Literature DB >> 28799862

Disrupted Topologic Efficiency of White Matter Structural Connectome in Individuals with Subjective Cognitive Decline.

Ni Shu1, Xiaoni Wang1, Qiuhui Bi1, Tengda Zhao1, Ying Han1.   

Abstract

Purpose To determine whether individuals with subjective cognitive decline (SCD), which is defined by memory complaints with normal performance at objective neuropsychologic examinations, exhibit disruptions of white matter (WM) connectivity and topologic alterations of the brain structural connectome. Materials and Methods Diffusion-tensor magnetic resonance imaging and graph theory approaches were used to investigate the topologic organization of the brain structural connectome in 36 participants with SCD (21 women: mean age, 62.0 years ± 8.6 [standard deviation]; age range, 42-76 years; 15 men: mean age, 65.5 years ± 8.9; age range, 51-80 years) and 51 age-, sex-, and years of education-matched healthy control participants (33 women: mean age, 63.7 years ± 8.8; age range, 46-83 years; 18 men: mean age, 59.4 years ± 9.3; age range, 43-75 years). Individual WM networks were constructed for each participant, and the network properties between two groups were compared with a linear regression model. Results Graph theory analyses revealed that the participants with SCD had less global efficiency (P = .001) and local efficiency (P = .008) compared with the healthy control participants. Lower regional efficiency was mainly distributed in the bilateral prefrontal regions and left thalamus (P < .05, corrected). Furthermore, a disrupted subnetwork was observed that consisted of widespread anatomic connections (P < .05, corrected), which has the potential to discriminate individuals with SCD from control participants. Moreover, similar hub distributions and less connection strength between the hub regions (P = .023) were found in SCD. Importantly, diminished strength of the rich-club and local connections was correlated with the impaired memory performance in patients with SCD (rich-club connection: r = 0.43, P = .011; local connection: r = 0.36, P = .037). Conclusion This study demonstrated disrupted topologic efficiency of the brain's structural connectome in participants with SCD and provided potential connectome-based biomarkers for the early detection of cognitive impairment in elderly individuals. © RSNA, 2017 Online supplemental material is available for this article.

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Year:  2017        PMID: 28799862     DOI: 10.1148/radiol.2017162696

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  37 in total

1.  Dynamic network connectivity predicts subjective cognitive decline: the Sino-Longitudinal Cognitive impairment and dementia study.

Authors:  Guozhao Dong; Liu Yang; Chiang-Shan R Li; Xiaoni Wang; Yihe Zhang; Wenying Du; Ying Han; Xiaoying Tang
Journal:  Brain Imaging Behav       Date:  2020-12       Impact factor: 3.978

2.  Diffusion tensor imaging reveals abnormal brain networks in elderly subjects with subjective cognitive deficits.

Authors:  Daegyeom Kim; Suji Lee; Myungwon Choi; HyunChul Youn; Sangil Suh; Hyun-Ghang Jeong; Cheol E Han
Journal:  Neurol Sci       Date:  2019-06-26       Impact factor: 3.307

3.  Aberrant topological organization and age-related differences in the human connectome in subjective cognitive decline by using regional morphology from magnetic resonance imaging.

Authors:  Zhenrong Fu; Mingyan Zhao; Yirong He; Xuetong Wang; Xin Li; Guixia Kang; Ying Han; Shuyu Li
Journal:  Brain Struct Funct       Date:  2022-05-17       Impact factor: 3.270

4.  Brain neurometabolites differences in individuals with subjective cognitive decline plus: a quantitative single- and multi-voxel proton magnetic resonance spectroscopy study.

Authors:  Zhongxian Yang; Xing Wan; Xinzhu Zhao; Yu Rong; Yi Wu; Zhen Cao; Qiuxia Xie; Min Luo; Yubao Liu
Journal:  Quant Imaging Med Surg       Date:  2021-09

5.  Early Changes in the White Matter Microstructure and Connectome Underlie Cognitive Deficit and Depression Symptoms After Mild Traumatic Brain Injury.

Authors:  Wenjing Huang; Wanjun Hu; Pengfei Zhang; Jun Wang; Yanli Jiang; Laiyang Ma; Yu Zheng; Jing Zhang
Journal:  Front Neurol       Date:  2022-06-30       Impact factor: 4.086

Review 6.  Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review.

Authors:  Fan Zhang; Alessandro Daducci; Yong He; Simona Schiavi; Caio Seguin; Robert E Smith; Chun-Hung Yeh; Tengda Zhao; Lauren J O'Donnell
Journal:  Neuroimage       Date:  2022-01-01       Impact factor: 7.400

7.  Impaired cerebral vascular and metabolic responses to parametric N-back tasks in subjective cognitive decline.

Authors:  Yaoyu Zhang; Wenying Du; Yayan Yin; Huanjie Li; Zhaowei Liu; Yang Yang; Ying Han; Jia-Hong Gao
Journal:  J Cereb Blood Flow Metab       Date:  2021-05-05       Impact factor: 6.200

8.  Morphological, Structural, and Functional Networks Highlight the Role of the Cortical-Subcortical Circuit in Individuals With Subjective Cognitive Decline.

Authors:  Xiaowen Xu; Tao Wang; Weikai Li; Hai Li; Boyan Xu; Min Zhang; Ling Yue; Peijun Wang; Shifu Xiao
Journal:  Front Aging Neurosci       Date:  2021-07-09       Impact factor: 5.750

9.  Abnormal white matter within brain structural networks is associated with high-impulse behaviour in codeine-containing cough syrup dependent users.

Authors:  Yunfan Wu; Zhihua Zhou; Meng Li; Xiaofen Ma; Zhihong Lan; Jin Fang; Shishun Fu; Kanghui Yu; Yi Yin; Shoujun Xu; Cuihua Gao; Jianneng Li; Guihua Jiang
Journal:  Eur Arch Psychiatry Clin Neurosci       Date:  2020-03-02       Impact factor: 5.270

10.  Machine learning based on the multimodal connectome can predict the preclinical stage of Alzheimer's disease: a preliminary study.

Authors:  Haifeng Chen; Weikai Li; Xiaoning Sheng; Qing Ye; Hui Zhao; Yun Xu; Feng Bai
Journal:  Eur Radiol       Date:  2021-06-10       Impact factor: 5.315

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