Timescales of Massive Human Entrainment

The past two decades have seen an upsurge of interest in the collective behaviors of complex systems composed of many agents entrained to each other and to external events. In this paper, we extend concepts of entrainment to the dynamics of human collective attention. We conducted a detailed investigation of the unfolding of human entrainment - as expressed by the content and patterns of hundreds of thousands of messages on Twitter - during the 2012 US presidential debates. By time locking these data sources, we quantify the impact of the unfolding debate on human attention. We show that collective social behavior covaries second-by-second to the interactional dynamics of the debates: A candidate speaking induces rapid increases in mentions of his name on social media and decreases in mentions of the other candidate. Moreover, interruptions by an interlocutor increase the attention received. We also highlight a distinct time scale for the impact of salient moments in the debate: Mentions in social media start within 5-10 seconds after the moment; peak at approximately one minute; and slowly decay in a consistent fashion across well-known events during the debates. Finally, we show that public attention after an initial burst slowly decays through the course of the debates. Thus we demonstrate that large-scale human entrainment may hold across a number of distinct scales, in an exquisitely time-locked fashion. The methods and results pave the way for careful study of the dynamics and mechanisms of large-scale human entrainment.Comment: 20 pages, 7 figures, 6 tables, 4 supplementary figures. 2nd version revised according to peer reviewers' comments: more detailed explanation of the methods, and grounding of the hypothese

Evolving Ensemble Fuzzy Classifier

The concept of ensemble learning offers a promising avenue in learning from data streams under complex environments because it addresses the bias and variance dilemma better than its single model counterpart and features a reconfigurable structure, which is well suited to the given context. While various extensions of ensemble learning for mining non-stationary data streams can be found in the literature, most of them are crafted under a static base classifier and revisits preceding samples in the sliding window for a retraining step. This feature causes computationally prohibitive complexity and is not flexible enough to cope with rapidly changing environments. Their complexities are often demanding because it involves a large collection of offline classifiers due to the absence of structural complexities reduction mechanisms and lack of an online feature selection mechanism. A novel evolving ensemble classifier, namely Parsimonious Ensemble pENsemble, is proposed in this paper. pENsemble differs from existing architectures in the fact that it is built upon an evolving classifier from data streams, termed Parsimonious Classifier pClass. pENsemble is equipped by an ensemble pruning mechanism, which estimates a localized generalization error of a base classifier. A dynamic online feature selection scenario is integrated into the pENsemble. This method allows for dynamic selection and deselection of input features on the fly. pENsemble adopts a dynamic ensemble structure to output a final classification decision where it features a novel drift detection scenario to grow the ensemble structure. The efficacy of the pENsemble has been numerically demonstrated through rigorous numerical studies with dynamic and evolving data streams where it delivers the most encouraging performance in attaining a tradeoff between accuracy and complexity.Comment: this paper has been published by IEEE Transactions on Fuzzy System

Developing Academic Persistence in the International Baccalaureate Diploma Programme: Educational Strategies, Associated Personality Traits and Outcomes

The aim of the study was to investigate the relationships between certain educational strategies and students' personality traits, on the one hand, and students' academic performance, on the other, respectively between the latter and two types of outcomes (i.e. students' academic performance and intentions to drop out of high school). These relationships were examined in two educational settings: in the Diploma Programme (DP), a two-year college-preparatory curriculum offered by the International Baccalaureate (IB), an international private educational system, and the traditional Romanian schools. A sample of IB students in 3 Eastern and Central European countries, and a comparison sample of non-IB students in Romania participated in the research. Results reveal several educational strategies and personality traits among those suggested by previous investigations that significantly sustain IB DP students’ academic persistence. Also, IB students’ academic performance and dropout intentions are influenced by these traits and educational strategies, and these effects are fully or partially mediated by academic persistence. A different pattern of associations emerged in the non-IB sample, with independent work style as the most important determinant of academic persistence, suggesting that relative to the traditional Romanian schools, the IB programme promotes a climate that better supports students in completing their education

The Parkinson's progression markers initiative (PPMI) - establishing a PD biomarker cohort.

ObjectiveThe Parkinson's Progression Markers Initiative (PPMI) is an observational, international study designed to establish biomarker-defined cohorts and identify clinical, imaging, genetic, and biospecimen Parkinson's disease (PD) progression markers to accelerate disease-modifying therapeutic trials.MethodsA total of 423 untreated PD, 196 Healthy Control (HC) and 64 SWEDD (scans without evidence of dopaminergic deficit) subjects were enrolled at 24 sites. To enroll PD subjects as early as possible following diagnosis, subjects were eligible with only asymmetric bradykinesia or tremor plus a dopamine transporter (DAT) binding deficit on SPECT imaging. Acquisition of data was standardized as detailed at www.ppmi-info.org.ResultsApproximately 9% of enrolled subjects had a single PD sign at baseline. DAT imaging excluded 16% of potential PD subjects with SWEDD. The total MDS-UPDRS for PD was 32.4 compared to 4.6 for HC and 28.2 for SWEDD. On average, PD subjects demonstrated 45% and 68% reduction in mean striatal and contralateral putamen Specific Binding Ratios (SBR), respectively. Cerebrospinal fluid (CSF) was acquired from >97% of all subjects. CSF (PD/HC/SWEDD pg/mL) α-synuclein (1845/2204/2141) was reduced in PD vs HC or SWEDD (P < 0.03). Similarly, t-tau (45/53) and p-tau (16/18) were reduced in PD versus HC (P < 0.01).InterpretationPPMI has detailed the biomarker signature for an early PD cohort defined by clinical features and imaging biomarkers. This strategy provides the framework to establish biomarker cohorts and to define longitudinal progression biomarkers to support future PD treatment trials

Domain specific traits predict achievement in music and multipotentiality

Previous research shows that individuals choose careers based on the relative strengths of various traits. More debated however, is how specific combinations of traits predict individual differences in professional achievements. General intelligence is often proposed to be the best predictor of eminence, but some studies suggest that more specific traits can be relatively important when performance depends on specific skills and expertise. Here we identified a comprehensive set of variables relevant for music achievement (intelligence, auditory ability, absolute pitch, Big-five personality traits, psychosis proneness, music flow proneness, childhood environment and music practice), and tested how they predicted level of musicianship (non-musicians vs. amateur musicians vs. professional musicians) and number of achievements among professional musicians. We used web survey data from a total of 2150 individuals, and generalized additive models that can also reveal non-linear relationships. The results largely confirmed our three main hypotheses: (i) non-musicians, amateur musicians, and professional musicians are best differentiated by domain specific abilities, personality traits, and childhood factors; (ii) largely the same significant predictors are also associated with the number of creative achievements within professional musicians; (iii) individuals who reach a professional level in two domains (here science and music) possess the union of the relevant traits of both domains. In addition, many of the associations between predictors and achievement were non-linear. This study confirms that in music, and potentially in other occupational fields where performance relies on specific competences, domain relevant characteristics may be better predictors of engagement and creative achievement than broad traits

A graph-based integration of multimodal brain imaging data for the detection of early mild cognitive impairment (E-MCI)

Alzheimer's disease (AD) is the most common cause of dementia in older adults. By the time an individual has been diagnosed with AD, it may be too late for potential disease modifying therapy to strongly influence outcome. Therefore, it is critical to develop better diagnostic tools that can recognize AD at early symptomatic and especially pre-symptomatic stages. Mild cognitive impairment (MCI), introduced to describe a prodromal stage of AD, is presently classified into early and late stages (E-MCI, L-MCI) based on severity. Using a graph-based semi-supervised learning (SSL) method to integrate multimodal brain imaging data and select valid imaging-based predictors for optimizing prediction accuracy, we developed a model to differentiate E-MCI from healthy controls (HC) for early detection of AD. Multimodal brain imaging scans (MRI and PET) of 174 E-MCI and 98 HC participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort were used in this analysis. Mean targeted region-of-interest (ROI) values extracted from structural MRI (voxel-based morphometry (VBM) and FreeSurfer V5) and PET (FDG and Florbetapir) scans were used as features. Our results show that the graph-based SSL classifiers outperformed support vector machines for this task and the best performance was obtained with 66.8% cross-validated AUC (area under the ROC curve) when FDG and FreeSurfer datasets were integrated. Valid imaging-based phenotypes selected from our approach included ROI values extracted from temporal lobe, hippocampus, and amygdala. Employing a graph-based SSL approach with multimodal brain imaging data appears to have substantial potential for detecting E-MCI for early detection of prodromal AD warranting further investigation

Genome-wide association study reveals new insights into the heritability and genetic correlates of developmental dyslexia

Developmental dyslexia (DD) is a learning disorder affecting the ability to read, with a heritability of 40-60%. A notable part of this heritability remains unexplained, and large genetic studies are warranted to identify new susceptibility genes and clarify the genetic bases of dyslexia. We carried out a genome-wide association study (GWAS) on 2274 dyslexia cases and 6272 controls, testing associations at the single variant, gene, and pathway level, and estimating heritability using single-nucleotide polymorphism (SNP) data. We also calculated polygenic scores (PGSs) based on large-scale GWAS data for different neuropsychiatric disorders and cortical brain measures, educational attainment, and fluid intelligence, testing them for association with dyslexia status in our sample. We observed statistically significant (p <2.8 x 10(-6)) enrichment of associations at the gene level, forLOC388780(20p13; uncharacterized gene), and forVEPH1(3q25), a gene implicated in brain development. We estimated an SNP-based heritability of 20-25% for DD, and observed significant associations of dyslexia risk with PGSs for attention deficit hyperactivity disorder (atp(T) = 0.05 in the training GWAS: OR = 1.23[1.16; 1.30] per standard deviation increase;p = 8 x 10(-13)), bipolar disorder (1.53[1.44; 1.63];p = 1 x 10(-43)), schizophrenia (1.36[1.28; 1.45];p = 4 x 10(-22)), psychiatric cross-disorder susceptibility (1.23[1.16; 1.30];p = 3 x 10(-12)), cortical thickness of the transverse temporal gyrus (0.90[0.86; 0.96];p = 5 x 10(-4)), educational attainment (0.86[0.82; 0.91];p = 2 x 10(-7)), and intelligence (0.72[0.68; 0.76];p = 9 x 10(-29)). This study suggests an important contribution of common genetic variants to dyslexia risk, and novel genomic overlaps with psychiatric conditions like bipolar disorder, schizophrenia, and cross-disorder susceptibility. Moreover, it revealed the presence of shared genetic foundations with a neural correlate previously implicated in dyslexia by neuroimaging evidence.Peer reviewe