ArticleProceedings of SPIE--the International Society for Optical Engineering2019
Predicting Cognitive Scores from Resting fMRI Data and Geometric Features of the Brain.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Exploring the link between body physiology and cognition: the role of the brain and aging.npj aging · 2026Article
- A NETWORK-BASED APPROACH TO STUDY OF ADHD USING TENSOR DECOMPOSITION OF RESTING STATE FMRI DATA.Proceedings. IEEE International Symposium on Biomedical Imaging · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Anatomical T1 weighted Magnetic Resonance Imaging (MRI) and functional magnetic resonance imaging collected during resting (rfMRI) are promising markers that offer insight into the structure and function of the human brain. The objective of this work is to explore the use of a deep learning neural network to predict cognitive performance scores for a population of normal controls and subjects with Attention Deficit Hyperactivity Disorder (ADHD). Specifically, we predict verbal and performance IQs and ADHD index from features derived from T1 and rfMRI imaging data. First, we processed the rfMRI and MRI data of subjects using the BrainSuite fMRI Processing (BFP) pipeline to perform anatomical and functional preprocessing. This produces for each subject fMRI and geometric (anatomical) features represented in a standardized grayordinate system. The geometric and functional cortical data corresponding to the two hemispheres were then transformed to 128×128 multichannel images and input to a convolutional component of the neural network. Subcortical data were presented in a standard vector form and inputted to a input layer of the network. The neural network was implemented in Python using the Keras library with a TensorFlow backend. Training was performed on 168 images with 90 images used for testing. We observed a high correlation between predicted and actual values of the indices tested: Performance IQ: 0.47; Verbal IQ: 0.41, ADHD: 0.57. Comparing these values to those from network trained on functional-only and structural-only data, we saw that rfMRI is more informative than MRI, but the two modalities are highly complementary in terms of predicting these indices.
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Registered trials
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