Peng, C.-H. | Search bioRxiv

bioRxiv2025

SimulScan and Partial Least Squares: Visualizing swallowing through functional and dynamic imaging correlations

PurposeSwallowing is a complex function involving the precise coordination of muscles, nerves, and brain areas, and can be disrupted in a variety of neurological conditions. Current imaging studies to visualize the central control of swallowing cannot examine both the biomechanics of the swallow and the brain activity associated with swallow events. An updated version of SimulScan is introduced that provides high-quality and high-speed dynamic imaging, together with fMRI acquisitions, to enable data-driven analysis of swallowing function through a partial least squares correlation (PLSC) analysis. MethodsIntegrating updated dynamic imaging approaches, SimulScan can achieve dynamic MRI at 23.75 frames per second and BOLD fMRI at a 1.6 s TR. Five subjects were recruited and scanned with SimulScan twice and with videofluoroscopy to compare the preliminary reliability of measuring swallowing biomechanics and the test-retest relationship in correlated functional and dynamic components of PLSC. ResultsHigh reliability of biomechanical measures of swallowing were achieved across the two SimulScan runs. In addition, these showed moderate correlation with videofluorscopy measures. Correlations between dynamic and functional imaging across runs also showed high reliability indicating that SimulScan with PLSC can extract maps of linked correlations between the brain and the oropharyngeal region. ConclusionThe updated version of SimulScan with PLSC analysis enables the study of central control of swallowing, providing simultaneous biomechanical visualization of the swallow along with brain functional signals.

neuroscience↗

bioRxiv2018

Decomposing the subclonal structure of tumors with two-way mixture models on copy number aberrations

Multistage tumorigenesis is a dynamic process characterized by the accumulation of mutations. Thus, a tumor mass is composed of genetically divergent cell subclones. With the advancement of next-generation sequencing (NGS), mathematical models have been recently developed to decompose tumor subclonal architecture from a collective genome sequencing data. Most of the methods focused on single-nucleotide variants (SNVs). However, somatic copy number aberrations (CNAs) also play critical roles in carcinogenesis. Therefore, further modeling subclonal CNAs composition would hold the promise to improve the analysis of tumor heterogeneity and cancer evolution. To address this issue, we developed a two-way mixture Poisson model, named CloneDeMix for the deconvolution of read-depth information. It can infer the subclonal copy number, mutational cellular prevalence (MCP), subclone composition, and the order in which mutations occurred in the evolutionary hierarchy. The performance of CloneDeMix was systematically assessed in simulations. As a result, the accuracy of CNA inference was nearly 93% and the MCP was also accurately restored. Furthermore, we also demonstrated its applicability using head and neck cancer samples from TCGA. Our results inform about the extent of subclonal CNA diversity, and a group of candidate genes that probably initiate lymph node metastasis during tumor evolution was also discovered. Most importantly, these driver genes are located at 11q13.3 which is highly susceptible to copy number change in head and neck cancer genomes. This study successfully estimates subclonal CNAs and exhibit the evolutionary relationships of mutation events. By doing so, we can track tumor heterogeneity and identify crucial mutations during evolution process. Hence, it facilitates not only understanding the cancer development but finding potential therapeutic targets. Briefly, this framework has implications for improved modeling of tumor evolution and the importance of inclusion of subclonal CNAs.

bioinformatics↗