Functional, Anatomical, and Morphological Networks Highlight the Role of Basal Ganglia-Thalamus-Cortex Circuits in Schizophrenia

被引:35
作者
Zhao, Wei [1 ]
Guo, Shuixia [1 ,2 ]
Linli, Zeqiang [1 ]
Yang, Albert C. [3 ,4 ]
Lin, Ching-Po [5 ,6 ,7 ]
Tsai, Shih-Jen [4 ,8 ,9 ]
机构
[1] Hunan Normal Univ, Sch Math & Stat, MOE LCSM, Changsha, Peoples R China
[2] Hunan Normal Univ, Sch Med, Key Lab Mol Epidemiol Hunan Prov, Changsha, Peoples R China
[3] Harvard Med Sch, Beth Israel Deaconess Med Ctr, Div Interdisciplinary Med & Biotechnol, Boston, MA 02115 USA
[4] Natl Yang Ming Univ, Inst Brain Sci, Taipei, Taiwan
[5] Natl Yang Ming Univ, Brain Res Ctr, Taipei, Taiwan
[6] Natl Yang Ming Univ, Inst Neurosci, Taipei, Taiwan
[7] Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence, Shanghai, Peoples R China
[8] Taipei Vet Gen Hosp, Dept Psychiat, Taipei, Taiwan
[9] Natl Yang Ming Univ, Sch Med, Div Psychiat, Taipei, Taiwan
基金
中国国家自然科学基金;
关键词
schizophrenia; functional network; structural network; morphological network; basal ganglia-thalamus-cortex circuits; STRUCTURAL BRAIN NETWORKS; CONNECTIVITY; RISK; DYSCONNECTIVITY; 1ST-EPISODE; PHENOTYPE; MACHINE; DISEASE;
D O I
10.1093/schbul/sbz062
中图分类号
R749 [精神病学];
学科分类号
100205 ;
摘要
Evidence from electrophysiological, functional, and structural research suggests that abnormal brain connectivity plays an important role in the pathophysiology of schizophrenia. However, most previous studies have focused on single modalities only, each of which is associated with its own limitations. Multimodal combinations can more effectively utilize various information, but previous multimodal research mostly focuses on extracting local features, rather than carrying out research based on network perspective. This study included 135 patients with schizophrenia and 148 sex- and age-matched healthy controls. Functional magnetic resonance imaging, diffusion tensor imaging, and structural magnetic resonance imaging data were used to construct the functional, anatomical, and morphological networks of each participant, respectively. These networks were used in combination with machine learning to identify more consistent biomarkers of brain connectivity and explore the relationships between different modalities. We found that although each modality had divergent connectivity biomarkers, the convergent pattern was that all were mostly located within the basal ganglia-thalamus-cortex circuit. Furthermore, using the biomarkers of these 3 modalities as a feature yielded the highest classification accuracy (91.75%, relative to a single modality), suggesting that the combination of multiple modalities could be effectively utilized to obtain complementary information regarding different mode networks; furthermore, this information could help distinguish patients. These findings provide direct evidence for the disconnection hypothesis of schizophrenia, suggesting that abnormalities in the basal ganglia-thalamus-cortex circuit can be used as a biomarker of schizophrenia.
引用
收藏
页码:422 / 431
页数:10
相关论文
共 63 条
  • [11] Disrupted white matter connectivity underlying developmental dyslexia: A machine learning approach
    Cui, Zaixu
    Xia, Zhichao
    Su, Mengmeng
    Shu, Hua
    Gong, Gaolang
    [J]. HUMAN BRAIN MAPPING, 2016, 37 (04) : 1443 - 1458
  • [12] PANDA: a pipeline toolbox for analyzing brain diffusion images
    Cui, Zaixu
    Zhong, Suyu
    Xu, Pengfei
    He, Yong
    Gong, Gaolang
    [J]. FRONTIERS IN HUMAN NEUROSCIENCE, 2013, 7
  • [13] IMPROVED ESTIMATION OF ULTRASOUND THERMAL STRAIN USING PULSE INVERSION HARMONIC IMAGING
    Ding, Xuan
    Nguyen, Man M.
    James, Isaac B.
    Marra, Kacey G.
    Rubin, J. Peter
    Leers, Steven A.
    Kim, Kang
    [J]. ULTRASOUND IN MEDICINE AND BIOLOGY, 2016, 42 (05) : 1182 - 1192
  • [14] Prediction of Individual Brain Maturity Using fMRI
    Dosenbach, Nico U. F.
    Nardos, Binyam
    Cohen, Alexander L.
    Fair, Damien A.
    Power, Jonathan D.
    Church, Jessica A.
    Nelson, Steven M.
    Wig, Gagan S.
    Vogel, Alecia C.
    Lessov-Schlaggar, Christina N.
    Barnes, Kelly Anne
    Dubis, Joseph W.
    Feczko, Eric
    Coalson, Rebecca S.
    Pruett, John R., Jr.
    Barch, Deanna M.
    Petersen, Steven E.
    Schlaggar, Bradley L.
    [J]. SCIENCE, 2010, 329 (5997) : 1358 - 1361
  • [15] Networks of anatomical covariance
    Evans, Alan C.
    [J]. NEUROIMAGE, 2013, 80 : 489 - 504
  • [16] The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture
    Fan, Lingzhong
    Li, Hai
    Zhuo, Junjie
    Zhang, Yu
    Wang, Jiaojian
    Chen, Liangfu
    Yang, Zhengyi
    Chu, Congying
    Xie, Sangma
    Laird, Angela R.
    Fox, Peter T.
    Eickhoff, Simon B.
    Yu, Chunshui
    Jiang, Tianzi
    [J]. CEREBRAL CORTEX, 2016, 26 (08) : 3508 - 3526
  • [17] Functional Dysconnectivity of Corticostriatal Circuitry as a Risk Phenotype for Psychosis
    Fornito, Alex
    Harrison, Ben J.
    Goodby, Emmeline
    Dean, Anna
    Ooi, Cinly
    Nathan, Pradeep J.
    Lennox, Belinda R.
    Jones, Peter B.
    Suckling, John
    Bullmore, Edward T.
    [J]. JAMA PSYCHIATRY, 2013, 70 (11) : 1143 - 1151
  • [18] Mapping Symptoms to Brain Networks with the Human Connectome
    Fox, Michael D.
    [J]. NEW ENGLAND JOURNAL OF MEDICINE, 2018, 379 (23) : 2237 - 2245
  • [19] The disconnection hypothesis
    Friston, KJ
    [J]. SCHIZOPHRENIA RESEARCH, 1998, 30 (02) : 115 - 125
  • [20] Convergence and divergence of thickness correlations with diffusion connections across the human cerebral cortex
    Gong, Gaolang
    He, Yong
    Chen, Zhang J.
    Evans, Alan C.
    [J]. NEUROIMAGE, 2012, 59 (02) : 1239 - 1248