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Infra-slow brain dynamics as a marker for cognitive function and decline.

Shagun Ajmera¹, Shreya Rajagopal¹, Razi Ur Rehman²

¹Centre for Neuroscience, Indian Institute of Science, Bangalore.

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|April 2, 2020

Summary

Slow brain dynamics measured by functional magnetic resonance imaging (fMRI) are relevant for cognition. These slow signals accurately predict cognitive tasks, behavioral differences, and disease progression in patients with dementia.

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Area of Science:

Neuroscience
Cognitive Science
Machine Learning

Background:

Functional magnetic resonance imaging (fMRI) measures brain activity, but its slow hemodynamic response ( <0.1-1 Hz) contrasts with rapid neural spiking (milliseconds).
The relevance of these slow fMRI dynamics for cognitive function remains unclear.

Purpose of the Study:

To investigate the relevance of slow fMRI dynamics for cognitive function and neurological disease.
To apply Gaussian Process Factor Analysis (GPFA) and machine learning to analyze fMRI data.

Main Methods:

Analyzed fMRI data from 1000 healthy participants (Human Connectome Project).
Applied GPFA to reduce dimensionality and extract latent dynamics from slowly sampled (1.4 Hz) fMRI data.
Investigated slow (<1 Hz) and infra-slow (<0.1 Hz) dynamics.

Main Results:

Slow GPFA dimensions accurately identified tasks performed by subjects (>95% accuracy).
Functional connectivity between slow latent dynamics predicted inter-individual differences in cognitive task performance.
Infra-slow latent dynamics predicted Clinical Dementia Rating (CDR) scores and identified patients with mild cognitive impairment (MCI) who progressed to Alzheimer's dementia (AD).

Conclusions:

Slow and infra-slow brain dynamics are relevant for cognitive function.
These findings suggest potential for fMRI-based biomarkers in diagnosing and predicting neurological conditions like Alzheimer's disease.