Biology subjects

Ngo, G. H.

Publications and source records attributed to Ngo, G. H..

3 recordsLinked to original sources

bioRxiv2018

Accurate Nonlinear Mapping between MNI Volumetric and FreeSurfer Surface Coordinate Systems

The results of most neuroimaging studies are reported in volumetric (e.g., MNI152) or surface (e.g., fsaverage) coordinate systems. Accurate mappings between volumetric and surface coordinate systems can facilitate many applications, such as projecting fMRI group analyses from MNI152/Colin27 to fsaverage for visualization, or projecting resting-state fMRI parcellations from fsaverage to MNI152/Colin27 for volumetric analysis of new data. However, there has been surprisingly little research on this topic. Here, we evaluated three approaches for mapping data between MNI152/Colin27 and fsaverage coordinate systems by simulating the above applications: projection of group-average data from MNI152/Colin27 to fsaverage and projection of fsaverage parcellations to MNI152/Colin27. Two of the approaches are currently widely used. A third approach (registration fusion) was previously proposed, but not widely adopted. Two implementations of the registration fusion (RF) approach were considered, with one implementation utilizing the Advanced Normalization Tools (ANTs). We found that RF-ANTs performed the best for mapping between fsaverage and MNI152/Colin27, even for new subjects registered to MNI152/Colin27 using a different software tool (FSL FNIRT). This suggests that RF-ANTs would be useful even for researchers not using ANTs. Finally, it is worth emphasizing that the most optimal approach for mapping data to a coordinate system (e.g., fsaverage) is to register individual subjects directly to the coordinate system, rather than via another coordinate system. Only in scenarios where the optimal approach is not possible (e.g., mapping previously published results from MNI152 to fsaverage), should the approaches evaluated in this manuscript be considered. In these scenarios, we recommend RF-ANTs (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/registration/Wu2017_RegistrationFusion).

neuroscience↗

bioRxiv2017

Beyond Consensus: Embracing Heterogeneity in Neuroimaging Meta-Analysis

Coordinate-based meta-analysis can provide important insights into mind-brain relationships. A popular approach for curated small-scale meta-analysis is activation likelihood estimation (ALE), which identifies brain regions consistently activated across a selected set of experiments, such as within a functional domain or mental disorder. ALE can also be utilized in meta-analytic co-activation modeling (MACM) to identify brain regions consistently co-activated with a seed region. Therefore, ALE aims to find consensus across experiments, treating heterogeneity across experiments as noise. However, heterogeneity within an ALE analysis of a functional domain might indicate the presence of functional sub-domains. Similarly, heterogeneity within a MACM analysis might indicate the involvement of a seed region in multiple co-activation patterns that are dependent on task contexts. Here, we demonstrate the use of the author-topic model to automatically determine if heterogeneities within ALE-type meta-analyses can be robustly explained by a small number of latent patterns. In the first application, the author-topic modeling of experiments involving self-generated thought (N = 179) revealed cognitive components fractionating the default network. In the second application, the author-topic model revealed that the left inferior frontal junction (IFJ) participated in multiple task-dependent co-activation patterns (N = 323). Furthermore, the author-topic model estimates compared favorably with spatial independent component analysis in both simulation and real data. Overall, the results suggest that the author-topic model is a flexible tool for exploring heterogeneity in ALE-type meta-analyses that might arise from functional sub-domains, mental disorder subtypes or task-dependent co-activation patterns. Code for this study is publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/meta-analysis/Ngo2019_AuthorTopic).

neuroscience↗

bioRxiv2017

Systematic analysis of the effects of the DNA damage response network in telomere defective budding yeast

Functional telomeres are critically important to eukaryotic genetic stability. Budding yeast is a powerful model organism for genetic analysis and yeast telomeres are maintained by very similar mechanisms to human telomeres. Scores of proteins and pathways are known to affect telomere function. Here, we report a series of related genome-wide genetic interaction screens performed on budding yeast cells with acute or chronic telomere defects. We examined genetic interactions in cells defective in Cdc13 and Stn1, affecting two components of CST, a single stranded DNA (ssDNA) binding complex that binds telomeric DNA. We investigated genetic interactions in cells with defects in Rfa3, affecting the major ssDNA binding protein, RPA, which has overlapping functions with CST at telomeres. We also examined genetic interactions in cells lacking EXO1 or RAD9, affecting different aspects of the DNA damage response in a cdc13-1 background. Comparing fitness profiles across the data sets allows us build up a picture of the specific responses to different types of dysfunctional telomeres. Our results show that there is no universal response to telomere defects. To help others engage with the large volumes of data we make the data available via two interactive web-based tools: Profilyzer and DIXY. Among numerous genetic interactions we found the chk1{Delta} mutation improved fitness of cdc13-1 exo1{Delta} cells more than other checkpoint mutations (ddc1{Delta}, rad9{Delta}, rad17{Delta}, rad24{Delta}), whereas in cdc13-1 cells the effects of all checkpoint mutations were similar. We find that Chk1 stimulates resection at defective telomeres, revealing a new role for Chk1 in the eukaryotic DNA damage response network.

genetics↗