Anatomy and Neuroscience - Research Publications
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ItemPrognosis in autoimmune encephalitis: Database(ELSEVIER SCIENCE BV, 2018-12)Autoimmune encephalitis is a rare and debilitating disease. An important question in clinical neurology is what factors may be correlated with outcomes in autoimmune encephalitis. There is observational data describing statistical analyses on such variables, but there are no review articles that collaborate and interpret this information. This data in brief article represents the data collection for such a review (Broadley et al., 2018). Herein we summarize clinical information from 44 research articles, in particular pertaining to outcomes and prognostic variables.
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ItemInnate Immunity in the Central Nervous System: A Missing Piece of the Autoimmune Encephalitis Puzzle?(FRONTIERS MEDIA SA, 2019-09-10)The autoimmune encephalitides are a group of autoimmune conditions targeting the central nervous system and causing severe clinical symptoms including drug-resistant seizures, cognitive dysfunction and psychiatric disturbance. Although these disorders appear to be antibody mediated, the role of innate immune responses needs further clarification. Infiltrating monocytes and microglial proliferation at the site of pathology could contribute to the pathogenesis of the disease with resultant blood brain barrier dysfunction, and subsequent activation of adaptive immune response. Both innate and adaptive immune cells can produce pro-inflammatory molecules which can perpetuate ongoing neuroinflammation and drive ongoing seizure activity. Ultimately neurodegenerative changes can ensue with resultant long-term neurological sequelae that can impact on ongoing patient morbidity and quality of life, providing a potential target for future translational research.
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Item7T-fMRI: Faster temporal resolution yields optimal BOLD sensitivity for functional network imaging specifically at high spatial resolution(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2018-01-01)Recent developments in accelerated imaging methods allow faster acquisition of high spatial resolution images. This could improve the applications of functional magnetic resonance imaging at 7 Tesla (7T-fMRI), such as neurosurgical planning and Brain Computer Interfaces (BCIs). However, increasing the spatial and temporal resolution will both lead to signal-to-noise ratio (SNR) losses due to decreased net magnetization per voxel and T1-relaxation effect, respectively. This could potentially offset the SNR efficiency gains made with increasing temporal resolution. We investigated the effects of varying spatial and temporal resolution on fMRI sensitivity measures and their implications on fMRI-based BCI simulations. We compared temporal signal-to-noise ratio (tSNR), observed percent signal change (%∆S), volumes of significant activation, Z-scores and decoding performance of linear classifiers commonly used in BCIs across a range of spatial and temporal resolution images acquired during an ankle-tapping task. Our results revealed an average increase of 22% in %∆S (p=0.006) and 9% in decoding performance (p=0.015) with temporal resolution only at the highest spatial resolution of 1.5×1.5×1.5mm3, despite a 29% decrease in tSNR (p<0.001) and plateaued Z-scores. Further, the volume of significant activation was indifferent (p>0.05) across spatial resolution specifically at the highest temporal resolution of 500ms. These results demonstrate that the overall BOLD sensitivity can be increased significantly with temporal resolution, granted an adequately high spatial resolution with minimal physiological noise level. This shows the feasibility of diffuse motor-network imaging at high spatial and temporal resolution with robust BOLD sensitivity with 7T-fMRI. Importantly, we show that this sensitivity improvement could be extended to an fMRI application such as BCIs.