Search

Current approaches to dynamic community detection in complex networks can fail to identify multi-scale community structure, or to resolve key features of community dynamics. We propose a targeted node removal technique to improve the resolution of community detection. Using synthetic oscillator networks with well-defined "ground truth" communities, we quantify the community detection performance of a common modularity maximization algorithm. We show that the performance of the algorithm on communities of a given size deteriorates when these communities are embedded in multi-scale networks with communities of different sizes, compared to the performance in a single-scale network. We demonstrate that targeted node removal during community detection improves performance on multi-scale networks, particularly when removing the most functionally cohesive nodes. Applying this approach to network neuroscience, we compare dynamic functional brain networks derived from fMRI data taken during both repetitive single-task and varied multi-task experiments. After the removal of regions in visual cortex, the most coherent functional brain area during the tasks, community detection is better able to resolve known functional brain systems into communities. In addition, node removal enables the algorithm to distinguish clear differences in brain network dynamics between these experiments, revealing task-switching behavior that was not identified with the visual regions present in the network. These results indicate that targeted node removal can improve spatial and temporal resolution in community detection, and they demonstrate a promising approach for comparison of network dynamics between neuroscientific data sets with different resolution parameters.

Individual differences in brain functional networks may be related to complex personal identifiers, including health, age, and ability. Dynamic network theory has been used to identify properties of dynamic brain function from fMRI data, but the majority of analyses and findings remain at the level of the group. Here, we apply hypergraph analysis, a method from dynamic network theory, to quantify individual differences in brain functional dynamics. Using a summary metric derived from the hypergraph formalism-hypergraph cardinality-we investigate individual variations in two separate, complementary data sets. The first data set ("multi-task") consists of 77 individuals engaging in four consecutive cognitive tasks. We observe that hypergraph cardinality exhibits variation across individuals while remaining consistent within individuals between tasks; moreover, the analysis of one of the memory tasks revealed a marginally significant correspondence between hypergraph cardinality and age. This finding motivated a similar analysis of the second data set ("age-memory"), in which 95 individuals, aged 18-75, performed a memory task with a similar structure to the multi-task memory task. With the increased age range in the age-memory data set, the correlation between hypergraph cardinality and age correspondence becomes significant. We discuss these results in the context of the well-known finding linking age with network structure, and suggest that hypergraph analysis should serve as a useful tool in furthering our understanding of the dynamic network structure of the brain.

Infectious prions contain a self-propagating, misfolded conformer of the prion protein termed PrPSc. A critical prediction of the protein-only hypothesis is that autocatalytic PrPSc molecules should be infectious. However, some autocatalytic recombinant PrPSc molecules have low or undetectable levels of specific infectivity in bioassays, and the essential determinants of recombinant prion infectivity remain obscure. To identify structural and functional features specifically associated with infectivity, we compared the properties of two autocatalytic recombinant PrP conformers derived from the same original template, which differ by >105-fold in specific infectivity for wild-type mice. Structurally, hydrogen/deuterium exchange mass spectrometry (DXMS) studies revealed that solvent accessibility profiles of infectious and non-infectious autocatalytic recombinant PrP conformers are remarkably similar throughout their protease-resistant cores, except for two domains encompassing residues 91-115 and 144-163. Raman spectroscopy and immunoprecipitation studies confirm that these domains adopt distinct conformations within infectious versus non-infectious autocatalytic recombinant PrP conformers. Functionally, in vitro prion propagation experiments show that the non-infectious conformer is unable to seed mouse PrPC substrates containing a glycosylphosphatidylinositol (GPI) anchor, including native PrPC. Taken together, these results indicate that having a conformation that can be specifically adopted by post-translationally modified PrPC molecules is an essential determinant of biological infectivity for recombinant prions, and suggest that this ability is associated with discrete features of PrPSc structure.

Activity in the human brain moves between diverse functional states to meet the demands of our dynamic environment, but fundamental principles guiding these transitions remain poorly understood. Here, we capitalize on recent advances in network science to analyze patterns of functional interactions between brain regions. We use dynamic network representations to probe the landscape of brain reconfigurations that accompany task performance both within and between four cognitive states: a task-free resting state, an attention-demanding state, and two memory-demanding states. Using the formalism of hypergraphs, we identify the presence of groups of functional interactions that fluctuate coherently in strength over time both within (task-specific) and across (task-general) brain states. In contrast to prior emphases on the complexity of many dyadic (region-to-region) relationships, these results demonstrate that brain adaptability can be described by common processes that drive the dynamic integration of cognitive systems. Moreover, our results establish the hypergraph as an effective measure for understanding functional brain dynamics, which may also prove useful in examining cross-task, cross-age, and cross-cohort functional change.

The anatomical connectivity of the human brain supports diverse patterns of correlated neural activity that are thought to underlie cognitive function. In a manner sensitive to underlying structural brain architecture, we examine the extent to which such patterns of correlated activity systematically vary across cognitive states. Anatomical white matter connectivity is compared with functional correlations in neural activity measured via blood oxygen level dependent (BOLD) signals. Functional connectivity is separately measured at rest, during an attention task, and during a memory task. We assess these structural and functional measures within previously-identified resting-state functional networks, denoted task-positive and task-negative networks, that have been independently shown to be strongly anticorrelated at rest but also involve regions of the brain that routinely increase and decrease in activity during task-driven processes. We find that the density of anatomical connections within and between task-positive and task-negative networks is differentially related to strong, task-dependent correlations in neural activity. The space mapped out by the observed structure-function relationships is used to define a quantitative measure of separation between resting, attention, and memory states. We find that the degree of separation between states is related to both general measures of behavioral performance and relative differences in task-specific measures of attention versus memory performance. These findings suggest that the observed separation between cognitive states reflects underlying organizational principles of human brain structure and function.

We carried out a genome-wide association study (GWAS) for general cognitive ability (GCA) plus three other analyses of GWAS data that aggregate the effects of multiple single-nucleotide polymorphisms (SNPs) in various ways. Our multigenerational sample comprised 7,100 Caucasian participants, drawn from two longitudinal family studies, who had been assessed with an age-appropriate IQ test and had provided DNA samples passing quality screens. We conducted the GWAS across ∼ 2.5 million SNPs (both typed and imputed), using a generalized least-squares method appropriate for the different family structures present in our sample, and subsequently conducted gene-based association tests. We also conducted polygenic prediction analyses under five-fold cross-validation, using two different schemes of weighting SNPs. Using parametric bootstrapping, we assessed the performance of this prediction procedure under the null. Finally, we estimated the proportion of variance attributable to all genotyped SNPs as random effects with software GCTA. The study is limited chiefly by its power to detect realistic single-SNP or single-gene effects, none of which reached genome-wide significance, though some genomic inflation was evident from the GWAS. Unit SNP weights performed about as well as least-squares regression weights under cross-validation, but the performance of both increased as more SNPs were included in calculating the polygenic score. Estimates from GCTA were 35% of phenotypic variance at the recommended biological-relatedness ceiling. Taken together, our results concur with other recent studies: they support a substantial heritability of GCA, arising from a very large number of causal SNPs, each of very small effect. We place our study in the context of the literature-both contemporary and historical-and provide accessible explication of our statistical methods.

Maintaining levels of calcium in the cytosol is important for many cellular events, including cell migration, where localized regions of high calcium are required to regulate cytoskeletal dynamics, contractility, and adhesion. Studies show inositol-trisphosphate receptors (IP3R) and ryanodine receptors (RyR), which release calcium into the cytosol, are important regulators of cell migration. Similarly, proteins that return calcium to secretory stores are likely to be important for cell migration. The secretory protein calcium ATPase (SPCA) is a Golgi-localized protein that transports calcium from the cytosol into secretory stores. SPCA has established roles in protein processing, metal homeostasis, and inositol-trisphosphate signaling. Defects in the human SPCA1/ATP2C1 gene cause Hailey-Hailey disease (MIM# 169600), a genodermatosis characterized by cutaneous blisters and fissures as well as keratinocyte cell adhesion defects. We have determined that PMR-1, the Caenorhabditis elegans ortholog of SPCA1, plays an essential role in embryogenesis. Pmr-1 strains isolated from genetic screens show terminal phenotypes, such as ventral and anterior enclosure failures, body morphogenesis defects, and an unattached pharynx, which are caused by earlier defects during gastrulation. In Pmr-1 embryos, migration rates are significantly reduced for cells moving along the embryo surface, such as ventral neuroblasts, C-derived, and anterior-most blastomeres. Gene interaction experiments show changing the activity of itr-1/IP3R and unc-68/RyR modulates levels of embryonic lethality in Pmr-1 strains, indicating pmr-1 acts with these calcium channels to regulate cell migration. This analysis reveals novel genes involved in C. elegans cell migration, as well as a new role in cell migration for the highly conserved SPCA gene family.

In a sample of 3,187 twins and 3,294 of their parents, we sought to investigate association of both individual variants and a genotype-based height score involving 176 of the 180 common genetic variants with adult height identified recently by the GIANT consortium. First, longitudinal observations on height spanning pre-adolescence through adulthood in the twin sample allowed us to investigate the separate effects of the previously identified SNPs on pre-pubertal height and pubertal growth spurt. We show that the effect of SNPs identified by the GIANT consortium is primarily on prepubertal height. Only one SNP, rs7759938 in LIN28B, approached a significant association with pubertal growth. Second, we show how using the twin data to control statistically for environmental variance can provide insight into the ultimate magnitude of SNP effects and consequently the genetic architecture of a phenotype. Specifically, we computed a genetic score by weighting SNPs according to their effects as assessed via meta-analysis. This weighted score accounted for 9.2% of the phenotypic variance in height, but 14.3% of the corresponding genetic variance. Longitudinal samples will be needed to understand the developmental context of common genetic variants identified through GWAS, while genetically informative designs will be helpful in accurately characterizing the extent to which these variants account for genetic, and not just phenotypic, variance.

Previous studies identified two mammalian prion protein (PrP) polybasic domains that bind the disease-associated conformer PrP(Sc), suggesting that these domains of cellular prion protein (PrP(C)) serve as docking sites for PrP(Sc) during prion propagation. To examine the role of polybasic domains in the context of full-length PrP(C), we used prion proteins lacking one or both polybasic domains expressed from Chinese hamster ovary (CHO) cells as substrates in serial protein misfolding cyclic amplification (sPMCA) reactions. After ∼5 rounds of sPMCA, PrP(Sc) molecules lacking the central polybasic domain (ΔC) were formed. Surprisingly, in contrast to wild-type prions, ΔC-PrP(Sc) prions could bind to and induce quantitative conversion of all the polybasic domain mutant substrates into PrP(Sc) molecules. Remarkably, ΔC-PrP(Sc) and other polybasic domain PrP(Sc) molecules displayed diminished or absent biological infectivity relative to wild-type PrP(Sc), despite their ability to seed sPMCA reactions of normal mouse brain homogenate. Thus, ΔC-PrP(Sc) prions interact with PrP(C) molecules through a novel interaction mechanism, yielding an expanded substrate range and highly efficient PrP(Sc) propagation. Furthermore, polybasic domain deficient PrP(Sc) molecules provide the first example of dissociation between normal brain homogenate sPMCA seeding ability from biological prion infectivity. These results suggest that the propagation of PrP(Sc) molecules may not depend on a single stereotypic mechanism, but that normal PrP(C)/PrP(Sc) interaction through polybasic domains may be required to generate prion infectivity.

Previous studies identified prion protein (PrP) mutants which act as dominant negative inhibitors of prion formation through a mechanism hypothesized to require an unidentified species-specific cofactor termed protein X. To study the mechanism of dominant negative inhibition in vitro, we used recombinant PrP(C) molecules expressed in Chinese hamster ovary cells as substrates in serial protein misfolding cyclic amplification (sPMCA) reactions. Bioassays confirmed that the products of these reactions are infectious. Using this system, we find that: (1) trans-dominant inhibition can be dissociated from conversion activity, (2) dominant-negative inhibition of prion formation can be reconstituted in vitro using only purified substrates, even when wild type (WT) PrP(C) is pre-incubated with poly(A) RNA and PrP(Sc) template, and (3) Q172R is the only hamster PrP mutant tested that fails to convert into PrP(Sc) and that can dominantly inhibit conversion of WT PrP at sub-stoichiometric levels. These results refute the hypothesis that protein X is required to mediate dominant inhibition of prion propagation, and suggest that PrP molecules compete for binding to a nascent seeding site on newly formed PrP(Sc) molecules, most likely through an epitope containing residue 172.

The identification of genes for monogenic disorders has proven to be highly effective for understanding disease mechanisms, pathways and gene function in humans. Nevertheless, while thousands of Mendelian disorders have not yet been mapped there has been a trend away from studying single-gene disorders. In part, this is due to the fact that many of the remaining single-gene families are not large enough to map the disease locus to a single site in the genome. New tools and approaches are needed to allow researchers to effectively tap into this genetic gold-mine. Towards this goal, we have used haploid cell lines to experimentally validate the use of high-density single nucleotide polymorphism (SNP) arrays to define genome-wide haplotypes and candidate regions, using a small amyotrophic lateral sclerosis (ALS) family as a prototype. Specifically, we used haploid-cell lines to determine if high-density SNP arrays accurately predict haplotypes across entire chromosomes and show that haplotype information significantly enhances the genetic information in small families. Panels of haploid-cell lines were generated and a 5 centimorgan (cM) short tandem repeat polymorphism (STRP) genome scan was performed. Experimentally derived haplotypes for entire chromosomes were used to directly identify regions of the genome identical-by-descent in 5 affected individuals. Comparisons between experimentally determined and in silico haplotypes predicted from SNP arrays demonstrate that SNP analysis of diploid DNA accurately predicted chromosomal haplotypes. These methods precisely identified 12 candidate intervals, which are shared by all 5 affected individuals. Our study illustrates how genetic information can be maximized using readily available tools as a first step in mapping single-gene disorders in small families.

IntroductionTranscranial direct current stimulation (tDCS) administers low-intensity direct current electrical stimulation to brain regions via electrodes arranged on the surface of the scalp. The core promise of tDCS is its ability to modulate brain activity and affect performance on diverse cognitive functions (affording causal inferences regarding regional brain activity and behavior), but the optimal methodological parameters for maximizing behavioral effects remain to be elucidated. Here we sought to examine the effects of 10 stimulation and experimental design factors across a series of five cognitive domains: motor performance, visual search, working memory, vigilance, and response inhibition. The objective was to identify a set of optimal parameter settings that consistently and reliably maximized the behavioral effects of tDCS within each cognitive domain.MethodsWe surveyed tDCS effects on these various cognitive functions in healthy young adults, ultimately resulting in 721 effects across 106 published reports. Hierarchical Bayesian meta-regression models were fit to characterize how (and to what extent) these design parameters differentially predict the likelihood of positive/negative behavioral outcomes.ResultsConsistent with many previous meta-analyses of tDCS effects, extensive variability was observed across tasks and measured outcomes. Consequently, most design parameters did not confer consistent advantages or disadvantages to behavioral effects—a domain-general model suggested an advantage to using within-subjects designs (versus between-subjects) and the tendency for cathodal stimulation (relative to anodal stimulation) to produce reduced behavioral effects, but these associations were scarcely-evident in domain-specific models.DiscussionThese findings highlight the urgent need for tDCS studies to more systematically probe the effects of these parameters on behavior to fulfill the promise of identifying causal links between brain function and cognition.

Individual genetic admixture estimates, determined both across the genome and at specific genomic regions, have been proposed for use in identifying specific genomic regions harboring loci influencing phenotypes in regional admixture mapping (RAM). Estimates of individual ancestry can be used in structured association tests (SAT) to reduce confounding induced by various forms of population substructure. Although presented as two distinct approaches, we provide a conceptual framework in which both RAM and SAT are special cases of a more general linear model. We clarify which variables are sufficient to condition upon in order to prevent spurious associations and also provide a simple closed form "semiparametric" method of evaluating the reliability of individual admixture estimates. An estimate of the reliability of individual admixture estimates is required to make an inherent errors-in-variables problem tractable. Casting RAM and SAT methods as a general linear model offers enormous flexibility enabling application to a rich set of phenotypes, populations, covariates, and situations, including interaction terms and multilocus models. This approach should allow far wider use of RAM and SAT, often using standard software, in addressing admixture as either a confounder of association studies or a tool for finding loci influencing complex phenotypes in species as diverse as plants, humans, and nonhuman animals.