Adaptive smoothing based on Gaussian processes regression increases the sensitivity and specificity of fMRI data.
Hum Brain Mapp
; 38(3): 1438-1459, 2017 03.
Article
en En
| MEDLINE
| ID: mdl-27943516
ABSTRACT
Temporal and spatial filtering of fMRI data is often used to improve statistical power. However, conventional methods, such as smoothing with fixed-width Gaussian filters, remove fine-scale structure in the data, necessitating a tradeoff between sensitivity and specificity. Specifically, smoothing may increase sensitivity (reduce noise and increase statistical power) but at the cost loss of specificity in that fine-scale structure in neural activity patterns is lost. Here, we propose an alternative smoothing method based on Gaussian processes (GP) regression for single subjects fMRI experiments. This method adapts the level of smoothing on a voxel by voxel basis according to the characteristics of the local neural activity patterns. GP-based fMRI analysis has been heretofore impractical owing to computational demands. Here, we demonstrate a new implementation of GP that makes it possible to handle the massive data dimensionality of the typical fMRI experiment. We demonstrate how GP can be used as a drop-in replacement to conventional preprocessing steps for temporal and spatial smoothing in a standard fMRI pipeline. We present simulated and experimental results that show the increased sensitivity and specificity compared to conventional smoothing strategies. Hum Brain Mapp 381438-1459, 2017. © 2016 Wiley Periodicals, Inc.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Procesamiento de Señales Asistido por Computador
/
Encéfalo
/
Mapeo Encefálico
/
Imagen por Resonancia Magnética
/
Distribución Normal
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Female
/
Humans
/
Male
Idioma:
En
Revista:
Hum Brain Mapp
Asunto de la revista:
CEREBRO
Año:
2017
Tipo del documento:
Article