Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses

Estimates of hemodynamic response functions (HRF) are often integral parts of event-related fMRI analyses. Although HRFs vary across individuals and brain regions, few studies have investigated how variations affect the results of statistical analyses using the general linear model (GLM). In this st...

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Published in	NeuroImage (Orlando, Fla.) Vol. 21; no. 4; pp. 1639 - 1651
Main Authors	Handwerker, Daniel A., Ollinger, John M., D'Esposito, Mark
Format	Journal Article
Language	English
Published	United States Elsevier Inc 01.04.2004 Elsevier Limited
Subjects	Activation latency Adolescent Adult Analysis of Variance Brain - blood supply Brain Mapping Cerebral Cortex - physiology Dominance, Cerebral - physiology Echo-Planar Imaging Estimates Evoked Potentials - physiology Experiments Female fMRI Frontal Lobe - physiology Functional neuroimaging Hemodynamic response function Hemodynamics - physiology Humans Image Enhancement Image Interpretation, Computer-Assisted Image Processing, Computer-Assisted Linear Models Linear regression Magnetic Resonance Imaging - statistics & numerical data Male Mathematical Computing Motor Cortex - physiology Oxygen - blood Pattern Recognition, Visual - physiology Psychomotor Performance - physiology Random effects Reaction Time - physiology Reflex - physiology Saccades Saccades - physiology Statistics as Topic Studies Time series Visual areas Visual Cortex - physiology fMRI Saccades Linear regression Functional neuroimaging Random effects Activation latency Hemodynamic response function Visual areas
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Abstract	Estimates of hemodynamic response functions (HRF) are often integral parts of event-related fMRI analyses. Although HRFs vary across individuals and brain regions, few studies have investigated how variations affect the results of statistical analyses using the general linear model (GLM). In this study, we empirically estimated HRFs from primary motor and visual cortices and frontal and supplementary eye fields (SEF) in 20 subjects. We observed more variability across subjects than regions and correlated variation of time-to-peak values across several pairs of regions. Simulations examined the effects of observed variability on statistical results and ways different experimental designs and statistical models can limit these effects. Widely spaced and rapid event-related experimental designs with two sampling rates were tested. Statistical models compared an empirically derived HRF to a canonical HRF and included the first derivative of the HRF in the GLM. Small differences between the estimated and true HRFs did not cause false negatives, but larger differences within an observed range of variation, such as a 2.5-s time-to-onset misestimate, led to false negatives. Although small errors minimally affected detection of activity, time-to-onset misestimates as small as 1 s influenced model parameter estimation and therefore random effects analyses across subjects. Experiment and analysis design methods such as decreasing the sampling rate or including the HRF's temporal derivative in the GLM improved results, but did not eliminate errors caused by HRF misestimates. These results highlight the benefits of determining the best possible HRF estimate and potential negative consequences of assuming HRF consistency across subjects or brain regions.
AbstractList	Estimates of hemodynamic response functions (HRF) are often integral parts of event-related fMRI analyses. Although HRFs vary across individuals and brain regions, few studies have investigated how variations affect the results of statistical analyses using the general linear model (GLM). In this study, we empirically estimated HRFs from primary motor and visual cortices and frontal and supplementary eye fields (SEF) in 20 subjects. We observed more variability across subjects than regions and correlated variation of time-to-peak values across several pairs of regions. Simulations examined the effects of observed variability on statistical results and ways different experimental designs and statistical models can limit these effects. Widely spaced and rapid event-related experimental designs with two sampling rates were tested. Statistical models compared an empirically derived HRF to a canonical HRF and included the first derivative of the HRF in the GLM. Small differences between the estimated and true HRFs did not cause false negatives, but larger differences within an observed range of variation, such as a 2.5-s time-to-onset misestimate, led to false negatives. Although small errors minimally affected detection of activity, time-to-onset misestimates as small as 1 s influenced model parameter estimation and therefore random effects analyses across subjects. Experiment and analysis design methods such as decreasing the sampling rate or including the HRF's temporal derivative in the GLM improved results, but did not eliminate errors caused by HRF misestimates. These results highlight the benefits of determining the best possible HRF estimate and potential negative consequences of assuming HRF consistency across subjects or brain regions. Estimates of hemodynamic response functions (HRF) are often integral parts of event-related fMRI analyses. Although HRFs vary across individuals and brain regions, few studies have investigated how variations affect the results of statistical analyses using the general linear model (GLM). In this study, we empirically estimated HRFs from primary motor and visual cortices and frontal and supplementary eye fields (SEF) in 20 subjects. We observed more variability across subjects than regions and correlated variation of time-to-peak values across several pairs of regions. Simulations examined the effects of observed variability on statistical results and ways different experimental designs and statistical models can limit these effects. Widely spaced and rapid event-related experimental designs with two sampling rates were tested. Statistical models compared an empirically derived HRF to a canonical HRF and included the first derivative of the HRF in the GLM. Small differences between the estimated and true HRFs did not cause false negatives, but larger differences within an observed range of variation, such as a 2.5-s time-to-onset misestimate, led to false negatives. Although small errors minimally affected detection of activity, time-to-onset misestimates as small as 1 s influenced model parameter estimation and therefore random effects analyses across subjects. Experiment and analysis design methods such as decreasing the sampling rate or including the HRF's temporal derivative in the GLM improved results, but did not eliminate errors caused by HRF misestimates. These results highlight the benefits of determining the best possible HRF estimate and potential negative consequences of assuming HRF consistency across subjects or brain regions.Estimates of hemodynamic response functions (HRF) are often integral parts of event-related fMRI analyses. Although HRFs vary across individuals and brain regions, few studies have investigated how variations affect the results of statistical analyses using the general linear model (GLM). In this study, we empirically estimated HRFs from primary motor and visual cortices and frontal and supplementary eye fields (SEF) in 20 subjects. We observed more variability across subjects than regions and correlated variation of time-to-peak values across several pairs of regions. Simulations examined the effects of observed variability on statistical results and ways different experimental designs and statistical models can limit these effects. Widely spaced and rapid event-related experimental designs with two sampling rates were tested. Statistical models compared an empirically derived HRF to a canonical HRF and included the first derivative of the HRF in the GLM. Small differences between the estimated and true HRFs did not cause false negatives, but larger differences within an observed range of variation, such as a 2.5-s time-to-onset misestimate, led to false negatives. Although small errors minimally affected detection of activity, time-to-onset misestimates as small as 1 s influenced model parameter estimation and therefore random effects analyses across subjects. Experiment and analysis design methods such as decreasing the sampling rate or including the HRF's temporal derivative in the GLM improved results, but did not eliminate errors caused by HRF misestimates. These results highlight the benefits of determining the best possible HRF estimate and potential negative consequences of assuming HRF consistency across subjects or brain regions.
Author	Ollinger, John M. Handwerker, Daniel A. D'Esposito, Mark
Author_xml	– sequence: 1 givenname: Daniel A. surname: Handwerker fullname: Handwerker, Daniel A. email: werker@socrates.berkeley.edu organization: Henry H. Wheeler Jr. Brain Imaging Center, Helen Wills Neuroscience Institute and Department of Psychology, University of California, Berkeley, CA 94720, USA – sequence: 2 givenname: John M. surname: Ollinger fullname: Ollinger, John M. organization: Henry H. Wheeler Jr. Brain Imaging Center, Helen Wills Neuroscience Institute and Department of Psychology, University of California, Berkeley, CA 94720, USA – sequence: 3 givenname: Mark surname: D'Esposito fullname: D'Esposito, Mark organization: Henry H. Wheeler Jr. Brain Imaging Center, Helen Wills Neuroscience Institute and Department of Psychology, University of California, Berkeley, CA 94720, USA
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/15050587$$D View this record in MEDLINE/PubMed
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Copyright	2004 Elsevier Inc. Copyright Elsevier Limited Apr 1, 2004
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SubjectTerms	Activation latency Adolescent Adult Analysis of Variance Brain - blood supply Brain Mapping Cerebral Cortex - physiology Dominance, Cerebral - physiology Echo-Planar Imaging Estimates Evoked Potentials - physiology Experiments Female fMRI Frontal Lobe - physiology Functional neuroimaging Hemodynamic response function Hemodynamics - physiology Humans Image Enhancement Image Interpretation, Computer-Assisted Image Processing, Computer-Assisted Linear Models Linear regression Magnetic Resonance Imaging - statistics & numerical data Male Mathematical Computing Motor Cortex - physiology Oxygen - blood Pattern Recognition, Visual - physiology Psychomotor Performance - physiology Random effects Reaction Time - physiology Reflex - physiology Saccades Saccades - physiology Statistics as Topic Studies Time series Visual areas Visual Cortex - physiology
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Title	Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses
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