Second-To-Fourth Digit Ratio (2D:4D) in Breast Cancer: A Systematic Review.

Because high-altitude (HA) environments have a reduced atmospheric oxygen partial pressure compared with low-altitude locations, continued exposure to HA conditions poses a unique physiological challenge to the human central nervous system. Accumulating evidence from recent empirical studies indicates that prolonged or acute HA exposure induces significant alterations in brain functional networks, which in turn contribute to observable behavioral deficits, such as impairments in attention, memory, and motor coordination (; ). These findings highlight the critical need for rigorous analytical frameworks to quantify and interpret the topological reorganization of brain networks under the hypoxic conditions of HA environments. Brain functional connectivity can be non-invasively quantified via resting-state fMRI (rs-fMRI) based on BOLD signal fluctuations coupled to spontaneous neuronal activity, and aberrant connectivity has been widely reported across multiple cognitive disease cohorts (; ; ). Advances in graph theory have made it a mainstream analytical tool for quantifying the topological properties of brain functional networks, including small-world attributes, modular organization, and hub-node distribution (), and it is commonly used to map network reorganization in healthy and clinical populations (; ; ). For example, using graph theory, demonstrated that node dysfunctions in prefrontal and parietal regions are associated closely with cognitive impairments in schizophrenia (SZ) patients, underscoring the method’s value for linking network topology to functional outcomes. In the context of HA exposure, prior research has begun to apply static graph theory to explore network reorganization. reported that after 2 years of HA residence, immigrants exhibited significant changes from baseline in degree centrality and nodal efficiency across multiple brain regions, including those involved in attention control, reaction, memory, and motor coordination. These findings suggest that HA exposure may disrupt the optimal balance between neural integration (inter-regional communication) and segregation (local specialization). However, static graph theory is inherently limited by its focus on “average” network properties derived from entire rs-fMRI scanning sessions. This limitation precludes the capture of time-varying fluctuations in functional connectivity, which are increasingly recognized as essential for understanding context-specific brain states (e.g., task engagement, pathological progression) and their relationship to network structure. Dynamic graph theory can address this gap by enabling analysis of how network topology evolves over time through the application of time-varying analytical techniques (). For example, employed dynamic graph theory to investigate topological changes in brain networks among patients with retinal detachment (RD) and identified significant alterations in both global network properties and node-specific characteristics. Importantly, these dynamic changes were found to correlate with clinical parameters, revealing mechanistic relationships that would not have been revealed by static models.