Neuroinformatics最新文献

A significant challenge for neurofeedback training research and related clinical applications, is participants' difficulty in learning to induce specific brain patterns during training. Here, we address this issue in the context of fMRI-based decoded neurofeedback (DecNef). Arguably, discrepancies between the data used to construct the decoder and the data used for neurofeedback training, such as differences in data distributions and experimental contexts, neural and non-neural noise, are likely the cause of the difficulties of the aforementioned participants. Here, we developed a co-adaptation procedure using standard machine learning algorithms. The procedure involves an adaptive decoder algorithm that is updated in real time based on its predictions across neurofeedback trials. First, we tested the procedure via simulations using a previous DecNef dataset and showed that decoder co-adaptation can improve performance during neurofeedback training. Importantly, a drift analysis demonstrated the stability of the co-adapted decoder throughout the neurofeedback training sessions. We then collected real time fMRI data in a DecNef training procedure to provide proof of concept evidence that co-adaptation enhances participant's ability to induce the target state during training. Thus, personalized decoders through co-adaptation can improve the precision and reliability of DecNef training protocols to target specific brain representations, with ramifications in translational research. The tools are made openly available to the scientific community.

Muscle weakness after immobilization often exceeds that explained by loss of muscle mass alone, suggesting a role for neuromuscular synaptic changes. To quantify these adaptations, we developed a composite transcriptomic Neuromuscular Junction (NMJ) Remodeling Score and evaluated its behavior relative to classical atrophy pathways during short-term unloading. We analyzed vastus lateralis RNA sequencing data from adults undergoing 10 days of unilateral lower-limb suspension followed by a 21-day recovery, generating NMJ and atrophy scores for 15 and 10 genes, respectively. Transcriptome-wide testing across more than twenty thousand genes identified a broad pattern of metabolic suppression. The NMJ score showed a large effect increase during unloading and partial normalization with recovery, while the atrophy score rose more strongly and reversed during recovery. The two scores demonstrated weak correlation, consistent with distinct biological processes. Individual NMJ-related genes displayed coordinated regulation, including marked upregulation of several acetylcholine receptor subunits and modest downregulation of muscle signaling kinase (MuSK), reflecting a denervation-like transcriptional pattern. Directional replication in a 60-day bed rest cohort supported generalizability across disuse conditions. Together, these findings indicate that limb unloading elicits measurable transcriptomic remodeling at the NMJ that is only partially aligned with atrophy signaling, providing a framework for investigating neural contributions to immobilization-induced weakness.

Tauopathies are characterised by a progressive accumulation of hyperphosphorylated tau. However, early and intermediate stages remain challenging to quantify due to subtle and heterogeneous morphological characteristics. This study evaluates a deep learning framework for classifying multiple temporal stages of tauopathy progression using AT8 (anti-phospho-tau antibody)-stained cortical micrographs in a controlled traumatic brain injury mouse model - an underexplored application. Three convolutional neural network (CNN) architectures were examined: a custom CNN and two transfer-learning models (InceptionV3 and DenseNet). Images were grouped into four post-injury stages: 1 day, 1 week, 1 month and 3 months. Preprocessing included normalisation, augmentation and oversampling to address imbalance. Performance was assessed using stratified k-fold cross-validation with accuracy, macro-F1, per-class F1, and one-vs-rest area under the receiver operating characteristic curve (AUC). DenseNet achieved the best overall performance (accuracy = 70.9%, macro-F1 = 0.68) with strong discrimination for the 1-week stage (F1 = 0.95). All models showed limited separability in the earliest post-injury stage (1 day), while intermediate to late stages (1-3 months) exhibited partial overlap, consistent with the progressive nature of tau accumulation. These results indicate that deep learning, particularly transfer learning, offers a scalable approach for automated temporal staging of tauopathy in preclinical histology. Although the results are based on internal cross-validation without independent animal-level identifiers or external cohorts, the proposed framework provides a reliable foundation for incorporating CNN-based analysis into digital neuropathology workflows. Larger multi-centre datasets and slide-level modelling will be required to assess generalisation and support applications in early detection, longitudinal tracking, and treatment evaluation of tau-related neurodegeneration.

The study of structural brain networks (SBNs) offers critical insights into brain-cognition relationships. However, a comprehensive comparison of these methods in terms of their topological properties, cognitive relevance, and sensitivity to connection density remains lacking. This study compares two types of individual-level SBNs-morphometric similarity networks (MSNs) and morphometric inverse divergence (MIND) networks-by analyzing their associations with cognitive performance using sMRI data from 29 male children. Group- and individual-level analyses were conducted to evaluate differences in hemispheric connectivity, topological features, and their correlations with cognitive performance across different connection densities. In our analyses, a connection density of [Formula: see text] appeared optimal for stabilizing network properties and maximizing cognitive correlations in both MSN and MIND. Moreover, advanced network segregation and integration metrics (such as local efficiency and node versatility, along with their global summaries) demonstrated greater sensitivity to cognitive performance. However, MSNs appeared to provide a more reliable framework, demonstrating more stable associations across connection densities in topological and hemispheric dimensions. Specifically, higher cognitive performance may be linked to stronger left intra-hemispheric connectivity, weaker inter-hemispheric connectivity, and more modular network organization-consistent with established theories of hemispheric specialization and efficient modularity. In contrast, MIND networks exhibit reduced effectiveness and stability across metrics and densities in our data. These preliminary insights enhance our understanding of brain-cognition relationships and provide practical guidelines for parameter selection and metric identification in network-based cognitive analyses.

This study addresses the important question of how neuron model choice and learning rules shape the classification performance of Spiking Neural Networks (SNNs) in bio-signal processing. By systematically contrasting Leaky Integrate-and-Fire, metaneurons, and probabilistic Levy-Baxter (LB) neurons across spike-timing dependent plasticity, tempotron, and reward-modulated learning, we identify model-rule combinations best suited for capturing the temporal richness of neural data. A novel contribution is the integration of a complexity-driven evaluation into the SNN pipeline. Using Lempel-Ziv Complexity (LZC), an entropy-related measure of spike-train regularity, we provide a consistent and interpretable benchmark of classification outcomes across architectures. To probe neural dynamics under controlled conditions, we employed synthetic datasets with varying temporal dependencies and stochasticity, including Markov and Poisson processes established models of neuronal spike-trains. Moreover, we validated the observed trends on real data by testing the same architectures on an MNIST dataset. Performance trends reveal strong dependence on the interaction between neuron model, learning rule, and network size. The LZC based evaluation highlights configurations resilient to weak or noisy signals. The LB-tempotron combination proved most effective for tasks with complex temporal patterns, leveraging adaptive neuronal dynamics and precise spike-timing exploitation. LIF-based architectures with Bio-inspired Active Learning delivered solid accuracy at lower computational cost, while hybrid models offered a versatile middle ground when paired with appropriate learning algorithms. This work delivers the first systematic mapping of neuron model learning rule synergies in SNNs and introduces complexity-based evaluation framework that sets a robust benchmark for biosignal classification. Beyond benchmarking, our results provide actionable guidelines for building next-generation SNNs capable of handling the variability and complexity of real neural data.

Central nervous system (CNS) involvement in primary Sjögren's syndrome (pSS), although less frequent, can lead to serious complications. Our study aimed to assess white matter (WM) tract integrity, identify specific regions of disruption, quantify diffusion tensor imaging (DTI) metrics, and correlate these findings with rheumatologic factors. Thirty-three patients with pSS and twenty-six healthy subjects included in the control group, matched by gender and age were studied by performing brain DTI, which was reprocessed by the TractSeg algorithm based on fully convolutional neural networks (FCNN). The result was the segmentation of 72 main WM tracts, which were used to calculate quantitative values (fractional anisotropy - FA) of WM integrity. Finally, correlations of these values with rheumatological factors were made. Considering all WM tracts collectively, we observed significant differences between the study group and the control group. Numerous areas showed significant reductions in FA values, including novel observations involving all cerebellar peduncles and optic radiations. There were numerous significant correlations between altered FA values and particular clinical factors such as CRP level, haemoglobin level, presence of cryoglobulins and more. Our work unquestionably confirms and emphasises CNS involvement in pSS patients. Multiple impaired WM tracts correspond with symptoms associated with CNS, moreover, there were areas of impaired WM tracts previously not reported in DTI studies. Finally, multiple significant correlations were found with particular rheumatological factors, can indirectly indicate the influence of the severity of pSS on the integrity of WM tracts of CNS.

Accurate classification of neuronal cell types is essential for understanding brain organization, but multimodal neuron datasets are scarce and strongly imbalanced across subclasses. We present a benchmark of synthetic data augmentation methods for predicting electrophysiology-defined neuronal classes (e-types) in the Allen Cell Types mouse visual cortex dataset. Two supervised tasks were evaluated over the same 17 e-type labels: prediction from electrophysiology features alone (E→e-type) and prediction from combined morphology plus electrophysiology features (M + E→e-type). We established real-data baselines across multiple classifier families under a unified preprocessing pipeline, then augmented only the training sets using matched per-class grids with Synthetic Minority Over-sampling Technique (SMOTE) and deep generative models: Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), masked autoregressive normalizing flows, and Denoising Diffusion Probabilistic Models (DDPM). Augmentation produced substantial generalization gains when applied in the native high-dimensional feature space, whereas introducing dimensionality reduction largely suppressed these benefits. SMOTE delivered the most robust and consistent improvements across tasks and augmentation levels. To assess biological realism, we introduced a fidelity framework combining feature-wise distribution comparisons, statistical concordance tests, and distance-based measures that compare synthetic-to-real variability against the natural variability between real classes. Most synthetic datasets stayed within biological diversity bounds, with deviations concentrated in the rarest subclasses. These results provide practical guidance on selecting and validating synthetic augmentation for neuronal subtype classification.

Neuroimaging open-data initiatives have led to increased availability of large scientific datasets. While these datasets are shifting the processing bottleneck from compute-intensive to data-intensive, current standardized analysis tools have yet to adopt strategies that mitigate the costs associated with large data transfers. A major challenge in adapting neuroimaging applications for data-intensive processing is that they must be entirely rewritten. To facilitate data management for standardized neuroimaging tools, we developed Sea, a library that intercepts and redirects application read and write calls to minimize data transfer time. In this paper, we investigate the performance of Sea on three preprocessing pipelines applied to three different neuroimaging datasets on two high-performance computing clusters. Our results demonstrate that Sea provides large speedups (up to 32×) when the shared file system's performance is deteriorated. When the shared file system is not overburdened by other users, performance is unaffected by Sea, suggesting that Sea's overhead is minimal even in cases where its benefits are limited.