Multilevel diffusion tensor imaging classification technique for characterizing neurobehavioral disorders
| Autor: | Josué Luiz Dalboni da Rocha, Fernanda Tovar Moll, Ivanei E. Bramati, Gabriel Coutinho, Ranganatha Sitaram |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Cognitive Neuroscience Feature selection computer.software_genre 050105 experimental psychology White matter 03 medical and health sciences Behavioral Neuroscience Cellular and Molecular Neuroscience 0302 clinical medicine ddc:150 Alzheimer Disease Voxel Fractional anisotropy medicine Humans Dementia 0501 psychology and cognitive sciences Radiology Nuclear Medicine and imaging business.industry 05 social sciences Dyslexia Neuropsychology Brain Pattern recognition medicine.disease Magnetic Resonance Imaging White Matter Psychiatry and Mental health Diffusion Tensor Imaging medicine.anatomical_structure Neurology Anisotropy Neurology (clinical) Artificial intelligence business computer 030217 neurology & neurosurgery Diffusion MRI |
| Zdroj: | Brain Imaging and Behavior, Vol. 14, No 3 (2020) pp. 641-652 |
| ISSN: | 1931-7557 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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