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Lao J, Zeng Y, Wu Z, Lin G, Wang Q, Yang M, Zhang S, Xu D, Zhang M, Yao K, Liang S, Liu Q, Li J, Zhong X, Ning Y. Abnormalities in Electroencephalographic Microstates in Patients with Late-Life Depression. Neuropsychiatr Dis Treat 2024; 20:1201-1210. [PMID: 38860214 PMCID: PMC11164213 DOI: 10.2147/ndt.s456486] [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: 12/24/2023] [Accepted: 05/24/2024] [Indexed: 06/12/2024] Open
Abstract
Background Late-life depression (LLD) is characterized by disrupted brain networks. Resting-state networks in the brain are composed of both stable and transient topological structures known as microstates, which reflect the dynamics of the neural activities. However, the specific pattern of EEG microstate in LLD remains unclear. Methods Resting-state EEG were recorded for 31 patients with episodic LLD (eLLD), 20 patients with remitted LLD (rLLD) and 32 healthy controls (HCs) using a 64-channel cap. The clinical data of the patients were collected and the 17-Item Hamilton Rating Scale for Depression (HAMD) was used for symptom assessment. Duration, occurrence, time coverage and syntax of the four microstate classes (A-D) were calculated. Group differences in EEG microstates and the relationship between microstates parameters and clinical features were analyzed. Results Compared with NC and patients with rLLD, patients with eLLD showed increased duration and time coverage of microstate class D. Besides, a decrease in occurrence of microstate C and transition probability between microstate B and C was observed. In addition, the time coverage of microstate D was positively correlated with the total score of HAMD, core symptoms, and miscellaneous items. Conclusion These findings suggest that disrupted EEG microstates may be associated with the pathophysiology of LLD and may serve as potential state markers for the monitoring of the disease.
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Affiliation(s)
- Jingyi Lao
- Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Yijie Zeng
- Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Zhangying Wu
- Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Gaohong Lin
- Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Qiang Wang
- Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Mingfeng Yang
- Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Si Zhang
- Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Danyan Xu
- Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Min Zhang
- Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Kexin Yao
- Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Shuang Liang
- Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Qin Liu
- Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Jiafu Li
- Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Xiaomei Zhong
- Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
| | - Yuping Ning
- Geriatric Neuroscience Center, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China
- The First School of Clinical Medicine, Southern Medical University, Guangzhou, People’s Republic of China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, People’s Republic of China
- Key Laboratory of Neurogenetics and Channelopathies of Guangdong Province and the Ministry of Education of China, Guangzhou, People’s Republic of China
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Tong X, Xie H, Wu W, Keller CJ, Fonzo GA, Chidharom M, Carlisle NB, Etkin A, Zhang Y. Individual deviations from normative electroencephalographic connectivity predict antidepressant response. J Affect Disord 2024; 351:220-230. [PMID: 38281595 PMCID: PMC10923099 DOI: 10.1016/j.jad.2024.01.177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 01/15/2024] [Accepted: 01/18/2024] [Indexed: 01/30/2024]
Abstract
BACKGROUND Antidepressant medications yield unsatisfactory treatment outcomes in patients with major depressive disorder (MDD) with modest advantages over the placebo, partly due to the elusive mechanisms of antidepressant responses and unexplained heterogeneity in patient's response to treatment. Here we develop a novel normative modeling framework to quantify individual deviations in psychopathological dimensions that offers a promising avenue for the personalized treatment for psychiatric disorders. METHODS We built a normative model with resting-state electroencephalography (EEG) connectivity data from healthy controls of three independent cohorts. We characterized the individual deviation of MDD patients from the healthy norms, based on which we trained sparse predictive models for treatment responses of MDD patients (102 sertraline-medicated and 119 placebo-medicated). Hamilton depression rating scale (HAMD-17) was assessed at both baseline and after the eight-week antidepressant treatment. RESULTS We successfully predicted treatment outcomes for patients receiving sertraline (r = 0.43, p < 0.001) and placebo (r = 0.33, p < 0.001). We also showed that the normative modeling framework successfully distinguished subclinical and diagnostic variabilities among subjects. From the predictive models, we identified key connectivity signatures in resting-state EEG for antidepressant treatment, suggesting differences in neural circuit involvement between sertraline and placebo responses. CONCLUSIONS Our findings and highly generalizable framework advance the neurobiological understanding in the potential pathways of antidepressant responses, enabling more targeted and effective personalized MDD treatment. TRIAL REGISTRATION Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC), NCT#01407094.
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Affiliation(s)
- Xiaoyu Tong
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Hua Xie
- Center for Neuroscience Research, Children's National Hospital, Washington, DC, USA; George Washington University School of Medicine, Washington, DC, USA
| | - Wei Wu
- Alto Neuroscience, Inc., Los Altos, CA, USA
| | - Corey J Keller
- Department of Psychiatry and Behavioral Sciences, Stanford University, CA, USA; Veterans Affairs Palo Alto Healthcare System, Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, USA
| | - Gregory A Fonzo
- Center for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, TX, USA
| | | | | | - Amit Etkin
- Alto Neuroscience, Inc., Los Altos, CA, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA; Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA.
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van der Wijk G, Enkhbold Y, Cnudde K, Szostakiwskyj MW, Blier P, Knott V, Jaworska N, Protzner AB. One size does not fit all: notable individual variation in brain activity correlates of antidepressant treatment response. Front Psychiatry 2024; 15:1358018. [PMID: 38628260 PMCID: PMC11018891 DOI: 10.3389/fpsyt.2024.1358018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 03/18/2024] [Indexed: 04/19/2024] Open
Abstract
Introduction To date, no robust electroencephalography (EEG) markers of antidepressant treatment response have been identified. Variable findings may arise from the use of group analyses, which neglect individual variation. Using a combination of group and single-participant analyses, we explored individual variability in EEG characteristics of treatment response. Methods Resting-state EEG data and Montgomery-Åsberg Depression Rating Scale (MADRS) symptom scores were collected from 43 patients with depression before, at 1 and 12 weeks of pharmacotherapy. Partial least squares (PLS) was used to: 1) identify group differences in EEG connectivity (weighted phase lag index) and complexity (multiscale entropy) between eventual medication responders and non-responders, and 2) determine whether group patterns could be identified in individual patients. Results Responders showed decreased alpha and increased beta connectivity, and early, widespread decreases in complexity over treatment. Non-responders showed an opposite connectivity pattern, and later, spatially confined decreases in complexity. Thus, as in previous studies, our group analyses identified significant differences between groups of patients with different treatment outcomes. These group-level EEG characteristics were only identified in ~40-60% of individual patients, as assessed quantitatively by correlating the spatiotemporal brain patterns between groups and individual results, and by independent raters through visualization. Discussion Our single-participant analyses suggest that substantial individual variation exists, and needs to be considered when investigating characteristics of antidepressant treatment response for potential clinical applicability. Clinical trial registration https://clinicaltrials.gov, identifier NCT00519428.
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Affiliation(s)
- Gwen van der Wijk
- Department of Psychology, University of Calgary, Calgary, AB, Canada
| | - Yaruuna Enkhbold
- Department of Psychology, University of Calgary, Calgary, AB, Canada
| | - Kelsey Cnudde
- Department of Psychology, University of Calgary, Calgary, AB, Canada
| | | | - Pierre Blier
- Institute of Mental Health Research, Affiliated with the University of Ottawa, Ottawa, ON, Canada
- Department of Cellular & Molecular Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Verner Knott
- Institute of Mental Health Research, Affiliated with the University of Ottawa, Ottawa, ON, Canada
- Department of Cellular & Molecular Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Natalia Jaworska
- Institute of Mental Health Research, Affiliated with the University of Ottawa, Ottawa, ON, Canada
- Department of Cellular & Molecular Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Andrea B. Protzner
- Department of Psychology, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Mathison Centre, University of Calgary, Calgary, AB, Canada
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4
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Yu L, Peng W, Lin W, Luo Y, Hu D, Zhao G, Xu H, Dou Z, Zhang Q, Hong X, Yu S. Electroencephalography connectome changes in chronic insomnia disorder are correlated with neurochemical signatures. Sleep 2024:zsae080. [PMID: 38520362 DOI: 10.1093/sleep/zsae080] [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: 12/15/2023] [Indexed: 03/25/2024] Open
Abstract
STUDY OBJECTIVES This study aimed to investigate the alterations in resting-state electroencephalography (EEG) global brain connectivity (GBC) in patients with chronic insomnia disorder (CID) and to explore the correlation between macroscale connectomic variances and microscale neurotransmitter distributions. METHODS We acquired 64-channel EEG from 35 female CID patients and 34 healthy females. EEG signals were source-localized using individual brain anatomy and orthogonalized to mitigate volume conduction. Correlation coefficients between band-limited source-space power envelopes of the DK 68 atlas were computed and averaged across regions to determine specific GBC values. A support vector machine (SVM) classifier utilizing GBC features was employed to differentiate CID patients from controls. We further used Neurosynth and a 3D atlas of neurotransmitter receptors/transporters to assess the cognitive functions and neurotransmitter landscape associated with CID cortical abnormality maps, respectively. RESULTS CID patients exhibited elevated GBC within the medial prefrontal cortex and limbic cortex, particularly at the gamma carrier frequency, compared to controls (pFDR<0.05). GBC patterns were found to effectively distinguish CID patients from controls with a precision of 90.8% in the SVM model. The cortical abnormality maps were significantly correlated with meta-analytic terms like "cognitive control" and "emotion regulation." Notably, GBC patterns were associated with neurotransmitter profiles (pspin<0.05), with neurotransmitter systems such as norepinephrine, dopamine, and serotonin making significant contributions. CONCLUSIONS This work characterizes the EEG connectomic profile of CID, facilitating the cost-effective clinical translation of EEG-derived markers. Additionally, the linkage between GBC patterns and neurotransmitter distribution offers promising avenues for developing targeted treatment strategies for CID.
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Affiliation(s)
- Liyong Yu
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Wei Peng
- Chongqing Hospital of Traditional Chinese Medicine, Chongqing, China
| | - Wenting Lin
- School of Rehabilitation and Health Preservation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Yucai Luo
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Daijie Hu
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Guangli Zhao
- School of Rehabilitation and Health Preservation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Hao Xu
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Zeyang Dou
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Qi Zhang
- Chongqing Hospital of Traditional Chinese Medicine, Chongqing, China
| | - Xiaojuan Hong
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Siyi Yu
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
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Paul SM, Potter WZ. Finding new and better treatments for psychiatric disorders. Neuropsychopharmacology 2024; 49:3-9. [PMID: 37582978 PMCID: PMC10700311 DOI: 10.1038/s41386-023-01690-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 07/07/2023] [Accepted: 07/24/2023] [Indexed: 08/17/2023]
Abstract
In contrast to most fields of medicine, progress to discover and develop new and improved psychiatric drugs has been slow and disappointing. The vast majority of currently prescribed drugs to treat schizophrenia, mood and anxiety disorders are arguably no more effective than the first generation of psychiatric drugs introduced well over 50 years ago. With only a few exceptions current psychiatric drugs work via the same fundamental mechanisms of action as first-generation agents. Here we describe the reasons for this slow progress and outline a number of areas of research that involve a greater reliance on experimental therapeutics utilizing recent advances in neuroscience to better understand disease biology. We exemplify the potential impact of these areas of research focus with several recent examples of novel agents that have emerged and which support our optimism that newer, more effective and better tolerated agents, are on the horizon. Together with existing drugs these newer agents and novel mechanisms could offer markedly improved functional outcomes for the millions of people still disabled by psychiatric disorders.
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Affiliation(s)
- Steven M Paul
- Karuna Therapeutics, Washington University School of Medicine, St. Louis, MO, USA.
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Berkovitch L, Lee K, Ji JL, Helmer M, Rahmati M, Demšar J, Kraljič A, Matkovič A, Tamayo Z, Murray JD, Repovš G, Krystal JH, Martin WJ, Fonteneau C, Anticevic A. A common symptom geometry of mood improvement under sertraline and placebo associated with distinct neural patterns. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.15.23300019. [PMID: 38168378 PMCID: PMC10760263 DOI: 10.1101/2023.12.15.23300019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Importance Understanding the mechanisms of major depressive disorder (MDD) improvement is a key challenge to determine effective personalized treatments. Objective To perform a secondary analysis quantifying neural-to-symptom relationships in MDD as a function of antidepressant treatment. Design Double blind randomized controlled trial. Setting Multicenter. Participants Patients with early onset recurrent depression from the public Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study. Interventions Either sertraline or placebo during 8 weeks (stage 1), and according to response a second line of treatment for 8 additional weeks (stage 2). Main Outcomes and Measures To identify a data-driven pattern of symptom variations during these two stages, we performed a Principal Component Analysis (PCA) on the variations of individual items of four clinical scales measuring depression, anxiety, suicidal ideas and manic-like symptoms, resulting in a univariate measure of clinical improvement. We then investigated how initial clinical and neural factors predicted this measure during stage 1. To do so, we extracted resting-state global brain connectivity (GBC) at baseline at the individual level using a whole-brain functional network parcellation. In turn, we computed a linear model for each brain parcel with individual data-driven clinical improvement scores during stage 1 for each group. Results 192 patients (127 women), age 37.7 years old (standard deviation: 13.5), were included. The first PC (PC1) capturing 20% of clinical variation was similar across treatment groups at stage 1 and stage 2, suggesting a reproducible pattern of symptom improvement. PC1 patients' scores significantly differed according to treatment during stage 1, whereas no difference of response was evidenced between groups with the Clinical Global Impressions (CGI). Baseline GBC correlated to stage 1 PC1 scores in the sertraline, but not in the placebo group. Conclusions and Relevance Using data-driven reduction of symptoms scales, we identified a common profile of symptom improvement across placebo and sertraline. However, the neural patterns of baseline that mapped onto symptom improvement distinguished between treatment and placebo. Our results underscore that mapping from data-driven symptom improvement onto neural circuits is vital to detect treatment-responsive neural profiles that may aid in optimal patient selection for future trials.
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Affiliation(s)
- Lucie Berkovitch
- Department of Psychiatry, Neuroscience, and Psychology, Yale University School of Medicine, New Haven, CT, USA
- Division of Neurocognition, Neurocomputation, Neurogenetics (N3), Yale University School of Medicine, New Haven, Connecticut, USA
- Université Paris Cité, Paris, France
- Department of Psychiatry, GHU Paris Psychiatrie et Neurosciences, Service Hospitalo-Universitaire, Paris, France
- Unicog, Saclay CEA Centre, Neurospin, Gif-Sur-Yvette Cedex, France
| | - Kangjoo Lee
- Department of Psychiatry, Neuroscience, and Psychology, Yale University School of Medicine, New Haven, CT, USA
- Division of Neurocognition, Neurocomputation, Neurogenetics (N3), Yale University School of Medicine, New Haven, Connecticut, USA
| | - Jie Lisa Ji
- Manifest Technologies, Inc. New Haven, CT, USA
| | | | | | - Jure Demšar
- Department of Psychology, University of Ljubljana, Ljubljana, Slovenia
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Aleksij Kraljič
- Department of Psychology, University of Ljubljana, Ljubljana, Slovenia
| | - Andraž Matkovič
- Department of Psychology, University of Ljubljana, Ljubljana, Slovenia
| | - Zailyn Tamayo
- Department of Psychiatry, Neuroscience, and Psychology, Yale University School of Medicine, New Haven, CT, USA
- Division of Neurocognition, Neurocomputation, Neurogenetics (N3), Yale University School of Medicine, New Haven, Connecticut, USA
| | - John D Murray
- Department of Psychological and Brain Science, Dartmouth College, Hanover, NH, USA
| | - Grega Repovš
- Department of Psychology, University of Ljubljana, Ljubljana, Slovenia
| | - John H Krystal
- Department of Psychiatry, Neuroscience, and Psychology, Yale University School of Medicine, New Haven, CT, USA
- Division of Neurocognition, Neurocomputation, Neurogenetics (N3), Yale University School of Medicine, New Haven, Connecticut, USA
| | | | - Clara Fonteneau
- Department of Psychiatry, Neuroscience, and Psychology, Yale University School of Medicine, New Haven, CT, USA
- Division of Neurocognition, Neurocomputation, Neurogenetics (N3), Yale University School of Medicine, New Haven, Connecticut, USA
| | - Alan Anticevic
- Department of Psychiatry, Neuroscience, and Psychology, Yale University School of Medicine, New Haven, CT, USA
- Division of Neurocognition, Neurocomputation, Neurogenetics (N3), Yale University School of Medicine, New Haven, Connecticut, USA
- Department of Psychology, Yale University School of Medicine, New Haven, CT, USA
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7
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Kavanaugh BC, Fukuda AM, Gemelli ZT, Thorpe R, Tirrell E, Vigne M, Jones SR, Carpenter LL. Pre-treatment frontal beta events are associated with executive dysfunction improvement after repetitive transcranial magnetic stimulation for depression: A preliminary report. J Psychiatr Res 2023; 168:71-81. [PMID: 37897839 DOI: 10.1016/j.jpsychires.2023.10.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 08/31/2023] [Accepted: 10/14/2023] [Indexed: 10/30/2023]
Abstract
Repetitive transcranial magnetic stimulation (rTMS) is an established clinical treatment for major depressive disorder (MDD) that has also been found to improve aspects of executive functioning. The objective of this study was to examine whether oscillatory burst-like events within the beta band (15-29 Hz) prior to treatment could predict subsequent change in self-reported executive dysfunction (EDF) across a clinical course of rTMS for MDD. Twenty-eight adults (64% female) with MDD completed the self-report Frontal Systems Behavior Scale (FrSBe) and provided eyes-closed resting-state electroencephalography (EEG) before and after a clinical course of rTMS therapy for primary MDD. The rate, power, duration, and frequency span of transient EEG measured oscillatory beta events were calculated. Events within delta/theta and alpha bands were examined to assess for beta specificity. After controlling for improvement in primary depressive symptoms, a lower rate of beta events at F3, Fz, F4, and Cz prior to rTMS treatment was associated with a larger improvement in EDF after rTMS treatment. In addition, a decrease in beta event rate at Fz pre-to-post treatment was associated with a larger improvement in EDF after treatment. Results were largely specific to the beta band. In this study, the rate of frontrocentral beta events prior to treatment significantly predicted the likelihood of subsequent improvement in EDF symptoms following a clinical course of rTMS for MDD. These preliminary findings suggest the potential utility of EEG measured beta events and rTMS for targeting EDF across an array of neuropsychiatric disorders.
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Affiliation(s)
- Brian C Kavanaugh
- E.P. Bradley Hospital, United States; Brown University, Department of Psychiatry & Human Behavior, United States.
| | - Andrew M Fukuda
- Brown University, Department of Psychiatry & Human Behavior, United States; Butler Hospital, United States
| | - Zachary T Gemelli
- Brown University, Department of Psychiatry & Human Behavior, United States; Rhode Island Hospital, United States
| | - Ryan Thorpe
- Brown University, Department of Neuroscience, United States
| | - Eric Tirrell
- Brown University, Department of Psychiatry & Human Behavior, United States; Butler Hospital, United States
| | - Megan Vigne
- Brown University, Department of Psychiatry & Human Behavior, United States; Butler Hospital, United States
| | - Stephanie R Jones
- Brown University, Department of Neuroscience, United States; Providence Veteran's Association Medical Center, Center for Neurorestoration and Neurotechnology, United States
| | - Linda L Carpenter
- Brown University, Department of Psychiatry & Human Behavior, United States; Butler Hospital, United States
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8
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Simmatis L, Russo EE, Geraci J, Harmsen IE, Samuel N. Technical and clinical considerations for electroencephalography-based biomarkers for major depressive disorder. NPJ MENTAL HEALTH RESEARCH 2023; 2:18. [PMID: 38609518 PMCID: PMC10955915 DOI: 10.1038/s44184-023-00038-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 09/21/2023] [Indexed: 04/14/2024]
Abstract
Major depressive disorder (MDD) is a prevalent and debilitating psychiatric disease that leads to substantial loss of quality of life. There has been little progress in developing new MDD therapeutics due to a poor understanding of disease heterogeneity and individuals' responses to treatments. Electroencephalography (EEG) is poised to improve this, owing to the ease of large-scale data collection and the advancement of computational methods to address artifacts. This review summarizes the viability of EEG for developing brain-based biomarkers in MDD. We examine the properties of well-established EEG preprocessing pipelines and consider factors leading to the discovery of sensitive and reliable biomarkers.
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Affiliation(s)
- Leif Simmatis
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Cove Neurosciences Inc., Toronto, ON, Canada
| | - Emma E Russo
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Cove Neurosciences Inc., Toronto, ON, Canada
| | - Joseph Geraci
- Cove Neurosciences Inc., Toronto, ON, Canada
- Department of Pathology and Molecular Medicine, Queen's University, Kingston, ON, Canada
| | - Irene E Harmsen
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Cove Neurosciences Inc., Toronto, ON, Canada
| | - Nardin Samuel
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
- Cove Neurosciences Inc., Toronto, ON, Canada.
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9
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Zandstra MG, Meijs H, Somers M, Stam CJ, de Wilde B, van Hecke J, Niemegeers P, Luykx JJ, van Dellen E. Associations between psychotropic drugs and rsEEG connectivity and network characteristics: a cross-sectional study in hospital-admitted psychiatric patients. Front Neurosci 2023; 17:1176825. [PMID: 37781262 PMCID: PMC10541222 DOI: 10.3389/fnins.2023.1176825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 08/22/2023] [Indexed: 10/03/2023] Open
Abstract
Introduction Resting-state EEG (rsEEG) characteristics, such as functional connectivity and network topology, are studied as potential biomarkers in psychiatric research. However, the presence of psychopharmacological treatment in study participants poses a potential confounding factor in biomarker research. To address this concern, our study aims to explore the impact of both single and multi-class psychotropic treatments on aforementioned rsEEG characteristics in a psychiatric population. Methods RsEEG was analyzed in a real-world cross-sectional sample of 900 hospital-admitted psychiatric patients. Patients were clustered into eight psychopharmacological groups: unmedicated, single-class treatment with antipsychotics (AP), antidepressants (AD) or benzodiazepines (BDZ), and multi-class combinations of these treatments. To assess the associations between psychotropic treatments and the macroscale rsEEG characteristics mentioned above, we employed a general linear model with post-hoc tests. Additionally, Spearman's rank correlation analyses were performed to explore potential dosage effects. Results Compared to unmedicated patients, single-class use of AD was associated with lower functional connectivity in the delta band, while AP was associated with lower functional connectivity in both the delta and alpha bands. Single-class use of BDZ was associated with widespread rsEEG differences, including lower functional connectivity across frequency bands and a different network topology within the beta band relative to unmedicated patients. All of the multi-class groups showed associations with functional connectivity or topology measures, but effects were most pronounced for concomitant use of all three classes of psychotropics. Differences were not only observed in comparison with unmedicated patients, but were also evident in comparisons between single-class, multi-class, and single/multi-class groups. Importantly, multi-class associations with rsEEG characteristics were found even in the absence of single-class associations, suggesting potential cumulative or interaction effects of different classes of psychotropics. Dosage correlations were only found for antipsychotics. Conclusion Our exploratory, cross-sectional study suggests small but significant associations between single and multi-class use of antidepressants, antipsychotics and benzodiazepines and macroscale rsEEG functional connectivity and network topology characteristics. These findings highlight the importance of considering the effects of specific psychotropics, as well as their interactions, when investigating rsEEG biomarkers in a medicated psychiatric population.
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Affiliation(s)
- Melissa G. Zandstra
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Hannah Meijs
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, Netherlands
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Metten Somers
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Cornelis J. Stam
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Bieke de Wilde
- Department of Psychiatry, Ziekenhuis Netwerk Antwerpen (ZNA), Antwerp, Belgium
| | - Jan van Hecke
- Department of Psychiatry, Ziekenhuis Netwerk Antwerpen (ZNA), Antwerp, Belgium
| | - Peter Niemegeers
- Department of Psychiatry, Ziekenhuis Netwerk Antwerpen (ZNA), Antwerp, Belgium
| | - Jurjen J. Luykx
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Research Institute Brainclinics, Brainclinics Foundation, Nijmegen, Netherlands
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Edwin van Dellen
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Department of Neurology, Universitair Ziekenhuis (UZ), Brussels, Belgium
- Vrije Universiteit Brussel, Brussels, Belgium
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10
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Zhang S, She S, Qiu Y, Li Z, Wu X, Hu H, Zheng W, Huang R, Wu H. Multi-modal MRI measures reveal sensory abnormalities in major depressive disorder patients: A surface-based study. Neuroimage Clin 2023; 39:103468. [PMID: 37473494 PMCID: PMC10372163 DOI: 10.1016/j.nicl.2023.103468] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 06/17/2023] [Accepted: 07/05/2023] [Indexed: 07/22/2023]
Abstract
BACKGROUND Multi-modal magnetic resonance imaging (MRI) measures are supposed to be able to capture different brain neurobiological aspects of major depressive disorder (MDD). A fusion analysis of structural and functional modalities may better reveal the disease biomarker specific to the MDD disease. METHODS We recruited 30 MDD patients and 30 matched healthy controls (HC). For each subject, we acquired high-resolution brain structural images and resting-state fMRI (rs-fMRI) data using a 3 T MRI scanner. We first extracted the brain morphometric measures, including the cortical volume (CV), cortical thickness (CT), and surface area (SA), for each subject from the structural images, and then detected the structural clusters showing significant between-group differences in each measure using the surface-based morphology (SBM) analysis. By taking the identified structural clusters as seeds, we performed seed-based functional connectivity (FC) analyses to determine the regions with abnormal FC in the patients. Based on a logistic regression model, we performed a classification analysis by selecting these structural and functional cluster-wise measures as features to distinguish the MDD patients from the HC. RESULTS The MDD patients showed significantly lower CV in a cluster involving the right superior temporal gyrus (STG) and middle temporal gyrus (MTG), and lower SA in three clusters involving the bilateral STG, temporal pole gyrus, and entorhinal cortex, and the left inferior temporal gyrus, and fusiform gyrus, than the controls. No significant difference in CT was detected between the two groups. By taking the above-detected clusters as seeds to perform the seed-based FC analysis, we found that the MDD patients showed significantly lower FC between STG/MTG (CV's cluster) and two clusters located in the bilateral visual cortices than the controls. The logistic regression model based on the structural and functional features reached a classification accuracy of 86.7% (p < 0.001) between MDD and controls. CONCLUSION The present study showed sensory abnormalities in MDD patients using the multi-modal MRI analysis. This finding may act as a disease biomarker distinguishing MDD patients from healthy individuals.
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Affiliation(s)
- Shufei Zhang
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Shenglin She
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China
| | - Yidan Qiu
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Zezhi Li
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China
| | - Xiaoyan Wu
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Huiqing Hu
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
| | - Wei Zheng
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China
| | - Ruiwang Huang
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China.
| | - Huawang Wu
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, China; Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, China.
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11
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Meyerson WU, Pieper CF, Hoyle RH. Use of Quantile Treatment Effects Analysis to Describe Antidepressant Response in Randomized Clinical Trials Submitted to the US Food and Drug Administration: A Secondary Analysis of Pooled Trial Data. JAMA Netw Open 2023; 6:e2317714. [PMID: 37294568 PMCID: PMC10257092 DOI: 10.1001/jamanetworkopen.2023.17714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 04/22/2023] [Indexed: 06/10/2023] Open
Abstract
Importance Major depressive disorder (MDD) is a leading cause of global distress and disability. Earlier studies have indicated that antidepressant therapy confers a modest reduction in depressive symptoms on average, but the distribution of this reduction requires more research. Objective To estimate the distribution of antidepressant response by depression severity. Design, Setting, and Participants In this secondary analysis of pooled trial data, quantile treatment effect (QTE) analysis was conducted from the US Food and Drug Administration (FDA) database of antidepressant monotherapy for patients with MDD, encompassing 232 positive and negative trials submitted to the FDA between 1979 and 2016. Analysis was restricted to participants with severe MDD (17-item Hamilton Rating Scale for Depression [HAMD-17] score ≥20). Data analysis was conducted from August 16, 2022, to April 16, 2023. Intervention Antidepressant monotherapy compared with placebo. Main Outcomes and Measures The distribution of percentage depression response was compared between the pooled treatment arm and pooled placebo arm. Percentage depression response was defined as 1 minus the ratio of final depression severity to baseline depression severity, expressed as a percentage. Depression severity was reported in HAMD-17-equivalent units. Results A total of 57 313 participants with severe depression were included in the analysis. There was no significant imbalance in baseline depression severity between the pooled treatment arm and pooled placebo arm, with a mean HAMD-17 difference of 0.037 points (P = .11 by Wilcoxon rank sum test). An interaction term test for rank similarity did not reject the rank similarity governing percentage depression response (P > .99). The entire distribution of depression response was more favorable in the pooled treatment arm than in the pooled placebo arm. The maximum separation between treatment and placebo occurred at the 55th quantile and corresponded to an absolute improvement in depression due to active drug of 13.5% (95% CI, 12.4%-14.4%). The separation between treatment and placebo diminished near the tails of the distribution. Conclusions and Relevance In this QTE analysis of pooled clinical trial data from the FDA, antidepressants were found to confer a small reduction in depression severity that was broadly distributed across participants with severe depression. Alternatively, if the assumptions behind the QTE analysis are not met, then the data are also compatible with antidepressants eliciting more complete response in a smaller subset of participants than is suggested by this QTE analysis.
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Affiliation(s)
- William U. Meyerson
- Department of Psychiatry & Behavioral Sciences, Duke University Medical Center, Durham, North Carolina
- Department of Molecular Biophysics & Biochemistry, Yale University, New Haven, Connecticut
| | - Carl F. Pieper
- Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina
- Center on Aging and Human Development, Duke University Medical Center, Durham, North Carolina
| | - Rick H. Hoyle
- Department of Psychology & Neuroscience, Duke University, Durham, North Carolina
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12
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Zhao K, Xie H, Fonzo GA, Tong X, Carlisle N, Chidharom M, Etkin A, Zhang Y. Individualized fMRI connectivity defines signatures of antidepressant and placebo responses in major depression. Mol Psychiatry 2023; 28:2490-2499. [PMID: 36732585 DOI: 10.1038/s41380-023-01958-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 01/04/2023] [Accepted: 01/11/2023] [Indexed: 02/04/2023]
Abstract
Though sertraline is commonly prescribed in patients with major depressive disorder (MDD), its superiority over placebo is only marginal. This is in part due to the neurobiological heterogeneity of the individuals. Characterizing individual-unique functional architecture of the brain may help better dissect the heterogeneity, thereby defining treatment-predictive signatures to guide personalized medication. In this study, we investigate whether individualized brain functional connectivity (FC) can define more predictable signatures of antidepressant and placebo treatment in MDD. The data used in the present work were collected by the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study. Patients (N = 296) were randomly assigned to antidepressant sertraline or placebo double-blind treatment for 8 weeks. The whole-brain FC networks were constructed from pre-treatment resting-state functional magnetic resonance imaging (rs-fMRI). Then, FC was individualized by removing the common components extracted from the raw baseline FC to train regression-based connectivity predictive models. With individualized FC features, the established prediction models successfully identified signatures that explained 22% variance for the sertraline group and 31% variance for the placebo group in predicting HAMD17 change. Compared with the raw FC-based models, the individualized FC-defined signatures significantly improved the prediction performance, as confirmed by cross-validation. For sertraline treatment, predictive FC metrics were predominantly located in the left middle temporal cortex and right insula. For placebo, predictive FC metrics were primarily located in the bilateral cingulate cortex and left superior temporal cortex. Our findings demonstrated that through the removal of common FC components, individualization of FC metrics enhanced the prediction performance compared to raw FC. Associated with previous MDD clinical studies, our identified predictive biomarkers provided new insights into the neuropathology of antidepressant and placebo treatment.
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Affiliation(s)
- Kanhao Zhao
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Hua Xie
- Center for Neuroscience Research, Children's National Hospital, Washington, DC, USA
| | - Gregory A Fonzo
- Center for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, Austin, TX, USA
| | - Xiaoyu Tong
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Nancy Carlisle
- Department of Psychology, Lehigh University, Bethlehem, PA, USA
| | | | - Amit Etkin
- Alto Neuroscience, Inc, Los Altos, CA, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA.
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA.
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13
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Tong X, Xie H, Wu W, Keller C, Fonzo G, Chidharom M, Carlisle N, Etkin A, Zhang Y. Individual Deviations from Normative Electroencephalographic Connectivity Predict Antidepressant Response. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.24.23290434. [PMID: 37292874 PMCID: PMC10246152 DOI: 10.1101/2023.05.24.23290434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Antidepressant medications yield unsatisfactory treatment outcomes in patients with major depressive disorder (MDD) with modest advantages over the placebo. This modest efficacy is partly due to the elusive mechanisms of antidepressant responses and unexplained heterogeneity in patient's response to treatment - the approved antidepressants only benefit a portion of patients, calling for personalized psychiatry based on individual-level prediction of treatment responses. Normative modeling, a framework that quantifies individual deviations in psychopathological dimensions, offers a promising avenue for the personalized treatment for psychiatric disorders. In this study, we built a normative model with resting-state electroencephalography (EEG) connectivity data from healthy controls of three independent cohorts. We characterized the individual deviation of MDD patients from the healthy norms, based on which we trained sparse predictive models for treatment responses of MDD patients. We successfully predicted treatment outcomes for patients receiving sertraline (r = 0.43, p < 0.001) and placebo (r = 0.33, p < 0.001). We also showed that the normative modeling framework successfully distinguished subclinical and diagnostic variabilities among subjects. From the predictive models, we identified key connectivity signatures in resting-state EEG for antidepressant treatment, suggesting differences in neural circuit involvement between treatment responses. Our findings and highly generalizable framework advance the neurobiological understanding in the potential pathways of antidepressant responses, enabling more targeted and effective MDD treatment.
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Affiliation(s)
- Xiaoyu Tong
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Hua Xie
- Center for Neuroscience Research, Children’s National Hospital, Washington, DC, USA
- George Washington University School of Medicine, Washington, DC, USA
| | - Wei Wu
- Alto Neuroscience, Inc., Los Altos, CA, USA
| | - Corey Keller
- Department of Psychiatry and Behavioral Sciences, Stanford University, CA, USA
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, USA
| | - Gregory Fonzo
- Center for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, TX, USA
| | | | - Nancy Carlisle
- Department of Psychology, Lehigh University, Bethlehem, PA, USA
| | - Amit Etkin
- Alto Neuroscience, Inc., Los Altos, CA, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA
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14
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Tong X, Xie H, Fonzo GA, Zhao K, Satterthwaite TD, Carlisle N, Zhang Y. Dissecting Symptom-linked Dimensions of Resting-State Electroencephalographic Functional Connectivity in Autism with Contrastive Learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.22.541841. [PMID: 37292736 PMCID: PMC10245871 DOI: 10.1101/2023.05.22.541841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by social interaction deficits, communication difficulties, and restricted/repetitive behaviors or fixated interests. Despite its high prevalence, development of effective therapy for ASD is hindered by its symptomatic and neurophysiological heterogeneities. To collectively dissect the ASD heterogeneity in neurophysiology and symptoms, we develop a new analytical framework combining contrastive learning and sparse canonical correlation analysis to identify resting-state EEG connectivity dimensions linked to ASD behavioral symptoms within 392 ASD samples. Two dimensions are successfully identified, showing significant correlations with social/communication deficits (r = 0.70) and restricted/repetitive behaviors (r = 0.45), respectively. We confirm the robustness of these dimensions through cross-validation and further demonstrate their generalizability using an independent dataset of 223 ASD samples. Our results reveal that the right inferior parietal lobe is the core region displaying EEG activity associated with restricted/repetitive behaviors, and functional connectivity between the left angular gyrus and the right middle temporal gyrus is a promising biomarker of social/communication deficits. Overall, these findings provide a promising avenue to parse ASD heterogeneity with high clinical translatability, paving the way for treatment development and precision medicine for ASD.
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Affiliation(s)
- Xiaoyu Tong
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Hua Xie
- Center for Neuroscience Research, Children’s National Hospital, Washington, DC, USA
| | - Gregory A. Fonzo
- Center for Psychedelic Research and Therapy, Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, TX, USA
| | - Kanhao Zhao
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
| | - Theodore D. Satterthwaite
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, PA, USA
- Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania Perelman School of Medicine, PA, USA
| | - Nancy Carlisle
- Department of Psychology, Lehigh University, Bethlehem, PA, USA
| | - Yu Zhang
- Department of Bioengineering, Lehigh University, Bethlehem, PA, USA
- Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA, USA
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15
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The potential of electroencephalography coherence to predict the outcome of repetitive transcranial magnetic stimulation in insomnia disorder. J Psychiatr Res 2023; 160:56-63. [PMID: 36774831 DOI: 10.1016/j.jpsychires.2023.02.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/27/2023] [Accepted: 02/04/2023] [Indexed: 02/09/2023]
Abstract
BACKGROUND It is unknown whether repetitive Transcranial Magnetic Stimulation (rTMS) could improve sleep quality by modulating electroencephalography (EEG) connectivity of insomnia disorder (ID) patients. Great heterogeneity had been found in the clinical outcomes of rTMS for ID. The study aimed to investigate the potential mechanisms of rTMS therapy for ID and develop models to predict clinical outcomes. METHODS In Study 1, 50 ID patients were randomly divided into active and sham groups, and subjected to 20 sessions of treatment with 1 Hz rTMS over the left dorsolateral prefrontal cortex. EEG during awake, Polysomnography, and clinical assessment were collected and analyzed before and after rTMS. In Study 2, 120 ID patients were subjected to active rTMS stimulation and were then separated into optimal and sub-optimal groups due to the median of Pittsburgh Sleep Quality Index reduction rate. Machine learning models were developed based on baseline EEG coherence to predict rTMS treatment effects. RESULTS In Study 1, decreased EEG coherence in theta and alpha bands were observed after rTMS treatment, and changes in theta band (F7-O1) coherence were correlated with changes in sleep efficiency. In Study 2, baseline EEG coherence in theta, alpha, and beta bands showed the potential to predict the treatment effects of rTMS for ID. CONCLUSION rTMS improved sleep quality of ID patients by modulating the abnormal EEG coherence. Baseline EEG coherence between certain channels in theta, alpha, and beta bands could act as potential biomarkers to predict the therapeutic effects.
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16
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Zhang Y, Ku Y, Sun J, Daskalakis ZJ, Yuan TF. Intermittent theta burst stimulation to the left dorsolateral prefrontal cortex improves working memory of subjects with methamphetamine use disorder. Psychol Med 2023; 53:2427-2436. [PMID: 37310309 DOI: 10.1017/s003329172100430x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND Repetitive transcranial magnetic stimulation has been employed to treat drug dependence, reduce drug use and improve cognition. The aim of the study was to analyze the effectiveness of intermittent theta-burst stimulation (iTBS) on cognition in individuals with methamphetamine use disorder (MUD). METHODS This was a secondary analysis of 40 MUD subjects receiving left dorsolateral prefrontal cortex (L-DLPFC) iTBS or sham iTBS for 20 times over 10 days (twice-daily). Changes in working memory (WM) accuracy, reaction time, and sensitivity index were analyzed before and after active and sham rTMS treatment. Resting-state EEG was also acquired to identify potential biological changes that may relate to any cognitive improvement. RESULTS The results showed that iTBS increased WM accuracy and discrimination ability, and improved reaction time relative to sham iTBS. iTBS also reduced resting-state delta power over the left prefrontal region. This reduction in resting-state delta power correlated with the changes in WM. CONCLUSIONS Prefrontal iTBS may enhance WM performance in MUD subjects. iTBS induced resting EEG changes raising the possibility that such findings may represent a biological target of iTBS treatment response.
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Affiliation(s)
- Yi Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Yixuan Ku
- Center for Brain and Mental Well-being, Department of Psychology, Sun Yat-sen University, Guangzhou, China
| | - Junfeng Sun
- School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Zafiris J Daskalakis
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Ti-Fei Yuan
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Co-innovation Center of Neuroregeneration, Nantong University, Nantong, Jiangsu, China
- Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital Affiliated to Tongji University School of Medicine, Shanghai, China
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17
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Chao ZC, Dillon DG, Liu YH, Barrick EM, Wu CT. Altered coordination between frontal delta and parietal alpha networks underlies anhedonia and depressive rumination in major depressive disorder. J Psychiatry Neurosci 2022; 47:E367-E378. [PMID: 36318983 PMCID: PMC9633055 DOI: 10.1503/jpn.220046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 04/03/2022] [Accepted: 08/27/2022] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND A hyperactive default mode network (DMN) has been observed in people with major depressive disorder (MDD), and weak DMN suppression has been linked to depressive symptoms. However, whether dysregulation of the DMN contributes to blunted positive emotional experience in people with MDD is unclear. METHODS We recorded 128-channel electroencephalograms (EEGs) from 24 participants with MDD and 31 healthy controls in a resting state (RS) and an emotion-induction state (ES), in which participants engaged with emotionally positive pictures. We combined Granger causality analysis and data-driven decomposition to extract latent brain networks shared among states and groups, and we further evaluated their interactions across individuals. RESULTS We extracted 2 subnetworks. Subnetwork 1 represented a delta (δ)-band (1~4 Hz) frontal network that was activated more in the ES than the RS (i.e., task-positive). Subnetwork 2 represented an alpha (α)-band (8~13 Hz) parietal network that was suppressed more in the ES than the RS (i.e., task-negative). These subnetworks were anticorrelated in both the healthy control and MDD groups, but with different sensitivities: for participants with MDD to achieve the same level of task-positive (subnetwork 1) activation as healthy controls, more suppression of task-negative (subnetwork 2) activation was necessary. Furthermore, the anticorrelation strength in participants with MDD correlated with the severity of 2 core MDD symptoms: anhedonia and rumination. LIMITATIONS The sample size was small. CONCLUSION Our findings revealed altered coordination between 2 functional networks in MDD and suggest that weak suppression of the task-negative α-band parietal network contributes to blunted positive emotional responses in adults with depression. The subnetworks identified here could be used for diagnosis or targeted for treatment in the future.
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Affiliation(s)
| | | | | | | | - Chien-Te Wu
- From the International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Tokyo, Japan (Chao, Wu); the Center for Depression, Anxiety and Stress Research, McLean Hospital, Belmont, Mass. (Dillon, Barrick); Harvard Medical School, Boston, Mass. (Dillon); the Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taiwan (Liu); the Yuan-Rung Medical System, Changhua, Taiwan (Liu)
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18
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Pinotsis DA, Fitzgerald S, See C, Sementsova A, Widge AS. Toward biophysical markers of depression vulnerability. Front Psychiatry 2022; 13:938694. [PMID: 36329919 PMCID: PMC9622949 DOI: 10.3389/fpsyt.2022.938694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Accepted: 09/22/2022] [Indexed: 11/13/2022] Open
Abstract
A major difficulty with treating psychiatric disorders is their heterogeneity: different neural causes can lead to the same phenotype. To address this, we propose describing the underlying pathophysiology in terms of interpretable, biophysical parameters of a neural model derived from the electroencephalogram. We analyzed data from a small patient cohort of patients with depression and controls. Using DCM, we constructed biophysical models that describe neural dynamics in a cortical network activated during a task that is used to assess depression state. We show that biophysical model parameters are biomarkers, that is, variables that allow subtyping of depression at a biological level. They yield a low dimensional, interpretable feature space that allowed description of differences between individual patients with depressive symptoms. They could capture internal heterogeneity/variance of depression state and achieve significantly better classification than commonly used EEG features. Our work is a proof of concept that a combination of biophysical models and machine learning may outperform earlier approaches based on classical statistics and raw brain data.
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Affiliation(s)
- D. A. Pinotsis
- Centre for Mathematical Neuroscience and Psychology, Department of Psychology, City, University of London, London, United Kingdom
- The Picower Institute for Learning and Memory, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - S. Fitzgerald
- Centre for Mathematical Neuroscience and Psychology, Department of Psychology, City, University of London, London, United Kingdom
| | - C. See
- Department of Computer Science, City, University of London, London, United Kingdom
| | - A. Sementsova
- Department of Computer Science, City, University of London, London, United Kingdom
| | - A. S. Widge
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN, United States
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19
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Winter NR, Leenings R, Ernsting J, Sarink K, Fisch L, Emden D, Blanke J, Goltermann J, Opel N, Barkhau C, Meinert S, Dohm K, Repple J, Mauritz M, Gruber M, Leehr EJ, Grotegerd D, Redlich R, Jansen A, Nenadic I, Nöthen MM, Forstner A, Rietschel M, Groß J, Bauer J, Heindel W, Andlauer T, Eickhoff SB, Kircher T, Dannlowski U, Hahn T. Quantifying Deviations of Brain Structure and Function in Major Depressive Disorder Across Neuroimaging Modalities. JAMA Psychiatry 2022; 79:879-888. [PMID: 35895072 PMCID: PMC9330277 DOI: 10.1001/jamapsychiatry.2022.1780] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
IMPORTANCE Identifying neurobiological differences between patients with major depressive disorder (MDD) and healthy individuals has been a mainstay of clinical neuroscience for decades. However, recent meta-analyses have raised concerns regarding the replicability and clinical relevance of brain alterations in depression. OBJECTIVE To quantify the upper bounds of univariate effect sizes, estimated predictive utility, and distributional dissimilarity of healthy individuals and those with depression across structural magnetic resonance imaging (MRI), diffusion-tensor imaging, and functional task-based as well as resting-state MRI, and to compare results with an MDD polygenic risk score (PRS) and environmental variables. DESIGN, SETTING, AND PARTICIPANTS This was a cross-sectional, case-control clinical neuroimaging study. Data were part of the Marburg-Münster Affective Disorders Cohort Study. Patients with depression and healthy controls were recruited from primary care and the general population in Münster and Marburg, Germany. Study recruitment was performed from September 11, 2014, to September 26, 2018. The sample comprised patients with acute and chronic MDD as well as healthy controls in the age range of 18 to 65 years. Data were analyzed from October 29, 2020, to April 7, 2022. MAIN OUTCOMES AND MEASURES Primary analyses included univariate partial effect size (η2), classification accuracy, and distributional overlapping coefficient for healthy individuals and those with depression across neuroimaging modalities, controlling for age, sex, and additional modality-specific confounding variables. Secondary analyses included patient subgroups for acute or chronic depressive status. RESULTS A total of 1809 individuals (861 patients [47.6%] and 948 controls [52.4%]) were included in the analysis (mean [SD] age, 35.6 [13.2] years; 1165 female patients [64.4%]). The upper bound of the effect sizes of the single univariate measures displaying the largest group difference ranged from partial η2 of 0.004 to 0.017, and distributions overlapped between 87% and 95%, with classification accuracies ranging between 54% and 56% across neuroimaging modalities. This pattern remained virtually unchanged when considering either only patients with acute or chronic depression. Differences were comparable with those found for PRS but substantially smaller than for environmental variables. CONCLUSIONS AND RELEVANCE Results of this case-control study suggest that even for maximum univariate biological differences, deviations between patients with MDD and healthy controls were remarkably small, single-participant prediction was not possible, and similarity between study groups dominated. Biological psychiatry should facilitate meaningful outcome measures or predictive approaches to increase the potential for a personalization of the clinical practice.
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Affiliation(s)
- Nils R. Winter
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Ramona Leenings
- University of Münster, Institute for Translational Psychiatry, Münster, Germany,University of Münster, Department of Mathematics and Computer Science, Münster, Germany
| | - Jan Ernsting
- University of Münster, Institute for Translational Psychiatry, Münster, Germany,University of Münster, Department of Mathematics and Computer Science, Münster, Germany
| | - Kelvin Sarink
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Lukas Fisch
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Daniel Emden
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Julian Blanke
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Janik Goltermann
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Nils Opel
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Carlotta Barkhau
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Susanne Meinert
- University of Münster, Institute for Translational Psychiatry, Münster, Germany,University of Münster, Institute for Translational Neuroscience, Münster, Germany
| | - Katharina Dohm
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Jonathan Repple
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Marco Mauritz
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Marius Gruber
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Elisabeth J. Leehr
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Dominik Grotegerd
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Ronny Redlich
- University of Münster, Institute for Translational Psychiatry, Münster, Germany,Institute of Psychology, University of Halle, Halle, Germany
| | - Andreas Jansen
- Department of Psychiatry and Psychotherapy, Philipps University Marburg, Marburg, Germany
| | - Igor Nenadic
- Department of Psychiatry and Psychotherapy, Philipps University Marburg, Marburg, Germany
| | - Markus M. Nöthen
- Institute of Human Genetics, University of Bonn, School of Medicine and University Hospital Bonn, Bonn, Germany
| | - Andreas Forstner
- Institute of Human Genetics, University of Bonn, School of Medicine and University Hospital Bonn, Bonn, Germany,Centre for Human Genetics, University of Marburg, Marburg, Germany,Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Joachim Groß
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
| | - Jochen Bauer
- Department of Clinical Radiology, University of Münster, Münster, Germany
| | - Walter Heindel
- Department of Clinical Radiology, University of Münster, Münster, Germany
| | - Till Andlauer
- Department of Neurology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Simon B. Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany,Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps University Marburg, Marburg, Germany
| | - Udo Dannlowski
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
| | - Tim Hahn
- University of Münster, Institute for Translational Psychiatry, Münster, Germany
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20
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Kabbara A, Robert G, Khalil M, Verin M, Benquet P, Hassan M. An electroencephalography connectome predictive model of major depressive disorder severity. Sci Rep 2022; 12:6816. [PMID: 35473962 PMCID: PMC9042869 DOI: 10.1038/s41598-022-10949-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 04/05/2022] [Indexed: 11/21/2022] Open
Abstract
Emerging evidence showed that major depressive disorder (MDD) is associated with disruptions of brain structural and functional networks, rather than impairment of isolated brain region. Thus, connectome-based models capable of predicting the depression severity at the individual level can be clinically useful. Here, we applied a machine-learning approach to predict the severity of depression using resting-state networks derived from source-reconstructed Electroencephalography (EEG) signals. Using regression models and three independent EEG datasets (N = 328), we tested whether resting state functional connectivity could predict individual depression score. On the first dataset, results showed that individuals scores could be reasonably predicted (r = 0.6, p = 4 × 10-18) using intrinsic functional connectivity in the EEG alpha band (8-13 Hz). In particular, the brain regions which contributed the most to the predictive network belong to the default mode network. We further tested the predictive potential of the established model by conducting two external validations on (N1 = 53, N2 = 154). Results showed statistically significant correlations between the predicted and the measured depression scale scores (r1 = 0.52, r2 = 0.44, p < 0.001). These findings lay the foundation for developing a generalizable and scientifically interpretable EEG network-based markers that can ultimately support clinicians in a biologically-based characterization of MDD.
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Affiliation(s)
- Aya Kabbara
- Lebanese Association for Scientific Research, Tripoli, Lebanon
- MINDig, F-35000, Rennes, France
| | - Gabriel Robert
- Academic Department of Psychiatry, Centre Hospitalier Guillaume Régnier, Rennes, France
- Empenn, U1228, IRISA, UMR 6074, Rennes, France
- Comportement et Noyaux Gris Centraux, EA 4712, CHU Rennes, Université de Rennes 1, 35000, Rennes, France
| | - Mohamad Khalil
- Azm Center for Research in Biotechnology and Its Applications, EDST, Tripoli, Lebanon
- CRSI Research Center, Faculty of Engineering, Lebanese University, Beirut, Lebanon
| | - Marc Verin
- Comportement et Noyaux Gris Centraux, EA 4712, CHU Rennes, Université de Rennes 1, 35000, Rennes, France
- Univ Rennes, Inserm, LTSI-U1099, F-35000, Rennes, France
| | - Pascal Benquet
- Univ Rennes, Inserm, LTSI-U1099, F-35000, Rennes, France
| | - Mahmoud Hassan
- MINDig, F-35000, Rennes, France.
- School of Science and Engineering, Reykjavik University, Reykjavik, Iceland.
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21
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Wang K, DiChiacchio T, Fang W, Lander L, Feinberg J, Xie C, Winstanley EL, Ubolrat Piamjariyakul. Longitudinal study of impact of medication for opioid use disorder on Hamilton Depression Rating Scale. J Affect Disord 2022; 297:148-155. [PMID: 34670131 DOI: 10.1016/j.jad.2021.10.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 10/10/2021] [Accepted: 10/15/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVE This study aimed to evaluate the longitudinal treatment effect on depression measured by Hamilton Depression Rating Scale (HAM-D) score in a randomized clinical trial for the treatment of opioid use disorder (OUD). METHODS We conducted a secondary data analysis of data from the National Institute on Drug Abuse's Clinical Trials Network Protocol-0051. Patients with OUD (N = 570) were randomized to receive buprenorphine/naloxone (BUP-NX, n = 287) or extended-release naltrexone injection (XR-NTX, n = 283). The HAM-D score was completed at baseline and follow-up visit up to 36 weeks. A linear mixed model analysis was performed for log transformed HAM-D score and a generalized linear mixed model analysis was conducted for depression status. RESULTS Compared with BUP-NX, subjects randomized to XR-NTX had higher HAM-D scores at weeks 1 and 3 (p<0.05). There were significant interactions between treatment and visit on HAM-D score and depression status during the first four weeks of treatments in individuals without lifetime major depressive disorder (MDD). Past year cocaine use was associated with HAM-D score and depression status just in individuals without MDD, whereas past year cannabis use was associated with HAM-D score and depression status just in individuals with MDD. Past year amphetamine use was associated with HAM-D score just in individuals without MDD, however, lifetime anxiety was associated with HAM-D scores regardless of MDD. CONCLUSION When prescribing XR-NTX, particularly in the first month of treatment, it is essential to monitor for depressive symptoms. Screening for depression and multiple substance abuse may help clinicians identify appropriate treatment.
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Affiliation(s)
- Kesheng Wang
- Department of Family and Community Health, School of Nursing, Health Sciences Center, West Virginia University, Morgantown, WV 26506, USA.
| | - Toni DiChiacchio
- School of Nursing, Health Sciences Center, West Virginia University, Morgantown, WV 26506, USA
| | - Wei Fang
- West Virginia Clinical and Translational Science Institute, Health Sciences Center, West Virginia University, Morgantown, WV 26506, USA
| | - Laura Lander
- Department of Behavioral Medicine and Psychiatry, School of Medicine, Rockefeller Neuroscience Institute, Morgantown, WV 26506, USA
| | - Judith Feinberg
- Department of Behavioral Medicine and Psychiatry, School of Medicine, Rockefeller Neuroscience Institute, Morgantown, WV 26506, USA; Department of Neuroscience, West Virginia University, Morgantown, WV 26506, USA
| | - Changchun Xie
- Division of Biostatistics and Bioinformatics, Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, OH 45267, USA
| | - Erin L Winstanley
- Department of Behavioral Medicine and Psychiatry, School of Medicine, Rockefeller Neuroscience Institute, Morgantown, WV 26506, USA; Department of Neuroscience, West Virginia University, Morgantown, WV 26506, USA
| | - Ubolrat Piamjariyakul
- School of Nursing, Health Sciences Center, West Virginia University, Morgantown, WV 26506, USA
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22
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Oscillatory brain network changes after transcranial magnetic stimulation treatment in patients with major depressive disorder. JOURNAL OF AFFECTIVE DISORDERS REPORTS 2022. [DOI: 10.1016/j.jadr.2021.100277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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23
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Wendt K, Denison T, Foster G, Krinke L, Thomson A, Wilson S, Widge AS. Physiologically informed neuromodulation. J Neurol Sci 2021; 434:120121. [PMID: 34998239 PMCID: PMC8976285 DOI: 10.1016/j.jns.2021.120121] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 12/19/2021] [Accepted: 12/21/2021] [Indexed: 01/09/2023]
Abstract
The rapid evolution of neuromodulation techniques includes an increasing amount of research into stimulation paradigms that are guided by patients' neurophysiology, to increase efficacy and responder rates. Treatment personalisation and target engagement have shown to be effective in fields such as Parkinson's disease, and closed-loop paradigms have been successfully implemented in cardiac defibrillators. Promising avenues are being explored for physiologically informed neuromodulation in psychiatry. Matching the stimulation frequency to individual brain rhythms has shown some promise in transcranial magnetic stimulation (TMS). Matching the phase of those rhythms may further enhance neuroplasticity, for instance when combining TMS with electroencephalographic (EEG) recordings. Resting-state EEG and event-related potentials may be useful to demonstrate connectivity between stimulation sites and connected areas. These techniques are available today to the psychiatrist to diagnose underlying sleep disorders, epilepsy, or lesions as contributing factors to the cause of depression. These technologies may also be useful in assessing the patient's brain network status prior to deciding on treatment options. Ongoing research using invasive recordings may allow for future identification of mood biomarkers and network structure. A core limitation is that biomarker research may currently be limited by the internal heterogeneity of psychiatric disorders according to the current DSM-based classifications. New approaches are being developed and may soon be validated. Finally, care must be taken when incorporating closed-loop capabilities into neuromodulation systems, by ensuring the safe operation of the system and understanding the physiological dynamics. Neurophysiological tools are rapidly evolving and will likely define the next generation of neuromodulation therapies.
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Affiliation(s)
- Karen Wendt
- Department of Engineering Science and MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK.
| | - Timothy Denison
- Department of Engineering Science and MRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK
| | - Gaynor Foster
- Welcony Inc., Plymouth, MN, United States of America
| | - Lothar Krinke
- Welcony Inc., Plymouth, MN, United States of America; Department of Neuroscience, School of Medicine, West Virginia University, Morgantown, WV, United States of America
| | - Alix Thomson
- Welcony Inc., Plymouth, MN, United States of America
| | - Saydra Wilson
- Department of Psychiatry and Behavioral Sciences, University of Minnesota-Twin Cities, Minneapolis, MN, United States of America
| | - Alik S Widge
- Department of Psychiatry and Behavioral Sciences, University of Minnesota-Twin Cities, Minneapolis, MN, United States of America; Medical Discovery Team on Additions, University of Minnesota, Minneapolis, MN, United States of America
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24
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Rodríguez-Nieto G, Dewitte M, Sack AT, Schuhmann T. Individual Differences in Testosterone and Self-Control Predict Compulsive Sexual Behavior Proneness in Young Males. Front Psychol 2021; 12:723449. [PMID: 34925132 PMCID: PMC8677662 DOI: 10.3389/fpsyg.2021.723449] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 11/08/2021] [Indexed: 11/13/2022] Open
Abstract
The ability to control sexual urges and impulses is essential to achieve individual and social harmony. Failing to regulate sexual behavior can lead to the interference with daily life goals, sexual diseases transmission and moral violations, among others. The dual control model of sexual response proposes that an imbalance between sexual excitation and sexual inhibition mechanisms may lead to sexual dysregulation, thereby explaining problematic sexual behavior. Interindividual differences in self-control and testosterone levels are likely to modulate sexual regulation mechanisms, but these individual features have scarcely been studied in the context of compulsive sexual behavior. This study investigated the role of sexual excitation and inhibition, self-control and testosterone levels in predicting individuals' proneness to display compulsive sexual behavior. Seventy healthy young males provided a saliva sample for testosterone measurements and filled in questionnaires on self-control, sexual excitation, sexual inhibition, sexual compulsivity and sexual behavior. High testosterone levels and low self-control were associated with higher sexual compulsivity scores. Additionally, testosterone levels and sexual inhibition negatively predicted the frequency of sexual behavior with a partner. The results of our study highlight the joint role of psychological traits and testosterone levels in compulsive sexual behavior proneness, providing implications regarding the prevention and treatment of this condition in young males.
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Affiliation(s)
- Geraldine Rodríguez-Nieto
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- Movement Control and Neuroplasticity Research Group, Group Biomedical Sciences, Department of Movement Sciences, KU Leuven, Heverlee, Belgium
| | - Marieke Dewitte
- Department of Clinical Psychological Science, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Alexander T. Sack
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Teresa Schuhmann
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
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25
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Ip CT, Ganz M, Dam VH, Ozenne B, Rüesch A, Köhler-Forsberg K, Jørgensen MB, Frokjaer VG, Søgaard B, Christensen SR, Knudsen GM, Olbrich S. NeuroPharm study: EEG wakefulness regulation as a biomarker in MDD. J Psychiatr Res 2021; 141:57-65. [PMID: 34175743 DOI: 10.1016/j.jpsychires.2021.06.021] [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] [Received: 09/14/2020] [Revised: 05/19/2021] [Accepted: 06/13/2021] [Indexed: 01/08/2023]
Abstract
While several electroencephalogram (EEG)-based biomarkers have been proposed as diagnostic or predictive tools in major depressive disorder (MDD), there is a clear lack of replication studies in this field. Markers that link clinical features such as disturbed wakefulness regulation in MDD with neurophysiological patterns are particularly promising candidates for e.g., EEG-informed choices of antidepressive treatment. We investigate if we in an independent MDD sample can replicate abnormal findings of EEG-vigilance regulation during rest and as a predictor for antidepressive treatment response. EEG-resting state was recorded in 91 patients and 35 healthy controls from the NeuroPharm trial. EEG-vigilance was assessed using the Vigilance Algorithm Leipzig (VIGALL). We compared the vigilance regulation during rest between patients and healthy controls and between remitters/responders and non-remitters/non-responders after eight weeks of SSRI/SNRI treatment using two different sets of response criteria (NeuroPharm and iSPOT-D). We replicated previous findings showing hyperstable EEG-wakefulness regulation in patients in comparison to healthy subjects. Responders defined by the iSPOT-D criteria showed a higher propensity toward low vigilance stages in comparison to patients with no response at pretreatment, however, this did not apply when using the NeuroPharm criteria. EEG-wakefulness regulation patterns normalized toward patterns of healthy controls after 8 weeks of treatment. This replication study supports the diagnostic value of EEG-vigilance regulation and its usefulness as a biomarker for the choice of treatment in MDD.
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Affiliation(s)
- Cheng-Teng Ip
- Department of Experimental Medicine, H. Lundbeck A/S, Valby, Denmark; Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Melanie Ganz
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark; Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Vibeke H Dam
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Brice Ozenne
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark; Department of Public Health, Section of Biostatistics, University of Copenhagen, Denmark
| | - Annia Rüesch
- Department for Psychiatry, Psychotherapy and Psychosomatic, University Zurich, Switzerland
| | - Kristin Köhler-Forsberg
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Martin B Jørgensen
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Psychiatry, Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Vibe G Frokjaer
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Psychiatry, Psychiatric Centre Copenhagen, Copenhagen, Denmark
| | - Birgitte Søgaard
- Department of Experimental Medicine, H. Lundbeck A/S, Valby, Denmark
| | | | - Gitte M Knudsen
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sebastian Olbrich
- Department for Psychiatry, Psychotherapy and Psychosomatic, University Zurich, Switzerland.
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26
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EEG extended source imaging with structured sparsity and $$L_1$$-norm residual. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05603-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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27
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O'Donnell P, Dijkstra FM, Damar U, Quanhong L, de Goede AA, Xu L, Pascual-Leone A, Buhl DL, Zuiker R, Ruijs TQ, Heuberger JAAC, MacMullin P, Lubell M, Asgharnejad M, Murthy V, Rotenberg A, Jacobs GE, Rosen L. Transcranial magnetic stimulation as a translational biomarker for AMPA receptor modulation. Transl Psychiatry 2021; 11:325. [PMID: 34045439 PMCID: PMC8160137 DOI: 10.1038/s41398-021-01451-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 04/05/2021] [Accepted: 05/12/2021] [Indexed: 11/09/2022] Open
Abstract
TAK-653 is a novel α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR)-positive allosteric modulator being developed as a potential therapeutic for major depressive disorder (MDD). Currently, there are no translational biomarkers that evaluate physiological responses to the activation of glutamatergic brain circuits available. Here, we tested whether noninvasive neurostimulation, specifically single-pulse or paired-pulse motor cortex transcranial magnetic stimulation (spTMS and ppTMS, respectively), coupled with measures of evoked motor response captures the pharmacodynamic effects of TAK-653 in rats and healthy humans. In the rat study, five escalating TAK-653 doses (0.1-50 mg/kg) or vehicle were administered to 31 adult male rats, while measures of cortical excitability were obtained by spTMS coupled with mechanomyography. Twenty additional rats were used to measure brain and plasma TAK-653 concentrations. The human study was conducted in 24 healthy volunteers (23 males, 1 female) to assess the impact on cortical excitability of 0.5 and 6 mg TAK-653 compared with placebo, measured by spTMS and ppTMS coupled with electromyography in a double-blind crossover design. Plasma TAK-653 levels were also measured. TAK-653 increased both the mechanomyographic response to spTMS in rats and the amplitude of motor-evoked potentials in humans at doses yielding similar plasma concentrations. TAK-653 did not affect resting motor threshold or paired-pulse responses in humans. This is the first report of a translational functional biomarker for AMPA receptor potentiation and indicates that TMS may be a useful translational platform to assess the pharmacodynamic profile of glutamate receptor modulators.
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Affiliation(s)
- Patricio O'Donnell
- Takeda Pharmaceuticals International, Inc., Cambridge, MA, USA.
- McLean Hospital, Department of Psychiatry, Harvard Medical School, Belmont, MA, USA.
| | - Francis M Dijkstra
- Centre for Human Drug Research (CHDR), Leiden, The Netherlands
- Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands
| | - Ugur Damar
- Neuromodulation Program, Department of Neurology and F.M. Kirby Center for Neurobiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Lei Quanhong
- Takeda Pharmaceuticals International, Inc., Cambridge, MA, USA
| | | | - Lin Xu
- Takeda Pharmaceuticals International, Inc., Cambridge, MA, USA
| | - Andres Pascual-Leone
- Neuromodulation Program, Department of Neurology and F.M. Kirby Center for Neurobiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Derek L Buhl
- Takeda Pharmaceuticals International, Inc., Cambridge, MA, USA
| | - Rob Zuiker
- Centre for Human Drug Research (CHDR), Leiden, The Netherlands
| | - Titia Q Ruijs
- Centre for Human Drug Research (CHDR), Leiden, The Netherlands
| | | | - Paul MacMullin
- Neuromodulation Program, Department of Neurology and F.M. Kirby Center for Neurobiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Martin Lubell
- Takeda Pharmaceuticals International, Inc., Cambridge, MA, USA
| | | | | | - Alexander Rotenberg
- Neuromodulation Program, Department of Neurology and F.M. Kirby Center for Neurobiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Gabriel E Jacobs
- Centre for Human Drug Research (CHDR), Leiden, The Netherlands
- Department of Psychiatry, Leiden University Medical Center, Leiden, The Netherlands
| | - Laura Rosen
- Takeda Pharmaceuticals International, Inc., Cambridge, MA, USA
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28
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de Bardeci M, Ip CT, Olbrich S. Deep learning applied to electroencephalogram data in mental disorders: A systematic review. Biol Psychol 2021; 162:108117. [PMID: 33991592 DOI: 10.1016/j.biopsycho.2021.108117] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 04/19/2021] [Accepted: 05/10/2021] [Indexed: 12/12/2022]
Abstract
In recent medical research, tremendous progress has been made in the application of deep learning (DL) techniques. This article systematically reviews how DL techniques have been applied to electroencephalogram (EEG) data for diagnostic and predictive purposes in conducting research on mental disorders. EEG-studies on psychiatric diseases based on the ICD-10 or DSM-V classification that used either convolutional neural networks (CNNs) or long -short-term-memory (LSTMs) networks for classification were searched and examined for the quality of the information they contained in three domains: clinical, EEG-data processing, and deep learning. Although we found that the description of EEG acquisition and pre-processing was sufficient in most of the studies, we found, that many of them lacked a systematic characterization of clinical features. Furthermore, many studies used misguided model selection procedures or flawed testing. It is recommended that the study of psychiatric disorders using DL in the future must improve the quality of clinical data and follow state of the art model selection and testing procedures so as to achieve a higher research standard and head toward a clinical significance.
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Affiliation(s)
- Mateo de Bardeci
- Department for Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich (PUK), Switzerland; University Hospital Zurich, Switzerland; University Zurich, Switzerland
| | - Cheng Teng Ip
- Neurobiology Research Unit, University Hospital Rigshospitalet, Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sebastian Olbrich
- Department for Psychiatry, Psychotherapy and Psychosomatics, Psychiatric University Hospital Zurich (PUK), Switzerland; University Hospital Zurich, Switzerland; University Zurich, Switzerland.
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29
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Lai CH. Fronto-limbic neuroimaging biomarkers for diagnosis and prediction of treatment responses in major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry 2021; 107:110234. [PMID: 33370569 DOI: 10.1016/j.pnpbp.2020.110234] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 12/02/2020] [Accepted: 12/21/2020] [Indexed: 12/23/2022]
Abstract
The neuroimaging is an important tool for understanding the biomarkers and predicting treatment responses in major depressive disorder (MDD). The potential biomarkers and prediction of treatment response in MDD will be addressed in the review article. The brain regions of cognitive control and emotion regulation, such as the frontal and limbic regions, might represent the potential targets for MDD biomarkers. The potential targets of frontal lobes might include anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC) and orbitofrontal cortex (OFC). For the limbic system, hippocampus and amygdala might be the potentially promising targets for MDD. The potential targets of fronto-limbic regions have been found in the studies of several major neuroimaging modalities, such as the magnetic resonance imaging, near-infrared spectroscopy, electroencephalography, positron emission tomography, and single-photon emission computed tomography. Additional regions, such as brainstem and midbrain, might also play a part in the MDD biomarkers. For the prediction of treatment response, the gray matter volumes, white matter tracts, functional representations and receptor bindings of ACC, DLPFC, OFC, amygdala, and hippocampus might play a role in the prediction of antidepressant responses in MDD. For the response prediction of psychotherapies, the fronto-limbic, reward regions, and insula will be the potential targets. For the repetitive transcranial magnetic stimulation, the DLPFC, ACC, limbic, and visuospatial regions might represent the predictive targets for treatment. The neuroimaging targets of MDD might be focused in the fronto-limbic regions. However, the neuroimaging targets for the prediction of treatment responses might be inconclusive and beyond the fronto-limbic regions.
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Affiliation(s)
- Chien-Han Lai
- Institute of Biophotonics, National Yang-Ming University, Taipei, Taiwan; PhD Psychiatry & Neuroscience Clinic, Taoyuan, Taiwan.
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30
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Homan S, Muscat W, Joanlanne A, Marousis N, Cecere G, Hofmann L, Ji E, Neumeier M, Vetter S, Seifritz E, Dierks T, Homan P. Treatment effect variability in brain stimulation across psychiatric disorders: A meta-analysis of variance. Neurosci Biobehav Rev 2021; 124:54-62. [PMID: 33482243 DOI: 10.1016/j.neubiorev.2020.11.033] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 10/26/2020] [Accepted: 11/29/2020] [Indexed: 02/07/2023]
Abstract
Noninvasive brain stimulation methods such as transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS) are promising add-on treatments for a number of psychiatric conditions. Yet, some of the initial excitement is wearing off. Randomized controlled trials (RCT) have found inconsistent results. This inconsistency is suspected to be the consequence of variation in treatment effects and solvable by identifying responders in RCTs and individualizing treatment. However, is there enough evidence from RCTs that patients respond differently to treatment? This question can be addressed by comparing the variability in the active stimulation group with the variability in the sham group. We searched MEDLINE/PubMed and included all double-blinded, sham-controlled RCTs and crossover trials that used TMS or tDCS in adults with a unipolar or bipolar depression, bipolar disorder, schizophrenia spectrum disorder, or obsessive compulsive disorder. In accordance with the PRISMA guidelines to ensure data quality and validity, we extracted a measure of variability of the primary outcome. A total of 130 studies with 5748 patients were considered in the analysis. We calculated variance-weighted variability ratios for each comparison of active stimulation vs sham and entered them into a random-effects model. We hypothesized that treatment effect variability in TMS or tDCS would be reflected by increased variability after active compared with sham stimulation, or in other words, a variability ratio greater than one. Across diagnoses, we found only a minimal increase in variability after active stimulation compared with sham that did not reach statistical significance (variability ratio = 1.03; 95% CI, 0.97, 1.08, P = 0.358). In conclusion, this study found little evidence for treatment effect variability in brain stimulation, suggesting that the need for personalized or stratified medicine is still an open question.
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Affiliation(s)
- Stephanie Homan
- University Hospital of Psychiatry Zurich, Zurich, Switzerland; University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland.
| | - Whitney Muscat
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY, USA; Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA; Department of Psychiatry, Zucker School of Medicine at Northwell/Hofstra, Hempstead, NY, USA
| | - Andrea Joanlanne
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY, USA; Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA; Department of Psychiatry, Zucker School of Medicine at Northwell/Hofstra, Hempstead, NY, USA
| | | | - Giacomo Cecere
- University Hospital of Psychiatry Zurich, Zurich, Switzerland
| | - Lena Hofmann
- University Hospital of Psychiatry Zurich, Zurich, Switzerland
| | - Ellen Ji
- University Hospital of Psychiatry Zurich, Zurich, Switzerland
| | - Maria Neumeier
- University Hospital of Psychiatry Zurich, Zurich, Switzerland
| | - Stefan Vetter
- University Hospital of Psychiatry Zurich, Zurich, Switzerland
| | - Erich Seifritz
- University Hospital of Psychiatry Zurich, Zurich, Switzerland
| | - Thomas Dierks
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Philipp Homan
- University Hospital of Psychiatry Zurich, Zurich, Switzerland; Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY, USA; Division of Psychiatry Research, Zucker Hillside Hospital, Northwell Health, New York, NY, USA; Department of Psychiatry, Zucker School of Medicine at Northwell/Hofstra, Hempstead, NY, USA.
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31
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Precision Psychiatry: Biomarker-Guided Tailored Therapy for Effective Treatment and Prevention in Major Depression. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1305:535-563. [PMID: 33834417 DOI: 10.1007/978-981-33-6044-0_27] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Depression contributes greatly to global disability and is a leading cause of suicide. It has multiple etiologies and therefore response to treatment can vary significantly. By applying the concepts of personalized medicine, precision psychiatry attempts to optimize psychiatric patient care by better predicting which individuals will develop an illness, by giving a more accurate biologically based diagnosis, and by utilizing more effective treatments based on an individual's biological characteristics (biomarkers). In this chapter, we discuss the basic principles underlying the role of biomarkers in psychiatric pathology and then explore multiple biomarkers that are specific to depression. These include endophenotypes, gene variants/polymorphisms, epigenetic factors such as methylation, biochemical measures, circadian rhythm dysregulation, and neuroimaging findings. We also examine the role of early childhood trauma in the development of, and treatment response to, depression. In addition, we review how new developments in technology may play a greater role in the determination of new biomarkers for depression.
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Song R, Shi Y, Li X, Zhu J, Zhang H, Li K, Wang B, Zhang H, Yang Y, Gao L, Zhao Y, Zhang Z. Potential of Antithrombin III as a Biomarker of Antidepressive Effect in Major Depressive Disorder. Front Psychiatry 2021; 12:678384. [PMID: 34777034 PMCID: PMC8580946 DOI: 10.3389/fpsyt.2021.678384] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 09/20/2021] [Indexed: 12/20/2022] Open
Abstract
Background: The evaluation of treatment response to antidepressant therapy commonly depends on neuropsychologic assessments, as there are currently no suitable biomarkers. Previous research has identified a panel of increased proteins in patients with major depressive disorder (MDD), including antithrombin III (ATIII), as potential biomarkers of depression. Methods: A total of 90 MDD patients were recruited. Of these, 74 patients received occipital repetitive transcranial magnetic stimulation (rTMS) as individualized, standard, or sham treatment for 5 days, and underwent the complete procedure, including clinical assessments, blood collection, and protein measurement. Results: After treatment, ATIII was significantly decreased in both the individualized and standard groups (both p < 0.001) relative to the sham group. In the individualized group, reduction in ATIII was associated with improvements in several neuropsychological assessments. Furthermore, ATIII at baseline in the standard group and after individualized rTMS showed good performance for evaluating or predicting the response to five-day treatment (AUC = 0.771, 95% CI, 0.571-0.971; AUC = 0.875, 95% CI, 0.714-1.000, respectively) and remission at follow-up (AUC = 0.736, 95% CI, 0.529-0.943; AUC = 0.828, 95% CI, 0.656-1.000, respectively). Lastly, both baseline ATIII and change in ATIII showed good predictive value for the 24-item Hamilton Depression Rating Scale at follow-up (p = 0.024 and 0.023, respectively). Conclusion: Our study revealed a reduction in ATIII after occipital rTMS in MDD patients and a relationship between change in ATIII and therapeutic response. Taken together, these findings provide evidence for the potential of ATIII as a biomarker for the evaluation and prediction of antidepressive effects.
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Affiliation(s)
- Ruize Song
- Department of Neurology, School of Medicine, Affiliated ZhongDa Hospital, Southeast University, Nanjing, China
| | - Yachen Shi
- Department of Neurology, School of Medicine, Affiliated ZhongDa Hospital, Southeast University, Nanjing, China
| | - Xianrui Li
- Department of Psychology, Xinxiang Medical University, Xinxiang, China
| | - Jianli Zhu
- Department of Psychology, Xinxiang Medical University, Xinxiang, China
| | - Hongxing Zhang
- Department of Psychology, Xinxiang Medical University, Xinxiang, China.,Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
| | - Kun Li
- Department of Psychology, Xinxiang Medical University, Xinxiang, China
| | - Bi Wang
- Department of Psychology, Xinxiang Medical University, Xinxiang, China
| | - Haisan Zhang
- Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
| | - Yongfeng Yang
- Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
| | - Lijuan Gao
- Department of Neurology, School of Medicine, Affiliated ZhongDa Hospital, Southeast University, Nanjing, China
| | - Yang Zhao
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Zhijun Zhang
- Department of Neurology, School of Medicine, Affiliated ZhongDa Hospital, Southeast University, Nanjing, China.,Department of Psychology, Xinxiang Medical University, Xinxiang, China
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Campanella S, Arikan K, Babiloni C, Balconi M, Bertollo M, Betti V, Bianchi L, Brunovsky M, Buttinelli C, Comani S, Di Lorenzo G, Dumalin D, Escera C, Fallgatter A, Fisher D, Giordano GM, Guntekin B, Imperatori C, Ishii R, Kajosch H, Kiang M, López-Caneda E, Missonnier P, Mucci A, Olbrich S, Otte G, Perrottelli A, Pizzuti A, Pinal D, Salisbury D, Tang Y, Tisei P, Wang J, Winkler I, Yuan J, Pogarell O. Special Report on the Impact of the COVID-19 Pandemic on Clinical EEG and Research and Consensus Recommendations for the Safe Use of EEG. Clin EEG Neurosci 2021; 52:3-28. [PMID: 32975150 PMCID: PMC8121213 DOI: 10.1177/1550059420954054] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
INTRODUCTION The global COVID-19 pandemic has affected the economy, daily life, and mental/physical health. The latter includes the use of electroencephalography (EEG) in clinical practice and research. We report a survey of the impact of COVID-19 on the use of clinical EEG in practice and research in several countries, and the recommendations of an international panel of experts for the safe application of EEG during and after this pandemic. METHODS Fifteen clinicians from 8 different countries and 25 researchers from 13 different countries reported the impact of COVID-19 on their EEG activities, the procedures implemented in response to the COVID-19 pandemic, and precautions planned or already implemented during the reopening of EEG activities. RESULTS Of the 15 clinical centers responding, 11 reported a total stoppage of all EEG activities, while 4 reduced the number of tests per day. In research settings, all 25 laboratories reported a complete stoppage of activity, with 7 laboratories reopening to some extent since initial closure. In both settings, recommended precautions for restarting or continuing EEG recording included strict hygienic rules, social distance, and assessment for infection symptoms among staff and patients/participants. CONCLUSIONS The COVID-19 pandemic interfered with the use of EEG recordings in clinical practice and even more in clinical research. We suggest updated best practices to allow safe EEG recordings in both research and clinical settings. The continued use of EEG is important in those with psychiatric diseases, particularly in times of social alarm such as the COVID-19 pandemic.
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Affiliation(s)
- Salvatore Campanella
- Laboratoire de Psychologie Médicale et d'Addictologie, ULB Neuroscience Institute (UNI), CHU Brugmann-Université Libre de Bruxelles (U.L.B.), Belgium
| | - Kemal Arikan
- Kemal Arıkan Psychiatry Clinic, Istanbul, Turkey
| | - Claudio Babiloni
- Department of Physiology and Pharmacology "Erspamer", Sapienza University of Rome, Italy.,San Raffaele Cassino, Cassino (FR), Italy
| | - Michela Balconi
- Research Unit in Affective and Social Neuroscience, Department of Psychology, Catholic University of Milan, Milan, Italy
| | - Maurizio Bertollo
- BIND-Behavioral Imaging and Neural Dynamics Center, Department of Neuroscience, Imaging and Clinical Sciences, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Viviana Betti
- Department of Psychology, Sapienza University of Rome, Fondazione Santa Lucia, Rome, Italy
| | - Luigi Bianchi
- Dipartimento di Ingegneria Civile e Ingegneria Informatica (DICII), University of Rome Tor Vergata, Rome, Italy
| | - Martin Brunovsky
- National Institute of Mental Health, Klecany Czech Republic.,Third Medical Faculty, Charles University, Prague, Czech Republic
| | - Carla Buttinelli
- Department of Neurosciences, Public Health and Sense Organs (NESMOS), Sapienza University of Rome, Rome, Italy
| | - Silvia Comani
- BIND-Behavioral Imaging and Neural Dynamics Center, Department of Neuroscience, Imaging and Clinical Sciences, University "G. d'Annunzio" of Chieti-Pescara, Chieti, Italy
| | - Giorgio Di Lorenzo
- Laboratory of Psychophysiology and Cognitive Neuroscience, Chair of Psychiatry, Department of Systems Medicine, School of Medicine and Surgery, University of Rome Tor Vergata, Rome, Italy.,IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Daniel Dumalin
- AZ Sint-Jan Brugge-Oostende AV, Campus Henri Serruys, Lab of Neurophysiology, Department Neurology-Psychiatry, Ostend, Belgium
| | - Carles Escera
- Brainlab-Cognitive Neuroscience Research Group, Department of Clinical Psychology and Psychobiology, Institute of Neurosciences, University of Barcelona, Barcelona, Spain
| | - Andreas Fallgatter
- Department of Psychiatry, University of Tübingen, Germany; LEAD Graduate School and Training Center, Tübingen, Germany.,German Center for Neurodegenerative Diseases DZNE, Tübingen, Germany
| | - Derek Fisher
- Department of Psychology, Mount Saint Vincent University, and Department of Psychiatry, Nova Scotia Health Authority, Halifax, Nova Scotia, Canada
| | | | - Bahar Guntekin
- Department of Biophysics, School of Medicine, Istanbul Medipol University, Istanbul, Turkey
| | - Claudio Imperatori
- Cognitive and Clinical Psychology Laboratory, Department of Human Science, European University of Rome, Rome, Italy
| | - Ryouhei Ishii
- Department of Psychiatry Osaka University Graduate School of Medicine, Osaka, Japan
| | - Hendrik Kajosch
- Laboratoire de Psychologie Médicale et d'Addictologie, ULB Neuroscience Institute (UNI), CHU Brugmann-Université Libre de Bruxelles (U.L.B.), Belgium
| | - Michael Kiang
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Eduardo López-Caneda
- Psychological Neuroscience Laboratory, Center for Research in Psychology, School of Psychology, University of Minho, Braga, Portugal
| | - Pascal Missonnier
- Mental Health Network Fribourg (RFSM), Sector of Psychiatry and Psychotherapy for Adults, Marsens, Switzerland
| | - Armida Mucci
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Sebastian Olbrich
- Psychotherapy and Psychosomatics, Department for Psychiatry, University Hospital Zurich, Zurich, Switzerland
| | | | - Andrea Perrottelli
- Department of Psychiatry, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Alessandra Pizzuti
- Department of Psychology, Sapienza University of Rome, Fondazione Santa Lucia, Rome, Italy
| | - Diego Pinal
- Psychological Neuroscience Laboratory, Center for Research in Psychology, School of Psychology, University of Minho, Braga, Portugal
| | - Dean Salisbury
- Clinical Neurophysiology Research Laboratory, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Paolo Tisei
- Department of Neurosciences, Public Health and Sense Organs (NESMOS), Sapienza University of Rome, Rome, Italy
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Istvan Winkler
- Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, Hungary
| | - Jiajin Yuan
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China
| | - Oliver Pogarell
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
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34
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Sánchez-Dinorín G, Rodríguez-Violante M, Cervantes-Arriaga A, Navarro-Roa C, Ricardo-Garcell J, Rodríguez-Camacho M, Solís-Vivanco R. Frontal functional connectivity and disease duration interactively predict cognitive decline in Parkinson's disease. Clin Neurophysiol 2020; 132:510-519. [PMID: 33450572 DOI: 10.1016/j.clinph.2020.11.035] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 09/25/2020] [Accepted: 11/16/2020] [Indexed: 01/27/2023]
Abstract
OBJECTIVE Cognitive decline does not always follow a predictable course in Parkinson's disease (PD), with some patients remaining stable while others meet criteria for dementia from early stages. Functional connectivity has been proposed as a good correlate of cognitive decline in PD, although it has not been explored whether the association between this connectivity and cognitive ability is influenced by disease duration, which was our objective. METHODS We included 30 patients with PD and 15 healthy controls (HC). Six cognitive domains were estimated based on neuropsychological assessment. Phase-based connectivity at frontal and posterior cortical regions was estimated from a resting EEG. RESULTS The PD group showed significant impairment for the executive, visuospatial, and language domains compared with HC. Increased connectivity at frontal regions was also found in the PD group. Frontal delta and theta connectivity negatively influenced general cognition and visuospatial performance, but this association was moderated by disease duration, with increased connectivity predicting worse performance after 8 years of disease duration. CONCLUSION Subtle neurophysiological changes underlie cognitive decline along PD progression, especially around a decade after motor symptoms onset. SIGNIFICANCE Connectivity of EEG slow waves at frontal regions might be used as a predictor of cognitive decline in PD.
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Affiliation(s)
- Gerardo Sánchez-Dinorín
- Neuropsychology Laboratory, Instituto Nacional de Neurología y Neurocirugía Manuel Velasco Suárez (INNN), Mexico City, Mexico; Faculty of Psychology, Universidad Nacional Autónoma de México (UNAM), Mexico City, Mexico
| | | | | | | | | | | | - Rodolfo Solís-Vivanco
- Neuropsychology Laboratory, Instituto Nacional de Neurología y Neurocirugía Manuel Velasco Suárez (INNN), Mexico City, Mexico; Faculty of Psychology, Universidad Nacional Autónoma de México (UNAM), Mexico City, Mexico.
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35
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Park SM, Jung HY. Respiratory sinus arrhythmia biofeedback alters heart rate variability and default mode network connectivity in major depressive disorder: A preliminary study. Int J Psychophysiol 2020; 158:225-237. [DOI: 10.1016/j.ijpsycho.2020.10.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 10/06/2020] [Accepted: 10/20/2020] [Indexed: 12/12/2022]
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36
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Hill AT, Hadas I, Zomorrodi R, Voineskos D, Farzan F, Fitzgerald PB, Blumberger DM, Daskalakis ZJ. Modulation of functional network properties in major depressive disorder following electroconvulsive therapy (ECT): a resting-state EEG analysis. Sci Rep 2020; 10:17057. [PMID: 33051528 PMCID: PMC7555809 DOI: 10.1038/s41598-020-74103-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 08/12/2020] [Indexed: 12/18/2022] Open
Abstract
Electroconvulsive therapy (ECT) is a highly effective neuromodulatory intervention for treatment-resistant major depressive disorder (MDD). Presently, however, understanding of its neurophysiological effects remains incomplete. In the present study, we utilised resting-state electroencephalography (RS-EEG) to explore changes in functional connectivity, network topology, and spectral power elicited by an acute open-label course of ECT in a cohort of 23 patients with treatment-resistant MDD. RS-EEG was recorded prior to commencement of ECT and again within 48 h following each patient’s final treatment session. Our results show that ECT was able to enhance connectivity within lower (delta and theta) frequency bands across subnetworks largely confined to fronto-central channels, while, conversely, more widespread subnetworks of reduced connectivity emerged within faster (alpha and beta) bands following treatment. Graph-based topological analyses revealed changes in measures of functional segregation (clustering coefficient), integration (characteristic path length), and small-world architecture following ECT. Finally, post-treatment enhancement of delta and theta spectral power was observed, which showed a positive association with the number of ECT sessions received. Overall, our findings indicate that RS-EEG can provide a sensitive measure of dynamic neural activity following ECT and highlight network-based analyses as a promising avenue for furthering mechanistic understanding of the effects of convulsive therapies.
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Affiliation(s)
- Aron T Hill
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, University of Toronto, 1001 Queen Street West, Unit 4-1, Toronto, ON, M6J 1H4, Canada
| | - Itay Hadas
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, University of Toronto, 1001 Queen Street West, Unit 4-1, Toronto, ON, M6J 1H4, Canada
| | - Reza Zomorrodi
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, University of Toronto, 1001 Queen Street West, Unit 4-1, Toronto, ON, M6J 1H4, Canada
| | - Daphne Voineskos
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, University of Toronto, 1001 Queen Street West, Unit 4-1, Toronto, ON, M6J 1H4, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Faranak Farzan
- School of Mechatronic Systems Engineering, Centre for Engineering-Led Brain Research, Simon Fraser University, Surrey, BC, Canada
| | - Paul B Fitzgerald
- Epworth Centre for Innovation in Mental Health, Epworth Healthcare and Monash Alfred Psychiatry Research Centre, The Alfred and Monash University Central Clinical School, Commercial Rd, Melbourne, VIC, Australia
| | - Daniel M Blumberger
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, University of Toronto, 1001 Queen Street West, Unit 4-1, Toronto, ON, M6J 1H4, Canada.,Institute of Medical Science, University of Toronto, Toronto, ON, Canada.,Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Zafiris J Daskalakis
- Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, University of Toronto, 1001 Queen Street West, Unit 4-1, Toronto, ON, M6J 1H4, Canada. .,Institute of Medical Science, University of Toronto, Toronto, ON, Canada. .,Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
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37
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Kennedy GJ. The Convergence of Biomedical and Psychosocial Approaches to Neural Network Connectivity in Depression. Am J Geriatr Psychiatry 2020; 28:869-871. [PMID: 32473874 DOI: 10.1016/j.jagp.2020.05.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 05/01/2020] [Indexed: 11/16/2022]
Affiliation(s)
- Gary J Kennedy
- Division of Geriatric Psychiatry, Department of Psychiatry and Behavioral Science, Montefiore Medical Center, Albert Einstein College of Medicine, 111 East 210th Street, Bronx NY 10467.
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38
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Assenza G, Tombini M, Lanzone J, Ricci L, Di Lazzaro V, Casciato S, Morano A, Giallonardo AT, Di Bonaventura C, Beghi E, Ferlazzo E, Gasparini S, Giuliano L, Pisani F, Benna P, Bisulli F, De Falco FA, Franceschetti S, La Neve A, Meletti S, Mostacci B, Sartucci F, Striano P, Villani F, Aguglia U, Avanzini G, Belcastro V, Bianchi A, Cianci V, Labate A, Magaudda A, Michelucci R, Verri A, Zaccara G, Pizza V, Tinuper P, Di Gennaro G. Antidepressant effect of vagal nerve stimulation in epilepsy patients: a systematic review. Neurol Sci 2020; 41:3075-3084. [PMID: 32524324 DOI: 10.1007/s10072-020-04479-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Accepted: 05/20/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND Vagal nerve stimulation (VNS) is an effective palliative therapy in drug-resistant epileptic patients and is also approved as a therapy for treatment-resistant depression. Depression is a frequent comorbidity in epilepsy and it affects the quality of life of patients more than the seizure frequency itself. The aim of this systematic review is to analyze the available literature about the VNS effect on depressive symptoms in epileptic patients. MATERIAL AND METHODS A comprehensive search of PubMed, Medline, Scopus, and Google Scholar was performed, and results were included up to January 2020. All studies concerning depressive symptom assessment in epileptic patients treated with VNS were included. RESULTS Nine studies were included because they fulfilled inclusion criteria. Six out of nine papers reported a positive effect of VNS on depressive symptoms. Eight out of nine studies did not find any correlation between seizure reduction and depressive symptom amelioration, as induced by VNS. Clinical scales for depression, drug regimens, and age of patients were broadly different among the examined studies. CONCLUSIONS Reviewed studies strongly suggest that VNS ameliorates depressive symptoms in drug-resistant epileptic patients and that the VNS effect on depression is uncorrelated to seizure response. However, more rigorous studies addressing this issue are encouraged.
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Affiliation(s)
- Giovanni Assenza
- Neurology, Neurophysiology and Neurobiology Unit, Department of Medicine, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Mario Tombini
- Neurology, Neurophysiology and Neurobiology Unit, Department of Medicine, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Jacopo Lanzone
- Neurology, Neurophysiology and Neurobiology Unit, Department of Medicine, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Lorenzo Ricci
- Neurology, Neurophysiology and Neurobiology Unit, Department of Medicine, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Vincenzo Di Lazzaro
- Neurology, Neurophysiology and Neurobiology Unit, Department of Medicine, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Sara Casciato
- Epilepsy Surgery Center, IRCCS NEUROMED, Via Atinense 18, 86170, Pozzilli (IS), Italy
| | - Alessandra Morano
- Epilepsy Unit, Department of Human Neurosciences, "Sapienza" University of Rome, Rome, Italy
| | - Anna Teresa Giallonardo
- Epilepsy Unit, Department of Human Neurosciences, "Sapienza" University of Rome, Rome, Italy
| | - Carlo Di Bonaventura
- Epilepsy Unit, Department of Human Neurosciences, "Sapienza" University of Rome, Rome, Italy
| | - Ettore Beghi
- Laboratory of Neurological Disorders, Department of Neuroscience, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Edoardo Ferlazzo
- Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy; Regional Epilepsy Centre, Great Metropolitan Hospital Bianchi-Melacrino-Morelli, Reggio Calabria, Italy
| | - Sara Gasparini
- Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy; Regional Epilepsy Centre, Great Metropolitan Hospital Bianchi-Melacrino-Morelli, Reggio Calabria, Italy
| | - Loretta Giuliano
- Department G.F. Ingrassia, Section of Neurosciences, University of Catania, Catania, Italy
| | - Francesco Pisani
- Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
| | - Paolo Benna
- Department of Neurosciences, University of Torino, Torino, Italy
| | - Francesca Bisulli
- IRCCS Institute of Neurological Sciences of Bologna, Bologna, Italy.,Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | | | - Silvana Franceschetti
- Department of Neurophysiopathology, Fondazione Istituto Neurologico Carlo Besta, Milan, Italy
| | - Angela La Neve
- Department of Neurological and Psychiatric Sciences, Centre for Epilepsy, University of Bari, Bari, Italy
| | - Stefano Meletti
- Neurology Unit, OCB Hospital, AOU Modena, Modena, Italy; Department of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Barbara Mostacci
- IRCCS Institute of Neurological Sciences of Bologna, Bologna, Italy
| | - Ferdinando Sartucci
- Section of Neurophysiopathology, Department of Clinical and Experimental Medicine, University of Pisa, Azienda Ospedaliero Universitaria Pisana and Neuroscience Institute, CNR, Pisa, Italy
| | - Pasquale Striano
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, and Maternal and Child Health, University of Genoa, Genoa, Italy.,Pediatric Neurology and Muscular Diseases Unit, IRCCS 'G. Gaslini' Institute, Genoa, Italy
| | - Flavio Villani
- Division of Clinical Neurophysiology and Epilepsy Center, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Umberto Aguglia
- Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy; Regional Epilepsy Centre, Great Metropolitan Hospital Bianchi-Melacrino-Morelli, Reggio Calabria, Italy
| | - Giuliano Avanzini
- Department of Neurophysiopathology, Fondazione Istituto Neurologico Carlo Besta, Milan, Italy
| | - Vincenzo Belcastro
- Child Neuropsychiatry Unit, Department of Mental Health, ASST-Lariana, Como, Italy
| | - Amedeo Bianchi
- Division of Neurology, Hospital San Donato Arezzo, Arezzo, Italy
| | - Vittoria Cianci
- Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy; Regional Epilepsy Centre, Great Metropolitan Hospital Bianchi-Melacrino-Morelli, Reggio Calabria, Italy
| | - Angelo Labate
- Institute of Neurology, University Magna Graecia, Germaneto (CZ), Italy
| | - Adriana Magaudda
- Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
| | | | - Annapia Verri
- Department of Behavioural Neurology and Laboratory of Cognitive Behavioural Psychology, Fondazione Istituto Neurologico Casimiro Mondino, Pavia, Italy
| | | | - Vincenzo Pizza
- Neurophysiopatology Unit, S. Luca Hospital, Vallo della Lucania (SA), Italy
| | - Paolo Tinuper
- IRCCS Institute of Neurological Sciences of Bologna, Bologna, Italy.,Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Giancarlo Di Gennaro
- Epilepsy Surgery Center, IRCCS NEUROMED, Via Atinense 18, 86170, Pozzilli (IS), Italy.
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Hong W, Zhao Z, Wang D, Li M, Tang C, Li Z, Xu R, Chan CCH. Altered gray matter volumes in post-stroke depressive patients after subcortical stroke. NEUROIMAGE-CLINICAL 2020; 26:102224. [PMID: 32146322 PMCID: PMC7063237 DOI: 10.1016/j.nicl.2020.102224] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Revised: 02/18/2020] [Accepted: 02/19/2020] [Indexed: 12/18/2022]
Abstract
Stroke survivors are known to suffer from post-stroke depression (PSD). However, the likelihood of structural changes in the brains of PSD patients has not been explored. This study aims to extract changes in the gray matter of these patients and test how these changes account for the PSD symptoms. High-resolution T1 weighted images were collected from 23 PSD patients diagnosed with subcortical stroke. Voxel-based morphometry and support vector machine analyses were used to analyze the data. The results were compared with those collected from 33 non-PSD patients. PSD group showed decreased gray matter volume (GMV) in the left middle frontal gyrus (MFG) when compared to the non-PSD patients. Together with the clinical and demographic variables, the MFG's GMV predictive model was able to distinguish PSD from the non-PSD patients (0•70 sensitivity and 0•88 specificity). The changes in the left inferior frontal gyrus (61%) and dorsolateral prefrontal cortex (39%) suggest that the somatic/affective symptoms in PSD is likely to be due to patients' problems with understanding and appraising negative emotional stimuli. The impact brought by the reduced prefrontal to limbic system connectivity needs further exploration. These findings indicate possible systemic involvement of the frontolimbic network resulting in PSD after brain lesions which is likely to be independent from the location of the lesion. The results inform specific clinical interventions to be provided for treating depressive symptoms in post-stroke patients.
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Affiliation(s)
- Wenjun Hong
- Department of Rehabilitation Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China.
| | - Zhiyong Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
| | - Dongmei Wang
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China.
| | - Ming Li
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China.
| | - Chaozheng Tang
- State Key Laboratory of Cognitive Neuroscience and Leaning, Beijing Normal University, Beijing, China.
| | - Zheng Li
- Department of Rehabilitation Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China.
| | - Rong Xu
- Department of Rehabilitation Medicine, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, 210008, China.
| | - Chetwyn C H Chan
- Applied Cognitive Neuroscience Laboratory, Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong; University Research Facility in Behavioral and Systems Neuroscience, The Hong Kong Polytechnic University, Hong Kong, China.
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