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Wang L, Zhou L, Liu S, Zheng Y, Liu Q, Yu M, Lu X, Lei W, Chen G. Identification of patients with internet gaming disorder via a radiomics-based machine learning model of subcortical structures in high-resolution T1-weighted MRI. Prog Neuropsychopharmacol Biol Psychiatry 2024; 133:111026. [PMID: 38735428 DOI: 10.1016/j.pnpbp.2024.111026] [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: 02/08/2024] [Revised: 04/26/2024] [Accepted: 05/09/2024] [Indexed: 05/14/2024]
Abstract
It is of vital importance to establish an objective and reliable model to facilitate the early diagnosis and intervention of internet gaming disorder (IGD). A total of 133 patients with IGD and 110 healthy controls (HCs) were included. We extracted radiomic features of subcortical structures in high-resolution T1-weighted MRI. Different combinations of four feature selection methods (analysis of variance, Kruskal-Wallis, recursive feature elimination and relief) and ten classification algorithms were used to identify the most robust combined models for distinguishing IGD patients from HCs. Furthermore, a nomogram incorporating radiomic signatures and independent clinical factors was developed. Calibration curve and decision curve analyses were used to evaluate the nomogram. The combination of analysis of variance selector and logistic regression classifier identified that the radiomic model constructed with 20 features from the right caudate nucleus and amygdala showed better IGD screening performance. The radiomic model produced good areas under the curves (AUCs) in the training, validation and test cohorts (AUCs of 0.961, 0.903 and 0.895, respectively). In addition, sex, internet addiction test scores and radiomic scores were included in the nomogram as independent risk factors for IGD. Analysis of the correction curve and decision curve showed that the clinical-radiomic model has good reliability (C-index: 0.987). The nomogram incorporating radiomic features of subcortical structures and clinical characteristics achieved satisfactory classification performance and could serve as an effective tool for distinguishing IGD patients from HCs.
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Affiliation(s)
- Li Wang
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China
| | - Li Zhou
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China
| | - Shengdan Liu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China
| | - Yurong Zheng
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China
| | - Qianhan Liu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China
| | - Minglin Yu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China
| | - Xiaofei Lu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China
| | - Wei Lei
- Department of Psychiatry, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China
| | - Guangxiang Chen
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou 646000, Sichuan, China.
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Xia X, Tang J, Peng Y, Liu Y, Chen Y, Yuan M, Yu R, Hou X, Fu Y. Brain alterations in adolescents with first-episode depression who have experienced adverse events: evidence from resting-state functional magnetic resonance imaging. Front Psychiatry 2024; 15:1358770. [PMID: 38654725 PMCID: PMC11036546 DOI: 10.3389/fpsyt.2024.1358770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 03/18/2024] [Indexed: 04/26/2024] Open
Abstract
Introduction Adverse life events constitute primary risk factors for major depressive disorder (MDD), influencing brain function and structure. Adolescents, with their brains undergoing continuous development, are particularly susceptible to enduring impacts of adverse events. Methods We investigated differences and correlations among childhood trauma, negative life events, and alterations of brain function in adolescents with first-episode MDD. The study included 23 patients with MDD and 19 healthy controls, aged 10-19 years. All participants underwent resting-state functional magnetic resonance imaging and were assessed using the beck depression inventory, childhood trauma questionnaire, and adolescent self-rating life events checklist. Results Compared with healthy controls, participants with first-episode MDD were more likely to have experienced emotional abuse, physical neglect, interpersonal relationship problems, and learning stress (all p' < 0.05). These adverse life events were significantly correlated with alterations in brain functions (all p < 0.05). Discussion This study contributes novel evidence on the underlying process between adverse life events, brain function, and depression, emphasizing the significant neurophysiological impact of environmental factors.
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Affiliation(s)
- Xiaodi Xia
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jinxiang Tang
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yadong Peng
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Ying Liu
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yingying Chen
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Meng Yuan
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Renqiang Yu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiao Hou
- Department of Clinical Medicine, Chongqing Medical and Pharmaceutical College, Chongqing, China
| | - Yixiao Fu
- Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Xiao P, Li Q, Gui H, Xu B, Zhao X, Wang H, Tao L, Chen H, Wang H, Lv F, Luo T, Cheng O, Luo J, Man Y, Xiao Z, Fang W. Combined brain topological metrics with machine learning to distinguish essential tremor and tremor-dominant Parkinson's disease. Neurol Sci 2024:10.1007/s10072-024-07472-1. [PMID: 38528280 DOI: 10.1007/s10072-024-07472-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 03/14/2024] [Indexed: 03/27/2024]
Abstract
BACKGROUND Essential tremor (ET) and Parkinson's disease (PD) are the two most prevalent movement disorders, sharing several overlapping tremor clinical features. Although growing evidence pointed out that changes in similar brain network nodes are associated with these two diseases, the brain network topological properties are still not very clear. OBJECTIVE The combination of graph theory analysis with machine learning (ML) algorithms provides a promising way to reveal the topological pathogenesis in ET and tremor-dominant PD (tPD). METHODS Topological metrics were extracted from Resting-state functional images of 86 ET patients, 86 tPD patients, and 86 age- and sex-matched healthy controls (HCs). Three steps were conducted to feature dimensionality reduction and four frequently used classifiers were adopted to discriminate ET, tPD, and HCs. RESULTS A support vector machine classifier achieved the best classification performance of four classifiers for discriminating ET, tPD, and HCs with 89.0% mean accuracy (mACC) and was used for binary classification. Particularly, the binary classification performances among ET vs. tPD, ET vs. HCs, and tPD vs. HCs were with 94.2% mACC, 86.0% mACC, and 86.3% mACC, respectively. The most power discriminative features were mainly located in the default, frontal-parietal, cingulo-opercular, sensorimotor, and cerebellum networks. Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with clinical characteristics. CONCLUSIONS These results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET, tPD, and HCs but also help to reveal the potential brain topological network pathogenesis in ET and tPD.
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Affiliation(s)
- Pan Xiao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Qin Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Honge Gui
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Bintao Xu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Xiaole Zhao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Hongyu Wang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Li Tao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Huiyue Chen
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Hansheng Wang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Tianyou Luo
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Oumei Cheng
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jin Luo
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yun Man
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zheng Xiao
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Weidong Fang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China.
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Yin X, Yang J, Xiang Q, Peng L, Song J, Liang S, Wu J. Brain network hierarchy reorganization in subthreshold depression. Neuroimage Clin 2024; 42:103594. [PMID: 38518552 PMCID: PMC10973537 DOI: 10.1016/j.nicl.2024.103594] [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: 02/14/2024] [Revised: 03/12/2024] [Accepted: 03/17/2024] [Indexed: 03/24/2024]
Abstract
BACKGROUND Hierarchy is the organizing principle of human brain network. How network hierarchy changes in subthreshold depression (StD) is unclear. The aim of this study was to investigate the altered brain network hierarchy and its clinical significance in patients with StD. METHODS A total of 43 patients with StD and 43 healthy controls matched for age, gender and years of education participated in this study. Alterations in the hierarchy of StD brain networks were depicted by connectome gradient analysis. We assessed changes in network hierarchy by comparing gradient scores in each network in patients with StD and healthy controls. The study compared different brain subdivisions if there was a different network. Finally, we analysed the relationship between the altered gradient scores and clinical characteristics. RESULTS Patients with StD had contracted network hierarchy and suppressed cortical range gradients. In the principal gradient, the gradient scores of default mode network were significantly reduced in patients with StD compared to controls. In the default network, the subdivisions of reduced gradient scores were mainly located in the precuneus, superior temporal gyrus, and anterior and posterior cingulate gyrus. Reduced gradient scores in the default mode network, the anterior and posterior cingulate gyrus were correlated with severity of depression. CONCLUSIONS The network hierarchy of the StD changed and was significantly correlated with depressive symptoms and severity. These results provided new insights into further understanding of the neural mechanisms of StD.
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Affiliation(s)
- Xiaolong Yin
- National-Local Joint Engineering Research Center of Rehabilitation Medicine Technology, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China; Rehabilitation Industry Institute, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China
| | - Junchao Yang
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China
| | - Qing Xiang
- National-Local Joint Engineering Research Center of Rehabilitation Medicine Technology, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China; Rehabilitation Industry Institute, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China
| | - Lixin Peng
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China
| | - Jian Song
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China
| | - Shengxiang Liang
- National-Local Joint Engineering Research Center of Rehabilitation Medicine Technology, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China; Rehabilitation Industry Institute, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China; Traditional Chinese Medicine Rehabilitation Research Center of State Administration of Traditional Chinese Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China.
| | - Jingsong Wu
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China.
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Dai P, Zhou Y, Shi Y, Lu D, Chen Z, Zou B, Liu K, Liao S. Classification of MDD using a Transformer classifier with large-scale multisite resting-state fMRI data. Hum Brain Mapp 2024; 45:e26542. [PMID: 38088473 PMCID: PMC10789197 DOI: 10.1002/hbm.26542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 10/27/2023] [Accepted: 11/09/2023] [Indexed: 01/16/2024] Open
Abstract
Major depressive disorder (MDD) is one of the most common psychiatric disorders worldwide with high recurrence rate. Identifying MDD patients, particularly those with recurrent episodes with resting-state fMRI, may reveal the relationship between MDD and brain function. We proposed a Transformer-Encoder model, which utilized functional connectivity extracted from large-scale multisite rs-fMRI datasets to classify MDD and HC. The model discarded the Transformer's Decoder part, reducing the model's complexity and decreasing the number of parameters to adapt to the limited sample size and it does not require a complex feature selection process and achieves end-to-end classification. Additionally, our model is suitable for classifying data combined from multiple brain atlases and has an optional unsupervised pre-training module to acquire optimal initial parameters and speed up the training process. The model's performance was tested on a large-scale multisite dataset and identified brain regions affected by MDD using the Grad-CAM method. After conducting five-fold cross-validation, our model achieved an average classification accuracy of 68.61% on a dataset consisting of 1611 samples. For the selected recurrent MDD dataset, the model reached an average classification accuracy of 78.11%. Abnormalities were detected in the frontal gyri and cerebral cortex of MDD patients in both datasets. Furthermore, the identified brain regions in the recurrent MDD dataset generally exhibited a higher contribution to the model's performance.
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Affiliation(s)
- Peishan Dai
- School of Computer Science and EngineeringCentral South UniversityChangshaChina
| | - Ying Zhou
- School of Computer Science and EngineeringCentral South UniversityChangshaChina
| | - Yun Shi
- School of Computer Science and EngineeringCentral South UniversityChangshaChina
| | - Da Lu
- School of Computer Science and EngineeringCentral South UniversityChangshaChina
| | - Zailiang Chen
- School of Computer Science and EngineeringCentral South UniversityChangshaChina
| | - Beiji Zou
- School of Computer Science and EngineeringCentral South UniversityChangshaChina
| | - Kun Liu
- Brain Hospital of Hunan Province (The Second People's Hospital of Hunan Province)ChangshaChina
| | - Shenghui Liao
- School of Computer Science and EngineeringCentral South UniversityChangshaChina
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Luo Y, Chen W, Zhan L, Qiu J, Jia T. Multi-feature concatenation and multi-classifier stacking: An interpretable and generalizable machine learning method for MDD discrimination with rsfMRI. Neuroimage 2024; 285:120497. [PMID: 38142755 DOI: 10.1016/j.neuroimage.2023.120497] [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: 08/13/2023] [Revised: 11/21/2023] [Accepted: 12/11/2023] [Indexed: 12/26/2023] Open
Abstract
Major depressive disorder (MDD) is a serious and heterogeneous psychiatric disorder that needs accurate diagnosis. Resting-state functional MRI (rsfMRI), which captures multiple perspectives on brain structure, function, and connectivity, is increasingly applied in the diagnosis and pathological research of MDD. Different machine learning algorithms are then developed to exploit the rich information in rsfMRI and discriminate MDD patients from normal controls. Despite recent advances reported, the MDD discrimination accuracy has room for further improvement. The generalizability and interpretability of the discrimination method are not sufficiently addressed either. Here, we propose a machine learning method (MFMC) for MDD discrimination by concatenating multiple features and stacking multiple classifiers. MFMC is tested on the REST-meta-MDD data set that contains 2428 subjects collected from 25 different sites. MFMC yields 96.9% MDD discrimination accuracy, demonstrating a significant improvement over existing methods. In addition, the generalizability of MFMC is validated by the good performance when the training and testing subjects are from independent sites. The use of XGBoost as the meta classifier allows us to probe the decision process of MFMC. We identify 13 feature values related to 9 brain regions including the posterior cingulate gyrus, superior frontal gyrus orbital part, and angular gyrus, which contribute most to the classification and also demonstrate significant differences at the group level. The use of these 13 feature values alone can reach 87% of MFMC's full performance when taking all feature values. These features may serve as clinically useful diagnostic and prognostic biomarkers for MDD in the future.
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Affiliation(s)
- Yunsong Luo
- College of Computer and Information Science, Southwest University, Chongqing, 400715, PR China.
| | - Wenyu Chen
- College of Computer and Information Science, Southwest University, Chongqing, 400715, PR China.
| | - Ling Zhan
- College of Computer and Information Science, Southwest University, Chongqing, 400715, PR China.
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, 400715, PR China; School of Psychology, Southwest University (SWU), Chongqing, 400715, PR China; Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing, 400715, PR China.
| | - Tao Jia
- College of Computer and Information Science, Southwest University, Chongqing, 400715, PR China.
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Qiu S, Luo X, Luo Y, Wei D, Mei G. State-dependent alterations of implicit emotional dominance during binocular rivalry in subthreshold depression. Psych J 2023; 12:809-823. [PMID: 37905936 DOI: 10.1002/pchj.686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 08/14/2023] [Indexed: 11/02/2023]
Abstract
Binocular rivalry, a visual perception phenomenon where two or more percepts alternate every few seconds when distinct stimuli are presented to the two eyes, has been reported as a biomarker in several psychiatric disorders. It is unclear whether abnormalities of binocular rivalry in depression could occur when emotional rivaling stimuli are used, and if so, whether an emotional binocular rivalry test could provide a trait-dependent or state-dependent biomarker. In the current study, 34 individuals with subthreshold depression and 31 non-depressed individuals performed a binocular rivalry task associated with implicit emotional processing. Participants were required to report their perceived orientations of the rival gratings in the foreground and to neglect emotional face stimuli in the background. The participants were retested after an approximately 4-month time interval. Compared to the non-depressed group, the subthreshold depression group showed significantly longer perceptual dominance durations of the grating with emotional faces as the background (i.e., implicit emotional dominance) at the initial assessment. However, the abnormality was not found at the follow-up assessment. More importantly, we found smaller changes in depressive severity at the follow-up assessment for individuals displaying longer emotional dominance at the initial assessment than for individuals with weaker emotional dominance. The current emotional binocular rivalry test may provide an objective, state-dependent biomarker for distinguishing individuals with subthreshold depression from non-depressed individuals.
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Affiliation(s)
- Shiming Qiu
- School of Psychology, Guizhou Normal University, Guiyang, People's Republic of China
- School of Psychology, Central China Normal University, Wuhan, People's Republic of China
| | - Xu Luo
- School of Psychology, Guizhou Normal University, Guiyang, People's Republic of China
| | - Yuhong Luo
- School of Psychology, Guizhou Normal University, Guiyang, People's Republic of China
| | - Dandan Wei
- School of Psychology, Guizhou Normal University, Guiyang, People's Republic of China
| | - Gaoxing Mei
- School of Psychology, Guizhou Normal University, Guiyang, People's Republic of China
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Wang X, Luo X, Pan H, Wang X, Xu S, Li H, Lin Z. Performance of hippocampal radiomics models based on T2-FLAIR images in mesial temporal lobe epilepsy with hippocampal sclerosis. Eur J Radiol 2023; 167:111082. [PMID: 37708677 DOI: 10.1016/j.ejrad.2023.111082] [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: 02/11/2023] [Revised: 07/14/2023] [Accepted: 09/04/2023] [Indexed: 09/16/2023]
Abstract
PURPOSE Preoperative identification of hippocampal sclerosis (HS) is crucial to successful surgery for mesial temporal lobe epilepsy (MTLE). We aimed to investigate the diagnostic performance of hippocampal radiomics models based on T2 fluid-attenuated inversion recovery (FLAIR) images in MTLE with HS. METHODS We analysed 210 cases, including 172 HS pathology-confirmed cases (100 magnetic resonance imaging [MRI]-positive cases [MRI + HS], 72 MRI-negative HS cases [MRI - HS]), and 38 healthy controls (HC). The hippocampus was delineated slice by slice on an oblique coronal plane by a T2-FLAIR sequence, perpendicular to the hippocampus's long axis, to obtain a three-dimensional region of interest. Radiomics were processed using Artificial Intelligence Kit software; logistic regression radiomics models were constructed. The model evaluation indexes included the area under the curve (AUC), accuracy, sensitivity, and specificity. RESULTS The respective AUC, accuracy, sensitivity, and specificity were 0.863, 81.4%, 78.0%, and 84.6% between the MRI - HS and HC groups in the training set and 0.855, 75.0%, 68.2%, and 81.8% in the test set; 0.975, 95.0%, 92.9%, and 98.0% between the MRI + HS and HC groups in the training set and 0.954, 88.7%, 90.0%, and 87.0% in the test set; and 0.912, 84.3%, 83.3%, and 86.5% between the MTLE and HC groups in the training set and 0.854, 79.7%, 80.8%, and 77.3% in the test set. The AUC values of the comparative radiomics models were > 0.85, indicating good diagnostic efficiency. CONCLUSION The hippocampal radiomics models based on T2-FLAIR images can help diagnose MTLE with HS. They can be used as biological markers for MTLE diagnosis.
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Affiliation(s)
- Xiaoyu Wang
- Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, Fujian Province, China; Department of Radiology, 900TH Hospital of Joint Logistics Support Force, Fuzhou, Fujian Province, China
| | - Xiaoting Luo
- Department of Radiology, the First Affiliated Hospital of Xiamen University, Xiamen, Fujian Province, China
| | - Haitao Pan
- Department of Radiology, Cangshan Branch of 900TH Hospital of Joint Logistics Support Force, Fuzhou, Fujian Province, China
| | - Xiaoyang Wang
- Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, Fujian Province, China; Department of Radiology, 900TH Hospital of Joint Logistics Support Force, Fuzhou, Fujian Province, China
| | - Shangwen Xu
- Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, Fujian Province, China; Department of Radiology, 900TH Hospital of Joint Logistics Support Force, Fuzhou, Fujian Province, China.
| | - Hui Li
- Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, Fujian Province, China; Department of Radiology, 900TH Hospital of Joint Logistics Support Force, Fuzhou, Fujian Province, China
| | - Zhiping Lin
- GE Healthcare, Guangzhou, Guangdong Province, China
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Gai Q, Chu T, Che K, Li Y, Dong F, Zhang H, Li Q, Ma H, Shi Y, Zhao F, Liu J, Mao N, Xie H. Classification of Major Depressive Disorder Based on Integrated Temporal and Spatial Functional MRI Variability Features of Dynamic Brain Network. J Magn Reson Imaging 2023; 58:827-837. [PMID: 36579618 DOI: 10.1002/jmri.28578] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 12/13/2022] [Accepted: 12/13/2022] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Characterization of the dynamics of functional brain network has gained increased attention in the study of depression. However, most studies have focused on single temporal dimension, while ignoring spatial dimensional information, hampering the discovery of validated biomarkers for depression. PURPOSE To integrate temporal and spatial functional MRI variability features of dynamic brain network in machine-learning techniques to distinguish patients with major depressive disorder (MDD) from healthy controls (HCs). STUDY TYPE Prospective. POPULATION A discovery cohort including 119 patients and 106 HCs and an external validation cohort including 126 patients and 124 HCs from Rest-meta-MDD consortium. FIELD STRENGTH/SEQUENCE A 3.0 T/resting-state functional MRI using the gradient echo sequence. ASSESSMENT A random forest (RF) model integrating temporal and spatial variability features of dynamic brain networks with separate feature selection method (MSFS ) was implemented for MDD classification. Its performance was compared with three RF models that used: temporal variability features (MTVF ), spatial variability features (MSVF ), and integrated temporal and spatial variability features with hybrid feature selection method (MHFS ). A linear regression model based on MSFS was further established to assess MDD symptom severity, with prediction performance evaluated by the correlations between true and predicted scores. STATISTICAL TESTS Receiver operating characteristic analyses with the area under the curve (AUC) were used to evaluate models' performance. Pearson's correlation was used to assess relationship of predicted scores and true scores. P < 0.05 was considered statistically significant. RESULTS The model with MSFS achieved the best performance, with AUCs of 0.946 and 0.834 in the discovery and validation cohort, respectively. Additionally, altered temporal and spatial variability could significantly predict the severity of depression (r = 0.640) and anxiety (r = 0.616) in MDD. DATA CONCLUSION Integration of temporal and spatial variability features provides potential assistance for clinical diagnosis and symptom prediction of MDD. EVIDENCE LEVEL 2. TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Qun Gai
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, People's Republic of China
| | - Tongpeng Chu
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, People's Republic of China
- Big Data & Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, People's Republic of China
| | - Kaili Che
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, People's Republic of China
| | - Yuna Li
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, People's Republic of China
| | - Fanghui Dong
- School of Medical Imaging, Binzhou Medical University, Yantai, Shandong, People's Republic of China
| | - Haicheng Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, People's Republic of China
- Big Data & Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, People's Republic of China
| | - Qinghe Li
- School of Medical Imaging, Binzhou Medical University, Yantai, Shandong, People's Republic of China
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, People's Republic of China
| | - Yinghong Shi
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, People's Republic of China
| | - Feng Zhao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, Shandong, People's Republic of China
| | - Jing Liu
- Department of Pediatrics, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, People's Republic of China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, People's Republic of China
- Big Data & Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, People's Republic of China
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, People's Republic of China
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Xiao P, Tao L, Zhang X, Li Q, Gui H, Xu B, Zhang X, He W, Chen H, Wang H, Lv F, Luo T, Cheng O, Luo J, Man Y, Xiao Z, Fang W. Using histogram analysis of the intrinsic brain activity mapping to identify essential tremor. Front Neurol 2023; 14:1165603. [PMID: 37404943 PMCID: PMC10317178 DOI: 10.3389/fneur.2023.1165603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 05/23/2023] [Indexed: 07/06/2023] Open
Abstract
Background Essential tremor (ET) is one of the most common movement disorders. Histogram analysis based on brain intrinsic activity imaging is a promising way to identify ET patients from healthy controls (HCs) and further explore the spontaneous brain activity change mechanisms and build the potential diagnostic biomarker in ET patients. Methods The histogram features based on the Resting-state functional magnetic resonance imaging (Rs-fMRI) data were extracted from 133 ET patients and 135 well-matched HCs as the input features. Then, a two-sample t-test, the mutual information, and the least absolute shrinkage and selection operator methods were applied to reduce the feature dimensionality. Support vector machine (SVM), logistic regression (LR), random forest (RF), and k-nearest neighbor (KNN) were used to differentiate ET and HCs, and classification performance of the established models was evaluated by the mean area under the curve (AUC). Moreover, correlation analysis was carried out between the selected histogram features and clinical tremor characteristics. Results Each classifier achieved a good classification performance in training and testing sets. The mean accuracy and area under the curve (AUC) of SVM, LR, RF, and KNN in the testing set were 92.62%, 0.948; 92.01%, 0.942; 93.88%, 0.941; and 92.27%, 0.939, respectively. The most power-discriminative features were mainly located in the cerebello-thalamo-motor and non-motor cortical pathways. Correlation analysis showed that there were two histogram features negatively and one positively correlated with tremor severity. Conclusion Our findings demonstrated that the histogram analysis of the amplitude of low-frequency fluctuation (ALFF) images with multiple machine learning algorithms could identify ET patients from HCs and help to understand the spontaneous brain activity pathogenesis mechanisms in ET patients.
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Affiliation(s)
- Pan Xiao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Li Tao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaoyu Zhang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Qin Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Honge Gui
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Bintao Xu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xueyan Zhang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Wanlin He
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Huiyue Chen
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Hansheng Wang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Tianyou Luo
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Oumei Cheng
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jin Luo
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yun Man
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zheng Xiao
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Weidong Fang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Franco-O´Byrne D, Gonzalez-Gomez R, Morales Sepúlveda JP, Vergara M, Ibañez A, Huepe D. The impact of loneliness and social adaptation on depressive symptoms: Behavioral and brain measures evidence from a brain health perspective. Front Psychol 2023; 14:1096178. [PMID: 37077845 PMCID: PMC10108715 DOI: 10.3389/fpsyg.2023.1096178] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 02/21/2023] [Indexed: 03/16/2023] Open
Abstract
Introduction Early detection of depression is a cost-effective way to prevent adverse outcomes on brain physiology, cognition, and health. Here we propose that loneliness and social adaptation are key factors that can anticipate depressive symptoms. Methods We analyzed data from two separate samples to evaluate the associations between loneliness, social adaptation, depressive symptoms, and their neural correlates. Results For both samples, hierarchical regression models on self-reported data showed that loneliness and social adaptation have negative and positive effects on depressive symptoms. Moreover, social adaptation reduces the impact of loneliness on depressive symptoms. Structural connectivity analysis showed that depressive symptoms, loneliness, and social adaptation share a common neural substrate. Furthermore, functional connectivity analysis demonstrated that only social adaptation was associated with connectivity in parietal areas. Discussion Altogether, our results suggest that loneliness is a strong risk factor for depressive symptoms while social adaptation acts as a buffer against the ill effects of loneliness. At the neuroanatomical level, loneliness and depression may affect the integrity of white matter structures known to be associated to emotion dysregulation and cognitive impairment. On the other hand, socio-adaptive processes may protect against the harmful effects of loneliness and depression. Structural and functional correlates of social adaptation could indicate a protective role through long and short-term effects, respectively. These findings may aid approaches to preserve brain health via social participation and adaptive social behavior.
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Affiliation(s)
- Daniel Franco-O´Byrne
- Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibáñez, Santiago de Chile, Chile
| | - Raul Gonzalez-Gomez
- Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibáñez, Santiago de Chile, Chile
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Juan Pablo Morales Sepúlveda
- Pontificia Universidad Católica de Chile Programa de Doctorado en Neurociencias Centro Interdisciplinario de Neurocienciass, Santiago, Chile
- Facultad de Educación Psicología y Familia, Universidad Finis Terrae, Santiago, Chile
| | - Mayte Vergara
- Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibáñez, Santiago de Chile, Chile
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Agustin Ibañez
- Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
- Global Brain Health Institute, University of California, San Francisco, San Francisco, CA, United States
- Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
| | - David Huepe
- Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibáñez, Santiago de Chile, Chile
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Du Y, Yu J, Liu M, Qiu Q, Fang Y, Zhao L, Wei W, Wang J, Lin X, Yan F, Li X. The relationship between depressive symptoms and cognitive function in Alzheimer's disease: The mediating effect of amygdala functional connectivity and radiomic features. J Affect Disord 2023; 330:101-109. [PMID: 36863470 DOI: 10.1016/j.jad.2023.02.129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 02/21/2023] [Accepted: 02/23/2023] [Indexed: 03/04/2023]
Abstract
BACKGROUND Depressive symptoms are common in Alzheimer's disease (AD) and are associated with cognitive function. Amygdala functional connectivity (FC) and radiomic features related to depression and cognition. However, studies have yet to explore the neural mechanisms underlying these associations. METHODS We enrolled eighty-two AD patients with depressive symptoms (ADD) and 85 healthy controls (HCs) in this study. We compared amygdala FC using the seed-based approach between ADD patients and HCs. The least absolute shrinkage and selection operator (LASSO) was used to select amygdala radiomic features. A support vector machine (SVM) model was constructed based on the identified radiomic features to distinguish ADD from HCs. We used mediation analyses to explore the mediating effects of amygdala radiomic features and amygdala FC on cognition. RESULTS We found that ADD patients showed decreased amygdala FC with posterior cingulate cortex, middle frontal gyrus (MFG), and parahippocampal gyrus involved in the default mode network compared to HCs. The area under the receiver operating characteristic curve (AUC) of the amygdala radiomic model was 0.95 for ADD patients and HCs. Notably, the mediation model demonstrated that amygdala FC with the MFG and amygdala-based radiomic features mediated the relationship between depressive symptoms and cognitive function in AD. LIMITATIONS This study is a cross-sectional study and lacks longitudinal data. CONCLUSION Our findings may not only expand existing biological knowledge of the relationship between cognition and depressive symptoms in AD from the perspective of brain function and structure but also may ultimately provide potential targets for personalized treatment strategies.
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Affiliation(s)
- Yang Du
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Jie Yu
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Manhua Liu
- MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Qi Qiu
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Yuan Fang
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Lu Zhao
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Wenjing Wei
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Jinghua Wang
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Xiang Lin
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Feng Yan
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai 200030, China.
| | - Xia Li
- Department of Geriatric Psychiatry, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China; Alzheimer's Disease and Related Disorders Center, Shanghai Jiao Tong University, Shanghai 200030, China.
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13
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Li Q, Wang W, Hu Z. Amygdala's T1-weighted image radiomics outperforms volume for differentiation of anxiety disorder and its subtype. Front Psychiatry 2023; 14:1091730. [PMID: 36911127 PMCID: PMC10001895 DOI: 10.3389/fpsyt.2023.1091730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 02/06/2023] [Indexed: 03/14/2023] Open
Abstract
INTRODUCTION Anxiety disorder is the most common psychiatric disorder among adolescents, with generalized anxiety disorder (GAD) being a common subtype of anxiety disorder. Current studies have revealed abnormal amygdala function in patients with anxiety compared with healthy people. However, the diagnosis of anxiety disorder and its subtypes still lack specific features of amygdala from T1-weighted structural magnetic resonance (MR) imaging. The purpose of our study was to investigate the feasibility of using radiomics approach to distinguish anxiety disorder and its subtype from healthy controls on T1-weighted images of the amygdala, and provide a basis for the clinical diagnosis of anxiety disorder. METHODS T1-weighted MR images of 200 patients with anxiety disorder (including 103 GAD patients) as well as 138 healthy controls were obtained in the Healthy Brain Network (HBN) dataset. We extracted 107 radiomics features for the left and right amygdala, respectively, and then performed feature selection using the 10-fold LASSO regression algorithm. For the selected features, we performed group-wise comparisons, and use different machine learning algorithms, including linear kernel support vector machine (SVM), to achieve the classification between the patients and healthy controls. RESULTS For the classification task of anxiety patients vs. healthy controls, 2 and 4 radiomics features were selected from left and right amygdala, respectively, and the area under receiver operating characteristic curve (AUC) of linear kernel SVM in cross-validation experiments was 0.6739±0.0708 for the left amygdala features and 0.6403±0.0519 for the right amygdala features; for classification task for GAD patients vs. healthy controls, 7 and 3 features were selected from left and right amygdala, respectively, and the cross-validation AUCs were 0.6755±0.0615 for the left amygdala features and 0.6966±0.0854 for the right amygdala features. In both classification tasks, the selected amygdala radiomics features had higher discriminatory significance and effect sizes compared with the amygdala volume. DISCUSSION Our study suggest that radiomics features of bilateral amygdala potentially could serve as a basis for the clinical diagnosis of anxiety disorder.
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Affiliation(s)
- Qingfeng Li
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wenzheng Wang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhishan Hu
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
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Kong Z, Zhu X, Chang S, Bao Y, Ma Y, Yu W, Zhu R, Sun Q, Sun W, Deng J, Sun H. Somatic symptoms mediate the association between subclinical anxiety and depressive symptoms and its neuroimaging mechanisms. BMC Psychiatry 2022; 22:835. [PMID: 36581819 PMCID: PMC9798660 DOI: 10.1186/s12888-022-04488-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 12/20/2022] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Subclinical anxiety, depressive and somatic symptoms appear closely related. However, it remains unclear whether somatic symptoms mediate the association between subclinical anxiety and depressive symptoms and what the underlying neuroimaging mechanisms are for the mediating effect. METHODS Data of healthy participants (n = 466) and participants in remission of major depressive disorder (n = 53) were obtained from the Human Connectome Project. The Achenbach Adult Self-Report was adopted to assess anxiety, depressive and somatic symptoms. All participants completed four runs of resting-state functional magnetic resonance imaging. Mediation analyses were utilized to explore the interactions among these symptoms and their neuroimaging mechanisms. RESULTS Somatic symptoms partially mediated the association between subclinical anxiety and depressive symptoms in healthy participants (anxiety→somatic→depression: effect: 0.2785, Boot 95% CI: 0.0958-0.3729; depression→somatic→anxiety: effect: 0.0753, Boot 95% CI: 0.0232-0.1314) and participants in remission of MDD (anxiety→somatic→depression: effect: 0.2948, Boot 95% CI: 0.0357-0.7382; depression→somatic→anxiety: effect: 0.0984, Boot 95% CI: 0.0007-0.2438). Resting-state functional connectivity (FC) between the right medial superior frontal gyrus and the left thalamus and somatic symptoms as chain mediators partially mediated the effect of subclinical depressive symptoms on subclinical anxiety symptoms in healthy participants (effect: 0.0020, Boot 95% CI: 0.0003-0.0043). The mean strength of common FCs of subclinical depressive and somatic symptoms, somatic symptoms, and the mean strength of common FCs of subclinical anxiety and somatic symptoms as chain mediators partially mediated the effect of subclinical depressive symptoms on subclinical anxiety symptoms in remission of MDD (effect: 0.0437, Boot 95% CI: 0.0024-0.1190). These common FCs mainly involved the insula, precentral gyri, postcentral gyri and cingulate gyri. Furthermore, FC between the triangular part of the left inferior frontal gyrus and the left postcentral gyrus was positively associated with subclinical anxiety, depressive and somatic symptoms in remission of MDD (FDR-corrected p < 0.01). CONCLUSIONS Somatic symptoms partially mediate the interaction between subclinical anxiety and depressive symptoms. FCs involving the right medial superior frontal gyrus, left thalamus, triangular part of left inferior frontal gyrus, bilateral insula, precentral gyri, postcentral gyri and cingulate gyri maybe underlie the mediating effect of somatic symptoms.
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Affiliation(s)
- Zhifei Kong
- grid.459847.30000 0004 1798 0615Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191 China
| | - Ximei Zhu
- grid.459847.30000 0004 1798 0615Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191 China
| | - Suhua Chang
- grid.459847.30000 0004 1798 0615Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191 China
| | - Yanping Bao
- grid.11135.370000 0001 2256 9319National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence, Peking University, Beijing, 100191 China ,grid.11135.370000 0001 2256 9319School of Public Health, Peking University, Beijing, 100191 China
| | - Yundong Ma
- grid.459847.30000 0004 1798 0615Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191 China
| | - Wenwen Yu
- grid.459847.30000 0004 1798 0615Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191 China
| | - Ran Zhu
- grid.459847.30000 0004 1798 0615Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191 China
| | - Qiqing Sun
- grid.459847.30000 0004 1798 0615Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191 China
| | - Wei Sun
- grid.459847.30000 0004 1798 0615Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191 China
| | - Jiahui Deng
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China.
| | - Hongqiang Sun
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, 100191, China.
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Common and unique neural activities in subclinical depression and major depressive disorder indicate the development of brain impairments in different depressive stages. J Affect Disord 2022; 317:278-286. [PMID: 36057285 DOI: 10.1016/j.jad.2022.08.128] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 07/19/2022] [Accepted: 08/28/2022] [Indexed: 11/20/2022]
Abstract
BACKGROUND Subclinical depression (SD) and major depressive disorder (MDD) can be considered as the early and late stages of depression, but the characteristics of intrinsic neural activity in different depressive stages are largely unknown. METHODS Twenty-six SD, 36 MDD subjects and 33 well-matched healthy controls (HCs) were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Voxel-wise regional homogeneity (ReHo) was analyzed to explore the alterations of intrinsic neural activity, and machine learning classification based on ReHo features was performed to assess potential performance for diagnostic classification. RESULTS Common alterations of ReHo in both SD and MDD groups were found in the bilateral middle temporal gyrus and the left middle occipital gyrus. Opposite alterations in SD and MDD groups were found in the right superior cerebellum. Moreover, increased ReHo in the bilateral precuneus was only found in MDD, while increased ReHo in the right middle frontal gyrus and precentral gyrus were unique to SD. The distinct ReHo values correctly identified SD, MDD, and HC by linear support vector machine (SVM) with an accuracy of 77.89 %, which further verified the discrimination ability of altered ReHo in these brain regions. LIMITATION The sample size is relatively small. CONCLUSION Common and unique ReHo alterations provided insights into the development of brain impairments in depression, and helped to understand the pathophysiology of SD and MDD.
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RP-Rs-fMRIomics as a Novel Imaging Analysis Strategy to Empower Diagnosis of Brain Gliomas. Cancers (Basel) 2022; 14:cancers14122818. [PMID: 35740484 PMCID: PMC9220978 DOI: 10.3390/cancers14122818] [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: 04/26/2022] [Revised: 05/29/2022] [Accepted: 06/01/2022] [Indexed: 12/07/2022] Open
Abstract
Rs-fMRI can provide rich information about functional processes in the brain with a large array of imaging parameters and is also suitable for investigating the biological processes in cerebral gliomas. We aimed to propose an imaging analysis method of RP-Rs-fMRIomics by adopting omics analysis on rs-fMRI with exhaustive regional parameters and subsequently estimating its feasibility on the prediction diagnosis of gliomas. In this retrospective study, preoperative rs-fMRI data were acquired from patients confirmed with diffuse gliomas (n = 176). A total of 420 features were extracted through measuring 14 regional parameters of rs-fMRI as much as available currently in 10 specific narrow frequency bins and three parts of gliomas. With a randomly split training and testing dataset (ratio 7:3), four classifiers were implemented to construct and optimize RP-Rs-fMRIomics models for predicting glioma grade, IDH status and Karnofsky Performance Status scores. The RP-Rs-fMRIomics models (AUROC 0.988, 0.905, 0.801) were superior to the corresponding traditional single rs-fMRI index (AUROC 0.803, 0.731, 0.632) in predicting glioma grade, IDH and survival. The RP-Rs-fMRIomics analysis, featuring high interpretability, was competitive for prediction of glioma grading, IDH genotype and prognosis. The method expanded the clinical application of rs-fMRI and also contributed a new imaging analysis for brain tumor research.
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Zhang L, Wei X, Zhao J. Amplitude of Low-Frequency Oscillations in First-Episode Drug-Naive Patients with Major Depressive Disorder: A Resting State Functional Magnetic Resonance Imaging Study. Neuropsychiatr Dis Treat 2022; 18:555-561. [PMID: 35330822 PMCID: PMC8938275 DOI: 10.2147/ndt.s348683] [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: 11/19/2021] [Accepted: 03/03/2022] [Indexed: 11/25/2022] Open
Abstract
OBJECTIVE To observe characteristics of the amplitudes of low-frequency oscillation (LFO) in first-episode drug-naive patients with major depressive disorder (MDD). METHODS Amplitudes of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) were computed using resting-state functional magnetic resonance imaging (rs-fMRI) data of 39 first-episode drug-naive patients with MDD and 37 healthy controls. RESULTS ALFF and fALFF in the left cerebellum were significantly higher in patients with MDD compared to control group, while ALFF in the right rolandic operculum was significantly lower (all p < 0.001, AlphaSim correction). CONCLUSION Abnormal neurological activity in multiple brain regions in first-episode drug-naive patients with MDD may be involved in the neurobiological mechanisms of MDD and should be considered in future studies.
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Affiliation(s)
- Lulu Zhang
- Department of Psychiatry, Guangzhou First People's Hospital, Guangzhou, Guangdong, People's Republic of China
| | - Xinghua Wei
- Department of Medical Imaging, Guangzhou First People's Hospital, Guangzhou, Guangdong, People's Republic of China
| | - Jingping Zhao
- Department of Psychiatry and Mental Health Institute of the Second Xiangya Hospital, Central South University, Chinese National Clinical Research Center on Mental Disorders, Chinese National Technology Institute on Mental Disorders, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan, People's Republic of China
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