Neuroinformatics最新文献

Axonal beading is a key morphological indicator of axonal degeneration, which plays a significant role in various neurodegenerative diseases and drug-induced neuropathies. Quantification of axonal susceptibility to beading using neuronal cell culture can be used as a facile assay to evaluate induced degenerative conditions, and thus aid in understanding mechanisms of beading and in drug development. Manual analysis of axonal beading for large datasets is labor-intensive and prone to subjectivity, limiting the reproducibility of results. To address these challenges, we developed a semi-automated Python-based tool to track axonal beading in time-lapse microscopy images. The software significantly reduces human effort by detecting the onset of axonal swelling. Our method is based on classical image processing techniques rather than an AI approach. This provides interpretable results while allowing the extraction of additional quantitative data, such as bead density, coarsening dynamics, and morphological changes over time. Comparison of results obtained through human analysis and the software shows strong agreement. The code can be easily extended to analyze diameter information of ridge-like structures in branched networks of rivers, road networks, blood vessels, etc.

The insula, a deeply situated cortical structure beneath the Sylvian sulcus, plays a critical role in sensory integration, emotion regulation, and cognitive control in the brain. Although several studies have described its anatomical and functional characteristics, mathematical models that quantitatively represent the insula's complex structure and connectivity are lacking. This study aimed to develop a mathematical model to represent the anatomical localization and functional organization of the insula, drawing on current neuroimaging findings and established anatomical data. A three-dimensional (3D) ellipsoid model was constructed to mathematically represent the anatomical boundaries of the insula using Montreal Neurological Institute (MNI) coordinate data. This geometric model adapts the ellipsoid equation to reflect the spatial configuration of the insula and is primarily based on cytoarchitectonic mapping and anatomical literature. Relevant findings from prior imaging research, particularly those reporting microstructural variations across insular subdivisions, were reviewed and conceptually integrated to guide the model's structural assumptions and interpretation of potential applications. The ellipsoid-based 3D model accurately represented the anatomical dimensions and spatial localization of the right insula, centered at the MNI coordinates (40, 5, 5 mm), and matched well with the known volumetric data. Functional regions (face, hand, and foot) were successfully plotted within the model, and statistical analysis confirmed significant differences along the anteroposterior and superoinferior axes (p < 0.01 and p < 0.05, respectively). Dynamic simulations revealed oscillatory patterns of excitatory and inhibitory neural activity, consistent with established insular neurophysiology. Additionally, connectivity modeling demonstrated strong bidirectional interactions between the insula and key regions, such as the prefrontal cortex and anterior cingulate cortex (ACC), reflecting its integrative role in brain networks. This study presents a scientifically validated mathematical model that captures the anatomical structure, functional subdivisions, and dynamic connectivity patterns of the insula. By integrating anatomical data with computational simulations, this model provides a foundation for future research in neuroimaging, functional mapping, and clinical applications involving insula-related disorders.

Brain slicing is a commonly used technique in brain science research. In order to study the spatial distribution of labeled information, such as specific types of neurons and neuronal circuits, it is necessary to register the brain slice images to the 3D standard brain space defined by the reference atlas. However, the registration of 2D brain slice images to a 3D reference brain atlas still faces challenges in terms of accuracy, computational throughput, and applicability. In this paper, we propose the SlicesMapi, an interactive 3D registration method for brain slice sequence. This method corrects linear and non-linear deformations in both 3D and 2D spaces by employing dual constraints from neighboring slices and corresponding reference atlas slices and guarantees precision by registering images with full resolution, which avoids the information loss of image down-sampling implemented in the deep learning based registration methods. This method was applied to deal the challenges of unknown slice angle registration and non-linear deformations between the 3D Allen Reference Atlas and slices with cytoarchitectonic or autofluorescence channels. Experimental results demonstrate Dice scores of 0.9 in major brain regions, highlighting significant advantages over existing methods. Compared with existing methods, our proposed method is expected to provide a more accurate, robust, and efficient spatial localization scheme for brain slices. Therefore, the proposed method is capable of achieving enhanced accuracy in slice image spatial positioning.

In T₁-weighted magnetic resonance imaging (MRI), cortical thickness (CT) and gray-white matter contrast (GWC) capture brain morphological traits and vary with age-related disease. To gain insight into genetic factors underlying brain structure and dynamics observed during neurodegeneration, this genome-wide association study (GWAS) quantifies the relationship between single nucleotide polymorphisms (SNPs) and both CT and GWC in UK Biobank participants (N = 43,002). To our knowledge, this is the first GWAS to investigate the genetic determinants of cortical T₁-MRI GWC in humans. We found 251 SNPs associated with CT or GWC for at least 1% of cortical locations, including 42 for both CT and GWC; 127 for only CT; and 82 for only GWC. Identified SNPs include rs1080066 (THSB1, featuring the strongest association with both CT and GWC), rs13107325 (SLC39A8, linked to CT at the largest number of cortical locations), and rs864736 (KCNK2, associated with GWC at the largest number of cortical locations). Dimensionality reduction reveals three major gene ontologies constraining CT (neural signaling, ion transport, cell migration) and four constraining GWC (neural cell development, cellular homeostasis, tissue repair, ion transport). Our findings provide insight into genetic determinants of GWC and CT, highlighting pathways associated with brain anatomy and dynamics of neurodegeneration. These insights can assist the development of gene therapies and treatments targeting brain diseases.

Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental condition characterized by a spectrum of behavioral and cognitive traits. As the characteristics of ASD are highly heterogeneous across individuals, a dimensional approach that overcomes the limitation of the categorical approach is preferred to reveal the symptomatology of ASD. Previous neuroimaging studies demonstrated strong links between large-scale brain networks and autism phenotypes. However, the existing studies have primarily focused on univariate association analysis, which limits our understanding of autism connectopathy. Using resting-state functional magnetic resonance imaging data from 309 participants (168 individuals with ASD and 141 typically developing controls) across a discovery dataset and two independent validation datasets, we identified multivariate associations between high-dimensional neuroimaging features and diverse phenotypic measures (20 or 7 measures). We generated low-dimensional representations of functional connectivity (i.e., gradients) and assessed their multivariate associations with autism-related phenotypes of social, behavioral, and cognitive problems using sparse canonical correlation analysis (SCCA). We selected three functional gradients that represented the cortical axes of the sensory-transmodal, motor-visual, and multiple demand-rests of the brain. The SCCA revealed multivariate associations between gradients and phenotypic measures, which were noted as linked dimensions. We identified three linked dimensions: the links between (1) the first gradient and social impairment, (2) the second and internalizing/externalizing problems, and (3) the third and metacognitive problems. Our findings were partially replicated in two independent validation datasets, indicating robustness. Multivariate association analysis linking high-dimensional neuroimaging and phenotypic features may offer promising avenues for establishing a dimensional approach to autism diagnosis.

Inflammation within the spinal subarachnoid space leads to arachnoid hypercellularity. Multiplex immunohistochemistry (MP-IHC) enables the quantification of immune cells to assess arachnoid inflammation, but manual counting is time-consuming, impractical for large datasets, and prone to operator bias. Although automated colocalization methods exist, many clinicians prefer manual counting due to challenges with diverse cell morphologies and imperfect colocalization. Object-based colocalization analysis (OBCA) tools address these issues, improving accuracy and efficiency. We evaluated semi-automated and automated OBCA techniques for quantifying colocalized immune cells in human arachnoid tissue sections. Both methods demonstrated sufficient reliability across morphologies (P < 0.0001). While automated counts differed significantly from manual counts, their strong correlation (R² = 0.7764-0.9954) supports their reliability for applications where exact counts are less critical. Additionally, both techniques significantly reduced analysis time compared to manual counting. Our findings support the use of automated and semi-automated colocalization analysis methods in histological samples, particularly as sample size increases.

Patch-clamp recordings are vital for investigating the electrical properties of excitable cells, yet the analysis of these recordings often involves time-consuming manual procedures prone to variability. To address this challenge, we developed the Auto ANT (Automated Analysis and Tables) open-source software, an automated, user-friendly graphical interface for the extraction of firing properties and passive membrane properties from patch-clamp recordings. Thanks to the novel built-in automation feature, Auto ANT enables batch analysis of multiple files recorded with the same protocol in minutes. Our tool is designed to streamline data analysis, providing a fast, efficient, and reproducible alternative to manual methods. With a focus on accessibility, Auto ANT allows the users to perform precise comprehensive electrophysiological analyses without requiring programming expertise. By combining automation with a user-centric design, Auto ANT offers a valuable resource for researchers to accelerate data analysis while promoting consistency and reproducibility across different studies.

Despite the vast number of publications reporting seizures and the reliance of the field on accurate seizure detection, there is a lack of open-source software tools in the scientific community for automating seizure detection based on electrographic recordings. Researchers instead rely on manual curation of seizure detection that is highly laborious, inefficient and can be error prone and heavily biased. Here we have developed - SeizyML - an open-source software that combines machine learning models with manual validation of detected events reducing bias and promoting efficient and accurate detection of electrographic seizures. We compared the validity of four interpretable machine learning classifiers (decision tree, gaussian naïve bayes, passive aggressive classifier, and stochastic gradient descent classifier) on an extensive electrographic seizure dataset that we collected from chronically epileptic mice. We find that the gaussian naïve bayes model detected all seizures in our dataset, had the lowest false detection rate, was robust to misclassifications, and only required a small amount of data to train. This approach has the potential to be a transformative research tool overcoming the analysis bottleneck that slows research progress.

In this Matters Arising, we critically examine the data processing and computational challenges highlighted under the high-resolution, three-dimensional reconstruction of human cortical tissue by Shapson-Coe et al. While the study represents a technical milestone in connectomics, involving a 1.4-petabyte dataset derived from mapping a cubic millimeter of temporal cortex, the findings also reveal the substantial obstacles inherent in scaling such approaches to the entire human brain. Beyond the application of artificial intelligence (AI) for segmentation and synapse detection, the study underscores the immense complexity of data acquisition, cleaning, alignment, and visualization at this scale. This article contextualizes these challenges by comparing the computational and infrastructural requirements of the Shapson-Coe work to other large-scale neuroscience initiatives, such as the fruit fly brain atlas, and explores emerging technologies like quantum computing and neuromorphic hardware as potential solutions. Additionally, we discuss the ethical and logistical implications of managing zettabyte-scale datasets and emphasize the necessity of international collaboration to achieve the ambitious goal of mapping the human connectome. By critically addressing these challenges and potential solutions, this article aims to guide future advancements in the field of connectomics and their transformative applications in neuroscience, artificial intelligence, and medicine.

Background: The occurrence of brain disorders correlates with detectable dysfunctions in the specialization of brain connectomics. While extensive research has explored this relationship, there is a lack of studies specifically examining the statistical correlation between the integration and segregation of psychotic brain networks using high-order networks, given the limitations of low-order networks. Moreover, these dysfunctions are believed to be linked to information imbalances in brain functions. However, our understanding of how these imbalances give rise to specific psychotic symptoms remains limited.

Methods: This study aims to address this gap by investigating variations at the topological high-order level of the system with regard to specialization in both healthy individuals and those diagnosed with schizophrenia. By employing graph-theoretic brain network analysis, we systematically examine information integration and segregation to delineate system-level differences in the connectivity patterns of brain networks.

Results: The findings indicate that topological high-order functional connectomics highlight differences in the connectome between healthy controls and schizophrenia, demonstrating increased cingulo-opercular task control and salience interactions, while the interaction between subcortical and default mode networks, dorsal attention and sensory/somatomotor mouth decreases in schizophrenia. Furthermore, we observed a reduction in the segregation of brain systems in healthy controls compared to individuals with schizophrenia, which means the balance between segregation and integration of brain networks is disrupted in schizophrenia, suggesting that restoring this balance may aid in the treatment of the disorder. Additionally, the increased segregation and decreased integration of brain systems in schizophrenia patients compared to healthy controls may serve as a novel indicator for early schizophrenia diagnosis.

Conclusion: We discovered that topological high-order functional connectivity highlights brain network interactions compared to low-order functional connectivity. Furthermore, we observed alterations in specific brain regions associated with schizophrenia, as well as changes in brain network information integration and segregation in individuals with schizophrenia.