Quantification of Tc-99m-ethyl cysteinate dimer brain single photon emission computed tomography images using statistical probabilistic brain atlas in depressive end-stage renal disease patients:Correlation with disease severity and symptom factors

This study adapted a statistical probabilistic anatomical map of the brain for single photon emission computed tomography images of depressive end-stage renal disease patients. This research aimed to investigate the relationship between symptom clusters, disease severity, and cerebral blood flow. Twenty-seven patients （16 males, 11 females） with stages 4 and 5 end-stage renal disease were enrolled, along with 25 healthy controls. All patients underwent depressive mood assessment and brain single photon emission computed tomography. The statistical probabilistic anatomical map images were used to calculate the brain single photon emission computed tomography counts. Asymmetric index was acquired and Pearson correlation analysis was performed to analyze the correlation between symptom factors, severity, and regional cerebral blood flow. The depression factors of the Hamilton Depression Rating Scale showed a negative correlation with cerebral blood flow in the left amygdale. The insomnia factor showed negative correlations with cerebral blood flow in the left amygdala, right superior frontal gyrus, right middle frontal gyrus, and left middle frontal gyrus. The anxiety factor showed a positive correlation with cerebral glucose metabolism in the cerebellar vermis and a negative correlation with cerebral glucose metabolism in the left globus pailidus, right inferior frontal gyrus, both temporal poles, and left parahippocampus. The overall depression severity （total scores of Hamilton Depression Rating Scale） was negatively correlated with the statistical probabilistic anatomical map results in the left amygdala and right inferior frontal gyrus. In conclusion, our results demonstrated that the disease severity and extent of cerebral blood flow quantified by a probabilistic brain atlas was related to various brain areas in terms of the overall severity and symptom factors in end-stage renal disease patients.

Multimodal Nature of the Single-cell Primate Brain Atlas:Morphology,Transcriptome,Electrophysiology,and Connectivity

Primates exhibit complex brain structures that augment cognitive function.The neocortex fulfills high-cognitive functions through billions of connected neurons.These neurons have distinct transcriptomic,morphological,and electrophysiological properties,and their connectivity principles vary.These features endow the primate brain atlas with a multimodal nature.The recent integration of next-generation sequencing with modified patch-clamp techniques is revolutionizing the way to census the primate neocortex,enabling a multimodal neuronal atlas to be established in great detail:(1)single-cell/single-nucleus RNA-seq technology establishes high-throughput transcriptomic references,covering all major transcriptomic cell types;(2)patch-seq links the morphological and electrophysiological features to the transcriptomic reference;(3)multicell patch-clamp delineates the principles of local connectivity.Here,we review the applications of these technologies in the primate neocortex and discuss the current advances and tentative gaps for a comprehensive understanding of the primate neocortex.

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.

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.

Age-related connectivity differences between attention deficit and hyperactivity disorder patients and typically developing subjects：a resting-state functional MRI study

Attention deficit and hyperactivity disorder（ADHD） is a disorder characterized by behavioral symptoms including hyperactivity/impulsivity among children,adolescents,and adults.These ADHD related symptoms are influenced by the complex interaction of brain networks which were under explored.We explored age-related brain network differences between ADHD patients and typically developing（TD） subjects using resting state f MRI（rs-f MRI） for three age groups of children,adolescents,and adults.We collected rs-f MRI data from 184 individuals（27 ADHD children and 31 TD children;32 ADHD adolescents and 32 TD adolescents;and 31 ADHD adults and 31 TD adults）.The Brainnetome Atlas was used to define nodes in the network analysis.We compared three age groups of ADHD and TD subjects to identify the distinct regions that could explain age-related brain network differences based on degree centrality,a well-known measure of nodal centrality.The left middle temporal gyrus showed significant interaction effects between disease status（i.e.,ADHD or TD） and age（i.e.,child,adolescent,or adult）（P 0.001）.Additional regions were identified at a relaxed threshold（P 0.05）.Many of the identified regions（the left inferior frontal gyrus,the left middle temporal gyrus,and the left insular gyrus） were related to cognitive function.The results of our study suggest that aberrant development in cognitive brain regions might be associated with age-related brain network changes in ADHD patients.These findings contribute to better understand how brain function influences the symptoms of ADHD.

猕猴脑网络组图谱:包含分区、连接和组织学的多层面全新大脑地图

The rhesus macaque(Macaca mulatta)is a crucial experimental animal that shares many genetic,brain organizational,and behavioral characteristics with humans.A macaque brain atlas is fundamental to biomedical and evolutionary research.However,even though connectivity is vital for understanding brain functions,a connectivity-based whole-brain atlas of the macaque has not previously been made.In this study,we created a new whole-brain map,the Macaque Brainnetome Atlas(MacBNA),based on the anatomical connectivity profiles provided by high angular and spatial resolution ex vivo diffusion MRI data.The new atlas consists of 248 cortical and 56 subcortical regions as well as their structural and functional connections.The parcellation and the diffusion-based tractography were evaluated with invasive neuronal-tracing and Nissl-stained images.As a demonstrative application,the structural connectivity divergence between macaque and human brains was mapped using the Brainnetome atlases of those two species to uncover the genetic underpinnings of the evolutionary changes in brain structure.The resulting resource includes:(1)the thoroughly delineated Macaque Brainnetome Atlas(MacBNA),(2)regional connectivity profiles,(3)the postmortem high-resolution macaque diffusion and T2-weighted MRI dataset(Brainnetome-8),and(4)multi-contrast MRI,neuronal-tracing,and histological images collected from a single macaque.MacBNA can serve as a common reference frame for mapping multifaceted features across modalities and spatial scales and for integrative investigation and characterization of brain organization and function.Therefore,it will enrich the collaborative resource platform for nonhuman primates and facilitate translational and comparative neuroscience research.

Constructing the rodent stereotaxic brain atlas:a survey

The stereotaxic brain atlas is a fundamental reference tool commonly used in the field of neuroscience.Here we provide a brief history of brain atlas development and clarify three key conceptual elements of stereotaxic brain atlasing:brain image,atlas,and stereotaxis.We also refine four technical indices for evaluating the construction of atlases:the quality of staining and labeling,the granularity of delineation,spatial resolution,and the precision of spatial location and orientation.Additionally,we discuss state-of-the-art technologies and their trends in the fields of image acquisition,stereotaxic coordinate construction,image processing,anatomical structure recognition,and publishing:the procedures of brain atlas illustration.We believe that the use of single-cell resolution and micron-level location precision will become a future trend in the study of the stereotaxic brain atlas,which will greatly benefit the development of neuroscience.

Application of Computational Biology to Decode Brain Transcriptomes

The rapid development of high-throughput sequencing technologies has generated massive valuable brain transcriptome atlases,providing great opportunities for systematically investigating gene expression characteristics across various brain regions throughout a series of developmental stages.Recent studies have revealed that the transcriptional architecture is the key to interpreting the molecular mechanisms of brain complexity.However,our knowledge of brain transcriptional characteristics remains very limited.With the immense efforts to generate high-quality brain transcriptome atlases,new computational approaches to analyze these highdimensional multivariate data are greatly needed.In this review,we summarize some public resources for brain transcriptome atlases and discuss the general computational pipelines that are commonly used in this field,which would aid in making new discoveries in brain development and disorders.

Stereotactic localization and visualization of the subthalamic nucleus

Background The subthalamic nucleus （STN） is widely recognized as one of the most important and commonly targeted nuclei in stereotactic and functional neurosurgery. The success of STN surgery depends on accuracy in target determination. Construction of a digitaiized atlas of STN based on stereotactic MRI will play an instrumental role in the accuracy of anatomical localization. The aim of this study was to investigate the three-dimensional （3D） target location of STN in stereotactic space and construct a digitalized atlas of STN to accomplish the visualization of the STN on stereotactic MRI, thus providing clinical guidance on the precise anatomical localization of STN. Methods One hundred and twenty healthy people volunteered to be scanned by 1.5 Tesla MRI scanning with 1-mm-thick slice in the standard stereotactic space between 2005 and 2006. One adult male was selected for 3D reconstruction of STN. The process of 3D reconstruction included identification, manual segmentation, extraction, conservation and reconstruction. Results There was a significant correlation between the coordinates and age （P 〈0.05）. The volume of left STN was significantly larger than the right STN, and there was a significant negative correlation between volume and age （P 〈0.05） The surface of the STN nucleus after 3D reconstruction appeared smooth, natural and realistic. The morphological feature of STN on the individual brain could be visualized directly in 3D. The 3D reconstructed STN could be rotated, zoomed and displayed at any direction in the stereotactic space. The anteroposterior diameter of the STN nucleus was longer than the vertical and transverse diameters in 3D space. The 3D reconstruction of STN manifested typical structure of the ＂dual lens＂. Conclusions The visualization of individual brain atlas based on stereotactic MRI is feasible. However, software for automated segmentation, extraction and registration of MR images need to be further developed.

Single-cell atlas of domestic pig cerebral cortex and hypothalamus

The brain of the domestic pig(Sus scrofa domesticus)has drawn considerable attention due to its high similarities to that of humans.However,the cellular compositions of the pig brain(PB)remain elusive.Here we investigated the single-nucleus transcriptomic profiles of five regions of the PB(frontal lobe,parietal lobe,temporal lobe,occipital lobe,and hypothalamus)and identified 21 cell subpopulations.The cross-species comparison of mouse and pig hypothalamus revealed the shared and specific gene expression patterns at the single-cell resolution.Furthermore,we identified cell types and molecular pathways closely associated with neurological disorders,bridging the gap between gene mutations and pathogenesis.We reported,to our knowledge,the first single-cell atlas of domestic pig cerebral cortex and hypothalamus combined with a comprehensive analysis across species,providing extensive resources for future research regarding neural science,evolutionary developmental biology,and regenerative medicine.

Cell-type-specific genes associated with cortical structural abnormalities in pediatric bipolar disorder

Background Pediatric bipolar disorder(PBD)has been proven to be related to abnormal brain structural connectivity,but how the abnormalities in PBD correlate with gene expression is debated.Objective This study aims at identification of cell-type-specific gene modules based on cortical structural differences in PBD.Methods Morphometric similarity networks(MSN)were computed as a marker of interareal cortical connectivity based on MRI data from 102 participants(59 patients and 43 controls).Partial least squares(PLS)regression was used to calculate MSN differences related to transcriptomic data in AHBA.The biological processes and cortical cell types associated with this gene expression profile were determined by gene enrichment tools.Results MSN analysis results demonstrated differences of cortical structure between individuals diagnosed with PBD and healthy control participants.MSN differences were spatially correlated with the PBD-related weighted genes.The weighted genes were enriched for“trans-synaptic signaling”and“regulation of ion transport”,and showed significant specific expression in excitatory and inhibitory neurons.Conclusions This study identified the genes that contributed to structural network aberrations in PBD.It was found that transcriptional changes of excitatory and inhibitory neurons might be associated with abnormal brain structural connectivity in PBD.