Structural neural connectivity of the vestibular nuclei in the human brain:a diffusion tensor imaging study

Many animal studies have reported on the neural connectivity of the vestibular nuclei（VN）.However,little is reported on the structural neural connectivity of the VN in the human brain.In this study,we attempted to investigate the structural neural connectivity of the VN in 37 healthy subjects using diffusion tensor tractography.A seed region of interest was placed on the isolated VN using probabilistic diffusion tensor tractography.Connectivity was defined as the incidence of connection between the VN and each brain region.The VN showed 100% connectivity with the cerebellum,thalamus,oculomotor nucleus,trochlear nucleus,abducens nucleus,and reticular formation,irrespective of thresholds.At the threshold of 5 streamlines,the VN showed connectivity with the primary motor cortex（95.9%）,primary somatosensory cortex（90.5%）,premotor cortex（87.8%）,hypothalamus（86.5%）,posterior parietal cortex（75.7%）,lateral prefrontal cortex（70.3%）,ventromedial prefrontal cortex（51.4%）,and orbitofrontal cortex（40.5%）,respectively.These results suggest that the VN showed high connectivity with the cerebellum,thalamus,oculomotor nucleus,trochlear nucleus,abducens nucleus,and reticular formation,which are the brain regions related to the functions of the VN,including equilibrium,control of eye movements,conscious perception of movement,and spatial orientation.

Unusual neural connection between injured cingulum and brainstem in a patient with subarachnoid hemorrhage

The human brain is known to have six cholinergic nudei （Selden et al., 1998; Nieuwenhuys et al., 2008）. The cerebral cortex obtains cholinergic innervation mainly from the basalis nucleus of Meynert （Ch 4） in the bas- al forebrain through the medial and lateral cholinergic pathways （Selden et al., 1998; Mesulam et al., 1983）. The cingulum, the neural fiber bundle connecting the basal forebrain and the medial temporal lobe, contains the medial cholinergic pathway （Selden et al., 1998; Hong and Jang, 2010）.

Fully Connected Feedforward Neural Networks Based CSI Feedback Algorithm

In modern wireless communication systems,the accurate acquisition of channel state information(CSI)is critical to the performance of beamforming,non-orthogonal multiple access(NOMA),etc.However,with the application of massive MIMO in 5G,the number of antennas increases by hundreds or even thousands times,which leads to excessive feedback overhead and poses a huge challenge to the conventional channel state information feedback scheme.In this paper,by using deep learning technology,we develop a system framework for CSI feedback based on fully connected feedforward neural networks(FCFNN),named CF-FCFNN.Through learning the training set composed of CSI,CF-FCFNN is able to recover the original CSI from the compressed CSI more accurately compared with the existing method based on deep learning without increasing the algorithm complexity.

Artificial neural network-based subgrid-scale models for LES of compressible turbulent channel flow

Fully connected neural networks(FCNNs)have been developed for the closure of subgrid-scale(SGS)stress and SGS heat flux in large-eddy simulations of compressible turbulent channel flow.The FCNNbased SGS model trained using data with Mach number Ma=3.0 and Reynolds number Re=3000 was applied to situations with different Mach numbers and Reynolds numbers.The input variables of the neural network model were the filtered velocity gradients and temperature gradients at a single spatial grid point.The a priori test showed that the FCNN model had a correlation coefficient larger than 0.91 and a relative error smaller than 0.43,with much better reconstructions of SGS unclosed terms than the dynamic Smagorinsky model(DSM).In a posteriori test,the behavior of the FCNN model was marginally better than that of the DSM in predicting the mean velocity profiles,mean temperature profiles,turbulent intensities,total Reynolds stress,total Reynolds heat flux,and mean SGS flux of kinetic energy,and outperformed the Smagorinsky model.

Perspectives on the neural connectivity of the fornix in the human brain

The fornix is involved in the transfer of information on episodic memory as a part of the Papez circuit. Diffusion tensor imaging enables to estimate the neural connectivity of the fornix. The anterior fornical body has high connectivity with the anterior commissure, and brain areas rele- vant to cholinergic nuclei （septal forebrain region and brainstem） and memory function （medial temporal lobe）. In the normal subjects, by contrast, the posterior fornical body has connectivity with the cerebral cortex and brainstem through the splenium of the corpus callosum. We believe that knowledge of the neural connectivity of the fornix would be helpful in investigation of the neural network associated with memory and recovery mechanisms following injury of the fornix.

Interpretation and characterization of rate of penetration intelligent prediction model

Accurate prediction of the rate of penetration(ROP)is significant for drilling optimization.While the intelligent ROP prediction model based on fully connected neural networks(FNN)outperforms traditional ROP equations and machine learning algorithms,its lack of interpretability undermines its credibility.This study proposes a novel interpretation and characterization method for the FNN ROP prediction model using the Rectified Linear Unit(ReLU)activation function.By leveraging the derivative of the ReLU function,the FNN function calculation process is transformed into vector operations.The FNN model is linearly characterized through further simplification,enabling its interpretation and analysis.The proposed method is applied in ROP prediction scenarios using drilling data from three vertical wells in the Tarim Oilfield.The results demonstrate that the FNN ROP prediction model with ReLU as the activation function performs exceptionally well.The relative activation frequency curve of hidden layer neurons aids in analyzing the overfitting of the FNN ROP model and determining drilling data similarity.In the well sections with similar drilling data,averaging the weight parameters enables linear characterization of the FNN ROP prediction model,leading to the establishment of a corresponding linear representation equation.Furthermore,the quantitative analysis of each feature's influence on ROP facilitates the proposal of drilling parameter optimization schemes for the current well section.The established linear characterization equation exhibits high precision,strong stability,and adaptability through the application and validation across multiple well sections.

Connectivity differences between adult male and female patients with attention deficit hyperactivity disorder according to resting-state functional MRI

Attention deficit hyperactivity disorder（ADHD） is a pervasive psychiatric disorder that affects both children and adults. Adult male and female patients with ADHD are differentially affected, but few studies have explored the differences. The purpose of this study was to quantify differences between adult male and female patients with ADHD based on neuroimaging and connectivity analysis. Resting-state functional magnetic resonance imaging scans were obtained and preprocessed in 82 patients. Group-wise differences between male and female patients were quantified using degree centrality for different brain regions. The medial-, middle-, and inferior-frontal gyrus, superior parietal lobule, precuneus, supramarginal gyrus, superior- and middle-temporal gyrus, middle occipital gyrus, and cuneus were identified as regions with significant group-wise differences. The identified regions were correlated with clinical scores reflecting depression and anxiety and significant correlations were found. Adult ADHD patients exhibit different levels of depression and anxiety depending on sex, and our study provides insight into how changes in brain circuitry might differentially impact male and female ADHD patients.

Central projections and connections of lumbar primary afferent fibers in adult rats:effectively revealed using Texas red-dextran amine tracing

Signals from lumbar primary afferent fibers are important for modulating locomotion of the hind-limbs.However,silver impregnation techniques,autoradiography,wheat germ agglutinin-horseradish peroxidase and cholera toxin B subunit-horseradish peroxidase cannot image the central projections and connections of the dorsal root in detail.Thus,we injected 3-k Da Texas red-dextran amine into the proximal trunks of L4 dorsal roots in adult rats.Confocal microscopy results revealed that numerous labeled arborizations and varicosities extended to the dorsal horn from T12–S4,to Clarke＇s column from T10–L2,and to the ventral horn from L1–5.The labeled varicosities at the L4 cord level were very dense,particularly in laminae I–Ⅲ,and the density decreased gradually in more rostral and caudal segments.In addition,they were predominately distributed in laminae I–IV,moderately in laminae V–VⅡ and sparsely in laminae VⅢ–X.Furthermore,direct contacts of lumbar afferent fibers with propriospinal neurons were widespread in gray matter.In conclusion,the projection and connection patterns of L4 afferents were illustrated in detail by Texas red-dextran amine-dorsal root tracing.

Applying Big Data Based Deep Learning System to Intrusion Detection

With vast amounts of data being generated daily and the ever increasing interconnectivity of the world’s internet infrastructures,a machine learning based Intrusion Detection Systems(IDS)has become a vital component to protect our economic and national security.Previous shallow learning and deep learning strategies adopt the single learning model approach for intrusion detection.The single learning model approach may experience problems to understand increasingly complicated data distribution of intrusion patterns.Particularly,the single deep learning model may not be effective to capture unique patterns from intrusive attacks having a small number of samples.In order to further enhance the performance of machine learning based IDS,we propose the Big Data based Hierarchical Deep Learning System(BDHDLS).BDHDLS utilizes behavioral features and content features to understand both network traffic characteristics and information stored in the payload.Each deep learning model in the BDHDLS concentrates its efforts to learn the unique data distribution in one cluster.This strategy can increase the detection rate of intrusive attacks as compared to the previous single learning model approaches.Based on parallel training strategy and big data techniques,the model construction time of BDHDLS is reduced substantially when multiple machines are deployed.