阈值
旋回作用
人工智能
分歧(语言学)
连接体
模式识别(心理学)
计算机科学
数学
统计
功能连接
心理学
大脑皮层
神经科学
图像(数学)
语言学
哲学
作者
Gang Yin,Ting Li,S. Jin,Ningkai Wang,Junle Li,Changwen Wu,Hongjian He,Jinhui Wang
出处
期刊:Cerebral Cortex
[Oxford University Press]
日期:2023-05-17
卷期号:33 (14): 9003-9019
被引量:5
标识
DOI:10.1093/cercor/bhad178
摘要
Abstract Despite the prevalence of research on single-subject cerebral morphological networks in recent years, whether they can offer a reliable way for multicentric studies remains largely unknown. Using two multicentric datasets of traveling subjects, this work systematically examined the inter-site test-retest (TRT) reliabilities of single-subject cerebral morphological networks, and further evaluated the effects of several key factors. We found that most graph-based network measures exhibited fair to excellent reliabilities regardless of different analytical pipelines. Nevertheless, the reliabilities were affected by choices of morphological index (fractal dimension > sulcal depth > gyrification index > cortical thickness), brain parcellation (high-resolution > low-resolution), thresholding method (proportional > absolute), and network type (binarized > weighted). For the factor of similarity measure, its effects depended on the thresholding method used (absolute: Kullback–Leibler divergence > Jensen–Shannon divergence; proportional: Jensen–Shannon divergence > Kullback–Leibler divergence). Furthermore, longer data acquisition intervals and different scanner software versions significantly reduced the reliabilities. Finally, we showed that inter-site reliabilities were significantly lower than intra-site reliabilities for single-subject cerebral morphological networks. Altogether, our findings propose single-subject cerebral morphological networks as a promising approach for multicentric human connectome studies, and offer recommendations on how to determine analytical pipelines and scanning protocols for obtaining reliable results.
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