Middlebrooks, Erik H

ABSTRACT Purpose Layer fMRI is an increasingly utilized technique that provides insights into the laminar organization of brain activity. However, both blood‐oxygen‐level‐dependent (BOLD) fMRI and vascular space occupancy data (VASO) have certain limitations, such as bias towards larger cortical veins in BOLD fMRI and high specific absorption rate in VASO. This study aims to explore the feasibility of whole‐brain laminar functional quantitative susceptibility mapping (fQSM) compared to laminar BOLD fMRI and VASO at ultra‐high field. Methods Data were acquired using 3D EPI techniques. Complex data were denoised with NORDIC and susceptibility maps were computed using 3D path‐based unwrapping, the variable‐kernel sophisticated harmonic artifact reduction, as well as the streaking artifact reduction for QSM algorithms. To assess layer‐specific activation, 20 layers were segmented in the somatosensory and motor cortices, obtained from a finger tapping paradigm, and further averaged into six anatomical cortical layers. The magnitude of signal change and z ‐scores were compared across layers for each technique. Results fQSM showed the largest activation‐dependent mean susceptibility decrease in Layers II/III in M1 and Layers I/II in S1 with up to −1.3 ppb while BOLD showed the strongest mean signal increase in Layer I. Our data suggest that fQSM demonstrates less bias towards activation in superficial layers compared to BOLD. Moreover, activation‐based susceptibility change was comparable to VASO data. Conclusion Studying whole‐brain, layer‐dependent activation with submillimeter fQSM is feasible and reduces bias towards venous drainage effects on the cortical surface compared to BOLD, thereby enabling better localization of laminar activation.

BACKGROUND The authors aimed to evaluate the feasibility/safety of stereo-electroencephalography (SEEG) and SEEG-guided radiofrequency thermocoagulation (RFTC) in patients with drug-resistant epilepsy previously implanted with neuromodulation devices, using a retrospective cohort design evaluating patients treated at the Mayo Clinic Florida Epilepsy Center. OBSERVATIONS Nine patients (mean age 36.3 ± 10.9 years, 33% female) with prior implantation of neuromodulation devices were included. A vagus nerve stimulator was present in 8 of 9 patients, and 4 patients had undergone previous intracranial neuromodulation. Five patients underwent SEEG-RFTC. The mean SEEG monitoring duration was 6.5 ± 2.9 days. No intraoperative or procedural complications occurred, including hardware-related issues. No permanent or significant neurological/cognitive deficits occurred. One patient experienced transient postoperative neurological symptoms attributed to perilesional edema. Seizure reduction ranged from 50% to 75% following SEEG-RFTC. LESSONS SEEG and SEEG-RFTC are feasible in patients who have implanted neuromodulation devices, including intracranial implants. These procedures may be performed without interference with existing hardware and provide meaningful seizure reduction in highly refractory populations. Further studies with larger cohorts and longer follow-up are needed to better define efficacy and patient selection. https://thejns.org/doi/10.3171/CASE26376

BACKGROUND AND OBJECTIVES: Access to the orbit, cavernous sinus, Meckel cave (MC), and infratemporal fossa (ITF) remains challenging due to the complex 3-dimensional anatomy and proximity to critical neurovascular structures. An extradural endoscopic orbitozygomatic (OZ) approach has the potential to provide a direct surgical corridor across these compartments while minimizing the limitations of compartment-specific corridors. METHODS: Five formalin-fixed, silicone-injected cadaveric heads (10 sides) were dissected using an extradural OZ approach under operative microscopy supplemented with 0° and 30° rigid endoscopes. After a two-piece OZ craniotomy, extradural dissection was performed toward the superior orbital fissure and foramen rotundum/ovale, combined with anterior petrosectomy and opening of MC. Anatomic exposure, corridor continuity, and visualization of key neurovascular structures were qualitatively assessed. RESULTS: The extradural endoscopic OZ approach created a direct working corridor from the lateral orbit to the ITF and petroclival region. Endoscopic augmentation enhanced visualization of the lateral cavernous sinus wall, trigeminal divisions (V 1 -V 3 ), cavernous internal carotid artery, medial orbital apex, deep infratemporal recesses, and the petroclival region. Anterior petrosectomy extended exposure toward the upper clivus and ventrolateral brainstem. Limitations included restricted microscopic visualization of the medial orbit and inferior ITF, which were partially mitigated by angled endoscopy. CONCLUSION: This cadaveric study demonstrates that the extradural OZ–anterior petrosectomy approach establishes a continuous anatomic corridor integrating the orbit, cavernous sinus/MC, ITF, and petroclival region, while defining its boundaries, areas of optimal exposure, and inherent limitations. As a qualitative study, the findings are descriptive in nature and do not include quantitative morphometric analysis.

Abstract Background Glioblastoma (GBM) remains a devastating cancer with limited treatment options, largely due to its heterogeneity. While supramaximal resection has recently provided survival benefits, therapeutic profiling of different tumor compartments, particularly its infiltrative edge remains largely unexplored. Methods Here, we leveraged magnetic resonance imaging (MRI)-guided multi-sampling, collecting 2 cores and 2 margins per case, to query GBM heterogeneity. Whole-exome and RNA-seq with drug testing in two patient-derived 3D models were used to reveal similarities and differences in genomic and transcriptomic makeups, cellular compositions, and drug responses across cores and margins. Bioinformatics interrogations further identified response biomarkers. Results Mutation analysis showed that oncogenes exhibited a higher degree of spatial heterogeneity than tumor suppressor genes, regardless of MRI status. While the mesenchymal transcriptional subtype with extracellular matrix remodeling, stress response, and immune programs were preferentially enriched in enhancing cores, proneural tumors with neurological processes favored non-enhancing margins. Using a 15-drug GBM-targeted panel, ERK (ulixertinib) and PI3K pathway (paxalisib, CC-115) inhibitors showed preferential efficacy in enhancing cores and non-enhancing margins, respectively. The anti-apoptosis, pan-Bcl2 agent navitoclax and the epigenetic drug trotabresib represented the most effective, tumor-wide monotherapies. Importantly, drug combinations generally outperformed single agents across all regions. Conclusions This work demonstrates the regional heterogeneity of therapeutic vulnerabilities in GBM ex vivo, showing various drugs with tumor-wide or MRI-enhancement informed activity. These findings offer preclinical bases of numerous monotherapies and drug combinations for future clinical trial design.

Abstract Background High‐resolution MRI is essential for accurate diagnosis and treatment planning, but its clinical acquisition is often constrained by long scanning times, which increase patient discomfort and reduce scanner throughput. While super‐resolution (SR) techniques offer a post‐acquisition solution to enhance resolution, existing deep learning approaches face trade‐offs between reconstruction fidelity and computational efficiency, limiting their clinical applicability. Purpose This study aims to develop an efficient and accurate deep learning framework for MRI SR that preserves fine anatomical detail while maintaining low computational overhead, enabling practical integration into clinical workflows. Materials and Methods We propose a novel SR framework based on multi‐head selective state‐space models (MHSSM) integrated with a lightweight channel multilayer perceptron (MLP). The model employs 2D patch extraction with hybrid scanning strategies (vertical, horizontal, and diagonal) to capture long‐range dependencies while mitigating pixel forgetting. Each MambaFormer block combines MHSSM, depthwise convolutions, and gated channel mixing to balance local and global feature representation. The framework was trained and evaluated on two distinct datasets: 7T brain T1 MP2RAGE maps (142 subjects) and 1.5T prostate T2w MRI (334 subjects). Performance was compared against multiple baselines including Bicubic interpolation, GAN‐based (CycleGAN, Pix2pix, SPSR), transformer‐based (SwinIR), Mamba‐based (MambaIR), and diffusion‐based (I 2 SB, Res‐SRDiff) methods. Results The proposed model demonstrated superior performance across all evaluation metrics while maintaining exceptional computational efficiency. On the 7T brain dataset, our method achieved the highest structural similarity (SSIM: ) and peak signal‐to‐noise ratio (PSNR: dB), along with the best perceptual quality scores (LPIPS: ; GMSD: ). These results represented statistically significant improvements over all baselines (), including a 2.1% SSIM gain over SPSR and a 2.4% PSNR improvement over Res‐SRDiff. For the prostate dataset, the model similarly outperformed competing approaches, achieving SSIM of , PSNR of dB, LPIPS of , and GMSD of . Notably, our framework accomplished these results with only 0.9 million parameters and 57 GFLOPs, representing reductions of 99.8% in parameters and 97.5% in computational operations compared to Res‐SRDiff, while also substantially outperforming SwinIR and MambaIR in both accuracy and efficiency metrics. Conclusion The proposed framework provides a computationally efficient yet accurate solution for MRI SR, delivering well‐defined anatomical details and improved perceptual fidelity across anatomically distinct datasets. By significantly reducing computational demands while maintaining state‐of‐the‐art performance, the model offers strong potential for feasibility toward clinical translation and scalable integration into future imaging workflows.

Abstract Background Ultra‐high‐field 7 Tesla (7T) magnetic resonance imaging (MRI) provides improved resolution and signal‐to‐noise ratio (SNR) over standard clinical field strengths (1.5T, 3T). However, 7T scanners are costly, scarce, and introduce additional challenges such as susceptibility artifacts. Purpose We propose an efficient transformer‐based model (7T‐Restormer) to synthesize 7T‐like MRI (quantitative T1 MP2RAGE maps) from routine 1.5T or 3T T1‐weighted (T1W) images. Methods The proposed method leverages an efficient restoration transformer backbone and spatial attention layers to capture long‐range dependencies and generate high‐quality 7T‐like images. Our model was validated on an institutional dataset, comprised of 35 1.5T and 108 3T T1w MRI paired with corresponding 7T T1 maps of patients with confirmed multiple sclerosis (MS). A total of 141 patient cases (32 128 slices) were randomly divided into 105 (25 1.5T and 80 3T) training cases (19 204 slices), 19 (5 1.5T and 14 3T) validation cases (3476 slices), and 17 (5 1.5T and 12 3T) test cases (3145 slices). The synthetic 7T T1 maps were evaluated by comparing their similarity to the ground truth volumes using peak signal‐to‐noise ratio (PSNR), structural similarity index measure (SSIM), and normalized mean squared error (NMSE), and were compared against ResViT and ResShift. In addition, a blinded reader study was performed in which three clinicians scored the diagnostic image quality of each method on a 5‐point Likert scale (1 = non‐acceptable, 5 = excellent). Statistical significance ( α = 0.05) was assessed using two‐sided paired t ‐tests with Holm correction for multiple comparisons and Wilcoxon signed‐rank tests with Holm correction for reader scores. Effect sizes (Cohen's for paired data) were also computed to quantify practical significance. We interpreted and as small, medium, and large effects. Results For 1.5T inputs, 7T‐Restormer achieved an NMSE of 0.018 ± 0.003, PSNR of 25.0 ± 0.6 dB, and SSIM of 0.870 ± 0.019; for 3T inputs, the corresponding values were 0.019 ± 0.006, 24.5 ± 1.2 dB, and 0.874 ± 0.027. Across both field strengths, 7T‐Restormer provided the lowest NMSE and highest PSNR and SSIM among the three methods, reducing NMSE by approximately 5% and 25% relative to ResShift and ResViT at 1.5T and by 14% and 24% at 3T, respectively, and increasing PSNR by 0.5–1.5 dB (Holm‐adjusted p < 0.05, paired Cohen's = 1.37–6.84). Training on mixed 1.5T+3T data improved performance for 1.5T inputs compared with 1.5T‐only training (e.g., NMSE 0.018 vs. 0.019; p = 0.001, ), without degrading performance for 3T inputs ( p = 0.630, for NMSE). In the blinded reader study, 7T‐Restormer received higher diagnostic quality scores (3.50 ± 0.28) than ResShift (3.00 ± 0.27) and ResViT (2.33 ± 0.27), corresponding to mean improvements of 0.5–1.2 points on the 5‐point scale (Holm‐adjusted p = 0.014 and 0.004; paired Cohen's and 3.24, respectively). Conclusion We propose a novel method for predicting quantitative 7T MP2RAGE maps from 1.5T and 3T T1W scans with higher quality than existing state‐of‐the‐art methods. Future development and application of our approach may enhance diagnostic accuracy, treatment planning, and facilitate downstream tasks by making the benefits of 7T MRI more accessible to standard clinical workflows.

Abstract Background Accurate synthesis of computed tomography (CT) images from magnetic resonance imaging (MRI) is clinically valuable for cranial applications such as attenuation correction, radiotherapy planning, and image‐guided interventions. However, the heterogeneity of MRI data across magnetic field strengths and pulse sequences limits the generalizability of existing methods, posing a barrier to clinical translation. Purpose This study aims to reformulate cranial CT synthesis as a modular, structurally coupled problem using a deep learning approach, enhancing adaptability across heterogeneous MRI conditions, including different field strengths and sequence protocols. Methods We implemented a cascaded multitask pipeline that jointly models skull segmentation and Hounsfield Unit (HU) regression in anatomically targeted regions. A 3D patch‐based training paradigm shifts the modeling focus from global image translation to localized bone feature extraction. The backbone leverages a residual Mamba‐based state space model within a 3D U‐Net structure to improve spatial representation, while a Transformer U‐Net serves as a widely recognized reference baseline comparison. The model was trained and evaluated in terms of multi‐modal (T1‐weighted and T2‐FLAIR) and cross‐domain transferability from a public 1.5 Tesla brain dataset ( n = 37) to an independent 7 Tesla clinical brain dataset ( n = 44). Performance was assessed using Dice and Jaccard indices for segmentation accuracy and mean absolute error (MAE) for HU regression. Results Quantitative analysis showed that our multitask pipeline, incorporating both morphological and HU map prediction stages, significantly outperformed conventional direct MRI‐to‐CT mapping across all metrics on the public 1.5T dataset (p < 0.05). Consistent performance on an external 7T clinical dataset (p < 0.001) further demonstrated the method's robustness and adaptability across field strengths. Conclusion These findings support that task‐structured training for modality transformation markedly improves both accuracy and generalizability of cranial CT synthesis across heterogeneous MRI conditions. The observed consistency across field strengths validates the robustness of the proposed methodology.

ABSTRACT Introduction/Aims There are no established biomarkers of upper motor neuron degeneration to aid in the diagnosis of motor neuron disease (MND). This study examines the diagnostic value of the motor band sign as a marker of upper motor neuron degeneration and its relationship to clinical findings in MND. Methods Records of consecutive patients who underwent 7T magnetic resonance imaging (MRI) between October 2021 and April 2025 for evaluation of MND or other neurologic indications were retrospectively reviewed. Clinical variables and plasma neurofilament light chain (pNfL) levels were recorded. An upper motor neuron score (Mayo UMNS) was derived from reflex scores. Blinded MRI review assessed the degree of susceptibility‐weighted imaging (SWI) hypointensity in the hand, foot, and bulbar motor cortex regions. Results An MBS was observed in 100 of 117 (85.5%) MND patients and in 16 (15.5%) patients with non‐MND diagnoses, corresponding to a sensitivity of 85.5% (78.0%–90.7%) and 84.5% (76.2%–90.2%) specificity. The MBS in 78 MND patients (70.9%) preferentially involved the middle and deep cortical layers, giving a trilaminar appearance, while only one non‐MND patient had this finding. Mayo UMNS ( β = 0.89, p < 0.001), pNfL ( β = 0.63, p = 0.033), and age at evaluation ( β = 0.68, p = 0.027) were independently associated with the summed SWI score. Discussion The 7T MRI MBS is a sensitive and specific marker for MND that complements established clinical evaluation. Using 7T, a trilaminar appearance of the motor cortex, reflecting known histopathological changes, can be visualized and may be specific to MND.

Objective This longitudinal study evaluated the long-term efficacy and safety of hematopoietic stem cell transplantation (HSCT) in slowing the progression of CSF1R-related disorder (CSF1R-RD).Methods Six symptomatic patients (mean follow-up, 6.6 years) were compared with six matched, untreated controls. The CSF1R Clinical Severity Score (CCSS), Montreal Cognitive Assessment, and Sundal radiological score were used for evaluation.Results Post-HSCT clinical progression slowed significantly from 14.1 to 3.7 CCSS/year, compared with 15.2 CCSS/year in the control group (p<0.01). Cognitive decline was substantially reduced (-1.5 points/year vs. -7.6 points/year), and radiological deterioration slowed (0.4 per year vs. 3.9 per year). Notably, during the observation period, all HSCT patients survived, whereas 50% of the patients in the control group died. No serious transplant-related complications were observed.Conclusion HSCT is a potent disease-modifying therapy for CSF1R-RD that drastically improves survival and slows deterioration. These findings underscore the necessity of early intervention during the symptomatic phase to maximize the preservation of quality of life for affected individuals.