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@INPROCEEDINGS{Chervonnyy:1018413,
author = {Chervonnyy, Alexey and Schiffer, Christian and Upschulte,
Eric and Bludau, Sebastian and Mohlberg, Hartmut and Amunts,
Katrin},
title = {{H}igh-{R}esolution 3{D} {M}apping of the {H}uman
{H}ypothalamus: {T}owards a {C}omprehensive
{C}ytoarchitectonic {A}tlas},
reportid = {FZJ-2023-04794},
year = {2023},
abstract = {The hypothalamus is a brain structure that plays a central
role in maintaining homeostasis and regulating various
physiological and behavioural processes. It encompasses
distinct nuclei with diverse microstructure, connectivity,
molecular structure and functions, including circadian
rhythm regulation, sleep-wake cycles, appetite control,
stress response, and thermoregulation. Dysfunctions of the
hypothalamus have been reported in the context of cluster
headaches, obesity, addictive behaviour, aggressive
disorders, anxiety disorders, sleep disorders, eating
disorders, hypertension, and epilepsy. Despite its
importance, the structural organization and precise
boundaries of the hypothalamus, as well as the functional
differentiation of its nuclei, are still not fully
understood. Currently, there are no maps available, which
inform neuroimaging studies about the microstructural
segregation of the hypothalamus in 3D space. Existing maps
of the hypothalamus lack the necessary spatial resolution
and morphological detail to provide a comprehensive
understanding of this complex region. Therefore, in our
project we aim to develop a high-resolution 3D map of the
human hypothalamus in order to determine its microstructure
and localization in the stereotaxic space.To create a
high-resolution 3D reconstruction of the hypothalamus in the
BigBrain models, its nuclei were delineated on a subsample
of the high-resolution digitized histological sections of
the BigBrain datasets (Amunts et al., 2013). To make
delineations on every remaining section a deep-learning
based brain mapping tool was applied (Schiffer et al.,
2021). The model was trained on manual expert annotations on
every 15th section in the BigBrain 1 to predict the
delineations of the hypothalamus on every remaining
sections. The automatically generated maps was re-evaluated
to exclude incorrectly delineated sections. The delineations
will then be converted into a 3D reconstructed BigBrain
space with the use of non-linear registration of the
high-resolution digital section (Amunts et al., 2020).
Therefore, the resulting map visualized the complex shape of
the hypothalamus and its 19 nuclei with high anatomical
details.The hypothalamus is divided into different zones,
both in the mediolateral and rostrocaudal directions.
Mediolaterally, there are three zones: the periventricular
zone next to the third ventricle, the medial zone, and the
lateral zone mainly occupied by the lateral hypothalamic
area. Rostrocaudally, there are four zones: the preoptic
zone bordering the lamina terminalis, the anterior zone, the
tuberal zone above the infundibulum, and the mammillary
zone.In the preoptic zone, we identified several nuclei,
including the periventricular hypothalamic nucleus, medial
preoptic nucleus, median preoptic nucleus, uncinate nucleus,
and intermediate hypothalamic nucleus. In the anterior
hypothalamic area we distinguished the suprachiasmatic
nucleus, paraventricular hypothalamic nucleus, anterior part
of the periventricular nucleus, and supraoptic nucleus. The
tuberal hypothalamic region contains the ventromedial
hypothalamic nucleus, dorsomedial hypothalamic nucleus,
arcuate nucleus, and tuberal part of the periventricular
nucleus. Lastly, in the mammillary region we identified the
medial and lateral mammillary nuclei, supramammillary
nucleus, tuberomammillary nucleus, and lateral tuberal
nucleus.These histology-based maps in 3D provide detailed
anatomical information of a complex region of the
hypothalamus and serve as a spatial and structural reference
for diagnostic, prognostic and therapeutic neuroimaging
studies of the healthy human brain and those of patients.},
month = {Oct},
date = {2023-10-04},
organization = {7th BigBrain Workshop, Reykjavík
(Iceland), 4 Oct 2023 - 6 Oct 2023},
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)
/ HBP SGA3 - Human Brain Project Specific Grant Agreement 3
(945539)},
pid = {G:(DE-HGF)POF4-5251 / G:(DE-HGF)POF4-5254 /
G:(DE-HGF)InterLabs-0015 / G:(EU-Grant)945539},
typ = {PUB:(DE-HGF)24},
url = {https://juser.fz-juelich.de/record/1018413},
}