Tissue optimization strategies for high-quality ex vivo diffusion imaging

Ultrahigh-resolution 7-Tesla anatomic magnetic resonance imaging and diffusion tensor imaging of ex vivo formalin-fixed human brainstem-cerebellum complex

Introduction

Brain cross-sectional images, tractography, and segmentation are valuable resources for neuroanatomical education and research but are also crucial for neurosurgical planning that may improve outcomes in cerebellar and brainstem interventions. Although ultrahigh-resolution 7-Tesla (7T) magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) reveal such structural brain details in living or fresh unpreserved brain tissue, imaging standard formalin-preserved cadaveric brain specimens often used for neurosurgical anatomic studies has proven difficult. This study sought to develop a practical protocol to provide anatomic information and tractography results of an ex vivo human brainstem-cerebellum specimen.

Materials and methods

A protocol was developed for specimen preparation and 7T MRI with image postprocessing on a combined brainstem-cerebellum specimen obtained from an 85-year-old male cadaver with a postmortem interval of 1 week that was stored in formalin for 6 months. Anatomic image series were acquired for detailed views and diffusion tractography to map neural pathways and segment major anatomic structures within the brainstem and cerebellum.

Results

Complex white matter tracts were visualized with high-precision segmentation of crucial brainstem structures, delineating the brainstem-cerebellum and mesencephalic-dentate connectivity, including the Guillain-Mollaret triangle. Tractography and fractional anisotropy mapping revealed the complexities of white matter fiber pathways, including the superior, middle, and inferior cerebellar peduncles and visible decussating fibers. 3-dimensional (3D) reconstruction and quantitative and qualitative analyses verified the anatomical precision of the imaging relative to a standard brain space.

Discussion

This novel imaging protocol successfully captured the intricate 3D architecture of the brainstem-cerebellum network. The protocol, unique in several respects (including tissue preservation and rehydration times, choice of solutions, preferred sequences, voxel sizes, and diffusion directions) aimed to balance high resolution and practical scan times. This approach provided detailed neuroanatomical imaging while avoiding impractically long scan times. The extended postmortem and fixation intervals did not compromise the diffusion imaging quality. Moreover, the combination of time efficiency and ultrahigh-resolution imaging results makes this protocol a strong candidate for optimal use in detailed neuroanatomical studies, particularly in presurgical trajectory planning.

Ultra-high-resolution mapping of myelin and g-ratio in a panel of Mbp enhancer-edited mouse strains using microstructural MRI

Non-invasive myelin water fraction (MWF) and g-ratio mapping using microstructural MRI have the potential to offer critical insights into brain microstructure and our understanding of neuroplasticity and neuroinflammation. By leveraging a unique panel of variably hypomyelinating mouse strains, we validated a high-resolution, model-free image reconstruction method for whole-brain MWF mapping. Further, by employing a bipolar gradient echo MRI sequence, we achieved high spatial resolution and robust mapping of MWF and g-ratio across the whole mouse brain. Our regional white matter-tract specific analyses demonstrated a graded decrease in MWF in white matter tracts which correlated strongly with myelin basic protein gene (Mbp) mRNA levels. Using these measures, we derived the first sensitive calibrations between MWF and Mbp mRNA in the mouse. Minimal changes in axonal density supported our hypothesis that observed MWF alterations stem from hypomyelination. Overall, our work strongly emphasizes the potential of non-invasive, MRI-derived MWF and g-ratio modeling for both preclinical model validation and ultimately translation to humans.

Sex matters: The MouseX DW-ALLEN Atlas for mice diffusion-weighted MR imaging

Overcoming sex bias in preclinical research requires not only including animals of both sexes in the experiments, but also developing proper tools to handle such data. Recent work revealed sensitivity of diffusion-weighted MRI to glia morphological changes in response to inflammatory stimuli, opening up exciting possibilities to characterize inflammation in a variety of preclinical models of pathologies, the great majority of them available in mice. However, there are limited resources dedicated to mouse imaging, like those required for the data processing and analysis. To fill this gap, we build a mouse MRI template of both structural and diffusion contrasts, with anatomical annotation according to the Allen Mouse Brain Atlas, the most detailed public resource for mouse brain investigation. To achieve a standardized resource, we use a large cohort of animals in vivo, and include animals of both sexes. To prove the utility of this resource to integrate imaging and molecular data, we demonstrate significant association between the mean diffusivity from MRI and gene expression-based glia density. To demonstrate the need of equitable sex representation, we compared across sexes the warp fields needed to match a male-based template, and our template built with both sexes. Then, we use both templates for analysing mice imaging data obtained in animals of different ages, demonstrating that using a male-based template creates spurious significant sex effects, not present otherwise. All in all, our MouseX DW-ALLEN Atlas will be a widely useful resource getting us one step closer to equitable healthcare.

A toolkit for stroke infarct volume estimation in rodents

Stroke volume is a key determinant of infarct severity and an important metric for evaluating treatments. However, accurate estimation of stroke volume can be challenging, due to the often confined 2-dimensional nature of available data. Here, we introduce a comprehensive semi-automated toolkit to reliably estimate stroke volumes based on (1) whole brains ex-vivo magnetic resonance imaging (MRI) and (2) brain sections that underwent immunofluorescence staining. We located and quantified infarct areas from MRI three days (acute) and 28 days (chronic) after photothrombotic stroke induction in whole mouse brains. MRI results were compared with measures obtained from immunofluorescent histologic sections of the same brains. We found that infarct volume determined by post-mortem MRI was highly correlated with a deviation of only 6.6 % (acute) and 4.9 % (chronic) to the measurements as determined in the histological brain sections indicating that both methods are capable of accurately assessing brain tissue damage (Pearson r > 0.9, p < 0.001). The Dice similarity coefficient (DC) showed a high degree of coherence (DC > 0.8) between MRI-delineated regions of interest (ROIs) and ROIs obtained from histologic sections at four to six pre-defined landmarks, with histology-based delineation demonstrating higher inter-operator similarity compared to MR images. We further investigated stroke-related scarring and post-ischemic angiogenesis in cortical peri‑infarct regions and described a negative correlation between GFAP+fluorescence intensity and MRI-obtained lesion size.

Mapping mesoscale connectivity within the human hippocampus

The connectivity of the hippocampus is essential to its functions. To gain a whole system view of intrahippocampal connectivity, ex vivo mesoscale (100 μm isotropic resolution) multi-shell diffusion MRI (11.7T) and tractography were performed on entire post-mortem human right hippocampi. Volumetric measurements indicated that the head region was largest followed by the body and tail regions. A unique anatomical organization in the head region reflected a complex organization of the granule cell layer (GCL) of the dentate gyrus. Tractography revealed the volumetric distribution of the perforant path, including both the tri-synaptic and temporoammonic pathways, as well as other well-established canonical connections, such as Schaffer collaterals. Visualization of the perforant path provided a means to verify the borders between the pro-subiculum and CA1, as well as between CA1/CA2. A specific angularity of different layers of fibers in the alveus was evident across the whole sample and allowed a separation of afferent and efferent connections based on their origin (i.e. entorhinal cortex) or destination (i.e. fimbria) using a cluster analysis of streamlines. Non-canonical translamellar connections running along the anterior-posterior axis were also discerned in the hilus. In line with "dentations" of the GCL, mossy fibers were bunching together in the sagittal plane revealing a unique lamellar organization and connections between these. In the head region, mossy fibers projected to the origin of the fimbria, which was distinct from the body and tail region. Mesoscale tractography provides an unprecedented systems view of intrahippocampal connections that underpin cognitive and emotional processing.

Differentiation of white matter histopathology using b-tensor encoding and machine learning

Diffusion-weighted magnetic resonance imaging (DW-MRI) is a non-invasive technique that is sensitive to microstructural geometry in neural tissue and is useful for the detection of neuropathology in research and clinical settings. Tensor-valued diffusion encoding schemes (b-tensor) have been developed to enrich the microstructural data that can be obtained through DW-MRI. These advanced methods have proven to be more specific to microstructural properties than conventional DW-MRI acquisitions. Additionally, machine learning methods are particularly useful for the study of multidimensional data sets. In this work, we have tested the reach of b-tensor encoding data analyses with machine learning in different histopathological scenarios. We achieved this in three steps: 1) We induced different levels of white matter damage in rodent optic nerves. 2) We obtained ex vivo DW-MRI data with b-tensor encoding schemes and calculated quantitative metrics using Q-space trajectory imaging. 3) We used a machine learning model to identify the main contributing features and built a voxel-wise probabilistic classification map of histological damage. Our results show that this model is sensitive to characteristics of microstructural damage. In conclusion, b-tensor encoded DW-MRI data analyzed with machine learning methods, have the potential to be further developed for the detection of histopathology and neurodegeneration.