A next-generation, histological atlas of the human brain and its application to automated brain MRI segmentation.

Magnetic resonance imaging (MRI) is the standard tool to image the human brain in vivo . In this domain, digital brain atlases are essential for subject-specific segmentation of anatomical regions of interest (ROIs) and spatial comparison of neuroanatomy from different subjects in a common coordinate frame. High-resolution, digital atlases derived from histology (e.g., Allen atlas [7], BigBrain [13], Julich [15]), are currently the state of the art and provide exquisite 3D cytoarchitectural maps, but lack probabilistic labels throughout the whole brain. Here we present NextBrain , a next - generation probabilistic atlas of human brain anatomy built from serial 3D histology and corresponding highly granular delineations of five whole brain hemispheres. We developed AI techniques to align and reconstruct ∼10,000 histological sections into coherent 3D volumes with joint geometric constraints (no overlap or gaps between sections), as well as to semi-automatically trace the boundaries of 333 distinct anatomical ROIs on all these sections. Comprehensive delineation on multiple cases enabled us to build the first probabilistic histological atlas of the whole human brain . Further, we created a companion Bayesian tool for automated segmentation of the 333 ROIs in any in vivo or ex vivo brain MRI scan using the NextBrain atlas. We showcase two applications of the atlas: automated segmentation of ultra-high-resolution ex vivo MRI and volumetric analysis of Alzheimer's disease and healthy brain ageing based on ∼4,000 publicly available in vivo MRI scans. We publicly release: the raw and aligned data (including an online visualisation tool); the probabilistic atlas; the segmentation tool; and ground truth delineations for a 100 µm isotropic ex vivo hemisphere (that we use for quantitative evaluation of our segmentation method in this paper). By enabling researchers worldwide to analyse brain MRI scans at a superior level of granularity without manual effort or highly specific neuroanatomical knowledge, NextBrain holds promise to increase the specificity of MRI findings and ultimately accelerate our quest to understand the human brain in health and disease.

Gliomatosis cerebri in children: A poor prognostic phenotype of diffuse gliomas with a distinct molecular profile.

BACKGROUND: The term gliomatosis cerebri (GC), a radiology-defined highly infiltrating diffuse glioma, has been abandoned since molecular GC-associated features could not be established. METHODS: We conducted a multinational retrospective study of 104 children and adolescents with GC providing comprehensive clinical and (epi-)genetic characterization. RESULTS: Median overall survival (OS) was 15.5 months (interquartile range, 10.9-27.7) with a 2-year survival rate of 28%. Histopathological grading correlated significantly with median OS: CNS WHO grade II: 47.8 months (25.2-55.7); grade III: 15.9 months (11.4-26.3); grade IV: 10.4 months (8.8-14.4). By DNA methylation profiling (n = 49), most tumors were classified as pediatric-type diffuse high-grade glioma (pedHGG), H3-/IDH-wild-type (n = 31/49, 63.3%) with enriched subclasses pedHGG_RTK2 (n = 19), pedHGG_A/B (n = 6), and pedHGG_MYCN (n = 5), but only one pedHGG_RTK1 case. Within the pedHGG, H3-/IDH-wild-type subgroup, recurrent alterations in EGFR (n = 10) and BCOR (n = 9) were identified. Additionally, we observed structural aberrations in chromosome 6 in 16/49 tumors (32.7%) across tumor types. In the pedHGG, H3-/IDH-wild-type subgroup TP53 alterations had a significant negative effect on OS. CONCLUSIONS: Contrary to previous studies, our representative pediatric GC study provides evidence that GC has a strong predilection to arise on the background of specific molecular features (especially pedHGG_RTK2, pedHGG_A/B, EGFR and BCOR mutations, chromosome 6 rearrangements).

A multimodal computational pipeline for 3D histology of the human brain.

Ex vivo imaging enables analysis of the human brain at a level of detail that is not possible in vivo with MRI. In particular, histology can be used to study brain tissue at the microscopic level, using a wide array of different stains that highlight different microanatomical features. Complementing MRI with histology has important applications in ex vivo atlas building and in modeling the link between microstructure and macroscopic MR signal. However, histology requires sectioning tissue, hence distorting its 3D structure, particularly in larger human samples. Here, we present an open-source computational pipeline to produce 3D consistent histology reconstructions of the human brain. The pipeline relies on a volumetric MRI scan that serves as undistorted reference, and on an intermediate imaging modality (blockface photography) that bridges the gap between MRI and histology. We present results on 3D histology reconstruction of whole human hemispheres from two donors.

Effect of Fluorinert on the Histological Properties of Formalin-Fixed Human Brain Tissue.

Fluorinert (perfluorocarbon) represents an inexpensive option for minimizing susceptibility artifacts in ex vivo brain MRI scanning, and provides an alternative to Fomblin. However, its impact on fixed tissue and histological analysis has not been rigorously and quantitatively validated. In this study, we excised tissue blocks from 2 brain regions (frontal pole and cerebellum) of 5 formalin-fixed specimens (2 progressive supranuclear palsy cases, 3 controls). We excised 2 blocks per region per case (20 blocks in total), one of which was subsequently immersed in Fluorinert for a week and then returned to a container with formalin. The other block from each region was kept in formalin for use as control. The tissue blocks were then sectioned and histological analysis was performed on each, including routine stains and immunohistochemistry. Visual inspection of the stained histological sections by an experienced neuropathologist through the microscope did not reveal any discernible differences between any of the samples. Moreover, quantitative analysis based on automated image patch classification showed that the samples were almost indistinguishable for a state-of-the-art classifier based on a deep convolutional neural network. The results showed that Fluorinert has no effect on subsequent histological analysis of the tissue even after a long (1 week) period of immersion, which is sufficient for even the lengthiest scanning protocols.