Original Research ARTICLE
Progressively Disrupted Brain Functional Connectivity Network in Subcortical Ischaemic Vascular Cognitive Impairment Patients
- 1Army Medical University, China
- 2Southwest Hospital, Third Military Medical University, China
Cognitive impairment caused by subcortical ischaemic vascular disease (SIVD) has been elucidated by many neuroimaging studies. However, little is known concerning the changes of brain functional connectivity networks in relation with severity of cognitive impairment in SIVD. In the present study, 20 subcortical ischaemic vascular cognitive impairment no dementia patients (SIVCIND) and 20 dementia patients (SIVaD) were enrolled, meanwhile, 19 normal controls were recruited. Each participant underwent a resting-state functional MRI scan. The whole-brain functional networks were examined by two approaches: graph theory for studying network functional organization and network-based statistics (NBS) for exploring functional connectivity amongst brain regions. After adjustment for age, gender and duration of formal education, there were significant group differences for two network functional organization indexes: global efficiency and local efficiency decreased (NC>SIVCIND>SIVaD) as cognitive impairment worsens. Between-group differences in functional connectivity (NBS corrected, p<0.01) mainly concerned the orbitofrontal, parietal, temporal cortex as well as basal ganglia. The brain connectivity network is progressively disrupted as cognitive impairment worsens, with increasing number of decreased connections between brain regions. We also observed more reduction of nodal efficiency in prefrontal and temporal cortex for SIVaD than SIVCIND. These findings suggested the progressively disrupted pattern of brain functional connectivity network with cognitive impairment increased and promise for developing reliable biomarkers of network metrics changes related to cognitive impairment caused by SIVD.
Keywords: subcortical ischaemic vascular cognitive impairment, graph theoretical analysis, Topological organization, network-based statistics, functional connections
Received: 24 Aug 2017;
Accepted: 09 Feb 2018.
Edited by:Hamid R. Sohrabi, Macquarie University, Australia
Reviewed by:Cristina Sanchez-Castañeda, Department of Psychiatry and Clinical Psychobiology, University of Barcelona, Spain
Samir Kumar-Singh, University of Antwerp, Belgium
Copyright: © 2018 Linqiong, Lin, Li, Jingna, Ye, Pengyue, Chuanming and Mingguo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Dr. Li Chuanming, Southwest Hospital, Third Military Medical University, Chongqing, China, firstname.lastname@example.org
Dr. Qiu Mingguo, Army Medical University, Chongqing, China, email@example.com