ArticleHuman brain mapping2017
Adaptive smoothing based on Gaussian processes regression increases the sensitivity and specificity of fMRI data.
Article in Human brain mapping, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Exploring Generative Artificial Intelligence and Data Augmentation Techniques for Spectroscopy Analysis.Chemical reviews · 2025Review
- Personalized neural responses to self and filial reward: Functional and structural responses to motivational cues.AIMS neuroscience · 2025Article
- Independent component analysis: a reliable alternative to general linear model for task-based fMRI.Frontiers in psychiatry · 2023Article
- Denoise Functional Magnetic Resonance Imaging With Random Matrix Theory Based Principal Component Analysis.IEEE transactions on bio-medical engineering · 2022Article
- Optic flow selectivity in the macaque parieto-occipital sulcus.Brain structure & function · 2021Article
- A common neural substrate for processing scenes and egomotion-compatible visual motion.Brain structure & function · 2020Article
- An artificial intelligence model for the simulation of visual effects in patients with visual field defects.Annals of translational medicine · 2020Article
- Neural bases of self- and object-motion in a naturalistic vision.Human brain mapping · 2020Article
- Egomotion-related visual areas respond to active leg movements.Human brain mapping · 2019Article
- Clinical application of advanced MR methods in children: points to consider.Annals of clinical and translational neurology · 2018Review
- Detailed spatiotemporal brain mapping of chromatic vision combining high-resolution VEP with fMRI and retinotopy.Human brain mapping · 2018Article
- Eight Weddings and Six Funerals: An fMRI Study on Autobiographical Memories.Frontiers in behavioral neuroscience · 2018Article
- Perceptual integration and attention in human extrastriate cortex.Scientific reports · 2017Article
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Authors and funding
7 authors.
Funding
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 38:1438-1459, 2017. © 2016 Wiley Periodicals, Inc.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.