Browsing by Author "Lee, Cheng-Chia"

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Early recovery of interhemispheric functional connectivity after corpus callosotomy
(2019-06-01)
Hung, Sheng-Che
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Lee, Cheng-Chia
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Chen, Hsin-Hung
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Chen, Chien
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Wu, Hsiu-Mei
Objective To investigate whether interhemispheric functional connectivity (FC) recovers in the first year after total callosotomy. Methods Eight epilepsy patients undergoing total callosotomy were recruited. Resting-state functional magnetic resonance imaging was acquired before and after surgery. The precallosotomy and postcallosotomy interhemispheric and intrahemispheric FC was analyzed by using graph theory and voxel-mirrored homotopic connectivity (VMHC). The seizure outcome was scored using the Engel surgical outcome scale. Results After callosotomy (mean postoperative interval = 4 months), the network density, average node degree, characteristic path length, and global efficiency of the whole interhemispheric networks were significantly decreased, compared to those in the precallosotomy networks. However, postcallosotomy interhemispheric FC and homotopic VMHC were not significantly reduced in bilateral frontal and temporal lobes. The network density and average node degree of the intrahemispheric networks were significantly increased. The characteristic path length and global efficiency of intrahemispheric networks were unchanged. Significance The interhemispheric FC may be preserved or recover early within the first postoperative year after total callosotomy, particularly in the frontal and anterior temporal lobes.
Ictal networks of temporal lobe epilepsy: views from high-frequency oscillations in stereoelectroencephalography
(2019-10-01)
Peng, Syu-Jyun
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Chou, Chien-Chen
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Yu, Hsiang-Yu
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Chen, Chien
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Yen, Der-Jen
OBJECTIVE In this study, the authors investigated high-frequency oscillation (HFO) networks during seizures in order to determine how HFOs spread from the focal cerebral cortex and become synchronized across various areas of the brain. METHODS All data were obtained from stereoelectroencephalography (SEEG) signals in patients with drug-resistant temporal lobe epilepsy (TLE). The authors calculated intercontact cross-coefficients between all pairs of contacts to construct HFO networks in 20 seizures that occurred in 5 patients. They then calculated HFO network topology metrics (i.e., network density and component size) after normalizing seizure duration data by dividing each seizure into 10 intervals of equal length (labeled I1-I10). RESULTS From the perspective of the dynamic topologies of cortical and subcortical HFO networks, the authors observed a significant increase in network density during intervals I5-I10. A significant increase was also observed in overall energy during intervals I3-I8. The results of subnetwork analysis revealed that the number of components continuously decreased following the onset of seizures, and those results were statistically significant during intervals I3-I10. Furthermore, the majority of nodes were connected to a single dominant component during the propagation of seizures, and the percentage of nodes within the largest component grew significantly until seizure termination. CONCLUSIONS The consistent topological changes that the authors observed suggest that TLE is affected by common epileptogenic patterns. Indeed, the findings help to elucidate the epileptogenic network that characterizes TLE, which may be of interest to researchers and physicians working to improve treatment modalities for epilepsy, including resection, cortical stimulation, and neuromodulation treatments that are responsive to network topologies.
Intervening Nidal Brain Parenchyma and Risk of Radiation-Induced Changes After Radiosurgery for Brain Arteriovenous Malformation: A Study Using an Unsupervised Machine Learning Algorithm
(2019-05-01)
Lee, Cheng-Chia
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Yang, Huai-Che
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Lin, Chung-Jung
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Chen, Ching-Jen
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Wu, Hsiu-Mei
OBJECTIVE: To assess the sensitivity and specificity of arteriovenous malformation (AVM) nidal component identification and quantification using an unsupervised machine learning algorithm and to evaluate the association between intervening nidal brain parenchyma and radiationinduced changes (RICs) after stereotactic radiosurgery. METHODS: Fully automated segmentation via unsupervised classification with fuzzy c-means clustering was used to analyze the AVM nidus on T2-weighted magnetic resonance imaging studies. The proportions of vasculature, brain parenchyma, and cerebrospinal fluid were quantified. These were compared with the results from manual segmentation. The association between the brain parenchyma component and RIC development was assessed. RESULTS: The proposed algorithm was applied to 39 unruptured AVMs in 39 patients (17 female and 22 male patients), with a median age of 27 years. The median proportion of the constituents was as follows: vasculature, 31.3%; brain parenchyma, 48.4%; and cerebrospinal fluid, 16.8%. RICs were identified in 17 of the 39 patients (43.6%). Compared with manual segmentation, the automated algorithm was able to achieve a Dice similarity index of 79.5% (sensitivity, 73.5%; specificity, 85.5%). RICs were associated with a greater proportion of intervening nidal brain parenchyma (52.0% vs. 45.3%; P = 0.015). Obliteration was not associated with greater proportions of nidal vasculature (36.0% vs. 31.2%; P = 0.152). CONCLUSIONS: The automated segmentation algorithm was able to achieve classification of the AVM nidus components with relative accuracy. Greater proportions of intervening nidal brain parenchyma were associated with RICs.
Signal-to-Interference-Plus-Noise Ratio Analysis for Direct-Sequence Ultra-Wideband Systems in Generalized Saleh-Valenzuela Channels
(2007-10-01)
Wu, Wei-De
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Lee, Cheng-Chia
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Wang, Chung-Hsuan
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Chao, Chi-chao
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電信工程研究所
In this paper, exact signal-to-interference-plus-noise ratio (SINR) analysis of direct-sequence ultra-wideband (UWB) systems with Rake receiving in the presence of inter-symbol interference and multiple-access interference over a generalized Saleh-Valenzuela (GSV) channel with a generic pulse shaping function is provided. The SINR expression, for synchronized multiple-access, is first obtained without assuming random spreading. The GSV channel structure under consideration is a generalization of the Saleh-Valenzuela channel structure with generalized path-gain and path-arrival models, examples of which can include all the IEEE 802.15.3a UWB channel models and some of the IEEE 802.15.4a models. Then, by the novel treatment of renewal processes, the exact average SINR over the GSV channel statistics is obtained. Our analytical results well match computer simulations and can readily be applied to evaluate and improve the performance of UWB systems over realistic channel and interference models.