A Bayesian segmentation of the amygdala in ALS and PLS: volumetric data

Published: 01-08-2020| Version 2 | DOI: 10.17632/28mhksbnsy.2
Contributors:
Peter Bede,
Rangariroyashe H. Chipika,
Kai Ming Chang,
Stacey Li Hi Shing,
Eoin Finegan,
Mary Clare McKenna,
Orla Hardiman

Description

Temporal lobe degeneration is a recognised feature of motor neuron disease which may manifest in a range of cognitive deficits contributing to the clinical heterogeneity of the condition. Computational neuroimaging allows the detailed characterisation of this region in vivo, but existing neuroimaging studies have invariably evaluated the amygdala as a single structure. In this data set we present the volumetric profile of specific amygdalar nuclei in 100 patients with ALS, 33 patients with PLS and 117 healthy controls. T1-weighted images were acquired with a 3D Inversion Recovery prepared Spoiled Gradient Recalled echo pulse sequence with an isometric spatial resolution of 1 mm3 on a 3 Tesla MRI system. A Bayesian inference was used to segment the amygdala into specific nuclei using a probabilistic atlas. The study was sponsored by the Spastic Paraplegia Foundation, Inc. (SPF), the Health Research Board (HRB EIA-2017-019), the EU Joint Programme – Neurodegenerative Disease Research (JPND), the Andrew Lydon scholarship, the Irish Institute of Clinical Neuroscience (IICN), the Iris O'Brien Foundation. Methodological details are provided in: Amygdala pathology in amyotrophic lateral sclerosis and primary lateral sclerosis. Chipika RH, Christidi F, Finegan E, Li Hi Shing S, McKenna MC, Chang KM, Karavasilis E, Doherty MA, Hengeveld JC, Vajda A, Pender N, Hutchinson S, Donaghy C, McLaughlin RL, Hardiman O, Bede P. J Neurol Sci. 2020 Jul 18:117039. doi: 10.1016/j.jns.2020.117039. Online ahead of print. PMID: 32713609

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