Biology subjects

Lee, T.-W.

Publications and source records attributed to Lee, T.-W..

6 recordsLinked to original sources

bioRxiv2025

RS-fMRI Evidence for Differential Within- and Between-Module Interactions Across Age

BackgroundPrevious resting-state functional MRI (rsfMRI) studies have identified a robust inverse relationship between nodal strength and amplitude of low-frequency fluctuations (ALFF) across cortical modules. This study examined whether this negative relationship exists within modules and further explored age-related effects on these associations. MethodsUsing MOSI (modularity analysis and similarity measurements), rsfMRI data from three public datasets spanning different age cohorts were analyzed. Correlations between ALFF and nodal strength between modules or voxel concordance within modules were calculated. Geometric mean p-values assessed robustness at the individual level. ResultsConfirming prior findings, a significant inverse correlation between nodal strength and ALFF was observed at the between-module level (geometric p-values 10-4 to 10-5). Within-module negative associations were nonsignificant in younger cohorts at individual level (mean ages 10 and 21) but became significant in the older cohort (mean age 33). The magnitude of this negative association increased with age, consistent with maturation of local inhibitory network mechanisms. ConclusionsThe findings support MOSI as a valid FP method for cortical network construction, with within-module inhibitory effects strengthening in adulthood. The age-dependent modulation reveals network maturation at the modular level, with implications for neurodevelopmental and neuropsychiatric conditions.

neuroscience↗

bioRxiv2024

Enolase-1 is essential for neutrophil recruitment during acute inflammation

Enolase-1 (ENO1) is a moonlighting protein with multiple functions. When expressed on the cell surface, ENO1 binds plasminogen (PLG) and promotes cell migration by facilitating plasmin (PLM)-mediated extracellular matrix degradation. Here, we observed that inflammatory stimulation significantly upregulated ENO1 expression on the neutrophil surface, both in vitro and in vivo. An anti-ENO1 monoclonal antibody (mAb), 7E5, which blocks the ENO1-PLG interaction, effectively suppressed neutrophil transmigration. In mouse models of acute inflammation, including lipopolysaccharide (LPS)-induced lung injury and necrotic cell challenge, 7E5 treatment markedly reduced neutrophil recruitment and neutrophil extracellular trap (NET) formation. Moreover, 7E5 neutralized the immunostimulatory activity of soluble ENO1, which was significantly elevated in circulation during acute inflammation. These findings highlight ENO1 as a key regulator of inflammation and neutrophil infiltration. Targeting ENO1 with antibodies could be a promising strategy to mitigate tissue damage caused by excessive neutrophilic inflammation.

immunology↗

bioRxiv2024

Separating biological variance from noise by applying EM algorithm to modified General Linear Model

IntroductionThe General Linear Model (GLM) has been widely used in research, where error term has been treated as noise. However, compelling evidence suggests that in biological systems, the target variables may possess their innate variances. MethodsA modified GLM was proposed to explicitly model biological variance and non-biological noise. Employing the Expectation and Maximization (EM) scheme can distinguish biological variance from noise, termed EMSEV (EM for Separating Variances). The performance of EMSEV was evaluated by varying noise levels, dimensions of the design matrix, and covariance structures of the target variables. ResultsThe deviation between EMSEV outputs and the pre-defined distribution parameters increased with noise level. With a proper initial guess, when the noise magnitude and the variance of the target variables were similar, there were deviations of 3% and 10-16% in the estimated mean and covariance of the target variables, respectively, along with a 1.7% deviation in noise estimation. ConclusionEMSEV appears promising for distinguishing signal variance from noise in biological systems. The potential applications and implications in biological science and statistical inference are discussed.

bioinformatics↗

bioRxiv2024

Negative relationship between inter-regional interaction and regional power: a resting fMRI study

BackgroundRegional neural response and network property used to be treated separately. However, evidence has suggested an intimate relationship between the regional and inter-regional profiles. This research aimed to investigate the influence of functional connectivity on regional spontaneous activity. MethodsThirty-six and sixty datasets of structural magnetic resonance imaging (sMRI) and resting state functional MRI (rsfMRI) were selected from the NKI and CAN-BIND database, respectively. The cerebral cortex in rsfMRI was parcellated by MOSI (modular analysis and similarity measurements), which enables multi-resolution exploration. For each parcellated cluster, the mean amplitude of low-frequency fluctuation (ALFF) and its average functional connectivity strength with the remaining cortical analogs were computed. Correlation analyses were exploited to examine their relationship. Supplementary analysis was applied to CAN-BIND EEG data (1 to 30 Hz). ResultsNegative correlation coefficients between inter-regional interaction and regional power were noticed in both MRI datasets. One-sample t-tests revealed robust statistics across different analytic resolutions yielded by MOSI, with individual P values at the level 10^-4 to 10^-5. The results suggested that the more intense crosstalk a neural node is embedded in, the less regional power it manifests, and vice versa. The negative relationship was replicated in EEG analysis but limited to delta (1 to 4 Hz) and theta (4 to 8 Hz) frequencies. ConclusionsWe postulate that inhibitory coupling is the mechanism that bridges the local and inter-regional properties, which is more prominent in the lower spectra. The interpretation warrants particular caution since noise may also contribute to the observation.

neuroscience↗

bioRxiv2023

Concordant dynamic changes of global network properties in the frontoparietal and limbic compartments: an EEG study

IntroductionDespite its complexity, deciphering nodal interaction is imperative to understanding a neural network. Network interaction is an even more complicated topic that must be addressed. This study aimed to examine the relationship between the brain waves of two canonical brain structures, i.e., the frontoparietal and limbic compartments, during a resting state. MethodsElectroencephalography (EEG) of 51 subjects in eye-closed condition was analyzed, and the eLORETA method was applied to convert the signals from the scalp to the brain. By way of community detection, representative neural nodes and the associated mean activities were retrieved. Total and lagged coherences were computed to indicate functional connectivity between those neural nodes. Two global network properties were elucidated based on the connectivity measures, i.e., global efficiency and mean functional connectivity strength. The temporal correlation of the global network indices between the two studied networks was explored. ResultsIt was found that there was a significant trend of positive correlation across the four metrics (lagged vs. total coherence x global efficiency vs. average connectivity). In other words, when the neural interaction in the FP network was stronger, so did that in the limbic network, and vice versa. Notably, the above interaction was not spectrally specific and only existed at a finer temporal scale (under hundreds of milliseconds level). ConclusionThe concordant change in network properties indicates an intricate balance between FP and LM compartments. Possible mechanisms and implications for the findings are discussed.

neuroscience↗

bioRxiv2020

Segmentation boosting with compensation methods in optical coherence tomography angiography images

Optical coherence tomography angiography is a noninvasive imaging modality to establish the diagnosis of retinal vascular diseases. However, angiography images are significantly interfered if patients jitter or blink. In this study, a novel retinal image analysis method to accurately detect blood vessels and compensate the effect of interference was proposed. We call this the patch U-Net compensation (PUC) system, which is based on the famous U-Net. Several techniques, including a better training mechanism, direction criteria, area criteria, gap criteria, and probability map criteria, have been proposed to improve its accuracy. Simulations show that the proposed PUC achieves much better performance than state-of-art methods.

bioengineering↗