Monte Carlo simulation is extensively utilized in functional magnetic resonance imaging (MRI) research to examine the behavior of an MR sequence in the presence of diffusion within complex microstructures. These simulations necessitate a substantial number of diffusing particles and time steps to be modeled to achieve convergence and produce robust and reliable results, which is computationally intensive. Incorporating additional parameters to enhance the realism of the simulations further intensifies this computational burden, particularly when simulating steady-state sequences, which require a long period of time to be simulated. To address this, we present SpinWalk, a high-performance Monte Carlo simulator for functional MRI. SpinWalk is free and open-source software, designed to offer a high-performance framework for facilitating the simulation of custom sequences. SpinWalk enables popular sequences in functional MRI to be efficiently simulated and ensures that results can be consistently reproduced. Key sequence and tissue parameters can be set, making SpinWalk flexible in examining different factors that contribute in signal formation. This versatility is demonstrated by replicating simulations from several previous studies, including GRE, SE, bSSFP, GRASE, and STE sequences. Performance evaluations demonstrate that SpinWalk can significantly reduce computation times, making it feasible to perform extensive simulations within a reasonable time frame.

The draining-vein bias of T2 * -weighted sequences, like gradient echo echo-planar imaging (GRE-EPI), can limit the spatial specificity of functional MRI (fMRI). The underlying extravascular signal changes increase with field strength (B 0) and the perpendicularity of draining veins to the main axis of B 0 , and are, therefore, particularly problematic at ultra-high field (UHF). In contrast, simulations showed that T2-weighted sequences are less affected by the draining-vein bias, depending on the amount of rephasing of extravascular signal. As large pial veins on the cortical surface follow the cortical folding tightly, their orientation can be approximated by the cortical orientation to B 0 → . In our work, we compare the influence of the cortical orientation to B 0 → on the resting-state fMRI signal of three sequences aiming to understand their macrovascular contribution. While 2D GRE-EPI and 3D GRE-EPI (both T2 * -weighted) showed a high dependence on the cortical orientation to B 0 → , especially on the cortical surface, this was not the case for 3D balanced steady-state free precession (bSSFP) (T2/T1-weighted). Here, a slight increase of orientation dependence was shown in depths closest to white matter (WM). And while orientation dependence decreased with increased distance to the veins for both EPI sequences, no change in orientation dependence was observed in bSSFP. This indicates the low macrovascular contribution to the bSSFP signal, making it a promising sequence for layer fMRI at UHF.

In functional magnetic resonance imaging (fMRI) studies, episodic memory is commonly investigated with the subsequent memory paradigm in which brain activity is recorded during encoding and analyzed as a function of subsequent remembering and forgetting. Impaired episodic memory is common in individuals with or at risk for Alzheimer’s disease (AD), but only few studies have reported subsequent memory effects in AD or its risk states like mild cognitive impairment (MCI). One reason for this might be that subsequent memory responses may be blunted in AD or MCI and thus less likely to manifest in fMRI signal differences. Here, we used Bayesian model selection of single-subject fMRI general linear models (GLMs) for a visual novelty and memory encoding experiment to compare the model performance of categorical and parametric subsequent memory models as well as memory-invariant models in a clinical cohort (N = 468) comprising healthy controls (HC) as well as individuals with subjective cognitive decline (SCD), MCI, and AD, plus healthy relatives of AD patients (AD-rel). We could replicate the previously reported superiority of parametric subsequent memory models over categorical models ( Soch, Richter, Schütze, Kizilirmak, Assmann, Knopf, et al., 2021 ) in the HC and also in the SCD and AD-rel groups. However, memory-invariant models outperformed any model assuming subsequent memory effects in the MCI and AD groups. In the AD group, we additionally found substantially lower model preference for models assuming novelty compared to models not differentiating between novel and familiar stimuli. Our results suggest that voxel-wise memory-related fMRI activity patterns in AD and also MCI should be interpreted with caution and point to the need for additional or alternative approaches to investigate memory function.

Laminar-specific functional magnetic resonance imaging (fMRI) has been widely used to study circuit-specific neuronal activity by mapping spatiotemporal fMRI response patterns across cortical layers. Hemodynamic responses reflect indirect neuronal activity given the limitation of spatial and temporal resolution. Previously, a gradient-echo-based line-scanning fMRI (GELINE) method was proposed with high temporal (50 ms) and spatial (50 µm) resolution to better characterize the fMRI onset time across cortical layers by employing two saturation RF pulses. However, the imperfect RF saturation performance led to poor boundary definition of the reduced region of interest (ROI) and aliasing problems outside of the ROI. Here, we propose an α (alpha)-180 spin-echo-based line-scanning fMRI (SELINE) method in animals to resolve this issue by employing a refocusing 180˚ RF pulse perpendicular to the excitation slice (without any saturation RF pulse) and also achieve high spatiotemporal resolution. In contrast to GELINE signals which peaked at the superficial layer, we detected varied peaks of laminar-specific BOLD signals across deeper cortical layers using the SELINE method, indicating the well-defined exclusion of the large draining-vein effect using the spin-echo sequence. Furthermore, we applied the SELINE method with a 200 ms repetition time (TR) to sample the fast hemodynamic changes across cortical layers with a less draining vein effect. In summary, this SELINE method provides a novel acquisition scheme to identify microvascular-sensitive laminar-specific BOLD responses across cortical depth.