Diffusion tensor imaging of the hippocampus reflects the severity of hippocampal injury induced by global cerebral ischemia/reperfusion injury

At present,predicting the severity of brain injury caused by global cerebral ischemia/reperfusion injury(GCI/RI)is a clinical problem.After such an injury,clinical indicators that can directly reflect neurological dysfunction are lacking.The change in hippocampal microstructure is the key to memory formation and consolidation.Diffusion tensor imaging is a highly sensitive tool for visualizing injury to hippocampal microstructure.Although hippocampal microstructure,brain-derived neurotrophic factor(BDNF),and tropomyosin-related kinase B(Trk B)levels are closely related to nerve injury and the repair process after GCI/RI,whether these indicators can reflect the severity of such hippocampal injury remains unknown.To address this issue,we established rat models of GCI/RI using the four-vessel occlusion method.Diffusion tensor imaging parameters,BDNF,and Trk B levels were correlated with modified neurological severity scores.The results revealed that after GCI/RI,while neurological function was not related to BDNF and Trk B levels,it was related to hippocampal fractional anisotropy.These findings suggest that hippocampal fractional anisotropy can reflect the severity of hippocampal injury after global GCI/RI.The study was approved by the Institutional Animal Care and Use Committee of Capital Medical University,China(approval No.AEEI-2015-139)on November 9,2015.

Computational model investigating the effect of magnetic field on neural–astrocyte microcircuit

Extremely low-frequency magnetic field is widely used as a noninvasive stimulation method in clinical practice and basic research. Electrical field induced from magnetic pulse can decrease or increase neuronal electrical activity. However, the cellular mechanism underlying the effects of magnetic field is not clear from experimental data. Recent studies have demonstrated that "non-neuronal" cells, especially astrocytes, may be the potential effector for transcranial magnetic stimulation(TMS). In the present study, we implemented a neural–astrocyte microcircuit computational model based on hippocampal architecture to investigate the biological effects of different magnetic field frequencies on cells. The purpose of the present study is to elucidate the main influencing factors of MS to allow a better understanding of its mechanisms.Our model reproduced the basic characteristics of the neuron and astrocyte response to different magnetic stimulation. The results predict that interneurons with lower firing thresholds were more active in magnetic fields by contrast to pyramidal neurons. And the synaptic coupling strength between the connected neurons may be one of the critical factor to affect the effect of magnetic field on cells. In addition, the simulations show that astrocytes can decrease or increase slow inward currents(SICs) to finely tune neuronal excitation, which suggests their key role in excitatory–inhibitory balance. The interaction between neurons and astrocytes may represent a novel target for effective therapeutic strategies involving magnetic stimulation.

The Neural Mechanism of Knowledge Assembly in the Human Brain Inspires Artificial Intelligence Algorithm

When new information enters the brain,a human's prior knowledge of the world can change rapidly through a process referred to as"knowledge assembly".Recently,Nelli et al.investigated the neural correlates of knowledge assembly in the human brain using functional MRI.Further,inspired by the neural mechanism,the authors developed an artificial neural network algorithm to permit rapid knowledge assembly,improving the flexibility of the system[1].Once again,this research demonstrates that studying how the brain works can lead to better computational algorithms.

New challenge for bionics--brain-inspired computing

By definition, bionics is the application of biological mechanisms found in nature to artificial systems in order to achieve specific functional goals. Successful examples range from Velcro, the touch fastener inspired by the hooks of burrs, to self-cleaning material, inspired by the surface of the lotus leaf. Recently, a new trend in bionics i Brain-Inspired Computing （BIC） - has captured increasing attention. Instead of learning from burrs and leaves, BIC aims to understand the brain and then utilize its operating principles to achieve powerful and efficient information processing.

MonkeyTrail:A scalable video-based method for tracking macaque movement trajectory in daily living cages

Behavioral analysis of macaques provides important experimental evidence in the field of neuroscience.In recent years,video-based automatic animal behavior analysis has received widespread attention.However,methods capable of extracting and analyzing daily movement trajectories of macaques in their daily living cages remain underdeveloped,with previous approaches usually requiring specific environments to reduce interference from occlusion or environmental change.Here,we introduce a novel method,called MonkeyTrail,which satisfies the above requirements by frequently generating virtual empty backgrounds and using background subtraction to accurately obtain the foreground of moving animals.The empty background is generated by combining the frame difference method(FDM)and deep learning-based model(YOLOv5).The entire setup can be operated with low-cost hardware and can be applied to the daily living environments of individually caged macaques.To test MonkeyTrail performance,we labeled a dataset containing>8000 video frames with the bounding boxes of macaques under various conditions as ground-truth.Results showed that the tracking accuracy and stability of MonkeyTrail exceeded that of two deep learningbased methods(YOLOv5 and Single-Shot MultiBox Detector),traditional frame difference method,and na?ve background subtraction method.Using MonkeyTrail to analyze long-term surveillance video recordings,we successfully assessed changes in animal behavior in terms of movement amount and spatial preference.Thus,these findings demonstrate that MonkeyTrail enables low-cost,large-scale daily behavioral analysis of macaques.

Decoding human brain functions: Multi-modal, multi-scale insights

Unraveling the intricate relationship between the structure and function of the human brain remains a central and unresolved question in neuroscience.Ethical considerations impose significant constraints on invasive techniques in human neuroscience research.Consequently,knowledge about human brain function often relies on animal models to provide valuable discoveries and insights.However,caution is warranted,as findings from animal studies may not always be directly translatable to humans,especially when investigating higher cognitive functions.

Grey-matter volume as a potential feature for the classification of Alzheimer's disease and mild cognitive impairment: an exploratory study

Specific patterns of brain atrophy may be helpful in the diagnosis of Alzheimer＇s disease （AD）. In the present study, we set out to evaluate the utility of grey-matter volume in the classification of AD and amnestic mild cognitive impairment （aMCI） compared to normal control （NC）individuals. Voxel-based morphometric analyses were performed on structural MRIs from 35 AD patients, 27 aMCI patients, and 27 NC participants. A two-sample two-tailed t-test was computed between the NC and AD groups to create a map of abnormal grey matter in AD. The brain areas with significant differences were extracted as regions of interest （ROIs）, and the grey-matter volumes in the ROIs of the aMCI patients were included to evaluate the patterns of change across different disease severities. Next, correlation analyses between the grey-matter volumes in the ROIs and all clinical variables were performed in aMCI and AD patients to determine whether they varied with disease progression. The results revealed significantly decreased grey matter in the bilateral hippocampus/ parahippocampus, the bilateral superior/middle temporal gyri, and the right precuneus in AD patients.The grey-matter volumes with clinical variables were positively correlated Finally, we performed exploratory linear discriminative analyses to assess the classifying capacity of grey-matter volumes in the bilateral hippocampus and parahippocampus among AD, aMCI, and NC. Leave-one-out cross- validation analyses demonstrated that grey-matter volumes in hippocampus and parahippocampus accurately distinguished AD from NC. These findings indicate that grey-matter volumes are useful in the classification of AD.

Brainnetome and related projects

The brain is organized as a hierarchy of complex networks on different temporal and spatial scales.The complex connectivities within the brain are presented in the anatomical architecture as well as dynamic activity.There have long been efforts to make a connection map of the brain[1],and this has now been achieved with the establishment of

Prefrontal cortex and the dysconnectivity hypothesis of schizophrenia

Schizophrenia is hypothesized to arise from disrupted brain connectivity. This ＂dysconnectivity hypothesis＂ has generated interest in discovering whether there is anatomical and functional dysconnectivity between the prefrontal cortex （PFC） and other brain regions, and how this dysconnectivity is linked to the impaired cognitive functions and aberrant behaviors of schizophrenia. Critical advances in neuroimaging technologies, including diffusion tensor imaging （DTI） and functional magnetic resonance imaging （fMRI）, make it possible to explore these issues. DTI affords the possibility to explore anatomical connectivity in the human brain in vivo and fMRI can be used to make inferences about functional connections between brain regions. In this review, we present major advances in the understanding of PFC anatomical and functional dysconnectivity and their implications in schizophrenia. We then briefly discuss future prospects that need to be explored in order to move beyond simple mapping of connectivity changes to elucidate the neuronal mechanisms underlying schizophrenia.

Spinal Cord Stimulation Frequency Influences the Hemodynamic Response in Patients with Disorders of Consciousness

Spinal cord stimulation （SCS） is a promising technique for treating disorders of consciousness （DOCs）. However, differences in the spatio-temporal responsiveness of the brain under varied SCS parameters remain unclear. In this pilot study, functional near-infrared spectroscopy was used to measure the hemodynamic responses of 10 DOC patients to different SCS frequencies （5 Hz, 10 Hz, 50 Hz, 70 Hz, and 100 Hz）. In the prefrontal cortex, a key area in consciousness circuits, we found significantly increased hemodynamic responses at 70 Hz and 100 Hz, and significantly different hemodynamic responses between 50 Hz and 70 Hz/100 Hz. In addition, the functional connectivity between prefrontal and occipital areas was significantly improved with SCS at 70 Hz. These results demonstrated that SCS modulates the hemodynamic responses and long-range connectivity in a frequency-specific manner （with 70 Hz apparently better）, perhaps by improving the cerebral blood volume and information transmission through the reticular formation-thalamus-cortex pathway.

Recent Progress in Basic and Clinical Research on Disorders of Consciousness

Severe brain injury can lead to disorders of consciousness （DOCs）. Since DOC patients cannot communicate functionally or behave purposefully, most remain bedridden and require laborious care. The medical community is often confronted with the expectations of the families of chronic DOC patients, and the social, economic, and ethical consequences are tremendous. Research on DOCs is attracting increasing attention from scientists and physicians in various fields. With the development of modern neuroimaging and neuromodulation techniques, much progress has been made in the diagnosis, prognosis, treatment, and rehabilitation of DOCs in the last decade.

Brain Network Studies in Chronic Disorders of Consciousness:Advances and Perspectives

Neuroimaging has opened new opportunities to study the neural correlates of consciousness, and provided additional information concerning diagnosis, prognosis, and therapeutic interventions in patients with disorders of consciousness. Here, we aim to review neuroimaging studies in chronic disorders of consciousness from the viewpoint of the brain network, focusing on positron emission tomogra- phy, functional MRI, functional near-infrared spectroscopy, electrophysiology, and diffusion MRI. To accelerate basic research on disorders of consciousness and provide a panoramic view of unconsciousness, we propose that it is urgent to integrate different techniques at various spatiotemporal scales, and to merge fragmented findings into a uniform ＂Brainnetome＂ （Brain-net-ome） research framework.

Reconstruction of behavior-relevant individual brain activity:an individualized fMRI study

Different patterns of brain activity are observed in various subjects across a wide functional domain.However,these individual differences,which are often neglected through the group average,are not yet completely understood.Based on the fundamental assumption that human behavior is rooted in the underlying brain function,we speculated that the individual differences in brain activity are reflected in the individual differences in behavior.Adopting 98 behavioral measures and assessing the brain activity induced at seven task functional magnetic resonance imaging states,we demonstrated that the individual differences in brain activity can be used to predict behavioral measures of individual subjects with high accuracy using the partial least square regression model.In addition,we revealed that behavior-relevant individual differences in brain activity transferred between different task states and can be used to reconstruct individual brain activity.Reconstructed individual brain activity retained certain individual differences which were lost in the group average and could serve as an individual functional localizer.Therefore,our results suggest that the individual differences in brain activity contain behavior-relevant information and should be included in group averaging.Moreover,reconstructed individual brain activity shows a potential use in precise and personalized medicine.

Fine-Grained Topography and Modularity of the Macaque Frontal Pole Cortex Revealed by Anatomical Connectivity Profiles

The frontal pole cortex(FPC)plays key roles in various higher-order functions and is highly developed in non-human primates.An essential missing piece of information is the detailed anatomical connections for finer parcellation of the macaque FPC than provided by the previous tracer results.This is important for understanding the functional architecture of the cerebral cortex.Here,combining cross-validation and principal component analysis,we formed a tractography-based parcellation scheme that applied a machine learning algorithm to divide the macaque FPC(2 males and 6 females)into eight subareas using high-resolution diffusion magnetic resonance imaging with the 9.4 T Bruker system,and then revealed their subregional connections.Furthermore,we applied improved hierarchical clustering to the obtained parcels to probe the modular structure of the subregions,and found that the dorsolateral FPC,which contains an extension to the medial FPC,was mainly connected to regions of the default-mode network.The ventral FPC was mainly involved in the social-interaction network and the dorsal FPC in the metacognitive network.These results enhance our understanding of the anatomy and circuitry of the macaque brain,and contribute to FPC-related clinical research.

How the coevolution of language and sociality has helped to shape the human brain

Understanding how neural circuits contribute to cognitive differences between hum ans and other species,including macaque monkeys,is a major issue in neuroscience.Language and tool use are the most prominent differences between humans and other primates.Many neuroimaging-based studies have explored the brain mechanisms un-

Changes of Effective Connectivity in the Alpha Band Characterize Differential Processing of Audiovisual Information in Cross-Modal Selective Attention

Cross-modal selective attention enhances the processing of sensory inputs that are most relevant to the task at hand.Such differential processing could be mediated by a swift network reconfiguration on the macroscopic level,but this remains a poorly understood process.To tackle this issue,we used a behavioral paradigm to introduce a shift of selective attention between the visual and auditory domains,and recorded scalp electroencephalographic signals from eight healthy participants.The changes in effective connectivity caused by the cross-modal attentional shift were delineated by analyzing spectral Granger Causality(GC),a metric of frequency-specific effective connectivity.Using data-driven methods of pattern-classification and feature-analysis,we found that a change in the a band(12 Hz-15 Hz) of GC is a stable feature across different individuals that can be used to decode the attentional shift.Specifically,auditory attention induces more pronounced information flow in the α band,especially from the parietal-occipital areas to the temporal-parietal areas,compared to the case of visual attention,reflecting a reconfiguration of interaction in the macroscopic brain network accompanying different processing.Our results support the role of α oscillation in organizing the information flow across spatially-separated brain areas and,thereby,mediating cross-modal selective attention.

Imaging Three-Dimensional Microvascular Networks of Brain with Synchrotron Radiation Microangiography

The adult human brain represents about 2%of the body weight,yet consumes about 20%of the body’s total energy.A key constituent in meeting this high energy demand is a complex cerebral vascular system.A detailed knowledge of this system is important for understanding the basic principles of cerebral blood flow and its coupling to neural processing and neuropathological alterations in brain diseases.

Authors' response to “Maternal age as a potential explanation of the role of the L allele of the serotonin transporter gene in anxiety and depression in Asians”

In the letter to the editor, Dr. Comings et al. proposed a potential explanation of our findings that the L allele rather than S allele of 5-HTTLPR was associated with higher anxiety levels and reduced amygdala-prefrontal cortex （PFC） connectivity in Han Chinese[1], which demonstrated an ＇allele reversal＇ in the genetics of the 5-HTTLPR gene in Asians versus Caucasians. The authors alleged that this ＇allele reversal＇ might simply result from maternal age and suggested that we test this on our datasets. Unfortunately,

Developing Neuroimaging Biomarker for Brain Diseases with a Machine Learning Framework and the Brainnetome Atlas

Neuroimaging made it possible to quantify brain structure and function.However,there are few neuroimaging biomarkers for the early diagnosis,prognosis,and evaluation of therapy for brain diseases.The development of neuroimaging biomarkers for brain diseases faces two major bottleneck problems.First,the neuroimaging datasets of brain diseases are always characterized by small sample size,high dimension,and large heterogeneity.Second,a fine-grained individualized human brain atlas for effective dimensionality reduction has always been lacking.

Voxel-based meta-analysis of grey matter changes in Alzheimer’s disease

Background:Voxel-based morphometry(VBM)using structural brain MRI has been widely used for the assessment of impairment in Alzheimer’s disease(AD),but previous studies in VBM studies on AD remain inconsistent.Objective:We conducted meta-analyses to integrate the reported studies to determine the consistent grey matter alterations in AD based on VBM method.Methods:The PubMed,ISI Web of Science,EMBASE and Medline database were searched for articles between 1995 and June 2014.Manual searches were also conducted,and authors of studies were contacted for additional data.Coordinates were extracted from clusters with significant grey matter difference between AD patients and healthy controls(HC).Meta-analysis was performed using a new improved voxel-based meta-analytic method,Effect Size Signed Differential Mapping(ES-SDM).Results:Thirty data-sets comprising 960 subjects with AD and 1195 HC met inclusion criteria.Grey matter volume(GMV)reduction at 334 coordinates in AD and no GMV increase were found in the current meta-analysis.Significant reductions in GMV were robustly localized in the limbic regions(left parahippocampl gyrus and left posterior cingulate gyrus).In addition,there were GM decreases in right fusiform gyrus and right superior frontal gyrus.The findings remain largely unchanged in the jackknife sensitivity analyses.Conclusions:Our meta-analysis clearly identified GMV atrophy in AD.These findings confirm that the most prominent and replicable structural abnormalities in AD are in the limbic regions and contributes to the understanding of pathophysiology underlying AD.