When performing statistical analysis of single-subject fMRI data, serial correlations need to be taken into account to allow for valid inference. Otherwise, the variability in the parameter estimates might be under-estimated resulting in increased false-positive rates. Serial correlations in fMRI data are commonly characterized in terms of a first-order autoregressive (AR) process and then removed via pre-whitening. The required noise model for the pre-whitening depends on a number of parameters, particularly the repetition time (TR). Here we investigate how the sub-second temporal resolution provided by simultaneous multislice (SMS) imaging changes the noise structure in fMRI time series. We fit a higher-order AR model and then estimate the optimal AR model order for a sequence with a TR of less than 600 ms providing whole brain coverage. We show that physiological noise modelling successfully reduces the required AR model order, but remaining serial correlations necessitate an advanced noise model. We conclude that commonly used noise models, such as the AR(1) model, are inadequate for modelling serial correlations in fMRI using sub-second TRs. Rather, physiological noise modelling in combination with advanced pre-whitening schemes enable valid inference in single-subject analysis using fast fMRI sequences.
Schira, M. M. (2016). The spatiotemporal hemodynamic response function for depth-dependent functional imaging of human cortex. NeuroImage, 139 (October), 240-248.The spatiotemporal hemodynamic response function for depthdependent functional imaging of human cortex AbstractThe gray matter of human cortex is characterized by depth-dependent differences in neuronal activity and connections (Shipp, 2007) as well as in the associated vasculature (Duvernoy et al., 1981). The resolution limit of functional magnetic resonance imaging (fMRI) measurements is now below a millimeter, promising the non-invasive measurement of these properties in awake and behaving humans (Muckli et al., 2015; Olman et al., 2012;Ress et al., 2007). To advance this endeavor, we present a detailed spatiotemporal hemodynamic response function (HRF) reconstructed through the use of high-resolution, submillimeter fMRI. We decomposed the HRF into directions tangential and perpendicular to the cortical surface and found that key spatial properties of the HRF change significantly with depth from the cortical surface. Notably, we found that the spatial spread of the HRF increases linearly from 4.8mm at the gray/white matter boundary to 6.6mm near the cortical surface. Using a hemodynamic model, we posit that this effect can be explained by the depth profile of the cortical vasculature, and as such, must be taken into account to properly estimate the underlying neuronal responses at different cortical depths. The gray matter of human cortex is characterized by depth-dependent 25 differences in neuronal activity and connections (Ship, 2007) as well as in the 26 associated vasculature (Duvernoy et al., 1981). The resolution limit of functional 27 magnetic resonance imaging (fMRI) measurements is now below a millimeter, 28 promising the non-invasive measurement of these properties in awake and 29 behaving humans (Muckli et al., 2015; Olman et al., 2012;Ress et al., 2007). To 30 advance this endeavor, we present a detailed spatiotemporal hemodynamic 31 response function (HRF) reconstructed through the use of high-resolution, 32 submillimeter fMRI. We decomposed the HRF into directions tangential and 33 perpendicular to the cortical surface and found that key spatial properties of the 34 HRF change significantly with depth from the cortical surface. Notably, we found 35 that the spatial spread of the HRF increases linearly from 4.8 mm at the 36 gray/white matter boundary to 6.6 mm near the cortical surface. Using a 37 hemodynamic model, we posit that this effect can be explained by the depth 38 profile of the cortical vasculature, and as such, must be taken into account to 39properly estimate the underlying neuronal responses at different cortical depths. 40 41
Recent studies have demonstrated significant regional variability in the hemodynamic response function (HRF), highlighting the difficulty of correctly interpreting functional MRI (fMRI) data without proper modeling of the HRF. The focus of this study was to investigate the HRF variability within visual cortex. The HRF was estimated for a number of cortical visual areas by deconvolution of fMRI blood oxygenation level dependent (BOLD) responses to brief, large-field visual stimulation. Significant HRF variation was found across visual areas V1, V2, V3, V4, VO-1,2, V3AB, IPS-0,1,2,3, LO-1,2, and TO-1,2. Additionally, a subpopulation of voxels was identified that exhibited an impulse response waveform that was similar, but not identical, to an inverted version of the commonly described and modeled positive HRF. These voxels were found within the retinotopic confines of the stimulus and were intermixed with those showing positive responses. The spatial distribution and variability of these HRFs suggest a vascular origin for the inverted waveforms. We suggest that the polarity of the HRF is a separate factor that is independent of the suppressive or activating nature of the underlying neuronal activity. Correctly modeling the polarity of the HRF allows one to recover an estimate of the underlying neuronal activity rather than discard the responses from these voxels on the assumption that they are artifactual. We demonstrate this approach on phase-encoded retinotopic mapping data as an example of the benefits of accurately modeling the HRF during the analysis of fMRI data.
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