Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
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
_version_ 1783353662644420608
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
work_keys_str_mv AT schutzemanuel useofmachinelearningtopredictcognitiveperformancebasedonbrainmetabolisminneurofibromatosistype1
AT desouzacostadanielle useofmachinelearningtopredictcognitiveperformancebasedonbrainmetabolisminneurofibromatosistype1
AT depaulajonasjardim useofmachinelearningtopredictcognitiveperformancebasedonbrainmetabolisminneurofibromatosistype1
AT malloydinizleandrofernandes useofmachinelearningtopredictcognitiveperformancebasedonbrainmetabolisminneurofibromatosistype1
AT malamutcarlos useofmachinelearningtopredictcognitiveperformancebasedonbrainmetabolisminneurofibromatosistype1
AT mamedemarcelo useofmachinelearningtopredictcognitiveperformancebasedonbrainmetabolisminneurofibromatosistype1
AT demirandadeboramarques useofmachinelearningtopredictcognitiveperformancebasedonbrainmetabolisminneurofibromatosistype1
AT brammermichael useofmachinelearningtopredictcognitiveperformancebasedonbrainmetabolisminneurofibromatosistype1
AT romanosilvamarcoaurelio useofmachinelearningtopredictcognitiveperformancebasedonbrainmetabolisminneurofibromatosistype1