Genotype specific age related changes in a transgenic rat model of Huntington's disease

Remote Corticospinal Tract Degeneration After Cortical Stroke in Rats May Not Preclude Spontaneous Sensorimotor Recovery

Background. Recovery of motor function after stroke appears to be related to the integrity of axonal connections in the corticospinal tract (CST) and corpus callosum, which may both be affected after cortical stroke. Objective. In the present study, we aimed to elucidate the relationship of changes in measures of the CST and transcallosal tract integrity, with the interhemispheric functional connectivity and sensorimotor performance after experimental cortical stroke. Methods. We conducted in vivo diffusion magnetic resonance imaging (MRI), resting-state functional MRI, and behavior testing in twenty-five male Sprague Dawley rats recovering from unilateral photothrombotic stroke in the sensorimotor cortex. Twenty-three healthy rats served as controls. Results. A reduction in the number of reconstructed fibers, a lower fractional anisotropy, and higher radial diffusivity in the ipsilesional but intact CST, reflected remote white matter degeneration. In contrast, transcallosal tract integrity remained preserved. Functional connectivity between the ipsi- and contralesional forelimb regions of the primary somatosensory cortex significantly reduced at week 8 post-stroke. Comparably, usage of the stroke-affected forelimb was normal at week 28, following significant initial impairment between day 1 and week 8 post-stroke. Conclusions. Our study shows that post-stroke motor recovery is possible despite degeneration in the CST and may be supported by intact neuronal communication between hemispheres.

Imaging Markers for the Characterization of Gray and White Matter Changes from Acute to Chronic Stages after Experimental Traumatic Brain Injury

Despite clinical symptoms, a large majority of people with mild traumatic brain injury (TBI) have normal computed tomography (CT) and magnetic resonance imaging (MRI) scans. Therefore, present-day neuroimaging tools are insufficient to diagnose or classify low grades of TBI. Advanced neuroimaging techniques, such as diffusion-weighted and functional MRI, may yield novel biomarkers that may aid in the diagnosis of TBI. Therefore, the present study had two aims: first, to characterize the development of MRI-based measures of structural and functional changes in gray and white matter regions from acute to chronic stages after mild and moderate TBI; and second, to identify the imaging markers that can most accurately predict outcome after TBI. To these aims, 52 rats underwent serial functional (resting-state) and structural (T1-, T2-, and diffusion-weighted) MRI before and 1 h, 1 day, 1 week, 1 month and 3-4 months after mild or moderate experimental TBI. All rats underwent behavioral testing. Histology was performed in subgroups of rats at different time points. Early after moderate TBI, axial and radial diffusivities were increased, and fractional anisotropy was reduced in the corpus callosum and bilateral hippocampi, which normalized over time and was paralleled by recovery of sensorimotor function. Correspondingly, histology revealed decreased myelin staining early after TBI, which was not detected at chronic stages. No significant changes in individual outcome measures were detected after mild TBI. However, multivariate analysis showed a significant additive contribution of diffusion parameters in the distinction between control and different grades of TBI-affected brains. Therefore, combining multiple imaging markers may increase the sensitivity for TBI-related pathology.

Diffusion Tensor Imaging in Preclinical and Human Studies of Huntington's Disease: What Have we Learned so Far?

BACKGROUND

Huntington's Disease is an irreversible neurodegenerative disease characterized by the progressive deterioration of specific brain nerve cells. The current evaluation of cellular and physiological events in patients with HD relies on the development of transgenic animal models. To explore such events in vivo, diffusion tensor imaging has been developed to examine the early macro and microstructural changes in brain tissue. However, the gap in diffusion tensor imaging findings between animal models and clinical studies and the lack of microstructural confirmation by histological methods has questioned the validity of this method.

OBJECTIVE

This review explores white and grey matter ultrastructural changes associated to diffusion tensor imaging, as well as similarities and differences between preclinical and clinical Huntington's Disease studies.

METHODS

A comprehensive review of the literature using online-resources was performed (Pub- Med search).

RESULTS

Similar changes in fractional anisotropy as well as axial, radial and mean diffusivities were observed in white matter tracts across clinical and animal studies. However, comparative diffusion alterations in different grey matter structures were inconsistent between clinical and animal studies.

CONCLUSION

Diffusion tensor imaging can be related to specific structural anomalies in specific cellular populations. However, some differences between animal and clinical studies could derive from the contrasting neuroanatomy or connectivity across species. Such differences should be considered before generalizing preclinical results into the clinical practice. Moreover, current limitations of this technique to accurately represent complex multicellular events at the single micro scale are real. Future work applying complex diffusion models should be considered.

Accelerating in vivo fast spin echo high angular resolution diffusion imaging with an isotropic resolution in mice through compressed sensing

PURPOSE

Echo planar imaging (EPI) is commonly used to acquire the many volumes needed for high angular resolution diffusion Imaging (HARDI), posing a higher risk for artifacts, such as distortion and deformation. An alternative to EPI is fast spin echo (FSE) imaging, which has fewer artifacts but is inherently slower. The aim is to accelerate FSE such that a HARDI data set can be acquired in a time comparable to EPI using compressed sensing.

METHODS

Compressed sensing was applied in either q-space or simultaneously in k-space and q-space, by undersampling the k-space in the phase-encoding direction or retrospectively eliminating diffusion directions for different degrees of undersampling. To test the replicability of the acquisition and reconstruction, brain data were acquired from six mice, and a numerical phantom experiment was performed. All HARDI data were analyzed individually using constrained spherical deconvolution, and the apparent fiber density and complexity metric were evaluated, together with whole-brain tractography.

RESULTS

The apparent fiber density and complexity metric showed relatively minor differences when only q-space undersampling was used, but deteriorate when k-space undersampling was applied. Likewise, the tract density weighted image showed good results when only q-space undersampling was applied using 15 directions or more, but information was lost when fewer volumes or k-space undersampling were used.

CONCLUSION

It was found that acquiring 15 to 20 diffusion directions with a full k-space and reconstructed using compressed sensing could suffice for a replicable measurement of quantitative measures in mice, where areas near the sinuses and ear cavities are untainted by signal loss.

Diffusion-weighted MRI in neurodegenerative and psychiatric animal models: Experimental strategies and main outcomes

Preclinical MRI approaches constitute a key tool to study a wide variety of neurological and psychiatric illnesses, allowing a more direct investigation of the disorder substrate and, at the same time, the possibility of back-translating such findings to human subjects. However, the lack of consensus on the optimal experimental scheme used to acquire the data has led to relatively high heterogeneity in the choice of protocols, which can potentially impact the comparison between results obtained by different groups, even using the same animal model. This is especially true for diffusion-weighted MRI data, where certain experimental choices can impact not only on the accuracy and precision of the extracted biomarkers, but also on their biological meaning. With this in mind, we extensively examined preclinical imaging studies that used diffusion-weighted MRI to investigate neurodegenerative, neurodevelopmental and psychiatric disorders in rodent models. In this review, we discuss the main findings for each preclinical model, with a special focus on the analysis and comparison of the different acquisition strategies used across studies and their impact on the heterogeneity of the findings.

A Critical Review of White Matter Changes in Huntington's Disease

Huntington’s disease is a genetic neurodegenerative disorder. White matter alterations have recently been identified as a relevant pathophysiological feature of Huntington’s disease, but their etiology and role in disease pathogenesis and progression remain unclear. Increasing evidence suggests that white matter changes in this disorder are attributed to alterations in myelin‐associated biological processes. This review first discusses evidence from neurochemical studies lending support to the demyelination hypothesis of Huntington’s disease, demonstrating aberrant myelination and changes in oligodendrocytes in the Huntington’s brain. Next, evidence from neuroimaging studies is reviewed, the limitations of the described methodologies are discussed, and suggested interpretations of findings from published studies are challenged. Although our understanding of Huntington’s associated pathological changes in the brain will increasingly rely on neuroimaging techniques, the shortcomings of these methodologies must not be forgotten. Advances in magnetic resonance imaging techniques and tissue modeling will enable a better in vivo, longitudinal characterization of the biological properties of white matter microstructure. This in turn will facilitate identification of disease‐related biomarkers and the specification of outcome measures in clinical trials. © 2020 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Impaired Decision Making and Loss of Inhibitory-Control in a Rat Model of Huntington Disease

Cognitive deficits associated with Huntington disease (HD) are generally dominated by executive function disorders often associated with disinhibition and impulsivity/compulsivity. Few studies have directly examined symptoms and consequences of behavioral disinhibition in HD and its relation with decision-making. To assess the different forms of impulsivity in a transgenic model of HD (tgHD rats), two tasks assessing cognitive/choice impulsivity were used: risky decision-making with a rat gambling task (RGT) and intertemporal choices with a delay discounting task (DD). To assess waiting or action impulsivity the differential reinforcement of low rate of responding task (DRL) was used. In parallel, the volume as well as cellular activity of the amygdala was analyzed. In contrast to WT rats, 15 months old tgHD rats exhibited a poor efficiency in the RGT task with difficulties to choose advantageous options, a steep DD curve as delays increased in the DD task and a high rate of premature and bursts responses in the DRL task. tgHD rats also demonstrated a concomitant and correlated presence of both action and cognitive/choice impulsivity in contrast to wild type (WT) animals. Moreover, a reduced volume associated with an increased basal cellular activity of the central nucleus of amygdala indicated a dysfunctional amygdala in tgHD rats, which could underlie inhibitory dyscontrol. In conclusion, tgHD rats are a good model for impulsivity disorder that could be used more widely to identify potential pharmacotherapies to treat these invasive symptoms in HD.

The evolving role of diffusion magnetic resonance imaging in movement disorders

Significant advances have allowed diffusion magnetic resonance imaging (MRI) to evolve into a powerful tool in the field of movement disorders that can be used to study disease states and connectivity between brain regions. Diffusion MRI is a promising potential biomarker for Parkinson's disease and other forms of parkinsonism, and may allow the distinction of different forms of parkinsonism. Techniques such as tractography have contributed to our current thinking regarding the pathophysiology of dystonia and possible mechanisms of penetrance. Diffusion MRI measures could potentially assist in monitoring disease progression in Huntington's disease, and in uncovering the nature of the processes and structures involved the development of essential tremor. The ability to represent structural connectivity in vivo also makes diffusion MRI an ideal adjunctive tool for the surgical treatment of movement disorders. We review recent studies using diffusion MRI in movement disorders research and present the current state of the science as well as future directions.

Spatiotemporal linear mixed effects modeling for the mass-univariate analysis of longitudinal neuroimage data

We present an extension of the Linear Mixed Effects (LME) modeling approach to be applied to the mass-univariate analysis of longitudinal neuroimaging (LNI) data. The proposed method, called spatiotemporal LME or ST-LME, builds on the flexible LME framework and exploits the spatial structure in image data. We instantiated ST-LME for the analysis of cortical surface measurements (e.g. thickness) computed by FreeSurfer, a widely-used brain Magnetic Resonance Image (MRI) analysis software package. We validate the proposed ST-LME method and provide a quantitative and objective empirical comparison with two popular alternative methods, using two brain MRI datasets obtained from the Alzheimer's disease neuroimaging initiative (ADNI) and Open Access Series of Imaging Studies (OASIS). Our experiments revealed that ST-LME offers a dramatic gain in statistical power and repeatability of findings, while providing good control of the false positive rate.

A novel BACHD transgenic rat exhibits characteristic neuropathological features of Huntington disease

Huntington disease (HD) is an inherited progressive neurodegenerative disorder, characterized by motor, cognitive, and psychiatric deficits as well as neurodegeneration and brain atrophy beginning in the striatum and the cortex and extending to other subcortical brain regions. The genetic cause is an expansion of the CAG repeat stretch in the HTT gene encoding huntingtin protein (htt). Here, we generated an HD transgenic rat model using a human bacterial artificial chromosome (BAC), which contains the full-length HTT genomic sequence with 97 CAG/CAA repeats and all regulatory elements. BACHD transgenic rats display a robust, early onset and progressive HD-like phenotype including motor deficits and anxiety-related symptoms. In contrast to BAC and yeast artificial chromosome HD mouse models that express full-length mutant huntingtin, BACHD rats do not exhibit an increased body weight. Neuropathologically, the distribution of neuropil aggregates and nuclear accumulation of N-terminal mutant huntingtin in BACHD rats is similar to the observations in human HD brains. Aggregates occur more frequently in the cortex than in the striatum and neuropil aggregates appear earlier than mutant htt accumulation in the nucleus. Furthermore, we found an imbalance in the striatal striosome and matrix compartments in early stages of the disease. In addition, reduced dopamine receptor binding was detectable by in vivo imaging. Our data demonstrate that this transgenic BACHD rat line may be a valuable model for further understanding the disease mechanisms and for preclinical pharmacological studies.

Statistical analysis of longitudinal neuroimage data with Linear Mixed Effects models

Longitudinal neuroimaging (LNI) studies are rapidly becoming more prevalent and growing in size. Today, no standardized computational tools exist for the analysis of LNI data and widely used methods are sub-optimal for the types of data encountered in real-life studies. Linear Mixed Effects (LME) modeling, a mature approach well known in the statistics community, offers a powerful and versatile framework for analyzing real-life LNI data. This article presents the theory behind LME models, contrasts it with other popular approaches in the context of LNI, and is accompanied with an array of computational tools that will be made freely available through FreeSurfer - a popular Magnetic Resonance Image (MRI) analysis software package. Our core contribution is to provide a quantitative empirical evaluation of the performance of LME and competing alternatives popularly used in prior longitudinal structural MRI studies, namely repeated measures ANOVA and the analysis of annualized longitudinal change measures (e.g. atrophy rate). In our experiments, we analyzed MRI-derived longitudinal hippocampal volume and entorhinal cortex thickness measurements from a public dataset consisting of Alzheimer's patients, subjects with mild cognitive impairment and healthy controls. Our results suggest that the LME approach offers superior statistical power in detecting longitudinal group differences.

Quantitative mouse brain phenotyping based on single and multispectral MR protocols

Sophisticated image analysis methods have been developed for the human brain, but such tools still need to be adapted and optimized for quantitative small animal imaging. We propose a framework for quantitative anatomical phenotyping in mouse models of neurological and psychiatric conditions. The framework encompasses an atlas space, image acquisition protocols, and software tools to register images into this space. We show that a suite of segmentation tools (Avants, Epstein et al., 2008) designed for human neuroimaging can be incorporated into a pipeline for segmenting mouse brain images acquired with multispectral magnetic resonance imaging (MR) protocols. We present a flexible approach for segmenting such hyperimages, optimizing registration, and identifying optimal combinations of image channels for particular structures. Brain imaging with T1, T2* and T2 contrasts yielded accuracy in the range of 83% for hippocampus and caudate putamen (Hc and CPu), but only 54% in white matter tracts, and 44% for the ventricles. The addition of diffusion tensor parameter images improved accuracy for large gray matter structures (by >5%), white matter (10%), and ventricles (15%). The use of Markov random field segmentation further improved overall accuracy in the C57BL/6 strain by 6%; so Dice coefficients for Hc and CPu reached 93%, for white matter 79%, for ventricles 68%, and for substantia nigra 80%. We demonstrate the segmentation pipeline for the widely used C57BL/6 strain, and two test strains (BXD29, APP/TTA). This approach appears promising for characterizing temporal changes in mouse models of human neurological and psychiatric conditions, and may provide anatomical constraints for other preclinical imaging, e.g. fMRI and molecular imaging. This is the first demonstration that multiple MR imaging modalities combined with multivariate segmentation methods lead to significant improvements in anatomical segmentation in the mouse brain.

Identification and characterization of Huntington related pathology: an in vivo DKI imaging study

An important focus of Huntington Disease (HD) research is the identification of symptom-independent biomarkers of HD neuropathology. There is an urgent need for reproducible, sensitive and specific outcome measures, which can be used to track disease onset as well as progression. Neuroimaging studies, in particular diffusion-based MRI methods, are powerful probes for characterizing the effects of disease and aging on tissue microstructure. We report novel diffusional kurtosis imaging (DKI) findings in aged transgenic HD rats. We demonstrate altered diffusion metrics in the (pre)frontal cerebral cortex, external capsule and striatum. Presence of increased diffusion complexity and restriction in the striatum is confirmed by an increased fiber dispersion in this region. Immunostaining of the same specimens reveals decreased number of microglia in the (pre)frontal cortex, and increased numbers of oligodendrocytes in the striatum. We conclude that DKI allows sensitive and specific characterization of altered tissue integrity in this HD rat model, indicating a promising potential for diagnostic imaging of gray and white matter pathology.

Altered diffusion tensor imaging measurements in aged transgenic Huntington disease rats

Rodent models of Huntington disease (HD) are valuable tools for investigating HD pathophysiology and evaluating new therapeutic approaches. Non-invasive characterization of HD-related phenotype changes is important for monitoring progression of pathological processes and possible effects of interventions. The first transgenic rat model for HD exhibits progressive late-onset affective, cognitive, and motor impairments, as well as neuropathological features reflecting observations from HD patients. In this report, we contribute to the anatomical phenotyping of this model by comparing high-resolution ex vivo DTI measurements obtained in aged transgenic HD rats and wild-type controls. By region of interest analysis supplemented by voxel-based statistics, we find little evidence of atrophy in basal ganglia regions, but demonstrate altered DTI measurements in the dorsal and ventral striatum, globus pallidus, entopeduncular nucleus, substantia nigra, and hippocampus. These changes are largely compatible with DTI findings in preclinical and clinical HD patients. We confirm earlier reports that HD rats express a moderate neuropathological phenotype, and provide evidence of altered DTI measures in specific HD-related brain regions, in the absence of pronounced morphometric changes.