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Background
Current anti-AIDS therapeutic agents and treatment regimens can provide a dramatically improved quality of life for HIV-positive people, many of whom have no detectable viral load for prolonged periods of time. Despite this, curing AIDS remains an elusive goal, partially due to the occurrence of drug resistance. Since the development of resistance is linked to, among other things, fluctuating drug levels, our long-term goal has been to develop nanotechnology-based drug delivery systems that can improve therapy by more precisely controlling drug concentrations in target cells. The theme of the current study is to investigate the value of combining AIDS drugs and modifiers of cellular uptake into macromolecular conjugates having novel pharmacological properties. 相似文献Functional magnetic resonance imaging (fMRI) in resting state can be used to evaluate the functional organization of the human brain in the absence of any task or stimulus. The functional connectivity (FC) has non-stationary nature and consented to be varying over time. By considering the dynamic characteristics of the FC and using graph theoretical analysis and a machine learning approach, we aim to identify the laterality in cases of temporal lobe epilepsy (TLE).
MethodsSix global graph measures are extracted from static and dynamic functional connectivity matrices using fMRI data of 35 unilateral TLE subjects. Alterations in the time trend of the graph measures are quantified. The random forest (RF) method is used for the determination of feature importance and selection of dynamic graph features including mean, variance, skewness, kurtosis, and Shannon entropy. The selected features are used in the support vector machine (SVM) classifier to identify the left and right epileptogenic sides in patients with TLE.
ResultsOur results for the performance of SVM demonstrate that the utility of dynamic features improves the classification outcome in terms of accuracy (88.5% for dynamic features compared with 82% for static features). Selecting the best dynamic features also elevates the accuracy to 91.5%.
ConclusionAccounting for the non-stationary characteristics of functional connectivity, dynamic connectivity analysis of graph measures along with machine learning approach can identify the temporal trend of some specific network features. These network features may be used as potential imaging markers in determining the epileptogenic hemisphere in patients with TLE.
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