101. Tensor Decomposition for Neurodevelopmental Disorder Prediction
- Author
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Rafal A. Angryk, Yubao Wu, Robin G. Morris, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi, and Lisa C. Krishnamurthy
- Subjects
medicine.diagnostic_test ,business.industry ,Computer science ,05 social sciences ,Pattern recognition ,medicine.disease ,050105 experimental psychology ,k-nearest neighbors algorithm ,Functional networks ,03 medical and health sciences ,0302 clinical medicine ,Neurodevelopmental disorder ,Tensor (intrinsic definition) ,Graph classification ,medicine ,Tensor decomposition ,0501 psychology and cognitive sciences ,Artificial intelligence ,Functional magnetic resonance imaging ,business ,030217 neurology & neurosurgery ,Tucker decomposition - Abstract
Functional Magnetic Resonance Imaging (fMRI) has been successfully used by the neuroscientists for diagnosis and analysis of neurological and neurodevelopmental disorders. After transforming fMRI data into functional networks, graph classification algorithms have been applied for distinguishing healthy controls from impaired subjects. Recently, classification followed by tensor decomposition has been used as an alternative, since the sparsity of the functional networks is still an open question. In this work, we present five tensor models of fMRI data, considering the time series of the brain regions as the raw form. After decomposing the tensor using CANDECOMP/PARAFAC (CP) and Tucker decomposition, we compared nearest neighbor classification accuracy on the resulting subject factor matrix. We show experimental results using an fMRI dataset from adult subjects with neurodevelopmental reading disabilities and normal controls.
- Published
- 2018
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