Frontiers in neuroimaging最新文献

Information Processing Pathway Maps (IPPMs) are a concise way to represent the evidence for the transformation of information as it travels around the brain. However, their construction currently relies on hand-drawn maps from electrophysical recordings such as magnetoencephalography (MEG) and electroencephalography (EEG). This is both inefficient and contains an element of subjectivity. A better approach would be to automatically generate IPPMs from the data and objectively evaluate their accuracy. In this work, we propose a range of possible strategies and compare them to select the best. To this end, we (a) provide a test dataset against which automatic IPPM creation procedures can be evaluated; (b) suggest two novel evaluation metrics-causality violation and transform recall-from which these proposed procedures can be evaluated; (c) conduct a simulation study to evaluate how well ground-truth IPPMs can be recovered using the automatic procedure; and (d) propose and evaluate a selection of different IPPM creation procedures. Our results suggest that the max pooling approach gives the best results on these metrics. We conclude with a discussion of the limitations of this framework, and possible future directions.

Background: Spinal cord cross-sectional area (CSA) is a biomarker of disability in multiple sclerosis (MS). Vertebral-based CSA suffers from anatomical variability and positional bias.

Objectives: To evaluate a fully automated PMJ-referenced approach, as implemented in the open-source Spinal Cord Toolbox, to assess cervical cord CSA at a fixed distance from the pontomedullary junction (PMJ) in MS.

Methods: Retrospective study performed at the MS center of Lugano (Switzerland). Inclusion criteria were treatment with natalizumab or ocrelizumab and absence of clinical/radiological disease activity over ≥2 years. CSA at 64 mm caudal to the PMJ (CSA PMJ) and at C2-C3 vertebral level (CSA C2-C3) were calculated using the Spinal Cord Toolbox.

Results: Seventy-five MS patients [females = 44 (58.7%), age = 45.1 (36.7-53.8) years, natalizumab = 36 (48%), ocrelizumab = 39 (52%)] were included. Median CSA PMJ and CSA C2-C3 were 57.7 (53.1-62.1) and 58.1 (53.2-62.6) mm², respectively. The two measures were highly correlated (rho = 0.95, p < 0.001), with some exceptions related to errors in vertebral labelling in CSA C2-C3 assessments. PMJ was correctly identified in all subjects. CSA PMJ measures were negatively associated with disability (β = -0.08, p = 0.002), independent of age and sex.

Conclusion: Automated measurement of spinal cord CSA at fixed distance from the PMJ is applicable in MS, performs better than vertebral-based CSA, and correlates with neurological disability.

Background: Computed tomography perfusion (CTP) is frequently used for the rapid assessment of suspected acute ischemic stroke (AIS). However, small lacunar infarcts often remain undetected by automated software, leading to false negatives and additional imaging. We compared the specificity of two commonly used CTP software packages in patients without evidence of stroke on follow-up diffusion-weighted imaging (DWI).

Methods: In this single-center retrospective study, 58 consecutive patients with suspected AIS but negative follow-up DWI-MRI were included. All patients underwent CTP on the same scanner. Perfusion data were processed using (1) syngo.via (Siemens Healthcare) with three parameter settings-A: CBV < 1.2 mL/100 mL, B: additional smoothing filter, and C: rCBF <30%-and (2) Cercare Medical Neurosuite (CMN). Software-reported ischemic core volumes were compared with the MRI findings.

Results: CMN showed the highest specificity, indicating zero infarct volume in 57/58 patients (98.3%). Conversely, all three syngo.via settings produced false-positive ischemic cores, with median volumes ranging from 21.3 mL (setting C) to 92.1 mL (setting A). Only syngo.via setting C reported zero infarct volume in some patients, yet still showed substantial overestimation (maximum 207.9 mL).

Conclusion: Our findings underscore the significant variability in the ability of different CTP software packages to reliably rule out small (lacunar) infarcts. CMN demonstrated good specificity, suggesting that dependable CTP-based stroke exclusion is achievable with advanced post-processing. High specificity could reduce reliance on follow-up MRI in acute stroke pathways if validated, thereby improving resource allocation and patient throughput.

Background: Glioblastoma (GBM) is the most common malignant brain tumor with an abysmal prognosis. Since complete tumor cell removal is impossible due to the infiltrative nature of GBM, accurate measurement is paramount for GBM assessment. Preoperative magnetic resonance images (MRIs) are crucial for initial diagnosis and surgical planning, while follow-up MRIs are vital for evaluating treatment response. The structural changes in the brain caused by surgical and therapeutic measures create significant differences between preoperative and follow-up MRIs. In clinical research, advanced deep learning models trained on preoperative MRIs are often applied to assess follow-up scans, but their effectiveness in this context remains underexplored. Our study evaluates the performance of these models on follow-up MRIs, revealing suboptimal results. To overcome this limitation, we developed a Bayesian deep segmentation model specifically designed for follow-up MRIs. This model is capable of accurately segmenting various GBM tumor sub-regions, including FLAIR hyperintensity regions, enhancing tumor areas, and non-enhancing central necrosis regions. By integrating uncertainty information, our model can identify and correct misclassifications, significantly improving segmentation accuracy. Therefore, the goal of this study is to provide an effective deep segmentation model for accurately segmenting GBM tumor sub-regions in follow-up MRIs, ultimately enhancing clinical decision-making and treatment evaluation.

Methods: A novel deep segmentation model was developed utilizing 311 follow-up MRIs to segment tumor subregions. This model integrates Bayesian learning to assess the uncertainty of its predictions and employs transfer learning techniques to effectively recognize and interpret textures and spatial details of regions that are typically underrepresented in follow-up MRI data.

Results: The proposed model significantly outperformed existing models, achieving DSC scores of 0.833, 0.901, and 0.931 for fluid attenuation inversion recovery hyperintensity, enhancing tumoral and non-enhancing central necrosis, respectively.

Conclusion: Our proposed model incorporates brain structural changes following surgical and therapeutic interventions and leverages uncertainty metrics to refine estimates of tumor, demonstrating the potential for improved patient management.

Introduction: Schizophrenia is extremely heterogenous, and the underlying brain mechanisms are not fully understood. Many attempts have been made to substantiate and delineate the relationship between schizophrenia and the brain through unbiased exploratory investigations of resting-state functional magnetic resonance imaging (rs-fMRI). The results of numerous data-driven rs-fMRI studies have converged in support of the disconnection hypothesis framework, reporting aberrant connectivity in cortical-subcortical-cerebellar circuitry. However, this model is vague and underspecified, encompassing a vast array of findings across studies. It is necessary to further refine this model to identify consistent patterns and establish stable imaging markers of schizophrenia and psychosis. The organizational structure of the NeuroMark atlas is especially well-equipped for describing functional units derived through independent component analysis (ICA) and uniting findings across studies utilizing data-driven whole-brain functional connectivity (FC) to characterize schizophrenia and psychosis.

Methods: Toward this goal, a systematic literature review was conducted on primary empirical articles published in English in peer-reviewed journals between January 2019-February 2025 which utilized cortical-subcortical-cerebellar terminology to describe schizophrenia-control comparisons of whole-brain FC in human rs-fMRI. The electronic databases utilized included Google scholar, PubMed, and APA PsycInfo, and search terms included ("schizophrenia" OR "psychosis") AND "resting-state fMRI" AND ("cortical-subcortical-cerebellar" OR "cerebello-thalamo-cortical").

Results: Ten studies were identified and NeuroMark nomenclature was utilized to describe findings within a common reference space. The most consistent patterns included cerebellar-thalamic hypoconnectivity, cerebellar-cortical (sensorimotor & insular-temporal) hyperconnectivity, subcortical (basal ganglia and thalamic)-cortical (sensorimotor, temporoparietal, insular-temporal, occipitotemporal, and occipital) hyperconnectivity, and cortical-cortical (insular-temporal and occipitotemporal) hypoconnectivity.

Discussion: Patterns implicating prefrontal cortex are largely inconsistent across studies and may not be effective targets for establishing stable imaging markers based on static FC in rs-fMRI. Instead, adapting new analytical strategies, or focusing on nodes in the cerebellum, thalamus, and primary motor and sensory cortex may prove to be a more effective approach.

Kernel Canonical Correlation Analysis (KCCA) is an effective method for globally detecting brain activation with reduced computational complexity. However, the current KCCA is limited to linear kernels, and the performance of more general types of kernels remains uncertain. This study aims to expand the current KCCA method to arbitrary nonlinear kernels. Our contributions are twofold: First, we propose an inverse mapping algorithm that works for general types of nonlinear kernels. Second, we demonstrate that nonlinear kernels yield improved performance, particularly when the true neural activation deviates from the hypothesized hemodynamic response function due to the complex nature of neural responses. Our results, based on a simulated fMRI dataset and two task-based fMRI datasets, indicate that nonlinear kernels outperform linear kernels and effectively reduce activation in undesired regions.

Introduction: Multiple sclerosis (MS) is a chronic neuroinflammatory disease marked by demyelination and axonal degeneration, processes that can be probed using diffusion tensor imaging (DTI). In the brain, white matter (WM) tractography enables anatomically specific analysis of microstructural changes. However, in the spinal cord (SC), anatomical localization is inherently defined by cervical levels, offering an alternative framework for regional analysis.

Methods: This study employed an along-level approach to assess both microstructural (e.g., fractional anisotropy) and macrostructural (e.g., cross-sectional area) features of the SC in persons with relapsing-remitting MS (pwRRMS) relative to healthy controls (HCs).

Results: Compared to conventional whole-cord averaging, along-level analyses provided enhanced sensitivity to group differences. Detailed segmentation of WM tracts and gray matter (GM) subregions revealed spatially discrete alterations along the cord and within axial cross-sections. Notably, while GM atrophy was associated with clinical disability, microstructural changes did not exhibit significant correlations with disability measures.

Discussion: These findings underscore the utility of level-specific analysis in detecting localized pathology and suggest a refined framework for characterizing SC alterations in MS.

Magnetic resonance spectroscopy (MRS) and magnetic resonance spectroscopic imaging (MRSI), are non-invasive techniques used to quantify biochemical compounds in tissue, such as choline, creatine, glutamate, glutamine, γ-aminobutyric acid, N-acetylaspartate, etc. However, reliable quantification of MRS and MRSI data is challenging due to the complex processing steps involved, often requiring advanced expertise. Existing data processing software solutions often demand MRS expertise or coding knowledge, presenting a steep learning curve for novel users. Mastering these tools typically requires a long training time, which can be a barrier for users with limited technical backgrounds. To address these challenges and create a tool that serves researchers using MRS/MRSI with a broad range of backgrounds, we developed MRSpecLAB-an open-access, user-friendly software platform for MRS and MRSI data analysis. MRSpecLAB is designed for easy installation and features an intuitive graphical pipeline editor that supports both predefined and customizable workflows. It also serves as a platform offering standardized pipelines while allowing users to integrate in-house functions for additional flexibility. Importantly, MRSpecLAB is envisioned as an open platform beyond the MRS community, bridging the gap between technical experts and practitioners. It facilitates contributions, collaboration, and the sharing of data workflows and processing methodologies for diverse MRS/MRSI applications, supporting reproducibility practices.