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.

Image processing for the investigation of glucose metabolism in patients of ALS

Cristina Campi (University of Padoa)

Predicting brain atrophy progression from the healthy brain connectome

Sara Garbarino (Universite de la Cote d'Azur and INRIA Sophia Antipolis)

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)

Organizers:

Anna Maria Massone (CNR - SPIN)

Keywords:

computed tomography, connectivity, image interpretation, image segmentation, inverse problems, machine learning, partial differential equation models