Biology subjects

Garcia, J. O.

Publications and source records attributed to Garcia, J. O..

8 recordsLinked to original sources

bioRxiv2024

A modulator of cognitive function: Cerebellum modifies human cortical network dynamics via integration

The cerebellum, with distinctive architecture and extensive cortical connections, has long been associated with motor control; however, evidence suggests its role extends beyond motor functions, playing a crucial role in cognitive processes. Despite these insights, how cerebellar computations modulate cortical networks remains elusive. Here, we evaluate dynamic network reconfigurations in the cerebral cortex connectivity following noninvasive inhibitory repetitive transcranial magnetic stimulation (rTMS) targeting the right cerebellum. Using dynamic community detection, we uncover the dynamic network properties by which cerebellar stimulation spreads through the cortex, inspecting the evolution of modular network structures prior to and after cerebellar stimulation. Our results indicate that: (1) flexibility, or the likelihood of network nodes to change module allegiances, increases post stimulation; (2) dynamic patterns in which module allegiances emerge and evolve are individualistic and do not follow a single functional prototype; and (3) cerebellar nodes play the role of integrators for distinct network modules. These results suggest that the cerebellum plays a pivotal role in modulating distributed cortical activity, seamlessly integrating and segregating information beyond motor control. This integrative capacity may underlie the cerebellums contributions to high-level cognitive functions and, more broadly, to the foundation of human intelligence.

neuroscience↗

bioRxiv2023

Cardiac-sympathetic contractility and neural alpha-band power: cross-modal collaboration during approach-avoidance conflict

As evidence mounts that the cardiac-sympathetic system reacts to challenging cognitive settings, we ask if these responses are passive companions or if they are instead fundamentally intertwined with cognitive function. Healthy human participants performed an approach-avoidance paradigm, trading off monetary reward for painful electric shock, while we recorded simultaneous neural and cardiac signals. Participants were reward-sensitive, but also experienced approach-avoidance "conflict" when the subjective appeal of the reward was near equivalent to the revulsion of the cost. Drift-diffusion model parameters revealed that participants managed conflict in part by integrating larger volumes of evidence into choices (wider decision boundaries). Late alpha-band (neural) dynamics suggested that widening decision boundaries served to combat reward-sensitivity and spread attention more fairly to all dimensions of available information. Independently, wider boundaries were also associated with cardiac "contractility" (an index of sympathetically-mediated positive inotropy). We also saw evidence of conflict-specific collaboration between the neural and cardiac-sympathetic signals. Specific to states of conflict, the alignment (i.e., product) of alpha dynamics and contractility were associated with a further widening of the boundary, independent of either signals singular influence. Cross-trial coherence analyses provided additional support for a direct role of cardiac-sympathetics in nurturing fair assessment of information streams during conflict by disrupting the prepotent reward signals. We conclude that cardiac-sympathetic activity is not a mere companion, rather it is a critical component collaborating with cognitive processes to combat reward-sensitivity during the approach-avoidance conflict.

physiology↗

bioRxiv2022

Within- and between-subject reproducibility and variability in multi-modal, longitudinal brain networks

Network analysis provides new and important insights into the function of complex systems such as the brain by examining structural and functional networks constructed from diffusion Magnetic Resonance Imaging (dMRI), functional MRI (fMRI) and Electro/Magnetoencephalography (E/MEG) data. Although network models can shed light on cognition and pathology, questions remain regarding the importance of these findings, due in part to the reproducibility of the core measurements and subsequent modeling strategies. In order to ensure that results are reproducible, we need a better understanding of within- and between-subject variability over long periods of time. Here, we analyze a longitudinal, 8 session, multi-modal (dMRI, and simultaneous EEG-fMRI), and multiple task imaging data set. We first investigate the reproducibility of individual brain connections and network measures and find that across all modalities, within-subject reproducibility is higher than between-subject reproducibility, reaffirming the ability to detect individual differences in network structure in both structural and functional human brain networks. We see high variability in the reproducibility of pairwise connections between brain regions, but observe that in EEG-derived networks, during both rest and task, alpha-band connectivity is consistently more reproducible than networks derived from other frequency bands. Further, reproducible connections correspond to strong connections. Structural networks show a higher reliability in network statistics than functional networks, and certain measures such as synchronizability and eigenvector centrality are consistently less reliable than other network measures across all modalities. Finally, we find that structural dMRI networks outperform functional networks in their ability to identify individuals using a fingerprinting analysis. Our results highlight that functional networks likely reflect state-dependent variability not present in structural networks, and that the analysis of either structural or functional networks to study individual differences should depend on whether or not one wants to take into account state dependencies of the observed networks.

neuroscience↗

bioRxiv2021

Reconfigurations in brain networks upon awakening from slow wave sleep: Interventions and implications in neural communication

Sleep inertia is the brief period of impaired alertness and performance experienced immediately after waking. While the neurobehavioral symptoms of sleep inertia are well-described, less is known about the neural mechanisms underlying this phenomenon. A better understanding of the neural processes during sleep inertia may offer insight into the cognitive impairments observed and the awakening process generally. We observed brain activity following abrupt awakening from slow wave sleep during the biological night. Using electroencephalography (EEG) and a network science approach, we evaluated power, clustering coefficient, and path length across frequency bands under both a control condition and a blue-enriched light intervention condition in a within-subject design. We found that under control conditions, the awakening brain is typified by an immediate reduction in global theta, alpha, and beta power. Simultaneously, we observed a decrease in the clustering coefficient and an increase in path length within the delta band. Exposure to blue-enriched light immediately after awakening ameliorated these changes, but only for clustering. Our results suggest that long-range network communication within the brain is crucial to the waking process and that the brain may prioritize these long-range connections during this transitional state. Our study highlights a novel neurophysiological signature of the awakening brain and provides a potential mechanistic explanation for the effect of light in improving performance after waking. One sentence summaryBlue-enriched light partially accelerates the rapid prioritization of long-range communication within the human brain that characterizes sleep inertia

neuroscience↗

bioRxiv2021

Brain network dynamics codify heterogeneity in seizure propagation

Dynamic functional brain connectivity facilitates adaptive cognition and behavior. Abnormal alterations within such connectivity could result in disrupted functions observed across various neurological conditions. As one of the most common neurological disorders, epilepsy is defined by the seemingly random occurrence of spontaneous seizures. A central but unresolved question concerns the mechanisms by which extraordinarily diverse dynamics of seizures emerge. Here, we apply a graph-theoretical approach to assess dynamic reconfigurations in the functional brain connectivity before, during, and after seizures that display heterogeneous propagation patterns despite sharing similar origins. We demonstrate unique reconfigurations in globally-defined network properties preceding seizure onset that predict propagation patterns of impending seizures, and in locally-defined network properties that differentiate post-onset dynamics. These results characterize quantitative network features underlying the heterogeneity of seizure dynamics and the accompanying clinical manifestations. Decoding these network properties could improve personalized preventative treatment strategies for epilepsy as well as other neurological disorders.

neuroscience↗

bioRxiv2018

Reconfigurations within resonating communities of brain regions following TMS reveal different scales of processing

1An overarching goal of neuroscience research is to understand how heterogeneous neuronal ensembles cohere into networks of coordinated activity to support cognition. To investigate how local activity harmonizes with global signals, we measured electroencephalography (EEG) while single pulses of transcranial magnetic stimulation (TMS) perturbed occipital and parietal cortices. We estimate the rapid network reconfigurations in dynamic network communities within specific frequency bands of the EEG, and characterize two distinct features of network reconfiguration, flexibility and allegiance, among spatially distributed neural sources following TMS. Using distance from the stimulation site to infer local and global effects, we find that alpha activity (8-12Hz) reflects concurrent local and global effects on network dynamics. Pair-wise allegiance of brain regions to communities on average increased near the stimulation site, whereas TMS-induced changes to flexibility were generally invariant to distance and stimulation site. In contrast, communities within the beta (13-20Hz) band demonstrated a high level of spatial specificity, particularly within a cluster comprising paracentral areas. Together, these results suggest that focal magnetic neurostimulation to distinct cortical sites can help identify both local and global effects on brain network dynamics, and highlight fundamental differences in the manifestation of network reconfigurations within alpha and beta frequency bands. 2 Author SummaryTMS may be used to probe the causal link between local regional activity and global brain dynamics. Using simultaneous TMS-EEG and dynamic community detection, we introduce what we call "resonating clusters", or frequency band-specific communities in the brain, as a way to index local and global processing. These resonating clusters within the alpha and beta bands brain display both global (or integrating) behavior and local specificity, highlighting fundamental differences in the manifestation of network reconfigurations.

neuroscience↗

bioRxiv2017

Applications of community detection techniques to brain graphs: Algorithmic considerations and implications for neural function

The human brain can be represented as a graph in which neural units such as cells or small volumes of tissue are heterogeneously connected to one another through structural or functional links. Brain graphs are parsimonious representations of neural systems that have begun to offer fundamental insights into healthy human cognition, as well as its alteration in disease. A critical open question in network neuroscience lies in how neural units cluster into densely interconnected groups that can provide the coordinated activity that is characteristic of perception, action, and adaptive behaviors. Tools that have proven particularly useful for addressing this question are community detection approaches, which can be used to identify communities or modules in brain graphs: groups of neural units that are densely interconnected with other units in their own group but sparsely interconnected with units in other groups. In this paper, we describe a common community detection algorithm known as modularity maximization, and we detail its applications to brain graphs constructed from neuroimaging data. We pay particular attention to important algorithmic considerations, especially in recent extensions of these techniques to graphs that evolve in time. After recounting a few fundamental insights that these techniques have provided into brain function, we highlight potential avenues of methodological advancements for future studies seeking to better characterize the patterns of coordinated activity in the brain that accompany human behavior. This tutorial provides a naive reader with an introduction to theoretical considerations pertinent to the generation of brain graphs, an understanding of modularity maximization for community detection, a resource of statistical measures that can be used to characterize community structure, and an appreciation of the utility of these approaches in uncovering behaviorally-relevant network dynamics in neuroimaging data.

neuroscience↗

bioRxiv2016

Brain connectivity dynamics during social interaction reflect social network structure

Social ties are crucial for humans. Disruption of ties through social exclusion has a marked effect on our thoughts and feelings; however, such effects can be tempered by broader social network resources. Here, we use functional magnetic resonance imaging data acquired from 80 male adolescents to investigate how social exclusion modulates functional connectivity within and across brain networks involved in social pain and understanding the mental states of others (i.e., mentalizing). Furthermore, using objectively logged friendship network data, we examine how individual variability in brain reactivity to social exclusion relates to the density of participants friendship networks, an important aspect of social network structure. We find increased connectivity within a set of regions previously identified as a mentalizing system during exclusion relative to inclusion. These results are consistent across the regions of interest as well as a whole-brain analysis. Next, examining how social network characteristics are associated with task-based connectivity dynamics, participants who showed greater changes in connectivity within the mentalizing system when socially excluded by peers had less dense friendship networks. This work provides novel insight to understand how distributed brain systems respond to social and emotional challenges, and how such brain dynamics might vary based on broader social network characteristics.

neuroscience↗