Pysche Loui, Ph.D.
Assistant Professor, Wesleyan University
Music is a fundamentally human activity that is celebrated worldwide and from a young age, but why we know and love music has remained a mystery. I will describe behavioral, structural and functional neuroimaging (MRI, DTI, fMRI using graph theory approaches), and brain-stimulation (tDCS) studies that use music as a model to understand the interaction of multiple systems in the human brain, and to apply our knowledge of these systems towards neurological disorders. Results suggest that much of what we know and love about music is learned from statistics of sounds in the environment, and that structural and functional connectivity among perceptual, motor, and cognitive systems subserve the musical experiences that may overlap in different extents with language, creativity, abstract reasoning, and affective communication. Capitalizing on these findings, I will also describe applications of music cognition towards neurological disorders characterized by disordered neural activity.
Pysche Loui, Ph.D.
Daniel Alexander, Ph.D.
Professor of Imaging Science
University College London
My talk will give an overview of the research in my group in developing microstructure imaging techniques using MRI and other activities. The aim of microstructure imaging is to estimate and map histological features of tissue non-invasively. Diffusion MRI sensitizes the MR signal to the dispersion of water arising from diffusion. It is a cornerstone of microstructure imaging, because it gives unique sensitivity to the cellular architecture of tissue, which determines the pattern of water dispersion. Other MR modalities, such as relaxometry, magnetization transfer, and susceptibility imaging can also contribute. I will talk about the work we have done towards development of the biophysical models that underpin parameter estimation (Panagiotaki et al Neuroimage 2012; Ferizi et al MRM 2014); the design of imaging sequences and protocols that provide and maximize sensitivity (Alexander MRM 2008; Drobnjak et al JMR 2010); specific techniques that emerge such as ActiveAx (Alexander et al Neuroimage 2010; Zhang et al Neuroimage 2011) and NODDI (Zhang et al Neuroimage 2012) for neuroimaging, and VERDICT (Panagiotaki et al Cancer Research 2014) for cancer imaging; and their validation and application. If time permits, I will also mention more recent work on the development of computational models for disease progression with application in Alzheimer's disease and other neurological conditions; see (Fonteijn et al Neuroimage 2012).
Students in this course will receive a firm grounding in the fundamentals of fMRI. This will include the basic physics of MR imaging, the biology and biophysics of the hemodynamic responses to neural activity, data analysis (including both exploratory and statistical analyses), stimulus presentation and response recording in the context of high magnetic fields and electromagnetic pulses, and the design of perceptual and cognitive experiments. Some advanced topics (especially related to issues of connectivity) have been added.
A special emphasis of the course will be the design, implementation, and execution of perceptual and/or cognitive experiments by the participants. Participants will break into small groups to design their own fMRI experiments. Barring unforeseen problems, some of these experiments will be executed, and the resulting data analyzed, on the final day of the course. The core faculty is drawn from the staff of the Athinoula A. Martinos Center (of the Massachusetts General Hospital and Massachusetts Institute of Technology) and affiliated faculty from Harvard University, Boston University, McLean Hospital and other institutions.
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