Search

While functional Magnetic Resonance Imaging (fMRI) offers valuable insights into cognitive processes, its inherent spatial limitations pose challenges for detailed analysis of the fine-grained functional architecture of the brain. More specifically, MRI scanner and sequence specifications impose a trade-off between temporal resolution, spatial resolution, signal-to-noise ratio, and scan time. Deep Learning (DL) Super-Resolution (SR) methods have emerged as a promising solution to enhance fMRI resolution, generating high-resolution (HR) images from low-resolution (LR) images typically acquired with lower scanning times. However, most existing SR approaches depend on supervised DL techniques, which require training ground truth (GT) HR data, which is often difficult to acquire and simultaneously sets a bound for how far SR can go. In this paper, we introduce a novel self-supervised DL SR model that combines a DL network with an analytical approach and Total Variation (TV) regularization. Our method eliminates the need for external GT images, achieving competitive performance compared to supervised DL techniques and preserving the functional maps., Comment: Preprint Submitted to ISBI 2025

High-resolution fMRI provides a window into the brain's mesoscale organization. Yet, higher spatial resolution increases scan times, to compensate for the low signal and contrast-to-noise ratio. This work introduces a deep learning-based 3D super-resolution (SR) method for fMRI. By incorporating a resolution-agnostic image augmentation framework, our method adapts to varying voxel sizes without retraining. We apply this innovative technique to localize fine-scale motion-selective sites in the early visual areas. Detection of these sites typically requires a resolution higher than 1 mm isotropic, whereas here, we visualize them based on lower resolution (2-3mm isotropic) fMRI data. Remarkably, the super-resolved fMRI is able to recover high-frequency detail of the interdigitated organization of these sites (relative to the color-selective sites), even with training data sourced from different subjects and experimental paradigms -- including non-visual resting-state fMRI, underscoring its robustness and versatility. Quantitative and qualitative results indicate that our method has the potential to enhance the spatial resolution of fMRI, leading to a drastic reduction in acquisition time., Comment: ISBI2024 final version

Personal space (PS) is the distance that people prefer to maintain between themselves and unfamiliar others. Interpersonal intrusion into a given persons PS evokes discomfort, and an urge to move further apart. We hypothesized that in parietal cortex: 1. PS processing involves a previously-described threat-sensitive brain circuit, and 2. the spatial encoding of distance is transformed accordingly, from purely sensory to PS-related. These hypotheses were tested using 7T fMRI at high spatial resolution. In response to images of different visual stimuli across a range of virtual distances, we found two categories of distance encoding in functionally corresponding columns within parietal cortex. First, PD (personal distance) columns responded selectively to moving and stationary face images presented at virtual distances nearer (but not further) than each subjects behaviorally-defined PS boundary. In the majority of PD columns, BOLD response amplitudes increased monotonically and nonlinearly with increasing virtual face proximity. In the remaining PD columns, BOLD responses decreased with increasing proximity. These fMRI response functions appear related to previously-reported variations in subjective discomfort levels, and physiologic arousal, during intrusion into but not beyond personal space. Secondly, DD (disparity distance) columns in parietal cortex responded selectively to disparity-based near or far distances, in random dot stimuli, like disparity-selective columns described previously in occipital cortex. In parietal cortex, maps of DD columns were systematically non-overlapping (interdigitated) with the PD columns. These results suggest that the transformation of spatial information, from visual to higher-order, may be computed in multiple small sites, rather than across a larger cortical gradient, in parietal cortex., Comment: 19 pages, 10 figures and 1 table. Tootell and Nasiriavanaki share joint first authorship

Purpose To alleviate the spatial encoding limitations of single-shot EPI by developing multi-shot segmented EPI for ultra-high-resolution fMRI with reduced ghosting artifacts from subject motion and respiration. Methods Segmented EPI can reduce readout duration and reduce acceleration factors, however, the time elapsed between segment acquisitions (on the order of seconds) can result in intermittent ghosting, limiting its use for fMRI. Here, "FLEET" segment ordering--where segments are looped over before slices--was combined with a variable flip angle progression (VFA-FLEET) to improve inter-segment fidelity and maximize signal for fMRI. Scaling a sinc pulse's flip angle for each segment (VFA-FLEET-Sinc) produced inconsistent slice profiles and ghosting, therefore, a recursive Shinnar-Le Roux (SLR) RF pulse design was developed (VFA-FLEET-SLR) to generate unique pulses for every segment that together produce consistent slice profiles and signals. Results The temporal stability of VFA-FLEET-SLR was compared against conventional-segmented EPI and VFA-FLEET-Sinc at 3 T and 7 T. VFA-FLEET-SLR showed reductions in both intermittent and stable ghosting compared to conventional-segmented and VFA-FLEET-Sinc, resulting in improved image quality with a minor trade-off in temporal SNR. Combining VFA-FLEET-SLR with acceleration, we achieved a 0.6-mm isotropic acquisition at 7 T--without zoomed imaging or partial Fourier--demonstrating reliable detection of BOLD responses to a visual stimulus. To counteract the increased repetition time from segmentation, simultaneous multi-slice VFA-FLEET-SLR was demonstrated using RF-encoded controlled aliasing. Conclusions VFA-FLEET with a recursive RF pulse design supports acquisitions with low levels of artifact and spatial blur, enabling fMRI at previously inaccessible spatial resolutions with a "full-brain" field of view., Comment: 51 pages (including supplement), 8 main figures, 6 supporting figures. For supporting videos (8), please visit https://github.com/aveberman/vfa-fleet. Note: this work has been accepted for publication at Magnetic Resonance in Medicine