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Zhang X, Cheng X, Chen J, Sun J, Yang X, Li W, Chen L, Mao Y, Liu Y, Zeng X, Ye B, Yang C, Li X, Cao L. Distinct global brain connectivity alterations in depressed adolescents with subthreshold mania and the relationship with processing speed: Evidence from sBEAD Cohort. J Affect Disord 2024; 357:97-106. [PMID: 38657768 DOI: 10.1016/j.jad.2024.04.063] [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: 02/19/2024] [Revised: 04/06/2024] [Accepted: 04/15/2024] [Indexed: 04/26/2024]
Abstract
BACKGROUND Bipolar disorder (BD) is a progressive condition. Investigating the neuroimaging mechanisms in depressed adolescents with subthreshold mania (SubMD) facilitates the early identification of BD. However, the global brain connectivity (GBC) patterns in SubMD patients, as well as the relationship with processing speed before the onset of full-blown BD, remain unclear. METHODS The study involved 72 SubMD, 77 depressed adolescents without subthreshold mania (nSubMD), and 69 gender- and age-matched healthy adolescents (HCs). All patients underwent a clinical follow-up ranging from six to twelve months. We calculated the voxel-based graph theory analysis of the GBC map and conducted the TMT-A test to measure the processing speed. RESULTS Compared to HCs and nSubMD, SubMD patients displayed distinctive GBC index patterns: GBC index decreased in the right Medial Superior Frontal Gyrus (SFGmed.R)/Superior Frontal Gyrus (SFG) while increased in the right Precuneus and left Postcentral Gyrus. Both patient groups showed increased GBC index in the right Inferior Temporal Gyrus. An increased GBC value in the right Supplementary Motor Area was exclusively observed in the nSubMD-group. There were opposite changes in the GBC index in SFGmed.R/SFG between two patient groups, with an AUC of 0.727. Additionally, GBC values in SFGmed.R/SFG exhibited a positive correlation with TMT-A scores in SubMD-group. LIMITATIONS Relatively shorter follow-up duration, medications confounding, and modest sample size. CONCLUSION These findings suggest that adolescents with subthreshold BD have specific impairments patterns at the whole brain connectivity level associated with processing speed impairments, providing insights into early identification and intervention strategies for BD.
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Affiliation(s)
- Xiaofei Zhang
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China; The First School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong province 510000, PR China
| | - Xiaofang Cheng
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China
| | - Jianshan Chen
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China
| | - Jiaqi Sun
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China
| | - Xiaoyong Yang
- Department of Psychiatry, Guangzhou Medical University, Guangdong province 510300, PR China
| | - Weiming Li
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China
| | - Lei Chen
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China
| | - Yimiao Mao
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China
| | - Yutong Liu
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China
| | - Xuanlin Zeng
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China
| | - Biyu Ye
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China
| | - Chanjuan Yang
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China
| | - Xuan Li
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China.
| | - Liping Cao
- Department of Child and Adolescent Psychiatry, Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, Guangdong province 510300, PR China.
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Sun H, Yan R, Hua L, Xia Y, Chen Z, Huang Y, Wang X, Xia Q, Yao Z, Lu Q. Abnormal stability of spontaneous neuronal activity as a predictor of diagnosis conversion from major depressive disorder to bipolar disorder. J Psychiatr Res 2024; 171:60-68. [PMID: 38244334 DOI: 10.1016/j.jpsychires.2024.01.028] [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: 11/12/2023] [Revised: 01/10/2024] [Accepted: 01/15/2024] [Indexed: 01/22/2024]
Abstract
OBJECTIVE Bipolar disorder (BD) is often misdiagnosed as major depressive disorder (MDD) in the early stage, which may lead to inappropriate treatment. This study aimed to characterize the alterations of spontaneous neuronal activity in patients with depressive episodes whose diagnosis transferred from MDD to BD. METHODS 532 patients with MDD and 132 healthy controls (HCs) were recruited over 10 years. During the follow-up period, 75 participants with MDD transferred to BD (tBD), and 157 participants remained with the diagnosis of unipolar depression (UD). After excluding participants with poor image quality and excessive head movement, 68 participants with the diagnosis of tBD, 150 participants with the diagnosis of UD, and 130 HCs were finally included in the analysis. The dynamic amplitude of low-frequency fluctuations (dALFF) of spontaneous neuronal activity was evaluated in tBD, UD and HC using functional magnetic resonance imaging at study inclusion. Receiver operating characteristic (ROC) analysis was performed to evaluate sensitivity and specificity of the conversion prediction from MDD to BD based on dALFF. RESULTS Compared to HC, tBD exhibited elevated dALFF at left premotor cortex (PMC_L), right lateral temporal cortex (LTC_R) and right early auditory cortex (EAC_R), and UD showed reduced dALFF at PMC_L, left paracentral lobule (PCL_L), bilateral medial prefrontal cortex (mPFC), right orbital frontal cortex (OFC_R), right dorsolateral prefrontal cortex (DLPFC_R), right posterior cingulate cortex (PCC_R) and elevated dALFF at LTC_R. Furthermore, tBD exhibited elevated dALFF at PMC_L, PCL_L, bilateral mPFC, bilateral OFC, DLPFC_R, PCC_R and LTC_R than UD. In addition, ROC analysis based on dALFF in differential areas obtained an area under the curve (AUC) of 72.7%. CONCLUSIONS The study demonstrated the temporal dynamic abnormalities of tBD and UD in the critical regions of the somatomotor network (SMN), default mode network (DMN), and central executive network (CEN). The differential abnormal patterns of temporal dynamics between the two diseases have the potential to predict the diagnosis transition from MDD to BD.
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Affiliation(s)
- Hao Sun
- Nanjing Brain Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, Nanjing, China; Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, 249 Guangzhou Road, Nanjing, 210029, China
| | - Rui Yan
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, 249 Guangzhou Road, Nanjing, 210029, China
| | - Lingling Hua
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, 249 Guangzhou Road, Nanjing, 210029, China
| | - Yi Xia
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, 249 Guangzhou Road, Nanjing, 210029, China
| | - Zhilu Chen
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, 249 Guangzhou Road, Nanjing, 210029, China
| | - Yinghong Huang
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, 249 Guangzhou Road, Nanjing, 210029, China
| | - Xiaoqin Wang
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, 249 Guangzhou Road, Nanjing, 210029, China
| | - Qiudong Xia
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, 249 Guangzhou Road, Nanjing, 210029, China
| | - Zhijian Yao
- Nanjing Brain Hospital, Clinical Teaching Hospital of Medical School, Nanjing University, Nanjing, China; Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, 249 Guangzhou Road, Nanjing, 210029, 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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Jiang X, Cao B, Li C, Jia L, Jing Y, Cai W, Zhao W, Sun Q, Wu F, Kong L, Tang Y. Identifying misdiagnosed bipolar disorder using support vector machine: feature selection based on fMRI of follow-up confirmed affective disorders. Transl Psychiatry 2024; 14:9. [PMID: 38191549 PMCID: PMC10774279 DOI: 10.1038/s41398-023-02703-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 11/27/2023] [Accepted: 12/05/2023] [Indexed: 01/10/2024] Open
Abstract
Nearly a quarter of bipolar disorder (BD) patients were misdiagnosed as major depressive disorder (MDD) patients, which cannot be corrected until mania/hypomania develops. It is important to recognize these obstacles so that the appropriate treatment can be initiated. Thus, we sought to distinguish patients with BD from MDD, especially to identify misdiagnosed BD before mania/hypomania, and further explore potential trait features that allow accurate differential diagnosis independent of state matters. Functional magnetic resonance imaging scans were performed at baseline on 92 MDD patients and 48 BD patients. The MDD patients were then followed up for more than two years. After follow-up, 23 patients transformed into BD (tBD), and 69 patients whose diagnoses remained unchanged were eligible for unipolar depression (UD). A support vector machine classifier was trained on the amygdala-based functional connectivity (FC) of 48 BD and 50 UD patients using a novel region-based feature selection. Then, the classifier was tested on the dataset, encompassing tBD and the remaining UD. It performed well for known BD and UD and can also distinguish tBD from UD with an accuracy of 81%, sensitivity of 82.6%, specificity of 79%, and AUC of 74.6%, respectively. Feature selection results revealed that ten regions within the cortico-limbic neural circuit contributed most to classification. Furthermore, in the FC comparisons among diseases, BD and tBD shared almost overlapped FC patterns in the cortico-limbic neural circuit, and both of them presented pronounced differences in most regions within the circuit compared with UD. The FC values of the most discriminating brain regions had no prominent correlations with the severity of depression, anxiety, and mania/hypomania (FDR correction). It suggests that BD possesses some trait features in the cortico-limbic neural circuit, rendering it dichotomized by the classifier based on known-diagnosis data.
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Affiliation(s)
- Xiaowei Jiang
- Brain Function Research Section, Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning, 110001, PR China
| | - Bo Cao
- Department of Psychiatry, Faculty of Medicine & Dentistry, University of Alberta, Edmonton, AB, T6G 2B7, Canada
| | - Chao Li
- Brain Function Research Section, Department of Radiology, The First Hospital of China Medical University, Shenyang, Liaoning, 110001, PR China
| | - Linna Jia
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, Liaoning, 110001, PR China
| | - Yi Jing
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd, Shenyang, Liaoning, 110167, PR China
| | - Wei Cai
- Neusoft Research of Intelligent Healthcare Technology, Co. Ltd, Shenyang, Liaoning, 110167, PR China
| | - Wenhui Zhao
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, Liaoning, 110001, PR China
| | - Qikun Sun
- Department of Radiation Oncology, The First Hospital of China Medical University, Shenyang, Liaoning, 110001, PR China
| | - Feng Wu
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, Liaoning, 110001, PR China
| | - Lingtao Kong
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, Liaoning, 110001, PR China
| | - Yanqing Tang
- Department of Psychiatry, The First Hospital of China Medical University, Shenyang, Liaoning, 110001, PR China.
- Department of Geriatric Medicine, The First Hospital of China Medical University, Shenyang, Liaoning, 110001, PR China.
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Xiao M, Luo Y, Ding C, Chen X, Liu Y, Tang Y, Chen H. Social support and overeating in young women: The role of altering functional network connectivity patterns and negative emotions. Appetite 2023; 191:107069. [PMID: 37837769 DOI: 10.1016/j.appet.2023.107069] [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: 03/16/2023] [Revised: 09/20/2023] [Accepted: 09/29/2023] [Indexed: 10/16/2023]
Abstract
Research suggests that social support has a protective effect on emotional health and emotionally induced overeating. Women are especially more sensitive to benefits from social support when facing eating problems. Although it has been demonstrated that social support can affect the neural processes of emotion regulation and reward perception, it is unclear how social support alters synergistic patterns in large-scale brain networks associated with negative emotions and overeating. We used a large sample of young women aged 17-22 years (N = 360) to examine how social support influences the synchrony of five intrinsic networks (executive control network [ECN], default mode network, salience network [SN], basal ganglia network, and precuneus network [PN]) and how these networks influence negative affect and overeating. Additionally, we explored these analyses in another sample of males (N = 136). After statistically controlling for differences in age and head movement, we observed significant associations of higher levels of social support with increased intra- and inter-network functional synchrony, particularly for ECN-centered network connectivity. Subsequent chain-mediated analyses showed that social support predicted overeating through the ECN-SN and ECN-PN network connectivity and negative emotions. However, these results were not found in men. These findings suggest that social support influences the synergistic patterns within and between intrinsic networks related to inhibitory control, emotion salience, self-referential thinking, and reward sensitivity. Furthermore, they reveal that social support and its neural markers may play a key role in young women's emotional health and eating behavior.
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Affiliation(s)
- Mingyue Xiao
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
| | - Yijun Luo
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
| | - Cody Ding
- Department of Educational Psychology, Research, and Evaluation, University of Missouri, St. Louis, USA
| | - Ximei Chen
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
| | - Yong Liu
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China
| | - Yutian Tang
- Faculty of Arts, University of British Columbia, Canada
| | - Hong Chen
- Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing, China; Research Center of Psychology and Social Development, Southwest University, Chongqing, China.
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Xu L, Ma H, Guan Y, Liu J, Huang H, Zhang Y, Tian L. A Siamese Network With Node Convolution for Individualized Predictions Based on Connectivity Maps Extracted From Resting-State fMRI Data. IEEE J Biomed Health Inform 2023; 27:5418-5429. [PMID: 37578917 DOI: 10.1109/jbhi.2023.3304974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/16/2023]
Abstract
Deep learning has demonstrated great potential for objective diagnosis of neuropsychiatric disorders based on neuroimaging data, which includes the promising resting-state functional magnetic resonance imaging (RS-fMRI). However, the insufficient sample size has long been a bottleneck for deep model training for the purpose. In this study, we proposed a Siamese network with node convolution (SNNC) for individualized predictions based on RS-fMRI data. With the involvement of Siamese network, which uses sample pair (rather than a single sample) as input, the problem of insufficient sample size can largely be alleviated. To adapt to connectivity maps extracted from RS-fMRI data, we applied node convolution to each of the two branches of the Siamese network. For regression purposes, we replaced the contrastive loss in classic Siamese network with the mean square error loss and thus enabled Siamese network to quantitatively predict label differences. The label of a test sample can be predicted based on any of the training samples, by adding the label of the training sample to the predicted label difference between them. The final prediction for a test sample in this study was made by averaging the predictions based on each of the training samples. The performance of the proposed SNNC was evaluated with age and IQ predictions based on a public dataset (Cam-CAN). The results indicated that SNNC can make effective predictions even with a sample size of as small as 40, and SNNC achieved state-of-the-art accuracy among a variety of deep models and standard machine learning approaches.
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Zheng R, Chen Y, Jiang Y, Zhou B, Han S, Wei Y, Wang C, Cheng J. Abnormal voxel-wise whole-brain functional connectivity in first-episode, drug-naïve adolescents with major depression disorder. Eur Child Adolesc Psychiatry 2023; 32:1317-1327. [PMID: 35318540 DOI: 10.1007/s00787-022-01959-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 02/06/2022] [Indexed: 12/24/2022]
Abstract
Major depression disorder (MDD) is one of the most common psychiatric disorders. Previous studies have demonstrated structural and functional abnormalities in adult depression. However, the neurobiology of adolescent depression has not been fully understood. The aim of this study was to investigate the intrinsic dysconnectivity pattern of voxel-level whole-brain functional networks in first-episode, drug-naïve adolescents with MDD. Resting-state functional magnetic resonance imaging data were acquired from 66 depressed adolescents and 47 matched healthy controls. Voxel-wise degree centrality (DC) analysis was performed to identify voxels that showed altered whole-brain functional connectivity (FC) with other voxels. We further conducted seed-based FC analysis to investigate in more detail the connectivity patterns of the identified DC changes. The relationship between altered DC and clinical variables in depressed adolescents was also analyzed. Compared with controls, depressed adolescents showed lower DC in the bilateral hippocampus, left superior temporal gyrus and right insula. Seed-based analysis revealed that depressed adolescents, relative to controls, showed hypoconnectivity between the hippocampus to the medial prefrontal regions and right precuneus. Furthermore, the DC values in the bilateral hippocampus were correlated with the Hamilton Depression Rating Scale score and duration of disease (all P < 0.05, false discovery rate corrected). Our study indicates abnormal intrinsic dysconnectivity patterns of whole-brain functional networks in drug-naïve, first-episode adolescents with MDD, and abnormal DC in the hippocampus may affect the association of prefrontal-hippocampus circuit. These findings may provide new insights into the pathophysiology of adolescent-onset MDD.
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Affiliation(s)
- Ruiping Zheng
- Functional and Molecular Imaging Key Laboratory of Henan Province, Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan, People's Republic of China
| | - Yuan Chen
- Functional and Molecular Imaging Key Laboratory of Henan Province, Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan, People's Republic of China
| | - Yu Jiang
- Functional and Molecular Imaging Key Laboratory of Henan Province, Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan, People's Republic of China
| | - Bingqian Zhou
- Functional and Molecular Imaging Key Laboratory of Henan Province, Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan, People's Republic of China
| | - Shaoqiang Han
- Functional and Molecular Imaging Key Laboratory of Henan Province, Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan, People's Republic of China
| | - Yarui Wei
- Functional and Molecular Imaging Key Laboratory of Henan Province, Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan, People's Republic of China
| | - Caihong Wang
- Functional and Molecular Imaging Key Laboratory of Henan Province, Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan, People's Republic of China
| | - Jingliang Cheng
- Functional and Molecular Imaging Key Laboratory of Henan Province, Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Henan, People's Republic of China.
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Liu S, Jia Y, Liu X, Ma R, Zheng S, Zhu H, Yin M, Jia H. Variation in self and familiar facial recognition in bipolar disorder patients at different clinical stages. Acta Psychol (Amst) 2023; 235:103903. [PMID: 37018931 DOI: 10.1016/j.actpsy.2023.103903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 02/14/2023] [Accepted: 03/31/2023] [Indexed: 04/05/2023] Open
Abstract
Previous studies suggest a close relationship between self-disorders and schizophrenia or unipolar depression. However, few studies have explored the characteristics of self-processing in bipolar disorder (BD) during different clinical states. This study compared the differences in self-face recognition (SFR) among patients with bipolar mania (BPM), bipolar depression (BPD), bipolar remission (RM), and healthy controls (HC). Images of subject's own face, a familiar face, and an unfamiliar face were combined in pairs at a certain proportion to obtain three types of blended images. We then compared the tendency between BD and HC while judging two kinds of blended faces emerging from presentation software. The results showed that the BPM and BPD groups seemed to lack an advantage in self-recognition. Self-processing and familiarity processing were significantly enhanced in BPM patients, while only familiarity processing was enhanced in BPD. The severity of clinical symptoms was not significantly correlated with self-bias or familiarity bias in BD.
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Yang X, Kumar P, Wang M, Zhao L, Du Y, Zhang BY, Qi S, Sui J, Li T, Ma X. Antidepressant treatment-related brain activity changes in remitted major depressive disorder. Psychiatry Res Neuroimaging 2023; 330:111601. [PMID: 36724678 DOI: 10.1016/j.pscychresns.2023.111601] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 12/21/2022] [Accepted: 01/24/2023] [Indexed: 01/29/2023]
Abstract
Recent evidence has shown that some brain regions are core hubs and play a key role in the treatment of depression. Twenty-five unmedicated patients with major depressive disorder (MDD) were included, and telephone follow-up was performed at 8, 24, and 48 weeks after enrollment. After reaching clinical remission, they were scheduled for a second magnetic resonance imaging scan and clinical evaluation. Thirty-one healthy controls were also investigated. The intrinsic functional connectivity (degree centrality) of each participant was mapped using a computationally efficient approach. Then, functional connectivity of patients was calculated between the identified regions of interest by degree centrality analysis and every voxel. Later, linear regression analysis was used to identify potential variables predictive of an improvement in disease severity. The prominent hubs identified by degree centrality analysis included the cerebellum, inferior temporal gyrus, lingual gyrus, dorsal medial prefrontal cortex (DMPFC), and dorsal lateral prefrontal cortex. We also found that the increased degree centrality of DMPFC was associated with improvement in depressive symptoms. The brain activity associated with antidepressant effects, especially brain connectivity changes in the left DMPFC, can potentially be used to monitor treatment response and predict treatment outcomes.
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Affiliation(s)
- Xiao Yang
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, 610041, Chengdu, China; Huaxi Brain Research Center, West China Hospital of Sichuan University, 610041, Chengdu, China
| | - Poornima Kumar
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, United States of America; Department of Psychiatry, Harvard Medical School, United States of America
| | - Min Wang
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, 610041, Chengdu, China; Huaxi Brain Research Center, West China Hospital of Sichuan University, 610041, Chengdu, China
| | - Liansheng Zhao
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, 610041, Chengdu, China; Huaxi Brain Research Center, West China Hospital of Sichuan University, 610041, Chengdu, China
| | - Yue Du
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, 610041, Chengdu, China; Huaxi Brain Research Center, West China Hospital of Sichuan University, 610041, Chengdu, China
| | - Belinda Y Zhang
- School of Nursing and Health Professions, University of San Francisco, CA, United States
| | - Shile Qi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) [Georgia State University, Georgia Institute of Technology, Emory University], 30303, Atlanta, GA, United States
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China; Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of Automation, 100190, Beijing, China
| | - Tao Li
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, 610041, Chengdu, China; Huaxi Brain Research Center, West China Hospital of Sichuan University, 610041, Chengdu, China
| | - Xiaohong Ma
- Psychiatric Laboratory and Mental Health Center, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, 610041, Chengdu, China; Huaxi Brain Research Center, West China Hospital of Sichuan University, 610041, Chengdu, China.
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Discriminating between bipolar and major depressive disorder using a machine learning approach and resting-state EEG data. Clin Neurophysiol 2023; 146:30-39. [PMID: 36525893 DOI: 10.1016/j.clinph.2022.11.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 09/28/2022] [Accepted: 11/27/2022] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Distinguishing major depressive disorder (MDD) from bipolar disorder (BD) is a crucial clinical challenge as effective treatment is quite different for each condition. In this study electroencephalography (EEG) was explored as an objective biomarker for distinguishing MDD from BD using an efficient machine learning algorithm (MLA) trained by a relatively large and balanced dataset. METHODS A 3 step MLA was applied: (1) a multi-step preprocessing method was used to improve the quality of the EEG signal, (2) symbolic transfer entropy (STE), an effective connectivity measure, was applied to the resultant EEG and (3) the MLA used the extracted STE features to distinguish MDD (N = 71) from BD (N = 71) subjects. RESULTS 14 connectivity features were selected by the proposed algorithm. Most of the selected features were related to the frontal, parietal, and temporal lobe electrodes. The major involved regions were the Broca region in the frontal lobe and the somatosensory association cortex in the parietal lobe. These regions are near electrodes FC5 and CPz and are involved in processing language and sensory information, respectively. The resulting classifier delivered an evaluation accuracy of 88.5% and a test accuracy of 89.3%, using 80% of the data for training and evaluation and the remaining 20% for testing, respectively. CONCLUSIONS The high evaluation and test accuracies of our algorithm, derived from a large balanced training sample suggests that this method may hold significant promise as a clinical tool. SIGNIFICANCE The proposed MLA may provide an inexpensive and readily available tool that clinicians may use to enhance diagnostic accuracy and shorten time to effective treatment.
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Xi C, Liu Z, Zeng C, Tan W, Sun F, Yang J, Palaniyappan L. The centrality of working memory networks in differentiating bipolar type I depression from unipolar depression: A task-fMRI study. CANADIAN JOURNAL OF PSYCHIATRY. REVUE CANADIENNE DE PSYCHIATRIE 2023; 68:22-32. [PMID: 35244484 PMCID: PMC9720478 DOI: 10.1177/07067437221078646] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
OBJECTIVES Up to 70%-80% of patients with bipolar disorder are misdiagnosed as having major depressive disorder (MDD), leading to both delayed intervention and worsening disability. Differences in the cognitive neurophysiology may serve to distinguish between the depressive phase of type 1 bipolar disorder (BDD-I) from MDD, though this remains to be demonstrated. To this end, we investigate the discriminatory signal in the topological organization of the functional connectome during a working memory (WM) task in BDD-I and MDD, as a candidate identification approach. METHODS We calculated and compared the degree centrality (DC) at the whole-brain voxel-wise level in 31 patients with BDD-I, 35 patients with MDD, and 80 healthy controls (HCs) during an n-back task. We further extracted the distinct DC patterns in the two patient groups under different WM loads and used machine learning approaches to determine the distinguishing ability of the DC map. RESULTS Patients with BDD-I had lower accuracy and longer reaction time (RT) than HCs at high WM loads. BDD-I is characterized by decreased DC in the default mode network (DMN) and the sensorimotor network (SMN) when facing high WM load. In contrast, MDD is characterized by increased DC in the DMN during high WM load. Higher WM load resulted in better classification performance, with the distinct aberrant DC maps under 2-back load discriminating the two disorders with 90.91% accuracy. CONCLUSIONS The distributed brain connectivity during high WM load provides novel insights into the neurophysiological mechanisms underlying cognitive impairment of depression. This could potentially distinguish BDD-I from MDD if replicated in future large-scale evaluations of first-episode depression with longitudinal confirmation of diagnostic transition.
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Affiliation(s)
- Chang Xi
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center for Mental Disorders, Changsha, China
| | - Zhening Liu
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center for Mental Disorders, Changsha, China
| | - Can Zeng
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center for Mental Disorders, Changsha, China
| | - Wenjian Tan
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center for Mental Disorders, Changsha, China
| | - Fuping Sun
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center for Mental Disorders, Changsha, China
| | - Jie Yang
- Department of Psychiatry, The Second Xiangya Hospital of Central South University, Changsha, China.,National Clinical Research Center for Mental Disorders, Changsha, China
| | - Lena Palaniyappan
- 113611Robarts Research Institute, Western University, London, Canada.,Departments of Psychiatry and Medical Biophysics, Schulich School of Medicine, Western University, London, Canada
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11
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Kandilarova S, Stoyanov D, Aryutova K, Paunova R, Mantarkov M, Mitrev I, Todeva-Radneva A, Specht K. Effective Connectivity Between the Orbitofrontal Cortex and the Precuneus Differentiates Major Psychiatric Disorders: Results from a Transdiagnostic Spectral DCM Study. CNS & NEUROLOGICAL DISORDERS DRUG TARGETS 2023; 22:180-190. [PMID: 34533450 DOI: 10.2174/1871527320666210917142815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 08/09/2021] [Accepted: 08/12/2021] [Indexed: 12/16/2022]
Abstract
BACKGROUND & OBJECTIVE We have previously identified aberrant connectivity of the left precuneus, ventrolateral prefrontal cortex, anterior cingulate cortex, and anterior insula in patients with either a paranoid (schizophrenia), or a depressive syndrome (both unipolar and bipolar). In the current study, we attempted to replicate and expand these findings by including a healthy control sample and separating the patients in a depressive episode into two groups: unipolar and bipolar depression. We hypothesized that the connections between those major nodes of the resting state networks would demonstrate different patterns in the three patient groups compared to the healthy subjects. METHODS Resting-state functional MRI was performed on a sample of 101 participants, of which 26 patients with schizophrenia (current psychotic episodes), 24 subjects with Bipolar Disorder (BD), 33 with Major Depressive Disorder (MDD) (both BD and MDD patients were in a current depressive episode), and 21 healthy controls. Spectral Dynamic Causal Modeling was used to calculate the coupling values between eight regions of interest, including the anterior precuneus (PRC), anterior hippocampus, anterior insula, angular gyrus, lateral Orbitofrontal Cortex (OFC), middle frontal gyrus, planum temporale, and anterior thalamus. RESULTS & CONCLUSION We identified disturbed effective connectivity from the left lateral orbitofrontal cortex to the left anterior precuneus that differed significantly between unipolar depression, where the influence was inhibitory, and bipolar depression, where the effect was excitatory. A logistic regression analysis correctly classified 75% of patients with unipolar and bipolar depression based solely on the coupling values of this connection. In addition, patients with schizophrenia demonstrated negative effective connectivity from the anterior PRC to the lateral OFC, which distinguished them from healthy controls and patients with major depression. Future studies with unmedicated patients will be needed to establish the replicability of our findings.
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Affiliation(s)
- Sevdalina Kandilarova
- Department of Psychiatry and Medical Psychology, Medical University-Plovdiv, Plovdiv, Bulgaria
- Division of Translational Neuroscience, Research Institute at Medical University-Plovdiv, Plovdiv, Bulgaria
| | - Drozdstoy Stoyanov
- Department of Psychiatry and Medical Psychology, Medical University-Plovdiv, Plovdiv, Bulgaria
- Division of Translational Neuroscience, Research Institute at Medical University-Plovdiv, Plovdiv, Bulgaria
| | - Katrin Aryutova
- Department of Psychiatry and Medical Psychology, Medical University-Plovdiv, Plovdiv, Bulgaria
- Division of Translational Neuroscience, Research Institute at Medical University-Plovdiv, Plovdiv, Bulgaria
| | - Rossitsa Paunova
- Department of Psychiatry and Medical Psychology, Medical University-Plovdiv, Plovdiv, Bulgaria
- Division of Translational Neuroscience, Research Institute at Medical University-Plovdiv, Plovdiv, Bulgaria
| | - Mladen Mantarkov
- Department of Psychiatry and Medical Psychology, Medical University-Plovdiv, Plovdiv, Bulgaria
| | - Ivo Mitrev
- Department of Psychiatry and Medical Psychology, Medical University-Plovdiv, Plovdiv, Bulgaria
| | - Anna Todeva-Radneva
- Department of Psychiatry and Medical Psychology, Medical University-Plovdiv, Plovdiv, Bulgaria
- Division of Translational Neuroscience, Research Institute at Medical University-Plovdiv, Plovdiv, Bulgaria
| | - Karsten Specht
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
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12
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Guan S, Wan D, Zhao R, Canario E, Meng C, Biswal BB. The complexity of spontaneous brain activity changes in schizophrenia, bipolar disorder, and ADHD was examined using different variations of entropy. Hum Brain Mapp 2022; 44:94-118. [PMID: 36358029 PMCID: PMC9783493 DOI: 10.1002/hbm.26129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Revised: 08/02/2022] [Accepted: 08/07/2022] [Indexed: 11/13/2022] Open
Abstract
Adult attention deficit/hyperactivity disorder (ADHD), schizophrenia (SCHZ), and bipolar disorder (BP) have common symptoms and differences, and the underlying neural mechanisms are still unclear. This article will thoroughly discuss the differences between ADHD, BP, and SCHZ (31 healthy control and 31 ADHD; 34 healthy control and 34 BP; 42 healthy control and 42 SCHZ) relative to healthy subjects in combination with three atlases (et al., the Brainnetome atlas, the Dosenbach atlas, the Power atlas) and seven entropies (et al., approximate entropy (ApEn), sample entropy (SaEn), permutation entropy (PeEn), fuzzy entropy (FuEn), differential entropy (DiffEn), range entropy (RaEn), and dispersion entropy (DispEn)), as well as the prominent significant brain regions, in the hope of giving information that is more suitable for analyzing different diseases' entropy. First, the reliability (et al., intraclass correlation coefficient [ICC]) of seven kinds of entropy is calculated and analyzed by using the MSC dataset (10 subjects and 100 sessions in total) and simulation data; then, seven types of entropy and multiscale entropy expanded based on seven kinds of entropy are used to explore the differences and brain regions of ADHD, BP, and SCHZ relative to healthy subjects; and finally, by verifying the classification performance of the seven information entropies on ADHD, BP, and SCHZ, the effectiveness of the seven entropy methods is evaluated through these three methods. The core brain regions that affect the classification are given, and DiffEn performed best on ADHD, SaEn for BP, and RaEn for SCHZ.
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Affiliation(s)
- Sihai Guan
- Key Laboratory of Electronic and Information EngineeringState Ethnic Affairs Commission, College of Electronic and Information, Southwest Minzu UniversityChengduChina
| | - Dongyu Wan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Rong Zhao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Edgar Canario
- Department of Biomedical EngineeringNew Jersey Institute of TechnologyNewarkNew JerseyUSA
| | - Chun Meng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Bharat B. Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina,Department of Biomedical EngineeringNew Jersey Institute of TechnologyNewarkNew JerseyUSA
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13
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Shao J, Zhang Y, Xue L, Wang X, Wang H, Zhu R, Yao Z, Lu Q. Shared and disease-sensitive dysfunction across bipolar and unipolar disorder during depressive episodes: a transdiagnostic study. Neuropsychopharmacology 2022; 47:1922-1930. [PMID: 35177806 PMCID: PMC9485137 DOI: 10.1038/s41386-022-01290-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Revised: 01/21/2022] [Accepted: 01/28/2022] [Indexed: 02/05/2023]
Abstract
Patients with depressive episodes (PDE), such as unipolar disorder (UD) and bipolar disorder (BD), are often defined as distinct diagnostic categories, but increasing converging evidence indicated shared etiologies and pathophysiological characteristics across different clinical diagnoses. We explored whether these transdiagnostic deficits are caused by the common neural substrates across diseases or disease-sensitive mechanisms, or a combination of both. In this study, we utilized a Bayesian model to decompose the resting-state brain activity into multiple hyper- and hypo-activity patterns (refer to as "factors"), so as to explore the shared and disease-sensitive alteration patterns in PDE. The model was constructed over a total of 259 patients (131 UD and 128 BD) with 100 healthy controls as the reference. The other 32 initial depressive episode BD (IDE-BD) patients who had symptoms of mania or hypomania during follow-up were taken as an independent set to estimate the factor composition using the established model for further analysis. We revealed three transdiagnostic alteration factors in PDE. Based on the distribution of factors and the tendency of factor composition at the group level, these factors were defined as BD sensitive factor, UD sensitive factor and shared basic alteration factor. We further found that the factor composition and the ROIs-based alteration degree (mainly involving in orbitofrontal gyrus and part of parietal lobe) were associated with the bipolar index in IDE-BD patients. Our findings contributed to understanding the core transdiagnostic shared and disease-sensitive alterations in PDE and to predicting the risk of emotional state transition in IDE-BD patients.
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Affiliation(s)
- Junneng Shao
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China
- Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, China
| | - Yujie Zhang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China
- Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, China
| | - Li Xue
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China
- Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, China
| | - Xinyi Wang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China
- Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, China
| | - Huan Wang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, 210096, China
- Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, China
| | - Rongxin Zhu
- 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.
- Key Laboratory of Child Development and Learning Science (Southeast University), Ministry of Education, Nanjing, China.
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14
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Siegel-Ramsay JE, Bertocci MA, Wu B, Phillips ML, Strakowski SM, Almeida JRC. Distinguishing between depression in bipolar disorder and unipolar depression using magnetic resonance imaging: a systematic review. Bipolar Disord 2022; 24:474-498. [PMID: 35060259 DOI: 10.1111/bdi.13176] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
OBJECTIVES Magnetic resonance imaging (MRI) studies comparing bipolar and unipolar depression characterize pathophysiological differences between these conditions. However, it is difficult to interpret the current literature due to differences in MRI modalities, analysis methods, and study designs. METHODS We conducted a systematic review of publications using MRI to compare individuals with bipolar and unipolar depression. We grouped studies according to MRI modality and task design. Within the discussion, we critically evaluated and summarized the functional MRI research and then further complemented these findings by reviewing the structural MRI literature. RESULTS We identified 88 MRI publications comparing participants with bipolar depression and unipolar depressive disorder. Compared to individuals with unipolar depression, participants with bipolar disorder exhibited heightened function, increased within network connectivity, and reduced grey matter volume in salience and central executive network brain regions. Group differences in default mode network function were less consistent but more closely associated with depressive symptoms in participants with unipolar depression but distractibility in bipolar depression. CONCLUSIONS When comparing mood disorder groups, the neuroimaging evidence suggests that individuals with bipolar disorder are more influenced by emotional and sensory processing when responding to their environment. In contrast, depressive symptoms and neurofunctional response to emotional stimuli were more closely associated with reduced central executive function and less adaptive cognitive control of emotionally oriented brain regions in unipolar depression. Researchers now need to replicate and refine network-level trends in these heterogeneous mood disorders and further characterize MRI markers associated with early disease onset, progression, and recovery.
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Affiliation(s)
- Jennifer E Siegel-Ramsay
- Department of Psychiatry and Behavioral Sciences, Dell Medical School, University of Texas, Austin, Texas, USA
| | - Michele A Bertocci
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Bryan Wu
- Department of Psychiatry and Behavioral Sciences, Dell Medical School, University of Texas, Austin, Texas, USA
| | - Mary L Phillips
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Stephen M Strakowski
- Department of Psychiatry and Behavioral Sciences, Dell Medical School, University of Texas, Austin, Texas, USA
| | - Jorge R C Almeida
- Department of Psychiatry and Behavioral Sciences, Dell Medical School, University of Texas, Austin, Texas, USA
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15
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Zhang S, Wang Y, Zheng S, Seger C, Zhong S, Huang H, Hu H, Chen G, Chen L, Jia Y, Huang L, Huang R. Multimodal MRI reveals alterations of the anterior insula and posterior cingulate cortex in bipolar II disorders: A surface-based approach. Prog Neuropsychopharmacol Biol Psychiatry 2022; 116:110533. [PMID: 35151795 DOI: 10.1016/j.pnpbp.2022.110533] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 11/29/2021] [Accepted: 02/05/2022] [Indexed: 10/19/2022]
Abstract
BACKGROUND Bipolar disorder (BD) is a mental disorder with severe implications for those affected and their families. Previous studies detected brain structural and functional alterations in BD patients. However, very few studies conducted a multimodal MRI fusion analysis, and little is known about the role of common anomalies in the connectivity of BD. METHODS We collected sMRI, rs-fMRI, and DTI data from 56 patients with unmedicated BD-II depression and 72 age-, sex- and handedness-matched healthy controls. We applied data-driven approaches to analyze multimodal MRI data and detected brain areas with significant group differences in cortical thickness (CT), amplitude of low frequency fluctuations (ALFF), and fractional anisotropy (FA) of the superficial white matter. We observed the common abnormal areas and took these areas as seeds to analyze the resting-state functional connectivity (RSFC) patterns in BD patients by overlapping these abnormal areas. RESULTS The BD patients showed two common abnormal areas: (1) the left anterior insula (AI) with abnormal CT and FA, and (2) the left posterior cingulate cortex (PCC) with abnormal CT and ALFF. Seed-based analyses showed RSFC between the left AI and left occipital sensory cortex, the left AI and left superior and inferior parietal cortex, and the left PCC and right medial prefrontal cortex were uniformly lower in the BD patients than controls. Correlation analyses showed negative correction between AI's FA and disease episodes and between AI's FA and disease duration in depressed BD-II patients. CONCLUSIONS We observed abnormal brain structural and functional properties in the left AI and left PCC in BD patients. The abnormal RSFC patterns may suggest sensory and cognitive dysfunction in BD.
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Affiliation(s)
- Shufei Zhang
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, South China Normal University, Guangzhou 510631, China
| | - Ying Wang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China.
| | - Senning Zheng
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, South China Normal University, Guangzhou 510631, China
| | - Carol Seger
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, South China Normal University, Guangzhou 510631, China; Department of Psychology and Program in Molecular, Cellular, and Integrative Neuroscience, Colorado State University, Fort Collins, CO 80523, USA
| | - Shuming Zhong
- Department of Psychiatry, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
| | - Huiyuan Huang
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, South China Normal University, Guangzhou 510631, China
| | - Huiqing Hu
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, South China Normal University, Guangzhou 510631, China
| | - Guanmao Chen
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
| | - Lixiang Chen
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, South China Normal University, Guangzhou 510631, China
| | - Yanbin Jia
- Department of Psychiatry, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
| | - Li Huang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
| | - Ruiwang Huang
- School of Psychology, Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, South China Normal University, Guangzhou 510631, China.
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16
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Zhang C, Li X, Zhao L, Liang R, Deng W, Guo W, Wang Q, Hu X, Du X, Sham PC, Luo X, Li T. Comprehensive and integrative analyses identify TYW5 as a schizophrenia risk gene. BMC Med 2022; 20:169. [PMID: 35527273 PMCID: PMC9082878 DOI: 10.1186/s12916-022-02363-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 04/04/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Identifying the causal genes at the risk loci and elucidating their roles in schizophrenia (SCZ) pathogenesis remain significant challenges. To explore risk variants associated with gene expression in the human brain and to identify genes whose expression change may contribute to the susceptibility of SCZ, here we report a comprehensive integrative study on SCZ. METHODS We systematically integrated the genetic associations from a large-scale SCZ GWAS (N = 56,418) and brain expression quantitative trait loci (eQTL) data (N = 175) using a Bayesian statistical framework (Sherlock) and Summary data-based Mendelian Randomization (SMR). We also measured brain structure of 86 first-episode antipsychotic-naive schizophrenia patients and 152 healthy controls with the structural MRI. RESULTS Both Sherlock (P = 3. 38 × 10-6) and SMR (P = 1. 90 × 10-8) analyses showed that TYW5 mRNA expression was significantly associated with risk of SCZ. Brain-based studies also identified a significant association between TYW5 protein abundance and SCZ. The single-nucleotide polymorphism rs203772 showed significant association with SCZ and the risk allele is associated with higher transcriptional level of TYW5 in the prefrontal cortex. We further found that TYW5 was significantly upregulated in the brain tissues of SCZ cases compared with controls. In addition, TYW5 expression was also significantly higher in neurons induced from pluripotent stem cells of schizophrenia cases compared with controls. Finally, combining analysis of genotyping and MRI data showed that rs203772 was significantly associated with gray matter volume of the right middle frontal gyrus and left precuneus. CONCLUSIONS We confirmed that TYW5 is a risk gene for SCZ. Our results provide useful information toward a better understanding of the genetic mechanism of TYW5 in risk of SCZ.
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Affiliation(s)
- Chengcheng Zhang
- Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Xiaojing Li
- Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Liansheng Zhao
- Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Rong Liang
- Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310013, People's Republic of China
| | - Wei Deng
- Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310013, People's Republic of China
| | - Wanjun Guo
- Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Qiang Wang
- Mental Health Center and Psychiatric Laboratory, the State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Xun Hu
- The Clinical Research Center and Department of Pathology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xiangdong Du
- Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Suzhou, China
| | - Pak Chung Sham
- Department of Psychiatry, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, SAR, China.,Centre for PanorOmic Sciences, The University of Hong Kong, Hong Kong, SAR, China.,State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, SAR, China
| | - Xiongjian Luo
- Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Tao Li
- Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310013, People's Republic of China. .,NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Science Center for Brain Science and Brain-machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
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17
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Claeys EHI, Mantingh T, Morrens M, Yalin N, Stokes PRA. Resting-state fMRI in depressive and (hypo)manic mood states in bipolar disorders: A systematic review. Prog Neuropsychopharmacol Biol Psychiatry 2022; 113:110465. [PMID: 34736998 DOI: 10.1016/j.pnpbp.2021.110465] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 10/01/2021] [Accepted: 10/27/2021] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Abnormalities in spontaneous brain activity, measured with resting-state functional magnetic resonance imaging (rs-fMRI), may be key biomarkers for bipolar disorders. This systematic review compares rs-fMRI findings in people experiencing a bipolar depressive or (hypo)manic episode to bipolar euthymia and/or healthy participants. METHODS Medline, Web of Science and Embase were searched up until April 2021. Studies without control group, or including minors, neurological co-morbidities or mixed episodes, were excluded. Qualitative synthesis was used to report results and risk of bias was assessed using the National Heart, Lung and Blood Institute tool for case-control studies. RESULTS Seventy-one studies were included (3167 bipolar depressed/706 (hypo)manic). In bipolar depression, studies demonstrated default-mode (DMN) and frontoparietal network (FPN) dysfunction, altered baseline activity in the precuneus, insula, striatum, cingulate, frontal and temporal cortex, and disturbed regional homogeneity in parietal, temporal and pericentral areas. Functional connectivity was altered in thalamocortical circuits and between the cingulate cortex and precuneus. In (hypo)mania, studies reported altered functional connectivity in the amygdala, frontal and cingulate cortex. Finally, rs-fMRI disturbances in the insula and putamen correlate with depressive symptoms, cerebellar resting-state alterations could evolve with disease progression and altered amygdala connectivity might mediate lithium effects. CONCLUSIONS Our results suggest DMN and FPN dysfunction in bipolar depression, whereas local rs-fMRI alterations might differentiate mood states. Future studies should consider controlling rs-fMRI findings for potential clinical confounding factors such as medication. Considerable heterogeneity of methodology between studies limits conclusions. Standardised clinical reporting and consistent analysis approaches would increase coherence in this promising field.
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Affiliation(s)
- Eva H I Claeys
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Department of Psychiatry, Collaborative Antwerp Psychiatric Research Institute (CAPRI), Faculty of Medicine and Health Sciences, University of Antwerp, Campus Drie Eiken, S.033, Universiteitsplein 1, 2610 Antwerpen, Belgium; Department of Psychiatry, University Psychiatric Centre Duffel, Stationsstraat 22, 2570 Duffel, Belgium
| | - Tim Mantingh
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Manuel Morrens
- Department of Psychiatry, Collaborative Antwerp Psychiatric Research Institute (CAPRI), Faculty of Medicine and Health Sciences, University of Antwerp, Campus Drie Eiken, S.033, Universiteitsplein 1, 2610 Antwerpen, Belgium; Department of Psychiatry, University Psychiatric Centre Duffel, Stationsstraat 22, 2570 Duffel, Belgium
| | - Nefize Yalin
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
| | - Paul R A Stokes
- Centre for Affective Disorders, Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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18
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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: 2] [Impact Index Per Article: 1.0] [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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A neural network approach to optimising treatments for depression using data from specialist and community psychiatric services in Australia, New Zealand and Japan. Neural Comput Appl 2022; 35:11497-11516. [PMID: 35039718 PMCID: PMC8754538 DOI: 10.1007/s00521-021-06710-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Accepted: 10/27/2021] [Indexed: 01/10/2023]
Abstract
This study investigated the application of a recurrent neural network for optimising pharmacological treatment for depression. A clinical dataset of 458 participants from specialist and community psychiatric services in Australia, New Zealand and Japan were extracted from an existing custom-built, web-based tool called Psynary . This data, which included baseline and self-completed reviews, was used to train and refine a novel algorithm which was a fully connected network feature extractor and long short-term memory algorithm was firstly trained in isolation and then integrated and annealed using slow learning rates due to the low dimensionality of the data. The accuracy of predicting depression remission before processing patient review data was 49.8%. After processing only 2 reviews, the accuracy was 76.5%. When considering a change in medication, the precision of changing medications was 97.4% and the recall was 71.4% . The medications with predicted best results were antipsychotics (88%) and selective serotonin reuptake inhibitors (87.9%). This is the first study that has created an all-in-one algorithm for optimising treatments for all subtypes of depression. Reducing treatment optimisation time for patients suffering with depression may lead to earlier remission and hence reduce the high levels of disability associated with the condition. Furthermore, in a setting where mental health conditions are increasing strain on mental health services, the utilisation of web-based tools for remote monitoring and machine/deep learning algorithms may assist clinicians in both specialist and primary care in extending specialist mental healthcare to a larger patient community.
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Guo X, Wang W, Kang L, Shu C, Bai H, Tu N, Bu L, Gao Y, Wang G, Liu Z. Abnormal degree centrality in first-episode medication-free adolescent depression at rest: A functional magnetic resonance imaging study and support vector machine analysis. Front Psychiatry 2022; 13:926292. [PMID: 36245889 PMCID: PMC9556654 DOI: 10.3389/fpsyt.2022.926292] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 07/28/2022] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Depression in adolescents is more heterogeneous and less often diagnosed than depression in adults. At present, reliable approaches to differentiating between adolescents who are and are not affected by depression are lacking. This study was designed to assess voxel-level whole-brain functional connectivity changes associated with adolescent depression in an effort to define an imaging-based biomarker associated with this condition. MATERIALS AND METHODS In total, 71 adolescents affected by major depressive disorder (MDD) and 71 age-, sex-, and education level-matched healthy controls were subjected to resting-state functional magnetic resonance imaging (rs-fMRI) based analyses of brain voxel-wise degree centrality (DC), with a support vector machine (SVM) being used for pattern classification analyses. RESULTS DC patterns derived from 16-min rs-fMRI analyses were able to effectively differentiate between adolescent MDD patients and healthy controls with 95.1% accuracy (136/143), and with respective sensitivity and specificity values of 92.1% (70/76) and 98.5% (66/67) based upon DC abnormalities detected in the right cerebellum. Specifically, increased DC was evident in the bilateral insula and left lingual area of MDD patients, together with reductions in the DC values in the right cerebellum and bilateral superior parietal lobe. DC values were not significantly correlated with disease severity or duration in these patients following correction for multiple comparisons. CONCLUSION These results suggest that whole-brain network centrality abnormalities may be present in many brain regions in adolescent depression patients. Accordingly, these DC maps may hold value as candidate neuroimaging biomarkers capable of differentiating between adolescents who are and are not affected by MDD, although further validation of these results will be critical.
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Affiliation(s)
- Xin Guo
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.,Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College of London, London, United Kingdom
| | - Wei Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Lijun Kang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Chang Shu
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Hanpin Bai
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Ning Tu
- PET/CT/MRI and Molecular Imaging Center, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Lihong Bu
- PET/CT/MRI and Molecular Imaging Center, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Yujun Gao
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Gaohua Wang
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Zhongchun Liu
- Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
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21
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Liu Y, Chen K, Luo Y, Wu J, Xiang Q, Peng L, Zhang J, Zhao W, Li M, Zhou X. Distinguish bipolar and major depressive disorder in adolescents based on multimodal neuroimaging: Results from the Adolescent Brain Cognitive Development study ®. Digit Health 2022; 8:20552076221123705. [PMID: 36090673 PMCID: PMC9452797 DOI: 10.1177/20552076221123705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 08/16/2022] [Indexed: 01/10/2023] Open
Abstract
Background Major depressive disorder and bipolar disorder in adolescents are prevalent and are associated with cognitive impairment, executive dysfunction, and increased mortality. Early intervention in the initial stages of major depressive disorder and bipolar disorder can significantly improve personal health. Methods We collected 309 samples from the Adolescent Brain Cognitive Development study, including 116 adolescents with bipolar disorder, 64 adolescents with major depressive disorder, and 129 healthy adolescents, and employed a support vector machine to develop classification models for identification. We developed a multimodal model, which combined functional connectivity of resting-state functional magnetic resonance imaging and four anatomical measures of structural magnetic resonance imaging (cortical thickness, area, volume, and sulcal depth). We measured the performances of both multimodal and single modality classifiers. Results The multimodal classifiers showed outstanding performance compared with all five single modalities, and they are 100% for major depressive disorder versus healthy controls, 100% for bipolar disorder versus healthy control, 98.5% (95% CI: 95.4–100%) for major depressive disorder versus bipolar disorder, 100% for major depressive disorder versus depressed bipolar disorder and the leave-one-site-out analysis results are 77.4%, 63.3%, 79.4%, and 81.7%, separately. Conclusions The study shows that multimodal classifiers show high classification performances. Moreover, cuneus may be a potential biomarker to differentiate major depressive disorder, bipolar disorder, and healthy adolescents. Overall, this study can form multimodal diagnostic prediction workflows for clinically feasible to make more precise diagnose at the early stage and potentially reduce loss of personal pain and public society.
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Affiliation(s)
- Yujun Liu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Kai Chen
- School of Public Health, University of Texas Health Science Center at Houston, Houston, USA
| | - Yangyang Luo
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Jiqiu Wu
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Qu Xiang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Li Peng
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Jian Zhang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Weiling Zhao
- Center for Computational Systems Medicine, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, USA
| | - Mingliang Li
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
- Med-X Center for Informatics, Sichuan University, Chengdu, China
| | - Xiaobo Zhou
- Center for Computational Systems Medicine, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, USA
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22
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Xiao M, Chen X, Yi H, Luo Y, Yan Q, Feng T, He Q, Lei X, Qiu J, Chen H. Stronger functional network connectivity and social support buffer against negative affect during the COVID-19 outbreak and after the pandemic peak. Neurobiol Stress 2021; 15:100418. [PMID: 34805450 PMCID: PMC8592855 DOI: 10.1016/j.ynstr.2021.100418] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 09/27/2021] [Accepted: 11/15/2021] [Indexed: 01/17/2023] Open
Abstract
Health and financial uncertainties, as well as enforced social distancing, during the COVID-19 pandemic have adversely affected the mental health of people. These impacts are expected to continue even after the pandemic, particularly for those who lack support from family and friends. The salience network (SN), default mode network (DMN), and frontoparietal network (FPN) function in an interconnected manner to support information processing and emotional regulation processes in stressful contexts. In this study, we examined whether functional connectivity of the SN, DMN, and FPN, measured using resting-state functional magnetic resonance imaging before the pandemic, is a neurobiological marker of negative affect (NA) during the COVID-19 pandemic and after its peak in a large sample (N = 496, 360 females); the moderating role of social support in the brain-NA association was also investigated. We found that participants reported an increase in NA during the pandemic compared to before the pandemic, and the NA did not decrease, even after the peak period. People with higher connectivity within the SN and between the SN and the other two networks reported less NA during and after the COVID-19 outbreak peak, and the buffer effect was stronger if their social support was greater. These findings suggest that the functional networks that are responsible for affective processing and executive functioning, as well as the social support from family and friends, play an important role in protecting against NA under stressful and uncontrollable situations.
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Affiliation(s)
- Mingyue Xiao
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.,Department of Psychology, Southwest University, Chongqing, China
| | - Ximei Chen
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.,Department of Psychology, Southwest University, Chongqing, China
| | - Haijing Yi
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.,Department of Psychology, Southwest University, Chongqing, China
| | - Yijun Luo
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.,Department of Psychology, Southwest University, Chongqing, China
| | - Qiaoling Yan
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.,Department of Psychology, Southwest University, Chongqing, China
| | - Tingyong Feng
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.,Department of Psychology, Southwest University, Chongqing, China
| | - Qinghua He
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.,Department of Psychology, Southwest University, Chongqing, China
| | - Xu Lei
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.,Department of Psychology, Southwest University, Chongqing, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.,Department of Psychology, Southwest University, Chongqing, China
| | - Hong Chen
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China.,Department of Psychology, Southwest University, Chongqing, China
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23
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Hsu CW, Tsai SY, Wang LJ, Liang CS, Carvalho AF, Solmi M, Vieta E, Lin PY, Hu CA, Kao HY. Predicting Serum Levels of Lithium-Treated Patients: A Supervised Machine Learning Approach. Biomedicines 2021; 9:biomedicines9111558. [PMID: 34829787 PMCID: PMC8615637 DOI: 10.3390/biomedicines9111558] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 10/23/2021] [Accepted: 10/25/2021] [Indexed: 11/16/2022] Open
Abstract
Routine monitoring of lithium levels is common clinical practice. This is because the lithium prediction strategies available developed by previous studies are still limited due to insufficient prediction performance. Thus, we used machine learning approaches to predict lithium concentration in a large real-world dataset. Real-world data from multicenter electronic medical records were used in different machine learning algorithms to predict: (1) whether the serum level was 0.6–1.2 mmol/L or 0.0–0.6 mmol/L (binary prediction), and (2) its concentration value (continuous prediction). We developed models from 1505 samples through 5-fold cross-validation and used 204 independent samples to test their performance by evaluating their accuracy. Moreover, we ranked the most important clinical features in different models and reconstructed three reduced models with fewer clinical features. For binary and continuous predictions, the average accuracy of these models was 0.70–0.73 and 0.68–0.75, respectively. Seven features were listed as important features related to serum lithium levels of 0.6–1.2 mmol/L or higher lithium concentration, namely older age, lower systolic blood pressure, higher daily and last doses of lithium prescription, concomitant psychotropic drugs with valproic acid and -pine drugs, and comorbid substance-related disorders. After reducing the features in the three new predictive models, the binary or continuous models still had an average accuracy of 0.67–0.74. Machine learning processes complex clinical data and provides a potential tool for predicting lithium concentration. This may help in clinical decision-making and reduce the frequency of serum level monitoring.
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Affiliation(s)
- Chih-Wei Hsu
- Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan; (P.-Y.L.); (C.-A.H.)
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 70101, Taiwan
- Correspondence: (C.-W.H.); (H.-Y.K.)
| | - Shang-Ying Tsai
- Department of Psychiatry, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110301, Taiwan;
- Department of Psychiatry and Psychiatric Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei 110301, Taiwan
| | - Liang-Jen Wang
- Department of Child and Adolescent Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan;
| | - Chih-Sung Liang
- National Defense Medical Center, Department of Psychiatry, Beitou Branch, Tri-Service General Hospital, Taipei 112003, Taiwan;
- National Defense Medical Center, Department of Psychiatry, Taipei 114201, Taiwan
| | - Andre F. Carvalho
- IMPACT (Innovation in Mental and Physical Health and Clinical Treatment) Strategic Research Centre, School of Medicine, Barwon Health, Deakin University, Geelong, VIC 3216, Australia;
| | - Marco Solmi
- Psychiatry Department, University of Ottawa, Ottawa, ON K1N 6N5, Canada;
- The Ottawa Hospital, University of Ottawa, Ottawa, ON K1H 8L6, Canada
- Clinical Epidemiology Program, Ottawa Hospital Research Institute (OHRI), University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Eduard Vieta
- Bipolar and Depressive Disorders Unit, Hospital Clinic, IDIBAPS, CIBERSAM, University of Barcelona, 08036 Barcelona, Catalonia, Spain;
| | - Pao-Yen Lin
- Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan; (P.-Y.L.); (C.-A.H.)
- Institute for Translational Research in Biomedical Sciences, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung 83301, Taiwan
| | - Chien-An Hu
- Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung 83301, Taiwan; (P.-Y.L.); (C.-A.H.)
| | - Hung-Yu Kao
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 70101, Taiwan
- Correspondence: (C.-W.H.); (H.-Y.K.)
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24
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Insula activity in resting-state differentiates bipolar from unipolar depression: a systematic review and meta-analysis. Sci Rep 2021; 11:16930. [PMID: 34417487 PMCID: PMC8379217 DOI: 10.1038/s41598-021-96319-2] [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] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 07/26/2021] [Indexed: 02/07/2023] Open
Abstract
Symptomatic overlap of depressive episodes in bipolar disorder (BD) and major depressive disorder (MDD) is a major diagnostic and therapeutic problem. Mania in medical history remains the only reliable distinguishing marker which is problematic given that episodes of depression compared to episodes of mania are more frequent and predominantly present at the beginning of BD. Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive, task-free, and well-tolerated method that may provide diagnostic markers acquired from spontaneous neural activity. Previous rs-fMRI studies focused on differentiating BD from MDD depression were inconsistent in their findings due to low sample power, heterogeneity of compared samples, and diversity of analytical methods. This meta-analysis investigated resting-state activity differences in BD and MDD depression using activation likelihood estimation. PubMed, Web of Science, Scopus and Google Scholar databases were searched for whole-brain rs-fMRI studies which compared MDD and BD currently depressed patients between Jan 2000 and August 2020. Ten studies were included, representing 234 BD and 296 MDD patients. The meta-analysis found increased activity in the left insula and adjacent area in MDD compared to BD. The finding suggests that the insula is involved in neural activity patterns during resting-state that can be potentially used as a biomarker differentiating both disorders.
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25
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Canario E, Chen D, Biswal B. A review of resting-state fMRI and its use to examine psychiatric disorders. PSYCHORADIOLOGY 2021; 1:42-53. [PMID: 38665309 PMCID: PMC10917160 DOI: 10.1093/psyrad/kkab003] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Revised: 02/17/2021] [Accepted: 03/08/2021] [Indexed: 04/28/2024]
Abstract
Resting-state fMRI (rs-fMRI) has emerged as an alternative method to study brain function in human and animal models. In humans, it has been widely used to study psychiatric disorders including schizophrenia, bipolar disorder, autism spectrum disorders, and attention deficit hyperactivity disorders. In this review, rs-fMRI and its advantages over task based fMRI, its currently used analysis methods, and its application in psychiatric disorders using different analysis methods are discussed. Finally, several limitations and challenges of rs-fMRI applications are also discussed.
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Affiliation(s)
- Edgar Canario
- Department of Biomedical Engineering, New Jersey Institute of Technology, 619 Fenster Hall, Newark, NJ, 07102, US
| | - Donna Chen
- Department of Biomedical Engineering, New Jersey Institute of Technology, 619 Fenster Hall, Newark, NJ, 07102, US
| | - Bharat Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, 619 Fenster Hall, Newark, NJ, 07102, US
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26
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Jun E, Na K, Kang W, Lee J, Suk H, Ham B. Identifying
resting‐state
effective connectivity abnormalities in
drug‐naïve
major depressive disorder diagnosis via graph convolutional networks. Hum Brain Mapp 2020. [DOI: 10.1002/hbm.25175 10.1002/hbm.25175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Affiliation(s)
- Eunji Jun
- Department of Brain and Cognitive Engineering Korea University Seoul Republic of Korea
| | - Kyoung‐Sae Na
- Department of Psychiatry Gachon University Gil Medical Center Incheon Republic of Korea
| | - Wooyoung Kang
- Department of Biomedical Sciences Korea University College of Medicine Seoul Republic of Korea
| | - Jiyeon Lee
- Department of Brain and Cognitive Engineering Korea University Seoul Republic of Korea
| | - Heung‐Il Suk
- Department of Brain and Cognitive Engineering Korea University Seoul Republic of Korea
- Department of Artificial Intelligence Korea University Seoul Republic of Korea
| | - Byung‐Joo Ham
- Department of Psychiatry Korea University Anam Hospital, Korea University College of Medicine Seoul Republic of Korea
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27
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EEG Frontal Asymmetry and Theta Power in Unipolar and Bipolar Depression. J Affect Disord 2020; 276:501-510. [PMID: 32871681 DOI: 10.1016/j.jad.2020.07.011] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 06/12/2020] [Accepted: 07/05/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Distinguishing between unipolar and bipolar depression is of high clinical relevance. However, there is sparse research directly comparing these groups in terms of EEG activity. METHOD We investigated 87 participants' left and right EEG frontal alpha-1, alpha-2, and theta activity related to happy and sad face stimuli in unipolar (UD, n=33) and bipolar (BD, n=22) depressed participants, and controls without depression (HC, n=32). RESULTS Post-hoc analysis of an observed hemisphere x group interaction (p< .037) showed significant differences in alpha-1 asymmetry only for the comparison of UD and HC (p< .006). Further analysis of a significant emotion x group interaction (p= .001) revealed a differential impact of stimulus valence on theta power between the groups (p< .001). The valence dependent theta power of the BD differed from that of the UD (p< .0002) and the HC (p< .004). Alpha-1 asymmetry classified HC and both depressed groups with an accuracy of .69. Valence-related theta classified BD from UD with an accuracy of .83. Leave-one-out cross validation resulted in slightly reduced accuracy. LIMITATIONS Important limitations were the small sample size and that subjects were not medication-free. CONCLUSIONS Our results demonstrate the value of simple, task related EEG activity for differentiating not only healthy individuals from those with depression, but also individuals with unipolar depression from those with bipolar depression.
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28
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Neuroanatomic and Functional Neuroimaging Findings. Curr Top Behav Neurosci 2020; 48:173-196. [PMID: 33040316 DOI: 10.1007/7854_2020_174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
The search for brain morphology findings that could explain behavioral disorders has gone through a long path in the history of psychiatry. With the advance of brain imaging technology, studies have been able to identify brain morphology and neural circuits associated with the pathophysiology of mental illnesses, such as bipolar disorders (BD). Promising results have also shown the potential of neuroimaging findings in the identification of outcome predictors and response to treatment among patients with BD. In this chapter, we present brain imaging structural and functional findings associated with BD, as well as their hypothesized relationship with the pathophysiological aspects of that condition and their potential clinical applications.
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29
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Yu H, Li ML, Li YF, Li XJ, Meng Y, Liang S, Li Z, Guo W, Wang Q, Deng W, Ma X, Coid J, Li DT. Anterior cingulate cortex, insula and amygdala seed-based whole brain resting-state functional connectivity differentiates bipolar from unipolar depression. J Affect Disord 2020; 274:38-47. [PMID: 32469830 DOI: 10.1016/j.jad.2020.05.005] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Revised: 04/02/2020] [Accepted: 05/05/2020] [Indexed: 12/24/2022]
Abstract
OBJECTIVE The frontal-limbic circuit is hypothesized as sub-serving emotional regulation. We performed whole brain resting-state functional connectivity (rs-FC) analysis by studying the key hubs of frontal-limbic circuit: anterior cingulate cortex (ACC), bilateral insula subregions, bilateral amygdala (Amy) as seeds, separately, to discriminate bipolar depression (BipD) from unipolar depression (UniD). METHODS We compared seed-based rs-FC of the frontal-limbic seeds with whole brain among 23 BipD participants; 23 age, gender, and depression severity matched patients with UniD, and 23 healthy controls (HCs). We also used support vector machine learning to study classification based on the rs-FC of ACC, bilateral insula subregions, and bilateral Amy seeds with whole brain. RESULTS BipD showed increased rs-FC between the left ventral anterior insula (vAI) seed and the left anterior supramarginal gyrus (aSMG) and left postcentral gyrus, as well as increased rs-FC between left amygdala seed and the left aSMG when compared to HCs and UniD. Compared to UniD, BipD was associated with increased rs-FC between right dorsal anterior insula seed and right superior frontal gyrus, as well as increased rs-FC between left posterior insula seed and right precentral gyrus and right thalamus. Combined rs-FC of ACC, bilateral insula subregions and bilateral Amy seeds with the whole brain discriminated BipD from UniD with an accuracy of 91.30%. CONCLUSIONS Rs-FC of the emotional regulation circuit is more widely disturbed in BipD than UniD. Using rs-FC with this circuit may lead to further developments in diagnostic decision-making.
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Affiliation(s)
- Hua Yu
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China; Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, PR China; Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Ming-Li Li
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China; Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, PR China; Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Yin-Fei Li
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China; Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, PR China; Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Xiao-Jing Li
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China; Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, PR China; Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Yajing Meng
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China; Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, PR China; Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Sugai Liang
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China; Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, PR China; Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Zhe Li
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China; Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, PR China; Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Wanjun Guo
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China; Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, PR China; Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Qiang Wang
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China; Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, PR China; Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Wei Deng
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China; Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, PR China; Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Xiaohong Ma
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China; Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, PR China; Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - Jeremy Coid
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China; Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, PR China; Brain Research Center, West China Hospital of Sichuan University, Chengdu, China
| | - D Tao Li
- Mental Health Center, West China Hospital of Sichuan University, Chengdu, China; Psychiatric Laboratory, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, PR China; Brain Research Center, West China Hospital of Sichuan University, Chengdu, China.
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Jun E, Na KS, Kang W, Lee J, Suk HI, Ham BJ. Identifying resting-state effective connectivity abnormalities in drug-naïve major depressive disorder diagnosis via graph convolutional networks. Hum Brain Mapp 2020; 41:4997-5014. [PMID: 32813309 PMCID: PMC7643383 DOI: 10.1002/hbm.25175] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Revised: 07/13/2020] [Accepted: 08/01/2020] [Indexed: 02/06/2023] Open
Abstract
Major depressive disorder (MDD) is a leading cause of disability; its symptoms interfere with social, occupational, interpersonal, and academic functioning. However, the diagnosis of MDD is still made by phenomenological approach. The advent of neuroimaging techniques allowed numerous studies to use resting-state functional magnetic resonance imaging (rs-fMRI) and estimate functional connectivity for brain-disease identification. Recently, attempts have been made to investigate effective connectivity (EC) that represents causal relations among regions of interest. In the meantime, to identify meaningful phenotypes for clinical diagnosis, graph-based approaches such as graph convolutional networks (GCNs) have been leveraged recently to explore complex pairwise similarities in imaging/nonimaging features among subjects. In this study, we validate the use of EC for MDD identification by estimating its measures via a group sparse representation along with a structured equation modeling approach in a whole-brain data-driven manner from rs-fMRI. To distinguish drug-naïve MDD patients from healthy controls, we utilize spectral GCNs based on a population graph to successfully integrate EC and nonimaging phenotypic information. Furthermore, we devise a novel sensitivity analysis method to investigate the discriminant connections for MDD identification in our trained GCNs. Our experimental results validated the effectiveness of our method in various scenarios, and we identified altered connectivities associated with the diagnosis of MDD.
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Affiliation(s)
- Eunji Jun
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Kyoung-Sae Na
- Department of Psychiatry, Gachon University Gil Medical Center, Incheon, Republic of Korea
| | - Wooyoung Kang
- Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Republic of Korea
| | - Jiyeon Lee
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Heung-Il Suk
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.,Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea
| | - Byung-Joo Ham
- Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Republic of Korea
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31
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Rashid B, Calhoun V. Towards a brain-based predictome of mental illness. Hum Brain Mapp 2020; 41:3468-3535. [PMID: 32374075 PMCID: PMC7375108 DOI: 10.1002/hbm.25013] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 04/06/2020] [Accepted: 04/06/2020] [Indexed: 01/10/2023] Open
Abstract
Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utilized features from a variety of neuroimaging modalities, including structural, functional, and diffusion magnetic resonance imaging data, as well as jointly estimated features from multiple modalities, to assess patients with heterogeneous mental disorders, such as schizophrenia and autism. We use the term "predictome" to describe the use of multivariate brain network features from one or more neuroimaging modalities to predict mental illness. In the predictome, multiple brain network-based features (either from the same modality or multiple modalities) are incorporated into a predictive model to jointly estimate features that are unique to a disorder and predict subjects accordingly. To date, more than 650 studies have been published on subject-level prediction focusing on psychiatric disorders. We have surveyed about 250 studies including schizophrenia, major depression, bipolar disorder, autism spectrum disorder, attention-deficit hyperactivity disorder, obsessive-compulsive disorder, social anxiety disorder, posttraumatic stress disorder, and substance dependence. In this review, we present a comprehensive review of recent neuroimaging-based predictomic approaches, current trends, and common shortcomings and share our vision for future directions.
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Affiliation(s)
- Barnaly Rashid
- Department of PsychiatryHarvard Medical SchoolBostonMassachusettsUSA
| | - Vince Calhoun
- Tri‐Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS)Georgia State University, Georgia Institute of Technology, and Emory UniversityAtlantaGeorgiaUSA
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32
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Russo D, Martino M, Magioncalda P, Inglese M, Amore M, Northoff G. Opposing Changes in the Functional Architecture of Large-Scale Networks in Bipolar Mania and Depression. Schizophr Bull 2020; 46:971-980. [PMID: 32047938 PMCID: PMC7342167 DOI: 10.1093/schbul/sbaa004] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
OBJECTIVE Manic and depressive phases of bipolar disorder (BD) show opposite symptoms in psychomotor, thought, and affective dimensions. Neuronally, these may depend on distinct patterns of alterations in the functional architecture of brain intrinsic activity. Therefore, the study aimed to characterize the spatial and temporal changes of resting-state activity in mania and depression, by investigating the regional homogeneity (ReHo) and degree of centrality (DC), in different frequency bands. METHODS Using resting-state functional magnetic resonance imaging (fMRI), voxel-wise ReHo and DC were calculated-in the standard frequency band (SFB: 0.01-0.10 Hz), as well as in Slow5 (0.01-0.027 Hz) and Slow4 (0.027-0.073 Hz)-and compared between manic (n = 36), depressed (n = 43), euthymic (n = 29) patients, and healthy controls (n = 112). Finally, clinical correlations were investigated. RESULTS Mania was mainly characterized by decreased ReHo and DC in Slow4 in the medial prefrontal cortex (as part of the default-mode network [DMN]), which in turn correlated with manic symptomatology. Conversely, depression was mainly characterized by decreased ReHo in SFB in the primary sensory-motor cortex (as part of the sensorimotor network [SMN]), which in turn correlated with depressive symptomatology. CONCLUSIONS Our data show a functional reconfiguration of the spatiotemporal structure of intrinsic brain activity to occur in BD. Mania might be characterized by a predominance of sensorimotor over associative networks, possibly driven by a deficit of the DMN (reflecting in internal thought deficit). Conversely, depression might be characterized by a predominance of associative over sensorimotor networks, possibly driven by a deficit of the SMN (reflecting in psychomotor inhibition).
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Affiliation(s)
- Daniel Russo
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, Section of Psychiatry, University of Genoa, Genoa, Italy,Ospedale Policlinico San Martino IRCCS, Genoa, Italy
| | - Matteo Martino
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Paola Magioncalda
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, Section of Psychiatry, University of Genoa, Genoa, Italy,Brain and Consciousness Research Center, Taipei Medical University – Shuang Ho Hospital, New Taipei City, Taiwan,Graduate Institute of Mind Brain and Consciousness, Taipei Medical University, Taipei, Taiwan,To whom correspondence should be addressed; Graduate Institute of Mind Brain and Consciousness, Taipei Medical University, No. 250 Wuxing Street, 11031 Taipei, Taiwan; tel: 00886-2-2736-1661-8601, fax: 00886-2-8732-5288, e-mail:
| | - Matilde Inglese
- Ospedale Policlinico San Martino IRCCS, Genoa, Italy,Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, Section of Neurology, University of Genoa, Genoa, Italy
| | - Mario Amore
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, Section of Psychiatry, University of Genoa, Genoa, Italy,Ospedale Policlinico San Martino IRCCS, Genoa, Italy
| | - Georg Northoff
- University of Ottawa Brain and Mind Research Institute, and Mind Brain Imaging and Neuroethics Royal’s Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada,Mental Health Centre, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China,Centre for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, China
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Claude LA, Houenou J, Duchesnay E, Favre P. Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions. Bipolar Disord 2020; 22:334-355. [PMID: 32108409 DOI: 10.1111/bdi.12895] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
OBJECTIVES The existence of anatomofunctional brain abnormalities in bipolar disorder (BD) is now well established by magnetic resonance imaging (MRI) studies. To create diagnostic and prognostic tools, as well as identifying biologically valid subtypes of BD, research has recently turned towards the use of machine learning (ML) techniques. We assessed both supervised ML and unsupervised ML studies in BD to evaluate their robustness, reproducibility and the potential need for improvement. METHOD We systematically searched for studies using ML algorithms based on MRI data of patients with BD until February 2019. RESULT We identified 47 studies, 45 using supervised ML techniques and 2 including unsupervised ML analyses. Among supervised studies, 43 focused on diagnostic classification. The reported accuracies for classification of BD ranged between (a) 57% and 100%, for BD vs healthy controls; (b) 49.5% and 93.1% for BD vs patients with major depressive disorder; and (c) 50% and 96.2% for BD vs patients with schizophrenia. Reported accuracies for discriminating subjects genetically at risk for BD (either from control or from patients with BD) ranged between 64.3% and 88.93%. CONCLUSIONS Although there are strong methodological limitations in previous studies and an important need for replication in large multicentric samples, the conclusions of our review bring hope of future computer-aided diagnosis of BD and pave the way for other applications, such as treatment response prediction. To reinforce the reliability of future results we provide methodological suggestions for good practice in conducting and reporting MRI-based ML studies in BD.
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Affiliation(s)
- Laurie-Anne Claude
- APHP, Mondor University Hospitals, DMU IMPACT Psychiatry and Addictology, UPEC, Créteil, France.,Neurospin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France.,INSERM Unit U955, IMRB, Team 15, "Neurotranslational Psychiatry", Créteil, France.,FondaMental Foundation, Créteil, France
| | - Josselin Houenou
- APHP, Mondor University Hospitals, DMU IMPACT Psychiatry and Addictology, UPEC, Créteil, France.,Neurospin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France.,INSERM Unit U955, IMRB, Team 15, "Neurotranslational Psychiatry", Créteil, France.,FondaMental Foundation, Créteil, France
| | | | - Pauline Favre
- Neurospin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France.,INSERM Unit U955, IMRB, Team 15, "Neurotranslational Psychiatry", Créteil, France.,FondaMental Foundation, Créteil, France
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Increased intrinsic default-mode network activity as a compensatory mechanism in aMCI: a resting-state functional connectivity MRI study. Aging (Albany NY) 2020; 12:5907-5919. [PMID: 32238610 PMCID: PMC7185142 DOI: 10.18632/aging.102986] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 03/24/2020] [Indexed: 11/25/2022]
Abstract
Numerous studies have investigated the differences in the mean functional connectivity (FC) strength between amnestic mild cognitive impairment (aMCI) patients and normal subjects using resting-state functional magnetic resonance imaging. However, whether the mean FC is increased, decreased or unchanged in aMCI patients compared to normal controls remains unclear. Two factors might lead to inconsistent results: the determination of regions of interest and the reliability of the FC. We explored differences in FC and the degree centrality (Dc) constructed by the bootstrap method, between and within networks (default-mode network (DN), frontoparietal control network (CN), dorsal attention network (AN)), and resulting from a hierarchical-clustering algorithm. The mean FC within the DN and CN was significantly increased (P < 0.05, uncorrected) in patients. Significant increases (P < 0.05, uncorrected) in the mean FC were found in patients between DN and CN and between DN and AN. Five pairs of FC (false discovery rate corrected) and the Dc of six regions (Bonferroni corrected) displayed a significant increase in patients. Lower cognitive ability was significantly associated with a greater increase in the Dc of the left superior temporal sulcus. Our results demonstrate that the early dysfunctions in aMCI disease are mainly compensatory impairments.
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Yan B, Xu X, Liu M, Zheng K, Liu J, Li J, Wei L, Zhang B, Lu H, Li B. Quantitative Identification of Major Depression Based on Resting-State Dynamic Functional Connectivity: A Machine Learning Approach. Front Neurosci 2020; 14:191. [PMID: 32292322 PMCID: PMC7118554 DOI: 10.3389/fnins.2020.00191] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 02/24/2020] [Indexed: 01/14/2023] Open
Abstract
Introduction Developing a machine learning-based approach which could provide quantitative identification of major depressive disorder (MDD) is essential for the diagnosis and intervention of this disorder. However, the performances of traditional algorithms using static functional connectivity (SFC) measures were unsatisfactory. In the present work, we exploit the hidden information embedded in dynamic functional connectivity (DFC) and developed an accurate and objective image-based diagnosis system for MDD. Methods MRI images were collected from 99 participants including 56 healthy controls and 43 MDD patients. DFC was calculated using a sliding-window algorithm. A non-linear support vector machine (SVM) approach was then used with the DFC matrices as features to distinguish MDD patients from healthy controls. The spatiotemporal characteristics of the most discriminative features were then investigated. Results The area under the curve (AUC) of the SVM classifier with DFC measures reached 0.9913, while this value is only 0.8685 for the algorithm using SFC measures. Spatially, the most discriminative 28 connections distributed in the visual network (VN), somatomotor network (SMN), dorsal attention network (DAN), ventral attention network (VAN), limbic network (LN), frontoparietal network (FPN), and default mode network (DMN), etc. Notably, a large portion of these connections were associated with the FPN, DMN, and VN. Temporally, the most discriminative connections transited from the cortex to deeper regions. Conclusion The results clearly suggested that DFC is superior to SFC and provide a reliable quantitative identification method for MDD. Our findings may furnish a better understanding of the neural mechanisms of MDD as well as improve accurate diagnosis and early intervention of this disorder.
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Affiliation(s)
- Baoyu Yan
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
| | - Xiaopan Xu
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
| | - Mengwan Liu
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
| | - Kaizhong Zheng
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
| | - Jian Liu
- Network Center, Air Force Medical University, Xi'an, China
| | - Jianming Li
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
| | - Lei Wei
- Network Center, Air Force Medical University, Xi'an, China
| | - Binjie Zhang
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
| | - Hongbing Lu
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
| | - Baojuan Li
- School of Biomedical Engineering, Air Force Medical University, Xi'an, China
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36
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Shao J, Dai Z, Zhu R, Wang X, Tao S, Bi K, Tian S, Wang H, Sun Y, Yao Z, Lu Q. Early identification of bipolar from unipolar depression before manic episode: Evidence from dynamic rfMRI. Bipolar Disord 2019; 21:774-784. [PMID: 31407477 DOI: 10.1111/bdi.12819] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
OBJECTIVE Misdiagnosis of bipolar disorder (BD) as unipolar disorder (UD) may cause improper treatment strategy to be chosen, especially in the early stages of disease. The aim of this study was to characterize alterations in specific brain networks for depressed patients who transformed into BD (tBD) from UD. METHOD The module allegiance from resting-fMRI by applying a multilayer modular method was estimated in 99 patients (33 tBD, 33 BD, 33 UD) and 33 healthy controls (HC). A classification model was trained on tBD and UD patients. HC was used to explore the functional declination patterns of BD, tBD, and UD. RESULTS Based on our classification model, difference mainly reflected in default-mode network (DMN). Compared with HC, both BD and tBD focused on the difference of somatomotor network (SMN), while UD on the abnormity of DMN. The patterns of brain network between patients with BD and tBD were well-overlapped, except for cognitive control network (CCN). CONCLUSION The functional declination of internal interaction in DMN was suggested to be useful for the identification of BD from UD in the early stage. The higher recruitment of DMN may predispose patients to depressive states, while higher recruitment of SMN makes them more sensitive to external stimuli and prone to mania. Furthermore, CCN may be a critical network for identifying different stages of BD, suggesting that the onset of mania in depressed patients is accompanied by CCN related cognitive impairments.
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Affiliation(s)
- Junneng Shao
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Zhongpeng Dai
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Rongxin Zhu
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, China
| | - Xinyi Wang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Shiwan Tao
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, China
| | - Kun Bi
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Shui Tian
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Huan Wang
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Yurong Sun
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Zhijian Yao
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China.,Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, China
| | - Qing Lu
- School of Biological Sciences & Medical Engineering, Southeast University, Nanjing, China.,Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
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Mitelman SA. Transdiagnostic neuroimaging in psychiatry: A review. Psychiatry Res 2019; 277:23-38. [PMID: 30639090 DOI: 10.1016/j.psychres.2019.01.026] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Revised: 01/07/2019] [Accepted: 01/07/2019] [Indexed: 01/10/2023]
Abstract
Transdiagnostic approach has a long history in neuroimaging, predating its recent ascendance as a paradigm for new psychiatric nosology. Various psychiatric disorders have been compared for commonalities and differences in neuroanatomical features and activation patterns, with different aims and rationales. This review covers both structural and functional neuroimaging publications with direct comparison of different psychiatric disorders, including schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorder, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, conduct disorder, anorexia nervosa, and bulimia nervosa. Major findings are systematically presented along with specific rationales for each comparison.
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Affiliation(s)
- Serge A Mitelman
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA; Department of Psychiatry, Division of Child and Adolescent Psychiatry, Elmhurst Hospital Center, 79-01 Broadway, Elmhurst, NY 11373, USA.
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38
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Sinha P, Reddy RV, Srivastava P, Mehta UM, Bharath RD. Network neurobiology of electroconvulsive therapy in patients with depression. Psychiatry Res Neuroimaging 2019; 287:31-40. [PMID: 30952030 DOI: 10.1016/j.pscychresns.2019.03.008] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Revised: 03/16/2019] [Accepted: 03/19/2019] [Indexed: 12/22/2022]
Abstract
Graph theory, a popular analytic tool for resting state fMRI (rsfMRI) has provided important insights in the neurobiology of depression. We aimed to analyze the changes in the network measures of segregation and integration associated with the administration of ECT in patients with depression and to correlate with both clinical response and cognitive deficits. Changes in normalised clustering coefficient (γ), path length (λ) and small-world (σ) index were explored in 17 patients with depressive episode before 1st and after 6th brief-pulse bifrontal ECT (BFECT) sessions. Significant brain regions were then correlated with differences in clinical and cognitive scales. There was significantly increased γ and σ despite significant increase in λ in several brain regions after ECT in patients with depression. The brain areas revealing significant differences in γ before and after ECT were medial left superior frontal gyrus, left paracentral lobule, right pallidum and left inferior frontal operculum; correlating with changes in verbal fluency, HAM-D scores and delayed verbal memory (last two regions) respectively. BFECT reorganized the brain network topology in patients with depression and made it more segregated and less integrated; these correlated with clinical improvement and associated cognitive deficits.
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Affiliation(s)
- Preeti Sinha
- Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Hosur Road, Bangalore 560029, India
| | - R Venkateswara Reddy
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences (NIMHANS), Hosur Road, Bangalore 560029, India; Cognitive Neuroscience Centre, National Institute of Mental Health and Neurosciences (NIMHANS), Hosur Road, Bangalore 560029, India
| | - Prerna Srivastava
- Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Hosur Road, Bangalore 560029, India
| | - Urvakhsh M Mehta
- Department of Psychiatry, National Institute of Mental Health and Neurosciences (NIMHANS), Hosur Road, Bangalore 560029, India
| | - Rose Dawn Bharath
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences (NIMHANS), Hosur Road, Bangalore 560029, India; Cognitive Neuroscience Centre, National Institute of Mental Health and Neurosciences (NIMHANS), Hosur Road, Bangalore 560029, India.
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Differentiating between bipolar and unipolar depression in functional and structural MRI studies. Prog Neuropsychopharmacol Biol Psychiatry 2019; 91:20-27. [PMID: 29601896 DOI: 10.1016/j.pnpbp.2018.03.022] [Citation(s) in RCA: 125] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Revised: 03/25/2018] [Accepted: 03/25/2018] [Indexed: 01/10/2023]
Abstract
Distinguishing depression in bipolar disorder (BD) from unipolar depression (UD) solely based on clinical clues is difficult, which has led to the exploration of promising neural markers in neuroimaging measures for discriminating between BD depression and UD. In this article, we review structural and functional magnetic resonance imaging (MRI) studies that directly compare UD and BD depression based on neuroimaging modalities including functional MRI studies on regional brain activation or functional connectivity, structural MRI on gray or white matter morphology, and pattern classification analyses using a machine learning approach. Numerous studies have reported distinct functional and structural alterations in emotion- or reward-processing neural circuits between BD depression and UD. Different activation patterns in neural networks including the amygdala, anterior cingulate cortex (ACC), prefrontal cortex (PFC), and striatum during emotion-, reward-, or cognition-related tasks have been reported between BD and UD. A stronger functional connectivity pattern in BD was pronounced in default mode and in frontoparietal networks and brain regions including the PFC, ACC, parietal and temporal regions, and thalamus compared to UD. Gray matter volume differences in the ACC, hippocampus, amygdala, and dorsolateral prefrontal cortex (DLPFC) have been reported between BD and UD, along with a thinner DLPFC in BD compared to UD. BD showed reduced integrity in the anterior part of the corpus callosum and posterior cingulum compared to UD. Several studies performed pattern classification analysis using structural and functional MRI data to distinguish between UD and BD depression using a supervised machine learning approach, which yielded a moderate level of accuracy in classification.
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40
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Gong J, Chen G, Jia Y, Zhong S, Zhao L, Luo X, Qiu S, Lai S, Qi Z, Huang L, Wang Y. Disrupted functional connectivity within the default mode network and salience network in unmedicated bipolar II disorder. Prog Neuropsychopharmacol Biol Psychiatry 2019; 88:11-18. [PMID: 29958116 DOI: 10.1016/j.pnpbp.2018.06.012] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Revised: 06/19/2018] [Accepted: 06/23/2018] [Indexed: 01/10/2023]
Abstract
BACKGROUND Recent studies demonstrate that functional disruption in resting-state networks contributes to cognitive and affective symptoms of bipolar disorder (BD), however, the functional connectivity (FC) pattern underlying BD II depression within the default mode network (DMN), salience network (SN), and frontoparietal network (FPN) is still not well understood. The primary aim of this study was to explore whether the pathophysiology of BD II derived from the pattern of FC within the DMN, SN, and FPN by using seed-based FC approach of resting-state functional magnetic resonance imaging (rs-fMRI). METHODS Ninety-six BD II patients and 100 HCs underwent rs-fMRI and three-dimensional structural data acquisition. All patients were either drug naive or unmedicated for at least 6 months. The following four regions of interest were used to conduct seed-based FC: the left posterior cingulate cortex (PCC) seed to probe the DMN, the left subgenual anterior cingulate cortex (sgACC) and amygdala seeds to probe the SN, the left dorsal lateral prefrontal cortex (dlPFC) seed to probe the FPN. RESULTS Compared with HCs, patients with BD II demonstrated hypoconnectivity of the left PCC to the bilateral medial prefrontal cortex (mPFC) and bilateral precuneus/PCC, and of the left sgACC to the right inferior temporal gyrus (ITG); nevertheless, the left amygdala and dlPFC had no within-network hypo- or hyperconnectivity to any other SN and FPN regions. CONCLUSION Our findings suggest that disrupted FC is located in the DMN and SN, especially in the PCC-mPFC and precuneus/PCC, and sgACC-ITG connectivity in BD II patients.
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Affiliation(s)
- JiaYing Gong
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China; Department of Radiology, Six Affiliated Hospital of Sun Yat-sen University, Guangzhou 510655, China
| | - Guanmao Chen
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
| | - Yanbin Jia
- Department of Psychiatry, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
| | - Shuming Zhong
- Department of Psychiatry, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
| | - Lianping Zhao
- Department of Radiology, Gansu Provincial Hospital, Gansu 730000, China
| | - Xiaomei Luo
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
| | - Shaojuan Qiu
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
| | - Shunkai Lai
- Department of Psychiatry, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
| | - Zhangzhang Qi
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
| | - Li Huang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China.
| | - Ying Wang
- Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China.
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Li J, Gong H, Xu H, Ding Q, He N, Huang Y, Jin Y, Zhang C, Voon V, Sun B, Yan F, Zhan S. Abnormal Voxel-Wise Degree Centrality in Patients With Late-Life Depression: A Resting-State Functional Magnetic Resonance Imaging Study. Front Psychiatry 2019; 10:1024. [PMID: 32082198 PMCID: PMC7005207 DOI: 10.3389/fpsyt.2019.01024] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 12/24/2019] [Indexed: 11/21/2022] Open
Abstract
OBJECTIVES Late-life depression (LLD) has negative impacts on somatic, emotional and cognitive domains of the lives of patients. Elucidating the abnormality in the brain networks of LLD patients could help to strengthen the understanding of LLD pathophysiology, however, the studies exploring the spontaneous brain activity in LLD during the resting state remain limited. This study aimed at identifying the voxel-level whole-brain functional connectivity changes in LLD patients. METHODS Fifty patients with late-life depression (LLD) and 33 healthy controls were recruited. All participants underwent a resting-state functional magnetic resonance imaging scan to assess the voxel-wise degree centrality (DC) changes in the patients. Furthermore, DC was compared between two patient subgroups, the late-onset depression (LOD) and the early-onset depression (EOD). RESULTS Compared with the healthy controls, LLD patients showed increased DC in the inferior parietal lobule, parahippocampal gyrus, brainstem and cerebellum (p < 0.05, AlphaSim-corrected). LLD patients also showed decreased DC in the somatosensory and motor cortices and cerebellum (p < 0.05, AlphaSim-corrected). Compared with EOD patients, LOD patients showed increased centrality in the superior and middle temporal gyrus and decreased centrality in the occipital region (p < 0.05, AlphaSim-corrected). No significant correlation was found between the DC value and the symptom severity or disease duration in the patients after the correction for multiple comparisons. CONCLUSIONS These findings indicate that the intrinsic abnormality of network centrality exists in a wide range of brain areas in LLD patients. LOD patients differ with EOD patients in cortical network centrality. Our study might help to strengthen the understanding of the pathophysiology of LLD and the potential neural substrates underlie related emotional and cognitive impairments observed in the patients.
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Affiliation(s)
- Jun Li
- Department of Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hengfen Gong
- Department of Psychiatry, Shanghai Pudong New Area Mental Health Center, Tongji University School of Medicine, Shanghai, China
| | - Hongmin Xu
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qiong Ding
- Neural and Intelligence Engineering Center, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Naying He
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ying Huang
- Department of Psychiatry, Shanghai Pudong New Area Mental Health Center, Tongji University School of Medicine, Shanghai, China
| | - Ying Jin
- Department of Psychiatry, Shanghai Pudong New Area Mental Health Center, Tongji University School of Medicine, Shanghai, China
| | - Chencheng Zhang
- Department of Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Valerie Voon
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Bomin Sun
- Department of Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shikun Zhan
- Department of Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Vai B, Bertocchi C, Benedetti F. Cortico-limbic connectivity as a possible biomarker for bipolar disorder: where are we now? Expert Rev Neurother 2019; 19:159-172. [PMID: 30599797 DOI: 10.1080/14737175.2019.1562338] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
INTRODUCTION The fronto-limbic network has been suggested as a key circuitry in the pathophysiology and maintenance of bipolar disorder. In the past decade, a disrupted connectivity within prefrontal-limbic structures was identified as a promising candidate biomarker for the disorder. Areas Covered: In this review, the authors examine current literature in terms of the structural, functional and effective connectivity in bipolar disorder, integrating recent findings of imaging genetics and machine learning. This paper profiles the current knowledge and identifies future perspectives to provide reliable and usable neuroimaging biomarkers for bipolar psychopathology in clinical practice. Expert Opinion: The replication and the translation of acquired knowledge into useful and usable tools represents one of the current greatest challenges in biomarker research applied to psychiatry.
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Affiliation(s)
- Benedetta Vai
- a Psychiatry & Clinical Psychobiology , Division of Neuroscience, Scientific Institute Ospedale San Raffaele , Milano , Italy.,b University Vita-Salute San Raffaele , Milano , Italy
| | - Carlotta Bertocchi
- a Psychiatry & Clinical Psychobiology , Division of Neuroscience, Scientific Institute Ospedale San Raffaele , Milano , Italy
| | - Francesco Benedetti
- a Psychiatry & Clinical Psychobiology , Division of Neuroscience, Scientific Institute Ospedale San Raffaele , Milano , Italy.,b University Vita-Salute San Raffaele , Milano , Italy
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Kling LR, Bessette KL, DelDonno SR, Ryan KA, Drevets WC, McInnis MG, Phillips ML, Langenecker SA. Cluster analysis with MOODS-SR illustrates a potential bipolar disorder risk phenotype in young adults with remitted major depressive disorder. Bipolar Disord 2018; 20:697-707. [PMID: 30294823 PMCID: PMC6319908 DOI: 10.1111/bdi.12693] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
OBJECTIVES Delays in the diagnosis and detection of bipolar disorder can lead to adverse consequences, including improper treatment and increased suicide risk. The Mood Spectrum Self-Report Measure (MOODS-SR) was designed to capture the full spectrum of lifetime mood symptomology with factor scores for depression and mania symptom constellations. The utility of the MOODS-SR as a tool to investigate homogeneous subgroups was examined, with particular focus on a possible bipolar risk subgroup. Moreover, potential patterns of differences in MOODS-SR subtypes were probed using cognitive vulnerabilities, neuropsychological functioning, and ventral striatum connectivity. METHODS K-mean cluster analysis based on factor scores of MOODS-SR was used to determine homogeneous subgroupings within a healthy and remitted depressed young adult sample (N = 86). Between-group comparisons (based on cluster subgroupings) were conducted on measures of cognitive vulnerabilities, neuropsychological functioning, and ventral striatum rs-fMRI connectivity. RESULTS Three groups of participants were identified: one with minimal symptomology, one with moderate primarily depressive symptomology, and one with more severe manic and depressive symptomology. Differences in impulsivity, neuroticism, conscientiousness, facial perception accuracy, and rs-fMRI connectivity exist between moderate and severe groups. CONCLUSIONS Within a sample of people with and without depression histories, a severe subgroup was identified with potentially increased risk of developing bipolar disorder through use of the MOODS-SR. This small subgroup had higher levels of lifetime depression and mania symptoms. Additionally, differences in traits, affective processing, and connectivity exist between those with a more prototypic unipolar subgrouping and those with potential risk for developing bipolar disorder.
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Affiliation(s)
| | | | | | - Kelly A Ryan
- University of Michigan Medical Center, Ann Arbor, MI,
USA
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Abnormal intrinsic cerebro-cerebellar functional connectivity in un-medicated patients with bipolar disorder and major depressive disorder. Psychopharmacology (Berl) 2018; 235:3187-3200. [PMID: 30206663 DOI: 10.1007/s00213-018-5021-6] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Accepted: 08/29/2018] [Indexed: 02/05/2023]
Abstract
OBJECTIVE The cerebellum plays an important role in depression. Cerebro-cerebellar circuits have been found to show aberrance in bipolar disorder (BD) and major depressive disorder (MDD). However, whether the cerebro-cerebellar connectivity contributes equally to the pathologic mechanisms of BD and MDD remains unknown. METHODS We recruited 33 patients with MDD, 32 patients with BD, and 43 healthy controls (HC). We selected six seed regions (three per hemisphere) in the cerebrum, corresponding to the affective, cognitive control, and default mode networks, to establish cerebro-cerebellar functional connectivity maps. RESULTS Relative to the HC, both the BD and MDD patients exhibited weaker negative connectivity between the right subgenual anterior cingulate cortex and the cerebellar vermis IV_V (pBD = 0.03, pMDD = 0.001) and weaker positive connectivity between the left precuneus and the left cerebellar lobule IX (pBD = 0.043, pMDD = 0.000). Moreover, the MDD patients showed weaker positive connectivity in the left precuneus-left cerebellar lobule IX circuit than the BD patients (p = 0.049). In addition, the BD patients showed weaker positive connectivity in the right dorsolateral prefrontal cortex-left cerebellar lobule Crus Ι circuit compared to the HC (p = 0.002) or the MDD patients (p = 0.013). Receiver operating characteristic curves analyses showed that the altered cerebro-cerebellar connectivities could be used to distinguish the patients from the HC with relatively high accuracy. CONCLUSIONS Our findings suggested that differences in connectivity of cerebro-cerebellar circuits, which are involved in affective or cognitive functioning, significantly contributed to BD and MDD.
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Osuch E, Gao S, Wammes M, Théberge J, Williamson P, Neufeld RJ, Du Y, Sui J, Calhoun V. Complexity in mood disorder diagnosis: fMRI connectivity networks predicted medication-class of response in complex patients. Acta Psychiatr Scand 2018; 138:472-482. [PMID: 30084192 PMCID: PMC6204076 DOI: 10.1111/acps.12945] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/10/2018] [Indexed: 12/20/2022]
Abstract
OBJECTIVE This study determined the clinical utility of an fMRI classification algorithm predicting medication-class of response in patients with challenging mood diagnoses. METHODS Ninety-nine 16-27-year-olds underwent resting state fMRI scans in three groups-BD, MDD and healthy controls. A predictive algorithm was trained and cross-validated on the known-diagnosis patients using maximally spatially independent components (ICs), constructing a similarity matrix among subjects, partitioning the matrix in kernel space and optimizing support vector machine classifiers and IC combinations. This classifier was also applied to each of 12 new individual patients with unclear mood disorder diagnoses. RESULTS Classification within the known-diagnosis group was approximately 92.4% accurate. The five maximally contributory ICs were identified. Applied to the complicated patients, the algorithm diagnosis was consistent with optimal medication-class of response to sustained recovery in 11 of 12 cases (i.e., almost 92% accuracy). CONCLUSION This classification algorithm performed well for the know-diagnosis but also predicted medication-class of response in difficult-to-diagnose patients. Further research can enhance this approach and extend these findings to be more clinically accessible.
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Affiliation(s)
- E. Osuch
- Lawson Health Research InstituteLondon Health Sciences CentreLondonONCanada,Department of PsychiatryUniversity of Western Ontario Schulich School of Medicine and DentistryLondonONCanada,Department of Medical BiophysicsUniversity of Western OntarioLondonONCanada
| | - S. Gao
- Brainnetome Center and National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijingChina,University of Chinese Academy of SciencesBeijingChina
| | - M. Wammes
- Department of PsychiatryUniversity of Western Ontario Schulich School of Medicine and DentistryLondonONCanada
| | - J. Théberge
- Lawson Health Research InstituteLondon Health Sciences CentreLondonONCanada,Department of PsychiatryUniversity of Western Ontario Schulich School of Medicine and DentistryLondonONCanada,Department of Medical BiophysicsUniversity of Western OntarioLondonONCanada
| | - P. Williamson
- Department of PsychiatryUniversity of Western Ontario Schulich School of Medicine and DentistryLondonONCanada,Department of Medical BiophysicsUniversity of Western OntarioLondonONCanada
| | - R. J. Neufeld
- Department of PsychologyUniversity of Western OntarioLondonONCanada
| | - Y. Du
- The Mind Research NetworkAlbuquerqueNMUSA,School of Computer and Information TechnologyShanxi UniversityTaiyuanChina
| | - J. Sui
- Brainnetome Center and National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijingChina,University of Chinese Academy of SciencesBeijingChina,The Mind Research NetworkAlbuquerqueNMUSA,CAS Center for Excellence in Brain Science and Intelligence TechnologyInstitute of AutomationChinese Academy of SciencesBeijingChina
| | - V. Calhoun
- The Mind Research NetworkAlbuquerqueNMUSA,Department of Electrical and Computer EngineeringUniversity of New MexicoAlbuquerqueNMUSA
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Association between catechol-O-methyltransferase genetic variation and functional connectivity in patients with first-episode schizophrenia. Schizophr Res 2018; 199:214-220. [PMID: 29730044 DOI: 10.1016/j.schres.2018.04.023] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2017] [Revised: 04/11/2018] [Accepted: 04/13/2018] [Indexed: 02/08/2023]
Abstract
Dopamine in the prefrontal cortex (PFC) plays an important role in cognitive performance and regulates by catechol-O-methyltransferase (COMT) expression. To clarify the effect of COMT genotype on cognitive function in patients with schizophrenia, we performed DNA genotyping, cognitive evaluations, and functional magnetic resonance imaging (fMRI) in antipsychotic-naïve patients with first-episode schizophrenia (FES) and matched healthy control subjects. We found that all cognitive domains were impaired in patients with FES compared with healthy subjects. Moreover, COMT genotype influenced the verbal learning performance in healthy subjects, but not in patients with FES. Resting-state fMRI data revealed that patients with FES exhibited higher functional connectivity degree centrality in the medial PFC and lower degree centrality in the parietal-occipital junction than healthy subjects. Furthermore, patients with FES who were COMT Met allele carriers had higher degree centrality in the medial PFC than those with the Val/Val genotype. In contrast, in healthy controls, Met allele carriers exhibited higher degree centrality than healthy controls with the Val/Val genotype in the left hippocampus and left amygdala. There was a negative correlation between the degree centrality value in medial PFC and score of the Hopkins Verbal Learning Test-Revised (HVLT-R) in FES patients with the Met allele. Our findings suggest that COMT genotype differentially influences pathways related to cognitive performance in patients with FES versus healthy individuals, providing an important insight into schizophrenia pathophysiology.
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47
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Korgaonkar MS, Erlinger M, Breukelaar IA, Boyce P, Hazell P, Antees C, Foster S, Grieve SM, Gomes L, Williams LM, Harris AWF, Malhi GS. Amygdala Activation and Connectivity to Emotional Processing Distinguishes Asymptomatic Patients With Bipolar Disorders and Unipolar Depression. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2018; 4:361-370. [PMID: 30343134 DOI: 10.1016/j.bpsc.2018.08.012] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Revised: 08/06/2018] [Accepted: 08/18/2018] [Indexed: 02/01/2023]
Abstract
BACKGROUND Mechanistically based neural markers, such as amygdala reactivity, offer one approach to addressing the challenges of differentiating bipolar and unipolar depressive disorders independently from mood state and acute symptoms. Although emotion-elicited amygdala reactivity has been found to distinguish patients with bipolar depression from patients with unipolar depression, it remains unknown whether this distinction is traitlike and present in the absence of an acutely depressed mood. We addressed this gap by investigating patients with bipolar disorder (BP) and unipolar major depressive disorder (MDD) in remission. METHODS Supraliminal and subliminal processing of faces exhibiting threat, sad, happy, and neutral emotions during functional magnetic resonance imaging was completed by 73 participants (23 BP patients and 25 MDD patients matched for age and gender, number of depressive episodes and severity; 25 age- and gender-matched healthy control subjects). We compared groups for activation and connectivity for the amygdala. RESULTS BP patients had lower left amygdala activation than MDD patients during supraliminal and subliminal threat, sad, and neutral emotion processing and for subliminal happy faces. BP patients also exhibited lower amygdala connectivity to the insula and hippocampus for threat and to medial orbitofrontal cortex for happy supraliminal and subliminal processing. BP patients also demonstrated greater amygdala-insula connectivity for sad supraliminal and subliminal face processing. Both patient groups were distinct from control subjects across several measures for activation and connectivity. CONCLUSIONS Independent of valence or level of emotional awareness, amygdala activation and connectivity during facial emotion processing can distinguish BP patients and MDD patients. These findings provide evidence that this neural substrate could be a potential trait marker to differentiate these two disorders largely independent of illness state.
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Affiliation(s)
- Mayuresh S Korgaonkar
- Brain Dynamics Centre, Westmead Institute for Medical Research, The University of Sydney, New South Wales, Australia; Discipline of Psychiatry, University of Sydney School of Medicine, New South Wales, Australia.
| | - May Erlinger
- Brain Dynamics Centre, Westmead Institute for Medical Research, The University of Sydney, New South Wales, Australia
| | - Isabella A Breukelaar
- Brain Dynamics Centre, Westmead Institute for Medical Research, The University of Sydney, New South Wales, Australia
| | - Philip Boyce
- Discipline of Psychiatry, University of Sydney School of Medicine, New South Wales, Australia
| | - Philip Hazell
- Discipline of Psychiatry, University of Sydney School of Medicine, New South Wales, Australia
| | - Cassandra Antees
- Brain Dynamics Centre, Westmead Institute for Medical Research, The University of Sydney, New South Wales, Australia
| | - Sheryl Foster
- Department of Radiology, Westmead Hospital, Westmead, New South Wales, Australia; Discipline of Medical Radiation Sciences, Faculty of Health Science, The University of Sydney, New South Wales, Australia
| | - Stuart M Grieve
- Sydney Translational Imaging Laboratory, Heart Research Institute, Charles Perkins Centre and University of Sydney School of Medicine, New South Wales, Australia; Department of Radiology, Royal Prince Alfred Hospital, Camperdown, New South Wales, Australia
| | - Lavier Gomes
- Department of Radiology, Westmead Hospital, Westmead, New South Wales, Australia
| | - Leanne M Williams
- Brain Dynamics Centre, Westmead Institute for Medical Research, The University of Sydney, New South Wales, Australia; Psychiatry and Behavioral Sciences, Stanford University, Stanford, Palo Alto, California; Sierra-Pacific Mental Illness Research, Education, and Clinical Center, Palo Alto Veterans Affairs Health Care System, Palo Alto, California
| | - Anthony W F Harris
- Brain Dynamics Centre, Westmead Institute for Medical Research, The University of Sydney, New South Wales, Australia; Discipline of Psychiatry, University of Sydney School of Medicine, New South Wales, Australia
| | - Gin S Malhi
- Discipline of Psychiatry, University of Sydney School of Medicine, New South Wales, Australia; Clinical Assessment Diagnostic Evaluation (CADE) Clinic, Department of Psychiatry, Royal North Shore Hospital, Sydney, New South Wales, Australia
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Gao S, Calhoun VD, Sui J. Machine learning in major depression: From classification to treatment outcome prediction. CNS Neurosci Ther 2018; 24:1037-1052. [PMID: 30136381 DOI: 10.1111/cns.13048] [Citation(s) in RCA: 145] [Impact Index Per Article: 24.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Revised: 07/19/2018] [Accepted: 07/21/2018] [Indexed: 01/10/2023] Open
Abstract
AIMS Major depression disorder (MDD) is the single greatest cause of disability and morbidity, and affects about 10% of the population worldwide. Currently, there are no clinically useful diagnostic biomarkers that are able to confirm a diagnosis of MDD from bipolar disorder (BD) in the early depressive episode. Therefore, exploring translational biomarkers of mood disorders based on machine learning is in pressing need, though it is challenging, but with great potential to improve our understanding of these disorders. DISCUSSIONS In this study, we review popular machine-learning methods used for brain imaging classification and predictions, and provide an overview of studies, specifically for MDD, that have used magnetic resonance imaging data to either (a) classify MDDs from controls or other mood disorders or (b) investigate treatment outcome predictors for individual patients. Finally, challenges, future directions, and potential limitations related to MDD biomarker identification are also discussed, with a goal of offering a comprehensive overview that may help readers to better understand the applications of neuroimaging data mining in depression. CONCLUSIONS We hope such efforts may highlight the need for an urgently needed paradigm shift in treatment, to guide personalized optimal clinical care.
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Affiliation(s)
- Shuang Gao
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, New Mexico.,Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, New Mexico
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China.,CAS Centre for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China
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49
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Qiu M, Zhang H, Mellor D, Shi J, Wu C, Huang Y, Zhang J, Shen T, Peng D. Aberrant Neural Activity in Patients With Bipolar Depressive Disorder Distinguishing to the Unipolar Depressive Disorder: A Resting-State Functional Magnetic Resonance Imaging Study. Front Psychiatry 2018; 9:238. [PMID: 29922189 PMCID: PMC5996277 DOI: 10.3389/fpsyt.2018.00238] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Accepted: 05/15/2018] [Indexed: 01/21/2023] Open
Abstract
This study aims to explore the intrinsic patterns of spontaneous activity of bipolar depression (BD) patients by analyzing the fractional amplitude of low frequency fluctuation (fALFF) that help differentiate BD from unipolar depressive disorder(UD). Twenty eight patients with BD, 47 patients with UD and 29 healthy controls were enrolled to receive the resting-state functional magnetic resonance imaging (rs-fMRI) scans. The group differences of fALFF values were calculated among three groups. In addition, the correlations between the clinical variables and mfALFF values were estimated. The brain regions with activation discrepancies among three groups are located in precuneus, the left middle temporal gyrus (MTG) and left inferior parietal lobe (IPL) and lingual gyrus. Compared with HC group, BD group shows decreased fALFF in precuneus, the left IPL and increased fALFF in lingual gyrus remarkably; UD group shows significantly decreased fALFF in precuneus, the left MTG and the left IPL. On the contrast of patients with UD, patients with BD have significantly increased fALFF value in the left precuneus, the left MGT and lingual gyrus. Furthermore, a negative correlation is found between the mfALFF values in precuneus and the scores of cognitive impairment factor in the UD group. The similar pattern of intrinsic activity in PCC suggests depressive state-dependent change. The aberrant patterns of intrinsic activity in precuneus, the IPL and lingual gyrus might be provide quantitative nodes that help to conduct further study for better distinguishing between BD and UD.
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Affiliation(s)
- Meihui Qiu
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huifeng Zhang
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - David Mellor
- School of Psychology, Faculty of Health, Deakin University, Melbourne, VIC, Australia
| | - Jun Shi
- Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Chuangxin Wu
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yueqi Huang
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jianye Zhang
- Department of Medical Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ting Shen
- Department of Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Daihui Peng
- Division of Mood Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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