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bioRxiv preprint doi: https://doi.org/10.1101/2023.03.11.532232; this version posted March 13, 2023. The copyright holder for this preprint (which was not certified by peer review) is the author/funder,
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Connectome-constrained deep mechanistic networks are computational models that incorporate connectivity data from the brain's connectome to enhance the understanding of neural mechanisms and functions.
Researchers and institutions involved in neuroscience research that involves the modeling of brain connectivity and mechanisms are typically required to file connectome-constrained deep mechanistic networks.
To fill out connectome-constrained deep mechanistic networks, one must provide detailed information on the connectivity data, the design of the model, and the methodologies used for analysis.
The purpose of connectome-constrained deep mechanistic networks is to better understand the relationships between brain structure and function, potentially leading to insights in neuroscience, psychology, and related fields.
Information that must be reported includes the model architecture, data sources, experimental conditions, results, and interpretations of the findings.
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