Biology subjects

McCusker, M. C.

Publications and source records attributed to McCusker, M. C..

3 recordsLinked to original sources

bioRxiv2026

Brain age prediction from structural and functional connectivity across the lifespan using connectome-based predictive modeling

Brain maturation and aging are characterized by widespread changes in structural and functional brain organization. The extent to which these modalities capture shared versus distinct signatures of lifespan development and aging remains unclear. In this study, we applied connectome-based predictive modeling (CPM) to connectomes derived from diffusion MRI and resting-state functional MRI in Human Connectome Project Development, Young Adult, Aging, and combined Lifespan cohorts to predict chronological age. Both structural and functional connectomes significantly predicted age across cohorts, demonstrating that age-related information is distributed throughout whole-brain connectivity patterns. Prediction performance varied across lifespan stages, with generally stronger performance in the Development, Aging, and Lifespan cohorts than in Young Adulthood. Structural and functional models produced significantly correlated predicted ages in several cohorts, indicating shared age-related information across modalities. Structural-functional convergence analyses revealed limited and variable correspondence between modality-specific predictive features, suggesting that structural and functional connectomes capture both shared and complementary aspects of brain maturation and aging. Network-level analyses demonstrated that age-predictive information was distributed across canonical brain networks, including cerebellar, frontoparietal, salience, somatomotor, and subcortical networks, with patterns varying across cohorts and modalities. Multimodal CPM improved prediction relative to unimodal models in several cohorts, supporting the complementary contribution of structural and functional connectivity to age prediction. Cross-cohort, cross-sex, and cross-modality analyses further characterized the generalizability and shared information of predictive models across lifespan stages, sexes, and imaging modalities. This work demonstrates the utility of CPM for characterizing distributed structural and functional connectivity patterns associated with chronological age across the lifespan and provides a foundation for future studies of individual variability in brain maturation and aging.

neuroscience↗

bioRxiv2024

Neural mechanisms of awareness of action

The origins of awareness of action (AoA), the ability to report an action just performed, remain elusive. Differing theories ascribe AoA to pre-action, efferent motor/volitional mechanisms versus post-action, afferent sensory/perceptual neural mechanisms. To study these two types of mechanisms and others, we developed a paradigm where very similar aware and unaware actions occur repeatedly. Aware actions demonstrated larger neurophysiological signals both preceding and following movement. The differences included well-known volitional and perceptual event related potentials (PMP, N140, P300), as well as frontal midline theta, event-related alpha/beta desynchronization, and post-move blink rates. On longer time scales, we identified a novel event related potential preceding unaware moves, and found behavioral and pupillometric evidence for decreased attention and arousal over minutes concurrent with AoA loss. Our findings suggest that both dynamic, individual action-associated volitional and perceptual neural activity, as well as long-term attention and arousal states play a role in maintaining AoA.

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↗