This proposed novel method consists of three levels of analyses of diffusion tensor imaging data: 1) voxel level analysis of fractional anisotropy of white matter tracks, 2) connection level analysis, based on fiber tracks between specific brain regions, and 3) network level analysis, based connections among multiple brain regions. Machine-learning techniques of (Fisher score) feature selection, (Support Vector Machine) pattern classification, and (Leave-one-out) cross-validation are performed, for recognition of the neural connectivity patterns for diagnostic purposes. For validation proposes, this multilevel approach achieved an average classification accuracy of 90% between Alzheimer’s disease and healthy controls, 83% between Alzheimer’s disease and mild cognitive impairment, and 83% between mild cognitive impairment and healthy controls. The results indicate that the multilevel diffusion tensor imaging approach used in this analysis is a potential diagnostic tool for clinical evaluations of brain disorders. The presented pipeline is now available as a tool for scientifically applications in a broad range of studies from both clinical and behavioral spectrum, which includes studies about autism, dyslexia, schizophrenia, dementia, motor body performance, among others.
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The senior author of this study was supported by the Indigo Project FKZ 01DQ13004, and Fondecyt Regular projects number 1171313 and number 1171320.
All procedures involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration.
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Dalboni da Rocha, J.L., Coutinho, G., Bramati, I. et al. Multilevel diffusion tensor imaging classification technique for characterizing neurobehavioral disorders. Brain Imaging and Behavior 14, 641–652 (2020). https://doi.org/10.1007/s11682-018-0002-2