1
|
Jensen KHR, Urdanibia-Centelles O, Dam VH, Köhler-Forsberg K, Frokjaer VG, Knudsen GM, Jørgensen MB, Ip CT. EEG abnormalities are not associated with poor antidepressant treatment outcome - A NeuroPharm study. Eur Neuropsychopharmacol 2024; 79:59-65. [PMID: 38128462 DOI: 10.1016/j.euroneuro.2023.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 11/24/2023] [Accepted: 11/26/2023] [Indexed: 12/23/2023]
Abstract
EEG brain abnormalities, such as slowing and isolated epileptiform discharges (IEDs), has previously been associated with non-response to antidepressant treatment with escitalopram and venlafaxine, suggesting a potential need for treatment with anticonvulsant property in some patients. The current study aims to replicate the reported association of EEG abnormality and treatment outcomes in an open-label trial of escitalopram for major depressive disorder (MDD) and explore its relationship to mood and cognition. Pretreatment, 6 min eyes-closed resting-state 256-channel EEG was recorded in 91 patients with MDD (age 18-57) who were treated with 10-20 mg escitalopram for 12 weeks; patients could switch to duloxetine after four weeks. A certified clinical neurophysiologist rated the EEGs. IED and EEG slowing was seen in 13.2%, and in 6.6% there were findings with unclear significance (i.e., Wicket spikes and theta activity). We saw no group-difference in remission or response rates after 8 and 12 weeks of treatment or switching to duloxetine. Patients with EEG abnormalities had higher pretreatment mood disturbances driven by greater anger (p=.039) and poorer verbal memory (p=.012). However, EEG abnormality was not associated with improved mood or verbal memory after treatment. Our findings should be interpreted in light of the rarity of EEG abnormalities and the sample size. While we cannot confirm that EEG abnormalities are associated with non-response to treatment, including escitalopram, abnormal EEG activity is associated with poor mood and verbal memory. The clinical utility of EEG abnormality in antidepressant treatment selection needs careful evaluation before deciding if useful for clinical implementation.
Collapse
Affiliation(s)
- Kristian H Reveles Jensen
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Department of Psychiatry, Psychiatric Centre Copenhagen, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Olalla Urdanibia-Centelles
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Department of Clinical Neurophysiology, Rigshospitalet, Copenhagen, Denmark
| | - Vibeke H Dam
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark
| | - Kristin Köhler-Forsberg
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Department of Psychiatry, Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Vibe G Frokjaer
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Department of Psychiatry, Psychiatric Centre Copenhagen, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Gitte M Knudsen
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Center for Cognitive and Brain Sciences, University of Macau, Taipa, Macau SAR, China
| | - Martin B Jørgensen
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Department of Psychiatry, Psychiatric Centre Copenhagen, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Cheng T Ip
- Neurobiology Research Unit, Rigshospitalet, Copenhagen, Denmark; Center for Cognitive and Brain Sciences, University of Macau, Taipa, Macau SAR, China
| |
Collapse
|
2
|
Amico F, De Canditiis D, Castiglione F, Pascarella A, Venerelli N, Fagan JV, Yek JH, Brophy J. A resting state EEG study on depressed persons with suicidal ideation. IBRO Neurosci Rep 2023; 14:346-352. [PMID: 37063608 PMCID: PMC10102403 DOI: 10.1016/j.ibneur.2023.03.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 03/23/2023] [Indexed: 04/03/2023] Open
Abstract
Background Major Depressive Disorder (MDD) is a psychiatric illness that is often associated with potentially life-threatening physiological changes and increased risk for suicidal behavior. Electroencephalography (EEG) research suggests an association between depression and specific frequency imbalances in the frontal brain region. Further, while recently developed technology has been proposed to simplify EEG data acquisition, more research is still needed to support its use in patients with MDD. Methods Using the 14-channel EMOTIV EPOC cap, we recorded resting state EEG from 15 MDD patients with and MDD persons with suicidal ideation (SI) vs. 12 healthy controls (HC) to investigate putative power spectral density (PSD) between-group differences at the F3 and F4 electrode sites. Specifically, we explored 1) between-group alpha power asymmetries (AA), 2) between-group differences in delta, theta, alpha and beta power, 3) between PSD data and the scores in the Beck's Depression Inventory-II (BDI-II), Beck's Anxiety Inventory (BAI), Reasons for Living Inventory (RFL), and Self-Disgust Questionnaire (SDS). Results When compared to HC, patients had higher scores on the BAI (p = 0.0018), BDI-II (p = 0.0001) or SDS (p = 0.0142) scale and lower scores in the RFL (p = 0.0006) scale. The PSD analysis revealed no between-group difference or correlation with questionnaire scores for any of the measures considered. Conclusions The present study could not confirm previous research suggesting frequency-specific anomalies in depressed persons with SI but might suggest that frontal EEG imbalances reflect greater anxiety and negative self-referencing. Future studies should confirm these findings in a larger population sample.
Collapse
Affiliation(s)
- Francesco Amico
- Newcastle Hospital, Newcastle, Co. Wicklow, Ireland
- Department of Psychiatry, Trinity Centre for Health Sciences, St. James Hospital, James's Street, Dublin 8, Ireland
- Corresponding author at: Department of Psychiatry, Trinity Centre for Health Sciences, St. James Hospital, James's Street, Dublin 8, Ireland.
| | - Daniela De Canditiis
- Centro Nazionale delle Ricerche (CNR), Istituto per le Applicazioni del Calcolo "M.Picone", Rome, Italy
| | - Filippo Castiglione
- Centro Nazionale delle Ricerche (CNR), Istituto per le Applicazioni del Calcolo "M.Picone", Rome, Italy
| | - Annalisa Pascarella
- Centro Nazionale delle Ricerche (CNR), Istituto per le Applicazioni del Calcolo "M.Picone", Rome, Italy
| | - Noemi Venerelli
- Dipartimento di Matematica G. Castelnuovo, Università La Sapienza, Rome, Italy
| | | | - John, H. Yek
- Newcastle Hospital, Newcastle, Co. Wicklow, Ireland
| | | |
Collapse
|
3
|
Wang B, Kang Y, Huo D, Chen D, Song W, Zhang F. Depression signal correlation identification from different EEG channels based on CNN feature extraction. Psychiatry Res Neuroimaging 2023; 328:111582. [PMID: 36565553 DOI: 10.1016/j.pscychresns.2022.111582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 11/24/2022] [Accepted: 12/17/2022] [Indexed: 12/24/2022]
Abstract
Depression is a mental illness and can even lead to suicide if not be diagnosed and treated. Electroencephalograph (EEG) is used to diagnose depression and it is more complexity to extract the features from all the multimodal channel information . In order to simplify the diagnose process and detect clinical depression, the EEG channels with strong depression information should be identified firstly. Therefore, a depression signal correlation identification method based on convolutional neural network (CNN) is proposed. In the method, the labeled multi-channel EEG is used as data. The EEG signals of each channel are divided into neural network training data set and these data is trained by AlexNet network. Then the correlation classification of each channel for depression is identified based on the trained sample. Accuracy and loss functions are used to evaluate classification index.Conversely, the correlation is lower. An experiments is conducted and the results show that the correlation is not consistent. A few of channels are strongly correlated with depression, such as 13, 17, 28, 40, 46, 66 and 69. These EEG channels are selected to diagnose depression.
Collapse
|
4
|
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.
Collapse
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
| |
Collapse
|
5
|
Swatzyna RJ, Arns M, Tarnow JD, Turner RP, Barr E, MacInerney EK, Hoffman AM, Boutros NN. Isolated epileptiform activity in children and adolescents: prevalence, relevance, and implications for treatment. Eur Child Adolesc Psychiatry 2022; 31:545-552. [PMID: 32666203 DOI: 10.1007/s00787-020-01597-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Accepted: 07/01/2020] [Indexed: 10/23/2022]
Abstract
In the field of psychiatry diagnoses are primarily based on the report of symptoms from either the patient, parents, or both, and a psychiatrist's observations. A psychiatric diagnosis is currently the most widely used basis for medication selection and the brain is seldom investigated directly as a source of those symptoms. This study addresses the request from the National Institute of Mental Health (NIMH) Research Domain Criteria Project (RDoC) for scientific research into neurological abnormalities that can be linked to psychiatric symptoms for the purpose of predicting medication response. One such neurological abnormality that has been the focus of many studies over the last three decades is isolated epileptiform discharges (IEDs) in children and adolescents without seizures. We conducted a systematic review of the literature to determine prevalence rates of IEDs within diagnostic categories. We then compared the prevalence of IEDs in the selected literature to our IRB-approved data archive. Our study found a consistent high prevalence of IEDs specifically for ADHD (majority > 25%) and ASD (majority > 59%), and consistent low prevalence rates were found for Depression (3%). If children and adolescents have failed multiple medication attempts, and more than one-third of them have IEDs, then an EEG would be justified within the RDoC paradigm.
Collapse
Affiliation(s)
- Ronald J Swatzyna
- Houston Neuroscience Brain Center, Houston, TX, USA. .,Clinical NeuroAnalytics, 1307 Oceanside Lane, League City, TX, 77573, USA.
| | - Martijn Arns
- Department of Experimental Psychology, Utrecht University, Utrecht, The Netherlands.,Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, The Netherlands
| | - Jay D Tarnow
- The Tarnow Center for Self-Management, Houston, USA
| | - Robert P Turner
- Network Neurology Health, Charleston, SC, USA.,Clinical Pediatrics and Neurology, USC School of Medicine, Columbia, SC, USA
| | - Emma Barr
- Houston Neuroscience Brain Center, Houston, TX, USA
| | | | | | - Nash N Boutros
- School of Medicine, RUSH University, Kansas City, MO, USA
| |
Collapse
|
6
|
Automated diagnosis of depression from EEG signals using traditional and deep learning approaches: A comparative analysis. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2021.12.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
|
7
|
Prediction of drug response in major depressive disorder using ensemble of transfer learning with convolutional neural network based on EEG. Biocybern Biomed Eng 2021. [DOI: 10.1016/j.bbe.2021.06.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
|
8
|
van der Vinne N, Vollebregt MA, Rush AJ, Eebes M, van Putten MJAM, Arns M. EEG biomarker informed prescription of antidepressants in MDD: a feasibility trial. Eur Neuropsychopharmacol 2021; 44:14-22. [PMID: 33509659 DOI: 10.1016/j.euroneuro.2020.12.005] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 12/02/2020] [Accepted: 12/15/2020] [Indexed: 12/28/2022]
Abstract
Using pre-treatment biomarkers to guide patients to the preferred antidepressant medication treatment could be a promising approach to enhance its current modest response and remission rates. This open-label prospective study assessed the feasibility of using such pre-treatment biomarkers, by using previously identified EEG features (paroxysmal activity; alpha peak frequency; frontal alpha asymmetry) to inform the clinician in selecting among three different antidepressants (ADs; escitalopram, sertraline, venlafaxine) as compared to Treatment As Usual (TAU). EEG data were obtained from 195 outpatients with major depressive disorder prior to eight weeks of AD treatment. Primary outcome measure was the percentage change between before and after treatment on the Beck Depression Inventory-II (BDI-II). We compared TAU and EEG-informed prescription through AN(C)OVAs. Recruitment started with patients receiving TAU to establish baseline effectiveness, after which we recruited patients receiving EEG-informed prescription. 108 patients received EEG-informed prescription and 87 patients received TAU. Clinicians and patients were satisfied with the protocol. Overall, 70 (65%) of the EEG-informed clinicians followed recommendations (compared to 52 (60%) following prescriptions in the TAU group), establishing feasibility. We here confirm that treatment allocation informed by EEG variables previously reported in correlational studies, was feasible.
Collapse
Affiliation(s)
- Nikita van der Vinne
- Synaeda Psycho Medisch Centrum, Leeuwarden, the Netherlands; Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands; Department of Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, the Netherlands.
| | - Madelon A Vollebregt
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands
| | - A John Rush
- National University of Singapore, Singapore; Duke University School of Medicine, Durham, NC, USA; Texas Tech Health Sciences Center, Odessa, TX, USA
| | - Michiel Eebes
- Synaeda Psycho Medisch Centrum, Leeuwarden, the Netherlands
| | - Michel J A M van Putten
- Department of Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, the Netherlands; Department of Clinical Neurophysiology and Neurology, Medisch Spectrum Twente, Enschede, the Netherlands
| | - Martijn Arns
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands; Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Location AMC, Amsterdam, the Netherlands
| |
Collapse
|
9
|
Rajpurkar P, Yang J, Dass N, Vale V, Keller AS, Irvin J, Taylor Z, Basu S, Ng A, Williams LM. Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment in Adults With Depression: A Prespecified Secondary Analysis of a Randomized Clinical Trial. JAMA Netw Open 2020; 3:e206653. [PMID: 32568399 PMCID: PMC7309440 DOI: 10.1001/jamanetworkopen.2020.6653] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
IMPORTANCE Despite the high prevalence and potential outcomes of major depressive disorder, whether and how patients will respond to antidepressant medications is not easily predicted. OBJECTIVE To identify the extent to which a machine learning approach, using gradient-boosted decision trees, can predict acute improvement for individual depressive symptoms with antidepressants based on pretreatment symptom scores and electroencephalographic (EEG) measures. DESIGN, SETTING, AND PARTICIPANTS This prognostic study analyzed data collected as part of the International Study to Predict Optimized Treatment in Depression, a randomized, prospective open-label trial to identify clinically useful predictors and moderators of response to commonly used first-line antidepressant medications. Data collection was conducted at 20 sites spanning 5 countries and including 518 adult outpatients (18-65 years of age) from primary care or specialty care practices who received a diagnosis of current major depressive disorder between December 1, 2008, and September 30, 2013. Patients were antidepressant medication naive or willing to undergo a 1-week washout period of any nonprotocol antidepressant medication. Statistical analysis was conducted from January 5 to June 30, 2019. EXPOSURES Participants with major depressive disorder were randomized in a 1:1:1 ratio to undergo 8 weeks of treatment with escitalopram oxalate (n = 162), sertraline hydrochloride (n = 176), or extended-release venlafaxine hydrochloride (n = 180). MAIN OUTCOMES AND MEASURES The primary objective was to predict improvement in individual symptoms, defined as the difference in score for each of the symptoms on the 21-item Hamilton Rating Scale for Depression from baseline to week 8, evaluated using the C index. RESULTS The resulting data set contained 518 patients (274 women; mean [SD] age, 39.0 [12.6] years; mean [SD] 21-item Hamilton Rating Scale for Depression score improvement, 13.0 [7.0]). With the use of 5-fold cross-validation for evaluation, the machine learning model achieved C index scores of 0.8 or higher on 12 of 21 clinician-rated symptoms, with the highest C index score of 0.963 (95% CI, 0.939-1.000) for loss of insight. The importance of any single EEG feature was higher than 5% for prediction of 7 symptoms, with the most important EEG features being the absolute delta band power at the occipital electrode sites (O1, 18.8%; Oz, 6.7%) for loss of insight. Over and above the use of baseline symptom scores alone, the use of both EEG and baseline symptom features was associated with a significant increase in the C index for improvement in 4 symptoms: loss of insight (C index increase, 0.012 [95% CI, 0.001-0.020]), energy loss (C index increase, 0.035 [95% CI, 0.011-0.059]), appetite changes (C index increase, 0.017 [95% CI, 0.003-0.030]), and psychomotor retardation (C index increase, 0.020 [95% CI, 0.008-0.032]). CONCLUSIONS AND RELEVANCE This study suggests that machine learning may be used to identify independent associations of symptoms and EEG features to predict antidepressant-associated improvements in specific symptoms of depression. The approach should next be prospectively validated in clinical trials and settings. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT00693849.
Collapse
Affiliation(s)
- Pranav Rajpurkar
- Department of Computer Science, Stanford University, Stanford, California
| | - Jingbo Yang
- Department of Computer Science, Stanford University, Stanford, California
| | - Nathan Dass
- Department of Computer Science, Stanford University, Stanford, California
| | - Vinjai Vale
- Department of Computer Science, Stanford University, Stanford, California
| | - Arielle S. Keller
- Stanford Center for Precision Mental Health and Wellness, Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
| | - Jeremy Irvin
- Department of Computer Science, Stanford University, Stanford, California
| | - Zachary Taylor
- Stanford Center for Precision Mental Health and Wellness, Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
| | - Sanjay Basu
- Center for Primary Care, Harvard Medical School, Boston, Massachusetts
- Research and Analytics, Collective Health, San Francisco, California
- Division of Primary Care and Public Health, Imperial College London School of Public Health, London, United Kingdom
| | - Andrew Ng
- Department of Computer Science, Stanford University, Stanford, California
| | - Leanne M. Williams
- Stanford Center for Precision Mental Health and Wellness, Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
| |
Collapse
|