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Hundreds of rodent gait studies have been published over the past two decades, according to a PubMed search. Treadmill gait data, for example from the DigiGait system, generates over 30 + spatial and temporal measures. Despite this multi-dimensional data, all but a handful of the published literature on rodent gait has conducted univariate analysis that reveals limited information on the relationships that are characteristic of different gait states. This study conducted rigorous multivariate analysis in the form of sequential feature selection and factor analysis on gait data from a variety of gait deviations (due to injury i.e. peripheral nerve transection and transplantation, disease i.e. IUGR and hyperoxia, and age-related changes) and used machine learning to train a classifier to distinguish among and score different gait states. Treadmill gait data (DigiGait) of three different types of gait deviations were collected. Data were collected from B6 mice using the DigiGait system, with gait measurements taken at standardized treadmill speeds of 10, 17, and 24 cm/s over a period of 3–4 s per observation. Each mouse underwent at least two trials at each speed. Data were collected on B6 mice that were healthy and had various types of gait deficit due to: (a) a peripheral nerve injury model with increasing degrees of damage to the neuromusculoskeletal sequence of gait i.e. nerve transection, total hind limb transplantation, (b) a central nerve injury model of increasing degrees of damage to the motor regions responsible for gait i.e. IUGR, IUGR + hyperoxia, and (c) gait changes due to increasing age. Multivariate factor analysis (using MATLAB’s factoran) and forward feature selection (with ten-fold cross-validation) were conducted to identify those features and factors most descriptive of each gait state for comparison. Various machine learning classifier models were trained with ten-fold cross-validation and evaluated (e.g. random forest, regression, discriminant analysis, support vector machine, and ensemble) in a 70 − 30 training-testing split for their accuracy, precision, recall, and F-score. The highest performing model was used to score each type of gait for direct comparison on a scale of -0.5 to 0.5. The score distributions were plotted on a histogram for direct comparisons of score populations among various gait states. Multivariate feature selection revealed that not all 30 + features were relevant to describing the gait states. Plotting misclassification error (MCE) as a function of number of features included revealed that there was a critical number of features (~ 16) that minimized MCE (0.17 via univariate feature selection vs. 0.12 via multivariate feature selection). Incorporating more than 16 features led MCE to increase linearly indicating overfitting. Relationships among the identified features were understood via factor analysis. The factor analysis results were consistent with the biological differences between the groups (e.g. total hind limb transplantation was distinguishable via features descriptive of the positioning of the paw in relation to the body while nerve transection injury alone was distinguishable via features descriptive of changes to fine motor movements). Across all gait states, there was significant conservation of features and factors. This suggests certain relationships may be fundamental to rodent gait analysis regardless of the gait pathology in question. The highest performing machine learning classifier model (ensemble) was able to distinguish between gait deficits with high performance (F-score, recall, precision, and accuracy all > 0.90). This included the ability to distinguish between peripheral vs. central gait deficit, between individual types of peripheral deficit, between individual types of central deficit, and between younger vs. older animals. Using the classifier to score individual animals and plot the scores by group revealed score distributions that were consistent with biological phenomena. For example, the multivariate gait score trends as a result of increasing central nerve injury were consistent with the trends of white matter volume loss in relevant motor regions of the brain as measured via MRI. Finally, the degrees of separation between multivariate gait scores were consistent with the degree of biological difference between gaits (e.g. central injury had greater separation from healthy vs. peripheral injury; older and younger animals had more moderate, yet still statistically significant, separation in scores vs. any of the injury / disease states did with each other). In conclusion, this study establishes a new methodology to quantify and evaluate gait deviations across a variety of different models. Its novelty is in using multivariate statistics to describe the features and factors that characterize gait states due to injury, disease, and age for use in machine learning model training. This includes statistically describing the differences in gait between diseases with vastly different etiologies of gait deficits (peripheral vs. central). In doing so the methodology’s novelty includes accounting for relationships between groupings of features in model training; something that traditional univariate analysis is unable to do. It used multivariate statistics and machine learning to reveal gait as a quantifiable, preclinical biomarker of injury, disease, and age. It collapsed a multi-dimensional biological phenomena (gait) into a single score by encoding revealed biological relationships allowing for direct, quantifiable comparisons of function as it pertains to ambulation. It revealed how these multivariate gait scores can visualize biologically consistent separation and combined effects. Finally, we demonstrate the application of this methodology to already published univariate study that is representative of the hundreds of univariate treadmill gait analysis published over the last two decades. Thereby, opening the door to a new class of multivariate gait analyses that provides greater insight and value than the current state-of-the art.

Animal models of nerve injury are important for studying nerve injury and repair, particularly for interventions that cannot be studied in humans. However, the vast majority of gait analysis in animals has been limited to univariate analysis even though gait data is highly multi-dimensional. As a result, little is known about how various spatiotemporal components of the gait relate to each other in the context of peripheral nerve injury and trauma. We hypothesize that a multivariate characterization of gait will reveal relationships among spatiotemporal components of gait with biological relevance to peripheral nerve injury and trauma. We further hypothesize that legitimate relationships among said components will allow for more accurate classification among distinct gait phenotypes than if attempted with univariate analysis alone. DigiGait data was collected of mice across groups representing increasing degrees of damage to the neuromusculoskeletal sequence of gait; that is (a) healthy controls, (b) nerve damage only via total nerve transection + reconnection of the femoral and sciatic nerves, and (c) nerve, muscle, and bone damage via total hind-limb transplantation. Multivariate relationships among the 30+ spatiotemporal measures were evaluated using exploratory factor analysis and forward feature selection to identify the features and latent factors that best described gait phenotypes. The identified features were then used to train classifier models and compared to a model trained with features identified using only univariate analysis. 10-15 features relevant to describing gait in the context of increasing degrees of traumatic peripheral nerve injury were identified. Factor analysis uncovered relationships among the identified features and enabled the extrapolation of a set of latent factors that further described the distinct gait phenotypes. The latent factors tied to biological differences among the groups (e.g. alterations to the anatomical configuration of the limb due to transplantation or aberrant fine motor function due to peripheral nerve injury). Models trained using the identified features generated values that could be used to distinguish among pathophysiological states with high statistical significance (p < .001) and accuracy (>80%) as compared to univariate analysis alone. This is the first performance evaluation of a multivariate approach to gait analysis and the first demonstration of superior performance as compared to univariate gait analysis in animals. It is also the first study to use multivariate statistics to characterize and distinguish among different gradations of gait deficit in animals. This study contributes a comprehensive, multivariate characterization pipeline for application in the study of any pathologies in which gait is a quantitative translational outcome metric.

Patients aged 65 years and older account for an increasing proportion of patients with traumatic brain injury (TBI). Older TBI patients experience increased morbidity and mortality compared to their younger counterparts. Our prior data demonstrated that by blocking α4 integrin, anti-CD49d antibody (aCD49d Ab) abrogates CD8 +  T-cell infiltration into the injured brain, improves survival, and attenuates neurocognitive deficits. Here, we aimed to uncover how aCD49d Ab treatment alters local cellular responses in the aged mouse brain. Consequently, mice incur age-associated toxic cytokine and chemokine responses long-term post-TBI. aCD49d Ab attenuates this response along with a T helper (Th)1/Th17 immunological shift and remediation of overall CD8 +  T cell cytotoxicity. Furthermore, aCD49d Ab reduces CD8 +  T cells exhibiting higher effector status, leading to reduced clonal expansion in aged, but not young, mouse brains with chronic TBI. Together, aCD49d Ab is a promising therapeutic strategy for treating TBI in the older people.

Mutations in the gene encoding ubiquilin2 ( UBQLN2 ) cause amyotrophic lateral sclerosis (ALS), frontotemporal type of dementia, or both. However, the molecular mechanisms are unknown. Here, we show that ALS/dementia-linked UBQLN2 ᴾ⁴⁹⁷ᴴ transgenic mice develop neuronal pathology with ubiquilin2/ubiquitin/p62-positive inclusions in the brain, especially in the hippocampus, recapitulating several key pathological features of dementia observed in human patients with UBQLN2 mutations. A major feature of the ubiquilin2-related pathology in these mice, and reminiscent of human disease, is a dendritic spinopathy with protein aggregation in the dendritic spines and an associated decrease in dendritic spine density and synaptic dysfunction. Finally, we show that the protein inclusions in the dendritic spines are composed of several components of the proteasome machinery, including Ub ᴳ⁷⁶ⱽ–GFP, a representative ubiquitinated protein substrate that is accumulated in the transgenic mice. Our data, therefore, directly link impaired protein degradation to inclusion formation that is associated with synaptic dysfunction and cognitive deficits. These data imply a convergent molecular pathway involving synaptic protein recycling that may also be involved in other neurodegenerative disorders, with implications for development of widely applicable rational therapeutics. Significance Mutations in the UBQLN2 gene, which encodes the ubiquitin-like protein ubiquilin2 (UBQLN2) have been shown to cause ALS and ALS/dementia. Ubiquilin2 links familial and sporadic forms of the disease through pathology observed in the spinal cords of all ALS cases and in the brains of ALS/dementia cases with or without UBQLN2 mutations. In this communication, we develop and characterize a mouse model of mutant UBQLN2 -linked dementia. We demonstrate that mutant mice develop impairment in the protein degradation pathway, abnormal protein aggregation, synaptic dysfunction, and cognitive deficits. This model provides a useful tool to further study dementia and develop rational therapies.

Dendritic spine morphogenesis contributes to brain function, cognition, and behavior, and is altered in psychiatric disorders. Kalirin is a brain-specific guanine-nucleotide exchange factor (GEF) for Rac-like GTPases and is a key regulator of spine morphogenesis. Here, we show that KALRN-knockout mice have specific reductions in cortical, but not hippocampal, Rac1 signaling and spine density, and exhibit reduced cortical glutamatergic transmission. These mice exhibit robust deficits in working memory, sociability, and prepulse inhibition, paralleled by locomotor hyperactivity reversible by clozapine in a kalirin-dependent manner. Several of these deficits are delayed and age-dependent. Our study thus links spine morphogenic signaling with age-dependent, delayed, disease-related phenotypes, including cognitive dysfunction.

Activity-induced manganese-dependent MRI (AIM-MRI) is a powerful tool to track system-wide neural activity using high resolution, quantitative T1-weighted MRI in animal models and has significant advantages for investigating neural activity over other modalities including BOLD fMRI. With AIM-MRI, Mn2+ ions enter neurons via voltage-gated calcium channels preferentially active during the time of experimental exposure. A broad range of AIM-MRI studies using different species studying different phenomena have been performed, but few of these studies provide a systematic evaluation of the factors influencing the detection of Mn2+ such as dosage and the temporal characteristics of Mn2+ uptake. We identified an optimal dose of Mn2+ (25mg/kg, s.c.) in order to characterize the time-course of Mn2+ accumulation in active neural regions in the rabbit. T1-weighted MRI and functional MRI were collected 0–3, 6–9, and 24–27h post-Mn2+ injection while the vibrissae on the right side were vibrated. Significant BOLD activation in the left somatosensory (SS) cortex and left ventral posteromedial (VPM) thalamic nucleus was detected during whisker vibration. T1-weighted signal intensities were extracted from these regions, their corresponding contralateral regions and the visual cortex (to serve as controls). A significant elevation in T1-weighted signal intensity in the left SS cortex (relative to right) was evident 6–9 and 24–27h post-Mn2+ injection while the left VPM thalamus showed a significant enhancement (relative to the right) only during the 24–27h session. Visual cortex showed no hemispheric difference at any timepoint. Our results suggest that studies employing AIM-MRI would benefit by conducting experimental manipulations 6–24h after subcutaneous MnCl2 injections to optimize the concentration of contrast agent in the regions active during the exposure. •First study of Mn2+-related efficacy, toxicology, and T1W enhancement in rabbits•Right whisker vibration induces BOLD activation in left somatosensory pathway.•These regions preferentially accumulate Mn2+ compared to contralateral counterparts.•Without BBB disruption, Mn2+ accumulation depends on spatial location of regions.

Exposure to exercise or to environmental enrichment increases the generation of new neurons in the adult hippocampus and promotes certain kinds of learning and memory. While the precise role of neurogenesis in cognition has been debated intensely, comparatively few studies have addressed the mechanisms linking environmental exposures to cellular and behavioral outcomes. Here we show that bone morphogenetic protein (BMP) signaling mediates the effects of exercise on neurogenesis and cognition in the adult hippocampus. Elective exercise reduces levels of hippocampal BMP signaling before and during its promotion of neurogenesis and learning. Transgenic mice with decreased BMP signaling or wild type mice infused with a BMP inhibitor both exhibit remarkable gains in hippocampal cognitive performance and neurogenesis, mirroring the effects of exercise. Conversely, transgenic mice with increased BMP signaling have diminished hippocampal neurogenesis and impaired cognition. Exercise exposure does not rescue these deficits, suggesting that reduced BMP signaling is required for environmental effects on neurogenesis and learning. Together, these observations show that BMP signaling is a fundamental mechanism linking environmental exposure with changes in cognitive function and cellular properties in the hippocampus.

Introduction Although mouse models of Alzheimer's disease (AD) have increased our understanding of the molecular basis of the disease, none of those models represent late‐onset Alzheimer's Disease which accounts for >90% of AD cases, and no therapeutics developed in the mouse (with the possible exceptions of aduhelm/aducanumab and gantenerumab) have succeeded in preventing or reversing the disease. This technology has allowed much progress in understanding the molecular basis of AD. To further enhance our understanding, we used wild‐type rabbit (with a nearly identical amino acid sequence for amyloid as in humans) to model LOAD by stressing risk factors including age, hypercholesterolemia, and elevated blood glucose levels (BGLs), upon an ε3‐like isoform of apolipoprotein. We report a combined behavioral, imaging, and metabolic study using rabbit as a non‐transgenic model to examine effects of AD‐related risk factors on cognition, intrinsic functional connectivity, and magnetic resonance‐based biomarkers of neuropathology. Methods Aging rabbits were fed a diet enriched with either 2% cholesterol or 10% fat/30% fructose. Monthly tests of novel object recognition (NOR) and object location memory (OLM) were administered to track cognitive impairment. Trace eyeblink conditioning (EBC) was administered as a final test of cognitive impairment. Magnetic resonance imaging (MRI) was used to obtain resting state connectivity and quantitative parametric data (R2*). Results Experimental diets induced hypercholesterolemia or elevated BGL. Both experimental diets induced statistically significant impairment of OLM (but not NOR) and altered intrinsic functional connectivity. EBC was more impaired by fat/fructose diet than by cholesterol. Whole brain and regional R2* MRI values were elevated in both experimental diet groups relative to rabbits on the control diet. Discussion We propose that mechanisms underlying LOAD can be assessed by stressing risk factors for inducing AD and that dietary manipulations can be used to assess etiological differences in the pathologies and effectiveness of potential therapeutics against LOAD. In addition, non‐invasive MRI in awake, non‐anesthetized rabbits further increases the translational value of this non‐transgenic model to study AD.

Introduction Amyloid‐beta oligomers (AβOs) accumulate in Alzheimer's disease and may instigate neuronal pathology and cognitive impairment. We examined the ability of a new probe for molecular magnetic resonance imaging (MRI) to detect AβOs in vivo, and we tested the behavioral impact of AβOs injected in rabbits, a species with an amino acid sequence that is nearly identical to the human sequence. Methods Intracerebroventricular (ICV) injection with stabilized AβOs was performed. Rabbits were probed for AβO accumulation using ACUMNS (an AβO‐selective antibody [ACU193] coupled to magnetic nanostructures). Immunohistochemistry was used to verify AβO presence. Cognitive impairment was evaluated using object location and object recognition memory tests and trace eyeblink conditioning. Results AβOs in the entorhinal cortex of ICV‐injected animals were detected by MRI and confirmed by immunohistochemistry. Injections of AβOs also impaired hippocampal‐dependent, but not hippocampal‐independent, tasks and the area fraction of bound ACUMNs correlated with the behavioral impairment. Discussion Accumulation of AβOs can be visualized in vivo by MRI of ACUMNS and the cognitive impairment induced by the AβOs can be followed longitudinally with the novel location memory test.

The way in which the brain is functionally connected into different networks has emerged as an important research topic in order to understand normal neural processing and signaling. Since some experimental manipulations are difficult or unethical to perform in humans, animal models are better suited to investigate this topic. Rabbits are a species that can undergo MRI scanning in an awake and conscious state with minimal preparation and habituation. In this study, we characterized the intrinsic functional networks of the resting New Zealand White rabbit brain using BOLD fMRI data. Group independent component analysis revealed seven networks similar to those previously found in humans, non-human primates and/or rodents including the hippocampus, default mode, cerebellum, thalamus, and visual, somatosensory, and parietal cortices. For the first time, the intrinsic functional networks of the resting rabbit brain have been elucidated demonstrating the rabbit's applicability as a translational animal model. Without the confounding effects of anesthetics or sedatives, future experiments may employ rabbits to understand changes in neural connectivity and brain functioning as a result of experimental manipulation (e.g., temporary or permanent network disruption, learning-related changes, and drug administration). [Display omitted] •First examination of intrinsic connectivity networks in rabbits.•Hippocampal, default mode, cerebellar, thalamic, and cortical networks identified.•Networks demonstrate reliability and robustness across and within rabbits.•Rabbit can be imaged in a conscious, awake state without anesthesia or sedation.