Human Brain Mapping最新文献

Ultra-low-field (ULF) MRI is emerging as an alternative modality to high field (HF) MRI due to its lower cost, minimal siting requirements, portability and enhanced accessibility—factors that enable large-scale deployment. Although ULF-MRI exhibits a lower signal-to-noise ratio (SNR), advanced imaging and data-driven denoising methods enabled by high-performance computing have made contrasts like diffusion-weighted imaging (DWI) feasible at ULF. This study investigates the potential and limitations of ULF tractography, using data acquired on a 0.064 T commercially available mobile point-of-care MRI scanner. The results demonstrate that most major white matter bundles can be successfully retrieved in healthy adult brains within clinically tolerable scan times. This study also examines the recovery of diffusion tensor imaging (DTI)-derived scalar maps, including fractional anisotropy and mean diffusivity. Strong correspondence is observed between scalar maps obtained with ULF-MRI and those acquired at high field strengths. Furthermore, fibre orientation distribution functions reconstructed from ULF data show good agreement with high-field references, supporting the feasibility of using ULF-MRI for reliable tractography. These findings open new opportunities to use ULF-MRI in studies of brain health, development and disease progression—particularly in populations traditionally underserved due to geographic or economic constraints. The results show that robust assessments of white matter microstructure can be achieved with ULF-MRI, effectively democratising microstructural MRI and extending advanced imaging capabilities to a broader range of research and clinical settings where resources are typically limited.

Using fMRI and pRF modeling, we show that visual spatial information is represented in the auditory cortex of congenital deaf individuals through deactivation signals. These negative BOLD responses suggest a novel mechanism of cross-modal plasticity.

Non-primary motor areas, including dorsal premotor cortex (PMd), ventral premotor cortex (PMv), and posterior parietal cortex (PPC), contribute to movement planning, but how these regions differentially shape kinematic features of goal-directed movements, and how this specialization is associated with functional connectivity within the frontoparietal network, remains of interest, particularly in relation to recovery after stroke. We used functional magnetic resonance imaging (fMRI), transcranial magnetic stimulation (TMS), and kinematic assessments to explore how these areas influence reaching performance in neurologically intact adults. Participants performed a goal-directed planar reaching task using the KINARM exoskeleton robot. Brief TMS pulse trains were initiated before movement onset to perturb cortical activity at subthreshold and suprathreshold intensities targeting bilateral PMd, PMv, and dorsomedial superior parietal lobule (SPL) within PPC. Resting-state fMRI quantified functional connectivity among these regions to assess whether connectivity explains stimulation-induced kinematic changes. Relative to the control target within the postcentral sulcus (PCS), subthreshold stimulation of contralateral PMd and PMv reduced reach efficiency and smoothness, while suprathreshold stimulation of contralateral PPC increased deviation error and reduced smoothness. Among ipsilateral targets, PMd showed consistent TMS-induced effects, and was the only target where resting-state connectivity predicted behavioral response. Stronger interhemispheric connectivity in the primary motor cortex and weaker interhemispheric PPC connectivity were associated with greater reductions in straightness and smoothness during subthreshold ipsilateral PMd stimulation. We found that perturbation of premotor and parietal targets led to distinct kinematic effects that varied by site, intensity, and laterality, with premotor stimulation showing the most consistent disruptions at subthreshold intensity and bilateral effects, whereas parietal effects were observed primarily for contralateral stimulation at suprathreshold intensity, and differences in network organization explain variability in behavioral response. Identifying contributions of cortical areas and connectivity patterns may help personalize interventions after stroke.

Trial Registration: This study was registered at ClinicalTrials.gov under ID NCT04286516

Enlarged perivascular spaces (PVS) are imaging markers of cerebral small vessel disease (CSVD) that are associated with age, disease phenotypes, and overall health. Quantification of PVS is challenging but necessary to expand an understanding of their role in cerebrovascular pathology. Accurate and automated segmentation of PVS on T₁-weighted images would be valuable given the widespread use of T₁-weighted imaging protocols in multisite clinical and research datasets. We introduce segcsvd_PVS, a convolutional neural network (CNN)-based tool for automated PVS segmentation on T₁-weighted images. segcsvd_PVS was developed using a novel hierarchical approach that builds on existing tools and incorporates robust training strategies to enhance the accuracy and consistency of PVS segmentation. Performance was evaluated using a comprehensive evaluation strategy that included comparison to existing benchmark methods, ablation-based validation, accuracy validation against manual ground truth annotations, correlation with age-related PVS burden as a biological benchmark, and extensive robustness testing. segcsvd_PVS achieved strong object-level performance for basal ganglia PVS (DSC = 0.78), exhibiting both high sensitivity (SNS = 0.80) and precision (PRC = 0.78). Although voxel-level precision was lower (PRC = 0.57), manual correction improved this by only ~3%, indicating that the additional voxels reflected primary boundary- or extent-related differences rather than correctable false positive error. For non-basal ganglia PVS, segcsvd_PVS outperformed benchmark methods, exhibiting higher voxel-level performance across several metrics (DSC = 0.60, SNS = 0.67, PRC = 0.57, NSD = 0.77), despite overall lower performance relative to basal ganglia PVS. Additionally, the association between age and segmentation-derived measures of PVS burden was consistently stronger and more reliable for segcsvd_PVS compared to benchmark methods across three cohorts (test6, ADNI, CAHHM), providing further evidence of the accuracy and consistency of its segmentation output. segcsvd_PVS demonstrates robust performance across diverse imaging conditions and improved sensitivity to biologically meaningful associations, supporting its utility as a T₁-based PVS segmentation tool.

Neurofibromatosis type 1 (NF1) is a rare, single-gene neurodevelopmental disorder. Atypical brain activation patterns have been linked to working memory difficulties in individuals with NF1. This work investigates the alterations in frontoparietal effective connectivity in regions with atypical activation during working memory performance, with particular attention to self-connections (intrinsic inhibitory influences each region exerts on itself). Forty-three adolescents with NF1 and 26 age-matched neurotypical controls completed functional magnetic resonance imaging scans during a verbal working memory task. Dynamic causal models (DCMs) were estimated for the bilateral frontoparietal network (dorsolateral and ventrolateral prefrontal cortices (dlPFC and vlPFC), superior and inferior parietal gyri (SPG and IPG)). The parametric empirical Bayes approach with Bayesian model reduction was used to test the hypothesis that NF1 diagnosis would be characterised by greater inhibitory intrinsic (self-) connections. Leave-one-out cross-validation (LOO-CV) was performed to test the generalisability of group differences. NF1 participants demonstrated greater endogenous self-connectivity of left dlPFC and IPG. The DCM that best explained the effects of working memory showed that the NF1 group has increased intrinsic connectivity of left vlPFC but weaker intrinsic connectivity of left dlPFC, left SPG and right IPG. The parameters of these connections showed a modest but positive predictive correlation coefficient of 0.34 (p = 0.002) with diagnosis status, suggesting a predictive value. Overall, increased endogenous self-connectivity of left dlPFC and IPG in NF1 suggests reduced overall sensitivity of these regions to inputs. Working memory evoked different patterns of input processing in NF1 that cannot be characterised by increased inhibition alone. Instead, modulatory connectivity related to working memory showed less inhibitory self-connectivity of left dlPFC, left SPG and right IPG and more inhibitory intrinsic connectivity of left vlPFC in NF1. This discrepancy between endogenous and modulatory connectivity suggests that overall NF1 participants are responsive to cognitive task-related inputs but may show atypical adaptation to the task demands of working memory.

Stoof et al. investigate why distinct brain regions exhibit characteristic oscillatory frequencies, such as occipital alpha and frontal beta rhythms. Their work elegantly links openly available intracranial EEG spectra from 106 epilepsy patients to synaptic receptor densities from available autoradiography maps in three healthy donors. In the framework of dynamic causal modelling, they show that regional oscillations emerge from balanced combinations of excitatory (AMPAR, NMDAR) and inhibitory receptors (GABAR A or B), while neuromodulatory receptors exert subtler influences.

Artificial intelligence and neuroimaging enable accurate dementia prediction but often involve ‘black box’ models that can be difficult to trust. Explainable artificial intelligence (XAI) aims to provide insights into the model's decisions; however, choosing the most appropriate method is non-trivial and often context-specific. We used T1-weighted MRI to train models on two tasks: Alzheimer's disease (AD) classification (diagnosis) and predicting conversion from mild-cognitive impairment (MCI) to all-cause dementia (prognosis). We applied eleven XAI methods across two popular image classification architectures, producing visualisations of the most salient regions. We also propose a framework for interpreting explanations produced by different XAI methods and predictive models. Models achieved balanced accuracies of 81% and 67% for diagnosis and prognosis. XAI outputs highlighted brain regions relevant to AD with strong convergence across gradient-based techniques. LIME produced explanations that were most similar across architectures. Mean saliency enhanced MCI prognosis prediction when included as an additional input feature. XAI can be used to verify that models are utilising relevant features and to generate valuable measures for further analysis.

This paper introduces an advanced framework for accelerated processing of diffusion-weighted imaging (DWI) data that utilizes an entire-image modeling approach to optimize the estimation of diffusion parameters from DWIs by mapping input diffusion data to predicted signals and estimating parameter values via a stochastic gradient descent optimizer (Adam). To validate this approach, we applied this framework to diffusion basis spectrum imaging (DBSI) and analyzed in vivo human brain and ex vivo mouse brain DWIs. Results demonstrate significant improvements to computational speed and signal-to-noise ratio (SNR) in estimated parameter maps compared to standard DBSI. Our approach is applicable to any diffusion signal representation and enables rapid and reliable signal partitioning in complex microstructural environments, demonstrating the potential of this framework for future neuroimaging research.

Internalizing symptoms are common in childhood and linked to meaningful differences in brain structure, yet their organization and neurobiological correlates during this developmental period remain poorly understood. An increasing number of studies conceptualize internalizing psychopathology as dimensional, transdiagnostic, and hierarchical, yet the factor structure of these symptoms in youth remains to be clearly defined. Additionally, the neurostructural underpinnings of internalizing factors warrants further investigation in younger samples. Using a large sample (N = 11,868) of 9- to 10-year-old children from the Adolescent Brain Cognitive Development^SM (ABCD Study), we examined the factor structure of internalizing symptoms and identified associated neurostructural correlates, focusing on regional gray matter volume, cortical thickness, and cortical surface area. Higher-order modeling was used, in which the correlations among first-order factors for distress, cognitive, fear, and somatic symptoms were accounted for by a higher-order general internalizing factor. After controlling for age, sex, income, parental education, and site/MRI scanner, we found that general internalizing, distress, and cognitive symptoms were associated with smaller gray matter volume and cortical surface area across most regions. Fear symptoms showed a more localized pattern of smaller surface area in the parietal, temporal, and insular cortices. Cortical thickness and somatic symptoms showed less consistent associations. These findings contribute to the growing literature on dimensional models of internalizing psychopathology in youth by linking higher- and lower-order internalizing symptom factors to distinct patterns of neurostructural variation. Our results support the utility of hierarchical dimensional approaches for elucidating the neural substrates of internalizing symptoms during middle childhood.

The brain matures rapidly during childhood and adolescence. The environment may calibrate the pace of this process to shape cognition and mental health. Extending its utility as a risk marker from older to younger populations, brain age has been proposed to capture relative brain maturity in youth. Multiple algorithms have been developed to estimate brain age in predominantly White advantaged adults. Whether these models are useful in youth, particularly in more representative cohorts, remains unclear. Here, we systematically compare five influential algorithms (Drobinin, Whitmore, Pyment, Kaufmann, Centile) in two population-based youth cohorts as a benchmark for future applied research. We examined (a) prediction accuracy (correlation with chronological age, mean absolute error), (b) sensitivity to scanning parameters (acquisition sequence, image quality), demographics (sex, puberty), and genetic similarity (intraclass correlations in pairs of monozygotic twins), and (c) strength of convergence between algorithms. In our primary sample of twins recruited from birth records to represent families in disadvantaged neighborhoods (N = 593; 9–19 years), three algorithms (Drobinin, Pyment, Centile) exhibited strong predictions from structural MRI data (correlations with chronological age = 0.51–0.68, mean absolute error = 1.60–3.02). These algorithms also generated correlated brain age values and gaps, and the expected pattern of strong but not identical intraclass correlations in monozygotic twins. Pyment exhibited the strongest correlation with age and was not sensitive to acquisition sequence, image quality, sex, and puberty. In a second sample of predominantly Black, low-income youth with a narrow age range (N = 198; 15–17 years), these five algorithms exhibited weak predictions. This study raises critical questions about what “brain age” means, how it can best be estimated depending on the research question and study population, and whether it can be universally applied across samples with heterogeneous backgrounds and age ranges that are narrow or misaligned with the training data.