Frontiers in neuroimaging最新文献

Low-field MRI is gaining interest, especially in low-resource settings, due to its low cost, portability, small footprint, and low power consumption. However, it suffers from significant noise, limiting its clinical utility. This study introduces native noise denoising (NND), which leverages the inherent noise characteristics of the acquired low-field data. By obtaining the noise characteristics from corner patches of low-field images, we iteratively added similar noise to high-field images to create a paired noisy-clean dataset. A U-Net based denoising autoencoder was trained on this dataset and evaluated on three low-field datasets: the M4Raw dataset (0.3T), in vivo brain MRI (0.05T), and phantom images (0.05T). The NND approach demonstrated improvements in signal-to-noise ratio (SNR) of 32.76%, 19.02%, and 8.16% across the M4Raw, in vivo and phantom datasets, respectively. Qualitative assessments, including difference maps, line intensity plots, and effective receptive fields, suggested that NND preserves structural details and edges compared to random noise denoising (RND), indicating potential enhancements in visual quality. This substantial improvement in low-field imaging quality addresses the fundamental challenge of diagnostic confidence in resource-constrained settings. By mitigating the primary technical limitation of these systems, our approach expands the clinical utility of low-field MRI scanners, potentially facilitating broader access to diagnostic imaging across resource-limited healthcare environments globally.

Over the past decade, functional magnetic resonance imaging (fMRI) has emerged as a widely adopted in vivo imaging technique for examining neural activity in the brain. A common preprocessing step in fMRI analysis is spatial smoothing, which helps in detecting cluster-like active regions. The use of a heuristically selected Gaussian filter for spatial smoothing is frequently preferred due to its simplicity and computational efficiency. Neurons in the cerebral cortex are located within a thin sheet of gray matter at the surface of the brain, and the human brain's gyrification results in a complex gray matter anatomy. For task-based fMRI activation analysis, isotropic Gaussian smoothing can reduce spatial specificity, introducing spatial blurring artifacts where inactive voxels near active regions are mistakenly identified as active. This blurring is beneficial for group-level analysis as it helps mitigate anatomical variability across subjects and inaccuracies in spatial normalization. However, it poses challenges in subject-level analysis, particularly in clinical applications such as presurgical planning and fMRI fingerprinting, which demand high spatial specificity. Previous studies have proposed several adaptive spatial smoothing techniques to address these issues. In this study, we introduce a versatile deep neural network (DNN) that builds on the strengths of previous approaches while overcoming their limitations. This method can incorporate additional neighboring voxels for estimating optimal spatial smoothing without significantly increasing computational costs, making it suitable for ultrahigh-resolution (sub-millimeter) task fMRI data. Furthermore, the proposed neural network incorporates brain tissue properties, enabling more accurate characterization of brain activation at the individual level.

Introduction: Functional connectivity (FC) is a metric of how different brain regions interact with each other. Although there have been some studies correlating learning and memory with FC, there have not yet been, to date, studies that use machine learning (ML) to explain how FC changes can be used to explain behavior not only in healthy mice, but also in mouse models of Alzheimer's Disease (AD). Here, we investigated changes in FC and their relationship to learning and memory in a mouse model of AD across disease progression.

Methods: We assessed the APP/PS1 mouse model of AD and wild-type controls at 3-, 6-, and 10-months of age. Using resting state functional magnetic resonance imaging (rs-fMRI) in awake, unanesthetized mice, we assessed FC between 30 brain regions. ML models were then used to define interactions between neuroimaging readouts with learning and memory performance.

Results: In the APP/PS1 mice, we identified a pattern of hyperconnectivity across all three time points, with 47 hyperconnected regions at 3 months, 46 at 6 months, and 84 at 10 months. Notably, FC changes were also observed in the Default Mode Network, exhibiting a loss of hyperconnectivity over time. Modeling revealed functional connections that support learning and memory performance differ between the 6- and 10-month groups.

Discussion: These ML models show potential for early disease detection by identifying connectivity patterns associated with cognitive decline. Additionally, ML may provide a means to begin to understand how FC translates into learning and memory performance.

Introduction: Anatomical variations in the posterior horns of the lateral ventricles are well-documented, with the horn presenting as open, constricted, or completely closed. However, the extent and nature of these variations across different demographics remain under-explored. This study aimed to investigate the anatomical variations of the posterior horn of the lateral ventricles across different age and sex groups and to compare the variations between the right and left sides.

Methods: We conducted a retrospective analysis of magnetic resonance imaging (MRI) scans from 217 adult participants across 15 age groups, utilizing a stratified random sampling from a radiology database. MRI scans were analyzed for ventricular dimensions, and horn types (open, constricted, and closed). Statistical significance was defined as p-value < 0.05.

Results: Variants of the posterior horn were observed frequently, with open posterior horn being the most common in the left lateral ventricle (41%) and constricted type being the most common in the right lateral ventricle (37%). A significant correlation existed between the right and left horn types, but in most cases, there was a difference in type between the right and the left horns in the same individual. No significant association between age and the type of the posterior horns was found. However, there was a significant difference in the width and length of the horns between the open and other types, with open horns being wider and longer. Lastly, the left horn appeared longer than the right one.

Discussion: The findings underline the high variability in posterior horn morphology, which is not significantly influenced by age or sex but varies between individuals and sides. Future studies should explore the functional impact of these anatomical variations.

Introduction: Electroencephalography (EEG) source localization (SL) has shown potential for various applications, from epilepsy and seizure focus localization to psychiatric disorder evaluation. However, questions remain about its neurophysiological plausibility in real-world settings where only EEG signals are available without subject-specific anatomical information. This study investigates whether established pre-processing and source localization methods can produce neurophysiologically plausible activation patterns when applied to naturalistic EEG data without structural magnetic resonance imaging (MRI) or digitized electrode positions.

Methods: Proven methods are aggregated into an end-to-end pipeline that includes automatic pre-processing, eLORETA for source estimation, and a shared forward model derived from the ICBM 2009c Nonlinear Symmetric template and its corresponding CerebrA atlas. The pipeline is validated using two distinct datasets: the Healthy Brain Network (HBN) dataset comparing resting and naturalistic video-watching states and the multi-session and multi-task EEG cognitive dataset (COGBCI) comparing different cognitive workload levels. The validation approach focuses on whether the reconstructed source activations exhibit expected neurophysiological patterns via permutation testing.

Results: Findings revealed significant differences between resting state and video-watching tasks, with greater activation in posterior regions during video-watching, consistent with known visual processing pathways. The cognitive workload analysis similarly showed progressive activation increases with task difficulty, mapping to regions associated with executive function.

Discussion: These results prove that established source localization methods can produce neurophysiologically plausible activation patterns without subject-specific information, highlighting the strengths and limitations of applying these methods to mid-length naturalistic EEG data. This research demonstrates the viability of template-based source analysis for research settings where individual structural imaging is unavailable or impractical.

Brain tumors represent a significant burden, particularly in low- and middle-income countries (LMICs) where access to neuroimaging techniques is often limited. Conventional MRI machines are expensive and bulky, posing a significant challenge in the diagnosis and treatment of brain tumors in LMICs. However, an emerging technology, ultra-low field magnetic resonance imaging (pULF-MRI), has the potential to address this limitation. This study aimed to evaluate the feasibility and effectiveness of post-contrast enhancement in a pULF-MRI scanner for brain tumor imaging in LMICs. A single case study was conducted, and post-contrast enhancement was successfully achieved, revealing the presence of a tumor which was subsequently confirmed on biopsy. To our knowledge, this is the first study to demonstrate the feasibility of post-contrast enhancement in a pULF-MRI scanner for brain tumor imaging. This technology has the potential to significantly improve access to neuroimaging in LMICs, leading to earlier diagnosis and more effective treatment of brain tumors. These promising results suggest that further studies are warranted to explore the potential of pULF-MRI for large-scale screening and diagnosis of brain tumors in LMICs. This can provide a future roadmap for neuroimaging in LMICs, providing a cost-effective and accessible way to diagnose and treat brain tumors, leading to improved healthcare outcomes with a further prospective clinical trial.

Cytotoxic lesions of the corpus callosum (CLOCC) are a clinicoradiological diagnosis, characterized by transient neurological symptoms and magnetic resonance imaging (MRI) changes in the splenium of the corpus callosum (SCC), which in most cases is completely reversible. However, the long-term pathophysiological trajectory and ultimate neurological outcomes of CLOCC remain largely unknown due to limited long-term follow-up data. We report an 11-year follow-up of a postpartum female with CLOCC, initially presenting with transient focal neurological symptoms and extensive diffusion-restricted white matter involvement including the SCC and surrounding area with diffusion restriction and low apparent diffusion coefficient values, indicative of cytotoxic edema. The edema regressed in days; over the years, she remained asymptomatic despite persistent white matter changes on MRI in the centrum semiovale. This case challenges the view of CLOCC as completely reversible and raises questions regarding the significance of lasting white matter changes. The enduring absence of neurological symptoms and stable radiological profile throughout the decade underscores the singular nature of CLOCC and the lasting, though isolated, impact on white matter. This report contributes a crucial perspective, suggesting that CLOCC may involve just an isolated episode without recurrent events or progressive neurological decline. By offering the first longitudinal analysis of a CLOCC episode with an extended follow-up of over a decade, our case enhances current knowledge about the long-term neurological and radiological landscape of this condition. It suggests a reevaluation of the conceptual understanding of CLOCC as an entirely reversible, non-relapsing disorder, highlighting the need for further research into its long-term impacts on cerebral white matter integrity.

Background and purpose: In the differential diagnostic workup of amyotrophic lateral sclerosis (ALS), magnetic resonance imaging (MRI) is primarily used to rule out significant differential diagnoses. So far, whole-brain T1-mapping has not been assessed as a diagnostic tool in this patient population.

Methods: We investigated the diagnostic potential of a novel T1-mapping method based on real-time MRI with 0.5 mm in-plane resolution and 4s acquisition time per slice. The study included patients aged 18 to 90 years who met the revised El Escorial criteria for at least possible ALS. T1-relaxation times were measured along the corticospinal tract in predefined regions of interest.

Results: Twenty-nine ALS-patients and 43 control group patients (CG) were included in the study. Median ALS Functional Rating Scale revised (ALSFRS-R) was 37 (IQR, 35-44) points and the mean duration from symptom onset to MRI was 21 ± 17 (SD) months. ALS patients showed significantly higher T1-relaxation times in all ROIs compared to CG with mean differences in the hand knob of 50 ms (p < 0.001), corona radiata 24 ms (p = 0.034), internal capsule 27 ms (p = 0.002) and midbrain peduncles 48 ms (p < 0.001). There was a consistent negative correlation between the ALSFRS-R and T1-relaxation times in all ROIs.

Conclusions: T1-relaxation times along the corticospinal tract are significantly elevated in ALS patients compared to CG and associated with lower ALSFRS-R. These results imply the analysis of T1-relaxation times as a promising diagnostic tool that can distinguish ALS patients from the control group. Ongoing longitudinal studies may provide deeper insights into disease progression and the effects of therapeutic interventions.

Abnormalities and alterations in the glycolytic pathway in the pathology of neurodegenerative diseases and brain aging have received much attention, as clinical applications of proton-based magnetic resonance spectroscopy (MRS) have recently illuminated the elevation of lactate concentrations in the brains of patients with neurodegenerative diseases, including Alzheimer's disease. Hyperpolarized [1-¹³C]pyruvate MRS has shown promise for neurological applications because it enables the real-time in vivo detection of glycolysis and oxidative phosphorylation flux. In studies of the mouse brain using hyperpolarized [1-¹³C]pyruvate, there are few reports that the signal of [¹³C]bicarbonate, a product of oxidative phosphorylation metabolized from [1-¹³C]pyruvate, was detected using MR spectroscopic imaging (MRSI) that allows spatial mapping of metabolism, although there have been reports of [¹³C]bicarbonate signals being detected by pulse-acquire sequences in the entire brain. In the present study, we compared hyperpolarized [1-¹³C]pyruvate metabolism between the brains of awake and isoflurane-anesthetized mice using a custom-made awake mouse restraint device with MRSI. Although the signal for [1-¹³C]lactate, a product of glycolysis metabolized from [1-¹³C]pyruvate, was detectable in multiple brain regions that include the orbitofrontal cortex and hippocampus in both awake and anesthetized mice, the signal for [¹³C]bicarbonate metabolized from [1-¹³C]pyruvate was only detectable in the brains of awake mice. Moreover, a comparison of hyperpolarized [1-¹³C]pyruvate metabolism in young and aged mouse brains using awake MRSI detected age-related decreases in oxidative phosphorylation flux in brain regions that include the hippocampus with variations in the extent of these changes across different brain regions. These results demonstrate that hyperpolarized [1-¹³C]pyruvate MRSI under awake conditions is useful for the spatial detection of abnormalities and alterations in glycolysis and oxidative phosphorylation flux in the brains of mice. Thus, the use of hyperpolarized [1-¹³C]pyruvate MRSI has potential in pathological and mechanistic studies of brain diseases and brain aging.