Radiomic Texture Analysis Mapping Predicts Areas of True Functional MRI Activity

Individual analysis of functional Magnetic Resonance Imaging (fMRI) scans requires user-adjustment of the statistical threshold in order to maximize true functional activity and eliminate false positives. In this study, we propose a novel technique that uses radiomic texture analysis (TA) features a...

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Bibliographic Details
Published in:Scientific reports 2016-05, Vol.6 (1), p.25295-25295, Article 25295
Main Authors: Hassan, Islam, Kotrotsou, Aikaterini, Bakhtiari, Ali Shojaee, Thomas, Ginu A., Weinberg, Jeffrey S., Kumar, Ashok J., Sawaya, Raymond, Luedi, Markus M., Zinn, Pascal O., Colen, Rivka R.
Format: Article
Language:English
Subjects:
59/57
692/700/1421/1628
692/700/1421/65
Adult
Aged
Area Under Curve
Brain Mapping - methods
Feasibility Studies
Female
Functional magnetic resonance imaging
Handedness
Healthy Volunteers
Heterogeneity
Humanities and Social Sciences
Humans
Logistic Models
Magnetic Resonance Imaging - methods
Male
Mapping
Middle Aged
multidisciplinary
NMR
Nuclear magnetic resonance
Prediction models
Radiomics
Science
Science (multidisciplinary)
Speech - physiology
Young Adult
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Summary:Individual analysis of functional Magnetic Resonance Imaging (fMRI) scans requires user-adjustment of the statistical threshold in order to maximize true functional activity and eliminate false positives. In this study, we propose a novel technique that uses radiomic texture analysis (TA) features associated with heterogeneity to predict areas of true functional activity. Scans of 15 right-handed healthy volunteers were analyzed using SPM8. The resulting functional maps were thresholded to optimize visualization of language areas, resulting in 116 regions of interests (ROIs). A board-certified neuroradiologist classified different ROIs into Expected (E) and Non-Expected (NE) based on their anatomical locations. TA was performed using the mean Echo-Planner Imaging (EPI) volume, and 20 rotation-invariant texture features were obtained for each ROI. Using forward stepwise logistic regression, we built a predictive model that discriminated between E and NE areas of functional activity, with a cross-validation AUC and success rate of 79.84% and 80.19% respectively (specificity/sensitivity of 78.34%/82.61%). This study found that radiomic TA of fMRI scans may allow for determination of areas of true functional activity, and thus eliminate clinician bias.
ISSN:2045-2322
2045-2322
DOI:10.1038/srep25295