Neuroinformatics最新文献

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.

Research in hearing science often relies on auditory models to describe listener's behaviour and its neural underpinning in acoustic environments. These models gather empirical evidence from behavioural data to address research questions on the neural mechanisms underlying sound perception. Despite seemingly similar statistical methods, auditory models are often implemented for each study separately, which hinders reproducibility and across-study comparisons, thus limiting the advancement at a field level. Here, we introduce a framework for studying neural mechanisms of sound perception by employing auditory modeling based on Bayesian inference (FrAMBI), a MATLAB/Octave toolbox. FrAMBI provides a standardized structure to implement an auditory model following the perception-action cycle and enables the automatic application of statistical analysis with behavioural data. We show FrAMBI's capabilities in several examples with increasing levels of complexity within the context of sound source localisation tasks: a basic implementation for a static scenario, iterating over the perception-action cycle with a moving sound source, the definition of multiple model variants testing different neural mechanisms, and the procedure for parameter estimation and model comparison. Being integrated into the widely used auditory modelling toolbox (AMT), FrAMBI is planned to be maintained in the long term and expanded accordingly, fostering reproducible research in the field of neuroscience.

The complementary properties of both modalities can be exploited through the fusion of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data. Thus, a joint analysis of both modalities can be used in brain studies to estimate brain activity's shared and unshared components. This study introduces a comprehensive approach for jointly analyzing EEG and fMRI data using the advanced coupled matrix tensor factorization (ACMTF) method. The similarity of the components based on normalized mutual information (NMI) was defined to overcome the restrictive equality assumption of shared components in the common dimension of the ACMTF method. Because the mutual information (MI) measure can identify both linear and nonlinear relationships between the components, the proposed method can be viewed as a generalization of the ACMTF method; thus, it is called the generalized coupled matrix tensor factorization (GCMTF). The proposed GCMTF method was applied to simulated data, in which the components exhibited a nonlinear relationship. The results demonstrate that the average match score increased by 23.46% compared with the ACMTF model, even with different noise levels. Furthermore, applying this method to real data from an auditory oddball paradigm demonstrated that three shared components with frequency responses in the alpha and theta bands were identified. The proposed MI-based method cannot only extract shared components with any nonlinear or linear relationship but can also identify more active brain areas corresponding to an auditory oddball paradigm compared to ACMTF and other similar methods.

The bidirectional interactions between brain and heart through autonomic nervous system is the prime focus of neuro-cardiology community. The computer models designed to analyze brain and heart signals are either complex in terms of molecular and cellular interactions or not capable of representing the complex ion channel dynamics. Therefore, scientists are unable to extract the overall behavior of organs by electrical response of heterogeneous cells of brain and heart. In this study, a unified model of excitable cells is proposed that can be modulated by adrenergic features. By implementing the proposed model, a network of one thousand sparsely coupled cardio-neural network is simulated. The major findings of study include i. cardiac heterogeneity in electrical behavior of cardiac myocytes is the prime factor of heart rate variability ii. Brain-heart interplay through electrical pulses holds the necessary information of brain and heart signals that can be analyzed through spiking neural networks iii. Heart rate variability can be predicted and monitored by spiking neural networks from electrophysiological recordings of brain and heart iv. Heart rate variability related to tachycardia and bradycardia depends upon the polarization protocols of cardiac myocytes during plateau phase of action potential. This study provides the modeling and simulation phase of brain-heart interface to predict the morbidity at early stages. The recent advancements in nano-electronics will make is possible to develop brain-heart interface as nano-chip to deploy in subject to stimulate the brain-heart interplay through electrophysiological signals.