Macroscale connections of the mouse lateral preoptic area and anterior lateral hypothalamic area

Abstract

The macroscale neuronal connections of the lateral preoptic area (LPO) and the caudally adjacent lateral hypothalamic area anterior region (LHAa) were investigated in mice by anterograde and retrograde axonal tracing. Both hypothalamic regions are highly and diversely connected, with connections to >200 gray matter regions spanning the forebrain, midbrain, and rhombicbrain. Intrahypothalamic connections predominate, followed by connections with the cerebral cortex and cerebral nuclei. A similar overall pattern of LPO and LHAa connections contrasts with substantial differences between their input and output connections. Strongest connections include outputs to the lateral habenula, medial septal and diagonal band nuclei, and inputs from rostral and caudal lateral septal nuclei; however, numerous additional robust connections were also observed. The results are discussed in relation to a current model for the mammalian forebrain network that associates LPO and LHAa with a range of functional roles, including reward prediction, innate survival behaviors (including integrated somatomotor and physiological control), and affect. The present data suggest a broad and intricate role for LPO and LHAa in behavioral control, similar in that regard to previously investigated LHA regions, contributing to the finely tuned sensory‐motor integration that is necessary for behavioral guidance supporting survival and reproduction.

Sex-Dimorphic Kidney-Brain Connectivity Map of Mice

“…4A-D). The connection weights were then displayed on a mouse brain flatmap created by Hahn [17]. To quantitatively characterize the sex differences in left and right kidney innervation bias, we analyzed the ratio of EGFP + neurons (retrogradely traced from the left kidney) or mRuby + neurons (retrogradely traced from the right kidney) among all brain regions connecting to the left and right kidneys in male and female mice.…”

A brain flatmap data visualization tool for mouse, rat, and human

“…Source: (a) Institute for Economics & Peace. Global Peace Index, 2022; (b) adapted from layer 4 of Supporting Information 2 in Hahn et al, 2022. corresponding abbreviations (sheet named "region names") in two arrangements: (1) alphabetical by abbreviation and (2) topographic, following a topographic arrangement used in the rat brain reference atlas (Swanson, 2018).…”

“…The flatmaps were originally modeled on a fate map of the topologically flat embryonic neural plate (Alvarez‐Bolado & Swanson, 1996; Swanson, 1992). They confer the ability to visualize whole brain data in a single diagram (for examples, see Hahn et al., 2022; Hahn et al., 2019; Munoz‐Castaneda et al., 2021), analogous to Mercator projection maps of Earth that are used to represent global data (see Snyder, 1993), and from which they draw inspiration (Alvarez‐Bolado & Swanson, 1996) (Figure 1). However, the previous flatmap versions require access to commercial software (Adobe Illustrator) to manipulate them for the purposes of data representation (Hahn et al., 2021; Swanson, 2004, 2018).…”

“…Source : (a) Institute for Economics & Peace. Global Peace Index, 2022; (b) adapted from layer 4 of Supporting Information 2 in Hahn et al., 2022.…”

“… Supporting Information 5 Data table (MS Excel spreadsheet file) of macroconnection number (degree) for output (out degree) and input (in degree) connections of cerebral cortical gray matter regions with other gray matter regions of the rat forebrain and midbrain (collated from Swanson et al., 2022). The original rat data were translated to human cerebral cortical nomenclature according to cerebral cortical gray matter division correspondence between rat and human (Swanson & Hof, 2019), with additional reference to rat and human brain nomenclature and reference atlas parcellation (Hahn et al., 2022; Swanson, 2015b, 2018). For human brain flatmap representation, the original data were assigned to corresponding reference divisions using the flatmap nomenclature and parcellation schema (Hahn et al., 2021, cross‐referenced with Ding et al., 2016 and Swanson & Hof, 2019 to enable flatmap transformation).…”