Extracting Multiple Nodes in a Brain Region of Interest for Brain Functional Network Estimation and Classification

Purpose: Brain functional networks (BFNs) has become important approach for diagnosis of some neurological or psychological disorders. Before estimating BFN, obtaining blood oxygen level dependent (BOLD) representative signals from brain regions of interest (ROIs) is important. In the past decades, the common method is generally to take a ROI as a node, averaging all the voxel time series inside it to extract a representative signal. However, one node does not represent the entire information of this ROI, and averaging method often leads to signal cancellation and information loss. Inspired by this, we propose a novel model extraction method based on an assumption that a ROI can be represented by multiple nodes. Methods: In this paper, we first extract multiple nodes (the number is user-defined) from the ROI based on two traditional methods, including principal component analysis (PCA), and K-means (Clustering according to the spatial position of voxels). Then, canonical correlation analysis (CCA) was issued to construct BFNs by maximizing the correlation between the representative signals corresponding to the nodes in any two ROIs. Finally, to further verify the effectiveness of the proposed method, the estimated BFNs are applied to identify subjects with autism spectrum disorder (ASD) and mild cognitive impairment (MCI) from health controls (HCs). Results: Experimental results on two benchmark databases demonstrate that the proposed method outperforms the baseline method in the sense of classification performance. Conclusions: We propose a novel method for obtaining nodes of ROId based on the hypothesis that a ROI can be represented by multiple nodes, that is, to extract the node signals of ROIs with K-means or PCA. Then, CCA is used to construct BFNs.

Role of the hippocampus on learning and memory functioning and pain modulation

The hippocampus, an important part of the limbic system, is considered to be an important region of the brain for learning and memory functioning. Recent studies have demonstrated that synaptic plasticity is thought to be the basis of learning and memory functioning. A series of studies report that similar synaptic plasticity also exists in the spinal cord in the conduction pathway of pain sensation, which may contribute to hyperalgesia, abnormal pain, and analgesia. The synaptic plasticity of learning and memory functioning and that of the pain conduction pathway have similar mechanisms, which are related to the N-methyl-D-aspartic acid receptor. The hippocampus also has a role in pain modulation. As pain signals can reach the hippocampus, the precise correlation between synaptic plasticity of the pain pathway and that of learning and memory functioning deserves further investigation. The role of the hippocampus in processing pain information requires to be identified.

Frameworked electrolytes:Ionic transport behavior and high mobility for solid state batteries

All solid-state batteries(ASSBs)are the holy grails of rechargeable batteries,where extensive searches are ongoing in the pursuit of ideal solid-state electrolytes.Nevertheless,there is still a long way off to the satisfactorily high(enough)ionic conductivity,long-term stability and especially being able to form compatible interfaces with the solid electrodes.Herein,we have explored ionic transport behavior and high mobility in the sub-nano pore networks in the framework structures.Macroscopically,the frameworked electrolyte behaves as a solid,and however in the(sub)-nano scales,the very limited number of solvent molecules in confinement makes them completely different from that in liquid electrolyte.Differentiated from a liquid-electrolyte counterpart,the interactions between the mobile ions and surrounding molecules are subject to dramatic changes,leading to a high ionic conductivity at room temperature with a low activation energy.Li+ions in the sub-nano cages of the network structure are highly mobile and diffuse rather independently,where the rate-limiting step of ions crossing cages is driven by the local concentration gradient and the electrostatic interactions between Li^(+)ions.This new class of frameworked electrolytes(FEs)with both high ionic conductivity and desirable interface with solid electrodes are demonstrated to work with Li-ion batteries,where the ASSB with LiFePO_(4)shows a highly stable electrochemical performance of over 450 cycles at 2℃ at room temperature,with an almost negligible capacity fade of 0.03‰ each cycle.In addition,the FE shows outstanding flexibility and anti-flammability,which are among the key requirements of large-scale applications.