Biology subjects

Barbey, A. K.

Publications and source records attributed to Barbey, A. K..

3 recordsLinked to original sources

bioRxiv2026

Concussion decreases brain signal variability

Sports-related concussion represents a major public health concern, with millions of cases diagnosed annually. Despite significant methodological advances in functional brain imaging, a clinically meaningful neuroimaging biomarker of concussion has yet to be established. This may be due in part to a reliance on traditional measures. For example, approaches examining mean activation across trials reduce responses to a single measure of central tendency and therefore fail to capture important individual differences. In contrast, blood oxygenation level dependent (BOLD) variability offers a promising new approach to measuring brain function, Specifically, it captures individual differences in the temporal variability in the BOLD signal over time. Further, it is sensitive to neural, vascular, and neurovascular coupling changes that accompany concussion. We collected baseline (prior to injury) and post-concussion (within 48 hours of injury) neuroimaging data in 56 collegiate athletes. Within this longitudinal dataset, we investigated changes in BOLD variability across multiple spatial scales. In addition, we examined their relationships with self-reported symptoms and cognitive performance. As expected, symptoms increased following concussion while computerized cognitive performance declined. BOLD variability decreased from baseline to post-concussion, particularly in the somatomotor and language networks. Moreover, changes in BOLD variability were related to changes in symptom reporting and cognitive performance. Together, these findings advance our understanding of how concussion affects brain function and highlight BOLD signal variability as a promising biomarker to aid ongoing efforts to improve the diagnosis and treatment of sports-related concussion.

neuroscience↗

bioRxiv2023

Human Intelligence and the Connectome are Driven by Structural Brain Network Control

Research in network neuroscience demonstrates that human intelligence is shaped by the structural brain connectome, which enables a globally coordinated and dynamic architecture for general intelligence. Building on this perspective, the network neuroscience theory proposes that intelligence arises from system-wide network dynamics and the capacity to flexibly transition between network states. According to this view, network flexibility is made possible by network controllers that move the system into specific network states, enabling solutions to familiar problems by accessing nearby, easy-to-reach network states and adapting to novel situations by engaging distant, difficult-to-reach network states. Although this framework predicts that general intelligence depends on network controllability, the specific cortical regions that serve as network controllers and the nature of their control operations remain to be established. We therefore conducted a comprehensive investigation of the relationship between regional measures of network controllability and general intelligence within a sample of 275 healthy young adults using structural and diffusion-weighted MRI data. Our findings revealed significant associations between intelligence and network controllers located within the frontal, temporal and parietal cortex. Furthermore, we discovered that these controllers collectively enable access to both easy- and difficult-to-reach network states, aligning with the predictions made by the network neuroscience framework. Additionally, our research demonstrated that the identified network controllers are primarily localized within the left hemisphere and do not reside within regions or connections that possess the highest capacity for structural control in general. This discovery suggests that the identified regions may facilitate specialized control operations and motivates further exploration of the network topology and dynamics underlying intelligence in the human brain. SummaryThis study examines the relationship between regional measures of network controllability and general intelligence within a sample of 275 healthy young adults using structural and diffusion-weighted MRI data. We report that individual differences in general intelligence are associated average and modal controllability in specific left-hemisphere cortical regions, and further show that controller regions associated with intelligence are distinct from regions with the highest, centrality, controllability, or communication. These findings reveal a significant structural role for individual regions in controlling the trajectory of the connectome, advancing our understanding of the nature and mechanisms of network controllability in general intelligence.

neuroscience↗

bioRxiv2017

How Sample Size Influences The Reproducibility Of Task-Based fMRI

Despite a growing body of research suggesting that task-based functional magnetic resonance imaging (fMRI) studies often suffer from a lack of statistical power due to too-small samples, the proliferation of such underpowered studies continues unabated. Using large independent samples across eleven distinct tasks, we demonstrate the impact of sample size on replicability, assessed at different levels of analysis relevant to fMRI researchers. We find that the degree of replicability for typical sample sizes is modest and that sample sizes much larger than typical (e.g., N = 100) produce results that fall well short of perfectly replicable. Thus, our results join the existing line of work advocating for larger sample sizes. Moreover, because we test sample sizes over a fairly large range and use intuitive metrics of replicability, our hope is that our results are more understandable and convincing to researchers who may have found previous results advocating for larger samples inaccessible.

neuroscience↗