Functional connectivity-based classification of rapid eye movement sleep behavior disorder.
Sleep Med
; 115: 5-13, 2024 Mar.
Article
em En
| MEDLINE
| ID: mdl-38295625
ABSTRACT
BACKGROUND:
Isolated rapid eye movement sleep behavior disorder (iRBD) is a clinically important parasomnia syndrome preceding α-synucleinopathies, thereby prompting us to develop methods for evaluating latent brain states in iRBD. Resting-state functional magnetic resonance imaging combined with a machine learning-based classification technology may help us achieve this purpose.METHODS:
We developed a machine learning-based classifier using functional connectivity to classify 55 patients with iRBD and 97 healthy elderly controls (HC). Selecting 55 HCs randomly from the HC dataset 100 times, we conducted a classification of iRBD and HC for each sampling, using functional connectivity. Random forest ranked the importance of functional connectivity, which was subsequently used for classification with logistic regression and a support vector machine. We also conducted correlation analysis of the selected functional connectivity with subclinical variations in motor and non-motor functions in the iRBDs.RESULTS:
Mean classification performance using logistic regression was 0.649 for accuracy, 0.659 for precision, 0.662 for recall, 0.645 for f1 score, and 0.707 for the area under the receiver operating characteristic curve (p < 0.001 for all). The result was similar in the support vector machine. The classifier used functional connectivity information from nine connectivities across the motor and somatosensory areas, parietal cortex, temporal cortex, thalamus, and cerebellum. Inter-individual variations in functional connectivity were correlated with the subclinical motor and non-motor symptoms of iRBD patients.CONCLUSIONS:
Machine learning-based classifiers using functional connectivity may be useful to evaluate latent brain states in iRBD.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Transtorno do Comportamento do Sono REM
Limite:
Aged
/
Humans
Idioma:
En
Revista:
Sleep Med
Assunto da revista:
NEUROLOGIA
/
PSICOFISIOLOGIA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Japão