Time-varying Analysis of Brain Networks Based on High-order Dynamic Functional Connections in Mild Cognitive Impairment

doi:10.11938/cjmr20243102

Abstract

Abstract:

Existing research commonly uses functional connectivity (FC) combined with graph theory analysis to accomplish the auxiliary diagnosis of mild cognitive impairment (MCI). Traditional FC analysis methods usually target low-order FC networks, while high-order FC networks can reveal higher-level interactions in brain networks. However, there are few studies involving graph theory in high-order FC networks, and traditional graph theory indicators have limitations in high-order FC networks. This paper constructs a high-order FC network through high-order dynamic functional connections, combines graph theory to analyze the brain network status of MCI and normal cognition (NC), and defines two new graph theory indicators, blocking coefficient and average transition time, to characterize temporal variability in brain networks. The results show that the application of graph theory in high-order FC network can effectively extract the differential information between MCI group and NC group. The proposed blocking coefficient and average conversion time index can both show significant differences, providing a new analysis method for the study of high-order brain network.

Key words: mild cognitive impairment (MCI), temporal variability, higher-order FC network, graph theory indicators

CLC Number:

TP301.6

WANG Xia, WANG Yong, LAN Qing. Time-varying Analysis of Brain Networks Based on High-order Dynamic Functional Connections in Mild Cognitive Impairment[J]. Chinese Journal of Magnetic Resonance, 2024, 41(3): 286-303.

Figures/Tables 16

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特征	组别（均值±标准差）		p值
特征	MCI(40)	NC(40)	p值
年龄	75.17±7.58	76.50±7.04	0.259
性别（男性/女性）	21/19	17/23	0.377
受教育年限	13.37±4.37	12.57±3.39	0.365
MMSE评分	23.68±1.89	29.48±0.78	0.001
CDR评分	≥1	0	-

参数	描述
数据库	ADNI-2
磁场强度	3.0 T
采集设备	Philips
翻转角	80°
TR	3000 ms
TE	30 ms
像素间距	3.3 mm×3.3 mm
切片数量	48
时间点	140

指标	意义
平均路径长度	每对节点之间的最短距离的均值
最大度数	入射到顶点的最大边数
图密度	边数与所有可能边数的比值
聚类系数	图中节点聚集程度的系数，代表节点间的交互关系程度
边连通性	获得非强连接图所需的最小边数
小世界属性	定量表达为$\sigma \text{=}\eta /\lambda$，其中$\eta$是真实网络与其对应随机网络的聚类系数平均值的比值，$\lambda$为真实网络与其对应随机网络的平均路径长度的比值

子网名称	英文简称	脑区数量	包含脑区名称
默认网络	DMN	20	背外侧额上回、内侧额上回、眶内额上回、回直肌、前扣带和旁扣带脑回、后扣带回、角回、楔前叶、颞中回、颞极：颞中回
注意网络	ATT	16	眶部额上回、额中回、眶部额中回、岛盖部额下回、三角部额下回、眶部额下回、顶上回、顶下缘角回
皮质下网络	SUB	20	嗅皮质、内侧和旁扣带脑回、海马、海马旁回、杏仁核、尾状核、豆状壳核、豆状苍白球、丘脑、颞下回
视觉网络	VIS	14	距状裂周围皮层、楔叶、舌回、枕上回、枕中回、枕下回、梭状回
听觉网络	AUD	12	中央沟盖、脑岛、缘上回、颞横回、颞上回、颞极：颞上回
感觉运动网络	SEN	8	中央前回、补充运动区、中央后回、中央旁小叶

参数	设置
L	30
T₀	1
W	101