September 11, 2020

Dr. Olivia Viessmann

B0 orientation effects in fMRI

Abstract: Many MRI contrast mechanisms are rooted in the susceptibility of the tissue composition (iron, haemoglobin, myelin and others). These susceptibility effects vary with the orientation of the tissue structure relative to the main magnetic field, B0. Of note, the geometry of blood vessels appears strongly coupled to the geometry of the tissue, with the tissue constraining the orientation of the vessels, and therefore blood susceptibility effects will vary with tissue orientation.  Systematic changes in the orientation of these structures, for example with cortical folding in the grey matter or fibre geometry in the white matter, thereby imparts a tissue orientation dependence on the amplitude of the gradient-echo BOLD fMRI response. This talk gives an overview of orientation effects with a focus on the impact in BOLD fMRI. We found orientation-dependent variations of up to 70% in the BOLD fMRI amplitudes in the cerebral cortex in high-resolution (1.1 mm) 7T data, and also demonstrate the effect in the popular 3T fMRI data (2 mm) of the Human Connectome project. We also present similar orientation effects in cerebral white matter fMRI, where vessels and fibre bundles are suspected to run in parallel; this finding has potential implications for the interpretation of recently observed fMRI activation patterns in the white matter. Overall, this work demonstrates how the gradient-echo BOLD fMRI signal can depend on the local brain anatomy and demonstrates a coupling between anatomical geometry and the fMRI signal.

October 1, 2020

Dr. Ev Fedorenko

The language system in the human mind and brain

Abstract: The goal of my research program is to decipher the representations and computations that support linguistic ability.  I will discuss three discoveries my lab has made over the last decade. First, I will show that the language network is selective for language processing over a wide range of non-linguistic processes. Next—challenging a common view whereby syntax is dissociable from meaning—I will show that every brain region that responds to syntactic processing is at least as sensitive to word meanings. Finally, I will show that linguistic composition is the core driver of the response in the language-selective areas: as long as nearby words can combine into phrases/clauses, the language areas respond as strongly as they do to their preferred stimulus—naturalistic sentences. Taken together, these results argue against an abstract and domain-general syntactic processing mechanism, and support strong integration between the lexicon and syntax. They further suggest that the language network is more concerned with meaning than structure, in line with the communicative function of language. November 6, 2020

November 2020 

Dr. Dwight J. Kravitz

December 4, 2020

Dr. Pradeep Reddy

Better biomarkers based on quality, reproducible and open science

Abstract: Mental health is a major public health challenge currently costing trillions of dollars, for which neuroscientific approaches are key to advancing the understanding of mechanisms, risk factors, biomarkers and to improve treatments. The rapid adoption of data sharing and open science practices present an unprecedented opportunity to offer better care at a lower cost. However, the quality and efficacy of potential biomarkers and treatments is critically dependent on the quality of multiple stages of data science. These stages include but are not limited to preprocessing, quality control, feature extraction, model building, and performance evaluation. Often overlooked, inaccuracies at these stages can get multiplied to produce suboptimal or irreproducible final results (so-called “garbage-in, garbage-out” and butterfly effects). Much research so far targeted only one of the aforementioned specific issues. Such studies require statistical comparison of relative efficacy to decide among many available options (e.g., biomarkers or treatment plans). However, due to the quality issues at each of the aforementioned stages, we haven’t fully leveraged the benefits from deep integration and proper application of machine learning and data science. In this talk, I present an outline of the research program at the Open MINDS lab to solve these challenges in two key areas: neuroimaging quality control and biomarker performance evaluation. I encourage you to visit the following websites to learn more: niQC SIG and crossinvalidation.com.