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@INPROCEEDINGS{Chervonnyy:1031460,
author = {Chervonnyy, Alexey and Schiffer, Christian and Upschulte,
Eric and Mohlberg, Hartmut and Amunts, Katrin and Bludau,
Sebastian},
title = {{H}igh-resolution 3{D} {M}apping of the {H}uman
{H}ypothalamus:{T}owards a {C}omprehensive
{C}ytoarchitectonic {A}tlas},
school = {Heinrich-Heine-University Düsseldorf},
reportid = {FZJ-2024-05679},
year = {2024},
abstract = {<b>Introduction</b><br><br>The hypothalamus is crucial for
maintaining homeostasis, regulating sleep-wake cycles,
appetite, circadian rhythm, and thermal regulation
(Nieuwenhuys et al., 2008). Despite its importance the
structural organization, precise boundaries, and functional
differentiation of its nuclei remain incompletely
understood. Existing anatomical maps of the hypothalamus do
not reflect interindividual variability in 3D space; they
often lack the spatial resolution and morphological detail
to provide a comprehensive understanding of this complex
region and to inform neuroimaging studies about the
brain’s microstructure. Therefore, we aimed to develop
probabilistic cytoarchitectonic maps to address intersubject
variability and provide a high-resolution 3D reference map
for informing studies in the living human
brain.<b>Methods</b><br><br>We delineated the hypothalamus
and its nuclei on every 15th cell-body stained brain
sections in 10 brains (5 female) including BigBrain (Amunts
et al., 2013). For creation the high-resolution BigBrain
model we used a deep-learning based tool (Schiffer et al.,
2021) that delineated the remaining sections. Other brains
were used to create probability maps that capture
intersubject variability in space and location of areas. To
do this, brains were 3D reconstructed and superimposed in
standard reference space (Amunts et. al., 2020).
Quantitative tools, including texture analysis
(Devakuruparan, 2024) and object instance segmentation
(Upschulte et al., 2021), were applied to analyse
subdivisions in more detail.<b>Results</b><br><br>We
generated high-resolution 3D map of 23 nuclei of the human
hypothalamus (Figure 1), that show their shapes and
neighbourhood relationships with high precision. These
nuclei were categorized into four rostro-caudal
zones:<br><br><b>Preoptic zone</b> includes the anterior
periventricular and median preoptic nuclei lining the third
ventricle, with the uncinate and intermediate nuclei forming
a cluster around the medial preoptic
nucleus.<br><br><b>Anterior zone</b> contains the
paraventricular nucleus with dark magnocellular neurons
ventrolaterally and less intense parvocellular neurons
medially, the supraoptic nucleus with densely packed
magnocellular neurons, and the retrochiasmatic,
suprachiasmatic and periventricular
nuclei.<br><br><b>Tuberal zone</b> features the ventromedial
nucleus with high peripheral cell density, the smaller
posteromedial nucleus between the ventromedial nucleus and
mammillary body, the dorsomedial nucleus with densely packed
small neurons at its centre, and the arcuate nucleus within
the tuber cinerium.<br><br><b>Mammillary zone</b> includes
the medial and lateral mammillary nuclei. The
tuberomammillary and supramammillary nuclei contain large
dark magnocellular neurons, and the lateral tuberal nucleus
housing medium-sized neurons in the basolateral mammillary
zone.<br><br>The mean hypothalamic volume was 1492 ± 264
mm³. The Lateral (514 ± 49 mm³) and Posterior
hypothalamic areas (262 ± 33 mm³) showed the highest
volumes, whereas the uncinate and lateral mammillary nuclei
exhibited the lowest values (0.845 ± 0.15 mm³; 1.8 ± 0.3
mm³). Permutation tests found no significant effects of
hemisphere, sex, or their interaction on the
shrinkage-corrected volumes for each nucleus. Intersubject
variability was reflected in the probabilistic maps that
will be part of the Julich-Brain Atlas (Amunts, 2020) and
available via EBRAINS and other
platforms.<br><br><b>Conclusions</b><br><br>In sum, we
provide a detailed microstructural map of the hypothalamus,
serving as a profound anatomical basis for interpreting and
comparing neuroimaging data helping to refine the functional
organization of the hypothalamus.},
month = {Sep},
date = {2024-09-09},
organization = {8th BigBrain Workshop, Padua (Italy),
9 Sep 2024 - 11 Sep 2024},
subtyp = {After Call},
cin = {INM-1},
cid = {I:(DE-Juel1)INM-1-20090406},
pnm = {5251 - Multilevel Brain Organization and Variability
(POF4-525) / 5254 - Neuroscientific Data Analytics and AI
(POF4-525) / HIBALL - Helmholtz International BigBrain
Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)
/ EBRAINS 2.0 - EBRAINS 2.0: A Research Infrastructure to
Advance Neuroscience and Brain Health (101147319) /
Helmholtz AI - Helmholtz Artificial Intelligence
Coordination Unit – Local Unit FZJ (E.40401.62)},
pid = {G:(DE-HGF)POF4-5251 / G:(DE-HGF)POF4-5254 /
G:(DE-HGF)InterLabs-0015 / G:(EU-Grant)101147319 /
G:(DE-Juel-1)E.40401.62},
typ = {PUB:(DE-HGF)6},
url = {https://juser.fz-juelich.de/record/1031460},
}