Feed aggregator

Annu Int Conf IEEE Eng Med Biol Soc. 2025 Jul;2025:1-5. doi: 10.1109/EMBC58623.2025.11252645.

ABSTRACT

Resting-state functional MRI (rs-fMRI) data analysis provides essential insights into early neurodevelopment through longitudinal assessment of functional connectivity (FC) patterns in infant brains, which may help uncover critical biomarkers for developmental monitoring. However, due to challenges in acquiring high-quality functional MRI (fMRI) data in infants, such as strong motion artifacts, short scan durations, and participant compliance, longitudinal FC of infants remain scarce, which significantly hampers the capacity to systematically investigate early functional brain development. To address this challenge, we propose MAD-Net, a novel diffusion model that predicts longitudinal FC from morphometric features derived from structural MRI (sMRI). Our framework integrates classifier-free guidance with a cross-modal attention mechanism, enabling the dynamic fusion of morphometric features and developmental age constraints during the diffusion process. A shared triplet encoder learns robust feature representations from longitudinal data, while a U-Net-based architecture ensures precise conditioning on individual morphometry and target age. We evaluate MAD-Net on 386 longitudinal infant fMRI scans and demonstrate its superior performance in FC prediction compared to state-of-the-art methods. By integrating diffusion-based learning, structural priors, and age-dependent constraints, MAD-Net represents a significant advancement in neuroimaging-based functional network reconstruction. The code is available at https://github.com/IPMI-NWU/MAD-Net.

PMID:41336914 | DOI:10.1109/EMBC58623.2025.11252645

Annu Int Conf IEEE Eng Med Biol Soc. 2025 Jul;2025:1-7. doi: 10.1109/EMBC58623.2025.11251603.

ABSTRACT

Alzheimer's Disease (AD) is characterized by progressive functional network disruptions that precede cognitive decline, yet traditional functional connectivity analyses often fail to capture transient network instabilities critical for early diagnosis. This study investigates the role of Quasi-Periodic Patterns (QPPs) in identifying disease-related connectivity changes across longitudinal stable disease stages (sNC, sMCI, sDAT) and transitioning (uNC, pMCI) AD cohorts using resting-state fMRI data from the Alzheimer's Disease Neuroimaging Initiative. By integrating QPP occurrences with intrinsic connectivity networks (ICNs), we assessed network integrity across disease stages, with statistical significance evaluated using the Kruskal-Wallis test and Dunn's test for post-hoc analysis. Results revealed a progressive decline in functional connectivity integrity, with early impairments in subcortical and executive function networks in stable groups, followed by widespread disconnection in higher cognition, sensorimotor, and visual networks at later stages. Transitioning AD groups exhibited earlier disruptions in visual and cerebellar networks, suggesting their potential as early biomarkers for disease onset. The occurrence of QPPs decreased significantly with disease progression, indicating an increase in functional disconnection. These findings highlight the synergy between QPPs and ICNs as a dynamic and sensitive biomarker framework for AD progression. Future research should further explore this integration within multimodal imaging and clinical diagnostic frameworks to enhance early detection and intervention strategies.

PMID:41336827 | DOI:10.1109/EMBC58623.2025.11251603

Annu Int Conf IEEE Eng Med Biol Soc. 2025 Jul;2025:1-4. doi: 10.1109/EMBC58623.2025.11252770.

ABSTRACT

In this study, we introduce Dynamic Inter-Modality Source Coupling (dIMSC), an extension of our earlier Inter-Modality Source Coupling (IMSC) method. While IMSC evaluated the coupling between source-based morphometry (SBM) from structural MRI (sMRI) and static functional network connectivity (sFNC) from resting-state fMRI (rs-fMRI), dIMSC incorporates the temporal dimension by linking SBM with dynamic functional network connectivity (dFNC). Using data from the Adolescent Brain Cognitive Development (ABCD) study, we applied dIMSC to examine brain connectivity and evaluate sex differences in children aged 9-11. Our analysis revealed significant sex-specific patterns: males exhibited stronger positive coupling in the putamen and hippocampus, while females showed stronger coupling in the superior parietal lobule and anterior cingulate cortex. On average, 27.12% of timecourses exhibited positive coupling, 46.63% neutral coupling, and 26.25% negative coupling, reflecting a balanced alignment between structural and functional features. Sex differences were also observed in neutral and negative coupling groups, with males demonstrating stronger coupling in the caudate and middle cingulate gyrus, and females in the cerebellum and inferior parietal lobule. These findings suggest distinct developmental trajectories in brain network organization between sexes, potentially reflecting sex-specific adaptations in functional integration and compensatory mechanisms. The dIMSC method advances our earlier work by enabling time-sensitive analysis of brain structure-function coupling, providing a powerful framework for investigating neurodevelopmental processes and their implications for cognitive and behavioral outcomes.Clinical RelevanceThis study identifies sex-specific patterns in brain connectivity during childhood, offering insights that could inform sex-tailored diagnostic and therapeutic approaches for neurodevelopmental disorders.

PMID:41336665 | DOI:10.1109/EMBC58623.2025.11252770

Annu Int Conf IEEE Eng Med Biol Soc. 2025 Jul;2025:1-5. doi: 10.1109/EMBC58623.2025.11253394.

ABSTRACT

Understanding the relationship between cognition and intrinsic brain activity through purely data-driven approaches remains a significant challenge in neuroscience. Resting-state functional magnetic resonance imaging (rs-fMRI) offers a non-invasive method to monitor regional neural activity, providing a rich and complex spatiotemporal data structure. Deep learning has shown promise in capturing these intricate representations. However, the limited availability of large datasets, particularly for disease-specific groups such as Alzheimer's Disease (AD), constrains the generalizability of deep learning models. In this study, we focus on multivariate time series forecasting of independent component networks derived from rs-fMRI as a form of data augmentation, using both a conventional LSTM-based model and the novel Transformer-based BrainLM model. We assess their utility in AD classification, demonstrating how generative forecasting enhances classification performance. Post-hoc interpretation of BrainLM reveals class-specific brain network sensitivities associated with AD.

PMID:41336569 | DOI:10.1109/EMBC58623.2025.11253394

Annu Int Conf IEEE Eng Med Biol Soc. 2025 Jul;2025:1-7. doi: 10.1109/EMBC58623.2025.11251833.

ABSTRACT

Visual hallucinations can severely impact the quality of life of affected individuals and are linked to greater disease severity in psychosis. To facilitate the detection of imaging biomarkers of visual hallucinations, we developed an automated pipeline to compare and evaluate feature extraction and classification methods using resting-state functional MRI scans from individuals with and without visual hallucinations. Five common functional connectivity features were assessed in this study: Regional Homogeneity, Voxel-Mirrored Homotopic Connectivity, Amplitude of Low Frequency Fluctuations, Fractional Amplitude of Low Frequency Fluctuations, and Eigenvector Centrality Mapping. We further evaluated the use of Pearson correlation in feature selection with different cutoff-values and employed a linear support vector machine for classification. The pipeline was validated on a dataset of 45 individuals, including people with psychosis and healthy controls. The model performance was evaluated based on the classification accuracy, sensitivity, specificity, as well as the interpretability of the feature weights. The code for the created pipeline is publicly available: https://github.com/LEO-UMCG/Visual_Hallucinations_Classification.Clinical relevance- Classification of patients based on biomarkers holds potential for complementing clinical measures, predicting future cases, and guiding personalized treatment of schizophrenia. The comparison of feature types and identification of imaging-based biomarkers in this study provide valuable insights for future research on VH classification and the underlying mechanisms of visual hallucinations.

PMID:41336506 | DOI:10.1109/EMBC58623.2025.11251833

Annu Int Conf IEEE Eng Med Biol Soc. 2025 Jul;2025:1-7. doi: 10.1109/EMBC58623.2025.11253094.

ABSTRACT

Autism Spectrum Disorder (ASD) is a complicated neurodevelopmental condition with numerous symptoms, making accurate diagnosis and the identification of reliable biomarkers particularly challenging. Recent advances in deep neural networks using connectivity features derived from resting-state functional magnetic resonance imaging have greatly extended our understanding of ASD and improved its diagnostic accuracy. However, most existing methods primarily focus on pairwise connections, limiting their ability to capture higher-order interactions in brain networks and resulting in suboptimal predictive performance. In this paper, to enhance the learning of higher-order relationships and improve model interpretability, we propose an Adaptive Hypergraph Contrastive Learning (AHCL) framework for ASD classification. Specifically, AHCL employs a trainable masking mechanism to adaptively estimate latent hyperedges, allowing the generation of two hypergraph views with distinct topological structures. Additionally, AHCL incorporates low-rank loss to improve the compactness of intra-class samples, effectively addressing the limitation of traditional contrastive learning in distinguishing negative samples. By jointly optimizing view similarity loss and contrastive loss, the framework ensures semantic consistency across views while enhancing topological differences, leading to robust and noise-resistant feature representations with minimal information redundancy. Experimental results demonstrate that AHCL outperforms competing methods in ASD classification. Furthermore, it identifies disease-related connections and regions, providing valuable insights into ASD and offering potential techniques for more precise and interpretable diagnostic strategies.

PMID:41336148 | DOI:10.1109/EMBC58623.2025.11253094

Annu Int Conf IEEE Eng Med Biol Soc. 2025 Jul;2025:1-5. doi: 10.1109/EMBC58623.2025.11253059.

ABSTRACT

Early infancy is a crucial period for brain development, during which fundamental functional and structural frameworks are established. Understanding the maturation of large-scale brain networks during this stage is essential for characterizing normative neurodevelopment and identifying potential deviations linked to neurodevelopmental disorders. In this study, we investigated developmental changes in the spatial organization of functional brain networks in infants using a longitudinal resting-state fMRI dataset comprising 137 scans from 74 low-likelihood developing infants aged 0-6 months. We applied independent component analysis to extract large-scale brain networks and utilized advanced spatial metrics, including network-averaged spatial similarity (NASS) to assess alignment with group-level patterns, network strength to quantify neural engagement based on voxel intensities, and network size to examine spatial distribution. Our findings reveal significant age-related increases in NASS across multiple networks, indicating greater consistency in functional organization over time. Additionally, most networks demonstrated increased network strength, reflecting heightened neural involvement, while network size exhibited distinct developmental trajectories, with some networks expanding and others remaining stable. These results highlight the dynamic evolution of functional brain architecture during early infancy, providing critical insights into neurodevelopmental processes.Clinical Relevance- This study provides critical insights into early brain network development, which is essential for identifying biomarkers of neurodevelopmental disorders such as autism and schizophrenia. By mapping typical maturation patterns using advanced spatial metrics, our findings offer a foundation for early detection of atypical development. Deviations in network organization and strength could serve as early indicators, supporting neuroimaging-based screening and intervention strategies to optimize neurodevelopmental outcomes.

PMID:41336104 | DOI:10.1109/EMBC58623.2025.11253059

Annu Int Conf IEEE Eng Med Biol Soc. 2025 Jul;2025:1-5. doi: 10.1109/EMBC58623.2025.11251640.

ABSTRACT

Brain tumors significantly disrupt brain network organization, yet the temporal dynamics of network reorganization following surgical intervention remain poorly understood. This study investigated longitudinal changes in functional brain network properties across pre-surgical, post-surgical, and follow-up time points in glioma patients. Using graph theory analysis of resting-state functional magnetic resonance imaging (fMRI) data, we examined whole-brain network metrics as well as the connections involving perilesional and contralesional regions. Results revealed significant alterations in network topology over time, with distinct patterns of reorganization in perilesional and contralesional regions, suggesting mechanisms of plasticity and recovery in brain network architecture following tumor resection.Clinical Relevance-These findings have significant implications for surgical planning and post-operative care, suggesting the need for therapeutic approaches that consider both local and distant network effects. The demonstrated importance of contralesional adaptation particularly warrants attention in rehabilitation strategies, potentially opening new avenues for targeted interventions in recovery.

PMID:41336078 | DOI:10.1109/EMBC58623.2025.11251640

Annu Int Conf IEEE Eng Med Biol Soc. 2025 Jul;2025:1-5. doi: 10.1109/EMBC58623.2025.11252604.

ABSTRACT

We present a novel topological framework for analyzing functional brain signals using time-frequency analysis. By integrating persistent homology with time-frequency representations, we capture multi-scale topological features that characterize the dynamic behavior of brain activity. This approach identifies 0D (connected components) and 1D (loops) topological structures in the signal's time-frequency domain, enabling robust extraction of features invariant to noise and temporal misalignments. The proposed method is demonstrated on resting-state functional magnetic resonance imaging (fMRI) data, showcasing its ability to discern critical topological patterns and provide insights into functional connectivity. This topological approach opens new avenues for analyzing complex brain signals, offering potential applications in neuroscience and clinical diagnostics.

PMID:41335918 | DOI:10.1109/EMBC58623.2025.11252604

Annu Int Conf IEEE Eng Med Biol Soc. 2025 Jul;2025:1-4. doi: 10.1109/EMBC58623.2025.11253645.

ABSTRACT

In neuroimaging-based Alzheimer's Disease (AD) diagnosis, effectively integrating structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) data while preserving clinical interpretability remains a significant challenge. To address this issue, we propose a novel transformer-based framework that unifies heterogeneous imaging features into coherent region-level representations. Our approach uniquely leverages prior anatomical knowledge to guide attention toward AD-relevant regions while employing a learnable mapping mechanism that transforms sMRI spatial features into biologically meaningful regional representations. We implement a consistency constraint to ensure optimal alignment between structural and functional coupling across modalities, followed by a Bayesian fusion strategy to integrate these aligned multi-modal features. Through comprehensive evaluation on the ADNI dataset, our method demonstrates not only superior diagnostic accuracy compared to existing state-of-the-art approaches but also provides clinically interpretable insights into AD-related brain connectivity patterns. This work represents a significant advancement in multi-modal neuroimaging analysis for AD diagnosis, successfully combining enhanced diagnostic performance with clinical interpretability.

PMID:41335802 | DOI:10.1109/EMBC58623.2025.11253645

Annu Int Conf IEEE Eng Med Biol Soc. 2025 Jul;2025:1-5. doi: 10.1109/EMBC58623.2025.11254461.

ABSTRACT

Tools for aiding in the diagnosis of Autism Spectrum Disorder (ASD) using machine learning (ML) and resting-state rs-fMRI (rs-fMRI) must encompass different phases such as data collection, preprocessing, feature extraction, model training, and validation. Many studies rely on a single preprocessing pipeline or use preprocessed data, which might not be optimal for the task at hand. This study investigates the impact of rs-fMRI preprocessing on the performance of ML models for ASD diagnosis. Using a subset of the Autism Brain Imaging Data Exchange (ABIDE) dataset, 72 subjects were preprocessed with 108 different configurations, and features were extracted to train 13 ML classifiers. Results indicate that preprocessing choices significantly influence model accuracy, with the best configurations achieving up to 95.83% accuracy. However, generalization tests on an extended dataset revealed a substantial performance drop, highlighting challenges in model robustness. Findings emphasize the need for adaptive preprocessing strategies and gender-balanced datasets to improve ASD classification reliability.

PMID:41335785 | DOI:10.1109/EMBC58623.2025.11254461

Annu Int Conf IEEE Eng Med Biol Soc. 2025 Jul;2025:1-6. doi: 10.1109/EMBC58623.2025.11254951.

ABSTRACT

Functional near-infrared spectroscopy (fNIRS) is an optical imaging modality which, similar to fMRI, measures cerebral hemodynamics associated with neural activity. It has several advantages over fMRI, including low cost, portability, compatibility with metal or electrical medical implants, and ease of integration with electroencephalography (EEG) and transcranial magnetic stimulation (TMS). However, fNIRS signal contains a number of confounding components. Physiological noises due to superficial absorption by the scalp and skull are present in all fNIRS data. Additionally, low-frequency oscillations of respiration, cardiac pulse and movements all obscure the underlying cerebral hemodynamic signals. Our previous work has developed an automatic processing pipeline that effectively removes these physiological noise components from data during voluntary tasks (e.g., a motor task) and an endogenous state (e.g., awake resting) [1], [2]. However, to date it has not been known if the noises behave similarly in recordings involving an externally injected stimulus such as TMS. Therefore, in a unique setup of concurrent fNIRS, EEG and TMS (fNET), this study examined the spatial and temporal profiles of fNIRS data and noises during motor, single pulse and repetitive TMS. Specifically, we compared the multichannel short separation recordings with the regularly distanced long separation data in a whole-head montage. The results showed that superficial fluctuations indeed were present in the TMS-concurrent fNIRS recordings and that the noise components behaved similarly across motor task, single pulse and repetitive TMS at individuals' alpha frequency, which warrants removal of such physiological noises.Clinical Relevance- Compared to fMRI, fNIRS offers a much less expensive alternative for measuring cortical hemodynamics. Importantly, fNIRS can provide a clinic accessible options for concurrent measurement with TMS when TMS is given as treatment to patients with depression or other neurological disorders. Our findings indicate that fNIRS data acquired during concurrent TMS are contaminated by superficial fluctuations and that careful removal of these physiological noises from fNIRS data is critical in obtaining accurate images of cerebral activity with fNIRS.

PMID:41335688 | DOI:10.1109/EMBC58623.2025.11254951

Annu Int Conf IEEE Eng Med Biol Soc. 2025 Jul;2025:1-7. doi: 10.1109/EMBC58623.2025.11254960.

ABSTRACT

Diagnosing Parkinson's disease (PD) is particularly challenging due to the intricate and variable nature of its biomarkers, which span motor and non-motor symptoms, differ across individuals, and evolve over time. While machine learning has been used to automate this process, most studies focus on limited biomarkers due to dataset constraints. This study introduces a Federated Learning (FL) framework that integrates electroencephalography (EEG) and resting-state functional magnetic resonance imaging (rs-fMRI) data for improved PD classification. Unlike traditional fusion-based studies integrating multiple biomarkers from the same subject group, the Federated Learning framework processes EEG and fMRI data separately from distinct subject groups. Client nodes treat these as independent datasets and utilize convolutional neural networks (CNNs), Graph CNNs, and ResNet-18 models for analysis. A central server then aggregates insights, simulating a diagnostic center to evaluate the relevance of additional biomarkers for enhanced PD detection utilizing support vector machines (SVM) and federated dynamic model aggregation (Fed-Dyn). Additionally, gender-specific evaluations suggest that male-exclusive models outperform female models in biomarker representation. The study underscores the necessity of demographic-aware frameworks and optimized fusion techniques for early-stage PD detection.Clinical Relevance- This study significantly enhances early PD diagnosis by integrating EEG and fMRI biomarkers through Federated Learning (FL), offering a more comprehensive view of neurodegenerative changes while preserving patient privacy. By addressing gender-specific biomarker differences and tailoring models to diverse patient profiles and disease stages, it supports precision medicine and equitable healthcare. The advanced fusion techniques improve diagnostic accuracy in terms of ROC-AUC score, aiding clinical decision-making and enabling scalable telemedicine solutions. Beyond PD, the framework holds potential for broader neurodegenerative research and sets benchmarks for biomarker-based diagnostics, paving the way for impactful advancements in precision neurology.

PMID:41335685 | DOI:10.1109/EMBC58623.2025.11254960

NMR Biomed. 2026 Jan;39(1):e70208. doi: 10.1002/nbm.70208.

ABSTRACT

Cerebrovascular reactivity (CVR) mapping based on resting-state BOLD fMRI can be widely available for research of vascular health not only in clinical studies but also in open databases. However, as it utilizes spontaneous CO2 fluctuations of blood as endogenous stimuli, resting-state CVR may be prone to low SNR and reproducibility if the CO2 fluctuation of an individual is small. The automatic identification of such poor-quality CVR datasets is crucial for large-scale research. Thus, in this work, we developed an automatic quality control algorithm for resting-state CVR mapping. Utilizing a total of 51 resting-state CVR maps acquired with three scanning protocols in each healthy participant, quality control parameters reflecting common characteristics of poor-quality CVR, including pooled variance of different tissue types, proportion of negative voxels in gray matter, and the sensitivity of the BOLD signal to CVR, were extracted and then combined into one comprehensive quality evaluation index (QEI). We further evaluated its performance by leave-one-out cross-validation and correlation analyses with test-retest reproducibility. Leave-one-out cross-validation showed that QEI was significantly correlated with the reference standard of quality evaluation in all left-out cases (r = 0.766). Correlation analyses with test-retest reproducibility revealed significant positive correlations between the worse QEI and similarity index of CVR maps from two tests (r = 0.809, 0.890, 0.396, and 0.654 for data from four open databases). The proposed QEI performed not only in good agreement with visual inspection but can also adapt in resting-state CVR from multiple age groups and scanning protocols, paving the way for the clinical applications of resting-state CVR mapping technology.

PMID:41334711 | DOI:10.1002/nbm.70208

Res Sq [Preprint]. 2025 Nov 23:rs.3.rs-7881643. doi: 10.21203/rs.3.rs-7881643/v1.

ABSTRACT

Delirium affects up to 50% of hospitalised, older patients and is linked to increased risk of death and long-term cognitive decline. Age-related changes in the ascending arousal system (AAS), including cholinergic and noradrenergic nuclei, may contribute to delirium vulnerability. Static and dynamic functional connectivity, across cortical and subcortical regions, was extracted from preoperative resting-state fMRI from 120 older adults (aged > 65 years old). Participants who developed delirium showed more segregated brain networks, cholinergic hyperconnectivity and noradrenergic hypoconnectivity. Dynamic patterns from these systems separated groups in low-dimensional space, suggesting altered temporal network dynamics. Normative maps of cholinergic gene expression density were associated with increased network segregation. These results suggest that aging-related AAS alterations-particularly compensatory cholinergic overactivity-may drive network changes that increase delirium risk. This work provides new insights into the neural mechanisms linking aging, arousal system dysfunction, and brain network disruption in delirium, mandating re-appraisal of leading delirium theories.

PMID:41333402 | PMC:PMC12668175 | DOI:10.21203/rs.3.rs-7881643/v1

bioRxiv [Preprint]. 2025 Nov 17:2025.09.22.677834. doi: 10.1101/2025.09.22.677834.

ABSTRACT

Understanding how the brain's functional architecture emerges prior to substantial postnatal visual experience is crucial for determining what initial capabilities infants possess and how they learn from their environment. Using resting-state fMRI from 584 neonates in the Developing Human Connectome Project, we provide the first comprehensive systems-level characterization of human visual cortex within hours of birth and across the third trimester of gestation. We discover that newborns possess a sophisticated visual architecture already functionally organized into three distinct pathways (ventral, lateral, and dorsal), each exhibiting posterior-to-anterior hierarchical structure and adult-like topographic organization. This tripartite visual organization differs from the bipartite organization observed in macaques, suggesting this architecture emerges through intrinsic developmental mechanisms rather than being a product of extensive postnatal experience and environmental adaptation. Moreover, pathway segregation, hierarchical ordering, and connectivity maturity all strengthen progressively with gestational age, revealing that visual cortical organization emerges through an active developmental program that unfolds across late gestation. Yet, despite this large-scale structure, individual pathways follow strikingly different maturation trajectories: dorsal areas exhibit a near-adult-like functional organization, even at the earliest gestational timepoints tested, whereas ventral areas remain immature and poised for experience-dependent refinement. These findings reframe our understanding of early visual development by revealing that complex functional networks emerge before substantial visual experience, yet are differentially prepared for plasticity, providing crucial insights into how evolution has optimized the brain for rapid learning while maintaining the flexibility needed for adaptation to diverse environments.

PMID:41332584 | PMC:PMC12667756 | DOI:10.1101/2025.09.22.677834

Physiol Rep. 2025 Dec;13(23):e70674. doi: 10.14814/phy2.70674.

ABSTRACT

Sensory processing disorder (SPD) is a neurodevelopmental condition characterized by impaired sensory discrimination and responsivity. Although the causes and neural correlates of SPD remain poorly understood, prenatal influences should be considered, as the prenatal environment is strongly implicated in the progression of neurodevelopmental disorders. One factor hypothesized to promote SPD is perinatal Western-style diet (WSD) exposure. This study explored the effects of perinatal WSD exposure on the proposed neural correlates of SPD in Japanese macaques. Functional connectivity between sensory and emotional processing areas was assessed at 4 months of age using resting-state functional magnetic resonance imaging (rs-fMRI). A machine learning model successfully predicted perinatal diet group based on functional connectivity strengths, indicating that differences in sensory connectivity exist between diet groups. Intra-somatomotor, visual-auditory, somatomotor-auditory, somatomotor-visual, and intra-visual network connections demonstrated the greatest differences between groups, with primary motor cortex connectivity being the most impacted. Connections to the amygdala were not major contributors to accurate model performance, but amygdala connectivity, especially to the somatomotor network, may still be a weak driver of model performance. These findings suggest that a proposed predictor of SPD, perinatal WSD exposure, impacts the functional connectivity of sensory processing areas relevant in SPD during early infancy.

PMID:41332183 | DOI:10.14814/phy2.70674

Acta Neuropathol Commun. 2025 Dec 1. doi: 10.1186/s40478-025-02183-w. Online ahead of print.

ABSTRACT

While neuroimaging studies have revealed notable white matter damage following mild traumatic brain injury (mTBI), the specific tracts and brain regions affected vary widely across studies. Here, we explored whether the spatial orientation of white matter tracts influences susceptibility to repeated mTBI, predicting that tracts oriented orthogonal to the axis of rotation of the head during impact (within the plane of rotation) would exhibit the most damage. Using a model of repeated rotational mTBI in mice, we acquired advanced diffusion MRI (diffusional kurtosis imaging using oscillating gradient encoding) and resting-state functional MRI (fMRI) data at baseline and 1-week post-injury. Consistent with our prediction, while both diffusivity and diffusional kurtosis decreased in the white matter of injured mice, only diffusional kurtosis revealed microstructural changes confined to tracts oriented orthogonal to the right-left axis of rotation. In addition, both region and subregion analyses showed functional connectivity (FC) deficits between regions connected via tracts running orthogonal to the rotation axis. The orientation-dependent changes in imaging metrics were validated by histopathological analyses. Females showed greater microstructural changes than males using diffusion MRI following injury, while no sex differences were detected by fMRI. Interestingly, the region-specific and subregion-specific FC analyses showed overlapping but non-identical changes in FC suggesting the utility of using both coarse and fine levels of brain parcellation for FC analyses in mTBI. These findings suggest that mTBI imaging studies may benefit from the consideration that damage after mTBI will predominate in tracts that are oriented orthogonal to the axis of rotation produced by the impact and that diffusivity and diffusional kurtosis as well as region and subregion-specific fMRI analyses can detect these changes.

PMID:41327381 | DOI:10.1186/s40478-025-02183-w

J Neurosci Methods. 2025 Nov 29:110646. doi: 10.1016/j.jneumeth.2025.110646. Online ahead of print.

ABSTRACT

BACKGROUND: Parkinson's disease (PD) involves concurrent changes in brain functional activity and white matter microstructure, yet single-modality analyses often fail to capture these complex alterations.

NEW METHODS: We propose a voxel-level dual-stream Swin Transformer fusion framework (DSTFP) to investigate multimodal structure-function relationships in PD. DSTFP employs parallel transformer branches to extract temporal dynamics from resting-state functional MRI (rs-fMRI) and topological features from diffusion tensor imaging (DTI) fractional anisotropy maps. A cross-modal attention fusion module establishes voxel-wise correspondence between functional and structural features.

RESULTS: Applied to the publicly available Parkinson's Progression Markers Initiative (PPMI) dataset, DSTFP discriminates PD, prodromal, and control groups with high robustness. Structural decoupling index (SDI) and structure-function coupling (SFC) analyses of fused features reveal distributed brain regions with characteristic alterations in functional-structural interactions.

COMPARISON WITH EXISTING METHODS: DSTFP outperforms conventional single-modality and baseline multimodal models in both classification accuracy and interpretability, providing more detailed insight into voxel-level structure-function relationships.

CONCLUSIONS: The proposed framework offers a robust, interpretable approach for multimodal neuroimaging analysis in PD. All source code is publicly available to support reproducibility (https://github.com/MAOmgg/DSTFP).

PMID:41325802 | DOI:10.1016/j.jneumeth.2025.110646

Clin Neurol Neurosurg. 2025 Nov 28;261:109277. doi: 10.1016/j.clineuro.2025.109277. Online ahead of print.

ABSTRACT

PURPOSE: The use of neuronavigation with superimposed mapping tools has enabled visualization of key fiber tracts and improved peri-operative planning. However, a limitation of these approaches is their reliance on a static underlying brain atlas, particularly in neurosurgical patients with brain tumors. A tool that enables qualification and quantification of brain region connectivity could refine approaches to surgical resection.

METHODS: We utilized a machine learning imaging platform, Quicktome™, to generate individualized functional parcels and tracts that dynamically adapt to perioperative change. The connectome was derived from a combination of diffusion tensor imaging and resting-state function magnetic resonance imaging. Matrices were generated from the functional MRI of four patients with intracranial neoplasms and the pre- and post-operative parcellation values were compared. The individual correlation and strength of regions were quantified. Hypo- and hyper-connected regions were marked as anomalous.

RESULTS: We present a case series of four patients to illustrate the correlation of the anomaly matrices with post-operative neurological changes. These include: post-operative delirium originating associated with salience network hypoconnectivity; visual hemineglect linked to hypoconnectivity in the dorsal attention network; and quantifiable improvements in the language network following the resolution of expressive aphasia. All differences between pre-and post-operative paired correlation values were statistically significant.

CONCLUSION: We demonstrate a novel approach to quantifying the extent to which anomalies in the functional connectome correlate with post-operative neurological changes. This has relevance in post-operative prognostication, provision of specialist therapy services, and could serve as a useful tool in surgical education and pre-operative planning.

PMID:41325661 | DOI:10.1016/j.clineuro.2025.109277