Neuroinformatics最新文献

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.

Accurately identifying the timing and frequency characteristics of impulse components in EEG signals is essential but limited by the Heisenberg uncertainty principle. Inspired by the visual system's ability to identify objects and their locations, we propose a new method that integrates a visual system model with wavelet analysis to calculate both time and frequency features of local impulses in EEG signals. We develop a mathematical model based on invariant pattern recognition by the visual system, combined with wavelet analysis using Krawtchouk functions as the mother wavelet. Our method precisely identifies the localization and frequency characteristics of the impulse components in EEG signals. Tested on task-related EEG data, it accurately detected blink components (0.5 to 1 Hz) and separated muscle artifacts (16 Hz). It also identified muscle response durations (298 ms) within the 1 to 31 Hz range in emotional reaction studies, offering insights into both individual and typical emotional responses. We further illustrated how the new method circumvents the uncertainty principle in low-frequency wavelet analysis. Unlike classical wavelet analysis, our method provides spectral characteristics of EEG impulses invariant to time shifts, improving the identification and classification of EEG components.

Blood flow velocity in the cerebral perforating arteries can be quantified in a two-dimensional plane with phase contrast magnetic imaging (2D PC-MRI). The velocity pulsatility index (PI) can inform on the stiffness of these perforating arteries, which is related to several cerebrovascular diseases. Currently, there is no open-source analysis tool for 2D PC-MRI data from these small vessels, impeding the usage of these measurements. In this study we present the Small vessEL MArker (SELMA) analysis software as a novel, user-friendly, open-source tool for velocity analysis in cerebral perforating arteries. The implementation of the analysis algorithm in SELMA was validated against previously published data with a Bland-Altman analysis. The inter-rater reliability of SELMA was assessed on PC-MRI data of sixty participants from three MRI vendors between eight different sites. The mean velocity (v_mean) and velocity PI of SELMA was very similar to the original results (v_mean: mean difference ± standard deviation: 0.1 ± 0.8 cm/s; velocity PI: mean difference ± standard deviation: 0.01 ± 0.1) despite the slightly higher number of detected vessels in SELMA (N_detected: mean difference ± standard deviation: 4 ± 9 vessels), which can be explained by the vessel selection paradigm of SELMA. The Dice Similarity Coefficient of drawn regions of interest between two operators using SELMA was 0.91 (range 0.69-0.95) and the overall intra-class coefficient for N_detected, v_mean, and velocity PI were 0.92, 0.84, and 0.85, respectively. The differences in the outcome measures was higher between sites than vendors, indicating the challenges in harmonizing the 2D PC-MRI sequence even across sites with the same vendor. We show that SELMA is a consistent and user-friendly analysis tool for small cerebral vessels.

Brain tumours are one of the most deadly and noticeable types of cancer, affecting both children and adults. One of the major drawbacks in brain tumour identification is the late diagnosis and high cost of brain tumour-detecting devices. Most existing approaches use ML algorithms to address problems, but they have drawbacks such as low accuracy, high loss, and high computing cost. To address these challenges, a novel U-Net model for tumour segmentation in magnetic resonance images (MRI) is proposed. Initially, images are claimed from the dataset and pre-processed with the Probabilistic Hybrid Wiener filter (PHWF) to remove unwanted noise and improve image quality. To reduce model complexity, the pre-processed images are submitted to a feature extraction procedure known as 3D Convolutional Vision Transformer (3D-VT). To perform the segmentation approach using chaotic optimization assisted Dilated Channel Gate attention U-Net (CDCG-UNet) model to segment brain tumour regions effectively. The proposed approach segments tumour portions as whole tumour (WT), tumour Core (TC), and Enhancing Tumour (ET) positions. The optimization loss function can be performed using the Chaotic Harris Shrinking Spiral optimization algorithm (CHSOA). The proposed CDCG-UNet model is evaluated with three datasets: BRATS 2021, BRATS 2020, and BRATS 2023. For the BRATS 2021 dataset, the proposed CDCG-UNet model obtained a dice score of 0.972 for ET, 0.987 for CT, and 0.98 for WT. For the BRATS 2020 dataset, the proposed CDCG-UNet model produced a dice score of 98.87% for ET, 98.67% for CT, and 99.1% for WT. The CDCG-UNet model is further evaluated using the BRATS 2023 dataset, which yields 98.42% for ET, 98.08% for CT, and 99.3% for WT.

Multiple sclerosis (MS) is a neurological disease causing myelin and axon damage through inflammatory and autoimmune processes. Despite affecting millions worldwide, understanding its genetic pathways remains limited. The choroid plexus (ChP) has been studied in neurodegenerative processes and diseases like MS due to its dysregulation, yet its role in MS pathophysiology remains unclear. Our work re-evaluates the ChP transcriptome in progressive MS patients and compares gene expression profiles using diverse methodological strategies. Samples from patient and healthy control RNASeq sequencing of brain tissue from post-mortem patients (GEO: GSE137619) were used. After an evaluation and quality control of these data, they had their transcripts mapped and quantified against the reference transcriptome GRCh38/hg38 of Homo sapiens using three strategies to identify differentially expressed genes in progressive MS patients. Functional analysis of genes revealed their involvement in immune processes, cell adhesion and migration, hormonal actions, amino acid transport, chemokines, metals, and signaling pathways. Our findings can offer valuable insights for progressive MS therapies, suggesting specific genes influence immune cell recruitment and potential ChP microenvironment changes. Combining complementary approaches maximizes literature coverage, facilitating a deeper understanding of the biological context in progressive MS.

The problem at hand is the significant global health challenge posed by children's diseases, where timely and accurate diagnosis is crucial for effective treatment and management. Conventional diagnosis techniques are typical, use tedious processes and generate inaccurate results since they are executed by human beings and cause delays in treatment that can be fatal. Considering these and other shortcomings there exists a need to have more efficient and accurate solutions based on artificial intelligence. Machine learning and more specifically, deep learning algorithms are of great help in analysing medical and clinical images to detect as well as classify diseases. In this paper, we propose a system for detecting various childhood diseases using a range of advanced Convolutional Neural Network models like EfficientNetB0, EfficientNetB3, Xception, InceptionV3, MobileNetV2, VGG19, DenseNet169, ResNet50V2, ResNet152V2, and the hybrid architecture InceptionResNetV2. These models are trained on MRI images of paediatric brain disorders to achieve high prediction accuracy. We use data visualization techniques such as segmentation and contour-based feature extraction to extract regions of interest before feeding the data into the models. The models are optimized using both ADAM and RMSprop optimizers. EfficientNetB0, when optimized with RMSprop, achieves an accuracy of 94.59%, a loss of 0.44, and an RMSE of 0.66. InceptionResNetV2, optimized with ADAM, achieves the highest accuracy of 97.59%, while EfficientNetB0 demonstrates the lowest loss (0.25) and RMSE (0.5). We also evaluate the models based on their precision, learning curves, recall, computational time, and F1 score, highlighting the effectiveness of AI-driven approaches for the diagnosis and management of children's diseases.

This study presents a thorough bibliometric analysis of Neuroinformatics over the past 20 years, offering insights into the journal's evolution at the intersection of neuroscience and computational science. Using advanced tools such as VOS viewer and methodologies like co-citation analysis, bibliographic coupling, and keyword co-occurrence, we examine trends in publication, citation patterns, and the journal's influence. Our analysis reveals enduring research themes like neuroimaging, data sharing, machine learning, and functional connectivity, which form the core of Neuroinformatics. These themes highlight the journal's role in addressing key challenges in neuroscience through computational methods. Emerging topics like deep learning, neuron reconstruction, and reproducibility further showcase the journal's responsiveness to technological advances. We also track the journal's rising impact, marked by a substantial growth in publications and citations, especially over the last decade. This growth underscores the relevance of computational approaches in neuroscience and the high-quality research the journal attracts. Key bibliometric indicators, such as publication counts, citation analysis, and the h-index, spotlight contributions from leading authors, papers, and institutions worldwide, particularly from the USA, China, and Europe. These metrics provide a clear view of the scientific landscape and collaboration patterns driving progress. This analysis not only celebrates Neuroinformatics's rich history but also offers strategic insights for future research, ensuring the journal remains a leader in innovation and advances both neuroscience and computational science.

Intracranial atherosclerotic stenosis (ICAS) and intracranial aneurysms are prevalent conditions in the cerebrovascular system. ICAS causes a narrowing of the arterial lumen, thereby restricting blood flow, while aneurysms involve the ballooning of blood vessels. Both conditions can lead to severe outcomes, such as stroke or vessel rupture, which can be fatal. Early detection is crucial for effective intervention. In this study, we introduced a method that combines classical computer vision techniques with deep learning to detect intracranial aneurysms and ICAS in time-of-flight magnetic resonance angiography images. The process began with skull-stripping, followed by an affine transformation to align the images to a common atlas space. We then focused on the region of interest, including the circle of Willis, by cropping the relevant area. A segmentation algorithm was used to isolate the arteries, after which a patch-wise residual neural network was applied across the image. A voting mechanism was then employed to identify the presence of atrophies. Our method achieved accuracies of 76.5% for aneurysms and 82.4% for ICAS. Notably, when occlusions were not considered, the accuracy for ICAS detection improved to 85.7%. While the algorithm performed well for localized pathological findings, it was less effective at detecting occlusions, which involved long-range dependencies in the MRIs. This limitation was due to the architectural design of the patch-wise deep learning approach. Regardless, this can, in the future, be mitigated in a multi-scale patch-wise algorithm.