Macrovascular Contribution in Activation Patterns of Working Memory

Abstract

Brain activation maps of blood oxygenation level dependent (BOLD) signals during functional magnetic resonance imaging (fMRI) are sensitive to unwanted contributions from large vessels. Most BOLD-fMRI studies are based on a stimulus-correlated modulation of the MRI signal amplitude that is sensitive to desired microvascular effects and unwanted macrovascular effects. Aiming to suppress macrovascular effects in activation patterns, this BOLD-fMRI study evaluates brain activation during a verbal working memory task (2-back) in healthy volunteers (n = 18) using the amplitude and phase components of the MRI signal. The use of the first time point as a phase reference allowed us to eliminate phase wrapping artifacts and increase the statistical power of 'phase' activation, and this information was used to filter out voxels with significant macrovascular (i.e., draining and pial veins) contribution in 'amplitude' activation patterns. Across subjects, the task produced large modulations of the relative phase in the occipital, dorsolateral prefrontal, and parietal cortices, suggesting a common distribution of draining veins in these regions across subjects, and in the rostral frontal cortex, probably associated to stimulus-correlated motion of the head. The phase filtering method partially suppressed BOLD responses in the superior and lateral prefrontal, parietal, and occipital cortices; therefore the commonly reported brain activation in these cortices during working memory tasks may include significant macrovascular contributions. This study suggests that the phase information embedded in the MRI signal can be used to suppress unwanted macrovascular contributions in fMRI studies.

Functional quantitative susceptibility mapping (fQSM)

“…This effect, which arises in part from the different contrast caused by the same susceptibility distribution in phase and magnitude images, can be reduced by increasing the spatial resolution, if the signal to thermal noise ratio is sufficient (Petridou et al, 2009). In line with this, an essential aspect of the present study relative to previous studies on activation-related BOLD phase (Arja et al, 2010;Bianciardi et al, 2011;Feng et al, 2009;Hagberg et al, 2008;Hagberg et al, 2012;Menon, 2002;Petridou et al, 2009;Rowe et al, 2007;Tomasi and Caparelli, 2007) and BOLD susceptibility changes in humans Bilgic et al, 2013;Chen et al, 2013), is the increased spatial resolution (1 mm isotropic) and the deliberate avoidance of spatial smoothing. Both the number of activated voxels and the number of common voxels increased significantly when the fMRI data were spatially smoothed before the GLM fit (Balla et al, 2012).…”

“…We will refer to this filter combination as DORK with SHARP. The three other alternative spatio-temporal filters were: (ii) complex regression of global phase changes in image space (NVR, ); (iii) 2D Gaussian homodyne high-pass filtering of the unwrapped phase with a filter width of 6 mm (homodyne, (Deistung et al, 2008;Haacke et al, 2004;Noll et al, 1991)) and (iv) removal of static phase components by complex division (Tomasi and Caparelli, 2007) in combination with 8 th order 2D polynomial high-pass filtering of the resulting relative phase images (RELPOLY, (Bianciardi et al, 2011)). The time-series from the multiple fMRI runs were co-registered using the FLIRT tool (Jenkinson et al, 2002) by aligning the motion correction reference volumes (i.e.…”

“…Only a few studies have investigated BOLD contrast by also using the phase information in time-series (Arja et al, 2010;Bianciardi et al, 2013;Chen et al, 2013;Hagberg et al, 2008;Hagberg et al, 2012;Hahn et al, 2009;Menon, 2002;Petridou et al, 2009;Rowe, 2005;Rowe and Logan, 2004;Rowe et al, 2007;Tomasi and Caparelli, 2007). At the spatial resolution allowed by conventional magnetic fields (e.g.…”

Adaptive independent vector analysis for multi-subject complex-valued fMRI data

“…Although magnitude-only fMRI data are extensively studied, fMRI data are initially acquired as complex-valued image pairs including magnitude and phase information Adali, 2012a, 2012b). Phase fMRI data contain useful and unique information such as blood oxygenation levels during functional activation (Hoogenraad et al, 1998;Arja et al, 2010), the effects of macro-and micro-vessels (Menon, 2002;Tomasi and Caparelli, 2007), and the orientation of large blood vessels (Klassen and Menon, 2005). Analysis of complex-valued fMRI data provides additional insights beyond magnitude-only fMRI data (Rowe, 2005;Adali and Calhoun, 2007;Adali, 2012a, 2012b;Rodriguez et al, 2011Rodriguez et al, , 2012Li et al, 2010Li et al, , 2011Yu et al, 2015).…”

Paramagnetic shimming for high-field MRI

“…In the present work and similarly to our previous study on macrovascular contributions in functional MRI studies (15), the differential phase accumulation between conditions was calculated from the complex ratio of two imaging experiments (Eq. [4]).…”