Use of machine learning to predict cognitive performance based on brain metabolism in Neurofibromatosis type 1

被引：11

作者：

Schutze, Manuel ^{[1
]}

Costa, Danielle de Souza ^{[1
]}

de Paula, Jonas Jardim ^{[1
]}

Malloy-Diniz, Leandro Fernandes ^{[1
]}

Malamut, Carlos ^{[1
,2
]}

Mamede, Marcelo ^{[1
,3
]}

de Miranda, Debora Marques ^{[1
,4
]}

Brammer, Michael ^{[1
,5
]}

Romano-Silva, Marco Aurelio ^{[1
,6
]}

机构：

[1] Univ Fed Minas Gerais, Inst Nacl Ciencia & Tecnol Med Mol, Belo Horizonte, MG, Brazil

[2] Comissao Nacl Energia Nucl, Ctr Desenvolvimento Tecnol Nucl, Belo Horizonte, MG, Brazil

[3] Univ Fed Minas Gerais, Fac Med, Dept Anat & Imagem, Belo Horizonte, MG, Brazil

[4] Univ Fed Minas Gerais, Fac Med, Dept Pediat, Belo Horizonte, MG, Brazil

[5] Inst Psychiat, Dept Neuroimaging, London, England

[6] Univ Fed Minas Gerais, Dept Saude Mental, Fac Med, Belo Horizonte, MG, Brazil

来源：

PLOS ONE | 2018年 / 13卷 / 09期

关键词：

POSITRON-EMISSION-TOMOGRAPHY; CEREBRAL GLUCOSE-METABOLISM; ALZHEIMERS-DISEASE; FDG-PET; CHILDREN; IMPAIRMENT; DEPRESSION; ATTENTION; ADULTS; STATES;

D O I：

10.1371/journal.pone.0203520

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页数：13