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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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van der Wijk G, Enkhbold Y, Cnudde K, Szostakiwskyj MW, Blier P, Knott V, Jaworska N, Protzner AB. One size does not fit all: notable individual variation in brain activity correlates of antidepressant treatment response. Front Psychiatry 2024; 15:1358018. [PMID: 38628260 PMCID: PMC11018891 DOI: 10.3389/fpsyt.2024.1358018] [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: 12/19/2023] [Accepted: 03/18/2024] [Indexed: 04/19/2024] Open
Abstract
Introduction To date, no robust electroencephalography (EEG) markers of antidepressant treatment response have been identified. Variable findings may arise from the use of group analyses, which neglect individual variation. Using a combination of group and single-participant analyses, we explored individual variability in EEG characteristics of treatment response. Methods Resting-state EEG data and Montgomery-Åsberg Depression Rating Scale (MADRS) symptom scores were collected from 43 patients with depression before, at 1 and 12 weeks of pharmacotherapy. Partial least squares (PLS) was used to: 1) identify group differences in EEG connectivity (weighted phase lag index) and complexity (multiscale entropy) between eventual medication responders and non-responders, and 2) determine whether group patterns could be identified in individual patients. Results Responders showed decreased alpha and increased beta connectivity, and early, widespread decreases in complexity over treatment. Non-responders showed an opposite connectivity pattern, and later, spatially confined decreases in complexity. Thus, as in previous studies, our group analyses identified significant differences between groups of patients with different treatment outcomes. These group-level EEG characteristics were only identified in ~40-60% of individual patients, as assessed quantitatively by correlating the spatiotemporal brain patterns between groups and individual results, and by independent raters through visualization. Discussion Our single-participant analyses suggest that substantial individual variation exists, and needs to be considered when investigating characteristics of antidepressant treatment response for potential clinical applicability. Clinical trial registration https://clinicaltrials.gov, identifier NCT00519428.
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Affiliation(s)
- Gwen van der Wijk
- Department of Psychology, University of Calgary, Calgary, AB, Canada
| | - Yaruuna Enkhbold
- Department of Psychology, University of Calgary, Calgary, AB, Canada
| | - Kelsey Cnudde
- Department of Psychology, University of Calgary, Calgary, AB, Canada
| | | | - Pierre Blier
- Institute of Mental Health Research, Affiliated with the University of Ottawa, Ottawa, ON, Canada
- Department of Cellular & Molecular Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Verner Knott
- Institute of Mental Health Research, Affiliated with the University of Ottawa, Ottawa, ON, Canada
- Department of Cellular & Molecular Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Natalia Jaworska
- Institute of Mental Health Research, Affiliated with the University of Ottawa, Ottawa, ON, Canada
- Department of Cellular & Molecular Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Andrea B. Protzner
- Department of Psychology, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Mathison Centre, University of Calgary, Calgary, AB, Canada
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Francis AM, Bissonnette JN, Hull KM, Leckey J, Pimer L, Lawrence MA, Berrigan LI, Fisher DJ. Measuring the attention networks and quantitative-electroencephalography correlates of attention in depression. Psychiatry Res Neuroimaging 2023; 333:111661. [PMID: 37331318 DOI: 10.1016/j.pscychresns.2023.111661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 03/06/2023] [Accepted: 05/22/2023] [Indexed: 06/20/2023]
Abstract
Given the association between major depressive disorder (MDD) and cortical inefficiency related to executive control, specifically in the sense that individuals with MDD may recruit more cognitive resources to complete tasks at the same capacity as those without MDD, the current study was interested in examining the attention networks and executive functioning of those with MDD. Past research has used the Attention Network Test (ANT) to measure changes of attention in clinical vs. healthy populations; however, theoretical concerns have been raised regarding the task. The Combined Attention Systems Task (CAST) was developed to address these concerns and was used in our study in combination with quantitative-electroencephalography (QEEG) to assess both behavioural and neurophysiological changes in participants with MDD (n = 18) compared to healthy controls (HCs; n = 22). We found no behavioural differences between MDD and HC groups suggesting individuals with MDD in our sample were not experiencing the executive functioning deficits previously reported in the literature. Neurophysiological measures of attention revealed that MDD participants had greater theta and alpha1 activity relative to HCs, suggesting that although individuals with MDD do not show deficits in behavioural attention, they exhibit altered neural processing which underlies cognitive function.
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Affiliation(s)
- Ashley M Francis
- Department of Psychology, Saint Mary's University, Halifax, NS, Canada; Department of Psychology, St. Francis Xavier University, Antigonish, Nova Scotia, Canada; Department of Psychiatry, Dalhousie University, Nova Scotia, Canada
| | - Jenna N Bissonnette
- Department of Psychology, Mount Saint Vincent University, Halifax, NS, Canada; Department of Psychology & Neuroscience, Dalhousie University, Halifax, NS, Canada
| | - Krista M Hull
- Department of Psychology, Mount Saint Vincent University, Halifax, NS, Canada
| | - Jennifer Leckey
- Department of Psychology, Mount Saint Vincent University, Halifax, NS, Canada
| | - Laura Pimer
- Department of Psychiatry, Dalhousie University, Nova Scotia, Canada; Department of Psychology, Mount Saint Vincent University, Halifax, NS, Canada
| | - Michael A Lawrence
- Department of Psychology & Neuroscience, Dalhousie University, Halifax, NS, Canada
| | - Lindsay I Berrigan
- Department of Psychology, St. Francis Xavier University, Antigonish, Nova Scotia, Canada; Department of Psychiatry, Dalhousie University, Nova Scotia, Canada
| | - Derek J Fisher
- Department of Psychology, Saint Mary's University, Halifax, NS, Canada; Department of Psychiatry, Dalhousie University, Nova Scotia, Canada; Department of Psychology, Mount Saint Vincent University, Halifax, NS, Canada; Department of Psychology & Neuroscience, Dalhousie University, Halifax, NS, Canada.
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Livinț Popa L, Chira D, Dăbală V, Hapca E, Popescu BO, Dina C, Cherecheș R, Strilciuc Ș, Mureșanu DF. Quantitative EEG as a Biomarker in Evaluating Post-Stroke Depression. Diagnostics (Basel) 2022; 13:diagnostics13010049. [PMID: 36611341 PMCID: PMC9818970 DOI: 10.3390/diagnostics13010049] [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: 11/07/2022] [Revised: 12/14/2022] [Accepted: 12/22/2022] [Indexed: 12/28/2022] Open
Abstract
Introduction: Post-stroke depression (PSD) has complex pathophysiology determined by various biological and psychological factors. Although it is a long-term complication of stroke, PSD is often underdiagnosed. Given the diagnostic role of quantitative electroencephalography (qEEG) in depression, it was investigated whether a possible marker of PSD could be identified by observing the evolution of the (Delta + Theta)/(Alpha + Beta) Ratio (DTABR), respectively the Delta/Alpha Ratio (DAR) values in post-stroke depressed patients (evaluated through the HADS-D subscale). Methods: The current paper analyzed the data of 57 patients initially selected from a randomized control trial (RCT) that assessed the role of N-Pep 12 in stroke rehabilitation. EEG recordings from the original trial database were analyzed using signal processing techniques, respecting the conditions (eyes open, eyes closed), and several cognitive tasks. Results: We observed two significant associations between the DTABR values and the HADS-D scores of post-stroke depressed patients for each of the two visits (V1 and V2) of the N-Pep 12 trial. We recorded the relationships in the Global (V1 = 30 to 120 days after stroke) and Frontal Extended (V2 = 90 days after stroke) regions during cognitive tasks that trained attention and working memory. For the second visit, the association between the analyzed variables was negative. Conclusions: As both our relationships were described during the cognitive condition, we can state that the neural networks involved in processing attention and working memory might go through a reorganization process one to four months after the stroke onset. After a period longer than six months, the process could localize itself at the level of frontal regions, highlighting a possible divergence between the local frontal dynamics and the subjective well-being of stroke survivors. QEEG parameters linked to stroke progression evolution (like DAR or DTABR) can facilitate the identification of the most common neuropsychiatric complication in stroke survivors.
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Affiliation(s)
- Livia Livinț Popa
- RoNeuro Institute for Neurological Research and Diagnostic, 400364 Cluj-Napoca, Romania
- Department of Neuroscience, Iuliu Hatieganu University of Medicine and Pharmacy, 400083 Cluj-Napoca, Romania
| | - Diana Chira
- RoNeuro Institute for Neurological Research and Diagnostic, 400364 Cluj-Napoca, Romania
- Correspondence:
| | - Victor Dăbală
- RoNeuro Institute for Neurological Research and Diagnostic, 400364 Cluj-Napoca, Romania
- Department of Neuroscience, Iuliu Hatieganu University of Medicine and Pharmacy, 400083 Cluj-Napoca, Romania
| | - Elian Hapca
- RoNeuro Institute for Neurological Research and Diagnostic, 400364 Cluj-Napoca, Romania
- Department of Neuroscience, Iuliu Hatieganu University of Medicine and Pharmacy, 400083 Cluj-Napoca, Romania
| | - Bogdan Ovidiu Popescu
- Department of Neuroscience, Carol Davila University of Medicine and Pharmacy, 050474 Bucharest, Romania
| | - Constantin Dina
- Faculty of Medicine, Ovidius University, 900527 Constanta, Romania
| | - Răzvan Cherecheș
- Department of Public Health, Babes-Bolyai University, 400294 Cluj-Napoca, Romania
| | - Ștefan Strilciuc
- RoNeuro Institute for Neurological Research and Diagnostic, 400364 Cluj-Napoca, Romania
- Department of Neuroscience, Iuliu Hatieganu University of Medicine and Pharmacy, 400083 Cluj-Napoca, Romania
| | - Dafin F. Mureșanu
- RoNeuro Institute for Neurological Research and Diagnostic, 400364 Cluj-Napoca, Romania
- Department of Neuroscience, Iuliu Hatieganu University of Medicine and Pharmacy, 400083 Cluj-Napoca, Romania
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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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Adamson M, Hadipour AL, Uyulan C, Erguzel T, Cerezci O, Kazemi R, Phillips A, Seenivasan S, Shah S, Tarhan N. Sex differences in rTMS treatment response: A deep learning-based EEG investigation. Brain Behav 2022; 12:e2696. [PMID: 35879921 PMCID: PMC9392544 DOI: 10.1002/brb3.2696] [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: 12/07/2021] [Revised: 06/01/2022] [Accepted: 06/08/2022] [Indexed: 11/22/2022] Open
Abstract
INTRODUCTION The present study aimed to investigate sex differences in response to repetitive transcranial magnetic stimulation (rTMS) in Major Depressive Disorder (MDD) patients. Identifying the factors that mediate treatment response to rTMS in MDD patients can guide clinicians to administer more appropriate, reliable, and personalized interventions. METHODS In this paper, we developed a novel pipeline based on convolutional LSTM-based deep learning (DL) to classify 25 female and 25 male patients based on their rTMS treatment response. RESULTS Five different classification models were generated, namely pre-/post-rTMS female (model 1), pre-/post-rTMS male (model 2), pre-rTMS female responder versus pre-rTMS female nonresponders (model 3), pre-rTMS male responder vs. pre-rTMS male nonresponder (model 4), and pre-rTMS responder versus nonresponder of both sexes (model 5), achieving 93.3%, 98%, 95.2%, 99.2%, and 96.6% overall test accuracy, respectively. CONCLUSION These results indicate the potential of our approach to be used as a response predictor especially regarding sex-specific antidepressant effects of rTMS in MDD patients.
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Affiliation(s)
- M Adamson
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, California.,Department of Rehabilitation Service, VA Palo Alto Healthcare System, Palo Alto, California
| | - A L Hadipour
- Department of Cognitive Sciences, University of Messina, Messina, Italy
| | - C Uyulan
- Department of Mechanical Engineering, İzmir Katip Çelebi University, İzmir, Turkey
| | - T Erguzel
- Faculty of Engineering and Natural Sciences, Üsküdar University, Istanbul, Turkey
| | - O Cerezci
- Faculty of Health Sciences, Üsküdar University, Istanbul, Turkey
| | - R Kazemi
- Department of Cognitive Psychology, Institute for Cognitive Science Studies, Tehran, Iran
| | - A Phillips
- Department of Rehabilitation Service, VA Palo Alto Healthcare System, Palo Alto, California
| | - S Seenivasan
- Department of Rehabilitation Service, VA Palo Alto Healthcare System, Palo Alto, California
| | - S Shah
- Department of Rehabilitation Service, VA Palo Alto Healthcare System, Palo Alto, California
| | - N Tarhan
- Faculty of Humanities and Social Sciences, Üsküdar University, Istanbul, Turkey
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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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Baptist J, Thompson DE, Spencer C, Mowla MR, Love HA, Su Y. Clinical efficacy of EMDR in unipolar depression: Changes in theta cordance. Psychiatry Res 2021; 296:113696. [PMID: 33387752 DOI: 10.1016/j.psychres.2020.113696] [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: 07/08/2020] [Accepted: 12/26/2020] [Indexed: 11/18/2022]
Abstract
Eye Movement Desensitization and Reprocessing (EMDR) has demonstrated efficacy in treating major depressive disorder. EMDR increases cerebral perfusion in the anterior cingulate cortex (ACC) and dorsolateral prefrontal cortex (dlPFC). Activity in the ACC and dlPFC can be measured by theta cordance (TC) but has not been examined in EMDR. Ten participants (3 men, 7 women, M age = 42.31 ± 15.03) received ten 75 ± 15 minute EMDR sessions over 6.5 ± .5 weeks. Results indicated that PHQ-9 depression scores reduced from T1 (M = 13.9 ± 3.31) to T11 (M = 6.30 ± 3.23) with EMDR (SMD = 2.30), and that fTC but not pfTC was significantly related to this change. Depression declined as fTC declined. EMDR may engage the dlPFC or ACC that modulates depression and aid in reducing fTC and thus depression levels.
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Affiliation(s)
| | | | | | | | | | - Yile Su
- Florida State University, USA
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Chen ST, Ku LC, Chen SJ, Shen TW. The Changes of qEEG Approximate Entropy during Test of Variables of Attention as a Predictor of Major Depressive Disorder. Brain Sci 2020; 10:brainsci10110828. [PMID: 33171848 PMCID: PMC7695214 DOI: 10.3390/brainsci10110828] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 10/30/2020] [Accepted: 11/05/2020] [Indexed: 01/30/2023] Open
Abstract
Evaluating brain function through biosignals remains challenging. Quantitative electroencephalography (qEEG) outcomes have emerged as a potential intermediate biomarker for diagnostic clarification in psychological disorders. The Test of Variables of Attention (TOVA) was combined with qEEG to evaluate biomarkers such as absolute power, relative power, cordance, and approximate entropy from covariance matrix images to predict major depressive disorder (MDD). EEG data from 18 healthy control and 18 MDD patients were monitored during the resting state and TOVA. TOVA was found to provide aspects for the evaluation of MDD beyond resting electroencephalography. The results showed that the prefrontal qEEG theta cordance of the control and MDD groups were significantly different. For comparison, the changes in qEEG approximate entropy (ApEn) patterns observed during TOVA provided features to distinguish between participants with or without MDD. Moreover, ApEn scores during TOVA were a strong predictor of MDD, and the ApEn scores correlated with the Beck Depression Inventory (BDI) scores. Between-group differences in ApEn were more significant for the testing state than for the resting state. Our results provide further understanding for MDD treatment selection and response prediction during TOVA.
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Affiliation(s)
- Shao-Tsu Chen
- Department of Psychiatry, Hualien Tzu Chi Hospital, Buddhist Tzu-Chi Medical Foundation, Hualien 970, Taiwan;
- Department of Psychiatry, Tzu Chi University, Hualien 970, Taiwan
| | - Li-Chi Ku
- Department of Medical Informatics, Tzu Chi University, Hualien 970, Taiwan;
| | - Shaw-Ji Chen
- Department of Psychiatry, Taitung MacKay Memorial Hospital, Taitung County 950, Taiwan;
- Department of Medicine, MacKay Medical College, New Taipei City 252, Taiwan
| | - Tsu-Wang Shen
- Department of Automatic Control Engineering, Feng Chia University, Taichung 40724, Taiwan
- Master’s Program Biomedical Informatics and Biomedical Engineering, Feng Chia University, Taichung 40724, Taiwan
- Correspondence: ; Tel.: +886-4-24517250 (ext. 3937)
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