TY - JOUR
T1 - The neuroanatomical organization of the hypothalamus is driven by spatial and topological efficiency
AU - Smith, Nathan R.
AU - Ameen, Shabeeb
AU - Miller, Sierra N.
AU - Kasper, James
AU - Schwarz, Jennifer M.
AU - Hommel, Jonathan D.
AU - Borzou, Ahmad
N1 - Publisher Copyright:
Copyright © 2024 Smith, Ameen, Miller, Kasper, Schwarz, Hommel and Borzou.
PY - 2024
Y1 - 2024
N2 - The hypothalamus in the mammalian brain is responsible for regulating functions associated with survival and reproduction representing a complex set of highly interconnected, yet anatomically and functionally distinct, sub-regions. It remains unclear what factors drive the spatial organization of sub-regions within the hypothalamus. One potential factor may be structural connectivity of the network that promotes efficient function with well-connected sub-regions placed closer together geometrically, i.e., the strongest axonal signal transferred through the shortest geometrical distance. To empirically test for such efficiency, we use hypothalamic data derived from the Allen Mouse Brain Connectivity Atlas, which provides a structural connectivity map of mouse brain regions derived from a series of viral tracing experiments. Using both cost function minimization and comparison with a weighted, sphere-packing ensemble, we demonstrate that the sum of the distances between hypothalamic sub-regions are not close to the minimum possible distance, consistent with prior whole brain studies. However, if such distances are weighted by the inverse of the magnitude of the connectivity, their sum is among the lowest possible values. Specifically, the hypothalamus appears within the top 94th percentile of neural efficiencies of randomly packed configurations and within one standard deviation of the median efficiency when packings are optimized for maximal neural efficiency. Our results, therefore, indicate that a combination of geometrical and topological constraints help govern the structure of the hypothalamus.
AB - The hypothalamus in the mammalian brain is responsible for regulating functions associated with survival and reproduction representing a complex set of highly interconnected, yet anatomically and functionally distinct, sub-regions. It remains unclear what factors drive the spatial organization of sub-regions within the hypothalamus. One potential factor may be structural connectivity of the network that promotes efficient function with well-connected sub-regions placed closer together geometrically, i.e., the strongest axonal signal transferred through the shortest geometrical distance. To empirically test for such efficiency, we use hypothalamic data derived from the Allen Mouse Brain Connectivity Atlas, which provides a structural connectivity map of mouse brain regions derived from a series of viral tracing experiments. Using both cost function minimization and comparison with a weighted, sphere-packing ensemble, we demonstrate that the sum of the distances between hypothalamic sub-regions are not close to the minimum possible distance, consistent with prior whole brain studies. However, if such distances are weighted by the inverse of the magnitude of the connectivity, their sum is among the lowest possible values. Specifically, the hypothalamus appears within the top 94th percentile of neural efficiencies of randomly packed configurations and within one standard deviation of the median efficiency when packings are optimized for maximal neural efficiency. Our results, therefore, indicate that a combination of geometrical and topological constraints help govern the structure of the hypothalamus.
KW - Allen Brain Atlas
KW - Monte - Carlo simulation
KW - computational biology
KW - connectivity
KW - connectome
KW - efficiency
KW - graph theory - graph algorithms
KW - hypothalamus
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UR - http://www.scopus.com/inward/citedby.url?scp=85201419757&partnerID=8YFLogxK
U2 - 10.3389/fnsys.2024.1417346
DO - 10.3389/fnsys.2024.1417346
M3 - Article
C2 - 39165582
AN - SCOPUS:85201419757
SN - 1662-5137
VL - 18
JO - Frontiers in Systems Neuroscience
JF - Frontiers in Systems Neuroscience
M1 - 1417346
ER -