Characterizing brain patterns in conversion from mild cognitive impairment (MCI) to Alzheimer's disease

Santiago S. Silva R.; Diana L. Giraldo; Eduardo Romero

doi:10.1117/12.2286102

17 November 2017 Characterizing brain patterns in conversion from mild cognitive impairment (MCI) to Alzheimer's disease

Santiago S. Silva R., Diana L. Giraldo, Eduardo Romero

Author Affiliations +

Proceedings Volume 10572, 13th International Conference on Medical Information Processing and Analysis; 105720I (2017) https://doi.org/10.1117/12.2286102
Event: 13th International Symposium on Medical Information Processing and Analysis, 2017, San Andres Island, Colombia

Abstract

Structural Magnetic Resonance (MR) brain images should provide quantitative information about the stage and progression of Alzheimer’s disease. However, the use of MRI is limited and practically reduced to corroborate a diagnosis already performed with neuropsychological tools. This paper presents an automated strategy for extraction of relevant anatomic patterns related with the conversion from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) using T1-weighted MR images. The process starts by representing each of the possible classes with models generated from a linear combination of volumes. The difference between models allows us to establish which are the regions where relevant patterns might be located. The approach searches patterns in a space of brain sulci, herein approximated by the most representative gradients found in regions of interest defined by the difference between the linear models. This hypothesis is assessed by training a conventional SVM model with the found relevant patterns under a leave-one-out scheme. The resultant AUC was 0.86 for the group of women and 0.61 for the group of men.

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Santiago S. Silva R., Diana L. Giraldo, and Eduardo Romero "Characterizing brain patterns in conversion from mild cognitive impairment (MCI) to Alzheimer's disease", Proc. SPIE 10572, 13th International Conference on Medical Information Processing and Analysis, 105720I (17 November 2017); https://doi.org/10.1117/12.2286102

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