Neuroinformatics最新文献

Neuroimaging studies have and continue to advance our understanding of the neurobiology of dyslexia. Integration of data from these studies has the potential to replicate findings, deepen understanding through theoretically focused research, and provide for unexpected discovery. This data integration can be important for questions where a sufficiently large and well-defined group of participants is necessary for sufficient experimental power, particularly for a complex disorder where age, language background, and cognitive profiles can impact imaging results. We have developed a data-sharing platform to provide a data repository, image processing resources, and data analysis tools, with an emphasis on data harmonization across retrospective datasets ( https://dyslexiadata.org ). Here, we summarize data sharing, download, imaging metrics, and quality and privacy considerations in the design of and resources available through this repository. By providing access to a relatively large multisite dataset, researchers can test hypotheses about reading development and disability, test novel data analysis methods, even within the platform, and advance understanding of dyslexia.

With recent developments in recording hardware, the processing of neuronal data must keep up with the increasing volumes and complexity by capturing the intrinsic relationships between instances of neuronal activity while remaining invariant to noise. Here, we explore a suite of non-linear manifold feature extraction methods - including PHATE, t-SNE, UMAP, TriMap - in an attempt to identify the most adequate method for automated spike sorting. Spike sorting is the process of clustering instances of neuronal activity, called spikes, based on similarity. By embedding high-dimensional spike shapes into low-dimensional manifolds that preserve local and global structure, we demonstrate more separable and robust clusters than those obtained via traditional feature extraction methods, such as PCA. We evaluated all feature extraction methods analyzed on 95 single-channel synthetic datasets and 2 single-channel real datasets spanning a range of cluster counts. Quantitative evaluation using clustering performance metrics (such as Adjusted Rand Index, Silhouette Score, etc.) indicates that several manifold feature extractions outperform other feature extraction methods. Our results suggest that the embeddings obtained by non-linear manifold approaches can offer a powerful, high-precision option in the spike sorting of the next-generation of electrophysiological recordings. While this study focuses on single-channel data and a subset of manifold learning techniques, a baseline has been established, and future avenues of research have been opened through this work. Future work may extend these insights to multi-channel settings, such as high-density probes and incorporate emerging manifold methods, such as hierarchical and multi-view extensions, which could further improve the robustness and accuracy of spike sorting.

Brain functional connectivity alterations, that is, pathological changes in the signal exchange between areas of the brain, are occurring in several neurological diseases, including neurodegenerative and neuropsychiatric ones. They consist in changes in how brain functional networks work. By conceptualising a brain space as a space whose points are connectome configurations representing brain functional states, changes in brain network functionality can be represented by paths between these points. Paths from a healthy state to a diseased one, or between diseased states as instances of disease progression, are modelled as the action of the Krankheit-Operator, that produces changes from a brain functional state to another one. This study proposes a formal representation of the space of brain states and presents its computational definition. Moreover, references to patients affected by Parkinson's disease, schizophrenia, and Alzheimer-Perusini's disease are included for discussing the proposed approach and possible developments of the research toward a generalisation.

Harmonizing multisite functional magnetic resonance imaging (fMRI) data is crucial for eliminating site-specific variability that hinders the generalizability of machine learning models. Traditional harmonization techniques, such as ComBat, depend on additive and multiplicative factors, and may struggle to capture the non-linear interactions between scanner hardware, acquisition protocols, and signal variations between different imaging sites. In addition, these statistical techniques require data from all the sites during their model training which may have the unintended consequence of data leakage for ML models trained using this harmonized data. The ML models trained using this harmonized data may result in low reliability and reproducibility when tested on unseen data sets, limiting their applicability for general clinical usage. In this study, we propose Autoencoders (AEs) as an alternative for harmonizing multisite fMRI data. Our designed and developed framework leverages the non-linear representation learning capabilities of AEs to reduce site-specific effects while preserving biologically meaningful features. Our evaluation using Autism Brain Imaging Data Exchange I (ABIDE-I) dataset, containing 1,035 subjects collected from 17 centers demonstrates statistically significant improvements in leave-one-site-out (LOSO) cross-validation evaluations. All AE variants (AE, SAE, TAE, and DAE) significantly outperformed the baseline mode (p < 0.01), with mean accuracy improvements ranging from 3.41% to 5.04%. Our findings demonstrate the potential of AEs to harmonize multisite neuroimaging data effectively enabling robust downstream analyses across various neuroscience applications while reducing data-leakage, and preservation of neurobiological features. Our open-source code is made available at https://github.com/pcdslab/Autoencoder-fMRI-Harmonization .

Early diagnosis of mild hepatic encephalopathy is important for the reversion of hepatic encephalopathy. Brain hyper-connectivity networks with hyperedges have showed good performance for diagnosis of neurological disorders. However, the previous hyper-connectivity networks is essentially low-level since the temporal synchronization of regional signal fluctuation is merely considered. Here, we propose a novel high-level hyper-connectivity network based on the resting state functional magnetic resonance imaging to capture the complex interactions among brain regions for better diagnosis of neurological disorders. Resting-state functional magnetic resonance imaging data from 36 mild hepatic encephalopathy patients and 36 cirrhotic patients with no mild hepatic encephalopathy are included in the study. Multi-level high-level hyper-connectivity networks are constructed firstly. Then, we define and extract node hyperdegree, hyperedge global importance and hyperedge dispersion from both low-level and high-level hyper-connectivity networks and combine them. Finally, gradient boosting decision tree is used for feature selection and classification. The leave-one-out cross-validation is used to evaluate the performance. The public ASD resting state functional magnetic resonance imaging datasets from 3 sites are also used as testing set to evaluate the generalization power of our method. Our method showed considerable performance in both experiments which confirms the effectiveness and generalization ability of the model. Besides, important regions and hyperedge features are identified for the interpretability.

Resting state functional magnetic resonance imaging (fMRI) is a useful technique to characterize functional connectivity patterns between regions of the brain, based on the Fisher-transformed Pearson correlations in the BOLD signal. Pinpointing how connectivity patterns change in neuropathies like early-stage psychosis (ESP) can help understand the disorders and track progression. Using study data from 21 ESP subjects with complete data for three consecutive scans, we examined connectivity changes throughout the whole brain with a region of interest (ROI) to ROI-based approach for ROI defined by the Harvard-Oxford cortical and subcortical atlases, supplemented by the AAL atlas for the cerebellum, and by networks defined by the CONN toolbox independent component analysis of the Human Connectome Project. We applied latent growth modelling, which is a type of structural equation modelling, to these connectivity measurements across baseline and follow-up visits. The models use age, community functioning, and negative symptoms at baselines as the covariates for subject-specific slope and intercept of the longitudinal measurements. After stringent thresholding cutoffs of root mean square error of approximation, standardized root mean square residual, comparative fit index, and Benjamini-Hochberg corrected p-value, we found a subset of connectivity measurements with significant longitudinal slopes (N = 18 atlas, N = 6 network), and used the subject's slope estimates to stratify these subjects into three clusters based on how the ROI-to-ROI correlations of functional connectivity change over time. The connections with significant slopes include atlas level regions like the temporal lobe, fronto-parietal lobe, and cerebellum, and network level patterns like the DMN, FPN, and Salience Networks. The structural equation modelling approach identifies ROIs whose functional connectivity changes over time, indicating the ROIs most dynamic during ESP. This highlights the utility of latent growth models for the analysis of longitudinal functional connectivity measures across the whole brain with relatively small sample sizes.

The amygdala plays a central role in emotion, memory, and decision-making and comprises approximately 13 distinct nuclei with connectivity. Despite its functional importance, high-resolution subnuclear mapping is challenging. This study aimed to construct a 3D model of the anatomical location of the amygdala in the brain and a functional dynamic model of the amygdala, integrating deep learning and elastic shape metrics. We used multimodal datasets from the Julich-Brain Atlas, BigBrain Project, and FreeSurfer, which were aligned with the Montreal Neurological Institute (MNI) and Colin 27 spaces. Subnuclei segmentation was performed using a Bayesian Fully Convolutional Network (FCN), and geometric morphometrics were analyzed using elastic shape analysis on the unit sphere. Functional dynamics were simulated using a MATLAB-based model of the amygdala incorporating theta (4-8 Hz) and gamma (30-40 Hz) oscillations with spike-timing-dependent plasticity (STDP). The mean MNI coordinates of the left and right amygdalae were (-20, -4, -15) and (22, -2, -15), respectively, with an inter-amygdalar distance of 42.48 mm. The Dice Similarity Coefficients (DSCs) for FCN-based subnuclear segmentation were as follows: basolateral amygdala (BLA) nucleus = 0.89 ± 0.03, centromedial nucleus = 0.83 ± 0.04, and cortical nucleus = 0.81 ± 0.05. Principal component analysis of elastic shape metrics revealed post-traumatic stress disorder (PTSD)-related morphological deviations, with the first principal component (PC1) accounting for 38% of the variance (p < 0.01). Oscillatory simulations captured the BLA rhythm dynamics and STDP-induced synaptic changes. This study presents a comprehensive 3D model of the human amygdala that bridges anatomical accuracy with computational modeling. Unlike prior models that focus solely on structural or functional domains, our approach integrates subnuclear segmentation, morphometrics, and real-time functional simulation. This study introduces a fully integrated anatomical-functional 3D model of the human amygdala, providing a translational platform for neuromodulation targeting, psychiatric diagnostics, and computational neuroengineering applications.

Multisensory integration is a fundamental neural mechanism crucial for understanding cognition. Multiple theoretical models exist to account for the computational processes underpinning this mechanism. However, there is an absence of a consolidated framework that facilitates the examination of multisensory integration across diverse experimental and computational contexts. We introduce Scikit-NeuroMSI, an accessible Python-based open-source framework designed to streamline the implementation and evaluation of computational models of multisensory integration. The capabilities of Scikit-NeuroMSI were demonstrated in enabling the implementation of multiple models of multisensory integration at different levels of analysis. Furthermore, we illustrate the utility of the software in systematically exploring the model's behavior in spatiotemporal causal inference tasks through parameter sweeps in simulations. Particularly, we conducted a comparative analysis of Bayesian and network models of multisensory integration to identify commonalities that may enable to bridge both levels of description, addressing a key research question within the field. We discuss the significance of this approach in generating computationally informed hypotheses in multisensory research. Recommendations for the improvement of this software and directions for future research using this framework are presented.