Assessing target optical camouflage effects using brain functional networks:A feasibility study

Brain functional networks model the brain's ability to exchange information across different regions,aiding in the understanding of the cognitive process of human visual attention during target searching,thereby contributing to the advancement of camouflage evaluation.In this study,images with various camouflage effects were presented to observers to generate electroencephalography(EEG)signals,which were then used to construct a brain functional network.The topological parameters of the network were subsequently extracted and input into a machine learning model for training.The results indicate that most of the classifiers achieved accuracy rates exceeding 70%.Specifically,the Logistic algorithm achieved an accuracy of 81.67%.Therefore,it is possible to predict target camouflage effectiveness with high accuracy without the need to calculate discovery probability.The proposed method fully considers the aspects of human visual and cognitive processes,overcomes the subjectivity of human interpretation,and achieves stable and reliable accuracy.

Brain Functional Network Changes in Patients with Poststroke Cognitive Impairment Following Acupuncture Therapy

Background: The mechanisms by which acupuncture affects poststroke cognitive impairment (PSCI) remain unclear. Objective: To investigate brain functional network (BFN) changes in patients with PSCI after acupuncture therapy. Methods: Twenty-two PSCI patients who underwent acupuncture therapy in our hospital were enrolled as research subjects. Another 14 people matched for age, sex, and education level were included in the normal control (HC) group. All the subjects underwent resting-state functional magnetic resonance imaging (rs-fMRI) scans;the PSCI patients underwent one scan before acupuncture therapy and another after. The network metric difference between PSCI patients and HCs was analyzed via the independent-sample t test, whereas the paired-sample t test was employed to analyze the network metric changes in PSCI patients before vs. after treatment. Results: Small-world network attributes were observed in both groups for sparsities between 0.1 and 0.28. Compared with the HC group, the PSCI group presented significantly lower values for the global topological properties (γ, Cp, and Eloc) of the brain;significantly greater values for the nodal attributes of betweenness centrality in the CUN. L and the HES. R, degree centrality in the SFGdor. L, PCG. L, IPL. L, and HES. R, and nodal local efficiency in the ORBsup. R, ORBsupmed. R, DCG. L, SMG. R, and TPOsup. L;and decreased degree centrality in the MFG. R, IFGoperc. R, and SOG. R. After treatment, PSCI patients presented increased degree centrality in the LING.L, LING.R, and IOG. L and nodal local efficiency in PHG. L, IOG. R, FFG. L, and the HES. L, and decreased betweenness centrality in the PCG. L and CUN. L, degree centrality in the ORBsupmed. R, and nodal local efficiency in ANG. R. Conclusion: Cognitive decline in PSCI patients may be related to BFN disorders;acupuncture therapy may modulate the topological properties of the BFNs of PSCI patients.

Brain Functional Network Based on Small-Worldness and Minimum Spanning Tree for Depression Analysis

Since the outbreak and spread of corona virus disease 2019(COVID-19),the prevalence of mental disorders,such as depression,has continued to increase.To explore the abnormal changes of brain functional connections in patients with depression,this paper proposes a depression analysis method based on brain function network(BFN).To avoid the volume conductor effect,BFN was constructed based on phase lag index(PLI).Then the indicators closely related to depression were selected from weighted BFN based on small-worldness(SW)characteristics and binarization BFN based on the minimum spanning tree(MST).Differences analysis between groups and correlation analysis between these indicators and diagnostic indicators were performed in turn.The resting state electroencephalogram(EEG)data of 24 patients with depression and 29 healthy controls(HC)was used to verify our proposed method.The results showed that compared with HC,the information processing of BFN in patients with depression decreased,and BFN showed a trend of randomization.

Brain Functional Networks with Dynamic Hypergraph Manifold Regularization for Classification of End-Stage Renal Disease Associated with Mild Cognitive Impairment

The structure and function of brain networks have been altered in patients with end-stage renal disease(ESRD).Manifold regularization(MR)only considers the pairing relationship between two brain regions and cannot represent functional interactions or higher-order relationships between multiple brain regions.To solve this issue,we developed a method to construct a dynamic brain functional network(DBFN)based on dynamic hypergraph MR(DHMR)and applied it to the classification of ESRD associated with mild cognitive impairment(ESRDaMCI).The construction of DBFN with Pearson’s correlation(PC)was transformed into an optimization model.Node convolution and hyperedge convolution superposition were adopted to dynamically modify the hypergraph structure,and then got the dynamic hypergraph to form the manifold regular terms of the dynamic hypergraph.The DHMR and L_(1) norm regularization were introduced into the PC-based optimization model to obtain the final DHMR-based DBFN(DDBFN).Experiment results demonstrated the validity of the DDBFN method by comparing the classification results with several related brain functional network construction methods.Our work not only improves better classification performance but also reveals the discriminative regions of ESRDaMCI,providing a reference for clinical research and auxiliary diagnosis of concomitant cognitive impairments.

Brain Functional Network Generation Using Distribution-Regularized Adversarial Graph Autoencoder with Transformer for Dementia Diagnosis

The topological connectivity information derived from the brain functional network can bring new insights for diagnosing and analyzing dementia disorders.The brain functional network is suitable to bridge the correlation between abnormal connectivities and dementia disorders.However,it is challenging to access considerable amounts of brain functional network data,which hinders the widespread application of data-driven models in dementia diagnosis.In this study,a novel distribution-regularized adversarial graph auto-Encoder(DAGAE)with transformer is proposed to generate new fake brain functional networks to augment the brain functional network dataset,improving the dementia diagnosis accuracy of data-driven models.Specifically,the label distribution is estimated to regularize the latent space learned by the graph encoder,which canmake the learning process stable and the learned representation robust.Also,the transformer generator is devised to map the node representations into node-to-node connections by exploring the long-term dependence of highly-correlated distant brain regions.The typical topological properties and discriminative features can be preserved entirely.Furthermore,the generated brain functional networks improve the prediction performance using different classifiers,which can be applied to analyze other cognitive diseases.Attempts on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset demonstrate that the proposed model can generate good brain functional networks.The classification results show adding generated data can achieve the best accuracy value of 85.33%,sensitivity value of 84.00%,specificity value of 86.67%.The proposed model also achieves superior performance compared with other related augmentedmodels.Overall,the proposedmodel effectively improves cognitive disease diagnosis by generating diverse brain functional networks.

Extracting Sub-Networks from Brain Functional Network Using Graph Regularized Nonnegative Matrix Factorization

Currently,functional connectomes constructed from neuroimaging data have emerged as a powerful tool in identifying brain disorders.If one brain disease just manifests as some cognitive dysfunction,it means that the disease may affect some local connectivity in the brain functional network.That is,there are functional abnormalities in the sub-network.Therefore,it is crucial to accurately identify them in pathological diagnosis.To solve these problems,we proposed a sub-network extraction method based on graph regularization nonnegative matrix factorization(GNMF).The dynamic functional networks of normal subjects and early mild cognitive impairment(eMCI)subjects were vectorized and the functional connection vectors(FCV)were assembled to aggregation matrices.Then GNMF was applied to factorize the aggregation matrix to get the base matrix,in which the column vectors were restored to a common sub-network and a distinctive sub-network,and visualization and statistical analysis were conducted on the two sub-networks,respectively.Experimental results demonstrated that,compared with other matrix factorization methods,the proposed method can more obviously reflect the similarity between the common subnetwork of eMCI subjects and normal subjects,as well as the difference between the distinctive sub-network of eMCI subjects and normal subjects,Therefore,the high-dimensional features in brain functional networks can be best represented locally in the lowdimensional space,which provides a new idea for studying brain functional connectomes.

Homeostatic regulation of brain functions by endocannabinoid signaling

Humans have been using Cannabis and its extracts for a few thousand years as a medicinal and recreational drug. How- ever, the chemical component in Cannabis sativa, △9-tet- rahydrocannabinol （△9-THC）, an exogenous cannabinoid, remained unknown until it was isolated and identified as the main psychoactive ingredient （Gaoni and Mechoulam, 1964）.

Anxiety Network of Brain Function in Patients with Acute Cerebral Infarction

Objective: To explore the characteristics of brain functional network with anxiety in patients with acute cerebral infarction. Methods: A total of 39 patients with acute cerebral infarction by cranial magnetic resonance examination were included, and all the patients were scored by the Hamilton Anxiety Scale. The anxiety scale is scored by a professional psychiatrist. There are a total of 14 items, including anxiety, nervousness, fear, insomnia, cognitive function, depressed mood, somatic anxiety, sensory system, etc. The total score ≥ 29 points may be severe;≥21 points, there must be obvious;≥14 points, there must be anxiety;a score of more than 7 may indicate anxiety. If the score is less than 7, there are no anxiety symptoms. All patients within 24 to 72 hours, complete the head examination magnetic resonance, computerized calculation of the DWI sequence images, according to the results of the calculation to superimpose the image of the lesion, image reconstruction in space, and carry out Binarization, defining the value of lesions as 1, and the value of non as 0. All lesions are superimposed into one image and integrated. The relationship between the lesions in this superimposed image and anxiety after cerebral infarction was analyzed. Results: The lesions were basically concentrated around the lateral ventricle, and they were mainly concentrated around the lateral ventricle. Conclusion: Patients with acute cerebral infarction in the lateral ventricle or basal ganglia are more prone to post-stroke anxiety. This has a certain evaluation value for the prognosis of future cerebral infarction, and has a certain understanding of the exploration of complications, and has a certain understanding of the exploration of complications.

Evaluation of Prognosis of Brain Function with Early Transcranial Color Doppler Ultrasound in Patients after Cardiopulmonary Resuscitation

Objective: To evaluate the clinical value of transcranial color Doppler ultrasound (TCCD) in assessing cerebral function after cardiopulmonary resuscitation (CPR). Methods: A prospective study was conducted in 52 patients with cardiac arrest treated by CPR from January 2018 to January 2020, and its clinical data were analyzed. According to classification of cerebral performance category (CPC), 31 cases (CPC grade 1 - 2) were selected in the good prognosis group and 21 cases (CPC grade 3 - 5) in the poor prognosis group. The cerebral blood flow was measured by transcranial Doppler ultrasound (TCCD) 24 h after CPR, and the differences were compared between the two groups in stroke index, diastolic blood flow velocity (Vd), systolic peak blood flow velocity (Vs) and mean peak blood flow velocity (Vm). The ROC curve of cerebral blood flow after CPR was drawn to predict the prognosis of brain function. Results: The data showed that the pulsatility index of middle cerebral artery of the poor prognosis group decreased within 24 h;the difference between the two groups was statistically significant (p < 0.05);the Vd, Vs, Vm increased in the good prognosis group;the difference between the two groups was statistically significant (p < 0.05). The ROC curve of cerebral blood flow after CPR was drawn to predict the prognosis of brain function, and the results showed that the area under the curve and the optimal critical value of cerebral blood flow were 0.731 and 5.69. The sensitivity and specificity were 67.3% and 79.1% respectively. Conclusion: The cerebral blood flow increase in the early stage of successful CPR is positively correlated with the prognosis of cerebral functional resuscitation. Monitoring intracranial blood flow after CPR by TCCD has clinical value to evaluate prognosis of brain function.

Brain Functional Network Improved by Magnetic Stimulation at Acupoints during Mental Fatigue

To investigate the effects of magnetic stimulation at acupoints on brain functional network during mental fatigue, magnetic stimulation was applied to stimulate SHENMEN (HT7), HEGU (LI4) and LAOGONG (PC8) acupoint in this paper. The brain functional networks of normal state, mental fatigue state and stimulated state were constructed and the characteristic parameters were comparatively studied based on the complex network theory. The results showed that the connection of the network was enhanced by stimulating the HT7, LI4 and PC8 acupoint. In conclusion, magnetic stimulation at acupoints can effectively relieve mental fatigue.

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.

Studies in Brain Functional Networks Based on Complex Networks

The purpose of the paper is to provide a way to model the brain functional network based on the complex networks with brain anatomical architecture. We introduce the brain structural and functional researches, and delineate the brain anatomical and functional networks based on complex networks, then we discuss the brain functional complex network models; at last we put forward the brain functional networks modeling process and the data processing with fMRI （functional magnetic resonance imaging） in detailed.

Effect of Ventriculoperitoneal Shunt on the Recovery of Brain Function in Children with Hydrocephalus

Objective:To analyze the effect of ventriculoperitoneal shunt on the recovery of brain function in children with hydrocephalus.Methods:The clinical data of 40 children with hydrocephalus were retrospectively analyzed.Ventriculoperitoneal shunt was performed with 9003 shunt tube and P.S.Shunt tube,B.C.E.shunt tube.Electroencephalogram(EEG),and brain CT/MRI were performed before and after surgery,and postoperative follow-up was carried out to observe the therapeutic effect.Results:In this study,there were seven cases of intracranial injury,seven cases of congenital hydrocephalus,11 cases of ventricular end obstruction,three cases of abdominal end obstruction,nine cases complicated with bacterial infection,and 3 cases of shunt entering the scrotum.The prognosis of all the children was good,and there were no significant changes in eight cases.Conclusion:Ventriculoperitoneal shunt is effective in the treatment of children with hydrocephalus.

Esophageal nasopharyngeal catheter in the wake-up anesthesia of the brain function area operations

Objective To evaluate the feasibility and safety of the self developed sound outside the ventilation device-esophageal nasopharynx catheter in brain functional areas surgery applications. Methods 13 patients involved functional areas of brain surgery were chosed. After induction of general anesthesia,the catheters were placed in the esophagus,then connected to anesthesia machines to an external

The development of brain functional connectivity networks revealed by resting-state functional magnetic resonance imaging

Previous studies on brain functional connectivity networks in children have mainly focused on changes in function in specific brain regions, as opposed to whole brain connectivity in healthy children. By analyzing the independent components of activation and network connectivity between brain regions, we examined brain activity status and development trends in children aged 3 and 5 years. These data could provide a reference for brain function rehabilitation in children with illness or abnormal function. We acquired functional magnetic resonance images from 15 3-year-old children and 15 5-year-old children under natural sleep cond让ions. The participants were recruited from five kindergartens in the Nanshan District of Shenzhen City, China. The parents of the participants signed an informed consent form with the premise that they had been fully informed regarding the experimental protocol. We used masked independent component analysis and BrainNet Viewer software to explore the independent components of the brain and correlation connections between brain regions. We identified seven independent components in the two groups of children, including the executive control network, the dorsal attention network, the default mode network, the left frontoparietal network, the right frontoparietal network, the salience network, and the motor network. In the default mode network, the posterior cingulate cortex, medial frontal gyrus, and inferior parietal lobule were activated in both 3- and 5-year-old children, supporting the "three-brain region theory” of the default mode network. In the frontoparietal network, the frontal and parietal gyri were activated in the two groups of children, and functional connectivity was strengthened in 5-year-olds compared with 3-year-olds, although the nodes and network connections were not yet mature. The high-correlation network connections in the default mode networks and dorsal attention networks had been significantly strengthened in 5-year-olds vs. 3-year-olds. Further, the salience network in the 3-year-old children included an activated insula/inferior frontal gyrus-anterior cingulate cortex network circu让 and an activated thalamus-parahippocampal-posterior cingulate cortex-subcortical regions network circuit. By the age of 5 years, no des and high-correlation network connections (edges) were reduced in the salience network. Overall, activation of the dorsal attention network, default mode network, left frontoparietal network, and right frontoparietal network increased (the volume of activation increased, the signals strengthened, and the high-correlation connections increased and strengthened) in 5-year-olds compared with 3-year-olds, but activation in some brain nodes weakened or disappeared in the salience network, and the network connections (edges) were reduced. Between the ages of 3 and 5 years, we observed a tendency for function in some brain regions to be strengthened and for the generalization of activation to be reduced, indicating that specialization begins to develop at this time. The study protocol was approved by the local ethics committee of the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences in China with approval No. SIAT-IRB- 131115-H0075 on November 15, 2013.

Effects of exercise on brain functions in diabetic animal models

Human life span has dramatically increased over several decades,and the quality of life has been considered to be equally important.However,diabetes mellitus(DM) characterized by problems related to insulin secretion and recognition has become a serious health problem in recent years that threatens human health by causing decline in brain functions and finally leading to neurodegenerative diseases.Exercise is recognized as an effective therapy for DM without medication administration.Exercise studiesusing experimental animals are a suitable option to overcome this drawback,and animal studies have improved continuously according to the needs of the experimenters.Since brain health is the most significant factor in human life,it is very important to assess brain functions according to the different exercise conditions using experimental animal models.Generally,there are two types of DM; insulin-dependent type 1 DM and an insulin-independent type 2 DM(T2DM); however,the author will mostly discuss brain functions in T2 DM animal models in this review.Additionally,many physiopathologic alterations are caused in the brain by DM such as increased adiposity,inflammation,hormonal dysregulation,uncontrolled hyperphagia,insulin and leptin resistance,and dysregulation of neurotransmitters and declined neurogenesis in the hippocampus and we describe how exercise corrects these alterations in animal models.The results of changes in the brain environment differ according to voluntary,involuntary running exercises and resistance exercise,and gender in the animal studies.These factors have been mentioned in this review,and this review will be a good reference for studying how exercise can be used with therapy for treating DM.

Changes in brain functional network connectivity after stroke

Studies have shown that functional network connection models can be used to study brain net- work changes in patients with schizophrenia. In this study, we inferred that these models could also be used to explore functional network connectivity changes in stroke patients. We used independent component analysis to find the motor areas of stroke patients, which is a novel way to determine these areas. In this study, we collected functional magnetic resonance imaging datasets from healthy controls and right-handed stroke patients following their first ever stroke. Using independent component analysis, six spatially independent components highly correlat- ed to the experimental paradigm were extracted. Then, the functional network connectivity of both patients and controls was established to observe the differences between them. The results showed that there were 11 connections in the model in the stroke patients, while there were only four connections in the healthy controls. Further analysis found that some damaged connections may be compensated for by new indirect connections or circuits produced after stroke. These connections may have a direct correlation with the degree of stroke rehabilitation. Our findings suggest that functional network connectivity in stroke patients is more complex than that in hea- lthy controls, and that there is a compensation loop in the functional network following stroke. This implies that functional network reorganization plays a very important role in the process of rehabilitation after stroke.

Elevated brain temperature under severe heat exposure impairs cortical motor activity and executive function

Background:Excessive heat exposure can lead to hyperthermia in humans,which impairs physical performance and disrupts cognitive function.While heat is a known physiological stressor,it is unclear how severe heat stress affects brain physiology and function.Methods:Eleven healthy participants were subjected to heat stress from prolonged exercise or warm water immersion until their rectal temperatures(T_(re))attained 39.5℃,inducing exertional or passive hyperthermia,respectively.In a separate trial,blended ice was ingested before and during exercise as a cooling strategy.Data were compared to a control condition with seated rest(normothermic).Brain temperature(T_(br)),cerebral perfusion,and task-based brain activity were assessed using magnetic resonance imaging techniques.Results:T_(br)in motor cortex was found to be tightly regulated at rest(37.3℃±0.4℃(mean±SD))despite fluctuations in T_(re).With the development of hyperthermia,T_(br)increases and dovetails with the rising T_(re).Bilateral motor cortical activity was suppressed during high-intensity plantarflexion tasks,implying a reduced central motor drive in hyperthermic participants(T_(re)=38.5℃±0.1℃).Global gray matter perfusion and regional perfusion in sensorimotor cortex were reduced with passive hyperthermia.Executive function was poorer under a passive hyperthermic state,and this could relate to compromised visual processing as indicated by the reduced activation of left lateral-occipital cortex.Conversely,ingestion of blended ice before and during exercise alleviated the rise in both T_(re)and T_(bc)and mitigated heat-related neural perturbations.Conclusion:Severe heat exposure elevates T_(br),disrupts motor cortical activity and executive function,and this can lead to impairment of physical and cognitive performance.

Resting-state brain network remodeling after different nerve reconstruction surgeries:a functional magnetic resonance imaging study in brachial plexus injury rats

Distinct brain remodeling has been found after different nerve reconstruction strategies,including motor representation of the affected limb.However,differences among reconstruction strategies at the brain network level have not been elucidated.This study aimed to explore intranetwork changes related to altered peripheral neural pathways after different nerve reconstruction surgeries,including nerve repair,endto-end nerve transfer,and end-to-side nerve transfer.Sprague–Dawley rats underwent complete left brachial plexus transection and were divided into four equal groups of eight:no nerve repair,grafted nerve repair,phrenic nerve end-to-end transfer,and end-to-side transfer with a graft sutured to the anterior upper trunk.Resting-state brain functional magnetic resonance imaging was obtained 7 months after surgery.The independent component analysis algorithm was utilized to identify group-level network components of interest and extract resting-state functional connectivity values of each voxel within the component.Alterations in intra-network resting-state functional connectivity were compared among the groups.Target muscle reinnervation was assessed by behavioral observation(elbow flexion)and electromyography.The results showed that alterations in the sensorimotor and interoception networks were mostly related to changes in the peripheral neural pathway.Nerve repair was related to enhanced connectivity within the sensorimotor network,while end-to-side nerve transfer might be more beneficial for restoring control over the affected limb by the original motor representation.The thalamic-cortical pathway was enhanced within the interoception network after nerve repair and end-to-end nerve transfer.Brain areas related to cognition and emotion were enhanced after end-to-side nerve transfer.Our study revealed important brain networks related to different nerve reconstructions.These networks may be potential targets for enhancing motor recovery.

Brain functional network connectivity based on a visual task: visual information processing-related brain regions are significantly activated in the task state

It is not clear whether the method used in functional brain-network related research can be applied to explore the feature binding mechanism of visual perception. In this study, we inves-tigated feature binding of color and shape in visual perception. Functional magnetic resonance imaging data were collected from 38 healthy volunteers at rest and while performing a visual perception task to construct brain networks active during resting and task states. Results showed that brain regions involved in visual information processing were obviously activated during the task. The components were partitioned using a greedy algorithm, indicating the visual network existed during the resting state.Z-values in the vision-related brain regions were calculated, conifrming the dynamic balance of the brain network. Connectivity between brain regions was determined, and the result showed that occipital and lingual gyri were stable brain regions in the visual system network, the parietal lobe played a very important role in the binding process of color features and shape features, and the fusiform and inferior temporal gyri were crucial for processing color and shape information. Experimental ifndings indicate that understanding visual feature binding and cognitive processes will help establish computational models of vision, improve image recognition technology, and provide a new theoretical mechanism for feature binding in visual perception.