Brain function complexity during dual-tasking is associated with cognitive impairment and age.

Area of Science:

• Neuroscience
• Cognitive Science
• Medical Imaging

Background:

• Early diagnosis of cognitive impairment is crucial for timely therapeutic intervention.
• Synaptic disconnections characterize dementia, and nonlinear complexity analysis can identify them.
• Functional magnetic resonance imaging (fMRI) combined with novel paradigms offers potential for early detection.

Purpose of the Study:

• To investigate the efficacy of a novel upper-extremity function (UEF) dual-task paradigm in fMRI to differentiate between cognitively normal (CN) individuals and those with mild cognitive impairment (MCI).
• To assess the utility of multiscale entropy (MSE) analysis of brain activity and motor function in identifying cognitive impairment.

Main Methods:

• Utilized a UEF dual-task paradigm during fMRI, involving arm movement and counting, to analyze blood oxygen-level dependent (BOLD) signals.
• Applied MSE complexity analysis to BOLD time-series across neural networks and brain regions.
• Measured motor function using elbow kinematics via motion sensors during an external UEF dual-task test.

Main Results:

• Individuals with MCI exhibited 34% lower MSE values compared to CN individuals across most brain regions and networks.
• A negative correlation was observed between MSE values and age, indicating age-related decline in brain complexity.
• Integrating brain function measures (MSE) with motor function scores improved the sensitivity of MCI prediction models by 14-24%.

Conclusions:

• Combining neural network activity (MSE) and motor function assessments may offer a robust tool for early cognitive impairment detection.
• This multimodal approach shows promise for assessing early-stage cognitive impairment and age-related cognitive decline.
• Further research can refine these methods for clinical application in diagnosing and managing cognitive disorders.