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@ARTICLE{Kassubek:285630,
      author       = {Kassubek, Jan and Höglinger, Günter U and Zůza, Adam and
                      Kreiser, Kornelia and Roselli, Francesco and Müller,
                      Hans-Peter},
      collaboration = {Group, DESCRIBE-PSP Study},
      othercontributors = {Brandt, Daniel Moritz and Bürger, Katharina and Düzel,
                          Emrah and Falkenburger, Björn and Flöel, Agnes and Glanz,
                          Wenzel and Janowitz, Daniel and Katzdobler, Sabrina and
                          Kilimann, Ingo and Kimmich, Okka and Levin, Johannes and
                          Peters, Oliver and Priller, Josef and Prudlo, Johannes and
                          Schneider, Luisa-Sophie and Spottke, Annika and Spruth, Eike
                          Jakob and Synofzik, Matthis and Teipel, Stefan and Wilke,
                          Carlo},
      title        = {{H}ypothalamic atrophy in progressive supranuclear palsy,
                      assessed by convolutional neural network-based automatic
                      segmentation.},
      journal      = {Journal of neurology},
      volume       = {273},
      number       = {3},
      issn         = {0367-004X},
      address      = {[Darmstadt]},
      publisher    = {Steinkopff},
      reportid     = {DZNE-2026-00269},
      pages        = {201},
      year         = {2026},
      abstract     = {The hypothalamus as one of the core structures in metabolic
                      control is increasingly recognized to be morphologically
                      altered in various neurodegenerative diseases.The purpose of
                      this study was to quantitatively investigate the
                      hypothalamic volumes in patients with progressive
                      supranuclear palsy (PSP) and to compare them with controls
                      and Parkinson disease (PD) patients.An automatic
                      hypothalamic volume quantification method based on the use
                      of convolutional neural networks (CNN) of U-Net architecture
                      was applied to the automatic segmentation of the
                      hypothalamus and intracranial volumes (ICV). This CNN-based
                      volumetric analysis was performed in high resolution T1
                      weighted MRI in two PSP cohorts: cohort A with 78 PSP
                      patients and 63 controls was recorded at 3.0 T at multiple
                      sites; the single site cohort B consisted of 66 PSP
                      patients, 66 PD patients, and 44 controls, recorded at 1.5
                      T.In cohort A, significant hypothalamic volume reduction was
                      observed in PSP (774 ± 83 mm3) when compared to controls
                      (817 ± 74 mm3). In cohort B, this result of significant
                      hypothalamic volume reduction was confirmed in PSP (745 ±
                      102 mm3) when compared to controls (831 ± 81 mm3); no
                      significant hypothalamic volume reduction was observed in PD
                      (797 ± 98 mm3), in support of previous studies.The
                      CNN-based hypothalamus volume quantification study
                      demonstrated significantly reduced hypothalamus volumes in
                      PSP patients compared to controls and PD, respectively;
                      future studies will address the metabolic profiles of PSP as
                      potential functional correlates.},
      keywords     = {Humans / Supranuclear Palsy, Progressive: diagnostic
                      imaging / Supranuclear Palsy, Progressive: pathology /
                      Supranuclear Palsy, Progressive: complications / Male /
                      Female / Aged / Hypothalamus: pathology / Hypothalamus:
                      diagnostic imaging / Magnetic Resonance Imaging: methods /
                      Middle Aged / Neural Networks, Computer / Atrophy:
                      diagnostic imaging / Atrophy: pathology / Parkinson Disease:
                      diagnostic imaging / Parkinson Disease: pathology / Cohort
                      Studies / Image Processing, Computer-Assisted: methods /
                      Convolutional Neural Networks / Hypothalamus (Other) /
                      Magnetic resonance imaging (Other) / Metabolism (Other) /
                      Neural networks (Other) / Progressive supranuclear palsy
                      (Other) / Volumetry (Other)},
      cin          = {Clinical Research (Munich) / AG Roselli},
      ddc          = {610},
      cid          = {I:(DE-2719)1111015 / I:(DE-2719)1910001},
      pnm          = {353 - Clinical and Health Care Research (POF4-353) / 352 -
                      Disease Mechanisms (POF4-352)},
      pid          = {G:(DE-HGF)POF4-353 / G:(DE-HGF)POF4-352},
      experiment   = {EXP:(DE-2719)DESCRIBE-PSP-20160101},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41811373},
      pmc          = {pmc:PMC12979338},
      doi          = {10.1007/s00415-026-13718-z},
      url          = {https://pub.dzne.de/record/285630},
}