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URL:https://murmitoyen.com/events/vanille/udem/detail/866314-towards-algori
 thmic-analytics-in-big-data-neuroimaging
LOCATION:CHU Sainte-Justine\, 3175\, Chemin de la Côte-Sainte-Catherine\, 
 Montréal\, QC\, Canada\, H3T 1C5
SUMMARY:Towards Algorithmic Analytics in Big-data Neuroimaging
DESCRIPTION:Conférence scientifique | Centre de recherche du CHU Sainte-Ju
 stine  Conférencier:
\nDanilo Bzdok\, MD\, PhD\, professeur adjoint de ne
 urosciences sociales et affectives\, Département de psychiatrie\, RWTH 
 Aachen University\, Allemagne. 
\nRésumé:Neuroscience datasets are const
 antly increasing in resolution\, sample size\, multi-modality\, and meta-i
 nformation complexity. This opens the brain imaging field to a more data-d
 riven machine-learning regime (e.g.\, autoencoders\, structured sparsity\,
  deep learning)\, while analysis methods from the domain of classical stat
 istics remain dominant (e.g.\, ANOVA\, Pearson correlation\, Student's t-t
 est).
\nSpecial interest may lie in the statistical learning of scalable g
 enerative models that explain brain function and structure. Instead of mer
 ely solving classification and regression tasks\, they could explicitly ca
 pture properties of the data-generating neurobiological mechanisms. Python
 -implemented examples for such supervised and semisupervised machine-learn
 ing techniques will be provided as applications to the currently biggest n
 euroimaging dataset from the Human Connectome Project (HCP) data-collectio
 n initiative as well as the prospective epidemiological UK Biobank. The em
 phasis will be put on the feasability of deep neural networks and semisupe
 rvised architectures in imaging neuroscience.
\nThe successful extraction 
 of structured knowledge from current and future large-scale neuroimaging d
 atasets will be a critical prerequisite for our understanding of human bra
 in organization in healthy populations and psychiatric disease.
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