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Cushing CA, Lau H, Kawato M, Craske MG, Taschereau-Dumouchel V. A double-blind trial of decoded neurofeedback intervention for specific phobias. Psychiatry Clin Neurosci 2024. [PMID: 39221769 DOI: 10.1111/pcn.13726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 07/25/2024] [Accepted: 07/29/2024] [Indexed: 09/04/2024]
Abstract
AIM A new closed-loop functional magnetic resonance imaging method called multivoxel neuroreinforcement has the potential to alleviate the subjective aversiveness of exposure-based interventions by directly inducing phobic representations in the brain, outside of conscious awareness. The current study seeks to test this method as an intervention for specific phobia. METHODS In a randomized, double-blind, controlled single-university trial, individuals diagnosed with at least two (one target, one control) animal subtype-specific phobias were randomly assigned (1:1:1) to receive one, three, or five sessions of multivoxel neuroreinforcement in which they were rewarded for implicit activation of a target animal representation. Amygdala response to phobic stimuli was assessed by study staff blind to target and control animal assignments. Pretreatment to posttreatment differences were analyzed with a two-way repeated-measures anova. RESULTS A total of 23 participants (69.6% female) were randomized to receive one (n = 8), three (n = 7), or five (n = 7) sessions of multivoxel neuroreinforcement. Eighteen (n = 6 each group) participants were analyzed for our primary outcome. After neuroreinforcement, we observed an interaction indicating a significant decrease in amygdala response for the target phobia but not the control phobia. No adverse events or dropouts were reported as a result of the intervention. CONCLUSION Results suggest that multivoxel neuroreinforcement can specifically reduce threat signatures in specific phobia. Consequently, this intervention may complement conventional psychotherapy approaches with a nondistressing experience for patients seeking treatment. This trial sets the stage for a larger randomized clinical trial to replicate these results and examine the effects on real-life exposure. CLINICAL TRIAL REGISTRATION The now-closed trial was prospectively registered at ClinicalTrials.gov with ID NCT03655262.
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Affiliation(s)
- Cody A Cushing
- Department of Psychology, UCLA, Los Angeles, California, USA
| | - Hakwan Lau
- RIKEN Center for Brain Science, Wako, Japan
| | - Mitsuo Kawato
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto, Japan
- XNef, Inc., Kyoto, Japan
| | | | - Vincent Taschereau-Dumouchel
- Department of Psychiatry and Addictology, Université de Montréal, Montreal, Québec, Canada
- Centre de Recherche de l'Institut Universitaire en Santé Mentale de Montréal, Montreal, Québec, Canada
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Pan Z, Qi H, Zhou J, Xu Y. Comparison of Brain Function and Structure in Patients with Major Depression: A Systematic Review and Meta-Analysis of MRI-Based Data. ACTAS ESPANOLAS DE PSIQUIATRIA 2024; 52:561-570. [PMID: 39129695 PMCID: PMC11319759 DOI: 10.62641/aep.v52i4.1636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
BACKGROUND Depression is a common mental illness worldwide. Neuroimaging techniques, such as magnetic resonance imaging and functional magnetic resonance imaging, play an essential role in diagnosing and evaluating depression. This study is based on magnetic resonance imaging (MRI)-related research to explore the comparison of brain function and structure between patients with severe depression and normal individuals, and to conduct meta-analysis. METHODS We conducted searches in various databases such as PubMed, Web of Science, Embase, and Cochrane Library to obtain research data on comparing brain function and structure between patients with severe depression and healthy individuals. The search keywords included "Major Depressive Disorder", "Brain Function", "Brain Structure", "Depression", "MRI", and "Magnetic Resonance". The quality assessment was conducted using the bias risk assessment tool recommended by the Cochrane Collaborative Network. Literature was screened following the predetermined inclusion and exclusion criteria, and Anisotropic Effect-Size Seed-Based Differential Mapping (AES-SDM) was used for systematic meta-analysis. Regression analysis was performed on age, gender, disease duration, years of education, and treatment status. RESULTS After a thorough screening process, 10 documents were selected for subsequent analysis. These studies consisted of 477 study subjects, including 231 depression patients and 246 healthy individuals. The proportion of women was 36%-75%, and the disease duration was 3-60 months. The patients in 4 documents had first attacks, and the patients in the other 6 documents had multiple attacks. The baseline conditions of the 10 included documents were consistent and comparable. None of the studies reported blinding methods, and none of the results had incomplete data. The Regional homogeneity (ReHo) levels in the left precuneus (BA7), lentiform nucleus (BA48), and left prefrontal lobe (BA32) were significantly increased in the depression group, with voxel numbers of 358, 116, and 181, respectively. Conversely, the left postcentral gyrus (BA4), left cerebellar area (hemispheric lobule I, IV/V, lingual gyrus, fusiform gyrus), left fusiform gyrus (BA30), and right cingulate gyrus (BA23) were significantly reduced, with voxel numbers of 17, 50, and 124, respectively. Furthermore, regression analysis showed that gender, age, disease duration, years of education, and disease severity were potential influencing factors, and the disease duration demonstrated the most significant impact on the left cingulate gyrus (SDM = 2.777). CONCLUSION There are significant differences in brain function and structure between patients with major depression and healthy individuals. Furthermore, our findings reveal a substantial correlation between the severity of depressive symptoms and brain function and structure indicators. These findings provide novel research directions and ideas for the diagnosis and treatment of depression.
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Affiliation(s)
- Zhenzhen Pan
- Department of Radiological, Shaoxing Seventh People's Hospital, 312000 Shaoxing, Zhejiang, China
| | - Hongyang Qi
- Department of Radiological, Shaoxing Seventh People's Hospital, 312000 Shaoxing, Zhejiang, China
| | - Jie Zhou
- Department of Radiological, Shaoxing Seventh People's Hospital, 312000 Shaoxing, Zhejiang, China
| | - Yuhua Xu
- Department of Radiological, Shaoxing Seventh People's Hospital, 312000 Shaoxing, Zhejiang, China
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Gall R, Mcdonald N, Huang X, Wears A, Price RB, Ostadabbas S, Akcakaya M, Woody ML. AttentionCARE: replicability of a BCI for the clinical application of augmented reality-guided EEG-based attention modification for adolescents at high risk for depression. Front Hum Neurosci 2024; 18:1360218. [PMID: 39045509 PMCID: PMC11264899 DOI: 10.3389/fnhum.2024.1360218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 06/11/2024] [Indexed: 07/25/2024] Open
Abstract
Affect-biased attention is the phenomenon of prioritizing attention to emotionally salient stimuli and away from goal-directed stimuli. It is thought that affect-biased attention to emotional stimuli is a driving factor in the development of depression. This effect has been well-studied in adults, but research shows that this is also true during adolescence, when the severity of depressive symptoms are correlated with the magnitude of affect-biased attention to negative emotional stimuli. Prior studies have shown that trainings to modify affect-biased attention may ameliorate depression in adults, but this research has also been stymied by concerns about reliability and replicability. This study describes a clinical application of augmented reality-guided EEG-based attention modification ("AttentionCARE") for adolescents who are at highest risk for future depressive disorders (i.e., daughters of depressed mothers). Our results (n = 10) indicated that the AttentionCARE protocol can reliably and accurately provide neurofeedback about adolescent attention to negative emotional distractors that detract from attention to a primary task. Through several within and cross-study replications, our work addresses concerns about the lack of reliability and reproducibility in brain-computer interface applications, offering insights for future interventions to modify affect-biased attention in high-risk adolescents.
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Affiliation(s)
- Richard Gall
- Signal Processing and Statistical Learning Laboratory, Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Nastasia Mcdonald
- Clinical Application of Neuroscience Laboratory, Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States
| | - Xiaofei Huang
- Augmented Cognition Laboratory, Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States
| | - Anna Wears
- Clinical Application of Neuroscience Laboratory, Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States
| | - Rebecca B. Price
- Clinical Application of Neuroscience Laboratory, Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States
| | - Sarah Ostadabbas
- Augmented Cognition Laboratory, Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States
| | - Murat Akcakaya
- Signal Processing and Statistical Learning Laboratory, Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Mary L. Woody
- Clinical Application of Neuroscience Laboratory, Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States
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Tornero-Costa R, Martinez-Millana A, Azzopardi-Muscat N, Lazeri L, Traver V, Novillo-Ortiz D. Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review. JMIR Ment Health 2023; 10:e42045. [PMID: 36729567 PMCID: PMC9936371 DOI: 10.2196/42045] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 11/02/2022] [Accepted: 11/20/2022] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges. OBJECTIVE This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality. METHODS A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided. RESULTS A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment-18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126). CONCLUSIONS These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management.
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Affiliation(s)
- Roberto Tornero-Costa
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas, Universitat Politècnica de València, Valencia, Spain
| | - Antonio Martinez-Millana
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas, Universitat Politècnica de València, Valencia, Spain
| | - Natasha Azzopardi-Muscat
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
| | - Ledia Lazeri
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
| | - Vicente Traver
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas, Universitat Politècnica de València, Valencia, Spain
| | - David Novillo-Ortiz
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
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Jia H, Lao H. Deep learning and multimodal feature fusion for the aided diagnosis of Alzheimer's disease. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07501-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Kumar M, Anderson MJ, Antony JW, Baldassano C, Brooks PP, Cai MB, Chen PHC, Ellis CT, Henselman-Petrusek G, Huberdeau D, Hutchinson JB, Li YP, Lu Q, Manning JR, Mennen AC, Nastase SA, Richard H, Schapiro AC, Schuck NW, Shvartsman M, Sundaram N, Suo D, Turek JS, Turner D, Vo VA, Wallace G, Wang Y, Williams JA, Zhang H, Zhu X, Capota˘ M, Cohen JD, Hasson U, Li K, Ramadge PJ, Turk-Browne NB, Willke TL, Norman KA. BrainIAK: The Brain Imaging Analysis Kit. APERTURE NEURO 2022; 1. [PMID: 35939268 PMCID: PMC9351935 DOI: 10.52294/31bb5b68-2184-411b-8c00-a1dacb61e1da] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
Functional magnetic resonance imaging (fMRI) offers a rich source of data for studying the neural basis of cognition. Here, we describe the Brain Imaging Analysis Kit (BrainIAK), an open-source, free Python package that provides computationally optimized solutions to key problems in advanced fMRI analysis. A variety of techniques are presently included in BrainIAK: intersubject correlation (ISC) and intersubject functional connectivity (ISFC), functional alignment via the shared response model (SRM), full correlation matrix analysis (FCMA), a Bayesian version of representational similarity analysis (BRSA), event segmentation using hidden Markov models, topographic factor analysis (TFA), inverted encoding models (IEMs), an fMRI data simulator that uses noise characteristics from real data (fmrisim), and some emerging methods. These techniques have been optimized to leverage the efficiencies of high-performance compute (HPC) clusters, and the same code can be seamlessly transferred from a laptop to a cluster. For each of the aforementioned techniques, we describe the data analysis problem that the technique is meant to solve and how it solves that problem; we also include an example Jupyter notebook for each technique and an annotated bibliography of papers that have used and/or described that technique. In addition to the sections describing various analysis techniques in BrainIAK, we have included sections describing the future applications of BrainIAK to real-time fMRI, tutorials that we have developed and shared online to facilitate learning the techniques in BrainIAK, computational innovations in BrainIAK, and how to contribute to BrainIAK. We hope that this manuscript helps readers to understand how BrainIAK might be useful in their research.
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Affiliation(s)
- Manoj Kumar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ
| | - Michael J. Anderson
- Work done while at Parallel Computing Lab, Intel Corporation, Santa Clara, CA
| | - James W. Antony
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ
| | | | - Paula P. Brooks
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ
| | - Ming Bo Cai
- International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Japan
| | - Po-Hsuan Cameron Chen
- Work done while at Princeton Neuroscience Institute, Princeton University, Princeton, NJ
| | | | | | | | | | - Y. Peeta Li
- Department of Psychology, University of Oregon, Eugene, OR
| | - Qihong Lu
- Department of Psychology, Princeton University, Princeton, NJ
| | - Jeremy R. Manning
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH
| | - Anne C. Mennen
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ
| | - Samuel A. Nastase
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ
| | - Hugo Richard
- Parietal Team, Inria, Neurospin, CEA, Université Paris-Saclay, France
| | - Anna C. Schapiro
- Department of Psychology, University of Pennsylvania, Philadelphia, PA
| | - Nicolas W. Schuck
- Max Planck Research Group NeuroCode, Max Planck Institute for Human Development, Berlin, Germany; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany
| | - Michael Shvartsman
- Work done while at Princeton Neuroscience Institute, Princeton University, Princeton, NJ
| | - Narayanan Sundaram
- Work done while at Parallel Computing Lab, Intel Corporation, Santa Clara, CA
| | - Daniel Suo
- epartment of Computer Science, Princeton University, Princeton, NJ
| | - Javier S. Turek
- Brain-Inspired Computing Lab, Intel Corporation, Hillsboro, OR
| | - David Turner
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ
| | - Vy A. Vo
- Brain-Inspired Computing Lab, Intel Corporation, Hillsboro, OR
| | - Grant Wallace
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ
| | - Yida Wang
- Work done while at Parallel Computing Lab, Intel Corporation, Santa Clara, CA
| | - Jamal A. Williams
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ; Department of Psychology, Princeton University, Princeton, NJ
| | - Hejia Zhang
- Work done while at Princeton Neuroscience Institute, Princeton University, Princeton, NJ
| | - Xia Zhu
- Brain-Inspired Computing Lab, Intel Corporation, Hillsboro, OR
| | - Mihai Capota˘
- Brain-Inspired Computing Lab, Intel Corporation, Hillsboro, OR
| | - Jonathan D. Cohen
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ; Department of Psychology, Princeton University, Princeton, NJ
| | - Uri Hasson
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ; Department of Psychology, Princeton University, Princeton, NJ
| | - Kai Li
- Department of Computer Science, Princeton University, Princeton, NJ
| | - Peter J. Ramadge
- Department of Electrical Engineering, and the Center for Statistics and Machine Learning, Princeton University, Princeton, NJ
| | | | | | - Kenneth A. Norman
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ; Department of Psychology, Princeton University, Princeton, NJ
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Singla RK, Joon S, Shen L, Shen B. Translational Informatics for Natural Products as Antidepressant Agents. Front Cell Dev Biol 2022; 9:738838. [PMID: 35127696 PMCID: PMC8811306 DOI: 10.3389/fcell.2021.738838] [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: 07/09/2021] [Accepted: 12/13/2021] [Indexed: 12/18/2022] Open
Abstract
Depression, a neurological disorder, is a universally common and debilitating illness where social and economic issues could also become one of its etiologic factors. From a global perspective, it is the fourth leading cause of long-term disability in human beings. For centuries, natural products have proven their true potential to combat various diseases and disorders, including depression and its associated ailments. Translational informatics applies informatics models at molecular, imaging, individual, and population levels to promote the translation of basic research to clinical applications. The present review summarizes natural-antidepressant-based translational informatics studies and addresses challenges and opportunities for future research in the field.
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Affiliation(s)
- Rajeev K. Singla
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- iGlobal Research and Publishing Foundation, New Delhi, India
| | - Shikha Joon
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
- iGlobal Research and Publishing Foundation, New Delhi, India
| | - Li Shen
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
| | - Bairong Shen
- Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China
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