Spatial–temporal patterns of brain disconnectome in Alzheimer's disease

• Published in: Human brain mapping Vol. 44; no. 11; pp. 4272 - 4286
• Main Authors: Liang, Li, Zhou, Pengzheng, Ye, Chenfei, Yang, Qi, Ma, Ting
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.08.2023
• Subjects: Alzheimer Disease - pathology; Alzheimer's disease; Brain; Brain - diagnostic imaging; Brain - pathology; brain disconnectome; Brain mapping; cerebral small vessel disease; Chi-square test; Cognitive ability; Cognitive Dysfunction - diagnostic imaging; Cognitive Dysfunction - etiology; Cognitive Dysfunction - pathology; Computational neuroscience; Dementia; Dementia disorders; Disease Progression; Evolution; Frontal gyrus; Hippocampus - pathology; Humans; Magnetic Resonance Imaging; Medical imaging; mild cognitive impairment; Neural networks; Neurodegeneration; Neurodegenerative diseases; Neuroimaging; Neuroimaging - methods; Occipital lobe; Parahippocampal gyrus; Pathophysiology; Signs and symptoms; spatial–temporal pattern; Substantia alba; Tau protein; Temporal cortex; white matter hyperintensities; spatial-temporal pattern; cerebral small vessel disease; mild cognitive impairment; Alzheimer's disease; white matter hyperintensities; brain disconnectome
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.26344

Mounting evidences have shown that progression of white matter hyperintensities (WMHs) with vascular origin might cause cognitive dysfunction symptoms through their effects on brain networks. However, the vulnerability of specific neural connection related to WMHs in Alzheimer's disease (AD) still remains unclear. In this study, we established an atlas‐guided computational framework based on brain disconnectome to assess the spatial–temporal patterns of WMH‐related structural disconnectivity within a longitudinal investigation. Alzheimer's Disease Neuroimaging Initiative (ADNI) database was adopted with 91, 90 and 44 subjects including in cognitive normal aging, stable and progressive mild cognitive impairment (MCI), respectively. The parcel‐wise disconnectome was computed by indirect mapping of individual WMHs onto population‐averaged tractography atlas. By performing chi‐square test, we discovered a spatial–temporal pattern of brain disconnectome along AD evolution. When applied such pattern as predictor, our models achieved highest mean accuracy of 0.82, mean sensitivity of 0.86, mean specificity of 0.82 and mean area under the receiver operating characteristic curve (AUC) of 0.91 for predicting conversion from MCI to dementia, which outperformed methods utilizing lesion volume as predictors. Our analysis suggests that brain WMH‐related structural disconnectome contributes to AD evolution mainly through attacking connections between: (1) parahippocampal gyrus and superior frontal gyrus, orbital gyrus, and lateral occipital cortex; and (2) hippocampus and cingulate gyrus, which are also vulnerable to Aβ and tau confirmed by other researches. All the results further indicate that a synergistic relationship exists between multiple contributors of AD as they attack similar brain connectivity at the prodromal stage of disease. We proposed an atlas‐guided computation framework to detect distinctive spatial‐temporal progressive WMH‐related brain disconnectome on the continuum of AD. We located the progressive disconnectome patterns within structural connectivity associated with parahippocampal gyrus and hippocampus. By developing prediction models, we further showed that brain disconnectome can be used as powerful predictor for conversion from MCI to dementia.