Neuroinformatics最新文献

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.

The open field test is widely used in behavioral neuroscience, providing insights into exploration, anxiety, and the learning processes associated with habituation to novelty. Analyses of exploratory behaviors in open field areas rely heavily on movement changes over time. These activity measures are susceptible to confounds from group differences in locomotor abilities and only provide an indirect measure of learning during exploration. Considerable effort has been placed on identifying additional measures of behavior that can better describe changes in exploration and habituation of novelty. Two measures for enhanced analysis of exploration are coverage and directional persistence (P₊₊). Coverage measures the number of visits to segments of the arena boundary and represents the number of opportunities to habituate to the novelty of this boundary. P₊₊ measures the probability of continued movement in the same direction, reflecting goal-directed exploration, which decreases as the animal habituates the novel arena. Our new Python package, opynfield, calculates coverage, P₊₊, and activity measures from open field tracking data. We further introduce versions of coverage and the analysis of additional motion probabilities. The package includes new, in-depth statistical approaches and data visualizations. We demonstrate the applicability of opynfield using experiments with Drosophila melanogaster in which we (1) validate opynfield's statistical tests, (2) substantiate coverage as a measure of novelty habituation, and (3) characterize behavioral differences in exploration. We also illustrate the utility of opynfield for analyzing rodent exploration by applying it to data from an experiment with Mus musculus. By leveraging full-density tracking data, opynfield facilitates a more nuanced understanding of exploration, potentially leading to improved insights into animal behavior and changes in learning, locomotor activity, and anxiety.

This computational study aimed to optimize the theta burst stimulation (TBS) protocols by systematically exploring the effects of novel frequency couplings combining alpha-band bursts (10 Hz) with pulses in beta (21-29 Hz) and gamma (30-100 Hz) ranges on cortical excitability. Utilizing a revised calcium-dependent plasticity model, we simulated intermittent (iTBS) and continuous (cTBS) TBS after-effects under conventional (5 Hz burst, 50 Hz pulse), Nyffeler's modified (6 Hz burst, 30 Hz pulse), and proposed alpha-beta/gamma frequency couplings. Model robustness was assessed via sensitivity analyses. Novel alpha-beta/gamma couplings consistently induced more pronounced Motor-Evoked Potential (MEP) after-effects. For iTBS/cTBS, alpha-beta coupling (10 Hz burst, 21 Hz pulse) yielded the highest facilitatory/inhibitory effect (14.25/-93.17), markedly surpassing Nyffeler's (7.71/-8.81) and conventional (5.48/-5.35). Alpha-gamma coupling (10 Hz burst, 30 Hz pulse) also showed superior effects. Sensitivity and uncertainty analyses confirmed higher responsiveness. Coupling alpha-band bursts with targeted beta/gamma pulse frequencies markedly enhances the efficacy of TBS-induced cortical plasticity. These findings provide a strong computational rationale for empirical validation and potential clinical translation to improve neuromodulation precision in neuropsychiatric disorders. This work introduces promising optimized TBS protocols that may elevate therapeutic outcomes and reduce treatment variability, advancing non-invasive brain stimulation interventions.

Modelling real-world networks allows investigating the structure and the dynamics of such networks, which led to significant developments in various scientific fields. One of the most used models in these investigations is the Watts-Strogatz, with a structure composed of high clustering and short path lengths known as small-world networks. This model proposes an interesting gradient between regular and random networks, but its generating process, which relies on a single rewiring probability parameter, is hard to access and to manipulate. In order to study the mechanics of the Watts-Strogatz model, the present work proposes a new method based on deep neural networks that could estimate its probability p. To illustrate its applicability, neuroimaging and phenotypic resting-state fMRI data were used from patients with ADHD and typical development children, obtained from the ADHD-200 database. The neural network efficiently estimated the probability parameter, resulting in small-world graphs for functional brain connectivity with a mean ± s.e.m. p distribution of 0.804 ± 0.003. Despite no difference was found considering the gender or diagnosis of participants, the generalized linear model revealed age as a significant predictor of p (mean ± s.e.m.: 4.410 ± 0.877; p < 0.001), indicating a great effect of neurodevelopment on the brain network's structure. The proposed approach is promising in estimating the probability of the Watts-Strogatz model, and its application has the potential to improve investigations of network connectivity with a relatively efficient and simple framework.

The fasciola cinerea (FC) is a slender archicortical band at the posterior hippocampal tail, and its human morphology and network role are poorly defined. To generate a reproducible in vivo three-dimensional (3D) model of the FC, quantify its geometry, characterize structural and functional connectivity within posterior-medial memory networks, and test a tractography-constrained computational model in which the FC acts as a multiplicative gate. Open 7 T datasets, structural, diffusion, and resting-state functional magnetic resonance imaging (fMRI) were anchored to BigBrain and Julich-Brain priors. A semi-automated, atlas-guided pipeline was used to segment the FC and derive morphometrics (volume, thickness, width, curvature, and Laplace-Beltrami spectral shape). Reliability was assessed using the Dice, 95% Hausdorff distance, and test-retest intraclass correlation coefficient (ICC). Diffusion tractography was used to estimate the FC structural pathways toward retrosplenial (RSC), parahippocampal (PHC), posterior cingulate (PCC), and thalamic targets. Resting-state coupling was summarized using Fisher-z correlations and narrowband coherence. A Wilson-Cowan neural mass model, constrained by tractography, simulated FC-dependent FC-RSC coherence with morphometric scaling of gating gain. Segmentation was reliable (Dice = 0.78 ± 0.05; 95% Hausdorff = 1.62 ± 0.41 mm; ICC_volume = 0.88; ICC_thickness = 0.82). Group morphometrics: volume 84.3 ± 17.9 mm³, mean thickness 0.92 ± 0.15 mm, width 1.86 ± 0.31 mm, centerline length 14.2 ± 2.1 mm. FC showed preferential connectivity: FC→RSC 0.21 ± 0.09; FC→PHC 0.18 ± 0.08; FC→PCC 0.11 ± 0.06; FC→Thalamus 0.06 ± 0.04. Resting-state coupling was strongest for FC-RSC (z = 0.24 ± 0.12) with a slow-band coherence enhancement. Thickness predicted the FC→RSC strength (β = 0.17 per 0.1 mm) and FC-RSC z (β = 0.08 per 0.1 mm), and higher curvature was negatively related. The gating model reproduced empirical FC-RSC coherence (r = 0.52 ± 0.11), and morphometric scaling improved the fit (Δr = + 0.06). We provide an anatomically grounded and mathematically validated 3D FC model that links microstructures to mesoscale connectivity. Preferential posterior-medial coupling and morphometry-dependent gating support the FC as a modulatory interface in human memory networks and yield testable markers for individualized mapping and clinical translation.

Background: Synthetic neuroimaging data has the potential to augment and improve the generalizability of deep learning models. However, memorization in generative models can lead to unintended leakage of sensitive patient information, limiting model utility and jeopardizing patient privacy.

Methods: We propose RELICT-NI (REpLIca deteCTion-NeuroImaging), a framework for detecting replicas in synthetic neuroimaging datasets. RELICT-NI evaluates image similarity using three complementary approaches: (1) image-level analysis, (2) feature-level analysis via a pretrained medical foundation model, and (3) segmentation-level analysis. RELICT-NI was validated on two clinically relevant neuroimaging use cases: non-contrast head CT with intracerebral hemorrhage (N = 774) and time-of-flight MR angiography of the Circle of Willis (N = 1,782). Expert visual scoring was used as the reference for identifying replicas. Balanced accuracy at the optimal threshold was reported to assess replica classification performance of each method.

Results: The reference visual rating identified 45 of 50 and 5 of 50 generated images as replicas for the NCCT and TOF-MRA use cases, respectively. For the NCCT use case, both image-level and feature-level analyses achieved perfect replica detection (balanced accuracy = 1) at optimal thresholds. A perfect classification of replicas for the TOF-MRA case was not possible at any threshold, with the segmentation-level analysis achieving the highest balanced accuracy (0.79).

Conclusions: Replica detection is a crucial but often neglected validation step in developing deep generative models in neuroimaging. The proposed RELICT-NI framework provides a standardized, easy-to-use tool for replica detection and aims to facilitate responsible and ethical synthesis of neuroimaging data.

Relevance statement: Our developed replica detection framework provides an important step towards standardized and rigorous validation practices of generative models in neuroimaging. Our method promotes the secure sharing of neuroimaging data and facilitates the development of robust deep learning models.

The recent advance in technology allows for the photorealistic digitalization of anatomical specimens that can now be presented through various dynamic visualization modalities, enabling a more interactive learning experience. This study explores a comprehensive workflow for reproducibly integrating photorealistic three-dimensional (3D) anatomical scans of the supra- and infratentorial venous system with stereoscopic visualization and virtual reality (VR) for anatomical learning. A formaldehyde-fixed head and neck specimen was injected with radiopaque dye into its vessels, and a post-mortem computed tomography (CT) venography was performed. A layered anatomical dissection of the intracranial venous system was performed. Photogrammetry surface scanning was employed to create 3D anatomical models, which were then post-processed to produce stereoscopic 3D images and videos using open-source software. In addition, the 3D models were formatted for immersive VR environment integration. Six photorealistic surface models and one CT venography-based reconstruction were generated. These were incorporated into several platforms: multiplayer VR environment using stand-alone headsets, and stereoscopic materials suitable for phone-based VR viewers, 3D multimedia projectors, or 3D monitors with passive or active glasses. These formats supported multiple learning scenarios (VR in single or multiplayer sessions), 3D stereoscopic lectures using 3D multimedia, real-time 3D stereoscopic visualization, or prerecorded videos for phone-based VR visualization. Building on these formats, the proposed workflow enables a realistic and spatially accurate representation of the anatomical data with photorealistic 3D models and facilitates the creation of accessible educational content for 3D stereoscopic presentations and immersive dedicated VR sessions, all through a user-friendly technical approach.