Call for Papers an Abstracts

International Workshop on Big Data Neuroimaging Analytics for Brain and Mental Health

in conjunction with 
2016 International Conference on Brain Informatics & Health

With rapid advances in neuroimaging techniques, the brain science is experiencing rapid changes with efficient noninvasive ways (such as fMRI, EEG, fNIRS, MEG, etc) for studying the functional activity of the human brain, either normal or in a diseased state. The research on brain and mental health from big brain data has become an emerging area for both data mining and neuroscience community. Big data analytics and machine learning plays an increasingly important role to aid clinical diagnosis, clarify underlying mechanisms, and inform neuroprotective interventions to slow or reverse neural injury for a broad spectrum of brain disorders and mental problems, including depression, epilepsy, autism, Alzheimer’s disease, Parkinson’s disease, etc. An accurate assessment of brain health status has the potential to greatly help patients with brain disorders and reduce medical costs largely to families and society. However, big brain imaging data poses extreme challenges for data mining research to detect subtle changes of complex dynamic brain networks, and understand how brain functions. There is an urgent need to develop the efficient data mining and knowledge discovery tools that allow one to make sense of massive brain imaging data, decode dynamic neural activity, and identify neural signatures of brain disorders and mental state robustly. 

This workshop will explore the advances in data analytics and computational methods for complex brain modeling and knowledge discovery from a variety of brain data with applications to brain diseases and mental health. We encourage submissions in, but not limited to, the following areas: 
  • Brain connectivity networks analysis 
  • Spatio-temporal brain imaging data modeling (fMRI, EEG, MEG, fNIRS, etc.) 
  • Integrative multi-modality brain imaging data analysis 
  • Feature analysis and feature selection for brain imaging data 
  • Structural and functional MRI data analysis
  • Neuro-feedback, neuro-stimulation, and brain computer interfaces 
  • Data analytics and pattern recognition for brain disorders, such as depression, epilepsy, Alzheimer's disease, Parkinson's disease, and autism, etc.