1. Use of machine learning to predict cognitive performance based on brain metabolism in Neurofibromatosis type 1.
- Author
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Schütze M, de Souza Costa D, de Paula JJ, Malloy-Diniz LF, Malamut C, Mamede M, Miranda DM, Brammer M, and Romano-Silva MA
- Subjects
- Adolescent, Adult, Brain diagnostic imaging, Child, Cognition Disorders metabolism, Female, Fluorodeoxyglucose F18 metabolism, Humans, Machine Learning, Male, Neurofibromatosis 1 diagnostic imaging, Neurofibromatosis 1 metabolism, Neuropsychological Tests, Positron Emission Tomography Computed Tomography, Principal Component Analysis, Young Adult, Brain metabolism, Cognition Disorders diagnosis, Neurofibromatosis 1 psychology
- Abstract
Neurofibromatosis Type 1 (NF1) can cause a wide range of cognitive deficits, but its underlying nature is still unknown. We investigated the correlation between cognitive performance and specific patterns of resting-state brain metabolism in a NF1 sample. Sixteen individuals diagnosed with NF1 underwent 18F-FDG PET/CT brain imaging followed by a neuropsychological assessment. Principal component analysis was performed on 17 measures of cognitive function and a machine learning approach based on Gaussian Process Regression was used to individually predict the components that represented most of the variance in the neuropsychological data. The accuracy of the method was estimated using leave-one-out cross-validation and its significance through permutation testing. We found that only the first component could be accurately predicted from resting state metabolism (r = 0.926, p<0.001). Multiple and heterogeneous measures contribute to the first component, mainly WISC/WAIS Procedure and Verbal IQ, verbal memory and fluency. Considering the accurate prediction of measures of neuropsychological performance based on brain metabolism in NF1 patients, this suggests an underlying metabolic pattern that relates to cognitive performance in this group., Competing Interests: The authors have declared that no competing interests exist.
- Published
- 2018
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