Brain Mapping Project Research Articles

CiftiStorm pipeline: facilitating reproducible EEG/MEG source connectomics.

We present CiftiStorm, an electrophysiological source imaging (ESI) pipeline incorporating recently developed methods to improve forward and inverse solutions. The CiftiStorm pipeline produces Human Connectome Project (HCP) and megconnectome-compliant outputs from dataset inputs with varying degrees of spatial resolution. The input data can range from low-sensor-density electroencephalogram (EEG) or magnetoencephalogram (MEG) recordings without structural magnetic resonance imaging (sMRI) to high-density EEG/MEG recordings with an HCP multimodal sMRI compliant protocol. CiftiStorm introduces a numerical quality control of the lead field and geometrical corrections to the head and source models for forward modeling. For the inverse modeling, we present a Bayesian estimation of the cross-spectrum of sources based on multiple priors. We facilitate ESI in the T1w/FSAverage32k high-resolution space obtained from individual sMRI. We validate this feature by comparing CiftiStorm outputs for EEG and MRI data from the Cuban Human Brain Mapping Project (CHBMP) acquired with technologies a decade before the HCP MEG and MRI standardized dataset.

An anatomical and connectivity atlas of the marmoset cerebellum

The cerebellum is essential for motor control and cognitive functioning, engaging in bidirectional communication with the cerebral cortex. The common marmoset, a small non-human primate, offers unique advantages for studying cerebello-cerebral circuits. However, the marmoset cerebellum is not well described in published resources. In this study, we present a comprehensive atlas of the marmoset cerebellum comprising (1) fine-detailed anatomical atlases and surface-analysis tools of the cerebellar cortex based on ultra-high-resolution exvivo MRI, (2) functional connectivity and gradient patterns of the cerebellar cortex revealed by awake resting-state fMRI, and (3) structural-connectivity mapping of cerebellar nuclei using high-resolution diffusion MRI tractography. The atlas elucidates the anatomical details of the marmoset cerebellum, reveals distinct gradient patterns of intra-cerebellar and cerebello-cerebral functional connectivity, and maps the topological relationship of cerebellar nuclei in cerebello-cerebral circuits. As version 5 of the Marmoset Brain Mapping project, this atlas is publicly available at https://marmosetbrainmapping.org/MBMv5.html.

The Cuban Human Brain Mapping Project, a young and middle age population-based EEG, MRI, and cognition dataset

The Cuban Human Brain Mapping Project (CHBMP) repository is an open multimodal neuroimaging and cognitive dataset from 282 young and middle age healthy participants (31.9 ± 9.3 years, age range 18–68 years). This dataset was acquired from 2004 to 2008 as a subset of a larger stratified random sample of 2,019 participants from La Lisa municipality in La Habana, Cuba. The exclusion criteria included the presence of disease or brain dysfunctions. Participant data that is being shared comprises i) high-density (64–120 channels) resting-state electroencephalograms (EEG), ii) magnetic resonance images (MRI), iii) psychological tests (MMSE, WAIS-III, computerized go-no go reaction time), as well as iv,) demographic information (age, gender, education, ethnicity, handedness, and weight). The EEG data contains recordings with at least 30 minutes in duration including the following conditions: eyes closed, eyes open, hyperventilation, and subsequent recovery. The MRI consists of anatomical T1 as well as diffusion-weighted (DWI) images acquired on a 1.5 Tesla system. The dataset presented here is hosted by Synapse.org and available at https://chbmp-open.loris.ca.

Russian-language neurosemantics: Clustering of word meaning and sense from oral narratives

This article is a part of a large-scale brain mapping project aimed at finding the relations among semantic categories in oral Russian-language texts and brain activity as measured using functional magnetic resonance imaging (fMRI). The goal of present study in particular is to examine the nature of lexical semantic relations and find an appropriate lexical space, homeomorphic to the activation patterns in the brain. Participants were presented with oral narratives, which described significant social issues from the first-person perspective. Stimuli were annotated using a dictionary and a vector approach. Results show that fMRI signal and clusters of related words have similar patterns of brain activation across participants. Results also show that annotation by a list of features more strongly contributes to prediction of the observed activation patterns. Findings confirm the hypothesis of situational semantic representation in the brain.

Marmoset Brain Mapping V3: Population multi-modal standard volumetric and surface-based templates

The standard anatomical brain template provides a common space and coordinate system for visualizing and analyzing neuroimaging data from large cohorts of subjects. Previous templates and atlases for the common marmoset brain were either based on data from a single individual or lacked essential functionalities for neuroimaging analysis. Here, we present new population-based in-vivo standard templates and tools derived from multi-modal data of 27 marmosets, including multiple types of T1w and T2w contrast images, DTI contrasts, and large field-of-view MRI and CT images. We performed multi-atlas labeling of anatomical structures on the new templates and constructed highly accurate tissue-type segmentation maps to facilitate volumetric studies. We built fully featured brain surfaces and cortical flat maps to facilitate 3D visualization and surface-based analyses, which are compatible with most surface analyzing tools, including FreeSurfer, AFNI/SUMA, and the Connectome Workbench. Analysis of the MRI and CT datasets revealed significant variations in brain shapes, sizes, and regional volumes of brain structures, highlighting substantial individual variabilities in the marmoset population. Thus, our population-based template and associated tools provide a versatile analysis platform and standard coordinate system for a wide range of MRI and connectome studies of common marmosets. These new template tools comprise version 3 of our Marmoset Brain Mapping Project and are publicly available via marmosetbrainmapping.org/v3.html.

A Quantitative EEG Toolbox for the MNI Neuroinformatics Ecosystem: Normative SPM of EEG Source Spectra.

The Tomographic Quantitative Electroencephalography (qEEGt) toolbox is integrated with the Montreal Neurological Institute (MNI) Neuroinformatics Ecosystem as a docker into the Canadian Brain Imaging Research Platform (CBRAIN). qEEGt produces age-corrected normative Statistical Parametric Maps of EEG log source spectra testing compliance to a normative database. This toolbox was developed at the Cuban Neuroscience Center as part of the first wave of the Cuban Human Brain Mapping Project (CHBMP) and has been validated and used in different health systems for several decades. Incorporation into the MNI ecosystem now provides CBRAIN registered users access to its full functionality and is accompanied by a public release of the source code on GitHub and Zenodo repositories. Among other features are the calculation of EEG scalp spectra, and the estimation of their source spectra using the Variable Resolution Electrical Tomography (VARETA) source imaging. Crucially, this is completed by the evaluation of z spectra by means of the built-in age regression equations obtained from the CHBMP database (ages 5–87) to provide normative Statistical Parametric Mapping of EEG log source spectra. Different scalp and source visualization tools are also provided for evaluation of individual subjects prior to further post-processing. Openly releasing this software in the CBRAIN platform will facilitate the use of standardized qEEGt methods in different research and clinical settings. An updated precis of the methods is provided in Appendix I as a reference for the toolbox. qEEGt/CBRAIN is the first installment of instruments developed by the neuroinformatic platform of the Cuba-Canada-China (CCC) project.

Mapping the mammalian brain

Integrative Omics The diverse physiology of the brain is reflected in its complex organization at regional, cellular, and subcellular levels. Sjostedt et al. combined data—both newly acquired and from other large-scale brain mapping projects—from transcriptomics, single-cell genomics, in situ hybridization, and antibody-based protein profiling to map the molecular profiles in human, pig, and mouse brain. The analysis is consistent with a conserved basic brain architecture during mammalian evolution, but it does show differences in regional gene expression profiles. Science , this issue p. [eaay5947][1] [1]: /lookup/doi/10.1126/science.aay5947

Crystallized and fluid intelligence are predicted by microstructure of specific white-matter tracts.

Studies of the neural basis of intelligence have focused on comparing brain imaging variables with global scales instead of the cognitive domains integrating these scales or quotients. Here, the relation between mean tract‐based fractional anisotropy (mTBFA) and intelligence indices was explored. Deterministic tractography was performed using a regions of interest approach for 10 white‐matter fascicles along which the mTBFA was calculated. The study sample included 83 healthy individuals from the second wave of the Cuban Human Brain Mapping Project, whose WAIS‐III intelligence quotients and indices were obtained. Inspired by the “Watershed model” of intelligence, we employed a regularized hierarchical Multiple Indicator, Multiple Causes model (MIMIC), to assess the association of mTBFA with intelligence scores, as mediated by latent variables summarizing the indices. Regularized MIMIC, used due to the limited sample size, selected relevant mTBFA by means of an elastic net penalty and achieved good fits to the data. Two latent variables were necessary to describe the indices: Fluid intelligence (Perceptual Organization and Processing Speed indices) and Crystallized Intelligence (Verbal Comprehension and Working Memory indices). Regularized MIMIC revealed effects of the forceps minor tract on crystallized intelligence and of the superior longitudinal fasciculus on fluid intelligence. The model also detected the significant effect of age on both latent variables.

Precise segmentation of densely interweaving neuron clusters using G-Cut

Characterizing the precise three-dimensional morphology and anatomical context of neurons is crucial for neuronal cell type classification and circuitry mapping. Recent advances in tissue clearing techniques and microscopy make it possible to obtain image stacks of intact, interweaving neuron clusters in brain tissues. As most current 3D neuronal morphology reconstruction methods are only applicable to single neurons, it remains challenging to reconstruct these clusters digitally. To advance the state of the art beyond these challenges, we propose a fast and robust method named G-Cut that is able to automatically segment individual neurons from an interweaving neuron cluster. Across various densely interconnected neuron clusters, G-Cut achieves significantly higher accuracies than other state-of-the-art algorithms. G-Cut is intended as a robust component in a high throughput informatics pipeline for large-scale brain mapping projects.

Unified Bayesian Estimator of EEG Reference at Infinity: rREST (Regularized Reference Electrode Standardization Technique).

The choice of reference for the electroencephalogram (EEG) is a long-lasting unsolved issue resulting in inconsistent usages and endless debates. Currently, both the average reference (AR) and the reference electrode standardization technique (REST) are two primary, apparently irreconcilable contenders. We propose a theoretical framework to resolve this reference issue by formulating both (a) estimation of potentials at infinity, and (b) determination of the reference, as a unified Bayesian linear inverse problem, which can be solved by maximum a posterior estimation. We find that AR and REST are very particular cases of this unified framework: AR results from biophysically non-informative prior; while REST utilizes the prior based on the EEG generative model. To allow for simultaneous denoising and reference estimation, we develop the regularized versions of AR and REST, named rAR and rREST, respectively. Both depend on a regularization parameter that is the noise to signal variance ratio. Traditional and new estimators are evaluated with this framework, by both simulations and analysis of real resting EEGs. Toward this end, we leverage the MRI and EEG data from 89 subjects which participated in the Cuban Human Brain Mapping Project. Generated artificial EEGs—with a known ground truth, show that relative error in estimating the EEG potentials at infinity is lowest for rREST. It also reveals that realistic volume conductor models improve the performances of REST and rREST. Importantly, for practical applications, it is shown that an average lead field gives the results comparable to the individual lead field. Finally, it is shown that the selection of the regularization parameter with Generalized Cross-Validation (GCV) is close to the “oracle” choice based on the ground truth. When evaluated with the real 89 resting state EEGs, rREST consistently yields the lowest GCV. This study provides a novel perspective to the EEG reference problem by means of a unified inverse solution framework. It may allow additional principled theoretical formulations and numerical evaluation of performance.

Fast assembling of neuron fragments in serial 3D sections

Reconstructing neurons from 3D image-stacks of serial sections of thick brain tissue is very time-consuming and often becomes a bottleneck in high-throughput brain mapping projects. We developed NeuronStitcher, a software suite for stitching non-overlapping neuron fragments reconstructed in serial 3D image sections. With its efficient algorithm and user-friendly interface, NeuronStitcher has been used successfully to reconstruct very large and complex human and mouse neurons.

Brain/MINDS: brain-mapping project in Japan.

There is an emerging interest in brain-mapping projects in countries across the world, including the USA, Europe, Australia and China. In 2014, Japan started a brain-mapping project called Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS). Brain/MINDS aims to map the structure and function of neuronal circuits to ultimately understand the vast complexity of the human brain, and takes advantage of a unique non-human primate animal model, the common marmoset (Callithrix jacchus). In Brain/MINDS, the RIKEN Brain Science Institute acts as a central institute. The objectives of Brain/MINDS can be categorized into the following three major subject areas: (i) structure and functional mapping of a non-human primate brain (the marmoset brain); (ii) development of innovative neurotechnologies for brain mapping; and (iii) human brain mapping; and clinical research. Brain/MINDS researchers are highly motivated to identify the neuronal circuits responsible for the phenotype of neurological and psychiatric disorders, and to understand the development of these devastating disorders through the integration of these three subject areas.

Simulating the human brain: Scientists have started various major projects to simulate and understand the brain, but many neuroscientists remain sceptical about their scope and aims.

Simulating the human brain: Scientists have started various major projects to simulate and understand the brain, but many neuroscientists remain sceptical about their scope and aims.

The Use of Neurodiagnostic Technologies in the 21st Century Neuroscientific Revolution

ABSTRACTNeuroscience is fascinating, mysterious, and truly medicine’s “final frontier,” but deciphering its marvels has historically been inhibited by its sheer complexity. The recent escalation of global neuroscientific endeavors and vast financial backing from governments, foundations, and industries, however, are changing this perspective. The sequencing of the human genome, development of innovative tools for mapping neuronal connectivities, and enhanced resolution capabilities of imaging techniques have made landmark contributions toward advancing neurotechnologies. Nations all around the world have initiated and launched brain mapping projects on such a profound and financially immense scale that research in 2015 and beyond are highly anticipated to revolutionize medicine and our interaction with the technological world.Although neurodiagnostic technology is not the vanguard of research interest in the scientific community, it will certainly ride the coattails of these new neuroscientific endeavors. And, in turn, these advancements will greatly impact how we diagnose, treat, and care for our patients in the future. Therefore, the purpose of this article is not only to introduce current neuroscientific enterprises, but to also explore some of the most interesting and instrumental findings using neurodiagnostic technology over the past year.

LittleQuickWarp: An ultrafast image warping tool

Warping images into a standard coordinate space is critical for many image computing related tasks. However, for multi-dimensional and high-resolution images, an accurate warping operation itself is often very expensive in terms of computer memory and computational time. For high-throughput image analysis studies such as brain mapping projects, it is desirable to have high performance image warping tools that are compatible with common image analysis pipelines. In this article, we present LittleQuickWarp, a swift and memory efficient tool that boosts 3D image warping performance dramatically and at the same time has high warping quality similar to the widely used thin plate spline (TPS) warping. Compared to the TPS, LittleQuickWarp can improve the warping speed 2–5 times and reduce the memory consumption 6–20 times. We have implemented LittleQuickWarp as an Open Source plug-in program on top of the Vaa3D system (http://vaa3d.org). The source code and a brief tutorial can be found in the Vaa3D plugin source code repository.

Brain-mapping projects to join forces

Brain-mapping projects to join forces

Towards a common format for storing electrophysiology data

Towards a common format for storing electrophysiology data

Brain Mapping Project: Clinical Aspects and Role of Neuroradiology

As experts in the realm of applying brain imaging technology for meaningful information on an individual patient's medical care, neuroradiologists can provide a physiologic, pathophysiologic, and clinical context for the advances achieved from brain-mapping. Precise identification of the abnormalities involved in brain conditions resulting from a multidisciplinary effort including neuroradiology will help to further define the nature of the pathology, evaluate the results of therapy, and improve health care.

Multimodal Quantitative Neuroimaging Databases and Methods: The Cuban Human Brain Mapping Project

This article reviews the contributions of the Cuban Neuroscience Center to the evolution of the statistical parametric mapping (SPM) of quantitative Multimodal Neuroimages (qMN), from its inception to more recent work. Attention is limited to methods that compare individual qMN to normative databases (n/qMN). This evolution is described in three successive stages: (a) the development of one variant of normative topographical quantitative EEG (n/qEEG-top) which carries out statistical comparison of individual EEG spectral topographies with regard to a normative database--as part of the now popular SPM of brain descriptive parameters; (b) the development of n/qEEG tomography (n/qEEG-TOM), which employs brain electrical tomography (BET) to calculate voxelwise SPM maps of source spectral features with respect to a norm; (c) the development of a more general n/qMN by substituting EEG parameters with other neuroimaging descriptive parameters to obtain SPM maps. The study also describes the creation of Cuban normative databases, starting with the Cuban EEG database obtained in the early 90s, and more recently, the Cuban Human Brain Mapping Project (CHBMP). This project has created a 240 subject database of the normal Cuban population, obtained from a population-based random sample, comprising clinical, neuropsychological, EEG, MRI and SPECT data for the same subjects. Examples of clinical studies using qMN are given and, more importantly, receiver operator characteristics (ROC) analyses of the different developments document a sustained effort to assess the clinical usefulness of the techniques.

Surface area and cortical thickness descriptors reveal different attributes of the structural human brain networks

Recently, a related morphometry-based connection concept has been introduced using local mean cortical thickness and volume to study the underlying complex architecture of the brain networks. In this article, the surface area is employed as a morphometric descriptor to study the concurrent changes between brain structures and to build binarized connectivity graphs. The statistical similarity in surface area between pair of regions was measured by computing the partial correlation coefficient across 186 normal subjects of the Cuban Human Brain Mapping Project. We demonstrated that connectivity matrices obtained follow a small-world behavior for two different parcellations of the brain gray matter. The properties of the connectivity matrices were compared to the matrices obtained using the mean cortical thickness for the same cortical parcellations. The topology of the cortical thickness and surface area networks were statistically different, demonstrating that both capture distinct properties of the interaction or different aspects of the same interaction (mechanical, anatomical, chemical, etc.) between brain structures. This finding could be explained by the fact that each descriptor is driven by distinct cellular mechanisms as result of a distinct genetic origin. To our knowledge, this is the first time that surface area is used to study the morphological connectivity of brain networks.