Brain Informatics最新文献

Infant brain magnetic resonance imaging (MRI) is a promising approach for studying early neurodevelopment. However, segmenting small regions such as limbic structures is challenging due to their low inter-regional contrast and high curvature. MRI studies of the adult brain have successfully applied deep learning techniques to segment limbic structures, and similar deep learning models are being leveraged for infant studies. However, these deep learning-based infant MRI segmentation models have generally been derived from small datasets, and may suffer from generalization problems. Moreover, the accuracy of segmentations derived from these deep learning models relative to more standard Expectation-Maximization approaches has not been characterized. To address these challenges, we leveraged a large, public infant MRI dataset (n = 473) and the transfer-learning technique to first pre-train a deep convolutional neural network model on two limbic structures: amygdala and hippocampus. Then we used a leave-one-out cross-validation strategy to fine-tune the pre-trained model and evaluated it separately on two independent datasets with manual labels. We term this new approach the Infant Deep learning SEGmentation Framework (ID-Seg). ID-Seg performed well on both datasets with a mean dice similarity score (DSC) of 0.87, a mean intra-class correlation (ICC) of 0.93, and a mean average surface distance (ASD) of 0.31 mm. Compared to the Developmental Human Connectome pipeline (dHCP) pipeline, ID-Seg significantly improved segmentation accuracy. In a third infant MRI dataset (n = 50), we used ID-Seg and dHCP separately to estimate amygdala and hippocampus volumes and shapes. The estimates derived from ID-seg, relative to those from the dHCP, showed stronger associations with behavioral problems assessed in these infants at age 2. In sum, ID-Seg consistently performed well on two different datasets with an 0.87 DSC, however, multi-site testing and extension for brain regions beyond the amygdala and hippocampus are still needed.

A deep understanding of the neuronal connectivity and networks with detailed cell typing across brain regions is necessary to unravel the mechanisms behind the emotional and memorial functions as well as to find the treatment of brain impairment. Brain-wide imaging with single-cell resolution provides unique advantages to access morphological features of a neuron and to investigate the connectivity of neuron networks, which has led to exciting discoveries over the past years based on animal models, such as rodents. Nonetheless, high-throughput systems are in urgent demand to support studies of neural morphologies at larger scale and more detailed level, as well as to enable research on non-human primates (NHP) and human brains. The advances in artificial intelligence (AI) and computational resources bring great opportunity to 'smart' imaging systems, i.e., to automate, speed up, optimize and upgrade the imaging systems with AI and computational strategies. In this light, we review the important computational techniques that can support smart systems in brain-wide imaging at single-cell resolution.

Brain informatics is a novel interdisciplinary area that focuses on scientifically studying the mechanisms of human brain information processing by integrating experimental cognitive neuroscience with advanced Web intelligence-centered information technologies. Web intelligence, which aims to understand the computational, cognitive, physical, and social foundations of the future Web, has attracted increasing attention to facilitate the study of brain informatics to promote human health. A large number of articles created in the recent few years are proof of the investment in Web intelligence-assisted human health. This study systematically reviews academic studies regarding article trends, top journals, subjects, countries/regions, and institutions, study design, artificial intelligence technologies, clinical tasks, and performance evaluation. Results indicate that literature is especially welcomed in subjects such as medical informatics and health care sciences and service. There are several promising topics, for example, random forests, support vector machines, and conventional neural networks for disease detection and diagnosis, semantic Web, ontology mining, and topic modeling for clinical or biomedical text mining, artificial neural networks and logistic regression for prediction, and convolutional neural networks and support vector machines for monitoring and classification. Additionally, future research should focus on algorithm innovations, additional information use, functionality improvement, model and system generalization, scalability, evaluation, and automation, data acquirement and quality improvement, and allowing interaction. The findings of this study help better understand what and how Web intelligence can be applied to promote healthcare procedures and clinical outcomes. This provides important insights into the effective use of Web intelligence to support informatics-enabled brain studies.

Background: Previous experiments in tacit coordination games hinted that some people are more successful in achieving coordination than others, although the variability in this ability has not yet been examined before. With that in mind, the overarching aim of our study is to model and describe the variability in human decision-making behavior in the context of tacit coordination games.

Methods: In this study, we conducted a large-scale experiment to collect behavioral data, characterized the distribution of tacit coordination ability, and modeled the decision-making behavior of players. First, we measured the multimodality in the data and described it by using a Gaussian mixture model. Then, using multivariate linear regression and dimensionality reduction (PCA), we have constructed a model linking between individual strategic profiles of players and their coordination ability. Finally, we validated the predictive performance of the model by using external validation.

Results: We demonstrated that coordination ability is best described by a multimodal distribution corresponding to the levels of coordination ability and that there is a significant relationship between the player's strategic profile and their coordination ability. External validation determined that our predictive model is robust.

Conclusions: The study provides insight into the amount of variability that exists in individual tacit coordination ability as well as in individual strategic profiles and shows that both are quite diverse. Our findings may facilitate the construction of improved algorithms for human-machine interaction in diverse contexts. Additional avenues for future research are discussed.

It has been a challenge for solving the motor imagery classification problem in the brain informatics area. Accuracy and efficiency are the major obstacles for motor imagery analysis in the past decades since the computational capability and algorithmic availability cannot satisfy complex brain signal analysis. In recent years, the rapid development of machine learning (ML) methods has empowered people to tackle the motor imagery classification problem with more efficient methods. Among various ML methods, the Graph neural networks (GNNs) method has shown its efficiency and accuracy in dealing with inter-related complex networks. The use of GNN provides new possibilities for feature extraction from brain structure connection. In this paper, we proposed a new model called MCGNet⁺, which improves the performance of our previous model MutualGraphNet. In this latest model, the mutual information of the input columns forms the initial adjacency matrix for the cosine similarity calculation between columns to generate a new adjacency matrix in each iteration. The dynamic adjacency matrix combined with the spatial temporal graph convolution network (ST-GCN) has better performance than the unchanged matrix model. The experimental results indicate that MCGNet⁺ is robust enough to learn the interpretable features and outperforms the current state-of-the-art methods.

Previous invasive studies indicate that human neocortical graymatter contains cytoarchitectonically distinct layers, with notable differences in their structural connectivity with the rest of the brain. Given recent improvements in the spatial resolution of anatomical and functional magnetic resonance imaging (fMRI), we hypothesize that resting state functional connectivity (FC) derived from fMRI is sensitive to layer-specific thalamo-cortical and cortico-cortical microcircuits. Using sub-millimeter resting state fMRI data obtained at 7 T, we found that: (1) FC between the entire thalamus and cortical layers I and VI was significantly stronger than between the thalamus and other layers. Furthermore, FC between somatosensory thalamus (ventral posterolateral nucleus, VPL) and layers IV, VI of the primary somatosensory cortex were stronger than with other layers; (2) Inter-hemispheric cortico-cortical FC between homologous regions in superficial layers (layers I-III) was stronger compared to deep layers (layers V-VI). These findings are in agreement with structural connections inferred from previous invasive studies that showed that: (i) M-type neurons in the entire thalamus project to layer-I; (ii) Pyramidal neurons in layer-VI target all thalamic nuclei, (iii) C-type neurons in the VPL project to layer-IV and receive inputs from layer-VI of the primary somatosensory cortex, and (iv) 80% of collosal projecting neurons between homologous cortical regions connect superficial layers. Our results demonstrate for the first time that resting state fMRI is sensitive to structural connections between cortical layers (previously inferred through invasive studies), specifically in thalamo-cortical and cortico-cortical networks.