A comprehensive evaluation of multicentric reliability of single-subject cortical morphological networks on traveling subjects

• Published in: Cerebral cortex (New York, N.Y. 1991) Vol. 33; no. 14; pp. 9003 - 9019
• Main Authors: Yin, Guole, Li, Ting, Jin, Suhui, Wang, Ningkai, Li, Junle, Wu, Changwen, He, Hongjian, Wang, Jinhui
• Format: Journal Article
• Language: English
• Published: United States Oxford University Press 05.07.2023
• Subjects: Brain - anatomy & histology; Connectome - methods; Humans; Magnetic Resonance Imaging - methods; Reproducibility of Results; multicentric study; structural MRI; test-retest reliability; morphological brain network; graph theory
• ISSN: 1047-3211; 1460-2199; 1460-2199
• DOI: 10.1093/cercor/bhad178

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.