Biology subjects

Rodriguez, R. X.

Publications and source records attributed to Rodriguez, R. X..

3 recordsLinked to original sources

bioRxiv2026

Multimodal Connectome Markers of Disruptive Behavior Problems in Youth: Distinct Patterns of Concurrent Severity and Prospective Symptom Change

Disruptive behaviors problems, such as irritability/anger and maladaptive aggression, are associated with altered connectivity across networks supporting emotion regulation. However, multimodal connectome markers of concurrent severity and prospective symptom change, including sex-specific patterns, remain unclear. Using a large, sex-balanced sample from the Adolescent Brain Cognitive Development (ABCD) Study, we applied multimodal connectome-based predictive modelling (CPM) to test whether resting-state functional (n = 7,556; 49.3% female) and structural (N = 8,978; 48.4%) connectivity predicted baseline disruptive behavior severity (at ages 9-10) and 2-year symptom change (from ages 9-10 to 11-12). Models were adjusted for covariates, permutation-tested, and FDR corrected. Secondary analyses tested sex-stratified models separately in males and females. In models emerging as significant, follow-up analyses tested for distinct vs shared predictive edges. Functional and structural connectomes predicted baseline severity ({rho} = 0.087 and {rho} = 0.071, respectively). Functional connectivity also predicted prospective symptom change ({rho} = 0.038), whereas prospective structural prediction was predominately with negative edges ({rho} = 0.026). Functional connectivity models were significant in both sexes, while prospective structural connectivity models were significant in males, but not females. Frontoparietal, medial frontal, sensorimotor, salience, cerebellar, and visual networks contributed across models. Cross-sectional models showed a more distributed pattern while prospective models showed a more focal pattern. These findings identify complementary, largely distinct functional and structural connectome patterns associated with current disruptive behavior risk and subsequent symptom change, supporting the importance of developmental timing, sex, and imaging modality in developing brain-based biomarkers of disruptive behavior risk in youth.

neuroscience↗

bioRxiv2024

Connectome caricatures: removing large-amplitude co-activation patterns in resting-state fMRI emphasizes individual differences

High-amplitude co-activation patterns are sparsely present during resting-state fMRI but drive functional connectivity1-5. Further, they resemble task activation patterns and are well-studied3,5-10. However, little research has characterized the remaining majority of the resting-state signal. In this work, we introduced caricaturing--a method to project resting-state data to a subspace orthogonal to a manifold of co-activation patterns estimated from the task fMRI data. Projecting to this subspace removes linear combinations of these co-activation patterns from the resting-state data to create Caricatured connectomes. We used rich task data from the Human Connectome Project (HCP)11 and the UCLA Consortium for Neuropsychiatric Phenomics12 to construct a manifold of task co-activation patterns. Caricatured connectomes were created by projecting resting-state data from the HCP and the Yale Test-Retest13 datasets away from this manifold. Like caricatures, these connectomes emphasized individual differences by reducing between-individual similarity and increasing individual identification14. They also improved predictive modeling of brain-phenotype associations. As caricaturing removes group-relevant task variance, it is an initial attempt to remove task-like co-activations from rest. Therefore, our results suggest that there is a useful signal beyond the dominating co-activations that drive resting-state functional connectivity, which may better characterize the brains intrinsic functional architecture.

neuroscience↗

bioRxiv2022

Trends in self-citation rates in neuroscience literature

Citation metrics influence academic reputation and career trajectories. Recent works have highlighted flaws in citation practices in the Neurosciences, such as the under-citation of women. However, self-citation rates--or how much authors cite themselves--have not yet been comprehensively investigated in the Neurosciences. This work characterizes self-citation rates in basic, translational, and clinical Neuroscience literature by collating 100,347 articles from 63 journals between the years 2000-2020. In analyzing over five million citations, we demonstrate four key findings: 1) increasing self-citation rates of Last Authors relative to First Authors, 2) lower self-citation rates in low- and middle-income countries, 3) gender differences in self-citation stemming from differences in the number of previously published papers, and 4) variations in self-citation rates by field. Our characterization of self-citation provides insight into citation practices that shape the perceived influence of authors in the Neurosciences, which in turn may impact what type of scientific research is done and who gets the opportunity to do it.

neuroscience↗