Department of
Epidemiology &
Biostatistics

Genetic Epidemiology

Global Health

Behavior and Prevention

Health Care

Modern Biostatistics

Biostatistics Seminar

Thursday, September, 19, 2013
12:00pm-1:00pm -- WG73
Bring your own lunch
Water and light refreshments (compliments of some faculty) will be served

Title:Dynamic adjustment of stimuli in real-time functional magnetic resonance imaging

Speaker: Dr. Curtis Tatsuoka, Director of Biostatistics, Neurological Institute and Neurological and Behavioral Outcome Center, UH and CWRU

Abstract: Functional magnetic resonance imaging (fMRI) is a powerful tool for localizing brain functioning. It relies on measuring blood oxygenation and flow in response to neural activity. These responses can be associated to stimuli through experiments. Conventional fMRI analysis is performed by carrying out a massive number of parallel regression analyses at the voxel level. However, fMRI signal data is known for its low signal-noise-ratio, and its complexity, such as reflected by spatial and temporal autocorrelation. In order to ensure accurate localization of brain activity, stimuli administration in an fMRI session is often lengthy and repetitive. In real time fMRI, signal processing is carried out while the signal is being observed. This method opens up the opportunity for the dynamic adjustment of stimuli through sequential experimental designs. We have developed a voxel-wise sequential probability ratio test (voxel-wise SPRT) approach for dynamically localizing activation associated with stimuli, as well as decision rules for the stopping of experimentation. Stopping is dynamically determined when sufficient statistical evidence is collected to assess the activation status of voxels across regions of interest. Simulation studies show that scan times can be reduced substantially compared to standard fMRI experimental designs that are fixed and predetermined, while still achieving comparably high levels of classification accuracy. An analysis based on actual brain imaging confirms the promise of this approach.

An interesting application of dynamic adjustment of fMRI stimuli is in the area of Alzheimer’s disease (AD). It is clear that there is a fair amount of heterogeneity in the cognitive course of the disease. This has led to the development of theories related to the notion of cognitive reserve, which posits that neural capacity, efficiency, and plasticity play a role in this heterogeneity. It has been further hypothesized that cognitive reserve levels at pre-symptomatic stage of AD will manifest specific neural activation patterns under carefully designed fMRI experimentation that systematically varies difficulty levels of a targeted task. A sequential testing approach is proposed for efficiently and accurately identifying and classifying such patterns. Methods for characterizing cognitive reserve that are studied here are comprised of two approaches. The first is sequential estimation through monitoring confidence interval lengths over a range of experimental conditions to assess efficiency and capacity. The other is sequential selection of difficulty levels, to detect neural compensation, which is a reflection of plasticity. Both approaches show high efficiencies and high detection accuracies in our fMRI simulation studies.

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