Real-time full correlation matrix analysis of fMRI data

Wang, Yida; Keller, Bryn; Capotă, Mihai; Anderson, Michael J.; Sundaram, Narayanan; Cohen, Jonathan D.; Li, Kai; Turk‐Browne, Nicholas B.; Willke, Theodore L.

doi:10.1109/bigdata.2016.7840728

Cited by 10 publications

(7 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…which for an arbitrary number of vectors becomes an inner product matrix multiplication. A more detailed derivation can be found in the appendix of [29]. To obtain the full correlation matrix for a given time epoch, corr(X, X) is calculated by taking the product of X and its transpose.…”

Section: Overviewmentioning

confidence: 99%

Full correlation matrix analysis of fMRI data on Intel® Xeon Phi™ coprocessors

Wang

Anderson²,

Cohen

et al. 2015

Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

Self Cite

View full text Add to dashboard Cite

Full correlation matrix analysis (FCMA) is an unbiased approach for exhaustively studying interactions among brain regions in functional magnetic resonance imaging (fMRI) data from human participants. In order to answer neuroscientific questions efficiently, we are developing a closedloop analysis system with FCMA on a cluster of nodes with Intel R Xeon Phi TM coprocessors. Here we propose several ideas for data-driven algorithmic modification to improve the performance on the coprocessor. Our experiments with real datasets show that the optimized single-node code runs 5x-16x faster than the baseline implementation using the well-known Intel R MKL and LibSVM libraries, and that the cluster implementation achieves near linear speedup on 5760 cores.

show abstract

Section: Overviewmentioning

confidence: 99%

Full correlation matrix analysis of fMRI data on Intel® Xeon Phi™ coprocessors

Wang

Anderson²,

Cohen

et al. 2015

Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

Self Cite

View full text Add to dashboard Cite

show abstract

“…There are also related multivariate techniques for functional connectivity and functional alignment, including: full correlation matrix analysis (FCMA; [7]), inter-subject correlation (ISC; [8,9]), inter-subject functional connectivity (ISFC; [10]), shared response modeling (SRM; [11]), and event segmentation [12]. These analyses can be run after data collection is complete or in realtime for neurofeedback training or adaptive design optimization [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

BrainIAK tutorials: User-friendly learning materials for advanced fMRI analysis

Kumar

Ellis

et al. 2020

PLoS Comput Biol

Self Cite

View full text Add to dashboard Cite

Advanced brain imaging analysis methods, including multivariate pattern analysis (MVPA), functional connectivity, and functional alignment, have become powerful tools in cognitive neuroscience over the past decade. These tools are implemented in custom code and separate packages, often requiring different software and language proficiencies. Although usable by expert researchers, novice users face a steep learning curve. These difficulties stem from the use of new programming languages (e.g., Python), learning how to apply machine-learning methods to high-dimensional fMRI data, and minimal documentation and training materials. Furthermore, most standard fMRI analysis packages (e.g., AFNI, FSL, SPM) focus on preprocessing and univariate analyses, leaving a gap in how to integrate with advanced tools. To address these needs, we developed BrainIAK (brainiak.org), an open-source Python software package that seamlessly integrates several cutting-edge, computationally efficient techniques with other Python packages (e.g., Nilearn, Scikit-learn) for file handling, visualization, and machine learning. To disseminate these powerful tools, we developed user-friendly tutorials (in Jupyter format; https://brainiak.org/tutorials/) for learning BrainIAK and advanced fMRI analysis in Python more generally. These materials cover techniques including: MVPA (pattern classification and representational similarity analysis); parallelized searchlight analysis; background connectivity; full correlation matrix analysis; inter-subject correlation; inter-subject functional connectivity; shared response modeling; event segmentation using hidden Markov models; and real-time fMRI. For longrunning jobs or large memory needs we provide detailed guidance on high-performance computing clusters. These notebooks were successfully tested at multiple sites, including as problem sets for courses at Yale and Princeton universities and at various workshops and hackathons. These materials are freely shared, with the hope that they become part of a pool of open-source software and educational materials for large-scale, reproducible fMRI analysis and accelerated discovery.

show abstract

“…By keeping track of intensity of each voxel over time, a time series is extracted out of each voxel which is used for further analysis. A popular technique for analyzing brain functional connectivity is Pearson’s correlation coefficient (PCC) [ 5 , 6 , 7 ]. The PCC computes linear association between two variables x and y using the following formula:

The value of PCC

can be in the range −1 and 1 [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…Wang et al [ 6 ] proposed a parallel technique based on a controller worker method with message passing interface (MPI) to compute pairwise Pearson’s correlations over multiple time windows. Another approach was proposed by Liu et al [ 18 ] to compute all pairwise correlation coefficients on Intel Xeon Phi clusters.…”

Section: Introductionmentioning

confidence: 99%

Fast-GPU-PCC: A GPU-Based Technique to Compute Pairwise Pearson’s Correlation Coefficients for Time Series Data—fMRI Study

Eslami

Saeed

2018

High-Throughput

View full text Add to dashboard Cite

Functional magnetic resonance imaging (fMRI) is a non-invasive brain imaging technique, which has been regularly used for studying brain’s functional activities in the past few years. A very well-used measure for capturing functional associations in brain is Pearson’s correlation coefficient. Pearson’s correlation is widely used for constructing functional network and studying dynamic functional connectivity of the brain. These are useful measures for understanding the effects of brain disorders on connectivities among brain regions. The fMRI scanners produce huge number of voxels and using traditional central processing unit (CPU)-based techniques for computing pairwise correlations is very time consuming especially when large number of subjects are being studied. In this paper, we propose a graphics processing unit (GPU)-based algorithm called Fast-GPU-PCC for computing pairwise Pearson’s correlation coefficient. Based on the symmetric property of Pearson’s correlation, this approach returns Nfalse(N−1false)/2 correlation coefficients located at strictly upper triangle part of the correlation matrix. Storing correlations in a one-dimensional array with the order as proposed in this paper is useful for further usage. Our experiments on real and synthetic fMRI data for different number of voxels and varying length of time series show that the proposed approach outperformed state of the art GPU-based techniques as well as the sequential CPU-based versions. We show that Fast-GPU-PCC runs 62 times faster than CPU-based version and about 2 to 3 times faster than two other state of the art GPU-based methods.

show abstract

Real-time full correlation matrix analysis of fMRI data

Cited by 10 publications

References 14 publications

Full correlation matrix analysis of fMRI data on Intel® Xeon Phi™ coprocessors

Full correlation matrix analysis of fMRI data on Intel® Xeon Phi™ coprocessors

BrainIAK tutorials: User-friendly learning materials for advanced fMRI analysis

Fast-GPU-PCC: A GPU-Based Technique to Compute Pairwise Pearson’s Correlation Coefficients for Time Series Data—fMRI Study

Contact Info

Product

Resources

About