Investigating the spatiotemporal characteristics of the deoxyhemoglobin-related and deoxyhemoglobin-unrelated functional hemodynamic response across cortical layers in awake marmosets

Abstract

Blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) has become a major tool to map neural activity. However, the spatiotemporal characteristics of the BOLD functional hemodynamic response across the cortical layers remain poorly understood. While human fMRI studies suffer from low spatiotemporal resolution, the use of anesthesia in animal models introduces confounding factors. Additionally, inflow contributions to the fMRI signal become non-negligible when short repetition times (TRs) are used. In the present work, we mapped the BOLD fMRI response to somatosensory stimulation in awake marmosets. To address the above technical concerns, we used a dual-echo gradient-recalled echo planar imaging (GR-EPI) sequence to separate the deoxyhemoglobin-related response (absolute T* differences) from the deoxyhemoglobin-unrelated response (relative S changes). We employed a spatial saturation pulse to saturate incoming arterial spins and reduce inflow effects. Functional GR-EPI images were obtained from a single coronal slice with two different echo times (13.5 and 40.5ms) and TR=0.2s. BOLD, T*, and S images were calculated and their functional responses were detected in both hemispheres of primary somatosensory cortex, from which five laminar regions (L1+2, L3, L4, L5, and L6) were derived. The spatiotemporal distribution of the BOLD response across the cortical layers was heterogeneous, with the middle layers having the highest BOLD amplitudes and shortest onset times. ΔT* also showed a similar trend. However, functional S changes were detected only in L1+2, with a fast onset time. Because inflow effects were minimized, the source of S functional changes in L1+2 could be attributed to a reduction of cerebrospinal fluid volume fraction due to the functional increase in cerebral blood volume and to unmodeled T* changes in the extra- and intra-venous compartments. Caution should be exercised when interpreting laminar BOLD fMRI changes in superficial layers as surrogates of underlying neural activity.

A dynamical model of the laminar BOLD response

“…This is because we considered a constant transit time across depths in the MVthen assuming an increase of CBV0 towards the surface in the MV translates to a spatial increase in the CBF0 in the MV as well. Although negative trends in laminar TTP or TTU were also reported (Hirano et al, 2011;Yen et al, 2018), they appear within much smaller ranges (about −0.2 s). Thus, they are most likely due to combination of different physiological mechanisms rather than CBV0 spatially increasing towards the surface in the MV (e.g.…”

Functional magnetic resonance imaging of auditory cortical fields in awake marmosets

“…By using high-resolution (7T) fMRI to identify distinct auditory cortical fields in awake marmosets, we show that this technique can complement existing neurophysiological experiments to expand our understanding of the primate auditory system. Similarly to recent fMRI experiments in marmoset vision (Hung et al, 2015a; Hung et al, 2015b) and somatosensory cortex (Yen et al, 2017), this preparation is amenable to techniques involving conditioned behavior and can, therefore, be extended to investigate relative contributions of multiple auditory cortical fields during behaviorally-dependent facets of audition (Remington et al, 2012; Song et al, 2016). Given the significance of marmosets as a neurobiological model of communication (Toarmino et al, 2017; Eliades & Wang, 2008; Miller et al, 2015; Eliades & Miller 2016; Miller, 2017; Nummela et al 2017), our approach could be implemented to identify areas of the auditory cortical system involved in vocalization processing (e.g., Sadagopan et al, 2015; Perrodin et al, 2011).…”

Axial variation of deoxyhemoglobin density as a source of the low-frequency time lag structure in blood oxygenation level-dependent signals

“…To our knowledge, the timing order of these events has not been studied at the precision of 681 the current data; further studies are needed to determine the source of this S 0 fluctuation 682 (Yen et al, 2017). The only available clue in our results is the spatial distribution, such as 683 the interesting anterior-posterior segmentation (resembling the unique "S 0 lag structure" in 684 has become a de facto standard.…”

“…Finally, we 183 used multi-echo imaging to assess the components of the BOLD signal that determine the 184 lag structure. One of the recent approaches toward fMRI denoising has focused on S 0 185 fluctuations (signal at TE = 0, which is the baseline MR signal from the fluid 186 compartments), as it is weakly associated with neural activity (Posse et al, 1999;Wu et al, 187 2012; Kundu et al, 2012;Yen et al, 2017). In contrast, the total-Hb sLFO, detected by 188 NIRS, is interpreted as a local CBV change under the assumption of a constant Hct, which 189 should also affect the non-BOLD component via changes in plasma volume and inflow 190 (Rostrup et al, 2005).…”