Oligodendrocyte precursor cells ingest axons in the mouse neocortex.

Neurons in the developing brain undergo extensive structural refinement as nascent circuits adopt their mature form. This physical transformation of neurons is facilitated by the engulfment and degradation of axonal branches and synapses by surrounding glial cells, including microglia and astrocytes. However, the small size of phagocytic organelles and the complex, highly ramified morphology of glia have made it difficult to define the contribution of these and other glial cell types to this crucial process. Here, we used large-scale, serial section transmission electron microscopy (TEM) with computational volume segmentation to reconstruct the complete 3D morphologies of distinct glial types in the mouse visual cortex, providing unprecedented resolution of their morphology and composition. Unexpectedly, we discovered that the fine processes of oligodendrocyte precursor cells (OPCs), a population of abundant, highly dynamic glial progenitors, frequently surrounded small branches of axons. Numerous phagosomes and phagolysosomes (PLs) containing fragments of axons and vesicular structures were present inside their processes, suggesting that OPCs engage in axon pruning. Single-nucleus RNA sequencing from the developing mouse cortex revealed that OPCs express key phagocytic genes at this stage, as well as neuronal transcripts, consistent with active axon engulfment. Although microglia are thought to be responsible for the majority of synaptic pruning and structural refinement, PLs were ten times more abundant in OPCs than in microglia at this stage, and these structures were markedly less abundant in newly generated oligodendrocytes, suggesting that OPCs contribute substantially to the refinement of neuronal circuits during cortical development.

Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy.

Understanding cells as integrated systems is central to modern biology. Although fluorescence microscopy can resolve subcellular structure in living cells, it is expensive, is slow, and can damage cells. We present a label-free method for predicting three-dimensional fluorescence directly from transmitted-light images and demonstrate that it can be used to generate multi-structure, integrated images. The method can also predict immunofluorescence (IF) from electron micrograph (EM) inputs, extending the potential applications.

3D in utero quantification of T2* relaxation times in human fetal brain tissues for age optimized structural and functional MRI.

PURPOSE: Maximization of the blood oxygen level-dependent (BOLD) functional MRI (fMRI) contrast requires the echo time of the MR sequence to match the T2* value of the tissue of interest, which is expected to be higher in the fetal brain compared with the brain of a child or an adult. METHODS: T2* values of the cortical plate/cortical gray matter tissue in utero in healthy fetuses from mid-gestation onward (20-36 gestational weeks) were measured using 3D T2* maps calculated from 2D dual-echo T2*-weighted data corrected for between-slice motion and reconstructed in 1.0 mm3 isotropic resolution from a sequence of multiple time points, together with 1.0 mm3 isotropic resolution T2-weighted structural data. RESULTS: Mean T2* relaxation times of the cortical tissue were about twice as high as those reported previously in adults. In a supporting experiment applying single seed analysis, default mode and auditory networks appeared better localized and less noisy while using an echo time of 100 ms versus 43 ms. The results of the previous study reporting a trend for T2* values to decrease with fetal age were reproduced and extended to include cortical tissues and subjects in earlier gestation (20-26 gestational weeks). CONCLUSION: The first measurement of T2* values in fetal cortical tissues suggested the appropriate echo time range for fetal BOLD fMRI protocol optimization to be 130-190 ms. Magn Reson Med 78:909-916, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Detecting default mode networks in utero by integrated 4D fMRI reconstruction and analysis.

Recently, there has been considerable interest, especially for in utero imaging, in the detection of functional connectivity in subjects whose motion cannot be controlled while in the MRI scanner. These cases require two advances over current studies: (1) multiecho acquisitions and (2) post processing and reconstruction that can deal with significant between slice motion during multislice protocols to allow for the ability to detect temporal correlations introduced by spatial scattering of slices into account. This article focuses on the estimation of a spatially and temporally regular time series from motion scattered slices of multiecho fMRI datasets using a full four-dimensional (4D) iterative image reconstruction framework. The framework which includes quantitative MRI methods for artifact correction is evaluated using adult studies with and without motion to both refine parameter settings and evaluate the analysis pipeline. ICA analysis is then applied to the 4D image reconstruction of both adult and in utero fetal studies where resting state activity is perturbed by motion. Results indicate quantitative improvements in reconstruction quality when compared to the conventional 3D reconstruction approach (using simulated adult data) and demonstrate the ability to detect the default mode network in moving adults and fetuses with single-subject and group analysis. Hum Brain Mapp 37:4158-4178, 2016. © 2016 Wiley Periodicals, Inc.

Ultrastructural differences impact cilia shape and external exposure across cell classes in the visual cortex.

A primary cilium is a membrane-bound extension from the cell surface that contains receptors for perceiving and transmitting signals that modulate cell state and activity. Primary cilia in the brain are less accessible than cilia on cultured cells or epithelial tissues because in the brain they protrude into a deep, dense network of glial and neuronal processes. Here, we investigated cilia frequency, internal structure, shape, and position in large, high-resolution transmission electron microscopy volumes of mouse primary visual cortex. Cilia extended from the cell bodies of nearly all excitatory and inhibitory neurons, astrocytes, and oligodendrocyte precursor cells (OPCs) but were absent from oligodendrocytes and microglia. Ultrastructural comparisons revealed that the base of the cilium and the microtubule organization differed between neurons and glia. Investigating cilia-proximal features revealed that many cilia were directly adjacent to synapses, suggesting that cilia are poised to encounter locally released signaling molecules. Our analysis indicated that synapse proximity is likely due to random encounters in the neuropil, with no evidence that cilia modulate synapse activity as would be expected in tetrapartite synapses. The observed cell class differences in proximity to synapses were largely due to differences in external cilia length. Many key structural features that differed between neuronal and glial cilia influenced both cilium placement and shape and, thus, exposure to processes and synapses outside the cilium. Together, the ultrastructure both within and around neuronal and glial cilia suggest differences in cilia formation and function across cell types in the brain.

Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex.

Mammalian cortex features a vast diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties. Synaptic connectivity powerfully shapes how each cell type participates in the cortical circuit, but mapping connectivity rules at the resolution of distinct cell types remains difficult. Here, we used millimeter-scale volumetric electron microscopy1 to investigate the connectivity of all inhibitory neurons across a densely-segmented neuronal population of 1352 cells spanning all layers of mouse visual cortex, producing a wiring diagram of inhibitory connections with more than 70,000 synapses. Taking a data-driven approach inspired by classical neuroanatomy, we classified inhibitory neurons based on the relative targeting of dendritic compartments and other inhibitory cells and developed a novel classification of excitatory neurons based on the morphological and synaptic input properties. The synaptic connectivity between inhibitory cells revealed a novel class of disinhibitory specialist targeting basket cells, in addition to familiar subclasses. Analysis of the inhibitory connectivity onto excitatory neurons found widespread specificity, with many interneurons exhibiting differential targeting of certain subpopulations spatially intermingled with other potential targets. Inhibitory targeting was organized into "motif groups," diverse sets of cells that collectively target both perisomatic and dendritic compartments of the same excitatory targets. Collectively, our analysis identified new organizing principles for cortical inhibition and will serve as a foundation for linking modern multimodal neuronal atlases with the cortical wiring diagram.

Nanometer-scale views of visual cortex reveal anatomical features of primary cilia poised to detect synaptic spillover.

A primary cilium is a thin membrane-bound extension off a cell surface that contains receptors for perceiving and transmitting signals that modulate cell state and activity. While many cell types have a primary cilium, little is known about primary cilia in the brain, where they are less accessible than cilia on cultured cells or epithelial tissues and protrude from cell bodies into a deep, dense network of glial and neuronal processes. Here, we investigated cilia frequency, internal structure, shape, and position in large, high-resolution transmission electron microscopy volumes of mouse primary visual cortex. Cilia extended from the cell bodies of nearly all excitatory and inhibitory neurons, astrocytes, and oligodendrocyte precursor cells (OPCs), but were absent from oligodendrocytes and microglia. Structural comparisons revealed that the membrane structure at the base of the cilium and the microtubule organization differed between neurons and glia. OPC cilia were distinct in that they were the shortest and contained pervasive internal vesicles only occasionally observed in neuron and astrocyte cilia. Investigating cilia-proximal features revealed that many cilia were directly adjacent to synapses, suggesting cilia are well poised to encounter locally released signaling molecules. Cilia proximity to synapses was random, not enriched, in the synapse-rich neuropil. The internal anatomy, including microtubule changes and centriole location, defined key structural features including cilium placement and shape. Together, the anatomical insights both within and around neuron and glia cilia provide new insights into cilia formation and function across cell types in the brain.

Expansion-assisted selective plane illumination microscopy for nanoscale imaging of centimeter-scale tissues.

Recent advances in tissue processing, labeling, and fluorescence microscopy are providing unprecedented views of the structure of cells and tissues at sub-diffraction resolutions and near single molecule sensitivity, driving discoveries in diverse fields of biology, including neuroscience. Biological tissue is organized over scales of nanometers to centimeters. Harnessing molecular imaging across three-dimensional samples on this scale requires new types of microscopes with larger fields of view and working distance, as well as higher imaging throughput. We present a new expansion-assisted selective plane illumination microscope (ExA-SPIM) with diffraction-limited and aberration-free performance over a large field of view (85 mm 2 ) and working distance (35 mm). Combined with new tissue clearing and expansion methods, the microscope allows nanoscale imaging of centimeter-scale samples, including entire mouse brains, with diffraction-limited resolutions and high contrast without sectioning. We illustrate ExA-SPIM by reconstructing individual neurons across the mouse brain, imaging cortico-spinal neurons in the macaque motor cortex, and tracing axons in human white matter.

Single-cell transcriptomic evidence for dense intracortical neuropeptide networks.

Seeking new insights into the homeostasis, modulation and plasticity of cortical synaptic networks, we have analyzed results from a single-cell RNA-seq study of 22,439 mouse neocortical neurons. Our analysis exposes transcriptomic evidence for dozens of molecularly distinct neuropeptidergic modulatory networks that directly interconnect all cortical neurons. This evidence begins with a discovery that transcripts of one or more neuropeptide precursor (NPP) and one or more neuropeptide-selective G-protein-coupled receptor (NP-GPCR) genes are highly abundant in all, or very nearly all, cortical neurons. Individual neurons express diverse subsets of NP signaling genes from palettes encoding 18 NPPs and 29 NP-GPCRs. These 47 genes comprise 37 cognate NPP/NP-GPCR pairs, implying the likelihood of local neuropeptide signaling. Here, we use neuron-type-specific patterns of NP gene expression to offer specific, testable predictions regarding 37 peptidergic neuromodulatory networks that may play prominent roles in cortical homeostasis and plasticity.

A method for handling intensity inhomogenieties in fMRI sequences of moving anatomy of the early developing brain.

This paper presents a method for intensity inhomogeniety removal in fMRI studies of a moving subject. In such studies, subtle changes in signal as the subject moves in the presence of a bias field can be a significant confound for BOLD signal analysis. The proposed method avoids the need for a specific tissue model or assumptions about tissue homogeneity by making use of the multiple views of the underlying bias field provided by the subject's motion. A parametric bias field model is assumed and a regression model is used to estimate the basis function weights of this model. Quantitative evaluation of the effects of motion and noise in motion estimates are performed using simulated data. Results demonstrate the strength and robustness of the new method compared to the state of the art 4D nonparametric bias estimator (N4ITK). We also qualitatively demonstrate the impact of the method on resting state neuroimage analysis of a moving adult brain with simulated motion and bias fields, as well as on in vivo moving fetal fMRI.

A unified approach to diffusion direction sensitive slice registration and 3-D DTI reconstruction from moving fetal brain anatomy.

This paper presents an approach to 3-D diffusion tensor image (DTI) reconstruction from multi-slice diffusion weighted (DW) magnetic resonance imaging acquisitions of the moving fetal brain. Motion scatters the slice measurements in the spatial and spherical diffusion domain with respect to the underlying anatomy. Previous image registration techniques have been described to estimate the between slice fetal head motion, allowing the reconstruction of 3D a diffusion estimate on a regular grid using interpolation. We propose Approach to Unified Diffusion Sensitive Slice Alignment and Reconstruction (AUDiSSAR) that explicitly formulates a process for diffusion direction sensitive DW-slice-to-DTI-volume alignment. This also incorporates image resolution modeling to iteratively deconvolve the effects of the imaging point spread function using the multiple views provided by thick slices acquired in different anatomical planes. The algorithm is implemented using a multi-resolution iterative scheme and multiple real and synthetic data are used to evaluate the performance of the technique. An accuracy experiment using synthetically created motion data of an adult head and an experiment using synthetic motion added to sedated fetal monkey dataset show a significant improvement in motion-trajectory estimation compared to current state-of-the-art approaches. The performance of the method is then evaluated on challenging but clinically typical in utero fetal scans of four different human cases, showing improved rendition of cortical anatomy and extraction of white matter tracts. While the experimental work focuses on DTI reconstruction (second-order tensor model), the proposed reconstruction framework can employ any 5-D diffusion volume model that can be represented by the spatial parameterizations of an orientation distribution function.

Adapting parcellation schemes to study fetal brain connectivity in serial imaging studies.

A crucial step in studying brain connectivity is the definition of the Regions Of Interest (ROI's) which are considered as nodes of a network graph. These ROI's identified in structural imaging reflect consistent functional regions in the anatomies being compared. However in serial studies of the developing fetal brain such functional and associated structural markers are not consistently present over time. In this study we adapt two non-atlas based parcellation schemes to study the development of connectivity networks of a fetal monkey brain using Diffusion Weighted Imaging techniques. Results demonstrate that the fetal brain network exhibits small-world characteristics and a pattern of increased cluster coefficients and decreased global efficiency. These findings may provide a route to creating a new biomarker for healthy fetal brain development.

Assessment of Crohn's disease lesions in wireless capsule endoscopy images.

Capsule endoscopy (CE) provides noninvasive access to a large part of the small bowel that is otherwise inaccessible without invasive and traumatic treatment. However, it also produces large amounts of data (approximately 50,000 images) that must be then manually reviewed by a clinician. Such large datasets provide an opportunity for application of image analysis and supervised learning methods. Automated analysis of CE images has only focused on detection, and often only for bleeding. Compared to these detection approaches, we explored assessment of discrete disease for lesions created by mucosal inflammation in Crohn's disease (CD). Our work is the first study to systematically explore supervised classification for CD lesions, a classifier cascade to classify discrete lesions, as well as quantitative assessment of lesion severity. We used a well-developed database of 47 studies for evaluation of these methods. The developed methods show high agreement with ground truth severity ratings manually assigned by an expert, and good precision ( > 90% for lesion detection) and recall ( > 90%) for lesions of varying severity.

Iterative refinement of point correspondences for 3D statistical shape models.

Statistical atlases of bone anatomy are traditionally constructed with point-based models. These methods establish initial point correspondences across the population of shapes and model variations in the shapes using a variety of statistical tools. A drawbacks of such methods is that initial point correspondences are not updated after their first establishment. This paper proposes an iterative method for refining point correspondences for statistical atlases. The statistical model is used to estimate the direction of "pull" along the surface and consistency checks are used to ensure that illegal shapes are not generated. Our method is much faster that previous methods since it does not rely on computationally expensive deformable registration. It is also generalizable and can be used with any statististical model. We perform experiments on a human pelvis atlas consisting of 110 healthy patients and demonstrate that the method can be used to re-estimate point correspondences which reduce the hausdorff distance from 3.2mm to 2.7mm and the surface error from 1.6mm to 1.4mm for PCA modelling with 20 modes.

A meta method for image matching.

This paper presents a novel system for image matching in optical endoscopy. The proposed metamatching system approaches the challenge of matching images in a complex scene by incorporating multiple matchers and a decision function. Experiments are presented for Crohn's disease lesion matching in capsule endoscopy with a metamatcher consisting of five independent matchers. We compare the performance of six different types of decision functions. Results show that the F-measure of the metamatching system containing all five matchers is 4%-7% greater than the performance of using the best matcher only, with a maximum F-measure of 0.811. The robustness of the method is validated using simulated data generated by controlled deformations of the image. We also demonstrate how the addition of simulated data to the training set can be used to augment the performance of the metamatcher by up to 10%.

A meta registration framework for lesion matching.

A variety of pixel and feature based methods have been proposed for registering multiple views of anatomy visible in studies obtained using diagnostic, minimally invasive imaging. A given registration method may outperform another depending on anatomical variations, imaging conditions, and imaging sensor performance, and it is often difficult a priori to determine the best registration method for a particular application. To address this problem, we propose a registration framework that pools the results of multiple registration methods using a decision function for validating registrations. We refer to this as meta registration. We demonstrate that our framework outperforms several individual registration methods on the task of registering multiple views of Crohn's disease lesions sampled from a Capsule Endoscopy (CE) study database. We also report on preliminary work on assessing the quality of registrations obtained, and the possibility of using such assessment in the registration framework.

Real-time endoscopic mosaicking.

With the advancement of minimally invasive techniques for surgical and diagnostic procedures, there is a growing need for the development of methods for improved visualization of internal body structures. Video mosaicking is one method for doing this. This approach provides a broader field of view of the scene by stitching together images in a video sequence. Of particular importance is the need for online processing to provide real-time feedback and visualization for image-guided surgery and diagnosis. We propose a method for online video mosaicking applied to endoscopic imagery, with examples in microscopic retinal imaging and catadioptric endometrial imaging.