Group-level impacts of within- and between-subject hemodynamic variability in fMRI

Abstract

Inter-subject fMRI analyses have specific issues regarding the reliability of the results concerning both the detection of brain activation patterns and the estimation of the underlying dynamics. Among these issues lies the variability of the hemodynamic response function (HRF), that is usually accounted for using functional basis sets in the general linear model context. Here, we use the joint detection-estimation approach (JDE) (Makni et al., 2008; Vincent et al., 2010) which combines regional nonparametric HRF inference with spatially adaptive regularization of activation clusters to avoid global smoothing of fMRI images. We show that the JDE-based inference brings a significant improvement in statistical sensitivity for detecting evoked activity in parietal regions. In contrast, the canonical HRF associated with spatially adaptive regularization is more sensitive in other regions, such as motor cortex. This different regional behavior is shown to reflect a larger discrepancy of HRF with the canonical model. By varying parallel imaging acceleration factor, SNR-specific region-based hemodynamic parameters (activation delay and duration) were extracted from the JDE inference. Complementary analyses highlighted their significant departure from the canonical parameters and the strongest between-subject variability that occurs in the parietal region, irrespective of the SNR value. Finally, statistical evidence that the fluctuation of the HRF shape is responsible for the significant change in activation detection performance is demonstrated using paired t-tests between hemodynamic parameters inferred by GLM and JDE.

Adaptive Mean Shift Based Hemodynamic Brain Parcellation in fMRI

“…The computed time to peak (TTP) for the HRF estimates was 5.4 s for all of them while the full width at half maximum (FWHM) was 4.2 s for parcels 1 and 3 and 4.8 s for parcel 2. The obtained results are coherent with the conclusion that the HRF estimates in the bilateral occipital cortex should be consistent with the canonical shape [19,23]. To verify these results, we also ran the JPDE model with the model selection procedure proposed in [18] on the same fMRI data using three candidate models with one, two and three parcels for initial parcellation.…”

Relationship between simultaneously acquired resting-state regional cerebral glucose metabolism and functional MRI: A PET/MR hybrid scanner study

“…Such 298 heterogeneities may indeed be expected due to possible regional differ-299 ences in the neurovascular coupling (Devonshire et al, 2012), aerobic 300 glycolysis (Vaishnavi et al, 2010) and/or in the hemodynamic function 301 (Badillo et al, 2013), which link functional activation and BOLD signal 302 changes. To this purpose, an anatomical parcellation, obtained applying 303 the automated anatomical labelling (AAL), a digital brain atlas (Tzourio- In order to investigate the relationship between the spatial distribu-311 tion of the four metrics obtained by the processing pipeline described…”

PyHRF: A Python Library for the Analysis of fMRI Data Based on Local Estimation of the Hemodynamic Response Function

“…The use of a canonical HRF is usually sufficient for activation detection. However, HRF functions have been found to have different shapes in different regions [HOM04], and to have different delays in specific populations [BVC13]. They change depending on pathologies such as stenosis.…”

“…However, several works (see [BVC13] for a survey) show that the HRF changes across different regions of the brain and across individuals, increasing thus the possibility of obtaining false negatives and decreasing the reliability of the results. The software PyHRF [VBR + 14] was developed to overcome the above limitation by analyzing fMRI data using a Joint Detection-Estimation (JDE) approach.…”