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Use of machine learning to predict cognitive performance based on brain metabolism in Neurofibromatosis type 1
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 under...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6128556/ https://www.ncbi.nlm.nih.gov/pubmed/30192842 http://dx.doi.org/10.1371/journal.pone.0203520 |
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author | Schütze, Manuel de Souza Costa, Danielle de Paula, Jonas Jardim Malloy-Diniz, Leandro Fernandes Malamut, Carlos Mamede, Marcelo de Miranda, Débora Marques Brammer, Michael Romano-Silva, Marco Aurélio |
author_facet | Schütze, Manuel de Souza Costa, Danielle de Paula, Jonas Jardim Malloy-Diniz, Leandro Fernandes Malamut, Carlos Mamede, Marcelo de Miranda, Débora Marques Brammer, Michael Romano-Silva, Marco Aurélio |
author_sort | Schütze, Manuel |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6128556 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-61285562018-09-15 Use of machine learning to predict cognitive performance based on brain metabolism in Neurofibromatosis type 1 Schütze, Manuel de Souza Costa, Danielle de Paula, Jonas Jardim Malloy-Diniz, Leandro Fernandes Malamut, Carlos Mamede, Marcelo de Miranda, Débora Marques Brammer, Michael Romano-Silva, Marco Aurélio PLoS One Research Article 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. Public Library of Science 2018-09-07 /pmc/articles/PMC6128556/ /pubmed/30192842 http://dx.doi.org/10.1371/journal.pone.0203520 Text en © 2018 Schütze et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Schütze, Manuel de Souza Costa, Danielle de Paula, Jonas Jardim Malloy-Diniz, Leandro Fernandes Malamut, Carlos Mamede, Marcelo de Miranda, Débora Marques Brammer, Michael Romano-Silva, Marco Aurélio Use of machine learning to predict cognitive performance based on brain metabolism in Neurofibromatosis type 1 |
title | Use of machine learning to predict cognitive performance based on brain metabolism in Neurofibromatosis type 1 |
title_full | Use of machine learning to predict cognitive performance based on brain metabolism in Neurofibromatosis type 1 |
title_fullStr | Use of machine learning to predict cognitive performance based on brain metabolism in Neurofibromatosis type 1 |
title_full_unstemmed | Use of machine learning to predict cognitive performance based on brain metabolism in Neurofibromatosis type 1 |
title_short | Use of machine learning to predict cognitive performance based on brain metabolism in Neurofibromatosis type 1 |
title_sort | use of machine learning to predict cognitive performance based on brain metabolism in neurofibromatosis type 1 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6128556/ https://www.ncbi.nlm.nih.gov/pubmed/30192842 http://dx.doi.org/10.1371/journal.pone.0203520 |
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