ArticleCognitive, affective & behavioral neuroscience2013
Harnessing graphics processing units for improved neuroimaging statistics.
Article in Cognitive, affective & behavioral neuroscience, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- cuBNM: GPU-Accelerated Brain Network Modeling.bioRxiv : the preprint server for biology · 2025Article
- Comparison of CPU and GPU bayesian estimates of fibre orientations from diffusion MRI.PloS one · 2022Article
- BROCCOLI: Software for fast fMRI analysis on many-core CPUs and GPUs.Frontiers in neuroinformatics · 2014Article
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
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Abstract
Simple models and algorithms based on restrictive assumptions are often used in the field of neuroimaging for studies involving functional magnetic resonance imaging, voxel based morphometry, and diffusion tensor imaging. Nonparametric statistical methods or flexible Bayesian models can be applied rather easily to yield more trustworthy results. The spatial normalization step required for multisubject studies can also be improved by taking advantage of more robust algorithms for image registration. A common drawback of algorithms based on weaker assumptions, however, is the increase in computational complexity. In this short overview, we will therefore present some examples of how inexpensive PC graphics hardware, normally used for demanding computer games, can be used to enable practical use of more realistic models and accurate algorithms, such that the outcome of neuroimaging studies really can be trusted.
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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.