Brain Connectivity & Machine Learning

Open datasets and code for multi-scale relations on structure, function and neuro-genetics in the human brain

Antonio Jimenez-Marin, Ibai Diez, Asier Erramuzpe, Sebastiano Stramaglia, Paolo Bonifazi, and Jesus M. Cortes. Open datasets and code for multi-scale relations on structure, function and neuro-genetics in the human brain. Scientific Data. In press, 2024. [pdf]

Abstract
The human brain is an extremely complex network of structural and functional connections that operate at multiple spatial and temporal scales. Investigating the relationship between these multi-scale connections is critical to advancing our comprehension of brain function and disorders. However, accurately predicting structural connectivity from its functional counterpart remains a challenging pursuit. One of the major impediments is the lack of public repositories that integrate structural and functional networks at diverse resolutions, in conjunction with modular transcriptomic profiles, which are essential for comprehensive biological interpretation. To mitigate this limitation, our contribution encompasses the provision of an open-access dataset consisting of derivative matrices of functional and structural connectivity across multiple scales, accompanied by code that facilitates the investigation of their interrelations. We also provide additional resources focused on neuro-genetic associations of module-level network metrics, which present promising opportunities to further advance research in the field of network neuroscience, particularly concerning brain disorders.

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