An abnormal resting-state functional brain network indicates progression towards Alzheimer's disease

Area of Science:

• Neuroscience
• Cognitive Neurology
• Medical Imaging

Background:

• Mild cognitive impairment (MCI) and Alzheimer's disease (AD) involve changes in brain structure and function, particularly in the temporal lobe, hippocampus, and prefrontal cortex.
• Existing research often compares patients with normal controls, but understanding the progression through MCI stages to AD is crucial.

Purpose of the Study:

• To analyze resting-state functional neural network characteristics in individuals across different cognitive stages: normal controls, early MCI, late MCI, and AD.
• To observe the progression of mild cognitive impairment before its transformation into Alzheimer's disease.

Main Methods:

• Utilized resting-state functional MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.
• Constructed and analyzed brain networks from four distinct populations: normal controls, early MCI, late MCI, and AD patients.
• Examined network efficiency metrics, including shortest path length and clustering coefficients.

Main Results:

• A gradual increase in the shortest path length and a decrease in clustering coefficients were observed as cognitive deficits progressed across the four groups.
• These changes indicate a significant decline in brain network efficiency associated with the progression of dementia.
• Functional network alterations demonstrated progressive deterioration from healthy aging through MCI to AD.

Conclusions:

• Dementia is strongly associated with a decline in overall brain network efficiency.
• Alterations in brain region node attributes may correlate with specific cognitive function impairments, such as memory, hearing, and language.
• Early cognitive impairments could precede and contribute to diffuse brain injury and broader cognitive deficits seen in later stages of dementia.