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Takasu K, Yawata Y, Tashima R, Aritomi H, Shimada S, Onodera T, Taishi T, Ogawa K. Distinct mechanisms of allopregnanolone and diazepam underlie neuronal oscillations and differential antidepressant effect. Front Cell Neurosci 2024; 17:1274459. [PMID: 38259500 PMCID: PMC10800935 DOI: 10.3389/fncel.2023.1274459] [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: 08/08/2023] [Accepted: 11/29/2023] [Indexed: 01/24/2024] Open
Abstract
The rapid relief of depressive symptoms is a major medical requirement for effective treatments for major depressive disorder (MDD). A decrease in neuroactive steroids contributes to the pathophysiological mechanisms associated with the neurological symptoms of MDD. Zuranolone (SAGE-217), a neuroactive steroid that acts as a positive allosteric modulator of synaptic and extrasynaptic δ-subunit-containing GABAA receptors, has shown rapid-onset, clinically effective antidepressant action in patients with MDD or postpartum depression (PPD). Benzodiazepines, on the other hand, act as positive allosteric modulators of synaptic GABAA receptors but are not approved for the treatment of patients with MDD. It remains unclear how differences in molecular mechanisms contribute to the alleviation of depressive symptoms and the regulation of associated neuronal activity. Focusing on the antidepressant-like effects and neuronal activity of the basolateral amygdala (BLA) and medial prefrontal cortex (mPFC), we conducted a head-to-head comparison study of the neuroactive steroid allopregnanolone and the benzodiazepine diazepam using a mouse social defeat stress (SDS) model. Allopregnanolone but not diazepam exhibited antidepressant-like effects in a social interaction test in SDS mice. This antidepressant-like effect of allopregnanolone was abolished in extrasynaptic GABAA receptor δ-subunit knockout mice (δko mice) subjected to the same SDS protocol. Regarding the neurophysiological mechanism associated with these antidepressant-like effects, allopregnanolone but not diazepam increased theta oscillation in the BLA of SDS mice. This increase did not occur in δko mice. Consistent with this, allopregnanolone potentiated tonic inhibition in BLA interneurons via δ-subunit-containing extrasynaptic GABAA receptors. Theta oscillation in the mPFC of SDS mice was also increased by allopregnanolone but not by diazepam. Finally, allopregnanolone but not diazepam increased frontal theta activity in electroencephalography recordings in naïve and SDS mice. Neuronal network alterations associated with MDD showed decreased frontal theta and beta activity in depressed SDS mice. These results demonstrated that, unlike benzodiazepines, neuroactive steroids increased theta oscillation in the BLA and mPFC through the activation of δ-subunit-containing GABAA receptors, and this change was associated with antidepressant-like effects in the SDS model. Our findings support the notion that the distinctive mechanism of neuroactive steroids may contribute to the rapid antidepressant effects in MDD.
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Affiliation(s)
- Keiko Takasu
- Laboratory for Drug Discovery and Disease Research, Shionogi Pharmaceutical Research Center, Shionogi & Co., Ltd., Osaka, Japan
| | - Yosuke Yawata
- Laboratory for Drug Discovery and Disease Research, Shionogi Pharmaceutical Research Center, Shionogi & Co., Ltd., Osaka, Japan
| | - Ryoichi Tashima
- Laboratory for Drug Discovery and Disease Research, Shionogi Pharmaceutical Research Center, Shionogi & Co., Ltd., Osaka, Japan
| | | | | | - Tsukasa Onodera
- Laboratory for Drug Discovery and Disease Research, Shionogi Pharmaceutical Research Center, Shionogi & Co., Ltd., Osaka, Japan
| | - Teruhiko Taishi
- Laboratory for Drug Discovery and Disease Research, Shionogi Pharmaceutical Research Center, Shionogi & Co., Ltd., Osaka, Japan
| | - Koichi Ogawa
- Laboratory for Drug Discovery and Disease Research, Shionogi Pharmaceutical Research Center, Shionogi & Co., Ltd., Osaka, Japan
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Chen Z, Feng Z, Dai Q, Fuschia S. Editorial: Cognitive-related and connectome-based biomarkers for depression: the application of state-of-the-art techniques and models to uncover. Front Psychiatry 2023; 14:1249049. [PMID: 37575561 PMCID: PMC10415064 DOI: 10.3389/fpsyt.2023.1249049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 07/18/2023] [Indexed: 08/15/2023] Open
Affiliation(s)
- Zhiyi Chen
- Experimental Research Center of Medical and Psychological Science, School of Psychology, Third Military Medical University, Chongqing, China
| | - Zhengzhi Feng
- Experimental Research Center of Medical and Psychological Science, School of Psychology, Third Military Medical University, Chongqing, China
| | - Qin Dai
- Experimental Research Center of Medical and Psychological Science, School of Psychology, Third Military Medical University, Chongqing, China
- School of Psychology, Third Military Medical University, Chongqing, China
| | - Sirois Fuschia
- Department of Psychology, Durham University, Durham, United Kingdom
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Sharma G, Rahmatkar SN, Rana AK, Sharma P, Patial V, Singh D, Roy Chowdhury S. Preclinical Validation of Electrodes for Single Anodal Transcranial Direct Current Stimulation on Rat Model With Chronic Stress-Induced Depression. IEEE SENSORS JOURNAL 2023; 23:12133-12145. [DOI: 10.1109/jsen.2023.3266235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/10/2024]
Affiliation(s)
- Gaurav Sharma
- Cognitive Brain Dynamics Laboratory, National Brain Research Centre (NBRC), Manesar, Haryana, India
| | - Shubham Nilkanth Rahmatkar
- Pharmacology and Toxicology Laboratory, Council of Scientific and Industrial Research (CSIR)-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India
| | - Anil Kumar Rana
- Pharmacology and Toxicology Laboratory, Council of Scientific and Industrial Research (CSIR)-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India
| | - Pallavi Sharma
- Pharmacology and Toxicology Laboratory, Council of Scientific and Industrial Research (CSIR)-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India
| | - Vikram Patial
- Pharmacology and Toxicology Laboratory, Council of Scientific and Industrial Research (CSIR)-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India
| | - Damanpreet Singh
- Pharmacology and Toxicology Laboratory, Council of Scientific and Industrial Research (CSIR)-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India
| | - Shubhajit Roy Chowdhury
- Biomedical Systems Laboratory, School of Computing and Electrical Engineering, Indian Institute of Technology Mandi, Kamand Campus, Mandi, Himachal Pradesh, India
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Park H, Petkova E, Tarpey T, Ogden RT. Functional additive models for optimizing individualized treatment rules. Biometrics 2023; 79:113-126. [PMID: 34704622 PMCID: PMC9043034 DOI: 10.1111/biom.13586] [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: 10/15/2020] [Revised: 10/01/2021] [Accepted: 10/14/2021] [Indexed: 11/29/2022]
Abstract
A novel functional additive model is proposed, which is uniquely modified and constrained to model nonlinear interactions between a treatment indicator and a potentially large number of functional and/or scalar pretreatment covariates. The primary motivation for this approach is to optimize individualized treatment rules based on data from a randomized clinical trial. We generalize functional additive regression models by incorporating treatment-specific components into additive effect components. A structural constraint is imposed on the treatment-specific components in order to provide a class of additive models with main effects and interaction effects that are orthogonal to each other. If primary interest is in the interaction between treatment and the covariates, as is generally the case when optimizing individualized treatment rules, we can thereby circumvent the need to estimate the main effects of the covariates, obviating the need to specify their form and thus avoiding the issue of model misspecification. The methods are illustrated with data from a depression clinical trial with electroencephalogram functional data as patients' pretreatment covariates.
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Affiliation(s)
- Hyung Park
- Division of Biostatistics, Department of Population Health, New York University, New York, NY 10016, USA
| | - Eva Petkova
- Division of Biostatistics, Department of Population Health, New York University, New York, NY 10016, USA
| | - Thaddeus Tarpey
- Division of Biostatistics, Department of Population Health, New York University, New York, NY 10016, USA
| | - R. Todd Ogden
- Department of Biostatistics, Columbia University, New York, NY 10032, USA
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Furlanis G, Busan P, Formaggio E, Menichelli A, Lunardelli A, Ajcevic M, Pesavento V, Manganotti P. Stuttering-Like Dysfluencies as a Consequence of Long COVID-19. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2023; 66:415-430. [PMID: 36749838 DOI: 10.1044/2022_jslhr-22-00381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
PURPOSE We present two patients who developed neurogenic stuttering after long COVID-19 related to SARS-CoV-2 infection. METHODS AND RESULTS Both patients experienced both physical (e.g., fatigue) and cognitive difficulties, which led to impaired function of attention, lexical retrieval, and memory consolidation. Both patients had new-onset stuttering-like speech dysfluencies: Blocks and repetitions were especially evident at the initial part of words and sentences, sometimes accompanied by effortful and associated movements (e.g., facial grimaces and oro-facial movements). Neuropsychological evaluations confirmed the presence of difficulties in cognitive tasks, while neurophysiological evaluations (i.e., electroencephalography) suggested the presence of "slowed" patterns of brain activity. Neurogenic stuttering and cognitive difficulties were evident for 4-5 months after negativization of SARS-CoV-2 nasopharyngeal swab, with gradual improvement and near-to-complete recovery. CONCLUSIONS It is now evident that SARS-CoV-2 infection may significantly involve the central nervous system, also resulting in severe and long-term consequences, even if the precise mechanisms are still unknown. In the present report, long COVID-19 resulted in neurogenic stuttering, as the likely consequence of a "slowed" metabolism of (pre)frontal and sensorimotor brain regions (as suggested by the present and previous clinical evidence). As a consequence, the pathophysiological mechanisms related to the appearance of neurogenic stuttering have been hypothesized, which help to better understand the broader and possible neurological consequences of COVID-19.
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Affiliation(s)
- Giovanni Furlanis
- Clinical Unit of Neurology, University Hospital and Health Services of Trieste, ASUGI, Italy
- Department of Medicine, Surgical and Health Sciences, University of Trieste, Italy
| | | | - Emanuela Formaggio
- Department of Neuroscience, Section of Rehabilitation, University of Padua, Italy
| | - Alina Menichelli
- Neuropsychological Service, Clinical Unit of Rehabilitation, University Hospital and Health Services of Trieste, ASUGI, Italy
| | - Alberta Lunardelli
- Neuropsychological Service, Clinical Unit of Rehabilitation, University Hospital and Health Services of Trieste, ASUGI, Italy
| | - Milos Ajcevic
- Department of Engineering and Architecture, University of Trieste, Italy
| | - Valentina Pesavento
- Neuropsychological Service, Clinical Unit of Rehabilitation, University Hospital and Health Services of Trieste, ASUGI, Italy
| | - Paolo Manganotti
- Clinical Unit of Neurology, University Hospital and Health Services of Trieste, ASUGI, Italy
- Department of Medicine, Surgical and Health Sciences, University of Trieste, Italy
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Shor O, Yaniv-Rosenfeld A, Valevski A, Weizman A, Khrennikov A, Benninger F. EEG-based spatio-temporal relation signatures for the diagnosis of depression and schizophrenia. Sci Rep 2023; 13:776. [PMID: 36641536 PMCID: PMC9840633 DOI: 10.1038/s41598-023-28009-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 01/11/2023] [Indexed: 01/15/2023] Open
Abstract
The diagnosis of psychiatric disorders is currently based on a clinical and psychiatric examination (intake). Ancillary tests are used minimally or only to exclude other disorders. Here, we demonstrate a novel mathematical approach based on the field of p-adic numbers and using electroencephalograms (EEGs) to identify and differentiate patients with schizophrenia and depression from healthy controls. This novel approach examines spatio-temporal relations of single EEG electrode signals and characterizes the topological structure of these relations in the individual patient. Our results indicate that the relational topological structures, characterized by either the personal universal dendrographic hologram (DH) signature (PUDHS) or personal block DH signature (PBDHS), form a unique range for each group of patients, with impressive correspondence to the clinical condition. This newly developed approach results in an individual patient signature calculated from the spatio-temporal relations of EEG electrodes signals and might help the clinician with a new objective tool for the diagnosis of a multitude of psychiatric disorders.
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Affiliation(s)
- Oded Shor
- Felsenstein Medical Research Centre, Petach Tikva, Israel.
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
| | - Amit Yaniv-Rosenfeld
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Shalvata Mental Health Centre, Hod Hasharon, Israel
| | - Avi Valevski
- Geha Mental Health Centre, Petach Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Abraham Weizman
- Felsenstein Medical Research Centre, Petach Tikva, Israel
- Geha Mental Health Centre, Petach Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Andrei Khrennikov
- Faculty of Technology, Department of Mathematics, Linnaeus University, Vaxjö, Sweden
| | - Felix Benninger
- Felsenstein Medical Research Centre, Petach Tikva, Israel
- Department of Neurology, Rabin Medical Centre, Petach Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
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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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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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EEG microstate temporal Dynamics Predict depressive symptoms in College Students. Brain Topogr 2022; 35:481-494. [PMID: 35790705 DOI: 10.1007/s10548-022-00905-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Accepted: 05/19/2022] [Indexed: 11/02/2022]
Abstract
Previous studies on resting-state electroencephalographic responses in patients with depressive disorders have identified electroencephalogram (EEG) parameters as potential biomarkers for the early detection and diagnosis of depressive disorders. However, these studies did not investigate the relationship between resting-state EEG microstates and the early detection of depressive symptoms in preclinical individuals. To explore the possible association between resting-state EEG microstate temporal dynamics and depressive symptoms among college students, EEG microstate analysis was performed on eyes-closed resting-state EEG data for approximately 5 min from 34 undergraduates with high intensity of depressive symptoms and 34 age- and sex-matched controls with low intensity of depressive symptoms. Five microstate classes (A-E) were identified to best explain the datasets of both groups. Compared to controls, the mean duration, occurrence, and coverage of microstate class B increased significantly, whereas the occurrence and coverage of microstate classes D and E decreased significantly in individuals with high intensity of depressive symptoms. Additionally, the presence of microstate class B was positively correlated with participants' Beck Depression Inventory-II (BDI-II) scores, and the presence of microstate classes D and E were negatively correlated with their BDI-II scores. Further, individuals with high intensity of depressive symptoms had higher transition probabilities of A→B, B→A, B→C, B→D, and C→B, with lower transition probabilities of A→D, A→E, D→A, D→E, E→A, E→C, and E→D than controls. These results highlight resting-state EEG microstate temporal dynamics as potential biomarkers for the early detection and timely treatment of depression in college students.
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Margarette Sanchez M, Borden L, Alam N, Noroozi A, Ravan M, Flor-Henry P, Hasey G. A Machine Learning Algorithm to Discriminating Between Bipolar and Major Depressive Disorders Based on Resting EEG Data. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:2635-2638. [PMID: 36085796 DOI: 10.1109/embc48229.2022.9871453] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Distinguishing major depressive disorder (MDD) from bipolar disorder (BD) is a crucial clinical challenge due to the lack of known biomarkers. Conventional methods of diagnosis rest exclusively on symptomatic presentation, and personal and family history. As a result, BD-depressed episode (BD-DE) is often misdiagnosed as MDD, and inappropriate therapy is given. Electroencephalography (EEG) has been widely studied as a potential source of biomarkers to differentiate these disorders. Previous attempts using machine learning (ML) methods have delivered insufficient sensitivity and specificity for clinical use, likely as a consequence of the small training set size, and inadequate ML methodology. We hope to overcome these limitations by employing a training dataset of resting-state EEG from 71 MDD and 71 BD patients. We introduce a robust 3 steps ML technique: 1) a multi-step preprocessing method is used to improve the quality of the EEG signal 2) symbolic transfer entropy (STE), which is an effective connectivity measure, is applied to the resultant EEG signals 3) the ML algorithm uses the extracted STE features to distinguish MDD from BD patients. Clinical Relevance--- The accuracy of our algorithm, derived from a large sample of patients, suggests that this method may hold significant promise as a clinical tool. The proposed method delivered total accuracy, sensitivity, and specificity of 84.9%, 83.4%, and 87.1%, respectively.
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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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12
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Mosabbir AA, Braun Janzen T, Al Shirawi M, Rotzinger S, Kennedy SH, Farzan F, Meltzer J, Bartel L. Investigating the Effects of Auditory and Vibrotactile Rhythmic Sensory Stimulation on Depression: An EEG Pilot Study. Cureus 2022; 14:e22557. [PMID: 35371676 PMCID: PMC8958118 DOI: 10.7759/cureus.22557] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/23/2022] [Indexed: 12/18/2022] Open
Abstract
Background Major depressive disorder (MDD) is a persistent psychiatric condition and one of the leading causes of global disease burden. In a previous study, we investigated the effects of a five-week intervention consisting of rhythmic gamma frequency (30-70 Hz) vibroacoustic stimulation in 20 patients formally diagnosed with MDD. In that study, the findings suggested a significant clinical improvement in depression symptoms as measured using the Montgomery-Asberg Depression Rating Scale (MADRS), with 37% of participants meeting the criteria for clinical response. The goal of the present research was to examine possible changes from baseline to posttreatment in resting-state electroencephalography (EEG) recordings using the same treatment protocol and to characterize basic changes in EEG related to treatment response. Materials and methods The study sample consisted of 19 individuals aged 18-70 years with a clinical diagnosis of MDD. The participants were assessed before and after a five-week treatment period, which consisted of listening to an instrumental musical track on a vibroacoustic device, delivering auditory and vibrotactile stimulus in the gamma-band range (30-70 Hz, with particular emphasis on 40 Hz). The primary outcome measure was the change in Montgomery-Asberg Depression Rating Scale (MADRS) from baseline to posttreatment and resting-state EEG. Results Analysis comparing MADRS score at baseline and post-intervention indicated a significant change in the severity of depression symptoms after five weeks (t = 3.9923, df = 18, p = 0.0009). The clinical response rate was 36.85%. Resting-state EEG power analysis revealed a significant increase in occipital alpha power (t = -2.149, df = 18, p = 0.04548), as well as an increase in the prefrontal gamma power of the responders (t = 2.8079, df = 13.431, p = 0.01442). Conclusions The results indicate that improvements in MADRS scores after rhythmic sensory stimulation (RSS) were accompanied by an increase in alpha power in the occipital region and an increase in gamma in the prefrontal region, thus suggesting treatment effects on cortical activity in depression. The results of this pilot study will help inform subsequent controlled studies evaluating whether treatment response to vibroacoustic stimulation constitutes a real and replicable reduction of depressive symptoms and to characterize the underlying mechanisms.
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Affiliation(s)
| | | | | | - Susan Rotzinger
- Department of Psychiatry, University Health Network, Toronto, CAN
| | - Sidney H Kennedy
- Centre for Depression and Suicide Studies, St. Michael's Hospital, Toronto, CAN
| | - Faranak Farzan
- School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, CAN
| | - Jed Meltzer
- Rotman Research Institute, Baycrest Health Sciences, Toronto, CAN
| | - Lee Bartel
- Faculty of Music, University of Toronto, Toronto, CAN
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13
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Antonoudiou P, Colmers PLW, Walton NL, Weiss GL, Smith AC, Nguyen DP, Lewis M, Quirk MC, Barros L, Melon LC, Maguire JL. Allopregnanolone Mediates Affective Switching Through Modulation of Oscillatory States in the Basolateral Amygdala. Biol Psychiatry 2022; 91:283-293. [PMID: 34561029 PMCID: PMC8714669 DOI: 10.1016/j.biopsych.2021.07.017] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 07/02/2021] [Accepted: 07/19/2021] [Indexed: 02/03/2023]
Abstract
BACKGROUND Brexanolone (allopregnanolone) was recently approved by the Food and Drug Administration for the treatment of postpartum depression, demonstrating long-lasting antidepressant effects. Despite our understanding of the mechanism of action of neurosteroids as positive allosteric modulators of GABAA (gamma-aminobutyric acid A) receptors, we still do not fully understand how allopregnanolone exerts persistent antidepressant effects. METHODS We used electroencephalogram recordings in rats and humans along with local field potential, functional magnetic resonance imaging, and behavioral tests in mice to assess the impact of neurosteroids on network states in brain regions implicated in mood and used optogenetic manipulations to directly examine their relationship to behavioral states. RESULTS We demonstrated that allopregnanolone and synthetic neuroactive steroid analogs with molecular pharmacology similar to allopregnanolone (SGE-516 [tool compound] and zuranolone [SAGE-217, investigational compound]) modulate oscillations across species. We further demonstrated a critical role for interneurons in generating oscillations in the basolateral amygdala (BLA) and a role for δ-containing GABAA receptors in mediating the ability of neurosteroids to modulate network and behavioral states. Allopregnanolone in the BLA enhances BLA high theta oscillations (6-12 Hz) through δ-containing GABAA receptors, a mechanism distinct from other GABAA positive allosteric modulators, such as benzodiazepines, and alters behavioral states. Treatment with the allopregnanolone analog SGE-516 protects mice from chronic stress-induced disruption of network and behavioral states, which is correlated with the modulation of theta oscillations in the BLA. Optogenetic manipulation of the network state influences the behavioral state after chronic unpredictable stress. CONCLUSIONS Our findings demonstrate a novel molecular and cellular mechanism mediating the well-established anxiolytic and antidepressant effects of neuroactive steroids.
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Affiliation(s)
- Pantelis Antonoudiou
- Department of Neuroscience, Tufts University School of Medicine, Boston, Massachusetts, 02111, USA
| | - Phillip LW Colmers
- Department of Neuroscience, Tufts University School of Medicine, Boston, Massachusetts, 02111, USA
| | - Najah L Walton
- Department of Neuroscience, Tufts University School of Medicine, Boston, Massachusetts, 02111, USA
| | - Grant L Weiss
- Department of Neuroscience, Tufts University School of Medicine, Boston, Massachusetts, 02111, USA
| | - Anne C Smith
- Sage Therapeutics, Inc., Cambridge, Massachusetts, 02142, USA
| | - David P Nguyen
- Sage Therapeutics, Inc., Cambridge, Massachusetts, 02142, USA
| | - Mike Lewis
- Sage Therapeutics, Inc., Cambridge, Massachusetts, 02142, USA
| | - Michael C Quirk
- Sage Therapeutics, Inc., Cambridge, Massachusetts, 02142, USA
| | - Lea Barros
- Department of Neuroscience, Tufts University School of Medicine, Boston, Massachusetts, 02111, USA,Department of Biology, Hamilton College, Clinton, NY. 13323, United States
| | - Laverne C Melon
- Department of Biology, Wesleyan University, Middletown, Connecticut, 06459, USA
| | - Jamie L Maguire
- Department of Neuroscience, Tufts University School of Medicine, Boston, Massachusetts.
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14
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White EJ, Nacke M, Akeman E, Cannon MJ, Mayeli A, Touthang J, Zoubi OA, McDermott TJ, Kirlic N, Santiago J, Kuplicki R, Bodurka J, Paulus MP, Craske MG, Wolitzky-Taylor K, Abelson J, Martell C, Clausen A, Stewart JL, Aupperle RL. P300 amplitude during a monetary incentive delay task predicts future therapy completion in individuals with major depressive disorder. J Affect Disord 2021; 295:873-882. [PMID: 34706458 PMCID: PMC8554135 DOI: 10.1016/j.jad.2021.08.106] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 07/24/2021] [Accepted: 08/28/2021] [Indexed: 11/22/2022]
Abstract
INTRODUCTION Treatment effectiveness for major depressive disorder (MDD) is often affected by client non-adherence, dropout, and non-response. Identification of client characteristics predicting successful treatment completion and/or response (i.e., symptom reduction) may be an important tool to increase intervention effectiveness. It is unclear whether neural attenuations in reward processing associated with MDD predict behavioral treatment outcome. METHODS This study aimed to determine whether blunted neural responses to reward at baseline differentiate MDD (n = 60; 41 with comorbid anxiety) and healthy control (HC; n = 40) groups; and predict MDD completion of and response to 7-10 sessions of behavior therapy. Participants completed a monetary incentive delay (MID) task. The N200, P300, contingent negative variation (CNV) event related potentials (ERPs) and behavioral responses (reaction time [RT], correct hits) were quantified and extracted for cross-sectional group analyses. ERPs and behavioral responses demonstrating group differences were then used to predict therapy completion and response within MDD. RESULTS MDD exhibited faster RT and smaller P300 amplitudes than HC across conditions. Within the MDD group, treatment completers (n = 37) exhibited larger P300 amplitudes than non-completers (n = 21). LIMITATIONS This study comprises secondary analyses of EEG data; thus task parameters are not optimized to examine feedback ERPs from the paradigm. We did not examine heterogenous presentations of MDD; however, severity and comorbidity did not influence findings. CONCLUSIONS Previous studies suggest that P300 is an index of motivational salience and stimulus resource allocation. In sum, individuals who deploy greater neural resources to task demands are more likely to persevere in behavioral therapy.
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Affiliation(s)
- Evan J White
- Laureate Institute for Brain Research, Tulsa, OK, United States.
| | - Mariah Nacke
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | | | | | - Ahmad Mayeli
- Laureate Institute for Brain Research, Tulsa, OK, United States; Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, United States
| | - James Touthang
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - Obada Al Zoubi
- Laureate Institute for Brain Research, Tulsa, OK, United States; Department of Psychiatry, Harvard Medical School/McLean Hospital, Boston MA, United States
| | - Timothy J McDermott
- Laureate Institute for Brain Research, Tulsa, OK, United States; Department of Psychology, University of Tulsa, Tulsa, OK, United States
| | - Namik Kirlic
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | | | - Rayus Kuplicki
- Laureate Institute for Brain Research, Tulsa, OK, United States
| | - Jerzy Bodurka
- Laureate Institute for Brain Research, Tulsa, OK, United States; Stephenson School of Biomedical Engineering, University of Oklahoma, Tulsa, OK, United States
| | - Martin P Paulus
- Laureate Institute for Brain Research, Tulsa, OK, United States; Department of Community Medicine, University of Tulsa, Tulsa, OK, United States
| | - Michelle G Craske
- Department of Psychology, University of California Los Angeles, Los Angeles, CA, United States; Department of Psychiatry and Biobehavioral science, University of California Los Angeles, Los Angeles, CA, United States
| | - Kate Wolitzky-Taylor
- Department of Psychiatry and Biobehavioral science, University of California Los Angeles, Los Angeles, CA, United States
| | - James Abelson
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, United States
| | - Christopher Martell
- Department of Psychological and Brain Sciences, University of Massachusetts- Amherst, Amherst, MA United States
| | - Ashley Clausen
- Kansas City VA Medical Center, Kansas City, MO, United States; Department of Psychiatry and Behavioral Science, University of Kansas Medical Center, Kansas City, Kansas United States
| | - Jennifer L Stewart
- Laureate Institute for Brain Research, Tulsa, OK, United States; Department of Community Medicine, University of Tulsa, Tulsa, OK, United States
| | - Robin L Aupperle
- Laureate Institute for Brain Research, Tulsa, OK, United States; Department of Community Medicine, University of Tulsa, Tulsa, OK, United States
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15
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Shor O, Glik A, Yaniv-Rosenfeld A, Valevski A, Weizman A, Khrennikov A, Benninger F. EEG p-adic quantum potential accurately identifies depression, schizophrenia and cognitive decline. PLoS One 2021; 16:e0255529. [PMID: 34351992 PMCID: PMC8341571 DOI: 10.1371/journal.pone.0255529] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 07/16/2021] [Indexed: 12/28/2022] Open
Abstract
No diagnostic or predictive instruments to help with early diagnosis and timely therapeutic intervention are available as yet for most neuro-psychiatric disorders. A quantum potential mean and variability score (qpmvs), to identify neuropsychiatric and neurocognitive disorders with high accuracy, based on routine EEG recordings, was developed. Information processing in the brain is assumed to involve integration of neuronal activity in various areas of the brain. Thus, the presumed quantum-like structure allows quantification of connectivity as a function of space and time (locality) as well as of instantaneous quantum-like effects in information space (non-locality). EEG signals reflect the holistic (nonseparable) function of the brain, including the highly ordered hierarchy of the brain, expressed by the quantum potential according to Bohmian mechanics, combined with dendrogram representation of data and p-adic numbers. Participants consisted of 230 participants including 28 with major depression, 42 with schizophrenia, 65 with cognitive impairment, and 95 controls. Routine EEG recordings were used for the calculation of qpmvs based on ultrametric analyses, closely coupled with p-adic numbers and quantum theory. Based on area under the curve, high accuracy was obtained in separating healthy controls from those diagnosed with schizophrenia (p<0.0001), depression (p<0.0001), Alzheimer's disease (AD; p<0.0001), and mild cognitive impairment (MCI; p<0.0001) as well as in differentiating participants with schizophrenia from those with depression (p<0.0001), AD (p<0.0001) or MCI (p<0.0001) and in differentiating people with depression from those with AD (p<0.0001) or MCI (p<0.0001). The novel EEG analytic algorithm (qpmvs) seems to be a useful and sufficiently accurate tool for diagnosis of neuropsychiatric and neurocognitive diseases and may be able to predict disease course and response to treatment.
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Affiliation(s)
- Oded Shor
- Felsenstein Medical Research Center, Petach Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Amir Glik
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Department of Neurology, Rabin Medical Center, Petach Tikva, Israel
- Cognitive Neurology Clinic, Rabin Medical Center, Petach Tikva, Israel
| | | | - Avi Valevski
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Geha Mental Health Center, Petach Tikva, Israel
| | - Abraham Weizman
- Felsenstein Medical Research Center, Petach Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Geha Mental Health Center, Petach Tikva, Israel
| | - Andrei Khrennikov
- Faculty of Technology, Department of Mathematics Linnaeus University, Växjö, Sweden
| | - Felix Benninger
- Felsenstein Medical Research Center, Petach Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Department of Neurology, Rabin Medical Center, Petach Tikva, Israel
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16
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Allawala A, Bijanki KR, Goodman W, Cohn JF, Viswanathan A, Yoshor D, Borton DA, Pouratian N, Sheth SA. A Novel Framework for Network-Targeted Neuropsychiatric Deep Brain Stimulation. Neurosurgery 2021; 89:E116-E121. [PMID: 33913499 PMCID: PMC8279838 DOI: 10.1093/neuros/nyab112] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 02/14/2021] [Indexed: 12/28/2022] Open
Abstract
Deep brain stimulation (DBS) has emerged as a promising therapy for neuropsychiatric illnesses, including depression and obsessive-compulsive disorder, but has shown inconsistent results in prior clinical trials. We propose a shift away from the empirical paradigm for developing new DBS applications, traditionally based on testing brain targets with conventional stimulation paradigms. Instead, we propose a multimodal approach centered on an individualized intracranial investigation adapted from the epilepsy monitoring experience, which integrates comprehensive behavioral assessment, such as the Research Domain Criteria proposed by the National Institutes of Mental Health. In this paradigm-shifting approach, we combine readouts obtained from neurophysiology, behavioral assessments, and self-report during broad exploration of stimulation parameters and behavioral tasks to inform the selection of ideal DBS parameters. Such an approach not only provides a foundational understanding of dysfunctional circuits underlying symptom domains in neuropsychiatric conditions but also aims to identify generalizable principles that can ultimately enable individualization and optimization of therapy without intracranial monitoring.
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Affiliation(s)
- Anusha Allawala
- School of Engineering, Brown University, Providence, Rhode Island, USA
| | - Kelly R Bijanki
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, USA
| | - Wayne Goodman
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, Texas, USA
| | - Jeffrey F Cohn
- Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Ashwin Viswanathan
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, USA
| | - Daniel Yoshor
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, USA.,Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - David A Borton
- School of Engineering, Brown University, Providence, Rhode Island, USA.,Carney Institute for Brain Science, Brown University, Providence, Rhode Island, USA.,Department of Veterans Affairs, Providence VA Medical Center for Neurorestoration and Neurotechnology, Providence, Rhode Island, USA
| | - Nader Pouratian
- Department of Neurological Surgery, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, USA
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17
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Bruun CF, Arnbjerg CJ, Kessing LV. Electroencephalographic Parameters Differentiating Melancholic Depression, Non-melancholic Depression, and Healthy Controls. A Systematic Review. Front Psychiatry 2021; 12:648713. [PMID: 34489747 PMCID: PMC8417250 DOI: 10.3389/fpsyt.2021.648713] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/01/2021] [Accepted: 07/27/2021] [Indexed: 01/03/2023] Open
Abstract
Introduction: The objective of this systematic review was to investigate whether electroencephalographic parameters can serve as a tool to distinguish between melancholic depression, non-melancholic depression, and healthy controls in adults. Methods: A systematic review comprising an extensive literature search conducted in PubMed, Embase, Google Scholar, and PsycINFO in August 2020 with monthly updates until November 1st, 2020. In addition, we performed a citation search and scanned reference lists. Clinical trials that performed an EEG-based examination on an adult patient group diagnosed with melancholic unipolar depression and compared with a control group of non-melancholic unipolar depression and/or healthy controls were eligible. Risk of bias was assessed by the Strengthening of Reporting of Observational Studies in Epidemiology (STROBE) checklist. Results: A total of 24 studies, all case-control design, met the inclusion criteria and could be divided into three subgroups: Resting state studies (n = 5), sleep EEG studies (n = 10), and event-related potentials (ERP) studies (n = 9). Within each subgroup, studies were characterized by marked variability on almost all levels, preventing pooling of data, and many studies were subject to weighty methodological problems. However, the main part of the studies identified one or several EEG parameters that differentiated the groups. Conclusions: Multiple EEG modalities showed an ability to distinguish melancholic patients from non-melancholic patients and/or healthy controls. The considerable heterogeneity across studies and the frequent methodological difficulties at the individual study level were the main limitations to this work. Also, the underlying premise of shifting diagnostic paradigms may have resulted in an inhomogeneous patient population. Systematic Review Registration: Registered in the PROSPERO registry on August 8th, 2020, registration number CRD42020197472.
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Affiliation(s)
- Caroline Fussing Bruun
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Copenhagen, Denmark
| | - Caroline Juhl Arnbjerg
- Department of Public Health, Center for Global Health, Aarhus University, Aarhus, Denmark
| | - Lars Vedel Kessing
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Copenhagen, Denmark.,Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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18
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Bučková B, Brunovský M, Bareš M, Hlinka J. Predicting Sex From EEG: Validity and Generalizability of Deep-Learning-Based Interpretable Classifier. Front Neurosci 2020; 14:589303. [PMID: 33192274 PMCID: PMC7652844 DOI: 10.3389/fnins.2020.589303] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 09/17/2020] [Indexed: 11/13/2022] Open
Abstract
Explainable artificial intelligence holds a great promise for neuroscience and plays an important role in the hypothesis generation process. We follow-up a recent machine learning-oriented study that constructed a deep convolutional neural network to automatically identify biological sex from EEG recordings in healthy individuals and highlighted the discriminative role of beta-band power. If generalizing, this finding would be relevant not only theoretically by pointing to some specific neurobiological sexual dimorphisms, but potentially also as a relevant confound in quantitative EEG diagnostic practice. To put this finding to test, we assess whether the automatic identification of biological sex generalizes to another dataset, particularly in the presence of a psychiatric disease, by testing the hypothesis of higher beta power in women compared to men on 134 patients suffering from Major Depressive Disorder. Moreover, we construct ROC curves and compare the performance of the classifiers in determining sex both before and after the antidepressant treatment. We replicate the observation of a significant difference in beta-band power between men and women, providing classification accuracy of nearly 77%. The difference was consistent across the majority of electrodes, however multivariate classification models did not generally improve the performance. Similar results were observed also after the antidepressant treatment (classification accuracy above 70%), further supporting the robustness of the initial finding.
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Affiliation(s)
- Barbora Bučková
- Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czechia.,Department of Complex Systems, Institute of Computer Science of the Czech Academy of Sciences, Prague, Czechia
| | - Martin Brunovský
- National Institute of Mental Health, Klecany, Czechia.,Third Faculty of Medicine, Charles University, Prague, Czechia
| | - Martin Bareš
- National Institute of Mental Health, Klecany, Czechia.,Third Faculty of Medicine, Charles University, Prague, Czechia
| | - Jaroslav Hlinka
- Department of Complex Systems, Institute of Computer Science of the Czech Academy of Sciences, Prague, Czechia.,National Institute of Mental Health, Klecany, Czechia
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19
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de la Salle S, Jaworska N, Blier P, Smith D, Knott V. Using prefrontal and midline right frontal EEG-derived theta cordance and depressive symptoms to predict the differential response or remission to antidepressant treatment in major depressive disorder. Psychiatry Res Neuroimaging 2020; 302:111109. [PMID: 32480044 PMCID: PMC10773969 DOI: 10.1016/j.pscychresns.2020.111109] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Revised: 02/21/2020] [Accepted: 03/30/2020] [Indexed: 10/24/2022]
Abstract
There is a growing need for optimizing treatment selection and response prediction in individuals with major depressive disorder (MDD). Prior investigations have shown that changes in electroencephalographic (EEG)-based measures precede symptom improvement and could serve as biomarkers of treatment outcome. One such method is cordance, a computation of regional brain activity based on a combination of absolute and relative resting EEG activity. Specifically, early reduction in prefrontal (PF) and midline right frontal (MRF) theta (4-8Hz) cordance has been shown to predict response to various antidepressants, though replication is required. Thus, this study examined early changes (baseline to week 1) in PF and MRF cordance in 47 MDD patients undergoing antidepressant treatment. Early changes in cordance and in Montgomery Åsberg Depression Rating Scale (MADRS) scores were assessed alone, and in combination, to predict eventual (by week 12) treatment response and remission. Models combining early changes in theta cordance (PF and MRF) and depressive symptoms were most predictive of response to treatment at week 12; remission models (cordance, MADRS, and their combination) were weaker, though provided modest prediction values. These results suggest that antidepressant response may be optimally predicted by combining both EEG and symptom-based measures after one week of treatment.
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Affiliation(s)
- Sara de la Salle
- University of Ottawa Institute of Mental Health Research, 1145 Carling Avenue, Ottawa K1Z 7K4, ON, Canada; School of Psychology, University of Ottawa, Ottawa, ON, Canada.
| | - Natalia Jaworska
- University of Ottawa Institute of Mental Health Research, 1145 Carling Avenue, Ottawa K1Z 7K4, ON, Canada; School of Psychology, University of Ottawa, Ottawa, ON, Canada
| | - Pierre Blier
- University of Ottawa Institute of Mental Health Research, 1145 Carling Avenue, Ottawa K1Z 7K4, ON, Canada
| | - Dylan Smith
- University of Ottawa Institute of Mental Health Research, 1145 Carling Avenue, Ottawa K1Z 7K4, ON, Canada
| | - Verner Knott
- University of Ottawa Institute of Mental Health Research, 1145 Carling Avenue, Ottawa K1Z 7K4, ON, Canada; School of Psychology, University of Ottawa, Ottawa, ON, Canada
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20
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Iosifescu DV. Are Electroencephalogram-Derived Predictors of Antidepressant Efficacy Closer to Clinical Usefulness? JAMA Netw Open 2020; 3:e207133. [PMID: 32568395 DOI: 10.1001/jamanetworkopen.2020.7133] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Affiliation(s)
- Dan V Iosifescu
- New York University School of Medicine, New York
- Clinical Research Division, Nathan Kline Institute for Psychiatric Research, Orangeburg, New York
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21
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Rolle CE, Fonzo GA, Wu W, Toll R, Jha MK, Cooper C, Chin-Fatt C, Pizzagalli DA, Trombello JM, Deckersbach T, Fava M, Weissman MM, Trivedi MH, Etkin A. Cortical Connectivity Moderators of Antidepressant vs Placebo Treatment Response in Major Depressive Disorder: Secondary Analysis of a Randomized Clinical Trial. JAMA Psychiatry 2020; 77:397-408. [PMID: 31895437 PMCID: PMC6990859 DOI: 10.1001/jamapsychiatry.2019.3867] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
IMPORTANCE Despite the widespread awareness of functional magnetic resonance imaging findings suggesting a role for cortical connectivity networks in treatment selection for major depressive disorder, its clinical utility remains limited. Recent methodological advances have revealed functional magnetic resonance imaging-like connectivity networks using electroencephalography (EEG), a tool more easily implemented in clinical practice. OBJECTIVE To determine whether EEG connectivity could reveal neural moderators of antidepressant treatment. DESIGN, SETTING, AND PARTICIPANTS In this nonprespecified secondary analysis, data were analyzed from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinic Care study, a placebo-controlled, double-blinded randomized clinical trial. Recruitment began July 29, 2011, and was completed December 15, 2015. A random sample of 221 outpatients with depression aged 18 to 65 years who were not taking medication for depression was recruited and assessed at 4 clinical sites. Analysis was performed on an intent-to-treat basis. Statistical analysis was performed from November 16, 2018, to May 23, 2019. INTERVENTIONS Patients received either the selective serotonin reuptake inhibitor sertraline hydrochloride or placebo for 8 weeks. MAIN OUTCOMES AND MEASURES Electroencephalographic orthogonalized power envelope connectivity analyses were applied to resting-state EEG data. Intent-to-treat prediction linear mixed models were used to determine which pretreatment connectivity patterns were associated with response to sertraline vs placebo. The primary clinical outcome was the total score on the 17-item Hamilton Rating Scale for Depression, administered at each study visit. RESULTS Of the participants recruited, 9 withdrew after first dose owing to reported adverse effects, and 221 participants (150 women; mean [SD] age, 37.8 [12.7] years) underwent EEG recordings and had high-quality pretreatment EEG data. After correction for multiple comparisons, connectome-wide analyses revealed moderation by connections within and between widespread cortical regions-most prominently parietal-for both the antidepressant and placebo groups. Greater alpha-band and lower gamma-band connectivity predicted better placebo outcomes and worse antidepressant outcomes. Lower connectivity levels in these moderating connections were associated with higher levels of anhedonia. Connectivity features that moderate treatment response differentially by treatment group were distinct from connectivity features that change from baseline to 1 week into treatment. The group mean (SD) score on the 17-item Hamilton Rating Scale for Depression was 18.35 (4.58) at baseline and 26.14 (30.37) across all time points. CONCLUSIONS AND RELEVANCE These findings establish the utility of EEG-based network functional connectivity analyses for differentiating between responses to an antidepressant vs placebo. A role emerged for parietal cortical regions in predicting placebo outcome. From a treatment perspective, capitalizing on the therapeutic components leading to placebo response differentially from antidepressant response should provide an alternative direction toward establishing a placebo signature in clinical trials, thereby enhancing the signal detection in randomized clinical trials. TRIAL REGISTRATION ClinicalTrials.gov identifier: NCT01407094.
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Affiliation(s)
- Camarin E. Rolle
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California,Wu Tsai Neuroscience Institute, Stanford University, Stanford, California,Veterans Affairs Palo Alto Healthcare System, Palo Alto, California,Sierra Pacific Mental Illness, Research, Education, and Clinical Center, Palo Alto, California
| | - Gregory A. Fonzo
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California,Wu Tsai Neuroscience Institute, Stanford University, Stanford, California,Veterans Affairs Palo Alto Healthcare System, Palo Alto, California,Sierra Pacific Mental Illness, Research, Education, and Clinical Center, Palo Alto, California,Department of Psychiatry, Dell Medical School, The University of Texas at Austin
| | - Wei Wu
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California,Wu Tsai Neuroscience Institute, Stanford University, Stanford, California,Veterans Affairs Palo Alto Healthcare System, Palo Alto, California,School of Automation Science and Engineering, South China University of Technology, Guangzhou, Guangdong, China
| | - Russ Toll
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California,Wu Tsai Neuroscience Institute, Stanford University, Stanford, California,Veterans Affairs Palo Alto Healthcare System, Palo Alto, California
| | - Manish K. Jha
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas
| | - Crystal Cooper
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas
| | - Cherise Chin-Fatt
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas
| | | | - Joseph M. Trombello
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas
| | - Thilo Deckersbach
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Maurizio Fava
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts
| | - Myrna M. Weissman
- New York State Psychiatric Institute, Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York
| | - Madhukar H. Trivedi
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas
| | - Amit Etkin
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California,Wu Tsai Neuroscience Institute, Stanford University, Stanford, California,Veterans Affairs Palo Alto Healthcare System, Palo Alto, California,Sierra Pacific Mental Illness, Research, Education, and Clinical Center, Palo Alto, California,now at Alto Neuroscience Inc, Los Altos, California
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22
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Wu W, Zhang Y, Jiang J, Lucas MV, Fonzo GA, Rolle CE, Cooper C, Chin-Fatt C, Krepel N, Cornelssen CA, Wright R, Toll RT, Trivedi HM, Monuszko K, Caudle TL, Sarhadi K, Jha MK, Trombello JM, Deckersbach T, Adams P, McGrath PJ, Weissman MM, Fava M, Pizzagalli DA, Arns M, Trivedi MH, Etkin A. An electroencephalographic signature predicts antidepressant response in major depression. Nat Biotechnol 2020; 38:439-447. [PMID: 32042166 PMCID: PMC7145761 DOI: 10.1038/s41587-019-0397-3] [Citation(s) in RCA: 138] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2018] [Revised: 12/06/2019] [Accepted: 12/17/2019] [Indexed: 12/21/2022]
Abstract
Antidepressants are widely prescribed, but their efficacy relative to placebo is modest, in part because the clinical diagnosis of major depression encompasses biologically heterogeneous conditions. Here, we sought to identify a neurobiological signature of response to antidepressant treatment as compared to placebo. We designed a latent-space machine-learning algorithm tailored for resting-state electroencephalography (EEG) and applied it to data from the largest imaging-coupled, placebo-controlled antidepressant study (n = 309). Symptom improvement was robustly predicted in a manner both specific for the antidepressant sertraline (versus placebo) and generalizable across different study sites and EEG equipment. This sertraline-predictive EEG signature generalized to two depression samples, wherein it reflected general antidepressant medication responsivity and related differentially to a repetitive transcranial magnetic stimulation treatment outcome. Furthermore, we found that the sertraline resting-state EEG signature indexed prefrontal neural responsivity, as measured by concurrent transcranial magnetic stimulation and EEG. Our findings advance the neurobiological understanding of antidepressant treatment through an EEG-tailored computational model and provide a clinical avenue for personalized treatment of depression.
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Affiliation(s)
- Wei Wu
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, Guangdong 510640, China
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Yu Zhang
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Jing Jiang
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Molly V. Lucas
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Gregory A. Fonzo
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Camarin E. Rolle
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Crystal Cooper
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX
| | - Cherise Chin-Fatt
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX
| | - Noralie Krepel
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, The Netherlands
- Department of Psychiatry, Harvard Medical School and McLean Hospital, Belmont, MA 02478
| | - Carena A. Cornelssen
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Rachael Wright
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Russell T. Toll
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Hersh M. Trivedi
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Karen Monuszko
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Trevor L. Caudle
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Kamron Sarhadi
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Manish K. Jha
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX
| | - Joseph M. Trombello
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX
| | - Thilo Deckersbach
- New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY
| | - Phil Adams
- New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY
| | - Patrick J. McGrath
- New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY
| | - Myrna M. Weissman
- New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY
| | - Maurizio Fava
- New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY
| | - Diego A. Pizzagalli
- New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY
| | - Martijn Arns
- Department of Psychiatry, Harvard Medical School and McLean Hospital, Belmont, MA 02478
- Department of Experimental Psychology, Utrecht University, Utrecht, the Netherlands
- neuroCare Group Netherlands, Nijmegen, the Netherlands
| | - Madhukar H. Trivedi
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX
| | - Amit Etkin
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
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23
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Jiang B, Petkova E, Tarpey T, Ogden RT. A Bayesian approach to joint modeling of matrix-valued imaging data and treatment outcome with applications to depression studies. Biometrics 2020; 76:87-97. [PMID: 31529701 PMCID: PMC7067625 DOI: 10.1111/biom.13151] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Accepted: 08/28/2019] [Indexed: 11/28/2022]
Abstract
In this paper, we propose a unified Bayesian joint modeling framework for studying association between a binary treatment outcome and a baseline matrix-valued predictor. Specifically, a joint modeling approach relating an outcome to a matrix-valued predictor through a probabilistic formulation of multilinear principal component analysis is developed. This framework establishes a theoretical relationship between the outcome and the matrix-valued predictor, although the predictor is not explicitly expressed in the model. Simulation studies are provided showing that the proposed method is superior or competitive to other methods, such as a two-stage approach and a classical principal component regression in terms of both prediction accuracy and estimation of association; its advantage is most notable when the sample size is small and the dimensionality in the imaging covariate is large. Finally, our proposed joint modeling approach is shown to be a very promising tool in an application exploring the association between baseline electroencephalography data and a favorable response to treatment in a depression treatment study by achieving a substantial improvement in prediction accuracy in comparison to competing methods.
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Affiliation(s)
- Bei Jiang
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2E8, Canada
| | - Eva Petkova
- Department of Population Health, New York University, New York, NY 10016, USA
- Department of Child and Adolescent Psychiatry, New York University, New York, NY 10016, USA
| | - Thaddeus Tarpey
- Department of Population Health, New York University, New York, NY 10016, USA
| | - R. Todd Ogden
- Department of Biostatistics, Columbia University, New York, NY 10032, USA
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24
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Smith EE, Tenke CE, Deldin PJ, Trivedi MH, Weissman MM, Auerbach RP, Bruder GE, Pizzagalli DA, Kayser J. Frontal theta and posterior alpha in resting EEG: A critical examination of convergent and discriminant validity. Psychophysiology 2019; 57:e13483. [PMID: 31578740 DOI: 10.1111/psyp.13483] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 08/28/2019] [Accepted: 08/29/2019] [Indexed: 12/22/2022]
Abstract
Prior research has identified two resting EEG biomarkers with potential for predicting functional outcomes in depression: theta current density in frontal brain regions (especially rostral anterior cingulate cortex) and alpha power over posterior scalp regions. As little is known about the discriminant and convergent validity of these putative biomarkers, a thorough evaluation of these psychometric properties was conducted toward the goal of improving clinical utility of these markers. Resting 71-channel EEG recorded from 35 healthy adults at two sessions (1-week retest) were used to systematically compare different quantification techniques for theta and alpha sources at scalp (surface Laplacian or current source density [CSD]) and brain (distributed inverse; exact low resolution electromagnetic tomography [eLORETA]) level. Signal quality was evaluated with signal-to-noise ratio, participant-level spectra, and frequency PCA covariance decomposition. Convergent and discriminant validity were assessed within a multitrait-multimethod framework. Posterior alpha was reliably identified as two spectral components, each with unique spatial patterns and condition effects (eyes open/closed), high signal quality, and good convergent and discriminant validity. In contrast, frontal theta was characterized by one low-variance component, low signal quality, lack of a distinct spectral peak, and mixed validity. Correlations between candidate biomarkers suggest that posterior alpha components constitute reliable, convergent, and discriminant biometrics in healthy adults. Component-based identification of spectral activity (CSD/eLORETA-fPCA) was superior to fixed, a priori frequency bands. Improved quantification and conceptualization of frontal theta is necessary to determine clinical utility.
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Affiliation(s)
- Ezra E Smith
- Division of Translational Epidemiology, New York State Psychiatric Institute, New York, New York, USA
| | - Craig E Tenke
- Division of Translational Epidemiology, New York State Psychiatric Institute, New York, New York, USA.,Department of Psychiatry, Vagelos College of Physicians & Surgeons, Columbia University, New York, New York, USA.,Division of Cognitive Neuroscience, New York State Psychiatric Institute, New York, New York, USA
| | - Patricia J Deldin
- Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, USA
| | - Madhukar H Trivedi
- Department of Psychiatry, University of Texas, Southwestern Medical Center, Dallas, Texas, USA
| | - Myrna M Weissman
- Division of Translational Epidemiology, New York State Psychiatric Institute, New York, New York, USA.,Department of Psychiatry, Vagelos College of Physicians & Surgeons, Columbia University, New York, New York, USA
| | - Randy P Auerbach
- Department of Psychiatry, Vagelos College of Physicians & Surgeons, Columbia University, New York, New York, USA
| | - Gerard E Bruder
- Department of Psychiatry, Vagelos College of Physicians & Surgeons, Columbia University, New York, New York, USA
| | - Diego A Pizzagalli
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA.,Center for Depression, Anxiety & Stress Research, McLean Hospital, Belmont, Massachusetts, USA
| | - Jürgen Kayser
- Division of Translational Epidemiology, New York State Psychiatric Institute, New York, New York, USA.,Department of Psychiatry, Vagelos College of Physicians & Surgeons, Columbia University, New York, New York, USA.,Division of Cognitive Neuroscience, New York State Psychiatric Institute, New York, New York, USA
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25
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Mithani K, Meng Y, Abrahao A, Mikhail M, Hamani C, Giacobbe P, Lipsman N. Electroencephalography in Psychiatric Surgery: Past Use and Future Directions. Stereotact Funct Neurosurg 2019; 97:141-152. [PMID: 31412334 DOI: 10.1159/000500994] [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: 11/06/2018] [Accepted: 05/08/2019] [Indexed: 11/19/2022]
Abstract
The last two decades have seen a re-emergence of surgery for intractable psychiatric disease, in large part due to increased use of deep brain stimulation. The development of more precise, image-guided, less invasive interventions has improved the safety of these procedures, even though the relative merits of modulation at various targets remain under investigation. With an increase in the number and type of interventions for modulating mood/anxiety circuits, the need for biomarkers to guide surgeries and predict treatment response is as critical as ever. Electroencephalography (EEG) has a long history in clinical neurology, cognitive neuroscience, and functional neurosurgery, but has limited prior usage in psychiatric surgery. MEDLINE, Embase, and Psyc-INFO searches on the use of EEG in guiding psychiatric surgery yielded 611 articles, which were screened for relevance and quality. We synthesized three important themes. First, considerable evidence supports EEG as a biomarker for response to various surgical and non-surgical therapies, but large-scale investigations are lacking. Second, intraoperative EEG is likely more valuable than surface EEG for guiding target selection, but comes at the cost of greater invasiveness. Finally, EEG may be a promising tool for objective functional feedback in developing "closed-loop" psychosurgeries, but more systematic investigations are required.
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Affiliation(s)
- Karim Mithani
- Sunnybrook Research Institute, Toronto, Ontario, Canada.,Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Ying Meng
- Sunnybrook Research Institute, Toronto, Ontario, Canada
| | | | - Mirriam Mikhail
- Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | | | | | - Nir Lipsman
- Sunnybrook Research Institute, Toronto, Ontario, Canada,
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26
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Dissanayaka NNW, Au TR, Angwin AJ, Iyer KK, O'Sullivan JD, Byrne GJ, Silburn PA, Marsh R, Mellick GD, Copland DA. Depression symptomatology correlates with event-related potentials in Parkinson's disease: An affective priming study. J Affect Disord 2019; 245:897-904. [PMID: 30699874 DOI: 10.1016/j.jad.2018.11.094] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Revised: 11/01/2018] [Accepted: 11/12/2018] [Indexed: 11/25/2022]
Abstract
BACKGROUND Depression is a predominant non-motor symptom of Parkinson's disease (PD), which is often under recognised and undertreated. To improve identification of depression in PD it is imperative to examine objective brain-related markers. The present study addresses this gap by using electroencephalography (EEG) to evaluate the processing of emotionally valanced words in PD. METHODS Fifty non-demented PD patients, unmedicated for depression or anxiety, completed an affective priming task while EEG was simultaneously recorded. Prime and target word pairs of negative or neutral valence were presented at a short 250 ms stimulus onset asynchrony. Participants were asked to evaluate the valence of the target word by button press. Depression was measured using an established rating scale. Repeated measures analysis of covariance and correlational analyses were performed to examine whether event-related potentials (ERP) varied as a function of depression scores. RESULTS Key ERP findings reveal reduced responses in parietal midline P300, N400 and Late Positive Potential (LPP) difference waves between congruent and incongruent neutral targets in patients with higher depression scores. LIMITATIONS Comparisons of ERPs were limited by insufficient classification of participants with and without clinical depression. A majority of PD patients who had high depression scores were excluded from the analysis as they were receiving antidepressant and/or anxiolytic medications which could interfere with ERP sensitivity. CONCLUSIONS The present study suggests that the Pz-P300, N400 and LPP are ERP markers relates to emotional dysfunction in PD. These findings thus advance current knowledge regarding the neurophysiological markers of a common neuropsychiatric deficit in PD.
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Affiliation(s)
- Nadeeka N W Dissanayaka
- UQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Royal Brisbane & Women's Hospital, Herston, Brisbane QLD4029, Australia; Department of Neurology, Royal Brisbane & Women's Hospital, Herston, Brisbane QLD4029, Australia; School of Psychology, The University of Queensland, St Lucia, Brisbane QLD4067, Australia.
| | - Tiffany R Au
- UQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Royal Brisbane & Women's Hospital, Herston, Brisbane QLD4029, Australia
| | - Anthony J Angwin
- School of Health & Rehabilitation Sciences, The University of Queensland, St Lucia, Brisbane QLD4067, Australia
| | - Kartik K Iyer
- UQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Royal Brisbane & Women's Hospital, Herston, Brisbane QLD4029, Australia
| | - John D O'Sullivan
- UQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Royal Brisbane & Women's Hospital, Herston, Brisbane QLD4029, Australia; Department of Neurology, Royal Brisbane & Women's Hospital, Herston, Brisbane QLD4029, Australia
| | - Gerard J Byrne
- UQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Royal Brisbane & Women's Hospital, Herston, Brisbane QLD4029, Australia; Mental Health Service, Royal Brisbane & Women's Hospital, Herston, Brisbane QLD4029, Australia
| | - Peter A Silburn
- Asia-Pacific Centre for Neuromodulation, Queensland Brain Institute, The University of Queensland, St Lucia, QLD, Brisbane QLD4067, Australia
| | - Rodney Marsh
- Asia-Pacific Centre for Neuromodulation, Queensland Brain Institute, The University of Queensland, St Lucia, QLD, Brisbane QLD4067, Australia
| | - George D Mellick
- Griffith Institute for Drug Discovery, Griffith University, Nathan, Brisbane QLD4111, Australia
| | - David A Copland
- UQ Centre for Clinical Research, Faculty of Medicine, The University of Queensland, Royal Brisbane & Women's Hospital, Herston, Brisbane QLD4029, Australia; School of Health & Rehabilitation Sciences, The University of Queensland, St Lucia, Brisbane QLD4067, Australia
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27
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Bailey NW, Hoy KE, Rogasch NC, Thomson RH, McQueen S, Elliot D, Sullivan CM, Fulcher BD, Daskalakis ZJ, Fitzgerald PB. Differentiating responders and non-responders to rTMS treatment for depression after one week using resting EEG connectivity measures. J Affect Disord 2019; 242:68-79. [PMID: 30172227 DOI: 10.1016/j.jad.2018.08.058] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 07/30/2018] [Accepted: 08/12/2018] [Indexed: 12/15/2022]
Abstract
BACKGROUND Non-response to repetitive transcranial magnetic stimulation (rTMS) treatment for depression is costly for both patients and clinics. Simple and cheap methods to predict response would reduce this burden. Resting EEG measures differentiate responders from non-responders, so may have utility for response prediction. METHODS Fifty patients with treatment resistant depression and 21 controls had resting electroencephalography (EEG) recorded at baseline (BL). Patients underwent 5-8 weeks of rTMS treatment, with EEG recordings repeated at week 1 (W1). Forty-two participants had valid BL and W1 EEG data, and 12 were responders. Responders and non-responders were compared at BL and W1 in measures of theta (4-8 Hz) and alpha (8-13 Hz) power and connectivity, frontal theta cordance and alpha peak frequency. Control group comparisons were made for measures that differed between responders and non-responders. A machine learning algorithm assessed the potential to differentiate responders from non-responders using EEG measures in combination with change in depression scores from BL to W1. RESULTS Responders showed elevated theta connectivity across BL and W1. No other EEG measures differed between groups. Responders could be distinguished from non-responders with a mean sensitivity of 0.84 (p = 0.001) and specificity of 0.89 (p = 0.002) using cross-validated machine learning classification on the combination of all EEG and mood measures. LIMITATIONS The low response rate limited our sample size to only 12 responders. CONCLUSION Resting theta connectivity at BL and W1 differ between responders and non-responders, and show potential for predicting response to rTMS treatment for depression.
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Affiliation(s)
- N W Bailey
- Monash Alfred Psychiatry Research Centre, Monash University Central Clinical School, Commercial Rd, Melbourne, Victoria, Australia..
| | - K E Hoy
- Monash Alfred Psychiatry Research Centre, Monash University Central Clinical School, Commercial Rd, Melbourne, Victoria, Australia
| | - N C Rogasch
- Brain and Mental Health Laboratory, Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Clayton 3168, Victoria, Australia
| | - R H Thomson
- Monash Alfred Psychiatry Research Centre, Monash University Central Clinical School, Commercial Rd, Melbourne, Victoria, Australia
| | - S McQueen
- Monash Alfred Psychiatry Research Centre, Monash University Central Clinical School, Commercial Rd, Melbourne, Victoria, Australia
| | - D Elliot
- Monash Alfred Psychiatry Research Centre, Monash University Central Clinical School, Commercial Rd, Melbourne, Victoria, Australia
| | - C M Sullivan
- Monash Alfred Psychiatry Research Centre, Monash University Central Clinical School, Commercial Rd, Melbourne, Victoria, Australia
| | - B D Fulcher
- Brain and Mental Health Laboratory, Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Clayton 3168, Victoria, Australia
| | - Z J Daskalakis
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - P B Fitzgerald
- Monash Alfred Psychiatry Research Centre, Monash University Central Clinical School, Commercial Rd, Melbourne, Victoria, Australia.; Epworth Healthcare, The Epworth Clinic, Camberwell 3004, Victoria, Australia
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28
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Widge AS, Bilge MT, Montana R, Chang W, Rodriguez CI, Deckersbach T, Carpenter LL, Kalin NH, Nemeroff CB. Electroencephalographic Biomarkers for Treatment Response Prediction in Major Depressive Illness: A Meta-Analysis. Am J Psychiatry 2019; 176:44-56. [PMID: 30278789 PMCID: PMC6312739 DOI: 10.1176/appi.ajp.2018.17121358] [Citation(s) in RCA: 105] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
OBJECTIVE Reducing unsuccessful treatment trials could improve depression treatment. Quantitative EEG (QEEG) may predict treatment response and is being commercially marketed for this purpose. The authors sought to quantify the reliability of QEEG for response prediction in depressive illness and to identify methodological limitations of the available evidence. METHOD The authors conducted a meta-analysis of diagnostic accuracy for QEEG in depressive illness, based on articles published between January 2000 and November 2017. The review included all articles that used QEEG to predict response during a major depressive episode, regardless of patient population, treatment, or QEEG marker. The primary meta-analytic outcome was the accuracy for predicting response to depression treatment, expressed as sensitivity, specificity, and the logarithm of the diagnostic odds ratio. Raters also judged each article on indicators of good research practice. RESULTS In 76 articles reporting 81 biomarkers, the meta-analytic estimates showed a sensitivity of 0.72 (95% CI=0.67-0.76) and a specificity of 0.68 (95% CI=0.63-0.73). The logarithm of the diagnostic odds ratio was 1.89 (95% CI=1.56-2.21), and the area under the receiver operator curve was 0.76 (95% CI=0.71-0.80). No specific QEEG biomarker or specific treatment showed greater predictive power than the all-studies estimate in a meta-regression. Funnel plot analysis suggested substantial publication bias. Most studies did not use ideal practices. CONCLUSIONS QEEG does not appear to be clinically reliable for predicting depression treatment response, as the literature is limited by underreporting of negative results, a lack of out-of-sample validation, and insufficient direct replication of previous findings. Until these limitations are remedied, QEEG is not recommended for guiding selection of psychiatric treatment.
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Affiliation(s)
- Alik S. Widge
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA,Picower Institute for Learning & Memory, Massachusetts Institute of Technology, Cambridge, MA,Correspondence to Alik S Widge, MD, PhD, 149 13th St, Room 2627, Charlestown, MA 02129, , 617-643-2580
| | - M. Taha Bilge
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Rebecca Montana
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Weilynn Chang
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | | | - Thilo Deckersbach
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Linda L. Carpenter
- Butler Hospital and Warren Alpert Medical School of Brown University, Providence, RI
| | - Ned H. Kalin
- Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, WI
| | - Charles B. Nemeroff
- Department of Psychiatry, University of Miami Miller School of Medicine, Miami, FL
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29
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Widge AS, Boggess M, Rockhill AP, Mullen A, Sheopory S, Loonis R, Freeman DK, Miller EK. Altering alpha-frequency brain oscillations with rapid analog feedback-driven neurostimulation. PLoS One 2018; 13:e0207781. [PMID: 30517149 PMCID: PMC6281199 DOI: 10.1371/journal.pone.0207781] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Accepted: 11/06/2018] [Indexed: 01/11/2023] Open
Abstract
Oscillations of the brain's local field potential (LFP) may coordinate neural ensembles and brain networks. It has been difficult to causally test this model or to translate its implications into treatments, because there are few reliable ways to alter LFP oscillations. We developed a closed-loop analog circuit to enhance brain oscillations by feeding them back into cortex through phase-locked transcranial electrical stimulation. We tested the system in a rhesus macaque with chronically implanted electrode arrays, targeting 8-15 Hz (alpha) oscillations. Ten seconds of stimulation increased alpha oscillatory power for up to 1 second after stimulation offset. In contrast, open-loop stimulation decreased alpha power. There was no effect in the neighboring 15-30 Hz (beta) LFP rhythm or on a neighboring array that did not participate in closed-loop feedback. Analog closed-loop neurostimulation might thus be a useful strategy for altering brain oscillations, both for basic research and the treatment of neuro-psychiatric disease.
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Affiliation(s)
- Alik S. Widge
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
- Picower Institute for Learning & Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Matthew Boggess
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Alexander P. Rockhill
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Andrew Mullen
- Picower Institute for Learning & Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Shivani Sheopory
- Picower Institute for Learning & Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- College of Engineering, Boston University, Boston, Massachusetts, United States of America
| | - Roman Loonis
- Picower Institute for Learning & Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Daniel K. Freeman
- The Charles Stark Draper Laboratory, Inc., Cambridge, Massachusetts, United States of America
| | - Earl K. Miller
- Picower Institute for Learning & Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
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Shalbaf R, Brenner C, Pang C, Blumberger DM, Downar J, Daskalakis ZJ, Tham J, Lam RW, Farzan F, Vila-Rodriguez F. Non-linear Entropy Analysis in EEG to Predict Treatment Response to Repetitive Transcranial Magnetic Stimulation in Depression. Front Pharmacol 2018; 9:1188. [PMID: 30425640 PMCID: PMC6218964 DOI: 10.3389/fphar.2018.01188] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2018] [Accepted: 09/28/2018] [Indexed: 12/12/2022] Open
Abstract
Background: Biomarkers that predict clinical outcomes in depression are essential for increasing the precision of treatments and clinical outcomes. The electroencephalogram (EEG) is a non-invasive neurophysiological test that has promise as a biomarker sensitive to treatment effects. The aim of our study was to investigate a novel non-linear index of resting state EEG activity as a predictor of clinical outcome, and compare its predictive capacity to traditional frequency-based indices. Methods: EEG was recorded from 62 patients with treatment resistant depression (TRD) and 25 healthy comparison (HC) subjects. TRD patients were treated with excitatory repetitive transcranial magnetic stimulation (rTMS) to the dorsolateral prefrontal cortex (DLPFC) for 4 to 6 weeks. EEG signals were first decomposed using the empirical mode decomposition (EMD) method into band-limited intrinsic mode functions (IMFs). Subsequently, Permutation Entropy (PE) was computed from the obtained second IMF to yield an index named PEIMF2. Receiver Operator Characteristic (ROC) curve analysis and ANOVA test were used to evaluate the efficiency of this index (PEIMF2) and were compared to frequency-band based methods. Results: Responders (RP) to rTMS exhibited an increase in the PEIMF2 index compared to non-responders (NR) at F3, FCz and FC3 sites (p < 0.01). The area under the curve (AUC) for ROC analysis was 0.8 for PEIMF2 index for the FC3 electrode. The PEIMF2 index was superior to ordinary frequency band measures. Conclusion: Our data show that the PEIMF2 index, yields superior outcome prediction performance compared to traditional frequency band indices. Our findings warrant further investigation of EEG-based biomarkers in depression; specifically entropy indices applied in band-limited EEG components. Registration in ClinicalTrials.Gov; identifiers NCT02800226 and NCT01887782.
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Affiliation(s)
- Reza Shalbaf
- Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Colleen Brenner
- Department of Psychology, Loma Linda University, Loma Linda, CA, United States
| | - Christopher Pang
- Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Daniel M Blumberger
- Temerty Centre for Therapeutic Brain Intervention and Campbell Family Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Jonathan Downar
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.,MRI-Guided rTMS Clinic and Krembil Research Institute, University Health Network, Toronto, ON, Canada
| | - Zafiris J Daskalakis
- Temerty Centre for Therapeutic Brain Intervention and Campbell Family Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Joseph Tham
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Raymond W Lam
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Faranak Farzan
- School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, BC, Canada
| | - Fidel Vila-Rodriguez
- Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
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31
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Yang Y, Connolly AT, Shanechi MM. A control-theoretic system identification framework and a real-time closed-loop clinical simulation testbed for electrical brain stimulation. J Neural Eng 2018; 15:066007. [DOI: 10.1088/1741-2552/aad1a8] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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32
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Bilge MT, Gosai AK, Widge AS. Deep Brain Stimulation in Psychiatry: Mechanisms, Models, and Next-Generation Therapies. Psychiatr Clin North Am 2018; 41:373-383. [PMID: 30098651 PMCID: PMC6092041 DOI: 10.1016/j.psc.2018.04.003] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Deep brain stimulation has preliminary evidence of clinical efficacy, but has been difficult to develop into a robust therapy, in part because its mechanisms are incompletely understood. We review evidence from movement and psychiatric disorder studies, with an emphasis on how deep brain stimulation changes brain networks. From this, we argue for a network-oriented approach to future deep brain stimulation studies. That network approach requires methods for identifying patients with specific circuit/network deficits. We describe how dimensional approaches to diagnoses may aid that identification. We discuss the use of network/circuit biomarkers to develop self-adjusting "closed loop" systems.
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Affiliation(s)
- Mustafa Taha Bilge
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, 149 13th Street, Charlestown, Boston, MA 02129, USA
| | - Aishwarya K Gosai
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, 149 13th Street, Charlestown, Boston, MA 02129, USA
| | - Alik S Widge
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, 149 13th Street, Charlestown, Boston, MA 02129, USA; Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA.
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33
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Carpenter G, Harbin HT, Smith RL, Hornberger J, Nash DB. A Promising New Strategy to Improve Treatment Outcomes for Patients with Depression. Popul Health Manag 2018; 22:223-228. [PMID: 30156460 PMCID: PMC6555180 DOI: 10.1089/pop.2018.0101] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Each year, ineffective medical management of patients with mental illness compromises the health and well-being of individuals, and also impacts communities and our society. A variety of interrelated factors have impeded the health system's ability to treat patients with behavior health conditions adequately. A key contributing factor is a lack of objective markers to help predict patient response to specific drugs that has led to patterns of “trial and error” prescribing. For many years, clinicians have sought objective data (eg, a laboratory or imaging test) to assist them in selecting appropriate treatments for individual patients. Electroencephalogram (EEG) findings coupled with medication outcomes data may provide a solution. “Crowdsourced” physician registries that reference clinical outcomes to individual patient physiology have been used successfully for cancers. These techniques are now being explored in the context of behavioral health care. The Psychiatric EEG Evaluation Registry (PEER) is one such approach. PEER is a clinical phenotypic database comprising more than 11,000 baseline EEGs and more than 39,000 outcomes of medication treatment for a variety of mental health diagnoses. Collective findings from 45 studies (3130 patients) provide compelling evidence for PEER as a relatively simple, inexpensive predictor of likely patient response to specific antidepressants and likely treatment-related side effects (including suicidal ideation).
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Affiliation(s)
| | | | | | - John Hornberger
- 3 Stanford University, Stanford, California.,4 Cedar Associates, Menlo Park, California
| | - David B Nash
- 5 Jefferson College of Population Health, Philadelphia, Pennsylvania
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Widge AS, Malone DA, Dougherty DD. Closing the Loop on Deep Brain Stimulation for Treatment-Resistant Depression. Front Neurosci 2018; 12:175. [PMID: 29618967 PMCID: PMC5871707 DOI: 10.3389/fnins.2018.00175] [Citation(s) in RCA: 83] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2018] [Accepted: 03/05/2018] [Indexed: 12/20/2022] Open
Abstract
Major depressive episodes are the largest cause of psychiatric disability, and can often resist treatment with medication and psychotherapy. Advances in the understanding of the neural circuit basis of depression, combined with the success of deep brain stimulation (DBS) in movement disorders, spurred several groups to test DBS for treatment-resistant depression. Multiple brain sites have now been stimulated in open-label and blinded studies. Initial open-label results were dramatic, but follow-on controlled/blinded clinical trials produced inconsistent results, with both successes and failures to meet endpoints. Data from follow-on studies suggest that this is because DBS in these trials was not targeted to achieve physiologic responses. We review these results within a technology-lifecycle framework, in which these early trial “failures” are a natural consequence of over-enthusiasm for an immature technology. That framework predicts that from this “valley of disillusionment,” DBS may be nearing a “slope of enlightenment.” Specifically, by combining recent mechanistic insights and the maturing technology of brain-computer interfaces (BCI), the next generation of trials will be better able to target pathophysiology. Key to that will be the development of closed-loop systems that semi-autonomously alter stimulation strategies based on a patient's individual phenotype. Such next-generation DBS approaches hold great promise for improving psychiatric care.
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Affiliation(s)
- Alik S Widge
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Donald A Malone
- Department of Psychiatry, Cleveland Clinic, Cleveland, OH, United States
| | - Darin D Dougherty
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
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35
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Schiller MJ. Quantitative Electroencephalography in Guiding Treatment of Major Depression. Front Psychiatry 2018; 9:779. [PMID: 30728787 PMCID: PMC6351457 DOI: 10.3389/fpsyt.2018.00779] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Accepted: 12/27/2018] [Indexed: 12/16/2022] Open
Abstract
This paper reviews significant contributions to the evidence for the use of quantitative electroencephalography features as biomarkers of depression treatment and examines the potential of such technology to guide pharmacotherapy. Frequency band abnormalities such as alpha and theta band abnormalities have shown promise as have combinatorial measures such as cordance (a measure combining alpha and theta power) and the Antidepressant Treatment Response Index in predicting medication treatment response. Nevertheless, studies have been hampered by methodological problems and inconsistencies, and these approaches have ultimately failed to elicit any significant interest in actual clinical practice. More recent machine learning approaches such as the Psychiatric Encephalography Evaluation Registry (PEER) technology and other efforts analyze large datasets to develop variables that may best predict response rather than test a priori hypotheses. PEER is a technology that may go beyond predicting response to a particular antidepressant and help to guide pharmacotherapy.
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Affiliation(s)
- Mark J Schiller
- Mind Therapy Clinic, San Francisco, CA, United States.,MYnd Analytics, Inc., Mission Viejo, CA, United States
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36
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Jiang B, Petkova E, Tarpey T, Ogden RT. LATENT CLASS MODELING USING MATRIX COVARIATES WITH APPLICATION TO IDENTIFYING EARLY PLACEBO RESPONDERS BASED ON EEG SIGNALS. Ann Appl Stat 2017; 11:1513-1536. [PMID: 29152032 PMCID: PMC5687521 DOI: 10.1214/17-aoas1044] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Latent class models are widely used to identify unobserved subgroups (i.e., latent classes) based upon one or more manifest variables. The probability of belonging to each subgroup is typically modeled as a function of a set of measured covariates. In this paper, we extend existing latent class models to incorporate matrix covariates. This research is motivated by a randomized placebo-controlled depression clinical trial. One study goal is to identify a subgroup of subjects who experience symptoms improvement early on during antidepressant treatment, which is considered to be an indication of a placebo rather than a true pharmacological response. We want to relate the likelihood of belonging to this subgroup of early responders to baseline electroencephalography (EEG) measurement that takes the form of a matrix. The proposed method is built upon a low rank Candecomp/Parafac (CP) decomposition of the target coefficient matrix through low-dimensional latent variables, which effectively reduces the model dimensionality. We adopt a Bayesian hierarchical modeling approach to estimate the latent variables, which allows a flexible way to incorporate prior knowledge about covariate effect heterogeneity and offers a data-driven method of regularization. Simulation studies suggest that the proposed method is robust against potentially misspecified rank in the CP decomposition. With the motivating example we show how the proposed method can be applied to extract valuable information from baseline EEG measurements that explains the likelihood of belonging to the early responder subgroup, helping to identify placebo responders and suggesting new targets for the study of placebo response.
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Affiliation(s)
| | - Eva Petkova
- New York University
- Nathan S. Kline Institute for Psychiatric Research
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37
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Computational Psychiatry: From Mechanistic Insights to the Development of New Treatments. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2016; 1:382-385. [DOI: 10.1016/j.bpsc.2016.08.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2016] [Accepted: 08/01/2016] [Indexed: 12/22/2022]
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