Sensory maps, such as the representation of mouse facial whiskers, are conveyed throughout the nervous system by topographic axonal projections that preserve neighboring relationships between adjacent neurons. In particular, the map transfer to the neocortex is ensured by thalamocortical axons (TCAs), whose terminals are topographically organized in response to intrinsic cortical signals. However, TCAs already show a topographic order early in development, as they navigate toward their target. Here, we show that this preordering of TCAs is required for the transfer of the whisker map to the neocortex. Using Ebf1 conditional inactivation that specifically perturbs the development of an intermediate target, the basal ganglia, we scrambled TCA topography en route to the neocortex without affecting the thalamus or neocortex. Notably, embryonic somatosensory TCAs were shifted toward the visual cortex and showed a substantial intermixing along their trajectory. Somatosensory TCAs rewired postnatally to reach the somatosensory cortex but failed to form a topographic anatomical or functional map. Our study reveals that sensory map transfer relies not only on positional information in the projecting and target structures but also on preordering of axons along their trajectory, thereby opening novel perspectives on brain wiring.
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http://dx.doi.org/10.1016/j.cub.2013.03.062 | DOI Listing |
Health Care Sci
December 2024
Centre for Quantitative Medicine, Duke-NUS Medical School Singapore.
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View Article and Find Full Text PDFNeural Netw
December 2024
Department of Physics, University of Trento, Via Sommarive 14, Trento, 38123, TN, Italy.
In this study, we address the challenge of analyzing electrophysiological measurements in neuronal networks. Our computational model, based on the Reservoir Computing Network (RCN) architecture, deciphers spatio-temporal data obtained from electrophysiological measurements of neuronal cultures. By reconstructing the network structure on a macroscopic scale, we reveal the connectivity between neuronal units.
View Article and Find Full Text PDFBMC Public Health
December 2024
Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, Unity Health Toronto, 30 Bond Street, Toronto, ON, M5B 1W8, Canada.
Background: Machine learning (ML) is increasingly used in population and public health to support epidemiological studies, surveillance, and evaluation. Our objective was to conduct a scoping review to identify studies that use ML in population health, with a focus on its use in non-communicable diseases (NCDs). We also examine potential algorithmic biases in model design, training, and implementation, as well as efforts to mitigate these biases.
View Article and Find Full Text PDFBMC Neurosci
December 2024
Department of Medicine, The University of Chicago, 5841 S Maryland Ave, Chicago, IL, 60637, USA.
Background: Understanding the neural basis of behavior requires insight into how different brain systems coordinate with each other. Existing connectomes for various species have highlighted brain systems essential to various aspects of behavior, yet their application to complex learned behaviors remains limited. Research on vocal learning in songbirds has extensively focused on the vocal control network, though recent work implicates a variety of circuits in contributing to important aspects of vocal behavior.
View Article and Find Full Text PDFAs the number and variety of assembled genomes continues to grow, the number of annotated genomes is falling behind, particularly for eukaryotes. DNA-based mapping tools help to address this challenge, but they are only able to transfer annotation between closely-related species. Here we introduce LiftOn, a homology-based software tool that integrates DNA and protein alignments to enhance the accuracy of genome-scale annotation and to allow mapping between relatively distant species.
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