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Gradwohl G, Snipes S, Walitza S, Huber R, Gerstenberg M. Timing and cortical region matter: theta power differences between teenagers affected by Major Depression and healthy controls. J Neural Transm (Vienna) 2024:10.1007/s00702-024-02810-1. [PMID: 39105815 DOI: 10.1007/s00702-024-02810-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 07/15/2024] [Indexed: 08/07/2024]
Abstract
In adults affected by Major Depressive Disorder (MDD), most findings point to higher electroencephalographic (EEG) theta power during wake compared to healthy controls (HC) as a potential biomarker aiding the diagnostic process or subgrouping for stratified treatment. Besides these group differences, theta power is modulated by time of day, sleep/wake history, and age. Thus, we aimed at assessing if the time of recording alters theta power in teenagers affected by MDD or HC. Standardized wake EEG power was assessed with high-density EEG in 15 children and adolescents with MDD and in 15 age- and sex-matched HC in the evening and morning. Using a two-way ANOVA, group, time, and their interaction were tested. In patients, the current severity of depression was rated using the Children's Depression Rating Scale. Broadband EEG power was lower in the morning after sleep, with a significant interaction (group x time) in central regions in the 4-6 Hz range. In MDD relative to HC, theta power was decreased over occipital areas in the evening and increased over frontal areas in the morning. A higher frontal theta power was correlated with more severe depressive mood in the morning but not in the evening. This was a cross-sectional study design, including patients on antidepressant medication. In conclusion, depending on time of recording, region-specific opposite differences of theta power were found between teenagers with MDD and HC. These findings stress the importance of the time of the recording when investigating theta power's relationship to psychopathology.
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Affiliation(s)
- Gideon Gradwohl
- Lev Academic Center, Department of Computer Sciences, Jerusalem College of Technology, Jerusalem, Israel
| | - Sophia Snipes
- Child Development Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland
- Children's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland
- Neural Control of Movement Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Susanne Walitza
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Neuroscicence Center Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
- Zurich Center for Integrative Human Physiology, University of Zurich, Zurich, Switzerland
| | - Reto Huber
- Child Development Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland
- Children's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Neuroscicence Center Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
| | - Miriam Gerstenberg
- Children's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland.
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland.
- Department of Child and Adolescent Psychiatry and Psychotherapy, Outpatient Services Winterthur, Psychiatric University Hospital Zurich, Albanistrasse 24, Winterthur, 8400, Switzerland.
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Berwian IM, Tröndle M, de Miquel C, Ziogas A, Stefanics G, Walter H, Stephan KE, Huys QJM. Emotion-Induced Frontal Alpha Asymmetry as a Candidate Predictor of Relapse After Discontinuation of Antidepressant Medication. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:809-818. [PMID: 38735534 DOI: 10.1016/j.bpsc.2024.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 02/13/2024] [Accepted: 05/03/2024] [Indexed: 05/14/2024]
Abstract
BACKGROUND One in 3 patients relapse after antidepressant discontinuation. Thus, the prevention of relapse after achieving remission is an important component in the long-term management of major depressive disorder. However, no clinical or other predictors are established. Frontal reactivity to sad mood as measured by functional magnetic resonance imaging has been reported to relate to relapse independently of antidepressant discontinuation and is an interesting candidate predictor. METHODS Patients (n = 56) who had remitted from a depressive episode while taking antidepressants underwent electroencephalography (EEG) recording during a sad mood induction procedure prior to gradually discontinuing their medication. Relapse was assessed over a 6-month follow-up period. Thirty five healthy control participants were also tested. Current source density of the EEG power in the alpha band (8-13 Hz) was extracted and alpha asymmetry was computed by comparing the power across 2 hemispheres at frontal electrodes (F5 and F6). RESULTS Sad mood induction was robust across all groups. Reactivity of alpha asymmetry to sad mood did not distinguish healthy control participants from patients with remitted major depressive disorder on medication. However, the 14 (25%) patients who relapsed during the follow-up period after discontinuing medication showed significantly reduced reactivity in alpha asymmetry compared with patients who remained well. This EEG signal provided predictive power (69% out-of-sample balanced accuracy and a positive predictive value of 0.75). CONCLUSIONS A simple EEG-based measure of emotional reactivity may have potential to contribute to clinical prediction models of antidepressant discontinuation. Given the very small sample size, this finding must be interpreted with caution and requires replication in a larger study.
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Affiliation(s)
- Isabel M Berwian
- Princeton Neuroscience Institute & Psychology Department, Princeton University, Princeton, New Jersey; Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zürich, Zurich, Switzerland.
| | - Marius Tröndle
- Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland
| | - Carlota de Miquel
- Research Innovation and Teaching Unit, Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Spain; Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain
| | - Anastasios Ziogas
- Faculty of Psychology, University Distance Suisse, Brig, Switzerland
| | - Gabor Stefanics
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Henrik Walter
- Charité-Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Psychiatry and Psychotherapy, Berlin, Germany
| | - Klaas E Stephan
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zürich, Zurich, Switzerland; Max Planck Institute for Metabolism Research, Cologne, Germany
| | - Quentin J M Huys
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zürich, Zurich, Switzerland; Department of Psychiatry, Psychotherapy and Psychosomatics, Hospital of Psychiatry, University of Zurich, Zurich, Switzerland; Applied Computational Psychiatry Lab, Mental Health Neuroscience Department, Division of Psychiatry and Max Planck Centre for Computational Psychiatry and Ageing Research, Queen Square Institute of Neurology, University College London, London, United Kingdom; Camden and Islington NHS Foundation Trust, London, United Kingdom
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Pettorruso M, Di Lorenzo G, Benatti B, d’Andrea G, Cavallotto C, Carullo R, Mancusi G, Di Marco O, Mammarella G, D’Attilio A, Barlocci E, Rosa I, Cocco A, Padula LP, Bubbico G, Perrucci MG, Guidotti R, D’Andrea A, Marzetti L, Zoratto F, Dell’Osso BM, Martinotti G. Overcoming treatment-resistant depression with machine-learning based tools: a study protocol combining EEG and clinical data to personalize glutamatergic and brain stimulation interventions (SelecTool Project). Front Psychiatry 2024; 15:1436006. [PMID: 39086731 PMCID: PMC11288917 DOI: 10.3389/fpsyt.2024.1436006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Accepted: 07/01/2024] [Indexed: 08/02/2024] Open
Abstract
Treatment-Resistant Depression (TRD) poses a substantial health and economic challenge, persisting as a major concern despite decades of extensive research into novel treatment modalities. The considerable heterogeneity in TRD's clinical manifestations and neurobiological bases has complicated efforts toward effective interventions. Recognizing the need for precise biomarkers to guide treatment choices in TRD, herein we introduce the SelecTool Project. This initiative focuses on developing (WorkPlane 1/WP1) and conducting preliminary validation (WorkPlane 2/WP2) of a computational tool (SelecTool) that integrates clinical data, neurophysiological (EEG) and peripheral (blood sample) biomarkers through a machine-learning framework designed to optimize TRD treatment protocols. The SelecTool project aims to enhance clinical decision-making by enabling the selection of personalized interventions. It leverages multi-modal data analysis to navigate treatment choices towards two validated therapeutic options for TRD: esketamine nasal spray (ESK-NS) and accelerated repetitive Transcranial Magnetic Stimulation (arTMS). In WP1, 100 subjects with TRD will be randomized to receive either ESK-NS or arTMS, with comprehensive evaluations encompassing neurophysiological (EEG), clinical (psychometric scales), and peripheral (blood samples) assessments both at baseline (T0) and one month post-treatment initiation (T1). WP2 will utilize the data collected in WP1 to train the SelecTool algorithm, followed by its application in a second, out-of-sample cohort of 20 TRD subjects, assigning treatments based on the tool's recommendations. Ultimately, this research seeks to revolutionize the treatment of TRD by employing advanced machine learning strategies and thorough data analysis, aimed at unraveling the complex neurobiological landscape of depression. This effort is expected to provide pivotal insights that will promote the development of more effective and individually tailored treatment strategies, thus addressing a significant void in current TRD management and potentially reducing its profound societal and economic burdens.
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Affiliation(s)
- Mauro Pettorruso
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D’Annunzio, Chieti, Italy
- Department of Mental Health, ASL02 Lanciano-Vasto-Chieti, Chieti, Italy
- Institute for Advanced Biomedical Technologies (ITAB), “G. d’Annunzio” University of Chieti-Pescara, Chieti, Italy
| | - Giorgio Di Lorenzo
- Laboratory of Psychophysiology and Cognitive Neuroscience, Chair of Psychiatry, Department of Systems Medicine, University of Rome Tor Vergata, Rome, Italy
- Institute of Hospitalization and Care With Scientific Character (IRCCS) Fondazione Santa Lucia, Rome, Italy
| | - Beatrice Benatti
- Department of Biomedical and Clinical Sciences Luigi Sacco and Aldo Ravelli Center for Neurotechnology and Brain Therapeutic, University of Milan, Milano, Italy
| | - Giacomo d’Andrea
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D’Annunzio, Chieti, Italy
- Department of Mental Health, ASL02 Lanciano-Vasto-Chieti, Chieti, Italy
| | - Clara Cavallotto
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D’Annunzio, Chieti, Italy
| | - Rosalba Carullo
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D’Annunzio, Chieti, Italy
| | - Gianluca Mancusi
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D’Annunzio, Chieti, Italy
| | - Ornella Di Marco
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D’Annunzio, Chieti, Italy
| | - Giovanna Mammarella
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D’Annunzio, Chieti, Italy
| | - Antonio D’Attilio
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D’Annunzio, Chieti, Italy
| | - Elisabetta Barlocci
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D’Annunzio, Chieti, Italy
| | - Ilenia Rosa
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D’Annunzio, Chieti, Italy
| | - Alessio Cocco
- Department of Mental Health, ASL02 Lanciano-Vasto-Chieti, Chieti, Italy
| | - Lorenzo Pio Padula
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D’Annunzio, Chieti, Italy
| | - Giovanna Bubbico
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D’Annunzio, Chieti, Italy
| | - Mauro Gianni Perrucci
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D’Annunzio, Chieti, Italy
- Institute for Advanced Biomedical Technologies (ITAB), “G. d’Annunzio” University of Chieti-Pescara, Chieti, Italy
| | - Roberto Guidotti
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D’Annunzio, Chieti, Italy
- Department of Mental Health, ASL02 Lanciano-Vasto-Chieti, Chieti, Italy
- Institute for Advanced Biomedical Technologies (ITAB), “G. d’Annunzio” University of Chieti-Pescara, Chieti, Italy
| | - Antea D’Andrea
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D’Annunzio, Chieti, Italy
| | - Laura Marzetti
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D’Annunzio, Chieti, Italy
- Institute for Advanced Biomedical Technologies (ITAB), “G. d’Annunzio” University of Chieti-Pescara, Chieti, Italy
| | - Francesca Zoratto
- Centre for Behavioural Sciences and Mental Health, Istituto Superiore di Sanità, Rome, Italy
| | - Bernardo Maria Dell’Osso
- Department of Biomedical and Clinical Sciences Luigi Sacco and Aldo Ravelli Center for Neurotechnology and Brain Therapeutic, University of Milan, Milano, Italy
| | - Giovanni Martinotti
- Department of Neurosciences, Imaging and Clinical Sciences, Università degli Studi G. D’Annunzio, Chieti, Italy
- Department of Mental Health, ASL02 Lanciano-Vasto-Chieti, Chieti, Italy
- Institute for Advanced Biomedical Technologies (ITAB), “G. d’Annunzio” University of Chieti-Pescara, Chieti, Italy
- Psychopharmacology, Drug Misuse and Novel Psychoactive Substances Research Unit, School of Life and Medical Sciences, University of Hertfordshire, Hatfield, United Kingdom
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Yang S, Kim JW. Electrophysiological Markers Predicting Antipsychotic Treatment Response in Patients with Schizophrenia: A Retrospective Study. Neuropsychiatr Dis Treat 2024; 20:1387-1394. [PMID: 39007072 PMCID: PMC11246663 DOI: 10.2147/ndt.s467180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Accepted: 06/29/2024] [Indexed: 07/16/2024] Open
Abstract
Purpose This study aimed to provide an objective means of predicting treatment responses in patients with schizophrenia using quantitative electroencephalography (qEEG) as an electrophysiological indicator. We obtained qEEG recordings from patients with schizophrenia and explored them for patterns indicative of treatment responsiveness. Patients and Methods The study included 68 patients had been diagnosed with schizophrenia spectrum disorder. After retrospectively gathering demographic information, clinical data such as qEEG, Positive and Negative Syndrome Scale (PANSS), a multiple regression analysis was performed. This analysis employed baseline qEEG findings as independent variables and PANSS score changes as dependent variables to discern causal relationships. Results The mean age of the participants was 38.4 years(SD =13.73). The mean PANSS score on admission was 92.97, decreasing to 67.41 at discharge. Multiple regression analysis revealed that delta waves in T4 (β=0.346, t=3.165, p=0.002), and high-beta waves in Fp2 (β=0.231, t=2.361, p=0.021) were associated with PANSS changes in absolute power. In addition, the delta waves of O2 (β=0.250, t=3.288, p=0.002); beta waves of T3 (β=-1.463, t=-5.423, p<0.001) and O2 (β=0.551, t=3.366, p=0.001); high beta waves of Fp1 (β=0.307, t=4.026, p<0.001), T3 (β=0.855, t=4.414, p<0.001) and T6 (β=-0.838, t=-4.559, p<0.001) of absolute power using the Z-score were also related to PANSS changes. Pearson's correlation analysis showed that only delta waves at Cz (r= 0.246, p=0.043) in absolute power correlated with changes in the PANSS. Conclusion We found that certain qEEG wave patterns in patients with schizophrenia prior to antipsychotic treatment were linked to PANSS changes before and after treatment. Delta waves and beta waves, primarily in the frontal and temporal regions, were found to be significantly associated with changes in PANSS scores. In the future, the qEEG indicators identified in this study could serve as electrophysiological markers for predicting antipsychotic treatment responses in patients with schizophrenia.
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Affiliation(s)
- Seungheon Yang
- Department of Psychiatry, Daegu Catholic University Medical Center, Daegu, Republic of Korea
| | - Jun Won Kim
- Department of Psychiatry, Daegu Catholic University Medical Center, Daegu, Republic of Korea
- Department of Psychiatry, Daegu Catholic University School of Medicine, Daegu, Republic of Korea
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5
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Prompiengchai S, Dunlop K. Breakthroughs and challenges for generating brain network-based biomarkers of treatment response in depression. Neuropsychopharmacology 2024:10.1038/s41386-024-01907-1. [PMID: 38951585 DOI: 10.1038/s41386-024-01907-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 05/17/2024] [Accepted: 06/13/2024] [Indexed: 07/03/2024]
Abstract
Treatment outcomes widely vary for individuals diagnosed with major depressive disorder, implicating a need for deeper understanding of the biological mechanisms conferring a greater likelihood of response to a particular treatment. Our improved understanding of intrinsic brain networks underlying depression psychopathology via magnetic resonance imaging and other neuroimaging modalities has helped reveal novel and potentially clinically meaningful biological markers of response. And while we have made considerable progress in identifying such biomarkers over the last decade, particularly with larger, multisite trials, there are significant methodological and practical obstacles that need to be overcome to translate these markers into the clinic. The aim of this review is to review current literature on brain network structural and functional biomarkers of treatment response or selection in depression, with a specific focus on recent large, multisite trials reporting predictive accuracy of candidate biomarkers. Regarding pharmaco- and psychotherapy, we discuss candidate biomarkers, reporting that while we have identified candidate biomarkers of response to a single intervention, we need more trials that distinguish biomarkers between first-line treatments. Further, we discuss the ways prognostic neuroimaging may help to improve treatment outcomes to neuromodulation-based therapies, such as transcranial magnetic stimulation and deep brain stimulation. Lastly, we highlight obstacles and technical developments that may help to address the knowledge gaps in this area of research. Ultimately, integrating neuroimaging-derived biomarkers into clinical practice holds promise for enhancing treatment outcomes and advancing precision psychiatry strategies for depression management. By elucidating the neural predictors of treatment response and selection, we can move towards more individualized and effective depression interventions, ultimately improving patient outcomes and quality of life.
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Affiliation(s)
| | - Katharine Dunlop
- Centre for Depression and Suicide Studies, Unity Health Toronto, Toronto, ON, Canada.
- Keenan Research Centre for Biomedical Science, Unity Health Toronto, Toronto, ON, Canada.
- Department of Psychiatry and Institute of Medical Science, University of Toronto, Toronto, ON, Canada.
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Jin MX, Qin PP, Xia AWL, Kan RLD, Zhang BBB, Tang AHP, Li ASM, Lin TTZ, Giron CG, Pei JJ, Kranz GS. Neurophysiological and neuroimaging markers of repetitive transcranial magnetic stimulation treatment response in major depressive disorder: A systematic review and meta-analysis of predictive modeling studies. Neurosci Biobehav Rev 2024; 162:105695. [PMID: 38710424 DOI: 10.1016/j.neubiorev.2024.105695] [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: 10/26/2023] [Revised: 04/10/2024] [Accepted: 04/26/2024] [Indexed: 05/08/2024]
Abstract
Predicting repetitive transcranial magnetic stimulation (rTMS) treatment outcomes in major depressive disorder (MDD) could reduce the financial and psychological risks of treatment failure. We systematically reviewed and meta-analyzed studies that leveraged neurophysiological and neuroimaging markers to predict rTMS response in MDD. Five databases were searched from inception to May 25, 2023. The primary meta-analytic outcome was predictive accuracy pooled from classification models. Regression models were summarized qualitatively. A promising marker was identified if it showed a sensitivity and specificity of 80% or higher in at least two independent studies. Searching yielded 36 studies. Twenty-two classification modeling studies produced an estimated area under the summary receiver operating characteristic curve of 0.87 (95% CI = 0.83-0.92), with 86.8% sensitivity (95% CI = 80.6-91.2%) and 81.9% specificity (95% CI = 76.1-86.4%). Frontal theta cordance measured by electroencephalography is closest to proof of concept. Predicting rTMS response using neurophysiological and neuroimaging markers is promising for clinical decision-making. However, replications by different research groups are needed to establish rigorous markers.
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Affiliation(s)
- Min Xia Jin
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China; Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai, China
| | - Penny Ping Qin
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China
| | - Adam Wei Li Xia
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China
| | - Rebecca Lai Di Kan
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China
| | - Bella Bing Bing Zhang
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China
| | - Alvin Hong Pui Tang
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China
| | - Ami Sin Man Li
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China
| | - Tim Tian Ze Lin
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China
| | - Cristian G Giron
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China
| | - Jun Jie Pei
- Department of Rehabilitation Medicine, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Zhejiang, China
| | - Georg S Kranz
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China; Mental Health Research Center, The Hong Kong Polytechnic University, Hong Kong, Special Administrative Region of China; Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Austria.
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7
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Mirjebreili SM, Shalbaf R, Shalbaf A. Prediction of treatment response in major depressive disorder using a hybrid of convolutional recurrent deep neural networks and effective connectivity based on EEG signal. Phys Eng Sci Med 2024; 47:633-642. [PMID: 38358619 DOI: 10.1007/s13246-024-01392-2] [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: 04/27/2023] [Accepted: 01/11/2024] [Indexed: 02/16/2024]
Abstract
In this study, we have developed a novel method based on deep learning and brain effective connectivity to classify responders and non-responders to selective serotonin reuptake inhibitors (SSRIs) antidepressants in major depressive disorder (MDD) patients prior to the treatment using EEG signal. The effective connectivity of 30 MDD patients was determined by analyzing their pretreatment EEG signals, which were then concatenated into delta, theta, alpha, and beta bands and transformed into images. Using these images, we then fine tuned a hybrid Convolutional Neural Network that is enhanced with bidirectional Long Short-Term Memory cells based on transfer learning. The Inception-v3, ResNet18, DenseNet121, and EfficientNet-B0 models are implemented as base models. Finally, the models are followed by BiLSTM and dense layers in order to classify responders and non-responders to SSRI treatment. Results showed that the EfficiencyNet-B0 has the highest accuracy of 98.33, followed by DensNet121, ResNet18 and Inception-v3. Therefore, a new method was proposed in this study that uses deep learning models to extract both spatial and temporal features automatically, which will improve classification results. The proposed method provides accurate identification of MDD patients who are responding, thereby reducing the cost of medical facilities and patient care.
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Affiliation(s)
| | - Reza Shalbaf
- Institute for Cognitive Science Studies, Tehran, Iran
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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McInnes AN, Olsen ST, Sullivan CR, Cooper DC, Wilson S, Sonmez AI, Albott SC, Olson SC, Peterson CB, Rittberg BR, Herman A, Bajzer M, Nahas Z, Widge AS. Trajectory Modeling and Response Prediction in Transcranial Magnetic Stimulation for Depression. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.30.24308258. [PMID: 38853937 PMCID: PMC11160841 DOI: 10.1101/2024.05.30.24308258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Repetitive transcranial magnetic stimulation (rTMS) therapy could be improved by better and earlier prediction of response. Latent class mixture (LCMM) and non-linear mixed effects (NLME) modelling have been applied to model the trajectories of antidepressant response (or non-response) to TMS, but it is not known whether such models can predict clinical outcomes. We compared LCMM and NLME approaches to model the antidepressant response to TMS in a naturalistic sample of 238 patients receiving rTMS for treatment resistant depression (TRD), across multiple coils and protocols. We then compared the predictive power of those models. LCMM trajectories were influenced largely by baseline symptom severity, but baseline symptoms provided little predictive power for later antidepressant response. Rather, the optimal LCMM model was a nonlinear two-class model that accounted for baseline symptoms. This model accurately predicted patient response at 4 weeks of treatment (AUC = 0.70, 95% CI = [0.52-0.87]), but not before. NLME offered slightly improved predictive performance at 4 weeks of treatment (AUC = 0.76, 95% CI = [0.58 - 0.94], but likewise, not before. In showing the predictive validity of these approaches to model response trajectories to rTMS, we provided preliminary evidence that trajectory modeling could be used to guide future treatment decisions.
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Affiliation(s)
- Aaron N. McInnes
- Corresponding authors: Aaron N. McInnes PhD and Alik S. Widge MD, PhD, Department of Psychiatry, University of Minnesota, Twin Cities, McGuire Translational Research Facility, 2001 6th St SE, Minneapolis, MN 55455,
| | | | - Christi R.P. Sullivan
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Dawson C. Cooper
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Saydra Wilson
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Ayse Irem Sonmez
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Sophia C. Albott
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Stephen C. Olson
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Carol B. Peterson
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Barry R. Rittberg
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Alexander Herman
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Matej Bajzer
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Ziad Nahas
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Alik S. Widge
- Department of Psychiatry and Behavioral Science, University of Minnesota Twin Cities, Minneapolis, MN, USA
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9
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Tong X, Zhao K, Fonzo GA, Xie H, Carlisle NB, Keller CJ, Oathes DJ, Sheline Y, Nemeroff CB, Williams LM, Trivedi M, Etkin A, Zhang Y. Optimizing Antidepressant Efficacy: Multimodal Neuroimaging Biomarkers for Prediction of Treatment Response. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.11.24305583. [PMID: 38645124 PMCID: PMC11030479 DOI: 10.1101/2024.04.11.24305583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Major depressive disorder (MDD) is a common and often severe condition that profoundly diminishes quality of life for individuals across ages and demographic groups. Unfortunately, current antidepressant and psychotherapeutic treatments exhibit limited efficacy and unsatisfactory response rates in a substantial number of patients. The development of effective therapies for MDD is hindered by the insufficiently understood heterogeneity within the disorder and its elusive underlying mechanisms. To address these challenges, we present a target-oriented multimodal fusion framework that robustly predicts antidepressant response by integrating structural and functional connectivity data (sertraline: R2 = 0.31; placebo: R2 = 0.22). Through the model, we identify multimodal neuroimaging biomarkers of antidepressant response and observe that sertraline and placebo show distinct predictive patterns. We further decompose the overall predictive patterns into constitutive network constellations with generalizable structural-functional co-variation, which exhibit treatment-specific association with personality traits and behavioral/cognitive task performance. Our innovative and interpretable multimodal framework provides novel insights into the intricate neuropsychopharmacology of antidepressant treatment and paves the way for advances in precision medicine and development of more targeted antidepressant therapeutics.
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Affiliation(s)
- Xiaoyu Tong
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Kanhao Zhao
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Gregory A. Fonzo
- Center for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, Austin, TX, USA
| | - Hua Xie
- Center for Neuroscience Research, Children’s National Hospital, Washington, DC, USA
| | | | - Corey J. Keller
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Sierra-Pacific Mental Illness Research, Education and Clinical Center (MIRECC), Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Desmond J. Oathes
- Center for Brain Imaging and Stimulation, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Yvette Sheline
- Center for Neuromodulation in Depression and Stress, Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Charles B. Nemeroff
- Center for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, Austin, TX, USA
| | - Leanne M. Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Sierra-Pacific Mental Illness Research, Education and Clinical Center (MIRECC), Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA
| | - Madhukar Trivedi
- Department of Psychiatry, Center for Depression Research and Clinical Care, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Amit Etkin
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Alto Neuroscience, Inc., Los Altos, CA, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA
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10
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Klooster D, Voetterl H, Baeken C, Arns M. Evaluating Robustness of Brain Stimulation Biomarkers for Depression: A Systematic Review of Magnetic Resonance Imaging and Electroencephalography Studies. Biol Psychiatry 2024; 95:553-563. [PMID: 37734515 DOI: 10.1016/j.biopsych.2023.09.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 08/30/2023] [Accepted: 09/06/2023] [Indexed: 09/23/2023]
Abstract
Noninvasive brain stimulation (NIBS) treatments have gained considerable attention as potential therapeutic intervention for psychiatric disorders. The identification of reliable biomarkers for predicting clinical response to NIBS has been a major focus of research in recent years. Neuroimaging techniques, such as electroencephalography (EEG) and functional magnetic resonance imaging (MRI), have been used to identify potential biomarkers that could predict response to NIBS. However, identifying clinically actionable brain biomarkers requires robustness. In this systematic review, we aimed to summarize the current state of brain biomarker research for NIBS in depression, focusing only on well-powered studies (N ≥ 88) and/or studies that aimed at independently replicating previous findings, either successfully or unsuccessfully. A total of 220 studies were initially identified, of which 18 MRI studies and 18 EEG studies met the inclusion criteria. All focused on repetitive transcranial magnetic stimulation treatment in depression. After reviewing the included studies, we found the following MRI and EEG biomarkers to be most robust: 1) functional MRI-based functional connectivity between the dorsolateral prefrontal cortex and subgenual anterior cingulate cortex, 2) functional MRI-based network connectivity, 3) task-induced EEG frontal-midline theta, and 4) EEG individual alpha frequency. Future prospective studies should further investigate the clinical actionability of these specific EEG and MRI biomarkers to bring biomarkers closer to clinical reality.
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Affiliation(s)
- Debby Klooster
- Ghent Experimental Psychiatry Laboratory, Department of Head and Skin, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium; 4BRAIN Team, Department of Head and Skin, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium; Center for Care and Cure, Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Helena Voetterl
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Chris Baeken
- Ghent Experimental Psychiatry Laboratory, Department of Head and Skin, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium; Center for Care and Cure, Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Department of Psychiatry, Brussels, Belgium
| | - Martijn Arns
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands.
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11
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Godfrey K, Muthukumaraswamy SD, Stinear CM, Hoeh NR. Resting-state EEG connectivity recorded before and after rTMS treatment in patients with treatment-resistant depression. Psychiatry Res Neuroimaging 2024; 338:111767. [PMID: 38183848 DOI: 10.1016/j.pscychresns.2023.111767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 11/12/2023] [Accepted: 12/08/2023] [Indexed: 01/08/2024]
Abstract
Repetitive transcranial magnetic stimulation (rTMS) has shown efficacy and tolerability in Major Depressive Disorder (MDD). However, the underlying mechanisms of its antidepressant effects remain unclear. This open-label study investigated electroencephalography (EEG) functional connectivity markers associated with response and the antidepressant effects of rTMS. Resting-state EEG data were collected from 28 participants with MDD before and after a four-week rTMS course. Source-space functional connectivity between 38 cortical regions was compared using an orthogonalised amplitude approach. Depressive symptoms significantly improved following rTMS, with 43 % of participants classified as responders. While the study's functional connectivity findings did not withstand multiple comparison corrections, exploratory analyses suggest an association between theta band connectivity and rTMS treatment mechanisms. Fronto-parietal theta connectivity increased after treatment but did not correlate with antidepressant response. Notably, low baseline theta connectivity was associated with greater response. However, due to the exploratory nature and small sample size, further replication is needed. The findings provide preliminary evidence that EEG functional connectivity, particularly within the theta band, may reflect the mechanisms by which rTMS exerts its therapeutic effects.
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Affiliation(s)
- Kate Godfrey
- School of Pharmacy, The University of Auckland, Auckland, New Zealand; Division of Psychiatry, Imperial College London, London, United Kingdom.
| | | | - Cathy M Stinear
- School of Medicine, The University of Auckland, Auckland, New Zealand
| | - Nicholas R Hoeh
- Department of Psychological Medicine, The University of Auckland, Auckland, New Zealand; Auckland District Health Board, Auckland, New Zealand
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12
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Jensen KHR, Urdanibia-Centelles O, Dam VH, Köhler-Forsberg K, Frokjaer VG, Knudsen GM, Jørgensen MB, Ip CT. EEG abnormalities are not associated with poor antidepressant treatment outcome - A NeuroPharm study. Eur Neuropsychopharmacol 2024; 79:59-65. [PMID: 38128462 DOI: 10.1016/j.euroneuro.2023.11.004] [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: 06/06/2023] [Revised: 11/24/2023] [Accepted: 11/26/2023] [Indexed: 12/23/2023]
Abstract
EEG brain abnormalities, such as slowing and isolated epileptiform discharges (IEDs), has previously been associated with non-response to antidepressant treatment with escitalopram and venlafaxine, suggesting a potential need for treatment with anticonvulsant property in some patients. The current study aims to replicate the reported association of EEG abnormality and treatment outcomes in an open-label trial of escitalopram for major depressive disorder (MDD) and explore its relationship to mood and cognition. Pretreatment, 6 min eyes-closed resting-state 256-channel EEG was recorded in 91 patients with MDD (age 18-57) who were treated with 10-20 mg escitalopram for 12 weeks; patients could switch to duloxetine after four weeks. A certified clinical neurophysiologist rated the EEGs. IED and EEG slowing was seen in 13.2%, and in 6.6% there were findings with unclear significance (i.e., Wicket spikes and theta activity). We saw no group-difference in remission or response rates after 8 and 12 weeks of treatment or switching to duloxetine. Patients with EEG abnormalities had higher pretreatment mood disturbances driven by greater anger (p=.039) and poorer verbal memory (p=.012). However, EEG abnormality was not associated with improved mood or verbal memory after treatment. Our findings should be interpreted in light of the rarity of EEG abnormalities and the sample size. While we cannot confirm that EEG abnormalities are associated with non-response to treatment, including escitalopram, abnormal EEG activity is associated with poor mood and verbal memory. The clinical utility of EEG abnormality in antidepressant treatment selection needs careful evaluation before deciding if useful for clinical implementation.
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Affiliation(s)
- Kristian H Reveles Jensen
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Department of Psychiatry, Psychiatric Centre Copenhagen, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Olalla Urdanibia-Centelles
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Department of Clinical Neurophysiology, Rigshospitalet, Copenhagen, Denmark
| | - Vibeke H Dam
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark
| | - Kristin Köhler-Forsberg
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Department of Psychiatry, Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Vibe G Frokjaer
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Department of Psychiatry, Psychiatric Centre Copenhagen, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Gitte M Knudsen
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Center for Cognitive and Brain Sciences, University of Macau, Taipa, Macau SAR, China
| | - Martin B Jørgensen
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Department of Psychiatry, Psychiatric Centre Copenhagen, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Cheng T Ip
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Center for Cognitive and Brain Sciences, University of Macau, Taipa, Macau SAR, China
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13
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Sellers KK, Cohen JL, Khambhati AN, Fan JM, Lee AM, Chang EF, Krystal AD. Closed-loop neurostimulation for the treatment of psychiatric disorders. Neuropsychopharmacology 2024; 49:163-178. [PMID: 37369777 PMCID: PMC10700557 DOI: 10.1038/s41386-023-01631-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 05/31/2023] [Accepted: 06/02/2023] [Indexed: 06/29/2023]
Abstract
Despite increasing prevalence and huge personal and societal burden, psychiatric diseases still lack treatments which can control symptoms for a large fraction of patients. Increasing insight into the neurobiology underlying these diseases has demonstrated wide-ranging aberrant activity and functioning in multiple brain circuits and networks. Together with varied presentation and symptoms, this makes one-size-fits-all treatment a challenge. There has been a resurgence of interest in the use of neurostimulation as a treatment for psychiatric diseases. Initial studies using continuous open-loop stimulation, in which clinicians adjusted stimulation parameters during patient visits, showed promise but also mixed results. Given the periodic nature and fluctuations of symptoms often observed in psychiatric illnesses, the use of device-driven closed-loop stimulation may provide more effective therapy. The use of a biomarker, which is correlated with specific symptoms, to deliver stimulation only during symptomatic periods allows for the personalized therapy needed for such heterogeneous disorders. Here, we provide the reader with background motivating the use of closed-loop neurostimulation for the treatment of psychiatric disorders. We review foundational studies of open- and closed-loop neurostimulation for neuropsychiatric indications, focusing on deep brain stimulation, and discuss key considerations when designing and implementing closed-loop neurostimulation.
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Affiliation(s)
- Kristin K Sellers
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Joshua L Cohen
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Ankit N Khambhati
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Joline M Fan
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
- Department of Neurology, University of California, San Francisco, CA, USA
| | - A Moses Lee
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Edward F Chang
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Andrew D Krystal
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA.
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA.
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14
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Widge AS. Closing the loop in psychiatric deep brain stimulation: physiology, psychometrics, and plasticity. Neuropsychopharmacology 2024; 49:138-149. [PMID: 37415081 PMCID: PMC10700701 DOI: 10.1038/s41386-023-01643-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 05/28/2023] [Accepted: 06/20/2023] [Indexed: 07/08/2023]
Abstract
Deep brain stimulation (DBS) is an invasive approach to precise modulation of psychiatrically relevant circuits. Although it has impressive results in open-label psychiatric trials, DBS has also struggled to scale to and pass through multi-center randomized trials. This contrasts with Parkinson disease, where DBS is an established therapy treating thousands of patients annually. The core difference between these clinical applications is the difficulty of proving target engagement, and of leveraging the wide range of possible settings (parameters) that can be programmed in a given patient's DBS. In Parkinson's, patients' symptoms change rapidly and visibly when the stimulator is tuned to the correct parameters. In psychiatry, those same changes take days to weeks, limiting a clinician's ability to explore parameter space and identify patient-specific optimal settings. I review new approaches to psychiatric target engagement, with an emphasis on major depressive disorder (MDD). Specifically, I argue that better engagement may come by focusing on the root causes of psychiatric illness: dysfunction in specific, measurable cognitive functions and in the connectivity and synchrony of distributed brain circuits. I overview recent progress in both those domains, and how it may relate to other technologies discussed in companion articles in this issue.
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Affiliation(s)
- Alik S Widge
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA.
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15
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Fu J, Cai X, Huang S, Qiu X, Li Z, Hong H, Qu S, Huang Y. Predicting acupuncture efficacy for major depressive disorder using baseline clinical variables: A machine learning study. J Psychiatr Res 2023; 168:64-70. [PMID: 37897838 DOI: 10.1016/j.jpsychires.2023.10.040] [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: 03/04/2023] [Revised: 08/05/2023] [Accepted: 10/18/2023] [Indexed: 10/30/2023]
Abstract
Acupuncture is a viable treatment option for major depressive disorder (MDD). However, its effectiveness varies among patients. This study aimed to develop a model to predict the efficacy of acupuncture therapy for MDD using machine learning and baseline clinical variables. A total of 124 patients with MDD from five research centers were included in our machine learning study. All patients underwent acupuncture treatment for 6 weeks and the efficacy of the treatment was evaluated using the Hamilton Depression Scale-17 (HAMD-17). The max-relevance and min-redundancy (mRMR) algorithm and Pearson correlation analysis were used for selecting 11 significant features from 26 baseline clinical variables for model training. We compared the performance of five machine learning models, including logistic regression, support vector machine, K-nearest neighbor, random forest, and XgBoost, in predicting the effect of acupuncture in relieving major depression. Among the five models, XgBoost performed the best with an area under the receiver operating characteristic curve (AUC) of 0.835, an accuracy of 0.730, a sensitivity of 0.670, a specificity of 0.774, and an F1 score of 0.751. The key predictive variables identified were anxiety score in the self-rating depression scale (SDS), the traditional Chinese medicine syndrome of deficiency in both heart and spleen, and body mass index (BMI). The study demonstrates that the developed model can help physicians predict the patients who will benefit from acupuncture treatment, which is of positive significance for improving the clinical efficacy of acupuncture on MDD.
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Affiliation(s)
- Jiani Fu
- School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China
| | - Xiaowen Cai
- School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China
| | - Shengtao Huang
- School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China
| | - Xiaoke Qiu
- School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China
| | - Zheng Li
- School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China
| | - Houyuan Hong
- School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China
| | - Shanshan Qu
- School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China.
| | - Yong Huang
- School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China.
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16
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Stolz LA, Kohn JN, Smith SE, Benster LL, Appelbaum LG. Predictive Biomarkers of Treatment Response in Major Depressive Disorder. Brain Sci 2023; 13:1570. [PMID: 38002530 PMCID: PMC10669981 DOI: 10.3390/brainsci13111570] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 11/02/2023] [Accepted: 11/07/2023] [Indexed: 11/26/2023] Open
Abstract
Major depressive disorder (MDD) is a highly prevalent, debilitating disorder with a high rate of treatment resistance. One strategy to improve treatment outcomes is to identify patient-specific, pre-intervention factors that can predict treatment success. Neurophysiological measures such as electroencephalography (EEG), which measures the brain's electrical activity from sensors on the scalp, offer one promising approach for predicting treatment response for psychiatric illnesses, including MDD. In this study, a secondary data analysis was conducted on the publicly available Two Decades Brainclinics Research Archive for Insights in Neurophysiology (TDBRAIN) database. Logistic regression modeling was used to predict treatment response, defined as at least a 50% improvement on the Beck's Depression Inventory, in 119 MDD patients receiving repetitive transcranial magnetic stimulation (rTMS). The results show that both age and baseline symptom severity were significant predictors of rTMS treatment response, with older individuals and more severe depression scores associated with decreased odds of a positive treatment response. EEG measures contributed predictive power to these models; however, these improvements in outcome predictability only trended towards statistical significance. These findings provide confirmation of previous demographic and clinical predictors, while pointing to EEG metrics that may provide predictive information in future studies.
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Affiliation(s)
- Louise A. Stolz
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA; (L.A.S.); (J.N.K.); (L.L.B.)
| | - Jordan N. Kohn
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA; (L.A.S.); (J.N.K.); (L.L.B.)
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA 92093, USA
| | - Sydney E. Smith
- Department of Cognitive Science, University of California San Diego, La Jolla, CA 92093, USA;
| | - Lindsay L. Benster
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA; (L.A.S.); (J.N.K.); (L.L.B.)
- Department Clinical Psychology, San Diego State University, San Diego, CA 92182, USA
| | - Lawrence G. Appelbaum
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA; (L.A.S.); (J.N.K.); (L.L.B.)
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Hacker C, Mocchi M, Xiao J, Metzger B, Adkinson J, Pascuzzi B, Mathura R, Oswalt D, Watrous A, Bartoli E, Allawala A, Pirtle V, Fan X, Danstrom I, Shofty B, Banks G, Zhang Y, Armenta-Salas M, Mirpour K, Provenza N, Mathew S, Cohn J, Borton D, Goodman W, Pouratian N, Sheth S, Bijanki K. Aperiodic neural activity is a biomarker for depression severity. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.07.23298040. [PMID: 37986996 PMCID: PMC10659509 DOI: 10.1101/2023.11.07.23298040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
A reliable physiological biomarker for Major Depressive Disorder (MDD) is necessary to improve treatment success rates by shoring up variability in outcome measures. In this study, we establish a passive biomarker that tracks with changes in mood on the order of minutes to hours. We record from intracranial electrodes implanted deep in the brain - a surgical setting providing exquisite temporal and spatial sensitivity to detect this relationship in a difficult-to-measure brain area, the ventromedial prefrontal cortex (VMPFC). The aperiodic slope of the power spectral density captures the balance of activity across all frequency bands and is construed as a putative proxy for excitatory/inhibitory balance in the brain. This study demonstrates how shifts in aperiodic slope correlate with depression severity in a clinical trial of deep brain stimulation for treatment-resistant depression (TRD). The correlation between depression severity scores and aperiodic slope is significant in N=5 subjects, indicating that flatter (less negative) slopes correspond to reduced depression severity, especially in the ventromedial prefrontal cortex. This biomarker offers a new way to track patient response to MDD treatment, facilitating individualized therapies in both intracranial and non-invasive monitoring scenarios.
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Affiliation(s)
- C. Hacker
- Baylor College of Medicine Department of Neurosurgery
- Washington University in St. Louis Department of Neurosurgery
| | - M.M Mocchi
- Baylor College of Medicine Department of Neurosurgery
| | - J. Xiao
- Baylor College of Medicine Department of Neurosurgery
| | - B.A. Metzger
- Baylor College of Medicine Department of Neurosurgery
| | - J.A. Adkinson
- Baylor College of Medicine Department of Neurosurgery
| | - B.R. Pascuzzi
- Baylor College of Medicine Department of Neurosurgery
| | - R.C. Mathura
- Baylor College of Medicine Department of Neurosurgery
| | - D. Oswalt
- University of Pennsylvania Department of Neurosurgery
| | - A. Watrous
- Baylor College of Medicine Department of Neurosurgery
| | - E. Bartoli
- Baylor College of Medicine Department of Neurosurgery
| | - A. Allawala
- Brown University Department of Biomedical Engineering and Carney Institute for Brain Science
| | - V. Pirtle
- Baylor College of Medicine Department of Neurosurgery
| | - X. Fan
- Baylor College of Medicine Department of Neurosurgery
| | - I. Danstrom
- Baylor College of Medicine Department of Neurosurgery
| | - B. Shofty
- Baylor College of Medicine Department of Neurosurgery
| | - G. Banks
- Baylor College of Medicine Department of Neurosurgery
| | - Y. Zhang
- Baylor College of Medicine Department of Neurosurgery
| | | | - K. Mirpour
- University of Texas Southwestern, Department of Neurosurgery
| | - N. Provenza
- Baylor College of Medicine Department of Neurosurgery
| | - S. Mathew
- Baylor College of Medicine Department of Psychiatry
| | - J. Cohn
- University of Pittsburgh Department of Psychology
| | - D. Borton
- Brown University Department of Biomedical Engineering and Carney Institute for Brain Science
- Brown University Department of Veterans Affairs Center for Neurorestoration and Neurotechnology
| | - W. Goodman
- Baylor College of Medicine Department of Psychiatry
| | - N. Pouratian
- University of Texas Southwestern, Department of Neurosurgery
| | - S.A. Sheth
- Baylor College of Medicine Department of Neurosurgery
| | - K.R. Bijanki
- Baylor College of Medicine Department of Neurosurgery
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18
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Zandstra MG, Meijs H, Somers M, Stam CJ, de Wilde B, van Hecke J, Niemegeers P, Luykx JJ, van Dellen E. Associations between psychotropic drugs and rsEEG connectivity and network characteristics: a cross-sectional study in hospital-admitted psychiatric patients. Front Neurosci 2023; 17:1176825. [PMID: 37781262 PMCID: PMC10541222 DOI: 10.3389/fnins.2023.1176825] [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: 02/28/2023] [Accepted: 08/22/2023] [Indexed: 10/03/2023] Open
Abstract
Introduction Resting-state EEG (rsEEG) characteristics, such as functional connectivity and network topology, are studied as potential biomarkers in psychiatric research. However, the presence of psychopharmacological treatment in study participants poses a potential confounding factor in biomarker research. To address this concern, our study aims to explore the impact of both single and multi-class psychotropic treatments on aforementioned rsEEG characteristics in a psychiatric population. Methods RsEEG was analyzed in a real-world cross-sectional sample of 900 hospital-admitted psychiatric patients. Patients were clustered into eight psychopharmacological groups: unmedicated, single-class treatment with antipsychotics (AP), antidepressants (AD) or benzodiazepines (BDZ), and multi-class combinations of these treatments. To assess the associations between psychotropic treatments and the macroscale rsEEG characteristics mentioned above, we employed a general linear model with post-hoc tests. Additionally, Spearman's rank correlation analyses were performed to explore potential dosage effects. Results Compared to unmedicated patients, single-class use of AD was associated with lower functional connectivity in the delta band, while AP was associated with lower functional connectivity in both the delta and alpha bands. Single-class use of BDZ was associated with widespread rsEEG differences, including lower functional connectivity across frequency bands and a different network topology within the beta band relative to unmedicated patients. All of the multi-class groups showed associations with functional connectivity or topology measures, but effects were most pronounced for concomitant use of all three classes of psychotropics. Differences were not only observed in comparison with unmedicated patients, but were also evident in comparisons between single-class, multi-class, and single/multi-class groups. Importantly, multi-class associations with rsEEG characteristics were found even in the absence of single-class associations, suggesting potential cumulative or interaction effects of different classes of psychotropics. Dosage correlations were only found for antipsychotics. Conclusion Our exploratory, cross-sectional study suggests small but significant associations between single and multi-class use of antidepressants, antipsychotics and benzodiazepines and macroscale rsEEG functional connectivity and network topology characteristics. These findings highlight the importance of considering the effects of specific psychotropics, as well as their interactions, when investigating rsEEG biomarkers in a medicated psychiatric population.
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Affiliation(s)
- Melissa G. Zandstra
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Hannah Meijs
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, Netherlands
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Metten Somers
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Cornelis J. Stam
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Bieke de Wilde
- Department of Psychiatry, Ziekenhuis Netwerk Antwerpen (ZNA), Antwerp, Belgium
| | - Jan van Hecke
- Department of Psychiatry, Ziekenhuis Netwerk Antwerpen (ZNA), Antwerp, Belgium
| | - Peter Niemegeers
- Department of Psychiatry, Ziekenhuis Netwerk Antwerpen (ZNA), Antwerp, Belgium
| | - Jurjen J. Luykx
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, Netherlands
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Edwin van Dellen
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Department of Neurology, Universitair Ziekenhuis (UZ), Brussels, Belgium
- Vrije Universiteit Brussel, Brussels, Belgium
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19
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Schwartzmann B, Dhami P, Uher R, Lam RW, Frey BN, Milev R, Müller DJ, Blier P, Soares CN, Parikh SV, Turecki G, Foster JA, Rotzinger S, Kennedy SH, Farzan F. Developing an Electroencephalography-Based Model for Predicting Response to Antidepressant Medication. JAMA Netw Open 2023; 6:e2336094. [PMID: 37768659 PMCID: PMC10539986 DOI: 10.1001/jamanetworkopen.2023.36094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/29/2023] Open
Abstract
Importance Untreated depression is a growing public health concern, with patients often facing a prolonged trial-and-error process in search of effective treatment. Developing a predictive model for treatment response in clinical practice remains challenging. Objective To establish a model based on electroencephalography (EEG) to predict response to 2 distinct selective serotonin reuptake inhibitor (SSRI) medications. Design, Setting, and Participants This prognostic study developed a predictive model using EEG data collected between 2011 and 2017 from 2 independent cohorts of participants with depression: 1 from the first Canadian Biomarker Integration Network in Depression (CAN-BIND) group and the other from the Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) consortium. Eligible participants included those aged 18 to 65 years who had a diagnosis of major depressive disorder. Data were analyzed from January to December 2022. Exposures In an open-label trial, CAN-BIND participants received an 8-week treatment regimen of escitalopram treatment (10-20 mg), and EMBARC participants were randomized in a double-blind trial to receive an 8-week sertraline (50-200 mg) treatment or placebo treatment. Main Outcomes and Measures The model's performance was estimated using balanced accuracy, specificity, and sensitivity metrics. The model used data from the CAN-BIND cohort for internal validation, and data from the treatment group of the EMBARC cohort for external validation. At week 8, response to treatment was defined as a 50% or greater reduction in the primary, clinician-rated scale of depression severity. Results The CAN-BIND cohort included 125 participants (mean [SD] age, 36.4 [13.0] years; 78 [62.4%] women), and the EMBARC sertraline treatment group included 105 participants (mean [SD] age, 38.4 [13.8] years; 72 [68.6%] women). The model achieved a balanced accuracy of 64.2% (95% CI, 55.8%-72.6%), sensitivity of 66.1% (95% CI, 53.7%-78.5%), and specificity of 62.3% (95% CI, 50.1%-73.8%) during internal validation with CAN-BIND. During external validation with EMBARC, the model achieved a balanced accuracy of 63.7% (95% CI, 54.5%-72.8%), sensitivity of 58.8% (95% CI, 45.3%-72.3%), and specificity of 68.5% (95% CI, 56.1%-80.9%). Additionally, the balanced accuracy for the EMBARC placebo group (118 participants) was 48.7% (95% CI, 39.3%-58.0%), the sensitivity was 50.0% (95% CI, 35.2%-64.8%), and the specificity was 47.3% (95% CI, 35.9%-58.7%), suggesting the model's specificity in predicting SSRIs treatment response. Conclusions and Relevance In this prognostic study, an EEG-based model was developed and validated in 2 independent cohorts. The model showed promising accuracy in predicting treatment response to 2 distinct SSRIs, suggesting potential applications for personalized depression treatment.
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Affiliation(s)
- Benjamin Schwartzmann
- eBrain Lab, School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, British Columbia, Canada
| | - Prabhjot Dhami
- eBrain Lab, School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, British Columbia, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Rudolf Uher
- Department of Psyciatry, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Raymond W Lam
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Benicio N Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
- Mood Disorders Program and Women's Health Concerns Clinic, St. Joseph's Healthcare, Hamilton, Ontario, Canada
| | - Roumen Milev
- Department of Psychiatry, Queen's University, Providence Care, Kingston, Ontario, Canada
- Department of Psychology, Queen's University, Providence Care, Kingston, Ontario, Canada
| | - Daniel J Müller
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Pierre Blier
- Mood Disorders Research Unit, University of Ottawa Institute of Mental Health Research, Ottawa, Ontario, Canada
| | - Claudio N Soares
- Department of Psychiatry, Queen's University, Providence Care, Kingston, Ontario, Canada
- Department of Psychology, Queen's University, Providence Care, Kingston, Ontario, Canada
| | | | - Gustavo Turecki
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Jane A Foster
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Ontario, Canada
- Center for Depression Research and Clinical Care, University of Texas Southwestern Medical Center, Dallas
| | - Susan Rotzinger
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Mood Disorders Program and Women's Health Concerns Clinic, St. Joseph's Healthcare, Hamilton, Ontario, Canada
| | - Sidney H Kennedy
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Unity Health Toronto, Toronto, Ontario, Canada
| | - Faranak Farzan
- eBrain Lab, School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, British Columbia, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
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20
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Ozger C, Chumachenko S, McVoy M, Croarkin PE, Doruk Camsari D. Evidence for Altered Electroencephalography Coherence in Depressed Adolescents with Suicidal Ideation and Behaviors. J Child Adolesc Psychopharmacol 2023; 33:287-293. [PMID: 37669028 PMCID: PMC10517320 DOI: 10.1089/cap.2023.0033] [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] [Indexed: 09/06/2023]
Abstract
Introduction: Major depressive disorder (MDD) is a primary risk factor for suicide, which is one of the leading causes of death among adolescents worldwide. Understanding the heterogeneity of suicidality in adolescents with MDD is critical for suicide prevention and intervention discovery. Quantitative electroencephalography (EEG) is a promising tool to address the knowledge gaps related to the neurophysiological characteristics of depression and suicidality. This study sought to examine resting-state EEG coherence differences in adolescents with MDD and suicidal ideation (SI)/behaviors (SB) and healthy controls (HC) to assess the utility of coherence as a biomarker of suicide. Methods: Twenty-six adolescents with MDD who were hospitalized for suicidality and 30 HC were recruited. The clinical sample was divided into SI (n = 9) and SB (n = 19) subgroups. Eyes closed resting-state EEG were recorded, and coherence was calculated. Depression severity and suicidality were assessed with the Children's Depression Rating Scale Revised and the Columbia Suicide Severity Rating Scale, respectively. Results: There were intrahemispheric differences in the right hemisphere across multiple electrode pairs. Delta, alpha, and beta coherence were higher in the SB group over the right prefrontofrontal and left parietooccipital electrode pairs, while alpha coherence was higher in the HC group over the right centroparietal electrode pair. There were no significant differences between HC and SI groups in any electrode pair. Conclusions: Patients with recent SB showed increased coherence in right frontal regions compared with patients with SI, suggesting altered cognitive states between those with SB and SI. These findings may have implications for suicide prevention in adolescents and could serve as useful biomarkers in clinical settings, but larger studies are needed to confirm the results.
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Affiliation(s)
- Can Ozger
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
| | - Serhiy Chumachenko
- Department of Psychiatry, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
| | - Molly McVoy
- Department of Psychiatry, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
| | - Paul E. Croarkin
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
| | - Deniz Doruk Camsari
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
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21
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Huang SS, Yu YH, Chen HH, Hung CC, Wang YT, Chang CH, Peng SJ, Kuo PH. Functional connectivity analysis on electroencephalography signals reveals potential biomarkers for treatment response in major depression. BMC Psychiatry 2023; 23:554. [PMID: 37528355 PMCID: PMC10394892 DOI: 10.1186/s12888-023-04958-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 06/13/2023] [Indexed: 08/03/2023] Open
Abstract
BACKGROUND The treatment efficacy varies across individual patients with major depressive disorder (MDD). It lacks robust electroencephalography (EEG) markers for an antidepressant-responsive phenotype. METHOD This is an observational study enrolling 28 patients with MDD and 33 healthy controls (mean age of 40.7 years, and 71.4% were women). Patients underwent EEG exams at baseline (week0) and week1, while controls' EEG recordings were acquired only at week0. A resting eye-closing EEG segment was analyzed for functional connectivity (FC). Four parameters were used in FC analysis: (1) node strength (NS), (2) global efficiency (GE), (3) clustering coefficient (CC), and (4) betweenness centrality (BC). RESULTS We found that controls had higher values in delta wave in the indices of NS, GE, BC, and CC than MDD patients at baseline. After treatment with antidepressants, patients' FC indices improved significantly, including GE, mean CC, and mean NS in the delta wave. The FC in the alpha and beta bands of the responders were higher than those of the non-responders. CONCLUSIONS The FC of the MDD patients at baseline without treatment was worse than that of controls. After treatment, the FC improved and was close to the values of controls. Responders showed better FC in the high-frequency bands than non-responders, and this feature exists in both pre-treatment and post-treatment EEG.
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Affiliation(s)
- Shiau-Shian Huang
- Department of Medical Education, Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
- College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Bali Psychiatric Center, Ministry of Health and Welfare, New Taipei, Taiwan
| | - Yu-Hsiang Yu
- College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Division of Neurology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - His-Han Chen
- Department of Psychiatry, Yang Ji Mental Hospital, Keelung, Taiwan
| | - Chia-Chun Hung
- Bali Psychiatric Center, Ministry of Health and Welfare, New Taipei, Taiwan
| | - Yao-Ting Wang
- Bali Psychiatric Center, Ministry of Health and Welfare, New Taipei, Taiwan
| | - Chieh Hsin Chang
- Bali Psychiatric Center, Ministry of Health and Welfare, New Taipei, Taiwan
| | - Syu-Jyun Peng
- Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
| | - Po-Hsiu Kuo
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
- Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan.
- Psychiatric Research Center, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.
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22
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Shanok NA, Rodriguez S, Muzac S, Del Pino CH, Brown L, Rodriguez R. Deep transcranial magnetic stimulation alters resting-state neurophysiological traits in major depressive disorder. J Affect Disord 2023:S0165-0327(23)00707-3. [PMID: 37230266 DOI: 10.1016/j.jad.2023.05.066] [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: 09/18/2022] [Revised: 05/17/2023] [Accepted: 05/19/2023] [Indexed: 05/27/2023]
Abstract
OBJECTIVE Major depressive disorder (MDD) is one of the most prevalent and debilitating health conditions worldwide; unfortunately, many patients do not respond to traditional antidepressant medication or talk therapy approaches. Deep transcranial magnetic stimulation (Deep TMS) has emerged as an effective treatment option for such "treatmentresistant" cases; however, the mechanisms by which Deep TMS attenuates depressive symptoms are still ambiguous. METHODS In the current study, resting-state quantitative electroencephalography (QEEG) measures were assessed pre-and-post treatment to illustrate neurophysiological changes resulting from Deep TMS. RESULTS The results showed reduced slow-frequency brain activity (delta and theta waves) in the prefrontal cortex following 36 treatments. Additionally, baseline QEEG measures predicted treatment response with approximately 90 % accuracy. CONCLUSIONS These findings provide preliminary evidence that TMS improves depressive symptoms by mitigating slow-wave brain activity in the prefrontal cortex. SIGNIFICANCE Deep TMS paired with QEEG should continue to be utilized for treatment of MDD in clinical practice and future studies should explore its potential for other neuropsychiatric conditions.
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Affiliation(s)
- Nathaniel A Shanok
- Delray Center for Brain Sciences, 103 SE 4th St., Delray Beach, FL 33483, United States of America.
| | - Santiago Rodriguez
- Delray Center for Brain Sciences, 103 SE 4th St., Delray Beach, FL 33483, United States of America
| | - Sabrina Muzac
- Delray Center for Brain Sciences, 103 SE 4th St., Delray Beach, FL 33483, United States of America
| | - Carla Huertes Del Pino
- Delray Center for Brain Sciences, 103 SE 4th St., Delray Beach, FL 33483, United States of America
| | - Leah Brown
- Delray Center for Brain Sciences, 103 SE 4th St., Delray Beach, FL 33483, United States of America
| | - Raul Rodriguez
- Delray Center for Brain Sciences, 103 SE 4th St., Delray Beach, FL 33483, United States of America
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23
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Nagrale SS, Yousefi A, Netoff TI, Widge AS. In silicodevelopment and validation of Bayesian methods for optimizing deep brain stimulation to enhance cognitive control. J Neural Eng 2023; 20:036015. [PMID: 37105164 PMCID: PMC10193041 DOI: 10.1088/1741-2552/acd0d5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 03/18/2023] [Accepted: 04/27/2023] [Indexed: 04/29/2023]
Abstract
Objective.deep brain stimulation (DBS) of the ventral internal capsule/striatum (VCVS) is a potentially effective treatment for several mental health disorders when conventional therapeutics fail. Its effectiveness, however, depends on correct programming to engage VCVS sub-circuits. VCVS programming is currently an iterative, time-consuming process, with weeks between setting changes and reliance on noisy, subjective self-reports. An objective measure of circuit engagement might allow individual settings to be tested in seconds to minutes, reducing the time to response and increasing patient and clinician confidence in the chosen settings. Here, we present an approach to measuring and optimizing that circuit engagement.Approach.we leverage prior results showing that effective VCVS DBS engages cognitive control circuitry and improves performance on the multi-source interference task, that this engagement depends primarily on which contact(s) are activated, and that circuit engagement can be tracked through a state space modeling framework. We develop a simulation framework based on those empirical results, then combine this framework with an adaptive optimizer to simulate a principled exploration of electrode contacts and identify the contacts that maximally improve cognitive control. We explore multiple optimization options (algorithms, number of inputs, speed of stimulation parameter changes) and compare them on problems of varying difficulty.Main results.we show that an upper confidence bound algorithm outperforms other optimizers, with roughly 80% probability of convergence to a global optimum when used in a majority-vote ensemble.Significance.we show that the optimization can converge even with lag between stimulation and effect, and that a complete optimization can be done in a clinically feasible timespan (a few hours). Further, the approach requires no specialized recording or imaging hardware, and thus could be a scalable path to expand the use of DBS in psychiatric and other non-motor applications.
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Affiliation(s)
- Sumedh S Nagrale
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States of America
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States of America
| | - Ali Yousefi
- Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA, United States of America
| | - Theoden I Netoff
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States of America
| | - Alik S Widge
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States of America
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24
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Tsai YC, Li CT, Juan CH. A review of critical brain oscillations in depression and the efficacy of transcranial magnetic stimulation treatment. Front Psychiatry 2023; 14:1073984. [PMID: 37260762 PMCID: PMC10228658 DOI: 10.3389/fpsyt.2023.1073984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 04/11/2023] [Indexed: 06/02/2023] Open
Abstract
Repetitive transcranial magnetic stimulation (rTMS) and intermittent theta burst stimulation (iTBS) have been proven effective non-invasive treatments for patients with drug-resistant major depressive disorder (MDD). However, some depressed patients do not respond to these treatments. Therefore, the investigation of reliable and valid brain oscillations as potential indices for facilitating the precision of diagnosis and treatment protocols has become a critical issue. The current review focuses on brain oscillations that, mostly based on EEG power analysis and connectivity, distinguish between MDD and controls, responders and non-responders, and potential depression severity indices, prognostic indicators, and potential biomarkers for rTMS or iTBS treatment. The possible roles of each biomarker and the potential reasons for heterogeneous results are discussed, and the directions of future studies are proposed.
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Affiliation(s)
- Yi-Chun Tsai
- Institute of Cognitive Neuroscience, College of Health Sciences and Technology, National Central University, Taoyuan City, Taiwan
| | - Cheng-Ta Li
- Institute of Cognitive Neuroscience, College of Health Sciences and Technology, National Central University, Taoyuan City, Taiwan
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Brain Science, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
- Division of Psychiatry, Faculty of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
| | - Chi-Hung Juan
- Institute of Cognitive Neuroscience, College of Health Sciences and Technology, National Central University, Taoyuan City, Taiwan
- Cognitive Intelligence and Precision Healthcare Center, National Central University, Taoyuan City, Taiwan
- Department of Psychology, Kaohsiung Medical University, Kaohsiung, Taiwan
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25
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Hitti FL, Widge AS, Riva-Posse P, Malone DA, Okun MS, Shanechi MM, Foote KD, Lisanby SH, Ankudowich E, Chivukula S, Chang EF, Gunduz A, Hamani C, Feinsinger A, Kubu CS, Chiong W, Chandler JA, Carbunaru R, Cheeran B, Raike RS, Davis RA, Halpern CH, Vanegas-Arroyave N, Markovic D, Bick SK, McIntyre CC, Richardson RM, Dougherty DD, Kopell BH, Sweet JA, Goodman WK, Sheth SA, Pouratian N. Future directions in psychiatric neurosurgery: Proceedings of the 2022 American Society for Stereotactic and Functional Neurosurgery meeting on surgical neuromodulation for psychiatric disorders. Brain Stimul 2023; 16:867-878. [PMID: 37217075 PMCID: PMC11189296 DOI: 10.1016/j.brs.2023.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 05/10/2023] [Accepted: 05/14/2023] [Indexed: 05/24/2023] Open
Abstract
OBJECTIVE Despite advances in the treatment of psychiatric diseases, currently available therapies do not provide sufficient and durable relief for as many as 30-40% of patients. Neuromodulation, including deep brain stimulation (DBS), has emerged as a potential therapy for persistent disabling disease, however it has not yet gained widespread adoption. In 2016, the American Society for Stereotactic and Functional Neurosurgery (ASSFN) convened a meeting with leaders in the field to discuss a roadmap for the path forward. A follow-up meeting in 2022 aimed to review the current state of the field and to identify critical barriers and milestones for progress. DESIGN The ASSFN convened a meeting on June 3, 2022 in Atlanta, Georgia and included leaders from the fields of neurology, neurosurgery, and psychiatry along with colleagues from industry, government, ethics, and law. The goal was to review the current state of the field, assess for advances or setbacks in the interim six years, and suggest a future path forward. The participants focused on five areas of interest: interdisciplinary engagement, regulatory pathways and trial design, disease biomarkers, ethics of psychiatric surgery, and resource allocation/prioritization. The proceedings are summarized here. CONCLUSION The field of surgical psychiatry has made significant progress since our last expert meeting. Although weakness and threats to the development of novel surgical therapies exist, the identified strengths and opportunities promise to move the field through methodically rigorous and biologically-based approaches. The experts agree that ethics, law, patient engagement, and multidisciplinary teams will be critical to any potential growth in this area.
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Affiliation(s)
- Frederick L Hitti
- Department of Neurosurgery, University of Texas Southwestern Medical Center, Dallas, TX, USA; Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA.
| | - Alik S Widge
- Department of Psychiatry and Behavioral Sciences, University of Minnesota-Twin Cities, Minneapolis, MN, USA
| | - Patricio Riva-Posse
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Donald A Malone
- Department of Psychiatry, Cleveland Clinic Lerner College of Medicine, Cleveland, OH, USA
| | - Michael S Okun
- Department of Neurology, Norman Fixel Institute for Neurological Diseases, Gainesville, FL, USA
| | - Maryam M Shanechi
- Departments of Electrical and Computer Engineering and Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Kelly D Foote
- Department of Neurosurgery, Norman Fixel Institute for Neurological Diseases, Gainesville, FL, USA
| | - Sarah H Lisanby
- Experimental Therapeutics and Pathophysiology Branch, National Institute of Mental Health, Bethesda, MD, USA
| | - Elizabeth Ankudowich
- Division of Translational Research, National Institute of Mental Health, Bethesda, MD, USA
| | - Srinivas Chivukula
- Department of Neurosurgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Edward F Chang
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Aysegul Gunduz
- Department of Biomedical Engineering and Fixel Institute for Neurological Disorders, University of Florida, Gainesville, FL, USA
| | - Clement Hamani
- Sunnybrook Research Institute, Hurvitz Brain Sciences Centre, Harquail Centre for Neuromodulation, Division of Neurosurgery, University of Toronto, Toronto, Canada
| | - Ashley Feinsinger
- Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Cynthia S Kubu
- Department of Neurology, Cleveland Clinic and Case Western Reserve University, School of Medicine, Cleveland, OH, USA
| | - Winston Chiong
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Jennifer A Chandler
- Faculty of Law, University of Ottawa, Ottawa, ON, USA; Affiliate Investigator, Bruyère Research Institute, Ottawa, ON, USA
| | | | | | - Robert S Raike
- Global Research Organization, Medtronic Inc. Neuromodulation, Minneapolis, MN, USA
| | - Rachel A Davis
- Departments of Psychiatry and Neurosurgery, University of Colorado Anschutz, Aurora, CO, USA
| | - Casey H Halpern
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; The Cpl Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
| | | | - Dejan Markovic
- Department of Electrical Engineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Sarah K Bick
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Cameron C McIntyre
- Departments of Biomedical Engineering and Neurosurgery, Duke University, Durham, NC, USA
| | - R Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | - Darin D Dougherty
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Brian H Kopell
- Department of Neurosurgery, Center for Neuromodulation, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jennifer A Sweet
- Department of Neurosurgery, University Hospitals Cleveland Medical Center, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Wayne K Goodman
- Department of Psychiatry and Behavior Sciences, Baylor College of Medicine, Houston, TX, USA
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Nader Pouratian
- Department of Neurosurgery, University of Texas Southwestern Medical Center, Dallas, TX, USA
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26
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Widge AS. Closed-Loop Deep Brain Stimulation for Psychiatric Disorders. Harv Rev Psychiatry 2023; 31:162-171. [PMID: 37171475 PMCID: PMC10188203 DOI: 10.1097/hrp.0000000000000367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
ABSTRACT Deep brain stimulation (DBS) is a well-established approach to treating medication-refractory neurological disorders and holds promise for treating psychiatric disorders. Despite strong open-label results in extremely refractory patients, DBS has struggled to meet endpoints in randomized controlled trials. A major challenge is stimulation "dosing"-DBS systems have many adjustable parameters, and clinicians receive little feedback on whether they have chosen the correct parameters for an individual patient. Multiple groups have proposed closed loop technologies as a solution. These systems sense electrical activity, identify markers of an (un)desired state, then automatically deliver or adjust stimulation to alter that electrical state. Closed loop DBS has been successfully deployed in movement disorders and epilepsy. The availability of that technology, as well as advances in opportunities for invasive research with neurosurgical patients, has yielded multiple pilot demonstrations in psychiatric illness. Those demonstrations split into two schools of thought, one rooted in well-established diagnoses and symptom scales, the other in the more experimental Research Domain Criteria (RDoC) framework. Both are promising, and both are limited by the boundaries of current stimulation technology. They are in turn driving advances in implantable recording hardware, signal processing, and stimulation paradigms. The combination of these advances is likely to change both our understanding of psychiatric neurobiology and our treatment toolbox, though the timeframe may be limited by the realities of implantable device development.
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Affiliation(s)
- Alik S Widge
- From the Department of Psychiatry & Behavioral Sciences and Medical Discovery Team on Addictions, University of Minnesota
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27
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Squires M, Tao X, Elangovan S, Gururajan R, Zhou X, Acharya UR, Li Y. Deep learning and machine learning in psychiatry: a survey of current progress in depression detection, diagnosis and treatment. Brain Inform 2023; 10:10. [PMID: 37093301 PMCID: PMC10123592 DOI: 10.1186/s40708-023-00188-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 03/08/2023] [Indexed: 04/25/2023] Open
Abstract
Informatics paradigms for brain and mental health research have seen significant advances in recent years. These developments can largely be attributed to the emergence of new technologies such as machine learning, deep learning, and artificial intelligence. Data-driven methods have the potential to support mental health care by providing more precise and personalised approaches to detection, diagnosis, and treatment of depression. In particular, precision psychiatry is an emerging field that utilises advanced computational techniques to achieve a more individualised approach to mental health care. This survey provides an overview of the ways in which artificial intelligence is currently being used to support precision psychiatry. Advanced algorithms are being used to support all phases of the treatment cycle. These systems have the potential to identify individuals suffering from mental health conditions, allowing them to receive the care they need and tailor treatments to individual patients who are mostly to benefit. Additionally, unsupervised learning techniques are breaking down existing discrete diagnostic categories and highlighting the vast disease heterogeneity observed within depression diagnoses. Artificial intelligence also provides the opportunity to shift towards evidence-based treatment prescription, moving away from existing methods based on group averages. However, our analysis suggests there are several limitations currently inhibiting the progress of data-driven paradigms in care. Significantly, none of the surveyed articles demonstrate empirically improved patient outcomes over existing methods. Furthermore, greater consideration needs to be given to uncertainty quantification, model validation, constructing interdisciplinary teams of researchers, improved access to diverse data and standardised definitions within the field. Empirical validation of computer algorithms via randomised control trials which demonstrate measurable improvement to patient outcomes are the next step in progressing models to clinical implementation.
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Affiliation(s)
- Matthew Squires
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD, Australia.
| | - Xiaohui Tao
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD, Australia
| | | | - Raj Gururajan
- School of Business, University of Southern Queensland, Springfield, QLD, Australia
| | - Xujuan Zhou
- School of Business, University of Southern Queensland, Springfield, QLD, Australia
| | - U Rajendra Acharya
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD, Australia
| | - Yuefeng Li
- School of Computer Science, Queensland University of Technology, Brisbane, QLD, Australia
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28
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Morris AT, Temereanca S, Zandvakili A, Thorpe R, Sliva DD, Greenberg BD, Carpenter LL, Philip NS, Jones SR. Fronto-central resting-state 15-29 Hz transient beta events change with therapeutic transcranial magnetic stimulation for posttraumatic stress disorder and major depressive disorder. Sci Rep 2023; 13:6366. [PMID: 37076496 PMCID: PMC10115889 DOI: 10.1038/s41598-023-32801-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 04/03/2023] [Indexed: 04/21/2023] Open
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is an established treatment for major depressive disorder (MDD) and shows promise for posttraumatic stress disorder (PTSD), yet effectiveness varies. Electroencephalography (EEG) can identify rTMS-associated brain changes. EEG oscillations are often examined using averaging approaches that mask finer time-scale dynamics. Recent advances show some brain oscillations emerge as transient increases in power, a phenomenon termed "Spectral Events," and that event characteristics correspond with cognitive functions. We applied Spectral Event analyses to identify potential EEG biomarkers of effective rTMS treatment. Resting 8-electrode EEG was collected from 23 patients with MDD and PTSD before and after 5 Hz rTMS targeting the left dorsolateral prefrontal cortex. Using an open-source toolbox ( https://github.com/jonescompneurolab/SpectralEvents ), we quantified event features and tested for treatment associated changes. Spectral Events in delta/theta (1-6 Hz), alpha (7-14 Hz), and beta (15-29 Hz) bands occurred in all patients. rTMS-induced improvement in comorbid MDD PTSD were associated with pre- to post-treatment changes in fronto-central electrode beta event features, including frontal beta event frequency spans and durations, and central beta event maxima power. Furthermore, frontal pre-treatment beta event duration correlated negatively with MDD symptom improvement. Beta events may provide new biomarkers of clinical response and advance the understanding of rTMS.
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Affiliation(s)
- Alexander T Morris
- VA RR&D Center for Neurorestoration and Neurotechnology, VA Providence, Providence, RI, USA
| | - Simona Temereanca
- VA RR&D Center for Neurorestoration and Neurotechnology, VA Providence, Providence, RI, USA.
- Department of Neuroscience, Brown University, Providence, RI, USA.
- Carney Institute for Brain Science, Brown University, Providence, RI, USA.
| | - Amin Zandvakili
- VA RR&D Center for Neurorestoration and Neurotechnology, VA Providence, Providence, RI, USA
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA
| | - Ryan Thorpe
- Department of Neuroscience, Brown University, Providence, RI, USA
- Carney Institute for Brain Science, Brown University, Providence, RI, USA
| | - Danielle D Sliva
- Department of Neuroscience, Brown University, Providence, RI, USA
- Carney Institute for Brain Science, Brown University, Providence, RI, USA
| | - Benjamin D Greenberg
- VA RR&D Center for Neurorestoration and Neurotechnology, VA Providence, Providence, RI, USA
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA
- COBRE Center for Neuromodulation, Butler Hospital, Providence, RI, USA
| | - Linda L Carpenter
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA
- COBRE Center for Neuromodulation, Butler Hospital, Providence, RI, USA
- Carney Institute for Brain Science, Brown University, Providence, RI, USA
| | - Noah S Philip
- VA RR&D Center for Neurorestoration and Neurotechnology, VA Providence, Providence, RI, USA
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA
- COBRE Center for Neuromodulation, Butler Hospital, Providence, RI, USA
| | - Stephanie R Jones
- VA RR&D Center for Neurorestoration and Neurotechnology, VA Providence, Providence, RI, USA.
- Department of Neuroscience, Brown University, Providence, RI, USA.
- Carney Institute for Brain Science, Brown University, Providence, RI, USA.
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29
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Morris AT, Temereanca S, Zandvakili A, Thorpe R, Sliva DD, Greenberg BD, Carpenter LL, Philip NS, Jones SR. Fronto-central resting-state 15-29Hz transient beta events change with therapeutic transcranial magnetic stimulation for posttraumatic stress disorder and major depressive disorder. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.11.23286902. [PMID: 36993547 PMCID: PMC10055566 DOI: 10.1101/2023.03.11.23286902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/28/2023]
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is an established treatment for major depressive disorder (MDD) and shows promise for posttraumatic stress disorder (PTSD), yet effectiveness varies. Electroencephalography (EEG) can identify rTMS-associated brain changes. EEG oscillations are often examined using averaging approaches that mask finer time-scale dynamics. Recent advances show some brain oscillations emerge as transient increases in power, a phenomenon termed "Spectral Events," and that event characteristics correspond with cognitive functions. We applied Spectral Event analyses to identify potential EEG biomarkers of effective rTMS treatment. Resting 8-electrode EEG was collected from 23 patients with MDD and PTSD before and after 5Hz rTMS targeting the left dorsolateral prefrontal cortex. Using an open-source toolbox ( https://github.com/jonescompneurolab/SpectralEvents ), we quantified event features and tested for treatment associated changes. Spectral Events in delta/theta (1-6 Hz), alpha (7-14 Hz), and beta (15-29 Hz) bands occurred in all patients. rTMS-induced improvement in comorbid MDD PTSD were associated with pre-to post-treatment changes in fronto-central electrode beta event features, including frontal beta event frequency spans and durations, and central beta event maxima power. Furthermore, frontal pre-treatment beta event duration correlated negatively with MDD symptom improvement. Beta events may provide new biomarkers of clinical response and advance the understanding of rTMS.
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30
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Tsai HJ, Yang WC, Tsai SJ, Lin CH, Yang AC. Right-side frontal-central cortical hyperactivation before the treatment predicts outcomes of antidepressant and electroconvulsive therapy responsivity in major depressive disorder. J Psychiatr Res 2023; 161:377-385. [PMID: 37012197 DOI: 10.1016/j.jpsychires.2023.03.023] [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: 01/04/2023] [Revised: 03/08/2023] [Accepted: 03/13/2023] [Indexed: 04/05/2023]
Abstract
Major depressive disorder places a great burden on healthcare resources worldwide. Antidepressants are the first-line treatment for major depressive disorder, but if patients don't respond adequately, brain stimulation therapy may be needed as second-line treatment. Digital phenotyping in patients with major depressive disorder will aid in the timely prediction of treatment effectiveness. This study explored electroencephalographic (EEG) signatures that diversify depression treatment responsivity, including antidepressant administration or brain stimulation therapy. Resting-state, pre-treatment EEG sequences from depressive patients who received fluoxetine treatment (n = 55; 26 remitters and 29 poor responders) or electroconvulsive therapy (ECT, n = 58; 36 remitters and 22 nonremitters) were recorded on 19 channels. Twenty-nine EEG segments were obtained from each patient per recording electrode. Power spectral analysis was conducted for feature extraction and showed the highest predictive accuracy for fluoxetine or ECT outcomes. Both occurred with beta-band oscillations within right-side frontal-central (F1-score = 0.9437) or prefrontal areas of the brain (F1-score = 0.9416), respectively. Significantly higher beta-band power was observed among patients who lacked adequate treatment response than the remitters, specifically at 19.2 Hz or 24.5 Hz for fluoxetine administration or ECT outcome, respectively. Our findings indicated that pre-treatment, right-side cortical hyperactivation is associated with poor outcomes of antidepressant-based or ECT-based treatment in major depression. Whether depression treatment response rates can be improved by reducing the high-frequency EEG power in corresponding areas of the brain to provide a protective effect against depression recurrence warrants further study.
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Affiliation(s)
- Hsin-Jung Tsai
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan; Digital Medicine and Smart Healthcare Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Wei-Cheng Yang
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Psychiatry, Tainan Hospital, Ministry of Health and Welfare, Tainan, Taiwan
| | - Shih-Jen Tsai
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Psychiatry, Taipei Veteran General Hospital, Taipei, Taiwan
| | - Ching-Hua Lin
- Kaohsiung Municipal Kai-Syuan Psychiatric Hospital, Kaohsiung, Taiwan.
| | - Albert C Yang
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan; Digital Medicine and Smart Healthcare Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan; Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan.
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31
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Jensen KHR, Dam VH, Ganz M, Fisher PM, Ip CT, Sankar A, Marstrand-Joergensen MR, Ozenne B, Osler M, Penninx BWJH, Pinborg LH, Frokjaer VG, Knudsen GM, Jørgensen MB. Deep phenotyping towards precision psychiatry of first-episode depression - the Brain Drugs-Depression cohort. BMC Psychiatry 2023; 23:151. [PMID: 36894940 PMCID: PMC9999625 DOI: 10.1186/s12888-023-04618-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 02/19/2023] [Indexed: 03/11/2023] Open
Abstract
BACKGROUND Major Depressive Disorder (MDD) is a heterogenous brain disorder, with potentially multiple psychosocial and biological disease mechanisms. This is also a plausible explanation for why patients do not respond equally well to treatment with first- or second-line antidepressants, i.e., one-third to one-half of patients do not remit in response to first- or second-line treatment. To map MDD heterogeneity and markers of treatment response to enable a precision medicine approach, we will acquire several possible predictive markers across several domains, e.g., psychosocial, biochemical, and neuroimaging. METHODS All patients are examined before receiving a standardised treatment package for adults aged 18-65 with first-episode depression in six public outpatient clinics in the Capital Region of Denmark. From this population, we will recruit a cohort of 800 patients for whom we will acquire clinical, cognitive, psychometric, and biological data. A subgroup (subcohort I, n = 600) will additionally provide neuroimaging data, i.e., Magnetic Resonance Imaging, and Electroencephalogram, and a subgroup of patients from subcohort I unmedicated at inclusion (subcohort II, n = 60) will also undergo a brain Positron Emission Tomography with the [11C]-UCB-J tracer binding to the presynaptic glycoprotein-SV2A. Subcohort allocation is based on eligibility and willingness to participate. The treatment package typically lasts six months. Depression severity is assessed with the Quick Inventory of Depressive Symptomatology (QIDS) at baseline, and 6, 12 and 18 months after treatment initiation. The primary outcome is remission (QIDS ≤ 5) and clinical improvement (≥ 50% reduction in QIDS) after 6 months. Secondary endpoints include remission at 12 and 18 months and %-change in QIDS, 10-item Symptom Checklist, 5-item WHO Well-Being Index, and modified Disability Scale from baseline through follow-up. We also assess psychotherapy and medication side-effects. We will use machine learning to determine a combination of characteristics that best predict treatment outcomes and statistical models to investigate the association between individual measures and clinical outcomes. We will assess associations between patient characteristics, treatment choices, and clinical outcomes using path analysis, enabling us to estimate the effect of treatment choices and timing on the clinical outcome. DISCUSSION The BrainDrugs-Depression study is a real-world deep-phenotyping clinical cohort study of first-episode MDD patients. TRIAL REGISTRATION Registered at clinicaltrials.gov November 15th, 2022 (NCT05616559).
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Affiliation(s)
- Kristian Høj Reveles Jensen
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Vibeke H Dam
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Melanie Ganz
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Patrick MacDonald Fisher
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Cheng-Teng Ip
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Center for Cognitive and Brain Sciences, University of Macau, Taipa, Macau SAR, China
| | - Anjali Sankar
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Maja Rou Marstrand-Joergensen
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Department of Neurology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Brice Ozenne
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Public Health, Section of Biostatistics, University of Copenhagen, Copenhagen, Denmark
| | - Merete Osler
- Center for Clinical Research and Prevention, Bispebjerg & Frederiksberg Hospitals, Copenhagen, Denmark.,Department of Public Health, Section of Epidemiology, University of Copenhagen, Copenhagen, Denmark
| | - Brenda W J H Penninx
- Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam, the Netherlands
| | - Lars H Pinborg
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Department of Neurology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Vibe Gedsø Frokjaer
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Gitte Moos Knudsen
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.,Department of Neurology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Martin Balslev Jørgensen
- BrainDrugs, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark. .,Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark. .,Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark. .,Psychiatric Centre Copenhagen, Copenhagen, Denmark.
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32
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Marcu GM, Szekely-Copîndean RD, Radu AM, Bucuță MD, Fleacă RS, Tănăsescu C, Roman MD, Boicean A, Băcilă CI. Resting-state frontal, frontlateral, and parietal alpha asymmetry:A pilot study examining relations with depressive disorder type and severity. Front Psychol 2023; 14:1087081. [PMID: 37008856 PMCID: PMC10062203 DOI: 10.3389/fpsyg.2023.1087081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 02/14/2023] [Indexed: 03/09/2023] Open
Abstract
IntroductionThe search for biomarkers has been central to efforts of improving clinical diagnosis and prognosis in psychopathology in the last decades. The main approach has been to validate biomarkers that could accurately discriminate between clinical diagnoses of very prevalent forms of psychopathology. One of the most popular electrophysiological markers proposed for discrimination in depressive disorders is the electroencephalography (EEG)-derived frontal alpha asymmetry. However, the validity, reliability and predictive value of this biomarker have been questioned in recent years, mainly due to conceptual and methodological heterogeneity.MethodsIn the current non-experimental, correlational study we investigated relationship of resting-state EEG alpha asymmetry from multiple sites (frontal, frontolateral, and parietal) with different forms of depressive disorders (varying in type or severity), in a clinical sample.ResultsResults showed that alpha asymmetry in the parietal (P3-P4) was significantly higher than in the frontal (F3-F4) and frontolateral sites (F7-F8). However, we did not find significant relations between alpha asymmetry indices and our depressive disorder measures, except for a moderate positive association between frontolateral alpha asymmetry (eyes-closed only) and depressive disorder severity (determined through clinical structured interview). We also found no significant differences in alpha asymmetry between participants, depending on their depression type.DiscussionBased on results, we propose the parietal and frontolateral asymmetry indices to form hypotheses that should not be abandoned in the depression markers research, but worth for further experimental research. Methodological and clinical implications of the current findings are discussed.
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Affiliation(s)
- Gabriela M. Marcu
- Department of Psychology, Lucian Blaga University of Sibiu, Sibiu, Romania
- Faculty of Medicine, Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
- Scientific Collective for Research in Neuroscience, Clinical Psychiatric Hospital “Dr. Gh. Preda”, Sibiu, Romania
| | - Raluca D. Szekely-Copîndean
- Scientific Collective for Research in Neuroscience, Clinical Psychiatric Hospital “Dr. Gh. Preda”, Sibiu, Romania
- Department of Social and Human Research, Romanian Academy, Cluj-Napoca, Romania
| | - Ana-Maria Radu
- Department of Psychology, Lucian Blaga University of Sibiu, Sibiu, Romania
- Scientific Collective for Research in Neuroscience, Clinical Psychiatric Hospital “Dr. Gh. Preda”, Sibiu, Romania
- *Correspondence: Ana-Maria Radu,
| | - Mihaela D. Bucuță
- Department of Psychology, Lucian Blaga University of Sibiu, Sibiu, Romania
- Center for Psychological Research, Lucian Blaga University of Sibiu, Sibiu, Romania
| | - Radu S. Fleacă
- Faculty of Medicine, Lucian Blaga University of Sibiu, Sibiu, Romania
| | - Ciprian Tănăsescu
- Faculty of Medicine, Lucian Blaga University of Sibiu, Sibiu, Romania
| | - Mihai D. Roman
- Faculty of Medicine, Lucian Blaga University of Sibiu, Sibiu, Romania
| | - Adrian Boicean
- Faculty of Medicine, Lucian Blaga University of Sibiu, Sibiu, Romania
| | - Ciprian I. Băcilă
- Scientific Collective for Research in Neuroscience, Clinical Psychiatric Hospital “Dr. Gh. Preda”, Sibiu, Romania
- Faculty of Medicine, Lucian Blaga University of Sibiu, Sibiu, Romania
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33
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van Dijk H, Koppenberg M, Arns M. Towards Robust, Reproducible, and Clinically Actionable EEG Biomarkers: Large Open Access EEG Database for Discovery and Out-of-sample Validation. Clin EEG Neurosci 2023; 54:103-105. [PMID: 35975621 DOI: 10.1177/15500594221120516] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Affiliation(s)
- Hanneke van Dijk
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands.,Faculty of Psychology & Neuroscience, Department of Cognitive Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Mark Koppenberg
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands
| | - Martijn Arns
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands.,Faculty of Psychology & Neuroscience, Department of Cognitive Neuroscience, Maastricht University, Maastricht, the Netherlands
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34
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A Narrative Review on REM Sleep Deprivation: A Promising Non-Pharmaceutical Alternative for Treating Endogenous Depression. J Pers Med 2023; 13:jpm13020306. [PMID: 36836540 PMCID: PMC9960519 DOI: 10.3390/jpm13020306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 02/03/2023] [Accepted: 02/04/2023] [Indexed: 02/12/2023] Open
Abstract
Endogenous depression represents a severe mental health condition projected to become one of the worldwide leading causes of years lived with disability. The currently available clinical and non-clinical interventions designed to alleviate endogenous depression-associated symptoms encounter a series of inconveniences, from the lack of intervention effectiveness and medication adherence to unpleasant side effects. In addition, depressive individuals tend to be more frequent users of primary care units, which markedly affects the overall treatment costs. In parallel with the growing incidence of endogenous depression, researchers in sleep science have discovered multiple links between rapid eye movement (REM) sleep patterns and endogenous depression. Recent findings suggest that prolonged periods of REM sleep are associated with different psychiatric disorders, including endogenous depression. In addition, a growing body of experimental work confidently describes REM sleep deprivation (REM-D) as the underlying mechanism of most pharmaceutical antidepressants, proving its utility as either an independent or adjuvant approach to alleviating the symptoms of endogenous depression. In this regard, REM-D is currently being explored for its potential value as a sleep intervention-based method for improving the clinical management of endogenous depression. Therefore, this narrative review represents a comprehensive inventory of the currently available evidence supporting the potential use of REM-D as a reliable, non-pharmaceutical approach for treating endogenous depression, or as an adjuvant practice that could improve the effectiveness of currently used medication.
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35
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Cohen SE, Zantvoord JB, Wezenberg BN, Daams JG, Bockting CLH, Denys D, van Wingen GA. Electroencephalography for predicting antidepressant treatment success: A systematic review and meta-analysis. J Affect Disord 2023; 321:201-207. [PMID: 36341804 DOI: 10.1016/j.jad.2022.10.042] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 10/19/2022] [Accepted: 10/22/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND Patients suffering from major depressive disorder (MDD) regularly experience non-response to treatment for their depressive episode. Personalized clinical decision making could shorten depressive episodes and reduce patient suffering. Although no clinical tools are currently available, machine learning analysis of electroencephalography (EEG) shows promise in treatment response prediction. METHODS With a systematic review and meta-analysis, we evaluated the accuracy of EEG for individual patient response prediction. Importantly, we included only prediction studies that used cross-validation. We used a bivariate model to calculate prediction success, as expressed by area-under the curve, sensitivity and specificity. Furthermore, we analyzed prediction success for separate antidepressant interventions. RESULTS 15 studies with 12 individual patient samples and a total of 479 patients were included. Research methods varied considerably between studies. Meta-analysis of results from this heterogeneous set of studies resulted in an area under the curve of 0.91, a sensitivity of 83 % (95 % CI 74-89 %), and a specificity of 86 % (95 % CI 81-90 %). Classification performance did not significantly differ between treatments. Although studies were all internally validated, no externally validated studies have been reported. We found substantial risk of bias caused by methodological shortcomings such as non-independent feature selection, though performance of non-biased studies was comparable. LIMITATIONS Sample sizes were relatively small and no study used external validation, increasing the risk of overestimation of accuracy. CONCLUSIONS Electroencephalography can predict the response to antidepressant treatment with high accuracy. However, future studies with more rigorous validation are needed to produce a clinical tool to guide interventions in MDD. PROSPERO REGISTRATION NUMBER CRD42021268169.
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Affiliation(s)
- S E Cohen
- Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Meibergdreef 9, Amsterdam, Netherlands; Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - J B Zantvoord
- Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Meibergdreef 9, Amsterdam, Netherlands; Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - B N Wezenberg
- Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Meibergdreef 9, Amsterdam, Netherlands; Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - J G Daams
- Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Meibergdreef 9, Amsterdam, Netherlands; Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - C L H Bockting
- Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Meibergdreef 9, Amsterdam, Netherlands; Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - D Denys
- Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Meibergdreef 9, Amsterdam, Netherlands; Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - G A van Wingen
- Amsterdam UMC location University of Amsterdam, Department of Psychiatry, Meibergdreef 9, Amsterdam, Netherlands; Amsterdam Neuroscience, Amsterdam, the Netherlands.
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36
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Ding X, Li Y, Zhang T, Li D, Luo SX, Liu X, Hao W. Electroencephalogram pattern association with drug-related cues in a long-duration virtual reality environment in patients with methamphetamine use disorder. Addict Biol 2023; 28:e13248. [PMID: 36577720 DOI: 10.1111/adb.13248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 08/28/2022] [Accepted: 10/12/2022] [Indexed: 11/06/2022]
Abstract
The cognitive processing of drug-related cues and the subsequent dysregulation of behaviour play a central role in the pathophysiology of substance use disorders. Prior studies are limited by small sample sizes and a lack of immersion in stimulus presentation. In the present study, we recruited patients with methamphetamine use disorder (MUD; N = 1099) from four compulsory isolated detoxification centres and healthy control participants (N = 305). With a 12-min-long virtual reality (VR) protocol stimulus, we discovered that patients showed a decrease in electroencephalogram (EEG) power across alpha to gamma bands in anterior scalp regions under methamphetamine-related VR stimuli (e.g. a glass pipe and medical tubing) compared with the control stimuli (e.g. balls and cubes). Analysis of variance (ANOVA) showed that the interaction effects of stimuli type and group were significant in five EEG bands. Using generalised linear models, we classified the stimuli type (i.e. drug-related vs. drug-unrelated cues) in MUD patients with an f1 score of 90% on an out-of-sample testing set. The decreases of EEG between drug-related cues and drug-unrelated cues in delta, theta and alpha frequency bands are more frequently seen in patients than in healthy controls, perhaps reflecting general arousal and attenuated impulsive control. Our results suggest that EEG responses elicited by long-duration methamphetamine-related VR cues showed a specific signature, which may have future clinical implications.
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Affiliation(s)
- Xinfang Ding
- Department of Medical Psychology, School of Medical Humanities, Capital Medical University, Beijing, China
| | - Yuanhui Li
- Adai Technology (Beijing), Ltd., Co, Beijing, China
| | | | - Dai Li
- Adai Technology (Beijing), Ltd., Co, Beijing, China
| | - Sean X Luo
- Department of Psychiatry, Columbia University, New York City, New York, USA
| | - Xiang Liu
- Department of Industrial Engineering, Tsinghua University, Beijing, China
| | - Wei Hao
- National Clinical Research Center on Mental Disorders and Mental Health Institute of the Second Xiangya Hospital, Central South University, Changsha, China
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37
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Lambert PM, Ni R, Benz A, Rensing NR, Wong M, Zorumski CF, Mennerick S. Non-sedative cortical EEG signatures of allopregnanolone and functional comparators. Neuropsychopharmacology 2023; 48:371-379. [PMID: 36168047 PMCID: PMC9751067 DOI: 10.1038/s41386-022-01450-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 08/13/2022] [Accepted: 08/31/2022] [Indexed: 12/26/2022]
Abstract
Neurosteroids that positively modulate GABAA receptors are among a growing list of rapidly acting antidepressants, including ketamine and psychedelics. To develop increasingly specific treatments with fewer side effects, we explored the possibility of EEG signatures in mice, which could serve as a cross-species screening tool. There are few studies of the impact of non-sedative doses of rapid antidepressants on EEG in either rodents or humans. Here we hypothesize that EEG features may separate a rapid antidepressant neurosteroid, allopregnanolone, from other GABAA positive modulators, pentobarbital and diazepam. Further, we compared the actions GABA modulators with those of ketamine, an NMDA antagonist and prototype rapid antidepressant. We examined EEG spectra during active exploration at two cortical locations and examined cross-regional and cross-frequency interactions. We found that at comparable doses, the effects of allopregnanolone, despite purported selectivity for certain GABAAR subtypes, was indistinguishable from pentobarbital during active waking exploration. The actions of diazepam had recognizable common features with allopregnanolone and pentobarbital but was also distinct, consistent with subunit selectivity of benzodiazepines. Finally, ketamine exhibited no distinguishing overlap with allopregnanolone in the parameters examined. Our results suggest that rapid antidepressants with different molecular substrates may remain separated at the level of large-scale ensemble activity, but the studies leave open the possibility of commonalities in more discrete circuits and/or in the context of a dysfunctional brain.
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Affiliation(s)
- Peter M Lambert
- Department of Psychiatry, Washington University in St. Louis School of Medicine, 660S. Euclid Ave., MSC 8134-0181-0G, St. Louis, MO, 63110, USA.,Medical Scientist Training Program, Washington University in St. Louis School of Medicine, 660S. Euclid Ave., MSC 8134-0181-0G, St. Louis, MO, 63110, USA
| | - Richard Ni
- Department of Psychiatry, Washington University in St. Louis School of Medicine, 660S. Euclid Ave., MSC 8134-0181-0G, St. Louis, MO, 63110, USA
| | - Ann Benz
- Department of Psychiatry, Washington University in St. Louis School of Medicine, 660S. Euclid Ave., MSC 8134-0181-0G, St. Louis, MO, 63110, USA
| | - Nicholas R Rensing
- Department of Neurology, Washington University in St. Louis School of Medicine, 660S. Euclid Ave., MSC 8134-0181-0G, St. Louis, MO, 63110, USA
| | - Michael Wong
- Department of Neurology, Washington University in St. Louis School of Medicine, 660S. Euclid Ave., MSC 8134-0181-0G, St. Louis, MO, 63110, USA
| | - Charles F Zorumski
- Department of Psychiatry, Washington University in St. Louis School of Medicine, 660S. Euclid Ave., MSC 8134-0181-0G, St. Louis, MO, 63110, USA.,Taylor Family Institute for Innovative Psychiatric Research, Washington University in St. Louis School of Medicine, 660S. Euclid Ave., MSC 8134-0181-0G, St. Louis, MO, 63110, USA
| | - Steven Mennerick
- Department of Psychiatry, Washington University in St. Louis School of Medicine, 660S. Euclid Ave., MSC 8134-0181-0G, St. Louis, MO, 63110, USA. .,Taylor Family Institute for Innovative Psychiatric Research, Washington University in St. Louis School of Medicine, 660S. Euclid Ave., MSC 8134-0181-0G, St. Louis, MO, 63110, USA.
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38
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Rost N, Binder EB, Brückl TM. Predicting treatment outcome in depression: an introduction into current concepts and challenges. Eur Arch Psychiatry Clin Neurosci 2023; 273:113-127. [PMID: 35587279 PMCID: PMC9957888 DOI: 10.1007/s00406-022-01418-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 04/11/2022] [Indexed: 12/19/2022]
Abstract
Improving response and remission rates in major depressive disorder (MDD) remains an important challenge. Matching patients to the treatment they will most likely respond to should be the ultimate goal. Even though numerous studies have investigated patient-specific indicators of treatment efficacy, no (bio)markers or empirical tests for use in clinical practice have resulted as of now. Therefore, clinical decisions regarding the treatment of MDD still have to be made on the basis of questionnaire- or interview-based assessments and general guidelines without the support of a (laboratory) test. We conducted a narrative review of current approaches to characterize and predict outcome to pharmacological treatments in MDD. We particularly focused on findings from newer computational studies using machine learning and on the resulting implementation into clinical decision support systems. The main issues seem to rest upon the unavailability of robust predictive variables and the lacking application of empirical findings and predictive models in clinical practice. We outline several challenges that need to be tackled on different stages of the translational process, from current concepts and definitions to generalizable prediction models and their successful implementation into digital support systems. By bridging the addressed gaps in translational psychiatric research, advances in data quantity and new technologies may enable the next steps toward precision psychiatry.
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Affiliation(s)
- Nicolas Rost
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804, Munich, Germany. .,International Max Planck Research School for Translational Psychiatry, Munich, Germany.
| | - Elisabeth B. Binder
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804 Munich, Germany
| | - Tanja M. Brückl
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Kraepelinstraße 2-10, 80804 Munich, Germany
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Zhao Y, Dahmani L, Li M, Hu Y, Ren J, Lui S, Wang D, Kuang W, Gong Q, Liu H. Individualized Functional Connectome Identified Replicable Biomarkers for Dysphoric Symptoms in First-Episode Medication-Naïve Patients With Major Depressive Disorder. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:42-51. [PMID: 34995770 DOI: 10.1016/j.bpsc.2021.12.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 12/03/2021] [Accepted: 12/19/2021] [Indexed: 01/11/2023]
Abstract
BACKGROUND Major depressive disorder (MDD) is a heterogeneous syndrome and can be conceptualized as a mixture of dimensional abnormalities across several specific brain circuits. The neural underpinnings of different symptom dimensions in MDD are not well understood. We aimed to identify robust, generalizable, functional connectivity (FC)-based biomarkers for different symptom dimensions in MDD using individualized functional connectomes. METHODS Patterns of FC associated with symptom severity were identified using a novel, individualized, functional network parcellation analysis in conjunction with hierarchical clustering. Dimension-specific prediction models were trained to estimate symptom severity in first-episode medication-naïve patients (discovery dataset, n = 95) and replicated in an independent validation dataset (n = 94). The correlation between FC changes and symptom changes was further explored in a treatment dataset (n = 55). RESULTS Two distinct symptom clusters previously identified in patients with MDD, namely dysphoric and anxiosomatic clusters, were robustly replicated in our data. A connectivity biomarker associated with dysphoric symptoms was identified, which mainly involved the default, dorsal attention, and limbic networks. Critically, this brain-symptom association was confirmed in the validation dataset. Moreover, the marker also tracked dysphoric symptom improvement following a 2-week antidepressant treatment. For comparison, we repeated our analyses using a nonindividualized approach and failed to identify replicable brain-symptom biomarkers. Further quantitative analysis indicated that the generalizability of the connectivity-symptom association was hampered when functional regions were not localized in individuals. CONCLUSIONS This work reveals robust, replicable FC biomarkers for dysphoric symptoms in MDD, demonstrates the advantage of individual-oriented approach, and emphasizes the importance of independent validation in psychiatric neuroimaging analysis.
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Affiliation(s)
- Youjin Zhao
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Louisa Dahmani
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts
| | - Meiling Li
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts
| | - Yongbo Hu
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Jianxun Ren
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts
| | - Su Lui
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China
| | - Danhong Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts
| | - Weihong Kuang
- Department of Psychiatry, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Qiyong Gong
- Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China; Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China.
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts; Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina.
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40
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Pinotsis DA, Fitzgerald S, See C, Sementsova A, Widge AS. Toward biophysical markers of depression vulnerability. Front Psychiatry 2022; 13:938694. [PMID: 36329919 PMCID: PMC9622949 DOI: 10.3389/fpsyt.2022.938694] [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: 05/07/2022] [Accepted: 09/22/2022] [Indexed: 11/13/2022] Open
Abstract
A major difficulty with treating psychiatric disorders is their heterogeneity: different neural causes can lead to the same phenotype. To address this, we propose describing the underlying pathophysiology in terms of interpretable, biophysical parameters of a neural model derived from the electroencephalogram. We analyzed data from a small patient cohort of patients with depression and controls. Using DCM, we constructed biophysical models that describe neural dynamics in a cortical network activated during a task that is used to assess depression state. We show that biophysical model parameters are biomarkers, that is, variables that allow subtyping of depression at a biological level. They yield a low dimensional, interpretable feature space that allowed description of differences between individual patients with depressive symptoms. They could capture internal heterogeneity/variance of depression state and achieve significantly better classification than commonly used EEG features. Our work is a proof of concept that a combination of biophysical models and machine learning may outperform earlier approaches based on classical statistics and raw brain data.
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Affiliation(s)
- D. A. Pinotsis
- Centre for Mathematical Neuroscience and Psychology, Department of Psychology, City, University of London, London, United Kingdom
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - S. Fitzgerald
- Centre for Mathematical Neuroscience and Psychology, Department of Psychology, City, University of London, London, United Kingdom
| | - C. See
- Department of Computer Science, City, University of London, London, United Kingdom
| | - A. Sementsova
- Department of Computer Science, City, University of London, London, United Kingdom
| | - A. S. Widge
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States
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41
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Meijs H, Prentice A, Lin BD, De Wilde B, Van Hecke J, Niemegeers P, van Eijk K, Luykx JJ, Arns M. A polygenic-informed approach to a predictive EEG signature empowers antidepressant treatment prediction: A proof-of-concept study. Eur Neuropsychopharmacol 2022; 62:49-60. [PMID: 35896057 DOI: 10.1016/j.euroneuro.2022.07.006] [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: 03/08/2022] [Revised: 07/05/2022] [Accepted: 07/08/2022] [Indexed: 11/04/2022]
Abstract
The treatment of major depressive disorder (MDD) is hampered by low chances of treatment response in each treatment step, which is partly due to a lack of firmly established outcome-predictive biomarkers. Here, we hypothesize that polygenic-informed EEG signatures may help predict antidepressant treatment response. Using a polygenic-informed electroencephalography (EEG) data-driven, data-reduction approach, we identify a brain network in a large cohort (N=1,123), and discover it is sex-specifically (male patients, N=617) associated with polygenic risk score (PRS) of antidepressant response. Subsequently, we demonstrate in three independent datasets the utility of the network in predicting response to antidepressant medication (male, N=232) as well as repetitive transcranial magnetic stimulation (rTMS) and concurrent psychotherapy (male, N=95). This network significantly improves a treatment response prediction model with age and baseline severity data (area under the curve, AUC=0.623 for medicaton; AUC=0.719 for rTMS). A predictive model for MDD patients, aimed at increasing the likelihood of being a responder to antidepressants or rTMS and concurrent psychotherapy based on only this network, yields a positive predictive value (PPV) of 69% for medication and 77% for rTMS. Finally, blinded out-of-sample validation of the network as predictor for psychotherapy response in another independent dataset (male, N=50) results in a within-subsample response rate of 50% (improvement of 56%). Overall, the findings provide a first proof-of-concept of a combined genetic and neurophysiological approach in the search for clinically-relevant biomarkers in psychiatric disorders, and should encourage researchers to incorporate genetic information, such as PRS, in their search for clinically relevant neuroimaging biomarkers.
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Affiliation(s)
- Hannah Meijs
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands; GGNet Mental Health, Warnsveld, the Netherlands.
| | - Amourie Prentice
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Bochao D Lin
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands; Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Bieke De Wilde
- Department of Psychiatry, Ziekenhuis Netwerk Antwerpen (ZNA), Antwerp, Belgium
| | - Jan Van Hecke
- Department of Psychiatry, Ziekenhuis Netwerk Antwerpen (ZNA), Antwerp, Belgium
| | - Peter Niemegeers
- Department of Psychiatry, Ziekenhuis Netwerk Antwerpen (ZNA), Antwerp, Belgium
| | - Kristel van Eijk
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands
| | - Jurjen J Luykx
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands; GGNet Mental Health, Warnsveld, the Netherlands; Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Martijn Arns
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
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42
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Watts D, Pulice RF, Reilly J, Brunoni AR, Kapczinski F, Passos IC. Predicting treatment response using EEG in major depressive disorder: A machine-learning meta-analysis. Transl Psychiatry 2022; 12:332. [PMID: 35961967 PMCID: PMC9374666 DOI: 10.1038/s41398-022-02064-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 06/27/2022] [Accepted: 07/06/2022] [Indexed: 11/09/2022] Open
Abstract
Selecting a course of treatment in psychiatry remains a trial-and-error process, and this long-standing clinical challenge has prompted an increased focus on predictive models of treatment response using machine learning techniques. Electroencephalography (EEG) represents a cost-effective and scalable potential measure to predict treatment response to major depressive disorder. We performed separate meta-analyses to determine the ability of models to distinguish between responders and non-responders using EEG across treatments, as well as a performed subgroup analysis of response to transcranial magnetic stimulation (rTMS), and antidepressants (Registration Number: CRD42021257477) in Major Depressive Disorder by searching PubMed, Scopus, and Web of Science for articles published between January 1960 and February 2022. We included 15 studies that predicted treatment responses among patients with major depressive disorder using machine-learning techniques. Within a random-effects model with a restricted maximum likelihood estimator comprising 758 patients, the pooled accuracy across studies was 83.93% (95% CI: 78.90-89.29), with an Area-Under-the-Curve (AUC) of 0.850 (95% CI: 0.747-0.890), and partial AUC of 0.779. The average sensitivity and specificity across models were 77.96% (95% CI: 60.05-88.70), and 84.60% (95% CI: 67.89-92.39), respectively. In a subgroup analysis, greater performance was observed in predicting response to rTMS (Pooled accuracy: 85.70% (95% CI: 77.45-94.83), Area-Under-the-Curve (AUC): 0.928, partial AUC: 0.844), relative to antidepressants (Pooled accuracy: 81.41% (95% CI: 77.45-94.83, AUC: 0.895, pAUC: 0.821). Furthermore, across all meta-analyses, the specificity (true negatives) of EEG models was greater than the sensitivity (true positives), suggesting that EEG models thus far better identify non-responders than responders to treatment in MDD. Studies varied widely in important features across models, although relevant features included absolute and relative power in frontal and temporal electrodes, measures of connectivity, and asymmetry across hemispheres. Predictive models of treatment response using EEG hold promise in major depressive disorder, although there is a need for prospective model validation in independent datasets, and a greater emphasis on replicating physiological markers. Crucially, standardization in cut-off values and clinical scales for defining clinical response and non-response will aid in the reproducibility of findings and the clinical utility of predictive models. Furthermore, several models thus far have used data from open-label trials with small sample sizes and evaluated performance in the absence of training and testing sets, which increases the risk of statistical overfitting. Large consortium studies are required to establish predictive signatures of treatment response using EEG, and better elucidate the replicability of specific markers. Additionally, it is speculated that greater performance was observed in rTMS models, since EEG is assessing neural networks more likely to be directly targeted by rTMS, comprising electrical activity primarily near the surface of the cortex. Prospectively, there is a need for models that examine the comparative effectiveness of multiple treatments across the same patients. However, this will require a thoughtful consideration towards cumulative treatment effects, and whether washout periods between treatments should be utilised. Regardless, longitudinal cross-over trials comparing multiple treatments across the same group of patients will be an important prerequisite step to both facilitate precision psychiatry and identify generalizable physiological predictors of response between and across treatment options.
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Affiliation(s)
- Devon Watts
- grid.25073.330000 0004 1936 8227Neuroscience Graduate Program, McMaster University, Hamilton, Canada
| | - Rafaela Fernandes Pulice
- grid.8532.c0000 0001 2200 7498School of Medicine, Universidade Federal Do Rio Grande Do Sul, Porto Alegre, RS Brasil ,grid.414449.80000 0001 0125 3761Laboratório de Molecular Psychiatry, Centro de Pesquisa Experimental (CPE) and Centro de Pesquisa Clínica (CPC), Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS Brasil
| | - Jim Reilly
- grid.25073.330000 0004 1936 8227Department of Electrical & Computer Engineering, McMaster University, Hamilton, ON Canada
| | - Andre R. Brunoni
- grid.11899.380000 0004 1937 0722Service of Interdisciplinary Neuromodulation, Laboratory of Neurosciences (LIM-27), Institute of Psychiatry, University of São Paulo, São Paulo, Brasil ,grid.11899.380000 0004 1937 0722Departamento de Clínica Médica, Faculdade de Medicina da USP, São Paulo, Brasil
| | - Flávio Kapczinski
- grid.25073.330000 0004 1936 8227Neuroscience Graduate Program, McMaster University, Hamilton, Canada ,grid.414449.80000 0001 0125 3761Laboratório de Molecular Psychiatry, Centro de Pesquisa Experimental (CPE) and Centro de Pesquisa Clínica (CPC), Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS Brasil ,Instituto Nacional de Ciência e Tecnologia Translacional em Medicina (INCT-TM), Porto Alegre, RS Brasil ,grid.25073.330000 0004 1936 8227Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON Canada
| | - Ives Cavalcante Passos
- School of Medicine, Universidade Federal Do Rio Grande Do Sul, Porto Alegre, RS, Brasil. .,Laboratório de Molecular Psychiatry, Centro de Pesquisa Experimental (CPE) and Centro de Pesquisa Clínica (CPC), Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brasil.
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Strafella R, Chen R, Rajji TK, Blumberger DM, Voineskos D. Resting and TMS-EEG markers of treatment response in major depressive disorder: A systematic review. Front Hum Neurosci 2022; 16:940759. [PMID: 35992942 PMCID: PMC9387384 DOI: 10.3389/fnhum.2022.940759] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 06/28/2022] [Indexed: 11/28/2022] Open
Abstract
Electroencephalography (EEG) is a non-invasive method to identify markers of treatment response in major depressive disorder (MDD). In this review, existing literature was assessed to determine how EEG markers change with different modalities of MDD treatments, and to synthesize the breadth of EEG markers used in conjunction with MDD treatments. PubMed and EMBASE were searched from 2000 to 2021 for studies reporting resting EEG (rEEG) and transcranial magnetic stimulation combined with EEG (TMS-EEG) measures in patients undergoing MDD treatments. The search yielded 966 articles, 204 underwent full-text screening, and 51 studies were included for a narrative synthesis of findings along with confidence in the evidence. In rEEG studies, non-linear quantitative algorithms such as theta cordance and theta current density show higher predictive value than traditional linear metrics. Although less abundant, TMS-EEG measures show promise for predictive markers of brain stimulation treatment response. Future focus on TMS-EEG measures may prove fruitful, given its ability to target cortical regions of interest related to MDD.
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Affiliation(s)
- Rebecca Strafella
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Temerty Centre for Therapeutic Brain Intervention, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Robert Chen
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
- Division of Neurology, Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Tarek K. Rajji
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Temerty Centre for Therapeutic Brain Intervention, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Toronto Dementia Research Alliance, University of Toronto, Toronto, ON, Canada
| | - Daniel M. Blumberger
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Temerty Centre for Therapeutic Brain Intervention, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Daphne Voineskos
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Temerty Centre for Therapeutic Brain Intervention, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- *Correspondence: Daphne Voineskos
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44
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Visual electrophysiology and neuropsychology in bipolar disorders: a review on current state and perspectives. Neurosci Biobehav Rev 2022; 140:104764. [DOI: 10.1016/j.neubiorev.2022.104764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 06/21/2022] [Accepted: 07/01/2022] [Indexed: 11/21/2022]
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45
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van Dijk H, van Wingen G, Denys D, Olbrich S, van Ruth R, Arns M. The two decades brainclinics research archive for insights in neurophysiology (TDBRAIN) database. Sci Data 2022; 9:333. [PMID: 35701407 PMCID: PMC9198070 DOI: 10.1038/s41597-022-01409-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 05/23/2022] [Indexed: 11/13/2022] Open
Abstract
In neuroscience, electroencephalography (EEG) data is often used to extract features (biomarkers) to identify neurological or psychiatric dysfunction or to predict treatment response. At the same time neuroscience is becoming more data-driven, made possible by computational advances. In support of biomarker development and methodologies such as training Artificial Intelligent (AI) networks we present the extensive Two Decades-Brainclinics Research Archive for Insights in Neurophysiology (TDBRAIN) EEG database. This clinical lifespan database (5–89 years) contains resting-state, raw EEG-data complemented with relevant clinical and demographic data of a heterogenous collection of 1274 psychiatric patients collected between 2001 to 2021. Main indications included are Major Depressive Disorder (MDD; N = 426), attention deficit hyperactivity disorder (ADHD; N = 271), Subjective Memory Complaints (SMC: N = 119) and obsessive-compulsive disorder (OCD; N = 75). Demographic-, personality- and day of measurement data are included in the database. Thirty percent of clinical and treatment outcome data will remain blinded for prospective validation and replication purposes. The TDBRAIN database and code are available on the Brainclinics Foundation website at www.brainclinics.com/resources and on Synapse at www.synapse.org/TDBRAIN. Measurement(s) | psychiatric and healthy resting state EEG | Technology Type(s) | electro-encephalography (EEG) | Sample Characteristic - Organism | human | Sample Characteristic - Environment | laboratory environment | Sample Characteristic - Location | Province (The Netherlands) |
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Affiliation(s)
- Hanneke van Dijk
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands. .,Faculty of Psychology & Neuroscience, Department of Cognitive Neuroscience, Maastricht University, Maastricht, the Netherlands.
| | - Guido van Wingen
- Amsterdam UMC, Department of psychiatry, University of Amsterdam, location AMC, Amsterdam, the Netherlands
| | - Damiaan Denys
- Amsterdam UMC, Department of psychiatry, University of Amsterdam, location AMC, Amsterdam, the Netherlands
| | - Sebastian Olbrich
- Department for Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry Zurich, Zurich, Switzerland
| | | | - Martijn Arns
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands.,Faculty of Psychology & Neuroscience, Department of Cognitive Neuroscience, Maastricht University, Maastricht, the Netherlands
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46
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A benchmark for prediction of psychiatric multimorbidity from resting EEG data in a large pediatric sample. Neuroimage 2022; 258:119348. [PMID: 35659998 DOI: 10.1016/j.neuroimage.2022.119348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 05/26/2022] [Accepted: 05/31/2022] [Indexed: 11/23/2022] Open
Abstract
Psychiatric disorders are among the most common and debilitating illnesses across the lifespan and begin usually during childhood and adolescence, which emphasizes the importance of studying the developing brain. Most of the previous pediatric neuroimaging studies employed traditional univariate statistics on relatively small samples. Multivariate machine learning approaches have a great potential to overcome the limitations of these approaches. On the other hand, the vast majority of existing multivariate machine learning studies have focused on differentiating between children with an isolated psychiatric disorder and typically developing children. However, this line of research does not reflect the real-life situation as the majority of children with a clinical diagnosis have multiple psychiatric disorders (multimorbidity), and consequently, a clinician has the task to choose between different diagnoses and/or the combination of multiple diagnoses. Thus, the goal of the present benchmark is to predict psychiatric multimorbidity in children and adolescents. For this purpose, we implemented two kinds of machine learning benchmark challenges: The first challenge targets the prediction of the seven most prevalent DSM-V psychiatric diagnoses for the available data set, of which each individual can exhibit multiple ones concurrently (i.e. multi-task multi-label classification). Based on behavioral and cognitive measures, a second challenge focuses on predicting psychiatric symptom severity on a dimensional level (i.e. multiple regression task). For the present benchmark challenges, we will leverage existing and future data from the biobank of the Healthy Brain Network (HBN) initiative, which offers a unique large-sample dataset (N = 2042) that provides a wide array of different psychiatric developmental disorders and true hidden data sets. Due to limited real-world practicability and economic viability of MRI measurements, the present challenge will permit only resting state EEG data and demographic information to derive predictive models. We believe that a community driven effort to derive predictive markers from these data using advanced machine learning algorithms can help to improve the diagnosis of psychiatric developmental disorders.
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47
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de la Salle S, Phillips JL, Blier P, Knott V. Electrophysiological correlates and predictors of the antidepressant response to repeated ketamine infusions in treatment-resistant depression. Prog Neuropsychopharmacol Biol Psychiatry 2022; 115:110507. [PMID: 34971723 DOI: 10.1016/j.pnpbp.2021.110507] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 12/03/2021] [Accepted: 12/23/2021] [Indexed: 12/28/2022]
Abstract
BACKGROUND Sub-anesthetic ketamine doses rapidly reduce depressive symptoms, although additional investigations of the underlying neural mechanisms and the prediction of response outcomes are needed. Electroencephalographic (EEG)-derived measures have shown promise in predicting antidepressant response to a variety of treatments, and are sensitive to ketamine administration. This study examined their utility in characterizing changes in depressive symptoms following single and repeated ketamine infusions. METHODS Recordings were obtained from patients with treatment-resistant major depressive disorder (MDD) (N = 24) enrolled in a multi-phase clinical ketamine trial. During the randomized, double-blind, crossover phase (Phase 1), patients received intravenous ketamine (0.5 mg/kg) and midazolam (30 μg/kg), at least 1 week apart. For each medication, three resting, eyes-closed recordings were obtained per session (pre-infusion, immediately post-infusion, 2 h post-infusion), and changes in power (delta, theta1/2/total, alpha1/2/total, beta, gamma), alpha asymmetry, theta cordance, and theta source-localized anterior cingulate cortex activity were quantified. The relationships between ketamine-induced changes with early (Phase 1) and sustained (Phases 2,3: open-label repeated infusions) decreases in depressive symptoms (Montgomery-Åsberg Depression Rating Score, MADRS) and suicidal ideation (MADRS item 10) were examined. RESULTS Both medications decreased alpha and theta immediately post-infusion, however, only midazolam increased delta (post-infusion), and only ketamine increased gamma (immediately post- and 2 h post-infusion). Regional- and frequency-specific ketamine-induced EEG changes were related to and predictive of decreases in depressive symptoms (theta, gamma) and suicidal ideation (alpha). Early and sustained treatment responders differed at baseline in surface-level and source-localized theta. CONCLUSIONS Ketamine exerts frequency-specific changes on EEG-derived measures, which are related to depressive symptom decreases in treatment-resistant MDD and provide information regarding early and sustained individual response to ketamine. CLINICAL TRIAL REGISTRATION ClinicalTrials.gov: Action of Ketamine in Treatment-Resistant Depression, NCT01945047.
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Affiliation(s)
- Sara de la Salle
- University of Ottawa Institute of Mental Health Research at the Royal, 1145 Carling Avenue, Ottawa, ON K1Z 7K4, Canada; School of Psychology, University of Ottawa, 136 Jean-Jacques Lussier, Ottawa, ON K1N6N5, Canada.
| | - Jennifer L Phillips
- University of Ottawa Institute of Mental Health Research at the Royal, 1145 Carling Avenue, Ottawa, ON K1Z 7K4, Canada; Department of Psychiatry, University of Ottawa, 1145 Carling Avenue, Ottawa, ON K1Z 7K4, Canada; Department of Biochemistry, Microbiology and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, ON K1H 8M5, Canada
| | - Pierre Blier
- University of Ottawa Institute of Mental Health Research at the Royal, 1145 Carling Avenue, Ottawa, ON K1Z 7K4, Canada; Department of Psychiatry, University of Ottawa, 1145 Carling Avenue, Ottawa, ON K1Z 7K4, Canada; Department of Cellular and Molecular Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON K1H 8M5, Canada
| | - Verner Knott
- University of Ottawa Institute of Mental Health Research at the Royal, 1145 Carling Avenue, Ottawa, ON K1Z 7K4, Canada; Department of Cellular and Molecular Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON K1H 8M5, Canada; School of Psychology, University of Ottawa, 136 Jean-Jacques Lussier, Ottawa, ON K1N6N5, Canada
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Swatzyna RJ, Arns M, Tarnow JD, Turner RP, Barr E, MacInerney EK, Hoffman AM, Boutros NN. Isolated epileptiform activity in children and adolescents: prevalence, relevance, and implications for treatment. Eur Child Adolesc Psychiatry 2022; 31:545-552. [PMID: 32666203 DOI: 10.1007/s00787-020-01597-2] [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: 02/12/2020] [Accepted: 07/01/2020] [Indexed: 10/23/2022]
Abstract
In the field of psychiatry diagnoses are primarily based on the report of symptoms from either the patient, parents, or both, and a psychiatrist's observations. A psychiatric diagnosis is currently the most widely used basis for medication selection and the brain is seldom investigated directly as a source of those symptoms. This study addresses the request from the National Institute of Mental Health (NIMH) Research Domain Criteria Project (RDoC) for scientific research into neurological abnormalities that can be linked to psychiatric symptoms for the purpose of predicting medication response. One such neurological abnormality that has been the focus of many studies over the last three decades is isolated epileptiform discharges (IEDs) in children and adolescents without seizures. We conducted a systematic review of the literature to determine prevalence rates of IEDs within diagnostic categories. We then compared the prevalence of IEDs in the selected literature to our IRB-approved data archive. Our study found a consistent high prevalence of IEDs specifically for ADHD (majority > 25%) and ASD (majority > 59%), and consistent low prevalence rates were found for Depression (3%). If children and adolescents have failed multiple medication attempts, and more than one-third of them have IEDs, then an EEG would be justified within the RDoC paradigm.
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Affiliation(s)
- Ronald J Swatzyna
- Houston Neuroscience Brain Center, Houston, TX, USA. .,Clinical NeuroAnalytics, 1307 Oceanside Lane, League City, TX, 77573, USA.
| | - Martijn Arns
- Department of Experimental Psychology, Utrecht University, Utrecht, The Netherlands.,Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, The Netherlands
| | - Jay D Tarnow
- The Tarnow Center for Self-Management, Houston, USA
| | - Robert P Turner
- Network Neurology Health, Charleston, SC, USA.,Clinical Pediatrics and Neurology, USC School of Medicine, Columbia, SC, USA
| | - Emma Barr
- Houston Neuroscience Brain Center, Houston, TX, USA
| | | | | | - Nash N Boutros
- School of Medicine, RUSH University, Kansas City, MO, USA
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Widge AS, Zhang F, Gosai A, Papadimitrou G, Wilson-Braun P, Tsintou M, Palanivelu S, Noecker AM, McIntyre CC, O’Donnell L, McLaughlin NCR, Greenberg BD, Makris N, Dougherty DD, Rathi Y. Patient-specific connectomic models correlate with, but do not reliably predict, outcomes in deep brain stimulation for obsessive-compulsive disorder. Neuropsychopharmacology 2022; 47:965-972. [PMID: 34621015 PMCID: PMC8882183 DOI: 10.1038/s41386-021-01199-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 08/11/2021] [Accepted: 09/23/2021] [Indexed: 12/15/2022]
Abstract
Deep brain stimulation (DBS) of the ventral internal capsule/ventral striatum (VCVS) is an emerging treatment for obsessive-compulsive disorder (OCD). Recently, multiple studies using normative connectomes have correlated DBS outcomes to stimulation of specific white matter tracts. Those studies did not test whether these correlations are clinically predictive, and did not apply cross-validation approaches that are necessary for biomarker development. Further, they did not account for the possibility of systematic differences between DBS patients and the non-diagnosed controls used in normative connectomes. To address these gaps, we performed patient-specific diffusion imaging in 8 patients who underwent VCVS DBS for OCD. We delineated tracts connecting thalamus and subthalamic nucleus (STN) to prefrontal cortex via VCVS. We then calculated which tracts were likely activated by individual patients' DBS settings. We fit multiple statistical models to predict both OCD and depression outcomes from tract activation. We further attempted to predict hypomania, a VCVS DBS complication. We assessed all models' performance on held-out test sets. With this best-practices approach, no model predicted OCD response, depression response, or hypomania above chance. Coefficient inspection partly supported prior reports, in that capture of tracts projecting to cingulate cortex was associated with both YBOCS and MADRS response. In contrast to prior reports, however, tracts connected to STN were not reliably correlated with response. Thus, patient-specific imaging and a guideline-adherent analysis were unable to identify a tractographic target with sufficient effect size to drive clinical decision-making or predict individual outcomes. These findings suggest caution in interpreting the results of normative connectome studies.
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Affiliation(s)
- Alik S. Widge
- grid.17635.360000000419368657Department of Psychiatry, University of Minnesota, Minneapolis, MN USA
| | - Fan Zhang
- grid.62560.370000 0004 0378 8294Department of Radiology, Brigham and Womens Hospital, Boston, MA USA
| | - Aishwarya Gosai
- grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Boston, MA USA
| | - George Papadimitrou
- grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Boston, MA USA
| | - Peter Wilson-Braun
- grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Boston, MA USA
| | - Magdalini Tsintou
- grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Boston, MA USA
| | - Senthil Palanivelu
- grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Boston, MA USA
| | - Angela M. Noecker
- grid.67105.350000 0001 2164 3847Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH USA
| | - Cameron C. McIntyre
- grid.67105.350000 0001 2164 3847Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH USA
| | - Lauren O’Donnell
- grid.62560.370000 0004 0378 8294Department of Radiology, Brigham and Womens Hospital, Boston, MA USA
| | - Nicole C. R. McLaughlin
- grid.40263.330000 0004 1936 9094Department of Psychiatry and Human Behavior, Alpert Medical School, Brown University, Providence, RI USA ,grid.273271.20000 0000 8593 9332Butler Hospital, Providence, RI USA
| | - Benjamin D. Greenberg
- grid.40263.330000 0004 1936 9094Department of Psychiatry and Human Behavior, Alpert Medical School, Brown University, Providence, RI USA ,grid.273271.20000 0000 8593 9332Butler Hospital, Providence, RI USA ,Center for Neurorestoration and Neurotechnology, VA Providence Healthcare System, Providence, RI USA
| | - Nikolaos Makris
- grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Boston, MA USA
| | - Darin D. Dougherty
- grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Boston, MA USA
| | - Yogesh Rathi
- grid.62560.370000 0004 0378 8294Department of Radiology, Brigham and Womens Hospital, Boston, MA USA ,grid.32224.350000 0004 0386 9924Department of Psychiatry, Massachusetts General Hospital, Boston, MA USA
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50
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Koller-Schlaud K, Ströhle A, Behr J, Bärwolf Dreysse E, Rentzsch J. Changes in Electric Brain Response to Affective Stimuli in the First Week of Antidepressant Treatment: An Exploratory Study. Neuropsychobiology 2022; 81:69-79. [PMID: 34515179 DOI: 10.1159/000517860] [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: 09/30/2020] [Accepted: 06/14/2021] [Indexed: 11/19/2022]
Abstract
INTRODUCTION Asymmetrical alpha and frontal theta activity have been discussed as neurobiological markers for antidepressant treatment response. While most studies focus on resting-state EEG, there is evidence that task-related activity assessed at multiple time points might be superior in detecting subtle early differences. METHODS This was a naturalistic study design assessing participants in a psychiatric in- and outpatient hospital setting. We investigated stimulus-related EEG asymmetry (frontal and occipital alpha-1 and alpha-2) and power (frontal midline theta) assessed at baseline and 1 week after initiation of pharmacological depression treatment while presenting affective stimuli. We then compared week 4 responders and nonresponders to antidepressant treatment. RESULTS Follow-up analyses of a significant group × emotion × time interaction (p < 0.04) for alpha-1 asymmetry showed that responders differed significantly at baseline in their asymmetry scores in response to sad compared to happy faces with a change in this pattern 1 week later. Nonresponders did not show this pattern. No significant results were found for alpha-2, occipital alpha-1, and occipital alpha-2 asymmetry or frontal midline theta power. DISCUSSION Our study addresses the gap in comparisons of task-related EEG activity changes measured at two time points and supports the potential value of this approach in detecting early differences in responders versus nonresponders to pharmacological treatment. Important limitations include the small sample size and the noncontrolled study design.
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Affiliation(s)
- Kristin Koller-Schlaud
- Department of Psychiatry and Neurosciences
- CCM, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany.,Department of Psychiatry, Psychotherapy and Psychosomatics, Brandenburg Medical School Theodor Fontane, Neuruppin, Germany
| | - Andreas Ströhle
- Department of Psychiatry and Neurosciences
- CCM, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany
| | - Joachim Behr
- Department of Psychiatry and Neurosciences
- CCM, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany.,Department of Psychiatry, Psychotherapy and Psychosomatics, Brandenburg Medical School Theodor Fontane, Neuruppin, Germany.,Faculty of Health Science Brandenburg, Joint Faculty of the University of Potsdam, Brandenburg University of Technology Cottbus-Senftenberg and Brandenburg Medical School Theodor Fontane, Potsdam, Germany
| | - Elisabeth Bärwolf Dreysse
- Department of Psychiatry and Neurosciences
- CCM, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany
| | - Johannes Rentzsch
- Department of Psychiatry and Neurosciences
- CCM, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany.,Department of Psychiatry, Psychotherapy and Psychosomatics, Brandenburg Medical School Theodor Fontane, Neuruppin, Germany
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