Deep learning classification of reading disability with regional brain volume features

•Deformation-based deep learning was used to classify reading disability.•Autoencoder pretraining optimized neural network classification accuracy.•Reading disability classification precision (0.75) and recall (0.78) was observed.•Brain regions were differentially predictive of cases and controls.•C...

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Veröffentlicht in:	NeuroImage (Orlando, Fla.) Jg. 273; S. 120075
Hauptverfasser:	Joshi, Foram, Wang, James Z., Vaden, Kenneth I., Eckert, Mark A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Inc 01.06.2023 Elsevier Limited Elsevier
Schlagworte:	Accuracy Brain Brain - diagnostic imaging Brain morphology Brain research Child Classification Comprehension Convolutional neural network Cortex Data Data compression Decoding Deep Learning Disability Discussion groups Dyslexia Dyslexia - diagnostic imaging Humans Individual differences Language Learning Learning disabilities Medical imaging Morphology Neural networks Neuroimaging Neuroimaging - methods Occipital lobe People with disabilities Phenotypic variations Phonology Reading comprehension Reading disabilities Reading disability Regions Specific learning disorder in reading Superior temporal sulcus Support vector machines Temporal lobe Specific learning disorder in reading Reading disability Dyslexia Brain morphology Convolutional neural network
ISSN:	1053-8119, 1095-9572, 1095-9572
Online-Zugang:	Volltext
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