J. Van Schependom
Vrije Universiteit Brussel
4 Papers
4 Citations
J. Van Schependom is an academic researcher from Vrije Universiteit Brussel. The author has contributed to research in topics: Medicine & Logistic regression. The author has an hindex of 3, co-authored 4 publications. Previous affiliations of J. Van Schependom include Université libre de Bruxelles & University of Mons.
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Papers
The Symbol Digit Modalities Test as sentinel test for cognitive impairment in multiple sclerosis.
J. Van Schependom,J. Van Schependom,Marie B. D'hooghe,Krista Cleynhens,Mieke D'hooge,Marie-Claire Haelewyck,de Jacques Keyser,Guy Nagels,Guy Nagels +8 more
TL;DR: This paper aims to assess the performance of the SDMT in predicting the outcome of an extensive battery of neuropsychological test batteries for cognitive impairment in MS.
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The role of hippocampal theta oscillations in working memory impairment in multiple sclerosis.
Lars Costers,J. Van Schependom,Jorne Laton,Jorne Laton,Johan Baijot,Martin Sjøgård,Vincent Wens,X. De Tiège,Serge Goldman,Miguel D'haeseleer,Marie B. D'hooghe,Mark W. Woolrich,Guy Nagels,Guy Nagels +13 more
TL;DR: The first neurophysiological evidence of the influence of hippocampal dysfunction on WM performance in MS is provided, using magnetoencephalography data from a visual‐verbal 2‐back task.
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The squares test as a measure of hand function in multiple sclerosis
TL;DR: The squares test (ST), a test of hand function that is used extensively in handedness research, is examined, showing a high and highly significant correlation with the standard 9HPT over a broad range of Expanded Disability Status Scale (EDSS) scores, and had high discriminatory power, also comparable to the 9H PT.
6
Brain age as a surrogate marker for information processing speed in multiple sclerosis
Stijn Denissen,Denis A. Engemann,Denis A. Engemann,A. de Cock,Lars Costers,Johan Baijot,Jorne Laton,Jorne Laton,Iris-Katharina Penner,M Grothe,M Kirsch,MB D'hooghe,Miguel D'haeseleer,Dominique Dive,J. de Mey,J. Van Schependom,DM Sima,Guy Nagels,Guy Nagels +18 more
TL;DR: In this paper, a ridge-regression model was trained to predict brain age from brain MRI volumetric features and sex in a healthy control dataset (HC_train, n=1690).