title: Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging.
abstract: We describe a new method to automatically discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly controls, based on multidimensional classification of hippocampal shape features. This approach uses spherical harmonics (SPHARM) coefficients to model the shape of the hippocampi, which are segmented from magnetic resonance images (MRI) using a fully automatic method that we previously developed. SPHARM coefficients are used as features in a classification procedure based on support vector machines (SVM). The most relevant features for classification are selected using a bagging strategy. We evaluate the accuracy of our method in a group of 23 patients with AD (10 males, 13 females, age+/-standard-deviation (SD)=73+/-6 years, mini-mental score (MMS)=24.4+/-2.8), 23 patients with amnestic MCI (10 males, 13 females, age+/-SD=74+/-8 years, MMS=27.3+/-1.4) and 25 elderly healthy controls (13 males, 12 females, age+/-SD=64+/-8 years), using leave-one-out cross-validation. For AD vs controls, we obtain a correct classification rate of 94%, a sensitivity of 96%, and a specificity of 92%. For MCI vs controls, we obtain a classification rate of 83%, a sensitivity of 83%, and a specificity of 84%. This accuracy is superior to that of hippocampal volumetry and is comparable to recently published SVM-based whole-brain classification methods, which relied on a different strategy. This new method may become a useful tool to assist in the diagnosis of Alzheimer's disease.
authors.affiliation: UPMC Université Paris 06, UMR 7225, UMR_S 975, Centre de Recherche de l'Institut Cerveau-Moelle (CRICM), Paris, France.
authors.forename: Emilie
authors.initials: E
authors.lastname: Gerardin
authors.forename: Gaël
authors.initials: G
authors.lastname: Chételat
authors.forename: Marie
authors.initials: M
authors.lastname: Chupin
authors.forename: Rémi
authors.initials: R
authors.lastname: Cuingnet
authors.forename: Béatrice
authors.initials: B
authors.lastname: Desgranges
authors.forename: Ho-Sung
authors.initials: HS
authors.lastname: Kim
authors.forename: Marc
authors.initials: M
authors.lastname: Niethammer
authors.forename: Bruno
authors.initials: B
authors.lastname: Dubois
authors.forename: Stéphane
authors.initials: S
authors.lastname: Lehéricy
authors.forename: Line
authors.initials: L
authors.lastname: Garnero
authors.forename: Francis
authors.initials: F
authors.lastname: Eustache
authors.forename: Olivier
authors.initials: O
authors.lastname: Colliot
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000375:Aging / Q000473:pathology*; D000465:Algorithms; D000544:Alzheimer Disease / Q000150:complications / Q000175:diagnosis*; D016000:Cluster Analysis; D003072:Cognition Disorders / Q000150:complications / Q000175:diagnosis*; D003937:Diagnosis, Differential; D005260:Female; D006624:Hippocampus / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity