Temporal-lobe epilepsy (TLE) involves seizures that typically originate in the hippocampus. There is evidence that seizures involve anatomically and functionally connected brain networks within and beyond the temporal lobe. Many studies have explored the effect of TLE on gray matter and resting-state functional connectivity in the brain. However, the relationship between structural and functional changes has not been fully explored. The goal of this study was to investigate the relationship between gray matter concentration (GMC) and functional connectivity in TLE at the voxel level. A voxel-wise linear regression analysis was performed between GMC maps and whole-brain resting-state functional connectivity maps to both the left thalamus (Lthal) and the left hippocampus (LH) in a group of 15 patients with left TLE. Twenty regions were found that exhibited GMC decreases linearly correlated with resting-state functional connectivity to either the LH or the Lthal in the patient group only. A subset of these regions had significantly reduced GMC, and one of these regions also had reduced functional connectivity to the LH in TLE compared to the controls. These results suggest a network of impairment in left TLE where more severe reductions in GMC accompany decreases (LH, Lthal, right midcingulate gyrus, left precuneus, and left postcentral gyrus) or increases (LH to right thalamus) in resting functional connectivity. However, direct relationships between these imaging parameters and disease characteristics in these regions have yet to be established.
Mapping the quantitative relationship between structure and function in the human brain is an important and challenging problem. Numerous volumetric, surface, regions of interest and voxelwise image processing techniques have been developed to statistically assess potential correlations between imaging and non-imaging metrices. Recently, biological parametric mapping has extended the widely popular statistical parametric mapping approach to enable application of the general linear model to multiple image modalities (both for regressors and regressands) along with scalar valued observations. This approach offers great promise for direct, voxelwise assessment of structural and functional relationships with multiple imaging modalities. However, as presented, the biological parametric mapping approach is not robust to outliers and may lead to invalid inferences (e.g., artifactual low p-values) due to slight mis-registration or variation in anatomy between subjects. To enable widespread application of this approach, we introduce robust regression and non-parametric regression in the neuroimaging context of application of the general linear model. Through simulation and empirical studies, we demonstrate that our robust approach reduces sensitivity to outliers without substantial degradation in power. The robust approach and associated software package provide a reliable way to quantitatively assess voxelwise correlations between structural and functional neuroimaging modalities.
To investigate the effects of microwave heating on decomposing epoxy resin, ReaxFF molecular dynamics simulations are performed. As one of its special effects, the thermal runaway phenomenon is studied and compared under microwave heating and under conventional heating. This study shows that this phenomenon results from the enhancement of system absorption of microwave energy, which is caused by the increasing number of small size polar species generated during the pyrolysis of epoxy resin under microwave heating. Meanwhile, non-thermal effects are investigated under microwave heating.Simulations indicate that, at the early stage of decomposition, average generating rates of H 2 O and H 2 obtained under microwave heating are always, or partly, lower than that obtained under conventional heating. To analyze the influence of microwave heating on reaction rates, collision theory is introduced.Further, several simplified collision models are constructed and formulated to study the effectiveness of collision orientations under microwave heating. Analyses illustrate that external microwave heating reduces the effectiveness of collision orientations between polar hydric fragments as well as hydroxyl radicals and polar hydric fragments, thus, decreasing relevant reaction rates.
Abstract-Robot awareness of human actions is an essential research problem in robotics with many important real-world applications, including human-robot collaboration and teaming. Over the past few years, depth sensors have become a standard device widely used by intelligent robots for 3D perception, which can also offer human skeletal data in 3D space. Several methods based on skeletal data were designed to enable robot awareness of human actions with satisfactory accuracy. However, previous methods treated all body parts and features equally important, without the capability to identify discriminative body parts and features. In this paper, we propose a novel simultaneous Feature And Body-part Learning (FABL) approach that simultaneously identifies discriminative body parts and features, and efficiently integrates all available information together to enable real-time robot awareness of human behaviors. We formulate FABL as a regression-like optimization problem with structured sparsityinducing norms to model interrelationships of body parts and features. We also develop an optimization algorithm to solve the formulated problem, which possesses a theoretical guarantee to find the optimal solution. To evaluate FABL, three experiments were performed using public benchmark datasets, including the MSR Action3D and CAD-60 datasets, as well as a Baxter robot in practical assistive living applications. Experimental results show that our FABL approach obtains a high recognition accuracy with a processing speed of the order-of-magnitude of 10 4 Hz, which makes FABL a promising method to enable realtime robot awareness of human behaviors in practical robotics applications.
Ultra-high field 7T magnetic resonance imaging (MRI) offers potentially unprecedented spatial resolution of functional activity within the human brain through increased signal and contrast to noise ratios over traditional 1.5T and 3T MRI scanners. However, the effects physiological and imaging artifacts are also greatly increased. Traditional statistical parametric mapping theories based on distributional properties representative of data acquired at lower fields may be inadequate for new 7T data. Herein, we investigate the model fitting residuals based on two 7T and one 3T protocols. We find that model residuals are substantively more non-Gaussian at 7T relative to 3T. Imaging slices that passed through regions with peak inhomogeneity problems (e.g., mid-brain acquisitions for the 7T hippocampus) exhibited visually higher degrees of distortion along with spatially correlated and extreme values of kurtosis (a measure of non-Gaussianity). The impacts of artifacts have been previously addressed for 3T data by estimating the covariance matrix of the regression errors. We further extend the robust estimation approach for autoregressive models and evaluate the qualitative impacts of this technique relative to traditional inference. Clear differences in statistical significance are shown between inferences based on classical versus robust assumptions, which suggest that inferences based on Gaussian assumptions are subject to practical (as well as theoretical) concerns regarding their power and validity. Hence, modern statistical approaches, such as the robust autoregressive model posed herein, are appropriate and suitable for inference with ultra-high field functional magnetic resonance imaging.
Quantification of β-amyloid (Aβ) in vivo is often accomplished using the distribution volume ratio (DVR), based on a simplified reference tissue model. We investigated the local relationships between DVR and cerebral blood flow (CBF), as well as relative blood flow (R1), in nondemented older adults. Methods Fifty-five nondemented participants (mean age 78.5 years) in the Baltimore Longitudinal Study of Aging underwent 15O-H2O PET CBF and dynamic 11C-PiB-PET. 15O-H2O PET images were normalized and smoothed using SPM. A simplified reference tissue model with linear regression and spatial constraints was used to generate parametric DVR images. The DVR images were regressed on CBF images on a voxel-by-voxel basis using robust Biological Parametric Mapping, adjusting for age and sex (FDR p=0.05, k=50). DVR images were also regressed on R1 images, a measure of the transport rate constant from vascular space to tissue. All analyses were performed in the entire sample, and in high and low tertiles of mean cortical DVR. Results Voxel-based analyses showed that increased DVR is associated with increased CBF in frontal, parietal, temporal, and occipital cortices. However, this association appears to spare regions that typically show early β-amyloid (Aβ) deposition. A more robust relationship between DVR and CBF was observed in the lowest tertile of DVR, i.e., negligible cortical Aβ load, compared to the highest tertile of cortical DVR and Aβ load. Spatial distributions of the DVR-CBF and DVR-R1 correlations showed similar patterns. No reliable negative voxel-wise relationships between DVR and CBF or R1 were observed. Conclusion Robust associations between DVR and CBF at negligible Aβ levels, together with similar spatial distributions of DVR-CBF and DVR-R1 correlations, suggest that regional distribution of DVR reflects blood flow and tracer influx rather than pattern of Aβ deposition in those with minimal Aβ load. DVR-CBF associations in individuals with higher DVR are more likely to reflect true associations between patterns of Aβ deposition and CBF or neural activity. These findings have important implications for analysis and interpretation of voxel-wise correlations with external variables in individuals with varying amounts of Aβ load.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.