1
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Chen Y, Zhao W, Yi S, Liu J. The diagnostic performance of machine learning based on resting-state functional magnetic resonance imaging data for major depressive disorders: a systematic review and meta-analysis. Front Neurosci 2023; 17:1174080. [PMID: 37811326 PMCID: PMC10559726 DOI: 10.3389/fnins.2023.1174080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 08/11/2023] [Indexed: 10/10/2023] Open
Abstract
Objective Machine learning (ML) has been widely used to detect and evaluate major depressive disorder (MDD) using neuroimaging data, i.e., resting-state functional magnetic resonance imaging (rs-fMRI). However, the diagnostic efficiency is unknown. The aim of the study is to conduct an updated meta-analysis to evaluate the diagnostic performance of ML based on rs-fMRI data for MDD. Methods English databases were searched for relevant studies. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) was used to assess the methodological quality of the included studies. A random-effects meta-analytic model was implemented to investigate the diagnostic efficiency, including sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC). Regression meta-analysis and subgroup analysis were performed to investigate the cause of heterogeneity. Results Thirty-one studies were included in this meta-analysis. The pooled sensitivity, specificity, DOR, and AUC with 95% confidence intervals were 0.80 (0.75, 0.83), 0.83 (0.74, 0.82), 14.00 (9, 22.00), and 0.86 (0.83, 0.89), respectively. Substantial heterogeneity was observed among the studies included. The meta-regression showed that the leave-one-out cross-validation (loocv) (sensitivity: p < 0.01, specificity: p < 0.001), graph theory (sensitivity: p < 0.05, specificity: p < 0.01), n > 100 (sensitivity: p < 0.001, specificity: p < 0.001), simens equipment (sensitivity: p < 0.01, specificity: p < 0.001), 3.0T field strength (Sensitivity: p < 0.001, specificity: p = 0.04), and Beck Depression Inventory (BDI) (sensitivity: p = 0.04, specificity: p = 0.06) might be the sources of heterogeneity. Furthermore, the subgroup analysis showed that the sample size (n > 100: sensitivity: 0.71, specificity: 0.72, n < 100: sensitivity: 0.81, specificity: 0.79), the different levels of disease evaluated by the Hamilton Depression Rating Scale (HDRS/HAMD) (mild vs. moderate vs. severe: sensitivity: 0.52 vs. 0.86 vs. 0.89, specificity: 0.62 vs. 0.78 vs. 0.82, respectively), the depression scales in patients with comparable levels of severity. (BDI vs. HDRS/HAMD: sensitivity: 0.86 vs. 0.87, specificity: 0.78 vs. 0.80, respectively), and the features (graph vs. functional connectivity: sensitivity: 0.84 vs. 0.86, specificity: 0.76 vs. 0.78, respectively) selected might be the causes of heterogeneity. Conclusion ML showed high accuracy for the automatic diagnosis of MDD. Future studies are warranted to promote the potential use of these classification algorithms in clinical settings.
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Affiliation(s)
- Yanjing Chen
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Wei Zhao
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha, Hunan, China
| | - Sijie Yi
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Jun Liu
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha, Hunan, China
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2
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Luo L, You W, DelBello MP, Gong Q, Li F. Recent advances in psychoradiology. Phys Med Biol 2022; 67. [PMID: 36279868 DOI: 10.1088/1361-6560/ac9d1e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 10/24/2022] [Indexed: 11/24/2022]
Abstract
Abstract
Psychiatry, as a field, lacks objective markers for diagnosis, progression, treatment planning, and prognosis, in part due to difficulties studying the brain in vivo, and diagnoses are based on self-reported symptoms and observation of patient behavior and cognition. Rapid advances in brain imaging techniques allow clinical investigators to noninvasively quantify brain features at the structural, functional, and molecular levels. Psychoradiology is an emerging discipline at the intersection of psychiatry and radiology. Psychoradiology applies medical imaging technologies to psychiatry and promises not only to improve insight into structural and functional brain abnormalities in patients with psychiatric disorders but also to have potential clinical utility. We searched for representative studies related to recent advances in psychoradiology through May 1, 2022, and conducted a selective review of 165 references, including 75 research articles. We summarize the novel dynamic imaging processing methods to model brain networks and present imaging genetics studies that reveal the relationship between various neuroimaging endophenotypes and genetic markers in psychiatric disorders. Furthermore, we survey recent advances in psychoradiology, with a focus on future psychiatric diagnostic approaches with dimensional analysis and a shift from group-level to individualized analysis. Finally, we examine the application of machine learning in psychoradiology studies and the potential of a novel option for brain stimulation treatment based on psychoradiological findings in precision medicine. Here, we provide a summary of recent advances in psychoradiology research, and we hope this review will help guide the practice of psychoradiology in the scientific and clinical fields.
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3
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Tong X, Xie H, Carlisle N, Fonzo GA, Oathes DJ, Jiang J, Zhang Y. Transdiagnostic connectome signatures from resting-state fMRI predict individual-level intellectual capacity. Transl Psychiatry 2022; 12:367. [PMID: 36068228 PMCID: PMC9448815 DOI: 10.1038/s41398-022-02134-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 08/18/2022] [Accepted: 08/22/2022] [Indexed: 11/22/2022] Open
Abstract
Medication and other therapies for psychiatric disorders show unsatisfying efficacy, in part due to the significant clinical/ biological heterogeneity within each disorder and our over-reliance on categorical clinical diagnoses. Alternatively, dimensional transdiagnostic studies have provided a promising pathway toward realizing personalized medicine and improved treatment outcomes. One factor that may influence response to psychiatric treatments is cognitive function, which is reflected in one's intellectual capacity. Intellectual capacity is also reflected in the organization and structure of intrinsic brain networks. Using a large transdiagnostic cohort (n = 1721), we sought to discover neuroimaging biomarkers by developing a resting-state functional connectome-based prediction model for a key intellectual capacity measure, Full-Scale Intelligence Quotient (FSIQ), across the diagnostic spectrum. Our cross-validated model yielded an excellent prediction accuracy (r = 0.5573, p < 0.001). The robustness and generalizability of our model was further validated on three independent cohorts (n = 2641). We identified key transdiagnostic connectome signatures underlying FSIQ capacity involving the dorsal-attention, frontoparietal and default-mode networks. Meanwhile, diagnosis groups showed disorder-specific biomarker patterns. Our findings advance the neurobiological understanding of cognitive functioning across traditional diagnostic categories and provide a new avenue for neuropathological classification of psychiatric disorders.
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Affiliation(s)
- Xiaoyu Tong
- grid.259029.50000 0004 1936 746XDepartment of Bioengineering, Lehigh University, Bethlehem, PA USA
| | - Hua Xie
- grid.164295.d0000 0001 0941 7177Department of Psychology, University of Maryland, College Park, MD USA
| | - Nancy Carlisle
- grid.259029.50000 0004 1936 746XDepartment of Psychology, Lehigh University, Bethlehem, PA USA
| | - Gregory A. Fonzo
- grid.89336.370000 0004 1936 9924Center for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, Austin, TX USA
| | - Desmond J. Oathes
- grid.25879.310000 0004 1936 8972Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA USA
| | - Jing Jiang
- grid.214572.70000 0004 1936 8294Departments of Pediatrics and Psychiatry, Carver College of Medicine, University of Iowa, Iowa, IA USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA.
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4
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Strigo IA, Spadoni AD, Simmons AN. Understanding Pain and Trauma Symptoms in Veterans From Resting-State Connectivity: Unsupervised Modeling. FRONTIERS IN PAIN RESEARCH 2022; 3:871961. [PMID: 35620636 PMCID: PMC9127988 DOI: 10.3389/fpain.2022.871961] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 04/07/2022] [Indexed: 01/19/2023] Open
Abstract
Trauma and posttraumatic stress are highly comorbid with chronic pain and are often antecedents to developing chronic pain conditions. Pain and trauma are associated with greater utilization of medical services, greater use of psychiatric medication, and increased total cost of treatment. Despite the high overlap in the clinic, the neural mechanisms of pain and trauma are often studied separately. In this study, resting-state functional magnetic resonance imaging (rs-fMRI) scans were completed among a diagnostically heterogeneous sample of veterans with a range of back pain and trauma symptoms. Using Group Iterative Multiple Model Estimation (GIMME), an effective functional connectivity analysis, we explored an unsupervised model deriving subgroups based on path similarity in a priori defined regions of interest (ROIs) from brain regions implicated in the experience of pain and trauma. Three subgroups were identified by patterns in functional connection and differed significantly on several psychological measures despite similar demographic and diagnostic characteristics. The first subgroup was highly connected overall, was characterized by functional connectivity from the nucleus accumbens (NAc), the anterior cingulate cortex (ACC), and the posterior cingulate cortex (PCC) to the insula and scored low on pain and trauma symptoms. The second subgroup did not significantly differ from the first subgroup on pain and trauma measures but was characterized by functional connectivity from the ACC and NAc to the thalamus and from ACC to PCC. The third subgroup was characterized by functional connectivity from the thalamus and PCC to NAc and scored high on pain and trauma symptoms. Our results suggest that, despite demographic and diagnostic similarities, there may be neurobiologically dissociable biotypes with different mechanisms for managing pain and trauma. These findings may have implications for the determination of appropriate biotype-specific interventions that target these neurological systems.
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Affiliation(s)
- Irina A. Strigo
- Emotion and Pain Laboratory, San Francisco Veterans Affairs Health Care Center, San Francisco, CA, United States
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
| | - Andrea D. Spadoni
- Stress and Neuroimaging Laboratory, San Diego Veterans Affairs Health Care Center, San Francisco, CA, United States
- Center of Excellence in Stress and Mental Health, San Diego Veterans Affairs Health Care Center, San Diego, CA, United States
- Department of Psychiatry, University of California, San Diego, San Diego, CA, United States
| | - Alan N. Simmons
- Stress and Neuroimaging Laboratory, San Diego Veterans Affairs Health Care Center, San Francisco, CA, United States
- Center of Excellence in Stress and Mental Health, San Diego Veterans Affairs Health Care Center, San Diego, CA, United States
- Department of Psychiatry, University of California, San Diego, San Diego, CA, United States
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5
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Large-scale functional brain networks of maladaptive childhood aggression identified by connectome-based predictive modeling. Mol Psychiatry 2022; 27:985-999. [PMID: 34690348 PMCID: PMC9035467 DOI: 10.1038/s41380-021-01317-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 09/07/2021] [Accepted: 09/22/2021] [Indexed: 01/05/2023]
Abstract
Disruptions in frontoparietal networks supporting emotion regulation have been long implicated in maladaptive childhood aggression. However, the association of connectivity between large-scale functional networks with aggressive behavior has not been tested. The present study examined whether the functional organization of the connectome predicts severity of aggression in children. This cross-sectional study included a transdiagnostic sample of 100 children with aggressive behavior (27 females) and 29 healthy controls without aggression or psychiatric disorders (13 females). Severity of aggression was indexed by the total score on the parent-rated Reactive-Proactive Aggression Questionnaire. During fMRI, participants completed a face emotion perception task of fearful and calm faces. Connectome-based predictive modeling with internal cross-validation was conducted to identify brain networks that predicted aggression severity. The replication and generalizability of the aggression predictive model was then tested in an independent sample of children from the Adolescent Brain Cognitive Development (ABCD) study. Connectivity predictive of aggression was identified within and between networks implicated in cognitive control (medial-frontal, frontoparietal), social functioning (default mode, salience), and emotion processing (subcortical, sensorimotor) (r = 0.31, RMSE = 9.05, p = 0.005). Out-of-sample replication (p < 0.002) and generalization (p = 0.007) of findings predicting aggression from the functional connectome was demonstrated in an independent sample of children from the ABCD study (n = 1791; n = 1701). Individual differences in large-scale functional networks contribute to variability in maladaptive aggression in children with psychiatric disorders. Linking these individual differences in the connectome to variation in behavioral phenotypes will advance identification of neural biomarkers of maladaptive childhood aggression to inform targeted treatments.
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6
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Singh NM, Harrod JB, Subramanian S, Robinson M, Chang K, Cetin-Karayumak S, Dalca AV, Eickhoff S, Fox M, Franke L, Golland P, Haehn D, Iglesias JE, O’Donnell LJ, Ou Y, Rathi Y, Siddiqi SH, Sun H, Westover MB, Whitfield-Gabrieli S, Gollub RL. How Machine Learning is Powering Neuroimaging to Improve Brain Health. Neuroinformatics 2022; 20:943-964. [PMID: 35347570 PMCID: PMC9515245 DOI: 10.1007/s12021-022-09572-9] [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] [Accepted: 02/07/2022] [Indexed: 12/31/2022]
Abstract
This report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. Towards this goal, we aresharing the information presented at a symposium, "Neuroimaging Indicators of Brain Structure and Function - Closing the Gap Between Research and Clinical Application", co-hosted by the McCance Center for Brain Health at Mass General Hospital and the MIT HST Neuroimaging Training Program on February 12, 2021. The symposium focused on the potential for machine learning approaches, applied to increasingly large-scale neuroimaging datasets, to transform healthcare delivery and change the trajectory of brain health by addressing brain care earlier in the lifespan. While not exhaustive, this overview uniquely addresses many of the technical challenges from image formation, to analysis and visualization, to synthesis and incorporation into the clinical workflow. Some of the ethical challenges inherent to this work are also explored, as are some of the regulatory requirements for implementation. We seek to educate, motivate, and inspire graduate students, postdoctoral fellows, and early career investigators to contribute to a future where neuroimaging meaningfully contributes to the maintenance of brain health.
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Affiliation(s)
- Nalini M. Singh
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Jordan B. Harrod
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Sandya Subramanian
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Mitchell Robinson
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Ken Chang
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Suheyla Cetin-Karayumak
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, 02115 USA
| | | | - Simon Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany ,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7) Research Centre Jülich, Jülich, Germany
| | - Michael Fox
- Center for Brain Circuit Therapeutics, Department of Neurology, Psychiatry, and Radiology, Brigham and Women’s Hospital and Harvard Medical School, 02115 Boston, USA
| | - Loraine Franke
- University of Massachusetts Boston, Boston, MA 02125 USA
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Daniel Haehn
- University of Massachusetts Boston, Boston, MA 02125 USA
| | - Juan Eugenio Iglesias
- Centre for Medical Image Computing, University College London, London, UK ,Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, 02114 USA ,Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Lauren J. O’Donnell
- Department of Radiology, Brigham and Women’s Hospital and Harvard Medical School, MA 02115 Boston, USA
| | - Yangming Ou
- Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115 USA
| | - Yogesh Rathi
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, 02115 USA
| | - Shan H. Siddiqi
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, 02115 USA
| | - Haoqi Sun
- Department of Neurology and McCance Center for Brain Health / Harvard Medical School, Massachusetts General Hospital, Boston, 02114 USA
| | - M. Brandon Westover
- Department of Neurology and McCance Center for Brain Health / Harvard Medical School, Massachusetts General Hospital, Boston, 02114 USA
| | | | - Randy L. Gollub
- Department of Psychiatry and Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA 02114 USA
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7
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Kesler SR, Sleurs C, McDonald BC, Deprez S, van der Plas E, Nieman BJ. Brain Imaging in Pediatric Cancer Survivors: Correlates of Cognitive Impairment. J Clin Oncol 2021; 39:1775-1785. [PMID: 33886371 DOI: 10.1200/jco.20.02315] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Affiliation(s)
- Shelli R Kesler
- School of Nursing, Department of Diagnostic Medicine, Dell School of Medicine, Livestrong Cancer Institutes, Austin, TX
| | - Charlotte Sleurs
- Department of Oncology, Catholic University of Leuven, Leuven, Belgium.,Leuven Cancer Institute, Leuven, Belgium
| | - Brenna C McDonald
- Center for Neuroimaging, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Center for Neuroimaging, Indiana University Melvin and Bren Simon Cancer Center, Indianapolis, IN
| | - Sabine Deprez
- Leuven Cancer Institute, Leuven, Belgium.,Department of Imaging and Pathology, Catholic University of Leuven, Leuven, Belgium
| | - Ellen van der Plas
- Department of Psychiatry, University of Iowa Hospital and Clinics, Iowa City, Iowa
| | - Brian J Nieman
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.,Ontario Institute for Cancer Research, Toronto, ON, Canada.,Translational Medicine, Hospital for Sick Children, Toronto, ON, Canada
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8
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Simpson S, Chen Y, Wellmeyer E, Smith LC, Aragon Montes B, George O, Kimbrough A. The Hidden Brain: Uncovering Previously Overlooked Brain Regions by Employing Novel Preclinical Unbiased Network Approaches. Front Syst Neurosci 2021; 15:595507. [PMID: 33967705 PMCID: PMC8097000 DOI: 10.3389/fnsys.2021.595507] [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: 08/16/2020] [Accepted: 03/26/2021] [Indexed: 12/18/2022] Open
Abstract
A large focus of modern neuroscience has revolved around preselected brain regions of interest based on prior studies. While there are reasons to focus on brain regions implicated in prior work, the result has been a biased assessment of brain function. Thus, many brain regions that may prove crucial in a wide range of neurobiological problems, including neurodegenerative diseases and neuropsychiatric disorders, have been neglected. Advances in neuroimaging and computational neuroscience have made it possible to make unbiased assessments of whole-brain function and identify previously overlooked regions of the brain. This review will discuss the tools that have been developed to advance neuroscience and network-based computational approaches used to further analyze the interconnectivity of the brain. Furthermore, it will survey examples of neural network approaches that assess connectivity in clinical (i.e., human) and preclinical (i.e., animal model) studies and discuss how preclinical studies of neurodegenerative diseases and neuropsychiatric disorders can greatly benefit from the unbiased nature of whole-brain imaging and network neuroscience.
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Affiliation(s)
- Sierra Simpson
- Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Yueyi Chen
- Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States.,Department of Basic Medical Sciences, College of Veterinary Medicine, Purdue University, West Lafayette, IN, United States
| | - Emma Wellmeyer
- Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Lauren C Smith
- Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Brianna Aragon Montes
- Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Olivier George
- Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Adam Kimbrough
- Department of Basic Medical Sciences, College of Veterinary Medicine, Purdue University, West Lafayette, IN, United States.,Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States.,Purdue Institute for Inflammation, Immunology, and Infectious Disease, West Lafayette, IN, United States
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9
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Wen Z, Marin MF, Blackford JU, Chen ZS, Milad MR. Fear-induced brain activations distinguish anxious and trauma-exposed brains. Transl Psychiatry 2021; 11:46. [PMID: 33441547 PMCID: PMC7806917 DOI: 10.1038/s41398-020-01193-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 12/11/2020] [Accepted: 12/16/2020] [Indexed: 01/29/2023] Open
Abstract
Translational models of fear conditioning and extinction have elucidated a core neural network involved in the learning, consolidation, and expression of conditioned fear and its extinction. Anxious or trauma-exposed brains are characterized by dysregulated neural activations within regions of this fear network. In this study, we examined how the functional MRI activations of 10 brain regions commonly activated during fear conditioning and extinction might distinguish anxious or trauma-exposed brains from controls. To achieve this, activations during four phases of a fear conditioning and extinction paradigm in 304 participants with or without a psychiatric diagnosis were studied. By training convolutional neural networks (CNNs) using task-specific brain activations, we reliably distinguished the anxious and trauma-exposed brains from controls. The performance of models decreased significantly when we trained our CNN using activations from task-irrelevant brain regions or from a brain network that is irrelevant to fear. Our results suggest that neuroimaging data analytics of task-induced brain activations within the fear network might provide novel prospects for development of brain-based psychiatric diagnosis.
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Affiliation(s)
- Zhenfu Wen
- Department of Psychiatry, New York University School of Medicine, New York, NY, USA
| | - Marie-France Marin
- Department of Psychology, Université du Québec à Montréal & Research Center of the Institut Universitaire en Santé Mentale de Montréal, Montreal, QC, Canada
| | - Jennifer Urbano Blackford
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Tennessee Valley Healthcare Services, Department of Veterans Affairs, Nashville, TN, USA
| | - Zhe Sage Chen
- Department of Psychiatry, New York University School of Medicine, New York, NY, USA.
- Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY, USA.
- The Neuroscience Institute, New York University School of Medicine, New York, NY, USA.
| | - Mohammed R Milad
- Department of Psychiatry, New York University School of Medicine, New York, NY, USA.
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10
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Park SM, Jeong B, Oh DY, Choi CH, Jung HY, Lee JY, Lee D, Choi JS. Identification of Major Psychiatric Disorders From Resting-State Electroencephalography Using a Machine Learning Approach. Front Psychiatry 2021; 12:707581. [PMID: 34483999 PMCID: PMC8416434 DOI: 10.3389/fpsyt.2021.707581] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 07/20/2021] [Indexed: 12/03/2022] Open
Abstract
We aimed to develop a machine learning (ML) classifier to detect and compare major psychiatric disorders using electroencephalography (EEG). We retrospectively collected data from medical records, intelligence quotient (IQ) scores from psychological assessments, and quantitative EEG (QEEG) at resting-state assessments from 945 subjects [850 patients with major psychiatric disorders (six large-categorical and nine specific disorders) and 95 healthy controls (HCs)]. A combination of QEEG parameters including power spectrum density (PSD) and functional connectivity (FC) at frequency bands was used to establish models for the binary classification between patients with each disorder and HCs. The support vector machine, random forest, and elastic net ML methods were applied, and prediction performances were compared. The elastic net model with IQ adjustment showed the highest accuracy. The best feature combinations and classification accuracies for discrimination between patients and HCs with adjusted IQ were as follows: schizophrenia = alpha PSD, 93.83%; trauma and stress-related disorders = beta FC, 91.21%; anxiety disorders = whole band PSD, 91.03%; mood disorders = theta FC, 89.26%; addictive disorders = theta PSD, 85.66%; and obsessive-compulsive disorder = gamma FC, 74.52%. Our findings suggest that ML in EEG may predict major psychiatric disorders and provide an objective index of psychiatric disorders.
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Affiliation(s)
- Su Mi Park
- Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul, South Korea
| | - Boram Jeong
- Department of Statistics, Ewha Womans University, Seoul, South Korea
| | - Da Young Oh
- Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul, South Korea
| | - Chi-Hyun Choi
- Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul, South Korea
| | - Hee Yeon Jung
- Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul, South Korea.,Department of Psychiatry and Behavioral Science, Seoul National University College of Medicine, Seoul, South Korea.,Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul, South Korea
| | - Jun-Young Lee
- Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul, South Korea.,Department of Psychiatry and Behavioral Science, Seoul National University College of Medicine, Seoul, South Korea
| | - Donghwan Lee
- Department of Statistics, Ewha Womans University, Seoul, South Korea
| | - Jung-Seok Choi
- Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul, South Korea.,Department of Psychiatry and Behavioral Science, Seoul National University College of Medicine, Seoul, South Korea
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11
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Abstract
International psychiatry is currently facing serious challenges triggered by the global economic crisis and the COVID-19 pandemic. These global events lead to the need to broaden our nosographic and therapeutic horizons, and to make use of the newest psychological approaches and the latest neuroscience acquisitions. The focus should be on the psychological consequences of the pandemic, not only on people suffering from mental disorders, but also on the general population, for which the risk of developing psychic symptoms appears to be increased. A population that needs special attention is that of health workers involved in the management of the pandemic. In facing these problems, psychiatry today can use numerous new clinical applications and technologies in the fields of precision medicine. These include genomics, neuroimaging, and microbiomics, which can also be integrated with each other through machine learning systems. They can provide new contributions both in treatment personalization and in the evolution of nosographic systems. Besides this, the contribution of psychotherapies and dynamic and clinical psychology appears to be indispensable for a complete understanding of the clinical and personological aspects of patients. This journal aims to include innovative studies deriving from original, clinical, and basic research in the fields of mental health, precision psychiatry, genomics, neuroimaging, neuropsychopharmacology, and dynamic and clinical psychology.
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12
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Paulus MP. Pragmatic and Explanatory Progress Using Statistical Models of Disturbed Mind, Brain, and Behavior to Improve Mental Health. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:723-725. [PMID: 32771178 DOI: 10.1016/j.bpsc.2020.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 06/09/2020] [Indexed: 11/30/2022]
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Barch DM. Understanding the Nature and Treatment of Psychopathology: Can the Data Guide the Way? BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:719-722. [PMID: 32771177 DOI: 10.1016/j.bpsc.2020.06.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 06/30/2020] [Indexed: 10/23/2022]
Affiliation(s)
- Deanna M Barch
- Departments of Psychological and Brain Sciences, Psychiatry, and Radiology, Washington University in St. Louis, St. Louis, Missouri.
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