1
|
Klooster D, Voetterl H, Baeken C, Arns M. Evaluating Robustness of Brain Stimulation Biomarkers for Depression: A Systematic Review of Magnetic Resonance Imaging and Electroencephalography Studies. Biol Psychiatry 2024; 95:553-563. [PMID: 37734515 DOI: 10.1016/j.biopsych.2023.09.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 08/30/2023] [Accepted: 09/06/2023] [Indexed: 09/23/2023]
Abstract
Noninvasive brain stimulation (NIBS) treatments have gained considerable attention as potential therapeutic intervention for psychiatric disorders. The identification of reliable biomarkers for predicting clinical response to NIBS has been a major focus of research in recent years. Neuroimaging techniques, such as electroencephalography (EEG) and functional magnetic resonance imaging (MRI), have been used to identify potential biomarkers that could predict response to NIBS. However, identifying clinically actionable brain biomarkers requires robustness. In this systematic review, we aimed to summarize the current state of brain biomarker research for NIBS in depression, focusing only on well-powered studies (N ≥ 88) and/or studies that aimed at independently replicating previous findings, either successfully or unsuccessfully. A total of 220 studies were initially identified, of which 18 MRI studies and 18 EEG studies met the inclusion criteria. All focused on repetitive transcranial magnetic stimulation treatment in depression. After reviewing the included studies, we found the following MRI and EEG biomarkers to be most robust: 1) functional MRI-based functional connectivity between the dorsolateral prefrontal cortex and subgenual anterior cingulate cortex, 2) functional MRI-based network connectivity, 3) task-induced EEG frontal-midline theta, and 4) EEG individual alpha frequency. Future prospective studies should further investigate the clinical actionability of these specific EEG and MRI biomarkers to bring biomarkers closer to clinical reality.
Collapse
Affiliation(s)
- Debby Klooster
- Ghent Experimental Psychiatry Laboratory, Department of Head and Skin, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium; 4BRAIN Team, Department of Head and Skin, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium; Center for Care and Cure, Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Helena Voetterl
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Chris Baeken
- Ghent Experimental Psychiatry Laboratory, Department of Head and Skin, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium; Center for Care and Cure, Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Department of Psychiatry, Brussels, Belgium
| | - Martijn Arns
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands.
| |
Collapse
|
2
|
Čukić M, Olejarzcyk E, Bachmann M. Fractal Analysis of Electrophysiological Signals to Detect and Monitor Depression: What We Know So Far? ADVANCES IN NEUROBIOLOGY 2024; 36:677-692. [PMID: 38468058 DOI: 10.1007/978-3-031-47606-8_34] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Depression is currently one of the most complicated public health problems with the rising number of patients, increasing partly due to pandemics, but also due to increased existential insecurities and complicated aetiology of disease. Besides the tsunami of mental health issues, there are limitations imposed by ambiguous clinical rules of assessment of the symptoms and obsolete and inefficient standard therapy approaches. Here we are summarizing the neuroimaging results pointing out the actual complexity of the disease and novel attempts to detect depression that are evidence-based, mostly related to electrophysiology. It is repeatedly shown that the complexity of resting-state EEG recorded in patients suffering from depression is increased compared to healthy controls. We are discussing here how that can be interpreted and what we can learn about future effective therapies. Also, there is evidence that novel options of treatment, like different modalities of electromagnetic stimulation, are successful just because they are capable of decreasing that aberrated complexity. And complexity measures extracted from electrophysiological signals of depression patients can serve as excellent features for further machine learning models in order to automatize detection. In addition, after initial detection and even selection of responders for further therapy route, it is possible to monitor the therapeutic flow for one person, which leads us to possible tailored treatment for patients suffering from depression.
Collapse
Affiliation(s)
- Milena Čukić
- Empa Swiss Federal Labs for Materials Science and Technology, St. Gallen, Switzerland.
| | - Elzbieta Olejarzcyk
- Nalez Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland
| | | |
Collapse
|
3
|
Ebrahimzadeh E, Dehghani A, Asgarinejad M, Soltanian-Zadeh H. Non-linear processing and reinforcement learning to predict rTMS treatment response in depression. Psychiatry Res Neuroimaging 2024; 337:111764. [PMID: 38043370 DOI: 10.1016/j.pscychresns.2023.111764] [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: 01/07/2023] [Revised: 11/05/2023] [Accepted: 11/09/2023] [Indexed: 12/05/2023]
Abstract
BACKGROUND Forecasting the efficacy of repetitive transcranial magnetic stimulation (rTMS) therapy can lead to substantial time and cost savings by preventing futile treatments. To achieve this objective, we've formulated a machine learning approach aimed at categorizing patients with major depressive disorder (MDD) into two groups: individuals who respond (R) positively to rTMS treatment and those who do not respond (NR). METHODS Preceding the commencement of treatment, we obtained resting-state EEG data from 106 patients diagnosed with MDD, employing 32 electrodes for data collection. These patients then underwent a 7-week course of rTMS therapy, and 54 of them exhibited positive responses to the treatment. Employing Independent Component Analysis (ICA) on the EEG data, we successfully pinpointed relevant brain sources that could potentially serve as markers of neural activity within the dorsolateral prefrontal cortex (DLPFC). These identified sources were further scrutinized to estimate the sources of activity within the sensor domain. Then, we integrated supplementary physiological data and implemented specific criteria to yield more realistic estimations when compared to conventional EEG analysis. In the end, we selected components corresponding to the DLPFC region within the sensor domain. Features were derived from the time-series data of these relevant independent components. To identify the most significant features, we used Reinforcement Learning (RL). In categorizing patients into two groups - R and NR to rTMS treatment - we utilized three distinct classification algorithms including K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Multilayer Perceptron (MLP). We assessed the performance of these classifiers through a ten-fold cross-validation method. Additionally, we conducted a statistical test to evaluate the discriminative capacity of these features between responders and non-responders, opening the door for further exploration in this field. RESULTS We identified EEG features that can anticipate the response to rTMS treatment. The most robust discriminators included EEG beta power, the sum of bispectrum diagonal elements in the delta and beta frequency bands. When these features were combined into a single vector, the classification of responders and non-responders achieved impressive performance, with an accuracy of 95.28 %, specificity at 94.23 %, sensitivity reaching 96.29 %, and precision standing at 94.54 %, all achieved using SVM. CONCLUSIONS The results of this study suggest that the proposed approach, utilizing power, non-linear, and bispectral features extracted from relevant independent component time-series, has the capability to forecast the treatment outcome of rTMS for MDD patients based solely on a single pre-treatment EEG recording session. The achieved findings demonstrate the superior performance of our method compared to previous techniques.
Collapse
Affiliation(s)
- Elias Ebrahimzadeh
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
| | - Amin Dehghani
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | | | - Hamid Soltanian-Zadeh
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| |
Collapse
|
4
|
Ebrahimzadeh E, Fayaz F, Rajabion L, Seraji M, Aflaki F, Hammoud A, Taghizadeh Z, Asgarinejad M, Soltanian-Zadeh H. Machine learning approaches and non-linear processing of extracted components in frontal region to predict rTMS treatment response in major depressive disorder. Front Syst Neurosci 2023; 17:919977. [PMID: 36968455 PMCID: PMC10034109 DOI: 10.3389/fnsys.2023.919977] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 02/13/2023] [Indexed: 03/12/2023] Open
Abstract
Predicting the therapeutic result of repetitive transcranial magnetic stimulation (rTMS) treatment could save time and costs as ineffective treatment can be avoided. To this end, we presented a machine-learning-based strategy for classifying patients with major depression disorder (MDD) into responders (R) and nonresponders (NR) to rTMS treatment. Resting state EEG data were recorded using 32 electrodes from 88 MDD patients before treatment. Then, patients underwent 7 weeks of rTMS, and 46 of them responded to treatment. By applying Independent Component Analysis (ICA) on EEG, we identified the relevant brain sources as possible indicators of neural activity in the dorsolateral prefrontal cortex (DLPFC). This was served through estimating the generators of activity in the sensor domain. Subsequently, we added physiological information and placed certain terms and conditions to offer a far more realistic estimation than the classic EEG. Ultimately, those components mapped in accordance with the region of the DLPFC in the sensor domain were chosen. Features extracted from the relevant ICs time series included permutation entropy (PE), fractal dimension (FD), Lempel-Ziv Complexity (LZC), power spectral density, correlation dimension (CD), features based on bispectrum, frontal and prefrontal cordance, and a combination of them. The most relevant features were selected by a Genetic Algorithm (GA). For classifying two groups of R and NR, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Multilayer Perceptron (MLP) were applied to predict rTMS treatment response. To evaluate the performance of classifiers, a 10-fold cross-validation method was employed. A statistical test was used to assess the capability of features in differentiating R and NR for further research. EEG characteristics that can predict rTMS treatment response were discovered. The strongest discriminative indicators were EEG beta power, the sum of bispectrum diagonal elements in delta and beta bands, and CD. The Combined feature vector classified R and NR with a high performance of 94.31% accuracy, 92.85% specificity, 95.65% sensitivity, and 92.85% precision using SVM. This result indicates that our proposed method with power and nonlinear and bispectral features from relevant ICs time-series can predict the treatment outcome of rTMS for MDD patients only by one session pretreatment EEG recording. The obtained results show that the proposed method outperforms previous methods.
Collapse
Affiliation(s)
- Elias Ebrahimzadeh
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
- *Correspondence: Elias Ebrahimzadeh
| | - Farahnaz Fayaz
- Biomedical Engineering Department, School of Electrical Engineering, Payame Noor University of North Tehran, Tehran, Iran
| | - Lila Rajabion
- School of Graduate Studies, SUNY Empire State College, Manhattan, NY, United States
| | - Masoud Seraji
- Department of Psychology, University of Texas at Austin, Austin, TX, United States
| | - Fatemeh Aflaki
- Department of Biomedical Engineering, Islamic Azad University Central Tehran Branch, Tehran, Iran
| | - Ahmad Hammoud
- Department of Medical and Technical Information Technology, Bauman Moscow State Technical University, Moscow, Russia
| | - Zahra Taghizadeh
- Department of Bioengineering, George Mason University, Fairfax, VA, United States
| | - Mostafa Asgarinejad
- Department of Cognitive Neuroscience, Institute for Cognitive Sciences Studies, Tehran, Iran
| | - Hamid Soltanian-Zadeh
- School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| |
Collapse
|
5
|
Nobakhsh B, Shalbaf A, Rostami R, Kazemi R, Rezaei E, Shalbaf R. An effective brain connectivity technique to predict repetitive transcranial magnetic stimulation outcome for major depressive disorder patients using EEG signals. Phys Eng Sci Med 2023; 46:67-81. [PMID: 36445618 DOI: 10.1007/s13246-022-01198-0] [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: 11/11/2021] [Accepted: 11/06/2022] [Indexed: 11/30/2022]
Abstract
One of the most effective treatments for drug-resistant Major depressive disorder (MDD) patients is repetitive transcranial magnetic stimulation (rTMS). To improve treatment efficacy and reduce health care costs, it is necessary to predict the treatment response. In this study, we intend to predict the rTMS treatment response in MDD patients from electroencephalogram (EEG) signals before starting the treatment using machine learning approaches. Effective brain connectivity of 19-channel EEG data of MDD patients was calculated by the direct directed transfer function (dDTF) method. Then, using three feature selection methods, the best features were selected and patients were classified as responders or non-responders to rTMS treatment by using the support vector machine (SVM). Results on the 34 MDD patients indicated that the Fp2 region in the delta and theta frequency bands has a significant difference between the two groups and can be used as a significant brain biomarker to assess the rTMS treatment response. Also, the highest accuracy (89.6%) using the SVM classifier for the best features of the dDTF method based on the area under the receiver operating characteristic curve (AUC-ROC) criteria was obtained by combining the delta and theta frequency bands. Consequently, the proposed method can accurately detect the rTMS treatment response in MDD patients before starting treatment on the EEG signal to avoid financial and time costs to patients and medical centers.
Collapse
Affiliation(s)
- Behrouz Nobakhsh
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ahmad Shalbaf
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Reza Rostami
- Department of Psychology, University of Tehran, Tehran, Iran
| | - Reza Kazemi
- Department of Cognitive Psychology, Institute for Cognitive Science Studies, Tehran, Iran
| | - Erfan Rezaei
- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Reza Shalbaf
- Institute for Cognitive Science Studies, Tehran, Iran
| |
Collapse
|
6
|
Meijs H, Prentice A, Lin BD, De Wilde B, Van Hecke J, Niemegeers P, van Eijk K, Luykx JJ, Arns M. A polygenic-informed approach to a predictive EEG signature empowers antidepressant treatment prediction: A proof-of-concept study. Eur Neuropsychopharmacol 2022; 62:49-60. [PMID: 35896057 DOI: 10.1016/j.euroneuro.2022.07.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 07/05/2022] [Accepted: 07/08/2022] [Indexed: 11/04/2022]
Abstract
The treatment of major depressive disorder (MDD) is hampered by low chances of treatment response in each treatment step, which is partly due to a lack of firmly established outcome-predictive biomarkers. Here, we hypothesize that polygenic-informed EEG signatures may help predict antidepressant treatment response. Using a polygenic-informed electroencephalography (EEG) data-driven, data-reduction approach, we identify a brain network in a large cohort (N=1,123), and discover it is sex-specifically (male patients, N=617) associated with polygenic risk score (PRS) of antidepressant response. Subsequently, we demonstrate in three independent datasets the utility of the network in predicting response to antidepressant medication (male, N=232) as well as repetitive transcranial magnetic stimulation (rTMS) and concurrent psychotherapy (male, N=95). This network significantly improves a treatment response prediction model with age and baseline severity data (area under the curve, AUC=0.623 for medicaton; AUC=0.719 for rTMS). A predictive model for MDD patients, aimed at increasing the likelihood of being a responder to antidepressants or rTMS and concurrent psychotherapy based on only this network, yields a positive predictive value (PPV) of 69% for medication and 77% for rTMS. Finally, blinded out-of-sample validation of the network as predictor for psychotherapy response in another independent dataset (male, N=50) results in a within-subsample response rate of 50% (improvement of 56%). Overall, the findings provide a first proof-of-concept of a combined genetic and neurophysiological approach in the search for clinically-relevant biomarkers in psychiatric disorders, and should encourage researchers to incorporate genetic information, such as PRS, in their search for clinically relevant neuroimaging biomarkers.
Collapse
Affiliation(s)
- Hannah Meijs
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands; GGNet Mental Health, Warnsveld, the Netherlands.
| | - Amourie Prentice
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Bochao D Lin
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands; Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Bieke De Wilde
- Department of Psychiatry, Ziekenhuis Netwerk Antwerpen (ZNA), Antwerp, Belgium
| | - Jan Van Hecke
- Department of Psychiatry, Ziekenhuis Netwerk Antwerpen (ZNA), Antwerp, Belgium
| | - Peter Niemegeers
- Department of Psychiatry, Ziekenhuis Netwerk Antwerpen (ZNA), Antwerp, Belgium
| | - Kristel van Eijk
- Department of Psychiatry, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands
| | - Jurjen J Luykx
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands; GGNet Mental Health, Warnsveld, the Netherlands; Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Martijn Arns
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
| |
Collapse
|
7
|
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
|
8
|
Liu S, Liu X, Yan D, Chen S, Liu Y, Hao X, Ou W, Huang Z, Su F, He F, Ming D. Alterations in patients with first-episode depression in the eyes-open and eyes-closed conditions: A resting-state EEG study. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1019-1029. [PMID: 35412986 DOI: 10.1109/tnsre.2022.3166824] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Altered resting-state EEG activity has been repeatedly reported in major depressive disorder (MDD), but no robust biomarkers have been identified until now. The poor consistency of EEG alterations may be due to inconsistent resting conditions; that is, the eyes-open (EO) and eyes-closed (EC) conditions. Here, we explored the effect of the EO and EC conditions on EEG biomarkers for discriminating MDD subjects and healthy control (HC) subjects. EEG data were recorded from 30 first-episode MDD and 26 HC subjects during an 8-min resting-state session. The features were extracted using spectral power, Lempel-Ziv complexity, and detrended fluctuation analysis. Significant features were further selected via the sequential backward feature selection algorithm. Support vector machine (SVM), logistic regression, and linear discriminate analysis were used to determine a better resting condition to provide more reliable estimates for identifying MDD. Compared with the HC group, we found that the MDD group exhibited widespread increased β and γ powers (p < 0.01) in both conditions. In the EO condition, the MDD group showed increased complexity and scaling exponents in the α band relative to HC subjects (p < 0.05). The best classification performance of the combined feature sets was found in the EO condition, with the leave-one-out classification accuracy of 89.29%, sensitivity of 90.00%, and specificity of 88.46% using SVM with the linear kernel classifier when the threshold was set to 0.7, followed by the β and γ spectral features with an average accuracy of 83.93%. Overall, EO and EC conditions indeed affected the between-group variance, and the EO condition is suggested as the more separable resting condition to identify depression. Specially, the β and γ powers are suggested as potential biomarkers for first-episode MDD.
Collapse
|
9
|
Faller J, Doose J, Sun X, Mclntosh JR, Saber GT, Lin Y, Teves JB, Blankenship A, Huffman S, Goldman RI, George MS, Brown TR, Sajda P. Daily prefrontal closed-loop repetitive transcranial magnetic stimulation (rTMS) produces progressive EEG quasi-alpha phase entrainment in depressed adults. Brain Stimul 2022; 15:458-471. [PMID: 35231608 PMCID: PMC8979612 DOI: 10.1016/j.brs.2022.02.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 01/31/2022] [Accepted: 02/17/2022] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation modality that can treat depression, obsessive-compulsive disorder, or help smoking cessation. Research suggests that timing the delivery of TMS relative to an endogenous brain state may affect efficacy and short-term brain dynamics. OBJECTIVE To investigate whether, for a multi-week daily treatment of repetitive TMS (rTMS), there is an effect on brain dynamics that depends on the timing of the TMS relative to individuals' prefrontal EEG quasi-alpha rhythm (between 6 and 13 Hz). METHOD We developed a novel closed-loop system that delivers personalized EEG-triggered rTMS to patients undergoing treatment for major depressive disorder. In a double blind study, patients received daily treatments of rTMS over a period of six weeks and were randomly assigned to either a synchronized or unsynchronized treatment group, where synchronization of rTMS was to their prefrontal EEG quasi-alpha rhythm. RESULTS When rTMS is applied over the dorsal lateral prefrontal cortex (DLPFC) and synchronized to the patient's prefrontal quasi-alpha rhythm, patients develop strong phase entrainment over a period of weeks, both over the stimulation site as well as in a subset of areas distal to the stimulation site. In addition, at the end of the course of treatment, this group's entrainment phase shifts to be closer to the phase that optimally engages the distal target, namely the anterior cingulate cortex (ACC). These entrainment effects are not observed in the group that is given rTMS without initial EEG synchronization of each TMS train. CONCLUSIONS The entrainment effects build over the course of days/weeks, suggesting that these effects engage neuroplastic changes which may have clinical consequences in depression or other diseases.
Collapse
Affiliation(s)
- Josef Faller
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
| | - Jayce Doose
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Xiaoxiao Sun
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA; US DEVCOM Army Research Laboratory, Aberdeen Proving Ground, MD, 20115, USA
| | - James R Mclntosh
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA; Department of Orthopaedic Surgery, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Golbarg T Saber
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, 29425, USA; Department of Neurology, University of Chicago, Chicago, IL, 60637, USA
| | - Yida Lin
- Department of Computer Science, Columbia University, New York, NY, 10027, USA
| | - Joshua B Teves
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Aidan Blankenship
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Sarah Huffman
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Robin I Goldman
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Mark S George
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, 29425, USA; Ralph H. Johnson VA Medical Center, Charleston, SC, 29401, USA
| | - Truman R Brown
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, 29425, USA; Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, 29425, USA
| | - Paul Sajda
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA; Department of Radiology, Columbia University Irving Medical Center, New York, NY, 10032, USA; Department of Electrical Engineering, Columbia University, New York, NY, 10027, USA; Data Science Institute, Columbia University, New York, NY, 10027, USA.
| |
Collapse
|
10
|
Yun S, Jeong B. Aberrant EEG signal variability at a specific temporal scale in major depressive disorder. Clin Neurophysiol 2021; 132:1866-1877. [PMID: 34147011 DOI: 10.1016/j.clinph.2021.05.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 04/17/2021] [Accepted: 05/18/2021] [Indexed: 11/28/2022]
Abstract
OBJECTIVE Signal variability is linked to irregularities in time series caused by intrinsic nonlinearities of the neural system and can be measured on variable temporal scales over short time series. By measuring refined complex multiscale permutation entropy (RCMPE) from resting-state electroencephalography (EEG) data, we investigated the presence of a specific range of time scales characterizing major depressive disorder (MDD). METHOD We used an EEG dataset acquired from 22 MDD patients and 22 healthy controls in the eyes-closed (EC) and eyes-open (EO) states available on the PRED + CT website. Signal variability in both the EC and EO states was compared between the two groups, and their relationship to depressive symptom severity was examined. RESULTS In the EC state, the RCMPE was higher in the MDD group than in the control group on a coarse temporal scale, approximately 20-32 ms, at almost all sensors. It also showed a negative correlation with depressive symptom severity on a fine temporal scale, approximately 2-26 ms, in the frontal, right temporal, and left parietal sensor areas in MDD. The EO state revealed a group difference but no relationship with depressive symptom severity. CONCLUSION Our results suggested that the diagnosis of MDD as a trait and the severity of depressive symptoms as a state are linked to EEG signal variability on the coarse temporal scale and the fine scale in the resting state, respectively. SIGNIFICANCE Signal variability reflects different characteristics of depression depending on the temporal scale.
Collapse
Affiliation(s)
- Seokho Yun
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Bumseok Jeong
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
| |
Collapse
|
11
|
Tsai HJ, Yang WC, Tsai SJ, Lin CH, Yang AC. Remission of depression is associated with asymmetric hemispheric variation in EEG complexity before antidepressant treatment. J Affect Disord 2021; 281:872-879. [PMID: 33220949 DOI: 10.1016/j.jad.2020.11.041] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 09/14/2020] [Accepted: 11/07/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND Intense increased alpha activity in the anterior brain has been recognized as one of the biomarkers of depression. This study aimed to explore whether complexity measures of electroencephalogram (EEG) signals vary across the frontal region between patients with or without remission in depression. METHODS Ninety-four patients with moderate-to-severe major depression underwent the experiment. Hilbert-Huang transform was applied to extract individual alpha EEG instantaneous amplitude. Averaged regional complexity (SampEnMean) on frontal and frontal-central areas as well as bilateral frontal areas were assessed according to multiscale entropy between the remitters (n = 26/94, 27.66%) and poor responders (n = 29/94, 30.85%). Standard deviation of regional complexity (SampEnSD) was used as a parameter to test the homogeneity of frontal alpha complexity. RESULTS No significant differences in SampEnMean for the frontal or frontal-central area were observed between the remitters and poor responders. However, the SampEnSD (i.e., regional homogeneity of frontal alpha complexity) significantly increased in the frontal-central area after treatment among the poor responders in comparison to their baseline, whereas the remitters showed an opposite trend. The remitters further showed significantly larger SampEnSD on the left frontal area at baseline, and this lateralization disappeared after antidepressant treatment except for the poor responders. LIMITATIONS This study was limited by a small sample size and without healthy controls. CONCLUSIONS Homogeneity in frontal-central alpha complexity and interhemispheric asymmetry normalization of anterior EEG complexity are associated with antidepressant efficacy, suggesting that homogeneity of dynamical brain activity is a key linked to the successful treatment of major depression.
Collapse
Affiliation(s)
- Hsin-Jung Tsai
- Department of Psychiatry, Taipei Veteran General Hospital, Taipei, Taiwan
| | - Wei-Cheng Yang
- Kaohsiung Municipal Kai-Syuan Psychiatric Hospital, Kaohsiung, Taiwan
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veteran General Hospital, Taipei, Taiwan; Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan
| | - Ching-Hua Lin
- Kaohsiung Municipal Kai-Syuan Psychiatric Hospital, Kaohsiung, Taiwan.
| | - Albert C Yang
- Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan; Digital Medicine Center, National Yang-Ming University, Taipei, Taiwan; Brain Medicine Center, Taoyuan Psychiatric Center, Taoyuan City, Taiwan.
| |
Collapse
|
12
|
Corlier J, Wilson A, Hunter AM, Vince-Cruz N, Krantz D, Levitt J, Minzenberg MJ, Ginder N, Cook IA, Leuchter AF. Changes in Functional Connectivity Predict Outcome of Repetitive Transcranial Magnetic Stimulation Treatment of Major Depressive Disorder. Cereb Cortex 2020; 29:4958-4967. [PMID: 30953441 DOI: 10.1093/cercor/bhz035] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Revised: 01/28/2019] [Accepted: 02/12/2019] [Indexed: 12/15/2022] Open
Abstract
Repetitive transcranial magnetic stimulation (rTMS) treatment of major depressive disorder (MDD) is associated with changes in brain functional connectivity (FC). These changes may be related to the mechanism of action of rTMS and explain the variability in clinical outcome. We examined changes in electroencephalographic FC during the first rTMS treatment in 109 subjects treated with 10 Hz stimulation to left dorsolateral prefrontal cortex. All subjects subsequently received 30 treatments and clinical response was defined as ≥40% improvement in the inventory of depressive symptomatology-30 SR score at treatment 30. Connectivity change was assessed with coherence, envelope correlation, and a novel measure, alpha spectral correlation (αSC). Machine learning was used to develop predictive models of outcome for each connectivity measure, which were compared with prediction based upon early clinical improvement. Significant connectivity changes were associated with clinical outcome (P < 0.001). Machine learning models based on αSC yielded the most accurate prediction (area under the curve, AUC = 0.83), and performance improved when combined with early clinical improvement measures (AUC = 0.91). The initial rTMS treatment session produced robust changes in FC, which were significant predictors of clinical outcome of a full course of treatment for MDD.
Collapse
Affiliation(s)
- Juliana Corlier
- TMS Clinical and Research Program, Neuromodulation Division, Semel Institute for Neuroscience and Human Behavior at UCLA, Los Angeles CA 90024, USA.,Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA 90024, USA
| | - Andrew Wilson
- TMS Clinical and Research Program, Neuromodulation Division, Semel Institute for Neuroscience and Human Behavior at UCLA, Los Angeles CA 90024, USA.,Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA 90024, USA
| | - Aimee M Hunter
- TMS Clinical and Research Program, Neuromodulation Division, Semel Institute for Neuroscience and Human Behavior at UCLA, Los Angeles CA 90024, USA.,Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA 90024, USA
| | - Nikita Vince-Cruz
- TMS Clinical and Research Program, Neuromodulation Division, Semel Institute for Neuroscience and Human Behavior at UCLA, Los Angeles CA 90024, USA.,Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA 90024, USA
| | - David Krantz
- TMS Clinical and Research Program, Neuromodulation Division, Semel Institute for Neuroscience and Human Behavior at UCLA, Los Angeles CA 90024, USA.,Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA 90024, USA
| | - Jennifer Levitt
- TMS Clinical and Research Program, Neuromodulation Division, Semel Institute for Neuroscience and Human Behavior at UCLA, Los Angeles CA 90024, USA.,Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA 90024, USA
| | - Michael J Minzenberg
- TMS Clinical and Research Program, Neuromodulation Division, Semel Institute for Neuroscience and Human Behavior at UCLA, Los Angeles CA 90024, USA.,Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA 90024, USA
| | - Nathaniel Ginder
- TMS Clinical and Research Program, Neuromodulation Division, Semel Institute for Neuroscience and Human Behavior at UCLA, Los Angeles CA 90024, USA.,Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA 90024, USA
| | - Ian A Cook
- TMS Clinical and Research Program, Neuromodulation Division, Semel Institute for Neuroscience and Human Behavior at UCLA, Los Angeles CA 90024, USA.,Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA 90024, USA.,Department of Bioengineering, Henry Samueli School of Engineering and Applied Sciences at UCLA, Los Angeles, CA 90024, USA
| | - Andrew F Leuchter
- TMS Clinical and Research Program, Neuromodulation Division, Semel Institute for Neuroscience and Human Behavior at UCLA, Los Angeles CA 90024, USA.,Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA 90024, USA
| |
Collapse
|
13
|
Čukić M, López V, Pavón J. Classification of Depression Through Resting-State Electroencephalogram as a Novel Practice in Psychiatry: Review. J Med Internet Res 2020; 22:e19548. [PMID: 33141088 PMCID: PMC7671839 DOI: 10.2196/19548] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 07/19/2020] [Accepted: 09/04/2020] [Indexed: 12/28/2022] Open
Abstract
Background Machine learning applications in health care have increased considerably in the recent past, and this review focuses on an important application in psychiatry related to the detection of depression. Since the advent of computational psychiatry, research based on functional magnetic resonance imaging has yielded remarkable results, but these tools tend to be too expensive for everyday clinical use. Objective This review focuses on an affordable data-driven approach based on electroencephalographic recordings. Web-based applications via public or private cloud-based platforms would be a logical next step. We aim to compare several different approaches to the detection of depression from electroencephalographic recordings using various features and machine learning models. Methods To detect depression, we reviewed published detection studies based on resting-state electroencephalogram with final machine learning, and to predict therapy outcomes, we reviewed a set of interventional studies using some form of stimulation in their methodology. Results We reviewed 14 detection studies and 12 interventional studies published between 2008 and 2019. As direct comparison was not possible due to the large diversity of theoretical approaches and methods used, we compared them based on the steps in analysis and accuracies yielded. In addition, we compared possible drawbacks in terms of sample size, feature extraction, feature selection, classification, internal and external validation, and possible unwarranted optimism and reproducibility. In addition, we suggested desirable practices to avoid misinterpretation of results and optimism. Conclusions This review shows the need for larger data sets and more systematic procedures to improve the use of the solution for clinical diagnostics. Therefore, regulation of the pipeline and standard requirements for methodology used should become mandatory to increase the reliability and accuracy of the complete methodology for it to be translated to modern psychiatry.
Collapse
Affiliation(s)
- Milena Čukić
- HealthInc 3EGA, Amsterdam Health and Technology Institute, Amsterdam, Netherlands
| | - Victoria López
- Instituto de Tecnología del Conocimiento, Institute of Knowledge Technology, Universidad Complutense Madrid, Ciudad Universitaria s/n, 28040, Madrid, Spain
| | - Juan Pavón
- Instituto de Tecnología del Conocimiento, Institute of Knowledge Technology, Universidad Complutense Madrid, Ciudad Universitaria s/n, 28040, Madrid, Spain
| |
Collapse
|
14
|
Čukić M, Stokić M, Radenković S, Ljubisavljević M, Simić S, Savić D. Nonlinear analysis of EEG complexity in episode and remission phase of recurrent depression. Int J Methods Psychiatr Res 2020; 29:e1816. [PMID: 31820528 PMCID: PMC7301286 DOI: 10.1002/mpr.1816] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Revised: 11/19/2019] [Accepted: 11/25/2019] [Indexed: 01/28/2023] Open
Abstract
OBJECTIVES Biomarkers of major depressive disorder (MDD), its phases and forms have long been sought. Objectives were to examine whether the complexity of EEG activity, measured by Higuchi's fractal dimension (HFD) and sample entropy (SampEn), differs between healthy subjects, patients in remission, and in episode phase of the recurrent depression and whether the changes are differentially distributed between hemispheres and cortical regions. METHODS Resting state EEG with eyes closed was recorded from 22 patients suffering from recurrent depression (11 in remission, 11 in the episode), and 20 age and sex-matched healthy control subjects. Artifact-free EEG epochs were analyzed by in-house developed programs running HFD and SampEn algorithms. RESULTS Depressed patients had higher HFD and SampEn complexity compared to healthy subjects. The complexity was higher in patients who were in remission than in those in the acute episode. Altered complexity was present in the frontal and centro-parietal regions when compared to control group. The complexity in frontal and parietal regions differed between the two phases of depressive disorder. CONCLUSIONS Complexity measures of EEG distinguish between the healthy controls, patients in remission and episode. Further studies are needed to establish whether these measures carry a potential to aid clinically relevant decisions about depression.
Collapse
Affiliation(s)
- Milena Čukić
- Department of General Physiology and Biophysics, School of Biology, University of Belgrade, Belgrade, Serbia
| | - Miodrag Stokić
- Cognitive Neuroscience Department, Life Activities Advancement Center, Belgrade, Serbia
| | | | - Miloš Ljubisavljević
- Department of Physiology, College of Medicine and Health Sciences, UAE University, Al Ain, United Arab Emirates
| | - Slobodan Simić
- Department for Forensic Psychiatry, Institute for Mental Health, Belgrade, Serbia
| | - Danka Savić
- Laboratory of Theoretical and Condensed Matter Physics 020/2, Vinča Institute, University of Belgrade, Belgrade, Serbia
| |
Collapse
|
15
|
Vlcek P, Bares M, Novak T, Brunovsky M. Baseline Difference in Quantitative Electroencephalography Variables Between Responders and Non-Responders to Low-Frequency Repetitive Transcranial Magnetic Stimulation in Depression. Front Psychiatry 2020; 11:83. [PMID: 32174854 PMCID: PMC7057228 DOI: 10.3389/fpsyt.2020.00083] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 02/03/2020] [Indexed: 12/13/2022] Open
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for depressive disorder, with outcomes approaching 45-55% response and 30-40% remission. Eligible predictors of treatment outcome, however, are still lacking. Few studies have investigated quantitative electroencephalography (QEEG) parameters as predictors of rTMS treatment outcome and none of them have addressed the source localization techniques to predict the response to low-frequency rTMS (LF rTMS). We investigated electrophysiological differences based on scalp EEG data and inverse solution method, exact low-resolution brain electromagnetic tomography (eLORETA), between responders and non-responders to LF rTMS in resting brain activity recorded prior to the treatment. Twenty-five unmedicated depressive patients (mean age of 45.7 years, 20 females) received a 4-week treatment of LF rTMS (1 Hz; 20 sessions per 600 pulses; 100% of the motor threshold) over the right dorsolateral prefrontal cortex. Comparisons between responders (≥50% reduction in Montgomery-Åsberg Depression Rating Scale score) and non-responders were made at baseline for measures of eLORETA current density, spectral absolute power, and inter-hemispheric and intra-hemispheric EEG asymmetry. Responders were found to have lower current source densities in the alpha-2 and beta-1 frequency bands bilaterally (with predominance on the left side) in the inferior, medial, and middle frontal gyrus, precentral gyrus, cingulate gyrus, anterior cingulate, and insula. The most pronounced difference was found in the left middle frontal gyrus for alpha-2 and beta-1 bands (p < 0.05). Using a spectral absolute power analysis, we found a negative correlation between the absolute power in beta and theta frequency bands on the left frontal electrode F7 and the change in depressive symptomatology. None of the selected asymmetries significantly differentiated responders from non-responders in any frequency band. Pre-treatment reduction of alpha-2 and beta-1 sources, but not QEEG asymmetry, was found in patients with major depressive disorder who responded to LF rTMS treatment. Prospective trials with larger groups of subjects are needed to further validate these findings.
Collapse
Affiliation(s)
- Premysl Vlcek
- National Institute of Mental Health, Klecany, Czechia.,Third Faculty of Medicine, Charles University, Prague, Czechia
| | - Martin Bares
- National Institute of Mental Health, Klecany, Czechia.,Third Faculty of Medicine, Charles University, Prague, Czechia
| | - Tomas Novak
- National Institute of Mental Health, Klecany, Czechia.,Third Faculty of Medicine, Charles University, Prague, Czechia
| | - Martin Brunovsky
- National Institute of Mental Health, Klecany, Czechia.,Third Faculty of Medicine, Charles University, Prague, Czechia
| |
Collapse
|
16
|
Hasanzadeh F, Mohebbi M, Rostami R. Prediction of rTMS treatment response in major depressive disorder using machine learning techniques and nonlinear features of EEG signal. J Affect Disord 2019; 256:132-142. [PMID: 31176185 DOI: 10.1016/j.jad.2019.05.070] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 05/15/2019] [Accepted: 05/28/2019] [Indexed: 12/18/2022]
Abstract
BACKGROUND Prediction of therapeutic outcome of repetitive transcranial magnetic stimulation (rTMS) treatment is an important purpose that eliminates financial and psychological consequences of applying inefficient therapy. To achieve this goal we proposed a method based on machine learning to classify responders (R) and non- responders (NR) to rTMS treatment for major depression disorder (MDD) patients. METHODS 19 electrodes resting state EEG was recorded from 46 MDD patients before treatment. Then patients underwent 7 weeks of rTMS, and 23 of them responded to treatment. Features extracted from EEG include Lempel-Ziv complexity (LZC), Katz fractal dimension (KFD), correlation dimension (CD), the power spectral density, features based on bispectrum, frontal and prefrontal cordance and combination of them. The most relevant features were selected by the minimal-redundancy-maximal-relevance (mRMR) feature selection algorithm. For classifying two groups of R and NR, k-nearest neighbors (KNN) were applied. The performance of the proposed method was evaluated by leave-1-out cross-validation. For further study, the capability of features in differentiating R and NR was investigated by a statistical test. RESULTS Effective EEG features for prediction of rTMS treatment response were found. EEG beta power, the sum of bispectrum diagonal elements in delta and beta bands and CD were the most discriminative features. Power of beta classified R and NR with the high performance of 91.3% accuracy, 91.3% specificity, and 91.3% sensitivity. LIMITATIONS Lack of large sample size restricted our method for using in clinical applications. CONCLUSION This considerable high accuracy indicates that our proposed method with power and some of the nonlinear and bispectral features can lead to promising results in predicting treatment outcome of rTMS for MDD patients only by one session pretreatment EEG recording.
Collapse
Affiliation(s)
- Fatemeh Hasanzadeh
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran
| | - Maryam Mohebbi
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran.
| | - Reza Rostami
- Department of Psychology, University of Tehran, Tehran, Iran
| |
Collapse
|
17
|
Nishida K, Koshikawa Y, Morishima Y, Yoshimura M, Katsura K, Ueda S, Ikeda S, Ishii R, Pascual-Marqui R, Kinoshita T. Pre-stimulus Brain Activity Is Associated With State-Anxiety Changes During Single-Session Transcranial Direct Current Stimulation. Front Hum Neurosci 2019; 13:266. [PMID: 31440149 PMCID: PMC6694795 DOI: 10.3389/fnhum.2019.00266] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Accepted: 07/16/2019] [Indexed: 12/15/2022] Open
Abstract
Transcranial direct current stimulation is a promising neuromodulation method for treating depression. However, compared with pharmacological treatment, previous studies have reported that a relatively limited proportion of patients respond to tDCS treatment. In addition, the neurophysiological mechanisms underlying tDCS treatment remain unclear, making it difficult to identify response predictors for tDCS treatment based on neurophysiological function. Because treatment effects are achieved by repetitive application of tDCS, studying the immediate effects of tDCS in depressive patients could extend understanding of its treatment mechanisms. However, immediate changes in a single session of tDCS are not well documented. Thus, in the current study, we focused on the immediate impact of tDCS and its association with pre-stimulus brain activity. To address this question, we applied anodal tDCS to the left dorsolateral prefrontal cortex (DLPFC) or dorsomedial prefrontal cortex (DMPFC) in 14 patients with major depressive disorder (MDD) and 19 healthy controls (HCs), at an intensity of 1.0 mA for 20 min in a single session. To evaluate anxiety, the state trait anxiety inventory was completed before and after tDCS. We recorded resting electroencephalography before tDCS, and calculated electrical neuronal activity in the theta and alpha frequency bands using standardized low-resolution electromagnetic tomography. We found that, during application of left DLPFC tDCS to patients with MDD, the anxiety reduction effect of tDCS was related to higher baseline theta-band activity in the rostral anterior cingulate cortex (rACC) and no medication with benzodiazepine used as hypnotic. For DMPFC stimulation in MDD, the anxiety reduction effect was associated with lower baseline alpha-band activity in the left inferior parietal lobule. In contrast, in HCs, the anxiety reduction effect was associated with higher baseline alpha activity in the precuneus during DMPFC stimulation. The current results suggest that the association between pre-tDCS brain activity and the anxiety reduction effect of tDCS depends on psychopathology (depressed or non-depressed) as well as the site of stimulation (DMPFC or left DLPFC) and insomnia. Furthermore, the results suggest that tDCS response might be associated with baseline resting state electrophysiological neural activity.
Collapse
Affiliation(s)
- Keiichiro Nishida
- Department of Neuropsychiatry, Kansai Medical University, Osaka, Japan
| | - Yosuke Koshikawa
- Department of Neuropsychiatry, Kansai Medical University, Osaka, Japan
| | - Yosuke Morishima
- Division of Systems Neuroscience of Psychopathology, Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | | | - Koji Katsura
- Department of Neuropsychiatry, Kansai Medical University, Osaka, Japan
| | - Satsuki Ueda
- Department of Neuropsychiatry, Kansai Medical University, Osaka, Japan
| | - Shunichiro Ikeda
- Department of Neuropsychiatry, Kansai Medical University, Osaka, Japan
| | - Ryouhei Ishii
- Osaka Prefecture University Graduate School of Comprehensive Rehabilitation, Osaka University, Osaka, Japan
| | - Roberto Pascual-Marqui
- Department of Neuropsychiatry, Kansai Medical University, Osaka, Japan.,The KEY Institute for Brain-Mind Research, University of Zurich, Zurich, Switzerland
| | | |
Collapse
|
18
|
Zandvakili A, Philip NS, Jones SR, Tyrka AR, Greenberg BD, Carpenter LL. Use of machine learning in predicting clinical response to transcranial magnetic stimulation in comorbid posttraumatic stress disorder and major depression: A resting state electroencephalography study. J Affect Disord 2019; 252:47-54. [PMID: 30978624 PMCID: PMC6520189 DOI: 10.1016/j.jad.2019.03.077] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Revised: 03/22/2019] [Accepted: 03/25/2019] [Indexed: 10/27/2022]
Abstract
BACKGROUND Repetitive transcranial magnetic stimulation (TMS) is clinically effective for major depressive disorder (MDD) and investigational for other conditions including posttraumatic stress disorder (PTSD). Understanding the mechanisms of TMS action and developing biomarkers predicting response remain important goals. We applied a combination of machine learning and electroencephalography (EEG), testing whether machine learning analysis of EEG coherence would (1) predict clinical outcomes in individuals with comorbid MDD and PTSD, and (2) determine whether an individual had received a TMS course. METHODS We collected resting-state 8-channel EEG before and after TMS (5 Hz to the left dorsolateral prefrontal cortex). We used Lasso regression and Support Vector Machine (SVM) to test the hypothesis that baseline EEG coherence predicted the outcome and to assess if EEG coherence changed after TMS. RESULTS In our sample, clinical response to TMS were predictable based on pretreatment EEG coherence (n = 29). After treatment, 13/29 had more than 50% reduction in MDD self-report score 12/29 had more than 50% reduction in PTSD self-report score. For MDD, area under roc curve was for MDD was 0.83 (95% confidence interval 0.69-0.94) and for PTSD was 0.71 (95% confidence interval 0.54-0.87). SVM classifier was able to accurately assign EEG recordings to pre- and post-TMS treatment. The accuracy for Alpha, Beta, Theta and Delta bands was 75.4 ± 1.5%, 77.4 ± 1.4%, 73.8 ± 1.5%, and 78.6 ± 1.4%, respectively, all significantly better than chance (50%, p < 0.001). LIMITATION Limitations of this work include lack of sham condition, modest sample size, and a sparse electrode array. Despite these methodological limitations, we found validated and clinically meaningful results. CONCLUSIONS Machine learning successfully predicted non-response to TMS with high specificity, and identified pre- and post-TMS status using EEG coherence. This approach may provide mechanistic insights and may also become a clinically useful screening tool for TMS candidates.
Collapse
Affiliation(s)
- Amin Zandvakili
- Butler Hospital, Providence, RI 02906, United States; Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI 02906, United States; Center for Neurorestoration and Neurotechnology, Providence VA Medical Center, Providence, RI 02908, United States.
| | - Noah S. Philip
- Butler Hospital, Providence, RI 02906,Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI 02906,Center for Neurorestoration and Neurotechnology, Providence VA Medical Center, Providence, RI 02908
| | - Stephanie R. Jones
- Center for Neurorestoration and Neurotechnology, Providence VA Medical Center, Providence, RI 02908,Department of Neuroscience, Brown University, Providence, RI 02906
| | - Audrey R. Tyrka
- Butler Hospital, Providence, RI 02906,Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI 02906
| | - Benjamin D. Greenberg
- Butler Hospital, Providence, RI 02906,Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI 02906,Center for Neurorestoration and Neurotechnology, Providence VA Medical Center, Providence, RI 02908
| | - Linda L. Carpenter
- Butler Hospital, Providence, RI 02906,Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI 02906
| |
Collapse
|
19
|
Zuchowicz U, Wozniak-Kwasniewska A, Szekely D, Olejarczyk E, David O. EEG Phase Synchronization in Persons With Depression Subjected to Transcranial Magnetic Stimulation. Front Neurosci 2019; 12:1037. [PMID: 30692906 PMCID: PMC6340356 DOI: 10.3389/fnins.2018.01037] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2018] [Accepted: 12/21/2018] [Indexed: 12/26/2022] Open
Abstract
Aim: The main objective of this work was to study the impact of repetitive Transcranial Magnetic Stimulation (rTMS) treatment on brain activity in 8 patients with major depressive disorder (MDD) and 10 patients with bipolar disorder (BP). Changes due to rTMS stimulation of the left dorsolateral prefrontal cortex (DLPFC) were investigated considering separately responders and non-responders to therapy in each of both groups. The aim of the research is to determine whether non-responders differ from responders suffered from both diseases, as well as if any change occurred due to rTMS across consecutive rTMS sessions. Methods: The graph-theory-based connectivity analysis of non-linearity measure of phase interdependencies—Phase Locking Value (PLV)—was examined from EEG data. The approximately 15-min EEG recordings from each of participants were recorded before and after 1st, 10th, and 20th session, respectively. PLV calculated from data was analyzed using principal graph theory indices (strength and degree) within five physiological frequency bands and in individual channels separately. The impact of rTMS on the EEG connectivity in every group of patients evaluated by PLV was assessed. Results: Each of four groups reacted differently to rTMS treatment. The strength and degree of PLV increased in gamma band in both groups of responders. Moreover, an increase of indices in beta band for BP-responders was observed. While, in MDD-non-responders the indices decreased in gamma band and increased in beta band. Moreover, the index strength was lower in alpha band for BP- non-responders. The rTMS stimulation caused topographically specific changes, i.e., the increase of the activity in the left DLPFC as well as in other brain regions such as right parieto-occipital areas. Conclusions: The analysis of PLV allowed for evaluation of the rTMS impact on the EEG activity in each group of patients. The changes of PLV under stimulation might be a good indicator of response to depression treatment permitting to improve the effectiveness of therapy.
Collapse
Affiliation(s)
- Urszula Zuchowicz
- Department of Automatics and Biomedical Engineering, AGH University of Science and Technology, Cracow, Poland
| | - Agata Wozniak-Kwasniewska
- Inserm, U1216, Grenoble, France.,Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, Grenoble, France
| | - David Szekely
- Inserm, U1216, Grenoble, France.,Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, Grenoble, France.,Centre Hospitalier Univ. Grenoble Alpes, Service de Psychiatrie, Grenoble, France
| | - Elzbieta Olejarczyk
- Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences, Warsaw, Poland
| | - Olivier David
- Inserm, U1216, Grenoble, France.,Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, Grenoble, France
| |
Collapse
|
20
|
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] [MESH Headings] [Grants] [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.
Collapse
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
| | - 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
| |
Collapse
|
21
|
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.
Collapse
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
| |
Collapse
|
22
|
Association of the loudness dependence of auditory evoked potentials with clinical changes to repetitive transcranial magnetic stimulation in patients with depression. J Affect Disord 2018; 238:451-457. [PMID: 29920440 DOI: 10.1016/j.jad.2018.05.023] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Revised: 04/09/2018] [Accepted: 05/17/2018] [Indexed: 11/20/2022]
Abstract
BACKGROUND Although the application of repetitive transcranial magnetic stimulation (rTMS) for treatment of depression has been well investigated, there are few biological predictors of clinical outcomes to rTMS treatment. Previous studies have suggested that the loudness dependence of auditory evoked potentials (LDAEP) can be used as a predictor of response to antidepressant treatment. However, little is known about the association between rTMS and LDAEP. The present study aimed to investigate whether baseline LDAEP is associated with clinical changes to rTMS treatment in patients with major depressive disorder (MDD), and to explore the effect of high-frequency rTMS on LDAEP. METHODS Thirty patients were randomized to receive 15 sessions of active (n = 15) or sham rTMS (n = 15). LDAEP and clinical measures of depression were assessed before and after 10 Hz rTMS treatment for 15 days. RESULTS Baseline LDAEP was associated with changes in scores on the Hamilton Rating Scale for Depression. There were no significant effects of rTMS on LDAEP. Patients with high LDAEP exhibited more favorable clinical changes than those with low LDAEP following treatment with rTMS. LIMITATIONS The sample was relatively small, and the participants were not divided into responders and non-responders group due to small sample. An influence of medication has not been controlled. CONCLUSIONS Our findings suggest that high baseline LDAEP may be associated with favorable clinical changes to rTMS treatment in patients with MDD. Further studies are required to replicate and validate the potential use of LDAEP as a predictor of clinical changes to rTMS treatment.
Collapse
|
23
|
Kazemi R, Rostami R, Khomami S, Baghdadi G, Rezaei M, Hata M, Aoki Y, Ishii R, Iwase M, Fitzgerald PB. Bilateral Transcranial Magnetic Stimulation on DLPFC Changes Resting State Networks and Cognitive Function in Patients With Bipolar Depression. Front Hum Neurosci 2018; 12:356. [PMID: 30233346 PMCID: PMC6135217 DOI: 10.3389/fnhum.2018.00356] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Accepted: 08/20/2018] [Indexed: 01/13/2023] Open
Abstract
Introduction: Bipolar patients have abnormalities in cognitive functions and emotional processing. Two resting state networks (RSNs), the default mode network (DMN) and the sensorimotor network (SMN), play a decisive role in these two functions. Dorsolateral prefrontal cortex (DLPFC) is one of the main areas in the central executive network (CEN), which is linked to the activities of each of the two networks. Studies have found DLPFC abnormalities in both hemispheres of patients with bipolar depression. We hypothesized that the bilateral repetitive transcranial magnetic stimulation (rTMS) of DLPFC would produce changes in the activity of both the SMN and DMN as well as relevant cognitive function in patients with bipolar depression that responded to treatment. Methods: 20 patients with bipolar depression underwent 10 sessions of 1 Hz rTMS on right DLPFC with subsequent 10 Hz rTMS on left DLPFC. Changes in electroencephalography resting networks between pre and post rTMS were evaluated utilizing low-resolution electromagnetic tomography (eLORETA). Depression symptom was assessed using the Beck Depression Inventory (BDI-II) and cognitive function was assessed by Verbal Fluency Test (VFT), Rey Auditory Verbal Learning Test (RAVLT), Stroop Test, and Wisconsin Card Sorting Test (WCST). Results: Responders to rTMS showed significantly lower DMN activity at baseline and a significant decrease in SMN connectivity after treatment. Non-responders did not significantly differ from the control group at the baseline and they showed higher activity in the SMN, visual network, and visual perception network compared to control group following treatment. Bilateral rTMS resulted in significant changes in the executive functions, verbal memory, and depression symptoms. No significant changes were observed in selective attention and verbal fluency. Conclusion: Bilateral stimulation of DLPFC, as the main node of CEN, results in changes in the activity of the SMN and consequently improves verbal memory and executive functions in patients with bipolar depression.
Collapse
Affiliation(s)
- Reza Kazemi
- Cognitive Lab, Department of Psychology, University of Tehran, Tehran, Iran.,Atieh Clinical Neuroscience Center, Tehran, Iran
| | - Reza Rostami
- Department of Psychology, University of Tehran, Tehran, Iran
| | - Sanaz Khomami
- Cognitive Lab, Department of Psychology, University of Tehran, Tehran, Iran
| | - Golnaz Baghdadi
- Department of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran
| | - Mehdi Rezaei
- Behavioral Sciences Research Center, Life Style Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Masahiro Hata
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Yasunori Aoki
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Ryouhei Ishii
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Masao Iwase
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Paul B Fitzgerald
- Epworth Healthcare, Epworth Clinic Camberwell, Victoria Australia and Monash Alfred Psychiatry Research Centre, Central Clinical School, Monash University, Melbourne, VIC, Australia
| |
Collapse
|
24
|
Garnaat SL, Yuan S, Wang H, Philip NS, Carpenter LL. Updates on Transcranial Magnetic Stimulation Therapy for Major Depressive Disorder. Psychiatr Clin North Am 2018; 41:419-431. [PMID: 30098655 PMCID: PMC6979370 DOI: 10.1016/j.psc.2018.04.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Transcranial magnetic stimulation has emerged as a treatment option for treatment-resistant depression. While existing data largely support efficacy of transcranial magnetic stimulation for major depressive disorder, ongoing research aims to optimize treatment parameters and identify biomarkers of treatment response. In this article, the authors describe data from controlled trials and ongoing efforts to enhance transcranial magnetic stimulation outcomes for major depressive disorder. Findings from preliminary research aimed at identifying neuroimaging and neurophysiological biomarkers of transcranial magnetic stimulation effects are discussed.
Collapse
Affiliation(s)
- Sarah L. Garnaat
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA,Butler Hospital, Providence, RI, USA
| | - Shiwen Yuan
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA
| | - Haizhi Wang
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA
| | - Noah S. Philip
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA,Butler Hospital, Providence, RI, USA,Providence VA Medical Center, Providence, RI, USA
| | - Linda L. Carpenter
- Department of Psychiatry and Human Behavior, Alpert Medical School of Brown University, Providence, RI, USA,Butler Hospital, Providence, RI, USA
| |
Collapse
|
25
|
Cerquera A, Vollebregt MA, Arns M. Nonlinear Recurrent Dynamics and Long-Term Nonstationarities in EEG Alpha Cortical Activity: Implications for Choosing Adequate Segment Length in Nonlinear EEG Analyses. Clin EEG Neurosci 2018; 49:71-78. [PMID: 28805079 DOI: 10.1177/1550059417724695] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Nonlinear analysis of EEG recordings allows detection of characteristics that would probably be neglected by linear methods. This study aimed to determine a suitable epoch length for nonlinear analysis of EEG data based on its recurrence rate in EEG alpha activity (electrodes Fz, Oz, and Pz) from 28 healthy and 64 major depressive disorder subjects. Two nonlinear metrics, Lempel-Ziv complexity and scaling index, were applied in sliding windows of 20 seconds shifted every 1 second and in nonoverlapping windows of 1 minute. In addition, linear spectral analysis was carried out for comparison with the nonlinear results. The analysis with sliding windows showed that the cortical dynamics underlying alpha activity had a recurrence period of around 40 seconds in both groups. In the analysis with nonoverlapping windows, long-term nonstationarities entailed changes over time in the nonlinear dynamics that became significantly different between epochs across time, which was not detected with the linear spectral analysis. Findings suggest that epoch lengths shorter than 40 seconds neglect information in EEG nonlinear studies. In turn, linear analysis did not detect characteristics from long-term nonstationarities in EEG alpha waves of control subjects and patients with major depressive disorder patients. We recommend that application of nonlinear metrics in EEG time series, particularly of alpha activity, should be carried out with epochs around 60 seconds. In addition, this study aimed to demonstrate that long-term nonlinearities are inherent to the cortical brain dynamics regardless of the presence or absence of a mental disorder.
Collapse
Affiliation(s)
- Alexander Cerquera
- 1 School of Electronics and Biomedical Engineering, Research Group Complex Systems, Universidad Antonio Nariño, Bogota, Colombia.,2 J. Crayton Pruitt Family Department of Biomedical Engineering, Brain Mapping Lab, University of Florida, Gainesville, FL, USA
| | - Madelon A Vollebregt
- 3 Research Institute Brainclinics, Nijmegen, The Netherlands.,4 Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Department of Cognitive Neuroscience, Nijmegen, The Netherlands
| | - Martijn Arns
- 3 Research Institute Brainclinics, Nijmegen, The Netherlands.,5 Department of Experimental Psychology, Utrecht University, Utrecht, The Netherlands
| |
Collapse
|
26
|
Cerquera A, Gjini K, Bowyer SM, Boutros N. Comparing EEG Nonlinearity in Deficit and Nondeficit Schizophrenia Patients: Preliminary Data. Clin EEG Neurosci 2017; 48:376-382. [PMID: 28618836 DOI: 10.1177/1550059417715388] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Electroencephalogram (EEG) contains valuable information obtained noninvasively that can be used for assessment of brain's processing capacity of patients with psychiatric disorders. The purpose of the present work was to evaluate possible differences in EEG complexity between deficit (DS) and nondeficit (NDS) subtypes of schizophrenia as a reflection of the cognitive processing capacities in these groups. A particular nonlinear metric known as Lempel-Ziv complexity (LZC) was used as a computational tool in order to determine the randomness in EEG alpha band time series from 3 groups (deficit schizophrenia [n = 9], nondeficit schizophrenia [n = 10], and healthy controls [n = 10]) according to time series randomness. There was a significant difference in frontal EEG complexity between the DS and NDS subgroups ( p = .013), with DS group showing less complexity. A significant positive correlation was found between LZC values and Positive and Negative Syndrome Scale (PANSS) general psychopathology scores (ie, larger frontal EEG complexity correlated with more severe psychopathology), explained partially by the emotional component subscore of the PANSS. These findings suggest that cognitive processing occurring in the frontal networks in DS is less complex compared to NDS patients as reflected by EEG complexity measures. The data also suggest that there may be a relationship between the degree of emotionality and the complexity of the frontal EEG signal.
Collapse
Affiliation(s)
- Alexander Cerquera
- 1 Facultad de Ingeniería Electrónica y Biomédica-Research Group Complex Systems, Universidad Antonio Nariño, Bogota, Colombia
| | - Klevest Gjini
- 2 Division of Neurosurgery, Seton Brain and Spine Institute, Austin, TX, USA
| | - Susan M Bowyer
- 3 Department of Neurology, Henry Ford Hospital and Wayne State University, Detroit, MI, USA
| | - Nash Boutros
- 4 Department of Psychiatry, University of Missouri-Kansas City, Kansas City, USA
| |
Collapse
|
27
|
Jaworska N, Wang H, Smith DM, Blier P, Knott V, Protzner AB. Pre-treatment EEG signal variability is associated with treatment success in depression. NEUROIMAGE-CLINICAL 2017; 17:368-377. [PMID: 29159049 PMCID: PMC5683802 DOI: 10.1016/j.nicl.2017.10.035] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/25/2017] [Revised: 10/27/2017] [Accepted: 10/30/2017] [Indexed: 11/20/2022]
Abstract
Background Previous work suggests that major depressive disorder (MDD) is associated with disturbances in global connectivity among brain regions, as well as local connectivity within regions. However, the relative importance of these global versus local changes for successful antidepressant treatment is unknown. We used multiscale entropy (MSE), a measure of brain signal variability, to examine how the propensity for local (fine scale MSE) versus global (coarse scale MSE) neural processing measured prior to antidepressant treatment is related to subsequent treatment response. Methods We collected resting-state EEG activity during eyes-open and closed conditions from unmedicated individuals with MDD prior to antidepressant pharmacotherapy (N = 36) as well as from non-depressed controls (N = 36). Treatment response was assessed after 12 weeks of treatment using the Montgomery-Åsberg Depression Rating Scale (MADRS), at which time participants with MDD were characterized as either responders (≥ 50% MADRS decrease) or non-responders. MSE was calculated from baseline EEG, and compared between controls, future treatment responders and non-responders. Putative interactions with the well-documented age effect on signal variability (increased reliance on local neural communication with increasing age, indexed by greater finer-scale variability) were assessed. Results Only in responders, we found that reduced MSE at fine temporal scales (especially fronto-centrally) and increased MSE diffusely at coarser temporal scales was related to the magnitude of the antidepressant response. In controls and MDD non-responders, but not MDD responders, there was an increase in MSE with age at fine temporal scales and a decrease in MSE with age at coarse temporal scales. Conclusion Our results suggest that an increased propensity toward global processing, indexed by greater MSE at coarser timescales, at baseline appears to facilitate eventual antidepressant treatment response. We measured resting-state EEG prior to antidepressant pharmacotherapy. We examined global vs. local processing in relation to antidepressant response. Greater response was linked with increased global processing. Age-related decreases in global communication were absent in future responders. Baseline brain dynamics in those who are/are not responsive to antidepressants differ.
Collapse
Affiliation(s)
- Natalia Jaworska
- Institute of Mental Health Research, Affiliated With the University of Ottawa, ON, Canada
| | - Hongye Wang
- Department of Psychology, University of Calgary, AB, Canada
| | - Dylan M Smith
- Center for Studies in Behavioral Neurobiology, Concordia University, Montreal, QC, Canada
| | - Pierre Blier
- Institute of Mental Health Research, Affiliated With the University of Ottawa, ON, Canada
| | - Verner Knott
- Institute of Mental Health Research, Affiliated With the University of Ottawa, ON, Canada
| | - Andrea B Protzner
- Department of Psychology, University of Calgary, AB, Canada; Hotchkiss Brain Institute, University of Calgary, AB, Canada.
| |
Collapse
|
28
|
Kobayashi B, Cook IA, Hunter AM, Minzenberg MJ, Krantz DE, Leuchter AF. Can neurophysiologic measures serve as biomarkers for the efficacy of repetitive transcranial magnetic stimulation treatment of major depressive disorder? Int Rev Psychiatry 2017; 29:98-114. [PMID: 28362541 DOI: 10.1080/09540261.2017.1297697] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for Major Depressive Disorder (MDD). There are clinical data that support the efficacy of many different approaches to rTMS treatment, and it remains unclear what combination of stimulation parameters is optimal to relieve depressive symptoms. Because of the costs and complexity of studies that would be necessary to explore and compare the large number of combinations of rTMS treatment parameters, it would be useful to establish reliable surrogate biomarkers of treatment efficacy that could be used to compare different approaches to treatment. This study reviews the evidence that neurophysiologic measures of cortical excitability could be used as biomarkers for screening different rTMS treatment paradigms. It examines evidence that: (1) changes in excitability are related to the mechanism of action of rTMS; (2) rTMS has consistent effects on measures of excitability that could constitute reliable biomarkers; and (3) changes in excitability are related to the outcomes of rTMS treatment of MDD. An increasing body of evidence indicates that these neurophysiologic measures have the potential to serve as reliable biomarkers for screening different approaches to rTMS treatment of MDD.
Collapse
Affiliation(s)
- Brian Kobayashi
- a David Geffen School of Medicine , University of California Los Angeles , Los Angeles , CA , USA.,b Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine , University of California Los Angeles , Los Angeles , CA , USA.,c Neuromodulation Division , Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles , Los Angeles , CA , USA
| | - Ian A Cook
- a David Geffen School of Medicine , University of California Los Angeles , Los Angeles , CA , USA.,b Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine , University of California Los Angeles , Los Angeles , CA , USA.,c Neuromodulation Division , Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles , Los Angeles , CA , USA.,d Department of Bioengineering , University of California Los Angeles , Los Angeles , CA , USA
| | - Aimee M Hunter
- a David Geffen School of Medicine , University of California Los Angeles , Los Angeles , CA , USA.,b Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine , University of California Los Angeles , Los Angeles , CA , USA.,c Neuromodulation Division , Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles , Los Angeles , CA , USA
| | - Michael J Minzenberg
- a David Geffen School of Medicine , University of California Los Angeles , Los Angeles , CA , USA.,b Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine , University of California Los Angeles , Los Angeles , CA , USA.,c Neuromodulation Division , Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles , Los Angeles , CA , USA
| | - David E Krantz
- a David Geffen School of Medicine , University of California Los Angeles , Los Angeles , CA , USA.,b Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine , University of California Los Angeles , Los Angeles , CA , USA.,c Neuromodulation Division , Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles , Los Angeles , CA , USA
| | - Andrew F Leuchter
- a David Geffen School of Medicine , University of California Los Angeles , Los Angeles , CA , USA.,b Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine , University of California Los Angeles , Los Angeles , CA , USA.,c Neuromodulation Division , Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles , Los Angeles , CA , USA
| |
Collapse
|
29
|
Beuzon G, Timour Q, Saoud M. Predictors of response to repetitive transcranial magnetic stimulation (rTMS) in the treatment of major depressive disorder. Encephale 2016; 43:3-9. [PMID: 28034451 DOI: 10.1016/j.encep.2016.11.002] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2016] [Revised: 06/28/2016] [Accepted: 11/16/2016] [Indexed: 01/01/2023]
Abstract
Repetitive transcranial magnetic stimulation (rTMS), based on the principle of electromagnetic induction, consists of applying series of magnetic impulses to the cerebral cortex so as to modulate neurone activity in a target zone. This technique, still experimental, could prove promising in the field of psychiatry, in particular for the treatment of major depressive disorder. It is important for the clinician to be able to assess the response potential of a given patient to rTMS, and this among other things requires relevant predictive factors to be available. This review of the literature aims to determine and analyse reported predictive factors for therapeutic response to rTMS treatment in major depressive disorder. Different parameters are studied, in particular age, the severity of the depressive episode, psychological dimensions, genetic factors, cerebral blood flows via cerebral imagery, and neuronavigation. The factors found to be associated with better therapeutic response were young age, low level of severity of the depressive episode, motor threshold intensity over 100%, more than 1000 stimulations per session, more than 10 days treatment, L/L genotype on the 5-HTTLPR transporter gene, C/C homozygosity on the promotor regions of the 5-HT1A receptor gene, Val/Val homozygosity on the BDNF gene, cordance analyses by EEG, and finally the accurate localisation provided by neuronavigation. The authors conclude that investigations in larger patient samples are required in the future, and that the work already achieved should provide lines of approach for the coming experimental studies.
Collapse
Affiliation(s)
- G Beuzon
- Pharmacologie médicale, EA 4312 neurocardiologie, physiopathologie des troubles du rythme cardiaque, hôpital Louis-Pradel, unité 50, 28, avenue du Doyen-Lépine, 69677 Bron, France.
| | - Q Timour
- Pharmacologie médicale, EA 4612 neurocardiologie, physiopathologie des troubles du rythme cardiaque hôpital Louis-Pradel, unité 50, 28, avenue du Doyen-Lépine, 69677 Bron, France.
| | - M Saoud
- Unité de recherche EA4615, PsyR, université Claude-Bernard-Lyon 1, 27-29, boulevard du 11-Novembre-1918, 69622 Lyon, France; Service de psychiatrie adultes liaison consultation, hospices civils de Lyon, 59, boulevard Pinel, 69500 Bron, France.
| |
Collapse
|
30
|
Abnormal EEG Complexity and Functional Connectivity of Brain in Patients with Acute Thalamic Ischemic Stroke. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2016; 2016:2582478. [PMID: 27403202 PMCID: PMC4923597 DOI: 10.1155/2016/2582478] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2016] [Revised: 03/26/2016] [Accepted: 04/10/2016] [Indexed: 11/28/2022]
Abstract
Ischemic thalamus stroke has become a serious cardiovascular and cerebral disease in recent years. To date the existing researches mostly concentrated on the power spectral density (PSD) in several frequency bands. In this paper, we investigated the nonlinear features of EEG and brain functional connectivity in patients with acute thalamic ischemic stroke and healthy subjects. Electroencephalography (EEG) in resting condition with eyes closed was recorded for 12 stroke patients and 11 healthy subjects as control group. Lempel-Ziv complexity (LZC), Sample Entropy (SampEn), and brain network using partial directed coherence (PDC) were calculated for feature extraction. Results showed that patients had increased mean LZC and SampEn than the controls, which implied the stroke group has higher EEG complexity. For the brain network, the stroke group displayed a trend of weaker cortical connectivity, which suggests a functional impairment of information transmission in cortical connections in stroke patients. These findings suggest that nonlinear analysis and brain network could provide essential information for better understanding the brain dysfunction in the stroke and assisting monitoring or prognostication of stroke evolution.
Collapse
|
31
|
Wade EC, Iosifescu DV. Using Electroencephalography for Treatment Guidance in Major Depressive Disorder. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2016; 1:411-422. [PMID: 29560870 DOI: 10.1016/j.bpsc.2016.06.002] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2016] [Revised: 05/06/2016] [Accepted: 06/01/2016] [Indexed: 01/12/2023]
Abstract
Given the high prevalence of treatment-resistant depression and the long delays in finding effective treatments via trial and error, valid biomarkers of treatment outcome with the ability to guide treatment selection represent one of the most important unmet needs in mood disorders. A large body of research has investigated, for this purpose, biomarkers derived from electroencephalography (EEG), using resting state EEG or evoked potentials. Most studies have focused on specific EEG features (or combinations thereof), whereas more recently machine-learning approaches have been used to define the EEG features with the best predictive abilities without a priori hypotheses. While reviewing these different approaches, we have focused on the predictor characteristics and the quality of the supporting evidence.
Collapse
Affiliation(s)
- Elizabeth C Wade
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Dan V Iosifescu
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York.
| |
Collapse
|
32
|
Olbrich S, Tränkner A, Surova G, Gevirtz R, Gordon E, Hegerl U, Arns M. CNS- and ANS-arousal predict response to antidepressant medication: Findings from the randomized iSPOT-D study. J Psychiatr Res 2016; 73:108-15. [PMID: 26714202 DOI: 10.1016/j.jpsychires.2015.12.001] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2015] [Revised: 12/04/2015] [Accepted: 12/04/2015] [Indexed: 11/26/2022]
Abstract
Arousal systems are one of the recently announced NIMH Research Domain Criteria to inform future diagnostics and treatment prediction. In major depressive disorder (MDD), altered central nervous system (CNS) wakefulness regulation and an increased sympathetic autonomic nervous system (ANS) activity have been identified as biomarkers with possible discriminative value for prediction of antidepressant treatment response. Therefore, the hypothesis of a more pronounced decline of CNS and ANS-arousal being predictive for a positive treatment outcome to selective-serotonin-reuptake-inhibitor (SSRI) treatment was derived from a small, independent exploratory dataset (N = 25) and replicated using data from the randomized international Study to Predict Optimized Treatment Response in Depression (iSPOT-D). There, 1008 MDD participants were randomized to either a SSRI (escitalopram or sertraline) or a serotonin-norepinephrine-reuptake-inhibitor (SNRI-venlafaxine) arm. Treatment response was established after eight weeks using the 17-item Hamilton Rating Scale for Depression. CNS-arousal (i.e. electroencephalogram-vigilance), ANS-arousal (heart rate) and their change across time were assessed during rest. Responders and remitters to SSRI treatment were characterized by a faster decline of CNS-arousal during rest whereas SNRI responders showed a significant increase of ANS-arousal. Furthermore, SSRI responders/remitters showed an association between ANS- and CNS-arousal regulation in comparison to non-responders/non-remitters while this was not the case for SNRI treatment arm. Since positive treatment outcome to SSRI and SNRI was linked to distinct CNS and ANS-arousal profiles, these predictive markers probably are not disorder specific alterations but reflect the responsiveness of the nervous system to specific drugs.
Collapse
Affiliation(s)
- Sebastian Olbrich
- Dept. of Psychiatry and Psychotherapy, University of Leipzig, Germany; Dept. of Psychiatry, Psychotherapy and Psychosomatic, University Zürich, Switzerland.
| | - Anja Tränkner
- Dept. of Psychiatry and Psychotherapy, University of Leipzig, Germany
| | - Galina Surova
- Dept. of Psychiatry and Psychotherapy, University of Leipzig, Germany
| | | | - Evian Gordon
- Brain Resource Ltd, Sydney, NSW, Australia; Brain Resource Ltd, San Francisco, CA, USA
| | - Ulrich Hegerl
- Dept. of Psychiatry and Psychotherapy, University of Leipzig, Germany
| | - Martijn Arns
- Dept. of Experimental Psychology, Utrecht University, Utrecht, The Netherlands; Research Institute Brainclinics, Nijmegen, The Netherlands
| |
Collapse
|
33
|
Silverstein WK, Noda Y, Barr MS, Vila-Rodriguez F, Rajji TK, Fitzgerald PB, Downar J, Mulsant BH, Vigod S, Daskalakis ZJ, Blumberger DM. NEUROBIOLOGICAL PREDICTORS OF RESPONSE TO DORSOLATERAL PREFRONTAL CORTEX REPETITIVE TRANSCRANIAL MAGNETIC STIMULATION IN DEPRESSION: A SYSTEMATIC REVIEW. Depress Anxiety 2015; 32:871-91. [PMID: 26382227 DOI: 10.1002/da.22424] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2015] [Revised: 08/19/2015] [Accepted: 08/24/2015] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND A significant proportion of patients with depression fail to respond to psychotherapy and standard pharmacotherapy, leading to treatment-resistant depression (TRD). Due to the significant prevalence of TRD, alternative therapies for depression have emerged as viable treatments in the armamentarium for this disorder. Repetitive transcranial magnetic stimulation (rTMS) is now being offered in clinical practice in broader numbers. Many studies have investigated various different neurobiological predictors of response of rTMS. However, a synthesis of this literature and an understanding of what biological targets predict response is lacking. This review aims to systematically synthesize the literature on the neurobiological predictors of rTMS in patients with depression. METHODS Medline (1996-2014), Embase (1980-2014), and PsycINFO (1806-2014) were searched under set terms. Two authors reviewed each article and came to consensus on the inclusion and exclusion criteria. All eligible studies were reviewed, duplicates were removed, and data were extracted individually. RESULTS The search identified 1,673 articles, 41 of which met both inclusion and exclusion criteria. Various biological factors at baseline appear to predict response to rTMS, including levels of certain molecular factors, blood flow in brain regions implicated in depression, electrophysiological findings, and specific genetic polymorphisms. CONCLUSIONS Significant methodological variability in rTMS treatment protocols limits the ability to generalize conclusions. However, response to treatment may be predicted by baseline frontal lobe blood flow, and presence of polymorphisms of the 5-hydroxytryptamine (5-HT) -1a gene, the LL genotype of the serotonin transporter linked polymorphic region (5-HTTLPR) gene, and Val/Val homozygotes of the brain-derived neurotrophic factor (BDNF) gene.
Collapse
Affiliation(s)
- William K Silverstein
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Yoshihiro Noda
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Mera S Barr
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.,Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Fidel Vila-Rodriguez
- Non-Invasive Neurostimulation Therapies Laboratory, Department of Psychiatry, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - Tarek K Rajji
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.,Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Paul B Fitzgerald
- Monash Alfred Psychiatry Research Centre, The Alfred and Monash University Central Clinical School, Melbourne, Victoria, Australia
| | - Jonathan Downar
- Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.,MRI-Guided rTMS Clinic, Department of Psychiatry, University Health Network, Toronto, Ontario, Canada
| | - Benoit H Mulsant
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.,Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Simone Vigod
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.,Reproductive Life Stages Program, Women's Mental Health Program, Women's College Hospital, Toronto, Ontario, Canada
| | - Zafiris J Daskalakis
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.,Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Daniel M Blumberger
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Ontario, Canada.,Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.,Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| |
Collapse
|
34
|
Erguzel TT, Ozekes S, Sayar GH, Tan O, Tarhan N. A hybrid artificial intelligence method to classify trichotillomania and obsessive compulsive disorder. Neurocomputing 2015. [DOI: 10.1016/j.neucom.2015.02.039] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
35
|
Erguzel TT, Ozekes S, Gultekin S, Tarhan N, Hizli Sayar G, Bayram A. Neural Network Based Response Prediction of rTMS in Major Depressive Disorder Using QEEG Cordance. Psychiatry Investig 2015; 12:61-5. [PMID: 25670947 PMCID: PMC4310922 DOI: 10.4306/pi.2015.12.1.61] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2013] [Revised: 02/27/2014] [Accepted: 03/25/2014] [Indexed: 12/05/2022] Open
Abstract
OBJECTIVE The combination of repetitive transcranial magnetic stimulation (rTMS), a non-pharmacological form of therapy for treating major depressive disorder (MDD), and electroencephalogram (EEG) is a valuable tool for investigating the functional connectivity in the brain. This study aims to explore whether pre-treating frontal quantitative EEG (QEEG) cordance is associated with response to rTMS treatment among MDD patients by using an artificial intelligence approach, artificial neural network (ANN). METHODS The artificial neural network using pre-treatment cordance of frontal QEEG classification was carried out to identify responder or non-responder to rTMS treatment among 55 MDD subjects. The classification performance was evaluated using k-fold cross-validation. RESULTS The ANN classification identified responders to rTMS treatment with a sensitivity of 93.33%, and its overall accuracy reached to 89.09%. Area under Receiver Operating Characteristic (ROC) curve (AUC) value for responder detection using 6, 8 and 10 fold cross validation were 0.917, 0.823 and 0.894 respectively. CONCLUSION Potential utility of ANN approach method can be used as a clinical tool in administering rTMS therapy to a targeted group of subjects suffering from MDD. This methodology is more potentially useful to the clinician as prediction is possible using EEG data collected before this treatment process is initiated. It is worth using feature selection algorithms to raise the sensitivity and accuracy values.
Collapse
Affiliation(s)
- Turker Tekin Erguzel
- Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Uskudar University, Istanbul, Turkey
| | - Serhat Ozekes
- Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Uskudar University, Istanbul, Turkey
| | - Selahattin Gultekin
- Department of Bioengineering, Faculty of Engineering and Natural Sciences, Uskudar University, Istanbul, Turkey
| | - Nevzat Tarhan
- Department of Psychiatry, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey
- Department of Psychiatry, NPIstanbul Hospital, Istanbul, Turkey
| | - Gokben Hizli Sayar
- Department of Psychiatry, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey
- Department of Psychiatry, NPIstanbul Hospital, Istanbul, Turkey
| | - Ali Bayram
- Biomedical Equipment Technology, Uskudar University, Istanbul, Turkey
| |
Collapse
|
36
|
McCarty DE, Punjabi NM, Kim PY, Frilot C, Marino AA. Recurrence analysis of the EEG during sleep accurately identifies subjects with mental health symptoms. Psychiatry Res 2014; 224:335-40. [PMID: 25456523 DOI: 10.1016/j.pscychresns.2014.10.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2014] [Revised: 09/05/2014] [Accepted: 10/03/2014] [Indexed: 11/16/2022]
Abstract
Analysis of brain recurrence (ABR) is a novel computational method that uses two variables for sleep depth and two for sleep fragmentation to quantify temporal changes in non-random brain electrical activity. We postulated that ABR of the sleep-staged EEG could identify an EEG signature specific for the presence of mental health symptoms. Using the Mental Health Inventory Questionnaire (MHI-5) as ground truth, psychological distress was assessed in a study cohort obtained from the Sleep Heart Health Study. Subjects with MHI-5 <50 (N=34) were matched for sex, BMI, age, and race with 34 subjects who had MHI-5 scores >50. Sixteen ABR markers derived from the EEG were analyzed using linear discriminant analysis to identify marker combinations that reliably classified individual subjects. A biomarker function computed from 12 of the markers accurately classified the subjects based on their MHI-5 scores (AUROC=82%). Use of additional markers did not improve classification accuracy. Subgroup analysis (20 highest and 20 lowest MHI-5 scores) improved classification accuracy (AUROC=89%). Biomarker values for individual subjects were significantly correlated with MHI-5 score (r=0.36, 0.54 for N=68, 40, respectively). ABR of EEGs obtained during sleep successfully classified subjects with regard to the severity of mental health symptoms, indicating that mood systems were reflected in brain electrical activity.
Collapse
Affiliation(s)
- David E McCarty
- Division of Sleep Medicine, Department of Neurology, LSU Health Sciences Center, Shreveport, LA, USA
| | - Naresh M Punjabi
- Department of Pulmonary & Critical Care Medicine, Johns Hopkins Medicine, Baltimore, MD, USA
| | - Paul Y Kim
- Division of Sleep Medicine, Department of Neurology, LSU Health Sciences Center, Shreveport, LA, USA
| | - Clifton Frilot
- School of Allied Health Professions, LSU Health Sciences Center, Shreveport, LA, USA
| | - Andrew A Marino
- Division of Sleep Medicine, Department of Neurology, LSU Health Sciences Center, Shreveport, LA, USA.
| |
Collapse
|
37
|
|
38
|
Ding L, Shou G, Yuan H, Urbano D, Cha YH. Lasting modulation effects of rTMS on neural activity and connectivity as revealed by resting-state EEG. IEEE Trans Biomed Eng 2014; 61:2070-80. [PMID: 24686227 DOI: 10.1109/tbme.2014.2313575] [Citation(s) in RCA: 50] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The long-lasting neuromodulatory effects of repetitive transcranial magnetic stimulation (rTMS) are of great interest for therapeutic applications in various neurological and psychiatric disorders, due to which functional connectivity among brain regions is profoundly disturbed. Classic TMS studies selectively alter neural activity in specific brain regions and observe neural activity changes on nonperturbed areas to infer underlying connectivity and its changes. Less has been indicated in direct measures of functional connectivity and/or neural network and on how connectivity/network alterations occur. Here, we developed a novel analysis framework to directly investigate both neural activity and connectivity changes induced by rTMS from resting-state EEG (rsEEG) acquired in a group of subjects with a chronic disorder of imbalance, known as the mal de debarquement syndrome (MdDS). Resting-state activity in multiple functional brain areas was identified through a data-driven blind source separation analysis on rsEEG data, and the connectivity among them was characterized using a phase synchronization measure. Our study revealed that there were significant long-lasting changes in resting-state neural activity, in theta, low alpha, and high alpha bands and neural networks in theta, low alpha, high alpha and beta bands, over broad cortical areas 4 to 5 h after the last application of rTMS in a consecutive five-day protocol. Our results of rsEEG connectivity further indicated that the changes, mainly in the alpha band, over the parietal and occipital cortices from pre- to post-TMS sessions were significantly correlated, in both magnitude and direction, to symptom changes in this group of subjects with MdDS. This connectivity measure not only suggested that rTMS can generate positive treatment effects in MdDS patients, but also revealed new potential targets for future therapeutic trials to improve treatment effects. It is promising that the new connectivity measure from rsEEG can be used to understand the variability in treatment response to rTMS in brain disorders with impaired functional connectivity and, eventually, to determine individually tailored stimulation parameters and treatment procedures in rTMS.
Collapse
|