Call for Papers

5th International Workshop on Pattern Recognition in NeuroImaging (PRNI 2015)

Stanford, CA, USA - June 10-12 2015

Pattern recognition techniques have become a staple of neuroimaging data analysis, with several open source dedicated toolboxes available to a large audience, in a variety of languages. In parallel, there has been a large increase in the availability of publicly available datasets, which routinely comprise several hundred subjects. Not only has the amount of data increased, but the variety of modalities available openly has also kept up, with behavioral test results, biomarker data, genotype and gene expression data now becoming abundant. This has created new challenges for storage and distribution of neuroimaging data, and given more importance to informatics infrastructures. This state of affairs has also further emphasized the critical role of multivariate modeling techniques, in particular those techniques that can handle multiple modalities, such as partial least squares, joint/parallel ICA, or canonical correlation analysis and their sparse and regularized versions. The dialogue between producers and consumers of multivariate, predictive modeling methods is ongoing, with great benefits for all involved.

After Istanbul, Seoul, London, Philadelphia, and Tübingen, the 5th International Workshop on Pattern Recognition in NeuroImaging will continue to facilitate exchange of ideas between scientific communities, with a particular interest in methods to deal with large-scale neuroimaging data, and what novel insights can be gained from datasets with hundreds of subjects. The single-track workshop will feature three tutorials, three keynote addresses, several contributed presentations of peer-reviewed papers, and a rich social programme.

** Topics of interest

PRNI welcomes original papers on predictive models of neuroimaging data, using e.g. fMRI, sMRI, EEG, MEG, ECoG modalities, including but not limited to the following:

* Learning & inference on neuroimaging data

Multimodal learning
Causal modeling
Dynamic and time-varying models
Graph kernels

* Large-scale open neuroimaging datasets

Distributed learning
Meta- and mega-analysis frameworks
Approximate inference for large-scale data
Tools and languages for efficient

* Applications

Imaging genomics
Affective sciences

** Submission Guidelines and Proceedings

Authors should prepare original full papers with a maximum length of 4 pages (double-column, PDF) for review. Proceedings will be submitted to the IEEExplore and CS Digital Library online repositories, and submitted for indexing in IET INSPEC, EI Compendex (Elsevier), Thomson ISI, and others. Selected papers will participate in an upcoming journal special issue.

More details:

** Important Dates

See the page Important Dates and Deadline.