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000285630 1001_ $$0P:(DE-2719)9001967$$aKassubek, Jan$$b0$$udzne
000285630 245__ $$aHypothalamic atrophy in progressive supranuclear palsy, assessed by convolutional neural network-based automatic segmentation.
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000285630 520__ $$aThe 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.
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000285630 650_7 $$2Other$$aHypothalamus
000285630 650_7 $$2Other$$aMagnetic resonance imaging
000285630 650_7 $$2Other$$aMetabolism
000285630 650_7 $$2Other$$aNeural networks
000285630 650_7 $$2Other$$aProgressive supranuclear palsy
000285630 650_7 $$2Other$$aVolumetry
000285630 650_2 $$2MeSH$$aHumans
000285630 650_2 $$2MeSH$$aSupranuclear Palsy, Progressive: diagnostic imaging
000285630 650_2 $$2MeSH$$aSupranuclear Palsy, Progressive: pathology
000285630 650_2 $$2MeSH$$aSupranuclear Palsy, Progressive: complications
000285630 650_2 $$2MeSH$$aMale
000285630 650_2 $$2MeSH$$aFemale
000285630 650_2 $$2MeSH$$aAged
000285630 650_2 $$2MeSH$$aHypothalamus: pathology
000285630 650_2 $$2MeSH$$aHypothalamus: diagnostic imaging
000285630 650_2 $$2MeSH$$aMagnetic Resonance Imaging: methods
000285630 650_2 $$2MeSH$$aMiddle Aged
000285630 650_2 $$2MeSH$$aNeural Networks, Computer
000285630 650_2 $$2MeSH$$aAtrophy: diagnostic imaging
000285630 650_2 $$2MeSH$$aAtrophy: pathology
000285630 650_2 $$2MeSH$$aParkinson Disease: diagnostic imaging
000285630 650_2 $$2MeSH$$aParkinson Disease: pathology
000285630 650_2 $$2MeSH$$aCohort Studies
000285630 650_2 $$2MeSH$$aImage Processing, Computer-Assisted: methods
000285630 650_2 $$2MeSH$$aConvolutional Neural Networks
000285630 693__ $$0EXP:(DE-2719)DESCRIBE-PSP-20160101$$5EXP:(DE-2719)DESCRIBE-PSP-20160101$$eDZNE Clinical Registry Study of Neurodegenerative Diseases in Patients with Progressive Supranuclear Paresis (PSP)$$x0
000285630 7001_ $$0P:(DE-2719)2811373$$aHöglinger, Günter U$$b1$$udzne
000285630 7001_ $$aZůza, Adam$$b2
000285630 7001_ $$aKreiser, Kornelia$$b3
000285630 7001_ $$0P:(DE-2719)2812851$$aRoselli, Francesco$$b4$$udzne
000285630 7001_ $$aGroup, DESCRIBE-PSP Study$$b5$$eCollaboration Author
000285630 7001_ $$aMüller, Hans-Peter$$b6
000285630 7001_ $$0P:(DE-2719)2812107$$aBrandt, Daniel Moritz$$b7$$eContributor$$udzne
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000285630 773__ $$0PERI:(DE-600)1421299-7$$a10.1007/s00415-026-13718-z$$gVol. 273, no. 3, p. 201$$n3$$p201$$tJournal of neurology$$v273$$x0367-004X$$y2026
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