GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signals

Towards developing effective and efficient brain-computer interface (BCI) systems, precise decoding of brain activity measured by electroencephalogram (EEG), is highly demanded. Traditional works classify EEG signals without considering the topological relationship among electrodes. However, neuroscience research has increasingly emphasized network patterns of brain dynamics. Thus, the Euclidean structure of electrodes might not adequately reflect the interaction between signals. To fill the gap, a novel deep learning framework based on the graph convolutional neural networks (GCNs) was presented to enhance the decoding performance of raw EEG signals during different types of motor imagery (MI) tasks while cooperating with the functional topological relationship of electrodes. Based on the absolute Pearson's matrix of overall signals, the graph Laplacian of EEG electrodes was built up. The GCNs-Net constructed by graph convolutional layers learns the generalized features. The followed pooling layers reduce dimensionality, and the fully-connected softmax layer derives the final prediction. The introduced approach has been shown to converge for both personalized and group-wise predictions. It has achieved the highest averaged accuracy, 93.056% and 88.57% (PhysioNet Dataset), 96.24% and 80.89% (High Gamma Dataset), at the subject and group level, respectively, compared with existing studies, which suggests adaptability and robustness to individual variability. Moreover, the performance was stably reproducible among repetitive experiments for cross-validation. To conclude, the GCNs-Net filters EEG signals based on the functional topological relationship, which manages to decode relevant features for brain motor imagery

Anatomy-Guided Dense Individualized and Common Connectivity-Based Cortical Landmarks (A-DICCCOL)

Establishment of structural and functional correspondences of human brain that can be quantitatively encoded and reproduced across different subjects and populations is one of the key issues in brain mapping. As an attempt to address this challenge, our recently developed Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL) system reported 358 connectional landmarks, each of which possesses consistent DTI-derived white matter fiber connection pattern that is reproducible in over 240 healthy brains. However, the DICCCOL system can be substantially improved by integrating anatomical and morphological information during landmark initialization and optimization procedures. In this paper, we present a novel anatomy-guided landmark discovery framework that defines and optimizes landmarks via integrating rich anatomical, morphological, and fiber connectional information for landmark initialization, group-wise optimization and prediction, which are formulated and solved as an energy minimization problem. The framework finally determined 555 consistent connectional landmarks. Validation studies demonstrated that the 555 landmarks are reproducible, predictable, and exhibited reasonably accurate anatomical, connectional, and functional correspondences across individuals and populations and thus are named anatomy-guided DICCCOL or A-DICCCOL. This A-DICCCOL system represents common cortical architectures with anatomical, connectional, and functional correspondences across different subjects and would potentially provide opportunities for various applications in brain science

Axonal Fiber Terminations Concentrate on Gyri

Convoluted cortical folding and neuronal wiring are 2 prominent attributes of the mammalian brain. However, the macroscale intrinsic relationship between these 2 general cross-species attributes, as well as the underlying principles that sculpt the architecture of the cerebral cortex, remains unclear. Here, we show that the axonal fibers connected to gyri are significantly denser than those connected to sulci. In human, chimpanzee, and macaque brains, a dominant fraction of axonal fibers were found to be connected to the gyri. This finding has been replicated in a range of mammalian brains via diffusion tensor imaging and high–angular resolution diffusion imaging. These results may have shed some lights on fundamental mechanisms for development and organization of the cerebral cortex, suggesting that axonal pushing is a mechanism of cortical folding

A tissue unbiased multimodal brain template

Buiding a tissue unbiased multimodal brain template with T1w, T2w, and Diffusion MRI. For, your interest, please read our first paper. If you use our template, please cite this paper. [1] Lv, J, Zeng, R, Ho, MP, D'Souza, A, Calamante, F. Building a tissue-unbiased brain template of fiber orientation distribution and tractography with multimodal registration. Magn Reson Med. 2022; 1- 14. doi:10.1002/mrm.2949

Research on Application System of Three-Dimensional Design of Transmission Line Based on Grid GIS Cloud Platform

Intelligent integration has been basically achieved in transmission line design in developed countries, while the current management design level in this field in China is backward and inefficient. According to the characteristics of the pilot transmission line project, it is necessary to build a transmission line 3D design application system based on the grid GIS cloud platform with the advantages of webgl cross platform and without installing plug-ins, to assist in the application of transmission line 3D design, and to develop the interface with professional line 3D design software, so as to realize the smooth operation of GIS elevation and image information and other information such as line selection and capital collection. Experiments prove that the construction of transmission line three-dimensional design application system based on grid GIS cloud platform provides a set of simple, fast, convenient and intelligent and efficient three-dimensional design support for planners and designers. It is an effective means for modern power enterprises to improve management design level and work efficiency, and has great significance in economic and social benefits

Hippocampal neuronal integrity and functional connectivity within the default mode network in mild cognitive impairment: a multimodal investigation

Background. In older people with Mild Cognitive Impairment (MCI), the relationship between early changes in functional connectivity and in vivo changes in key neurometabolites is not known. Two established correlates of MCI diagnosis are decreased in N-Acetylaspartate (NAA) in the hippocampus, indicative of decreased neuronal integrity, and changes in the Default Mode Network (DMN) functional network. If and how these measures interrelate is yet to be established, and this understanding may provide insight into the processes underpinning observed cognitive decline. Objectives. To determine the relationship between NAA levels in the left hippocampus and functional connectivity within the DMN in an aging cohort. Methods. In a sample of 51 MCI participants and 30 controls, hippocampal NAA was determined using magnetic resonance spectroscopy, and DMN connectivity was quantified using resting state functional MRI. The association between hippocampal NAA and the DMN functional connectivity was tested independently within in the MCI and separately within the control group. Results. In the DMN, we showed a significant inverse association between functional connectivity and hippocampal NAA in 20 specific brain connections for patients with MCI. This was despite no evidence of any associations in the healthy control group or group differences in either of these measures alone. Conclusions. This study suggests that decreased neuronal integrity in the hippocampus is associated with functional change within the DMN for those with MCI, in contrast to healthy older adults. These results highlight the potential of multimodal investigations to better understand the processes associated with cognitive decline

Correction to: Regional brain volume predicts response to methylphenidate treatment in individuals with ADHD

An amendment to this paper has been published and can be accessed via the original article

Sparse coding reveals greater functional connectivity in female brains during naturalistic emotional experience

<div><p>Functional neuroimaging is widely used to examine changes in brain function associated with age, gender or neuropsychiatric conditions. FMRI (functional magnetic resonance imaging) studies employ either laboratory-designed tasks that engage the brain with abstracted and repeated stimuli, or resting state paradigms with little behavioral constraint. Recently, novel neuroimaging paradigms using naturalistic stimuli are gaining increasing attraction, as they offer an ecologically-valid condition to approximate brain function in real life. Wider application of naturalistic paradigms in exploring individual differences in brain function, however, awaits further advances in statistical methods for modeling dynamic and complex dataset. Here, we developed a novel data-driven strategy that employs group sparse representation to assess gender differences in brain responses during naturalistic emotional experience. Comparing to independent component analysis (ICA), sparse coding algorithm considers the intrinsic sparsity of neural coding and thus could be more suitable in modeling dynamic whole-brain fMRI signals. An online dictionary learning and sparse coding algorithm was applied to the aggregated fMRI signals from both groups, which was subsequently factorized into a common time series signal dictionary matrix and the associated weight coefficient matrix. Our results demonstrate that group sparse representation can effectively identify gender differences in functional brain network during natural viewing, with improved sensitivity and reliability over ICA-based method. Group sparse representation hence offers a superior data-driven strategy for examining brain function during naturalistic conditions, with great potential for clinical application in neuropsychiatric disorders.</p></div