Functional neuroimaging methods for experimental dataMS18

Computation is crucial for information extraction in functional neuroimaging. Indeed, functional images provide representations of complex conditions whose interpretation requires the use of sophisticated computational techniques. This mini-symposium provides an overview of up-to-date methods and applications concerning a wide spectrum of functional neuroimaging problems. As for applications, imaging modalities will range from neurophysiology, through PET to fMRI. As for methodologies, the mathematics considered will involve inverse and forward modeling, pattern recognition, image integration, connectivity analysis. Common trait of these contributions will be the use of experimental measurements, acquired in health and disease, under lab and clinical conditions.

Wed 06 June at 09:30 in Room 2 (Redenti floor 1)
Image processing for the investigation of glucose metabolism in patients of ALS
Cristina Campi ( CNR - SPIN )
Predicting brain atrophy progression from the healthy brain connectome
Sara Garbarino ( Centre for Medical Image Computing, University College London )
Conductivity models for functional neuroimaging
Maureen Clerc ( INRIA Sophia Antipolis-Méditerranée )
Brain imaging from MEG data: an unsupervised clustering approach for source space reduction
Sara Sommariva ( Aalto University )
Anna Maria Massone ( CNR - SPIN )
computed tomography, connectivity, image interpretation, image segmentation, inverse problems, machine learning, partial differential equation models