Bigdata 2016 - Symposium on Big Data Analysis and Challenges in Neuro-Imaging

IEEE GlobalSIP 2016 Symposium on Big Data Analysis and Challenges in Neuroimaging is a one day symposium which will be held as part of the IEEE Global Conference on Signal and Information Processing in Washington D.C., USA from December 7-9, 2016. The symposium will feature one two invited key note speaker, two invited talks, one oral, and one poster session.
Today, neuroscience research is being actively carried out to understand structural and functional brain networks with a view to understanding brain functioning, early disease diagnosis, disease prognosis, and pre-surgical planning. With the advances in computing hardware, signal processing methods, and imaging technologies, research in this area has gained momentum. Today, a huge amount of neuroimaging data is being generated from different modalities MRI, fMRI, PET, NIRS, DTI, EEG/MEG. This data is also shared as free resources with the view to push research. Broadly, two issues are emerging- 1) to handle this big data efficiently via advanced signal processing methods and 2) to provide validation across subjects and across data from different modalities. This symposium is aimed at addressing these two broad issues.
Submissions are welcome on topics including:
Big Data Approaches to NeuroimagingAdvanced machine learning approaches to brain data analysis including network building, parcellation, and brain state decodingStatistical machine learningBrain data analysis using signal processing on graphsDistributed signal processing on networks/graphs in neuroimaging dataStatistical inference, dictionary learning, sparse recovery, matrix factorization, blind source separation methods applied to neuroimaging applicationStructured data recovery, e.g., sparse + low-rank matrix factorization, robust PCA, compressive sensing, structured sparsityHigher order data analysis, e.g. tensor-based approaches to neuroimaging data analysisFunctional/effective Connectivity of evolving networksAnatomical imaging and structural connectivityMultimodal (EEG, MEG, MRI, fMRI, PET, NIRS, DTI) neuroimaging data analysisJoint study of Structural and functional networks via fMRI plus DTI analysisStudy of altered brain networks in neuropsychiatric disordersVisual scene reconstruction using brain imagingDynamic and non-linear time-series analysis