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Telesheva K, Savenkova V, Morozova I, Ochneva A, Zeltser A, Andreyuk D, Reznik A, Mukhin V, Melkonyan G, Lytkina K, Mitrofanov A, Morozova A. Potential Neurophysiological Markers of Combat-Related Post-Traumatic Stress Disorder: A Cross-Sectional Diagnostic Study. CONSORTIUM PSYCHIATRICUM 2024; 5:31-44. [PMID: 39072002 PMCID: PMC11272305 DOI: 10.17816/cp15512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 06/19/2024] [Indexed: 07/30/2024] Open
Abstract
BACKGROUND Studies suggest that the components of brain-evoked potentials (EPs) may serve as biomarkers of the post-traumatic stress disorder (PTSD) caused by participation in combat operations; however, to date, research remains fragmented, with no studies that have attempted to combine different paradigms. In addition, the mismatch negativity component has not been studied in a Russian sample of veterans with PTSD. AIM To identify objective neurophysiological markers of combat-related PTSD using the method of auditory-evoked potentials in active and passive listening paradigms. METHODS The study included a recording of auditory EPs in an oddball paradigm in three settings: 1) directed attention to auditory stimuli, 2) passive listening while viewing a neutral video sequence, and 3) viewing a video sequence associated with a traumatic event. Combatants diagnosed with PTSD (18 people) were compared with mentally healthy civilian volunteers (22 people). RESULTS An increase in the latency period of the early components of auditory EP (N100 and P200), an increase in the amplitude of the P200 component to a deviant stimulus, and a decrease to a standard one in the active listening paradigm were established in the PTSD group. There were no significant differences in the parameters of the P300 component. The characteristics of mismatch negativity in the passive paradigm were revealed: an increase in the phenomenon amplitude, both when shown a video sequence associated with a traumatic event and when shown a neutral video sequence. A binary logistic regression model constructed using the selected parameters showed that the identified characteristics can potentially be considered as diagnostic markers of PTSD in combatants, as the classification accuracy stood at 87% (sensitivity - 81%, specificity - 91%). CONCLUSION Potential neurophysiological markers of PTSD are the following: the amplitude and latency of early components of auditory EPs in the paradigm of directed attention to stimuli and the amplitude of mismatch negativity during passive attention.
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Arıkan MK, İlhan R, Orhan Ö, Esmeray MT, Turan Ş, Gica Ş, Bakay H, Pogarell O, Tarhan KN, Metin B. P300 parameters in major depressive disorder: A systematic review and meta-analysis. World J Biol Psychiatry 2024; 25:255-266. [PMID: 38493361 DOI: 10.1080/15622975.2024.2321554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 02/17/2024] [Indexed: 03/18/2024]
Abstract
OBJECTIVES Event-related potential measures have been extensively studied in mental disorders. Among them, P300 amplitude and latency reflect impaired cognitive abilities in major depressive disorder (MDD). The present systematic review and meta-analysis was conducted to investigate whether patients with MDD differ from healthy controls (HCs) with respect to P300 amplitude and latency. METHODS PubMed and Web of Science databases were searched from inception to 15 January 2023 for case-control studies comparing P300 amplitude and latency in patients with MDD and HCs. The primary outcome was the standard mean difference. A total of 13 articles on P300 amplitude and latency were included in the meta-analysis. RESULTS Random effect models indicated that MDD patients had decreased P300 amplitude, but similar latency compared to healthy controls. According to regression analysis, the effect size increased with the severity of depression and decreased with the proportion of women in the MDD samples. Funnel plot asymmetry was not significant for publication bias. CONCLUSIONS Decreased P300 amplitude may be a candidate diagnostic biomarker for MDD. However, prospective studies testing P300 amplitude as a monitoring biomarker for MDD are needed.
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Affiliation(s)
| | - Reyhan İlhan
- Prof. Dr. Mehmet Kemal Arıkan Psychiatry Clinic, Istanbul, Turkey
| | - Özden Orhan
- Prof. Dr. Mehmet Kemal Arıkan Psychiatry Clinic, Istanbul, Turkey
| | | | - Şenol Turan
- Department of Psychiatry, Cerrahpasa Medical School, Istanbul University, Istanbul, Turkey
| | - Şakir Gica
- Department of Mental Health and Disease, MERAM School of Medicine, Necmettin Erbakan University, Konya, Turkey
| | - Hasan Bakay
- Department of Mental Health and Disease, MERAM School of Medicine, Necmettin Erbakan University, Konya, Turkey
| | - Oliver Pogarell
- Department of Psychiatry, Division of Clinical Neurophysiology, Ludwig-Maximilians-University of Munich, Munich, Germany
| | - Kâşif Nevzat Tarhan
- Department of Neurology, Medical Faculty, Uskudar University, Istanbul, Turkey
| | - Barış Metin
- Department of Neurology, Medical Faculty, Uskudar University, Istanbul, Turkey
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Lee S, Kim J. Testing the bipolar assumption of Singer-Loomis Type Deployment Inventory for Korean adults using classification and multidimensional scaling. Front Psychol 2024; 14:1249185. [PMID: 38356992 PMCID: PMC10864660 DOI: 10.3389/fpsyg.2023.1249185] [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: 06/28/2023] [Accepted: 12/26/2023] [Indexed: 02/16/2024] Open
Abstract
In this study, we explored whether the Korean version of Singer Loomis Type Deployment Inventory II (K-SLTDI) captures the opposing tendencies of Jung's theory of psychological type. The types are Extroverted Sensing, Extroverted Intuition, Extroverted Feeling, Extroverted Thinking, Introverted Sensing, Introverted Intuition, Introverted Feeling, and Introverted Thinking. A nationwide online survey was conducted in South Korea. We performed multidimensional scaling and classification analyses based on 521 Korean adult profiles with eight psychological types to test the bipolarity assumption. The results showed that the Procrustes-rotated four-dimensional space successfully represented four types of opposing tendencies. Moreover, the bipolarity assumption in the four dimensions of Jungian typology was tested and compared between lower and higher psychological distress populations via cluster analysis. Lastly, we explored patterns of responses in lower and higher psychological distress populations using intersubject correlation. Both similarity analyses and classification results consistently support the theoretical considerations on the conceptualization of Jung's type in independent order that the types could be derived without bipolar assumption as Singer and Loomis expected in their Type Development Inventory. Limitations in our study include the sample being randomly selected internet users during the COVID-19 pandemic, despite excellence in the use of the internet in the general Korean population.
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Affiliation(s)
| | - Jongwan Kim
- Psychology Department, Jeonbuk National University, Jeonju, Republic of Korea
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Wu Y, Mao K, Dennett L, Zhang Y, Chen J. Systematic review of machine learning in PTSD studies for automated diagnosis evaluation. NPJ MENTAL HEALTH RESEARCH 2023; 2:16. [PMID: 38609504 PMCID: PMC10955977 DOI: 10.1038/s44184-023-00035-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 08/18/2023] [Indexed: 04/14/2024]
Abstract
Post-traumatic stress disorder (PTSD) is frequently underdiagnosed due to its clinical and biological heterogeneity. Worldwide, many people face barriers to accessing accurate and timely diagnoses. Machine learning (ML) techniques have been utilized for early assessments and outcome prediction to address these challenges. This paper aims to conduct a systematic review to investigate if ML is a promising approach for PTSD diagnosis. In this review, statistical methods were employed to synthesize the outcomes of the included research and provide guidance on critical considerations for ML task implementation. These included (a) selection of the most appropriate ML model for the available dataset, (b) identification of optimal ML features based on the chosen diagnostic method, (c) determination of appropriate sample size based on the distribution of the data, and (d) implementation of suitable validation tools to assess the performance of the selected ML models. We screened 3186 studies and included 41 articles based on eligibility criteria in the final synthesis. Here we report that the analysis of the included studies highlights the potential of artificial intelligence (AI) in PTSD diagnosis. However, implementing AI-based diagnostic systems in real clinical settings requires addressing several limitations, including appropriate regulation, ethical considerations, and protection of patient privacy.
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Affiliation(s)
- Yuqi Wu
- Electrical & Computer Engineering Department, Faculty of Engineering, University of Alberta, Edmonton, AB, Canada
| | - Kaining Mao
- Electrical & Computer Engineering Department, Faculty of Engineering, University of Alberta, Edmonton, AB, Canada
| | - Liz Dennett
- Scott Health Sciences Library, University of Alberta, Edmonton, AB, Canada
| | - Yanbo Zhang
- Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada.
| | - Jie Chen
- Electrical & Computer Engineering Department, Faculty of Engineering, University of Alberta, Edmonton, AB, Canada.
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Chen Z, Hu B, Liu X, Becker B, Eickhoff SB, Miao K, Gu X, Tang Y, Dai X, Li C, Leonov A, Xiao Z, Feng Z, Chen J, Chuan-Peng H. Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry. BMC Med 2023; 21:241. [PMID: 37400814 DOI: 10.1186/s12916-023-02941-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 06/13/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. METHODS Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. RESULTS A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (β = - 2.75, p < .001, R2adj = 0.40; r = - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. CONCLUSIONS Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.
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Affiliation(s)
- Zhiyi Chen
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China.
- Faculty of Psychology, Southwest University, Chongqing, China.
| | - Bowen Hu
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Xuerong Liu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, Chengdu, China
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Kuan Miao
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Xingmei Gu
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Yancheng Tang
- School of Business and Management, Shanghai International Studies University, Shanghai, China
| | - Xin Dai
- Faculty of Psychology, Southwest University, Chongqing, China
| | - Chao Li
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangdong, China
| | - Artemiy Leonov
- School of Psychology, Clark University, Worcester, MA, USA
| | - Zhibing Xiao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zhengzhi Feng
- Experimental Research Center for Medical and Psychological Science (ERC-MPS), School of Psychology, Third Military Medical University, Chongqing, China
| | - Ji Chen
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou, China.
- Department of Psychiatry, The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China.
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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Hámori G, File B, Fiáth R, Pászthy B, Réthelyi JM, Ulbert I, Bunford N. Adolescent ADHD and electrophysiological reward responsiveness: A machine learning approach to evaluate classification accuracy and prognosis. Psychiatry Res 2023; 323:115139. [PMID: 36921508 DOI: 10.1016/j.psychres.2023.115139] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 02/12/2023] [Accepted: 02/28/2023] [Indexed: 03/18/2023]
Abstract
We evaluated event-related potential (ERP) indices of reinforcement sensitivity as ADHD biomarkers by examining, in N=306 adolescents (Mage=15.78, SD=1.08), the extent to which ERP amplitude and latency variables measuring reward anticipation and response (1) differentiate, in age- and sex-matched subsamples, (i) youth with vs. without ADHD, (ii) youth at-risk for vs. not at-risk for ADHD, and, in the with ADHD subsample, (iii) youth with the inattentive vs. the hyperactive/impulsive (H/I) and combined presentations. We further examined the extent to which ERP variables (2) predict, in the ADHD subsample, substance use (i) concurrently and (ii) prospectively at 18-month follow-up. Linear support vector machine analyses indicated ERPs weakly differentiate youth with/without (65%) - and at-risk for/not at-risk for (63%) - ADHD but better differentiate ADHD presentations (78%). Regression analyses showed in adolescents with ADHD, ERPs explain a considerable proportion of variance (50%) in concurrent alcohol use and, controlling for concurrent marijuana and tobacco use, explain a considerable proportion of variance (87 and 87%) in, and predict later marijuana and tobacco use. Findings are consistent with the dual-pathway model of ADHD. Results also highlight limitations of a dichotomous, syndromic classification and indicate differences in neural reinforcement sensitivity are a promising ADHD prognostic biomarker.
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Affiliation(s)
- György Hámori
- Clinical and Developmental Neuropsychology Research Group, Research Centre for Natural Sciences, Institute of Cognitive Neuroscience and Psychology, Magyar Tudósok körútja 2, Budapest 1117, Hungary; Department of Cognitive Science, Faculty of Natural Sciences, Budapest University of Technology and Economics, Műegyetem rkp. 3., Budapest H-1111, Hungary
| | - Bálint File
- Research Centre for Natural Sciences, Integrative Neuroscience Research Group, Institute of Cognitive Neuroscience and Psychology, Magyar Tudósok körútja 2, Budapest 1117, Hungary; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 50/A, Budapest 1083, Hungary; Theoretical Neuroscience and Complex Systems Research Group, Wigner Research Centre for Physics, Konkoly-Tege Miklós út 29-33, Budapest 1121, Hungary
| | - Richárd Fiáth
- Research Centre for Natural Sciences, Integrative Neuroscience Research Group, Institute of Cognitive Neuroscience and Psychology, Magyar Tudósok körútja 2, Budapest 1117, Hungary; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 50/A, Budapest 1083, Hungary
| | - Bea Pászthy
- Department of Paediatrics, Semmelweis University, Faculty of Medicine, Bókay János u. 53-54, Budapest 1083, Hungary
| | - János M Réthelyi
- Department of Psychiatry and Psychotherapy, Semmelweis University, Faculty of Medicine, Balassa u. 6, Budapest 1083, Hungary
| | - István Ulbert
- Research Centre for Natural Sciences, Integrative Neuroscience Research Group, Institute of Cognitive Neuroscience and Psychology, Magyar Tudósok körútja 2, Budapest 1117, Hungary; Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter utca 50/A, Budapest 1083, Hungary
| | - Nóra Bunford
- Clinical and Developmental Neuropsychology Research Group, Research Centre for Natural Sciences, Institute of Cognitive Neuroscience and Psychology, Magyar Tudósok körútja 2, Budapest 1117, Hungary.
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Rountree-Harrison D, Berkovsky S, Kangas M. Heart and brain traumatic stress biomarker analysis with and without machine learning: A scoping review. Int J Psychophysiol 2023; 185:27-49. [PMID: 36720392 DOI: 10.1016/j.ijpsycho.2023.01.009] [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: 06/14/2022] [Revised: 01/22/2023] [Accepted: 01/25/2023] [Indexed: 01/31/2023]
Abstract
The enigma of post-traumatic stress disorder (PTSD) is embedded in a complex array of physiological responses to stressful situations that result in disruptions in arousal and cognitions that characterise the psychological disorder. Deciphering these physiological patterns is complex, which has seen the use of machine learning (ML) grow in popularity. However, it is unclear to what extent ML has been used with physiological data, specifically, the electroencephalogram (EEG) and electrocardiogram (ECG) to further understand the physiological responses associated with PTSD. To better understand the use of EEG and ECG biomarkers, with and without ML, a scoping review was undertaken. A total of 124 papers based on adult samples were identified comprising 19 ML studies involving EEG and ECG. A further 21 studies using EEG data, and 84 studies employing ECG meeting all other criteria but not employing ML were included for comparison. Identified studies indicate classical ML methodologies currently dominate EEG and ECG biomarkers research, with derived biomarkers holding clinically relevant diagnostic implications for PTSD. Discussion of the emerging trends, algorithms used and their success is provided, along with areas for future research.
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Affiliation(s)
- Darius Rountree-Harrison
- Macquarie University, Balaclava Road, Macquarie Park, New South Wales 2109, Australia; New South Wales Service for the Rehabilitation and Treatment of Torture and Trauma Survivors (STARTTS), 152-168 The Horsley Drive Carramar, New South Wales 2163, Australia.
| | - Shlomo Berkovsky
- Macquarie University, Balaclava Road, Macquarie Park, New South Wales 2109, Australia
| | - Maria Kangas
- Macquarie University, Balaclava Road, Macquarie Park, New South Wales 2109, Australia
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Chen Z, Liu X, Yang Q, Wang YJ, Miao K, Gong Z, Yu Y, Leonov A, Liu C, Feng Z, Chuan-Peng H. Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review. JAMA Netw Open 2023; 6:e231671. [PMID: 36877519 PMCID: PMC9989906 DOI: 10.1001/jamanetworkopen.2023.1671] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2023] Open
Abstract
IMPORTANCE Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated. OBJECTIVE To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis. EVIDENCE REVIEW PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality. FINDINGS A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%). CONCLUSIONS AND RELEVANCE This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.
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Affiliation(s)
- Zhiyi Chen
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Xuerong Liu
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Qingwu Yang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Yan-Jiang Wang
- Department of Neurology, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Kuan Miao
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Zheng Gong
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Yang Yu
- School of Psychology, Third Military Medical University, Chongqing, China
| | - Artemiy Leonov
- Department of Psychology, Clark University, Worcester, Massachusetts
| | - Chunlei Liu
- School of Psychology, Qufu Normal University, Qufu, China
| | - Zhengzhi Feng
- School of Psychology, Third Military Medical University, Chongqing, China
- Experimental Research Center for Medical and Psychological Science, Third Military Medical University, Chongqing, China
| | - Hu Chuan-Peng
- School of Psychology, Nanjing Normal University, Nanjing, China
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Identifying Patients with Epilepsy Having Depression/Anxiety Disorder Using Common Spatial Patterns of Functional EEG Networks. J Med Biol Eng 2022. [DOI: 10.1007/s40846-022-00726-3] [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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Abstract
BACKGROUND In this modern era, depression is one of the most prevalent mental disorders from which millions of individuals are affected today. The symptoms of depression are heterogeneous and often coincide with other disorders such as bipolar disorder, Parkinson's, schizophrenia, etc. It is a serious mental illness that may lead to other health problems if left untreated. Currently, identifying individuals with depression is totally based on the expertise of the clinician's experience. In order to assist clinicians in identifying the characteristics and classifying depressed people, different types of data modalities and machine learning techniques have been incorporated by researchers in this field. This study aims to find the answers to some important questions related to the trend of publications, data modality, machine learning models, dataset usage, pre-processing techniques and feature extraction and selection techniques that are prevalent and guide the direction of future research on depression diagnosis. METHODS This systematic review was conducted using a broad range of articles from two major databases: IEEE Xplore and PubMed. Studies ranging from the years 2011 to April 2021 were retrieved from the databases resulting in a total of 590 articles (53 articles from the IEEE Xplore database and 537 articles from the PubMed database). Out of those, the articles which satisfied the defined inclusion criteria were investigated for further analysis. RESULTS A total of 135 articles were identified and analysed for this review. High growth in the number of publications has been observed in recent years. Furthermore, significant diversity in the use of data modalities and machine learning classifiers has also been noted in this study. fMRI data with an SVM classifier was found to be the most popular choice among researchers. In most of the studies, data scarcity and small sample size, particularly for neuroimaging data are major concerns. The use of identical data pre-processing tools for similar data modalities can be seen. This study also provides statistical analysis of the current framework with respect to the modality, machine learning classifier, sample size and accuracy by applying one-way ANOVA and the Tukey - Kramer test. CONCLUSION The results indicate that an effective fusion of machine learning techniques with a potential data modality has a promising future for assisting clinicians in automatic depression diagnosis.
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Affiliation(s)
- Sweta Bhadra
- Department of CS & IT, Cotton University, Guwahati, India
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Shim M, Im CH, Lee SH, Hwang HJ. Enhanced Performance by Interpretable Low-Frequency Electroencephalogram Oscillations in the Machine Learning-Based Diagnosis of Post-traumatic Stress Disorder. Front Neuroinform 2022; 16:811756. [PMID: 35571868 PMCID: PMC9094422 DOI: 10.3389/fninf.2022.811756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 03/28/2022] [Indexed: 11/13/2022] Open
Abstract
Electroencephalography (EEG)-based diagnosis of psychiatric diseases using machine-learning approaches has made possible the objective diagnosis of various psychiatric diseases. The objective of this study was to improve the performance of a resting-state EEG-based computer-aided diagnosis (CAD) system to diagnose post-traumatic stress disorder (PTSD), by optimizing the frequency bands used to extract EEG features. We used eyes-closed resting-state EEG data recorded from 77 PTSD patients and 58 healthy controls (HC). Source-level power spectrum densities (PSDs) of the resting-state EEG data were extracted from 6 frequency bands (delta, theta, alpha, low-beta, high-beta, and gamma), and the PSD features of each frequency band and their combinations were independently used to discriminate PTSD and HC. The classification performance was evaluated using support vector machine with leave-one-out cross validation. The PSD features extracted from slower-frequency bands (delta and theta) showed significantly higher classification performance than those of relatively higher-frequency bands. The best classification performance was achieved when using delta PSD features (86.61%), which was significantly higher than that reported in a recent study by about 13%. The PSD features selected to obtain better classification performances could be explained from a neurophysiological point of view, demonstrating the promising potential to develop a clinically reliable EEG-based CAD system for PTSD diagnosis.
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Affiliation(s)
- Miseon Shim
- Department of Electronics and Information, Korea University, Sejong, South Korea
- Industry Development Institute, Korea University, Sejong, South Korea
| | - Chang-Hwan Im
- Department of Biomedical Engineering, Hanyang University, Seoul, South Korea
| | - Seung-Hwan Lee
- Department of Psychiatry, Ilsan Paik Hospital, Inje University, Goyang, South Korea
- Clinical Emotion and Cognition Research Laboratory, Goyang, South Korea
| | - Han-Jeong Hwang
- Department of Electronics and Information, Korea University, Sejong, South Korea
- Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology, Korea University, Sejong, South Korea
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Diao Y, Geng M, Fu Y, Wang H, Liu C, Gu J, Dong J, Mu J, Liu X, Wang C. A combination of P300 and eye movement data improves the accuracy of auxiliary diagnoses of depression. J Affect Disord 2022; 297:386-395. [PMID: 34710500 DOI: 10.1016/j.jad.2021.10.028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 10/09/2021] [Accepted: 10/20/2021] [Indexed: 12/27/2022]
Abstract
BACKGROUND Exploratory eye movements (EEMs) and P300 are often used to facilitate the clinical diagnosis of depression. However, There were few studies using the combination of EEMs and P300 to build a model for detecting depression and predicting a curative effect. METHODS Sixty patients were recruited for 2 groups: high frequency repetitive transcranial magnetic stimulation(rTMS) combined with paroxetine group and simple paroxetine group. Clinical efficacy was evaluated by the Hamilton Depression scale-24(HAMD-24), EEMs and P300. The classification model of the auxiliary diagnosis of depression and the prediction model of the two treatments were developed based on a machine learning algorithm. RESULTS The classification model with the greatest accuracy for patients with depression and healthy controls was 95.24% (AUC = 0.75, recall = 1.00, precision = 0.95, F1-score = 0.97). The root mean square error (RMSE) of the model for predicting the efficacy of high frequency rTMS combined with paroxetine was 3.54 (MAE [mean absolute error] = 2.56, R2 = -0.53). The RMSE of the model for predicting the efficacy of paroxetine was 4.97 (MAE = 4.00, R2 = -0.91). CONCLUSION Based on the machine learning algorithm, P300 and EEMs data was suitable for modeling to distinguish depression patients and healthy individuals. However, it was not suitable for predicting the efficacy of high frequency rTMS combined with paroxetine or to predict the efficacy of paroxetine.
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Affiliation(s)
- Yunheng Diao
- Department of Psychiatry, The Second Affiliated Hospital of Xinxiang Medical University, Henan Mental Hospital, Xinxiang, Henan, 453002, PR China; The Second Clinical College, Xinxiang Medical University, Xinxiang, Henan, 453003, PR China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, Henan, 453002, PR China
| | - Mengjun Geng
- Department of Psychiatry, The Second Affiliated Hospital of Xinxiang Medical University, Henan Mental Hospital, Xinxiang, Henan, 453002, PR China; The Second Clinical College, Xinxiang Medical University, Xinxiang, Henan, 453003, PR China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, Henan, 453002, PR China
| | - Yifang Fu
- Department of Psychiatry, The Second Affiliated Hospital of Xinxiang Medical University, Henan Mental Hospital, Xinxiang, Henan, 453002, PR China; The Second Clinical College, Xinxiang Medical University, Xinxiang, Henan, 453003, PR China
| | - Huiying Wang
- Department of Psychiatry, The Second Affiliated Hospital of Xinxiang Medical University, Henan Mental Hospital, Xinxiang, Henan, 453002, PR China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, Henan, 453002, PR China
| | - Cong Liu
- Department of Psychiatry, The Second Affiliated Hospital of Xinxiang Medical University, Henan Mental Hospital, Xinxiang, Henan, 453002, PR China
| | - Jingyang Gu
- Department of Psychiatry, The Second Affiliated Hospital of Xinxiang Medical University, Henan Mental Hospital, Xinxiang, Henan, 453002, PR China
| | - Jiao Dong
- Department of Psychiatry, The Second Affiliated Hospital of Xinxiang Medical University, Henan Mental Hospital, Xinxiang, Henan, 453002, PR China
| | - Junlin Mu
- Department of Psychiatry, The Second Affiliated Hospital of Xinxiang Medical University, Henan Mental Hospital, Xinxiang, Henan, 453002, PR China
| | - Xianhua Liu
- Department of Psychiatry, The Second Affiliated Hospital of Xinxiang Medical University, Henan Mental Hospital, Xinxiang, Henan, 453002, PR China
| | - Changhong Wang
- Department of Psychiatry, The Second Affiliated Hospital of Xinxiang Medical University, Henan Mental Hospital, Xinxiang, Henan, 453002, PR China; The Second Clinical College, Xinxiang Medical University, Xinxiang, Henan, 453003, PR China; Henan Key Lab of Biological Psychiatry, Xinxiang Medical University, Xinxiang, Henan, 453002, PR China.
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13
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Yang J, Lei D, Qin K, Pinaya WHL, Suo X, Li W, Li L, Kemp GJ, Gong Q. Using deep learning to classify pediatric posttraumatic stress disorder at the individual level. BMC Psychiatry 2021; 21:535. [PMID: 34711200 PMCID: PMC8555083 DOI: 10.1186/s12888-021-03503-9] [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: 11/17/2020] [Accepted: 09/28/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Children exposed to natural disasters are vulnerable to developing posttraumatic stress disorder (PTSD). Previous studies using resting-state functional neuroimaging have revealed alterations in graph-based brain topological network metrics in pediatric PTSD patients relative to healthy controls (HC). Here we aimed to apply deep learning (DL) models to neuroimaging markers of classification which may be of assistance in diagnosis of pediatric PTSD. METHODS We studied 33 pediatric PTSD and 53 matched HC. Functional connectivity between 90 brain regions from the automated anatomical labeling atlas was established using partial correlation coefficients, and the whole-brain functional connectome was constructed by applying a threshold to the resultant 90 * 90 partial correlation matrix. Graph theory analysis was used to examine the topological properties of the functional connectome. A DL algorithm then used this measure to classify pediatric PTSD vs HC. RESULTS Graphic topological measures using DL provide a potentially clinically useful classifier for differentiating pediatric PTSD and HC (overall accuracy 71.2%). Frontoparietal areas (central executive network), cingulate cortex, and amygdala contributed the most to the DL model's performance. CONCLUSIONS Graphic topological measures based on fMRI data could contribute to imaging models of clinical utility in distinguishing pediatric PTSD from HC. DL model may be a useful tool in the identification of brain mechanisms PTSD participants.
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Affiliation(s)
- Jing Yang
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, 610041, China
- Department of Radiology, Chongqing University Cancer Hospital, School of Medicine, Chongqing University, Chongqing, China
| | - Du Lei
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, 45219, USA
| | - Kun Qin
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Walter H L Pinaya
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE5 8AF, UK
| | - Xueling Suo
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Wenbin Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Lingjiang Li
- Mental Health Institute, the Second Xiangya Hospital of Central South University, Changsha, 410008, Hunan, China
| | - Graham J Kemp
- Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, L9 7AL, UK
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, 610041, China.
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China.
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14
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Machine-learning-based diagnosis of drug-naive adult patients with attention-deficit hyperactivity disorder using mismatch negativity. Transl Psychiatry 2021; 11:484. [PMID: 34537812 PMCID: PMC8449778 DOI: 10.1038/s41398-021-01604-3] [Citation(s) in RCA: 12] [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: 12/07/2020] [Revised: 08/23/2021] [Accepted: 09/01/2021] [Indexed: 02/08/2023] Open
Abstract
Relatively little is investigated regarding the neurophysiology of adult attention-deficit/hyperactivity disorder (ADHD). Mismatch negativity (MMN) is an event-related potential component representing pre-attentive auditory processing, which is closely associated with cognitive status. We investigated MMN features as biomarkers to classify drug-naive adult patients with ADHD and healthy controls (HCs). Sensor-level features (amplitude and latency) and source-level features (source activation) of MMN were investigated and compared between the electroencephalograms of 34 patients with ADHD and 45 HCs using a passive auditory oddball paradigm. Correlations between MMN features and ADHD symptoms were analyzed. Finally, we applied machine learning to differentiate the two groups using sensor- and source-level features of MMN. Adult patients with ADHD showed significantly lower MMN amplitudes at the frontocentral electrodes and reduced MMN source activation in the frontal, temporal, and limbic lobes, which were closely associated with MMN generators and ADHD pathophysiology. Source activities were significantly correlated with ADHD symptoms. The best classification performance for adult ADHD patients and HCs showed an 81.01% accuracy, 82.35% sensitivity, and 80.00% specificity based on MMN source activity features. Our results suggest that abnormal MMN reflects the adult ADHD patients' pathophysiological characteristics and might serve clinically as a neuromarker of adult ADHD.
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15
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Teasing apart trauma: neural oscillations differentiate individual cases of mild traumatic brain injury from post-traumatic stress disorder even when symptoms overlap. Transl Psychiatry 2021; 11:345. [PMID: 34088901 PMCID: PMC8178364 DOI: 10.1038/s41398-021-01467-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 05/08/2021] [Accepted: 05/19/2021] [Indexed: 01/21/2023] Open
Abstract
Post-traumatic stress disorder (PTSD) and mild traumatic brain injury (mTBI) are highly prevalent and closely related disorders. Affected individuals often exhibit substantially overlapping symptomatology - a major challenge for differential diagnosis in both military and civilian contexts. According to our symptom assessment, the PTSD group exhibited comparable levels of concussion symptoms and severity to the mTBI group. An objective and reliable system to uncover the key neural signatures differentiating these disorders would be an important step towards translational and applied clinical use. Here we explore use of MEG (magnetoencephalography)-multivariate statistical learning analysis in identifying the neural features for differential PTSD/mTBI characterisation. Resting state MEG-derived regional neural activity and coherence (or functional connectivity) across seven canonical neural oscillation frequencies (delta to high gamma) were used. The selected features were consistent and largely confirmatory with previously established neurophysiological markers for the two disorders. For regional power from theta, alpha and high gamma bands, the amygdala, hippocampus and temporal areas were identified. In line with regional activity, additional connections within the occipital, parietal and temporal regions were selected across a number of frequency bands. This study is the first to employ MEG-derived neural features to reliably and differentially stratify the two disorders in a multi-group context. The features from alpha and beta bands exhibited the best classification performance, even in cases where distinction by concussion symptom profiles alone were extremely difficult. We demonstrate the potential of using 'invisible' neural indices of brain functioning to understand and differentiate these debilitating conditions.
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16
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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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17
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Jones C, Smith-MacDonald L, Miguel-Cruz A, Pike A, van Gelderen M, Lentz L, Shiu MY, Tang E, Sawalha J, Greenshaw A, Rhind SG, Fang X, Norbash A, Jetly R, Vermetten E, Brémault-Phillips S. Virtual Reality-Based Treatment for Military Members and Veterans With Combat-Related Posttraumatic Stress Disorder: Protocol for a Multimodular Motion-Assisted Memory Desensitization and Reconsolidation Randomized Controlled Trial. JMIR Res Protoc 2020; 9:e20620. [PMID: 33118957 PMCID: PMC7661230 DOI: 10.2196/20620] [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: 07/10/2020] [Revised: 08/26/2020] [Accepted: 09/01/2020] [Indexed: 12/12/2022] Open
Abstract
Background Military members are at elevated risk of operational stress injuries, including posttraumatic stress disorder (PTSD) and moral injury. Although psychotherapy can reduce symptoms, some military members may experience treatment-resistant PTSD. Multimodular motion-assisted memory desensitization and reconsolidation (3MDR) has been introduced as a virtual reality (VR) intervention for military members with PTSD related to military service. The 3MDR intervention incorporates exposure therapy, psychotherapy, eye movement desensitization and reconsolidation, VR, supportive counselling, and treadmill walking. Objective The objective of this study is to investigate whether 3MDR reduces PTSD symptoms among military members with combat-related treatment-resistant PTSD (TR-PTSD); examine the technology acceptance and usability of the Computer Assisted Rehabilitation ENvironment (CAREN) and 3MDR interventions by Canadian Armed Forces service members (CAF-SMs), veterans, 3MDR clinicians, and operators; and evaluate the impact on clinicians and operators of delivering 3MDR. Methods This is a mixed-methods waitlist controlled crossover design randomized controlled trial. Participants include both CAF-SMs and veterans (N=40) aged 18-60 years with combat-related TR-PTSD (unsuccessful experience of at least 2 evidence-based trauma treatments). Participants will also include clinicians and operators (N=12) who have been trained in 3MDR and subsequently utilized this intervention with patients. CAF-SMs and veterans will receive 6 weekly 90-minute 3MDR sessions. Quantitative and qualitative data will be collected at baseline and at 1, 3, and 6 months postintervention. Quantitative data collection will include multiomic biomarkers (ie, blood and salivary proteomic and genomic profiles of neuroendocrine, immune-inflammatory mediators, and microRNA), eye tracking, electroencephalography, and physiological data. Data from outcome measures will capture self-reported symptoms of PTSD, moral injury, resilience, and technology acceptance and usability. Qualitative data will be collected from audiovisual recordings of 3MDR sessions and semistructured interviews. Data analysis will include univariate and multivariate approaches, and thematic analysis of treatment sessions and interviews. Machine learning analysis will be included to develop models for the prediction of diagnosis, symptom severity, and treatment outcomes. Results This study commenced in April 2019 and is planned to conclude in April 2021. Study results will guide the further evolution and utilization of 3MDR for military members with TR-PTSD and will have utility in treating other trauma-affected populations. Conclusions The goal of this study is to utilize qualitative and quantitative primary and secondary outcomes to provide evidence for the effectiveness and feasibility of 3MDR for treating CAF-SMs and veterans with combat-related TR-PTSD. The results will inform a full-scale clinical trial and stimulate development and adaptation of the protocol to mobile VR apps in supervised clinical settings. This study will add to knowledge of the clinical effectiveness of 3MDR, and provide the first comprehensive analysis of biomarkers, technology acceptance and usability, moral injury, resilience, and the experience of clinicians and operators delivering 3MDR. Trial Registration ISRCTN Registry 11264368; http://www.isrctn.com/ISRCTN11264368. International Registered Report Identifier (IRRID) DERR1-10.2196/20620
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Affiliation(s)
- Chelsea Jones
- Heroes in Mind, Advocacy and Research Consortium, Faculty of Rehabilitation, University of Alberta, Edmonton, AB, Canada
| | - Lorraine Smith-MacDonald
- Heroes in Mind, Advocacy and Research Consortium, Faculty of Rehabilitation, University of Alberta, Edmonton, AB, Canada
| | - Antonio Miguel-Cruz
- Department of Occupational Therapy, Faculty of Rehabilitation, University of Alberta, Edmonton, AB, Canada.,Glenrose Rehabilitation Hospital Research Innovation and Technology (GRRIT), Glenrose Rehabilitation Hospital, Edmonton, AB, Canada
| | - Ashley Pike
- Heroes in Mind, Advocacy and Research Consortium, Faculty of Rehabilitation, University of Alberta, Edmonton, AB, Canada
| | - Marieke van Gelderen
- ARQ Centrum'45, Diemen, Netherlands.,Department of Psychiatry, Leiden University Medical Center, Leiden, Netherlands
| | - Liana Lentz
- School of Health Studies, Western University, London, ON, Canada
| | - Maria Y Shiu
- Defence Research and Development Canada, Toronto Research Centre, Toronto, ON, Canada
| | - Emily Tang
- Heroes in Mind, Advocacy and Research Consortium, Faculty of Rehabilitation, University of Alberta, Edmonton, AB, Canada
| | - Jeffrey Sawalha
- Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada
| | - Andrew Greenshaw
- Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada
| | - Shawn G Rhind
- Defence Research and Development Canada, Toronto Research Centre, Toronto, ON, Canada
| | - Xin Fang
- Department of Obstetrics and Gynecology, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada
| | - Adrian Norbash
- Canadian Forces Health Services, Department of National Defense, Edmonton, AB, Canada
| | - Rakesh Jetly
- Department of Mental Health, Canadian Forces Health Services, Department of National Defense, Ottawa, ON, Canada
| | - Eric Vermetten
- Department of Psychiatry, Leiden University Medical Center, Leiden, Netherlands.,Military Mental Health Research, Ministry of Defense, Utrecht, Netherlands.,ARQ National Psychotrauma Centre, Deimen, Netherlands
| | - Suzette Brémault-Phillips
- Heroes in Mind, Advocacy and Research Consortium, Faculty of Rehabilitation, University of Alberta, Edmonton, AB, Canada.,Department of Occupational Therapy, Faculty of Rehabilitation, University of Alberta, Edmonton, AB, Canada
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