The goal of this paper is to use graph theory network measures derived from non-invasive electroencephalography (EEG) to develop neural decoders that can differentiate Parkinson's disease (PD) patients from healthy controls (HC). EEG signals from 27 patients and 27 demographically matched controls from New Mexico were analyzed by estimating their functional networks. Data recorded from the patients during ON and OFF levodopa sessions were included in the analysis for comparison. We used betweenness centrality of estimated functional networks to classify the HC and PD groups. The classifiers were evaluated using leave-one-out cross-validation. We observed that the PD patients (on and off medication) could be distinguished from healthy controls with 89% accuracy – approximately 4% higher than the state-of-the-art on the same dataset. This work shows that brain network analysis using extracranial resting-state EEG can discover patterns of interactions indicative of PD. This approach can also be extended to other neurological disorders.
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Assessing the performance of the bootstrap in simulated assemblage networks
Archaeologists are increasingly interested in networks constructed from site assemblage data, in which weighted network ties reflect sites’ assemblage similarity. Equivalent networks would arise in other scientific fields where actors’ similarity is assessed by comparing distributions of observed counts, so the assemblages studied here can represent other kinds of distributions in other domains. One concern with such work is that sampling variability in the assemblage network and, in turn, sampling variability in measures calculated from the network must be recognized in any comprehensive analysis. In this study, we investigated the use of the bootstrap as a means of estimating sampling variability in measures of assemblage networks. We evaluated the performance of the bootstrap in simulated assemblage networks, using a probability structure based on the actual distribution of sherds of ceramic wares in a region with 25 archaeological sites. Results indicated that the bootstrap was successful in estimating the true sampling variability of eigenvector centrality for the 25 sites. This held both for centrality scores and for centrality ranks, as well as the ratio of first to second eigenvalues of the network (similarity) matrix. Findings encourage the use of the bootstrap as a tool in analyses of network data derived from counts.
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- NSF-PAR ID:
- 10280572
- Date Published:
- Journal Name:
- Social Networks
- Volume:
- 65
- ISSN:
- 0378-8733
- Page Range / eLocation ID:
- 98-109
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Inclusion of edaphic conditions in biogeographical studies typically provides a better fit and deeper understanding of plant distributions. Increased reliance on soil data calls for easily accessible data layers providing continuous soil predictions worldwide. Although SoilGrids provides a potentially useful source of predicted soil data for biogeographic applications, its accuracy for estimating the soil characteristics experienced by individuals in small‐scale populations is unclear. We used a biogeographic sampling approach to obtain soil samples from 212 sites across the midwestern and eastern United States, sampling only at sites where there was a population of one of the 22 species in
Lobelia sect.Lobelia . We analyzed six physical and chemical characteristics in our samples and compared them with predicted values from SoilGrids. Across all sites and species, soil texture variables (clay, silt, sand) were better predicted by SoilGrids (R 2: .25–.46) than were soil chemistry variables (carbon and nitrogen,R 2 ≤ .01; pH,R 2: .19). While SoilGrids predictions rarely matched actual field values for any variable, we were able to recover qualitative patterns relating species means and population‐level plant characteristics to soil texture and pH. Rank order of species mean values from SoilGrids and direct measures were much more consistent for soil texture (Spearmanr S = .74–.84; allp < .0001) and pH (r S = .61,p = .002) than for carbon and nitrogen (p > .35). Within the speciesL. siphilitica , a significant association, known from field measurements, between soil texture and population sex ratios could be detected using SoilGrids data, but only with large numbers of sites. Our results suggest that modeled soil texture values can be used with caution in biogeographic applications, such as species distribution modeling, but that soil carbon and nitrogen contents are currently unreliable, at least in the region studied here. -
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Abstract Aim Efforts to predict the responses of soil fungal communities to climate change are hindered by limited information on how fungal niches are distributed across environmental hyperspace. We predict the climate sensitivity of North American soil fungal assemblage composition by modelling the ecological niches of several thousand fungal species.
Location One hundred and thirteen sites in the United States and Canada spanning all biomes except tropical rain forest.
Major Taxa Studied Fungi.
Time Period 2011–2018.
Methods We combine internal transcribed spacer (ITS) sequences from two continental‐scale sampling networks in North America and cluster them into operational taxonomic units (OTUs) at 97% similarity. Using climate and soil data, we fit ecological niche models (ENMs) based on logistic ridge regression for all OTUs present in at least 10 sites (
n = 8597). To describe the compositional turnover of soil fungal assemblages over climatic gradients, we introduce a novel niche‐based metric of climate sensitivity, the Sørensen climate sensitivity index. Finally, we map climate sensitivity across North America.Results ENMs have a mean out‐of‐sample predictive accuracy of 73.8%, with temperature variables being strong predictors of fungal distributions. Soil fungal climate niches clump together across environmental space, which suggests common physiological limits and predicts abrupt changes in composition with respect to changes in climate. Soil fungi in North American climates are more likely to be limited by cold and dry conditions than by warm and wet conditions, and ectomycorrhizal fungi generally tolerate colder temperatures than saprotrophic fungi. Sørensen climate sensitivity exhibits a multimodal distribution across environmental space, with a peak in climates corresponding to boreal forests.
Main Conclusions The boreal forest occupies an especially precarious region of environmental space for the composition of soil fungal assemblages in North America, as even small degrees of warming could trigger large compositional changes characterized mainly by an influx of warm‐adapted species.
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Abstract Heterogeneous distributions are a fundamental principle of ecology, manifesting as natural variability within ecological levels of organization from individuals to ecosystems. In disease ecology, variability in biotic and abiotic factors can result in heterogeneous patterns of transmission and virulence—broadly defined here as the negative consequences of infection. Still, our classic theoretical understanding of disease dynamics comes from models that assume homogeneous transmission and virulence. Here, we test this assumption by assessing the contribution of various sources of individual and spatial heterogeneity to patterns of transmission and sublethal measurements of virulence in two lizard–malaria systems: a three‐parasite assemblage (
Plasmodium floridense ,Plasmodium leukocytica , andPlasmodium azurophilum ) infecting the lizardAnolis gundlachi in the rainforest of Puerto Rico and a single‐parasite system (P. floridense –Anolis sagrei ) in Florida. Using a Bayesian model selection framework, we evaluated whether individual host differences (i.e., body size and sex) or spatial variability (i.e., habitat type and local‐scale host spatial structure) drive heterogeneity in the probability of infection or its associated health costs (i.e., body condition, blood chemistry). We found that the probability of infection increases with increasing lizard body size in both systems. However, in Florida, we found the relationship to be subdued in deforested habitats compared to the adjacent urban hydric forests. Furthermore, infection was spatially clustered within sampling sites, with “hot” and “cold” spots across the landscape. Nevertheless, we found no clear evidence of costs of infection on lizard health in any of the measures assessed and hence no grounds for inference regarding heterogeneous virulence. Ultimately, the consistency of our results across systems suggests prominent roles of individual and spatial heterogeneities as driving factors of transmission of vector‐borne diseases. -
Grilli, Jacopo (Ed.)A major strategy to prevent the spread of COVID-19 is the limiting of in-person contacts. However, limiting contacts is impractical or impossible for the many disabled people who do not live in care facilities but still require caregivers to assist them with activities of daily living. We seek to determine which interventions can best prevent infections of disabled people and their caregivers. To accomplish this, we simulate COVID-19 transmission with a compartmental model that includes susceptible, exposed, asymptomatic, symptomatically ill, hospitalized, and removed/recovered individuals. The networks on which we simulate disease spread incorporate heterogeneity in the risk levels of different types of interactions, time-dependent lockdown and reopening measures, and interaction distributions for four different groups (caregivers, disabled people, essential workers, and the general population). Of these groups, we find that the probability of becoming infected is largest for caregivers and second largest for disabled people. Consistent with this finding, our analysis of network structure illustrates that caregivers have the largest modal eigenvector centrality of the four groups. We find that two interventions—contact-limiting by all groups and mask-wearing by disabled people and caregivers—most reduce the number of infections in disabled and caregiver populations. We also test which group of people spreads COVID-19 most readily by seeding infections in a subset of each group and comparing the total number of infections as the disease spreads. We find that caregivers are the most potent spreaders of COVID-19, particularly to other caregivers and to disabled people. We test where to use limited infection-blocking vaccine doses most effectively and find that (1) vaccinating caregivers better protects disabled people from infection than vaccinating the general population or essential workers and that (2) vaccinating caregivers protects disabled people from infection about as effectively as vaccinating disabled people themselves. Our results highlight the potential effectiveness of mask-wearing, contact-limiting throughout society, and strategic vaccination for limiting the exposure of disabled people and their caregivers to COVID-19.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.socnet.2020.11.005
