Imperial College London
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ProfessorAdamHampshire

Faculty of Medicine, Department of Brain Sciences

Visiting Professor
 
 
 
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Contact

 

+44 (0)20 7594 7993a.hampshire

 
 
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Location

 

Burlington DanesHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Mason:2018:10.1002/ana.25171,
author = {Mason, SL and Daws, RE and Soreq, E and Johnson, EB and Scahill, RI and Tabrizi, SJ and Barker, RA and Hampshire, A},
doi = {10.1002/ana.25171},
journal = {ANNALS OF NEUROLOGY},
pages = {532--543},
title = {Predicting clinical diagnosis in Huntington's disease: An imaging polymarker},
url = {http://dx.doi.org/10.1002/ana.25171},
volume = {83},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - ObjectiveHuntington's disease (HD) gene carriers can be identified before clinical diagnosis; however, statistical models for predicting when overt motor symptoms will manifest are too imprecise to be useful at the level of the individual. Perfecting this prediction is integral to the search for disease modifying therapies. This study aimed to identify an imaging marker capable of reliably predicting reallife clinical diagnosis in HD.MethodA multivariate machine learning approach was applied to restingstate and structural magnetic resonance imaging scans from 19 premanifest HD gene carriers (preHD, 8 of whom developed clinical disease in the 5 years postscanning) and 21 healthy controls. A classification model was developed using crossgroup comparisons between preHD and controls, and within the preHD group in relation to “estimated” and “actual” proximity to disease onset. Imaging measures were modeled individually, and combined, and permutation modeling robustly tested classification accuracy.ResultsClassification performance for preHDs versus controls was greatest when all measures were combined. The resulting polymarker predicted converters with high accuracy, including those who were not expected to manifest in that time scale based on the currently adopted statistical models.InterpretationWe propose that a holistic multivariate machine learning treatment of brain abnormalities in the premanifest phase can be used to accurately identify those patients within 5 years of developing motor features of HD, with implications for prognostication and preclinical trials.
AU  - Mason,SL
AU  - Daws,RE
AU  - Soreq,E
AU  - Johnson,EB
AU  - Scahill,RI
AU  - Tabrizi,SJ
AU  - Barker,RA
AU  - Hampshire,A
DO  - 10.1002/ana.25171
EP  - 543
PY  - 2018///
SN  - 0364-5134
SP  - 532
TI  - Predicting clinical diagnosis in Huntington's disease: An imaging polymarker
T2  - ANNALS OF NEUROLOGY
UR  - http://dx.doi.org/10.1002/ana.25171
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000428350800012&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/59863
VL  - 83
ER  -
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