Early detection of dementia with default-mode network effective connectivity

Area of Science:

• Neuroscience
• Medical Imaging
• Gerontology

Background:

• Dementia, particularly Alzheimer's disease, significantly impacts brain function, disrupting the default-mode network (DMN).
• Altered functional connectivity is an early indicator of neurological changes, often preceding structural alterations and clinical symptoms.

Purpose of the Study:

• To determine if a neurobiological model of DMN effective connectivity can predict future dementia diagnoses at an individual level.
• To assess the predictive power of this model against existing methods based on brain structure and functional connectivity.

Main Methods:

• Utilized spectral dynamic causal modeling on resting-state functional MRI data from the UK Biobank.
• Analyzed data from 81 individuals who later developed dementia and 1,030 matched controls.

Main Results:

• The neurobiological model accurately predicted future dementia incidence (AUC = 0.82) and time to diagnosis (R = 0.53).
• This predictive accuracy surpassed models based solely on brain structure or functional connectivity.
• DMN dysconnectivity showed strong associations with Alzheimer's disease polygenic risk and social isolation.

Conclusions:

• Neurobiological modeling of effective connectivity offers a promising tool for early dementia detection.
• This approach can aid in the timely implementation of targeted dementia prevention strategies at a population level.