2009
DOI: 10.1016/j.neuroimage.2008.10.065
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Modeling the hemodynamic response function in fMRI: Efficiency, bias and mis-modeling

Abstract: Most brain research to date has focused on studying the amplitude of evoked fMRI responses, though there has recently been an increased interest in measuring onset, peak latency and duration of the responses as well. A number of modeling procedures provide measures of the latency and duration of fMRI responses. In this work we compare several techniques that vary in their assumptions, model complexity, and interpretation. For each method, we introduce methods for estimating amplitude, peak latency, and duratio… Show more

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Cited by 488 publications
(480 citation statements)
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“…The Escape Delay Incentive ( however, can lead to fitting implausible shapes (Calhoun et al, 2004) and decreased power (Lindquist et al, 2009). …”
Section: Task Designmentioning
confidence: 99%
“…The Escape Delay Incentive ( however, can lead to fitting implausible shapes (Calhoun et al, 2004) and decreased power (Lindquist et al, 2009). …”
Section: Task Designmentioning
confidence: 99%
“…A number of studies have addressed the problem of systematically assessing the quality of fMRI experimental designs, both in terms of the ability to detect stimulus/task-related BOLD activation (detection power) and the ability to estimate the HRF model (estimation efficiency) in a given amount of imaging time (Dale, 1999;Liu et al, 2001). Different methodologies have been proposed to determine the optimal design of fMRI experiments for maximal estimation efficiency (Buracas and Boynton, 2002;Wager and Nichols, 2003;Maus et al, 2012), and a few studies have compared different HRF models and the associated estimation efficiency, focusing on specific parameters of interest such as the response latency and duration (Lindquist and Wager, 2007;Lindquist et al, 2009). Importantly, the authors were concerned with the physiological plausibility of the estimated HRF parameters and with their independence, such that differences in one parameter are not confounded with differences in another parameter.…”
Section: Introductionmentioning
confidence: 99%
“…First, the PETCO 2 signal and the BOLD signal were cross-correlated to find time lag that maximizes the cross correlation. The BOLD signal was shifted one frame at a time, and was assumed to always lag the PETCO 2 signal (Lindquist et al, 2009). In addition, voxels with lag >100 frames (~40 s) do not conform with physiological restrictions and thus were ignored.…”
mentioning
confidence: 99%