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  • Connectome-based reservoir computing with the conn2res toolbox. Nature communications Suarez, L. E., Mihalik, A., Milisav, F., Marshall, K., Li, M., Vertes, P. E., Lajoie, G., Misic, B. 2024; 15 (1): 656

    Abstract

    The connection patterns of neural circuits form a complex network. How signaling in these circuits manifests as complex cognition and adaptive behaviour remains the central question in neuroscience. Concomitant advances in connectomics and artificial intelligence open fundamentally new opportunities to understand how connection patterns shape computational capacity in biological brain networks. Reservoir computing is a versatile paradigm that uses high-dimensional, nonlinear dynamical systems to perform computations and approximate cognitive functions. Here we present conn2res: an open-source Python toolbox for implementing biological neural networks as artificial neural networks. conn2res is modular, allowing arbitrary network architecture and dynamics to be imposed. The toolbox allows researchers to input connectomes reconstructed using multiple techniques, from tract tracing to noninvasive diffusion imaging, and to impose multiple dynamical systems, from spiking neurons to memristive dynamics. The versatility of the conn2res toolbox allows us to ask new questions at the confluence of neuroscience and artificial intelligence. By reconceptualizing function as computation, conn2res sets the stage for a more mechanistic understanding of structure-function relationships in brain networks.

    View details for DOI 10.1038/s41467-024-44900-4

    View details for PubMedID 38253577

  • Tractography passes the test: results from the diffusion-simulated connectivity (DiSCo) challenge. NeuroImage Girard, G., Rafael-Patiño, J., Truffet, R., Aydogan, D. B., Adluru, N., Nair, V. A., Prabhakaran, V., Bendlin, B. B., Alexander, A. L., Bosticardo, S., Gabusi, I., Ocampo-Pineda, M., Battocchio, M., Piskorova, Z., Bontempi, P., Schiavi, S., Daducci, A., Stafiej, A., Ciupek, D., Bogusz, F., Pieciak, T., Frigo, M., Sedlar, S., Deslauriers-Gauthier, S., Kojcic, I., Zucchelli, M., Laghrissi, H., Ji, Y., Deriche, R., Schilling, K. G., Landman, B. A., Cacciola, A., Antonio, G., Bertino, S., Newlin, N., Kanakaraj, P., Rheault, F., Filipiak, P., Shepherd, T., Lin, Y. C., Placantonakis, D. G., Boada, F. E., Baete, S. H., Hernández-Gutiérrez, E., Ramírez-Manzanares, A., Coronado-Leija, R., Stack-Sánchez, P., Concha, L., Descoteaux, M., MansourL, S., Seguin, C., Zalesky, A., Marshall, K., Canales-Rodríguez, E. J., Wu, Y., Ahmad, S., Yap, P. T., Théberge, A., Gagnon, F., Massi, F., Fischi-Gomez, E., Gardier, R., Haro, J. L., Pizzolato, M., Caruyer, E., Thiran, J. P. 2023: 120231

    Abstract

    Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was carried out to evaluate state-of-the-art connectivity methods using novel large-scale numerical phantoms. The diffusion signal for the phantoms was obtained from Monte Carlo simulations. The results of the challenge suggest that methods selected by the 14 teams participating in the challenge can provide high correlations between estimated and ground-truth connectivity weights, in complex numerical environments. Additionally, the methods used by the participating teams were able to accurately identify the binary connectivity of the numerical dataset. However, specific false positive and false negative connections were consistently estimated across all methods. Although the challenge dataset doesn't capture the complexity of a real brain, it provided unique data with known macrostructure and microstructure ground-truth properties to facilitate the development of connectivity estimation methods.

    View details for DOI 10.1016/j.neuroimage.2023.120231

    View details for PubMedID 37330025