Journal article
Automatic detection of preclinical neurodegeneration : Presymptomatic Huntington disease
Neurology, Vol.72(5), pp.426-431
2009
DOI: 10.1212/01.wnl.0000341768.28646.b6
PMCID: PMC2817939
PMID: 19188573
Abstract
Background: Treatment of neurodegenerative diseases is likely to be most beneficial in the very early, possibly preclinical stages of degeneration. We explored the usefulness of fully automatic structural MRI classification methods for detecting subtle degenerative change. The availability of a definitive genetic test for Huntington disease (HD) provides an excellent metric for judging the performance of such methods in gene mutation carriers who are free of symptoms.
Methods: Using the gray matter segment of MRI scans, this study explored the usefulness of a multivariate support vector machine to automatically identify presymptomatic HD gene mutation carriers (PSCs) in the absence of any a priori information. A multicenter data set of 96 PSCs and 95 age- and sex-matched controls was studied. The PSC group was subclassified into three groups based on time from predicted clinical onset, an estimate that is a function of DNA mutation size and age.
Results: Subjects with at least a 33% chance of developing unequivocal signs of HD in 5 years were correctly assigned to the PSC group 69% of the time. Accuracy improved to 83% when regions affected by the disease were selected a priori for analysis. Performance was at chance when the probability of developing symptoms in 5 years was less than 10%.
Conclusions: Presymptomatic Huntington disease gene mutation carriers close to estimated diagnostic onset were successfully separated from controls on the basis of single anatomic scans, without additional a priori information. Prior information is required to allow separation when degenerative changes are either subtle or variable.
Details
- Title: Subtitle
- Automatic detection of preclinical neurodegeneration : Presymptomatic Huntington disease
- Creators
- S Klöppel - Department of Psychiatry and Psychotherapy (S.K.), Freiburg Brain Imaging, University Clinic Freiburg, GermanyC Chu - Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, United KingdomG. C Tan - Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, United KingdomB Draganski - Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, United KingdomH Johnson - Department of Psychiatry The University of Iowa, Iowa City, IA, United StatesJ. S Paulsen - Department of Psychiatry The University of Iowa, Iowa City, IA, United StatesW Kienzle - Max Planck Institute for Biological Cybernetics, Tübingen, GermanyS. J Tabrizi - Department of Clinical Neurology, Institute of Neurology, University College London, United KingdomJ Ashburner - Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, United KingdomR. S. J Frackowiak - Département d'études cognitives Ecole Normale Supérieure, Paris, FrancePREDICT-HD Investigators of the Huntington Study Group
- Resource Type
- Journal article
- Publication Details
- Neurology, Vol.72(5), pp.426-431
- DOI
- 10.1212/01.wnl.0000341768.28646.b6
- PMID
- 19188573
- PMCID
- PMC2817939
- NLM abbreviation
- Neurology
- ISSN
- 0028-3878
- eISSN
- 1526-632X
- Publisher
- Lippincott Williams & Wilkins
- Language
- English
- Date published
- 2009
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Psychiatry; Psychological and Brain Sciences; The Iowa Institute for Biomedical Imaging; The Iowa Initiative for Artificial Intelligence; Iowa Informatics Initiative
- Record Identifier
- 9984221629702771
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