An official website of the United States government
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.
more »
« less
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.
more »
« less
- 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
More Like this
-
-
The National Ecological Observation Network (NEON) is a thirty-year, open-source, continental-scale ecological observation platform. The objective of the NEON project is to provide data to facilitate the understanding and forecasting of the ecological impacts of anthropogenic change at a continental scale. Fish are sentinel taxa in freshwater systems, and the NEON has been sampling and collecting fish assemblage data at wadable stream sites for six years. One to two NEON wadable stream sites are located in sixteen domains from Alaska to Puerto Rico. The goal of site selection was that sites represent local conditions but with the intention that site data be analyzed at a continental observatory level. Site selection did not include fish assemblage criteria. Without using fish assemblage criteria, anomalies in fish assemblages at the site level may skew the expected spatial patterns of North American stream fish assemblages, thereby hindering change detection in subsequent years. However, if NEON stream sites are representative of the current spatial distributions of North American stream fish assemblages, we could expect to find the most diverse sites in Atlantic drainages and the most depauperate sites in Pacific drainages. Therefore, we calculated the alpha and regional (beta) diversities of wadable stream sites to highlight spatial patterns. As expected, NEON sites followed predictable spatial diversity patterns, which could facilitate future change detection and attribution to changes in environmental drivers, if any.more » « less
-
Choosing effective methods to restore habitat for the diverse faunal assemblages of tropical forests is hampered by lack of long-term data comparing multiple restoration treatments. We conducted area counts of bird assemblages over 12 years (~5–17 years since restoration) in a blocked experiment with two active planted treatments (tree plantations and applied nucleation) and a passive restoration treatment (natural regeneration) replicated at 11 sites in Costa Rica. We also surveyed six pastures and five remnant forest sites to assess recovery of avian species richness, composition, forest specialists, and range-restricted species in restoration plots relative to degraded and reference systems. Restoration treatments showed increased resemblance of avian assemblages to remnant forest over time. Applied nucleation proved equally effective as plantation, despite a reduced planted area, whereas natural regeneration recovered more slowly. Assemblage-level trends in avian species richness and compositional similarity to reference forest are underpinned by reductions in use by pasture birds and by gradual increases in richness of forest-affiliated species. Because forest-affiliated species tend to have narrower distributions than the open-country species they replace, forest restoration can reduce biotic homogenization at the local scale. Restoration practitioners should consider applied nucleation as an alternative to standard plantations if seeking rapid recovery of bird assemblages. However, the ecological return on investment from natural regeneration increases over a couple of decades. Managers should monitor trends in forest-affiliated and rangerestricted species to track the recovery of the full avian assemblages, since coarse metrics like species richness and overall compositional similarity may plateau relatively quicklymore » « less
-
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 inLobeliasect.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 (R2: .25–.46) than were soil chemistry variables (carbon and nitrogen,R2 ≤ .01; pH,R2: .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 (SpearmanrS = .74–.84; allp < .0001) and pH (rS = .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.more » « less
-
Abstract Knowledge of someone’s friendships can powerfully impact how one interacts with them. Previous research suggests that information about others’ real-world social network positions—e.g. how well-connected they are (centrality), ‘degrees of separation’ (relative social distance)—is spontaneously encoded when encountering familiar individuals. However, many types of information covary with where someone sits in a social network. For instance, strangers’ face-based trait impressions are associated with their social network centrality, and social distance and centrality are inherently intertwined with familiarity, interpersonal similarity and memories. To disentangle the encoding of the social network position from other social information, participants learned a novel social network in which the social network position was decoupled from other factors and then saw each person’s image during functional magnetic resonance imaging scanning. Using representational similarity analysis, we found that social network centrality was robustly encoded in regions associated with visual attention and mentalizing. Thus, even when considering a social network in which one is not included and where centrality is unlinked from perceptual and experience-based features to which it is inextricably tied in naturalistic contexts, the brain encodes information about others’ importance in that network, likely shaping future perceptions of and interactions with those individuals.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1016/j.socnet.2020.11.005
