Magnetic resonance spectroscopy (MRS) enables non-invasive assessment of brain metabolites and is commonly implemented using single-voxel spectroscopy (SVS) or magnetic resonance spectroscopic imaging (MRSI). This study directly compares the reproducibility of SVS-based semi-localization by adiabatic selective refocusing (sLASER) and 3D-Concentric Ring Trajectory-based Free Induction Decay MRSI (3D-CRT-FID-MRSI) at 3T and 7T in the same cohort of healthy participants. To explore MRSI's capabilities for regional metabolite quantification and reproducibility assessment, three masking strategies were applied. Additionally, two spatial averaging approaches for MRSI, averaging before vs. after spectral fitting, were evaluated. Coefficients of variation (CV) and voxel-wise correlation analyses were used to assess intra-subject and inter-session reproducibility. Results showed good-to-excellent reproducibility across both techniques, with SVS generally providing lower CVs at 7T, while MRSI outperformed SVS in several metabolites at 3T. MRSI allowed tissue-specific analysis, with lower CVs observed in WM compared to GM, especially at 7T. Although MRSI reproducibility was slightly reduced at 7T likely due to longer scan times and lack of prospective motion correction, the spatial coverage and retrospective region analysis makes it an attractive alternative to SVS for many brain regions. This study demonstrates that both sLASER and CRT-FID-MRSI provide reproducible metabolite measurements at 3T and 7T. The findings highlight MRSI's advantages for retrospective multi-regional and tissue-specific analysis, facilitating its integration into future clinical research.

PURPOSE:To measure magnetic susceptibility (χ) with Quantitative Susceptibility Mapping (QSM) and evaluate its repeatability using four phantom doping materials relevant to QSM applications. METHODS:A cylindrical phantom was constructed containing vials of agarose gel doped with two paramagnetic materials (ferritin, USPIO) and two diamagnetic materials (CaCl2, CaCO3) at five concentrations each. Single orientation QSM measurements (MEDI+0) were carried out on the phantom at 3 and 7 T. We measured molar susceptibility (χmol) from QSM and evaluated the precision of QSM measurements using the standard deviation of the ROI measurement (SDROI). We evaluated material lifespan by conducting a t-test of χmol at various timepoints. RESULTS:χmol (ppm L mmol-1) were measured as 1.67 ± 0.24 and 0.74 ± 0.09 (USPIO: 3 and 7 T, respectively), 10-2 × (8.13 ± 1.35; 8.13 ± 1.19) (Ferritin: 3; 7 T), 10-4 × (-2.68 ± 0.24; -2.71 ± 0.37) (CaCl2: 3; 7 T), and 10-5 × (-9.52 ± 1.44; -9.53 ± 1.18) (CaCO3: 3; 7 T). We observed no significant changes in molar susceptibility for ferritin and CaCO3 over the measured timeframes (24 and 15 months, respectively). CONCLUSION:We recommend using ferritin as a paramagnetic dopant. Further research is required to identify a diamagnetic dopant with a lower electrical conductivity and a lower ratio of R2*/B0 to χ.

Purpose: To develop and evaluate a multi-model consensus deep learning approach for automated gold fiducial marker (FM) segmentation in T1-weighted prostate MRI. Materials and Methods: In this retrospective study, T1-weighted MRI and CT-derived reference standard segmentations were collected from 127 prostate cancer patients (all male; mean age, 70 years +/- 7 [standard deviation]; age range, 50-88 years; collected between October 2020 and January 2026) who each had three implanted gold FMs. A 3D U-Net was trained on 93 subjects using four random seeds to produce an ensemble. At inference, marker-class probability maps were averaged across models and the top three connected components selected. Performance was evaluated on 34 temporally held-out subjects (9 tuning, 25 test) using marker-level sensitivity and precision with exact (Clopper-Pearson) 95% confidence intervals (CIs). A model count ablation study was performed. The pipeline was deployed for on-scanner processing on Siemens MRI systems via the OpenRecon framework and as a browser-based application using WebAssembly, executing entirely client-side. Results: The four-model consensus achieved 96% (70 of 73) sensitivity and 95% (70 of 74) precision on 25 test subjects, with 29 of 34 (85%) subjects achieving perfect marker detection. Single models had a mean sensitivity of 84% (SD, 9%), improving to 96% with four-model consensus (SD, <1%). Conclusion: Multi-model consensus deep learning substantially improved FM segmentation reliability over individual models, achieving high sensitivity and precision using only routinely acquired T1-weighted MRI.

This dataset was acquired and curated to explore the spectrum of Motor Neuron Disease (MND) and Fronto-Temporal Dementia (FTD) with Ultra-High Field Magnetic Resonance Imaging (7 Tesla) and compare these to non-neurodegenerative disease controls (known colloquially as "The 7 T hEalthy Ageing study [7TEA]"). Twenty people living with neurodegenerative disease and 14 non-neurodegenerative controls underwent a comprehensive multimodal MRI protocol including structural, diffusion, quantitative MRI, resting state, and task fMRI, alongside cognitive testing and genetic screening. This dataset combines detailed imaging phenotypes with extensive clinical characterisations. It facilitates investigations into the spectrum of MND and FTD, has provided a basis for developing novel quantitative biomarkers, and supports the exploration of interactions between imaging features and clinical progression. The availability of this dataset supports various research avenues, from detailed hippocampal subfield analyses, network connectivity assessments, and multimodal genetic, cognitive, and imaging studies. The dataset is published on OpenNeuro (dataset ds007036) and is curated in the Brain Imaging Data Structure (BIDS) standard.

Ultra-low-field (ULF) point-of-care (PoC) Magnetic Resonance Imaging (MRI) offers a promising pathway to improve accessibility in medical imaging due to its portability and lower cost. However, the diagnostic utility of ULF MRI is currently limited by lower image quality, particularly in signal-to-noise ratio, resolution, and contrast. To address this, we introduce SynPoC, a generative diffusion model designed to enhance ULF MRI by synthesizing high-field MRI-like images. SynPoC employs a conditional adversarial diffusion framework that leverages both noise and contrast-specific features to model inter-field representations. We evaluated SynPoC across a multi-site dataset of 180 participants, including both healthy individuals and patients with a variety of brain conditions. The enhanced images exhibited improved anatomical clarity and structural alignment with corresponding high-field MRI, as supported by quantitative and volumetric analyses. Our model demonstrates promise for image quality enhancement and research applications; however, as with other generative approaches, there is a non-zero risk of hallucinated or misleading features, particularly near low-SNR boundaries and fine structures. We therefore provide synchronized slice-by-slice comparison videos (3T, PoC, SynPoC) to aid reader inspection and emphasize that SynPoC is not intended for diagnostic decision-making without additional safeguards and validation. Further validation is warranted before diagnostic use.

Abstract Quantitative Susceptibility Mapping (QSM) relies on a tissue’s underlying macroscopic geometry to lead to measurable orientation-dependent field perturbations. To understand and assess QSM error in vivo, anthropomorphic phantoms provide a useful model that mimic the electromagnetic properties and morphology of underlying tissue. Herein, we designed and manufactured an MRI compatible anthropomorphic phantom with cylindrical and spheroid compartments containing realistic susceptibilities to mimic hemorrhages, calcifications, and blood vessels. We estimated accuracy (ε, bias, RMSE) and repeatability (RC) of MEDI-susceptibility measurements within ROIs. We evaluated voxel-based agreement to validate susceptibility mapping under different acquisition conditions (3T versus 7T) and reconstruction algorithms (COSMOS versus MEDI). Reliable MEDI-based susceptibility measurements were obtained from ellipsoids but not from straws. The ellipsoids (|ε| = 0.007 to 0.083 ppm at 3T; 0.050 to 0.118 ppm at 7T) were more accurate than the straws (|ε| = 0.084 to 0.190 ppm at 3T; 0.105 to 0.160 ppm at 7T). The repeatability coefficient across all 6 ROIs (RC = 0.652 ppm at 3T; 0.459 ppm at 7T) was substantially larger than across the 4 ellipsoid ROIs only (RC’ = 0.168 ppm at 3T; 0.141 ppm at 7T). The accuracy at 3T (bias = -0.002 ppm, RMSE = 0.082 ppm) was better than the accuracy at 7T (bias = -0.056 ppm, RMSE = 0.092 ppm). Using voxels from the 4 ellipsoid ROIs, we observed excellent agreement between COSMOS and MEDI susceptibility maps at 3T, with linear regression of y=1.00x-0.01 (r=0.99). We observed some underestimation of MEDI susceptibility maps relative to COSMOS at 7T, with linear regression and y=0.93x-0.04 (r=0.99). The results imply that QSM reconstructions are reliable with 3T scanners but can be challenging with 7T scanners at high magnetic susceptibilities.

PURPOSE:Using parallel transmission (pTx) in ultra-high field MRI allows efficient radiofrequency pulse designs that can produce uniform excitations. Here, we demonstrate that pTx can simultaneously mitigate B 1 + $$ {\mathrm{B}}_1^{+} $$ non-uniformity and off-resonance artifacts in balanced steady-state free precession sequences at 7 T. THEORY AND METHODS:The standard hard excitation pulses of the balanced steady-state free precession sequence were replaced with two pTx pulses, played out in alternating repetition times. Based on the kT-point trajectory, each pulse was designed to produce spatially varying phase distributions that compensate for off-resonance phase variations accumulated over one repetition time duration, while also mitigating B 1 + $$ {\mathrm{B}}_1^{+} $$ non-uniformities. Depending on the local ΔB0, the different steady-state signals formed were distributed over two aliased images with a half field-of-view shift relative to one another. Using slice-GRAPPA, these images were disentangled and recombined to produce artifact-free images. RESULTS:Simulations validated the concept showing that a pair of strategically designed pTx pulses can impose a favorable distribution of spatially varying steady-state. As a proof of principle, experiments performed demonstrated the technical feasibility of the concept in a phantom in the presence of induced phase variations and in vivo. CONCLUSION:With a tailored pair of pTx pulses, it was possible to concurrently mitigate B 1 + $$ {\mathrm{B}}_1^{+} $$ and ΔB0 non-uniformities in a thin slab through the brain in a single acquisition. Although further work is needed to extend the coverage and robustness of this method before being adopted more widely, it highlights the potential of utilizing pTx capabilities beyond B 1 + $$ {\mathrm{B}}_1^{+} $$ correction.

This dataset provides the first annotated, openly available MRI-based imaging dataset for investigations of tongue musculature, including multi-contrast and multi-site MRI data from non-disease participants. The present dataset includes 47 participants collated from three studies: BeLong (four participants; T2-weighted images), EATT4MND (19 participants; T2-weighted images), and BMC (24 participants; T1-weighted images). We provide manually corrected segmentations of five key tongue muscles: the superior longitudinal, combined transverse/vertical, genioglossus, and inferior longitudinal muscles. Other phenotypic measures, including age, sex, weight, height, and tongue muscle volume, are also available for use. This dataset will benefit researchers across domains interested in the structure and function of the tongue in health and disease. For instance, researchers can use this data to train new machine learning models for tongue segmentation, which can be leveraged for segmentation and tracking of different tongue muscles engaged in speech formation in health and disease. Altogether, this dataset provides the means to the scientific community for investigation of the intricate tongue musculature and its role in physiological processes and speech production.

Clinical research emphasizes the implementation of rigorous and reproducible study designs that rely on between-group matching or controlling for sources of biological variation such as subject's sex and age. However, corrections for body size (i.e., height and weight) are mostly lacking in clinical neuroimaging designs. This study investigates the importance of body size parameters in their relationship with spinal cord (SC) and brain magnetic resonance imaging (MRI) metrics. Data were derived from a cosmopolitan population of 267 healthy human adults (age 30.1 ± 6.6 years old, 125 females). We show that body height correlates with brain gray matter (GM) volume, cortical GM volume, total cerebellar volume, brainstem volume, and cross-sectional area (CSA) of cervical SC white matter (CSA-WM; 0.44 ≤ r ≤ 0.62). Intracranial volume (ICV) correlates with body height (r = 0.46) and the brain volumes and CSA-WM (0.37 ≤ r ≤ 0.77). In comparison, age correlates with cortical GM volume, precentral GM volume, and cortical thickness (-0.21 ≥ r ≥ -0.27). Body weight correlates with magnetization transfer ratio in the SC WM, dorsal columns, and lateral corticospinal tracts (-0.20 ≥ r ≥ -0.23). Body weight further correlates with the mean diffusivity derived from diffusion tensor imaging (DTI) in SC WM (r = -0.20) and dorsal columns (-0.21), but only in males. CSA-WM correlates with brain volumes (0.39 ≤ r ≤ 0.64), and with precentral gyrus thickness and DTI-based fractional anisotropy in SC dorsal columns and SC lateral corticospinal tracts (-0.22 ≥ r ≥ -0.25). Linear mixture of age, sex, or sex and age, explained 2 ± 2%, 24 ± 10%, or 26 ± 10%, of data variance in brain volumetry and SC CSA. The amount of explained variance increased to 33 ± 11%, 41 ± 17%, or 46 ± 17%, when body height, ICV, or body height and ICV were added into the mixture model. In females, the explained variances halved suggesting another unidentified biological factor(s) determining females' central nervous system (CNS) morphology. In conclusion, body size and ICV are significant biological variables. Along with sex and age, body size should therefore be included as a mandatory variable in the design of clinical neuroimaging studies examining SC and brain structure; and body size and ICV should be considered as covariates in statistical analyses. Normalization of different brain regions with ICV diminishes their correlations with body size, but simultaneously amplifies ICV-related variance (r = 0.72 ± 0.07) and suppresses volume variance of the different brain regions (r = 0.12 ± 0.19) in the normalized measurements.

PURPOSE:This study aims to investigate the feasibility of using T1-weighted fMRI with an iron oxide nanoparticle contrast agent and Ultrashort Echo Time (UTE) imaging at 9.4T to measure functional hyperaemia in the mouse visual cortex. The goal is to capture positive signal changes in both the parenchyma and pial surface, and to test whether surface vessels respond during neuronal activation. METHODS:The study involved scanning of nine mice after administration of iron oxide-based superparamagnetic contrast agent (Molday ION) via the tail vein. Two functional imaging experiments were conducted: one to investigate the effect of echo time on the functional response, and another to characterize the impact of higher resolution on UTE functional contrast. Regions of interest (ROIs) were defined in the parenchyma and pial surface of the visual cortex. RESULTS:The administration of the contrast agent produced a bright-blood signal in the vasculature in structural MRI when using a UTE acquisition. Positive signal changes were observed at the shortest echo time (0.164 ms) in both the parenchyma (0.2% +/- 0.08) and pial surface (0.2% +/- 0.1 %), providing evidence that UTE fMRI experiments can detect changes in both pial and parenchymal vessels. Measurements using longer echo times (≥1 ms) showed negative signal changes. Higher spatial resolution resulted in increased percent signal change at the pial surface, suggesting less partial volume effects and better delineation of surface vessels. CONCLUSION:The findings demonstrate that T1-weighted fMRI with UTE imaging and iron oxide nanoparticles captures positive signal changes across all vascular compartments, providing additional insights into the involvement of surface vessels during functional hyperemia.

Long COVID and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) patients share similar symptoms including post-exertional malaise, neurocognitive impairment, and memory loss. The neurocognitive impairment in both conditions might be linked to alterations in the hippocampal subfields. Therefore, this study compared alterations in hippocampal subfields of 17 long COVID, 29 ME/CFS patients, and 15 healthy controls (HC). Structural MRI data was acquired with sub-millimeter isotropic resolution on a 7 Telsa MRI scanner and hippocampal subfield volumes were then estimated for each participant using FreeSurfer software. Our study found significantly larger volumes in the left hippocampal subfields of both long COVID and ME/CFS patients compared to HC. These included the left subiculum head (long COVID; p = 0.01, ME/CFS; p = 0.002,), presubiculum head (long COVID; p = 0.004, ME/CFS; p = 0.005), molecular layer hippocampus head (long COVID; p = 0.014, ME/CFS; p = 0.011), and whole hippocampal head (long COVID; p = 0.01, ME/CFS; p = 0.01). Notably, hippocampal subfield volumes were similar between long COVID and ME/CFS patients. Additionally, we found significant associations between hippocampal subfield volumes and severity measures of ‘Pain’, ‘Duration of illness’, ‘Severity of fatigue’, ‘Impaired concentration’, ‘Unrefreshing sleep’, and ‘Physical function’ in both conditions. These findings suggest that hippocampal alterations may contribute to the neurocognitive impairment experienced by long COVID and ME/CFS patients. Furthermore, our study highlights similarities between these two conditions.

This study compares volumetric measurements of various brain regions using different magnetic resonance imaging (MRI) modalities and deep learning models, specifically 3T MRI, ultra-low field (ULF) MRI at 64mT, and AI-enhanced ULF MRI using SynthSR and HiLoResGAN. The aim is to evaluate the alignment and agreement among field strengths and ULF MRI with and without AI. Descriptive statistics, paired t-tests, effect size analyses, and regression analyses are employed to assess the relationships and differences between modalities. The results indicate that volumetric measurements derived from 64mT MRI deviate significantly from those obtained using 3T MRI. By leveraging SynthSR and LoHiResGAN models, these deviations are reduced, bringing the volumetric estimates closer to those obtained from 3T MRI, which serves as the reference standard for brain volume quantification. These findings highlight that deep learning models can reduce systematic differences in brain volume measurements across field strengths, providing potential solutions to minimize bias in imaging studies.

Proton magnetic resonance spectroscopy (1H MRS) is a valuable non-invasive technique for quantifying biochemical compounds in vivo, facilitating the monitoring of disease progression and treatment. This study evaluates the reliability and reproducibility of 1H MRS measurements by comparing data acquired with STEAM and sLASER sequences at 3 T and 7 T. We assessed test-retest reliability and reproducibility by scanning healthy participants twice with STEAM and sLASER at 3 T and 7 T in two different voxel locations about 1 week apart, investigating the impact of measurement conditions on results. Reliability was measured using the intraclass correlation coefficients (ICC), whereas reproducibility was assessed with the coefficients of variation (CV). Our findings reveal that data acquired with the sLASER sequence show superior reliability and reproducibility compared to STEAM for most metabolites at both fields. Although the higher field strength of 7 T provides advantages in signal-to-noise ratio and resolution as expected, our study highlights that a field strength of 3 T provides a suitable alternative when ultra-high-field scanners are unavailable. This study provides valuable insights for researchers regarding the selection of appropriate MRS sequences and field strengths based on reliability and reproducibility. Our findings underscore the importance of consistent measurements over time, guiding decisions in longitudinal studies and enhancing data consistency.

40 Hz auditory steady-state responses (ASSR) can be evoked by brief auditory clicks delivered at 40 Hz. While the neuropharmacology behind the generation of ASSR is well examined, the link between ASSR and microstructural properties of the brain is unclear. Further, whether the 40 Hz ASSR can be manipulated through processes involving top-down control, such as prediction, is currently unknown. We recorded EEG in 50 neurotypical participants while they engaged in a 40 Hz auditory steady-state paradigm. We manipulated the predictability of the stimuli to test the modulatory effect of prediction on 40 Hz steady-state responses. Further, we acquired T1w and T2w structural MRI on the same individuals and used the T1/T2 ratio as a proxy to determine myelination content in gray matter. The phase locking of the 40 Hz ASSR was indeed modulated by prediction, suggesting that prediction violation directly affects phase locking to the 40 Hz ASSR. We found that the prediction violation of the phase locking at 40 Hz (gamma) was associated with the degree of gray matter myelination in the right cerebellum, such that greater myelin led to less desynchronization induced by prediction violations. We demonstrate that prediction violations modulate steady-state activity at 40 Hz and suggest that the efficiency of this process is promoted by greater cerebellar myelin. Our findings provide a structural-functional relationship for myelin and phase locking of auditory oscillatory activity. These results introduce a framework for investigating the interaction of predictive processes and ASSR in disorders where these processes are impaired, such as in psychosis.

This work addresses the challenge of reliably measuring the muscles of the human tongue, which are difficult to quantify due to complex interwoven muscle types. We introduce a new semi-automated method, enabled by a manually curated dataset of MRI scans to accurately measure five key tongue muscles, combining AI-assisted, atlas-based, and manual segmentation approaches. The method was tested and validated in a dataset of 178 scans and included segmentation validation (n = 103) and clinical application (n = 132) in individuals with motor neuron disease. We show that people with speech and swallowing deficits tend to have smaller muscle volumes and present a normalisation strategy that removes confounding demographic factors, enabling broader application to large MRI datasets. As the tongue is generally covered in neuroimaging protocols, our multi-contrast pipeline will allow for the post-hoc analysis of a vast number of datasets. We expect this work to enable the investigation of tongue muscle morphology as a marker in a wide range of diseases that implicate tongue function, including neurodegenerative diseases and pathological speech disorders.

Proton magnetic resonance spectroscopy (1H MRS) is a valuable non-invasive technique for quantifying biochemical compounds in vivo, facilitating the monitoring of disease progression and treatment. This study evaluates the reliability and reproducibility of 1H MRS measurements by comparing data acquired with STEAM and sLASER sequences at 3 T and 7 T. We assessed test-retest reliability and reproducibility by scanning healthy participants twice with STEAM and sLASER at 3 T and 7 T in two different voxel locations about 1 week apart, investigating the impact of measurement conditions on results. Reliability was measured using the intraclass correlation coefficients (ICC), whereas reproducibility was assessed with the coefficients of variation (CV). Our findings reveal that data acquired with the sLASER sequence show superior reliability and reproducibility compared to STEAM for most metabolites at both fields. Although the higher field strength of 7 T provides advantages in signal-to-noise ratio and resolution as expected, our study highlights that a field strength of 3 T provides a suitable alternative when ultra-high-field scanners are unavailable. This study provides valuable insights for researchers regarding the selection of appropriate MRS sequences and field strengths based on reliability and reproducibility. Our findings underscore the importance of consistent measurements over time, guiding decisions in longitudinal studies and enhancing data consistency.

Physical exercise may reduce dementia risk in aging, but varying reports on its effectiveness make it challenging to ascribe what level of exercise will have significant longer-term effects on important functions such as hippocampal-based learning and memory. This study compared the effect of three different 6-month exercise regimens on hippocampal-dependent cognition in healthy, elderly individuals. Participants, aged 65-85 with no cognitive deficits, were randomly assigned to one of three exercise interventions (low (LIT), medium (MIT), and High intensity interval training (HIIT), respectively). Each participant attended 72 supervised exercise sessions over a 6month period. A total of 151 participants completed all sessions. Cognitive testing for hippocampal performance occurred monthly, as did blood collection, and continued for up to 5 years following initiation of the study. Multimodal 7 Tesla MRI scans were taken at commencement, 6 and 12 months. After 6 months, only the HIIT group displayed significant improvement in hippocampal function, as measured by paired associative learning (PAL). MRI from the HIIT group showed abrogation of the age-dependent volumetric decrease within several cortical regions including the hippocampus and improved functional connectivity between multiple neural networks not seen in the other groups. HIIT-mediated changes in the circulating levels of brain-derived neurotrophic factor (BDNF) and cortisol correlated to improved hippocampal-dependent cognitive ability. These findings demonstrate that HIIT significantly improves and prolongs the hippocampal-dependent cognitive health of aged individuals. Importantly, improvement was retained for at least 5 years following initiation of HIIT, suggesting that the changes seen in hippocampal volume and connectivity underpin this long-term maintenance. Sustained improvement in hippocampal function to this extent confirms that such exercise-based interventions can provide significant protection against hippocampal cognitive decline in the aged population. The changes in specific blood factor levels also may provide useful biomarkers for choosing the optimal exercise regimen to promote cognitive improvement.

Magnetic resonance angiography (MRA) performed at ultra-high magnetic field provides a unique opportunity to study the arteries of the living human brain at the mesoscopic level. From this, we can gain new insights into the brain’s blood supply and vascular disease affecting small vessels. However, for quantitative characterization and precise representation of human angioarchitecture to, for example, inform blood-flow simulations, detailed segmentations of the smallest vessels are required. Given the success of deep learning-based methods in many segmentation tasks, we explore their application to high-resolution MRA data and address the difficulty of obtaining large data sets of correctly and comprehensively labelled data. We introduce VesselBoost, a vessel segmentation toolbox, which utilizes deep learning and imperfect training labels for accurate vasculature segmentation. To enhance the segmentation models’ robustness and accuracy, VesselBoost employs an innovative data augmentation technique, which captures the resemblance of vascular structures across scales by zooming in or out on input image patches—virtually creating diverse scale vascular data. This approach enables detailed vascular segmentation and ensures the model’s ability to generalize across various scales of vascular structures.

PURPOSE:QSM provides insight into healthy brain aging and neuropathologies such as multiple sclerosis (MS), traumatic brain injuries, brain tumors, and neurodegenerative diseases. Phase data for QSM are usually acquired from 3D gradient-echo (3D GRE) scans with long acquisition times that are detrimental to patient comfort and susceptible to patient motion. This is particularly true for scans requiring whole-brain coverage and submillimeter resolutions. In this work, we use a multishot 3D echo plannar imaging (3D EPI) sequence with shot-selective 2D CAIPIRIHANA to acquire high-resolution, whole-brain data for QSM with minimal distortion and blurring. METHODS:To test clinical viability, the 3D EPI sequence was used to image a cohort of MS patients at 1-mm isotropic resolution at 3 T. Additionally, 3D EPI data of healthy subjects were acquired at 1-mm, 0.78-mm, and 0.65-mm isotropic resolution with varying echo train lengths (ETLs) and compared with a reference 3D GRE acquisition. RESULTS:The appearance of the susceptibility maps and the susceptibility values for segmented regions of interest were comparable between 3D EPI and 3D GRE acquisitions for both healthy and MS participants. Additionally, all lesions visible in the MS patients on the 3D GRE susceptibility maps were also visible on the 3D EPI susceptibility maps. The interplay among acquisition time, resolution, echo train length, and the effect of distortion on the calculated susceptibility maps was investigated. CONCLUSION:We demonstrate that the 3D EPI sequence is capable of rapidly acquiring submillimeter resolutions and providing high-quality, clinically relevant susceptibility maps.

Whole-brain network modeling (WBM) offers a pivotal tool to explore the large-scale spatiotemporal dynamics of the brain at rest, during cognitive tasks, and under external stimulation. However, it is unclear how to fuse multi-modal neural dynamics in a united WBM framework and predict the whole-brain spatiotemporal neural responses to electrical stimulation. In this study, we present a computational framework with whole-brain network modeling, parameter optimization, and model validation using simultaneous EEG-SEEG data during intracranial brain stimulation. To test the efficacy of WBM in revealing brain-wide neural dynamics, our experiments utilize synthetic electrophysiological data, real EEG data, and real EEG-SEEG signals. Experimental results demonstrate that our WBM framework accurately captures the spatiotemporal brain activities by jointly leveraging the higher spatial resolution from SEEG and the whole-brain coverage from EEG. Notably, our model shows a higher correlation between the functional connectivity (FC) matrix of EEG and that of the inferred whole-brain neural dynamics from WBM (r=0.86), compared to the FC from EEG source localization (r=0.48). Together, we demonstrate the capability and flexibility of WBM framework to uncover the whole-brain spatiotemporal neural activity and its potential to provide new insights into the input-response mechanism of the brain.