Scanning ultrasound-mediated memory and functional improvements do not require amyloid-ß reduction.

A prevalent view in treating age-dependent disorders including Alzheimer's disease (AD) is that the underlying amyloid plaque pathology must be targeted for cognitive improvements. In contrast, we report here that repeated scanning ultrasound (SUS) treatment at 1 MHz frequency can ameliorate memory deficits in the APP23 mouse model of AD without reducing amyloid-ß (Aß) burden. Different from previous studies that had shown Aß clearance as a consequence of blood-brain barrier (BBB) opening, here, the BBB was not opened as no microbubbles were used. Quantitative SWATH proteomics and functional magnetic resonance imaging revealed that ultrasound induced long-lasting functional changes that correlate with the improvement in memory. Intriguingly, the treatment was more effective at a higher frequency (1 MHz) than at a frequency within the range currently explored in clinical trials in AD patients (286 kHz). Together, our data suggest frequency-dependent bio-effects of ultrasound and a dissociation of cognitive improvement and Aß clearance, with important implications for the design of trials for AD therapies.

Anti-inflammatory interleukin 1 receptor antagonist concentration in plasma correlates with blood-brain barrier integrity in the primary lesion area in traumatic brain injury patients.

Purpose: Blood-brain barrier (BBB) dysregulation and pro-inflammatory signalling molecules are secondary factors that have been associated with injury severity and long-term clinical outcome following traumatic brain injury (TBI). However, the association between BBB permeability and inflammation is unknown in human TBI patients. In this study, we investigated whether BBI integrity as measured by Dynamic Contrast-Enhanced (DCE) Magnetic Resonance Imaging (MRI) correlates with plasma levels of immunological markers following TBI. Methods: Thirty-two TBI patients recruited from a neurosurgical unit were included in the study. Structural three-dimensional T1-weighted and DCE-MRI images were acquired on a 3T MRI at the earliest opportunity once the participant was sufficiently stable after patient admission to hospital. Blood sampling was performed on the same day as the MRI. The location and extents of the haemorrhagic and contusional lesions were identified. Immunological biomarkers were quantified from the participants' plasma using a multiplex immunoassay. Demographic and clinical information, including age and Glasgow Coma Scale (GCS) were also collected and the immunological biomarker profiles were compared across controls and the TBI severity sub-groups. Contrast agent leakiness through blood-brain barriers (BBB) in the contusional lesions were assessed by fitting DCE-MRI using Patlak model and BBB leakiness characteristics of the participants were correlated with the immunological biomarker profiles. Results: TBI patients showed reduced plasma levels of interleukin (IL)-1ß, IFN-γ, IL-13, and chemokine (C-C motif) ligands (CCL)2 compared to controls and significantly higher levels of platelet-derived growth factor (PDGF-BB), IL-6, and IL-8. BBB leakiness of the contusional lesions did not significantly differ across different TBI severity sub-groups. IL-1ra levels significantly and positively correlated with the contusional lesion's BBB integrity as measured with DCE-MRI via an exponential curve relationship. Discussion: This is the first study to combine DCE-MRI with plasma markers of inflammation in acute TBI patients. Our finding that plasma levels of the anti-inflammatory cytokine IL-1ra correlated negatively with increased leakiness of the BBB.

Cascaded Multi-Modal Mixing Transformers for Alzheimer's Disease Classification with Incomplete Data.

Accurate medical classification requires a large number of multi-modal data, and in many cases, different feature types. Previous studies have shown promising results when using multi-modal data, outperforming single-modality models when classifying diseases such as Alzheimer's Disease (AD). However, those models are usually not flexible enough to handle missing modalities. Currently, the most common workaround is discarding samples with missing modalities which leads to considerable data under-utilisation. Adding to the fact that labelled medical images are already scarce, the performance of data-driven methods like deep learning can be severely hampered. Therefore, a multi-modal method that can handle missing data in various clinical settings is highly desirable. In this paper, we present Multi-Modal Mixing Transformer (3MT), a disease classification transformer that not only leverages multi-modal data but also handles missing data scenarios. In this work, we test 3MT for AD and Cognitively normal (CN) classification and mild cognitive impairment (MCI) conversion prediction to progressive MCI (pMCI) or stable MCI (sMCI) using clinical and neuroimaging data. The model uses a novel Cascaded Modality Transformers architecture with cross-attention to incorporate multi-modal information for more informed predictions. We propose a novel modality dropout mechanism to ensure an unprecedented level of modality independence and robustness to handle missing data scenarios. The result is a versatile network that enables the mixing of arbitrary numbers of modalities with different feature types and also ensures full data utilization in missing data scenarios. The model is trained and evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with the state-of-the-art performance and further evaluated with The Australian Imaging Biomarker & Lifestyle Flagship Study of Ageing (AIBL) dataset with missing data.

Hippocampal demyelination is associated with increased magnetic susceptibility in a mouse model of concussion.

Structural and functional deficits in the hippocampus are a prominent feature of moderate-severe traumatic brain injury (TBI). In this work, we investigated the potential of Quantitative Susceptibility Imaging (QSM) to reveal the temporal changes in myelin integrity in a mouse model of concussion (mild TBI). We employed a cross-sectional design wherein we assigned 43 mice to cohorts undergoing either a concussive impact or a sham procedure, with QSM imaging at day 2, 7, or 14 post-injury, followed by Luxol Fast Blue (LFB) myelin staining to assess the structural integrity of hippocampal white matter (WM). We assessed spatial learning in the mice using the Active Place Avoidance Test (APA), recording their ability to use visual cues to locate and avoid zone-dependent mild electrical shocks. QSM and LFB staining indicated changes in the stratum lacunosum-molecular layer of the hippocampus in the concussion groups, suggesting impairment of this key relay between the entorhinal cortex and the CA1 regions. These imaging and histology findings were consistent with demyelination, namely increased magnetic susceptibility to MR imaging and decreased LFB staining. In the APA test, sham animals showed fewer entries into the shock zone compared to the concussed cohort. Thus, we present radiological, histological, and behavioral findings that concussion can induce significant and alterations in hippocampal integrity and function that evolve over time after the injury.

PREdiction and Diagnosis using Imaging and Clinical biomarkers Trial in Traumatic Brain Injury (PREDICT-TBI) study protocol: an observational, prospective, multicentre cohort study for the prediction of outcome in moderate-to-severe TBI.

INTRODUCTION: Traumatic brain injury (TBI) is a heterogeneous condition with a broad spectrum of injury severity, pathophysiological processes and variable outcomes. For moderate-to-severe TBI survivors, recovery is often protracted and outcomes can range from total dependence to full recovery. Despite advances in medical treatment options, prognosis remains largely unchanged. The objective of this study is to develop a machine learning predictive model for neurological outcomes at 6 months in patients with a moderate-to-severe TBI, incorporating longitudinal clinical, multimodal neuroimaging and blood biomarker predictor variables. METHODS AND ANALYSIS: A prospective, observational, cohort study will enrol 300 patients with moderate-to-severe TBI from seven Australian hospitals over 3 years. Candidate predictors including demographic and general health variables, and longitudinal clinical, neuroimaging (CT and MRI), blood biomarker and patient-reported outcome measures will be collected at multiple time points within the acute phase of injury. The predictor variables will populate novel machine learning models to predict the Glasgow Outcome Scale Extended 6 months after injury. The study will also expand on current prognostic models by including novel blood biomarkers (circulating cell-free DNA), and the results of quantitative neuroimaging such as Quantitative Susceptibility Mapping and Dynamic Contrast Enhanced MRI as predictor variables. ETHICS AND DISSEMINATION: Ethical approval has been obtained by the Royal Brisbane and Women's Hospital Human Research Ethics Committee, Queensland. Participants or their substitute decision-maker/s will receive oral and written information about the study before providing written informed consent. Study findings will be disseminated by peer-review publications and presented at national and international conferences and clinical networks. TRIAL REGISTRATION NUMBER: ACTRN12620001360909.

An end-end deep learning framework for lesion segmentation on multi-contrast MR images-an exploratory study in a rat model of traumatic brain injury.

Traumatic brain injury (TBI) engenders traumatic necrosis and penumbra-areas of secondary neural injury which are crucial targets for therapeutic interventions. Segmenting manually areas of ongoing changes like necrosis, edema, hematoma, and inflammation is tedious, error-prone, and biased. Using the multi-parametric MR data from a rodent model study, we demonstrate the effectiveness of an end-end deep learning global-attention-based UNet (GA-UNet) framework for automatic segmentation and quantification of TBI lesions. Longitudinal MR scans (2 h, 1, 3, 7, 14, 30, and 60 days) were performed on eight Sprague-Dawley rats after controlled cortical injury was performed. TBI lesion and sub-regions segmentation was performed using 3D-UNet and GA-UNet. Dice statistics (DSI) and Hausdorff distance were calculated to assess the performance. MR scan variations-based (bias, noise, blur, ghosting) data augmentation was performed to develop a robust model.Training/validation median DSI for U-Net was 0.9368 with T2w and MPRAGE inputs, whereas GA-UNet had 0.9537 for the same. Testing accuracies were higher for GA-UNet than U-Net with a DSI of 0.8232 for the T2w-MPRAGE inputs.Longitudinally, necrosis remained constant while oligemia and penumbra decreased, and edema appearing around day 3 which increased with time. GA-UNet shows promise for multi-contrast MR image-based segmentation/quantification of TBI in large cohort studies.

Diffusion tensor imaging and plasma immunological biomarker panel in a rat traumatic brain injury (TBI) model and in human clinical TBI.

Introduction: Neuroinflammatory reactions play a significant role in the pathology and long-term consequences of traumatic brain injury (TBI) and may mediate salutogenic processes that white matter integrity. This study aimed to investigate the relationship between inflammatory markers and white matter integrity following TBI in both a rat TBI model and clinical TBI cases. Methods: In the rat model, blood samples were collected following a controlled cortical impact (CCI) to assess a panel of inflammatory markers; MR-based diffusion tensor imaging (DTI) was employed to evaluate white matter integrity 60 days post-injury. 15 clinical TBI patients were similarly assessed for a panel of inflammatory markers and DTI post-intensive care unit discharge. Blood samples from healthy controls were used for comparison of the inflammatory markers. Results: Time-dependent elevations in immunological markers were observed in TBI rats, with a correlation to preserved fractional anisotropy (FA) in white matter. Specifically, TBI-induced increased plasma levels of IL-1ß, IL-6, G-CSF, CCL3, CCL5, and TNF-α were associated with higher white matter integrity, as measured by FA. Clinical cases had similar findings: elevated inflammatory markers (relative to controls) were associated with preservation of FA in vulnerable white matter regions. Discussion: Inflammatory markers in post-TBI plasma samples are ambivalent with respect to prediction of favourable outcome versus a progression to more pervasive pathology and morbidity.

A mice resting-state functional magnetic resonance imaging dataset on the effects of medetomidine dosages and prior-stimulation on functional connectivity.

Nine 8 C57Bl6 mice (9 ± 0.5 months) were utilised for this dataset. Each animal was scanned twice on a 9.4T Bruker Magnetic Resonance Imaging (MRI) scanner using a cryogenically cooled coil with 0.1 mg/kg body weight/h (low) or 0.3 mg/kg body weight/h (high) medetomidine doses; 0.5% isoflurane was used in conjunction with both doses. The scans were one week apart, and the first session's dose was decided randomly. In each session, the animal had a pre-stimulation resting-state functional Magnetic Resonance Imaging (rs-fMRI) scan followed by 10 min where mild, constant electrical stimulation to the forepaw was applied, and a post-stimulation rs-fMRI scan. Each fMRI scan lasted 10 min, and there was 5 min break between fMRI scans. The dataset included, for each animal, a pair of forward-phase and reverse-phase gradient echo Echo-Planar-Imaging (EPI) images for EPI distortion correction purpose and three (unprocessed) functional MRI images acquired using the same EPI sequence: prior, during, and post-stimulation. The MRI data was saved in compressed NIFTI format converted from Bruker DICOMs. The dataset also included the pre-processed functional MRI images, with the following pre-processing steps: slice-timing correction, temporal despiking, motion correction, distortion correction, band-pass filtration at 0.01-0.2 Hz, and spatial normalisation. This dataset adds to the publicly available collection of resting-state functional MRI in the mice and facilitates reproducibility and validation of functional imaging and its analysis.

Towards data-driven group inferences of resting-state fMRI data in rodents: Comparison of group ICA, GIG-ICA, and IVA-GL.

BACKGROUND: A trend in the development of resting-state fMRI (rsfMRI) data analysis is the drive towards more data-driven methods. Group Independent Component Analysis (GICA) is a well-proven data-driven method for performing fMRI group analysis, though not without issues, especially the back-reconstruction from group-level independent components to individual-level components. Group information-guided ICA (GIG-ICA) and Independent Vector Analysis (IVA) are recent extensions of GICA that were shown to outperform GICA in the identification of unique rsfMRI biomarkers in psychiatric conditions. NEW METHOD: In this work, GICA, GIG-ICA, and IVA-GL analysis methods were applied to rsfMRI data acquired from 9 mice under different doses of medetomidine (0.1 - 0.3 mg/kg/h) in the before and after forepaw stimulation, and their performance was compared to determine whether GIG-ICA and IVA-GL outperform GICA in identifying robust and reliable resting-state networks in the rodent brain. RESULTS: Our results showed IVA-GL method had certain desirable performance characteristics over the other two methods under minimal data pre-processing and data-driven assumptions in application to analysis of mouse resting-state functional MRI. COMPARISON WITH EXISTING METHODS: IVA-GL provides better stability towards detecting group differences at different model order assumptions and performed better at separating data well-defined and functionally reasonable components in mouse resting-state fMRI. At higher model order and more likely functional component assumptions, GIG-ICA and IVA-GL were found to have greater sensitivity at detecting functional connectivity changes due to physiological challenges compared to GICA. CONCLUSIONS: This study indicates that IVA-GL yields better detection of resting-state networks in the rodent brain compared to other ICA methods and a promising data-driven analysis method for rodent rsfMRI.

Association of sub-acute changes in plasma amino acid levels with long-term brain pathologies in a rat model of moderate-severe traumatic brain injury.

Introduction: Traumatic brain injury (TBI) induces a cascade of cellular alterations that are responsible for evolving secondary brain injuries. Changes in brain structure and function after TBI may occur in concert with dysbiosis and altered amino acid fermentation in the gut. Therefore, we hypothesized that subacute plasma amino acid levels could predict long-term microstructural outcomes as quantified using neurite orientation dispersion and density imaging (NODDI). Methods: Fourteen 8-10-week-old male rats were randomly assigned either to sham (n = 6) or a single moderate-severe TBI (n = 8) procedure targeting the primary somatosensory cortex. Venous blood samples were collected at days one, three, seven, and 60 post-procedure and NODDI imaging were carried out at day 60. Principal Component Regression analysis was used to identify time dependent plasma amino acid concentrations after in the subacute phase post-injury that predicted NODDI metric outcomes at day 60. Results: The TBI group had significantly increased plasma levels of glutamine, arginine, alanine, proline, tyrosine, valine, isoleucine, leucine, and phenylalanine at days three-seven post-injury. Higher levels of several neuroprotective amino acids, especially the branched-chain amino acids (valine, isoleucine, leucine) and phenylalanine, as well as serine, arginine, and asparagine at days three-seven post-injury were also associated with lower isotropic diffusion volume fraction measures in the ventricles and thus lesser ventricular dilation at day 60. Discussion: In the first such study, we examined the relationship between the long-term post-TBI microstructural outcomes across whole brain and the subacute changes in plasma amino acid concentrations. At days three to seven post-injury, we observed that increased plasma levels of several amino acids, particularly the branched-chain amino acids and phenylalanine, were associated with lesser degrees of ventriculomegaly and hydrocephalus TBI neuropathology at day 60 post-injury. The results imply that altered amino acid fermentation in the gut may mediate neuroprotection in the aftermath of TBI.

Multi-modal magnetic resonance imaging in a mouse model of concussion.

This data collection contains Magnetic Resonance Imaging (MRI) data, including structural, diffusion, stimulus-evoked, and resting-state functional MRI and behavioural assessment results, including acute post-impact Loss-of-Righting Reflex time and acute, subacute, and longer-term Neural Severity Score, and Open Field Behaviour obtained from a mouse model of concussion. Four cohorts with 43 3-4 months old male mice in total were used: Sham (n = 14, n = 6 day 2, n = 3 day 7, n = 5 day 14), concussion day 2 (CON 2; n = 9), concussion day 7 (CON 7; n = 10), concussion day 14 (CON 14; n = 10). The data collection contains the aforementioned MRI data in compressed NIFTI format, data sheets on animal's backgrounds and behavioural outcomes and is made publicly available from a data repository. The available data are intended to facility cross-study comparisons, meta-analysis, and science reproducibility.

Diffusion Tensor Imaging Detects Acute Pathology-Specific Changes in the P301L Tauopathy Mouse Model Following Traumatic Brain Injury.

Traumatic brain injury (TBI) has been linked with tauopathy. However, imaging methods that can non-invasively detect tau-protein abnormalities following TBI need further investigation. This study aimed to investigate the potential of diffusion tensor imaging (DTI) to detect tauopathy following TBI in P301L mutant-tau-transgenic-pR5-mice. A total of 24 9-month-old pR5 mice were randomly assigned to sham and TBI groups. Controlled cortical injuries/craniotomies were performed for TBI/sham groups followed by DTI data acquisition on days 1 and 7 post-injury. DTI data were analyzed by using voxelwise analysis and track-based spatial statistics for gray matter and white matter. Further, immunohistochemistry was performed for total-tau and phosphorylated-tau, astrocytes, and microglia. To detect the association of DTI with these pathological markers, a correlation analysis was performed between DTI and histology findings. At day 1 post-TBI, DTI revealed a widespread reduction in fractional anisotropy (FA) and axial diffusivity (AxD) in the TBI group compared to shams. On day 7, further reduction in FA, AxD, and mean diffusivity and increased radial diffusivity were observed. FA was significantly increased in the amygdala and cortex. Correlation results showed that in the ipsilateral hemisphere FA reduction was associated with increased phosphorylated-tau and glial-immunoreactivity, whereas in the contralateral regions, the FA increase was associated with increased immunostaining for astrocytes. This study is the first to exploit DTI to investigate the effect of TBI in tau-transgenic mice. We show that alterations in the DTI signal were associated with glial activity following TBI and would most likely reflect changes that co-occur with/without phosphorylated-tau. In addition, FA may be a promising measure to identify discrete pathological processes such as increased astroglia activation, tau-hyperphosphorylation or both in the brain following TBI.

A roadmap of brain recovery in a mouse model of concussion: insights from neuroimaging.

Concussion or mild traumatic brain injury is the most common form of traumatic brain injury with potentially long-term consequences. Current objective diagnosis and treatment options are limited to clinical assessment, cognitive rest, and symptom management, which raises the real danger of concussed patients being released back into activities where subsequent and cumulative injuries may cause disproportionate damages. This study conducted a cross-sectional multi-modal examination investigation of the temporal changes in behavioural and brain changes in a mouse model of concussion using magnetic resonance imaging. Sham and concussed mice were assessed at day 2, day 7, and day 14 post-sham or injury procedures following a single concussion event for motor deficits, psychological symptoms with open field assessment, T2-weighted structural imaging, diffusion tensor imaging (DTI), neurite orientation density dispersion imaging (NODDI), stimulus-evoked and resting-state functional magnetic resonance imaging (fMRI). Overall, a mismatch in the temporal onsets and durations of the behavioural symptoms and structural/functional changes in the brain was seen. Deficits in behaviour persisted until day 7 post-concussion but recovered at day 14 post-concussion. DTI and NODDI changes were most extensive at day 7 and persisted in some regions at day 14 post-concussion. A persistent increase in connectivity was seen at day 2 and day 14 on rsfMRI. Stimulus-invoked fMRI detected increased cortical activation at day 7 and 14 post-concussion. Our results demonstrate the capabilities of advanced MRI in detecting the effects of a single concussive impact in the brain, and highlight a mismatch in the onset and temporal evolution of behaviour, structure, and function after a concussion. These results have significant translational impact in developing methods for the detection of human concussion and the time course of brain recovery.

Ultra-High-Field Diffusion Tensor Imaging Identifies Discrete Patterns of Concussive Injury in the Rodent Brain.

Although concussions can result in persistent neurological post-concussion symptoms, they are typically invisible on routine magnetic resonance imaging (MRI) scans. Our study aimed to investigate the use of ultra-high-field diffusion tensor imaging (UHF-DTI) in discerning severity-dependent microstructural changes in the mouse brain following a concussion. Twenty-three C57BL/6 mice were randomly allocated into three groups: the low concussive (LC, n = 9) injury group, the high concussive (HC, n = 6) injury group, and the sham control (SC, n = 7) group. Mice were perfused on day 2 post-injury, and the brains were scanned on a 16.4T MRI scanner with UHF-DTI and neurite orientation dispersion imaging (NODDI). Finite element analysis (FEA) was performed to determine the pattern and extent of the physical impact on the brain tissue. MRI findings were correlated with histopathological analysis in a subset of mice. In the LC group, increased fractional anisotropy (FA) and decreased orientation dispersion index (ODI) but limited neurite density index (NDI) changes were found in the gray matter, and minimal changes to white matter (WM) were observed. The HC group presented increased mean diffusivity (MD), decreased NDI, and decreased ODI in the WM and gray matter (GM); decreased FA was also found in a small area of the WM. WM changes were associated with WM degeneration and neuroinflammation. FEA showed varying region-dependent degrees of stress, in line with the different imaging findings. This study provides evidence that UHF-DTI combined with NODDI can detect concussions of variable intensities. This has significant implications for the diagnosis of concussion in humans.

Combined Diffusion Tensor Imaging and Quantitative Susceptibility Mapping Discern Discrete Facets of White Matter Pathology Post-injury in the Rodent Brain.

Early loss of white matter microstructure integrity is a significant cause of long-term neurological disorders following traumatic brain injury (TBI). White matter abnormalities typically involve axonal loss and demyelination. In-vivo imaging tools to detect and differentiate such microstructural changes are not well-explored. This work utilizes the conjoint potential offered by advanced magnetic resonance imaging techniques, including quantitative susceptibility mapping (QSM) and diffusion tensor imaging (DTI), to discern the underlying white matter pathology at specific time points (5 h, 1, 3, 7, 14, and 30 days) post-injury in the controlled cortical impact mouse model. A total of 42 animals were randomized into six TBI groups (n = 6 per group) and one sham group (n = 6). Histopathology was performed to validate in-vivo findings by performing myelin basic protein (MBP) and glial fibrillary acidic protein (GFAP) immunostaining for the assessment of changes to myelin and astrocytes. After 5 h of injury radial diffusivity (RD) was increased in white matter without a significant change in axial diffusivity (AxD) and susceptibility values. After 1 day post-injury RD was decreased. AxD and susceptibility changes were seen after 3 days post-injury. Susceptibility increases in white matter were observed in both ipsilateral and contralateral regions and persisted for 30 days. In histology, an increase in GFAP immunoreactivity was observed after 3 days post-injury and remained high for 30 days in both ipsilateral and contralateral white matter regions. A loss in MBP signal was noted after 3 days post-injury that continued up to 30 days. In conclusion, these results demonstrate the complementary ability of DTI and QSM in discerning the micro-pathological processes triggered following TBI. While DTI revealed acute and focal white matter changes, QSM mirrored the temporal demyelination in the white matter tracts and diffuse regions at the chronic state.

MEMRI detects neuronal activity and connectivity in hypothalamic neural circuit responding to leptin.

Hypothalamus plays the central role in regulating energy homeostasis. To understand the hypothalamic neurocircuit in responding to leptin, Manganese-Enhanced MRI (MEMRI) was applied. Highly elevated signal could be mapped in major nuclei of the leptin signaling pathway, including the arcuate nucleus (ARC), paraventricular nucleus (PVN), ventromedial hypothalamus (VMH) and dorsomedial hypothalamus (DMH) in fasted mice and the enhancement was reduced by leptin administration. However, whether changes in MEMRI signal reflect Ca2+ channel activity, neuronal activation or connectivity in the leptin signaling pathway are not clear. By blocking L-type Ca2+ channels, the signal enhancement in the ARC, PVN and DMH, but not VMH, was reduced. By disrupting microtubule with colchicine, signal enhancement of the secondary neural areas like DMH and PVN was delayed which is consistent with the known projection density from ARC into these regions. Finally, strong correlation between c-fos expression and MEMRI signal increase rate was observed in the ARC, VMH and DMH. Together, we provide experimental evidence that MEMRI signal could represent activity and connectivity in certain hypothalamic nuclei and hence may be used for mapping activated neuronal pathway in vivo. This understanding would facilitate the application of MEMRI for evaluation of hypothalamic dysfunction in metabolic diseases.

Laquinimod rescues striatal, cortical and white matter pathology and results in modest behavioural improvements in the YAC128 model of Huntington disease.

Increasing evidence supports a role for abnormal immune activation and inflammatory responses in Huntington disease (HD). In this study, we evaluated the therapeutic potential of laquinimod (1 and 10 mg/kg), a novel immunomodulatory agent shown to be protective in a number of neuroinflammatory conditions, in the YAC128 mouse model of HD. Treatment with laquinimod for 6 months rescued atrophy in the striatum, in certain cortical regions, and in the corpus callosum of YAC128 HD mice. Diffusion tensor imaging showed that white matter microstructural abnormalities in the posterior corpus callosum were improved following treatment with low dose (1 mg/kg) laquinimod, and were paralleled by reduced levels of interleukin-6 in the periphery of YAC128 HD mice. Functionally, treatment with laquinimod (1 and 10 mg/kg) led to modest improvements in motor function and in depressive-like behaviour. Taken together, these results suggest that laquinimod may improve some features of pathology in HD, and provides support for the role of immune activation in the pathogenesis of HD.

Resting state functional connectivity data supports detection of cognition in the rodent brain.

Learning is a process which induces plastic changes in the synapses and connections across different regions of the brain. It is hypothesized that these new connections can be tracked with resting state functional connectivity MRI. While most of the evidence of learning-induced plasticity arises from previous human data, data from sedated rats that had undergone training for either 1 day or 5 days in a Morris Watermaze is presented. Seed points were taken from the somatosensory and visual cortices, and the hippocampal CA3 to detect connectivity changes. The data demonstrates that 5-day trained rats showed increased correlations between the hippocampal CA3 and thalamus, septum and cingulate cortex, compared to swim control or naïve animals. Seven days after the training, persistent but reorganized networks toward the cortex were observed. Data from the 1-day trained rats, on the contrary, showed connectivity similar to the swim control and less persistent. The connectivity in several regions was highly correlated with the behavioral performance in these animals. The data demonstrates that longitudinal changes following learning-induced plasticity can be detected and tracked with resting state connectivity.

Structural and molecular myelination deficits occur prior to neuronal loss in the YAC128 and BACHD models of Huntington disease.

White matter (WM) atrophy is a significant feature of Huntington disease (HD), although its aetiology and early pathological manifestations remain poorly defined. In this study, we aimed to characterize WM-related features in the transgenic YAC128 and BACHD models of HD. Using diffusion tensor magnetic resonance imaging (DT-MRI), we demonstrate that microstructural WM abnormalities occur from an early age in YAC128 mice. Similarly, electron microscopy analysis of myelinated fibres of the corpus callosum indicated that myelin sheaths are thinner in YAC128 mice as early as 1.5 months of age, well before any neuronal loss can be detected. Transcript levels of myelin-related genes in striatal and cortical tissues were significantly lower in YAC128 mice from 2 weeks of age, and these findings were replicated in differentiated primary oligodendrocytes from YAC128 mice, suggesting a possible mechanistic explanation for the observed structural deficits. Concordant with these observations, we demonstrate reduced expression of myelin-related genes at 3 months of age and WM microstructural abnormalities using DT-MRI at 12 months of age in the BACHD rats. These findings indicate that WM deficits in HD are an early phenotype associated with cell-intrinsic effects of mutant huntingtin on myelin-related transcripts in oligodendrocytes, and raise the possibility that WM abnormalities may be an early contributing factor to the pathogenesis of HD.

Functional connectivity MRI tracks memory networks after maze learning in rodents.

Learning and memory employs a series of cognitive processes which require the coordination of multiple areas across the brain. However in vivo imaging of cognitive function has been challenging in rodents. Since these processes involve synchronous firing among different brain loci we explored functional connectivity imaging with resting-state fMRI. After 5-day training on a hidden platform watermaze task, notable signal correlations were seen between the hippocampal CA3 and other structures, including thalamus, septum and cingulate cortex, compared to swim control or naïve animals. The connectivity sustained 7 days after training and was reorganized toward the cortex, consistent with views of memory trace distribution leading to memory consolidation. These data demonstrates that, after a cognitive task, altered functional connectivity can be detected in the subsequently sedated rodent using in vivo imaging. This approach paves the way to understand dynamics of area-dependent distribution processes in animal models of cognition.