Terminal H-reflex Measurements in Mice

The Hoffmann reflex (H-reflex), as an electrical analog to the stretch reflex, allows electrophysiological validation of the integrity of neural circuits ...

University of Cologne

Magnetic resonance imaging (MRI) is a key technology in multimodal animal studies of brain connectivity and disease pathology.  MRI provides non-invasive, whole brain macroscopic images containing structural and functional information, thereby complementing invasive  high-resolution microscopy and  molecular techniques. Brain mapping, the correlation of corresponding regions between multiple brains in a standard brain atlas system, is widely used in human MRI. For small animal MRI, however, there is no scientific consensus on pre-processing strategies and atlas-based neuroinformatics. Thus, it remains difficult to compare and validate results from different pre-clinical studies which were processed using custom-made code or individual adjustments of clinical MRI software and without a standard brain reference atlas. Here, we describe AIDAmri, a novel Atlas-based Imaging Data Analysis pipeline to process structural and functional mouse brain data including anatomical MRI, fiber tracking using diffusion tensor imaging (DTI) and functional connectivity analysis using resting-state functional MRI (rs-fMRI). The AIDAmri pipeline includes automated pre-processing steps, such as raw data conversion, skull-stripping and bias-field correction as well as image registration with the Allen Mouse Brain Reference Atlas (ARA). Following a modular structure developed in Python scripting language, the pipeline integrates established and newly developed algorithms. Each processing step was optimized for efficient data processing requiring minimal user-input and user programming skills. The raw data is analyzed and results transferred to the ARA coordinate system in order to allow an efficient and highly-accurate region-based analysis. AIDAmri is intended to fill the gap of a missing open-access and cross-platform toolbox for the most relevant mouse brain MRI sequences thereby facilitating data processing in large cohorts and multi-center studies.

Processing Pipeline for Atlas-Based Imaging Data Analysis of Structural and Functional Mouse Brain MRI (AIDAmri).

Cell counting in neuroscience is a routine method of utmost importance to support descriptive in vivo findings with quantitative data on the cellular level. Although known to be error- and bias-prone, manual cell counting of histological stained brain slices remains the gold standard in the field. While the manual approach is limited to small regions-of-interest in the brain, automated tools are needed to up-scale translational approaches and generate whole mouse brain counts in an atlas framework. Our goal was to develop an algorithm which requires no pre-training such as machine learning algorithms, only minimal user input, and adjustable variables to obtain reliable cell counting results for stitched mouse brain slices registered to a common atlas such as the Allen Mouse Brain atlas. We adapted filter banks to extract the maxima from round-shaped cell nuclei and various cell structures. In a qualitative as well as quantitative comparison to other tools and two expert raters, AIDAhisto provides accurate and fast results for cell nuclei as well as immunohistochemical stainings of various types of cells in the mouse brain.

Atlas-based imaging data analysis tool for quantitative mouse brain histology (AIDAhisto).

Brain lesions caused by cerebral ischemia or hemorrhage lead to a local breakdown of energy homeostasis followed by irreversible cell death and long-term impairment. Importantly, local brain lesions also generate remote functional and structural disturbances, which contribute to the behavioral deficit but also impact the recovery of function. While spontaneous recovery has been associated with endogenous repair mechanisms at the vascular, neural, and immune cell levels, the impact of structural plasticity on sensory-motor dysfunction and recovery thereof remains to be elucidated by longitudinal imaging in a mouse model. Here, we applied behavioral assessments, in vivo fiber tracking, and histological validation in a photothrombotic stroke mouse model. Atlas-based whole-brain structural connectivity analysis and ex vivo histology revealed secondary neurodegeneration in the ipsilesional brain areas, mostly in the dorsal sensorimotor area of the thalamus. Furthermore, we describe for the first time a lesion size-dependent increase in structural connectivity between the contralesional primary motor cortex and thalamus with the ipsilesional cortex. The involvement of the contralesional hemisphere was associated with improved functional recovery relative to lesion size. This study highlights the importance of in vivo fiber tracking and the role of the contralesional hemisphere during spontaneous functional improvement as a potential novel stroke biomarker and therapeutic targets.

Lesion Size- and Location-Dependent Recruitment of Contralesional Thalamus and Motor Cortex Facilitates Recovery after Stroke in Mice.

Stroke is a devastating disease leading to cell death and disconnection between neurons both locally and remote, often resulting in severe long-term disability. Spontaneous reorganization of areas and pathways not primarily affected by ischemia is, however, associated with albeit limited recovery of function. Quantitative mapping of whole-brain changes of structural connectivity concerning the ischemia-induced sensorimotor deficit and recovery thereof would help to target structural plasticity in order to improve rehabilitation. Currently, only in vivo diffusion MRI can extract the structural whole-brain connectome noninvasively. This approach is, however, used primarily in human studies. Here, we applied atlas-based MRI analysis and graph theory to DTI in wild-type mice with cortical stroke lesions. Using a DTI network approach and graph theory, we aimed at gaining insights into the dynamics of the spontaneous reorganization after stroke related to the recovery of function. We found evidence for altered structural integrity of connections of specific brain regions, including the breakdown of connections between brain regions directly affected by stroke as well as long-range rerouting of intra- and transhemispheric connections related to improved sensorimotor behavior.

Graph theoretical quantification of white matter reorganization after cortical stroke in mice.

Although spasticity is one of the most common causes of motor disability worldwide, its precise definition and pathophysiology remain elusive, which to date renders its experimental targeting tricky. At least in part, this difficulty is caused by heterogeneous phenotypes of spasticity-causing neurological disorders, all causing spasticity by involving upper motor neurons. The most common clinical symptoms are a series of rapid muscle contractions (clonus), an increased muscle tone (hypertonia), and augmented tendon reflex activity (hyperreflexia). This muscle overactivity is due to disturbed inhibition of spinal reflexes following upper motor neuron dysfunction. Despite a range of physical and pharmacological therapies ameliorating the symptoms, their targeted application remains difficult. Therefore, to date, spasticity impacts rehabilitative therapy, and no therapy exists that reverses the pathology completely. In contrast to the incidence and importance of spasticity, only very little pre-clinical work in animal models exists, and this research is focused on the cat or the rat spastic tail model to decipher altered reflexes and excitability of the motor neurons in the spinal cord. Meanwhile, the characterization of spasticity in clinically more relevant mouse models of neurological disorders, such as stroke, remains understudied. Here, we provide a brief introduction into the clinical knowledge and therapy of spasticity and an in-depth review of pre-clinical studies of spasticity in mice including the current experimental challenges for clinical translation.

Introduction to spasticity and related mouse models.

Structural integrity and remodeling underlying functional recovery after stroke.

A poor understanding of statistical analysis has been proposed as a key reason for lack of replicability of many studies in experimental biomedicine. While several authors have demonstrated the fickleness of calculated p values based on simulations, we have experienced that such simulations are difficult to understand for many biomedical scientists and often do not lead to a sound understanding of the role of variability between random samples in statistical analysis. Therefore, we as trainees and trainers in a course of statistics for biomedical scientists have used real data from a large published study to develop a tool that allows scientists to directly experience the fickleness of p values. A tool based on a commonly used software package was developed that allows using random samples from real data. The tool is described and together with the underlying database is made available. The tool has been tested successfully in multiple other groups of biomedical scientists. It can also let trainees experience the impact of randomness, sample sizes and choice of specific statistical test on measured p values. We propose that live exercises based on real data will be more impactful in the training of biomedical scientists on statistical concepts.

A teaching tool about the fickle p value and other statistical principles based on real-life data.

The gut microbiome has been implicated as a key regulator of brain function in health and disease. But the impact of gut microbiota on functional brain connectivity is unknown. We used resting-state functional magnetic resonance imaging in germ-free and normally colonized mice under naive conditions and after ischemic stroke. We observed a strong, brain-wide increase of functional connectivity in germ-free animals. Graph theoretical analysis revealed significant higher values in germ-free animals, indicating a stronger and denser global network but with less structural organization. Breakdown of network function after stroke equally affected germ-free and colonized mice. Results from histological analyses showed changes in dendritic spine densities, as well as an immature microglial phenotype, indicating impaired microglia-neuron interaction in germ-free mice as potential cause of this phenomenon. These results demonstrate the substantial impact of bacterial colonization on brain-wide function and extend our so far mainly (sub) cellular understanding of the gut-brain axis.

The gut microbiota modulates brain network connectivity under physiological conditions and after acute brain ischemia.

Restoration of functional connectivity is a major contributor to functional recovery after stroke. We investigated the role of reactive astrocytes in functional connectivity and recovery after photothrombotic stroke in mice with attenuated reactive gliosis (GFAPVim). Infarct volume and longitudinal functional connectivity changes were determined by in vivo T2-weighted magnetic resonance imaging (MRI) and resting-state functional MRI. Sensorimotor function was assessed with behavioral tests, and glial and neural plasticity responses were quantified in the peri-infarct region. Four weeks after stroke, GFAPVim mice showed impaired recovery of sensorimotor function and aberrant restoration of global neuronal connectivity. These mice also exhibited maladaptive plasticity responses, shown by higher number of lost and newly formed functional connections between primary and secondary targets of cortical stroke regions and increased peri-infarct expression of the axonal plasticity marker Gap43. We conclude that reactive astrocytes modulate recovery-promoting plasticity responses after ischemic stroke.

Article	Year
Processing Pipeline for Atlas-Based Imaging Data Analysis of Structural and Functional Mouse Brain MRI (AIDAmri). Frontiers in neuroinformatics\| PubMed ID: 31231202	2019
Atlas-based imaging data analysis tool for quantitative mouse brain histology (AIDAhisto). Journal of neuroscience methods\| PubMed ID: 31415844	2019
Lesion Size- and Location-Dependent Recruitment of Contralesional Thalamus and Motor Cortex Facilitates Recovery after Stroke in Mice. Translational stroke research\| PubMed ID: 32166716	2021
Graph theoretical quantification of white matter reorganization after cortical stroke in mice. NeuroImage\| PubMed ID: 32380139	2020
Introduction to spasticity and related mouse models. Experimental neurology\| PubMed ID: 33007294	2021
Structural integrity and remodeling underlying functional recovery after stroke. Neural regeneration research\| PubMed ID: 33318437	2021
A teaching tool about the fickle p value and other statistical principles based on real-life data. Naunyn-Schmiedeberg's archives of pharmacology\| PubMed ID: 33443615	2021
The gut microbiota modulates brain network connectivity under physiological conditions and after acute brain ischemia. iScience\| PubMed ID: 34622150	2021
Reactive astrocytes prevent maladaptive plasticity after ischemic stroke. Progress in neurobiology\| PubMed ID: 34921928	2022

Frederique Wieters

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