Recovering high-quality fiber orientation distributions from a reduced number of diffusion-weighted images using a model-driven deep learning architecture.

PURPOSE: The aim of this study was to develop a model-based deep learning architecture to accurately reconstruct fiber orientation distributions (FODs) from a reduced number of diffusion-weighted images (DWIs), facilitating accurate analysis with reduced acquisition times. METHODS: Our proposed architecture, Spherical Deconvolution Network (SDNet), performed FOD reconstruction by mapping 30 DWIs to fully sampled FODs, which have been fit to 288 DWIs. SDNet included DWI-consistency blocks within the network architecture, and a fixel-classification penalty within the loss function. SDNet was trained on a subset of the Human Connectome Project, and its performance compared with FOD-Net, and multishell multitissue constrained spherical deconvolution. RESULTS: SDNet achieved the strongest results with respect to angular correlation coefficient and sum of squared errors. When the impact of the fixel-classification penalty was increased, we observed an improvement in performance metrics reliant on segmenting the FODs into the correct number of fixels. CONCLUSION: Inclusion of DWI-consistency blocks improved reconstruction performance, and the fixel-classification penalty term offered increased control over the angular separation of fixels in the reconstructed FODs.

Hybrid adiabatic pulse with asymmetry (HAPY): An asymmetric adiabatic pulse with an application in pulsed arterial spin labeling at 7T.

PURPOSE: A new class of asymmetric adiabatic radiofrequency (RF) pulses, Hybrid Adiabatic Pulse with asYmmetry (HAPY), is designed to be used as the labeling pulse for Pulsed Arterial Spin labeling (PASL) at 7T to reduce overall specific absorption rate (SAR) while maintaining high labeling efficiency with B 0 $$ {\mathrm{B}}_0 $$ and B 1 + $$ {\mathrm{B}}_1^{+} $$ inhomogeneities. METHODS: Realistic Δ B 0 $$ \Delta {\mathrm{B}}_0 $$ and B 1 + $$ {\mathrm{B}}_1^{+} $$ distributions were extracted from multiple in vivo scans. The proposed class of asymmetric pulses was parameterized and optimized considering these conditions. Simulation and phantoms experiments were performed to compare the optimized pulses with HS-3, GOIA, and trFOCI pulses. In vivo experiments were conducted to demonstrate the application of HAPY in PICORE PASL at 7T, compared with the GOIA and trFOCI pulses. RESULTS: HAPYs with different amounts of pulse energy reduction are obtained by the proposed optimization framework. Both simulation and phantom experiments demonstrate that HAPY achieves high labeling efficiency and high selectivity along the critical side despite B 0 $$ {\mathrm{B}}_0 $$ off-resonance and low B 1 + $$ {\mathrm{B}}_1^{+} $$ amplitude. In vivo experiments reveal that HAPY is able to generate robust perfusion signal with less overall SAR or shorter pulse repetition time, compared to the GOIA and trFOCI pulses. CONCLUSION: The HAPY class of pulses, obtained via systematic optimization tailored to the application of PASL at 7T, reduces power deposition without affecting labeling efficiency, which provides a prospect of further exploiting the benefits of ultra-high field in ASL.

The "Spin-3/2 Bloch Equation": System matrix formalism of excitation, relaxation, and off-resonance effects in biological tissue.

PURPOSE: This work proposes "Spin-3/2 Bloch Equation" (SBE), a consolidated formalism for spin-3/2 dynamics in biological environments. The formalism encapsulates excitation, relaxation, and off-resonance with accessible matrix representation for a straightforward implementation with high computational efficiency. THEORY: The SBE is derived using spherical tensor operators to encapsulate the spin-3/2 dynamics in biological systems in a single system matrix, a formalism akin to the well-known Bloch Equations (BE). METHODS: Using the proposed SBE, simulations of three classical 23 Na pulse sequences were performed to demonstrate the versatility and applicability of the model, returning the evolution of the 23 Na spin system during these experiments: soft rectangular and adiabatic inversion recovery (IR) and triple-quantum filtering. IR simulations were compared with two existing spin-3/2 simulators and the adaptive BE as a first-order approximation. RESULTS: The proposed SBE is straightforward to implement and facilitates accurate and fast simulations of the underlying higher order coherence in sodium experiments of biological tissues. SBE simulations and comparison spin-3/2 simulators outperform the BE simulations as expected, with the SBE offering superior computational efficiency achieved by the single system matrix formalism. CONCLUSION: The proposed SBE enables comprehensive and accurate simulations for spin-3/2 systems in biological tissue. With a one-line call to an ordinary differential equation solver, it offers a computationally efficient and accessible method for use in 23 Na pulse sequence design.

A gyrification analysis approach based on Laplace Beltrami eigenfunction level sets.

An accurate measure of the complexity of patterns of cortical folding or gyrification is necessary for understanding normal brain development and neurodevelopmental disorders. Conventional gyrification indices (GIs) are calculated based on surface curvature (curvature-based GI) or an outer hull surface of the cortex (outer surface-based GI). The latter is dependent on the definition of the outer hull surface and a corresponding function between surfaces. In the present study, we propose the Laplace Beltrami-based gyrification index (LB-GI). This is a new curvature-based local GI computed using the first three Laplace Beltrami eigenfunction level sets. As with outer surface-based GI methods, this method is based on the hypothesis that gyrification stems from a flat surface during development. However, instead of quantifying gyrification with reference to corresponding points on an outer hull surface, LB-GI quantifies the gyrification at each point on the cortical surface with reference to their surrounding gyral points, overcoming several shortcomings of existing methods. The LB-GI was applied to investigate the cortical maturation profile of the human brain from preschool to early adulthood using the PING database. The results revealed more detail in patterns of cortical folding than conventional curvature-based methods, especially on frontal and posterior tips of the brain, such as the frontal pole, lateral occipital, lateral cuneus, and lingual. Negative associations of cortical folding with age were observed at cortical regions, including bilateral lingual, lateral occipital, precentral gyrus, postcentral gyrus, and superior frontal gyrus. The results also indicated positive significant associations between age and the LB-GI of bilateral insula, the medial orbitofrontal, frontal pole and rostral anterior cingulate regions. It is anticipated that the LB-GI will be advantageous in providing further insights in the understanding of brain development and degeneration in large clinical neuroimaging studies.

QSMART: Quantitative susceptibility mapping artifact reduction technique.

PURPOSE: Quantitative susceptibility mapping (QSM) is a novel MR technique that allows mapping of tissue susceptibility values from MR phase images. QSM is an ill-conditioned inverse problem, and although several methods have been proposed in the field, in the presence of a wide range of susceptibility sources, streaking artifacts appear around high susceptibility regions and contaminate the whole QSM map. QSMART is a post-processing pipeline that uses two-stage parallel inversion to reduce the streaking artifacts and remove banding artifact at the cortical surface and around the vasculature. METHOD: Tissue and vein susceptibility values were separately estimated by generating a mask of vasculature driven from the magnitude data using a Frangi filter. Spatially dependent filtering was used for the background field removal step and the two susceptibility estimates were combined in the final QSM map. QSMART was compared to RESHARP/iLSQR and V-SHARP/iLSQR inversion in a numerical phantom, 7T in vivo single and multiple-orientation scans, 9.4T ex vivo mouse data, and 4.7T in vivo rat brain with induced focal ischemia. RESULTS: Spatially dependent filtering showed better suppression of phase artifacts near cortex compared to RESHARP and V-SHARP, while preserving voxels located within regions of interest without brain edge erosion. QSMART showed successful reduction of streaking artifacts as well as improved contrast between different brain tissues compared to the QSM maps obtained by RESHARP/iLSQR and V-SHARP/iLSQR. CONCLUSION: QSMART can reduce QSM artifacts to enable more robust estimation of susceptibility values in vivo and ex vivo.

Microstructural correlates of 23Na relaxation in human brain at 7 Tesla.

23Na provides the second strongest MR-observable signal in biological tissue and exhibits bi-exponential T2∗ relaxation in micro-environments such as the brain. There is significant interest in developing 23Na biomarkers for neurological diseases that are associated with sodium channel dysfunction such as multiple sclerosis and epilepsy. We have previously reported methods for acquisition of multi-echo sodium MRI and continuous distribution modelling of sodium relaxation properties as surrogate markers of brain microstructure. This study aimed to compare 23Na T2∗ relaxation times to more established measures of tissue microstructure derived from advanced diffusion MRI at 7 â€‹T. Six healthy volunteers were scanned using a 3D multi-echo radial ultra-short TE sequence using a dual-tuned 1H/23Na birdcage coil, and a high-resolution multi-shell, high angular resolution diffusion imaging sequence using a 32-channel 1H receive coil. 23Na T2∗ relaxation parameters [mean T2∗ (T2∗mean) and fast relaxation fraction (T2∗ff)] were calculated from a voxel-wise continuous gamma distribution signal model. White matter (restricted anisotropic diffusion) and grey matter (restricted isotropic diffusion) density were calculated from multi-shell multi-tissue constrained spherical deconvolution. Sodium parameters were compared with white and grey matter diffusion properties. Sodium T2∗mean and T2∗ff showed little variation across a range of white matter axonal fibre and grey matter densities. We conclude that sodium T2∗ relaxation parameters are not greatly influenced by relative differences in intra- and extracellular partial volumes. We suggest that care be taken when interpreting sodium relaxation changes in terms of tissue microstructure in healthy tissue.

Predicting individual improvement in schizophrenia symptom severity at 1-year follow-up: Comparison of connectomic, structural, and clinical predictors.

In a machine learning setting, this study aims to compare the prognostic utility of connectomic, brain structural, and clinical/demographic predictors of individual change in symptom severity in individuals with schizophrenia. Symptom severity at baseline and 1-year follow-up was assessed in 30 individuals with a schizophrenia-spectrum disorder using the Brief Psychiatric Rating Scale. Structural and functional neuroimaging was acquired in all individuals at baseline. Machine learning classifiers were trained to predict whether individuals improved or worsened with respect to positive, negative, and overall symptom severity. Classifiers were trained using various combinations of predictors, including regional cortical thickness and gray matter volume, static and dynamic resting-state connectivity, and/or baseline clinical and demographic variables. Relative change in overall symptom severity between baseline and 1-year follow-up varied markedly among individuals (interquartile range: 55%). Dynamic resting-state connectivity measured within the default-mode network was the most accurate single predictor of change in positive (accuracy: 87%), negative (83%), and overall symptom severity (77%) at follow-up. Incorporating predictors based on regional cortical thickness, gray matter volume, and baseline clinical variables did not markedly improve prediction accuracy and the prognostic utility of these predictors in isolation was moderate (<70%). Worsening negative symptoms at 1-year follow-up were predicted by hyper-connectivity and hypo-dynamism within the default-mode network at baseline assessment, while hypo-connectivity and hyper-dynamism predicted worsening positive symptoms. Given the modest sample size investigated, we recommend giving precedence to the relative ranking of the predictors investigated in this study, rather than the prediction accuracy estimates.

Compressed sensing effects on quantitative analysis of undersampled human brain sodium MRI.

PURPOSE: The clinical application of sodium MRI is hampered due to relatively low image quality and associated long acquisition times. Compressed sensing (CS) aims at a reduction of measurement time, but has been found to encompass quantitative estimation bias when used in low SNR x-Nuclei imaging. This work analyses CS in quantitative human brain sodium MRI from undersampled acquisitions and provides recommendations for tissue sodium concentration (TSC) estimation. METHODS: CS reconstructions from 3D radial acquisitions of 5 healthy volunteers were investigated over varying undersampling factors (USFs) and CS penalty weights on different sparsity domains, Wavelet, Discrete Cosine Transform (DCT), and Identity. Resulting images were compared with highly sampled and undersampled NUFFT-based images and evaluated for image quality (i.e. structural similarity), image intensity bias, and its effect on TSC estimates in gray and white matter. RESULTS: Wavelet-based CS reconstructions show highest image quality with stable TSC estimates for most USFs. Up to an USF of 4, images showed good structural detail. DCT and Identity-based CS enable good image quality, however show a bias in TSC with a reduction in estimates across USFs. CONCLUSIONS: The image intensity bias is lowest in Wavelet-based reconstructions and enables an up to fourfold acquisition speed up while maintaining good structural detail. The associated acquisition time reduction can facilitate a translation of sodium MRI into clinical routine.

Brain network dynamics in schizophrenia: Reduced dynamism of the default mode network.

Complex human behavior emerges from dynamic patterns of neural activity that transiently synchronize between distributed brain networks. This study aims to model the dynamics of neural activity in individuals with schizophrenia and to investigate whether the attributes of these dynamics associate with the disorder's behavioral and cognitive deficits. A hidden Markov model (HMM) was inferred from resting-state functional magnetic resonance imaging (fMRI) data that was temporally concatenated across individuals with schizophrenia (n = 41) and healthy comparison individuals (n = 41). Under the HMM, fluctuations in fMRI activity within 14 canonical resting-state networks were described using a repertoire of 12 brain states. The proportion of time spent in each state and the mean length of visits to each state were compared between groups, and canonical correlation analysis was used to test for associations between these state descriptors and symptom severity. Individuals with schizophrenia activated default mode and executive networks for a significantly shorter proportion of the 8-min acquisition than healthy comparison individuals. While the default mode was activated less frequently in schizophrenia, the duration of each activation was on average 4-5 s longer than the comparison group. Severity of positive symptoms was associated with a longer proportion of time spent in states characterized by inactive default mode and executive networks, together with heightened activity in sensory networks. Furthermore, classifiers trained on the state descriptors predicted individual diagnostic status with an accuracy of 76-85%.

A continuum of T2* components: Flexible fast fraction mapping in sodium MRI.

PURPOSE: Parameter mapping in sodium MRI data is challenging due to inherently low SNR and spatial resolution, prompting the need to employ robust models and estimation techniques. This work aims to develop a continuum model of sodium T2* -decay to overcome the limitations of the commonly employed bi-exponential models. Estimates of mean T2* -decay and fast component fraction in tissue are emergent from the inferred continuum model. METHODS: A closed-form continuum model was derived assuming a gamma distribution of T2* components. Sodium MRI was performed on four healthy human subjects and a phantom consisting of closely packed vials filled with an aqueous solution of varying sodium and agarose concentrations. The continuum model was applied to the phantom and in vivo human multi-echo 7T data. Parameter maps by voxelwise model-fitting were obtained. RESULTS: The continuum model demonstrated comparable estimation performance to the bi-exponential model. The parameter maps provided improved contrast between tissue structures. The fast component fraction, an indicator of the heterogeneity of localised sodium motion regimes in tissue, was zero in CSF and high in WM structures. CONCLUSIONS: The continuum distribution model provides high quality, high contrast parameter maps, and informative voxelwise estimates of the relative weighting between fast and slow decay components.

Zero-gradient-excitation ramped hybrid encoding (zGRF -RHE) sodium MRI.

PURPOSE: Fast bi-exponential transverse signal decay compounds sodium image quality. This work aims at enhancing image characteristics using a special case of ramped hybrid encoding (RHE). Zero-gradient-excitation (zGRF )-RHE provides (1) gradient-free excitation for high flip angle, artifact-free excitation profiles and (2) gradient ramping during dead-time for the optimization of encoding time (tenc ) to reduce T2* signal decay influence during acquisition. METHODS: Radial zGRF -RHE and standard radial UTE were investigated over a range of receiver bandwidths in simulations, phantom and in vivo brain experiments. Central k-space in zGRF -RHE was acquired through single point measurements at the minimum achievable TE. T2* blurring artifacts and image SNR and CNR were assessed. RESULTS: zGRF -RHE enabled 90° flip angle artifact-free excitation, whereas gradient pre-ramping provided greater tenc efficiency for any readout bandwidths. Experiments confirmed simulation results, revealing sharper edge characteristics particularly at short readout durations (TRO ). Significant SNR improvements of up to 4.8% were observed for longer TRO . CONCLUSION: zGRF -RHE allows for artifact-free high flip angle excitation with time-efficient encoding improving on image characteristics. This hybrid encoding concept with gradient pre-ramping is trajectory independent and can be introduced in any center-out UTE trajectory design.

3D-multi-echo radial imaging of 23 Na (3D-MERINA) for time-efficient multi-parameter tissue compartment mapping.

PURPOSE: This work demonstrates a 3D radial multi-echo acquisition scheme for time-efficient sodium (23 Na) MR-signal acquisition and analysis. Echo reconstructions were used to produce signal-to-noise ratio (SNR)-enhanced 23 Na-images and parameter maps of the biexponential observed transverse relaxation time ( T2*) decay. METHODS: A custom-built sequence for radial multi-echo acquisition was proposed for acquisition of a series of 3D volumetric 23 Na-images. Measurements acquired in a phantom and in vivo human brains were analyzed for SNR enhancement and multi-component T2* estimation. RESULTS: Rapid gradient refocused imaging acquired 38 echoes within a repetition time of 160 ms. Signal averaging of multi-echo time (TE) measurements showed an average brain tissue SNR enhancement of 34% compared to single-TE images across subjects. Phantom and in vivo measurements detected distinguishable signal decay characteristics for fluid and solid media. Mapping results were investigated in phantom and in vivo experiments for sequence timing optimization and signal decay analysis. The T2* mapping results were consistent with previously reported values and facilitated fluid-signal discrimination. CONCLUSION: The proposed method offers an efficient 23 Na-imaging scheme that extends existing 23 Na-MRI sequences by acquiring signal decay information with no increase in time or specific absorption rate. The resultant SNR-enhanced 23 Na-images and estimated T2* signal decay characteristics offer great potential for detailed investigation of tissue compartment characterization and clinical application. Magn Reson Med 79:1950-1961, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Traumatic Brain Injury Results in Cellular, Structural and Functional Changes Resembling Motor Neuron Disease.

Traumatic brain injury (TBI) has been suggested to increase the risk of amyotrophic lateral sclerosis (ALS). However, this link remains controversial and as such, here we performed experimental moderate TBI in rats and assessed for the presence of ALS-like pathological and functional abnormalities at both 1 and 12 weeks post-injury. Serial in-vivo magnetic resonance imaging (MRI) demonstrated that rats given a TBI had progressive atrophy of the motor cortices and degeneration of the corticospinal tracts compared with sham-injured rats. Immunofluorescence analyses revealed a progressive reduction in neurons, as well as increased phosphorylated transactive response DNA-binding protein 43 (TDP-43) and cytoplasmic TDP-43, in the motor cortex of rats given a TBI. Rats given a TBI also had fewer spinal cord motor neurons, increased expression of muscle atrophy markers, and altered muscle fiber contractile properties compared with sham-injured rats at 12 weeks, but not 1 week, post-injury. All of these changes occurred in the presence of persisting motor deficits. These findings resemble some of the pathological and functional abnormalities common in ALS and support the notion that TBI can result in a progressive neurodegenerative disease process pathologically bearing similarities to a motor neuron disease.

Differences in white matter structure between seizure prone (FAST) and seizure resistant (SLOW) rat strains.

Alterations in white matter integrity have been well documented in chronic epilepsy and during epileptogenesis. However, the relationship between white matter integrity and a predisposition towards epileptogenesis has been understudied. The FAST rat strain exhibit heightened susceptibility towards kindling epileptogenesis whereas SLOW rats are highly resistant. FAST rats also display behavioral phenotypes reminiscent of those observed in neurodevelopmental disorders that commonly comorbid with epilepsy. In this study, we aim to identify differences in white matter integrity that may contribute to a predisposition towards epileptogenesis and its associated comorbidities in 6month old FAST (n=10) and SLOW (n=10) male rats. Open field and water consumption tests were conducted to confirm the behavioral phenotype difference between FAST and SLOW rats followed by ex-vivo diffusion-weighted magnetic resonance imaging to identify differences in white matter integrity. Diffusion tensor imaging scalar values namely fractional anisotropy, mean diffusivity, axial diffusivity and radial diffusivity were compared in the anterior commissure, corpus callosum, external capsule, internal capsule, fimbria and optic tract. Electron microscopy was used to evaluate microstructural alterations in myelinated axons. Behavioral phenotyping confirmed higher activity levels (distance moved on days 2-4, p<0.001; number of rearings on days 2 and 4, p<0.05 at both days) and polydipsia (p<0.001) in FAST rats. Comparative analysis of diffusion tensor imaging scalars found a significant decrease in fractional anisotropy in the corpus callosum (p<0.05) of FAST versus SLOW rats. Using electron microscopy, alterations in myelinated axons including increased axon diameter (p<0.001) and reduced g-ratio (p<0.001) in the midline of the corpus callosum in 6month old FAST (n=3) versus SLOW (n=4) male rats. These findings suggest that differences in white matter integrity between FAST and SLOW rats could be a contributing factor to the differential seizure susceptibility and behavioral phenotypes observed in these strains.

Sodium selenate retards epileptogenesis in acquired epilepsy models reversing changes in protein phosphatase 2A and hyperphosphorylated tau.

There are no treatments in clinical practice known to mitigate the neurobiological processes that convert a healthy brain into an epileptic one, a phenomenon known as epileptogenesis. Downregulation of protein phosphatase 2A, a protein that causes the hyperphosphorylation of tau, is implicated in neurodegenerative diseases commonly associated with epilepsy, such as Alzheimer's disease and traumatic brain injury. Here we used the protein phosphatase 2A activator sodium selenate to investigate the role of protein phosphatase 2A in three different rat models of epileptogenesis: amygdala kindling, post-kainic acid status epilepticus, and post-traumatic epilepsy. Protein phosphatase 2A activity was decreased, and tau phosphorylation increased, in epileptogenic brain regions in all three models. Continuous sodium selenate treatment mitigated epileptogenesis and prevented the biochemical abnormalities, effects which persisted after drug withdrawal. Our studies indicate that limbic epileptogenesis is associated with downregulation of protein phosphatase 2A and the hyperphosphorylation of tau, and that targeting this mechanism with sodium selenate is a potential anti-epileptogenic therapy.

Sodium selenate reduces hyperphosphorylated tau and improves outcomes after traumatic brain injury.

Traumatic brain injury is a common and serious neurodegenerative condition that lacks a pharmaceutical intervention to improve long-term outcome. Hyperphosphorylated tau is implicated in some of the consequences of traumatic brain injury and is a potential pharmacological target. Protein phosphatase 2A is a heterotrimeric protein that regulates key signalling pathways, and protein phosphatase 2A heterotrimers consisting of the PR55 B-subunit represent the major tau phosphatase in the brain. Here we investigated whether traumatic brain injury in rats and humans would induce changes in protein phosphatase 2A and phosphorylated tau, and whether treatment with sodium selenate-a potent PR55 activator-would reduce phosphorylated tau and improve traumatic brain injury outcomes in rats. Ninety young adult male Long-Evans rats were administered either a fluid percussion injury or sham-injury. A proportion of rats were killed at 2, 24, and 72 h post-injury to assess acute changes in protein phosphatase 2A and tau. Other rats were given either sodium selenate or saline-vehicle treatment that was continuously administered via subcutaneous osmotic pump for 12 weeks. Serial magnetic resonance imaging was acquired prior to, and at 1, 4, and 12 weeks post-injury to assess evolving structural brain damage and axonal injury. Behavioural impairments were assessed at 12 weeks post-injury. The results showed that traumatic brain injury in rats acutely reduced PR55 expression and protein phosphatase 2A activity, and increased the expression of phosphorylated tau and the ratio of phosphorylated tau to total tau. Similar findings were seen in post-mortem brain samples from acute human traumatic brain injury patients, although many did not reach statistical significance. Continuous sodium selenate treatment for 12 weeks after sham or fluid percussion injury in rats increased protein phosphatase 2A activity and PR55 expression, and reduced the ratio of phosphorylated tau to total tau, attenuated brain damage, and improved behavioural outcomes in rats given a fluid percussion injury. Notably, total tau levels were decreased in rats 12 weeks after fluid percussion injury, and several other factors, including the use of anaesthetic, the length of recovery time, and that some brain injury and behavioural dysfunction still occurred in rats treated with sodium selenate must be considered in the interpretation of this study. However, taken together these data suggest protein phosphatase 2A and hyperphosphorylated tau may be involved in the neurodegenerative cascade of traumatic brain injury, and support the potential use of sodium selenate as a novel traumatic brain injury therapy.

Neuroanatomical differences in FAST and SLOW rat strains with differential vulnerability to kindling and behavioral comorbidities.

OBJECTIVE: The neurobiological factors underlying a predisposition towards developing epilepsy and its common behavioral comorbidities are poorly understood. FAST rats are a strain that has been selectively bred for enhanced vulnerability to kindling, while the SLOW strain has been bred to be resistant to kindling. FAST rats also exhibit behavioral traits reminiscent of those observed in neurodevelopmental disorders (autism spectrum disorder (ASD)/attention-deficit/hyperactivity disorder (ADHD)) commonly comorbid with epilepsy. In this study, we aimed to investigate neuroanatomical differences between these strains that may be associated with a differential vulnerability towards these interrelated disorders. METHODS: Ex vivo high-resolution magnetic resonance imaging on adult male FAST and SLOW rat brains was performed to identify morphological differences in regions of interest between the two strains. Behavioral examination using open-field, water consumption, and restraint tests was also conducted on a subgroup of these rats to document their differential ASD/ADHD-like behavior phenotype. Using optical stereological methods, the volume of cerebellar granule, white matter, and molecular layer and number of Purkinje cells were compared in a separate cohort of adult FAST and SLOW rats. RESULTS: Behavioral testing demonstrated hyperactivity, impulsivity, and polydipsia in FAST versus SLOW rats, consistent with an ASD/ADHD-like phenotype. Magnetic resonance imaging analysis identified brain structural differences in FAST compared with SLOW rats, including increased volume of the cerebrum, corpus callosum, third ventricle, and posterior inferior cerebellum, while decreased volume of the anterior cerebellar vermis. Stereological measurements on histological slices indicated significantly larger white matter layer volume, reduced number of Purkinje cells, and smaller molecular layer volume in the cerebellum in FAST versus SLOW rats. SIGNIFICANCE: These findings provide evidence of structural differences between the brains of FAST and SLOW rats that may be mechanistically related to their differential vulnerability to kindling and associated comorbid ASD/ADHD-like behaviors.

Fourier Tract Sampling (FouTS): A framework for improved inference of white matter tracts from diffusion MRI by explicitly modelling tract volume.

Diffusion MRI tractography algorithm development is increasingly moving towards global techniques to incorporate "downstream" information and conditional probabilities between neighbouring tracts. Such approaches also enable white matter to be represented more tangibly than the abstract lines generated by the most common approaches to fibre tracking. However, previously proposed algorithms still use fibre-like models of white matter corresponding to thin strands of white matter tracts rather than the tracts themselves, and therefore require many components for accurate representations, which leads to poorly constrained inverse problems. We propose a novel tract-based model of white matter, the 'Fourier tract', which is able to represent rich tract shapes with a relatively low number of parameters, and explicitly decouples the spatial extent of the modelled tract from its 'Apparent Connection Strength (ACS)'. The Fourier tract model is placed within a novel Bayesian framework, which relates the tract parameters directly to the observed signal, enabling a wide range of acquisition schemes to be used. The posterior distribution of the Bayesian framework is characterised via Markov-chain Monte-Carlo sampling to infer probable values of the ACS and spatial extent of the imaged white matter tracts, providing measures that can be directly applied to many research and clinical studies. The robustness of the proposed tractography algorithm is demonstrated on simulated basic tract configurations, such as curving, twisting, crossing and kissing tracts, and sections of more complex numerical phantoms. As an illustration of the approach in vivo, fibre tracking is performed on a central section of the brain in three subjects from 60 direction HARDI datasets.

Anti-lysophosphatidic acid antibodies improve traumatic brain injury outcomes.

BACKGROUND: Lysophosphatidic acid (LPA) is a bioactive phospholipid with a potentially causative role in neurotrauma. Blocking LPA signaling with the LPA-directed monoclonal antibody B3/Lpathomab is neuroprotective in the mouse spinal cord following injury. FINDINGS: Here we investigated the use of this agent in treatment of secondary brain damage consequent to traumatic brain injury (TBI). LPA was elevated in cerebrospinal fluid (CSF) of patients with TBI compared to controls. LPA levels were also elevated in a mouse controlled cortical impact (CCI) model of TBI and B3 significantly reduced lesion volume by both histological and MRI assessments. Diminished tissue damage coincided with lower brain IL-6 levels and improvement in functional outcomes. CONCLUSIONS: This study presents a novel therapeutic approach for the treatment of TBI by blocking extracellular LPA signaling to minimize secondary brain damage and neurological dysfunction.

Transfer learning for auto-segmentation of 17 organs-at-risk in the head and neck: Bridging the gap between institutional and public datasets.

BACKGROUND: Auto-segmentation of organs-at-risk (OARs) in the head and neck (HN) on computed tomography (CT) images is a time-consuming component of the radiation therapy pipeline that suffers from inter-observer variability. Deep learning (DL) has shown state-of-the-art results in CT auto-segmentation, with larger and more diverse datasets showing better segmentation performance. Institutional CT auto-segmentation datasets have been small historically (n < 50) due to the time required for manual curation of images and anatomical labels. Recently, large public CT auto-segmentation datasets (n > 1000 aggregated) have become available through online repositories such as The Cancer Imaging Archive. Transfer learning is a technique applied when training samples are scarce, but a large dataset from a closely related domain is available. PURPOSE: The purpose of this study was to investigate whether a large public dataset could be used in place of an institutional dataset (n > 500), or to augment performance via transfer learning, when building HN OAR auto-segmentation models for institutional use. METHODS: Auto-segmentation models were trained on a large public dataset (public models) and a smaller institutional dataset (institutional models). The public models were fine-tuned on the institutional dataset using transfer learning (transfer models). We assessed both public model generalizability and transfer model performance by comparison with institutional models. Additionally, the effect of institutional dataset size on both transfer and institutional models was investigated. All DL models used a high-resolution, two-stage architecture based on the popular 3D U-Net. Model performance was evaluated using five geometric measures: the dice similarity coefficient (DSC), surface DSC, 95th percentile Hausdorff distance, mean surface distance (MSD), and added path length. RESULTS: For a small subset of OARs (left/right optic nerve, spinal cord, left submandibular), the public models performed significantly better (p < 0.05) than, or showed no significant difference to, the institutional models under most of the metrics examined. For the remaining OARs, the public models were inferior to the institutional models, although performance differences were small (DSC ≤ 0.03, MSD < 0.5 mm) for seven OARs (brainstem, left/right lens, left/right parotid, mandible, right submandibular). The transfer models performed significantly better than the institutional models for seven OARs (brainstem, right lens, left/right optic nerve, left/right parotid, spinal cord) with a small margin of improvement (DSC ≤ 0.02, MSD < 0.4 mm). When numbers of institutional training samples were limited, public and transfer models outperformed the institutional models for most OARs (brainstem, left/right lens, left/right optic nerve, left/right parotid, spinal cord, and left/right submandibular). CONCLUSION: Training auto-segmentation models with public data alone was suitable for a small number of OARs. Using only public data incurred a small performance deficit for most other OARs, when compared with institutional data alone, but may be preferable over time-consuming curation of a large institutional dataset. When a large institutional dataset was available, transfer learning with models pretrained on a large public dataset provided a modest performance improvement for several OARs. When numbers of institutional samples were limited, using the public dataset alone, or as a pretrained model, was beneficial for most OARs.