Axonal degeneration is a major cause of permanent disability in multiple sclerosis (MS). Recent observations from our and other laboratories suggest that sodium accumulation within compromised axons is a key, early step in the degenerative process, and hence that limiting axonal sodium influx may represent a mechanism for axonal protection in MS. Here we assess whether lamotrigine, a sodium channel-blocking agent, is effective in preventing axonal degeneration in an animal model of MS, namely chronic-relapsing experimental autoimmune encephalomyelitis (CR-EAE). When administered from 7 days post-inoculation, lamotrigine provided a small but significant reduction in the neurological deficit present at the termination of the experiments (averaged over three independent experiments; vehicle: 3.5+/-2.7; lamotrigine: 2.6+/-2.0, P<0.05) and preserved more functional axons in the spinal cord (measured as mean compound action potential area; vehicle: 31.7 microV.ms+/-23.0; lamotrigine: 42.9+/-27.4, P<0.05). Histological examination of the thoracic spinal cord (n=71) revealed that lamotrigine treatment also provided significant protection against axonal degeneration (percentage degeneration in dorsal column; vehicle: 33.5 %+/-38.5; lamotrigine: 10.4 %+/-12.5, P<0.01). The findings suggest that lamotrigine may provide a novel avenue for axonal protection in MS.

Purpose To validate a random forest method for segmenting cerebral white matter lesions (WMLs) on computed tomographic (CT) images in a multicenter cohort of patients with acute ischemic stroke, by comparison with fluid-attenuated recovery (FLAIR) magnetic resonance (MR) images and expert consensus. Materials and Methods A retrospective sample of 1082 acute ischemic stroke cases was obtained that was composed of unselected patients who were treated with thrombolysis or who were undergoing contemporaneous MR imaging and CT, and a subset of International Stroke Thrombolysis-3 trial participants. Automated delineations of WML on images were validated relative to experts' manual tracings on CT images, and co-registered FLAIR MR imaging, and ratings were performed by using two conventional ordinal scales. Analyses included correlations between CT and MR imaging volumes, and agreements between automated and expert ratings. Results Automated WML volumes correlated strongly with expert-delineated WML volumes at MR imaging and CT (r = 0.85 and 0.71 respectively; P < .001). Spatial-similarity of automated maps, relative to WML MR imaging, was not significantly different to that of expert WML tracings on CT images. Individual expert WML volumes at CT correlated well with each other (r = 0.85), but varied widely (range, 91% of mean estimate; median estimate, 11 mL; range of estimated ranges, 0.2-68 mL). Agreements (κ) between automated ratings and consensus ratings were 0.60 (Wahlund system) and 0.64 (van Swieten system) compared with agreements between individual pairs of experts of 0.51 and 0.67, respectively, for the two rating systems (P < .01 for Wahlund system comparison of agreements). Accuracy was unaffected by established infarction, acute ischemic changes, or atrophy (P > .05). Automated preprocessing failure rate was 4%; rating errors occurred in a further 4%. Total automated processing time averaged 109 seconds (range, 79-140 seconds). Conclusion An automated method for quantifying CT cerebral white matter lesions achieves a similar accuracy to experts in unselected and multicenter cohorts.

Cerebral small vessel disease (SVD) is a common cause of ageing-associated physical and cognitive impairment. Identifying SVD is important for both clinical and research purposes but is usually dependent on radiologists' evaluation of brain scans. Computer tomography (CT) is the most widely used brain imaging technique but for SVD it shows a low signal-to-noise ratio, and consequently poor inter-rater reliability. We therefore propose a novel framework based on multiple instance learning (MIL) to distinguish between absent/mild SVD and moderate/severe SVD. Intensity patches are extracted from regions with high probability of containing lesions. These are then used as instances in MIL for the identification of SVD. A large baseline CT dataset, consisting of 590 CT scans, was used for evaluation. We achieved approximately 75% accuracy in classifying two different types of SVD, which is high for this challenging problem. Our results outperform those obtained by either standard machine learning methods or current clinical practice.