4 resultados para temporal lobe epilepsy

em University of Queensland eSpace - Australia


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It has been shown that P auxiliary subunits increase current amplitude in voltage-dependent calcium channels. In this study, however, we found a hovel inhibitory effect of beta3 Subunit on macroscopic Ba2+ currents through recombinant N- and R-type calcium channels expressed in Xenopus oocytes. Overexpressed beta3 (12.5 ng/ cell cRNA) significantly suppressed N- and R-type, but not L-type, calcium channel currents at physiological holding potentials (HPs) of -60 and -80 mV At a HP of -80 mV, coinjection of various concentrations (0-12.5 ng) of the beta3 with Ca,.2.2alpha(1) and alpha(2)delta enhanced the maximum conductance of expressed channels at lower beta3 concentrations but at higher concentrations (>2.5 ng/cell) caused a marked inhibition. The beta3-induced Current suppression was reversed at a HP of - 120 mV, suggesting that the inhibition was voltage dependent. A high concentration of Ba-2divided by (40 mM) as a charge carrier also largely diminished the effect of P3 at -80 mV Therefore, experimental conditions (HP, divalent cation concentration, and P3 subunit concentration) approaching normal physiological conditions were critical to elucidate the full extent of this novel P3 effect. Steady-state inactivation curves revealed that N-type channels exhibited closed-state inactivation without P3, and that P3 caused an similar to40 mV negative shift of the inactivation, producing a second component with an inactivation midpoint of approximately -85 mV The inactivation of N-type channels in the presence of a high concentration (12.5 ng/cell) of P3 developed slowly and the time-dependent inactivation curve was best fit by the sum of two exponential functions with time constants of 14 s and 8.8 min at -80 mV Similar ultra-slow inactivation was observed for N-type channels Without P3. Thus, P3 can have a profound negative regulatory effect on N-type (and also R-type) calcium channels by Causing a hyperpolarizing shift of the inactivation without affecting ultra-slow and closed-state inactivation properties.

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Mixture models implemented via the expectation-maximization (EM) algorithm are being increasingly used in a wide range of problems in pattern recognition such as image segmentation. However, the EM algorithm requires considerable computational time in its application to huge data sets such as a three-dimensional magnetic resonance (MR) image of over 10 million voxels. Recently, it was shown that a sparse, incremental version of the EM algorithm could improve its rate of convergence. In this paper, we show how this modified EM algorithm can be speeded up further by adopting a multiresolution kd-tree structure in performing the E-step. The proposed algorithm outperforms some other variants of the EM algorithm for segmenting MR images of the human brain. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

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We developed an anatomical mapping technique to detect hippocampal and ventricular changes in Alzheimer disease (AD). The resulting maps are sensitive to longitudinal changes in brain structure as the disease progresses. An anatomical surface modeling approach was combined with surface-based statistics to visualize the region and rate of atrophy in serial MRI scans and isolate where these changes link with cognitive decline. Fifty-two high-resolution MRI scans were acquired from 12 AD patients (age: 68.4 +/- 1.9 years) and 14 matched controls (age: 71.4 +/- 0.9 years), each scanned twice (2.1 +/- 0.4 years apart). 3D parametric mesh models of the hippocampus and temporal horns were created in sequential scans and averaged across subjects to identify systematic patterns of atrophy. As an index of radial atrophy, 3D distance fields were generated relating each anatomical surface point to a medial curve threading down the medial axis of each structure. Hippocampal atrophic rates and ventricular expansion were assessed statistically using surface-based permutation testing and were faster in AD than in controls. Using color-coded maps and video sequences, these changes were visualized as they progressed anatomically over time. Additional maps localized regions where atrophic changes linked with cognitive decline. Temporal horn expansion maps were more sensitive to AD progression than maps of hippocampal atrophy, but both maps correlated with clinical deterioration. These quantitative, dynamic visualizations of hippocampal atrophy and ventricular expansion rates in aging and AD may provide a promising measure to track AD progression in drug trials. (C) 2004 Elsevier Inc. All rights reserved.

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Semantic priming occurs when a subject is faster in recognising a target word when it is preceded by a related word compared to an unrelated word. The effect is attributed to automatic or controlled processing mechanisms elicited by short or long interstimulus intervals (ISIs) between primes and targets. We employed event-related functional magnetic resonance imaging (fMRI) to investigate blood oxygen level dependent (BOLD) responses associated with automatic semantic priming using an experimental design identical to that used in standard behavioural priming tasks. Prime-target semantic strength was manipulated by using lexical ambiguity primes (e.g., bank) and target words related to dominant or subordinate meaning of the ambiguity. Subjects made speeded lexical decisions (word/nonword) on dominant related, subordinate related, and unrelated word pairs presented randomly with a short ISI. The major finding was a pattern of reduced activity in middle temporal and inferior prefrontal regions for dominant versus unrelated and subordinate versus unrelated comparisons, respectively. These findings are consistent with both a dual process model of semantic priming and recent repetition priming data that suggest that reductions in BOLD responses represent neural priming associated with automatic semantic activation and implicate the left middle temporal cortex and inferior prefrontal cortex in more automatic aspects of semantic processing.