Normative brain-state trajectories reveal deviation from healthy aging in AD.

Area of Science:

• Neuroscience
• Medical Imaging
• Computational Biology

Background:

• Differentiating healthy brain aging from early Alzheimer's disease (AD) neurodegeneration is difficult.
• Resting-state functional magnetic resonance imaging (fMRI) offers insights into brain dynamics.

Purpose of the Study:

• To model large-scale brain-state dynamics in cognitively normal aging.
• To identify deviations from healthy aging trajectories in mild cognitive impairment (MCI) and AD.
• To develop a candidate biomarker for altered brain organization.

Main Methods:

• Utilized resting-state fMRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI).
• Employed a Hidden Markov Model (HMM) to define brain connectivity states in healthy adults.
• Applied a generalized additive model to establish an age-adjusted reference trajectory of transition entropy.

Main Results:

• Cognitively normal adults exhibited a reference trajectory of brain-state dynamics.
• Mild cognitive impairment and Alzheimer's disease showed significant, progressive deviations from this healthy aging reference.
• A single absolute-deviation score effectively captured predictive information from complex dynamic features.

Conclusions:

• Alzheimer's disease is characterized by a measurable departure from healthy dynamic brain aging patterns.
• The proposed absolute-deviation score serves as a compact and interpretable candidate biomarker.
• This framework supports future longitudinal studies and clinical validation for Alzheimer's disease diagnosis.