Analysis of Brain Structural Connectivity Networks and White Matter Integrity in Patients With Mild Cognitive Impairment

Abstract

White matter integrity and structural connectivity may be altered in mild cognitive impairment (MCI), and these changes may closely reflect decline in specific cognitive domains. Multi-shell diffusion data in healthy control (HC, n = 31) and mild cognitive impairment (MCI, n = 19) cohorts were downloaded from the ADNI3 database. The data were analyzed using an advanced approach to assess both white matter microstructural integrity and structural connectivity. Compared with HC, lower intracellular compartment (IC) and higher isotropic (ISO) values were found in MCI. Additionally, significant correlations were found between IC and Montreal Cognitive Assessment (MoCA) scores in the MCI cohort. Network analysis detected structural connectivity differences between the two groups, with lower connectivity in MCI. Additionally, significant differences between HC and MCI were observed for global network efficiency. Our results demonstrate the potential of advanced diffusion MRI biomarkers for understanding brain changes in MCI.

Structural connectivity and brain network analyses in Parkinson's disease: A cross-sectional and longitudinal study

“…Combining dMRI and tractography enables the assessment of brain structural connectivity, which represents the WM tracts that physically interconnect brain regions in vivo ( 14 ). Additionally, tractography, in combination with graph theory, can been used to reconstruct and analyze the structural whole-brain connectivity in PD ( 10 , 15 ), as well as other neurodegenerative disorders ( 16 , 17 ). These studies in PD have revealed the existence of novel PD-specific anatomic networks, characterized by alterations in cortical–subcortical loops, reduced modular organization, and compensatory changes ( 15 , 18 ).…”

Developmental differences in canonical cortical networks: insights from microstructure-informed tractography

“…The Convex Optimization Modelling for Microstructure Informed Tractography (COMMIT) (Daducci et al, 2015; Daducci et al, 2013) approach address this problem by deconvolving specific microstructural features on each streamline to recover individual contributions to the measured signal. By replacing the commonly used streamline count with intra-axonal signal fraction (IASF), it offers a quantitative and more biologically informative assessment of brain connectivity (Bergamino et al, 2022; Gabusi et al, 2022; Schiavi et al, 2022; Schiavi, Ocampo-Pineda, et al, 2020; Schiavi, Petracca, et al, 2020).…”

“…By replacing the commonly used streamline count with intra-axonal signal fraction (IASF), it offers a quantitative and more biologically informative assessment of brain connectivity (Bergamino et al, 2022;Gabusi et al, 2022;Schiavi et al, 2022;Schiavi, Ocampo-Pineda, et al, 2020;Schiavi, Petracca, et al, 2020).…”