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2. Transcriptomic decoding of regional cortical vulnerability to major depressive disorder

Author

Zhu, Jiajia, Chen, Xiao, Lu, Bin, Li, Xue-Ying, Wang, Zi-Han, Cao, Li-Ping, Chen, Guan-Mao, Chen, Jian-Shan, Chen, Tao, Chen, Tao-Lin, Cheng, Yu-Qi, Chu, Zhao-Song, Cui, Shi-Xian, Cui, Xi-Long, Deng, Zhao-Yu, Gong, Qi-Yong, Guo, Wen-Bin, He, Can-Can, Hu, Zheng-Jia-Yi, Huang, Qian, Ji, Xin-Lei, Jia, Feng-Nan, Kuang, Li, Li, Bao-Juan, Li, Feng, Li, Hui-Xian, Li, Tao, Lian, Tao, Liao, Yi-Fan, Liu, Xiao-Yun, Liu, Yan-Song, Liu, Zhe-Ning, Long, Yi-Cheng, Lu, Jian-Ping, Qiu, Jiang, Shan, Xiao-Xiao, Si, Tian-Mei, Sun, Peng-Feng, Wang, Chuan-Yue, Wang, Hua-Ning, Wang, Xiang, Wang, Ying, Wang, Yu-Wei, Wu, Xiao-Ping, Wu, Xin-Ran, Wu, Yan-Kun, Xie, Chun-Ming, Xie, Guang-Rong, Xie, Peng, Xu, Xiu-Feng, Xue, Zhen-Peng, Yang, Hong, Yu, Hua, Yuan, Min-Lan, Yuan, Yong-Gui, Zhang, Ai-Xia, Zhao, Jing-Ping, Zhang, Ke-Rang, Zhang, Wei, Zhang, Zi-Jing, Yan, Chao-Gan, and Yu, Yongqiang

Published
2024
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4. Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo

Author

Fu, Cynthia H. Y., Antoniades, Mathilde, Erus, Guray, Garcia, Jose A., Fan, Yong, Arnone, Danilo, Arnott, Stephen R., Chen, Taolin, Choi, Ki Sueng, Fatt, Cherise Chin, Frey, Benicio N., Frokjaer, Vibe G., Ganz, Melanie, Godlewska, Beata R., Hassel, Stefanie, Ho, Keith, McIntosh, Andrew M., Qin, Kun, Rotzinger, Susan, Sacchet, Matthew D., Savitz, Jonathan, Shou, Haochang, Singh, Ashish, Stolicyn, Aleks, Strigo, Irina, Strother, Stephen C., Tosun, Duygu, Victor, Teresa A., Wei, Dongtao, Wise, Toby, Zahn, Roland, Anderson, Ian M., Craighead, W. Edward, Deakin, J. F. William, Dunlop, Boadie W., Elliott, Rebecca, Gong, Qiyong, Gotlib, Ian H., Harmer, Catherine J., Kennedy, Sidney H., Knudsen, Gitte M., Mayberg, Helen S., Paulus, Martin P., Qiu, Jiang, Trivedi, Madhukar H., Whalley, Heather C., Yan, Chao-Gan, Young, Allan H., and Davatzikos, Christos

Published
2024
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5. Publisher Correction: Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo

Author

Fu, Cynthia H. Y., Antoniades, Mathilde, Erus, Guray, Garcia, Jose A., Fan, Yong, Arnone, Danilo, Arnott, Stephen R., Chen, Taolin, Choi, Ki Sueng, Fatt, Cherise Chin, Frey, Benicio N., Frokjaer, Vibe G., Ganz, Melanie, Godlewska, Beata R., Hassel, Stefanie, Ho, Keith, McIntosh, Andrew M., Qin, Kun, Rotzinger, Susan, Sacchet, Matthew D., Savitz, Jonathan, Shou, Haochang, Singh, Ashish, Stolicyn, Aleks, Strigo, Irina, Strother, Stephen C., Tosun, Duygu, Victor, Teresa A., Wei, Dongtao, Wise, Toby, Zahn, Roland, Anderson, Ian M., Craighead, W. Edward, Deakin, J. F. William, Dunlop, Boadie W., Elliott, Rebecca, Gong, Qiyong, Gotlib, Ian H., Harmer, Catherine J., Kennedy, Sidney H., Knudsen, Gitte M., Mayberg, Helen S., Paulus, Martin P., Qiu, Jiang, Trivedi, Madhukar H., Whalley, Heather C., Yan, Chao-Gan, Young, Allan H., and Davatzikos, Christos

Published
2024
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6. AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale

Author

Fu, Cynthia HY, Erus, Guray, Fan, Yong, Antoniades, Mathilde, Arnone, Danilo, Arnott, Stephen R, Chen, Taolin, Choi, Ki Sueng, Fatt, Cherise Chin, Frey, Benicio N, Frokjaer, Vibe G, Ganz, Melanie, Garcia, Jose, Godlewska, Beata R, Hassel, Stefanie, Ho, Keith, McIntosh, Andrew M, Qin, Kun, Rotzinger, Susan, Sacchet, Matthew D, Savitz, Jonathan, Shou, Haochang, Singh, Ashish, Stolicyn, Aleks, Strigo, Irina, Strother, Stephen C, Tosun, Duygu, Victor, Teresa A, Wei, Dongtao, Wise, Toby, Woodham, Rachel D, Zahn, Roland, Anderson, Ian M, Deakin, JF William, Dunlop, Boadie W, Elliott, Rebecca, Gong, Qiyong, Gotlib, Ian H, Harmer, Catherine J, Kennedy, Sidney H, Knudsen, Gitte M, Mayberg, Helen S, Paulus, Martin P, Qiu, Jiang, Trivedi, Madhukar H, Whalley, Heather C, Yan, Chao-Gan, Young, Allan H, and Davatzikos, Christos

Subjects
Biological Psychology, Biomedical and Clinical Sciences, Clinical Sciences, Psychology, Biomedical Imaging, Neurosciences, Brain Disorders, Clinical Research, Depression, Bioengineering, Serious Mental Illness, Major Depressive Disorder, Mental Health, 4.1 Discovery and preclinical testing of markers and technologies, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, Neurological, Mental health, Humans, Depressive Disorder, Major, Prospective Studies, Reproducibility of Results, Brain, Neuroimaging, Magnetic Resonance Imaging, Artificial Intelligence, Classification, Biomarkers, Deep learning, Harmonization, Predictors, MRI, Public Health and Health Services, Psychiatry, Clinical sciences, Epidemiology, Clinical and health psychology
Abstract

BackgroundEfforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states.MethodsWe describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants.ResultsWe are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites.ConclusionWe describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.

Published
2023

12. A longitudinal resource for population neuroscience of school-age children and adolescents in China

Author

Fan, Xue-Ru, Wang, Yin-Shan, Chang, Da, Yang, Ning, Rong, Meng-Jie, Zhang, Zhe, He, Ye, Hou, Xiaohui, Zhou, Quan, Gong, Zhu-Qing, Cao, Li-Zhi, Dong, Hao-Ming, Nie, Jing-Jing, Chen, Li-Zhen, Zhang, Qing, Zhang, Jia-Xin, Zhang, Lei, Li, Hui-Jie, Bao, Min, Chen, Antao, Chen, Jing, Chen, Xu, Ding, Jinfeng, Dong, Xue, Du, Yi, Feng, Chen, Feng, Tingyong, Fu, Xiaolan, Ge, Li-Kun, Hong, Bao, Hu, Xiaomeng, Huang, Wenjun, Jiang, Chao, Li, Li, Li, Qi, Li, Su, Liu, Xun, Mo, Fan, Qiu, Jiang, Su, Xue-Quan, Wei, Gao-Xia, Wu, Yiyang, Xia, Haishuo, Yan, Chao-Gan, Yan, Zhi-Xiong, Yang, Xiaohong, Zhang, Wenfang, Zhao, Ke, Zhu, Liqi, and Zuo, Xi-Nian

Published
2023
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16. Atypicalities in the Developmental Trajectory of Cortico-Striatal Functional Connectivity in Autism Spectrum Disorder

Author

Ma, Zeng-Hui, Lu, Bin, Li, Xue, Mei, Ting, Guo, Yan-Qing, Yang, Liu, Wang, Hui, Tang, Xin-Zhou, Ji, Zhao-Zheng, Liu, Jing-Ran, Xu, Ling-Zi, Yang, Yu-Lu, Cao, Qing-Jiu, Yan, Chao-Gan, and Liu, Jing

Abstract

The last decades of neuroimaging research has revealed atypical development of intrinsic functional connectivity within and between large-scale cortical networks in autism spectrum disorder, but much remains unknown about cortico-subcortical developmental connectivity atypicalities. This study examined cortico-striatal developmental intrinsic functional connectivity changes in autism spectrum disorder and explored how those changes may be correlated with autistic traits. We studied 49 individuals with autism spectrum disorder and 52 age-, sex-, and head motion-matched typically developing individuals (5-30 years old (14.0 ± 5.6)) using resting-state functional magnetic resonance imaging. Age-related differences in striatal intrinsic functional connectivity were compared between the two groups by adopting functional network--based parcellations of the striatum as seeds. Relative to typically developing individuals, autism spectrum disorder individuals showed atypical developmental changes in intrinsic functional connectivities between almost all striatal networks and sensorimotor network/default network, with connectivity increasing with age in the autism spectrum disorder group and decreasing or constant in typically developing individuals. Age-related degree centrality and voxel-mirrored homotopic connectivity atypicalities in sensorimotor network/default network and voxel-mirrored homotopic connectivity disruptions in striatal regions were also observed in autism spectrum disorder. Significant correlations were found between cortico-striatal intrinsic functional connectivities and Autism Diagnostic Observation Schedule communication/repetitive and restricted-behavior subscores in autism spectrum disorder. Our results indicated that developmental atypicalities of cortico-striatal intrinsic functional connectivities might contribute to the neuropathology of autism spectrum disorder.

Published
2022
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22. Reduced nucleus accumbens functional connectivity in reward network and default mode network in patients with recurrent major depressive disorder

Author

Ding, Yu-Dan, Chen, Xiao, Chen, Zuo-Bing, Li, Le, Li, Xue-Ying, Castellanos, Francisco Xavier, Bai, Tong-Jian, Bo, Qi-Jing, Cao, Jun, Chang, Zhi-Kai, Chen, Guan-Mao, Chen, Ning-Xuan, Chen, Wei, Cheng, Chang, Cheng, Yu-Qi, Cui, Xi-Long, Duan, Jia, Fang, Yi-Ru, Gong, Qi-Yong, Hou, Zheng-Hua, Hu, Lan, Kuang, Li, Li, Feng, Li, Hui-Xian, Li, Kai-Ming, Li, Tao, Liu, Yan-Song, Liu, Zhe-Ning, Long, Yi-Cheng, Lu, Bin, Luo, Qing-Hua, Meng, Hua-Qing, Peng, Dai-Hui, Qiu, Hai-Tang, Qiu, Jiang, Shen, Yue-Di, Shi, Yu-Shu, Si, Tian-Mei, Tang, Yan-Qing, Wang, Chuan-Yue, Wang, Fei, Wang, Kai, Wang, Li, Wang, Xiang, Wang, Ying, Wang, Yu-Wei, Wu, Xiao-Ping, Wu, Xin-Ran, Xie, Chun-Ming, Xie, Guang-Rong, Xie, Hai-Yan, Xie, Peng, Xu, Xiu-Feng, Yang, Hong, Yang, Jian, Yao, Jia-Shu, Yao, Shu-Qiao, Yin, Ying-Ying, Yuan, Yong-Gui, Zang, Yu-Feng, Zhang, Ai-Xia, Zhang, Hong, Zhang, Ke-Rang, Zhang, Lei, Zhang, Zhi-Jun, Zhao, Jing-Ping, Zhou, Ru-Bai, Zhou, Yi-Ting, Zhu, Jun-Juan, Zhu, Zhi-Chen, Zou, Chao-Jie, Zuo, Xi-Nian, Yan, Chao-Gan, and Guo, Wen-Bin

Published
2022
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25. Disrupted intrinsic functional brain topology in patients with major depressive disorder

Author

Yang, Hong, Chen, Xiao, Chen, Zuo-Bing, Li, Le, Li, Xue-Ying, Castellanos, Francisco Xavier, Bai, Tong-Jian, Bo, Qi-Jing, Cao, Jun, Chang, Zhi-Kai, Chen, Guan-Mao, Chen, Ning-Xuan, Chen, Wei, Cheng, Chang, Cheng, Yu-Qi, Cui, Xi-Long, Duan, Jia, Fang, Yiru, Gong, Qi-Yong, Guo, Wen-Bin, Hou, Zheng-Hua, Hu, Lan, Kuang, Li, Li, Feng, Li, Hui-Xian, Li, Kai-Ming, Li, Tao, Liu, Yan-Song, Liu, Zhe-Ning, Long, Yi-Cheng, Lu, Bin, Luo, Qing-Hua, Meng, Hua-Qing, Peng, Daihui, Qiu, Hai-Tang, Qiu, Jiang, Shen, Yue-Di, Shi, Yu-Shu, Si, Tian-Mei, Tang, Yan-Qing, Wang, Chuan-Yue, Wang, Fei, Wang, Kai, Wang, Li, Wang, Xiang, Wang, Ying, Wang, Yu-Wei, Wu, Xiao-Ping, Wu, Xin-Ran, Xie, Chun-Ming, Xie, Guang-Rong, Xie, Hai-Yan, Xie, Peng, Xu, Xiu-Feng, Yang, Jian, Yao, Jia-Shu, Yao, Shu-Qiao, Yin, Ying-Ying, Yuan, Yong-Gui, Zang, Yu-Feng, Zhang, Ai-Xia, Zhang, Hong, Zhang, Ke-Rang, Zhang, Lei, Zhang, Zhi-Jun, Zhao, Jing-Ping, Zhou, Rubai, Zhou, Yi-Ting, Zhu, Jun-Juan, Zhu, Zhi-Chen, Zou, Chao-Jie, Zuo, Xi-Nian, and Yan, Chao-Gan

Published
2021
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26. Brain structural alterations in MDD patients with gastrointestinal symptoms: Evidence from the REST-meta-MDD project

Author

Liu, Peng-hong, Li, Yan, Zhang, Ai-Xia, Sun, Ning, Li, Gai-Zhi, Chen, Xiao, Bai, Tong-Jian, Bo, Qi-Jing, Chen, Guan-Mao, Chen, Ning-Xuan, Chen, Tao-Lin, Chen, Wei, Cheng, Chang, Cheng, Yu-Qi, Cui, Xi-Long, Duan, Jia, Fang, Yi-Ru, Gong, Qi-Yong, Guo, Wen-Bin, Hou, Zheng-Hua, Hu, Lan, Kuang, Li, Li, Feng, Li, Kai-Ming, Li, Tao, Liu, Yan-Song, Liu, Zhe-Ning, Long, Yi-Cheng, Luo, Qing-Hua, Meng, Hua-Qing, Peng, Dai-Hui, Qiu, Hai-Tang, Qiu, Jiang, Shen, Yue-Di, Shi, Yu-Shu, Wang, Fei, Wang, Kai, Wang, Li, Wang, Xiang, Wang, Ying, Wu, Xiao-Ping, Wu, Xin-Ran, Xie, Chun-Ming, Xie, Guang-Rong, Xie, Hai-Yan, Xie, Peng, Xu, Xiu-Feng, Yang, Hong, Yang, Jian, Yao, Jia-Shu, Yao, Shu-Qiao, Yin, Ying-Ying, Yuan, Yong-Gui, Zhang, Hong, Zhang, Lei, Zhang, Zhi-Jun, Zhou, Ru-Bai, Zhou, Yi-Ting, Zhu, Jun-Juan, Zou, Chao-Jie, Si, Tian-Mei, Zuo, Xi-Nian, Yan, Chao-Gan, and Zhang, Ke-Rang

Published
2021
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28. Disrupted hemispheric connectivity specialization in patients with major depressive disorder: Evidence from the REST-meta-MDD Project

Author

Ding, Yu-Dan, Yang, Ru, Yan, Chao-Gan, Chen, Xiao, Bai, Tong-Jian, Bo, Qi-Jing, Chen, Guan-Mao, Chen, Ning-Xuan, Chen, Tao-Lin, Chen, Wei, Cheng, Chang, Cheng, Yu-Qi, Cui, Xi-Long, Duan, Jia, Fang, Yi-Ru, Gong, Qi-Yong, Hou, Zheng-Hua, Hu, Lan, Kuang, Li, Li, Feng, Li, Tao, Liu, Yan-Song, Liu, Zhe-Ning, Long, Yi-Cheng, Luo, Qing-Hua, Meng, Hua-Qing, Peng, Dai-Hui, Qiu, Hai-Tang, Qiu, Jiang, Shen, Yue-Di, Shi, Yu-Shu, Tang, Yanqing, Wang, Chuan-Yue, Wang, Fei, Wang, Kai, Wang, Li, Wang, Xiang, Wang, Ying, Wu, Xiao-Ping, Wu, Xin-Ran, Xie, Chun-Ming, Xie, Guang-Rong, Xie, Hai-Yan, Xie, Peng, Xu, Xiu-Feng, Yang, Hong, Yang, Jian, Yao, Jia-Shu, Yao, Shu-Qiao, Yin, Ying-Ying, Yuan, Yong-Gui, Zhang, Ai-Xia, Zhang, Hong, Zhang, Ke-Rang, Zhang, Lei, Zhang, Zhi-Jun, Zhou, Ru-Bai, Zhou, Yi-Ting, Zhu, Jun-Juan, Zou, Chao-Jie, Si, Tian-Mei, Zang, Yu-Feng, Zhao, Jing-Ping, and Guo, Wen-Bin

Published
2021
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35. Reduced default mode network functional connectivity in patients with recurrent major depressive disorder

Author

Yan, Chao-Gan, Chen, Xiao, Li, Le, Castellanos, Francisco Xavier, Bai, Tong-Jian, Bo, Qi-Jing, Cao, Jun, Chen, Guan-Mao, Chen, Ning-Xuan, Chen, Wei, Cheng, Chang, Cheng, Yu-Qi, Cui, Xi-Long, Duan, Jia, Fang, Yi-Ru, Gong, Qi-Yong, Guo, Wen-Bin, Hou, Zheng-Hua, Hu, Lan, Kuang, Li, Li, Feng, Li, Kai-Ming, Li, Tao, Liu, Yan-Song, Liu, Zhe-Ning, Long, Yi-Cheng, Luo, Qing-Hua, Meng, Hua-Qing, Peng, Dai-Hui, Qiu, Hai-Tang, Qiu, Jiang, Shen, Yue-Di, Shi, Yu-Shu, Wang, Chuan-Yue, Wang, Fei, Wang, Kai, Wang, Li, Wang, Xiang, Wang, Ying, Wu, Xiao-Ping, Wu, Xin-Ran, Xie, Chun-Ming, Xie, Guang-Rong, Xie, Hai-Yan, Xie, Peng, Xu, Xiu-Feng, Yang, Hong, Yang, Jian, Yao, Jia-Shu, Yao, Shu-Qiao, Yin, Ying-Ying, Yuan, Yong-Gui, Zhang, Ai-Xia, Zhang, Hong, Zhang, Ke-Rang, Zhang, Lei, Zhang, Zhi-Jun, Zhou, Ru-Bai, Zhou, Yi-Ting, Zhu, Jun-Juan, Zou, Chao-Jie, Si, Tian-Mei, Zuo, Xi-Nian, Zhao, Jing-Ping, and Zang, Yu-Feng

Published
2019

36. Brain functional changes across mood states in bipolar disorder: from a large-scale network perspective.

Author

Wu, Yan-Kun, Su, Yun-Ai, Li, Le, Zhu, Lin-Lin, Li, Ke, Li, Ji-Tao, Mitchell, Philip B., Yan, Chao-Gan, and Si, Tian-Mei

Subjects
BIPOLAR disorder, EMOTION regulation, FUNCTIONAL connectivity, RESEARCH funding, BRAIN, MAGNETIC resonance imaging, LARGE-scale brain networks, NERVOUS system, RESEARCH, AFFECT (Psychology), MANIA, DISEASE complications
Abstract

Background: Exploring the neural basis related to different mood states is a critical issue for understanding the pathophysiology underlying mood switching in bipolar disorder (BD), but research has been scarce and inconsistent. Methods: Resting-state functional magnetic resonance imaging data were acquired from 162 patients with BD: 33 (hypo)manic, 64 euthymic, and 65 depressive, and 80 healthy controls (HCs). The differences of large-scale brain network functional connectivity (FC) between the four groups were compared and correlated with clinical characteristics. To validate the generalizability of our findings, we recruited a small longitudinal independent sample of BD patients (n = 11). In addition, we examined topological nodal properties across four groups as exploratory analysis. Results: A specific strengthened pattern of network FC, predominantly involving the default mode network (DMN), was observed in (hypo)manic patients when compared with HCs and bipolar patients in other mood states. Longitudinal observation revealed an increase in several network FCs in patients during (hypo)manic episode. Both samples evidenced an increase in the FC between the DMN and ventral attention network, and between the DMN and limbic network (LN) related to (hypo)mania. The altered network connections were correlated with mania severity and positive affect. Bipolar depressive patients exhibited decreased FC within the LN compared with HCs. The exploratory analysis also revealed an increase in degree in (hypo)manic patients. Conclusions: Our findings identify a distributed pattern of large-scale network disturbances in the unique context of (hypo)mania and thus provide new evidence for our understanding of the neural mechanism of BD. [ABSTRACT FROM AUTHOR]

Published
2024
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37. Age- and gender-related dispersion of brain networks across the lifespan.

Author

Wang, Qing, Qi, Lingyu, He, Cancan, Feng, Haixia, Xie, Chunming, Depression Imaging REsearch ConsorTium, Yan, Chao-Gan, Chen, Xiao, Li, Le, Castellanos, Francisco Xavier, Bai, Tong-Jian, Chen, Ning-Xuan, Chen, Wei, Cheng, Chang, Cheng, Yu-Qi, Cui, Xi-Long, Duan, Jia, Fang, Yi-Ru, Gong, Qi-Yong, and Guo, Wen-Bin

Subjects
LARGE-scale brain networks, SALIENCE network, ALZHEIMER'S disease, COGNITIVE aging, TOPOLOGICAL property, LOW-calorie diet
Abstract

The effects of age and gender on large-scale resting-state networks (RSNs) reflecting within- and between-network connectivity in the healthy brain remain unclear. This study investigated how age and gender influence the brain network roles and topological properties underlying the ageing process. Ten RSNs were constructed based on 998 participants from the REST-meta-MDD cohort. Multivariate linear regression analysis was used to examine the independent and interactive influences of age and gender on large-scale RSNs and their topological properties. A support vector regression model integrating whole-brain network features was used to predict brain age across the lifespan and cognitive decline in an Alzheimer's disease spectrum (ADS) sample. Differential effects of age and gender on brain network roles were demonstrated across the lifespan. Specifically, cingulo-opercular, auditory, and visual (VIS) networks showed more incohesive features reflected by decreased intra-network connectivity with ageing. Further, females displayed distinctive brain network trajectory patterns in middle-early age, showing enhanced network connectivity within the fronto-parietal network (FPN) and salience network (SAN) and weakened network connectivity between the FPN-somatomotor, FPN-VIS, and SAN-VIS networks. Age — but not gender — induced widespread decrease in topological properties of brain networks. Importantly, these differential network features predicted brain age and cognitive impairment in the ADS sample. By showing that age and gender exert specific dispersion of dynamic network roles and trajectories across the lifespan, this study has expanded our understanding of age- and gender-related brain changes with ageing. Moreover, the findings may be useful for detecting early-stage dementia. [ABSTRACT FROM AUTHOR]

Published
2024
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38. Aberrant degree centrality profiles during rumination in major depressive disorder

Author

Jia, Feng‐Nan, primary, Chen, Xiao, additional, Du, Xiang‐Dong, additional, Tang, Zhen, additional, Ma, Xiao‐Yun, additional, Ning, Tian‐Tian, additional, Zou, Si‐Yun, additional, Zuo, Shang‐Fu, additional, Li, Hui‐Xian, additional, Cui, Shi‐Xian, additional, Deng, Zhao‐Yu, additional, Fu, Jia‐Lin, additional, Fu, Xiao‐Qian, additional, Huang, Yue‐Xiang, additional, Li, Xue‐Ying, additional, Lian, Tao, additional, Liao, Yi‐Fan, additional, Liu, Li‐Li, additional, Lu, Bin, additional, Wang, Yan, additional, Wang, Yu‐Wei, additional, Wang, Zi‐Han, additional, Ye, Gang, additional, Zhang, Xin‐Zhu, additional, Zhu, Hong‐Liang, additional, Quan, Chuan‐Sheng, additional, Sun, Hong‐Yan, additional, Yan, Chao‐Gan, additional, and Liu, Yan‐Song, additional

Published
2023
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46. Altered intrinsic functional brain architecture in female patients with bulimia nervosa

Author

Wang, Li, Kong, Qing-Mei, Li, Ke, Li, Xue-Ni, Zeng, Ya-Wei, Chen, Chao, Qian, Ying, Feng, Shi-Jie, Li, Ji-Tao, Su, Yun'Ai, Correll, Christoph U., Mitchell, Philip B., Yan, Chao-Gan, Zhang, Da-Rong, and Si, Tian-Mei

Subjects
Brain -- Health aspects -- Structure -- Physiological aspects, Bulimia -- Physiological aspects -- Health aspects, Health, Psychology and mental health
Abstract

Background: Bulimia nervosa is a severe psychiatric syndrome with uncertain pathogenesis. Neural systems involved in sensorimotor and visual processing, reward and impulsive control may contribute to the binge eating and purging behaviours characterizing bulimia nervosa. However, little is known about the alterations of functional organization of whole brain networks in individuals with this disorder. Methods: We used resting-state functional MRI and graph theory to characterize functional brain networks of unmedicated women with bulimia nervosa and healthy women. Results: We included 44 unmedicated women with bulimia nervosa and 44 healthy women in our analyses. Women with bulimia nervosa showed increased clustering coefficient and path length compared with control women. The nodal strength in patients with the disorder was higher in the sensorimotor and visual regions as well as the precuneus, but lower in several subcortical regions, such as the hippocampus, parahippocampal gyrus and orbitofrontal cortex. Patients also showed hyperconnectivity primarily involving sensorimotor and unimodal visual association regions, but hypoconnectivity involving subcortical (striatum, thalamus), limbic (amygdala, hippocampus) and paralimbic (orbitofrontal cortex, parahippocampal gyrus) regions. The topological aberrations correlated significantly with scores of bulimia and drive for thinness and with body mass index. Limitations: We reruited patients with only acute bulimia nervosa, so it is unclear whether the topological abnormalities comprise vulnerability markers for the disorder developing or the changes associated with illness state. Conclusion: Our findings show altered intrinsic functional brain architecture, specifically abnormal global and local efficiency, as well as nodal- and network-level connectivity across sensorimotor, visual, subcortical and limbic systems in women with bulimia nervosa, suggesting that it is a disorder of dysfunctional integration among large-scale distributed brain regions. These abnormalities contribute to more comprehensive understanding of the neural mechanism underlying pathological eating and body perception in women with bulimia nervosa., Introduction Bulimia nervosa is a severe psychiatric syndrome that usually begins during adolescence in girls. It is characterized by episodic binge eating followed by purging behaviours to avoid weight gain. [...]

Published
2017
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47. The Complexity of Functional Connectivity Profiles of the Subgenual Anterior Cingulate Cortex and Dorsal Lateral Prefrontal Cortex in Major Depressive Disorder: a DIRECT Consortium Study

Author

Yan, Chao-Gan, primary, Chen, Xiao, additional, Lu, Bin, additional, Wang, Yu-Wei, additional, Li, Xue-Ying, additional, Wang, Zi-Han, additional, Li, Hui-Xian, additional, Liao, Yi-Fan, additional, Blumberger, Daniel, additional, Castellanos, Francisco, additional, Cao, Li-Ping, additional, Chen, Guan-Mao, additional, Chen, Jian-Shan, additional, Chen, Tao, additional, Chen, Tao-Lin, additional, Cheng, Yu-Qi, additional, Chu, Zhao-Song, additional, Cui, Shi-Xian, additional, Cui, Xi-Long, additional, Deng, Zhao-Yu, additional, Gong, Qiyong, additional, Guo, Wen-Bin, additional, He, Can-Can, additional, Hu, Zheng-Jia-Yi, additional, Huang, Qian, additional, Ji, Xin-Lei, additional, Jia, Feng-Nan, additional, Kuang, Li, additional, Li, Bao-Juan, additional, Li, Feng, additional, Li, Tao, additional, Lian, Tao, additional, Liu, Xiao-Yun, additional, Liu, Yan-song, additional, Liu, Zhe-Ning, additional, Long, Yi-Cheng, additional, Lu, Jian-Ping, additional, Qiu, Jiang, additional, Shan, Xiao-Xiao, additional, Si, Tianmei, additional, Sun, Peng-Feng, additional, Wang, Chuan-Yue, additional, Wang, Huaning, additional, Wang, Xiang, additional, Wang, Ying, additional, Wu, Xiao-Ping, additional, Wu, Xinran, additional, Wu, Yan-Kun, additional, Xie, Chun-Ming, additional, Xie, Guang-Rong, additional, Xie, Peng, additional, Xu, Xiu-Feng, additional, Xue, Zhen-Peng, additional, Yang, Hong, additional, Yang, Jian, additional, Yu, Hua, additional, Yu, Yong-Qiang, additional, Yuan, Min-Lan, additional, Yuan, Yong-Gui, additional, Zang, Yu-Feng, additional, Zhang, Ai-Xia, additional, Zhang, Ke-Rang, additional, Zhang, Wei, additional, Zhang, Zi-Jing, additional, Zhao, Jing-Ping, additional, Zhu, Jia-Jia, additional, Zuo, Xi-Nian, additional, and Consortium, the DIRECT, additional

Published
2023
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50. AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale.

Author

Fu, Cynthia, Fu, Cynthia, Erus, Guray, Fan, Yong, Antoniades, Mathilde, Arnone, Danilo, Arnott, Stephen, Chen, Taolin, Choi, Ki, Fatt, Cherise, Frey, Benicio, Frokjaer, Vibe, Ganz, Melanie, Garcia, Jose, Godlewska, Beata, Hassel, Stefanie, Ho, Keith, McIntosh, Andrew, Qin, Kun, Rotzinger, Susan, Sacchet, Matthew, Savitz, Jonathan, Shou, Haochang, Singh, Ashish, Stolicyn, Aleks, Strother, Stephen, Tosun, Duygu, Victor, Teresa, Wei, Dongtao, Wise, Toby, Woodham, Rachel, Zahn, Roland, Anderson, Ian, Deakin, J, Dunlop, Boadie, Elliott, Rebecca, Gong, Qiyong, Gotlib, Ian, Harmer, Catherine, Kennedy, Sidney, Knudsen, Gitte, Mayberg, Helen, Paulus, Martin, Qiu, Jiang, Trivedi, Madhukar, Whalley, Heather, Yan, Chao-Gan, Young, Allan, Davatzikos, Christos, Strigo, Irina, Fu, Cynthia, Fu, Cynthia, Erus, Guray, Fan, Yong, Antoniades, Mathilde, Arnone, Danilo, Arnott, Stephen, Chen, Taolin, Choi, Ki, Fatt, Cherise, Frey, Benicio, Frokjaer, Vibe, Ganz, Melanie, Garcia, Jose, Godlewska, Beata, Hassel, Stefanie, Ho, Keith, McIntosh, Andrew, Qin, Kun, Rotzinger, Susan, Sacchet, Matthew, Savitz, Jonathan, Shou, Haochang, Singh, Ashish, Stolicyn, Aleks, Strother, Stephen, Tosun, Duygu, Victor, Teresa, Wei, Dongtao, Wise, Toby, Woodham, Rachel, Zahn, Roland, Anderson, Ian, Deakin, J, Dunlop, Boadie, Elliott, Rebecca, Gong, Qiyong, Gotlib, Ian, Harmer, Catherine, Kennedy, Sidney, Knudsen, Gitte, Mayberg, Helen, Paulus, Martin, Qiu, Jiang, Trivedi, Madhukar, Whalley, Heather, Yan, Chao-Gan, Young, Allan, Davatzikos, Christos, and Strigo, Irina

Abstract

BACKGROUND: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective lon

Published
2023

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