Biology subjects

Vergara, V. M.

Publications and source records attributed to Vergara, V. M..

5 recordsLinked to original sources

bioRxiv2024

LOCAL SPATIAL FLOWS AND PROPAGATIVE ATTRACTORS: A NOVEL 'FLOWNECTOME' FRAMEWORK FOR ANALYZING BOLD FMRI DYNAMICS

Although the analysis of temporal signal fluctuations and co-fluctuations has long been a fixture of blood oxygenation-level dependent (BOLD) functional magnetic resonance imaging (fMRI) research, the role of local directional flows in both signal propagation and healthy functional integration remains almost entirely neglected. We are introducing an extensible framework, based on localized directional signal flows, to capture and analyze spatial signal propagation and propagative attractor patterns in BOLD fMRI. Novel features derived from this approach are validated in a large resting-state fMRI schizophrenia study where they reveal significant relationships between spatially directional flows, propagative attractor patterns and subject diagnostic status. Plausibly, we find that spatial signal inflow to functional regions tends to positively correlate with net gain/loss in the region's temporal contribution BOLD signal reconstruction. We also find that group ICA (pdGICA) performed on time-varying propagative density maps, which are whole-brain maps of spatial signal inflow to each voxel on successive 30 second windows (ie. propagative attractor maps), produce components that tend to concentrate predominantly in at most six or seven functional regions, in some cases focusing on as few as two. The relationship between the propagative attractor group ICA component maps and functional regions is not sharp, but is focused enough to render the pdGICA component maps functionally tractable. Temporal correlations between pdGICA component timeseries and net gain/loss functional region timeseries on corresponding windows echoes those traditionally observed between functional region timeseries when aligning each pdGICA component with the functional region with which it has greatest spatial overlap. Schizophrenia strongly disrupts the average correlative relationship between pdGICA components and certain functional regions, some of which tend to be implicated in schizophrenia e.g. the thalamus and the anterior cingulate cortex. Schizophrenia also strongly and pervasively linked to the importance of specific pdGICA components in reconstructing subjects' observed time-varying propagative density maps. Over half of the 35 pdGICA component make significantly different average contributions to patient propagative density maps than to those of controls, with the functional footprints of impacted pdGICA components spreading over diverse functional domains. Finally, the magnitudes of local directional flows that carry propagation have spatially structured averages and structured, pervasive schizophrenia effects. The framework introduced here follows a new and fundamentally different data-driven approach to the BOLD fMRI signal. We believe that the empirical measurement of local directional flows and wider spatial signal propagation opens a plethora of new avenues through which to investigate healthy and disordered brain function using BOLD fMRI.

neuroscience↗

bioRxiv2023

Markov Spatial Flows in BOLD fMRI: a Novel Lens on the BOLD Signal Reveals Attracting Patterns of Signal Intensity

While the analysis of temporal signal fluctuations and cofluctuations has long been a fixture of blood oxygenation-level dependent (BOLD) functional magnetic resonance imaging (fMRI) research, the role and implications of spatial propagation within the 4D neurovascular BOLD signal has been almost entirely neglected. As part of a larger research program aimed at capturing and analyzing spatially propagative dynamics in BOLD fMRI, we report here a method that exposes large-scale functional attractors of spatial flows formulated as Markov processes defined at the voxel level. The brainwide stationary distributions of these voxel-level Markov processes represent patterns of signal accumulation toward which we find evidence that the brain exerts a probabilistic propagative undertow. These probabilistic propagative attractors are spatially structured and organized interpretably over functional regions. They also differ significantly between schizophrenia patients and controls.

neuroscience↗

bioRxiv2021

Multiframe Evolving Dynamic Functional Connectivity (EVOdFNCs): A Method for Constructing and Investigating Functional Brain Motifs

The study of brain network connectivity as a time-varying property began relatively recently and to date has remained primarily concerned with capturing a handful of discrete static states that characterize connectivity as measured on a timescale shorter than that of the full scan. Capturing group-level representations of temporally evolving patterns of connectivity is a challenging and important next step in fully leveraging the information available in large resting state functional magnetic resonance imaging (rs-fMRI) studies. We introduce a flexible, extensible data-driven framework for the stable identification of group-level multiframe (movie-style) dynamic functional network connectivity (dFNC) states. Our approach employs uniform manifold approximation and embedding (UMAP) to produce a continuity-preserving planar embedding of high-dimensional time-varying measurements of whole-brain functional network connectivity. Planar linear exemplars summarizing dominant dynamic trends across the population are computed from local linear approximations to the 2D embedded trajectories. A high-dimensional representation of each 2D exemplar segment is obtained by averaging the dFNC observations corresponding to the n planar nearest neighbors of {tau} evenly spaced points along the 2D line segment representation (where n is the UMAP number-of-neighbors parameter and {tau} is the temporal duration of trajectory segments being approximated). Each of the 2D exemplars thus "lifts" to a multiframe high-dimensional dFNC trajectory of length {tau}. The collection of high-dimensional temporally evolving dFNC representations (EVOdFNCs) derived in this manner are employed as dynamic basis objects with which to characterize observed high-dimensional dFNC trajectories, which are then expressed as weighted combination of these basis objects. Our approach yields new insights into anomalous patterns of fluidly varying whole brain connectivity that are significantly associated with schizophrenia as a broad diagnosis as well as with certain symptoms of this serious disorder. Importantly, we show that relative to conventional hidden Markov modeling with single-frame unvarying dFNC summary states, EVOdFNCs are more sensitive to positive symptoms of schizophrenia including hallucinations and delusions, suggesting a more dynamic characterization is needed to help illuminate such a complex brain disorder.

neuroscience↗

bioRxiv2021

A METHOD FOR INTEGRATIVE ANALYSIS OF LOCAL AND GLOBAL BRAIN DYNAMICS

The most common pipelines for studying time-varying network connectivity in resting state functional magnetic resonance imaging (rs-fMRI) operate at the whole brain level, capturing a small discrete set of "states" that best represent time-resolved joint measures of connectivity over all network pairs in the brain. This whole-brain hidden Markov model (HMM) approach "uniformizes" the dynamics over what is typically more than 1000 pairs of networks, forcing each time-resolved high-dimensional observation into its best-matched high-dimensional state. While straightforward and convenient, this HMM simplification obscures functional and temporal nonstationarities that could reveal systematic, informative features of resting state brain dynamics at a more granular scale. We introduce a framework for studying functionally localized dynamics that intrinsically embeds them within a whole-brain HMM frame of reference. The approach is validated in a large rs-fMRI schizophrenia study where it identifies group differences in localized patterns of entropy and dynamics that help explain consistently observed differences between schizophrenia patients and controls in occupancy of whole-brain dFNC states more mechanistically.

neuroscience↗

bioRxiv2020

Modular and state-relevant connectivity in high-frequency resting-state BOLD fMRI data: An independent component analysis

Resting-state fMRI (rs-fMRI) data are typically filtered at different frequency bins between 0.008[~]0.2 Hz (varies across the literature) prior to analysis to mitigate nuisance variables (e.g., drift, motion, cardiac, and respiratory) and maximize the sensitivity to neuronal-mediated BOLD signal. However, multiple lines of evidence suggest meaningful BOLD signal may also be parsed at higher frequencies. To test this notion, a functional network connectivity (FNC) analysis based on a spatially informed independent component analysis (ICA) was performed at seven different bandpass frequency bins to examine FNC matrices across spectra. Further, eyes open (EO) vs. eyes closed (EC) resting-state acquisitions from the same participants were compared across frequency bins to examine if EO vs. EC FNC matrices and randomness estimations of FNC matrices are distinguishable at different frequencies. Results show that FNCs in higher-frequency bins display modular FNC similar to the lowest frequency bin, while r-to-z FNC and FNC-based measures indicating matrix non-randomness were highest in the 0.31-0.46 Hz range relative to all frequency bins above and below this range. As such, the FNC within this range appears to be the most temporally correlated, but the mechanisms facilitating this coherence require further analyses. Compared to EO, EC displayed greater FNC (involved in visual, cognitive control, somatomotor, and auditory domains) and randomness values at lower frequency bins, but this phenomenon flipped (EO > EC) at frequency bins greater than 0.46 Hz, particularly within visual regions. While the effect sizes range from small to large specific to frequency range and resting state (EO vs. EC), with little influence from common artifacts. These differences indicate that unique information can be derived from FNC between BOLD signals at different frequencies relative to a given restingstate acquisition and support the hypothesis meaningful BOLD signal is present at higher frequency ranges.

neuroscience↗