Anatomy and Neuroscience - Research Publications
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ItemNo Preview AvailableIDH1 mutation is associated with seizures and protoplasmic subtype in patients with low-grade gliomas(WILEY, 2014-09)OBJECTIVE: The isocitrate dehydrogenase 1 (IDH1) R132H mutation is the most common mutation in World Health Organization (WHO) grade II gliomas, reported to be expressed in 70-80%, but only 5-10% of high grade gliomas. Low grade tumors, especially the protoplasmic subtype, have the highest incidence of tumor associated epilepsy (TAE). The IDH1 mutation leads to the accumulation of 2-hydroxyglutarate (2HG), a metabolite that bears a close structural similarity to glutamate, an excitatory neurotransmitter that has been implicated in the pathogenesis of TAE. We hypothesized that expression of mutated IDH1 may play a role in the pathogenesis of TAE in low grade gliomas. METHODS: Thirty consecutive patients with WHO grade II gliomas were analyzed for the presence of the IDH1-R132H mutation using immunohistochemistry. The expression of IDH1 mutation was semiquantified using open-source biologic-imaging analysis software. RESULTS: The percentage of cells positive for the IDH1-R132H mutation was found to be higher in patients with TAE compared to those without TAE (median and interquartile range (IQR) 25.3% [8.6-53.5] vs. 5.2% [0.6-13.4], p = 0.03). In addition, we found a significantly higher median IDH1 mutation expression level in the protoplasmic subtype of low grade glioma (52.2% [IQR 19.9-58.6] vs. 13.8% [IQR 3.9-29.4], p = 0.04). SIGNIFICANCE: Increased expression of the IDH1-R132H mutation is associated with seizures in low grade gliomas and also with the protoplasmic subtype. This supports the hypothesis that this mutation may play a role in the pathogenesis of both TAE and low grade gliomas.
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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.
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ItemDevelopment and Implementation of a Corriedale Ovine Brain Atlas for Use in Atlas-Based Segmentation(PUBLIC LIBRARY SCIENCE, 2016-06-10)Segmentation is the process of partitioning an image into subdivisions and can be applied to medical images to isolate anatomical or pathological areas for further analysis. This process can be done manually or automated by the use of image processing computer packages. Atlas-based segmentation automates this process by the use of a pre-labelled template and a registration algorithm. We developed an ovine brain atlas that can be used as a model for neurological conditions such as Parkinson's disease and focal epilepsy. 17 female Corriedale ovine brains were imaged in-vivo in a 1.5T (low-resolution) MRI scanner. 13 of the low-resolution images were combined using a template construction algorithm to form a low-resolution template. The template was labelled to form an atlas and tested by comparing manual with atlas-based segmentations against the remaining four low-resolution images. The comparisons were in the form of similarity metrics used in previous segmentation research. Dice Similarity Coefficients were utilised to determine the degree of overlap between eight independent, manual and atlas-based segmentations, with values ranging from 0 (no overlap) to 1 (complete overlap). For 7 of these 8 segmented areas, we achieved a Dice Similarity Coefficient of 0.5-0.8. The amygdala was difficult to segment due to its variable location and similar intensity to surrounding tissues resulting in Dice Coefficients of 0.0-0.2. We developed a low resolution ovine brain atlas with eight clinically relevant areas labelled. This brain atlas performed comparably to prior human atlases described in the literature and to intra-observer error providing an atlas that can be used to guide further research using ovine brains as a model and is hosted online for public access.