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Prompiengchai S, Dunlop K. Breakthroughs and challenges for generating brain network-based biomarkers of treatment response in depression. Neuropsychopharmacology 2024:10.1038/s41386-024-01907-1. [PMID: 38951585 DOI: 10.1038/s41386-024-01907-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 05/17/2024] [Accepted: 06/13/2024] [Indexed: 07/03/2024]
Abstract
Treatment outcomes widely vary for individuals diagnosed with major depressive disorder, implicating a need for deeper understanding of the biological mechanisms conferring a greater likelihood of response to a particular treatment. Our improved understanding of intrinsic brain networks underlying depression psychopathology via magnetic resonance imaging and other neuroimaging modalities has helped reveal novel and potentially clinically meaningful biological markers of response. And while we have made considerable progress in identifying such biomarkers over the last decade, particularly with larger, multisite trials, there are significant methodological and practical obstacles that need to be overcome to translate these markers into the clinic. The aim of this review is to review current literature on brain network structural and functional biomarkers of treatment response or selection in depression, with a specific focus on recent large, multisite trials reporting predictive accuracy of candidate biomarkers. Regarding pharmaco- and psychotherapy, we discuss candidate biomarkers, reporting that while we have identified candidate biomarkers of response to a single intervention, we need more trials that distinguish biomarkers between first-line treatments. Further, we discuss the ways prognostic neuroimaging may help to improve treatment outcomes to neuromodulation-based therapies, such as transcranial magnetic stimulation and deep brain stimulation. Lastly, we highlight obstacles and technical developments that may help to address the knowledge gaps in this area of research. Ultimately, integrating neuroimaging-derived biomarkers into clinical practice holds promise for enhancing treatment outcomes and advancing precision psychiatry strategies for depression management. By elucidating the neural predictors of treatment response and selection, we can move towards more individualized and effective depression interventions, ultimately improving patient outcomes and quality of life.
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Affiliation(s)
| | - Katharine Dunlop
- Centre for Depression and Suicide Studies, Unity Health Toronto, Toronto, ON, Canada.
- Keenan Research Centre for Biomedical Science, Unity Health Toronto, Toronto, ON, Canada.
- Department of Psychiatry and Institute of Medical Science, University of Toronto, Toronto, ON, Canada.
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Kasperk N, Haen E, Hiemke C, Frodl T, Schoretsanitis G, Paulzen M, Kuzo N. Pharmacokinetic correlates of clinical response in a naturalistic sample of escitalopram-treated patients. Expert Rev Clin Pharmacol 2024; 17:247-253. [PMID: 38299560 DOI: 10.1080/17512433.2024.2314211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 01/31/2024] [Indexed: 02/02/2024]
Abstract
OBJECTIVE We assessed pharmacokinetic correlates of treatment response to escitalopram using a large therapeutic drug monitoring database. METHODS A large naturalistic sample of patients receiving escitalopram was analyzed. Responders were defined as 'very much improved' or 'much improved' based on the Clinical Global Impression - Improvement score, CGI-I. We compared responders (n = 83) vs. non-responders (n = 388) with the primary outcome being the escitalopram plasma concentration and concentration corrected by the daily dose (C/D ratio). Effects of age, sex, body-mass-index (BMI), and C/D ratio were assessed in a multivariate logistic regression model predicting response. RESULTS There were no statistically significant differences in clinical and demographic characteristics between responders vs. non-responders. There were also no differences between escitalopram daily doses or plasma concentrations, while C/D ratios were significantly higher in non-responders than in responders (1.6 ± 1.7 vs. 1.2 ± 0.9 (ng/mL)/(mg/day), p = 0.007); C/D ratios (odds ratio 0.52, 95% confidence interval 0.34-0.80, p < 0.003) were associated with response to escitalopram, after controlling for age, sex, and BMI. CONCLUSIONS Patients with low clearance of escitalopram as reflected upon high C/D ratios may be less likely respond to escitalopram. Identifying these patients during dose titration may support clinical decision-making, including switching to a different antidepressant instead of increasing daily dose.
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Affiliation(s)
- Nicholas Kasperk
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, and JARA - Translational Brain Medicine, Aachen, Germany
| | - Ekkehard Haen
- Department of Psychiatry and Psychotherapy, Clinical Pharmacology, University of Regensburg, Regensburg, Germany
- Department of Pharmacology and Toxicology, University of Regensburg, Regensburg, Germany
- Clinical Pharmacology, Institute AGATE gGmbH, Pentling, Germany
| | - Christoph Hiemke
- Department of Psychiatry and Psychotherapy, University Medical Center of Mainz, Mainz, Germany
- Institute of Clinical Chemistry and Laboratory Medicine, University Medical Center of Mainz, Mainz, Germany
| | - Thomas Frodl
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, and JARA - Translational Brain Medicine, Aachen, Germany
| | - Georgios Schoretsanitis
- The Zucker Hillside Hospital, Department of Psychiatry Research, Northwell Health, Glen Oaks, New York, USA
- Department of Psychiatry, Zucker School of Medicine at Northwell/Hofstra, Hempstead, NY, USA
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry, University of Zurich, Zurich, Switzerland
| | - Michael Paulzen
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, and JARA - Translational Brain Medicine, Aachen, Germany
- Alexianer Hospital Aachen, Aachen, Germany
| | - Nazar Kuzo
- Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital of Psychiatry, University of Zurich, Zurich, Switzerland
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Li CT, Chen CS, Cheng CM, Chen CP, Chen JP, Chen MH, Bai YM, Tsai SJ. Prediction of antidepressant responses to non-invasive brain stimulation using frontal electroencephalogram signals: Cross-dataset comparisons and validation. J Affect Disord 2023; 343:86-95. [PMID: 37579885 DOI: 10.1016/j.jad.2023.08.059] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 08/10/2023] [Accepted: 08/11/2023] [Indexed: 08/16/2023]
Abstract
BACKGROUND 10-Hz repetitive transcranial magnetic stimulation(rTMS) and intermittent theta-burst stimulation(iTBS) over left prefrontal cortex are FDA-approved, effective options for treatment-resistant depression (TRD). Optimal prediction models for iTBS and rTMS remain elusive. Therefore, our primary objective was to compare prediction accuracy between classification by frontal theta activity alone and machine learning(ML) models by linear and non-linear frontal signals. The second objective was to study an optimal ML model for predicting responses to rTMS and iTBS. METHODS Two rTMS and iTBS datasets (n = 163) were used: one randomized controlled trial dataset (RCTD; n = 96) and one outpatient dataset (OPD; n = 67). Frontal theta and non-linear EEG features that reflect trend, stability, and complexity were extracted. Pretreatment frontal EEG and ML algorithms, including classical support vector machine(SVM), random forest(RF), XGBoost, and CatBoost, were analyzed. Responses were defined as ≥50 % depression improvement after treatment. Response rates between those with and without pretreatment prediction in another independent outpatient cohort (n = 208) were compared. RESULTS Prediction accuracy using combined EEG features by SVM was better than frontal theta by logistic regression. The accuracy for OPD patients significantly dropped using the RCTD-trained SVM model. Modern ML models, especially RF (rTMS = 83.3 %, iTBS = 88.9 %, p-value(ACC > NIR) < 0.05 for iTBS), performed significantly above chance and had higher accuracy than SVM using both selected features (p < 0.05, FDR corrected for multiple comparisons) or all EEG features. Response rates among those receiving prediction before treatment were significantly higher than those without prediction (p = 0.035). CONCLUSION The first study combining linear and non-linear EEG features could accurately predict responses to left PFC iTBS. The bootstraps-based ML model (i.e., RF) had the best predictive accuracy for rTMS and iTBS.
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Affiliation(s)
- Cheng-Ta Li
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan; Division of Psychiatry, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan; Institute of Brain Science and Brain Research Center, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan; Institute of Cognitive Neuroscience, National Central University, Jhongli, Taiwan.
| | - Chi-Sheng Chen
- Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science, National Taiwan University, Taipei, Taiwan
| | - Chih-Ming Cheng
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Chung-Ping Chen
- Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science, National Taiwan University, Taipei, Taiwan
| | - Jen-Ping Chen
- Division of Psychiatry, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
| | - Mu-Hong Chen
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan; Division of Psychiatry, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan; Institute of Brain Science and Brain Research Center, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
| | - Ya-Mei Bai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan; Division of Psychiatry, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan; Institute of Brain Science and Brain Research Center, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan; Division of Psychiatry, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan; Institute of Brain Science and Brain Research Center, School of Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
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Li K, Lu X, Xiao C, Zheng K, Sun J, Dong Q, Wang M, Zhang L, Liu B, Liu J, Zhang Y, Guo H, Zhao F, Ju Y, Li L. Aberrant Resting-State Functional Connectivity in MDD and the Antidepressant Treatment Effect-A 6-Month Follow-Up Study. Brain Sci 2023; 13:brainsci13050705. [PMID: 37239177 DOI: 10.3390/brainsci13050705] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 04/10/2023] [Accepted: 04/19/2023] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND The mechanism by which antidepressants normalizing aberrant resting-state functional connectivity (rsFC) in patients with major depressive disorder (MDD) is still a matter of debate. The current study aimed to investigate aberrant rsFC and whether antidepressants would restore the aberrant rsFC in patients with MDD. METHODS A total of 196 patients with MDD and 143 healthy controls (HCs) received the resting-state functional magnetic resonance imaging and clinical assessments at baseline. Patients with MDD received antidepressant treatment after baseline assessment and were re-scanned at the 6-month follow-up. Network-based statistics were employed to identify aberrant rsFC and rsFC changes in patients with MDD and to compare the rsFC differences between remitters and non-remitters. RESULTS We identified a significantly decreased sub-network and a significantly increased sub-network in MDD at baseline. Approximately half of the aberrant rsFC remained significantly different from HCs after 6-month treatment. Significant overlaps were found between baseline reduced sub-network and follow-up increased sub-network, and between baseline increased sub-network and follow-up decreased sub-network. Besides, rsFC at baseline and rsFC changes between baseline and follow-up in remitters were not different from non-remitters. CONCLUSIONS Most aberrant rsFC in patients with MDD showed state-independence. Although antidepressants may modulate aberrant rsFC, they may not specifically target these aberrations to achieve therapeutic effects, with only a few having been directly linked to treatment efficacy.
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Affiliation(s)
- Kangning Li
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China
| | - Xiaowen Lu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China
| | - Chuman Xiao
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China
| | - Kangning Zheng
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China
| | - Jinrong Sun
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China
| | - Qiangli Dong
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China
| | - Mi Wang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China
| | - Liang Zhang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China
| | - Bangshan Liu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China
| | - Jin Liu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China
| | - Yan Zhang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China
| | - Hua Guo
- Zhumadian Psychiatric Hospital, Zhumadian 463000, China
| | - Futao Zhao
- Zhumadian Psychiatric Hospital, Zhumadian 463000, China
| | - Yumeng Ju
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China
| | - Lingjiang Li
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha 410011, China
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Hart XM, Amann F, Brand J, Eichentopf L, Gründer G. Low Escitalopram Concentrations in Patients with Depression predict Treatment Failure: A Naturalistic Retrospective Study. PHARMACOPSYCHIATRY 2023; 56:73-80. [PMID: 36944330 PMCID: PMC10030201 DOI: 10.1055/a-2039-2829] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
INTRODUCTION Cross sectional therapeutic drug monitoring (TDM) data mining introduces new opportunities for the investigation of medication treatment effects to find optimal therapeutic windows. Medication discontinuation has been proven useful as an objective surrogate marker to assess treatment failure. This study aimed to investigate the treatment effects of escitalopram and pharmacokinetic influences on blood levels using retrospectively assessed data from a TDM database. METHODS Data was collected from 134 patients longitudinally treated with escitalopram for whom TDM was requested to guide drug therapy. Escitalopram metabolism was estimated by the log-transformed dose-corrected concentrations and compared within subpopulations differing in age, gender, renal function, smoking status, body mass index, and comedication. RESULTS Patients with a depressive episode who were treated with escitalopram and discontinued the treatment within the hospital stay showed lower serum concentrations compared to patients who continued escitalopram treatment with a concentration of 15 ng/mL separating both groups. Variability was high between individuals and factors influencing blood levels, including dose, sex, and age. Comedication that inhibits cytochrome P450 (CYP) 2C19 isoenzymes were further found to influence escitalopram pharmacokinetics independent of dose, age or sex. DISCUSSION Medication switch is a valuable objective surrogate marker to assess treatment effects under real-world conditions. Of note, treatment discontinuation is not always a cause of insufficient response but may also be related to other factors such as medication side effects. TDM might not only be useful in addressing these issues but titrating drug concentrations into the currently recommended reference range for escitalopram will also increase response in non-responders and avoid treatment failure in underdosed patients.
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Affiliation(s)
- Xenia M Hart
- Central Institute of Mental Health, Department of Molecular Neuroimaging, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Friederike Amann
- Central Institute of Mental Health, Department of Molecular Neuroimaging, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Jonas Brand
- Laboratory Limbach Analytics GmbH, Heidelberg, Germany
| | - Luzie Eichentopf
- Central Institute of Mental Health, Department of Molecular Neuroimaging, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Gerhard Gründer
- Central Institute of Mental Health, Department of Molecular Neuroimaging, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
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Verma D, Bach K, Mork PJ. External validation of prediction models for patient-reported outcome measurements collected using the selfBACK mobile app. Int J Med Inform 2023; 170:104936. [PMID: 36459835 DOI: 10.1016/j.ijmedinf.2022.104936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 11/18/2022] [Accepted: 11/22/2022] [Indexed: 11/27/2022]
Abstract
BACKGROUND External validation is essential in examining the disparities in the training and validation cohorts during the development of prediction models, especially when the application domain is healthcare-oriented. Currently, the use of prediction models in healthcare research aimed at utilising the under-explored potential of patient-reported outcome measurements (PROMs) is limited, and few are validated using external datasets. OBJECTIVE To validate the machine learning prediction models developed in our previous work [29] for predicting four pain-related patient-reported outcomes from the selfBACK clinical trial datasets. METHODS We evaluate the validity of three pre-trained prediction models based on three methods- Case-Based Reasoning, Support Vector Regression, and XGBoost Regression-using an external dataset that contains PROMs collected from patients with non-specific neck and or low back pain using the selfBACK mobile application. RESULTS Overall, the predictive power was low, except for prediction of one of the outcomes. The results indicate that while the predictions are far from immaculate in either case, the models show ability to generalise and predict outcomes for a new dataset. CONCLUSION External validation of the prediction models presents modest results and highlights the individual differences and need for external validation of prediction models in clinical settings. There is need for further development in this area of machine learning application and patient-centred care.
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Affiliation(s)
- Deepika Verma
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway.
| | - Kerstin Bach
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway.
| | - Paul Jarle Mork
- Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway.
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Strafella R, Chen R, Rajji TK, Blumberger DM, Voineskos D. Resting and TMS-EEG markers of treatment response in major depressive disorder: A systematic review. Front Hum Neurosci 2022; 16:940759. [PMID: 35992942 PMCID: PMC9387384 DOI: 10.3389/fnhum.2022.940759] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 06/28/2022] [Indexed: 11/28/2022] Open
Abstract
Electroencephalography (EEG) is a non-invasive method to identify markers of treatment response in major depressive disorder (MDD). In this review, existing literature was assessed to determine how EEG markers change with different modalities of MDD treatments, and to synthesize the breadth of EEG markers used in conjunction with MDD treatments. PubMed and EMBASE were searched from 2000 to 2021 for studies reporting resting EEG (rEEG) and transcranial magnetic stimulation combined with EEG (TMS-EEG) measures in patients undergoing MDD treatments. The search yielded 966 articles, 204 underwent full-text screening, and 51 studies were included for a narrative synthesis of findings along with confidence in the evidence. In rEEG studies, non-linear quantitative algorithms such as theta cordance and theta current density show higher predictive value than traditional linear metrics. Although less abundant, TMS-EEG measures show promise for predictive markers of brain stimulation treatment response. Future focus on TMS-EEG measures may prove fruitful, given its ability to target cortical regions of interest related to MDD.
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Affiliation(s)
- Rebecca Strafella
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Temerty Centre for Therapeutic Brain Intervention, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Robert Chen
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
- Division of Neurology, Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Tarek K. Rajji
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Temerty Centre for Therapeutic Brain Intervention, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Toronto Dementia Research Alliance, University of Toronto, Toronto, ON, Canada
| | - Daniel M. Blumberger
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Temerty Centre for Therapeutic Brain Intervention, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Daphne Voineskos
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Temerty Centre for Therapeutic Brain Intervention, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- *Correspondence: Daphne Voineskos
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Adamson M, Hadipour AL, Uyulan C, Erguzel T, Cerezci O, Kazemi R, Phillips A, Seenivasan S, Shah S, Tarhan N. Sex differences in rTMS treatment response: A deep learning-based EEG investigation. Brain Behav 2022; 12:e2696. [PMID: 35879921 PMCID: PMC9392544 DOI: 10.1002/brb3.2696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 06/01/2022] [Accepted: 06/08/2022] [Indexed: 11/22/2022] Open
Abstract
INTRODUCTION The present study aimed to investigate sex differences in response to repetitive transcranial magnetic stimulation (rTMS) in Major Depressive Disorder (MDD) patients. Identifying the factors that mediate treatment response to rTMS in MDD patients can guide clinicians to administer more appropriate, reliable, and personalized interventions. METHODS In this paper, we developed a novel pipeline based on convolutional LSTM-based deep learning (DL) to classify 25 female and 25 male patients based on their rTMS treatment response. RESULTS Five different classification models were generated, namely pre-/post-rTMS female (model 1), pre-/post-rTMS male (model 2), pre-rTMS female responder versus pre-rTMS female nonresponders (model 3), pre-rTMS male responder vs. pre-rTMS male nonresponder (model 4), and pre-rTMS responder versus nonresponder of both sexes (model 5), achieving 93.3%, 98%, 95.2%, 99.2%, and 96.6% overall test accuracy, respectively. CONCLUSION These results indicate the potential of our approach to be used as a response predictor especially regarding sex-specific antidepressant effects of rTMS in MDD patients.
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Affiliation(s)
- M Adamson
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, California.,Department of Rehabilitation Service, VA Palo Alto Healthcare System, Palo Alto, California
| | - A L Hadipour
- Department of Cognitive Sciences, University of Messina, Messina, Italy
| | - C Uyulan
- Department of Mechanical Engineering, İzmir Katip Çelebi University, İzmir, Turkey
| | - T Erguzel
- Faculty of Engineering and Natural Sciences, Üsküdar University, Istanbul, Turkey
| | - O Cerezci
- Faculty of Health Sciences, Üsküdar University, Istanbul, Turkey
| | - R Kazemi
- Department of Cognitive Psychology, Institute for Cognitive Science Studies, Tehran, Iran
| | - A Phillips
- Department of Rehabilitation Service, VA Palo Alto Healthcare System, Palo Alto, California
| | - S Seenivasan
- Department of Rehabilitation Service, VA Palo Alto Healthcare System, Palo Alto, California
| | - S Shah
- Department of Rehabilitation Service, VA Palo Alto Healthcare System, Palo Alto, California
| | - N Tarhan
- Faculty of Humanities and Social Sciences, Üsküdar University, Istanbul, Turkey
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Henssler J, Alexander D, Schwarzer G, Bschor T, Baethge C. Combining Antidepressants vs Antidepressant Monotherapy for Treatment of Patients With Acute Depression: A Systematic Review and Meta-analysis. JAMA Psychiatry 2022; 79:300-312. [PMID: 35171215 PMCID: PMC8851370 DOI: 10.1001/jamapsychiatry.2021.4313] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Accepted: 12/08/2021] [Indexed: 01/08/2023]
Abstract
IMPORTANCE Combining antidepressants is frequently done in the treatment of acute depression, but studies have yielded conflicting results. OBJECTIVE To conduct a systematic review and meta-analysis assessing efficacy and tolerability of combination therapy. Combinations using presynaptic α2-autoreceptor antagonists or bupropion were investigated separately. DATA SOURCES MEDLINE, Embase, PsycINFO, and the Cochrane Central Register of Controlled Trials were systematically searched from each database inception through January 2020. STUDY SELECTION Randomized clinical trials (RCTs) comparing combinations of antidepressants with antidepressant monotherapy in adult patients with acute depression were included. DATA EXTRACTION AND SYNTHESIS Following guidelines from Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) and recommendations from the Cochrane Handbook, 2 reviewers independently performed a literature search, study selection, data extraction, and evaluation of risk of bias. Data were pooled in random-effects analyses. MAIN OUTCOMES AND MEASURES Primary outcome was efficacy measured as standardized mean difference (SMD); secondary outcomes were response, remission, change from baseline in rating scale scores, number of dropouts, and number of dropouts due to adverse events. RESULTS Thirty-nine RCTs including 6751 patients were eligible. Combination treatment was statistically significantly associated with superior treatment outcomes relative to monotherapy (SMD = 0.31; 95% CI, 0.19-0.44). Combining a reuptake inhibitor with an antagonist of presynaptic α2-autoreceptors was superior to other combinations (SMD = 0.37; 95% CI, 0.19-0.55). Bupropion combinations were not superior to monotherapy (SMD = 0.10; 95% CI, -0.07 to 0.27). Numbers of dropouts and dropouts due to adverse events did not differ between treatments. Studies were heterogeneous, and there was indication of publication bias (Egger test result was positive; P = .007, df = 36), but results remained robust across prespecified secondary outcomes and sensitivity and subgroup analyses, including analyses restricted to studies with low risk of bias. CONCLUSIONS AND RELEVANCE In this meta-analysis of RCTs comparing combinations of antidepressants with antidepressant monotherapy, combining antidepressants was associated with superior treatment outcomes but not with more patients dropping out of treatment. Combinations using an antagonist of presynaptic α2-autoreceptors may be preferable and may be applied as a first-line treatment in severe cases of depression and for patients considered nonresponders.
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Affiliation(s)
- Jonathan Henssler
- Department of Psychiatry and Psychotherapy, University of Cologne Medical School, Cologne, Germany
- Charité University Medicine, St Hedwig-Krankenhaus, Clinic for Psychiatry and Psychotherapy, Berlin, Germany
| | - David Alexander
- Department of Psychiatry and Psychotherapy, University of Cologne Medical School, Cologne, Germany
| | - Guido Schwarzer
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Tom Bschor
- Department of Psychiatry and Psychotherapy, University Hospital of Dresden, Dresden, Germany
| | - Christopher Baethge
- Department of Psychiatry and Psychotherapy, University of Cologne Medical School, Cologne, Germany
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Mosabbir AA, Braun Janzen T, Al Shirawi M, Rotzinger S, Kennedy SH, Farzan F, Meltzer J, Bartel L. Investigating the Effects of Auditory and Vibrotactile Rhythmic Sensory Stimulation on Depression: An EEG Pilot Study. Cureus 2022; 14:e22557. [PMID: 35371676 PMCID: PMC8958118 DOI: 10.7759/cureus.22557] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/23/2022] [Indexed: 12/18/2022] Open
Abstract
Background Major depressive disorder (MDD) is a persistent psychiatric condition and one of the leading causes of global disease burden. In a previous study, we investigated the effects of a five-week intervention consisting of rhythmic gamma frequency (30-70 Hz) vibroacoustic stimulation in 20 patients formally diagnosed with MDD. In that study, the findings suggested a significant clinical improvement in depression symptoms as measured using the Montgomery-Asberg Depression Rating Scale (MADRS), with 37% of participants meeting the criteria for clinical response. The goal of the present research was to examine possible changes from baseline to posttreatment in resting-state electroencephalography (EEG) recordings using the same treatment protocol and to characterize basic changes in EEG related to treatment response. Materials and methods The study sample consisted of 19 individuals aged 18-70 years with a clinical diagnosis of MDD. The participants were assessed before and after a five-week treatment period, which consisted of listening to an instrumental musical track on a vibroacoustic device, delivering auditory and vibrotactile stimulus in the gamma-band range (30-70 Hz, with particular emphasis on 40 Hz). The primary outcome measure was the change in Montgomery-Asberg Depression Rating Scale (MADRS) from baseline to posttreatment and resting-state EEG. Results Analysis comparing MADRS score at baseline and post-intervention indicated a significant change in the severity of depression symptoms after five weeks (t = 3.9923, df = 18, p = 0.0009). The clinical response rate was 36.85%. Resting-state EEG power analysis revealed a significant increase in occipital alpha power (t = -2.149, df = 18, p = 0.04548), as well as an increase in the prefrontal gamma power of the responders (t = 2.8079, df = 13.431, p = 0.01442). Conclusions The results indicate that improvements in MADRS scores after rhythmic sensory stimulation (RSS) were accompanied by an increase in alpha power in the occipital region and an increase in gamma in the prefrontal region, thus suggesting treatment effects on cortical activity in depression. The results of this pilot study will help inform subsequent controlled studies evaluating whether treatment response to vibroacoustic stimulation constitutes a real and replicable reduction of depressive symptoms and to characterize the underlying mechanisms.
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Affiliation(s)
| | | | | | - Susan Rotzinger
- Department of Psychiatry, University Health Network, Toronto, CAN
| | - Sidney H Kennedy
- Centre for Depression and Suicide Studies, St. Michael's Hospital, Toronto, CAN
| | - Faranak Farzan
- School of Mechatronic Systems Engineering, Simon Fraser University, Surrey, CAN
| | - Jed Meltzer
- Rotman Research Institute, Baycrest Health Sciences, Toronto, CAN
| | - Lee Bartel
- Faculty of Music, University of Toronto, Toronto, CAN
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11
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Eichentopf L, Hiemke C, Conca A, Engelmann J, Gerlach M, Havemann-Reinecke U, Hefner G, Florio V, Kuzin M, Lieb K, Reis M, Riemer TG, Serretti A, Schoretsanitis G, Zernig G, Gründer G, Hart XM. Systematic review and meta-analysis on the therapeutic reference range for escitalopram: Blood concentrations, clinical effects and serotonin transporter occupancy. Front Psychiatry 2022; 13:972141. [PMID: 36325531 PMCID: PMC9621321 DOI: 10.3389/fpsyt.2022.972141] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 09/28/2022] [Indexed: 11/13/2022] Open
Abstract
INTRODUCTION A titration within a certain therapeutic reference range presupposes a relationship between the blood concentration and the therapeutic effect of a drug. However, this has not been systematically investigated for escitalopram. Furthermore, the recommended reference range disagrees with mean steady state concentrations (11-21 ng/ml) that are expected under the approved dose range (10-20 mg/day). This work systematically investigated the relationships between escitalopram dose, blood levels, clinical effects, and serotonin transporter occupancy. METHODS Following our previously published methodology, relevant articles were systematically searched and reviewed for escitalopram. RESULTS Of 1,032 articles screened, a total of 30 studies met the eligibility criteria. The included studies investigated escitalopram blood levels in relationship to clinical effects (9 studies) or moderating factors on escitalopram metabolism (12 studies) or serotonin transporter occupancy (9 studies). Overall, the evidence for an escitalopram concentration/effect relationship is low (level C). CONCLUSION Based on our findings, we propose a target range of 20-40 ng/ml for antidepressant efficacy of escitalopram. In maintenance treatment, therapeutic response is expected, when titrating patients above the lower limit. The lower concentration threshold is strongly supported by findings from neuroimaging studies. The upper limit for escitalopram's reference range rather reflects a therapeutic maximum than a tolerability threshold, since the incidence of side effects in general is low. Concentrations above 40 ng/ml should not necessarily result in dose reductions in case of good clinical efficacy and tolerability. Dose-related escitalopram concentrations in different trials were more than twice the expected concentrations from guideline reports. SYSTEMATIC REVIEW REGISTRATION [https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=215873], identifier [CRD42020215873].
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Affiliation(s)
- Luzie Eichentopf
- Department of Molecular Neuroimaging, Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, Mannheim, Germany
| | - Christoph Hiemke
- Department of Psychiatry and Psychotherapy, Institute of Clinical Chemistry and Laboratory Medicine, University Medical Center of Mainz, Mainz, Germany.,Arbeitsgemeinschaft für Neuropsychopharmakologie und Pharmakopsychiatrie (AGNP)-Work Group "Therapeutic Drug Monitoring", Nürnberg, Germany
| | - Andreas Conca
- Arbeitsgemeinschaft für Neuropsychopharmakologie und Pharmakopsychiatrie (AGNP)-Work Group "Therapeutic Drug Monitoring", Nürnberg, Germany.,Department of Psychiatry, Central Hospital, Sanitary Agency of South Tyrol, Bolzano, Italy
| | - Jan Engelmann
- Department of Psychiatry and Psychotherapy, Johannes Gutenberg University Medical Center Mainz, Mainz, Germany
| | - Manfred Gerlach
- Arbeitsgemeinschaft für Neuropsychopharmakologie und Pharmakopsychiatrie (AGNP)-Work Group "Therapeutic Drug Monitoring", Nürnberg, Germany.,Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital Würzburg, Würzburg, Germany
| | - Ursula Havemann-Reinecke
- Arbeitsgemeinschaft für Neuropsychopharmakologie und Pharmakopsychiatrie (AGNP)-Work Group "Therapeutic Drug Monitoring", Nürnberg, Germany.,Department of Psychiatry and Psychosomatics, University of Göttingen, Göttingen, Germany
| | - Gudrun Hefner
- Arbeitsgemeinschaft für Neuropsychopharmakologie und Pharmakopsychiatrie (AGNP)-Work Group "Therapeutic Drug Monitoring", Nürnberg, Germany.,Vitos Clinic for Forensic Psychiatry, Forensic Psychiatry, Eltville, Germany
| | - Vincenzo Florio
- Department of Psychiatry, Comprensorio Sanitario di Bolzano, Bolzano, Italy
| | - Maxim Kuzin
- Arbeitsgemeinschaft für Neuropsychopharmakologie und Pharmakopsychiatrie (AGNP)-Work Group "Therapeutic Drug Monitoring", Nürnberg, Germany.,Clienia Schlössli AG, Psychiatric and Psychotherapeutic Private Clinic, Academic Teaching Hospital of the University of Zurich, Oetwil am See, Switzerland
| | - Klaus Lieb
- Department of Psychiatry and Psychotherapy, University Medical Center Mainz, Mainz, Germany
| | - Margareta Reis
- Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden.,Department of Clinical Chemistry and Pharmacology, Skåne University Hospital, Lund, Sweden
| | - Thomas G Riemer
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Clinical Pharmacology and Toxicology, Berlin, Germany
| | - Alessandro Serretti
- Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Georgios Schoretsanitis
- Arbeitsgemeinschaft für Neuropsychopharmakologie und Pharmakopsychiatrie (AGNP)-Work Group "Therapeutic Drug Monitoring", Nürnberg, Germany.,Department of Psychiatry, Psychotherapy and Psychosomatics, Hospital of Psychiatry, University of Zurich, Zurich, Switzerland.,Department of Psychiatry, Behavioral Health Pavilion, Northwell Health, The Zucker Hillside Hospital, Glen Oaks, NY, United States.,Department of Psychiatry, Zucker School of Medicine at Northwell/Hofstra, Hempstead, NY, United States
| | - Gerald Zernig
- Arbeitsgemeinschaft für Neuropsychopharmakologie und Pharmakopsychiatrie (AGNP)-Work Group "Therapeutic Drug Monitoring", Nürnberg, Germany.,Department of Pharmacology, Medical University of Innsbruck, Innsbruck, Austria.,Private Practice for Psychotherapy and Court-Certified Witness, Hall in Tirol, Austria
| | - Gerhard Gründer
- Department of Molecular Neuroimaging, Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, Mannheim, Germany.,Arbeitsgemeinschaft für Neuropsychopharmakologie und Pharmakopsychiatrie (AGNP)-Work Group "Therapeutic Drug Monitoring", Nürnberg, Germany
| | - Xenia M Hart
- Department of Molecular Neuroimaging, Medical Faculty Mannheim, Central Institute of Mental Health, Heidelberg University, Mannheim, Germany.,Arbeitsgemeinschaft für Neuropsychopharmakologie und Pharmakopsychiatrie (AGNP)-Work Group "Therapeutic Drug Monitoring", Nürnberg, Germany
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12
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Davey CG, Cearns M, Jamieson A, Harrison BJ. Suppressed activity of the rostral anterior cingulate cortex as a biomarker for depression remission. Psychol Med 2021; 53:1-8. [PMID: 36762975 PMCID: PMC10123826 DOI: 10.1017/s0033291721004323] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 08/08/2021] [Accepted: 10/04/2021] [Indexed: 01/22/2023]
Abstract
BACKGROUND Suppression of the rostral anterior cingulate cortex (rACC) has shown promise as a prognostic biomarker for depression. We aimed to use machine learning to characterise its ability to predict depression remission. METHODS Data were obtained from 81 15- to 25-year-olds with a major depressive disorder who had participated in the YoDA-C trial, in which they had been randomised to receive cognitive behavioural therapy plus either fluoxetine or placebo. Prior to commencing treatment patients performed a functional magnetic resonance imaging (fMRI) task to assess rACC suppression. Support vector machines were trained on the fMRI data using nested cross-validation, and were similarly trained on clinical data. We further tested our fMRI model on data from the YoDA-A trial, in which participants had completed the same fMRI paradigm. RESULTS Thirty-six of 81 (44%) participants in the YoDA-C trial achieved remission. Our fMRI model was able to predict remission status (AUC = 0.777 [95% confidence interval (CI) 0.638-0.916], balanced accuracy = 67%, negative predictive value = 74%, p < 0.0001). Clinical models failed to predict remission status at better than chance levels. Testing the model on the alternative YoDA-A dataset confirmed its ability to predict remission (AUC = 0.776, balanced accuracy = 64%, negative predictive value = 70%, p < 0.0001). CONCLUSIONS We confirm that rACC activity acts as a prognostic biomarker for depression. The machine learning model can identify patients who are likely to have difficult-to-treat depression, which might direct the earlier provision of enhanced support and more intensive therapies.
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Affiliation(s)
| | - Micah Cearns
- Discipline of Psychiatry, School of Medicine, The University of Adelaide, Adelaide, Australia
| | - Alec Jamieson
- Department of Psychiatry, The University of Melbourne, Melbourne, Australia
| | - Ben J. Harrison
- Department of Psychiatry, The University of Melbourne, Melbourne, Australia
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13
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Machine Learning-Based Definition of Symptom Clusters and Selection of Antidepressants for Depressive Syndrome. Diagnostics (Basel) 2021; 11:diagnostics11091631. [PMID: 34573974 PMCID: PMC8468112 DOI: 10.3390/diagnostics11091631] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 09/03/2021] [Accepted: 09/03/2021] [Indexed: 12/30/2022] Open
Abstract
The current polythetic and operational criteria for major depression inevitably contribute to the heterogeneity of depressive syndromes. The heterogeneity of depressive syndrome has been criticized using the concept of language game in Wittgensteinian philosophy. Moreover, “a symptom- or endophenotype-based approach, rather than a diagnosis-based approach, has been proposed” as the “next-generation treatment for mental disorders” by Thomas Insel. Understanding the heterogeneity renders promise for personalized medicine to treat cases of depressive syndrome, in terms of both defining symptom clusters and selecting antidepressants. Machine learning algorithms have emerged as a tool for personalized medicine by handling clinical big data that can be used as predictors for subtype classification and treatment outcome prediction. The large clinical cohort data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D), Combining Medications to Enhance Depression Outcome (CO-MED), and the German Research Network on Depression (GRND) have recently began to be acknowledged as useful sources for machine learning-based depression research with regard to cost effectiveness and generalizability. In addition, noninvasive biological tools such as functional and resting state magnetic resonance imaging techniques are widely combined with machine learning methods to detect intrinsic endophenotypes of depression. This review highlights recent studies that have used clinical cohort or brain imaging data and have addressed machine learning-based approaches to defining symptom clusters and selecting antidepressants. Potentially applicable suggestions to realize machine learning-based personalized medicine for depressive syndrome are also provided herein.
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14
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McCallum RT, Perreault ML. Glycogen Synthase Kinase-3: A Focal Point for Advancing Pathogenic Inflammation in Depression. Cells 2021; 10:cells10092270. [PMID: 34571919 PMCID: PMC8470361 DOI: 10.3390/cells10092270] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 08/26/2021] [Accepted: 08/28/2021] [Indexed: 01/03/2023] Open
Abstract
Increasing evidence indicates that the host immune response has a monumental role in the etiology of major depressive disorder (MDD), motivating the development of the inflammatory hypothesis of depression. Central to the involvement of chronic inflammation in MDD is a wide range of signaling deficits induced by the excessive secretion of pro-inflammatory cytokines and imbalanced T cell differentiation. Such signaling deficits include the glutamatergic, cholinergic, insulin, and neurotrophin systems, which work in concert to initiate and advance the neuropathology. Fundamental to the communication between such systems is the protein kinase glycogen synthase kinase-3 (GSK-3), a multifaceted protein critically linked to the etiology of MDD and an emerging target to treat pathogenic inflammation. Here, a consolidated overview of the widespread multi-system involvement of GSK-3 in contributing to the neuropathology of MDD will be discussed, with the feed-forward mechanistic links between all major neuronal signaling pathways highlighted.
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Affiliation(s)
- Ryan T. McCallum
- Department of Biomedical Sciences, Ontario Veterinary College, University of Guelph, Guelph, ON N1G 2W1, Canada;
| | - Melissa L. Perreault
- Department of Biomedical Sciences, Ontario Veterinary College, University of Guelph, Guelph, ON N1G 2W1, Canada;
- Collaborative Program in Neuroscience, University of Guelph, Guelph, ON N1G 2W1, Canada
- Correspondence: ; Tel.: +1-(519)-824-4120 (ext. 52013)
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15
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The clinical effectiveness of using a predictive algorithm to guide antidepressant treatment in primary care (PReDicT): an open-label, randomised controlled trial. Neuropsychopharmacology 2021; 46:1307-1314. [PMID: 33637837 PMCID: PMC8134561 DOI: 10.1038/s41386-021-00981-z] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 01/25/2021] [Accepted: 01/27/2021] [Indexed: 11/25/2022]
Abstract
Depressed patients often do not respond to the first antidepressant prescribed, resulting in sequential trials of different medications. Personalised medicine offers a means of reducing this delay; however, the clinical effectiveness of personalised approaches to antidepressant treatment has not previously been tested. We assessed the clinical effectiveness of using a predictive algorithm, based on behavioural tests of affective cognition and subjective symptoms, to guide antidepressant treatment. We conducted a multicentre, open-label, randomised controlled trial in 913 medication-free depressed patients. Patients were randomly assigned to have their antidepressant treatment guided by a predictive algorithm or treatment as usual (TaU). The primary outcome was the response of depression symptoms, defined as a 50% or greater reduction in baseline score of the QIDS-SR-16 scale, at week 8. Additional prespecified outcomes included symptoms of anxiety at week 8, and symptoms of depression and functional outcome at weeks 8, 24 and 48. The response rate of depressive symptoms at week 8 in the PReDicT (55.9%) and TaU (51.8%) arms did not differ significantly (odds ratio: 1.18 (95% CI: 0.89-1.56), P = 0.25). However, there was a significantly greater reduction of anxiety in week 8 and a greater improvement in functional outcome at week 24 in the PReDicT arm. Use of the PReDicT test did not increase the rate of response to antidepressant treatment estimated by depressive symptoms but did improve symptoms of anxiety at week 8 and functional outcome at week 24. Our findings indicate that personalisation of antidepressant treatment may improve outcomes in depressed patients.
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16
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Chiarenza GA. Quantitative EEG in Childhood Attention Deficit Hyperactivity Disorder and Learning Disabilities. Clin EEG Neurosci 2021; 52:144-155. [PMID: 33012168 DOI: 10.1177/1550059420962343] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
The clinical use of the quantitative EEG (QEEG) from the pioneering work of John has received a new impetus thanks to new neuroimaging techniques and the possibility of using a number of normative databases both of normal subjects and of subjects with definite pathologies. In this direction, the term personalized medicine is becoming more and more common, a medical procedure that separates patients into different groups based on their predicted response to the quantitative EEG. This has allowed the study of single subjects and to customize health care, with decisions and treatments tailored to each individual patient, as well as improvement of knowledge of the pathophysiological mechanisms of specific diseases. This review article will present the most recent evidence in the field of developmental neuropsychiatric disorders obtained from the application of quantitative EEG both in clinical group studies (attention deficit hyperactivity disorder, developmental dyslexia, oppositional defiant disorder) and in individual case studies not yet published.
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17
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Davidson B, Suresh H, Goubran M, Rabin JS, Meng Y, Mithani K, Pople CB, Giacobbe P, Hamani C, Lipsman N. Predicting response to psychiatric surgery: a systematic review of neuroimaging findings. J Psychiatry Neurosci 2020; 45:387-394. [PMID: 32293838 PMCID: PMC7595737 DOI: 10.1503/jpn.190208] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Psychiatric surgery, including deep brain stimulation and stereotactic ablation, is an important treatment option in severe refractory psychiatric illness. Several large trials have demonstrated response rates of approximately 50%, underscoring the need to identify and select responders preoperatively. Recent advances in neuroimaging have brought this possibility into focus. We systematically reviewed the psychiatric surgery neuroimaging literature to assess the current state of evidence for preoperative imaging predictors of response. METHODS We performed this study in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Meta-analysis of Observational Studies in Epidemiology (MOOSE) frameworks, and preregistered it using PROSPERO. We systematically searched the Medline, Embase and Cochrane databases for studies reporting preoperative neuroimaging analyses correlated with clinical outcomes in patients who underwent psychiatric surgery. We recorded and synthesized the methodological details, imaging results and clinical correlations from these studies. RESULTS After removing duplicates, the search yielded 8388 unique articles, of which 7 met the inclusion criteria. The included articles were published between 2001 and 2018 and reported on the outcomes of 101 unique patients. Of the 6 studies that reported significant findings, all identified clusters of hypermetabolism, hyperconnectivity or increased size in the frontostriatal limbic circuitry. LIMITATIONS The included studies were few and highly varied, spanning 2 decades. CONCLUSION Although few studies have analyzed preoperative imaging for predictors of response to psychiatric surgery, we found consistency among the reported results: most studies implicated overactivity in the frontostriatal limbic network as being correlated with clinical response. Larger prospective studies are needed. REGISTRATION www.crd.york.ac.uk/prospero/display_record.php?RecordID=131151.
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Affiliation(s)
- Benjamin Davidson
- From the Division of Neurosurgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada (Davidson, Suresh, Hamani, Lipsman); and the Harquail Centre for Neuromodulation, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada (Davidson, Goubran, Rabin, Meng, Mithani, Pople, Giacobbe, Hamani, Lipsman)
| | - Hrishikesh Suresh
- From the Division of Neurosurgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada (Davidson, Suresh, Hamani, Lipsman); and the Harquail Centre for Neuromodulation, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada (Davidson, Goubran, Rabin, Meng, Mithani, Pople, Giacobbe, Hamani, Lipsman)
| | - Maged Goubran
- From the Division of Neurosurgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada (Davidson, Suresh, Hamani, Lipsman); and the Harquail Centre for Neuromodulation, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada (Davidson, Goubran, Rabin, Meng, Mithani, Pople, Giacobbe, Hamani, Lipsman)
| | - Jennifer S Rabin
- From the Division of Neurosurgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada (Davidson, Suresh, Hamani, Lipsman); and the Harquail Centre for Neuromodulation, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada (Davidson, Goubran, Rabin, Meng, Mithani, Pople, Giacobbe, Hamani, Lipsman)
| | - Ying Meng
- From the Division of Neurosurgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada (Davidson, Suresh, Hamani, Lipsman); and the Harquail Centre for Neuromodulation, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada (Davidson, Goubran, Rabin, Meng, Mithani, Pople, Giacobbe, Hamani, Lipsman)
| | - Karim Mithani
- From the Division of Neurosurgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada (Davidson, Suresh, Hamani, Lipsman); and the Harquail Centre for Neuromodulation, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada (Davidson, Goubran, Rabin, Meng, Mithani, Pople, Giacobbe, Hamani, Lipsman)
| | - Christopher B Pople
- From the Division of Neurosurgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada (Davidson, Suresh, Hamani, Lipsman); and the Harquail Centre for Neuromodulation, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada (Davidson, Goubran, Rabin, Meng, Mithani, Pople, Giacobbe, Hamani, Lipsman)
| | - Peter Giacobbe
- From the Division of Neurosurgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada (Davidson, Suresh, Hamani, Lipsman); and the Harquail Centre for Neuromodulation, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada (Davidson, Goubran, Rabin, Meng, Mithani, Pople, Giacobbe, Hamani, Lipsman)
| | - Clement Hamani
- From the Division of Neurosurgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada (Davidson, Suresh, Hamani, Lipsman); and the Harquail Centre for Neuromodulation, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada (Davidson, Goubran, Rabin, Meng, Mithani, Pople, Giacobbe, Hamani, Lipsman)
| | - Nir Lipsman
- From the Division of Neurosurgery, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada (Davidson, Suresh, Hamani, Lipsman); and the Harquail Centre for Neuromodulation, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada (Davidson, Goubran, Rabin, Meng, Mithani, Pople, Giacobbe, Hamani, Lipsman)
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Vitkauskas M, Mathuru AS. Total Recall: Lateral Habenula and Psychedelics in the Study of Depression and Comorbid Brain Disorders. Int J Mol Sci 2020; 21:ijms21186525. [PMID: 32906643 PMCID: PMC7555763 DOI: 10.3390/ijms21186525] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 08/24/2020] [Accepted: 09/04/2020] [Indexed: 12/12/2022] Open
Abstract
Depression impacts the lives and daily activities of millions globally. Research into the neurobiology of lateral habenula circuitry and the use of psychedelics for treating depressive states has emerged in the last decade as new directions to devise interventional strategies and therapies. Several clinical trials using deep brain stimulation of the habenula, or using ketamine, and psychedelics that target the serotonergic system such as psilocybin are also underway. The promising early results in these fields require cautious optimism as further evidence from experiments conducted in animal systems in ecologically relevant settings, and a larger number of human studies with improved spatiotemporal neuroimaging, accumulates. Designing optimal methods of intervention will also be aided by an improvement in our understanding of the common genetic and molecular factors underlying disorders comorbid with depression, as well as the characterization of psychedelic-induced changes at a molecular level. Advances in the use of cerebral organoids offers a new approach for rapid progress towards these goals. Here, we review developments in these fast-moving areas of research and discuss potential future directions.
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Affiliation(s)
| | - Ajay S. Mathuru
- Yale-NUS College, Singapore 637551, Singapore;
- Institute of Molecular and Cell Biology (IMCB), Singapore 637551, Singapore
- Department of Physiology, Yong Loo Lin School of Medicine, NUS, Singapore 637551, Singapore
- Correspondence:
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de la Salle S, Jaworska N, Blier P, Smith D, Knott V. Using prefrontal and midline right frontal EEG-derived theta cordance and depressive symptoms to predict the differential response or remission to antidepressant treatment in major depressive disorder. Psychiatry Res Neuroimaging 2020; 302:111109. [PMID: 32480044 PMCID: PMC10773969 DOI: 10.1016/j.pscychresns.2020.111109] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Revised: 02/21/2020] [Accepted: 03/30/2020] [Indexed: 10/24/2022]
Abstract
There is a growing need for optimizing treatment selection and response prediction in individuals with major depressive disorder (MDD). Prior investigations have shown that changes in electroencephalographic (EEG)-based measures precede symptom improvement and could serve as biomarkers of treatment outcome. One such method is cordance, a computation of regional brain activity based on a combination of absolute and relative resting EEG activity. Specifically, early reduction in prefrontal (PF) and midline right frontal (MRF) theta (4-8Hz) cordance has been shown to predict response to various antidepressants, though replication is required. Thus, this study examined early changes (baseline to week 1) in PF and MRF cordance in 47 MDD patients undergoing antidepressant treatment. Early changes in cordance and in Montgomery Åsberg Depression Rating Scale (MADRS) scores were assessed alone, and in combination, to predict eventual (by week 12) treatment response and remission. Models combining early changes in theta cordance (PF and MRF) and depressive symptoms were most predictive of response to treatment at week 12; remission models (cordance, MADRS, and their combination) were weaker, though provided modest prediction values. These results suggest that antidepressant response may be optimally predicted by combining both EEG and symptom-based measures after one week of treatment.
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Affiliation(s)
- Sara de la Salle
- University of Ottawa Institute of Mental Health Research, 1145 Carling Avenue, Ottawa K1Z 7K4, ON, Canada; School of Psychology, University of Ottawa, Ottawa, ON, Canada.
| | - Natalia Jaworska
- University of Ottawa Institute of Mental Health Research, 1145 Carling Avenue, Ottawa K1Z 7K4, ON, Canada; School of Psychology, University of Ottawa, Ottawa, ON, Canada
| | - Pierre Blier
- University of Ottawa Institute of Mental Health Research, 1145 Carling Avenue, Ottawa K1Z 7K4, ON, Canada
| | - Dylan Smith
- University of Ottawa Institute of Mental Health Research, 1145 Carling Avenue, Ottawa K1Z 7K4, ON, Canada
| | - Verner Knott
- University of Ottawa Institute of Mental Health Research, 1145 Carling Avenue, Ottawa K1Z 7K4, ON, Canada; School of Psychology, University of Ottawa, Ottawa, ON, Canada
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Kang HJ, Kim KT, Yoo KH, Park Y, Kim JW, Kim SW, Shin IS, Kim JH, Kim JM. Genetic Markers for Later Remission in Response to Early Improvement of Antidepressants. Int J Mol Sci 2020; 21:ijms21144884. [PMID: 32664413 PMCID: PMC7402334 DOI: 10.3390/ijms21144884] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 07/01/2020] [Accepted: 07/08/2020] [Indexed: 12/11/2022] Open
Abstract
Planning subsequent treatment strategies based on early responses rather than waiting for delayed antidepressant action can be helpful. We identified genetic markers for later non-remission in patients exhibiting poor early improvement using whole-exome sequencing data of depressive patients treated in a naturalistic manner. Among 1000 patients, early improvement at 2 weeks (reduction in Hamilton Depression Rating Scale [HAM-D] score ≥ 20%) and remission at 12 weeks (HAM-D score ≤ 7) were evaluated. Gene- and variant-level analyses were conducted to compare patients who did not exhibit early improvement and did not eventually achieve remission (n = 126) with those who exhibited early improvement and achieved remission (n = 385). Genes predicting final non-remission in patients who exhibited poor early improvement (COMT, PRNP, BRPF3, SLC25A40, and CGREF1 in males; PPFIBPI, LZTS3, MEPCE, MAP1A, and PFAS in females; ST3GAL5 in the total population) were determined. Among the significant genes, variants in the PRNP (rs1800014), COMT (rs6267), BRPF3 (rs200565609), and SLC25A40 genes (rs3213633) were identified. However, interpretations should be made cautiously, as complex pharmacotherapy involves various genes and pathways. Early detection of poor early improvement and final non-remission based on genetic risk would be helpful for decision-making in a clinical setting.
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Affiliation(s)
- Hee-Ju Kang
- Department of Psychiatry, Chonnam National University Medical School, Gwangju 61469, Korea; (H.-J.K.); (J.-W.K.); (S.-W.K.); (I.-S.S.)
| | - Ki-Tae Kim
- Department of Laboratory Medicine, Korea University Anam Hospital, Seoul 02841, Korea;
| | - Kyung-Hun Yoo
- Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul 151-742, Korea; (K.-H.Y.); (Y.P.)
| | - Yoomi Park
- Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul 151-742, Korea; (K.-H.Y.); (Y.P.)
| | - Ju-Wan Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju 61469, Korea; (H.-J.K.); (J.-W.K.); (S.-W.K.); (I.-S.S.)
| | - Sung-Wan Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju 61469, Korea; (H.-J.K.); (J.-W.K.); (S.-W.K.); (I.-S.S.)
| | - Il-Seon Shin
- Department of Psychiatry, Chonnam National University Medical School, Gwangju 61469, Korea; (H.-J.K.); (J.-W.K.); (S.-W.K.); (I.-S.S.)
| | - Ju Han Kim
- Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul 151-742, Korea; (K.-H.Y.); (Y.P.)
- Correspondence: (J.H.K.); (J.-M.K.)
| | - Jae-Min Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju 61469, Korea; (H.-J.K.); (J.-W.K.); (S.-W.K.); (I.-S.S.)
- Correspondence: (J.H.K.); (J.-M.K.)
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Predicting second-generation antidepressant effectiveness in treating sadness using demographic and clinical information: A machine learning approach. J Affect Disord 2020; 272:295-304. [PMID: 32553371 DOI: 10.1016/j.jad.2020.04.010] [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: 10/05/2019] [Revised: 03/24/2020] [Accepted: 04/17/2020] [Indexed: 11/21/2022]
Abstract
INTRODUCTION Current guidelines for choosing antidepressant medications involve a trial-and-error process. Most patients try multiple antidepressants before finding an effective antidepressant. This study uses demographic and clinical information to create models predicting effectiveness of different antidepressants in treating sadness in a nationally representative sample of US adults. METHODS A secondary analysis of the Collaborative Psychiatric Epidemiology Survey (CPES) was performed. Participants with or without a mental health diagnosis who reported sadness as a symptom, and were taking fluoxetine (n=156), sertraline (n=224), citalopram (n=91), paroxetine (n=156), venlafaxine (n=69), bupropion (n=92), or trazadone (n=26) within the past year were included. Two sets of principal component analyses (PCAs) and logistic regressions were performed: one determined associations between symptom clusters and antidepressant effectiveness for sadness, and the other created models to predict effectiveness. Both PCAs controlled for psychiatric and medical diagnoses, substance use, psychiatric medications, alternative treatments, and demographics. RESULTS Anxiety was associated with ineffectiveness of fluoxetine in treating sadness. Low mood scores were associated with ineffectiveness of paroxetine and venlafaxine, and fatigue was associated with ineffectiveness of sertraline. The models for predicting drug effectiveness had a mean accuracy of 83% and internal validity of 72%. LIMITATIONS CPES data were collected from 2001-2003, so newer drugs were not included. Effectiveness was for sadness, so results are not directly comparable to studies using overall depressive symptom reductions as outcomes. CONCLUSION Since fewer than 50% of patients currently respond to their first antidepressant, this model could provide modest improvement to choosing starting antidepressants in treating sadness.
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Prospective testing of a neurophysiologic biomarker for treatment decisions in major depressive disorder: The PRISE-MD trial. J Psychiatr Res 2020; 124:159-165. [PMID: 32169689 PMCID: PMC7141143 DOI: 10.1016/j.jpsychires.2020.02.028] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Revised: 01/03/2020] [Accepted: 02/24/2020] [Indexed: 12/12/2022]
Abstract
Management of Major Depressive Disorder (MDD) might be improved by a biomarker to predict whether a selected medication is likely to lead to remission. We previously reported on a quantitative electroencephalogram-based biomarker, the Antidepressant Treatment Response (ATR) index, that integrated recordings at baseline and after one week of treatment. The present study prospectively tested whether treatment directed by the biomarker increased the likelihood of remission; we hypothesized that continued treatment with a drug predicted to lead to remission (i.e., high ATR values) would be associated with better outcomes than if the drug was predicted not to lead to remission (i.e., low ATR values). We enrolled 180 adult outpatients with unipolar MDD from the community. After one week of escitalopram treatment to determine the biomarker, stratified randomization (high vs. low ATR) was used to assign subjects to either continued escitalopram or a switch to bupropion as a blinded control condition, for seven additional weeks. For the 73 evaluable subjects assigned to continued escitalopram treatment, the remission rate was significantly higher for those in whom ATR had predicted remission versus non-remission (60.4% vs. 30.0%, respectively, p = 0.01). Accuracy was enhanced by combining 1-week depressive symptom change with ATR (68.6% vs 28.9%). This prospective validation study supports further development of the ATR biomarker, alone or together with early symptom change, to improve care by identifying individuals unlikely to remit with their current treatment, and support the decision to change treatment after one week rather than after failing a full, prolonged course of medication.
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23
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Wu W, Zhang Y, Jiang J, Lucas MV, Fonzo GA, Rolle CE, Cooper C, Chin-Fatt C, Krepel N, Cornelssen CA, Wright R, Toll RT, Trivedi HM, Monuszko K, Caudle TL, Sarhadi K, Jha MK, Trombello JM, Deckersbach T, Adams P, McGrath PJ, Weissman MM, Fava M, Pizzagalli DA, Arns M, Trivedi MH, Etkin A. An electroencephalographic signature predicts antidepressant response in major depression. Nat Biotechnol 2020; 38:439-447. [PMID: 32042166 PMCID: PMC7145761 DOI: 10.1038/s41587-019-0397-3] [Citation(s) in RCA: 138] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2018] [Revised: 12/06/2019] [Accepted: 12/17/2019] [Indexed: 12/21/2022]
Abstract
Antidepressants are widely prescribed, but their efficacy relative to placebo is modest, in part because the clinical diagnosis of major depression encompasses biologically heterogeneous conditions. Here, we sought to identify a neurobiological signature of response to antidepressant treatment as compared to placebo. We designed a latent-space machine-learning algorithm tailored for resting-state electroencephalography (EEG) and applied it to data from the largest imaging-coupled, placebo-controlled antidepressant study (n = 309). Symptom improvement was robustly predicted in a manner both specific for the antidepressant sertraline (versus placebo) and generalizable across different study sites and EEG equipment. This sertraline-predictive EEG signature generalized to two depression samples, wherein it reflected general antidepressant medication responsivity and related differentially to a repetitive transcranial magnetic stimulation treatment outcome. Furthermore, we found that the sertraline resting-state EEG signature indexed prefrontal neural responsivity, as measured by concurrent transcranial magnetic stimulation and EEG. Our findings advance the neurobiological understanding of antidepressant treatment through an EEG-tailored computational model and provide a clinical avenue for personalized treatment of depression.
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Affiliation(s)
- Wei Wu
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, Guangdong 510640, China
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Yu Zhang
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Jing Jiang
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Molly V. Lucas
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Gregory A. Fonzo
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Camarin E. Rolle
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Crystal Cooper
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX
| | - Cherise Chin-Fatt
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX
| | - Noralie Krepel
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, The Netherlands
- Department of Psychiatry, Harvard Medical School and McLean Hospital, Belmont, MA 02478
| | - Carena A. Cornelssen
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Rachael Wright
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Russell T. Toll
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Hersh M. Trivedi
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Karen Monuszko
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Trevor L. Caudle
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Kamron Sarhadi
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
| | - Manish K. Jha
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX
| | - Joseph M. Trombello
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX
| | - Thilo Deckersbach
- New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY
| | - Phil Adams
- New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY
| | - Patrick J. McGrath
- New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY
| | - Myrna M. Weissman
- New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY
| | - Maurizio Fava
- New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY
| | - Diego A. Pizzagalli
- New York State Psychiatric Institute & Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY
| | - Martijn Arns
- Department of Psychiatry, Harvard Medical School and McLean Hospital, Belmont, MA 02478
- Department of Experimental Psychology, Utrecht University, Utrecht, the Netherlands
- neuroCare Group Netherlands, Nijmegen, the Netherlands
| | - Madhukar H. Trivedi
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX
| | - Amit Etkin
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
- Wu Tsai Neuroscience Institute Stanford University, Stanford, CA 94305
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
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Iznak AF, Iznak EV, Abramova LI, Lozhnikov MA. Models for the Quantitative Prediction of Therapeutic Responses Based on the Baseline EEG Parameters in Depressive Patients. ACTA ACUST UNITED AC 2020. [DOI: 10.1134/s0362119719060057] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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25
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Scangos KW, Ahmad HS, Shafi A, Sellers KK, Dawes HE, Krystal A, Chang EF. Pilot Study of An Intracranial Electroencephalography Biomarker of Depressive Symptoms in Epilepsy. J Neuropsychiatry Clin Neurosci 2020; 32:185-190. [PMID: 31394989 PMCID: PMC7429560 DOI: 10.1176/appi.neuropsych.19030081] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
OBJECTIVES Adult patients with epilepsy have an increased prevalence of major depressive disorder (MDD). Intracranial EEG (iEEG) captured during extended inpatient monitoring of patients with treatment-resistant epilepsy offers a particularly promising method to study MDD networks in epilepsy. METHODS The authors used 24 hours of resting-state iEEG to examine the neural activity patterns within corticolimbic structures that reflected the presence of depressive symptoms in 13 adults with medication-refractory epilepsy. Principal component analysis was performed on the z-scored mean relative power in five standard frequency bands averaged across electrodes within a region. RESULTS Principal component 3 was a statistically significant predictor of the presence of depressive symptoms (R2=0.35, p=0.014). A balanced logistic classifier model using principal component 3 alone correctly classified 78% of patients as belonging to the group with a high burden of depressive symptoms or a control group with minimal depressive symptoms (sensitivity, 75%; specificity, 80%; area under the curve=0.8, leave-one-out cross validation). Classification was dependent on beta power throughout the corticolimbic network and low-frequency cingulate power. CONCLUSIONS These finding suggest, for the first time, that neural features across circuits involved in epilepsy may distinguish patients who have depressive symptoms from those who do not. Larger studies are required to validate these findings and to assess their diagnostic utility in MDD.
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26
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Smith EE, Tenke CE, Deldin PJ, Trivedi MH, Weissman MM, Auerbach RP, Bruder GE, Pizzagalli DA, Kayser J. Frontal theta and posterior alpha in resting EEG: A critical examination of convergent and discriminant validity. Psychophysiology 2019; 57:e13483. [PMID: 31578740 DOI: 10.1111/psyp.13483] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 08/28/2019] [Accepted: 08/29/2019] [Indexed: 12/22/2022]
Abstract
Prior research has identified two resting EEG biomarkers with potential for predicting functional outcomes in depression: theta current density in frontal brain regions (especially rostral anterior cingulate cortex) and alpha power over posterior scalp regions. As little is known about the discriminant and convergent validity of these putative biomarkers, a thorough evaluation of these psychometric properties was conducted toward the goal of improving clinical utility of these markers. Resting 71-channel EEG recorded from 35 healthy adults at two sessions (1-week retest) were used to systematically compare different quantification techniques for theta and alpha sources at scalp (surface Laplacian or current source density [CSD]) and brain (distributed inverse; exact low resolution electromagnetic tomography [eLORETA]) level. Signal quality was evaluated with signal-to-noise ratio, participant-level spectra, and frequency PCA covariance decomposition. Convergent and discriminant validity were assessed within a multitrait-multimethod framework. Posterior alpha was reliably identified as two spectral components, each with unique spatial patterns and condition effects (eyes open/closed), high signal quality, and good convergent and discriminant validity. In contrast, frontal theta was characterized by one low-variance component, low signal quality, lack of a distinct spectral peak, and mixed validity. Correlations between candidate biomarkers suggest that posterior alpha components constitute reliable, convergent, and discriminant biometrics in healthy adults. Component-based identification of spectral activity (CSD/eLORETA-fPCA) was superior to fixed, a priori frequency bands. Improved quantification and conceptualization of frontal theta is necessary to determine clinical utility.
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Affiliation(s)
- Ezra E Smith
- Division of Translational Epidemiology, New York State Psychiatric Institute, New York, New York, USA
| | - Craig E Tenke
- Division of Translational Epidemiology, New York State Psychiatric Institute, New York, New York, USA.,Department of Psychiatry, Vagelos College of Physicians & Surgeons, Columbia University, New York, New York, USA.,Division of Cognitive Neuroscience, New York State Psychiatric Institute, New York, New York, USA
| | - Patricia J Deldin
- Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, USA
| | - Madhukar H Trivedi
- Department of Psychiatry, University of Texas, Southwestern Medical Center, Dallas, Texas, USA
| | - Myrna M Weissman
- Division of Translational Epidemiology, New York State Psychiatric Institute, New York, New York, USA.,Department of Psychiatry, Vagelos College of Physicians & Surgeons, Columbia University, New York, New York, USA
| | - Randy P Auerbach
- Department of Psychiatry, Vagelos College of Physicians & Surgeons, Columbia University, New York, New York, USA
| | - Gerard E Bruder
- Department of Psychiatry, Vagelos College of Physicians & Surgeons, Columbia University, New York, New York, USA
| | - Diego A Pizzagalli
- Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA.,Center for Depression, Anxiety & Stress Research, McLean Hospital, Belmont, Massachusetts, USA
| | - Jürgen Kayser
- Division of Translational Epidemiology, New York State Psychiatric Institute, New York, New York, USA.,Department of Psychiatry, Vagelos College of Physicians & Surgeons, Columbia University, New York, New York, USA.,Division of Cognitive Neuroscience, New York State Psychiatric Institute, New York, New York, USA
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27
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Leighton SP, Upthegrove R, Krishnadas R, Benros ME, Broome MR, Gkoutos GV, Liddle PF, Singh SP, Everard L, Jones PB, Fowler D, Sharma V, Freemantle N, Christensen RHB, Albert N, Nordentoft M, Schwannauer M, Cavanagh J, Gumley AI, Birchwood M, Mallikarjun PK. Development and validation of multivariable prediction models of remission, recovery, and quality of life outcomes in people with first episode psychosis: a machine learning approach. Lancet Digit Health 2019; 1:e261-e270. [PMID: 33323250 DOI: 10.1016/s2589-7500(19)30121-9] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Accepted: 08/12/2019] [Indexed: 12/15/2022]
Abstract
BACKGROUND Outcomes for people with first-episode psychosis are highly heterogeneous. Few reliable validated methods are available to predict the outcome for individual patients in the first clinical contact. In this study, we aimed to build multivariable prediction models of 1-year remission and recovery outcomes using baseline clinical variables in people with first-episode psychosis. METHODS In this machine learning approach, we applied supervised machine learning, using regularised regression and nested leave-one-site-out cross-validation, to baseline clinical data from the English Evaluating the Development and Impact of Early Intervention Services (EDEN) study (n=1027), to develop and internally validate prediction models at 1-year follow-up. We assessed four binary outcomes that were recorded at 1 year: symptom remission, social recovery, vocational recovery, and quality of life (QoL). We externally validated the prediction models by selecting from the top predictor variables identified in the internal validation models the variables shared with the external validation datasets comprised of two Scottish longitudinal cohort studies (n=162) and the OPUS trial, a randomised controlled trial of specialised assertive intervention versus standard treatment (n=578). FINDINGS The performance of prediction models was robust for the four 1-year outcomes of symptom remission (area under the receiver operating characteristic curve [AUC] 0·703, 95% CI 0·664-0·742), social recovery (0·731, 0·697-0·765), vocational recovery (0·736, 0·702-0·771), and QoL (0·704, 0·667-0·742; p<0·0001 for all outcomes), on internal validation. We externally validated the outcomes of symptom remission (AUC 0·680, 95% CI 0·587-0·773), vocational recovery (0·867, 0·805-0·930), and QoL (0·679, 0·522-0·836) in the Scottish datasets, and symptom remission (0·616, 0·553-0·679), social recovery (0·573, 0·504-0·643), vocational recovery (0·660, 0·610-0·710), and QoL (0·556, 0·481-0·631) in the OPUS dataset. INTERPRETATION In our machine learning analysis, we showed that prediction models can reliably and prospectively identify poor remission and recovery outcomes at 1 year for patients with first-episode psychosis using baseline clinical variables at first clinical contact. FUNDING Lundbeck Foundation.
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Affiliation(s)
- Samuel P Leighton
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Rachel Upthegrove
- Institute for Mental Health, University of Birmingham, Birmingham, UK
| | - Rajeev Krishnadas
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, UK
| | - Michael E Benros
- Copenhagen Research Center for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - Matthew R Broome
- Institute for Mental Health, University of Birmingham, Birmingham, UK
| | - Georgios V Gkoutos
- Institute of Cancer and Genomics, University of Birmingham, Birmingham, UK; Institute of Translational Medicine, University of Birmingham, Birmingham, UK; Health Data Research UK Midlands, UK; University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Peter F Liddle
- Institute of Mental Health, University of Nottingham, Nottingham, UK
| | - Swaran P Singh
- Mental Health and Wellbeing, Warwick Medical School, University of Warwick, Coventry, UK
| | | | - Peter B Jones
- Wolfson College, University of Cambridge, Cambridge, UK
| | - David Fowler
- School of Psychology, University of Sussex, Brighton, UK
| | - Vimal Sharma
- Department of Health and Social Care, University of Chester, Chester, UK
| | | | - Rune H B Christensen
- Copenhagen Research Center for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - Nikolai Albert
- Copenhagen Research Center for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - Merete Nordentoft
- Copenhagen Research Center for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark; Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Matthias Schwannauer
- School of Health in Social Science, Clinical Psychology, University of Edinburgh, Edinburgh, UK
| | - Jonathan Cavanagh
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Andrew I Gumley
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Max Birchwood
- Mental Health and Wellbeing, Warwick Medical School, University of Warwick, Coventry, UK
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28
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Cook IA, Congdon E, Krantz DE, Hunter AM, Coppola G, Hamilton SP, Leuchter AF. Time Course of Changes in Peripheral Blood Gene Expression During Medication Treatment for Major Depressive Disorder. Front Genet 2019; 10:870. [PMID: 31620172 PMCID: PMC6760033 DOI: 10.3389/fgene.2019.00870] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 08/20/2019] [Indexed: 12/11/2022] Open
Abstract
Changes in gene expression (GE) during antidepressant treatment may increase understanding of the action of antidepressant medications and serve as biomarkers of efficacy. GE changes in peripheral blood are desirable because they can be assessed easily on multiple occasions during treatment. We report here on GE changes in 68 individuals who were treated for 8 weeks with either escitalopram alone, or escitalopram followed by bupropion. GE changes were assessed after 1, 2, and 8 weeks of treatment, with significant changes observed in 156, 121, and 585 peripheral blood gene transcripts, respectively. Thirty-one transcript changes were shared between the 1- and 8-week time points (seven upregulated, 24 downregulated). Differences were detected between the escitalopram- and bupropion-treated subjects, although there was no significant association between GE changes and clinical outcome. A subset of 18 genes overlapped with those previously identified as differentially expressed in subjects with MDD compared with healthy control subjects. There was statistically significant overlap between genes differentially expressed in the current and previous studies, with 10 genes overlapping in at least two previous studies. There was no enrichment for genes overexpressed in nervous system cell types, but there was a trend toward enrichment for genes in the WNT/β-catenin pathway in the anterior thalamus; three genes in this pathway showed differential expression in the present and in three previous studies. Our dataset and other similar studies will provide an important source of information about potential biomarkers of recovery and for potential dysregulation of GE in MDD.
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Affiliation(s)
- Ian A Cook
- Neuromodulation Division, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Psychiatry & Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Bioengineering, Henry Samueli School of Engineering at Applied Science, University of California, Los Angeles, Los Angeles, CA, United States
| | - Eliza Congdon
- Neuromodulation Division, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Psychiatry & Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - David E Krantz
- Neuromodulation Division, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Psychiatry & Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Aimee M Hunter
- Neuromodulation Division, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Psychiatry & Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Giovanni Coppola
- Neuromodulation Division, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Psychiatry & Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Steven P Hamilton
- Department of Psychiatry, Kaiser Permanente Northern California, San Francisco, CA, United States.,Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States
| | - Andrew F Leuchter
- Neuromodulation Division, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Psychiatry & Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
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Sari Gokten E, Tulay EE, Beser B, Elagoz Yuksel M, Arikan K, Tarhan N, Metin B. Predictive Value of Slow and Fast EEG Oscillations for Methylphenidate Response in ADHD. Clin EEG Neurosci 2019; 50:332-338. [PMID: 31304784 DOI: 10.1177/1550059419863206] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder and is characterized by symptoms of inattention and/or hyperactivity and impulsivity. In the current study, we obtained quantitative EEG (QEEG) recordings of 51 children aged between 6 and 12 years before the initiation of methylphenidate treatment. The relationship between changes in the scores of ADHD symptoms and initial QEEG features (power/power ratios values) were assessed. In addition, the children were classified as responder and nonresponder according to the ratio of their response to the medication (>25% improvement after medication). Logistic regression analyses were performed to analyze the accuracy of QEEG features for predicting responders. The findings indicate that patients with increased delta power at F8, theta power at Fz, F4, C3, Cz, T5, and gamma power at T6 and decreased beta powers at F8 and P3 showed more improvement in ADHD hyperactivity symptoms. In addition, increased delta/beta power ratio at F8 and theta/beta power ratio at F8, F3, Fz, F4, C3, Cz, P3, and T5 showed negative correlations with Conners' score difference of hyperactivity as well. This means, those with greater theta/beta and delta/beta powers showed more improvement in hyperactivity following medication. Theta power at Cz and T5 and theta/beta power ratios at C3, Cz, and T5 have significantly classified responders and nonresponders according to the logistic binary regression analysis. The results show that slow and fast oscillations may have predictive value for treatment response in ADHD. Future studies should seek for more sensitive biomarkers.
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Affiliation(s)
- Emel Sari Gokten
- 1 Department of Child and Adolescent Psychiatry, NPIstanbul Brain Hospital, Istanbul, Turkey
| | - Emine Elif Tulay
- 2 Technology Transfer Office, Uskudar University, Istanbul, Turkey
| | - Birsu Beser
- 3 Neuroscience Department, Istanbul University, Istanbul, Turkey
| | - Mine Elagoz Yuksel
- 1 Department of Child and Adolescent Psychiatry, NPIstanbul Brain Hospital, Istanbul, Turkey
| | - Kemal Arikan
- 4 Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey
| | - Nevzat Tarhan
- 4 Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,5 Department of Psychiatry, NPIstanbul Brain Hospital, Istanbul, Turkey
| | - Baris Metin
- 4 Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey
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Allen TA, Lam RW, Milev R, Rizvi SJ, Frey BN, MacQueen GM, Müller DJ, Uher R, Kennedy SH, Quilty LC. Early change in reward and punishment sensitivity as a predictor of response to antidepressant treatment for major depressive disorder: a CAN-BIND-1 report. Psychol Med 2019; 49:1629-1638. [PMID: 30220263 DOI: 10.1017/s0033291718002441] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
BACKGROUND In an effort to optimize patient outcomes, considerable attention is being devoted to identifying patient characteristics associated with major depressive disorder (MDD) and its responsiveness to treatment. In the current study, we extend this work by evaluating whether early change in these sensitivities is associated with response to antidepressant treatment for MDD. METHODS Participants included 210 patients with MDD who were treated with 8 weeks of escitalopram and 112 healthy comparison participants. Of the original 210 patients, 90 non-responders received adjunctive aripiprazole for an additional 8 weeks. Symptoms of depression and anhedonia were assessed at the beginning of treatment and 8 weeks later in both samples. Reward and punishment sensitivity were assessed using the BIS/BAS scales measured at the initiation of treatment and 2 weeks later. RESULTS Individuals with MDD exhibited higher punishment sensitivity and lower reward sensitivity compared with healthy comparison participants. Change in reward sensitivity during the first 2 weeks of treatment was associated with improved depressive symptoms and anhedonia following 8 weeks of treatment with escitalopram. Similarly, improvement in reward responsiveness during the first 2 weeks of adjunctive therapy with aripiprazole was associated with fewer symptoms of depression at post-treatment. CONCLUSIONS Findings highlight the predictive utility of early change in reward sensitivity during antidepressant treatment for major depression. In a clinical setting, a lack of change in early reward processing may signal a need to modify a patient's treatment plan with alternative or augmented treatment approaches.
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Affiliation(s)
- Timothy A Allen
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health,Toronto, ON,Canada
| | - Raymond W Lam
- Department of Psychiatry,University of British Columbia,Vancouver, BC,Canada
| | - Roumen Milev
- Departments of Psychiatry and Psychology,Queen's University,Kingston, ON,Canada
| | - Sakina J Rizvi
- Department of Psychiatry,University of Toronto,Toronto, ON,Canada
| | - Benicio N Frey
- Department of Psychiatry & Behavioural Neurosciences,McMaster University,Hamilton, ON,Canada
| | | | - Daniel J Müller
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health,Toronto, ON,Canada
| | - Rudolf Uher
- Department of Psychiatry,Dalhousie University,Halifax, NS,Canada
| | - Sidney H Kennedy
- Department of Psychiatry,University of Toronto,Toronto, ON,Canada
| | - Lena C Quilty
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health,Toronto, ON,Canada
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The Association between Serum Magnesium Levels and Depression in an Adult Primary Care Population. Nutrients 2019; 11:nu11071475. [PMID: 31261707 PMCID: PMC6683054 DOI: 10.3390/nu11071475] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 06/13/2019] [Accepted: 06/26/2019] [Indexed: 12/21/2022] Open
Abstract
Depression is common, places a large burden on the patient, their family and community, and is often difficult to treat. Magnesium supplementation is associated with improved depressive symptoms, but because the mechanism is unknown, it is unclear whether serum magnesium levels act as a biological predictor of the treatment outcome. Therefore, we sought to describe the relationship between serum magnesium and the Patient Health Questionnaire (PHQ, a measure of depression) scores. A cross-sectional analysis of medical records from 3604 adults (mean age 62 years; 42% men) seen in primary care clinics between 2015 and 2018, with at least one completed PHQ were included. The relationship between serum magnesium and depression using univariate analyses showed a significant effect when measured by the PHQ-2 (−0.19 points/mg/dL; 95% CI −0.31, −0.07; P = 0.001) and the PHQ-9 (−0.93 points/mg/dL; 95% CI −1.81, −0.06; P = 0.037). This relationship was strengthened after adjusting for covariates (age, gender, race, time between serum magnesium and PHQ tests, and presence of diabetes and chronic kidney disease) (PHQ-2: −0.25 points/mg/dL; 95% CI −3.33, −0.09; P < 0.001 and PHQ-9: −1.09 95% CI −1.96 −0.21; P = 0.015). For adults seen in primary care, lower serum magnesium levels are associated with depressive symptoms, supporting the use of supplemental magnesium as therapy. Serum magnesium may help identify the biological mechanism of depressive symptoms and identify patients likely to respond to magnesium supplementation.
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Abstract
OBJECTIVE Electroconvulsive therapy (ECT) is the most effective treatment for major depression but also carries risk of cognitive side effects. The ability to predict whether treatment will be effective before initiation of treatment could significantly improve quality of care, reduce suffering, and diminish costs. We sought to carry out a comprehensive and definitive study of the relationship between the background electroencephalography (EEG) and therapeutic response to ECT. METHODS Twenty-one channel resting EEG was collected pre-ECT and 2 to 3 days after ECT course from 2 separate data sets, one to develop an EEG model of therapeutic response (n = 30) and a second to test this model (n = 40). A 3-way principal components analysis was applied and coherence and spectral amplitude across 6 frequency bands were examined. The primary outcome measure was the Montgomery-Asberg Rating Scale (MADRS). RESULTS Four patterns of amplitude and coherence along with baseline MADRS score accounted for 85% of the variance in posttreatment course MADRS score in study 1 (R = 0.85, F = 11.7, P < 0.0002) and 53% of the variance in MADRS score in study 2 (R = 0.53, F = 5.5, P < 0.003). Greater pre-ECT course anterior delta coherence accounted for the majority of variance in therapeutic response (study 1: R = 0.44, P = 0.01; study 2: R = 0.16, P = 0.008). CONCLUSIONS These results suggest a putative electrophysiological biomarker that can predict therapeutic response before a course of ECT. Greater baseline anterior delta coherence is significantly associated with a better subsequent therapeutic response and could be indicative of intact circuitry allowing for improved seizure propagation.
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Hack LM, Fries GR, Eyre HA, Bousman CA, Singh AB, Quevedo J, John VP, Baune BT, Dunlop BW. Moving pharmacoepigenetics tools for depression toward clinical use. J Affect Disord 2019; 249:336-346. [PMID: 30802699 PMCID: PMC6763314 DOI: 10.1016/j.jad.2019.02.009] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 02/01/2019] [Accepted: 02/05/2019] [Indexed: 12/13/2022]
Abstract
BACKGROUND Major depressive disorder (MDD) is a leading cause of disability worldwide, and over half of patients do not achieve symptom remission following an initial antidepressant course. Despite evidence implicating a strong genetic basis for the pathophysiology of MDD, there are no adequately validated biomarkers of treatment response routinely used in clinical practice. Pharmacoepigenetics is an emerging field that has the potential to combine both genetic and environmental information into treatment selection and further the goal of precision psychiatry. However, this field is in its infancy compared to the more established pharmacogenetics approaches. METHODS We prepared a narrative review using literature searches of studies in English pertaining to pharmacoepigenetics and treatment of depressive disorders conducted in PubMed, Google Scholar, PsychINFO, and Ovid Medicine from inception through January 2019. We reviewed studies of DNA methylation and histone modifications in both humans and animal models of depression. RESULTS Emerging evidence from human and animal work suggests a key role for epigenetic marks, including DNA methylation and histone modifications, in the prediction of antidepressant response. The challenges of heterogeneity of patient characteristics and loci studied as well as lack of replication that have impacted the field of pharmacogenetics also pose challenges to the development of pharmacoepigenetic tools. Additionally, given the tissue specific nature of epigenetic marks as well as their susceptibility to change in response to environmental factors and aging, pharmacoepigenetic tools face additional challenges to their development. LIMITATIONS This is a narrative and not systematic review of the literature on the pharmacoepigenetics of antidepressant response. We highlight key studies pertaining to pharmacoepigenetics and treatment of depressive disorders in humans and depressive-like behaviors in animal models, regardless of sample size or methodology. While we discuss DNA methylation and histone modifications, we do not cover microRNAs, which have been reviewed elsewhere recently. CONCLUSIONS Utilization of genome-wide approaches and reproducible epigenetic assays, careful selection of the tissue assessed, and integration of genetic and clinical information into pharmacoepigenetic tools will improve the likelihood of developing clinically useful tests.
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Affiliation(s)
- Laura M Hack
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Emory University, Atlanta, GA, USA; Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Road, Palo Alto, CA 94305, USA; Sierra Pacific Mental Illness Research Education and Clinical Centers, VA Palo Alto Health Care System, Palo Alto, CA, USA.
| | - Gabriel R Fries
- Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA
| | - Harris A Eyre
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Road, Palo Alto, CA 94305, USA; Innovation Institute, Texas Medical Center, Houston, TX, USA; IMPACT SRC, School of Medicine, Deakin University, Geelong, Victoria, Australia; Department of Psychiatry, University of Melbourne, Melbourne, Victoria, Australia
| | - Chad A Bousman
- Departments of Medical Genetics, Psychiatry, Physiology & Pharmacology, University of Calgary, Calgary, AB, Canada
| | - Ajeet B Singh
- IMPACT SRC, School of Medicine, Deakin University, Geelong, Victoria, Australia
| | - Joao Quevedo
- Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA
| | - Vineeth P John
- Department of Psychiatry and Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston (UTHealth), Houston, TX, USA
| | - Bernhard T Baune
- Department of Psychiatry, University of Melbourne, Melbourne, Victoria, Australia
| | - Boadie W Dunlop
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Emory University, Atlanta, GA, USA
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Hippocampal subfield-specific connectivity findings in major depressive disorder: A 7 Tesla diffusion MRI study. J Psychiatr Res 2019; 111:186-192. [PMID: 30798080 PMCID: PMC7325444 DOI: 10.1016/j.jpsychires.2019.02.008] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Revised: 02/07/2019] [Accepted: 02/14/2019] [Indexed: 11/23/2022]
Abstract
OBJECTIVE Diffusion magnetic resonance imaging (dMRI) enables non-invasive characterization of white matter (WM) structures in vivo. Prior studies suggest that certain WM tracts may be affected in major depressive disorder (MDD), however, hippocampal subfield-specific dMRI measures have not yet been explored in MDD. We use 7 Tesla dMRI to investigate differences in hippocampal subfield connectivity of MDD patients. METHODS Eighteen MDD patients and eighteen matched healthy volunteers underwent 7 Tesla MRI. The hippocampal formations were segmented by subfields and tractography was performed to determine streamline count (SC), fractional anisotropy (FA), and mean diffusivity (MD) in patients and controls. Significant subfield connectivity measures were also correlated with age at depression onset. RESULTS Compared with controls, MDD patients exhibited reduced SC in the molecular layer of the left dentate gyrus (p < 0.001). SC count in the left dentate gyrus was shown to positively correlate with age at disease onset (p < 0.05). Increased MD was observed in streamlines emanating from both the left (p = 0.0001) and right (p < 0.001) fimbriae in MDD patients. CONCLUSIONS Increased MD of tracts in the fimbriae suggests compromised neuronal membranes in the major hippocampal output gate. Reduced SC of the dentate gyri indexes a disruption of normal cellular processes such as neurogenesis. These findings may have significant implications for identifying biomarkers of MDD and elucidating the neurobiological underpinnings of depression.
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Vitinius F, Escherich S, Deter HC, Hellmich M, Jünger J, Petrowski K, Ladwig KH, Lambertus F, Michal M, Weber C, de Zwaan M, Herrmann-Lingen C, Ronel J, Albus C. Somatic and sociodemographic predictors of depression outcome among depressed patients with coronary artery disease - a secondary analysis of the SPIRR-CAD study. BMC Psychiatry 2019; 19:57. [PMID: 30717711 PMCID: PMC6360727 DOI: 10.1186/s12888-019-2026-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Accepted: 01/15/2019] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Depressive symptoms are common in patients with coronary artery disease (CAD) and are associated with an unfavourable outcome. Establishing prognostic patient profiles prior to the beginning of mental health care may facilitate higher efficacy of targeted interventions. The aim of the current study was to identify sociodemographic and somatic predictors of depression outcome among depressed patients with CAD. METHODS Based on the dataset of the multicentre SPIRR-CAD randomised controlled trial (n = 570 patients with CAD and ≥ 8 points on the Hospital Anxiety and Depression Scale (HADS)), 141 potential sociodemographic and somatic predictors of the change in the HADS-D depression score from baseline to 18-month-follow-up were derived in two different ways. We screened for univariable association with response, using either analysis of (co)variance or logistic regression, respectively, both adjusted for baseline HADS-D value and treatment group. To guard against overfitting, multivariable association was evaluated by a linear or binomial (generalised) linear model with lasso regularisation, a machine learning approach. Outcome measures were the change in continuous HADS-D depression scores, as well as three established binary criteria. The Charlson Comorbidity Index (CCI) was calculated to assess possible influences of comorbidities on our results and was also entered in our machine learning approach. RESULTS Higher age (p = 0.002), unknown previous myocardial infarction (p = 0.013), and a higher heart rate variability during numeracy tests (p = .020) were univariably associated with a favourable depression outcome, whereas hyperuricemia (p ≤ 0.003), higher triglycerides (p = 0.014), NYHA class III (p ≤ 0.028), state after resuscitation (p ≤ 0.042), intake of thyroid hormones (p = 0.007), antidiabetic drugs (p = 0.015), analgesic drugs (p = 0.027), beta blockers (p = 0.035), uric acid drugs (p ≤ 0.039), and anticholinergic drugs (p = 0.045) were associated with an adverse effect on the HADS-D depression score. In all analyses, no significant differences between study arms could be found and physical comorbidities also had no significant influence on our results. CONCLUSION Our findings may contribute to identification of somatic and sociodemographic predictors of depression outcome in patients with CAD. The unexpected effects of specific medication require further clarification and further research is needed to establish a causal association between depression outcome and our predictors. TRIAL REGISTRATION www.clinicaltrials.gov NCT00705965 (registered 27th of June, 2008). www.isrctn.com ISRCTN76240576 (registered 27th of March, 2008).
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Affiliation(s)
- Frank Vitinius
- Department of Psychosomatics and Psychotherapy, University of Cologne, Cologne, Germany.
| | - Steffen Escherich
- Department of Psychiatry and Psychotherapy, University of Cologne, Cologne, Germany.
| | - Hans-Christian Deter
- grid.412753.6Department of Psychosomatics and Psychotherapy, Charité Universitaetsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Martin Hellmich
- 0000 0000 8580 3777grid.6190.eInstitute of Medical Statistics and Computational Biology (IMSB), University of Cologne, Cologne, Germany
| | - Jana Jünger
- German National Institute for state examinations in Medicine, Pharmacy and Psychotherapy, Mainz, Germany
| | - Katja Petrowski
- 0000 0001 2111 7257grid.4488.0Department of Psychotherapy and Psychosomatic Medicine, Technical University Dresden, Dresden, Germany
| | - Karl-Heinz Ladwig
- German Research Center of Environmental Health, Helmholtz Zentrum Muenchen, Institute of Epidemiology, Oberschleißheim, Germany
| | - Frank Lambertus
- 0000 0000 8580 3777grid.6190.eDepartment of Psychosomatics and Psychotherapy, University of Cologne, Cologne, Germany
| | - Matthias Michal
- grid.410607.4Department of Psychosomatic Medicine and Psychotherapy, University Hospital of Mainz, Mainz, Germany
| | - Cora Weber
- grid.412753.6Department of Psychosomatics and Psychotherapy, Charité Universitaetsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Martina de Zwaan
- 0000 0000 9529 9877grid.10423.34Department of Psychosomatic Medicine and Psychotherapy, Hannover Medical School, Hannover, Germany
| | - Christoph Herrmann-Lingen
- 0000 0001 2364 4210grid.7450.6Department of Psychosomatic Medicine and Psychotherapy, University of Goettingen Medical Center and German Center for Cardiovascular Research (DZHK), Partner Site Goettingen, Goettingen, Germany
| | - Joram Ronel
- 0000000123222966grid.6936.aDepartment of Psychosomatic Medicine and Psychotherapy, University Hospital Rechts der Isar, Technische Universitaet München, Munich, Germany
| | - Christian Albus
- 0000 0000 8580 3777grid.6190.eDepartment of Psychosomatics and Psychotherapy, University of Cologne, Cologne, Germany
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Browning M, Kingslake J, Dourish CT, Goodwin GM, Harmer CJ, Dawson GR. Predicting treatment response to antidepressant medication using early changes in emotional processing. Eur Neuropsychopharmacol 2019; 29:66-75. [PMID: 30473402 DOI: 10.1016/j.euroneuro.2018.11.1102] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2018] [Revised: 10/02/2018] [Accepted: 11/09/2018] [Indexed: 12/28/2022]
Abstract
Antidepressants must be taken for weeks before response can be assessed with many patients not responding to the first medication prescribed. This often results in long delays before effective treatment is started. Antidepressants induce changes in the processing of emotional stimuli early in the course of treatment. In the current study we assessed whether changes in emotional processing and subjective symptoms over the first week of antidepressant treatment predicted clinical response after 4-8 weeks of treatment. Such a predictive test may shorten the time taken to initiate effective treatment in depressed patients. Seventy-four depressed primary care patients completed measures of emotional bias and subjective symptoms before starting antidepressant treatment and then again 1 week later. Response to treatment was assessed after 4-6 weeks. The performance of classifiers based on these measures was assessed using a leave-one-out validation procedure with the best classifier then tested in an independent sample from a second study of 239 patients. The combination of a facial emotion recognition task and subjective symptoms predicted response with 77% accuracy in the training sample and 60% accuracy in the independent study, significantly better than possible using baseline response rates. The face based measure of emotional bias provided good quality data with high acceptability ratings. Changes in emotional processing can provide a sensitive early measure of antidepressant efficacy for individual patients. Early treatment induced changes in emotional processing may be used to guide antidepressant therapy and reduce the time taken for depressed patients to return to good mental health.
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Affiliation(s)
- Michael Browning
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, United Kingdom; Oxford Health NHS Trust, Warneford Hospital, Oxford, United Kingdom; P1vital Ltd, Manor House, Howbery Park, Wallingford, Oxfordshire, United Kingdom.
| | - Jonathan Kingslake
- P1vital Ltd, Manor House, Howbery Park, Wallingford, Oxfordshire, United Kingdom
| | - Colin T Dourish
- P1vital Ltd, Manor House, Howbery Park, Wallingford, Oxfordshire, United Kingdom
| | - Guy M Goodwin
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, United Kingdom; Oxford Health NHS Trust, Warneford Hospital, Oxford, United Kingdom
| | - Catherine J Harmer
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, United Kingdom; Oxford Health NHS Trust, Warneford Hospital, Oxford, United Kingdom
| | - Gerard R Dawson
- P1vital Ltd, Manor House, Howbery Park, Wallingford, Oxfordshire, United Kingdom
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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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Mora C, Zonca V, Riva MA, Cattaneo A. Blood biomarkers and treatment response in major depression. Expert Rev Mol Diagn 2018; 18:513-529. [DOI: 10.1080/14737159.2018.1470927] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Cristina Mora
- Biological Psychiatry Unit, IRCCS Fatebenefratelli S. Giovanni di Dio, Brescia, Italy
| | - Valentina Zonca
- Biological Psychiatry Unit, IRCCS Fatebenefratelli S. Giovanni di Dio, Brescia, Italy
- Department of Pharmacological and Biomolecular Sciences, University of Milan, Milan, Italy
| | - Marco A. Riva
- Department of Pharmacological and Biomolecular Sciences, University of Milan, Milan, Italy
| | - Annamaria Cattaneo
- Biological Psychiatry Unit, IRCCS Fatebenefratelli S. Giovanni di Dio, Brescia, Italy
- Stress, Psychiatry and Immunology Laboratory, Department of Psychological Medicine, Institute of Psychiatry, King’s College, London, UK
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Yokota T, Struzik ZR, Jurica P, Horiuchi M, Hiroyama S, Li J, Takahara Y, Ogawa K, Nishitomi K, Hasegawa M, Cichocki A. Semi-Automated Biomarker Discovery from Pharmacodynamic Effects on EEG in ADHD Rodent Models. Sci Rep 2018; 8:5202. [PMID: 29581452 PMCID: PMC5980101 DOI: 10.1038/s41598-018-23450-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2016] [Accepted: 03/13/2018] [Indexed: 11/16/2022] Open
Abstract
We propose a novel semi-automatic approach to design biomarkers for capturing pharmacodynamic effects induced by pharmacological agents on the spectral power of electroencephalography (EEG) recordings. We apply this methodology to investigate the pharmacodynamic effects of methylphenidate (MPH) and atomoxetine (ATX) on attention deficit/hyperactivity disorder (ADHD), using rodent models. We inject the two agents into the spontaneously hypertensive rat (SHR) model of ADHD, the Wistar-Kyoto rat (WKY), and the Wistar rat (WIS), and record their EEG patterns. To assess individual EEG patterns quantitatively, we use an integrated methodological approach, which consists of calculating the mean, slope and intercept parameters of temporal records of EEG spectral power using a smoothing filter, outlier truncation, and linear regression. We apply Fisher discriminant analysis (FDA) to identify dominant discriminants to be heuristically consolidated into several new composite biomarkers. Results of the analysis of variance (ANOVA) and t-test show benefits in pharmacodynamic parameters, especially the slope parameter. Composite biomarker evaluation confirms their validity for genetic model stratification and the effects of the pharmacological agents used. The methodology proposed is of generic use as an approach to investigating thoroughly the dynamics of the EEG spectral power.
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Affiliation(s)
- Tatsuya Yokota
- RIKEN Brain Science Institute, Hirosawa, Wako, Saitama, Japan
| | | | - Peter Jurica
- RIKEN Brain Science Institute, Hirosawa, Wako, Saitama, Japan
| | | | | | - Junhua Li
- RIKEN Brain Science Institute, Hirosawa, Wako, Saitama, Japan
| | - Yuji Takahara
- SHIONOGI & Co., Ltd., Futaba, Toyonaka, Osaka, Japan
| | - Koichi Ogawa
- SHIONOGI & Co., Ltd., Futaba, Toyonaka, Osaka, Japan.
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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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41
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Schiller MJ. Quantitative Electroencephalography in Guiding Treatment of Major Depression. Front Psychiatry 2018; 9:779. [PMID: 30728787 PMCID: PMC6351457 DOI: 10.3389/fpsyt.2018.00779] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/03/2018] [Accepted: 12/27/2018] [Indexed: 12/16/2022] Open
Abstract
This paper reviews significant contributions to the evidence for the use of quantitative electroencephalography features as biomarkers of depression treatment and examines the potential of such technology to guide pharmacotherapy. Frequency band abnormalities such as alpha and theta band abnormalities have shown promise as have combinatorial measures such as cordance (a measure combining alpha and theta power) and the Antidepressant Treatment Response Index in predicting medication treatment response. Nevertheless, studies have been hampered by methodological problems and inconsistencies, and these approaches have ultimately failed to elicit any significant interest in actual clinical practice. More recent machine learning approaches such as the Psychiatric Encephalography Evaluation Registry (PEER) technology and other efforts analyze large datasets to develop variables that may best predict response rather than test a priori hypotheses. PEER is a technology that may go beyond predicting response to a particular antidepressant and help to guide pharmacotherapy.
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Affiliation(s)
- Mark J Schiller
- Mind Therapy Clinic, San Francisco, CA, United States.,MYnd Analytics, Inc., Mission Viejo, CA, United States
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43
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Iznak AF, Iznak EV, Klyushnik TP, Kobel'kov GM, Damjanovich EV, Oleichik IV, Abramova LI. Neurobiological parameters in quantitative prediction of treatment outcome in schizophrenic patients. J Integr Neurosci 2017; 17:317-329. [PMID: 29081418 DOI: 10.3233/jin-170054] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
The aim of the study was to reveal the set of neurobiological parameters informative for individual quantitative prediction of therapeutic response in schizophrenic patients. Correlation and regression analyses of quantitative clinical scores (by Positive And Negative Syndromes Scale - PANSS), together with background EEG spectral power values and four immunological parameters: enzymatic activity of leukocyte elastase and of alpha-1 proteinase inhibitor, as well as serum levels of autoantibodies to common myelin protein and to nerve growth factor, were performed in 50 patients (all females, aged 32.9±10.8 years) with hallucinatory-delusional disorders in the frames of attack-like paranoid schizophrenia. Background neurobiological data obtained before the beginning of syndrome based treatment course (at visit 1) were matched with PANSS clinical scores of the same patients after treatment course at the stage of remission establishment (at visit 2). The multiple linear regression equations were created which contained only 3 to 4 (from initial 80) background EEG parameters and one of four immunological parameters. These mathematical models allowed prediction from 65% to 76% of PANSS scores variance after treatment course (at visit 2). The data obtained may be used for elaboration of methods of individual quantitative prediction of treatment outcome in schizophrenic patients.
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Affiliation(s)
- Andrey F Iznak
- Laboratory of Neurophysiology, Mental Health Research Center, Moscow, Russia
| | - Ekaterina V Iznak
- Laboratory of Neurophysiology, Mental Health Research Center, Moscow, Russia
| | - Tatiana P Klyushnik
- Laboratory of Neuroimmunology, Mental Health Research Center, Moscow, Russia
| | - Georgy M Kobel'kov
- Department of Computational Mathematics, Faculty of Mechanics and Mathematics, M.V. Lomonosov Moscow State University, Moscow, Russia
| | - Elena V Damjanovich
- Laboratory of Neurophysiology, Mental Health Research Center, Moscow, Russia.,Department of Brain Research, Research Center of Neurology, Moscow, Russia
| | - Igor V Oleichik
- Department of Endogenous Mental Disorders and Affective Conditions, Mental Health Research Center, Moscow, Russia
| | - Lilia I Abramova
- Department of Endogenous Mental Disorders and Affective Conditions, Mental Health Research Center, Moscow, Russia
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Tenke CE, Kayser J, Pechtel P, Webb CA, Dillon DG, Goer F, Murray L, Deldin P, Kurian BT, McGrath PJ, Parsey R, Trivedi M, Fava M, Weissman MM, McInnis M, Abraham K, E Alvarenga J, Alschuler DM, Cooper C, Pizzagalli DA, Bruder GE. Demonstrating test-retest reliability of electrophysiological measures for healthy adults in a multisite study of biomarkers of antidepressant treatment response. Psychophysiology 2017; 54:34-50. [PMID: 28000259 DOI: 10.1111/psyp.12758] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2016] [Accepted: 08/16/2016] [Indexed: 01/13/2023]
Abstract
Growing evidence suggests that loudness dependency of auditory evoked potentials (LDAEP) and resting EEG alpha and theta may be biological markers for predicting response to antidepressants. In spite of this promise, little is known about the joint reliability of these markers, and thus their clinical applicability. New standardized procedures were developed to improve the compatibility of data acquired with different EEG platforms, and used to examine test-retest reliability for the three electrophysiological measures selected for a multisite project-Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC). Thirty-nine healthy controls across four clinical research sites were tested in two sessions separated by about 1 week. Resting EEG (eyes-open and eyes-closed conditions) was recorded and LDAEP measured using binaural tones (1000 Hz, 40 ms) at five intensities (60-100 dB SPL). Principal components analysis of current source density waveforms reduced volume conduction and provided reference-free measures of resting EEG alpha and N1 dipole activity to tones from auditory cortex. Low-resolution electromagnetic tomography (LORETA) extracted resting theta current density measures corresponding to rostral anterior cingulate (rACC), which has been implicated in treatment response. There were no significant differences in posterior alpha, N1 dipole, or rACC theta across sessions. Test-retest reliability was .84 for alpha, .87 for N1 dipole, and .70 for theta rACC current density. The demonstration of good-to-excellent reliability for these measures provides a template for future EEG/ERP studies from multiple testing sites, and an important step for evaluating them as biomarkers for predicting treatment response.
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Affiliation(s)
- Craig E Tenke
- Department of Psychiatry, Columbia University College of Physicians & Surgeons and New York State Psychiatric Institute, New York, NY, USA
| | - Jürgen Kayser
- Department of Psychiatry, Columbia University College of Physicians & Surgeons and New York State Psychiatric Institute, New York, NY, USA
| | - Pia Pechtel
- Department of Psychiatry, Harvard Medical School and McLean Hospital, Belmont, Massachusetts, USA
| | - Christian A Webb
- Department of Psychiatry, Harvard Medical School and McLean Hospital, Belmont, Massachusetts, USA
| | - Daniel G Dillon
- Department of Psychiatry, Harvard Medical School and McLean Hospital, Belmont, Massachusetts, USA
| | - Franziska Goer
- Center For Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts, USA
| | - Laura Murray
- Center For Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Massachusetts, USA
| | - Patricia Deldin
- Departments of Psychology and Psychiatry, University of Michigan Health System, Ann Arbor, Michigan, USA
| | - Benji T Kurian
- Department of Psychiatry, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Patrick J McGrath
- Department of Psychiatry, Columbia University College of Physicians & Surgeons and New York State Psychiatric Institute, New York, NY, USA
| | - Ramin Parsey
- Department of Psychiatry, SUNY Stony Brook, Stony Brook, New York, USA
| | - Madhukar Trivedi
- Department of Psychiatry, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Maurizio Fava
- Department of Psychiatry, Harvard Medical School and McLean Hospital, Belmont, Massachusetts, USA.,Depression Clinical and Research Program, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Myrna M Weissman
- Department of Psychiatry, Columbia University College of Physicians & Surgeons and New York State Psychiatric Institute, New York, NY, USA
| | - Melvin McInnis
- Departments of Psychology and Psychiatry, University of Michigan Health System, Ann Arbor, Michigan, USA
| | - Karen Abraham
- Department of Psychiatry, Columbia University College of Physicians & Surgeons and New York State Psychiatric Institute, New York, NY, USA
| | - Jorge E Alvarenga
- Department of Psychiatry, Columbia University College of Physicians & Surgeons and New York State Psychiatric Institute, New York, NY, USA
| | - Daniel M Alschuler
- Department of Psychiatry, Columbia University College of Physicians & Surgeons and New York State Psychiatric Institute, New York, NY, USA
| | - Crystal Cooper
- Department of Psychiatry, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Diego A Pizzagalli
- Department of Psychiatry, Harvard Medical School and McLean Hospital, Belmont, Massachusetts, USA
| | - Gerard E Bruder
- Department of Psychiatry, Columbia University College of Physicians & Surgeons and New York State Psychiatric Institute, New York, NY, USA
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45
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Petkova E, Ogden RT, Tarpey T, Ciarleglio A, Jiang B, Su Z, Carmody T, Adams P, Kraemer HC, Grannemann BD, Oquendo MA, Parsey R, Weissman M, McGrath PJ, Fava M, Trivedi MH. Statistical Analysis Plan for Stage 1 EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care) Study. Contemp Clin Trials Commun 2017; 6:22-30. [PMID: 28670629 PMCID: PMC5485858 DOI: 10.1016/j.conctc.2017.02.007] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2016] [Revised: 02/08/2017] [Accepted: 02/13/2017] [Indexed: 12/28/2022] Open
Abstract
Antidepressant medications are commonly used to treat depression, but only about 30% of patients reach remission with any single first-step antidepressant. If the first-step treatment fails, response and remission rates at subsequent steps are even more limited. The literature on biomarkers for treatment response is largely based on secondary analyses of studies designed to answer primary questions of efficacy, rather than on a planned systematic evaluation of biomarkers for treatment decision. The lack of evidence-based knowledge to guide treatment decisions for patients with depression has lead to the recognition that specially designed studies with the primary objective being to discover biosignatures for optimizing treatment decisions are necessary. Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) is one such discovery study. Stage 1 of EMBARC is a randomized placebo controlled clinical trial of 8 week duration. A wide array of patient characteristics is collected at baseline, including assessments of brain structure, function and connectivity along with electrophysiological, biological, behavioral and clinical features. This paper reports on the data analytic strategy for discovering biosignatures for treatment response based on Stage 1 of EMBARC.
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Affiliation(s)
- Eva Petkova
- New York University, New York, NY, USA
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | | | - Thaddeus Tarpey
- New York University, New York, NY, USA
- Wright State University, Dayton, OH, USA
| | - Adam Ciarleglio
- New York University, New York, NY, USA
- Columbia University, New York, NY, USA
| | - Bei Jiang
- University of Alberta, Edmonton, Alberta, Canada
| | - Zhe Su
- New York University, New York, NY, USA
| | - Thomas Carmody
- University of Texas, Southwestern Medical Center, Dallas, TX, USA
| | - Philip Adams
- New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY, USA
| | | | | | - Maria A. Oquendo
- New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY, USA
| | | | - Myrna Weissman
- New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY, USA
| | - Patrick J. McGrath
- New York State Psychiatric Institute, New York, NY, USA
- Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY, USA
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46
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Kobayashi B, Cook IA, Hunter AM, Minzenberg MJ, Krantz DE, Leuchter AF. Can neurophysiologic measures serve as biomarkers for the efficacy of repetitive transcranial magnetic stimulation treatment of major depressive disorder? Int Rev Psychiatry 2017; 29:98-114. [PMID: 28362541 DOI: 10.1080/09540261.2017.1297697] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for Major Depressive Disorder (MDD). There are clinical data that support the efficacy of many different approaches to rTMS treatment, and it remains unclear what combination of stimulation parameters is optimal to relieve depressive symptoms. Because of the costs and complexity of studies that would be necessary to explore and compare the large number of combinations of rTMS treatment parameters, it would be useful to establish reliable surrogate biomarkers of treatment efficacy that could be used to compare different approaches to treatment. This study reviews the evidence that neurophysiologic measures of cortical excitability could be used as biomarkers for screening different rTMS treatment paradigms. It examines evidence that: (1) changes in excitability are related to the mechanism of action of rTMS; (2) rTMS has consistent effects on measures of excitability that could constitute reliable biomarkers; and (3) changes in excitability are related to the outcomes of rTMS treatment of MDD. An increasing body of evidence indicates that these neurophysiologic measures have the potential to serve as reliable biomarkers for screening different approaches to rTMS treatment of MDD.
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Affiliation(s)
- Brian Kobayashi
- a David Geffen School of Medicine , University of California Los Angeles , Los Angeles , CA , USA.,b Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine , University of California Los Angeles , Los Angeles , CA , USA.,c Neuromodulation Division , Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles , Los Angeles , CA , USA
| | - Ian A Cook
- a David Geffen School of Medicine , University of California Los Angeles , Los Angeles , CA , USA.,b Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine , University of California Los Angeles , Los Angeles , CA , USA.,c Neuromodulation Division , Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles , Los Angeles , CA , USA.,d Department of Bioengineering , University of California Los Angeles , Los Angeles , CA , USA
| | - Aimee M Hunter
- a David Geffen School of Medicine , University of California Los Angeles , Los Angeles , CA , USA.,b Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine , University of California Los Angeles , Los Angeles , CA , USA.,c Neuromodulation Division , Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles , Los Angeles , CA , USA
| | - Michael J Minzenberg
- a David Geffen School of Medicine , University of California Los Angeles , Los Angeles , CA , USA.,b Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine , University of California Los Angeles , Los Angeles , CA , USA.,c Neuromodulation Division , Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles , Los Angeles , CA , USA
| | - David E Krantz
- a David Geffen School of Medicine , University of California Los Angeles , Los Angeles , CA , USA.,b Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine , University of California Los Angeles , Los Angeles , CA , USA.,c Neuromodulation Division , Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles , Los Angeles , CA , USA
| | - Andrew F Leuchter
- a David Geffen School of Medicine , University of California Los Angeles , Los Angeles , CA , USA.,b Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine , University of California Los Angeles , Los Angeles , CA , USA.,c Neuromodulation Division , Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles , Los Angeles , CA , USA
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Alpha Wavelet Power as a Biomarker of Antidepressant Treatment Response in Bipolar Depression. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 968:79-94. [DOI: 10.1007/5584_2016_180] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/17/2023]
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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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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: 64] [Impact Index Per Article: 9.1] [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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Escitalopram but not placebo modulates brain rhythmic oscillatory activity in the first week of treatment of Major Depressive Disorder. J Psychiatr Res 2017; 84:174-183. [PMID: 27770740 DOI: 10.1016/j.jpsychires.2016.10.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Revised: 09/23/2016] [Accepted: 10/06/2016] [Indexed: 01/15/2023]
Abstract
Serotonin modulates brain oscillatory activity, and serotonergic projections to the thalamus and cortex modulate the frequency of prefrontal rhythmic oscillations. Changes in serotonergic tone have been reported to shift oscillations between the combined delta-theta (2.5-8 Hz) and the alpha (8-12 Hz) frequency ranges. Such frequency shifts may constitute a useful biomarker for the effects of selective serotonin reuptake inhibitor (SSRI) medications in Major Depressive Disorder (MDD). We utilized quantitative electroencephalography (qEEG) to measure shifts in prefrontal rhythmic oscillations early in treatment with either the SSRI escitalopram or placebo, and examined the relationship between these changes and remission of depressive symptoms. Prefrontal delta-theta and alpha power were calculated for 194 subjects with moderate MDD prior to and one week after start of treatment. Changes at one week in delta-theta and alpha power, as well as the delta-theta/alpha ratio, were examined in three cohorts: initial (N = 70) and replication (N = 76) cohorts treated with escitalopram, and a cohort treated with placebo (N = 48). Mean delta-theta power significantly increased and alpha power decreased after one week of escitalopram treatment, but did not significantly change with placebo treatment. The delta-theta/alpha ratio change was a specific predictor of the likelihood of remission after seven weeks of medication treatment: a large increase in this ratio was associated with non-remission in escitalopram-treated subjects, but not placebo-treated subjects. Escitalopram and placebo treatment have differential effects on delta-theta and alpha frequency oscillations. Early increase in delta-theta/alpha may constitute a replicable biomarker for non-remission during SSRI treatment of MDD.
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