SLIMM: Slice localization integrated MRI monitoring

Comparison of fMRI language laterality with and without sedation in pediatric epilepsy

Functional MRI is an essential component of presurgical language mapping. In clinical settings, young children may be sedated for the MRI with the functional stimuli presented passively. Research has found that sedation changes language activation in healthy adults and children. However, there is limited research comparing sedated and unsedated functional MRI in pediatric epilepsy patients. We compared language activation patterns in children with epilepsy who received sedation for functional MRI to the ones who did not. We retrospectively identified the patients with focal epilepsy who underwent presurgical functional MRI including Auditory Descriptive Decision Task at Boston Children's Hospital from 2014 to 2022. Patients were divided into sedated and awake groups, based on their sedation status during functional MRI. Auditory Descriptive Decision Task stimuli were presented passively to the sedated group per clinical protocol. We extracted language activation maps contrasted against a control task (reverse speech) in the Frontal and Temporal language regions and calculated separate language laterality indexes for each region. We considered positive laterality indexes as left dominant, negative laterality indexes as right dominant, and absolute laterality indexes <0.2 as bilateral. We defined 2 language patterns: typical (i.e., primarily left-sided) and atypical. Typical pattern required at least one left dominant region (either frontal or temporal) and no right dominant region. We then compared the language patterns between the sedated and awake groups. Seventy patients met the inclusion criteria, 25 sedated, and 45 awake. Using the Auditory Descriptive Decision Task paradigm, when adjusted for age, handedness, gender, and laterality of lesion in a weighted logistic regression model, the odds of the atypical pattern were 13.2 times higher in the sedated group compared to the awake group (Confidence Interval: 2.55-68.41, p-value < 0.01). Sedation may alter language activation patterns in pediatric epilepsy patients. Language patterns on sedated functional MRI with passive tasks may not represent language networks during wakefulness, sedation may differentially suppress some networks, or require a different task or method of analysis to capture the awake language network. Given the critical surgical implication of these findings, additional studies are needed to better understand how sedation impacts the functional MRI blood oxygenation level-dependent signal. Consistent with current practice, sedated functional MRI should be interpreted with greater caution and requires additional validation as well as research on post-surgical language outcomes.

Scan-Specific Generative Neural Network for MRI Super-Resolution Reconstruction

The interpretation and analysis of Magnetic resonance imaging (MRI) benefit from high spatial resolution. Unfortunately, direct acquisition of high spatial resolution MRI is time-consuming and costly, which increases the potential for motion artifact, and suffers from reduced signal-to-noise ratio (SNR). Super-resolution reconstruction (SRR) is one of the most widely used methods in MRI since it allows for the trade-off between high spatial resolution, high SNR, and reduced scan times. Deep learning has emerged for improved SRR as compared to conventional methods. However, current deep learning-based SRR methods require large-scale training datasets of high-resolution images, which are practically difficult to obtain at a suitable SNR. We sought to develop a methodology that allows for dataset-free deep learning-based SRR, through which to construct images with higher spatial resolution and of higher SNR than can be practically obtained by direct Fourier encoding. We developed a dataset-free learning method that leverages a generative neural network trained for each specific scan or set of scans, which in turn, allows for SRR tailored to the individual patient. With the SRR from three short duration scans, we achieved high quality brain MRI at an isotropic spatial resolution of 0.125 cubic mm with six minutes of imaging time for T2 contrast and an average increase of 7.2 dB (34.2%) in SNR to these short duration scans. Motion compensation was achieved by aligning the three short duration scans together. We assessed our technique on simulated MRI data and clinical data acquired from 15 subjects. Extensive experimental results demonstrate that our approach achieved superior results to state-of-the-art methods, while in parallel, performed at reduced cost as scans delivered with direct high-resolution acquisition.

Gradient-Guided Isotropic MRI Reconstruction from Anisotropic Acquisitions

The trade-off between image resolution, signal-to-noise ratio (SNR), and scan time in any magnetic resonance imaging (MRI) protocol is inevitable and unavoidable. Super-resolution reconstruction (SRR) has been shown effective in mitigating these factors, and thus, has become an important approach in addressing the current limitations of MRI. In this work, we developed a novel, image-based MRI SRR approach based on anisotropic acquisition schemes, which utilizes a new gradient guidance regularization method that guides the high-resolution (HR) reconstruction via a spatial gradient estimate. Further, we designed an analytical solution to propagate the spatial gradient fields from the low-resolution (LR) images to the HR image space and exploited these gradient fields over multiple scales with a dynamic update scheme for more accurate edge localization in the reconstruction. We also established a forward model of image formation and inverted it along with the proposed gradient guidance. The proposed SRR method allows subject motion between volumes and is able to incorporate various acquisition schemes where the LR images are acquired with arbitrary orientations and displacements, such as orthogonal and through-plane origin-shifted scans. We assessed our proposed approach on simulated data as well as on the data acquired on a Siemens 3T MRI scanner containing 45 MRI scans from 14 subjects. Our experimental results demonstrate that our approach achieved superior reconstructions compared to state-of-the-art methods, both in terms of local spatial smoothness and edge preservation, while, in parallel, at reduced, or at the same cost as scans delivered with direct HR acquisition.

MRI Super-Resolution Through Generative Degradation Learning

Spatial resolution plays a critically important role in MRI for the precise delineation of the imaged tissues. Unfortunately, acquisitions with high spatial resolution require increased imaging time, which increases the potential of subject motion, and suffers from reduced signal-to-noise ratio (SNR). Super-resolution reconstruction (SRR) has recently emerged as a technique that allows for a trade-off between high spatial resolution, high SNR, and short scan duration. Deconvolution-based SRR has recently received significant interest due to the convenience of using the image space. The most critical factor to succeed in deconvolution is the accuracy of the estimated blur kernels that characterize how the image was degraded in the acquisition process. Current methods use handcrafted filters, such as Gaussian filters, to approximate the blur kernels, and have achieved promising SRR results. As the image degradation is complex and varies with different sequences and scanners, handcrafted filters, unfortunately, do not necessarily ensure the success of the deconvolution. We sought to develop a technique that enables accurately estimating blur kernels from the image data itself. We designed a deep architecture that utilizes an adversarial scheme with a generative neural network against its degradation counterparts. This design allows for the SRR tailored to an individual subject, as the training requires the scan-specific data only, i.e., it does not require auxiliary datasets of high-quality images, which are practically challenging to obtain. With this technique, we achieved high-quality brain MRI at an isotropic resolution of 0.125 cubic mm with six minutes of imaging time. Extensive experiments on both simulated low-resolution data and clinical data acquired from ten pediatric patients demonstrated that our approach achieved superior SRR results as compared to state-of-the-art deconvolution-based methods, while in parallel, at substantially reduced imaging time in comparison to direct high-resolution acquisitions.

Matched neurofeedback during fMRI differentially activates reward-related circuits in active and sham groups

BACKGROUND AND PURPOSE

Functional MRI neurofeedback (fMRI-nf) leverages the brain's ability to self-regulate its own activity. However, self-regulation processes engaged during fMRI-nf are incompletely understood. Here, we used matched feedback in an fMRI-nf experimental protocol to investigate whether brain processes recognize true neurofeedback signals.

METHODS

We implemented an existing fMRI-nf protocol to train lateralized motor activity using a finger-tap task in conjunction with real-time feedback. Twelve healthy, right-handed, adult participants were assigned into age- and sex-matched active and sham study groups. Matched participant pairs received the same visual feedback, based on brain activity of the participant from the active group. We compared group-averaged activation maps before, during, and after neurofeedback, and analyzed changes in lateralized motor activity due to neurofeedback.

RESULTS

Active and sham groups demonstrated different brain activation to the same feedback during neurofeedback. In particular, there was higher activation in visual cortex, secondary somatosensory cortex, and right inferior frontal gyrus in the active group compared to the sham group. Conversely, sham participants demonstrated higher activation in anterior cingulate cortex, left frontal pole, and posterior superior temporal gyrus. Despite differing brain activations during neurofeedback, neither group demonstrated significant improvement in lateralized motor activity from pre to postfeedback scan in the same session. We also observed no significant difference between pre and postfeedback activation maps, suggesting that no significant finger-tap related functional reorganization had occurred.

CONCLUSIONS

These findings suggest that fMRI neurofeedback paradigms that monitor or incorporate activity from regions reported here would provide enhanced efficacy for research investigation and clinical intervention.