Statistical Complexity Analysis of Neurovascular Coupling with Cognitive Stimulation in Healthy Participants

Area of Science:

• Neuroscience
• Biomedical Engineering
• Signal Processing

Background:

• Neurovascular coupling (NVC) links brain activity with blood flow changes.
• Transcranial Doppler ultrasound (TCD) non-invasively measures cerebral blood velocity (CBv) for NVC assessment.
• Existing NVC analysis methods, like coherent averaging, have limitations.

Purpose of the Study:

• To introduce and evaluate a novel analytical approach for TCD-measured CBv signals.
• To assess the utility of information entropy and statistical complexity measures in analyzing NVC during cognitive tasks.
• To differentiate between resting and cognitively active brain states using these novel measures.

Main Methods:

• Analyzed CBv changes in 40 healthy individuals performing 20 tasks from the Addenbrooke's Cognitive Examination III.
• Applied permutation, Tsallis, and Rényi entropy, alongside statistical complexity measures.
• Utilized receiver operating characteristic curves on the entropy-complexity plane to assess discriminative power.

Main Results:

• Most cognitive tasks (attention, visuospatial, memory) exhibited lower statistical complexity compared to the resting state.
• The entropy-complexity analysis achieved a high area under the curve (0.91 ± 0.04, p = .001) in distinguishing resting from cognitive states.
• Demonstrated the potential of entropy and complexity measures to differentiate brain states based on hemodynamic signals.

Conclusions:

• Entropy and statistical complexity offer a viable alternative to traditional methods for analyzing TCD-derived NVC.
• This approach effectively distinguishes between resting and cognitive effort states using hemodynamic signals.
• Further research is needed to directly compare these novel methods with existing techniques for optimal NVC analysis.