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.

RF power design optimization in MRI system

Magnetic resonance image quality and patient safety have been the focus of engineering and research ever since the invention of equipment in the early 1970s.In high field(or ultrahigh field)MRI systems,the emerging techniques induced by B1 field challenges have promoted various potential solutions.This paper describes the relationship between RF power and B1þfield performance,and the overall requirements considered in RF subsystem design.The design of the RF in the MR system is systematically summarized,including the entire transmission chain,sequence algorithm and RF pulse design,and the probabilities for improvement and optimization in the system design are indicated.At the same time,the radio frequency related issues of the human whole-body 7 T MR and animal MR systems are discussed,especially the promising future showed by the technologies such as radio frequency parallel transmission technology in the ultrahigh field.

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.

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.

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.

Multimodal Fusion of Brain Imaging Data: Methods and Applications

Neuroimaging data typically include multiple modalities,such as structural or functional magnetic resonance imaging,dif-fusion tensor imaging,and positron emission tomography,which provide multiple views for observing and analyzing the brain.To lever-age the complementary representations of different modalities,multimodal fusion is consequently needed to dig out both inter-modality and intra-modality information.With the exploited rich information,it is becoming popular to combine multiple modality data to ex-plore the structural and functional characteristics of the brain in both health and disease status.In this paper,we first review a wide spectrum of advanced machine learning methodologies for fusing multimodal brain imaging data,broadly categorized into unsupervised and supervised learning strategies.Followed by this,some representative applications are discussed,including how they help to under-stand the brain arealization,how they improve the prediction of behavioral phenotypes and brain aging,and how they accelerate the biomarker exploration of brain diseases.Finally,we discuss some exciting emerging trends and important future directions.Collectively,we intend to offer a comprehensive overview of brain imaging fusion methods and their successful applications,along with the chal-lenges imposed by multi-scale and big data,which arises an urgent demand on developing new models and platforms.

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.

Individualized brain mapping for navigated neuromodulation

The brain is a complex organ that requires precise mapping to understand its structure and function.Brain atlases provide a powerful tool for studying brain circuits,discovering biological markers for early diagnosis,and developing personalized treatments for neuropsychiatric disorders.Neuromodulation techniques,such as transcranial magnetic stimulation and deep brain stimulation,have revolutionized clinical therapies for neuropsychiatric disorders.However,the lack of fine-scale brain atlases limits the precision and effectiveness of these techniques.Advances in neuroimaging and machine learning techniques have led to the emergence of stereotactic-assisted neurosurgery and navigation systems.Still,the individual variability among patients and the diversity of brain diseases make it necessary to develop personalized solutions.The article provides an overview of recent advances in individualized brain mapping and navigated neuromodulation and discusses the methodological profiles,advantages,disadvantages,and future trends of these techniques.The article concludes by posing open questions about the future development of individualized brain mapping and navigated neuromodulation.

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.

Uncovering hidden nodes and hidden links in complex dynamic networks

Inferring network structures from available data has attracted much interest in network science;however,in many realistic networks,only some of the nodes are perceptible while others are hidden,making it a challenging task.In this work,we develop a method for reconstructing the network with hidden nodes and links,taking account of fast-varying noise and time-delay interactions.By calculating the correlations of available data with different derivative orders for multiple pairs of accessible nodes,analyzing and integrating the relationships between different correlations,and defining diverse hidden-node-related reconstruction motifs,we can effectively identify the hidden nodes and hidden links in the network.

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

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.

Reproducible Abnormalities and Diagnostic Generalizability of White Matter in Alzheimer’s Disease

Alzheimer’s disease(AD)is associated with the impairment of white matter(WM)tracts.The current study aimed to verify the utility of WM as the neuroimaging marker of AD with multisite diffusion tensor imaging datasets[321 patients with AD,265 patients with mild cognitive impairment(MCI),279 normal controls(NC)],a unified pipeline,and independent site cross-validation.Automated fiber quantification was used to extract diffusion profiles along tracts.Random-effects meta-analyses showed a reproducible degeneration pattern in which fractional anisotropy significantly decreased in the AD and MCI groups compared with NC.Machine learning models using tract-based features showed good generalizability among independent site cross-validation.The diffusion metrics of the altered regions and the AD probability predicted by the models were highly correlated with cognitive ability in the AD and MCI groups.We highlighted the reproducibility and generalizability of the degeneration pattern of WM tracts in AD.

EM-fMRI:A Promising Method for Mapping the Brain Functional Connectome

Electrical microstimulation(EM)can be used to locally stimulate the cerebral cortex or subcortical nuclei.Meanwhile,functional magnetic resonance imaging(fMRI)can noninvasively visualize the activity of the whole brain.When EM is combined with fMRI(EM-fMRI),it is possible to measure the changes of the whole-brain neural activity using fMRI while applying electrical stimulation to a specific brain site,and accordingly infer the causal links between the stimulated site and the activated brain areas.

Different Regional Patterns in Gray Matter-based Age Prediction

Dear Editor,The brain experiences ongoing changes across different ages to support brain development and functional reorganization.During the span of adulthood,although the brain has matured from a neurobiological perspective,it is still continuously shaped by external factors such as living habit.

Retraction Note to:Gray Matter-Based Age Prediction Characterizes Different Regional Patterns

The authors have retracted this article.After publication we found an error in the implementation code that resulted in data leakage in the age-prediction model training process.We have redesigned the prediction model and tested the mode with an extended dataset(around 2000 subjects,in contrast to the 600 subjects in this article).

Effects of childhood trauma on aggressive behaviors andhippocampal function:the modulation of COMT haplotypes

Background:Aggression is a commonly hostile behavior linked to the hippocampal activity.Childhood trauma(CT)exposure has been associated with altered sensitization of the hypothalamic-pituitary-adrenal(HPA)axis and hippocampal volumewhich could increase violent aggressive behaviors.Additionally,Catechol-O-methyltransferase(COMT),the major dopamine metabolism enzyme,is impli-cated in stress responsivity,including aggression.Hence,CT exposure may affect aggression through the effect on the hippocampal function,which might also be modulated by the COMT variations.Objectives:This study examined whether both CT and haplotypes of COMT moderate hippocampal function and thus affect human aggressive behavior.Methods:We obtained bilateral hippocampal functional connectivity maps using resting state functional magnetic resonance imag-ing(MRI)data.COMT haplotype estimation was performed using Haploview 4.2 and PHASE 2.1.Then we constructed a moderated mediation model to study the effect of the CTQ×COMT on aggressive behavior.Results:Three major haplotypes were generated from thirteen single nucleotide polymorphisms(SNPs)within the COMT gene and formed three haplotypes corresponding to high,medium,and low enzymatic activity of COMT.The results showed interactive re-lationships between the Childhood Trauma Questionnaire(CTQ)and COMT with respect to the functional connectivity(FC)of the bilateral hippocampus(HIP)-orbital frontal cortex(OFC).Specifically,CT experience predicted lower negative HIP-OFC coupling in the APS and HPS haplotypes corresponding to the medium and high enzymatic activity of COMT,but greater FC in the LPS haplotypes corresponding to the low enzymatic activity.We also observed a conditional mediation effect of the right HIP-OFC coupling in the link between COMT and aggressive behavior that was moderated by CT experience.Conclusions:These results suggest that CT and COMT have a combined effect on aggressive behavior through hippocampal function.This mediation analysis sheds light on the influence of childhood experience on aggressive behavior in different genetic backgrounds.

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.

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.