Evolution of Human Brain Atlases in Terms of Content, Applications, Functionality, and Availability

A quantitative comparison of atlas parcellations on the human superior temporal sulcus

The superior temporal sulcus (STS) has a functional topography that has been difficult to characterize through traditional approaches. Automated atlas parcellations may be one solution while also being beneficial for both dimensional reduction and standardizing regions of interest, but they yield very different boundary definitions along the STS. Here we evaluate how well machine learning classifiers can correctly identify six social cognitive tasks from STS activation patterns dimensionally reduced using four popular atlases (Glasser et al., 2016; Gordon et al., 2016; Power et al., 2011 as projected onto the surface by Arslan et al., 2018; Schaefer et al., 2018). Functional data was summarized within each STS parcel in one of four ways, then subjected to leave-one-subject-out cross-validation SVM classification. We found that the classifiers could readily label conditions when data was parcellated using any of the four atlases, evidence that dimensional reduction to parcels did not compromise functional fingerprints. Mean activation for the social conditions was the most effective metric for classification in the right STS, whereas all the metrics classified equally well in the left STS. Interestingly, even atlases constructed from random parcellation schemes (null atlases) classified the conditions with high accuracy. We therefore conclude that the complex activation maps on the STS are readily differentiated at a coarse granular level, despite a strict topography having not yet been identified. Further work is required to identify what features have greatest potential to improve the utility of atlases in replacing functional localizers.

A Narrative Review on 3-Dimensional Visualization Techniques in Neurosurgical Education, Simulation, and Planning

BACKGROUND

High-fidelity visualization of anatomical organs is crucial for neurosurgical education, simulation, and planning. This becomes much more important for minimally invasive neurosurgical procedures. Realistic anatomical visualization can allow resident surgeons to learn visual cues and orient themselves with the complex 3-dimensional (3D) anatomy. Achieving full fidelity in 3D medical visualization is an active area of research; however, the prior reviews focus on the application area and lack the underlying technical principles. Accordingly, the present study attempts to bridge this gap by providing a narrative review of the techniques used for 3D visualization.

METHODS

We conducted a literature review on 3D medical visualization technology from 2018 to 2023 using the PubMed and Google Scholar search engines. The cross-referenced manuscripts were extensively studied to find literature that discusses technology relevant to 3D medical visualization. We also compiled and ran software applications that were accessible to us in order to better understand them.

RESULTS

We present the underlying fundamental technology used in 3D medical visualization in the context of neurosurgical education, simulation, and planning. Further, we discuss and categorize a few important applications based on the 3D visualization techniques they use.

CONCLUSIONS

The visualization of virtual human organs has not yet achieved a level of realism close to reality. This gap is largely due to the interdisciplinary nature of this research, population diversity, and validation complexities. With the advancements in computational resources and automation of 3D visualization pipelines, next-gen applications may offer enhanced medical 3D visualization fidelity.

Taxonomy of Acute Stroke: Imaging, Processing, and Treatment

Stroke management employs a variety of diagnostic imaging modalities, image processing and analysis methods, and treatment procedures. This work categorizes methods for stroke imaging, image processing and analysis, and treatment, and provides their taxonomies illustrated by a state-of-the-art review. Imaging plays a critical role in stroke management, and the most frequently employed modalities are computed tomography (CT) and magnetic resonance (MR). CT includes unenhanced non-contrast CT as the first-line diagnosis, CT angiography, and CT perfusion. MR is the most complete method to examine stroke patients. MR angiography is useful to evaluate the severity of artery stenosis, vascular occlusion, and collateral flow. Diffusion-weighted imaging is the gold standard for evaluating ischemia. MR perfusion-weighted imaging assesses the penumbra. The stroke image processing methods are divided into non-atlas/template-based and atlas/template-based. The non-atlas/template-based methods are subdivided into intensity and contrast transformations, local segmentation-related, anatomy-guided, global density-guided, and artificial intelligence/deep learning-based. The atlas/template-based methods are subdivided into intensity templates and atlases with three atlas types: anatomy atlases, vascular atlases, and lesion-derived atlases. The treatment procedures for arterial and venous strokes include intravenous and intraarterial thrombolysis and mechanical thrombectomy. This work captures the state-of-the-art in stroke management summarized in the form of comprehensive and straightforward taxonomy diagrams. All three introduced taxonomies in diagnostic imaging, image processing and analysis, and treatment are widely illustrated and compared against other state-of-the-art classifications.

On presentation of cerebral arteries via cortical openings

The presentation of cortical arteries is challenging, as most of their course is hidden in the depth of the sulci. Despite that, demonstrating the arteries on the cortical surface is a standard way of their presentation. To keep advantages of surface presentation while lessening its limitation, we propose a novel context-related method of cerebrovasculature presentation by cortical openings consisting in the removal of a selected region from the cortical mantle and exposing underlying structures. We also introduce a reverse than standard vessel-to-context mapping from a gyrus/lobule to vessels supplying it.The method has the following steps: define a cortical opening, develop a tool to perform them, create cortical openings for gyri and lobules with underlying white matter and intracranial arteries, generate labeled and parcellated images for the created openings, and integrate the cortical opening images with the NOWinBRAIN public repository of 8600 3D neuroimages.The cortical openings are created for 64 gyri and six lobules for the left and right cerebral hemispheres resulting in 210 images arranged in triples as spatially corresponding non-parcellated and unlabeled, parcellated by color and unlabeled, and parcellated and labeled images.The cortical opening approach, generally, increases vessel exposure in a higher number of depicted branches, revealing arteries otherwise hidden deep in sulci, a more complete vessel course, and a lower number of required views.The gyrus/lobule-to-arteries mapping facilitates exploration of a studied region, encapsulates all local arteries, and reduces vascular complexity by decomposing the entire vascular system into smaller sets involved in the studied region.

Exploring the neural basis and modulating factors of implicit altercentric spatial perspective-taking with fNIRS

Humans spontaneously take the perspective of others when encoding spatial information in a scene, especially with agentive action cues present. This functional near-infrared spectroscopy (fNIRS) study explored how action observation influences implicit spatial perspective-taking (SPT) by adapting a left-right spatial judgment task to investigate whether transformation strategies underlying altercentric SPT can be predicted on the basis of cortical activation. Strategies associated with two opposing neurocognitive accounts (embodied versus disembodied) and their proposed neural correlates (human mirror neuron system; hMNS versus cognitive control network; CCN) are hypothesized. Exploratory analyses with 117 subjects uncover an interplay between perspective-taking and post-hoc factor, consistency of selection, in regions alluding to involvement of the CCN. Descriptively, inconsistent altercentric SPT elicited greater activation than consistent altercentric SPT and/or inconsistent egocentric SPT in the left inferior frontal gyrus (IFG), left dorsolateral prefrontal cortex (DLPFC) and left motor cortex (MC), but not the inferior parietal lobules (IPL). Despite the presence of grasping cues, spontaneous embodied strategies were not evident during implicit altercentric SPT. Instead, neural trends in the inconsistent subgroups (22 subjects; 13 altercentric; 9 egocentric) suggest that inconsistency in selection modulates the decision-making process and plausibly taps on deliberate and effortful disembodied strategies driven by the CCN. Implications for future research are discussed.

Statistical Learning Methods for Neuroimaging Data Analysis with Applications

The aim of this review is to provide a comprehensive survey of statistical challenges in neuroimaging data analysis, from neuroimaging techniques to large-scale neuroimaging studies and statistical learning methods. We briefly review eight popular neuroimaging techniques and their potential applications in neuroscience research and clinical translation. We delineate four themes of neuroimaging data and review major image processing analysis methods for processing neuroimaging data at the individual level. We briefly review four large-scale neuroimaging-related studies and a consortium on imaging genomics and discuss four themes of neuroimaging data analysis at the population level. We review nine major population-based statistical analysis methods and their associated statistical challenges and present recent progress in statistical methodology to address these challenges.

AtOM, an ontology model to standardize use of brain atlases in tools, workflows, and data infrastructures

Brain atlases are important reference resources for accurate anatomical description of neuroscience data. Open access, three-dimensional atlases serve as spatial frameworks for integrating experimental data and defining regions-of-interest in analytic workflows. However, naming conventions, parcellation criteria, area definitions, and underlying mapping methodologies differ considerably between atlases and across atlas versions. This lack of standardized description impedes use of atlases in analytic tools and registration of data to different atlases. To establish a machine-readable standard for representing brain atlases, we identified four fundamental atlas elements, defined their relations, and created an ontology model. Here we present our Atlas Ontology Model (AtOM) and exemplify its use by applying it to mouse, rat, and human brain atlases. We discuss how AtOM can facilitate atlas interoperability and data integration, thereby increasing compliance with the FAIR guiding principles. AtOM provides a standardized framework for communication and use of brain atlases to create, use, and refer to specific atlas elements and versions. We argue that AtOM will accelerate analysis, sharing, and reuse of neuroscience data.

Mapping cross-species connectome atlas of human and macaque striatum

Cross-species connectome atlas (CCA) that can provide connectionally homogeneous and homologous brain nodes is essential and customized for cross-species neuroscience. However, existing CCAs were flawed in design and coarse-grained in results. In this study, a normative mapping framework of CCA was proposed and applied on human and macaque striatum. Specifically, all striatal voxels in the 2 species were mixed together and classified based on their represented and characterized feature of within-striatum resting-state functional connectivity, which was shared between the species. Six pairs of striatal parcels in these species were delineated in both hemispheres. Furthermore, this striatal parcellation was demonstrated by the best-matched whole-brain functional and structural connectivity between interspecies corresponding subregions. Besides, detailed interspecies differences in whole-brain multimodal connectivities and involved brain functions of these subregions were described to flesh out this CCA of striatum. In particular, this flexible and scalable mapping framework enables reliable construction of CCA of the whole brain, which would enable reliable findings in future cross-species research and advance our understandings into how the human brain works.

A Brief History of Stereotactic Atlases: Their Evolution and Importance in Stereotactic Neurosurgery

Following the recent acquisition of unprecedented anatomical details through state-of-the-art neuroimaging, stereotactic procedures such as microelectrode recording (MER) or deep brain stimulation (DBS) can now rely on direct and accurately individualized topographic targeting. Nevertheless, both modern brain atlases derived from appropriate histological techniques involving post-mortem studies of human brain tissue and the methods based on neuroimaging and functional information represent a valuable tool to avoid targeting errors due to imaging artifacts or insufficient anatomical details. Hence, they have thus far been considered a reference guide for functional neurosurgical procedures by neuroscientists and neurosurgeons. In fact, brain atlases, ranging from the ones based on histology and histochemistry to the probabilistic ones grounded on data derived from large clinical databases, are the result of a long and inspiring journey made possible thanks to genial intuitions of great minds in the field of neurosurgery and to the technical advancement of neuroimaging and computational science. The aim of this text is to review the principal characteristics highlighting the milestones of their evolution.

Neuromorphological Atlas of Human Prenatal Brain Development: White Paper

Recent morphological data on human brain development are quite fragmentary. However, they are highly requested for a number of medical practices, educational programs, and fundamental research in the fields of embryology, cytology and histology, neurology, physiology, path anatomy, neonatology, and others. This paper provides the initial information on the new online Human Prenatal Brain Development Atlas (HBDA). The Atlas will start with forebrain annotated hemisphere maps, based on human fetal brain serial sections at the different stages of prenatal ontogenesis. Spatiotemporal changes in the regional-specific immunophenotype profiles will also be demonstrated on virtual serial sections. The HBDA can serve as a reference database for the neurological research, which provides opportunity to compare the data obtained by noninvasive techniques, such as neurosonography, X-ray computed tomography and magnetic resonance imaging, functional magnetic resonance imaging, 3D high-resolution phase-contrast computed tomography visualization techniques, as well as spatial transcriptomics data. It could also become a database for the qualitative and quantitative analysis of individual variability in the human brain. Systemized data on the mechanisms and pathways of prenatal human glio- and neurogenesis could also contribute to the search for new therapy methods for a large spectrum of neurological pathologies, including neurodegenerative and cancer diseases. The preliminary data are now accessible on the special HBDA website.

High-resolution atlasing and segmentation of the subcortex: Review and perspective on challenges and opportunities created by machine learning

This paper reviews almost three decades of work on atlasing and segmentation methods for subcortical structures in human brain MRI. In writing this survey, we have three distinct aims. First, to document the evolution of digital subcortical atlases of the human brain, from the early MRI templates published in the nineties, to the complex multi-modal atlases at the subregion level that are available today. Second, to provide a detailed record of related efforts in the automated segmentation front, from earlier atlas-based methods to modern machine learning approaches. And third, to present a perspective on the future of high-resolution atlasing and segmentation of subcortical structures in in vivo human brain MRI, including open challenges and opportunities created by recent developments in machine learning.

Whole-Brain Network Models: From Physics to Bedside

Computational neuroscience has come a long way from its humble origins in the pioneering work of Hodgkin and Huxley. Contemporary computational models of the brain span multiple spatiotemporal scales, from single neuronal compartments to models of social cognition. Each spatial scale comes with its own unique set of promises and challenges. Here, we review models of large-scale neural communication facilitated by white matter tracts, also known as whole-brain models (WBMs). Whole-brain approaches employ inputs from neuroimaging data and insights from graph theory and non-linear systems theory to model brain-wide dynamics. Over the years, WBM models have shown promise in providing predictive insights into various facets of neuropathologies such as Alzheimer's disease, Schizophrenia, Epilepsy, Traumatic brain injury, while also offering mechanistic insights into large-scale cortical communication. First, we briefly trace the history of WBMs, leading up to the state-of-the-art. We discuss various methodological considerations for implementing a whole-brain modeling pipeline, such as choice of node dynamics, model fitting and appropriate parcellations. We then demonstrate the applicability of WBMs toward understanding various neuropathologies. We conclude by discussing ways of augmenting the biological and clinical validity of whole-brain models.

NOWinBRAIN: a Large, Systematic, and Extendable Repository of 3D Reconstructed Images of a Living Human Brain Cum Head and Neck

Despite the tremendous development of various brain-related resources, a large, systematic, comprehensive, extendable, and beautiful repository of 3D reconstructed images of a living human brain expanded to the head and neck is not yet available. I have created such a novel repository and populated it with images derived from a 3D atlas constructed from 3/7 Tesla MRI and high-resolution CT scans. This web-based repository contains 6 galleries hierarchically organized in 444 albums and sub-albums with 5,156 images. Its original features include a systematic design in terms of multiple standard views, modes of presentation, and spatially co-registered image sequences; multi-tissue class galleries constructed from 26 primary tissue classes and 199 sub-classes; and a unique image naming syntax enabling image searching based solely on the image name. Anatomic structures are displayed in 6 standard views (anterior, left, posterior, right, superior, inferior), all views having the same brain size, and optionally with additional arbitrary views. In each view, the images are shown as sequences in three standard modes of presentation, non-parcellated unlabeled, parcellated unlabeled, and parcellated labeled. There are two types of spatially co-registered image sequences (imitating image layers and enabling animation creation), the appearance image sequence (for standard views) and the context image sequence (with a growing number of tissue classes). Color-coded neuroanatomic content makes the brain beautiful and facilitates its learning and understanding. This unique repository is freely available and easily accessible online at www.nowinbrain.org for a wide spectrum of users in medicine and beyond. Its future extensions are in progress.

Towards an Architecture of a Multi-purpose, User-Extendable Reference Human Brain Atlas

Human brain atlas development is predominantly research-oriented and the use of atlases in clinical practice is limited. Here I introduce a new definition of a reference human brain atlas that serves education, research and clinical applications, and is extendable by its user. Subsequently, an architecture of a multi-purpose, user-extendable reference human brain atlas is proposed and its implementation discussed. The human brain atlas is defined as a vehicle to gather, present, use, share, and discover knowledge about the human brain with highly organized content, tools enabling a wide range of its applications, massive and heterogeneous knowledge database, and means for content and knowledge growing by its users. The proposed architecture determines major components of the atlas, their mutual relationships, and functional roles. It contains four functional units, core cerebral models, knowledge database, research and clinical data input and conversion, and toolkit (supporting processing, content extension, atlas individualization, navigation, exploration, and display), all united by a user interface. Each unit is described in terms of its function, component modules and sub-modules, data handling, and implementation aspects. This novel architecture supports brain knowledge gathering, presentation, use, sharing, and discovery and is broadly applicable and useful in student- and educator-oriented neuroeducation for knowledge presentation and communication, research for knowledge acquisition, aggregation and discovery, and clinical applications in decision making support for prevention, diagnosis, treatment, monitoring, and prediction. It establishes a backbone for designing and developing new, multi-purpose and user-extendable brain atlas platforms, serving as a potential standard across labs, hospitals, and medical schools.

Ischemic infarct detection, localization, and segmentation in noncontrast CT human brain scans: review of automated methods

Noncontrast Computed Tomography (NCCT) of the brain has been the first-line diagnosis for emergency evaluation of acute stroke, so a rapid and automated detection, localization, and/or segmentation of ischemic lesions is of great importance. We provide the state-of-the-art review of methods for automated detection, localization, and/or segmentation of ischemic lesions on NCCT in human brain scans along with their comparison, evaluation, and classification. Twenty-two methods are (1) reviewed and evaluated; (2) grouped into image processing and analysis-based methods (11 methods), brain atlas-based methods (two methods), intensity template-based methods (1 method), Stroke Imaging Marker-based methods (two methods), and Artificial Intelligence-based methods (six methods); and (3) properties of these groups of methods are characterized. A new method classification scheme is proposed as a 2 × 2 matrix with local versus global processing and analysis, and density versus spatial sampling. Future studies are necessary to develop more efficient methods directed toward deep learning methods as well as combining the global methods with a high sampling both in space and density for the merged radiologic and neurologic data.