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HyperCOCO: Multi-sensory HyperCOgnitive COmputing for learning population level brain connectivity.

Mayssa Soussia1, Mohamed Ali Mahjoub2, Islem Rekik3

  • 1BASIRA Lab, Imperial-X (I-X) and Department of Computing, Imperial College London, United Kingdom; National Engineering School of Sousse, University of Sousse, LATIS-Laboratory of Advanced Technology and Intelligent Systems, 4023, Sousse, Tunisia.

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Summary
This summary is machine-generated.

This study introduces HyperCOCO, a novel framework using reservoir computing to create high-order connectional brain templates (CBTs) with cognitive abilities. This method enhances understanding of brain function and identifies cognition-related biomarkers.

Keywords:
Cognitive computingConnectional brain template (CBT)Functional connectivityReservoir computing (RC)

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • Higher-order connectional brain templates (CBTs) are vital for identifying cognition-related biomarkers and understanding brain function.
  • Existing methods for estimating CBTs often focus on pairwise interactions and structural features, neglecting higher-order organization and cognitive capacities.

Purpose of the Study:

  • To develop a novel framework, HyperCOCO, for learning high-order CBTs that are population-level centered and possess cognitive capacities.
  • To address the limitations of current machine-learning and graph-neural-network approaches in capturing complex brain dynamics and cognitive properties.

Main Methods:

  • Utilized reservoir computing (RC), a biologically inspired framework mimicking brain information processing and short-term memory via the Echo State Property (ESP).
  • Introduced a two-stage HyperCOCO framework: 1) generating high-order functional connectomes from BOLD signals via a random reservoir and aggregating them into a population template, and 2) instantiating this template into a hyper-cognitive reservoir stimulated with multi-sensory inputs.
  • Measured the memory capacity of the resulting CBT as a proxy for its cognitive information encoding and retention abilities.

Main Results:

  • Successfully generated high-order cognitively enhanced CBTs using the HyperCOCO framework.
  • Demonstrated the framework's ability to incorporate higher-order organization and cognitive properties, overcoming limitations of previous methods.
  • Quantified the memory capacity of the learned CBTs, indicating their potential for encoding cognitive information.

Conclusions:

  • HyperCOCO offers a novel approach to learning high-order CBTs with integrated cognitive capacities, advancing the understanding of neural function.
  • The framework provides a promising tool for identifying cognition-related biomarkers and distinguishing between clinical and control populations.
  • This work highlights the potential of reservoir computing in modeling complex brain dynamics and cognitive processes.