Author:
Yadav Neha,Gupta Niraj Kumar,Thakar Darshit,Tiwari Vivek
Abstract
AbstractIn an aging population, a subset of individuals at a given age group have low white matter hyperintensity (WMH) while another subset has intermediate to high WMH load. WMH load quantification together with comprehensive neuroanatomic volumetry needs to be examined together for establishing a unique precise and optimal number of brain features as a noninvasive indicator of ‘Brain Age’ and cognitive status. Here, a comprehensive neuroanatomic volumetry together with WMH quantification using longitudinal MRI and cognitive measurements from two aging cohorts have been performed together with machine learning modeling of the quantitative changes with aging to establish Optimal unique brain events discriminative of cognitive status and estimative of Brain Age. A set of Three optimal brain-associated quantities; wherein two are neuroanatomic features Total brain volume; CSF volume, and the third is the extent of microvascular pathology WMH load, provide highly precise discrimination of cognitive status as cognitively normal (CN), impaired (CI) and AD (CI-AD). While medial cortical thinning of Parahippocampal gyrus is an ‘early age event’ discriminative between CI and CI-AD but loss of hippocampus, gray matter and white matter volume lacks sensitivity to discriminate between CI and CI-AD. The Brain Age estimation using the neuroanatomic volumetry and periventricular and deep WMH load indicates that elevated WMH load in the brain led to an increased Brain Age gap than the brain with low WMH at a given chronological age. Increased Brain Age gap with elevated WMH load at the early age groups is suggestive of profound vascular insult arising from WMH to the brain structure and function.
Publisher
Cold Spring Harbor Laboratory