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

Learning a high-order connectional brain template (CBT) endowed with cognitive capacities such as visual or auditory memory is crucial for identifying cognition-related biomarkers and distinguishing between control and clinical populations. Higher-order CBTs provide a population-level representation that captures not only structural or topological regularities but also the multi-regional interactions and cognitive processes that conventional pairwise models fail to reflect. Because the brain operates through complex, coordinated dynamics, estimating CBTs that incorporate such higher-order and cognitively meaningful organization is essential for advancing our understanding of neural function and dysfunction. While recent machine-learning and graph-neural-network approaches have improved CBT estimation, they remain limited by their focus on pairwise interactions and purely structural features, overlooking both higher-order organization and cognitive properties. This gap raises a central question: How can we learn a high-order CBT that is well-centered at the population level and also endowed with cognitive capacities? We tackle this challenge using reservoir computing (RC), a biologically inspired framework that mimics how the brain processes information. RC exhibits dynamic properties similar to those of the prefrontal cortex, an area associated with working memory and features a fading memory mechanism, known as the Echo State Property (ESP), which mirrors the brain's short-term memory function. Building on these properties, we introduce HyperCOCO, a novel framework for generating high-order cognitively enhanced CBTs in two stages. First, BOLD signals are processed through a random reservoir to generate high-order individual functional connectomes, which are then aggregated into a population-level template. Second, this template is instantiated into a hyper-cognitive reservoir and stimulated with multi-sensory inputs (visual, auditory, and linguistic). Finally, we measure the memory capacity of the resulting CBT as a proxy for its ability to encode and retain cognitive information. Our source code is available at https://github.com/basiralab/HyperCOCO.