Dataset Information

Evaluation of NEON Data to Model Spatio-Temporal Tick Dynamics in Florida.

ABSTRACT: In 2013, the National Ecological Observatory Network (NEON) started collecting 30-year multi-faceted ecological data at various spatial and temporal scales across the US including ticks. Understanding the abundance and dynamics of disease vectors under changing environmental conditions in the long-term is important to societies, but sustained long-term collection efforts are sparse. Using hard-bodied tick data collected by NEON, the vegetation and atmospheric data and a statistical state-space model, which included a detection probability component, this study estimated the abundance of tick nymphs and adult ticks across a Florida NEON location. It took into account the spatial and temporal variation, and factors affecting detection. Its purpose was to test the applicability of data collected thus far and evaluate tick abundance. The study found an increase in tick abundance at this Florida location, and was able to explain spatial and temporal variability in abundance and detection. This approach shows the potential of NEON data. The NEON data collection is unique in scale, and promises to be of great value to understand tick and disease dynamics across the US. From a public health perspective, the detection probability of vectors can be interpreted as the probability of encountering that vector, making these types of analyses useful for estimating disease risk.

SUBMITTER: Klarenberg G

PROVIDER: S-EPMC6836180 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Evaluation of NEON Data to Model Spatio-Temporal Tick Dynamics in Florida.

Klarenberg Geraldine G Wisely Samantha M SM

Insects 20190927 10

In 2013, the National Ecological Observatory Network (NEON) started collecting 30-year multi-faceted ecological data at various spatial and temporal scales across the US including ticks. Understanding the abundance and dynamics of disease vectors under changing environmental conditions in the long-term is important to societies, but sustained long-term collection efforts are sparse. Using hard-bodied tick data collected by NEON, the vegetation and atmospheric data and a statistical state-space m ...[more]

PMID: 31569729

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Project description:Resting-state brain activities have been extensively investigated to understand the macro-scale network architecture of the human brain using non-invasive imaging methods such as fMRI, EEG, and MEG. Previous studies revealed a mechanistic origin of resting-state networks (RSNs) using the connectome dynamics modeling approach, where the neural mass dynamics model constrained by the structural connectivity is simulated to replicate the resting-state networks measured with fMRI and/or fast synchronization transitions with EEG/MEG. However, there is still little understanding of the relationship between the slow fluctuations measured with fMRI and the fast synchronization transitions with EEG/MEG. In this study, as a first step toward evaluating experimental evidence of resting state activity at two different time scales but in a unified way, we investigate connectome dynamics models that simultaneously explain resting-state functional connectivity (rsFC) and EEG microstates. Here, we introduce empirical rsFC and microstates as evaluation criteria of simulated neuronal dynamics obtained by the Larter-Breakspear model in one cortical region connected with those in other cortical regions based on structural connectivity. We optimized the global coupling strength and the local gain parameter (variance of the excitatory and inhibitory threshold) of the simulated neuronal dynamics by fitting both rsFC and microstate spatial patterns to those of experimental ones. As a result, we found that simulated neuronal dynamics in a narrow optimal parameter range simultaneously reproduced empirical rsFC and microstates. Two parameter groups had different inter-regional interdependence. One type of dynamics was synchronized across the whole brain region, and the other type was synchronized between brain regions with strong structural connectivity. In other words, both fast synchronization transitions and slow BOLD fluctuation changed based on structural connectivity in the two parameter groups. Empirical microstates were similar to simulated microstates in the two parameter groups. Thus, fast synchronization transitions correlated with slow BOLD fluctuation based on structural connectivity yielded characteristics of microstates. Our results demonstrate that a bottom-up approach, which extends the single neuronal dynamics model based on empirical observations into a neural mass dynamics model and integrates structural connectivity, effectively reveals both macroscopic fast, and slow resting-state network dynamics.

Dataset Information

Evaluation of NEON Data to Model Spatio-Temporal Tick Dynamics in Florida.

Publications

Evaluation of NEON Data to Model Spatio-Temporal Tick Dynamics in Florida.

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