Neuroscience Laboratories, Department of Decoded Neurofeedback, Advanced Telecommunications Research Institute International, Kyoto, Japan.,Flower of Light Clinic for Mind and Body, Tokyo, Japan
| | - Vincent Taschereau-Dumouchel
- Computational Neuroscience Laboratories, Department of Decoded Neurofeedback, Advanced Telecommunications Research Institute International, Kyoto, Japan.,Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Shuken Boku
- Department of Psychiatry, Graduate School of Medicine, Kobe University, Kobe, Japan
| | - Akitoyo Hishimoto
- Department of Psychiatry, Graduate School of Medicine, Kobe University, Kobe, Japan
| | | | - Ichiro Sora
- Department of Psychiatry, Graduate School of Medicine, Kobe University, Kobe, Japan
| | - Hakwan Lau
- Computational Neuroscience Laboratories, Department of Decoded Neurofeedback, Advanced Telecommunications Research Institute International, Kyoto, Japan.,Department of Psychology, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Brain Research Institute, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Psychology, University of Hong Kong, Pokfulam, Hong Kong
| | - Hiroshi Yoneda
- Department of Neuropsychiatry, Osaka Medical College, Osaka, Japan
| | - Mitsuo Kawato
- Computational Neuroscience Laboratories, Department of Decoded Neurofeedback, Advanced Telecommunications Research Institute International, Kyoto, Japan.,RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan
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46
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Chen J, Liu J, Calhoun VD. The Translational Potential of Neuroimaging Genomic Analyses To Diagnosis And Treatment In The Mental Disorders. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2019; 107:912-927. [PMID: 32051642 PMCID: PMC7015534 DOI: 10.1109/jproc.2019.2913145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Imaging genomics focuses on characterizing genomic influence on the variation of neurobiological traits, holding promise for illuminating the pathogenesis, reforming the diagnostic system, and precision medicine of mental disorders. This paper aims to provide an overall picture of the current status of neuroimaging-genomic analyses in mental disorders, and how we can increase their translational potential into clinical practice. The review is organized around three perspectives. (a) Towards reliability, generalizability and interpretability, where we summarize the multivariate models and discuss the considerations and trade-offs of using these methods and how reliable findings may be reached, to serve as ground for further delineation. (b) Towards improved diagnosis, where we outline the advantages and challenges of constructing a dimensional transdiagnostic model and how imaging genomic analyses map into this framework to aid in deconstructing heterogeneity and achieving an optimal stratification of patients that better inform treatment planning. (c) Towards improved treatment. Here we highlight recent efforts and progress in elucidating the functional annotations that bridge between genomic risk and neurobiological abnormalities, in detecting genomic predisposition and prodromal neurodevelopmental changes, as well as in identifying imaging genomic biomarkers for predicting treatment response. Providing an overview of the challenges and promises, this review hopefully motivates imaging genomic studies with multivariate, dimensional and transdiagnostic designs for generalizable and interpretable findings that facilitate development of personalized treatment.
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Affiliation(s)
- Jiayu Chen
- The Mind Research Network, Albuquerque, NM 87106 USA
| | - Jingyu Liu
- The Mind Research Network, Albuquerque, NM 87106 USA, and also with the Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131 USA
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM 87106 USA, and also with the Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131 USA
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47
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Mak KK, Lee K, Park C. Applications of machine learning in addiction studies: A systematic review. Psychiatry Res 2019; 275:53-60. [PMID: 30878857 DOI: 10.1016/j.psychres.2019.03.001] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Revised: 03/02/2019] [Accepted: 03/02/2019] [Indexed: 02/09/2023]
Abstract
This study aims to provide a systematic review of the applications of machine learning methods in addiction research. In this study, multiple searches on MEDLINE, Embase and the Cochrane Database of Systematic Reviews were performed. 23 full-text articles were assessed and 17 articles met the inclusion criteria for the final review. The selected studies covered mainly substance addiction (N = 14, 82.4%), including smoking (N = 4), alcohol drinking (N = 3), as well as uses of cocaine (N = 4), opioids (N = 1), and multiple substances (N = 2). Other studies were non-substance addiction (N = 3, 17.6%), including gambling (N = 2) and internet gaming (N = 1). There were eight cross-sectional, seven cohort, one non-randomized controlled, and one crossover trial studies. Majority of the studies employed supervised learning (N = 13), and others employed unsupervised learning (N = 2) and reinforcement learning (N = 2). Among the supervised learning studies, five studies used ensemble learning methods or multiple algorithm comparisons, six used regression, and two used classification. The two included reinforcement learning studies used the direct methods. These results suggest that machine learning methods, particularly supervised learning are increasingly used in addiction psychiatry for informing medical decisions.
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Affiliation(s)
- Kwok Kei Mak
- Department of Statistics, Keimyung University, Republic of Korea.
| | - Kounseok Lee
- Department of Psychiatry, Hanyang University Hospital, Hanyang University, Republic of Korea
| | - Cheolyong Park
- Department of Statistics, Keimyung University, Republic of Korea
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48
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Yamashita A, Yahata N, Itahashi T, Lisi G, Yamada T, Ichikawa N, Takamura M, Yoshihara Y, Kunimatsu A, Okada N, Yamagata H, Matsuo K, Hashimoto R, Okada G, Sakai Y, Morimoto J, Narumoto J, Shimada Y, Kasai K, Kato N, Takahashi H, Okamoto Y, Tanaka SC, Kawato M, Yamashita O, Imamizu H. Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias. PLoS Biol 2019; 17:e3000042. [PMID: 30998673 PMCID: PMC6472734 DOI: 10.1371/journal.pbio.3000042] [Citation(s) in RCA: 85] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2018] [Accepted: 03/14/2019] [Indexed: 01/07/2023] Open
Abstract
When collecting large amounts of neuroimaging data associated with psychiatric disorders, images must be acquired from multiple sites because of the limited capacity of a single site. However, site differences represent a barrier when acquiring multisite neuroimaging data. We utilized a traveling-subject dataset in conjunction with a multisite, multidisorder dataset to demonstrate that site differences are composed of biological sampling bias and engineering measurement bias. The effects on resting-state functional MRI connectivity based on pairwise correlations because of both bias types were greater than or equal to psychiatric disorder differences. Furthermore, our findings indicated that each site can sample only from a subpopulation of participants. This result suggests that it is essential to collect large amounts of neuroimaging data from as many sites as possible to appropriately estimate the distribution of the grand population. Finally, we developed a novel harmonization method that removed only the measurement bias by using a traveling-subject dataset and achieved the reduction of the measurement bias by 29% and improvement of the signal-to-noise ratios by 40%. Our results provide fundamental knowledge regarding site effects, which is important for future research using multisite, multidisorder resting-state functional MRI data.
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Affiliation(s)
- Ayumu Yamashita
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- * E-mail: (HI); (OY); or (AY)
| | - Noriaki Yahata
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Department of Molecular Imaging and Theranostics, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan
| | - Takashi Itahashi
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Giuseppe Lisi
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
| | - Takashi Yamada
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Naho Ichikawa
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Masahiro Takamura
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Yujiro Yoshihara
- Department of Psychiatry, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Akira Kunimatsu
- Department of Radiology, IMSUT Hospital, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Naohiro Okada
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- The International Research Center for Neurointelligence (WPI-IRCN) at the University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, Japan
| | - Hirotaka Yamagata
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Yamaguchi, Japan
| | - Koji Matsuo
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Yamaguchi, Japan
- Department of Psychiatry, Faculty of Medicine, Saitama Medical University, Saitama, Japan
| | - Ryuichiro Hashimoto
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
- Department of Language Sciences, Tokyo Metropolitan University, Tokyo, Japan
| | - Go Okada
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Yuki Sakai
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Jun Morimoto
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
| | - Jin Narumoto
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Yasuhiro Shimada
- Brain Activity Imaging Center, ATR-Promotions Inc., Kyoto, Japan
| | - Kiyoto Kasai
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- The International Research Center for Neurointelligence (WPI-IRCN) at the University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, Japan
| | - Nobumasa Kato
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Hidehiko Takahashi
- Department of Psychiatry, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yasumasa Okamoto
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Saori C. Tanaka
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
| | - Mitsuo Kawato
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
| | - Okito Yamashita
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
- * E-mail: (HI); (OY); or (AY)
| | - Hiroshi Imamizu
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Department of Psychology, Graduate School of Humanities and Sociology, The University of Tokyo, Tokyo, Japan
- * E-mail: (HI); (OY); or (AY)
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49
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Popovych OV, Manos T, Hoffstaedter F, Eickhoff SB. What Can Computational Models Contribute to Neuroimaging Data Analytics? Front Syst Neurosci 2019; 12:68. [PMID: 30687028 PMCID: PMC6338060 DOI: 10.3389/fnsys.2018.00068] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Accepted: 12/17/2018] [Indexed: 01/12/2023] Open
Abstract
Over the past years, nonlinear dynamical models have significantly contributed to the general understanding of brain activity as well as brain disorders. Appropriately validated and optimized mathematical models can be used to mechanistically explain properties of brain structure and neuronal dynamics observed from neuroimaging data. A thorough exploration of the model parameter space and hypothesis testing with the methods of nonlinear dynamical systems and statistical physics can assist in classification and prediction of brain states. On the one hand, such a detailed investigation and systematic parameter variation are hardly feasible in experiments and data analysis. On the other hand, the model-based approach can establish a link between empirically discovered phenomena and more abstract concepts of attractors, multistability, bifurcations, synchronization, noise-induced dynamics, etc. Such a mathematical description allows to compare and differentiate brain structure and dynamics in health and disease, such that model parameters and dynamical regimes may serve as additional biomarkers of brain states and behavioral modes. In this perspective paper we first provide very brief overview of the recent progress and some open problems in neuroimaging data analytics with emphasis on the resting state brain activity. We then focus on a few recent contributions of mathematical modeling to our understanding of the brain dynamics and model-based approaches in medicine. Finally, we discuss the question stated in the title. We conclude that incorporating computational models in neuroimaging data analytics as well as in translational medicine could significantly contribute to the progress in these fields.
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Affiliation(s)
- Oleksandr V. Popovych
- 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
| | - Thanos Manos
- 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
| | - Felix Hoffstaedter
- 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
| | - Simon B. Eickhoff
- 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
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50
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Kato T, Kanba S. Making psychiatry a clinical neuroscience-based medicine. Psychiatry Clin Neurosci 2019; 73:1. [PMID: 30609238 DOI: 10.1111/pcn.12798] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/11/2018] [Accepted: 11/11/2018] [Indexed: 12/23/2022]
Affiliation(s)
- Tadafumi Kato
- Laboratory for Molecular Dynamics of Mental Disorders, RIKEN Center for Brain Science, Saitama, Japan
| | - Shigenobu Kanba
- Department of Neuropsychiatry, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
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