Question

diffusion of sensory ​

Answer 1

Answer:

iffusion tensor tractography in children with sensory processing disorder: Potentials for devising machine learning classifiers☆

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 < 0.001). Among different combinations of machine learning algorithms and DTI/connectivity metrics, random forest models using tract-based TD yielded the highest accuracy in classification of SPD – 77.5% accuracy, 73.8% sensitivity, and 81.6% specificity. Our findings demonstrate impaired microstructural and connectivity/connectomic integrity in children with SPD, predominantly in posterior white matter tracts, and with reduced TD of the splenium of corpus callosum as the most distinctive pattern. Applying machine learning algorithms, these connectivity metrics can be used to devise novel imaging biomarkers for neurodevelopmental disorders.

Keywords: Probabilistic tractography, Edge density imaging, Diffusion tensor imaging, Machine learning, Neurodevelopmental disorders, Sensory processing disorders

Abbreviations: AD, axial diffusivity; ADHD, attention-deficit hyperactivity disorder; ASD, autism spectrum disorder; AUC, area under the curve; BET, brain extraction tool; ED, edge density; EDI, edge density imaging; DTI, diffusion tensor imaging; FA, fractional anisotropy; GLM, general linear model; NPV, negative predictive value; MD, mean diffusivity; PPV, positive predictive value; RD, radial diffusivity; ROC, receiver operating characteristic; ROI, region of interest; SPD, sensory processing disorders; SVM, support vector machine; TBSS, tract-based spatial statistics; TD, track density; TDC, typically developing children; TFCE, threshold-free cluster enhancement; VBM, voxel-based morphometry

Answer 2

Answer:

There are 3 main types of sensory processing disorders:

Sensory Modulation Disorder (SMD)

Sensory-Based Motor Disorder (SBMD)

Sensory Discrimination Disorder

Explanation:

i think so

mark me brainiest plzz and follow me

thanks my answer