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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.
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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
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Gao M, Sang W, Mi K, Liu J, Liu Y, Zhen W, An B. The Relationship Between Theta Power, Theta Asymmetry and the Effect of Escitalopram in the Treatment of Depression. Neuropsychiatr Dis Treat 2023; 19:2241-2249. [PMID: 37900670 PMCID: PMC10612517 DOI: 10.2147/ndt.s425506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 10/05/2023] [Indexed: 10/31/2023] Open
Abstract
Objective Only about one-third of depressed patients respond to initial antidepressant treatment. Therefore, it is crucial to find effective predictors of antidepressants. The purpose of our study was to learn the relationship between EEG theta power, theta asymmetry, and the efficacy of escitalopram. Methods The study included 34 patients with depression. Before and after each patient's course of treatment, EEG data was gathered. Both the Hamilton Anxiety Scale (HAMA) and the 17-item Hamilton Depression Scale (HAMD-17) were evaluated simultaneously. The natural logarithm of right frontal theta power minus left frontal theta power was used to calculate inter-electrode theta asymmetry (AT). Results First, our study found no statistically significant difference between intra-electrode theta power and inter-electrode AT before and after treatment (P ≥ 0.05). When we later looked at the data regarding treatment effects, the findings revealed that patients (n = 9) who did not respond to treatment had lower baseline theta power at C4 [6.190 (2.000, 12.990) vs 15.800 (7.255, 22.330), z = -2.166, P = 0.030]. The two groups had no difference in other electrodes (P ≥ 0.05). The AT of C3/C4 in non-responders (n = 9) was lower [0.012 (0.795) vs 0.733 (0.539), t = -3.224, P = 0.005]. However, there was no difference in inter-electrode AT between the two groups in F3/F4 and F7/F8 (P ≥ 0.05). We finally show that the theta power at C4 was negatively correlated with HAMD scores before treatment (r = -0.346, P = 0.045). Conclusion Our findings determined that increased theta power and positive asymmetry in the right frontal-central area correlate with favourable escitalopram treatment, providing a basis for finding predictive markers for antidepressants.
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Affiliation(s)
- Min Gao
- Department of Affective Disorders II, Hebei Provincial Mental Health Center, Baoding, People’s Republic of China
- Xianyang Central Hospital, Xianyang Mental Health Center, Xianyang, People’s Republic of China
| | - Wenhua Sang
- Department of Affective Disorders II, Hebei Provincial Mental Health Center, Baoding, People’s Republic of China
- Hebei Key Laboratory of Major Mental and Behavioral Disorders, Baoding, People's Republic of China
- The Sixth Clinical Medical College of Hebei University, Baoding, People's Republic of China
| | - Kun Mi
- Department of Affective Disorders II, Hebei Provincial Mental Health Center, Baoding, People’s Republic of China
- Hebei Key Laboratory of Major Mental and Behavioral Disorders, Baoding, People's Republic of China
- The Sixth Clinical Medical College of Hebei University, Baoding, People's Republic of China
| | - Jiancong Liu
- Department of Affective Disorders II, Hebei Provincial Mental Health Center, Baoding, People’s Republic of China
- Hebei Key Laboratory of Major Mental and Behavioral Disorders, Baoding, People's Republic of China
- The Sixth Clinical Medical College of Hebei University, Baoding, People's Republic of China
| | - Yudong Liu
- Department of Affective Disorders II, Hebei Provincial Mental Health Center, Baoding, People’s Republic of China
- Hebei Key Laboratory of Major Mental and Behavioral Disorders, Baoding, People's Republic of China
- The Sixth Clinical Medical College of Hebei University, Baoding, People's Republic of China
| | - Wenge Zhen
- Department of Affective Disorders II, Hebei Provincial Mental Health Center, Baoding, People’s Republic of China
- Hebei Key Laboratory of Major Mental and Behavioral Disorders, Baoding, People's Republic of China
- The Sixth Clinical Medical College of Hebei University, Baoding, People's Republic of China
| | - Bang An
- Xianyang Central Hospital, Xianyang Mental Health Center, Xianyang, People’s Republic of China
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Zelada MI, Garrido V, Liberona A, Jones N, Zúñiga K, Silva H, Nieto RR. Brain-Derived Neurotrophic Factor (BDNF) as a Predictor of Treatment Response in Major Depressive Disorder (MDD): A Systematic Review. Int J Mol Sci 2023; 24:14810. [PMID: 37834258 PMCID: PMC10572866 DOI: 10.3390/ijms241914810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/16/2023] [Accepted: 09/25/2023] [Indexed: 10/15/2023] Open
Abstract
Brain-derived neurotrophic factor (BDNF) has been studied as a biomarker of major depressive disorder (MDD). Besides diagnostic biomarkers, clinically useful biomarkers can inform response to treatment. We aimed to review all studies that sought to relate BDNF baseline levels, or BDNF polymorphisms, with response to treatment in MDD. In order to achieve this, we performed a systematic review of studies that explored the relation of BDNF with both pharmacological and non-pharmacological treatment. Finally, we reviewed the evidence that relates peripheral levels of BDNF and BDNF polymorphisms with the development and management of treatment-resistant depression.
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Affiliation(s)
- Mario Ignacio Zelada
- Escuela de Medicina, Facultad de Medicina, Universidad de Chile, Santiago 8380453, Chile
| | - Verónica Garrido
- Escuela de Medicina, Facultad de Medicina, Universidad de Chile, Santiago 8380453, Chile
| | - Andrés Liberona
- Escuela de Medicina, Facultad de Medicina, Universidad de Chile, Santiago 8380453, Chile
| | - Natalia Jones
- Escuela de Medicina, Facultad de Medicina, Universidad de Chile, Santiago 8380453, Chile
| | - Karen Zúñiga
- Escuela de Medicina, Facultad de Medicina, Universidad de Chile, Santiago 8380453, Chile
| | - Hernán Silva
- Clínica Psiquiátrica Universitaria, Hospital Clínico de la Universidad de Chile, Universidad de Chile, Santiago 8380453, Chile
- Departamento de Psiquiatría y Salud Mental Norte, Facultad de Medicina, Universidad de Chile, Santiago 8380453, Chile
| | - Rodrigo R. Nieto
- Clínica Psiquiátrica Universitaria, Hospital Clínico de la Universidad de Chile, Universidad de Chile, Santiago 8380453, Chile
- Departamento de Psiquiatría y Salud Mental Norte, Facultad de Medicina, Universidad de Chile, Santiago 8380453, Chile
- Departamento de Neurociencias, Facultad de Medicina, Universidad de Chile, Santiago 8380453, Chile
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Schwartzmann B, Quilty LC, Dhami P, Uher R, Allen TA, Kloiber S, Lam RW, Frey BN, Milev R, Müller DJ, Soares CN, Foster JA, Rotzinger S, Kennedy SH, Farzan F. Resting-state EEG delta and alpha power predict response to cognitive behavioral therapy in depression: a Canadian biomarker integration network for depression study. Sci Rep 2023; 13:8418. [PMID: 37225718 DOI: 10.1038/s41598-023-35179-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 05/14/2023] [Indexed: 05/26/2023] Open
Abstract
Cognitive behavioral therapy (CBT) is often recommended as a first-line treatment in depression. However, access to CBT remains limited, and up to 50% of patients do not benefit from this therapy. Identifying biomarkers that can predict which patients will respond to CBT may assist in designing optimal treatment allocation strategies. In a Canadian Biomarker Integration Network for Depression (CAN-BIND) study, forty-one adults with depression were recruited to undergo a 16-week course of CBT with thirty having resting-state electroencephalography (EEG) recorded at baseline and week 2 of therapy. Successful clinical response to CBT was defined as a 50% or greater reduction in Montgomery-Åsberg Depression Rating Scale (MADRS) score from baseline to post-treatment completion. EEG relative power spectral measures were analyzed at baseline, week 2, and as early changes from baseline to week 2. At baseline, lower relative delta (0.5-4 Hz) power was observed in responders. This difference was predictive of successful clinical response to CBT. Furthermore, responders exhibited an early increase in relative delta power and a decrease in relative alpha (8-12 Hz) power compared to non-responders. These changes were also found to be good predictors of response to the therapy. These findings showed the potential utility of resting-state EEG in predicting CBT outcomes. They also further reinforce the promise of an EEG-based clinical decision-making tool to support treatment decisions for each patient.
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Affiliation(s)
- Benjamin Schwartzmann
- eBrain Lab, School of Mechatronic Systems Engineering, Simon Fraser University, 13750-96 Ave, Surrey, BC, V3V 1Z2, Canada
| | - Lena C Quilty
- University of Toronto, 27 King's College Circle, Toronto, ON, M5S 1A1, Canada
- Centre for Addiction and Mental Health, 1001 Queen St. W, Toronto, ON, M6J 1H4, Canada
| | - Prabhjot Dhami
- eBrain Lab, School of Mechatronic Systems Engineering, Simon Fraser University, 13750-96 Ave, Surrey, BC, V3V 1Z2, Canada
- University of Toronto, 27 King's College Circle, Toronto, ON, M5S 1A1, Canada
- Centre for Addiction and Mental Health, 1001 Queen St. W, Toronto, ON, M6J 1H4, Canada
| | - Rudolf Uher
- Department of Psychiatry, Dalhousie University, 5909 Veterans' Memorial Lane, Halifax, NS, B3H 2E2, Canada
| | - Timothy A Allen
- Centre for Addiction and Mental Health, 1001 Queen St. W, Toronto, ON, M6J 1H4, Canada
| | - Stefan Kloiber
- University of Toronto, 27 King's College Circle, Toronto, ON, M5S 1A1, Canada
- Centre for Addiction and Mental Health, 1001 Queen St. W, Toronto, ON, M6J 1H4, Canada
| | - Raymond W Lam
- Department of Psychiatry, University of British Columbia, 2255 Wesbrook Mall, Vancouver, BC, V6T 2A1, Canada
| | - Benicio N Frey
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, 100 West 5th St., Hamilton, ON, L8N 3K7, Canada
- Mood Disorders Program and Women's Health Concerns Clinic, St. Joseph's Healthcare, 100 West 5th St., Hamilton, ON, L8N 3K7, Canada
| | - Roumen Milev
- Department of Psychiatry, Providence Care, Queen's University, 752 King Street West, Kingston, ON, K7L 4X3, Canada
| | - Daniel J Müller
- University of Toronto, 27 King's College Circle, Toronto, ON, M5S 1A1, Canada
- Centre for Addiction and Mental Health, 1001 Queen St. W, Toronto, ON, M6J 1H4, Canada
| | - Claudio N Soares
- Department of Psychiatry, Providence Care, Queen's University, 752 King Street West, Kingston, ON, K7L 4X3, Canada
| | - Jane A Foster
- Department of Psychiatry and Behavioural Neurosciences, McMaster University, 100 West 5th St., Hamilton, ON, L8N 3K7, Canada
| | - Susan Rotzinger
- University of Toronto, 27 King's College Circle, Toronto, ON, M5S 1A1, Canada
- Unity Health Toronto, Toronto, ON, Canada
- University Health Network, 399 Bathurst Street, Toronto, ON, M5T 2S8, Canada
| | - Sidney H Kennedy
- University of Toronto, 27 King's College Circle, Toronto, ON, M5S 1A1, Canada
- Unity Health Toronto, Toronto, ON, Canada
- University Health Network, 399 Bathurst Street, Toronto, ON, M5T 2S8, Canada
| | - Faranak Farzan
- eBrain Lab, School of Mechatronic Systems Engineering, Simon Fraser University, 13750-96 Ave, Surrey, BC, V3V 1Z2, Canada.
- University of Toronto, 27 King's College Circle, Toronto, ON, M5S 1A1, Canada.
- Centre for Addiction and Mental Health, 1001 Queen St. W, Toronto, ON, M6J 1H4, Canada.
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Alterations in EEG functional connectivity in individuals with depression: A systematic review. J Affect Disord 2023; 328:287-302. [PMID: 36801418 DOI: 10.1016/j.jad.2023.01.126] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 01/22/2023] [Accepted: 01/30/2023] [Indexed: 02/19/2023]
Abstract
The brain works as an organised, network-like structure of functionally interconnected regions. Disruptions to interconnectivity in certain networks have been linked to symptoms of depression and impairments in cognition. Electroencephalography (EEG) is a low-burden tool by which differences in functional connectivity (FC) can be assessed. This systematic review aims to provide a synthesis of evidence relating to EEG FC in depression. A comprehensive electronic literature search for terms relating to depression, EEG, and FC was conducted on studies published before the end of November 2021, according to PRISMA guidelines. Studies comparing EEG measures of FC of individuals with depression to that of healthy control groups were included. Data was extracted by two independent reviewers, and the quality of EEG FC methods was assessed. Fifty-two studies assessing EEG FC in depression were identified: 36 assessed resting-state FC, and 16 assessed task-related or other (i.e., sleep) FC. Somewhat consistent findings in resting-state studies suggest for no differences between depression and control groups in EEG FC in the delta and gamma frequencies. However, while most resting-state studies noted a difference in alpha, theta, and beta, no clear conclusions could be drawn about the direction of the difference, due to considerable inconsistencies between study design and methodology. This was also true for task-related and other EEG FC. More robust research is needed to understand the true differences in EEG FC in depression. Given that the FC between brain regions drives behaviour, cognition, and emotion, characterising how FC differs in depression is essential for understanding the aetiology of depression.
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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: 4] [Impact Index Per Article: 4.0] [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.
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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
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Rostami R, Kazemi R, Nasiri Z, Ataei S, Hadipour AL, Jaafari N. Cold Cognition as Predictor of Treatment Response to rTMS; A Retrospective Study on Patients With Unipolar and Bipolar Depression. Front Hum Neurosci 2022; 16:888472. [PMID: 35959241 PMCID: PMC9358278 DOI: 10.3389/fnhum.2022.888472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 06/06/2022] [Indexed: 01/10/2023] Open
Abstract
BackgroundCognitive impairments are prevalent in patients with unipolar and bipolar depressive disorder (UDD and BDD, respectively). Considering the fact assessing cognitive functions is increasingly feasible for clinicians and researchers, targeting these problems in treatment and using them at baseline as predictors of response to treatment can be very informative.MethodIn a naturalistic, retrospective study, data from 120 patients (Mean age: 33.58) with UDD (n = 56) and BDD (n = 64) were analyzed. Patients received 20 sessions of bilateral rTMS (10 Hz over LDLPFC and 1 HZ over RDLPFC) and were assessed regarding their depressive symptoms, sustained attention, working memory, and executive functions, using the Beck Depression Inventory (BDI-II) and Neuropsychological Test Automated Battery Cambridge, at baseline and after the end of rTMS treatment course. Generalized estimating equations (GEE) and logistic regression were used as the main statistical methods to test the hypotheses.ResultsFifty-three percentage of all patients (n = 64) responded to treatment. In particular, 53.1% of UDD patients (n = 34) and 46.9% of BDD patients (n = 30) responded to treatment. Bilateral rTMS improved all cognitive functions (attention, working memory, and executive function) except for visual memory and resulted in more modulations in the working memory of UDD compared to BDD patients. More improvements in working memory were observed in responded patients and visual memory, age, and sex were determined as treatment response predictors. Working memory, visual memory, and age were identified as treatment response predictors in BDD and UDD patients, respectively.ConclusionBilateral rTMS improved cold cognition and depressive symptoms in UDD and BDD patients, possibly by altering cognitive control mechanisms (top-down), and processing negative emotional bias.
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Affiliation(s)
- Reza Rostami
- Department of Psychology, University of Tehran, Tehran, Iran
- *Correspondence: Reza Rostami
| | - Reza Kazemi
- Department of Cognitive Psychology, Institute for Cognitive Science Studies>, Tehran, Iran
| | - Zahra Nasiri
- Convergent Technologies Research Center, University of Tehran, Tehran, Iran
| | - Somayeh Ataei
- Department of Neuropsychology, Faculty of Psychology, Institute of Cognitive Neuroscience, Ruhr-University Bochum, Bochum, Germany
| | - Abed L. Hadipour
- Department of Cognitive Sciences, University of Messina, Messina, Italy
| | - Nematollah Jaafari
- Unité de Recherche Clinique Intersectorielle en Psychiatrie Pierre Deniker, Centre Hospitalier Henri Laborit, Poitiers, France
- University Poitiers & CHU Poitiers, INSERM U1084, Laboratoire Expérimental et Clinique en Neurosciences, Poitiers, France
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Sun S, Yang P, Chen H, Shao X, Ji S, Li X, Li G, Hu B. Electroconvulsive Therapy-Induced Changes in Functional Brain Network of Major Depressive Disorder Patients: A Longitudinal Resting-State Electroencephalography Study. Front Hum Neurosci 2022; 16:852657. [PMID: 35664348 PMCID: PMC9158117 DOI: 10.3389/fnhum.2022.852657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 04/05/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectivesSeveral studies have shown abnormal network topology in patients with major depressive disorder (MDD). However, changes in functional brain networks associated with electroconvulsive therapy (ECT) remission based on electroencephalography (EEG) signals have yet to be investigated.MethodsNineteen-channel resting-state eyes-closed EEG signals were collected from 24 MDD patients pre- and post-ECT treatment. Functional brain networks were constructed by using various coupling methods and binarization techniques. Changes in functional connectivity and network metrics after ECT treatment and relationships between network metrics and clinical symptoms were explored.ResultsECT significantly increased global efficiency, edge betweenness centrality, local efficiency, and mean degree of alpha band after ECT treatment, and an increase in these network metrics had significant correlations with decreased depressive symptoms in repeated measures correlation. In addition, ECT regulated the distribution of hubs in frontal and occipital lobes.ConclusionECT modulated the brain’s global and local information-processing patterns. In addition, an ECT-induced increase in network metrics was associated with clinical remission.SignificanceThese findings might present the evidence for us to understand how ECT regulated the topology organization in functional brain networks of clinically remitted depressive patients.
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Affiliation(s)
- Shuting Sun
- Brain Health Engineering Laboratory, School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Peng Yang
- Shandong Daizhuang Hospital, Jining, China
| | - Huayu Chen
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Xuexiao Shao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Shanling Ji
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Xiaowei Li
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
- Shandong Academy of Intelligent Computing Technology, Jinan, China
- *Correspondence: Xiaowei Li,
| | - Gongying Li
- Department of Psychiatry, Huai’an Third People’s Hospital, Huai’an, China
- Gongying Li,
| | - Bin Hu
- Brain Health Engineering Laboratory, School of Medical Technology, Beijing Institute of Technology, Beijing, China
- Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
- Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University and Institute of Semiconductors, Chinese Academy of Sciences, Lanzhou, China
- Open Source Software and Real-Time System, Lanzhou University, Ministry of Education, Lanzhou, China
- Bin Hu,
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Zolezzi DM, Alonso-Valerdi LM, Ibarra-Zarate DI. Chronic neuropathic pain is more than a perception: Systems and methods for an integral characterization. Neurosci Biobehav Rev 2022; 136:104599. [PMID: 35271915 DOI: 10.1016/j.neubiorev.2022.104599] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 03/01/2022] [Accepted: 03/02/2022] [Indexed: 10/18/2022]
Abstract
The management of chronic neuropathic pain remains a challenge, because pain is subjective, and measuring it objectively is usually out of question. However, neuropathic pain is also a signal provided by maladaptive neuronal activity. Thus, the integral management of chronic neuropathic pain should not only rely on the subjective perception of the patient, but also on objective data that measures the evolution of neuronal activity. We will discuss different objective and subjective methods for the characterization of neuropathic pain. Additionally, the gaps and proposals for an integral management of chronic neuropathic pain will also be discussed. The current management that relies mostly on subjective measures has not been sufficient, therefore, this has hindered advances in pain management and clinical trials. If an integral characterization is achieved, clinical management and stratification for clinical trials could be based on both questionnaires and neuronal activity. Appropriate characterization may lead to an increased effectiveness for new therapies, and a better quality of life for neuropathic pain sufferers.
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Affiliation(s)
- Daniela M Zolezzi
- Escuela de Ingeniería y Ciencias, Tecnológico de Monterrey, Monterrey 64849, Nuevo León, México; Center for Neuroplasticity and Pain, Department of Health Science and Technology, Aalborg University, Aalborg 9220, Denmark.
| | | | - David I Ibarra-Zarate
- Escuela de Ingeniería y Ciencias, Tecnológico de Monterrey, Puebla 72453, Puebla, México
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de la Salle S, Phillips JL, Blier P, Knott V. Electrophysiological correlates and predictors of the antidepressant response to repeated ketamine infusions in treatment-resistant depression. Prog Neuropsychopharmacol Biol Psychiatry 2022; 115:110507. [PMID: 34971723 DOI: 10.1016/j.pnpbp.2021.110507] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 12/03/2021] [Accepted: 12/23/2021] [Indexed: 12/28/2022]
Abstract
BACKGROUND Sub-anesthetic ketamine doses rapidly reduce depressive symptoms, although additional investigations of the underlying neural mechanisms and the prediction of response outcomes are needed. Electroencephalographic (EEG)-derived measures have shown promise in predicting antidepressant response to a variety of treatments, and are sensitive to ketamine administration. This study examined their utility in characterizing changes in depressive symptoms following single and repeated ketamine infusions. METHODS Recordings were obtained from patients with treatment-resistant major depressive disorder (MDD) (N = 24) enrolled in a multi-phase clinical ketamine trial. During the randomized, double-blind, crossover phase (Phase 1), patients received intravenous ketamine (0.5 mg/kg) and midazolam (30 μg/kg), at least 1 week apart. For each medication, three resting, eyes-closed recordings were obtained per session (pre-infusion, immediately post-infusion, 2 h post-infusion), and changes in power (delta, theta1/2/total, alpha1/2/total, beta, gamma), alpha asymmetry, theta cordance, and theta source-localized anterior cingulate cortex activity were quantified. The relationships between ketamine-induced changes with early (Phase 1) and sustained (Phases 2,3: open-label repeated infusions) decreases in depressive symptoms (Montgomery-Åsberg Depression Rating Score, MADRS) and suicidal ideation (MADRS item 10) were examined. RESULTS Both medications decreased alpha and theta immediately post-infusion, however, only midazolam increased delta (post-infusion), and only ketamine increased gamma (immediately post- and 2 h post-infusion). Regional- and frequency-specific ketamine-induced EEG changes were related to and predictive of decreases in depressive symptoms (theta, gamma) and suicidal ideation (alpha). Early and sustained treatment responders differed at baseline in surface-level and source-localized theta. CONCLUSIONS Ketamine exerts frequency-specific changes on EEG-derived measures, which are related to depressive symptom decreases in treatment-resistant MDD and provide information regarding early and sustained individual response to ketamine. CLINICAL TRIAL REGISTRATION ClinicalTrials.gov: Action of Ketamine in Treatment-Resistant Depression, NCT01945047.
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Affiliation(s)
- Sara de la Salle
- University of Ottawa Institute of Mental Health Research at the Royal, 1145 Carling Avenue, Ottawa, ON K1Z 7K4, Canada; School of Psychology, University of Ottawa, 136 Jean-Jacques Lussier, Ottawa, ON K1N6N5, Canada.
| | - Jennifer L Phillips
- University of Ottawa Institute of Mental Health Research at the Royal, 1145 Carling Avenue, Ottawa, ON K1Z 7K4, Canada; Department of Psychiatry, University of Ottawa, 1145 Carling Avenue, Ottawa, ON K1Z 7K4, Canada; Department of Biochemistry, Microbiology and Immunology, University of Ottawa, 451 Smyth Road, Ottawa, ON K1H 8M5, Canada
| | - Pierre Blier
- University of Ottawa Institute of Mental Health Research at the Royal, 1145 Carling Avenue, Ottawa, ON K1Z 7K4, Canada; Department of Psychiatry, University of Ottawa, 1145 Carling Avenue, Ottawa, ON K1Z 7K4, Canada; Department of Cellular and Molecular Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON K1H 8M5, Canada
| | - Verner Knott
- University of Ottawa Institute of Mental Health Research at the Royal, 1145 Carling Avenue, Ottawa, ON K1Z 7K4, Canada; Department of Cellular and Molecular Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON K1H 8M5, Canada; School of Psychology, University of Ottawa, 136 Jean-Jacques Lussier, Ottawa, ON K1N6N5, Canada
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11
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Koller-Schlaud K, Ströhle A, Behr J, Bärwolf Dreysse E, Rentzsch J. Changes in Electric Brain Response to Affective Stimuli in the First Week of Antidepressant Treatment: An Exploratory Study. Neuropsychobiology 2022; 81:69-79. [PMID: 34515179 DOI: 10.1159/000517860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 06/14/2021] [Indexed: 11/19/2022]
Abstract
INTRODUCTION Asymmetrical alpha and frontal theta activity have been discussed as neurobiological markers for antidepressant treatment response. While most studies focus on resting-state EEG, there is evidence that task-related activity assessed at multiple time points might be superior in detecting subtle early differences. METHODS This was a naturalistic study design assessing participants in a psychiatric in- and outpatient hospital setting. We investigated stimulus-related EEG asymmetry (frontal and occipital alpha-1 and alpha-2) and power (frontal midline theta) assessed at baseline and 1 week after initiation of pharmacological depression treatment while presenting affective stimuli. We then compared week 4 responders and nonresponders to antidepressant treatment. RESULTS Follow-up analyses of a significant group × emotion × time interaction (p < 0.04) for alpha-1 asymmetry showed that responders differed significantly at baseline in their asymmetry scores in response to sad compared to happy faces with a change in this pattern 1 week later. Nonresponders did not show this pattern. No significant results were found for alpha-2, occipital alpha-1, and occipital alpha-2 asymmetry or frontal midline theta power. DISCUSSION Our study addresses the gap in comparisons of task-related EEG activity changes measured at two time points and supports the potential value of this approach in detecting early differences in responders versus nonresponders to pharmacological treatment. Important limitations include the small sample size and the noncontrolled study design.
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Affiliation(s)
- Kristin Koller-Schlaud
- Department of Psychiatry and Neurosciences
- CCM, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany.,Department of Psychiatry, Psychotherapy and Psychosomatics, Brandenburg Medical School Theodor Fontane, Neuruppin, Germany
| | - Andreas Ströhle
- Department of Psychiatry and Neurosciences
- CCM, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany
| | - Joachim Behr
- Department of Psychiatry and Neurosciences
- CCM, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany.,Department of Psychiatry, Psychotherapy and Psychosomatics, Brandenburg Medical School Theodor Fontane, Neuruppin, Germany.,Faculty of Health Science Brandenburg, Joint Faculty of the University of Potsdam, Brandenburg University of Technology Cottbus-Senftenberg and Brandenburg Medical School Theodor Fontane, Potsdam, Germany
| | - Elisabeth Bärwolf Dreysse
- Department of Psychiatry and Neurosciences
- CCM, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany
| | - Johannes Rentzsch
- Department of Psychiatry and Neurosciences
- CCM, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Berlin, Germany.,Department of Psychiatry, Psychotherapy and Psychosomatics, Brandenburg Medical School Theodor Fontane, Neuruppin, Germany
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12
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Cannard C, Wahbeh H, Delorme A. Electroencephalography Correlates of Well-Being Using a Low-Cost Wearable System. Front Hum Neurosci 2021; 15:745135. [PMID: 35002651 PMCID: PMC8740323 DOI: 10.3389/fnhum.2021.745135] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 11/15/2021] [Indexed: 12/02/2022] Open
Abstract
Electroencephalography (EEG) alpha asymmetry is thought to reflect crucial brain processes underlying executive control, motivation, and affect. It has been widely used in psychopathology and, more recently, in novel neuromodulation studies. However, inconsistencies remain in the field due to the lack of consensus in methodological approaches employed and the recurrent use of small samples. Wearable technologies ease the collection of large and diversified EEG datasets that better reflect the general population, allow longitudinal monitoring of individuals, and facilitate real-world experience sampling. We tested the feasibility of using a low-cost wearable headset to collect a relatively large EEG database (N = 230, 22-80 years old, 64.3% female), and an open-source automatic method to preprocess it. We then examined associations between well-being levels and the alpha center of gravity (CoG) as well as trait EEG asymmetries, in the frontal and temporoparietal (TP) areas. Robust linear regression models did not reveal an association between well-being and alpha (8-13 Hz) asymmetry in the frontal regions, nor with the CoG. However, well-being was associated with alpha asymmetry in the TP areas (i.e., corresponding to relatively less left than right TP cortical activity as well-being levels increased). This effect was driven by oscillatory activity in lower alpha frequencies (8-10.5 Hz), reinforcing the importance of dissociating sub-components of the alpha band when investigating alpha asymmetries. Age was correlated with both well-being and alpha asymmetry scores, but gender was not. Finally, EEG asymmetries in the other frequency bands were not associated with well-being, supporting the specific role of alpha asymmetries with the brain mechanisms underlying well-being levels. Interpretations, limitations, and recommendations for future studies are discussed. This paper presents novel methodological, experimental, and theoretical findings that help advance human neurophysiological monitoring techniques using wearable neurotechnologies and increase the feasibility of their implementation into real-world applications.
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Affiliation(s)
- Cédric Cannard
- Centre de Recherche Cerveau et Cognition (CerCo), Centre National de la Recherche Scientifique (CNRS), Paul Sabatier University, Toulouse, France
- Institute of Noetic Sciences (IONS), Petaluma, CA, United States
| | - Helané Wahbeh
- Institute of Noetic Sciences (IONS), Petaluma, CA, United States
| | - Arnaud Delorme
- Centre de Recherche Cerveau et Cognition (CerCo), Centre National de la Recherche Scientifique (CNRS), Paul Sabatier University, Toulouse, France
- Institute of Noetic Sciences (IONS), Petaluma, CA, United States
- Swartz Center for Computational Neuroscience (SCCN), Institute of Neural Computation (INC), University of California, San Diego, San Diego, CA, United States
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13
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Ip CT, Olbrich S, Ganz M, Ozenne B, Köhler-Forsberg K, Dam VH, Beniczky S, Jørgensen MB, Frokjaer VG, Søgaard B, Christensen SR, Knudsen GM. Pretreatment qEEG biomarkers for predicting pharmacological treatment outcome in major depressive disorder: Independent validation from the NeuroPharm study. Eur Neuropsychopharmacol 2021; 49:101-112. [PMID: 33910154 DOI: 10.1016/j.euroneuro.2021.03.024] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 02/24/2021] [Accepted: 03/30/2021] [Indexed: 02/04/2023]
Abstract
Several electroencephalogram (EEG) biomarkers for prediction of drug response in major depressive disorder (MDD) have been proposed, but validations in larger independent datasets are missing. In the current study, we investigated the prognostic value of previously suggested EEG biomarkers. We gathered data that matched prior studies in terms of EEG methodology, clinical criteria for MDD, and statistical approach as closely as possible. The NeuroPharm study is a non-randomized and open label prospective clinical trial. One hundred antidepressant free patients with MDD were enrolled in the study and 79 (57 female) were included in the per-protocol analysis. The biomarkers candidates for cross-validation were derived from prior studies such as iSPOT-D and EMBARC and include frontal and occipital alpha power and asymmetry and delta and theta activity at anterior cingulate cortex (ACC). The alpha asymmetry, reported in two out of six prior studies, could be partially validated. We found that in female patients, larger right than left frontal alpha power prior to drug treatment was associated with better clinical outcome 8 weeks later. Moreover, female non-responder had higher central left alpha power relative to the right. In contrast to prior reports, we found that lower theta activity at ACC was present in remitters and was associated with greater improvement at week 8. We provide evidence that in women with MDD, alpha asymmetry seems to be the most promising EEG biomarker for prediction of treatment response. Registration number: NCT02869035.
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Affiliation(s)
- Cheng-Teng Ip
- Department of Clinical Pharmacology, H. Lundbeck A/S, Valby, Denmark; Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sebastian Olbrich
- Department of Psychiatry, Psychotherapy and Psychosomatic, University Zürich, Switzerland
| | - Melanie Ganz
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark; Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Brice Ozenne
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark; Section of Biostatistics, Department of Public Health, University of Copenhagen, Denmark
| | - Kristin Köhler-Forsberg
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Vibeke H Dam
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund, Denmark; Department of Clinical Medicine, University of Aarhus, Aarhus, Denmark; Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
| | - Martin B Jørgensen
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Psychiatry, Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Vibe G Frokjaer
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Birgitte Søgaard
- Department of Clinical Pharmacology, H. Lundbeck A/S, Valby, Denmark
| | | | - Gitte M Knudsen
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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14
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Arns M, Voetterl H. Neurophysiological effects of rTMS: Revisiting the role of the N100 as a clinically useful marker in depression. Clin Neurophysiol 2021; 132:2259-2260. [PMID: 34284973 DOI: 10.1016/j.clinph.2021.06.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 06/25/2021] [Indexed: 11/17/2022]
Affiliation(s)
- Martijn Arns
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands; Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Location AMC, Amsterdam Neuroscience, the Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands.
| | - Helena Voetterl
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, the Netherlands; Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Location AMC, Amsterdam Neuroscience, the Netherlands
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15
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Iznak AF, Iznak EV. [EEG predictors of therapeutic response in psychiatry]. Zh Nevrol Psikhiatr Im S S Korsakova 2021; 121:145-151. [PMID: 34037368 DOI: 10.17116/jnevro2021121041145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The literature review provides data on one of the types of biomarkers - EEG predictors of the therapeutic response of patients with different types of mental pathology. It has been shown that the quantitative parameters of the electroencephalogram (EEG) recorded before the start of the treatment course reflect not only the current functional state of the patient's brain, but also its adaptive resources in terms of the possibility and magnitude of response to therapy. The identified EEG predictors of the therapeutic response in patients with depression, schizophrenia and some other mental disorders have a sufficiently high prognostic ability, sensitivity and specificity in determining responders and non-responders, make it possible to carry out a quantitative prediction of the patient's condition after a course of treatment, and also to assist the clinician in choosing medications for optimal therapy.
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Affiliation(s)
- A F Iznak
- Mental Health Research Centre, Moscow, Russia
| | - E V Iznak
- Mental Health Research Centre, Moscow, Russia
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16
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Lenoir D, Willaert W, Coppieters I, Malfliet A, Ickmans K, Nijs J, Vonck K, Meeus M, Cagnie B. Electroencephalography During Nociceptive Stimulation in Chronic Pain Patients: A Systematic Review. PAIN MEDICINE 2021; 21:3413-3427. [PMID: 32488229 DOI: 10.1093/pm/pnaa131] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND With its high temporal resolution, electroencephalography (EEG), a technique that records electrical activity of cortical neuronal cells, is a potentially suitable technique to investigate human somatosensory processing. By using EEG, the processing of (nociceptive) stimuli can be investigated, along with the functionality of the nociceptive pathway. Therefore, it can be applied in chronic pain patients to objectify whether changes have occurred in nociceptive processing. Typically, so-called event-related potential (ERP) recordings are used, where EEG signals are recorded in response to specific stimuli and characterized by latency and amplitude. OBJECTIVE To summarize whether differences in somatosensory processing occur between chronic pain patients and healthy controls, measured with ERPs, and determine whether this response is related to the subjective pain intensity. DESIGN Systematic review. SETTING AND METHODS PubMed, Web of Science, and Embase were consulted, and 18 case-control studies were finally included. SUBJECTS The chronic pain patients suffered from tension-type headache, back pain, migraine, fibromyalgia, carpal tunnel syndrome, prostatitis, or complex regional pain syndrome. RESULTS Chronic neuropathic pain patients showed increased latencies of the N2 and P2 components, along with a decreased amplitude of the N2-P2 complex, which was also obtained in FM patients with small fiber dysfunction. The latter also showed a decreased amplitude of the N2-P3 and N1-P1 complex. For the other chronic pain patients, the latencies and the amplitudes of the ERP components did not seem to differ from healthy controls. One paper indicated that the N2-P3 peak-to-peak amplitude correlates with the subjective experience of the stimulus. CONCLUSIONS Differences in ERPs with healthy controls can mostly be found in chronic pain populations that suffer from neuropathic pain or where fiber dysfunction is present. In chronic pain populations with other etiological mechanisms, limited differences were found or agreed upon across studies.
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Affiliation(s)
- Dorine Lenoir
- Pain in Motion International Research Group.,Department of Physiotherapy, Human Physiology and Anatomy (KIMA), Faculty of Physical Education & Physiotherapy, Pain in Motion International Research Group, Vrije Universiteit Brussel, Brussel, Belgium.,Department of Physical Medicine and Physiotherapy, Universitair Ziekenhuis Brussel, Brussels, Belgium.,Research Foundation - Flanders (FWO), Brussels, Belgium
| | - Ward Willaert
- Pain in Motion International Research Group.,Department of Physiotherapy, Human Physiology and Anatomy (KIMA), Faculty of Physical Education & Physiotherapy, Pain in Motion International Research Group, Vrije Universiteit Brussel, Brussel, Belgium.,Department of Physical Medicine and Physiotherapy, Universitair Ziekenhuis Brussel, Brussels, Belgium.,Research Foundation - Flanders (FWO), Brussels, Belgium.,Department of Rehabilitation Sciences and Physiotherapy, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Iris Coppieters
- Pain in Motion International Research Group.,Department of Physiotherapy, Human Physiology and Anatomy (KIMA), Faculty of Physical Education & Physiotherapy, Pain in Motion International Research Group, Vrije Universiteit Brussel, Brussel, Belgium.,Department of Rehabilitation Sciences and Physiotherapy, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
| | - Anneleen Malfliet
- Pain in Motion International Research Group.,Department of Physiotherapy, Human Physiology and Anatomy (KIMA), Faculty of Physical Education & Physiotherapy, Pain in Motion International Research Group, Vrije Universiteit Brussel, Brussel, Belgium
| | - Kelly Ickmans
- Pain in Motion International Research Group.,Department of Physiotherapy, Human Physiology and Anatomy (KIMA), Faculty of Physical Education & Physiotherapy, Pain in Motion International Research Group, Vrije Universiteit Brussel, Brussel, Belgium.,Department of Physical Medicine and Physiotherapy, Universitair Ziekenhuis Brussel, Brussels, Belgium.,Research Foundation - Flanders (FWO), Brussels, Belgium
| | - Jo Nijs
- Department of Physiotherapy, Human Physiology and Anatomy (KIMA), Faculty of Physical Education & Physiotherapy, Pain in Motion International Research Group, Vrije Universiteit Brussel, Brussel, Belgium
| | - Kristl Vonck
- Department of Neurology, 4Brain, Ghent University Hospital, Ghent, Belgium
| | - Mira Meeus
- Pain in Motion International Research Group.,Department of Rehabilitation Sciences and Physiotherapy, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium.,Department of Rehabilitation Sciences and Physiotherapy - MOVANT Research Group, Faculty of Medicine and Health Science, University of Antwerp, Antwerp, Belgium
| | - Barbara Cagnie
- Department of Rehabilitation Sciences and Physiotherapy, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium
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17
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Thériault RK, Manduca JD, Perreault ML. Sex differences in innate and adaptive neural oscillatory patterns link resilience and susceptibility to chronic stress in rats. J Psychiatry Neurosci 2021; 46:E258-E270. [PMID: 33769022 PMCID: PMC8061734 DOI: 10.1503/jpn.200117] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Major depressive disorder is a chronic illness with a higher incidence in women. Dysregulated neural oscillatory activity is an emerging mechanism thought to underlie major depressive disorder, but whether sex differences in these rhythms contribute to the development of symptoms is unknown. METHODS We exposed male and female rats to chronic unpredictable stress and characterized them as stress-resilient or stress-susceptible based on behavioural output in the forced swim test and the sucrose preference test. To identify sex-specific neural oscillatory patterns associated with stress response, we recorded local field potentials from the prefrontal cortex, cingulate cortex, nucleus accumbens and dorsal hippocampus throughout stress exposure. RESULTS At baseline, female stress-resilient rats innately exhibited higher theta coherence in hippocampal connections compared with stress-susceptible female rats. Following stress exposure, additional oscillatory changes manifested: stress-resilient females were characterized by increased dorsal hippocampal theta power and cortical gamma power, and stress-resilient males were characterized by a widespread increase in high gamma coherence. In stress-susceptible animals, we observed a pattern of increased delta and reduced theta power; the changes were restricted to the cingulate cortex and dorsal hippocampus in males but occurred globally in females. Finally, stress exposure was accompanied by the time-dependent recruitment of specific neural pathways, which culminated in system-wide changes that temporally coincided with the onset of depression-like behaviour. LIMITATIONS We could not establish causality between the electrophysiological changes and behaviours with the methodology we employed. CONCLUSION Sex-specific neurophysiological patterns can function as early markers for stress vulnerability and the onset of depression-like behaviours in rats.
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Affiliation(s)
- Rachel-Karson Thériault
- From the department of Molecular and Cellular Biology, University of Guelph, Guelph, Ont., Canada (Thériault, Manduca, Perreault) and the Collaborative Neuroscience Program, University of Guelph, Guelph, Ont., Canada (Thériault, Perreault)
| | - Joshua D Manduca
- From the department of Molecular and Cellular Biology, University of Guelph, Guelph, Ont., Canada (Thériault, Manduca, Perreault) and the Collaborative Neuroscience Program, University of Guelph, Guelph, Ont., Canada (Thériault, Perreault)
| | - Melissa L Perreault
- From the department of Molecular and Cellular Biology, University of Guelph, Guelph, Ont., Canada (Thériault, Manduca, Perreault) and the Collaborative Neuroscience Program, University of Guelph, Guelph, Ont., Canada (Thériault, Perreault)
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18
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Amidfar M, Kim YK. EEG Correlates of Cognitive Functions and Neuropsychiatric Disorders: A Review of Oscillatory Activity and Neural Synchrony Abnormalities. CURRENT PSYCHIATRY RESEARCH AND REVIEWS 2021. [DOI: 10.2174/2666082216999201209130117] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
A large body of evidence suggested that disruption of neural rhythms and
synchronization of brain oscillations are correlated with a variety of cognitive and perceptual processes.
Cognitive deficits are common features of psychiatric disorders that complicate treatment of
the motivational, affective and emotional symptoms.
Objective:
Electrophysiological correlates of cognitive functions will contribute to understanding of
neural circuits controlling cognition, the causes of their perturbation in psychiatric disorders and
developing novel targets for the treatment of cognitive impairments.
Methods:
This review includes a description of brain oscillations in Alzheimer’s disease, bipolar
disorder, attention-deficit/hyperactivity disorder, major depression, obsessive compulsive disorders,
anxiety disorders, schizophrenia and autism.
Results:
The review clearly shows that the reviewed neuropsychiatric diseases are associated with
fundamental changes in both spectral power and coherence of EEG oscillations.
Conclusion:
In this article, we examined the nature of brain oscillations, the association of brain
rhythms with cognitive functions and the relationship between EEG oscillations and neuropsychiatric
diseases. Accordingly, EEG oscillations can most likely be used as biomarkers in psychiatric
disorders.
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Affiliation(s)
- Meysam Amidfar
- Department of Neuroscience, Tehran University of Medical Sciences, Tehran, Iran
| | - Yong-Ku Kim
- Department of Psychiatry, College of Medicine, Korea University, Seoul, South Korea
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19
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Barkley CM, Hu Z, Fieberg AM, Eberly LE, Birnbaum AK, Leppik IE, Marino SE. An association between resting state EEG parameters and the severity of topiramate-related cognitive impairment. Epilepsy Behav 2021; 114:107598. [PMID: 33268020 DOI: 10.1016/j.yebeh.2020.107598] [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: 08/07/2020] [Revised: 10/19/2020] [Accepted: 10/24/2020] [Indexed: 11/27/2022]
Abstract
INTRODUCTION Many commonly prescribed drugs cause cognitive deficits. We investigated whether parameters of the resting-state electroencephalogram (rsEEG) are related to the severity of cognitive impairments associated with administration of the antiseizure drug topiramate (TPM) and the benzodiazepine lorazepam (LZP). METHODS We conducted a double-blind, randomized, placebo-controlled crossover study. After a baseline visit, subjects completed three sessions at which they received either a single dose of TPM, LZP, or placebo. Four-hours after drug administration and at baseline, subjects completed a working memory (WM) task after their rsEEG was recorded. After quantifying drug-related behavioral (WM accuracy (ACC)/reaction time (RT)) and electrophysiological (alpha, theta, beta (1,2), gamma power) change for each subject, we constructed drug-specific mixed effects models of change for each WM and EEG measure. Regression models were constructed to characterize the relationship between baseline rsEEG measures and drug-related performance changes. RESULTS Linear mixed effects models showed theta power increases in response to TPM administration. The results of the regression models revealed a number of robust relationships between baseline rsEEG parameters and TPM-related, but not LZP-related, WM impairment. CONCLUSIONS We showed for the first time that parameters of the rsEEG are associated with the severity of TPM-related WM deficits; this suggests that rsEEG measures may have novel clinical applications in the future.
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Affiliation(s)
- Christopher M Barkley
- Center for Clinical and Cognitive Neuropharmacology, Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, 308 Harvard Street, Minneapolis, MN 55455, United States.
| | - Zhenhong Hu
- Department of Biomedical Engineering, University of Florida, 1275 Center Drive, Gainesville, FL 32611, United States
| | - Ann M Fieberg
- Division of Biostatistics, University of Minnesota, 429 Delaware Street SE, Minneapolis, MN 55455, United States.
| | - Lynn E Eberly
- Division of Biostatistics, University of Minnesota, 429 Delaware Street SE, Minneapolis, MN 55455, United States.
| | - Angela K Birnbaum
- Center for Clinical and Cognitive Neuropharmacology, Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, 308 Harvard Street, Minneapolis, MN 55455, United States.
| | - Ilo E Leppik
- Center for Clinical and Cognitive Neuropharmacology, Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, 308 Harvard Street, Minneapolis, MN 55455, United States.
| | - Susan E Marino
- Center for Clinical and Cognitive Neuropharmacology, Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, 308 Harvard Street, Minneapolis, MN 55455, United States.
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Conley AC, Key AP, Taylor WD, Albert KM, Boyd BD, Vega JN, Newhouse PA. EEG as a Functional Marker of Nicotine Activity: Evidence From a Pilot Study of Adults With Late-Life Depression. Front Psychiatry 2021; 12:721874. [PMID: 35002791 PMCID: PMC8732868 DOI: 10.3389/fpsyt.2021.721874] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 11/15/2021] [Indexed: 11/13/2022] Open
Abstract
Late-life depression (LLD) is a debilitating condition that is associated with poor response to antidepressant medications and deficits in cognitive performance. Nicotinic cholinergic stimulation has emerged as a potentially effective candidate to improve cognitive performance in patients with cognitive impairment. Previous studies of nicotinic stimulation in animal models and human populations with cognitive impairment led to examining potential cognitive and mood effects of nicotinic stimulation in older adults with LLD. We report results from a pilot study of transdermal nicotine in LLD testing whether nicotine treatment would enhance cognitive performance and mood. The study used electroencephalography (EEG) recordings as a tool to test for potential mechanisms underlying the effect of nicotine. Eight non-smoking participants with LLD completed EEG recordings at baseline and after 12 weeks of transdermal nicotine treatment (NCT02816138). Nicotine augmentation treatment was associated with improved performance on an auditory oddball task. Analysis of event-related oscillations showed that nicotine treatment was associated with reduced beta desynchronization at week 12 for both standard and target trials. The change in beta power on standard trials was also correlated with improvement in mood symptoms. This pilot study provides preliminary evidence for the impact of nicotine in modulating cortical activity and improving mood in depressed older adults and shows the utility of using EEG as a marker of functional engagement in nicotinic interventions in clinical geriatric patients.
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Affiliation(s)
- Alexander C Conley
- Department of Psychiatry, Center for Cognitive Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Alexandra P Key
- Department of Psychiatry, Center for Cognitive Medicine, Vanderbilt University Medical Center, Nashville, TN, United States.,Vanderbilt Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Warren D Taylor
- Department of Psychiatry, Center for Cognitive Medicine, Vanderbilt University Medical Center, Nashville, TN, United States.,Department of Veterans Affairs Medical Center, Geriatric Research, Education and Clinical Center, Tennessee Valley Healthcare System, Nashville, TN, United States
| | - Kimberly M Albert
- Department of Psychiatry, Center for Cognitive Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Brian D Boyd
- Department of Psychiatry, Center for Cognitive Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Jennifer N Vega
- Department of Psychiatry, Center for Cognitive Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Paul A Newhouse
- Department of Psychiatry, Center for Cognitive Medicine, Vanderbilt University Medical Center, Nashville, TN, United States.,Department of Veterans Affairs Medical Center, Geriatric Research, Education and Clinical Center, Tennessee Valley Healthcare System, Nashville, TN, United States
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21
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Sumner RL, McMillan R, Spriggs MJ, Campbell D, Malpas G, Maxwell E, Deng C, Hay J, Ponton R, Sundram F, Muthukumaraswamy SD. Ketamine improves short-term plasticity in depression by enhancing sensitivity to prediction errors. Eur Neuropsychopharmacol 2020; 38:73-85. [PMID: 32763021 DOI: 10.1016/j.euroneuro.2020.07.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 06/02/2020] [Accepted: 07/16/2020] [Indexed: 12/17/2022]
Abstract
Major depressive disorder negatively impacts the sensitivity and adaptability of the brain's predictive coding framework. The current electroencephalography study into the antidepressant properties of ketamine investigated the downstream effects of ketamine on predictive coding and short-term plasticity in thirty patients with depression using the auditory roving mismatch negativity (rMMN). The rMMN paradigm was run 3-4 h after a single 0.44 mg/kg intravenous dose of ketamine or active placebo (remifentanil infused to a target plasma concentration of 1.7 ng/mL) in order to measure the neural effects of ketamine in the period when an improvement in depressive symptoms emerges. Depression symptomatology was measured using the Montgomery-Asberg Depression Rating Scale (MADRS); 70% of patients demonstrated at least a 50% reduction their MADRS global score. Ketamine significantly increased the MMN and P3a event related potentials, directly contrasting literature demonstrating ketamine's acute attenuation of the MMN. This effect was only reliable when all repetitions of the post-deviant tone were used. Dynamic causal modelling showed greater modulation of forward connectivity in response to a deviant tone between right primary auditory cortex and right inferior temporal cortex, which significantly correlated with antidepressant response to ketamine at 24 h. This is consistent with the hypothesis that ketamine increases sensitivity to unexpected sensory input and restores deficits in sensitivity to prediction error that are hypothesised to underlie depression. However, the lack of repetition suppression evident in the MMN evoked data compared to studies of healthy adults suggests that, at least within the short term, ketamine does not improve deficits in adaptive internal model calibration.
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Affiliation(s)
| | | | - Meg J Spriggs
- Centre for Psychedelic Research, Department of Medicine, Imperial College London, UK; Brain Research New Zealand; School of Psychology, University of Auckland, New Zealand
| | - Doug Campbell
- Department of Anaesthesia and Perioperative Medicine, Auckland District Health Board, New Zealand
| | - Gemma Malpas
- Department of Anaesthesia and Perioperative Medicine, Auckland District Health Board, New Zealand
| | - Elizabeth Maxwell
- Department of Anaesthesia and Perioperative Medicine, Auckland District Health Board, New Zealand
| | - Carolyn Deng
- Department of Anaesthesia and Perioperative Medicine, Auckland District Health Board, New Zealand
| | - John Hay
- Department of Anaesthesia and Perioperative Medicine, Auckland District Health Board, New Zealand
| | - Rhys Ponton
- School of Pharmacy, University of Auckland, New Zealand
| | - Frederick Sundram
- Department of Psychological Medicine, University of Auckland, New Zealand
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22
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Rajpurkar P, Yang J, Dass N, Vale V, Keller AS, Irvin J, Taylor Z, Basu S, Ng A, Williams LM. Evaluation of a Machine Learning Model Based on Pretreatment Symptoms and Electroencephalographic Features to Predict Outcomes of Antidepressant Treatment in Adults With Depression: A Prespecified Secondary Analysis of a Randomized Clinical Trial. JAMA Netw Open 2020; 3:e206653. [PMID: 32568399 PMCID: PMC7309440 DOI: 10.1001/jamanetworkopen.2020.6653] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
IMPORTANCE Despite the high prevalence and potential outcomes of major depressive disorder, whether and how patients will respond to antidepressant medications is not easily predicted. OBJECTIVE To identify the extent to which a machine learning approach, using gradient-boosted decision trees, can predict acute improvement for individual depressive symptoms with antidepressants based on pretreatment symptom scores and electroencephalographic (EEG) measures. DESIGN, SETTING, AND PARTICIPANTS This prognostic study analyzed data collected as part of the International Study to Predict Optimized Treatment in Depression, a randomized, prospective open-label trial to identify clinically useful predictors and moderators of response to commonly used first-line antidepressant medications. Data collection was conducted at 20 sites spanning 5 countries and including 518 adult outpatients (18-65 years of age) from primary care or specialty care practices who received a diagnosis of current major depressive disorder between December 1, 2008, and September 30, 2013. Patients were antidepressant medication naive or willing to undergo a 1-week washout period of any nonprotocol antidepressant medication. Statistical analysis was conducted from January 5 to June 30, 2019. EXPOSURES Participants with major depressive disorder were randomized in a 1:1:1 ratio to undergo 8 weeks of treatment with escitalopram oxalate (n = 162), sertraline hydrochloride (n = 176), or extended-release venlafaxine hydrochloride (n = 180). MAIN OUTCOMES AND MEASURES The primary objective was to predict improvement in individual symptoms, defined as the difference in score for each of the symptoms on the 21-item Hamilton Rating Scale for Depression from baseline to week 8, evaluated using the C index. RESULTS The resulting data set contained 518 patients (274 women; mean [SD] age, 39.0 [12.6] years; mean [SD] 21-item Hamilton Rating Scale for Depression score improvement, 13.0 [7.0]). With the use of 5-fold cross-validation for evaluation, the machine learning model achieved C index scores of 0.8 or higher on 12 of 21 clinician-rated symptoms, with the highest C index score of 0.963 (95% CI, 0.939-1.000) for loss of insight. The importance of any single EEG feature was higher than 5% for prediction of 7 symptoms, with the most important EEG features being the absolute delta band power at the occipital electrode sites (O1, 18.8%; Oz, 6.7%) for loss of insight. Over and above the use of baseline symptom scores alone, the use of both EEG and baseline symptom features was associated with a significant increase in the C index for improvement in 4 symptoms: loss of insight (C index increase, 0.012 [95% CI, 0.001-0.020]), energy loss (C index increase, 0.035 [95% CI, 0.011-0.059]), appetite changes (C index increase, 0.017 [95% CI, 0.003-0.030]), and psychomotor retardation (C index increase, 0.020 [95% CI, 0.008-0.032]). CONCLUSIONS AND RELEVANCE This study suggests that machine learning may be used to identify independent associations of symptoms and EEG features to predict antidepressant-associated improvements in specific symptoms of depression. The approach should next be prospectively validated in clinical trials and settings. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT00693849.
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Affiliation(s)
- Pranav Rajpurkar
- Department of Computer Science, Stanford University, Stanford, California
| | - Jingbo Yang
- Department of Computer Science, Stanford University, Stanford, California
| | - Nathan Dass
- Department of Computer Science, Stanford University, Stanford, California
| | - Vinjai Vale
- Department of Computer Science, Stanford University, Stanford, California
| | - Arielle S. Keller
- Stanford Center for Precision Mental Health and Wellness, Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
| | - Jeremy Irvin
- Department of Computer Science, Stanford University, Stanford, California
| | - Zachary Taylor
- Stanford Center for Precision Mental Health and Wellness, Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
| | - Sanjay Basu
- Center for Primary Care, Harvard Medical School, Boston, Massachusetts
- Research and Analytics, Collective Health, San Francisco, California
- Division of Primary Care and Public Health, Imperial College London School of Public Health, London, United Kingdom
| | - Andrew Ng
- Department of Computer Science, Stanford University, Stanford, California
| | - Leanne M. Williams
- Stanford Center for Precision Mental Health and Wellness, Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California
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Nikolin S, Martin D, Loo CK, Iacoviello BM, Boonstra TW. Assessing neurophysiological changes associated with combined transcranial direct current stimulation and cognitive-emotional training for treatment-resistant depression. Eur J Neurosci 2020; 51:2119-2133. [PMID: 31859397 DOI: 10.1111/ejn.14656] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Revised: 11/20/2019] [Accepted: 12/11/2019] [Indexed: 12/15/2022]
Abstract
Transcranial direct current stimulation (tDCS), a form of non-invasive brain stimulation, is a promising treatment for depression. Recent research suggests that tDCS efficacy can be augmented using concurrent cognitive-emotional training (CET). However, the neurophysiological changes associated with this combined intervention remain to be elucidated. We therefore examined the effects of tDCS combined with CET using electroencephalography (EEG). A total of 20 participants with treatment-resistant depression took part in this open-label study and received 18 sessions over 6 weeks of tDCS and concurrent CET. Resting-state and task-related EEG during a 3-back working memory task were acquired at baseline and immediately following the treatment course. Results showed an improvement in mood and working memory accuracy, but not response time, following the intervention. We did not find significant effects of the intervention on resting-state power spectral density (frontal theta and alpha asymmetry), time-frequency power (alpha event-related desynchronisation and theta event-related synchronisation) or event-related potentials (P2 and P3 components). We therefore identified little evidence of neurophysiological changes associated with treatment using tDCS and concurrent CET, despite significant improvements in mood and near-transfer effects of cognitive training to working memory accuracy. Further research incorporating a sham-controlled group may be necessary to identify the neurophysiological effects of the intervention.
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Affiliation(s)
- Stevan Nikolin
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.,Black Dog Institute, Sydney, NSW, Australia
| | - Donel Martin
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.,Black Dog Institute, Sydney, NSW, Australia
| | - Colleen K Loo
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.,Black Dog Institute, Sydney, NSW, Australia.,St. George Hospital, Sydney, NSW, Australia
| | - Brian M Iacoviello
- Click Therapeutics, Inc., New York, NY, USA.,Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tjeerd W Boonstra
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.,Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
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24
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Normalization of EEG in depression after antidepressant treatment with sertraline? A preliminary report. J Affect Disord 2019; 259:67-72. [PMID: 31437703 DOI: 10.1016/j.jad.2019.08.016] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 08/08/2019] [Accepted: 08/12/2019] [Indexed: 12/20/2022]
Abstract
BACKGROUND MDD patients with abnormal EEG patterns seem more likely to be non-responsive to the antidepressants escitalopram and venlafaxine, but not sertraline, than patients without EEG abnormalities. This finding suggests that patients with both MDD and abnormal EEGs may differentially respond to antidepressant treatment. In the current study, we investigated whether depressed patients with an abnormal EEG show a normalization of the EEG related to antidepressant treatment and response and whether such effect is drug specific, and whether having had early life stress (ELS) increases the chance of abnormal activity. METHODS Baseline and week 8 EEGs and depression symptoms were extracted from a large multicenter study (iSPOT-D, n = 1008) where depressed patients were randomized to escitalopram, sertraline, or venlafaxine-XR treatment. We calculated Odds Ratios of EEG normalization and depression response in patients with an abnormal EEG at baseline, comparing sertraline versus other antidepressants. RESULTS Fifty seven patients with abnormal EEGs were included. EEGs did not normalize significantly more with sertraline compared to other antidepressants (OR = 1.9, p = .280). However, patients with a normalized EEG taking sertraline were 5.2 times more likely to respond than subjects taking other antidepressants (p = .019). ELS was not significantly related to abnormal activity. LIMITATIONS Neurophysiological recordings were limited in time (two times 2-minute EEGs) and statistical power (n = 57 abnormal EEGs). CONCLUSIONS Response rates in patients with normalized EEG taking sertraline were significantly larger than in subjects treated with escitalopram/venlafaxine. This adds to personalized medicine and suggests a possible drug repurposing for sertraline.
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25
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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.
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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
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26
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Bares M, Novak T, Vlcek P, Hejzlar M, Brunovsky M. Early change of prefrontal theta cordance and occipital alpha asymmetry in the prediction of responses to antidepressants. Int J Psychophysiol 2019; 143:1-8. [PMID: 31195067 DOI: 10.1016/j.ijpsycho.2019.06.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Revised: 06/07/2019] [Accepted: 06/07/2019] [Indexed: 12/18/2022]
Abstract
BACKGROUND The study evaluated the effectiveness of EEG alpha 1, alpha 2 and theta power, along with prefrontal theta cordance (PFC), frontal and occipital alpha 1, alpha 2 asymmetry (FAA1/2, OAA1/2) at baseline and their changes at week 1 in predicting response to antidepressants. METHOD Resting-state EEG data were recorded from 103 depressive patients that were treated in average for 5.1 ± 0.9 weeks with SSRIs (n = 57) and SNRIs (n = 46). RESULTS Fifty-five percent of patients (n = 56) responded to treatment (i.e.reduction of Montgomery-Åsberg Depression Rating Scale score ≥ 50%) and 45% (n = 47) of treated subjects did not reach positive treatment outcome. No differences in EEG baseline alpha and theta power or changes at week 1 for prefrontal, frontal, central, temporal and occipital regions were found between responders and non-responders. Both groups showed no differences at baseline PFC, FAA1/2 and OAA1/2 as well as change of FAA1/2 at week 1. The only parameters associated with treatment outcome were decrease of PFC in responders and increase of OAA1/2 at week 1 in non-responders. There was no influence of the used antidepressant classes on the results. The PFC change at week 1 (PFCC) (area under curve-AUC = 0.75) showed only a numerically higher predictive ability than OAA change in alpha 1 (OAA1C, AUC = 0.64)/alpha 2 (OAA2C, AUC = 0.63). A combined model, where OAA1C was added to PFCC (AUC = 0.79), did not significantly improve response prediction. CONCLUSION Besides PFCC, we found that OAA1C/OAA2C might be another candidate for EEG predictors of antidepressant response.
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Affiliation(s)
- Martin Bares
- National Institute of Mental Health Czech Republic, Topolova 748, 250 67 Klecany, Czech Republic; Department of Psychiatry and Medical Psychology of Third Medical Faculty, Charles University, Ruská 87, 100 00 Prague 10, Czech Republic.
| | - Tomas Novak
- National Institute of Mental Health Czech Republic, Topolova 748, 250 67 Klecany, Czech Republic; Department of Psychiatry and Medical Psychology of Third Medical Faculty, Charles University, Ruská 87, 100 00 Prague 10, Czech Republic.
| | - Premysl Vlcek
- National Institute of Mental Health Czech Republic, Topolova 748, 250 67 Klecany, Czech Republic; Department of Psychiatry and Medical Psychology of Third Medical Faculty, Charles University, Ruská 87, 100 00 Prague 10, Czech Republic.
| | - Martin Hejzlar
- National Institute of Mental Health Czech Republic, Topolova 748, 250 67 Klecany, Czech Republic; Department of Psychiatry and Medical Psychology of Third Medical Faculty, Charles University, Ruská 87, 100 00 Prague 10, Czech Republic.
| | - Martin Brunovsky
- National Institute of Mental Health Czech Republic, Topolova 748, 250 67 Klecany, Czech Republic; Department of Psychiatry and Medical Psychology of Third Medical Faculty, Charles University, Ruská 87, 100 00 Prague 10, Czech Republic.
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27
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Pharmacogenetics of Antidepressants: from Genetic Findings to Predictive Strategies. ACTA BIOMEDICA SCIENTIFICA 2019. [DOI: 10.29413/abs.2019-4.2.5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
The constantly growing contribution of depressive disorders to the global disease statistics calls for a growth of treatment effectiveness and optimization. Antidepressants are the most frequently prescribed medicines for depressive disorders. However, development of a standardized pharmacotherapeutic approach is burdened by the genomic heterogeneity, lack of reliable predictive biomarkers and variability of the medicines metabolism aggravated by multiple side effects of antidepressants. According to modern assessments up to 20 % of the genes expressed in our brain are involved in the pathogenesis of depression. Large-scale genetic and genomic research has found a number of potentially prognostic genes. It has also been proven that the effectiveness and tolerability of antidepressants directly depend on the variable activity of the enzymes that metabolize medicines. Almost all modern antidepressants are metabolized by the cytochrome P450 family enzymes. The most promising direction of research today is the GWAS (Genome-Wide Association Study) method that is aimed to link genomic variations with phenotypical manifestations. In this type of research genomes of depressive patients with different phenotypes are compared to the genomes of the control group containing same age, sex and other parameters healthy people. Notably, regardless of the large cohorts of patients analyzed, none of the GWA studies conducted so far can reliably reproduce the results of other analogous studies. The explicit heterogeneity of the genes associated with the depression pathogenesis and their pleiotropic effects are strongly influenced by environmental factors. This may explain the difficulty of obtaining clear and reproducible results. However, despite any negative circumstances, the active multidirectional research conducted today, raises the hope of clinicians and their patients to get a whole number of schedules how to achieve remission faster and with guaranteed results
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28
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Thériault RK, Perreault ML. Hormonal regulation of circuit function: sex, systems and depression. Biol Sex Differ 2019; 10:12. [PMID: 30819248 PMCID: PMC6394099 DOI: 10.1186/s13293-019-0226-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Accepted: 02/18/2019] [Indexed: 01/10/2023] Open
Abstract
Major depressive disorder (MDD) is a debilitating chronic illness that is two times more prevalent in women than in men. The mechanisms associated with the increased female susceptibility to depression remain poorly characterized. Aberrant neuronal oscillatory activity within the putative depression network is an emerging mechanism underlying MDD. However, innate sex differences in network activity and its contribution to depression vulnerability have not been well described. In this review, current evidence of sex differences in neuronal oscillatory activity, including the influence of sex hormones and female cycling, will first be described followed by evidence of disrupted neuronal circuit function in MDD and the effects of antidepressant treatment. Lastly, current knowledge of sex differences in MDD-associated aberrant circuit function and oscillatory activity will be highlighted, with an emphasis on the role of sex steroids and female cycling. Collectively, it is clear that there are significant gaps in the literature regarding innate and pathologically associated sex differences in network activity and that the elucidation of these differences is invaluable to our understanding of sex-specific vulnerabilities and therapies for MDD.
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Affiliation(s)
- Rachel-Karson Thériault
- Department of Molecular and Cellular Biology, University of Guelph (ON), 50 Stone Rd. E, Guelph, Ontario, N1G 2W1, Canada.,Collaborative Neuroscience Program, University of Guelph (ON), Guelph, Canada
| | - Melissa L Perreault
- Department of Molecular and Cellular Biology, University of Guelph (ON), 50 Stone Rd. E, Guelph, Ontario, N1G 2W1, Canada. .,Collaborative Neuroscience Program, University of Guelph (ON), Guelph, Canada.
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29
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Bailey NW, Hoy KE, Rogasch NC, Thomson RH, McQueen S, Elliot D, Sullivan CM, Fulcher BD, Daskalakis ZJ, Fitzgerald PB. Differentiating responders and non-responders to rTMS treatment for depression after one week using resting EEG connectivity measures. J Affect Disord 2019; 242:68-79. [PMID: 30172227 DOI: 10.1016/j.jad.2018.08.058] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 07/30/2018] [Accepted: 08/12/2018] [Indexed: 12/15/2022]
Abstract
BACKGROUND Non-response to repetitive transcranial magnetic stimulation (rTMS) treatment for depression is costly for both patients and clinics. Simple and cheap methods to predict response would reduce this burden. Resting EEG measures differentiate responders from non-responders, so may have utility for response prediction. METHODS Fifty patients with treatment resistant depression and 21 controls had resting electroencephalography (EEG) recorded at baseline (BL). Patients underwent 5-8 weeks of rTMS treatment, with EEG recordings repeated at week 1 (W1). Forty-two participants had valid BL and W1 EEG data, and 12 were responders. Responders and non-responders were compared at BL and W1 in measures of theta (4-8 Hz) and alpha (8-13 Hz) power and connectivity, frontal theta cordance and alpha peak frequency. Control group comparisons were made for measures that differed between responders and non-responders. A machine learning algorithm assessed the potential to differentiate responders from non-responders using EEG measures in combination with change in depression scores from BL to W1. RESULTS Responders showed elevated theta connectivity across BL and W1. No other EEG measures differed between groups. Responders could be distinguished from non-responders with a mean sensitivity of 0.84 (p = 0.001) and specificity of 0.89 (p = 0.002) using cross-validated machine learning classification on the combination of all EEG and mood measures. LIMITATIONS The low response rate limited our sample size to only 12 responders. CONCLUSION Resting theta connectivity at BL and W1 differ between responders and non-responders, and show potential for predicting response to rTMS treatment for depression.
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Affiliation(s)
- N W Bailey
- Monash Alfred Psychiatry Research Centre, Monash University Central Clinical School, Commercial Rd, Melbourne, Victoria, Australia..
| | - K E Hoy
- Monash Alfred Psychiatry Research Centre, Monash University Central Clinical School, Commercial Rd, Melbourne, Victoria, Australia
| | - N C Rogasch
- Brain and Mental Health Laboratory, Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Clayton 3168, Victoria, Australia
| | - R H Thomson
- Monash Alfred Psychiatry Research Centre, Monash University Central Clinical School, Commercial Rd, Melbourne, Victoria, Australia
| | - S McQueen
- Monash Alfred Psychiatry Research Centre, Monash University Central Clinical School, Commercial Rd, Melbourne, Victoria, Australia
| | - D Elliot
- Monash Alfred Psychiatry Research Centre, Monash University Central Clinical School, Commercial Rd, Melbourne, Victoria, Australia
| | - C M Sullivan
- Monash Alfred Psychiatry Research Centre, Monash University Central Clinical School, Commercial Rd, Melbourne, Victoria, Australia
| | - B D Fulcher
- Brain and Mental Health Laboratory, Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Clayton 3168, Victoria, Australia
| | - Z J Daskalakis
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - P B Fitzgerald
- Monash Alfred Psychiatry Research Centre, Monash University Central Clinical School, Commercial Rd, Melbourne, Victoria, Australia.; Epworth Healthcare, The Epworth Clinic, Camberwell 3004, Victoria, Australia
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30
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Widge AS, Bilge MT, Montana R, Chang W, Rodriguez CI, Deckersbach T, Carpenter LL, Kalin NH, Nemeroff CB. Electroencephalographic Biomarkers for Treatment Response Prediction in Major Depressive Illness: A Meta-Analysis. Am J Psychiatry 2019; 176:44-56. [PMID: 30278789 PMCID: PMC6312739 DOI: 10.1176/appi.ajp.2018.17121358] [Citation(s) in RCA: 98] [Impact Index Per Article: 19.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
OBJECTIVE Reducing unsuccessful treatment trials could improve depression treatment. Quantitative EEG (QEEG) may predict treatment response and is being commercially marketed for this purpose. The authors sought to quantify the reliability of QEEG for response prediction in depressive illness and to identify methodological limitations of the available evidence. METHOD The authors conducted a meta-analysis of diagnostic accuracy for QEEG in depressive illness, based on articles published between January 2000 and November 2017. The review included all articles that used QEEG to predict response during a major depressive episode, regardless of patient population, treatment, or QEEG marker. The primary meta-analytic outcome was the accuracy for predicting response to depression treatment, expressed as sensitivity, specificity, and the logarithm of the diagnostic odds ratio. Raters also judged each article on indicators of good research practice. RESULTS In 76 articles reporting 81 biomarkers, the meta-analytic estimates showed a sensitivity of 0.72 (95% CI=0.67-0.76) and a specificity of 0.68 (95% CI=0.63-0.73). The logarithm of the diagnostic odds ratio was 1.89 (95% CI=1.56-2.21), and the area under the receiver operator curve was 0.76 (95% CI=0.71-0.80). No specific QEEG biomarker or specific treatment showed greater predictive power than the all-studies estimate in a meta-regression. Funnel plot analysis suggested substantial publication bias. Most studies did not use ideal practices. CONCLUSIONS QEEG does not appear to be clinically reliable for predicting depression treatment response, as the literature is limited by underreporting of negative results, a lack of out-of-sample validation, and insufficient direct replication of previous findings. Until these limitations are remedied, QEEG is not recommended for guiding selection of psychiatric treatment.
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Affiliation(s)
- Alik S. Widge
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA,Picower Institute for Learning & Memory, Massachusetts Institute of Technology, Cambridge, MA,Correspondence to Alik S Widge, MD, PhD, 149 13th St, Room 2627, Charlestown, MA 02129, , 617-643-2580
| | - M. Taha Bilge
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Rebecca Montana
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Weilynn Chang
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | | | - Thilo Deckersbach
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Linda L. Carpenter
- Butler Hospital and Warren Alpert Medical School of Brown University, Providence, RI
| | - Ned H. Kalin
- Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, WI
| | - Charles B. Nemeroff
- Department of Psychiatry, University of Miami Miller School of Medicine, Miami, FL
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Gonda X, Sharma SR, Tarazi FI. Vortioxetine: a novel antidepressant for the treatment of major depressive disorder. Expert Opin Drug Discov 2018; 14:81-89. [DOI: 10.1080/17460441.2019.1546691] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Xenia Gonda
- Department of Psychiatry and Psychotherapy, Kutvolgyi Clinical Centre, Semmelweis University, Budapest, Hungary
- MTA-SE Neurochemistry and Neuropsychopharmacology Research Group, Hungarian Academy of Sciences and Semmelweis University, Budapest, Hungary
- NAP-2-SE New Antidepressant Target Research Group, Semmelweis University, Budapest, Hungary
| | - Samata R. Sharma
- Department of Psychiatry, Harvard Medical School, Brigham and Women’s Hospital, Boston, USA
| | - Frank I. Tarazi
- Department of Psychiatry and Neuroscience Program, Harvard Medical School, McLean Hospital, Belmont, USA
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Identifying Ketamine Responses in Treatment-Resistant Depression Using a Wearable Forehead EEG. IEEE Trans Biomed Eng 2018; 66:1668-1679. [PMID: 30369433 DOI: 10.1109/tbme.2018.2877651] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
This study explores responses to ketamine in patients with treatment-resistant depression (TRD) using a wearable forehead electroencephalography (EEG) device. We recruited and randomly assigned 55 outpatients with TRD into three approximately equal-sized groups (A: 0.5-mg/kg ketamine; B: 0.2-mg/kg ketamine; and C: normal saline) under double-blind conditions. The ketamine responses were measured by EEG signals and Hamilton depression rating scale scores. At baseline, the responders showed significantly weaker EEG theta power than the non-responders (p < 0.05). Compared to the baseline, the responders exhibited higher EEG alpha power but lower EEG alpha asymmetry and theta cordance post-treatment (p < 0.05). Furthermore, our baseline EEG predictor classified the responders and non-responders with 81.3 ± 9.5% accuracy, 82.1 ± 8.6% sensitivity, and 91.9 ± 7.4% specificity. In conclusion, the rapid antidepressant effects of mixed doses of ketamine are associated with prefrontal EEG power, asymmetry, and cordance at baseline and early post-treatment changes. Prefrontal EEG patterns at baseline may serve as indicators of ketamine effects. Our randomized double-blind placebo-controlled study provides information regarding the clinical impacts on the potential targets underlying baseline identification and early changes from the effects of ketamine in patients with TRD.
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Yang Y, Connolly AT, Shanechi MM. A control-theoretic system identification framework and a real-time closed-loop clinical simulation testbed for electrical brain stimulation. J Neural Eng 2018; 15:066007. [DOI: 10.1088/1741-2552/aad1a8] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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34
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Gonda X, Petschner P, Eszlari N, Baksa D, Edes A, Antal P, Juhasz G, Bagdy G. Genetic variants in major depressive disorder: From pathophysiology to therapy. Pharmacol Ther 2018; 194:22-43. [PMID: 30189291 DOI: 10.1016/j.pharmthera.2018.09.002] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
In spite of promising preclinical results there is a decreasing number of new registered medications in major depression. The main reason behind this fact is the lack of confirmation in clinical studies for the assumed, and in animals confirmed, therapeutic results. This suggests low predictive value of animal studies for central nervous system disorders. One solution for identifying new possible targets is the application of genetics and genomics, which may pinpoint new targets based on the effect of genetic variants in humans. The present review summarizes such research focusing on depression and its therapy. The inconsistency between most genetic studies in depression suggests, first of all, a significant role of environmental stress. Furthermore, effect of individual genes and polymorphisms is weak, therefore gene x gene interactions or complete biochemical pathways should be analyzed. Even genes encoding target proteins of currently used antidepressants remain non-significant in genome-wide case control investigations suggesting no main effect in depression, but rather an interaction with stress. The few significant genes in GWASs are related to neurogenesis, neuronal synapse, cell contact and DNA transcription and as being nonspecific for depression are difficult to harvest pharmacologically. Most candidate genes in replicable gene x environment interactions, on the other hand, are connected to the regulation of stress and the HPA axis and thus could serve as drug targets for depression subgroups characterized by stress-sensitivity and anxiety while other risk polymorphisms such as those related to prominent cognitive symptoms in depression may help to identify additional subgroups and their distinct treatment. Until these new targets find their way into therapy, the optimization of current medications can be approached by pharmacogenomics, where metabolizing enzyme polymorphisms remain prominent determinants of therapeutic success.
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Affiliation(s)
- Xenia Gonda
- Department of Psychiatry and Psychotherapy, Kutvolgyi Clinical Centre, Semmelweis University, Budapest, Hungary; NAP-2-SE New Antidepressant Target Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary; MTA-SE Neuropsychopharmacology and Neurochemistry Research Group, Hungarian Academy of Sciences, Semmelweis University, Budapest, Hungary.
| | - Peter Petschner
- MTA-SE Neuropsychopharmacology and Neurochemistry Research Group, Hungarian Academy of Sciences, Semmelweis University, Budapest, Hungary; Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary
| | - Nora Eszlari
- NAP-2-SE New Antidepressant Target Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary; Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary
| | - Daniel Baksa
- Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary; SE-NAP 2 Genetic Brain Imaging Migraine Research Group, Hungarian Academy of Sciences, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
| | - Andrea Edes
- Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary; SE-NAP 2 Genetic Brain Imaging Migraine Research Group, Hungarian Academy of Sciences, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary
| | - Peter Antal
- Department of Measurement and Information Systems, Budapest University of Technology and Economics, Budapest, Hungary
| | - Gabriella Juhasz
- Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary; SE-NAP 2 Genetic Brain Imaging Migraine Research Group, Hungarian Academy of Sciences, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary; Neuroscience and Psychiatry Unit, University of Manchester, Manchester Academic Health Sciences Centre, Manchester, UK
| | - Gyorgy Bagdy
- NAP-2-SE New Antidepressant Target Research Group, Hungarian Brain Research Program, Semmelweis University, Budapest, Hungary; MTA-SE Neuropsychopharmacology and Neurochemistry Research Group, Hungarian Academy of Sciences, Semmelweis University, Budapest, Hungary; Department of Pharmacodynamics, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary.
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Carpenter G, Harbin HT, Smith RL, Hornberger J, Nash DB. A Promising New Strategy to Improve Treatment Outcomes for Patients with Depression. Popul Health Manag 2018; 22:223-228. [PMID: 30156460 PMCID: PMC6555180 DOI: 10.1089/pop.2018.0101] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Each year, ineffective medical management of patients with mental illness compromises the health and well-being of individuals, and also impacts communities and our society. A variety of interrelated factors have impeded the health system's ability to treat patients with behavior health conditions adequately. A key contributing factor is a lack of objective markers to help predict patient response to specific drugs that has led to patterns of “trial and error” prescribing. For many years, clinicians have sought objective data (eg, a laboratory or imaging test) to assist them in selecting appropriate treatments for individual patients. Electroencephalogram (EEG) findings coupled with medication outcomes data may provide a solution. “Crowdsourced” physician registries that reference clinical outcomes to individual patient physiology have been used successfully for cancers. These techniques are now being explored in the context of behavioral health care. The Psychiatric EEG Evaluation Registry (PEER) is one such approach. PEER is a clinical phenotypic database comprising more than 11,000 baseline EEGs and more than 39,000 outcomes of medication treatment for a variety of mental health diagnoses. Collective findings from 45 studies (3130 patients) provide compelling evidence for PEER as a relatively simple, inexpensive predictor of likely patient response to specific antidepressants and likely treatment-related side effects (including suicidal ideation).
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Affiliation(s)
| | | | | | - John Hornberger
- 3 Stanford University, Stanford, California.,4 Cedar Associates, Menlo Park, California
| | - David B Nash
- 5 Jefferson College of Population Health, Philadelphia, Pennsylvania
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Kesebir S, Yosmaoğlu A. QEEG in affective disorder: about to be a biomarker, endophenotype and predictor of treatment response. Heliyon 2018; 4:e00741. [PMID: 30148219 PMCID: PMC6106696 DOI: 10.1016/j.heliyon.2018.e00741] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Revised: 06/22/2018] [Accepted: 08/13/2018] [Indexed: 12/28/2022] Open
Abstract
QEEG is a relatively easy to apply, cost effective method among many electrophysiologic and functional brain imaging techniques used to assess individuals for diagnosis and determination of the most suitable treatment. Its temporal resolution provides an important advantage. Many specific EEG indicators play a role in the differential diagnosis of neuropsychiatric disorders. QEEG has advantages over EEG in the dimensional approach to symptomatology of psychiatric disorders. The prognostic value of EEG has a long history. Slow wave EEG rhythm has been reported as a predictor and measure of clinical improvement under ECT. The induction level in delta band activity predicts the long term effect of ECT. Current studies focus on the predictive power of EEG on response to pharmacotherapy and somatic treatments other than ECT. This paper discusses either QEEG can be a biomarker and/or an endophenotype in affective disorders, if it has diagnostic and prognostic value and if it can contribute to personalized treatment design, through a review of relevant literature.
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Affiliation(s)
- Sermin Kesebir
- Üsküdar University, NPİstanbul Brain Hospital, İstanbul, Turkey
| | - Ahmet Yosmaoğlu
- Üsküdar University, NPİstanbul Brain Hospital, İstanbul, Turkey
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37
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Voegeli G, Cléry-Melin ML, Ramoz N, Gorwood P. Progress in Elucidating Biomarkers of Antidepressant Pharmacological Treatment Response: A Systematic Review and Meta-analysis of the Last 15 Years. Drugs 2018; 77:1967-1986. [PMID: 29094313 DOI: 10.1007/s40265-017-0819-9] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
BACKGROUND Antidepressant drugs are widely prescribed, but response rates after 3 months are only around one-third, explaining the importance of the search of objectively measurable markers predicting positive treatment response. These markers are being developed in different fields, with different techniques, sample sizes, costs, and efficiency. It is therefore difficult to know which ones are the most promising. OBJECTIVE Our purpose was to compute comparable (i.e., standardized) effect sizes, at study level but also at marker level, in order to conclude on the efficacy of each technique used and all analyzed markers. METHODS We conducted a systematic search on the PubMed database to gather all articles published since 2000 using objectively measurable markers to predict antidepressant response from five domains, namely cognition, electrophysiology, imaging, genetics, and transcriptomics/proteomics/epigenetics. A manual screening of the abstracts and the reference lists of these articles completed the search process. RESULTS Executive functioning, theta activity in the rostral Anterior Cingular Cortex (rACC), and polysomnographic sleep measures could be considered as belonging to the best objectively measured markers, with a combined d around 1 and at least four positive studies. For inter-category comparisons, the approaches that showed the highest effect sizes are, in descending order, imaging (combined d between 0.703 and 1.353), electrophysiology (0.294-1.138), cognition (0.929-1.022), proteins/nucleotides (0.520-1.18), and genetics (0.021-0.515). CONCLUSION Markers of antidepressant treatment outcome are numerous, but with a discrepant level of accuracy. Many biomarkers and cognitions have sufficient predictive value (d ≥ 1) to be potentially useful for clinicians to predict outcome and personalize antidepressant treatment.
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Affiliation(s)
- G Voegeli
- CMME, Hôpital Sainte-Anne, Université Paris Descartes, 100 rue de la Santé, 75014, Paris, France.
- Centre de Psychiatrie et Neuroscience (INSERM UMR 894), 2 ter rue d'Alésia, 75014, Paris, France.
| | - M L Cléry-Melin
- CMME, Hôpital Sainte-Anne, Université Paris Descartes, 100 rue de la Santé, 75014, Paris, France
- Centre de Psychiatrie et Neuroscience (INSERM UMR 894), 2 ter rue d'Alésia, 75014, Paris, France
| | - N Ramoz
- CMME, Hôpital Sainte-Anne, Université Paris Descartes, 100 rue de la Santé, 75014, Paris, France
- Centre de Psychiatrie et Neuroscience (INSERM UMR 894), 2 ter rue d'Alésia, 75014, Paris, France
| | - P Gorwood
- CMME, Hôpital Sainte-Anne, Université Paris Descartes, 100 rue de la Santé, 75014, Paris, France
- Centre de Psychiatrie et Neuroscience (INSERM UMR 894), 2 ter rue d'Alésia, 75014, Paris, France
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38
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Shim M, Im CH, Kim YW, Lee SH. Altered cortical functional network in major depressive disorder: A resting-state electroencephalogram study. NEUROIMAGE-CLINICAL 2018; 19:1000-1007. [PMID: 30003037 PMCID: PMC6039896 DOI: 10.1016/j.nicl.2018.06.012] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/09/2018] [Revised: 05/31/2018] [Accepted: 06/11/2018] [Indexed: 01/16/2023]
Abstract
Background Electroencephalogram (EEG)-based brain network analysis is a useful biological correlate reflecting brain function. Sensor-level network analysis might be contaminated by volume conduction and does not explain regional brain characteristics. Source-level network analysis could be a useful alternative. We analyzed EEG-based source-level network in major depressive disorder (MDD). Method Resting-state EEG was recorded in 87 MDD and 58 healthy controls, and cortical source signals were estimated. Network measures were calculated: global indices (strength, clustering coefficient (CC), path length (PL), and efficiency) and nodal indices (eigenvector centrality and nodal CC) in six frequency. Correlation analyses were performed between network indices and symptom scales. Results At the global level, MDD showed decreased strength, CC in theta and alpha bands, and efficiency in alpha band, while enhanced PL in alpha band. At nodal level, eigenvector centrality of alpha band showed region dependent changes in MDD. Nodal CCs of alpha band were reduced in MDD and were negatively correlated with depression and anxiety scales. Conclusion Disturbances in EEG-based brain network indices might reflect altered emotional processing in MDD. These source-level network indices might provide useful biomarkers to understand regional brain pathology in MDD. We investigated the altered function of electroencephalogram-based cortical network for major depressive disorder (MDD). MDD showed altered cortical brain network at both global and nodal level network in alpha frequency band. Abnormal network indices in MDD were significantly correlated with depression and anxiety symptom scale scores. Disrupted cortical networks band might reflect altered neural pathway during emotional processing in patients with MDD.
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Affiliation(s)
- Miseon Shim
- Psychiatry Department, University of Missouri, Kansas City, USA; Clinical Emotion and Cognition Research Laboratory, Goyang, Republic of Korea
| | - Chang-Hwan Im
- Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea
| | - Yong-Wook Kim
- Department of Biomedical Engineering, Hanyang University, Seoul, Republic of Korea; Clinical Emotion and Cognition Research Laboratory, Goyang, Republic of Korea
| | - Seung-Hwan Lee
- Clinical Emotion and Cognition Research Laboratory, Goyang, Republic of Korea; Psychiatry Department, Ilsan Paik Hospital, Inje University, Goyang, Republic of Korea.
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39
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Peripheral biomarkers of major depression and antidepressant treatment response: Current knowledge and future outlooks. J Affect Disord 2018; 233:3-14. [PMID: 28709695 PMCID: PMC5815949 DOI: 10.1016/j.jad.2017.07.001] [Citation(s) in RCA: 99] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2017] [Revised: 06/19/2017] [Accepted: 07/03/2017] [Indexed: 12/28/2022]
Abstract
BACKGROUND In recent years, we have accomplished a deeper understanding about the pathophysiology of major depressive disorder (MDD). Nevertheless, this improved comprehension has not translated to improved treatment outcome, as identification of specific biologic markers of disease may still be crucial to facilitate a more rapid, successful treatment. Ongoing research explores the importance of screening biomarkers using neuroimaging, neurophysiology, genomics, proteomics, and metabolomics measures. RESULTS In the present review, we highlight the biomarkers that are differentially expressed in MDD and treatment response and place a particular emphasis on the most recent progress in advancing technology which will continue the search for blood-based biomarkers. LIMITATIONS Due to space constraints, we are unable to detail all biomarker platforms, such as neurophysiological and neuroimaging markers, although their contributions are certainly applicable to a biomarker review and valuable to the field. CONCLUSIONS Although the search for reliable biomarkers of depression and/or treatment outcome is ongoing, the rapidly-expanding field of research along with promising new technologies may provide the foundation for identifying key factors which will ultimately help direct patients toward a quicker and more effective treatment for MDD.
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40
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Baskaran A, Farzan F, Milev R, Brenner CA, Alturi S, Pat McAndrews M, Blier P, Evans K, Foster JA, Frey BN, Giacobbe P, Lam RW, Leri F, MacQueen GM, Müller DJ, Parikh SV, Rotzinger S, Soares CN, Strother SC, Turecki G, Kennedy SH. The comparative effectiveness of electroencephalographic indices in predicting response to escitalopram therapy in depression: A pilot study. J Affect Disord 2018; 227:542-549. [PMID: 29169123 DOI: 10.1016/j.jad.2017.10.028] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2016] [Revised: 09/25/2017] [Accepted: 10/16/2017] [Indexed: 01/21/2023]
Abstract
BACKGROUND This study aims to compare the effectiveness of EEG frequency band activity including interhemispheric asymmetry and prefrontal theta cordance in predicting response to escitalopram therapy at 8-weeks post-treatment, in a multi-site initiative. METHODS Resting state 64-channel EEG data were recorded from 44 patients with a diagnosis of major depressive disorder (MDD) as part of a larger, multisite discovery study of biomarkers in antidepressant treatment response, conducted by the Canadian Biomarker Integration Network in Depression (CAN-BIND). Clinical response was measured at 8-weeks post-treatment as change from baseline Montgomery-Asberg Depression Rating Scale (MADRS) score of 50% or more. EEG measures were analyzed at (1) pre-treatment baseline (2) 2 weeks post-treatment and (3) as an ''early change" variable defined as change in EEG from baseline to 2 weeks post-treatment. RESULTS At baseline, treatment responders showed elevated absolute alpha power in the left hemisphere while non-responders showed the opposite. Responders further exhibited a cortical asymmetry in the parietal region. Groups also differed in pre-treatment relative delta power with responders showing greater power in the right hemisphere over the left while non-responders showed the opposite. At 2 weeks post-treatment, responders exhibited greater absolute beta power in the left hemisphere relative to the right and the opposite was noted for non-responders. A reverse pattern was noted for absolute and relative delta power at 2 weeks post-treatment. Responders exhibited early reductions in relative alpha power and early increments in relative theta power. Non-responders showed a significant early increase in prefrontal theta cordance. CONCLUSIONS Hemispheric asymmetries in the alpha and delta bands at baseline and at 2 weeks post-treatment have moderately strong predictive utility in predicting response to antidepressant treatment.
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Affiliation(s)
- Anusha Baskaran
- Centre for Neuroscience Studies, Queen's Unviersty, Kingston, Canada; Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada.
| | - Faranak Farzan
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Canada; Department of Psychiatry, University of Toronto, Toronto, Canada; School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, Canada
| | - Roumen Milev
- Centre for Neuroscience Studies, Queen's Unviersty, Kingston, Canada; Department of Psychiatry, Queen's University, Kingston, Canada
| | - Colleen A Brenner
- Department of Psychology, Loma Linda University, Loma Linda, United States
| | - Sravya Alturi
- Department of Psychiatry, Queen's University, Kingston, Canada
| | | | - Pierre Blier
- Brain and Mind Research Institute, University of Ottawa, Ottawa, Canada
| | | | - Jane A Foster
- Krembil Research Institute, University Health Network, Toronto, Canada; Department of Psychiatry & Behavioural Neurosciences, McMaster University, Hamilton, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada
| | - Benicio N Frey
- Psychiatry & Behavioural Neurosciences, McMaster University, Hamilton, Canada; Mood Disorders Program & Women's Health Concerns Clinic, St. Joseph's Healthcare, Hamilton, Canada
| | - Peter Giacobbe
- Department of Psychiatry, University of Toronto, Toronto, Canada; Department of Psychiatry, University Health Network, Toronto, Canada
| | - Raymond W Lam
- Department of Psychiatry, University of British Columbia, Vancouver, Canada
| | - Francesco Leri
- Department of Psychology, University of Guelph, Guelph, Canada
| | - Glenda M MacQueen
- The Hotchkiss Brain Institute, University of Calgary, Calgary, Canada
| | - Daniel J Müller
- Department of Psychiatry, University of Toronto, Toronto, Canada; Pharmacogenetics Research Clinic, Centre for Addiction and Mental Health, Toronto, Canada
| | - Sagar V Parikh
- Department of Psychiatry, University of Michigan, Ann Arbor, United States
| | - Susan Rotzinger
- Department of Psychiatry, University of Toronto, Toronto, Canada; Department of Psychiatry, University Health Network, Toronto, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada
| | - Claudio N Soares
- Department of Psychiatry, Queen's University, Kingston, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada
| | | | - Gustavo Turecki
- Department of Psychiatry, McGill University, Montreal, Canada
| | - Sidney H Kennedy
- Department of Psychiatry, University of Toronto, Toronto, Canada; Department of Psychiatry, University Health Network, Toronto, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Canada
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Busch Y, Menke A. Blood-based biomarkers predicting response to antidepressants. J Neural Transm (Vienna) 2018; 126:47-63. [PMID: 29374800 DOI: 10.1007/s00702-018-1844-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2017] [Accepted: 01/11/2018] [Indexed: 01/04/2023]
Abstract
Major depressive disorder is a common, serious and in some cases, life-threatening condition and affects approximately 350 million people globally. Although there is effective treatment available for it, more than 50% of the patients fail to respond to the first antidepressant they receive. The selection of a distinct treatment is still exclusively based on clinical judgment without incorporating lab-derived objective measures. However, there is growing evidence of biomarkers that it helps to improve diagnostic processes and treatment algorithms. Here genetic markers and blood-based biomarkers of the monoamine pathways, inflammatory pathways and the hypothalamic-pituitary-adrenal (HPA) axis are reviewed. Promising findings arise from studies investigating inflammatory pathways and immune markers that may identify patients suitable for anti-inflammatory based treatment regimes. Next, an early normalization of a disturbed HPA axis or depleted neurotrophic factors may predict stable treatment response. Genetic markers within the serotonergic system may identify patients who are vulnerable because of stressful life events, but evidence for guiding treatment regimes still is inconsistent. Therefore, there is still a great need for studies investigating and validating biomarkers for the prediction of treatment response to facilitate the treatment selection and shorten the time to remission and thus provide personalized medicine in psychiatry.
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Affiliation(s)
- Yasmin Busch
- Department of Psychiatry, Psychosomatics and Psychotherapy, University Hospital of Wuerzburg, Margarete-Hoeppel-Platz 1, 97080, Würzburg, Germany
| | - Andreas Menke
- Department of Psychiatry, Psychosomatics and Psychotherapy, University Hospital of Wuerzburg, Margarete-Hoeppel-Platz 1, 97080, Würzburg, Germany. .,Comprehensive Heart Failure Center, University Hospital of Wuerzburg, Am Schwarzenberg 15, 97080, Würzburg, Germany.
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42
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Löpprich M, Karmen C, Ganzinger M, Gietzelt M. Models and Data Sources Used in Systems Medicine. Methods Inf Med 2018; 55:107-13. [DOI: 10.3414/me15-01-0151] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Accepted: 01/18/2016] [Indexed: 12/11/2022]
Abstract
SummaryBackground: Systems medicine is a new approach for the development and selection of treatment strategies for patients with complex diseases. It is often referred to as the application of systems biology methods for decision making in patient care. For systems medicine computer applications, many different data sources have to be integrated and included into models. This is a challenging task for Medical Informatics since the approach exceeds traditional systems like Electronic Health Records. To prioritize research activities for systems medicine applications, it is necessary to get an overview over modelling methods and data sources already used in this field.Objectives: We performed a systematic literature review with the objective to capture current use of 1) modelling methods and 2) data sources in systems medicine related research projects.Methods: We queried the MEDLINE and ScienceDirect databases for papers associated with the search term systems medicine and related terms. Papers were screened and assessed in full text in a two-step process according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement guidelines.Results: The queries returned 698 articles of which 34 papers were finally included into the study. A multitude of modelling approaches such as machine learning and network analysis was identified and classified. Since these approaches are also used in other domains, no methods specific for systems medicine could be identified. Omics data are the most widely used data types followed by clinical data. Most studies only include a rather limited number of data sources.Conclusions: Currently, many different modelling approaches are used in systems medicine. Thus, highly flexible modular solutions are necessary for systems medicine clinical applications. However, the number of data sources included into the models is limited and most projects currently focus on prognosis. To leverage the potential of systems medicine further, it will be necessary to focus on treatment strategies for patients and consider a broader range of data.
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Jaworska N, de la Salle S, Ibrahim MH, Blier P, Knott V. Leveraging Machine Learning Approaches for Predicting Antidepressant Treatment Response Using Electroencephalography (EEG) and Clinical Data. Front Psychiatry 2018; 9:768. [PMID: 30692945 PMCID: PMC6339954 DOI: 10.3389/fpsyt.2018.00768] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Accepted: 12/21/2018] [Indexed: 12/28/2022] Open
Abstract
Background: Individuals with major depressive disorder (MDD) vary in their response to antidepressants. However, identifying objective biomarkers, prior to or early in the course of treatment that can predict antidepressant efficacy, remains a challenge. Methods: Individuals with MDD participated in a 12-week antidepressant pharmacotherapy trial. Electroencephalographic (EEG) data was collected before and 1 week post-treatment initiation in 51 patients. Response status at week 12 was established with the Montgomery-Asberg Depression Scale (MADRS), with a ≥50% decrease characterizing responders (N = 27/24 responders/non-responders). We used a machine learning (ML)-approach for predicting response status. We focused on Random Forests, though other ML methods were compared. First, we used a tree-based estimator to select a relatively small number of significant features from: (a) demographic/clinical data (age, sex, individual item/total MADRS scores at baseline, week 1, change scores); (b) scalp-level EEG power; (c) source-localized current density (via exact low-resolution electromagnetic tomography [eLORETA] software). Second, we applied kernel principal component analysis to reduce and map important features. Third, a set of ML models were constructed to classify response outcome based on mapped features. For each dataset, predictive features were extracted, followed by a model of all predictive features, and finally by a model of the most predictive features. Results: Fifty eLORETA features were predictive of response (across bands, both time-points); alpha1/theta eLORETA features showed the highest predictive value. Eighty-eight scalp EEG features were predictive of response (across bands, both time-points), with theta/alpha2 being most predictive. Clinical/demographic data consisted of 31 features, with the most important being week 1 "concentration difficulty" scores. When all features were included into one model, its predictive utility was high (88% accuracy). When the most important features were extracted in the final model, 12 predictive features emerged (78% accuracy), including baseline scalp-EEG frontopolar theta, parietal alpha2 and frontopolar alpha1. Conclusions: These findings suggest that ML models of pre- and early treatment-emergent EEG profiles and clinical features can serve as tools for predicting antidepressant response. While this must be replicated using large independent samples, it lays the groundwork for research on personalized, "biomarker"-based treatment approaches.
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Affiliation(s)
- Natalia Jaworska
- Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada.,Cellular & Molecular Medicine, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada.,Brain and Mind Research Institute, University of Ottawa, Ottawa, ON, Canada
| | - Sara de la Salle
- Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada
| | | | - Pierre Blier
- Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada.,Cellular & Molecular Medicine, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada.,Brain and Mind Research Institute, University of Ottawa, Ottawa, ON, Canada
| | - Verner Knott
- Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada.,Cellular & Molecular Medicine, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada.,Brain and Mind Research Institute, University of Ottawa, Ottawa, ON, Canada
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Groves SJ, Douglas KM, Porter RJ. A Systematic Review of Cognitive Predictors of Treatment Outcome in Major Depression. Front Psychiatry 2018; 9:382. [PMID: 30210368 PMCID: PMC6121150 DOI: 10.3389/fpsyt.2018.00382] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Accepted: 07/30/2018] [Indexed: 12/28/2022] Open
Abstract
Background: Research suggests that only 50% of patients with major depression respond to psychotherapy or pharmacological treatment, and relapse is common. Therefore, there is interest in elucidating factors that help predict clinical response. Cognitive impairment is a key feature of depression, which often persists beyond remission; thus, the aim of this systematic review was to determine whether baseline cognitive functioning can predict treatment outcomes in individuals with depression. Method: Studies examining cognitive predictors of treatment response in depression were identified using Pub Med and Web of Science databases. Given the heterogeneity of outcome measures, the variety of treatment protocols, and the differing ways in which data was presented and analyzed, a narrative rather than meta-analytic review technique was used. Results: 39 studies met inclusion criteria. Findings in younger adult samples were inconclusive. There was some evidence for a predictive effect of executive function and to a lesser extent, psychomotor speed, on treatment response. There was no evidence of learning or memory being associated with treatment response. In older-aged samples, the evidence was much more consistent, suggesting that poor executive function predicts poor response to SSRIs. Conclusions: Findings from the present review suggest that certain aspects of cognitive functioning, particularly executive function, may be useful in predicting treatment response in depression. This is certainly the case in elderly samples, with evidence suggesting that poor executive functioning predicts poor response to SSRIs. With further research, baseline cognitive functioning may serve as a factor which helps guide clinical decision making. Moreover, cognitive deficits may become targets for specific pharmacological or psychological treatments, with the hope of improving overall outcome.
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Affiliation(s)
- Samantha J Groves
- Department of Psychological Medicine, University of Otago, Christchurch, New Zealand
| | - Katie M Douglas
- Department of Psychological Medicine, University of Otago, Christchurch, New Zealand
| | - Richard J Porter
- Department of Psychological Medicine, University of Otago, Christchurch, New Zealand.,Specialist Mental Health Services, Canterbury District Health Board, Christchurch, New Zealand
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Tian Y, Yang L, Xu W, Zhang H, Wang Z, Zhang H, Zheng S, Shi Y, Xu P. Predictors for drug effects with brain disease: Shed new light from EEG parameters to brain connectomics. Eur J Pharm Sci 2017; 110:26-36. [PMID: 28456573 DOI: 10.1016/j.ejps.2017.04.019] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Revised: 04/24/2017] [Accepted: 04/24/2017] [Indexed: 01/21/2023]
Abstract
Though researchers spent a lot of effort to develop treatments for neuropsychiatric disorders, the poor translation of drug efficacy data from animals to human hampered the success of these therapeutic approaches in human. Pharmaceutical industry is challenged by low clinical success rates for new drug registration. To maximize the success in drug development, biomarkers are required to act as surrogate end points and predictors of drug effects. The pathology of brain disease could be in part due to synaptic dysfunction. Electroencephalogram (EEG), generating from the result of the postsynaptic potential discharge between cells, could be a potential measure to bridge the gaps between animal and human data. Here we discuss recent progress on using relevant EEG characteristics and brain connectomics as biomarkers to monitor drug effects and measure cognitive changes on animal models and human in real-time. It is expected that the novel approach, i.e. EEG connectomics, will offer a deeper understanding on the drug efficacy at a microcirculatory level, which will be useful to support the development of new treatments for neuropsychiatric disorders.
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Affiliation(s)
- Yin Tian
- Biomedical Engineering Department, Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, High School Innovation Team of Architecture and Core Technologies of Smart Medical System, ChongQing University of Posts and Telecommunications, ChongQing 400065, China.
| | - Li Yang
- Biomedical Engineering Department, Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, High School Innovation Team of Architecture and Core Technologies of Smart Medical System, ChongQing University of Posts and Telecommunications, ChongQing 400065, China
| | - Wei Xu
- Biomedical Engineering Department, Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, High School Innovation Team of Architecture and Core Technologies of Smart Medical System, ChongQing University of Posts and Telecommunications, ChongQing 400065, China
| | - Huiling Zhang
- Biomedical Engineering Department, Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, High School Innovation Team of Architecture and Core Technologies of Smart Medical System, ChongQing University of Posts and Telecommunications, ChongQing 400065, China
| | - Zhongyan Wang
- Biomedical Engineering Department, Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, High School Innovation Team of Architecture and Core Technologies of Smart Medical System, ChongQing University of Posts and Telecommunications, ChongQing 400065, China
| | - Haiyong Zhang
- Biomedical Engineering Department, Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, High School Innovation Team of Architecture and Core Technologies of Smart Medical System, ChongQing University of Posts and Telecommunications, ChongQing 400065, China
| | - Shuxing Zheng
- Biomedical Engineering Department, Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, High School Innovation Team of Architecture and Core Technologies of Smart Medical System, ChongQing University of Posts and Telecommunications, ChongQing 400065, China
| | - Yupan Shi
- Biomedical Engineering Department, Key Laboratory of Photoelectronic Information Sensing and Transmitting Technology, High School Innovation Team of Architecture and Core Technologies of Smart Medical System, ChongQing University of Posts and Telecommunications, ChongQing 400065, China
| | - Peng Xu
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
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Taranu A, Asmar KE, Colle R, Ferreri F, Polosan M, David D, Becquemont L, Corruble E, Verstuyft C. The Catechol-O-methyltransferase Val(108/158)Met Genetic Polymorphism cannot be Recommended as a Biomarker for the Prediction of Venlafaxine Efficacy in Patients Treated in Psychiatric Settings. Basic Clin Pharmacol Toxicol 2017. [DOI: 10.1111/bcpt.12827] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
- Adela Taranu
- INSERM UMR1178; Team ‘Depression and Antidepressants’; University of Medicine of Paris-Sud; University Paris-Sud; Le Kremlin Bicetre France
| | - Khalil El Asmar
- INSERM UMR1178; Team ‘Depression and Antidepressants’; University of Medicine of Paris-Sud; University Paris-Sud; Le Kremlin Bicetre France
| | - Romain Colle
- INSERM UMR1178; Team ‘Depression and Antidepressants’; University of Medicine of Paris-Sud; University Paris-Sud; Le Kremlin Bicetre France
- Service of Psychiatry; Group of Hospitals of Paris Sud; AP-HP, Hospital Bicetre; Le Kremlin Bicetre France
| | - Florian Ferreri
- Service of Psychiatry; Group of Hospitals of Paris Est; AP-HP, Hospital Saint-Antoine; Paris France
| | - Mircea Polosan
- Grenoble Institute of Neurosciences; Inserm U1216 GIN; University of Grenoble Alpes; Grenoble France
- Hospital of Grenoble; Grenoble France
| | - Denis David
- INSERM UMR1178; Team ‘Depression and Antidepressants’; University of Medicine of Paris-Sud; University Paris-Sud; Le Kremlin Bicetre France
| | - Laurent Becquemont
- INSERM UMR1178; Team ‘Depression and Antidepressants’; University of Medicine of Paris-Sud; University Paris-Sud; Le Kremlin Bicetre France
- Clinical Research Center (CRC); Group of Hospitals of Paris Sud; AP-HP, Hospital Bicetre; Le Kremlin Bicetre France
| | - Emmanuelle Corruble
- INSERM UMR1178; Team ‘Depression and Antidepressants’; University of Medicine of Paris-Sud; University Paris-Sud; Le Kremlin Bicetre France
- Service of Psychiatry; Group of Hospitals of Paris Sud; AP-HP, Hospital Bicetre; Le Kremlin Bicetre France
| | - Céline Verstuyft
- INSERM UMR1178; Team ‘Depression and Antidepressants’; University of Medicine of Paris-Sud; University Paris-Sud; Le Kremlin Bicetre France
- Service of Molecular Genetics, Pharmacogenetics and Hormonology; Group of Hospitals of Paris Sud; AP-HP, Hospital Bicetre; Le Kremlin Bicetre France
- Center of Biological Ressources of Paris-Sud; Group of Hospitals of Paris Sud; AP-HP, Hospital Bicetre; Le Kremlin Bicetre France
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The Comparison of Effectiveness of Various Potential Predictors of Response to Treatment With SSRIs in Patients With Depressive Disorder. J Nerv Ment Dis 2017; 205:618-626. [PMID: 27660994 DOI: 10.1097/nmd.0000000000000574] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The substantial non-response rate in depressive patients indicates a continuing need to identify predictors of treatment outcome. The aim of this 6-week, open-label study was (1) to compare the efficacy of a priori defined predictors: ≥20% reduction in MADRS score at week 1, ≥20% reduction in MADRS score at week 2 (RM ≥ 20% W2), decrease of cordance (RC), and increase of serum and plasma level of brain-derived neurotrophic factor at week 1; and (2) to assess whether their combination yields higher efficacy in the prediction of response to selective serotonin re-uptake inhibitors (SSRIs) than when used singly. Twenty-one patients (55%) achieved a response to SSRIs. The RM ≥20% W2 (areas under curve-AUC = 0.83) showed better predictive efficacy compared to all other predictors with the exception of RC. The identified combined model (RM ≥ 20% W2 + RC), which predicted response with an 84% accuracy (AUC = 0.92), may be a useful tool in the prediction of response to SSRIs.
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Schmidt FM, Sander C, Dietz ME, Nowak C, Schröder T, Mergl R, Schönknecht P, Himmerich H, Hegerl U. Brain arousal regulation as response predictor for antidepressant therapy in major depression. Sci Rep 2017; 7:45187. [PMID: 28345662 PMCID: PMC5366924 DOI: 10.1038/srep45187] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Accepted: 02/20/2017] [Indexed: 12/11/2022] Open
Abstract
A tonically high level of brain arousal and its hyperstable regulation is supposed to be a pathogenic factor in major depression. Preclinical studies indicate that most antidepressants may counteract this dysregulation. Therefore, it was hypothesized that responders to antidepressants show a) a high level of EEG-vigilance (an indicator of brain arousal) and b) a more stable EEG-vigilance regulation than non-responders. In 65 unmedicated depressed patients 15-min resting-state EEGs were recorded off medication (baseline). In 57 patients an additional EEG was recorded 14 ± 1 days following onset of antidepressant treatment (T1). Response was defined as a ≥50% HAMD-17-improvement after 28 ± 1 days of treatment (T2), resulting in 29 responders and 36 non-responders. Brain arousal was assessed using the Vigilance Algorithm Leipzig (VIGALL 2.1). At baseline responders and non-responders differed in distribution of overall EEG-vigilance stages (F2,133 = 4.780, p = 0.009), with responders showing significantly more high vigilance stage A and less low vigilance stage B. The 15-minutes Time-course of EEG-vigilance did not differ significantly between groups. Exploratory analyses revealed that responders showed a stronger decline in EEG-vigilance levels from baseline to T1 than non-responders (F2,130 = 4.978, p = 0.005). Higher brain arousal level in responders to antidepressants supports the concept that dysregulation of brain arousal is a possible predictor of treatment response in affective disorders.
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Affiliation(s)
- Frank M. Schmidt
- Department of Psychiatry and Psychotherapy, University Hospital Leipzig, Semmelweisstr. 10, D-04103, Leipzig, Germany
| | - Christian Sander
- Department of Psychiatry and Psychotherapy, University Hospital Leipzig, Semmelweisstr. 10, D-04103, Leipzig, Germany
- Research Center of the German Depression Foundation, Leipzig, Germany
| | - Marie-Elisa Dietz
- Department of Psychiatry and Psychotherapy, University Hospital Leipzig, Semmelweisstr. 10, D-04103, Leipzig, Germany
| | - Claudia Nowak
- Department of Psychiatry and Psychotherapy, University Hospital Leipzig, Semmelweisstr. 10, D-04103, Leipzig, Germany
| | - Thomas Schröder
- Department of Psychiatry and Psychotherapy, University Hospital Leipzig, Semmelweisstr. 10, D-04103, Leipzig, Germany
| | - Roland Mergl
- Department of Psychiatry and Psychotherapy, University Hospital Leipzig, Semmelweisstr. 10, D-04103, Leipzig, Germany
| | - Peter Schönknecht
- Department of Psychiatry and Psychotherapy, University Hospital Leipzig, Semmelweisstr. 10, D-04103, Leipzig, Germany
- Saxonian Hospital Arnsdorf, Arnsdorf, Germany
| | - Hubertus Himmerich
- Department of Psychiatry and Psychotherapy, University Hospital Leipzig, Semmelweisstr. 10, D-04103, Leipzig, Germany
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Ulrich Hegerl
- Department of Psychiatry and Psychotherapy, University Hospital Leipzig, Semmelweisstr. 10, D-04103, Leipzig, Germany
- Research Center of the German Depression Foundation, Leipzig, Germany
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49
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Corral-Frías NS, Pizzagalli DA, Carré JM, Michalski LJ, Nikolova YS, Perlis RH, Fagerness J, Lee MR, Conley ED, Lancaster TM, Haddad S, Wolf A, Smoller JW, Hariri AR, Bogdan R. COMT Val(158) Met genotype is associated with reward learning: a replication study and meta-analysis. GENES BRAIN AND BEHAVIOR 2017; 15:503-13. [PMID: 27138112 DOI: 10.1111/gbb.12296] [Citation(s) in RCA: 59] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2016] [Revised: 03/25/2016] [Accepted: 04/14/2016] [Indexed: 02/06/2023]
Abstract
Identifying mechanisms through which individual differences in reward learning emerge offers an opportunity to understand both a fundamental form of adaptive responding as well as etiological pathways through which aberrant reward learning may contribute to maladaptive behaviors and psychopathology. One candidate mechanism through which individual differences in reward learning may emerge is variability in dopaminergic reinforcement signaling. A common functional polymorphism within the catechol-O-methyl transferase gene (COMT; rs4680, Val(158) Met) has been linked to reward learning, where homozygosity for the Met allele (linked to heightened prefrontal dopamine function and decreased dopamine synthesis in the midbrain) has been associated with relatively increased reward learning. Here, we used a probabilistic reward learning task to asses response bias, a behavioral form of reward learning, across three separate samples that were combined for analyses (age: 21.80 ± 3.95; n = 392; 268 female; European-American: n = 208). We replicate prior reports that COMT rs4680 Met allele homozygosity is associated with increased reward learning in European-American participants (β = 0.20, t = 2.75, P < 0.01; ΔR(2) = 0.04). Moreover, a meta-analysis of 4 studies, including the current one, confirmed the association between COMT rs4680 genotype and reward learning (95% CI -0.11 to -0.03; z = 3.2; P < 0.01). These results suggest that variability in dopamine signaling associated with COMT rs4680 influences individual differences in reward which may potentially contribute to psychopathology characterized by reward dysfunction.
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Affiliation(s)
- N S Corral-Frías
- Psychiatry Department, Washington University in St. Louis, St. Louis, MO, USA.,BRAIN Laboratory, Department of Psychology, Washington University in St. Louis, St. Louis, MO, USA
| | - D A Pizzagalli
- Center For Depression, Anxiety and Stress Research and Neuroimaging Center, McLean Hospital and Harvard Medical School, Belmont, MA, USA
| | - J M Carré
- Nipissing University, North Bay, Ontario, Canada
| | - L J Michalski
- BRAIN Laboratory, Department of Psychology, Washington University in St. Louis, St. Louis, MO, USA
| | - Y S Nikolova
- Centre for Addiction and Mental Health Toronto, Ontario, Canada
| | - R H Perlis
- Massachusetts General Hospital and Harvard Medical School, Cambridge, MA, USA.,Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
| | - J Fagerness
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
| | - M R Lee
- National Institute on Drug Abuse, Intramural Research Program, Baltimore, MD, USA
| | | | - T M Lancaster
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, UK
| | - S Haddad
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
| | - A Wolf
- Department of Psychiatry Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - J W Smoller
- Massachusetts General Hospital and Harvard Medical School, Cambridge, MA, USA.,Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
| | - A R Hariri
- Laboratory of NeuroGenetics, Department of Psychology and Neuroscience, Duke University, Durham, NC, USA
| | - R Bogdan
- BRAIN Laboratory, Department of Psychology, Washington University in St. Louis, St. Louis, MO, USA.,Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, MO, USA
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50
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Mumtaz W, Xia L, Mohd Yasin MA, Azhar Ali SS, Malik AS. A wavelet-based technique to predict treatment outcome for Major Depressive Disorder. PLoS One 2017; 12:e0171409. [PMID: 28152063 PMCID: PMC5289714 DOI: 10.1371/journal.pone.0171409] [Citation(s) in RCA: 58] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2016] [Accepted: 01/20/2017] [Indexed: 11/18/2022] Open
Abstract
Treatment management for Major Depressive Disorder (MDD) has been challenging. However, electroencephalogram (EEG)-based predictions of antidepressant’s treatment outcome may help during antidepressant’s selection and ultimately improve the quality of life for MDD patients. In this study, a machine learning (ML) method involving pretreatment EEG data was proposed to perform such predictions for Selective Serotonin Reuptake Inhibitor (SSRIs). For this purpose, the acquisition of experimental data involved 34 MDD patients and 30 healthy controls. Consequently, a feature matrix was constructed involving time-frequency decomposition of EEG data based on wavelet transform (WT) analysis, termed as EEG data matrix. However, the resultant EEG data matrix had high dimensionality. Therefore, dimension reduction was performed based on a rank-based feature selection method according to a criterion, i.e., receiver operating characteristic (ROC). As a result, the most significant features were identified and further be utilized during the training and testing of a classification model, i.e., the logistic regression (LR) classifier. Finally, the LR model was validated with 100 iterations of 10-fold cross-validation (10-CV). The classification results were compared with short-time Fourier transform (STFT) analysis, and empirical mode decompositions (EMD). The wavelet features extracted from frontal and temporal EEG data were found statistically significant. In comparison with other time-frequency approaches such as the STFT and EMD, the WT analysis has shown highest classification accuracy, i.e., accuracy = 87.5%, sensitivity = 95%, and specificity = 80%. In conclusion, significant wavelet coefficients extracted from frontal and temporal pre-treatment EEG data involving delta and theta frequency bands may predict antidepressant’s treatment outcome for the MDD patients.
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Affiliation(s)
- Wajid Mumtaz
- Centre for Intelligent Signal and Imaging Research (CISIR),Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak, Malaysia
| | - Likun Xia
- Beijing Institute of Technology, Beijing, China
| | - Mohd Azhar Mohd Yasin
- Department of Psychiatry,Universiti Sains Malaysia, Jalan Hospital Universiti Sains Malaysia, Kubang Kerian, Kota Bharu, Kelantan, Malaysia
| | - Syed Saad Azhar Ali
- Centre for Intelligent Signal and Imaging Research (CISIR),Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak, Malaysia
| | - Aamir Saeed Malik
- Centre for Intelligent Signal and Imaging Research (CISIR),Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak, Malaysia
- * E-mail:
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