Affiliations: [a]
Department of Signal Theory, Networking and Communications, University of Granada, Spain
| [b]
Department of Psychiatry, University of Cambridge, UK
| [c]
Department of Communications Engineering, University of Malaga, Spain
Correspondence:
[*]
Correspondence to: Francisco Jesus Martinez-Murcia, C/ Periodista Rafael Gomez 2, D1-5, 18071 Granada, Spain. Tel.: +34 958 241717; E-mail: [email protected].
Note: [1] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
Abstract: Background:The early diagnosis of Alzheimer’s Disease (AD), particularly in its prodromal stage, mild cognitive impairment (MCI), still remains a challenge. Many computational tools have been developed to successfully explore and predict the disease progression. In this context, the Spherical Brain Mapping (SBM) proved its ability in detecting differences between AD and aged subjects without symptoms of dementia. Being a very visual tool, its application in predicting MCI conversion to AD could be of great help to understand neurodegeneration and the disease progression. Objective:In this work, we aim at predicting the conversion of MCI affected subjects to AD more than 6 months in advance of their conversion session and understanding the progression of the disease by predicting neuropsychological test outcomes from MRI data. Methods:In order to do so, SBM is applied to a series of MRI scans from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The resulting spherical brain maps show statistical and morphological information of the brain in a bidimensional plane, performing at the same time a significant feature reduction that provides a feature vector used in classification analysis. Results:The study achieves up to 92.3% accuracy in the AD versus normal controls (CTL) detection, and up to a 77.6% in detection a of MCI conversions when trained with AD and CTL subjects. The prediction of neuropsychological test outcomes achieved R2 rates up to more than 0.5. Significant regions according to t-test and correlation analysis match reported brain areas in the literature. Conclusion:The results prove that Spherical Brain Mapping offers good ability to predict conversion patterns and cognitive state, at the same time that provides an additional aid for visualizing a two-dimensional abstraction map of the brain.
