From Neurons to Networks: Opportunities & Challenges for Laminar fMRI
Member-Initiated Symposium

ORGANIZERS: Kamil Uludag, Essa Yacoub

Tuesday, 19 June 2018

Session Number: MIS-09

Overview
In recent years, there have been several highly publicized studies showing the value of laminar fMRI for human neuroscience. fMRI at the laminar scale revolutionizes our ability to tackle cortical computations at the mesoscale (< 1mm), closing the gap with invasive animal electrophysiology. However, the fMRI signal stems from vascular changes associated with neuronal activity prohibiting direct interpretation of fMRI data. In addition, even at high-spatial resolution, the geometry of the cortical vasculature reduces laminar information in the fMRI signal, severely limiting its spatial specificity and subsequent interpretability. Thus, in this symposium, we will educate MRI scientists on the neuronal underpinnings of the fMRI signal, by reflecting on the cellular origin of neuronal signals and how these signals translate to the laminar fMRI scale. Internationally leading experts will present overviews and future prospects on: 1) the electrophysiological basis of laminar processing, 2) MRI acquisition schemes for high-spatial resolution, 3) analysis approaches for laminar fMRI in humans, and 4) neurovascular modeling of the fMRI signal at high-spatial resolution to remove vascular biases, which are limiting the interpretability of laminar fMRI data. This symposium perfectly fits to and complements the recurrent ISMRM annual conference sessions on the temporal and spatial properties of the fMRI signal.

Target Audience
Cognitive neuroscientists, MRI sequence developers for functional MRI, data analysts, software developers, High-Field MRI scientists and engineers.

Educational Objectives
As a result of attending this course, participants should be able to:
- Recognize the indirect nature of the fMRI signal;
- Judge benefits and limitations of various fMRI acquisition schemes;
- Identify advanced analysis methods for super-resolution fMRI; and
- Demonstrate how to model high-spatial resolution fMRI signal.