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 evolut...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.展开更多
Objective Accurate infant brain parcellation is crucial for understanding early brain development;however,it is challenging due to the inherent low tissue contrast,high noise,and severe partial volume effects in infan...Objective Accurate infant brain parcellation is crucial for understanding early brain development;however,it is challenging due to the inherent low tissue contrast,high noise,and severe partial volume effects in infant magnetic resonance images(MRIs).The aim of this study was to develop an end-to-end pipeline that enabled accurate parcellation of infant brain MRIs.Methods We proposed an end-to-end pipeline that employs a two-stage global-to-local approach for accurate parcellation of infant brain MRIs.Specifically,in the global regions of interest(ROIs)localization stage,a combination of transformer and convolution operations was employed to capture both global spatial features and fine texture features,enabling an approximate localization of the ROIs across the whole brain.In the local ROIs refinement stage,leveraging the position priors from the first stage along with the raw MRIs,the boundaries of the ROIs are refined for a more accurate parcellation.Results We utilized the Dice ratio to evaluate the accuracy of parcellation results.Results on 263 subjects from National Database for Autism Research(NDAR),Baby Connectome Project(BCP)and Cross-site datasets demonstrated the better accuracy and robustness of our method than other competing methods.Conclusion Our end-to-end pipeline may be capable of accurately parcellating 6-month-old infant brain MRIs.展开更多
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 pa...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.展开更多
Deep learning approaches,especially convolutional neural networks(CNNs),have become the method of choice in the field of medical image analysis over the last few years.This prevalence is attributed to their excellent ...Deep learning approaches,especially convolutional neural networks(CNNs),have become the method of choice in the field of medical image analysis over the last few years.This prevalence is attributed to their excellent abilities to learn features in a more effective and efficient manner,not only for 2D/3D images in the Euclidean space,but also for meshes and graphs in non-Euclidean space such as cortical surfaces in neuroimaging analysis field.The brain cerebral cortex is a highly convoluted and thin sheet of gray matter(GM)that is thus typically represented by triangular surface meshes with an intrinsic spherical topology for each hemisphere.Accordingly,novel tailored deep learning methods have been developed for cortical surface-based analysis of neuroimaging data.This paper reviewsed the representative deep learning techniques relevant to cortical surface-based analysis and summarizes recent major contributions to the field.Specifically,we surveyed the use of deep learning techniques for cortical surface reconstruction,registration,parcellation,prediction,and other applications.We concluded by discussing the open challenges,limitations,and potentials of these techniques,and suggested directions for future research.展开更多
Recently, restingstate functional magnetic resonance imaging has been used to parcellate the brain into functionally distinct regions based on the information available in functional connectivity maps. However, brain ...Recently, restingstate functional magnetic resonance imaging has been used to parcellate the brain into functionally distinct regions based on the information available in functional connectivity maps. However, brain voxels are not independent units and adjacent voxels are always highly correlated, so functional connectivity maps contain redundant information, which not only impairs the computational efficiency during clustering, but also reduces the accuracy of clustering results. The aim of this study was to propose featurereduction approaches to reduce the redundancy and to develop semisimulated data with defined ground truth to evaluate these approaches. We proposed a featurereduction approach based on the Affinity Propagation Algorithm （APA） and compared it with the classic feature reduction approach based on Principal Component Analysis （PCA）. We tested the two approaches to the parcellation of both semisimulated and real seed regions using the Kmeans algorithm and designed two experiments to evaluate their noise resistance. We found that all functional connectivitymaps （with/without feature reduction） provided correct information for the parcellation of the semi simulated seed region and the computational efficiency was greatly improved by both feature reduction approaches. Meanwhile, the APAbased featurereduction approach outperformed the PCA based approach in noiseresistance. The results suggested that functional connectivity maps can provide correct information for cortical parcellation, and featurereduction does not significantly change the information. Considering the improvement in computational efficiency and the noiseresistance, featurereduction of functional connectivity maps before cortical parcellation is both feasible and necessary.展开更多
The human striatum is essential for both lowand high-level functions and has been implicated in the pathophysiology of various prevalent disorders,including Parkinson's disease(PD)and schizophrenia(SCZ).It is know...The human striatum is essential for both lowand high-level functions and has been implicated in the pathophysiology of various prevalent disorders,including Parkinson's disease(PD)and schizophrenia(SCZ).It is known to consist of structurally and functionally divergent subdivisions.However,previous parcellations are based on a single neuroimaging modality,leaving the extent of the multi-modal organization of the striatum unknown.Here,we investigated the organization of the striatum across three modalities—resting-state functional connectivity,probabilistic diffusion tractography,and structural covariance—to provide a holistic convergent view of its structure and function.We found convergent clusters in the dorsal,dorsolateral,rostral,ventral,and caudal striatum.Functional characterization revealed the anterior striatum to be mainly associated with cognitive and emotional functions,while the caudal striatum was related to action execution.Interestingly,significant structural atrophy in the rostral and ventral striatum was common to both PD and SCZ,but atrophy in the dorsolateral striatum was specifically attributable to PD.Our study revealed a cross-modal convergent organization of the striatum,representing a fundamental topographical model that can be useful for investigating structural and functional variability in aging and in clinical conditions.展开更多
基金partially supported by the Science and Technology Innovation 2030-Brain Science and Brain-Inspired Intelligence Project(2021ZD0200200)the National Natural Science Foundation of China(62327805,82151307,82072099,82202253)。
文摘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.
基金funded by National Institutes of Health(Grant Nos.MH117943,MH109773,MH116225,and MH123202)Additionally,the work leverages approaches developed through an National Institutes of Health(Grant No.1U01MH110274)the efforts of the Baby Connectome Project Consortium at UNC/UMIN.
文摘Objective Accurate infant brain parcellation is crucial for understanding early brain development;however,it is challenging due to the inherent low tissue contrast,high noise,and severe partial volume effects in infant magnetic resonance images(MRIs).The aim of this study was to develop an end-to-end pipeline that enabled accurate parcellation of infant brain MRIs.Methods We proposed an end-to-end pipeline that employs a two-stage global-to-local approach for accurate parcellation of infant brain MRIs.Specifically,in the global regions of interest(ROIs)localization stage,a combination of transformer and convolution operations was employed to capture both global spatial features and fine texture features,enabling an approximate localization of the ROIs across the whole brain.In the local ROIs refinement stage,leveraging the position priors from the first stage along with the raw MRIs,the boundaries of the ROIs are refined for a more accurate parcellation.Results We utilized the Dice ratio to evaluate the accuracy of parcellation results.Results on 263 subjects from National Database for Autism Research(NDAR),Baby Connectome Project(BCP)and Cross-site datasets demonstrated the better accuracy and robustness of our method than other competing methods.Conclusion Our end-to-end pipeline may be capable of accurately parcellating 6-month-old infant brain MRIs.
基金the National Natural Science Foundation of China(91432302 and 31620103905)the Science Frontier Program of the Chinese Academy of Sciences(QYZDJ-SSW-SMC019)+3 种基金the National Key R&D Program of China(2017YFA0105203)Beijing Municipal Science and Technology Commission(Z161100000216152,Z161100000216139,Z181100001518004and Z171100000117002)the Beijing Brain Initiative of Beijing Municipal Science and Technology Commission(Z181100001518004)the Guangdong Pearl River Talents Plan(2016ZT06S220)。
文摘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.
基金the National Institutes of Health(NIH)(Grant Nos.MH116225,MH117943,MH123202,and AG075582).
文摘Deep learning approaches,especially convolutional neural networks(CNNs),have become the method of choice in the field of medical image analysis over the last few years.This prevalence is attributed to their excellent abilities to learn features in a more effective and efficient manner,not only for 2D/3D images in the Euclidean space,but also for meshes and graphs in non-Euclidean space such as cortical surfaces in neuroimaging analysis field.The brain cerebral cortex is a highly convoluted and thin sheet of gray matter(GM)that is thus typically represented by triangular surface meshes with an intrinsic spherical topology for each hemisphere.Accordingly,novel tailored deep learning methods have been developed for cortical surface-based analysis of neuroimaging data.This paper reviewsed the representative deep learning techniques relevant to cortical surface-based analysis and summarizes recent major contributions to the field.Specifically,we surveyed the use of deep learning techniques for cortical surface reconstruction,registration,parcellation,prediction,and other applications.We concluded by discussing the open challenges,limitations,and potentials of these techniques,and suggested directions for future research.
基金supported by the National Basic Research Development Program (973 Program) of China (2012CBA01304, 2011CB707800)the National High Technology Research and Development Program (863 Program) of China (2012AA020701)+2 种基金the National Natural Science Foundation of China (31271167, 31271168, 81271495, 31070963, 31070965)the Strategic Priority Research Program of the Chinese Academy of Science, China (XDB02020500)the Development and Reform Project of Yunnan Province, China
文摘Recently, restingstate functional magnetic resonance imaging has been used to parcellate the brain into functionally distinct regions based on the information available in functional connectivity maps. However, brain voxels are not independent units and adjacent voxels are always highly correlated, so functional connectivity maps contain redundant information, which not only impairs the computational efficiency during clustering, but also reduces the accuracy of clustering results. The aim of this study was to propose featurereduction approaches to reduce the redundancy and to develop semisimulated data with defined ground truth to evaluate these approaches. We proposed a featurereduction approach based on the Affinity Propagation Algorithm （APA） and compared it with the classic feature reduction approach based on Principal Component Analysis （PCA）. We tested the two approaches to the parcellation of both semisimulated and real seed regions using the Kmeans algorithm and designed two experiments to evaluate their noise resistance. We found that all functional connectivitymaps （with/without feature reduction） provided correct information for the parcellation of the semi simulated seed region and the computational efficiency was greatly improved by both feature reduction approaches. Meanwhile, the APAbased featurereduction approach outperformed the PCA based approach in noiseresistance. The results suggested that functional connectivity maps can provide correct information for cortical parcellation, and featurereduction does not significantly change the information. Considering the improvement in computational efficiency and the noiseresistance, featurereduction of functional connectivity maps before cortical parcellation is both feasible and necessary.
基金This work was supported by the Deutsche Forschungsgemeinschaft(GE 2835/1-1,El 816/4-1)the Helmholtz Portfolio Theme 4 Supercomputing and Modelling for the Human Brain'and the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No.785907(HBP SGA2)We gratefully acknowledge financial support from the China Scholarship Council(201606750003).
文摘The human striatum is essential for both lowand high-level functions and has been implicated in the pathophysiology of various prevalent disorders,including Parkinson's disease(PD)and schizophrenia(SCZ).It is known to consist of structurally and functionally divergent subdivisions.However,previous parcellations are based on a single neuroimaging modality,leaving the extent of the multi-modal organization of the striatum unknown.Here,we investigated the organization of the striatum across three modalities—resting-state functional connectivity,probabilistic diffusion tractography,and structural covariance—to provide a holistic convergent view of its structure and function.We found convergent clusters in the dorsal,dorsolateral,rostral,ventral,and caudal striatum.Functional characterization revealed the anterior striatum to be mainly associated with cognitive and emotional functions,while the caudal striatum was related to action execution.Interestingly,significant structural atrophy in the rostral and ventral striatum was common to both PD and SCZ,but atrophy in the dorsolateral striatum was specifically attributable to PD.Our study revealed a cross-modal convergent organization of the striatum,representing a fundamental topographical model that can be useful for investigating structural and functional variability in aging and in clinical conditions.
