Biology subjects

Mejia, A. F.

Publications and source records attributed to Mejia, A. F..

3 recordsLinked to original sources

bioRxiv2023

Individual patterns of functional connectivity in neonates as revealed by surfaced-based Bayesian modeling

Resting-state functional connectivity is a widely used approach to study the functional brain network organization during early brain development. However, the estimation of functional connectivity networks in individual infants has been rather elusive due to the unique challenges involved with functional magnetic resonance imaging (fMRI) data from young populations. Here, we use fMRI data from the developing Human Connectome Project (dHCP) database to characterize individual variability in a large cohort of term-born infants (N = 289) using a novel data-driven Bayesian framework. To enhance alignment across individuals, the analysis was conducted exclusively on the cortical surface, employing surface-based registration guided by age-matched neonatal atlases. Using 10 minutes of resting-state fMRI data, we successfully estimated subject-level maps for fourteen brain networks/subnetworks along with individual functional parcellation maps that revealed differences between subjects. We also found a significant relationship between age and mean connectivity strength in all brain regions, including previously unreported findings in higher-order networks. These results illustrate the advantages of surface-based methods and Bayesian statistical approaches in uncovering individual variability within very young populations.

neuroscience↗

bioRxiv2022

Highlight Results, Don't Hide Them: Enhance interpretation, reduce biases and improve reproducibility

Most neuroimaging studies display results that represent only a tiny fraction of the collected data. While it is conventional to present "only the significant results" to the reader, here we suggest that this practice has several negative consequences for both reproducibility and understanding. This practice hides away most of the results of the dataset and leads to problems of selection bias and irreproducibility, both of which have been recognized as major issues in neuroimaging studies recently. Opaque, all-or-nothing thresholding, even if well-intentioned, places undue influence on arbitrary filter values, hinders clear communication of scientific results, wastes data, is antithetical to good scientific practice, and leads to conceptual inconsistencies. It is also inconsistent with the properties of the acquired data and the underlying biology being studied. Instead of presenting only a few statistically significant locations and hiding away the remaining results, we propose that studies should "highlight" the former while also showing as much as possible of the rest. This is distinct from but complementary to utilizing data sharing repositories: the initial presentation of results has an enormous impact on the interpretation of a study. We present practical examples for voxelwise, regionwise and cross-study analyses using publicly available data that was analyzed previously by 70 teams (NARPS; Botvinik-Nezer, et al., 2020), showing that it is possible to balance the goals of displaying a full set of results with providing the reader reasonably concise and "digestible" findings. In particular, the highlighting approach sheds useful light on the kind of variability present among the NARPS teams results, which is primarily a varied strength of agreement rather than disagreement. Using a meta-analysis built on the informative "highlighting" approach shows this relative agreement, while one using the standard "hiding" approach does not. We describe how this simple but powerful change in practice---focusing on highlighting results, rather than hiding all but the strongest ones---can help address many large concerns within the field, or at least to provide more complete information about them. We include a list of practical suggestions for results reporting to improve reproducibility, cross-study comparisons and meta-analyses. HighlightsO_LIMost studies do not present all results of their analysis, hiding subthreshold ones. C_LIO_LIHiding results negatively affects the interpretation and understanding of the study. C_LIO_LINeuroimagers should present all results of their study, highlighting key ones. C_LIO_LIUsing the public NARPS data, we show several benefits of the "highlighting" approach. C_LIO_LIThe highlighting approach improves individual studies and meta-analyses. C_LI

neuroscience↗

bioRxiv2022

An updated C. elegans body muscle transcriptome for studies in muscle formation and function

The body muscle is an important tissue used in organisms for proper viability and locomotion. Although this tissue is generally well studied and characterized, and many pathways have been elucidated throughout the years, we still lack a comprehensive understanding of its transcriptome and how it controls muscle development and function. Here, we have updated a nuclear FACS sorting-based methodology to isolate and sequence a high-quality muscle transcriptome from C. elegans mixed-stage animals. We have identified 2,848 muscle-specific protein-coding genes, including 78 transcription factors and 206 protein-coding genes containing an RNA binding domain. We studied their interaction network, performed a detailed promoter analysis, and identified novel muscle-specific cis-acting elements. We have also identified 16 high-quality muscle-specific miRNAs, studied their function in vivo using fluorochrome-based analyses, and developed a high-quality C. elegans miRNA Interactome incorporating other muscle-specific datasets produced by our lab and others. Our study expands our understanding of how muscle tissue functions in C. elegans and in turn, provide results that can in the future be applied to humans to study muscular-related diseases.

genetics↗