Morphometric network differences in ageing versus Alzheimer's disease dementia.

Area of Science:

• Neuroimaging
• Neuroscience
• Gerontology

Background:

• Distinguishing normal brain aging from Alzheimer's disease (AD) is challenging due to age being a primary risk factor for AD.
• Previous studies often focused only on older adults and predefined brain regions.
• A comprehensive understanding requires analyzing structural changes across the lifespan and in relation to AD pathology.

Purpose of the Study:

• To investigate grey matter organization and volume differences in normal aging versus Alzheimer's disease.
• To determine if structural brain differences can discriminate between aging and AD.
• To explore the relationship between brain structure and clinical progression.

Main Methods:

• Utilized a large, multi-cohort dataset including young adults, cognitively intact older adults, and individuals with mild cognitive impairment or AD dementia.
• Applied independent component analysis (ICA) on structural MRI data to derive morphometric networks and grey matter volumes.
• Assessed whole-brain grey matter pattern organization, interindividual heterogeneity, and used logistic regression and ROC analyses for discrimination.

Main Results:

• Both aging and AD showed additive effects on grey matter volume in most networks; frontal lobe differences were specific to aging.
• No specific networks discriminated AD from aging, but heterogeneity in grey matter across networks and whole-brain patterns characterized cognitive impairment.
• Preservation of the whole-brain grey matter pattern was a stronger predictor of lower cognitive decline risk than grey matter volume.

Conclusions:

• Both aging and AD involve widespread grey matter atrophy.
• Disruption in morphometric organization, particularly whole-brain pattern heterogeneity, uniquely characterizes Alzheimer's disease.
• Brain organization, more than volume, is crucial for understanding cognitive impairment and predicting clinical progression in AD.