Automatic detection of preclinical neurodegeneration: presymptomatic Huntington disease

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 genet...

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Vydáno v:Neurology Ročník 72; číslo 5; s. 426
Hlavní autoři: Klöppel, S, Chu, C, Tan, G C, Draganski, B, Johnson, H, Paulsen, J S, Kienzle, W, Tabrizi, S J, Ashburner, J, Frackowiak, R S J
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 03.02.2009
Témata:
Adult
Age Distribution
Age of Onset
Aged
Automatic Data Processing - methods
Brain - pathology
Brain - physiopathology
Disease Progression
Early Diagnosis
Female
Genetic Testing
Heterozygote
Humans
Huntington Disease - diagnosis
Huntington Disease - physiopathology
Image Processing, Computer-Assisted - methods
Magnetic Resonance Imaging - methods
Male
Middle Aged
Nerve Degeneration - diagnosis
Nerve Degeneration - physiopathology
Predictive Value of Tests
Young Adult
ISSN:1526-632X, 1526-632X
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Abstract 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. 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. 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%. 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.
AbstractList 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.BACKGROUNDTreatment 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.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.METHODSUsing 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.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%.RESULTSSubjects 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%.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.CONCLUSIONSPresymptomatic 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.
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. 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. 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%. 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.
Author Johnson, H
Frackowiak, R S J
Tan, G C
Chu, C
Draganski, B
Tabrizi, S J
Ashburner, J
Paulsen, J S
Klöppel, S
Kienzle, W
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  surname: Klöppel
  fullname: Klöppel, S
  email: stefan.kloeppel@uniklinik-freiburg.de
  organization: Department of Psychiatry and Psychotherapy, Freiburg Brain Imaging, University Clinic Freiburg, Germany. stefan.kloeppel@uniklinik-freiburg.de
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  surname: Chu
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  surname: Johnson
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  surname: Frackowiak
  fullname: Frackowiak, R S J
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Snippet Treatment of neurodegenerative diseases is likely to be most beneficial in the very early, possibly preclinical stages of degeneration. We explored the...
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StartPage 426
SubjectTerms Adult
Age Distribution
Age of Onset
Aged
Automatic Data Processing - methods
Brain - pathology
Brain - physiopathology
Disease Progression
Early Diagnosis
Female
Genetic Testing
Heterozygote
Humans
Huntington Disease - diagnosis
Huntington Disease - physiopathology
Image Processing, Computer-Assisted - methods
Magnetic Resonance Imaging - methods
Male
Middle Aged
Nerve Degeneration - diagnosis
Nerve Degeneration - physiopathology
Predictive Value of Tests
Young Adult
Title Automatic detection of preclinical neurodegeneration: presymptomatic Huntington disease
URI https://www.ncbi.nlm.nih.gov/pubmed/19188573
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