NeuroImage最新文献

The detection of Alzheimer's Disease (AD) using structural Magnetic Resonance Imaging (MRI) and Machine Learning (ML) often focuses on late-stage atrophy patterns. End-to-end deep learning models address this by considering MRI signal intensities. However, their explainability components typically focus on attention regions, neglecting underlying patterns. This work overcomes both problems by training and explaining time-to-event models utilizing Radiomics features. SHapley Additive exPlanations (SHAP) and high-level explanations were combined to interpret the effects of MRI texture, shape, and volumes, as well as neuro-psychological and cognitive tests, and socio-demographic features on the AD risk score. All models were trained and internally validated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. External validation was performed on the Australian Imaging Biomarkers and Lifestyle flagship study of Ageing (AIBL) and the Open Access Series of Imaging Studies version 3 (OASIS-3). The results demonstrate that Radiomics features add value to models trained on cognitive tests, socio-demographics, genetics, and MRI volumes, particularly for long-term AD predictions. On average, the Radiomics-based models slightly outperformed the comparison models by between 0.11% points and 3.02% points in terms of Brier scores for the eight-year prediction. Despite varying data distributions during external validation, the models demonstrate moderate to high reproducibility. The analysis of Radiomics features uncovered complex associations with AD, including tissue with complex texture in the left entorhinal cortex, an irregular shape of the right amygdala, and a fine-granular texture of the left middle temporal gyrus. All models showed reasonable concordance with the Voxel-Based Morphometry (VBM).

Due to significant differences in brain volume, morphology, and white matter integrity among neonates of varying gestational ages, using a single full-term template for preterm analysis inevitably introduces analytical errors. To address this, we aimed to develop gestational-age-specific stereotaxic DTI templates using retrospective diffusion MRI scans from 161 neonates acquired between August 2021 and January 2024. The cohort was stratified into four WHO-defined subgroups: extremely preterm (n = 31), very preterm (n = 29), moderate to late preterm (n = 28), and full-term (n = 73). Templates were constructed via iterative registration, with corresponding atlases transformed from JHU space and manually corrected. Quantitative evaluation using the Jacobian determinant and standard deviation revealed that our age-specific templates demonstrated significantly lower deformation magnitude and registration error compared to a standard full-term template. When applied to investigate developmental differences, we observed progressively more extensive fractional anisotropy reductions from moderate-to-late to extremely preterm neonates. Notably, commissural fibers, particularly the corpus callosum body (0.194 ± 0.005 in extremely preterm vs. 0.230 ± 0.003 in full-term, p < 0.001), exhibited significant developmental gradients. Consequently, these constructed gestational-age-specific DTI templates offer a robust tool to improve the accuracy of morbidity risk predictions and facilitate multicenter studies of preterm neonates.

Objective: To investigate the pathway-specific structure-function coupling induced by focal subcortical infarction and its influence on clinical symptoms.

Methods: In this prospective study, 50 patients with unilateral subcortical infarction and motor impairment and 50 matched controls underwent resting state fMRI, DTI, and Fugl-Meyer-Assessment lower-extremity (FMA-LE) at 7-14- and 30-days post-infarction. To analyze the pathway-specific structure-function coupling, we evaluated the association between structural integrity of the corticospinal tract (CST), dentate thalamocortical tract (DTCT), cortico-pontocerebellar tract (CPCT), and dorsal spinocerebellar tract (DSCT) and functional connectivity (FC) of corresponding subregions. Moderation analysis assesses whether the structure-function coupling pathway moderates FMA-LE.

Results: At baseline, patients exhibited significantly lower structural integrity of DTCT, DSCT, and CST than controls. We found structure-function couplings in the three motor pathways of the cerebro-cerebellar circuit: (1) contralesional thalamus to ipsilesional cerebellum-crus_2 with dentate thalamocortical tract (DTCT), (2) contralesional thalamus to cerebellum vermis_10 with dorsal spinocerebellar tract (DSCT), (3) ipsilesional precentral gyrus to frontal medial gyrus with CST. The baseline DSCT structural integrity specificity modulates the relationship between FC and FMA-LE over 30 days.

Conclusions: We observed that cerebro-cerebellar circuit structure-function coupling after infarction, based on its anatomy and mapped to motor function (with DSCT as the key pathway mediating/moderating prognosis), serves as a potent biomarker for lower limb prognosis and a basis for precise rehabilitation.

Electroencephalography (EEG) is a powerful tool for investigating neural processes underlying cognition and neuropsychiatric disorders. Yet, variability in EEG preprocessing strategies restricts reproducibility and data integration across study sites and laboratories, particularly in larger research consortia. This paper introduces the CLEAN-EEG preprocessing pipeline, designed to standardize data processing and documentation across multiple sites. The CLEAN pipeline is implemented in MATLAB using EEGLAB. It comprises three modular, script-based stages: main preprocessing (including down-sampling, filtering, line noise removal, and channel interpolation), independent component analysis preparation and decomposition with flexible options for artifact rejection or neural component extraction, and component exclusion with support for automated classification and dipole fitting. Emphasis is placed on transparency through comprehensive logging and quality-control plotting, as well as on minimizing rank reduction to preserve data suitability for advanced analyses such as source localization and connectivity modeling. By providing clear, adaptable recommendations while ensuring detailed documentation of every step, the CLEAN pipeline aims to harmonize EEG preprocessing in large-scale, multi-center studies. This open and reproducible approach facilitates high throughput analyses, supports the training of researchers, and enables the rigorous integration of neurophysiological data across study sites, study designs, and populations.

Facial age serves as a socially salient cue that shapes impression formation and social cognition, yet its neurocognitive mechanism remains unclear. This study aimed to establish a three-stage model for facial age processing: structural encoding, prototype matching, and affective evaluation. We recorded electroencephalography (EEG) during age judgments of faces from four age groups (10, 30, 50, and 70 years) and combined event-related potential (ERP) analyses (component-based and mass-univariate), time-frequency analysis, and functional connectivity. ERPs showed stage-specific age effects: older faces evoked larger N170 amplitudes, reduced P2 responses, and enhanced late positive potentials (LPP). Mass-univariate analysis (MUA) further confirmed these effects, identifying three significant time bands (70-168 ms, 228-286 ms, and 342-800 ms) over occipital and temporo-occipital sensors, with strongest differentiation for the oldest versus younger faces. Time-frequency analysis revealed increased theta (4-8 Hz) and alpha (8-13 Hz) power during early encoding (∼100-200 ms), accompanied by widespread theta/alpha phase-based connectivity, indicating global coordination for initial age encoding. During prototype matching (∼200-300 ms), only local theta activity remained, suggesting localized processing without large-scale network engagement. The late stage (>300 ms) was indexed by LPP modulations, reflecting age-related affective processing. Overall, facial age processing shows a dynamic shift from early global coordination to later localized processing, providing a mechanistic account of how the brain extracts age information from faces.

Studying flexible, adaptive transitions between cognitive tasks and serial-parallel processing under changing task demands has been central to understanding human cognition. Advances in neuroimaging analysis have improved the ability to link cognition with brain function, motivating methods that characterize dynamic brain activity to quantify emergent cognitive properties during task-based fMRI. Probabilistic Cognitive State Modeling (PCSM) combines Finite Impulse Response modeling of BOLD activity with a Gaussian Mixture Model-Hidden Markov Model to infer recurring multivariate patterns of task-evoked BOLD responses across spatially distributed regions over time ("brain states"). From the resulting posterior structure, PCSM deterministically derives interpretable processing metrics, including serial-parallel deviation, cognitive demand, and serial bottleneck. Data-informed generative simulations evaluated PCSM across systematically varied noise levels and transition regimes. Results show that PCSM reliably recovers latent structure (∼98 % state-alignment accuracy under known generative conditions) and produces stable parameter estimates across simulation regimes. Threshold analyses identify reliable boundaries between parallel, mixed, and serial processing modes and recover expected relationships among demand, and bottleneck. Together, these results demonstrate that PCSM provides a principled framework for characterizing dynamic task-evoked processing architectures and estimating individual-level cognitive dynamics from task-based fMRI, supporting future investigation of cognitive processing constraints across tasks and populations.

Movie-watching studies have shown that specific cortical areas are tuned to stimulus segments of certain durations. However, increases in stimulus duration naturally co-occur with increases in content complexity. This study aimed to disentangle the effects of stimulus content and duration to determine whether hierarchically nested, complex, naturalistic stimuli, like movies, are processed primarily on the basis of their underlying temporal or content structure. To this end, 48 participants watched six equal-length blocks of movie frames presented at a constant frame rate in an fMRI experiment. Frames were extracted from either movie scenes or movie shots (Content Level) and displayed as continuous segments for 4s, 12s or 36s (Duration). We applied inter-subject correlation and three-dimensional linear mixed-effects modeling with crossed random effects to identify cortical areas selectively modulated by Content Level and Duration. Effects along the visual processing hierarchy were additionally assessed in a ROI analysis. Whole-brain results were located predominately within distinct subnetworks of the scene network: Movie scenes, compared to shots, elicited stronger engagement in the visually-attuned posterior subnetwork containing the occipital and posterior parahippocampal place areas. Longer stimuli whereas additionally engaged the memory-related anterior scene network including the anterior parahippocampal place area, retrosplenial cortex, and caudal inferior parietal lobe. ROI analyses confirmed that temporally extended, content-rich stimuli preferentially engaged hierarchically higher areas. Overall, these findings support a functional differentiation within the scene network while expanding on its relation to temporal receptive windows, demonstrating that Content Level and Duration interact in shaping the cortical processing of naturalistic movie stimuli.

Background: Mounting evidence highlights the critical role of the brain's glymphatic system in cerebral waste clearance, yet its alterations in Wilson's disease (WD) remain unclear. This study aimed to systematically evaluate structural and functional alterations of the glymphatic system across WD clinical phenotypes and their associations with neurological impairment.

Methods: Nineteen patients with neurological WD (neuro-WD), 13 with hepatic WD (hep-WD), and 25 healthy controls (HCs) were enrolled. Quantitative MRI metrics included choroid plexus (ChP) volume and diffusion parameters, basal ganglia perivascular space (PVS_BG) volume, and free water-eliminated diffusion tensor imaging analysis along the perivascular space (FWE-DTI-ALPS) index. Group differences were analyzed using ANCOVA, post hoc t-tests, and receiver operating characteristic analyses. Partial correlation analyses were performed to examine associations between MRI and clinical parameters.

Results: ChP and PVS_BG volumes increased progressively across HC, hep-WD, and neuro-WD groups, whereas the FWE-DTI-ALPS index decreased (all p < 0.01), accompanied by elevated free water content and altered diffusion properties in the ChP. The combination of ChP and PVS markers distinguished WD from HC (AUC = 0.939), while ChP volume alone effectively differentiated neuro-WD from hep-WD (AUC = 0.799). ChP volume correlated negatively with the FWE-DTI-ALPS index (r = -0.619), and PVS_BG volume was inversely associated with FWE-DTI-ALPS (r = -0.320). Clinically, ChP enlargement correlated with higher urinary copper levels, whereas fractional anisotropy values, both before and after free water correction, were negatively correlated with serum iron.

Conclusions: These findings provide preliminary imaging evidence of alterations in glymphatic-related MRI markers in WD and suggest that these markers may help differentiate neurological from hepatic phenotypes in research settings. In addition, we emphasize that the observed ALPS changes should be interpreted cautiously, and future longitudinal and multi-shell studies are required before clinical translation can be considered.

Children's ability to process emotional information is central for social development and for understanding risk factors for affective disorders. Prior neuroimaging studies have identified brain systems underlying emotional processing, but most have relied on functional MRI, which cannot capture rapid neural dynamics. Moreover, these studies utilized emotional stimulus sets with outdated, adult-focused content, which may not effectively engage children, thereby reducing sensitivity to developmental effects. Here, we used magnetoencephalography (MEG) to examine the spatiotemporal dynamics of emotional processing in children and adolescents. Fifty-seven participants viewed pleasant, unpleasant, and neutral pictures from the Nencki Affective Picture System (NAPS), a recent database with child-appropriate content. Source-reconstructed responses were analysed using cluster-based permutation tests. Both pleasant and unpleasant pictures elicited stronger activity than neutral ones in salience and prefrontal regions, including the insula and orbitofrontal cortex, as early as 50-100 ms. Unpleasant pictures evoked stronger and more sustained activity than pleasant pictures in salience and default mode network regions, consistent with negativity bias. Finally, developmental analyses revealed that younger children exhibited greater medial prefrontal response amplitude to pleasant than to unpleasant pictures between 650-700 ms post-stimulus, whereas older adolescents showed greater medial prefrontal response amplitude to unpleasant than to pleasant pictures during the same time window. Overall, these results suggest that children rapidly differentiate emotional from neutral input, prioritize negative information in salience and default mode network systems, and that age influences emotional processing in prefrontal and default mode network regions. Our findings clarify the timing of affective brain responses across development and inform pathways of risk for affective disorders.