Analysis of functional magnetic resonance imaging (fMRI) data is becoming ever more computationally demanding as temporal and spatial resolutions improve, and large, publicly available data sets proliferate. Moreover, methodological improvements in the neuroimaging pipeline, such as non-linear spatial normalization, non-parametric permutation tests and Bayesian Markov Chain Monte Carlo approaches, can dramatically increase the computational burden. Despite these challenges, there do not yet exist any fMRI software packages which leverage inexpensive and powerful graphics processing units (GPUs) to perform these analyses. Here, we therefore present BROCCOLI, a free software package written in OpenCL (Open Computing Language) that can be used...
Studying dynamic-functional connectivity (DFC) using fMRI data of the brain gives much richer inform...
In this thesis, the main aims are to accelerate algorithms in diffusion tractography and functional ...
With the performance of central processing units (CPUs) having effectively reached a limit, parallel...
Analysis of functional magnetic resonance imaging (fMRI) data is becoming ever more computationally ...
Functional magnetic resonance imaging (fMRI) makes it possible to non-invasively measure brain activ...
especially bioinformatics have been made practical by the recent advances in Graphical Processing Un...
Recent advances in multi-core processors and graphics card based computational technologies have pav...
<p>This thesis covers two brain computer interfaces (BCIs) using real-time fMRI, and how to use grap...
Functional magnetic resonance imaging (fMRI) is a non-invasive brain imaging technique, which has be...
Functional magnetic resonance imaging (fMRI) is a prime example of multi-disciplinary research. With...
Functional connectivity analysis is a way to investigate how different parts of the brain are connec...
<p>The poster for our ICIP paper "A GPU Accelerated Interactive Interface for Exploratory Functional...
Dedicated computing environments are becoming increasingly important in neuroimaging applications. ...
The searchlight algorithm is a popular choice for locally-multivariate decoding of fMRI data. A subs...
Parametric statistical methods, such as Z-, t-, and F-values are traditionally employed in functiona...
Studying dynamic-functional connectivity (DFC) using fMRI data of the brain gives much richer inform...
In this thesis, the main aims are to accelerate algorithms in diffusion tractography and functional ...
With the performance of central processing units (CPUs) having effectively reached a limit, parallel...
Analysis of functional magnetic resonance imaging (fMRI) data is becoming ever more computationally ...
Functional magnetic resonance imaging (fMRI) makes it possible to non-invasively measure brain activ...
especially bioinformatics have been made practical by the recent advances in Graphical Processing Un...
Recent advances in multi-core processors and graphics card based computational technologies have pav...
<p>This thesis covers two brain computer interfaces (BCIs) using real-time fMRI, and how to use grap...
Functional magnetic resonance imaging (fMRI) is a non-invasive brain imaging technique, which has be...
Functional magnetic resonance imaging (fMRI) is a prime example of multi-disciplinary research. With...
Functional connectivity analysis is a way to investigate how different parts of the brain are connec...
<p>The poster for our ICIP paper "A GPU Accelerated Interactive Interface for Exploratory Functional...
Dedicated computing environments are becoming increasingly important in neuroimaging applications. ...
The searchlight algorithm is a popular choice for locally-multivariate decoding of fMRI data. A subs...
Parametric statistical methods, such as Z-, t-, and F-values are traditionally employed in functiona...
Studying dynamic-functional connectivity (DFC) using fMRI data of the brain gives much richer inform...
In this thesis, the main aims are to accelerate algorithms in diffusion tractography and functional ...
With the performance of central processing units (CPUs) having effectively reached a limit, parallel...