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Diffusion tensor tractography in children with sensory processing disorder: Potentials for devising machine learning classifiers.


ABSTRACT: The "sensory processing disorder" (SPD) refers to brain's inability to organize sensory input for appropriate use. In this study, we determined the diffusion tensor imaging (DTI) microstructural and connectivity correlates of SPD, and apply machine learning algorithms for identification of children with SPD based on DTI/tractography metrics. A total of 44 children with SPD and 41 typically developing children (TDC) were prospectively recruited and scanned. In addition to fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD), we applied probabilistic tractography to generate edge density (ED) and track density (TD) from DTI maps. For identification of children with SPD, accurate classification rates from a combination of DTI microstructural (FA, MD, AD, and RD), connectivity (TD) and connectomic (ED) metrics with different machine learning algorithms - including naïve Bayes, random forest, support vector machine, and neural networks - were determined. In voxel-wise analysis, children with SPD had lower FA, ED, and TD but higher MD and RD compared to TDC - predominantly in posterior white matter tracts including posterior corona radiata, posterior thalamic radiation, and posterior body and splenium of corpus callosum. In stepwise penalized logistic regression, the only independent variable distinguishing children with SPD from TDC was the average TD in the splenium (p?

SUBMITTER: Payabvash S 

PROVIDER: S-EPMC6488562 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Diffusion tensor tractography in children with sensory processing disorder: Potentials for devising machine learning classifiers.

Payabvash Seyedmehdi S   Palacios Eva M EM   Owen Julia P JP   Wang Maxwell B MB   Tavassoli Teresa T   Gerdes Molly M   Brandes-Aitken Anne A   Marco Elysa J EJ   Mukherjee Pratik P  

NeuroImage. Clinical 20190424


The "sensory processing disorder" (SPD) refers to brain's inability to organize sensory input for appropriate use. In this study, we determined the diffusion tensor imaging (DTI) microstructural and connectivity correlates of SPD, and apply machine learning algorithms for identification of children with SPD based on DTI/tractography metrics. A total of 44 children with SPD and 41 typically developing children (TDC) were prospectively recruited and scanned. In addition to fractional anisotropy (F  ...[more]

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