Fetal brain MRI atlases and datasets: A review

Fetal brain development is a complex process involving different stages of growth and organization which are crucial for the development of brain circuits and neural connections. Fetal atlases and labeled datasets are promising tools to investigate prenatal brain development. They support the identification of atypical brain patterns, providing insights into potential early signs of clinical conditions. In a nutshell, prenatal brain imaging and post-processing via modern tools are a cutting-edge field that will significantly contribute to the advancement of our understanding of fetal development. In this work, we first provide terminological clarification for specific terms (i.e., "brain template" and "brain atlas"), highlighting potentially misleading interpretations related to inconsistent use of terms in the literature. We discuss the major structures and neurodevelopmental milestones characterizing fetal brain ontogenesis. Our main contribution is the systematic review of 18 prenatal brain atlases and 3 datasets. We also tangentially focus on clinical, research, and ethical implications of prenatal neuroimaging.

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.

Macaque Brainnetome Atlas: A multifaceted brain map with parcellation, connection, and histology

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.

Where do you measure the Bregma for rodent stereotaxic surgery?

The advent of the stereotaxic apparatus developed by Clarke and Horsley revolutionized neuroscience research, enabling precise 3D navigation along the skull mediolateral, anteroposterior, and dorsoventral axes. In rodents, the Bregma is widely used as the origin reference point for the stereotaxic coordinates, but the specific procedure for its measurement varies among different laboratories. Notably, the renowned brain atlas developed by Paxinos and Franklin lacks explicit instructions on the Bregma determination. Recent studies have found discrepancies in skull and brain landmark measurements. This review describes the commonly used brain atlases and highlights the limitations in accurately measuring the stereotaxic coordinates. In addition, we propose alternative and more reliable approaches to measure the Bregma. It is imperative to address the misconceptions about the accuracy of stereotaxic surgeries, as it can significantly impact a substantial portion of neuroscience research.

Mapping Cytoarchitectonics and Receptor Architectonics to Understand Brain Function and Connectivity

This review focuses on cytoarchitectonics and receptor architectonics as biological correlates of function and connectivity. It introduces the 3-dimensional cytoarchitectonic probabilistic maps of cortical areas and nuclei of the Julich-Brain Atlas, available at EBRAINS, to study structure-function relationships. The maps are linked to the BigBrain as microanatomical reference model and template space. The siibra software tool suite enables programmatic access to the maps and to receptor architectonic data that are anchored to brain areas. Such cellular and molecular data are tools for studying magnetic resonance connectivity including modeling and simulation. At the end, we highlight perspectives of the Julich-Brain as well as methodological considerations. Thus, microstructural maps as part of a multimodal atlas help elucidate the biological correlates of large-scale networks and brain function with a high level of anatomical detail, which provides a basis to study brains of patients with psychiatric disorders.

Multimodal 3D Mouse Brain Atlas Framework with the Skull-Derived Coordinate System

Magnetic resonance imaging (MRI) and light-sheet fluorescence microscopy (LSFM) are technologies that enable non-disruptive 3-dimensional imaging of whole mouse brains. A combination of complementary information from both modalities is desirable for studying neuroscience in general, disease progression and drug efficacy. Although both technologies rely on atlas mapping for quantitative analyses, the translation of LSFM recorded data to MRI templates has been complicated by the morphological changes inflicted by tissue clearing and the enormous size of the raw data sets. Consequently, there is an unmet need for tools that will facilitate fast and accurate translation of LSFM recorded brains to in vivo, non-distorted templates. In this study, we have developed a bidirectional multimodal atlas framework that includes brain templates based on both imaging modalities, region delineations from the Allen's Common Coordinate Framework, and a skull-derived stereotaxic coordinate system. The framework also provides algorithms for bidirectional transformation of results obtained using either MR or LSFM (iDISCO cleared) mouse brain imaging while the coordinate system enables users to easily assign in vivo coordinates across the different brain templates.

Variability of Single Pulse Electrical Stimulation Responses Recorded with Intracranial Electroencephalography in Epileptic Patients

Cohort studies of brain stimulations performed with stereo-electroencephalographic (SEEG) electrodes in epileptic patients allow to derive large scale functional connectivity. It is known, however, that brain responses to electrical or magnetic stimulation techniques are not always reproducible. Here, we study variability of responses to single pulse SEEG electrical stimulation. We introduce a second-order probability analysis, i.e. we extend estimation of connection probabilities, defined as the proportion of responses trespassing a statistical threshold (determined in terms of Z-score with respect to spontaneous neuronal activity before stimulation) over all responses and derived from a number of individual measurements, to an analysis of pairs of measurements.Data from 445 patients were processed. We found that variability between two equivalent measurements is substantial in particular conditions. For long ( > ~ 90 mm) distances between stimulating and recording sites, and threshold value Z = 3, correlation between measurements drops almost to zero. In general, it remains below 0.5 when the threshold is smaller than Z = 4 or the stimulating current intensity is 1 mA. It grows with an increase of either of these factors. Variability is independent of interictal spiking rates in the stimulating and recording sites.We conclude that responses to SEEG stimulation in the human brain are variable, i.e. in a subject at rest, two stimulation trains performed at the same electrode contacts and with the same protocol can give discrepant results. Our findings highlight an advantage of probabilistic interpretation of such results even in the context of a single individual.

A hybrid high-resolution anatomical MRI atlas with sub-parcellation of cortical gyri using resting fMRI

We present a new high-quality, single-subject atlas with sub-millimeter voxel resolution, high SNR, and excellent gray-white tissue contrast to resolve fine anatomical details. The atlas is labeled into two parcellation schemes: 1) the anatomical BCI-DNI atlas, which is manually labeled based on known morphological and anatomical features, and 2) the hybrid USCBrain atlas, which incorporates functional information to guide the sub-parcellation of cerebral cortex. In both cases, we provide consistent volumetric and cortical surface-based parcellation and labeling. The intended use of the atlas is as a reference template for structural coregistration and labeling of individual brains. A single-subject T1-weighted image was acquired five times at a resolution of 0.547 mm × 0.547 mm × 0.800 mm and averaged. Images were processed by an expert neuroanatomist using semi-automated methods in BrainSuite to extract the brain, classify tissue-types, and render anatomical surfaces. Sixty-six cortical and 29 noncortical regions were manually labeled to generate the BCI-DNI atlas. The cortical regions were further sub-parcellated into 130 cortical regions based on multi-subject connectivity analysis using resting fMRI (rfMRI) data from the Human Connectome Project (HCP) database to produce the USCBrain atlas. In addition, we provide a delineation between sulcal valleys and gyral crowns, which offer an additional set of 26 sulcal subregions per hemisphere. Lastly, a probabilistic map is provided to give users a quantitative measure of reliability for each gyral subdivision. Utility of the atlas was assessed by computing Adjusted Rand Indices (ARIs) between individual sub-parcellations obtained through structural-only coregistration to the USCBrain atlas and sub-parcellations obtained directly from each subject's resting fMRI data. Both atlas parcellations can be used with the BrainSuite, FreeSurfer, and FSL software packages.

A framework For brain atlases: Lessons from seizure dynamics

Brain maps, or atlases, are essential tools for studying brain function and organization. The abundance of available atlases used across the neuroscience literature, however, creates an implicit challenge that may alter the hypotheses and predictions we make about neurological function and pathophysiology. Here, we demonstrate how parcellation scale, shape, anatomical coverage, and other atlas features may impact our prediction of the brain’s function from its underlying structure. We show how network topology, structure–function correlation (SFC), and the power to test specific hypotheses about epilepsy pathophysiology may change as a result of atlas choice and atlas features. Through the lens of our disease system, we propose a general framework and algorithm for atlas selection. This framework aims to maximize the descriptive, explanatory, and predictive validity of an atlas. Broadly, our framework strives to provide empirical guidance to neuroscience research utilizing the various atlases published over the last century.

A 4D infant brain volumetric atlas based on the UNC/UMN baby connectome project (BCP) cohort

Spatiotemporal (four-dimensional) infant-dedicated brain atlases are essential for neuroimaging analysis of early dynamic brain development. However, due to the substantial technical challenges in the acquisition and processing of infant brain MR images, 4D atlases densely covering the dynamic brain development during infancy are still scarce. Few existing ones generally have fuzzy tissue contrast and low spatiotemporal resolution, leading to degraded accuracy of atlas-based normalization and subsequent analyses. To address this issue, in this paper, we construct a 4D structural MRI atlas for infant brains based on the UNC/UMN Baby Connectome Project (BCP) dataset, which features a high spatial resolution, extensive age-range coverage, and densely sampled time points. Specifically, 542 longitudinal T1w and T2w scans from 240 typically developing infants up to 26-month of age were utilized for our atlas construction. To improve the co-registration accuracy of the infant brain images, which typically exhibit dynamic appearance with low tissue contrast, we employed the state-of-the-art registration method and leveraged our generated reliable brain tissue probability maps in addition to the intensity images to improve the alignment of individual images. To achieve consistent region labeling on both infant and adult brain images for facilitating region-based analysis across ages, we mapped the widely used Desikan cortical parcellation onto our atlas by following an age-decreasing mapping manner. Meanwhile, the typical subcortical structures were manually delineated to facilitate the studies related to the subcortex. Compared with the existing infant brain atlases, our 4D atlas has much higher spatiotemporal resolution and preserves more structural details, and thus can boost accuracy in neurodevelopmental analysis during infancy.

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.

Anterior Nucleus of Thalamus Deep Brain Stimulation: A Clinical-Based Analysis of the Ideal Target in Drug-Resistant Epilepsy

INTRODUCTION

Deep brain stimulation of the anterior nucleus of thalamus (ANT-DBS) is an approved procedure for drug-resistant epilepsy. However, the preferred location inside ANT is not well known. In this study, we investigated the relationship between stereotactical coordinates of stimulated contacts and clinical improvement, in order to define the ideal target for ANT-DBS.

METHODS

Individual contact's coordinates were obtained in the Montreal Neurological Institute (MNI) 152 space, with the utilization of advanced normalization tools and co-registration of pre- and postoperative MRI and CT images in open-source toolbox lead-DBS with the "Atlas of the Human Thalamus." Each contact's pair was either classified as a responder (≥50% seizure reduction and absence of intolerable adverse effects) or nonresponder, with a minimum follow-up of 11 continuous months of stimulation.

RESULTS

A total of 19 contacts' pairs were tested in 14 patients. The responder rate was 9 out of 14 patients (64.3%). In 4 patients, a change in contacts' pairs was needed to achieve this result. A highly encouraging location inside ANT (HELIA) was delimited in MNI space, corresponding to an area in the anterior and inferior portion of the anteroventral (AV) nucleus, medially to the endpoint of the mammillothalamic tract (ANT-mtt junction) (x [3.8; 5.85], y [-2.1; -6.35] and z [6.2; 10.1] in MNI space). Statistically significant difference was observed between responders and nonresponders, in terms of the number of coordinates inside this volume. Seven responders and two nonresponders had at least 5 of 6 coordinates (2 electrodes) inside HELIA (77.8% sensitivity and 80% specificity). In 3 patients, changing to contacts that were better placed inside HELIA changed the status from nonresponder to responder.

CONCLUSIONS

A relationship between stimulated contacts' coordinates and responder status was observed in drug-resistant epilepsy. The possibility to target different locations inside HELIA may help surpass anatomical variations and eventually obtain increased clinical benefit.

An Optimized Mouse Brain Atlas for Automated Mapping and Quantification of Neuronal Activity Using iDISCO+ and Light Sheet Fluorescence Microscopy

In recent years, the combination of whole-brain immunolabelling, light sheet fluorescence microscopy (LSFM) and subsequent registration of data with a common reference atlas, has enabled 3D visualization and quantification of fluorescent markers or tracers in the adult mouse brain. Today, the common coordinate framework version 3 developed by the Allen’s Institute of Brain Science (AIBS CCFv3), is widely used as the standard brain atlas for registration of LSFM data. However, the AIBS CCFv3 is based on histological processing and imaging modalities different from those used for LSFM imaging and consequently, the data differ in both tissue contrast and morphology. To improve the accuracy and speed by which LSFM-imaged whole-brain data can be registered and quantified, we have created an optimized digital mouse brain atlas based on immunolabelled and solvent-cleared brains. Compared to the AIBS CCFv3 atlas, our atlas resulted in faster and more accurate mapping of neuronal activity as measured by c-Fos expression, especially in the hindbrain. We further demonstrated utility of the LSFM atlas by comparing whole-brain quantitative changes in c-Fos expression following acute administration of semaglutide in lean and diet-induced obese mice. In combination with an improved algorithm for c-Fos detection, the LSFM atlas enables unbiased and computationally efficient characterization of drug effects on whole-brain neuronal activity patterns. In conclusion, we established an optimized reference atlas for more precise mapping of fluorescent markers, including c-Fos, in mouse brains processed for LSFM.

A historical perspective on training students to create standardized maps of novel brain structure: Newly-uncovered resonances between past and present research-based neuroanatomy curricula

Recent efforts to reform postsecondary STEM education in the U.S. have resulted in the creation of course-based undergraduate research experiences (CUREs), which, among other outcomes, have successfully retained freshmen in their chosen STEM majors and provided them with a greater sense of identity as scientists by enabling them to experience how research is conducted in a laboratory setting. In 2014, we launched our own laboratory-based CURE, Brain Mapping & Connectomics (BMC). Now in its seventh year, BMC trains University of Texas at El Paso (UTEP) undergraduates to identify and label neuron populations in the rat brain, analyze their cytoarchitecture, and draw their detailed chemoarchitecture onto standardized rat brain atlas maps in stereotaxic space. Significantly, some BMC students produce atlas drawings derived from their coursework or from further independent study after the course that are being presented and/or published in the scientific literature. These maps should prove useful to neuroscientists seeking to experimentally target elusive neuron populations. Here, we review the procedures taught in BMC that have empowered students to learn about the scientific process. We contextualize our efforts with those similarly carried out over a century ago to reform U.S. medical education. Notably, we have uncovered historical records that highlight interesting resonances between our curriculum and that created at the Johns Hopkins University Medical School (JHUMS) in the 1890s. Although the two programs are over a century apart and were created for students of differing career levels, many aspects between them are strikingly similar, including the unique atlas-based brain mapping methods they encouraged students to learn. A notable example of these efforts was the brain atlas maps published by Florence Sabin, a JHUMS student who later became the first woman to be elected to the U.S. National Academy of Sciences. We conclude by discussing how the revitalization of century-old methods and their dissemination to the next generation of scientists in BMC not only provides student benefit and academic development, but also acts to preserve what are increasingly becoming "lost arts" critical for advancing neuroscience - brain histology, cytoarchitectonics, and atlas-based mapping of novel brain structure.

An Optimized Mouse Brain Atlas for Automated Mapping and Quantification of Neuronal Activity Using iDISCO+ and Light Sheet Fluorescence Microscopy

In recent years, the combination of whole-brain immunolabelling, light sheet fluorescence microscopy (LSFM) and subsequent registration of data with a common reference atlas, has enabled 3D visualization and quantification of fluorescent markers or tracers in the adult mouse brain. Today, the common coordinate framework version 3 developed by the Allen's Institute of Brain Science (AIBS CCFv3), is widely used as the standard brain atlas for registration of LSFM data. However, the AIBS CCFv3 is based on histological processing and imaging modalities different from those used for LSFM imaging and consequently, the data differ in both tissue contrast and morphology. To improve the accuracy and speed by which LSFM-imaged whole-brain data can be registered and quantified, we have created an optimized digital mouse brain atlas based on immunolabelled and solvent-cleared brains. Compared to the AIBS CCFv3 atlas, our atlas resulted in faster and more accurate mapping of neuronal activity as measured by c-Fos expression, especially in the hindbrain. We further demonstrated utility of the LSFM atlas by comparing whole-brain quantitative changes in c-Fos expression following acute administration of semaglutide in lean and diet-induced obese mice. In combination with an improved algorithm for c-Fos detection, the LSFM atlas enables unbiased and computationally efficient characterization of drug effects on whole-brain neuronal activity patterns. In conclusion, we established an optimized reference atlas for more precise mapping of fluorescent markers, including c-Fos, in mouse brains processed for LSFM.

Myelo- and cytoarchitectonic microstructural and functional human cortical atlases reconstructed in common MRI space

The parcellation of the brain's cortical surface into anatomically and/or functionally distinct areas is a topic of ongoing investigation and interest. We provide digital versions of six classical human brain atlases in common MRI space. The cortical atlases represent a range of modalities, including cyto- and myeloarchitecture (Campbell, Smith, Brodmann and Von Economo), myelogenesis (Flechsig), and mappings of symptomatic information in relation to the spatial location of brain lesions (Kleist). Digital reconstructions of these important cortical atlases widen the range of modalities for which cortex-wide imaging atlases are currently available and offer the opportunity to compare and combine microstructural and lesion-based functional atlases with in-vivo imaging-based atlases.

Histology-driven model of the macaque motor hyperdirect pathway

Emerging appreciation for the hyperdirect pathway (HDP) as an important cortical glutamatergic input to the subthalamic nucleus (STN) has motivated a wide range of recent investigations on its role in motor control, as well as the mechanisms of subthalamic deep brain stimulation (DBS). However, the pathway anatomy and terminal arbor morphometry by which the HDP links cortical and subthalamic activity are incompletely understood. One critical hindrance to advancing understanding is the lack of anatomically detailed population models which can help explain how HDP pathway anatomy and neuronal biophysics give rise to spatiotemporal patterns of stimulus-response activity observed in vivo. Therefore, the goal of this study was to establish a population model of motor HDP axons through application of generative algorithms constrained by recent histology and imaging data. The products of this effort include a de novo macaque brain atlas, detailed statistical analysis of histological reconstructions of macaque motor HDP axons, and the generation of 10,000 morphometrically constrained synthetic motor HDP axons. The synthetic HDP axons exhibited a 3.8% mean error with respect to parametric distributions of the fiber target volume, total length, number of bifurcations, bifurcation angles, meander angles, and segment lengths measured in BDA-labeled HDP axon reconstructions. As such, this large population of synthetic motor HDP axons represents an anatomically based foundation for biophysical simulations that can be coupled to electrophysiological and/or behavioral measurements, with the goal of better understanding the role of the HDP in motor system activity.

Inter-subject and inter-parcellation variability of resting-state whole-brain dynamical modeling

Modern approaches to investigate complex brain dynamics suggest to represent the brain as a functional network of brain regions defined by a brain atlas, while edges represent the structural or functional connectivity among them. This approach is also utilized for mathematical modeling of the resting-state brain dynamics, where the applied brain parcellation plays an essential role in deriving the model network and governing the modeling results. There is however no consensus and empirical evidence on how a given brain atlas affects the model outcome, and the choice of parcellation is still rather arbitrary. Accordingly, we explore the impact of brain parcellation on inter-subject and inter-parcellation variability of model fitting to empirical data. Our objective is to provide a comprehensive empirical evidence of potential influences of parcellation choice on resting-state whole-brain dynamical modeling. We show that brain atlases strongly influence the quality of model validation and propose several variables calculated from empirical data to account for the observed variability. A few classes of such data variables can be distinguished depending on their inter-subject and inter-parcellation explanatory power.

Atlas-informed computational processing pipeline for individual targeting of brain areas for therapeutic navigated transcranial magnetic stimulation

OBJECTIVE

Navigated transcranial magnetic stimulation (nTMS) is targeted at different cortical sites for diagnostic, therapeutic, and neuroscientific purposes. Correct identification of the cortical target areas is important for achieving desired effects, but it is challenging when no direct responses arise upon target area stimulation. We aimed at utilizing atlas-based marking of cortical areas for nTMS targeting to present a convenient, rater-independent method for overlaying the individual target sites with brain anatomy.

METHODS

We developed a pipeline, which fits a brain atlas to the individual brain and enables visualization of the target areas during the nTMS session. We applied the pipeline to our previous nTMS data, focusing on depression and schizophrenia patients. Furthermore, we included examples of Tourette syndrome and tinnitus therapies, as well as neurosurgical and motor mappings.

RESULTS

In depression and schizophrenia patients, the visually selected dorsolateral prefrontal cortex (DLPFC) targets were close to the border between atlas areas A9/46 and A8. In the other areas, the atlas-based areas were in agreement with the treatment targets.

CONCLUSIONS

The atlas-based target areas agreed well with the cortical targets selected by experts during the treatments.

SIGNIFICANCE

Overlaying atlas information over the navigation view is a convenient and useful add-on for improving nTMS targeting.

High-resolution magnetic resonance imaging-based atlases for the young and adolescent domesticated pig (Sus scrofa)

BACKGROUND

Neurodevelopmental studies utilize the pig as a translational animal model due to anatomical and morphological similarities between the pig and human brain. However, neuroimaging resources are not as well developed for the pig as they are for humans and other animal models. We established a magnetic resonance imaging-based brain atlas at two different ages for biomedical studies utilizing the pig as a preclinical model.

NEW METHOD

Twenty artificially-reared domesticated male pigs (Sus scrofa) and thirteen sow-reared adolescent domesticated male pigs (Sus scrofa) underwent a series of scans measuring brain macrostructure, microstructure, and arterial cerebral blood volume.

RESULTS

An atlas for the 4-week-old and 12-week-old pig were created along with twenty-six regions of interest. Normative data for brain measures were obtained and detailed descriptions of the data processing pipelines were provided.

COMPARISON WITH EXISTING METHOD

Atlases at the two different ages were created for the pig utilizing newer imaging technology and software. This facilitates the performance of longitudinal studies and enables more precise volume measurements in pigs of various ages by appropriately representing the neuroanatomical features of younger and older pigs and accommodating the proportion differences of the brain over time.

CONCLUSION

Two high-resolution MRI brain atlases specific to the domesticated young and adolescent pig were created using defined image acquisition and data processing methods to facilitate the generation of high-quality normative data for neurodevelopmental research.

Current Status of Tissue Clearing and the Path Forward in Neuroscience

Due to the complexity and limited availability of human brain tissues, for decades, pathologists have sought to maximize information gained from individual samples, based on which (patho)physiological processes could be inferred. Recently, new understandings of chemical and physical properties of biological tissues and multiple chemical profiling have given rise to the development of scalable tissue clearing methods allowing superior optical clearing of across-the-scale samples. In the past decade, tissue clearing techniques, molecular labeling methods, advanced laser scanning microscopes, and data visualization and analysis have become commonplace. Combined, they have made 3D visualization of brain tissues with unprecedented resolution and depth widely accessible. To facilitate further advancements and applications, here we provide a critical appraisal of these techniques. We propose a classification system of current tissue clearing and expansion methods that allows users to judge the applicability of individual ones to their questions, followed by a review of the current progress in molecular labeling, optical imaging, and data processing to demonstrate the whole 3D imaging pipeline based on tissue clearing and downstream techniques for visualizing the brain. We also raise the path forward of tissue-clearing-based imaging technology, that is, integrating with state-of-the-art techniques, such as multiplexing protein imaging, in situ signal amplification, RNA detection and sequencing, super-resolution imaging techniques, multiomics studies, and deep learning, for drawing the complete atlas of the human brain and building a 3D pathology platform for central nervous system disorders.

Optical Tissue Clearing: Illuminating Brain Function and Dysfunction

Tissue optical clearing technology has been developing rapidly in the past decade due to advances in microscopy equipment and various labeling techniques. Consistent modification of primary methods for optical tissue transparency has allowed observation of the whole mouse body at single-cell resolution or thick tissue slices at the nanoscale level, with the final aim to make intact primate and human brains or thick human brain tissues optically transparent. Optical clearance combined with flexible large-volume tissue labeling technology can not only preserve the anatomical structure but also visualize multiple molecular information from intact samples in situ. It also provides a new strategy for studying complex tissues, which is of great significance for deciphering the functional structure of healthy brains and the mechanisms of neurological pathologies. In this review, we briefly introduce the existing optical clearing technology and discuss its application in deciphering connection and structure, brain development, and brain diseases. Besides, we discuss the standard computational analysis tools for large-scale imaging dataset processing and information extraction. In general, we hope that this review will provide a valuable reference for researchers who intend to use optical clearing technology in studying the brain.

Automated Brain Region Segmentation for Single Cell Resolution Histological Images Based on Markov Random Field

The brain consists of massive regions with different functions and the precise delineation of brain region boundaries is important for brain region identification and atlas illustration. In this paper we propose a hierarchical Markov random field (MRF) model for brain region segmentation, where a MRF is applied to the downsampled low-resolution images and the result is used to initialize another MRF for the original high-resolution images. A fractional differential feature and a gray level co-occurrence matrix are extracted as the observed vector for the MRF and a new potential energy function, which can capture the spatial characteristic of brain regions, is proposed as well. A fuzzy entropy criterion is used to fine-tune the boundary from the hierarchical MRF model. We test the model both on synthetic images and real histological mouse brain images. The result suggests that the model can accurately identify target regions and even the whole mouse brain outline as a special case. An interesting observation is that the model cannot only segment regions with different cell density but also can segment regions with similar cell density and different cell morphology texture. Thus this model shows great potential for building the high-resolution 3D brain atlas.

Comparison of Automatic Segmentation Algorithms for the Subthalamic Nucleus

INTRODUCTION

Various automatic segmentation algorithms for the subthalamic nucleus (STN) have been published recently. However, most of the available software tools are not approved for clinical use.

OBJECTIVE

The aim of this study is to evaluate a clinically available automatic segmentation tool of the navigation planning software Brainlab Elements (BL-E) by comparing the output to manual segmentation and a nonclinically approved research method using the DISTAL atlas (DA) and the Horn electrophysiological atlas (HEA).

METHODS

Preoperative MRI data of 30 patients with idiopathic Parkinson's disease were used, resulting in 60 STN segmentations. The segmentations were created manually by two clinical experts. Automatic segmentations of the STN were obtained from BL-E and Advanced Normalization Tools using DA and HEA. Differences between manual and automatic segmentations were quantified by Dice and Jaccard coefficient, target overlap, and false negative/positive value (FNV/FPV) measurements. Statistical differences between similarity measures were assessed using the Wilcoxon signed-rank test with continuity correction, and comparison with interrater results was performed using the Mann-Whitney U test.

RESULTS

For manual segmentation, the mean size of the segmented STN was 133 ± 24 mm3. The mean size of the STN was 121 ± 18 mm3 for BL-E, 162 ± 21 mm3 for DA, and 130 ± 17 mm3 for HEA. The Dice coefficient for the interrater comparison was 0.63 and 0.54 ± 0.12, 0.59 ± 0.13, and 0.52 ± 0.14 for BL-E, DA, and HEA, respectively. Significant differences between similarity measures were found for Dice and Jaccard coefficient, target overlap and FNV between BL-E and DA; and FPV between BL-E and HEA. However, none of the differences were significant compared to interrater variability. The analysis of the center of gravity of the segmentations revealed that the BL-E STN ROI was located more medially, superior and posterior compared to other segmentations. Regarding the target overlap for beta power within the STN ROI included with the HEA, the BL-E segmentation showed a significantly higher value compared to manual segmentation.

CONCLUSION

Automatic image segmentation by means of the clinically approved software BL-E provides STN segmentations with similar accuracy like research tools, and differences are in the range of observed interrater variability. Further studies are required to investigate the clinical validity, for example, by comparing segmentation results of BL-E with electrophysiological data.

Targeting brain functions from the scalp: Transcranial brain atlas based on large-scale fMRI data synthesis

Transcranial brain mapping techniques, such as functional near-infrared spectroscopy (fNIRS) and transcranial magnetic stimulation (TMS), have been playing an increasingly important role in studies of human brain functions. Given a brain function of interest, fNIRS probes and TMS coils should be properly placed on the scalp to ensure that the function is effectively measured or modulated. However, since brain activity is inside the skull and invisible to the researcher during placement, this blind targeting may cause the device to partially or completely miss the functional target, resulting in inconsistent experimental results and divergent clinical outcomes, especially when participants' structural MRI data are not available. To address this issue, we propose here a framework for targeting a designated function directly from the scalp. First, a functional brain atlas for the targeted brain function is constructed via a meta-analysis of large-scale functional magnetic resonance imaging datasets. Second, the functional brain atlas is presented on the scalp surface by using a transcranial mapping previously established from an structural MRI dataset (n ​= ​114), resulting in a novel functional transcranial brain atlas (fTBA). Finally, a low-cost, portable scalp-navigation system is used to localize the transcranial device on the individual's scalp with the guidance of the fTBA. To demonstrate the feasibility of the targeting framework, both fNIRS and TMS mapping experiments were conducted. The results show that fTBA-guided fNIRS positioning can detect functional activity with high sensitivity and specificity for working memory and motor systems; Moreover, compared with traditional TMS targeting approaches (e.g. the International 10-20 System and the conventional 5-cm rule), the fTBA suggested motor stimulation site is closesr to both the motor hotspot and the center of gravity of motor evoked potentials (MEP-COG). In summary, the proposed method unblinds the transcranial function targeting process using prior information, providing an effective and straightforward approach to transcranial brain mapping studies, especially those without participants' structural MRI data.