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The advent of event-related functional magnetic resonance imaging (fMRI) has resulted in many exciting studies that have exploited its unique capability. However, the utility of event-related fMRI is still limited by several technical difficulties. One significant limitation in event-related fMRI is the low signal-to-noise ratio (SNR). In this work, a(More)
Commonly used methods in analyzing functional magnetic resonance imaging (fMRI) data, such as the Student's t-test and cross-correlation analysis, are model-based approaches. Although these methods are easy to implement and are effective in analyzing data obtained with simple paradigms, they are not applicable in situations in which patterns of neuronal(More)
To circumvent the problem of low signal-to-noise ratio (SNR) in event-related fMRI data, the fMRI experiment is typically designed to consist of repeated presentations of the stimulus and measurements of the response, allowing for subsequent averaging of the resulting data. Due to factors such as time limitation, subject motion, habituation, and fatigue,(More)
Template-based activation detection methods, such as cross-correlation, could be difficult to apply in event-related functional MRI data because accurate a priori knowledge about the activation signal patterns is often not available. As a result, several categories of template-free data analysis techniques have been introduced in the fMRI literature. One(More)
A significant recent development in functional magnetic resonance imaging (fMRI) is the introduction of event-related fMRI, also known as time-resolved fMRI. Because the exact shape of the MR response in an event-related fMRI experiment is often not known, traditional methods developed for block design experiments, such as t test and correlation analysis,(More)
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