Simplifying functional network representation and interpretation through causality clustering

SMART (Splitting-Merging Assisted Reliable) Independent Component Analysis for Extracting Accurate Brain Functional Networks

Functional networks (FNs) hold significant promise in understanding brain function. Independent component analysis (ICA) has been applied in estimating FNs from functional magnetic resonance imaging (fMRI). However, determining an optimal model order for ICA remains challenging, leading to criticism about the reliability of FN estimation. Here, we propose a SMART (splitting-merging assisted reliable) ICA method that automatically extracts reliable FNs by clustering independent components (ICs) obtained from multi-model-order ICA using a simplified graph while providing linkages among FNs deduced from different-model orders. We extend SMART ICA to multi-subject fMRI analysis, validating its effectiveness using simulated and real fMRI data. Based on simulated data, the method accurately estimates both group-common and group-unique components and demonstrates robustness to parameters. Using two age-matched cohorts of resting fMRI data comprising 1,950 healthy subjects, the resulting reliable group-level FNs are greatly similar between the two cohorts, and interestingly the subject-specific FNs show progressive changes while age increases. Furthermore, both small-scale and large-scale brain FN templates are provided as benchmarks for future studies. Taken together, SMART ICA can automatically obtain reliable FNs in analyzing multi-subject fMRI data, while also providing linkages between different FNs.

Analyzing international events through the lens of statistical physics: The case of Ukraine

During the last few years, statistical physics has received increasing attention as a framework for the analysis of real complex systems; yet, this is less clear in the case of international political events, partly due to the complexity in securing relevant quantitative data on them. Here, we analyze a detailed dataset of violent events that took place in Ukraine since January 2021 and analyze their temporal and spatial correlations through entropy and complexity metrics and functional networks. Results depict a complex scenario with events appearing in a non-random fashion but with eastern-most regions functionally disconnected from the remainder of the country-something opposing the widespread "two Ukraines" view. We further draw some lessons and venues for future analyses.

Preimplantation Endometrial Transcriptomics in Natural Conception Cycle of the Rhesus Monkey

There is no report on preimplantation phase endometrial transcriptomics in natural conception cycles of primates. In the present study, the whole-genome expression array of endometrium on Days 2, 4, and 6 post-ovulation (pov) in proven natural conception (Group 1; n = 12) and non-mated, ovulatory (Group 2; n = 12) cycles of rhesus monkeys was examined, compared, and validated. Of fifteen (15) genes showing differential expression (>2-fold; pFDR < 0.05), six genes (CHRND, FOXD3, GJD4, MAPK8IP3, MKS1, and NUP50) were upregulated, while eight genes (ADCY5, ADIPOR1, NNMT, PATL1, PIGV, TGFBR2, TOX2, and VWA5B1) were down regulated on Day 6 pov as compared to Day 2 pov in conception cycles. On Day 6 pov, four genes (ADCY5, NNMT, TOX2, and VWA5B1) were down regulated, and AVEN was upregulated in conception cycles compared with the non-conception cycle. These observations were orthogonally validated at protein expression level. Group-specifically expressed unique genes in conception cycles influence the process of induction of immune-tolerance, while the genes expressed in both groups influence processes of protein targeting and metabolism. A triad of timed-actions of progesterone, seminal plasma, and preimplantation embryo putatively regulate several input molecules to CREB, NF-kB, and STAT regulatory networks during secretory phase towards evolution of endometrial receptivity in the rhesus monkey.

Conceptualizing ecosystem services using social-ecological networks

Social-ecological networks (SENs) represent the complex relationships between ecological and social systems and are a useful tool for analyzing and managing ecosystem services. However, mainstreaming the application of SENs in ecosystem service research has been hindered by a lack of clarity about how to match research questions to ecosystem service conceptualizations in SEN (i.e., as nodes, links, attributes, or emergent properties). Building from different disciplines, we propose a typology to represent ecosystem service in SENs and identify opportunities and challenges of using SENs in ecosystem service research. Our typology provides guidance for this growing field to improve research design and increase the breadth of questions that can be addressed with SEN to understand human-nature interdependencies in a changing world.