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

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 A beta 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.

Automated summary

Recent evidence suggests that white matter hyperintensities (WMHs) may contribute to cognitive dysfunction in Alzheimer's disease (AD) through their effects on brain networks. However, the vulnerability of specific neural connections related to WMHs in AD is not well understood. This study used a computational framework to assess the spatial-temporal patterns of WMH-related structural disconnectivity in AD. The results showed a specific pattern of brain disconnectome that predicted conversion from mild cognitive impairment (MCI) to dementia with high accuracy.