功能磁共振脑连接度中心性对精神分裂症症状的预测评估

doi:10.3969/j.issn.0258-8021.2020.01.03

Abstract
Figure/Table
References
Related Citation (8)

Download: PDF (4311 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Schizophrenia is a kind of serious mental disorder. Most of patients with schizophrenia need lifetime treatment of antipsychotic drugs. It is very important to evaluate the effects of drugs for the choice of therapeutic approaches. In this study, the degree centrality (DC) of brain functional connectivity was used to investigate the effects of taking two clinical different kinds of antipsychotic drugs: risperidone and clozapine, aiming to construct a model to predict their effects. The resting-state functional magnetic resonance imaging data were collected from 20 schizophrenia patients therapized with risperidone, 24 patients were treated with clozapine and 30 healthy as controls. The voxel-based DC was evaluated using analysis of variance. The DC in the regions with difference between patients and healthy controls was used in support vector regression (SVR) to construct predictive model for the scores of syndromes in patients with different drugs respectively. Experimental results indicated that the DC was altered in thalamus, insula and primary perception and motor-related cortexes in schizophrenia contrast to healthy controls (P< 0.05). In addition, The SVR model based on the DC in these regions could predict the negative syndrome of patients’ treatment with clozapine (r=0.448, P<0.05), while the syndrome of patients was not predicted in the risperidone group. Therefore, using SVR for the regions with difference in analysis of variance, several main regions related with antipsychotic drugs in schizophrenia were determined, and these findings would contribute to the choice antipsychotic drugs in the future.

Key words： functional MRI degree centrality support vector regression schizophrenia antipsychotics drugs

Received: 23 January 2019

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Duan Mingjun
	Li Ning
	Chang Xin
	Li Cheng
	Zhang Nan
	Ren Youwei
	Yang Huanghao
	He Hui
	Luo Cheng
	Yao Dezhong

Cite this article:

Duan Mingjun,Li Ning,Chang Xin, et al. Prediction and Evaluation of Schizophrenia Symptom by fMRI Brain Connectivity Centrality[J]. Chinese Journal of Biomedical Engineering, 2020, 39(1): 19-25.

URL:

http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2020.01.03 OR http://cjbme.csbme.org/EN/Y2020/V39/I1/19

[1] Sullivan PF, Kendler KS, Neale MC. Schizophrenia as a complex trait-Evidence from a meta-analysis of twin studies [J]. Archives of General Psychiatry, 2003, 60(12): 1187-1192.
[2] Su TW, Hsu TW, Lin YC, et al., Schizophrenia symptoms and brain network efficiency: A resting-state fMRI study [J]. Psychiatry Research-Neuroimaging, 2015, 234(2): 208-218.
[3] Duan M, Chen X, He H, et al. Altered Basal Ganglia Network Integration in Schizophrenia [J]. Front Hum Neurosci, 2015, 9:7.
[4] Torres US, Portela-Oliveira E, Borgwardt S, et al. Structural brain changes associated with antipsychotic treatment in schizophrenia as revealed by voxel-based morphometric MRI: An activation likelihood estimation meta-analysis [J]. BMC Psychiatry, 2013, 13:14.
[5] Hashimoto N, Ito YM, Okada N, et al. The effect of duration of illness and antipsychotics on subcortical volumes in schizophrenia: Analysis of 778 subjects [J]. Neuroimage-Clinical, 2018, 17: 563-569.
[6] Desamericq G, Schurhoff F, Meary A, et al. Long-term neurocognitive effects of antipsychotics in schizophrenia: a network meta-analysis [J]. European Journal of Clinical Pharmacology, 2014, 70(2):127-134.
[7] Huhtaniska S, Jaaskelainen E, Hirvonen N, et al. Long-term antipsychotic use and brain changes in schizophrenia - a systematic review and meta-analysis [J]. Human Psychopharmacology-Clinical and Experimental, 2017, 32(2):1-23.
[8] Shen H, Wang LB, Liu YD, et al. Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI [J]. Neuroimage, 2010, 49(4): 3110-3121.
[9] Zuo XN, Ehmke R, Mennes M, et al. Network Centrality in the Human Functional Connectome [J]. Cerebral Cortex, 2012, 22(8): 1862-1875.
[10] Garcia-Garcia I, Jurado MA, Garolera, et al., Functional network centrality in obesity: A resting-state and task fMRI study [J]. Psychiatry Res Neuroimaging, 2017, 233(3):331-338.
[11] Zhu JJ, Zhu DM, Qian YF, et al. Altered spatial and temporal concordance among intrinsic brain activity measures in schizophrenia [J]. Journal of Psychiatric Research, 2018, 106: 91-98.
[12] Buckner RL, Sepulcre J, Talukdar T, et al. Cortical hubs revealed by intrinsic functional connectivity: Mapping, assessment of stability, and relation to Alzheimer′s disease [J]. Journal of Neuroscience, 2009, 29(6): 1860-1873.
[13] Dai ZJ, Yan CG, Li KC, et al. Identifying and mapping connectivity patterns of brain network hubs in Alzheimer′s disease [J]. Cerebral Cortex, 2015, 25(10): 3723-3742.
[14] Zhang D, Liu X, Chen J, et al. Widespread increase of functional connectivity in Parkinson′s disease with tremor: A resting-state fMRI study [J]. Frontiers in Aging Neuroscience, 2015. 7:12.
[15] Smola AJ, Scholkopf B. A tutorial on support vector regression [J]. Statistics and Computing, 2004, 14(3): 199-222.
[16] Ellison-Wright I, Glahn DC, Laird AR, et al. The anatomy of first-episode and chronic schizophrenia: An anatomical likelihood estimation meta-analysis [J]. American Journal of Psychiatry, 2008, 165(8): 1015-1023.
[17] 常鑫, 罗程, 侯昌月, 等, 阿立哌唑和利培酮对精神分裂症患者自发脑活动的不同影响[J]. 四川精神卫生, 2015(6): 492-495.
[18] 李家凤, 分析利培酮与氯氮平联合治疗精神分裂症的疗效[J]. 北方药学, 2017. 14(5): 131-131.
[19] Machielsen MWJ, Veltman DJ, van den Brink W, et al. The effect of clozapine and risperidone on attentional bias in patients with schizophrenia and a cannabis use disorder: An fMRI study [J]. Journal of Psychopharmacology, 2014, 28(7): 633-642.