Long term irreversible disability in multiple sclerosis (MS) is thought to be primarily driven by axonal degeneration. Axonal degeneration leads to degenerative atrophy, therefore early markers of axonal degeneration are required to predict clinical disability and treatment efficacy. Given that additional pathologies such as inflammation, demyelination and oedema are also present in MS, it is essential to develop axonal markers that are not confounded by these processes. The present study investigated a novel method for measuring axonal degeneration in MS based on high angular resolution diffusion magnetic resonance imaging. Unlike standard methods, this novel method involved advanced acquisition and modelling for improved axonal sensitivity and specificity. Recent work has developed analytical methods, two novel axonal markers, fibre density and cross-section, that can be estimated for each fibre direction in each voxel (termed a "fixel"). This technique, termed fixel-based analysis, thus simultaneously estimates axonal density and white matter atrophy from specific white matter tracts. Diffusion-weighted imaging datasets were acquired for 17 patients with a history of acute unilateral optic neuritis (35.3 ± 10.2 years, 11 females) and 14 healthy controls (32.7 ± 4.8 years, 8 females) on a 3 T scanner. Fibre density values were compared to standard diffusion tensor imaging parameters (fractional anisotropy and mean diffusivity) in lesions and normal appearing white matter. Group comparisons were performed for each fixel to assess putative differences in fibre density and fibre cross-section. Fibre density was observed to have a comparable sensitivity to fractional anisotropy for detecting white matter pathology in MS, but was not affected by crossing axonal fibres. Whole brain fixel-based analysis revealed significant reductions in fibre density and fibre cross-section in the inferior fronto-occipital fasciculus (including the optic radiations) of patients compared to controls. We interpret this result to indicate that this fixel-based approach is able to detect early loss of fibre density and cross-section in the optic radiations in MS patients with a history of optic neuritis. Fibre-specific markers of axonal degeneration should be investigated further for use in early stage therapeutic trials, or to monitor axonal injury in early stage MS.

Autologous bone grafts are considered the gold standard grafting material for the treatment of nonunion, but in very large bone defects, traditional autograft alone is insufficient to induce repair. Recombinant human bone morphogenetic protein 2 (rhBMP-2) can stimulate bone regeneration and enhance the healing efficacy of bone grafts. The delivery of rhBMP-2 may even enable engineered synthetic scaffolds to be used in place of autologous bone grafts for the treatment of critical size defects, eliminating risks associated with autologous tissue harvest. We here demonstrate that an osteoinductive scaffold, fabricated by combining a 3D printed rigid polymer/ceramic composite scaffold with an rhBMP-2-eluting collagen sponge can treat extremely large-scale segmental defects in a pilot feasibility study using a new sheep metatarsus fracture model stabilized with an intramedullary nail. Bone regeneration after 24 weeks was evaluated by micro-computed tomography, mechanical testing, and histological characterization. Load-bearing cortical bridging was achieved in all animals, with increased bone volume observed in sheep that received osteoinductive scaffolds compared to sheep that received an rhBMP-2-eluting collagen sponge alone.

Temporal lobe epilepsy, a common drug-resistant epilepsy in adults, is primarily a limbic network disorder associated with predominant unilateral hippocampal pathology. Structural MRI has provided an in vivo window into whole-brain grey matter structural alterations in temporal lobe epilepsy relative to controls, by either mapping (i) atypical inter-hemispheric asymmetry; or (ii) regional atrophy. However, similarities and differences of both atypical asymmetry and regional atrophy measures have not been systematically investigated. Here, we addressed this gap using the multisite ENIGMA-Epilepsy dataset comprising MRI brain morphological measures in 732 temporal lobe epilepsy patients and 1418 healthy controls. We compared spatial distributions of grey matter asymmetry and atrophy in temporal lobe epilepsy, contextualized their topographies relative to spatial gradients in cortical microstructure and functional connectivity calculated using 207 healthy controls obtained from Human Connectome Project and an independent dataset containing 23 temporal lobe epilepsy patients and 53 healthy controls and examined clinical associations using machine learning. We identified a marked divergence in the spatial distribution of atypical inter-hemispheric asymmetry and regional atrophy mapping. The former revealed a temporo-limbic disease signature while the latter showed diffuse and bilateral patterns. Our findings were robust across individual sites and patients. Cortical atrophy was significantly correlated with disease duration and age at seizure onset, while degrees of asymmetry did not show a significant relationship to these clinical variables. Our findings highlight that the mapping of atypical inter-hemispheric asymmetry and regional atrophy tap into two complementary aspects of temporal lobe epilepsy-related pathology, with the former revealing primary substrates in ipsilateral limbic circuits and the latter capturing bilateral disease effects. These findings refine our notion of the neuropathology of temporal lobe epilepsy and may inform future discovery and validation of complementary MRI biomarkers in temporal lobe epilepsy.

Derangements in brain glutamate, glutathione, and γ-amino butyric acid (GABA) are implicated in a range of neurological disorders. Reliable methods to measure these compounds non-invasively in vivo are needed. We evaluated the reproducibility of their measurements in brain regions involved in the default mode network using quantitative MRS at 7-Tesla in healthy individuals.

Background/Objective: Core decompression (CD) with scaffold and cell-based therapies is a promising strategy for providing both mechanical support and regeneration of the osteonecrotic area for early stage osteonecrosis of the femoral head (ONFH). We designed a new 3D printed porous functionally-graded scaffold (FGS) with a central channel to facilitate delivery of transplanted cells in a hydrogel to the osteonecrotic area. However, the optimal porous structural design for the FGS for the engineering of bone in ONFH has not been elucidated. The aim of this study was to fabricate and evaluate two different porous structures (30% or 60% porosity) of the FGSs in corticosteroid-associated ONFH in rabbits.

Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with ("lesional") and without ("non-lesional") radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68-75%) compared to models to lateralize the side of TLE (56-73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67-75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68-76%) than models that stratified non-lesional patients (53-62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care.

We evaluated the effect of electron beam (E-beam) sterilization (25 kGy, ISO 11137) on the degradation of β-tricalcium phosphate/polycaprolactone (β-TCP/PCL) composite filaments of various ratios (0:100, 20:80, 40:60, and 60:40 TCP:PCL by mass) in a rat subcutaneous model for 24 weeks. Volumes of the samples before implantation and after explantation were measured using micro-computed tomography (micro-CT). The filament volume changes before sacrifice were also measured using a live micro-CT. In our micro-CT analyses, there was no significant difference in volume change between the E-beam treated groups and non-E-beam treated groups of the same β-TCP to PCL ratios, except for the 0% β-TCP group. However, the average volume reduction differences between the E-beam and non-E-beam groups in the same-ratio samples were 0.76% (0% TCP), 3.30% (20% TCP), 4.65% (40% TCP), and 3.67% (60% TCP). The E-beam samples generally had more volume reduction in all experimental groups. Therefore, E-beam treatment may accelerate degradation. In our live micro-CT analyses, most volume reduction arose in the first four weeks after implantation and slowed between 4 and 20 weeks in all groups. E-beam groups showed greater volume reduction at every time point, which is consistent with the results by micro-CT analysis. Histology results suggest the biocompatibility of TCP/PCL composite filaments.

The default mode network (DMN) is the main large-scale network of the resting brain and the PCC/precuneus is a major hub of this network. Glutamate and GABA (γ-amino butyric acid) are the main excitatory and inhibitory neurotransmitters in the CNS, respectively. We studied glutamate and GABA concentrations in the PCC/precuneus via magnetic resonance spectroscopy (MRS) at 7T in relation to age and correlated them with functional connectivity between this region and other DMN nodes in ten healthy right-handed volunteers ranging in age between 23-68 years. Mean functional connectivity of the PCC/precuneus to the other DMN nodes and the glutamate/GABA ratio significantly correlated with age (r = 0.802, p = 0.005 and r = 0.793, p = 0.006, respectively) but not with each other. Glutamate and GABA alone did not significantly correlate with age nor with functional connectivity within the DMN. The glutamate/GABA ratio and functional connectivity of the PCC/precuneus are, therefore, independent age-related biomarkers of the DMN and may be combined in a multimodal pipeline to study DMN alterations in various disease states.

Approximately one third of patients undergoing core decompression (CD) for early-stage osteonecrosis of the femoral head (ONFH) experience progression of the disease, and subsequently require total hip arthroplasty (THA). Thus, identifying adjunctive treatments to optimize bone regeneration during CD is an unmet clinical need. Platelet-derived growth factor (PDGF)-BB plays a central role in cell growth and differentiation. The aim of this study was to characterize mesenchymal stromal cells (MSCs) that were genetically modified to overexpress PDGF-BB (PDGF-BB-MSCs) in vitro and evaluate their therapeutic effect when injected into the bone tunnel at the time of CD in an in vivo rabbit model of steroid-associated ONFH.

Recent work has shown that people with common epilepsies have characteristic patterns of cortical thinning, and that these changes may be progressive over time. Leveraging a large multicenter cross-sectional cohort, we investigated whether regional morphometric changes occur in a sequential manner, and whether these changes in people with mesial temporal lobe epilepsy and hippocampal sclerosis (MTLE-HS) correlate with clinical features.

Bone transport is a surgery-driven procedure for the treatment of large bone defects. However, challenging complications include prolonged consolidation, docking site nonunion and pin tract infection. Here, we develop an osteoinductive and biodegradable intramedullary implant by a hybrid tissue engineering construct technique to enable sustained delivery of bone morphogenetic protein-2 as an adjunctive therapy. In a male rat bone transport model, the eluting bone morphogenetic protein-2 from the implants accelerates bone formation and remodeling, leading to early bony fusion as shown by imaging, mechanical testing, histological analysis, and microarray assays. Moreover, no pin tract infection but tight osseointegration are observed. In contrast, conventional treatments show higher proportion of docking site nonunion and pin tract infection. The findings of this study demonstrate that the novel intramedullary implant holds great promise for advancing bone transport techniques by promoting bone regeneration and reducing complications in the treatment of bone defects.

Drug-resistant focal epilepsy is often caused by focal cortical dysplasias (FCDs). The distribution of these lesions across the cerebral cortex and the impact of lesion location on clinical presentation and surgical outcome are largely unknown. We created a neuroimaging cohort of patients with individually mapped FCDs to determine factors associated with lesion location and predictors of postsurgical outcome.

One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted 'gold-standard' subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy.