Integrating multimodal data to understand cortical circuit architecture and function

Arkhipov, A.; da, Costa N.; de, Vries S.; Bakken, T.; Bennett, C.; Bernard, A.; Berg, J.; Buice, M.; Collman, F.; Daigle, T.; Garrett, M.; Gouwens, N.; Groblewski, P.A.; Harris, J.; Hawrylycz, M.; Hodge, R.; Jarsky, T.; Kalmbach, B.; Lecoq, J.; Lee, B.; Lein, E.; Levi, B.; Mihalas, S.; Ng, L.; Olsen, S.; Reid, C.; Siegle, J.H.; Sorensen, S.; Tasic, B.; Thompson, C.; Ting, J.T.; van, Velthoven C.; Yao, S.; Yao, Z.; Koch, C.; Zeng, H.

Angol nyelvű Sokszerzős vagy csoportos szerzőségű szakcikk (Folyóiratcikk) Tudományos
Megjelent: NATURE NEUROSCIENCE 1097-6256 1546-1726 Paper: adk4858 2025
  • SJR Scopus - Neuroscience (miscellaneous): D1
Azonosítók
In recent years there has been a tremendous growth in new technologies that allow large-scale investigation of different characteristics of the nervous system at an unprecedented level of detail. There is a growing trend to use combinations of these new techniques to determine direct links between different modalities. In this Perspective, we focus on the mouse visual cortex, as this is one of the model systems in which much progress has been made in the integration of multimodal data to advance understanding. We review several approaches that allow integration of data regarding various properties of cortical cell types, connectivity at the level of brain areas, cell types and individual cells, and functional neural activity in vivo. The increasingly crucial contributions of computation and theory in analyzing and systematically modeling data are also highlighted. Together with open sharing of data, tools and models, integrative approaches are essential tools in modern neuroscience for improving our understanding of the brain architecture, mechanisms and function. © Springer Nature America, Inc. 2025.
Hivatkozás stílusok: IEEEACMAPAChicagoHarvardCSLMásolásNyomtatás
2025-05-16 23:40