Browsing by Author "Kuo, Li-Wei"

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Attention and Functional Connectivity Among Patients With Early-Stage Subcortical Ischemic Vascular Disease and Alzheimer's Disease
(2020-08-13)
Tu, Min-Chien
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Hsu, Yen-Hsuan
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Yang, Jir-Jei
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Huang, Wen-Hui
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Deng, Jie Fu
The current study compared attention profiles and functional connectivity of frontal regions in patients with early-stage subcortical ischemic vascular disease (SIVD) and Alzheimer's disease (AD). Twenty patients with SIVD, 32 patients with AD, and 23 subjects with normal cognition (NC) received cognition and resting-state functional MRI (rs-fMRI) evaluations. The Cognitive Abilities Screening Instrument (CASI) was used to assess global cognition, and simple attention, processing speed, divided attention, and vigilance/sustained attention were evaluated using the Digit Span Forward, Trail Making Test, Symbol Digit Modality Test, and Conners Continuous Performance Test, respectively. Voxel-based regional homogeneity (ReHo) derived from rs-fMRI data was analyzed to identify significant clusters, which were further correlated with attention profiles. Although the patients with SIVD and AD had comparable global cognitive ability, those with SIVD exhibited worse divided attention and vigilance/sustained attention than those with AD. Compared with the NC group, the patients with SIVD exhibited decreased ReHo within the right middle frontal gyrus (MFG) and left anterior cingulate gyrus (ACG), whereas the patients with AD exhibited increased ReHo within the right orbital part of frontal regions. Correlations between these three clusters with attention exhibited distinct patterns according to the dementia subtype, as did attention indices with significance in predicting global cognition. In summary, our study suggested that worse attention performance was associated with functional disconnection within the frontal regions among patients with SIVD than in those with AD. Frontal functional disconnection may underlie the pathogenesis responsible for defective divided attention, vigilance/sustained attention, and notable within-group variations identified in SIVD.
Effects of signal averaging, gradient encoding scheme, and spatial resolution on diffusion kurtosis imaging: An empirical study using 7T MRI
(2019-11-01)
Chiang, Chia-Wen
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Lin, Shih-Yen
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Cho, Kuan-Hung
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Wu, Kuo-Jen
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Wang, Yun
Background Although diffusion gradient directions and b-values have been optimized for diffusion kurtosis imaging (DKI), little is known about the effect of signal averaging on DKI reliability. Purpose To evaluate how signal averaging influences the reliability of DKI indices using two gradient encoding schemes with three spatial resolutions. Study Type Prospective. Animal Model Fifteen naive Sprague-Dawley rats. Field Strength/Sequence DKI was performed at 7T using two schemes (30 directions with three b-values [30d-3b] and six directions with 15 b-values [6d-15b]), three resolutions, and eight repetitions. Assessment DKI reliability was assessed using voxelwise relative error (sigma) and test-retest error of fractional anisotropy (FA), mean diffusivity (MD), and mean kurtosis (MK) within gray matter (GM) and white matter (WM). The number of excitations (NEX) was optimized by considering DKI reliability. The influence of the partial volume effect (PVE) was also assessed. Statistical Test One-way analysis of variance. Results The 30d-3b scheme, compared with the 6d-15b scheme, exhibited apparently smaller sigma(FA) and sigma(MK) (eg, at NEX 1, in GM, for three resolutions, sigma(FA): 19.9-38.2% vs. 34.2-61.4%, sigma(MK): 6.9-11.4% vs. 14.1-15.4%) and similar sigma(MD) (all differences between two schemes <1.6%). The optimal NEX was determined as 2 for enabling a reliable measurement of DKI-derived indices. The PVE at the lowest resolution apparently increased sigma(FA) for both schemes (19.9% for 30d-3b and 34.2% for 6d-15b) and sigma(MK) for the 6d-15b scheme (14.7%) in GM, and exerted lower effects on MK values for the 30d-3b scheme (P > 0.05). Data Conclusion A higher number of diffusion directions would benefit FA and MK estimation. A higher spatial resolution helps to reduce PVE. By using the 30d-3b scheme, MK is considered a robust index to reflect microstructural changes in GM and WM. We propose a systematic approach to determine the optimal DKI protocols for appropriate preclinical settings. Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1593-1603.
Functional Correlates of Resting-State Connectivity in the Default Mode Network of Heroin Users on Methadone Treatment and Medication-Free Therapeutic Community Program
(2019-06-06)
Kuo, Li-Wei
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Lin, Pei-Sheng
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Lin, Shih-Yen
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Liu, Ming-Fang
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Jan, Hengtai
The treatment of heroin addiction is a complex process involving changes in addictive behavior and brain functioning. The goal of this study was to explore the brain default mode network (DMN) functional connectivity using resting-state functional magnetic resonance imaging (rs-fMRI) and decision-making performance based on the Cambridge gambling task in heroin-dependent individuals undergoing methadone treatment (MT, n = 11) and medication-free faith-based therapeutic community program (TC, n = 11). The DMN involved the medial prefrontal cortex (mPFC), left inferior parietal lobe (IPLL), right inferior parietal lobe (IPLR), and posterior cingulate cortex (PCC) subregions for all participants in both the MT and TC groups. Compared with MT, TC had an increased functional connectivity in IPLL-IPLR and IPLR-PCC and decreased functional connectivity in mPFC-IPLL and IPLL-PCC. Both groups exhibited no significant difference in the regional rs-fMRI metric [i.e., amplitude of low-frequency fluctuation (ALFF)]. In the analysis of the neural correlates for decision-making performance, risk adjustment was positively associated with ALFF in IPLL for all participants considering the group effects. The involvement of IPL in decision-making performance and treatment response among heroin-dependent patients warrants further investigation.
Investigation of functional brain network reconfiguration during vocal emotional processing using graph-theoretical analysis
(2019-05-01)
Lin, Shih-Yen
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Lee, Chi-Chun
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Chen, Yong-Sheng
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Kuo, Li-Wei
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資訊工程學系
Vocal expression is essential for conveying the emotion during social interaction. Although vocal emotion has been explored in previous studies, little is known about how perception of different vocal emotional expressions modulates the functional brain network topology. In this study, we aimed to investigate the functional brain networks under different attributes of vocal emotion by graph-theoretical network analysis. Functional magnetic resonance imaging (fMRI) experiments were performed on 36 healthy participants. We utilized the Power-264 functional brain atlas to calculate the interregional functional connectivity (FC) from fMRI data under resting state and vocal stimuli at different arousal and valence levels. The orthogonal minimal spanning trees method was used for topological filtering. The paired-sample t-test with Bonferroni correction across all regions and arousal-valence levels were used for statistical comparisons. Our results show that brain network exhibits significantly altered network attributes at FC, nodal and global levels, especially under high-arousal or negative-valence vocal emotional stimuli. The alterations within/between well-known large-scale functional networks were also investigated. Through the present study, we have gained more insights into how comprehending emotional speech modulates brain networks. These findings may shed light on how the human brain processes emotional speech and how it distinguishes different emotional conditions.
Multifunctional 3D Patternable Drug-Embedded Nanocarrier-Based Interfaces to Enhance Signal Recording and Reduce Neuron Degeneration in Neural Implantation
(2015-07-22)
Huang, Wei-Chen
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Lai, Hsin-Yi
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Kuo, Li-Wei
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Liao, Chia-Hsin
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Chang, Po-Hsieh
Multiparametric graph theoretical analysis reveals altered structural and functional network topology in Alzheimer's disease
(2019-01-01)
Lin, Shih-Yen
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Lin, Chen-Pei
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Hsieh, Tsung-Jen
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Lin, Chung-Fen
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Chen, Sih-Huei
Alzheimer's disease (AD), an irreversible neurodegenerative disease, is the most common type of dementia in elderly people. This present study incorporated multiple structural and functional connectivity metrics into a graph theoretical analysis framework and investigated alterations in brain network topology in patients with mild cognitive impairment (MCI) and AD. By using this multiparametric analysis, we expected different connectivity metrics may reflect additional or complementary information regarding the topological changes in brain networks in MCI or AD. In our study, a total of 73 subjects participated in this study and underwent the magnetic resonance imaging scans. For the structural network, we compared commonly used connectivity metrics, including fractional anisotropy and normalized streamline count, with multiple diffusivity-based metrics. We compared Pearson correlation and covariance by investigating their sensitivities to functional network topology. Significant disruption of structural network topology in MCI and AD was found predominantly in regions within the limbic system, prefrontal and occipital regions, in addition to widespread alterations of local efficiency. At a global scale, our results showed that the disruption of the structural network was consistent across different edge definitions and global network metrics from the MCI to AD stages. Significant changes in connectivity and tract-specific diffusivity were also found in several limbic connections. Our findings suggest that tract-specific metrics (e.g., fractional anisotropy and diffusivity) provide more sensitive and interpretable measurements than does metrics based on streamline count. Besides, the use of inversed radial diffusivity provided additional information for understanding alterations in network topology caused by AD progression and its possible origins. Use of this proposed multiparametric network analysis framework may facilitate early MCI diagnosis and AD prevention.