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Gradwohl G, Snipes S, Walitza S, Huber R, Gerstenberg M. Timing and cortical region matter: theta power differences between teenagers affected by Major Depression and healthy controls. J Neural Transm (Vienna) 2024:10.1007/s00702-024-02810-1. [PMID: 39105815 DOI: 10.1007/s00702-024-02810-1] [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: 01/22/2024] [Accepted: 07/15/2024] [Indexed: 08/07/2024]
Abstract
In adults affected by Major Depressive Disorder (MDD), most findings point to higher electroencephalographic (EEG) theta power during wake compared to healthy controls (HC) as a potential biomarker aiding the diagnostic process or subgrouping for stratified treatment. Besides these group differences, theta power is modulated by time of day, sleep/wake history, and age. Thus, we aimed at assessing if the time of recording alters theta power in teenagers affected by MDD or HC. Standardized wake EEG power was assessed with high-density EEG in 15 children and adolescents with MDD and in 15 age- and sex-matched HC in the evening and morning. Using a two-way ANOVA, group, time, and their interaction were tested. In patients, the current severity of depression was rated using the Children's Depression Rating Scale. Broadband EEG power was lower in the morning after sleep, with a significant interaction (group x time) in central regions in the 4-6 Hz range. In MDD relative to HC, theta power was decreased over occipital areas in the evening and increased over frontal areas in the morning. A higher frontal theta power was correlated with more severe depressive mood in the morning but not in the evening. This was a cross-sectional study design, including patients on antidepressant medication. In conclusion, depending on time of recording, region-specific opposite differences of theta power were found between teenagers with MDD and HC. These findings stress the importance of the time of the recording when investigating theta power's relationship to psychopathology.
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Affiliation(s)
- Gideon Gradwohl
- Lev Academic Center, Department of Computer Sciences, Jerusalem College of Technology, Jerusalem, Israel
| | - Sophia Snipes
- Child Development Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland
- Children's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland
- Neural Control of Movement Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Susanne Walitza
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Neuroscicence Center Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
- Zurich Center for Integrative Human Physiology, University of Zurich, Zurich, Switzerland
| | - Reto Huber
- Child Development Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland
- Children's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Neuroscicence Center Zurich, University of Zurich and Swiss Federal Institute of Technology Zurich, Zurich, Switzerland
| | - Miriam Gerstenberg
- Children's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland.
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric University Hospital Zurich, University of Zurich, Zurich, Switzerland.
- Department of Child and Adolescent Psychiatry and Psychotherapy, Outpatient Services Winterthur, Psychiatric University Hospital Zurich, Albanistrasse 24, Winterthur, 8400, Switzerland.
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Li L, Wang X, Li J, Zhao Y. An EEG-based marker of functional connectivity: detection of major depressive disorder. Cogn Neurodyn 2024; 18:1671-1687. [PMID: 39104678 PMCID: PMC11297863 DOI: 10.1007/s11571-023-10041-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 09/15/2023] [Accepted: 11/09/2023] [Indexed: 08/07/2024] Open
Abstract
Major depressive disorder (MDD) is a prevalent psychiatric disorder globally. There are many assays for MDD, but rapid and reliable detection remains a pressing challenge. In this study, we present a fusion feature called P-MSWC, as a novel marker to construct brain functional connectivity matrices and utilize the convolutional neural network (CNN) to identify MDD based on electroencephalogram (EEG) signal. Firstly, we combine synchrosqueezed wavelet transform and coherence theory to get synchrosqueezed wavelet coherence. Then, we obtain the fusion feature by incorporating synchrosqueezed wavelet coherence value and phase-locking value, which outperforms conventional functional connectivity markers by comprehensively capturing the original EEG signal's information and demonstrating notable noise-resistance capabilities. Finally, we propose a lightweight CNN model that effectively utilizes the high-dimensional connectivity matrix of the brain, constructed using our novel marker, to enable more accurate and efficient detection of MDD. The proposed method achieves 99.92% accuracy on a single dataset and 97.86% accuracy on a combined dataset. Moreover, comparison experiments have shown that the performance of the proposed method is superior to traditional machine learning methods. Furthermore, visualization experiments reveal differences in the distribution of brain connectivity between MDD patients and healthy subjects, including decreased connectivity in the T7, O1, F8, and C3 channels of the gamma band. The results of the experiments indicate that the fusion feature can be utilized as a new marker for constructing functional brain connectivity, and the combination of deep learning and functional connectivity matrices can provide more help for the detection of MDD.
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Affiliation(s)
- Ling Li
- College of Communication Engineering, Jilin University, Changchun, Jilin China
| | - Xianshuo Wang
- College of Communication Engineering, Jilin University, Changchun, Jilin China
| | - Jiahui Li
- College of Communication Engineering, Jilin University, Changchun, Jilin China
| | - Yanping Zhao
- College of Communication Engineering, Jilin University, Changchun, Jilin China
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Wang Y, Chen Y, Cui Y, Zhao T, Wang B, Zheng Y, Ren Y, Sha S, Yan Y, Zhao X, Zhang L, Wang G. Alterations in electroencephalographic functional connectivity in individuals with major depressive disorder: a resting-state electroencephalogram study. Front Neurosci 2024; 18:1412591. [PMID: 39055996 PMCID: PMC11270625 DOI: 10.3389/fnins.2024.1412591] [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: 04/05/2024] [Accepted: 06/19/2024] [Indexed: 07/28/2024] Open
Abstract
Background Major depressive disorder (MDD) is the leading cause of disability among all mental illnesses with increasing prevalence. The diagnosis of MDD is susceptible to interference by several factors, which has led to a trend of exploring objective biomarkers. Electroencephalography (EEG) is a non-invasive procedure that is being gradually applied to detect and diagnose MDD through some features such as functional connectivity (FC). Methods In this research, we analyzed the resting-state EEG of patients with MDD and healthy controls (HCs) in both eyes-open (EO) and eyes-closed (EC) conditions. The phase locking value (PLV) method was utilized to explore the connection and synchronization of neuronal activities spatiotemporally between different brain regions. We compared the PLV between participants with MDD and HCs in five frequency bands (theta, 4-8 Hz; alpha, 8-12 Hz; beta1, 12-16 Hz; beta2, 16-24 Hz; and beta3, 24-40 Hz) and further analyzed the correlation between the PLV of connections with significant differences and the severity of depression (via the scores of 17-item Hamilton Depression Rating Scale, HDRS-17). Results During the EO period, lower PLVs were found in the right temporal-left midline occipital cortex (RT-LMOC; theta, alpha, beta1, and beta2) and posterior parietal-right temporal cortex (PP-RT; beta1 and beta2) in the MDD group compared with the HC group, while PLVs were higher in the MDD group in LT-LMOC (beta2). During the EC period, for the MDD group, lower theta and beta (beta1, beta2, and beta3) PLVs were found in PP-RT, as well as lower theta, alpha, and beta (beta1, beta2, and beta3) PLVs in RT-LMOC. Additionally, in the left midline frontal cortex-right temporal cortex (LMFC-RT) and posterior parietal cortex-right temporal cortex (PP-RMOC), higher PLVs were observed in beta2. There were no significant correlations between PLVs and HDRS-17 scores when connections with significantly different PLVs (all p > 0.05) were checked. Conclusion Our study confirmed the presence of differences in FC between patients with MDD and healthy individuals. Lower PLVs in the connection of the right temporal-left occipital cortex were mostly observed, whereas an increase in PLVs was observed in patients with MDD in the connections of the left temporal with occipital lobe (EO), the circuits of the frontal-temporal lobe, and the parietal-occipital lobe. The trends in FC involved in this study were not correlated with the level of depression. Limitations The study was limited due to the lack of further analysis of confounding factors and follow-up data. Future studies with large-sampled and long-term designs are needed to further explore the distinguishable features of EEG FC in individuals with MDD.
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Affiliation(s)
- Yingtan Wang
- National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yu Chen
- National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yi Cui
- Gnosis Healthineer Co. Ltd, Beijing, China
| | - Tong Zhao
- Gnosis Healthineer Co. Ltd, Beijing, China
| | - Bin Wang
- National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yunxi Zheng
- National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yanping Ren
- National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Sha Sha
- National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | | | - Xixi Zhao
- National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Ling Zhang
- National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Gang Wang
- National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
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Singh MK, Gorelik AJ, Stave C, Gotlib IH. Genetics, epigenetics, and neurobiology of childhood-onset depression: an umbrella review. Mol Psychiatry 2024; 29:553-565. [PMID: 38102485 DOI: 10.1038/s41380-023-02347-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 11/21/2023] [Accepted: 11/27/2023] [Indexed: 12/17/2023]
Abstract
Depression is a serious and persistent psychiatric disorder that commonly first manifests during childhood. Depression that starts in childhood is increasing in frequency, likely due both to evolutionary trends and to increased recognition of the disorder. In this umbrella review, we systematically searched the extant literature for genetic, epigenetic, and neurobiological factors that contribute to a childhood onset of depression. We searched PubMed, EMBASE, OVID/PsychInfo, and Google Scholar with the following inclusion criteria: (1) systematic review or meta-analysis from a peer-reviewed journal; (2) inclusion of a measure assessing early age of onset of depression; and (3) assessment of neurobiological, genetic, environmental, and epigenetic predictors of early onset depression. Findings from 89 systematic reviews of moderate to high quality suggest that childhood-onset depressive disorders have neurobiological, genetic, environmental, and epigenetic roots consistent with a diathesis-stress theory of depression. This review identified key putative markers that may be targeted for personalized clinical decision-making and provide important insights concerning candidate mechanisms that might underpin the early onset of depression.
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Liu X, Zhang H, Cui Y, Zhao T, Wang B, Xie X, Liang S, Sha S, Yan Y, Zhao X, Zhang L. EEG-based major depressive disorder recognition by neural oscillation and asymmetry. Front Neurosci 2024; 18:1362111. [PMID: 38419668 PMCID: PMC10899403 DOI: 10.3389/fnins.2024.1362111] [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: 12/27/2023] [Accepted: 01/30/2024] [Indexed: 03/02/2024] Open
Abstract
Background Major Depressive Disorder (MDD) is a pervasive mental health issue with significant diagnostic challenges. Electroencephalography (EEG) offers a non-invasive window into the neural dynamics associated with MDD, yet the diagnostic efficacy is contingent upon the appropriate selection of EEG features and brain regions. Methods In this study, resting-state EEG signals from both eyes-closed and eyes-open conditions were analyzed. We examined band power across various brain regions, assessed the asymmetry of band power between the hemispheres, and integrated these features with clinical characteristics of MDD into a diagnostic regression model. Results Regression analysis found significant predictors of MDD to be beta2 (16-24 Hz) power in the Prefrontal Cortex (PFC) with eyes open (B = 20.092, p = 0.011), beta3 (24-40 Hz) power in the Medial Occipital Cortex (MOC) (B = -12.050, p < 0.001), and beta2 power in the Right Medial Frontal Cortex (RMFC) with eyes closed (B = 24.227, p < 0.001). Asymmetries in beta1 (12-16 Hz) power with eyes open (B = 28.047, p = 0.018), and in alpha (8-12 Hz, B = 9.004, p = 0.013) and theta (4-8 Hz, B = -13.582, p = 0.008) with eyes closed were also significant predictors. Conclusion The study confirms the potential of multi-region EEG analysis in improving the diagnostic precision for MDD. By including both neurophysiological and clinical data, we present a more robust approach to understanding and identifying this complex disorder. Limitations The research is limited by the sample size and the inherent variability in EEG signal interpretation. Future studies with larger cohorts and advanced analytical techniques are warranted to validate and refine these findings.
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Affiliation(s)
- Xinyu Liu
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Haoran Zhang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yi Cui
- Gnosis Healthineer Co. Ltd., Beijing, China
| | - Tong Zhao
- Gnosis Healthineer Co. Ltd., Beijing, China
| | - Bin Wang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Xiaomeng Xie
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Sixiang Liang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Sha Sha
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | | | - Xixi Zhao
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Ling Zhang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders and National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
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Yang K, Hu Y, Zeng Y, Tong L, Gao Y, Pei C, Li Z, Yan B. EEG Network Analysis of Depressive Emotion Interference Spatial Cognition Based on a Simulated Robotic Arm Docking Task. Brain Sci 2023; 14:44. [PMID: 38248259 PMCID: PMC10813131 DOI: 10.3390/brainsci14010044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 12/24/2023] [Accepted: 12/29/2023] [Indexed: 01/23/2024] Open
Abstract
Depressive emotion (DE) refers to clinically relevant depressive symptoms without meeting the diagnostic criteria for depression. Studies have demonstrated that DE can cause spatial cognition impairment. However, the brain network mechanisms underlying DE interference spatial cognition remain unclear. This study aimed to reveal the differences in brain network connections between DE and healthy control (HC) groups during resting state and a spatial cognition task. The longer operation time of the DE group during spatial cognition task indicated DE interference spatial cognition. In the resting state stage, the DE group had weaker network connections in theta and alpha bands than the HC group had. Specifically, the electrodes in parietal regions were hubs of the differential networks, which are related to spatial attention. Moreover, in docking task stages, the left frontoparietal network connections in delta, beta, and gamma bands were stronger in the DE group than those of the HC group. The enhanced left frontoparietal connections in the DE group may be related to brain resource reorganization to compensate for spatial cognition decline and ensure the completion of spatial cognition tasks. Thus, these findings might provide new insights into the neural mechanisms of depressive emotion interference spatial cognition.
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Affiliation(s)
- Kai Yang
- Henan Province Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou 450001, China; (K.Y.)
| | - Yidong Hu
- Henan Province Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou 450001, China; (K.Y.)
| | - Ying Zeng
- Henan Province Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou 450001, China; (K.Y.)
- Key Laboratory for Neuroinformation of Ministry of Education, University of Electronic Science and Technology of China, Chengdu 611730, China
| | - Li Tong
- Henan Province Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou 450001, China; (K.Y.)
| | - Yuanlong Gao
- Henan Province Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou 450001, China; (K.Y.)
| | - Changfu Pei
- Henan Province Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou 450001, China; (K.Y.)
| | - Zhongrui Li
- Henan Province Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou 450001, China; (K.Y.)
| | - Bin Yan
- Henan Province Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, Zhengzhou 450001, China; (K.Y.)
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Tian S, Wang Q, Zhang S, Chen Z, Dai Z, Zhang W, Yao Z, Lu Q. Local and large-scale resting-state oscillatory dysfunctions for early antidepressant response prediction in major depressive disorder. J Affect Disord 2023; 340:751-757. [PMID: 37597781 DOI: 10.1016/j.jad.2023.08.096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 08/05/2023] [Accepted: 08/16/2023] [Indexed: 08/21/2023]
Abstract
BACKGROUND Magnetoencephalography (MEG) could explore and resolve brain signals with realistic temporal resolution to investigate the underlying electrophysiology of major depressive disorder (MDD) and the treatment efficacy. Here, we explore whether neuro-electrophysiological features of MDD at baseline can be used as a neural marker to predict their early antidepressant response. METHODS Sixty-six medication-free patients with MDD and 48 healthy controls were enrolled and underwent resting-state MEG scans. Hamilton depression rating scale (HAMD-17) was assessed at both baseline and after two-week pharmacotherapy. We measured local and large-scale resting-state oscillatory dysfunctions with a data-driven model, the Fitting Oscillations & One-Over F algorithm. Then, we quantified band-limited regional power and functional connectivity between brain regions. RESULTS After two-week follow-up, 52 patients completed the re-interviews. Thirty-one patients showed early response (ER) to pharmacotherapy and 21 patients did not. Treatment response was defined as at least 50 % reduction of severity reflected by HAMD-17. We observed decreased regional periodic power in patients with MDD comparing to controls. However, patients with ER exhibited that functional couplings across brain regions in both alpha and beta band were increased and significantly correlated with severity of depressive symptoms after treatment. Receiver operating characteristic curves (ROC) further confirmed the predictive ability of baseline large-scale functional connectivity for early antidepressant efficacy (AUC = 0.9969). LIMITATIONS Relatively small sample size and not a double-blind design. CONCLUSIONS The current study demonstrated the electrophysiological dysfunctions of local neural oscillatory related with depression and highlighted the identification ability of large-scale couplings biomarkers in early antidepressant response prediction.
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Affiliation(s)
- Shui Tian
- Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Qiang Wang
- Department of Medical Psychology, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China
| | - Siqi Zhang
- Insitut des Sciences Cognitives, Marc Jeannerod, CNRS, France
| | - Zhilu Chen
- Department of Psychiatry, the Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Zhongpeng Dai
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China
| | - Wei Zhang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China
| | - Zhijian Yao
- Department of Psychiatry, the Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing 210029, China; School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing 210093, China.
| | - Qing Lu
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, China.
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Ozger C, Chumachenko S, McVoy M, Croarkin PE, Doruk Camsari D. Evidence for Altered Electroencephalography Coherence in Depressed Adolescents with Suicidal Ideation and Behaviors. J Child Adolesc Psychopharmacol 2023; 33:287-293. [PMID: 37669028 PMCID: PMC10517320 DOI: 10.1089/cap.2023.0033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/06/2023]
Abstract
Introduction: Major depressive disorder (MDD) is a primary risk factor for suicide, which is one of the leading causes of death among adolescents worldwide. Understanding the heterogeneity of suicidality in adolescents with MDD is critical for suicide prevention and intervention discovery. Quantitative electroencephalography (EEG) is a promising tool to address the knowledge gaps related to the neurophysiological characteristics of depression and suicidality. This study sought to examine resting-state EEG coherence differences in adolescents with MDD and suicidal ideation (SI)/behaviors (SB) and healthy controls (HC) to assess the utility of coherence as a biomarker of suicide. Methods: Twenty-six adolescents with MDD who were hospitalized for suicidality and 30 HC were recruited. The clinical sample was divided into SI (n = 9) and SB (n = 19) subgroups. Eyes closed resting-state EEG were recorded, and coherence was calculated. Depression severity and suicidality were assessed with the Children's Depression Rating Scale Revised and the Columbia Suicide Severity Rating Scale, respectively. Results: There were intrahemispheric differences in the right hemisphere across multiple electrode pairs. Delta, alpha, and beta coherence were higher in the SB group over the right prefrontofrontal and left parietooccipital electrode pairs, while alpha coherence was higher in the HC group over the right centroparietal electrode pair. There were no significant differences between HC and SI groups in any electrode pair. Conclusions: Patients with recent SB showed increased coherence in right frontal regions compared with patients with SI, suggesting altered cognitive states between those with SB and SI. These findings may have implications for suicide prevention in adolescents and could serve as useful biomarkers in clinical settings, but larger studies are needed to confirm the results.
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Affiliation(s)
- Can Ozger
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
| | - Serhiy Chumachenko
- Department of Psychiatry, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
| | - Molly McVoy
- Department of Psychiatry, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
| | - Paul E. Croarkin
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
| | - Deniz Doruk Camsari
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
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Huang SS, Yu YH, Chen HH, Hung CC, Wang YT, Chang CH, Peng SJ, Kuo PH. Functional connectivity analysis on electroencephalography signals reveals potential biomarkers for treatment response in major depression. BMC Psychiatry 2023; 23:554. [PMID: 37528355 PMCID: PMC10394892 DOI: 10.1186/s12888-023-04958-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 06/13/2023] [Indexed: 08/03/2023] Open
Abstract
BACKGROUND The treatment efficacy varies across individual patients with major depressive disorder (MDD). It lacks robust electroencephalography (EEG) markers for an antidepressant-responsive phenotype. METHOD This is an observational study enrolling 28 patients with MDD and 33 healthy controls (mean age of 40.7 years, and 71.4% were women). Patients underwent EEG exams at baseline (week0) and week1, while controls' EEG recordings were acquired only at week0. A resting eye-closing EEG segment was analyzed for functional connectivity (FC). Four parameters were used in FC analysis: (1) node strength (NS), (2) global efficiency (GE), (3) clustering coefficient (CC), and (4) betweenness centrality (BC). RESULTS We found that controls had higher values in delta wave in the indices of NS, GE, BC, and CC than MDD patients at baseline. After treatment with antidepressants, patients' FC indices improved significantly, including GE, mean CC, and mean NS in the delta wave. The FC in the alpha and beta bands of the responders were higher than those of the non-responders. CONCLUSIONS The FC of the MDD patients at baseline without treatment was worse than that of controls. After treatment, the FC improved and was close to the values of controls. Responders showed better FC in the high-frequency bands than non-responders, and this feature exists in both pre-treatment and post-treatment EEG.
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Affiliation(s)
- Shiau-Shian Huang
- Department of Medical Education, Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
- College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Bali Psychiatric Center, Ministry of Health and Welfare, New Taipei, Taiwan
| | - Yu-Hsiang Yu
- College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Division of Neurology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - His-Han Chen
- Department of Psychiatry, Yang Ji Mental Hospital, Keelung, Taiwan
| | - Chia-Chun Hung
- Bali Psychiatric Center, Ministry of Health and Welfare, New Taipei, Taiwan
| | - Yao-Ting Wang
- Bali Psychiatric Center, Ministry of Health and Welfare, New Taipei, Taiwan
| | - Chieh Hsin Chang
- Bali Psychiatric Center, Ministry of Health and Welfare, New Taipei, Taiwan
| | - Syu-Jyun Peng
- Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
| | - Po-Hsiu Kuo
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan.
- Department of Psychiatry, National Taiwan University Hospital, Taipei, Taiwan.
- Psychiatric Research Center, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.
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10
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Chumachenko SY, McVoy M. A narrative review and discussion of concepts and ongoing data regarding quantitative EEG as a childhood mood disorder biomarker. Biomark Neuropsychiatry 2023. [DOI: 10.1016/j.bionps.2022.100060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
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11
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Alterations in EEG functional connectivity in individuals with depression: A systematic review. J Affect Disord 2023; 328:287-302. [PMID: 36801418 DOI: 10.1016/j.jad.2023.01.126] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 01/22/2023] [Accepted: 01/30/2023] [Indexed: 02/19/2023]
Abstract
The brain works as an organised, network-like structure of functionally interconnected regions. Disruptions to interconnectivity in certain networks have been linked to symptoms of depression and impairments in cognition. Electroencephalography (EEG) is a low-burden tool by which differences in functional connectivity (FC) can be assessed. This systematic review aims to provide a synthesis of evidence relating to EEG FC in depression. A comprehensive electronic literature search for terms relating to depression, EEG, and FC was conducted on studies published before the end of November 2021, according to PRISMA guidelines. Studies comparing EEG measures of FC of individuals with depression to that of healthy control groups were included. Data was extracted by two independent reviewers, and the quality of EEG FC methods was assessed. Fifty-two studies assessing EEG FC in depression were identified: 36 assessed resting-state FC, and 16 assessed task-related or other (i.e., sleep) FC. Somewhat consistent findings in resting-state studies suggest for no differences between depression and control groups in EEG FC in the delta and gamma frequencies. However, while most resting-state studies noted a difference in alpha, theta, and beta, no clear conclusions could be drawn about the direction of the difference, due to considerable inconsistencies between study design and methodology. This was also true for task-related and other EEG FC. More robust research is needed to understand the true differences in EEG FC in depression. Given that the FC between brain regions drives behaviour, cognition, and emotion, characterising how FC differs in depression is essential for understanding the aetiology of depression.
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12
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McVoy M, Chumachenko S, Briggs F, Kaffashi F, Loparo K. A Predictive Biomarker Model Using Quantitative Electroencephalography in Adolescent Major Depressive Disorder. J Child Adolesc Psychopharmacol 2022; 32:460-466. [PMID: 36251778 DOI: 10.1089/cap.2022.0041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Background: With evolving understanding of psychiatric diagnosis and treatment, demand for biomarkers for psychiatric disorders in children and adolescents has grown dramatically. This study utilized quantitative electroencephalography (qEEG) to develop a predictive model for adolescent major depressive disorder (MDD). We hypothesized that youth with MDD compared to healthy controls (HCs) could be differentiated using a singular logistic regression model that utilized qEEG data alone. Methods: qEEG data and psychometric measures were obtained in adolescents aged 14-17 years with MDD (n = 35) and age- and gender-matched HCs (n = 14). qEEG in four frequency bands (alpha, beta, theta, and delta) was collected and coherence, cross-correlation, and power data streams obtained. A two-stage analytical framework was then used to develop the final logistic regression model, which was then evaluated using a receiver-operating characteristic curve (ROC) analysis. Results: Within the initial analysis, six qEEG dyads (all coherence) had significant predictive values. Within the final biomarkers, just four predictors, including F3-C3 (R frontal) alpha coherence, P3-O1 (R parietal) theta coherence, CZ-PZ (central) beta coherence, and P8-O2 (L parietal occipital) theta power were used in the final model, which yielded an ROC area of 0.8226. Conclusions: We replicated our previous findings of qEEG differences between adolescents and HCs and successfully developed a single-value predictive model with a robust ROC area. Furthermore, the brain areas involved in behavioral disinhibition and resting state/default mode networks were again shown to be involved in the observed differences. Thus, qEEG appears to be a potential low-cost and effective intermediate biomarker for MDD in youth.
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Affiliation(s)
- Molly McVoy
- Department of Psychiatry, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
- Neurological and Behavioral Outcomes Center, University Hospitals Cleveland Medical Center, Cleveland, Ohio, USA
| | - Serhiy Chumachenko
- Department of Psychiatry, Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
| | - Farren Briggs
- Case Western Reserve University School of Medicine, Population and Quantitative Health Sciences, Cleveland, Ohio, USA
| | - Farhad Kaffashi
- Department of Electrical Engineering and Computer Science, Case Western Reserve University School of Engineering, Cleveland, Ohio, USA
| | - Kenneth Loparo
- Department of Electrical Engineering and Computer Science, Case Western Reserve University School of Engineering, Cleveland, Ohio, USA
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13
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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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14
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Yao S, Zhu J, Li S, Zhang R, Zhao J, Yang X, Wang Y. Bibliometric Analysis of Quantitative Electroencephalogram Research in Neuropsychiatric Disorders From 2000 to 2021. Front Psychiatry 2022; 13:830819. [PMID: 35677873 PMCID: PMC9167960 DOI: 10.3389/fpsyt.2022.830819] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 04/05/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND With the development of quantitative electroencephalography (QEEG), an increasing number of studies have been published on the clinical use of QEEG in the past two decades, particularly in the diagnosis, treatment, and prognosis of neuropsychiatric disorders. However, to date, the current status and developing trends of this research field have not been systematically analyzed from a macroscopic perspective. The present study aimed to identify the hot spots, knowledge base, and frontiers of QEEG research in neuropsychiatric disorders from 2000 to 2021 through bibliometric analysis. METHODS QEEG-related publications in the neuropsychiatric field from 2000 to 2021 were retrieved from the Web of Science Core Collection (WOSCC). CiteSpace and VOSviewer software programs, and the online literature analysis platform (bibliometric.com) were employed to perform bibliographic and visualized analysis. RESULTS A total of 1,904 publications between 2000 and 2021 were retrieved. The number of QEEG-related publications in neuropsychiatric disorders increased steadily from 2000 to 2021, and research in psychiatric disorders requires more attention in comparison to research in neurological disorders. During the last two decades, QEEG has been mainly applied in neurodegenerative diseases, cerebrovascular diseases, and mental disorders to reveal the pathological mechanisms, assist clinical diagnosis, and promote the selection of effective treatments. The recent hot topics focused on QEEG utilization in neurodegenerative disorders like Alzheimer's and Parkinson's disease, traumatic brain injury and related cerebrovascular diseases, epilepsy and seizure, attention-deficit hyperactivity disorder, and other mental disorders like major depressive disorder and schizophrenia. In addition, studies to cross-validate QEEG biomarkers, develop new biomarkers (e.g., functional connectivity and complexity), and extract compound biomarkers by machine learning were the emerging trends. CONCLUSION The present study integrated bibliometric information on the current status, the knowledge base, and future directions of QEEG studies in neuropsychiatric disorders from a macroscopic perspective. It may provide valuable insights for researchers focusing on the utilization of QEEG in this field.
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Affiliation(s)
- Shun Yao
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China.,Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Jieying Zhu
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Shuiyan Li
- Department of Rehabilitation Medicine, School of Rehabilitation Medicine, Southern Medical University, Guangzhou, China
| | - Ruibin Zhang
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China
| | - Jiubo Zhao
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China.,Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Xueling Yang
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China.,Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - You Wang
- Department of Psychology, School of Public Health, Southern Medical University, Guangzhou, China.,Department of Psychiatry, Zhujiang Hospital, Southern Medical University, Guangzhou, China
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15
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Thériault RK, Manduca JD, Perreault ML. Sex differences in innate and adaptive neural oscillatory patterns link resilience and susceptibility to chronic stress in rats. J Psychiatry Neurosci 2021; 46:E258-E270. [PMID: 33769022 PMCID: PMC8061734 DOI: 10.1503/jpn.200117] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Major depressive disorder is a chronic illness with a higher incidence in women. Dysregulated neural oscillatory activity is an emerging mechanism thought to underlie major depressive disorder, but whether sex differences in these rhythms contribute to the development of symptoms is unknown. METHODS We exposed male and female rats to chronic unpredictable stress and characterized them as stress-resilient or stress-susceptible based on behavioural output in the forced swim test and the sucrose preference test. To identify sex-specific neural oscillatory patterns associated with stress response, we recorded local field potentials from the prefrontal cortex, cingulate cortex, nucleus accumbens and dorsal hippocampus throughout stress exposure. RESULTS At baseline, female stress-resilient rats innately exhibited higher theta coherence in hippocampal connections compared with stress-susceptible female rats. Following stress exposure, additional oscillatory changes manifested: stress-resilient females were characterized by increased dorsal hippocampal theta power and cortical gamma power, and stress-resilient males were characterized by a widespread increase in high gamma coherence. In stress-susceptible animals, we observed a pattern of increased delta and reduced theta power; the changes were restricted to the cingulate cortex and dorsal hippocampus in males but occurred globally in females. Finally, stress exposure was accompanied by the time-dependent recruitment of specific neural pathways, which culminated in system-wide changes that temporally coincided with the onset of depression-like behaviour. LIMITATIONS We could not establish causality between the electrophysiological changes and behaviours with the methodology we employed. CONCLUSION Sex-specific neurophysiological patterns can function as early markers for stress vulnerability and the onset of depression-like behaviours in rats.
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Affiliation(s)
- Rachel-Karson Thériault
- From the department of Molecular and Cellular Biology, University of Guelph, Guelph, Ont., Canada (Thériault, Manduca, Perreault) and the Collaborative Neuroscience Program, University of Guelph, Guelph, Ont., Canada (Thériault, Perreault)
| | - Joshua D Manduca
- From the department of Molecular and Cellular Biology, University of Guelph, Guelph, Ont., Canada (Thériault, Manduca, Perreault) and the Collaborative Neuroscience Program, University of Guelph, Guelph, Ont., Canada (Thériault, Perreault)
| | - Melissa L Perreault
- From the department of Molecular and Cellular Biology, University of Guelph, Guelph, Ont., Canada (Thériault, Manduca, Perreault) and the Collaborative Neuroscience Program, University of Guelph, Guelph, Ont., Canada (Thériault, Perreault)
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16
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Dobson ET, Croarkin PE, Schroeder HK, Varney ST, Mossman SA, Cecil K, Strawn JR. Bridging Anxiety and Depression: A Network Approach in Anxious Adolescents. J Affect Disord 2021; 280:305-314. [PMID: 33221716 PMCID: PMC7744436 DOI: 10.1016/j.jad.2020.11.027] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Revised: 09/22/2020] [Accepted: 11/07/2020] [Indexed: 12/26/2022]
Abstract
BACKGROUND The phenomenology and neurobiology of depressive symptoms in anxious youth is poorly understood. METHODS Association networks of anxiety and depressive symptoms were developed in adolescents with generalized anxiety disorder (GAD; N=52, mean age: 15.4±1.6 years) who had not yet developed major depressive disorder. Community analyses were used to create consensus clusters of depressive and anxiety symptoms and to identify "bridge" symptoms between the clusters. In a subset of this sample (n=39), correlations between cortical thickness and depressive symptom severity was examined. RESULTS Ten symptoms clustered into an anxious community, 5 clustered into a depressive community and 5 bridged the two communities: impaired schoolwork, excessive weeping, low self-esteem, disturbed appetite, and physical symptoms of depression. Patients with more depressive cluster burden had altered cortical thickness in prefrontal, inferior and medial parietal (e.g., precuneus, supramarginal) regions and had decreases in cortical thickness-age relationships in prefrontal, temporal and parietal cortices. LIMITATIONS Data are cross-sectional and observational. Limited sample size precluded secondary analysis of comorbidities and demographics. CONCLUSIONS In youth with GAD, a sub-set of symptoms not directly related to anxiety bridge anxiety and depression. Youth with greater depressive cluster burden had altered cortical thickness in cortical structures within the default mode and central executive networks. These alternations in cortical thickness may represent a distinct neurostructural fingerprint in anxious youth with early depressive symptoms. Finally, youth with GAD and high depressive symptoms had reduced age-cortical thickness correlations. The emergence of depressive symptoms in early GAD and cortical development may have bidirectional, neurobiological relationships.
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Affiliation(s)
- Eric T Dobson
- Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina.
| | | | - Heidi K Schroeder
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, College of Medicine, Cincinnati, OH 45219
| | - Sara T Varney
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, College of Medicine, Cincinnati, OH 45219
| | - Sarah A Mossman
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, College of Medicine, Cincinnati, OH 45219
| | - Kim Cecil
- Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio 45267
| | - Jeffrey R Strawn
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, College of Medicine, Cincinnati, OH 45219
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17
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He Y, Yu Q, Yang T, Zhang Y, Zhang K, Jin X, Wu S, Gao X, Huang C, Cui X, Luo X. Abnormalities in Electroencephalographic Microstates Among Adolescents With First Episode Major Depressive Disorder. Front Psychiatry 2021; 12:775156. [PMID: 34975577 PMCID: PMC8718790 DOI: 10.3389/fpsyt.2021.775156] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 11/24/2021] [Indexed: 12/28/2022] Open
Abstract
Background: Recent studies have reported changes in the electroencephalograms (EEG) of patients with major depressive disorder (MDD). However, little research has explored EEG differences between adolescents with MDD and healthy controls, particularly EEG microstates differences. The aim of the current study was to characterize EEG microstate activity in adolescents with MDD and healthy controls (HCs). Methods: A total of 35 adolescents with MDD and 35 HCs were recruited in this study. The depressive symptoms were assessed by Hamilton Depression Scale (HAMD) and Children's Depression Inventory (CDI), and the anxiety symptoms were assessed by Chinese version of DSM-5 Level 2-Anxiety-Child scale. A 64-channel EEG was recorded for 5 min (eye closed, resting-state) and analyzed using microstate analysis. Microstate properties were compared between groups and correlated with patients' depression scores. Results: We found increased occurrence and contribution of microstate B in MDD patients compared to HCs, and decreased occurrence and contribution of microstate D in MDD patients compared to HCs. While no significant correlation between depression severity (HAMD score) and the microstate metrics (occurrence and contribution of microstate B and D) differing between MDD adolescents and HCs was found. Conclusions: Adolescents with MDD showed microstate B and microstate D changes. The obtained results may deepen our understanding of dynamic EEG changes among adolescents with MDD and provide some evidence of changes in brain development in adolescents with MDD.
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Affiliation(s)
- Yuqiong He
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Qianting Yu
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Tingyu Yang
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Yaru Zhang
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Kun Zhang
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Xingyue Jin
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Shuxian Wu
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Xueping Gao
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Autism Center of the Second Xiangya Hospital, Central South University, Changsha, China
| | - Chunxiang Huang
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Autism Center of the Second Xiangya Hospital, Central South University, Changsha, China
| | - Xilong Cui
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Autism Center of the Second Xiangya Hospital, Central South University, Changsha, China
| | - Xuerong Luo
- National Clinical Research Center for Mental Disorders, Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,Autism Center of the Second Xiangya Hospital, Central South University, Changsha, China
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18
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Mohammadi Y, Moradi MH. Prediction of Depression Severity Scores Based on Functional Connectivity and Complexity of the EEG Signal. Clin EEG Neurosci 2021; 52:52-60. [PMID: 33040603 DOI: 10.1177/1550059420965431] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND Depression is one of the most common mental disorders and the leading cause of functional disabilities. This study aims to specify whether functional connectivity and complexity of brain activity can predict the severity of depression (Beck Depression Inventory-II scores). METHODS Resting-state, eyes-closed EEG data were recorded from 60 depressed patients. A phase synchronization measure was used to estimate functional connectivity between all pairs of the EEG channels in the delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) frequency bands. To quantify the local value of functional connectivity, 2 graph theory metrics, degree, and clustering coefficient (CC), were measured. Moreover, Lempel-Ziv complexity (LZC) and fuzzy entropy (FuzzyEn) were used to measure the complexity of the EEG signal. RESULTS Through correlation analysis, a significant negative relationship was found between graph metrics and depression severity in the alpha band. This association was strongly positive for the complexity measures in alpha and delta bands. Also, the linear regression model represented a substantial performance of depression severity prediction based on EEG features of the alpha band (r = 0.839; P < .0001, root mean square error score of 7.69). CONCLUSION We found that the brain activity of patients with depression was related to depression severity. Abnormal brain activity reflects an increase in the severity of depression. The presented regression model provides a quantitative depression severity prediction, which can inform the development of EEG state and exhibit potential desirable application for the medical treatment of the depressive disorder.
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Affiliation(s)
- Yousef Mohammadi
- Biomedical Engineering Department, Amirkabir University of Technology, Tehran, Islamic Republic of Iran
| | - Mohammad Hassan Moradi
- Biomedical Engineering Department, Amirkabir University of Technology, Tehran, Islamic Republic of Iran
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19
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Chen ST, Ku LC, Chen SJ, Shen TW. The Changes of qEEG Approximate Entropy during Test of Variables of Attention as a Predictor of Major Depressive Disorder. Brain Sci 2020; 10:brainsci10110828. [PMID: 33171848 PMCID: PMC7695214 DOI: 10.3390/brainsci10110828] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 10/30/2020] [Accepted: 11/05/2020] [Indexed: 01/30/2023] Open
Abstract
Evaluating brain function through biosignals remains challenging. Quantitative electroencephalography (qEEG) outcomes have emerged as a potential intermediate biomarker for diagnostic clarification in psychological disorders. The Test of Variables of Attention (TOVA) was combined with qEEG to evaluate biomarkers such as absolute power, relative power, cordance, and approximate entropy from covariance matrix images to predict major depressive disorder (MDD). EEG data from 18 healthy control and 18 MDD patients were monitored during the resting state and TOVA. TOVA was found to provide aspects for the evaluation of MDD beyond resting electroencephalography. The results showed that the prefrontal qEEG theta cordance of the control and MDD groups were significantly different. For comparison, the changes in qEEG approximate entropy (ApEn) patterns observed during TOVA provided features to distinguish between participants with or without MDD. Moreover, ApEn scores during TOVA were a strong predictor of MDD, and the ApEn scores correlated with the Beck Depression Inventory (BDI) scores. Between-group differences in ApEn were more significant for the testing state than for the resting state. Our results provide further understanding for MDD treatment selection and response prediction during TOVA.
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Affiliation(s)
- Shao-Tsu Chen
- Department of Psychiatry, Hualien Tzu Chi Hospital, Buddhist Tzu-Chi Medical Foundation, Hualien 970, Taiwan;
- Department of Psychiatry, Tzu Chi University, Hualien 970, Taiwan
| | - Li-Chi Ku
- Department of Medical Informatics, Tzu Chi University, Hualien 970, Taiwan;
| | - Shaw-Ji Chen
- Department of Psychiatry, Taitung MacKay Memorial Hospital, Taitung County 950, Taiwan;
- Department of Medicine, MacKay Medical College, New Taipei City 252, Taiwan
| | - Tsu-Wang Shen
- Department of Automatic Control Engineering, Feng Chia University, Taichung 40724, Taiwan
- Master’s Program Biomedical Informatics and Biomedical Engineering, Feng Chia University, Taichung 40724, Taiwan
- Correspondence: ; Tel.: +886-4-24517250 (ext. 3937)
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20
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Thériault RK, Manduca JD, Blight CR, Khokhar JY, Akhtar TA, Perreault ML. Acute mitragynine administration suppresses cortical oscillatory power and systems theta coherence in rats. J Psychopharmacol 2020; 34:759-770. [PMID: 32248751 DOI: 10.1177/0269881120914223] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND Mitragynine is the major alkaloid of Mitragyna speciosa (kratom) with potential as a therapeutic in pain management and in depression. There has been debate over the potential side effects of the drug including addiction risk and cognitive decline. AIMS To evaluate the effects of mitragynine on neurophysiological systems function in the prefrontal cortex (PFC), cingulate cortex (Cg), orbitofrontal cortex, nucleus accumbens (NAc), hippocampus (HIP), thalamus (THAL), basolateral amygdala (BLA) and ventral tegmental area of rats. METHODS Local field potential recordings were taken from animals at baseline and for 45 min following mitragynine administration (10 mg/kg, intraperitoneally). Drug-induced changes in spectral power and coherence between regions at specific frequencies were evaluated. Mitragynine-induced changes in c-fos expression were also analyzed. RESULTS Mitragynine increased delta power and reduced theta power in all three cortical regions that were accompanied by increased c-fos expression. A transient suppression of gamma power in PFC and Cg was also evident. There were no effects of mitragynine on spectral power in any of the other regions. Mitragynine induced a widespread reduction in theta coherence (7-9 Hz) that involved disruptions in cortical and NAc connectivity with the BLA, HIP and THAL. CONCLUSIONS These findings show that mitragynine induces frequency-specific changes in cortical neural oscillatory activity that could potentially impact cognitive functioning. However, the absence of drug effects within regions of the mesolimbic pathway may suggest either a lack of addiction potential, or an underlying mechanism of addiction that is distinct from other opioid analgesic agents.
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Affiliation(s)
| | - Joshua D Manduca
- Department of Molecular and Cellular Biology, University of Guelph, Guelph, Ontario, Canada
| | - Colin R Blight
- Department of Molecular and Cellular Biology, University of Guelph, Guelph, Ontario, Canada
| | - Jibran Y Khokhar
- Department of Biomedical Sciences, University of Guelph, Guelph, Ontario, Canada RKT, JYK and MLP are part of the Collaborative Neuroscience Program
| | - Tariq A Akhtar
- Department of Molecular and Cellular Biology, University of Guelph, Guelph, Ontario, Canada
| | - Melissa L Perreault
- Department of Molecular and Cellular Biology, University of Guelph, Guelph, Ontario, Canada
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