Frequency-Specific Changes of Resting Brain Activity in Parkinson's Disease: A Machine Learning Approach.
Neuroscience
; 436: 170-183, 2020 06 01.
Article
en En
| MEDLINE
| ID: mdl-32059985
ABSTRACT
The application of resting state functional MRI (RS-fMRI) in Parkinson's disease (PD) was widely performed using standard statistical tests, however, the machine learning (ML) approach has not yet been investigated in PD using RS-fMRI. In current study, we utilized the mean regional amplitude values as the features in patients with PD (nâ¯=â¯72) and in healthy controls (HC, nâ¯=â¯89). The t-test and linear support vector machine were employed to select the features and make prediction, respectively. Three frequency bins (Slow-5 0.0107-0.0286â¯Hz; Slow-4 0.0286-0.0821â¯Hz; conventional 0.01-0.08â¯Hz) were analyzed. Our results showed that the Slow-4 may provide important information than Slow-5 in PD, and it had almost identical classification performance compared with the Combined (Slow-5 and Slow-4) and conventional frequency bands. Similar with previous neuroimaging studies in PD, the discriminative regions were mainly included the disrupted motor system, aberrant visual cortex, dysfunction of paralimbic/limbic and basal ganglia networks. The lateral parietal lobe, such as right inferior parietal lobe (IPL) and supramarginal gyrus (SMG), was detected as the discriminative features exclusively in Slow-4. Our findings, at the first time, indicated that the ML approach is a promising choice for detecting abnormal regions in PD, and a multi-frequency scheme would provide us more specific information.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Enfermedad de Parkinson
/
Corteza Visual
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Neuroscience
Año:
2020
Tipo del documento:
Article
País de afiliación:
China