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Pan Y, Wang P, Xue B, Liu Y, Shen X, Wang S, Wang X. Machine learning for the diagnosis accuracy of bipolar disorder: a systematic review and meta-analysis. Front Psychiatry 2025; 15:1515549. [PMID: 39935623 PMCID: PMC11810903 DOI: 10.3389/fpsyt.2024.1515549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2024] [Accepted: 12/20/2024] [Indexed: 02/13/2025] Open
Abstract
Background Diagnosing bipolar disorder poses a challenge in clinical practice and demands a substantial time investment. With the growing utilization of artificial intelligence in mental health, researchers are endeavoring to create AI-based diagnostic models. In this context, some researchers have sought to develop machine learning models for bipolar disorder diagnosis. Nevertheless, the accuracy of these diagnoses remains a subject of controversy. Consequently, we conducted this systematic review to comprehensively assess the diagnostic value of machine learning in the context of bipolar disorder. Methods We searched PubMed, Embase, Cochrane, and Web of Science, with the search ending on April 1, 2023. QUADAS-2 was applied to assess the quality of the literature included. In addition, we employed a bivariate mixed-effects model for the meta-analysis. Results 18 studies were included, covering 3152 participants, including 1858 cases of bipolar disorder. 28 machine learning models were encompassed. Sensitivity and specificity in discriminating between bipolar disorder and normal individuals were 0.88 (9.5% CI: 0.74~0.95) and 0.89 (95% CI: 0.73~0.96) respectively, and the SROC curve was 0.94(95% CI: 0.92~0.96). The sensitivity and specificity for distinguishing between bipolar disorder and depression were 0.84 (95%CI: 0.80~0.87) and 0.82 (95%CI: 0.75~0.88) respectively. The SROC curve was 0.89 (95%CI: 0.86~0.91). Conclusions Machine learning methods can be employed for discriminating and diagnosing bipolar disorder. However, in current research, they are predominantly utilized for binary classification tasks, limiting their progress in clinical practice. Therefore, in future studies, we anticipate the development of more multi-class classification tasks to enhance the clinical applicability of these methods. Systematic review registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023427290, identifier CRD42023427290.
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Affiliation(s)
- Yi Pan
- Department of Neurosis and Psychosomatic Diseases, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, Zhejiang, China
| | - Pushi Wang
- Department of Mental Disorders, National Center for Mental Health, NCMHC, Beijing, China
| | - Bowen Xue
- Affiliated Mental Health Center & Hangzhou Seventh People’s Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yanbin Liu
- Department of Mental Disorders, National Center for Mental Health, NCMHC, Beijing, China
| | - Xinhua Shen
- Department of Neurosis and Psychosomatic Diseases, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, Zhejiang, China
| | - Shiliang Wang
- Department of Neurosis and Psychosomatic Diseases, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, Zhejiang, China
| | - Xing Wang
- Department of Neurosis and Psychosomatic Diseases, Huzhou Third Municipal Hospital, The Affiliated Hospital of Huzhou University, Huzhou, Zhejiang, China
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Xue L, Hu X, Zhang S, Dai Z, Zhou H, Chen Z, Yao Z, Lu Q. Abnormal beta bursts of depression in the orbitofrontal cortex and its relationship with clinical symptoms. J Affect Disord 2025; 369:1168-1177. [PMID: 39490422 DOI: 10.1016/j.jad.2024.10.092] [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: 04/27/2024] [Revised: 10/16/2024] [Accepted: 10/20/2024] [Indexed: 11/05/2024]
Abstract
BACKGROUND Recent researches have reported that frequency-specific patterns of neural activity contain not only rhythmically sustained oscillations but also transient-bursts of isolated events. The aim of this study was to investigated the correlation between beta burst and depression in order to explore depressive disease and the neurological underpinnings of disease-related symptoms. METHODS We collected resting-state MEG recordings from 30 depressive patients and a matched 40 healthy controls. A Hidden Markov Model (HMM) was applied on source-space time courses for 78 cortical regions of the AAL atlas and the temporal characteristics of beta burst from the matched HMM states were captured. Group differences were evaluated on these beta burst characteristics after permutation tests and, for the depressive group, associations between burst characteristics and clinical symptom severity were determined using Spearman correlation coefficients. RESULTS At a threshold of p=0.05corrected, burst characteristics revealed significant differences between depression patients and controls at the group level, including increased burst amplitude in frontal lobe, decreased burst duration in occipital regions, increased burst rate and decreased burst interval time in some brain regions. Furthermore, burst amplitude in the orbitofrontal cortex (OFC) was positively related to the severity of sleep disturbance and burst rate in the OFC was negatively related to the severity of anxiety in depression patients. CONCLUSIONS The findings highlight OFC may be a targeted area responsible for the anxiety and sleep disturbance symptom by abnormal beta burst in depressive patients and beta burst characteristics of OFC might serve as a neuro-marker for the depression.
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Affiliation(s)
- Li Xue
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing 210096, China
| | - Xiaowen Hu
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing 210096, China
| | - Siqi Zhang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing 210096, 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, Nanjing 210096, China
| | - Hongliang Zhou
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Zhilu Chen
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Zhijian Yao
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, 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, Nanjing 210096, China.
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Tang H, Xia Y, Hua L, Dai Z, Wang X, Yao Z, Lu Q. Electrophysiological predictors of early response to antidepressants in major depressive disorder. J Affect Disord 2024; 365:509-517. [PMID: 39187184 DOI: 10.1016/j.jad.2024.08.118] [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: 03/20/2024] [Revised: 07/16/2024] [Accepted: 08/23/2024] [Indexed: 08/28/2024]
Abstract
BACKGROUND Psychomotor retardation (PMR) is a core feature of major depressive disorder (MDD), which is characterized by abnormalities in motor control and cognitive processes. PMR in MDD can predict a poor antidepressant response, suggesting that PMR may serve as a marker of the antidepressant response. However, the neuropathological relationship between treatment outcomes and PMR remains uncertain. Thus, this study examined electrophysiological biomarkers associated with poor antidepressant response in MDD. METHODS A total of 142 subjects were enrolled in this study, including 49 healthy controls (HCs) and 93 MDD patients. All participants performed a simple right-hand visuomotor task during magnetoencephalography (MEG) scanning. Patients who exhibited at least a 50 % reduction in disorder severity at the endpoint (>2 weeks) were considered to be responders. Motor-related beta desynchronization (MRBD) and inter- and intra-hemispheric functional connectivity were measured in the bilateral motor network. RESULTS An increased MRBD and decreased inter- and intra-hemispheric functional connectivity in the motor network during movement were observed in non-responders, relative to responders and HCs. This dysregulation predicted the potential antidepressant response. CONCLUSION Abnormal local activity and functional connectivity in the motor network indicate poor psychomotor function, which might cause insensitivity to antidepressant treatment. This could be regarded as a potential neural mechanism for the prediction of a patient's treatment response.
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Affiliation(s)
- Hao Tang
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Yi Xia
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Lingling Hua
- Department of Psychiatry, the Affiliated 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, Southeast University, Nanjing 210096, China
| | - Xiaoqin Wang
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - ZhiJian Yao
- Department of Psychiatry, the Affiliated 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, Southeast University, Nanjing 210096, China.
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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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Xia Y, Hua L, Dai Z, Han Y, Du Y, Zhao S, Zhou H, Wang X, Yan R, Wang X, Zou H, Sun H, Huang Y, Yao Z, Lu Q. Attenuated post-movement beta rebound reflects psychomotor alterations in major depressive disorder during a simple visuomotor task: a MEG study. BMC Psychiatry 2023; 23:395. [PMID: 37270511 DOI: 10.1186/s12888-023-04844-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Accepted: 05/04/2023] [Indexed: 06/05/2023] Open
Abstract
BACKGROUND Psychomotor alterations are a common symptom in patients with major depressive disorder (MDD). The primary motor cortex (M1) plays a vital role in the mechanism of psychomotor alterations. Post-movement beta rebound (PMBR) in the sensorimotor cortex is abnormal in patients with motor abnormalities. However, the changes in M1 beta rebound in patients with MDD remain unclear. This study aimed to primarily explore the relationship between psychomotor alterations and PMBR in MDD. METHODS One hundred thirty-two subjects were enrolled in the study, comprising 65 healthy controls (HCs) and 67 MDD patients. All participants performed a simple right-hand visuomotor task during MEG scanning. PMBR was measured in the left M1 at the source reconstruction level with the time-frequency analysis method. Retardation factor scores and neurocognitive test performance, including the Digit Symbol Substitution Test (DSST), the Making Test Part A (TMT-A), and the Verbal Fluency Test (VFT), were used to measure psychomotor functions. Pearson correlation analyses were used to assess relationships between PMBR and psychomotor alterations in MDD. RESULTS The MDD group showed worse neurocognitive performance than the HC group in all three neurocognitive tests. The PMBR was diminished in patients with MDD compared to HCs. In a group of MDD patients, the reduced PMBR was negatively correlated with retardation factor scores. Further, there was a positive correlation between the PMBR and DSST scores. PMBR is negatively associated with the TMT-A scores. CONCLUSION Our findings suggested that the attenuated PMBR in M1 could illustrate the psychomotor disturbance in MDD, possibly contributing to clinical psychomotor symptoms and deficits of cognitive functions.
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Affiliation(s)
- Yi Xia
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Lingling Hua
- Department of Psychiatry, the Affiliated 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, Southeast University, Nanjing, 210096, China
| | - Yinglin Han
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Yishan Du
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Shuai Zhao
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Hongliang Zhou
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Xiaoqin Wang
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Rui Yan
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
- Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, 210093, China
| | - Xumiao Wang
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - HaoWen Zou
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
- Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, 210093, China
| | - Hao Sun
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
- Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, 210093, China
| | - YingHong Huang
- Department of Psychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029, China
- Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, 210093, China
| | - ZhiJian Yao
- Department of Psychiatry, the Affiliated 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, Southeast University, Nanjing, 210096, China.
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Xia Y, Sun H, Hua L, Dai Z, Wang X, Tang H, Han Y, Du Y, Zhou H, Zou H, Yao Z, Lu Q. Spontaneous beta power, motor-related beta power and cortical thickness in major depressive disorder with psychomotor disturbance. Neuroimage Clin 2023; 38:103433. [PMID: 37216848 PMCID: PMC10209543 DOI: 10.1016/j.nicl.2023.103433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 05/05/2023] [Accepted: 05/11/2023] [Indexed: 05/24/2023]
Abstract
INTRODUCTION The psychomotor disturbance is a common symptom in patients with major depressive disorder (MDD). The neurological mechanisms of psychomotor disturbance are intricate, involving alterations in the structure and function of motor-related regions. However, the relationship among changes in the spontaneous activity, motor-related activity, local cortical thickness, and psychomotor function remains unclear. METHOD A total of 140 patients with MDD and 68 healthy controls performed a simple right-hand visuomotor task during magnetoencephalography (MEG) scanning. All patients were divided into two groups according to the presence of psychomotor slowing. Spontaneous beta power, movement-related beta desynchronization (MRBD), absolute beta power during movement and cortical characteristics in the bilateral primary motor cortex were compared using general linear models with the group as a fixed effect and age as a covariate. Finally, the moderated mediation model was tested to examine the relationship between brain metrics with group differences and psychomotor performance. RESULTS The patients with psychomotor slowing showed higher spontaneous beta power, movement-related beta desynchronization and absolute beta power during movement than patients without psychomotor slowing. Compared with the other two groups, significant decreases were found in cortical thickness of the left primary motor cortex in patients with psychomotor slowing. Our moderated mediation model showed that the increased spontaneous beta power indirectly affected impaired psychomotor performance by abnormal MRBD, and the indirect effects were moderated by cortical thickness. CONCLUSION These results suggest that patients with MDD have aberrant cortical beta activity at rest and during movement, combined with abnormal cortical thickness, contributing to the psychomotor disturbance observed in this patient population.
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Affiliation(s)
- Yi Xia
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Hao Sun
- Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing 210093, China
| | - Lingling Hua
- Department of Psychiatry, The Affiliated 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, Southeast University, Nanjing 210096, China
| | - Xiaoqin Wang
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Hao Tang
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Yinglin Han
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Yishan Du
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Hongliang Zhou
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Haowen Zou
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China; Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing 210093, China
| | - Zhijian Yao
- Department of Psychiatry, The Affiliated 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, Southeast University, Nanjing 210096, China.
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Wang H, Tian S, Yan R, Tang H, Shi J, Zhu R, Chen Y, Han Y, Chen Z, Zhou H, Zhao S, Yao Z, Lu Q. Convergent and divergent cognitive impairment of unipolar and bipolar depression: A magnetoencephalography resting-state study. J Affect Disord 2023; 321:8-15. [PMID: 36181913 DOI: 10.1016/j.jad.2022.09.126] [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: 06/30/2022] [Revised: 09/14/2022] [Accepted: 09/26/2022] [Indexed: 11/20/2022]
Abstract
BACKGROUND Unipolar depression (UD) and bipolar depression (BD) showed convergent and divergent cognitive impairments. Neural oscillations are linked to the foundational cognitive processes. We aimed to investigate the underpinning spectral neuronal power patterns by magnetoencephalography (MEG), which combinates high spatial and temporal resolution. We hypothesized that patients with UD and BD exhibit common and distinct patterns, which may contribute to their cognitive impairments. METHODS Group cognitive tests were performed. Eyes closed resting-state MEG data were collected from 61 UD, 55 BD, and 52 healthy controls (HC). Nonparametric cluster-based permutation tests were performed to deal with the multiple comparison problem on channel-frequency MEG data. Correlation analysis of cognitive dysfunction scores and MEG oscillation were conducted by Spearman or partial correlation analysis. RESULTS Wisconsin Card Sorting Test showed similar cognitive impairment in patients with UD and BD. Moreover, patients with BD exhibited extensive cognitive deficits in verbal executive functions and visuospatial processing. Compare to HC, both patients with UD and BD showed increased frontal-central beta power while high gamma power was decreased in UD groups during the resting-state. The significant correlations between cognitive function and average beta power were observed. CONCLUSIONS Patients with BD had more cognitive impairments on different dimensions than those with UD, involving disrupted beta power modulations. Our investigation provides a better understanding of the neuroelectrophysiological process underlying cognitive impairments in patients with UD and BD.
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Affiliation(s)
- HaoFei Wang
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China; Department of Clinical Psychology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Shui Tian
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Rui Yan
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Hao Tang
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - JiaBo Shi
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - RongXin Zhu
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Yu Chen
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - YingLin Han
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - ZhiLu Chen
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - HongLiang Zhou
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Shuai Zhao
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China
| | - ZhiJian Yao
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210029, China; Nanjing Brain Hospital, Medical School, Nanjing University, Nanjing 210093, China; School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, China.
| | - Qing Lu
- School of Biological Sciences and Medical Engineering, Southeast University, Nanjing 210096, China; Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing 210096, China.
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Liu D, Liu B, Lin T, Liu G, Yang G, Qi D, Qiu Y, Lu Y, Yuan Q, Shuai SC, Li X, Liu O, Tang X, Shuai J, Cao Y, Lin H. Measuring depression severity based on facial expression and body movement using deep convolutional neural network. Front Psychiatry 2022; 13:1017064. [PMID: 36620657 PMCID: PMC9810804 DOI: 10.3389/fpsyt.2022.1017064] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 12/02/2022] [Indexed: 12/24/2022] Open
Abstract
Introduction Real-time evaluations of the severity of depressive symptoms are of great significance for the diagnosis and treatment of patients with major depressive disorder (MDD). In clinical practice, the evaluation approaches are mainly based on psychological scales and doctor-patient interviews, which are time-consuming and labor-intensive. Also, the accuracy of results mainly depends on the subjective judgment of the clinician. With the development of artificial intelligence (AI) technology, more and more machine learning methods are used to diagnose depression by appearance characteristics. Most of the previous research focused on the study of single-modal data; however, in recent years, many studies have shown that multi-modal data has better prediction performance than single-modal data. This study aimed to develop a measurement of depression severity from expression and action features and to assess its validity among the patients with MDD. Methods We proposed a multi-modal deep convolutional neural network (CNN) to evaluate the severity of depressive symptoms in real-time, which was based on the detection of patients' facial expression and body movement from videos captured by ordinary cameras. We established behavioral depression degree (BDD) metrics, which combines expression entropy and action entropy to measure the depression severity of MDD patients. Results We found that the information extracted from different modes, when integrated in appropriate proportions, can significantly improve the accuracy of the evaluation, which has not been reported in previous studies. This method presented an over 74% Pearson similarity between BDD and self-rating depression scale (SDS), self-rating anxiety scale (SAS), and Hamilton depression scale (HAMD). In addition, we tracked and evaluated the changes of BDD in patients at different stages of a course of treatment and the results obtained were in agreement with the evaluation from the scales. Discussion The BDD can effectively measure the current state of patients' depression and its changing trend according to the patient's expression and action features. Our model may provide an automatic auxiliary tool for the diagnosis and treatment of MDD.
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Affiliation(s)
- Dongdong Liu
- Department of Physics, Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, China
| | - Bowen Liu
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
- Department of Psychiatry, Baoan Mental Health Center, Shenzhen Baoan Center for Chronic Disease Control, Shenzhen, China
| | - Tao Lin
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, China
| | - Guangya Liu
- Integrated Chinese and Western Therapy of Depression Ward, Hunan Brain Hospital, Changsha, China
| | - Guoyu Yang
- Department of Physics, Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, China
| | - Dezhen Qi
- Department of Physics, Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, China
| | - Ye Qiu
- Department of Physics, Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, China
| | - Yuer Lu
- Department of Physics, Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, China
| | - Qinmei Yuan
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Stella C. Shuai
- Department of Biological Sciences, Northwestern University, Evanston, IL, United States
| | - Xiang Li
- Department of Physics, Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, China
| | - Ou Liu
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, China
| | - Xiangdong Tang
- Sleep Medicine Center, Mental Health Center, Department of Respiratory and Critical Care Medicine, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Jianwei Shuai
- Department of Physics, Fujian Provincial Key Laboratory for Soft Functional Materials Research, Xiamen University, Xiamen, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, China
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, China
| | - Yuping Cao
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Hai Lin
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, China
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9
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Abstract
BACKGROUND In this modern era, depression is one of the most prevalent mental disorders from which millions of individuals are affected today. The symptoms of depression are heterogeneous and often coincide with other disorders such as bipolar disorder, Parkinson's, schizophrenia, etc. It is a serious mental illness that may lead to other health problems if left untreated. Currently, identifying individuals with depression is totally based on the expertise of the clinician's experience. In order to assist clinicians in identifying the characteristics and classifying depressed people, different types of data modalities and machine learning techniques have been incorporated by researchers in this field. This study aims to find the answers to some important questions related to the trend of publications, data modality, machine learning models, dataset usage, pre-processing techniques and feature extraction and selection techniques that are prevalent and guide the direction of future research on depression diagnosis. METHODS This systematic review was conducted using a broad range of articles from two major databases: IEEE Xplore and PubMed. Studies ranging from the years 2011 to April 2021 were retrieved from the databases resulting in a total of 590 articles (53 articles from the IEEE Xplore database and 537 articles from the PubMed database). Out of those, the articles which satisfied the defined inclusion criteria were investigated for further analysis. RESULTS A total of 135 articles were identified and analysed for this review. High growth in the number of publications has been observed in recent years. Furthermore, significant diversity in the use of data modalities and machine learning classifiers has also been noted in this study. fMRI data with an SVM classifier was found to be the most popular choice among researchers. In most of the studies, data scarcity and small sample size, particularly for neuroimaging data are major concerns. The use of identical data pre-processing tools for similar data modalities can be seen. This study also provides statistical analysis of the current framework with respect to the modality, machine learning classifier, sample size and accuracy by applying one-way ANOVA and the Tukey - Kramer test. CONCLUSION The results indicate that an effective fusion of machine learning techniques with a potential data modality has a promising future for assisting clinicians in automatic depression diagnosis.
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Affiliation(s)
- Sweta Bhadra
- Department of CS & IT, Cotton University, Guwahati, India
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10
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Liu S, Liu X, Yan D, Chen S, Liu Y, Hao X, Ou W, Huang Z, Su F, He F, Ming D. Alterations in patients with first-episode depression in the eyes-open and eyes-closed conditions: A resting-state EEG study. IEEE Trans Neural Syst Rehabil Eng 2022; 30:1019-1029. [PMID: 35412986 DOI: 10.1109/tnsre.2022.3166824] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Altered resting-state EEG activity has been repeatedly reported in major depressive disorder (MDD), but no robust biomarkers have been identified until now. The poor consistency of EEG alterations may be due to inconsistent resting conditions; that is, the eyes-open (EO) and eyes-closed (EC) conditions. Here, we explored the effect of the EO and EC conditions on EEG biomarkers for discriminating MDD subjects and healthy control (HC) subjects. EEG data were recorded from 30 first-episode MDD and 26 HC subjects during an 8-min resting-state session. The features were extracted using spectral power, Lempel-Ziv complexity, and detrended fluctuation analysis. Significant features were further selected via the sequential backward feature selection algorithm. Support vector machine (SVM), logistic regression, and linear discriminate analysis were used to determine a better resting condition to provide more reliable estimates for identifying MDD. Compared with the HC group, we found that the MDD group exhibited widespread increased β and γ powers (p < 0.01) in both conditions. In the EO condition, the MDD group showed increased complexity and scaling exponents in the α band relative to HC subjects (p < 0.05). The best classification performance of the combined feature sets was found in the EO condition, with the leave-one-out classification accuracy of 89.29%, sensitivity of 90.00%, and specificity of 88.46% using SVM with the linear kernel classifier when the threshold was set to 0.7, followed by the β and γ spectral features with an average accuracy of 83.93%. Overall, EO and EC conditions indeed affected the between-group variance, and the EO condition is suggested as the more separable resting condition to identify depression. Specially, the β and γ powers are suggested as potential biomarkers for first-episode MDD.
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11
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Machine learning approaches for prediction of bipolar disorder based on biological, clinical and neuropsychological markers: a systematic review and meta-analysis. Neurosci Biobehav Rev 2022; 135:104552. [PMID: 35120970 DOI: 10.1016/j.neubiorev.2022.104552] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 01/11/2022] [Accepted: 01/30/2022] [Indexed: 01/10/2023]
Abstract
Applying machine learning (ML) to objective markers may overcome prognosis uncertainty due to the subjective nature of the diagnosis of bipolar disorder (BD). This PRISMA-compliant meta-analysis provides new systematic evidence of the BD classification accuracy reached by different markers and ML algorithms. We focused on neuroimaging, electrophysiological techniques, peripheral biomarkers, genetic data, neuropsychological or clinical measures, and multimodal approaches. PubMed, Embase and Scopus were searched through 3rd December 2020. Meta-analyses were performed using random-effect models. Overall, 81 studies were included in this systematic review and 65 in the meta-analysis (11,336 participants, 3,903 BD). The overall pooled classification accuracy was 0.77 (95%CI[0.75;0.80]). Despite subgroup analyses for diagnostic comparison group, psychiatric disorders, marker, ML algorithm, and validation procedure were not significant, linear discriminant analysis significantly outperformed support vector machine for peripheral biomarkers (p=0.03). Sample size was inversely related to accuracy. Evidence of publication bias was detected. Ultimately, although ML reached a high accuracy in differentiating BD from other psychiatric disorders, best practices in methodology are needed for the advancement of future studies.
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12
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Wang Y, Huang YW, Ablikim D, Lu Q, Zhang AJ, Dong YQ, Zeng FC, Xu JH, Wang W, Hu ZH. Efficacy of acupuncture at ghost points combined with fluoxetine in treating depression: A randomized study. World J Clin Cases 2022; 10:929-938. [PMID: 35127907 PMCID: PMC8790430 DOI: 10.12998/wjcc.v10.i3.929] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 12/11/2021] [Accepted: 12/25/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Depression affects more than 350 million people worldwide. In China, 4.2% (54 million people) of the total population suffers from depression. Psychotherapy has been shown to change cognition, improve personality, and enhance the ability to cope with difficulties and setbacks. While pharmacotherapy can reduce symptoms, it is also associated with adverse reactions and relapse after drug withdrawal. Therefore, there has been an increasing emphasis placed on the use of non-pharmacological therapies for depression. The hypothesis of this study was that acupuncture at ghost points combined with fluoxetine would be more effective than fluoxetine alone for the treatment of depression.
AIM To investigate the efficacy of acupuncture at ghost points combined with fluoxetine for the treatment of patients with depression.
METHODS This randomized controlled trial included patients with mild to moderate depression (n = 160). Patients received either acupuncture at ghost points combined with fluoxetine (n = 80) or fluoxetine alone (control group, n = 80). Needles were retained in place for 30 min, 5 times a week; three treatment cycles were administered. The Mann–Whitney U test was used to compare functional magnet resonance imaging parameters, Hamilton depression rating scale (HAMD) scores, and self-rating depression scale (SDS) scores between the acupuncture group and control group.
RESULTS There were no significant differences in HAMD or SDS scores between the acupuncture group and control group, before or after 4 wk of treatment. The acupuncture group exhibited significantly lower HAMD and SDS scores than the control group after 8 wk of treatment (P < 0.05). The acupuncture group had significantly lower fractional Amplitude of Low Frequency Fluctuations values for the left anterior wedge leaf, left posterior cingulate gyrus, left middle occipital gyrus, and left inferior occipital gyrus after 8 wk. The acupuncture group also had significantly higher values for the right inferior frontal gyrus, right insula, and right hippocampus (P < 0.05). After 8 wk of treatment, the effective rates of the acupuncture and control groups were 51.25% and 36.25%, respectively (P < 0.05).
CONCLUSION The study results suggest that acupuncture at ghost points combined with fluoxetine is more effective than fluoxetine alone for the treatment of patients with mild to moderate depression.
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Affiliation(s)
- Yi Wang
- Department of Acupuncture and Moxibustion, Shanghai TCM-Integrated Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200082, China
| | - Yu-Wei Huang
- Department of Acupuncture and Moxibustion, Shanghai TCM-Integrated Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200082, China
| | - Dilnur Ablikim
- Department of Acupuncture and Moxibustion, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China
| | - Qun Lu
- Department of Clinical Laboratory, Shanghai TCM-Integrated Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200082, China
| | - Ai-Jia Zhang
- Department of Acupuncture and Moxibustion, Shanghai TCM-Integrated Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200082, China
| | - Ye-Qing Dong
- Department of Traditional Chinese Medicine, Jiangwan Hospital, Shanghai 200081, China
| | - Fei-Cui Zeng
- Department of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200081, China
| | - Jing-Hua Xu
- Department of Acupuncture and Moxibustion, Shanghai TCM-Integrated Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200082, China
| | - Wen Wang
- Department of Acupuncture and Moxibustion, Shanghai TCM-Integrated Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200082, China
| | - Zhi-Hai Hu
- Department of Acupuncture and Moxibustion, Shanghai TCM-Integrated Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200082, China
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13
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Structural-functional decoupling predicts suicide attempts in bipolar disorder patients with a current major depressive episode. Neuropsychopharmacology 2020; 45:1735-1742. [PMID: 32604403 PMCID: PMC7421902 DOI: 10.1038/s41386-020-0753-5] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 06/14/2020] [Accepted: 06/22/2020] [Indexed: 12/19/2022]
Abstract
Bipolar disorder (BD) is associated with a high risk of suicidality, and it is challenging to predict suicide attempts in clinical practice to date. Although structural and functional connectivity alterations from neuroimaging studies have been previously reported in BD with suicide attempts, little is known about how abnormal structural and functional connectivity relates to each other. Here, we hypothesize that structure connectivity constrains functional connectivity, and structural-functional coupling is a more sensitive biomarker to detect subtle brain abnormalities than any single modality in BD patients with a current major depressive episode who had attempted suicide. By investigating structural and resting-state fMRI connectivity, as well as their coupling among 191 BD depression patients with or without a history of suicide attempts and 113 healthy controls, we found that suicide attempters in BD depression patients showed significantly decreased central-temporal structural connectivity, increased frontal-temporal functional connectivity, along with decreased structural-functional coupling compared with non-suicide attempters. Crucially, the altered structural connectivity network predicted the abnormal functional connectivity network profile, and the structural-functional coupling was significantly correlated with suicide risk but not with depression or anxiety severity. Our findings suggest that the structural connectome is the key determinant of brain dysfunction, and structural-functional coupling could serve as a valuable trait-like biomarker for BD suicidal predication over and above the intramodality network connectivity. Such a measure can have clinical implications for early identification of suicide attempters with BD depression and inform strategies for prevention.
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14
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Sunaga M, Takei Y, Kato Y, Tagawa M, Suto T, Hironaga N, Ohki T, Takahashi Y, Fujihara K, Sakurai N, Ujita K, Tsushima Y, Fukuda M. Frequency-Specific Resting Connectome in Bipolar Disorder: An MEG Study. Front Psychiatry 2020; 11:597. [PMID: 32670117 PMCID: PMC7330711 DOI: 10.3389/fpsyt.2020.00597] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 06/09/2020] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Bipolar disorder (BD) is a serious psychiatric disorder that is associated with a high suicide rate, and for which no clinical biomarker has yet been identified. To address this issue, we investigated the use of magnetoencephalography (MEG) as a new prospective tool. MEG has been used to evaluate frequency-specific connectivity between brain regions; however, no previous study has investigated the frequency-specific resting-state connectome in patients with BD. This resting-state MEG study explored the oscillatory representations of clinical symptoms of BD via graph analysis. METHODS In this prospective case-control study, 17 patients with BD and 22 healthy controls (HCs) underwent resting-state MEG and evaluations for depressive and manic symptoms. After estimating the source current distribution, orthogonalized envelope correlations between multiple brain regions were evaluated for each frequency band. We separated regions-of-interest into seven left and right network modules, including the frontoparietal network (FPN), limbic network (LM), salience network (SAL), and default mode network (DMN), to compare the intra- and inter-community edges between the two groups. RESULTS In the BD group, we found significantly increased inter-community edges of the right LM-right DMN at the gamma band, and decreased inter-community edges of the right SAL-right FPN at the delta band and the left SAL-right SAL at the theta band. Intra-community edges in the left LM at the high beta band were significantly higher in the BD group than in the HC group. The number of connections in the left LM at the high beta band showed positive correlations with the subjective and objective depressive symptoms in the BD group. CONCLUSION We introduced graph theory into resting-state MEG studies to investigate the functional connectivity in patients with BD. To the best of our knowledge, this is a novel approach that may be beneficial in the diagnosis of BD. This study describes the spontaneous oscillatory brain networks that compensate for the time-domain issues associated with functional magnetic resonance imaging. These findings suggest that the connectivity of the LM at the beta band may be a good objective biological biomarker of the depressive symptoms associated with BD.
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Affiliation(s)
- Masakazu Sunaga
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Yuichi Takei
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Yutaka Kato
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Maebashi, Japan.,Tsutsuji Mental Hospital, Tatebayashi, Japan
| | - Minami Tagawa
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Maebashi, Japan.,Gunma Prefectural Psychiatric Medical Center, Isesaki, Japan
| | - Tomohiro Suto
- Gunma Prefectural Psychiatric Medical Center, Isesaki, Japan
| | - Naruhito Hironaga
- Brain Center, Faculty of Medicine, Kyushu University, Fukuoka, Japan
| | - Takefumi Ohki
- Department of Neurosurgery, Osaka University Medical School, Suita, Japan
| | - Yumiko Takahashi
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Kazuyuki Fujihara
- Department of Genetic and Behavioral Neuroscience, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Noriko Sakurai
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Koichi Ujita
- Department of Diagnostic Radiology and Nuclear Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Yoshito Tsushima
- Department of Diagnostic Radiology and Nuclear Medicine, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Masato Fukuda
- Department of Psychiatry and Neuroscience, Gunma University Graduate School of Medicine, Maebashi, Japan
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15
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Han YL, Dai ZP, Ridwan MC, Lin PH, Zhou HL, Wang HF, Yao ZJ, Lu Q. Connectivity of the Frontal Cortical Oscillatory Dynamics Underlying Inhibitory Control During a Go/No-Go Task as a Predictive Biomarker in Major Depression. Front Psychiatry 2020; 11:707. [PMID: 32848905 PMCID: PMC7416643 DOI: 10.3389/fpsyt.2020.00707] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Accepted: 07/06/2020] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Major depressive disorder (MDD) is characterized by core functional deficits in cognitive inhibition, which is crucial for emotion regulation. To assess the response to ruminative and negative mood states, it was hypothesized that MDD patients have prolonged disparities in the oscillatory dynamics of the frontal cortical regions across the life course of the disease. METHOD A "go/no-go" response inhibition paradigm was tested in 31 MDD patients and 19 age-matched healthy controls after magnetoencephalography (MEG) scanning. The use of minimum norm estimates (MNE) examined the changes of inhibitory control network which included the right inferior frontal gyrus (rIFG), pre-supplementary motor area (preSMA), and left primary motor cortex (lM1). The power spectrum (PS) within each node and the functional connectivity (FC) between nodes were compared between two groups. Furthermore, Pearson correlation was calculated to estimate the relationship between altered FC and clinical features. RESULT PS was significantly reduced in left motor and preSMA of MDD patients in both beta (13-30 Hz) and low gamma (30-50 Hz) bands. Compared to the HC group, the MDD group demonstrated higher connectivity between lM1 and preSMA in the beta band (t = 3.214, p = 0.002, FDR corrected) and showed reduced connectivity between preSMA and rIFG in the low gamma band (t = -2.612, p = 0.012, FDR corrected). The FC between lM1 and preSMA in the beta band was positively correlated with illness duration (r = 0.475, p = 0.005, FDR corrected), while the FC between preSMA and rIFG in the low gamma band was negatively correlated with illness duration (r = -0.509, p = 0.002, FDR corrected) and retardation factor scores (r = -0.288, p = 0.022, uncorrected). CONCLUSION In this study, a clinical neurophysiological signature of cognitive inhibition leading to sustained negative affect as well as functional non-recovery in MDD patients is highlighted. Duration of illness (DI) plays a key role in negative emotional processing, heighten rumination, impulsivity, and disinhibition.
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Affiliation(s)
- Ying-Lin Han
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Zhong-Peng Dai
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China.,Key Laboratory of Child Development and Learning Science, Ministry of Education, Southeast University, Nanjing, China
| | - Mohammad Chattun Ridwan
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Pin-Hua Lin
- Medical School of Nanjing University, Nanjing Brain Hospital, Nanjing, China
| | - Hong-Liang Zhou
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Hao-Fei Wang
- Department of Psychology, Jiangsu Province Hospital Affiliated to Nanjing Medical University , Nanjing, China
| | - Zhi-Jian Yao
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China.,Medical School of Nanjing University, Nanjing Brain Hospital, Nanjing, China
| | - Qing Lu
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China.,Key Laboratory of Child Development and Learning Science, Ministry of Education, Southeast University, Nanjing, China
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