Functional brain network efficiency predicts intelligence

The neuronal causes of individual differences in mental abilities such as intelligence are complex and profoundly important. Understanding these abilities has the potential to facilitate their enhancement. The purpose of this study was to identify the functional brain network characteristics and their relation to psychometric intelligence. In particular, we examined whether the functional network exhibits efficient small-world network attributes (high clustering and short path length) and whether these small-world network parameters are associated with intellectual performance. High-density resting state electroencephalography (EEG) was recorded in 74 healthy subjects to analyze graph-theoretical functional network characteristics at an intracortical level. Ravens advanced progressive matrices were used to assess intelligence. We found that the clustering coefficient and path length of the functional network are strongly related to intelligence. Thus, the more intelligent the subjects are the more the functional brain network resembles a small-world network. We further identified the parietal cortex as a main hub of this resting state network as indicated by increased degree centrality that is associated with higher intelligence. Taken together, this is the first study that substantiates the neural efficiency hypothesis as well as the Parieto-Frontal Integration Theory (P-FIT) of intelligence in the context of functional brain network characteristics. These theories are currently the most established intelligence theories in neuroscience. Our findings revealed robust evidence of an efficiently organized resting state functional brain network for highly productive cognitions.

Relations among fluid intelligence, sensory discrimination and working memory in middle to late childhood – A latent variable approach

Two factors that have been suggested as key in explaining individual differences in fluid intelligence are working memory and sensory discrimination ability. A latent variable approach was used to explore the relative contributions of these two variables to individual differences in fluid intelligence in middle to late childhood. A sample of 263 children aged 7–12 years was examined. Correlational analyses showed that general discrimination ability (GDA)and working memory (WM) were related to each other and to fluid intelligence. Structural equation modeling showed that within both younger and older age groups and the sample as a whole, the relation between GDA and fluid intelligence could be accounted for by WM. While WM was able to predict variance in fluid intelligence above and beyond GDA, GDA was not able to explain significant amounts of variance in fluid intelligence, either in the whole sample or within the younger or older age group. We concluded that compared to GDA, WM should be considered the better predictor of individual differences in fluid intelligence in childhood. WM and fluid intelligence, while not being separable in middle childhood, develop at different rates, becoming more separable with age.

Cancer intelligence acquired (CIA): tumor glycosylation and sialylation codes dismantling antitumor defense

Aberrant glycosylation is a key feature of malignant transformation and reflects epigenetic and genetic anomalies among the multitude of molecules involved in glycan biosynthesis. Although glycan biosynthesis is not template bound, altered tumor glycosylation is not random, but associated with common glycosylation patterns. Evidence suggests that acquisition of distinct glycosylation patterns evolves from a ‘microevolutionary’ process conferring advantages in terms of tumor growth, tumor dissemination, and immune escape. Such glycosylation modifications also involve xeno- and hypersialylation. Xeno-autoantigens such as Neu5Gc-gangliosides provide potential targets for immunotherapy. Hypersialylation may display ‘enhanced self’ to escape immunosurveillance and involves several not mutually exclusive inhibitory pathways that all rely on protein–glycan interactions. A better understanding of tumor ‘glycan codes’ as deciphered by lectins, such as siglecs, selectins, C-type lectins and galectins, may lead to novel treatment strategies, not only in cancer, but also in autoimmune disease or transplantation.

Ambient cosmology and spacetime singularities

We present a new approach to the issues of spacetime singularities and cosmic censorship in general relativity. This is based on the idea that standard 4-dimensional spacetime is the conformal infinity of an ambient metric for the 5-dimensional Einstein equations with fluid sources. We then find that the existence of spacetime singularities in four dimensions is constrained by asymptotic properties of the ambient 5-metric, while the non-degeneracy of the latter crucially depends on cosmic censorship holding on the boundary.

Emotional stability promotes intelligence

Emotional stability promotes intelligence In the research field of the relationship between intelligence and personality factors, one of the most consistent findings is that intelligence is positively correlated with emotional stability. However, few studies have considered this relationship in children, and very few have differentiated between types of intelligence as well as underlying differences in working memory capacity when explaining the relationship between intelligence scores and emotional stability. In this study, the level of emotional stability and performance in a proxy for fluid and crystallized intelligence as well as in two working memory tasks was assessed in a sample of 397 primary school children. Results reveal that emotional stability is significantly positively related to vocabulary (crystallized intelligence), moderated by high working memory performance, but unrelated to abstract reasoning (fluid intelligence). This was interpreted as indicating that the positive relationship between intelligence and emotional stability is mainly due to learning advantages starting in early age, due to high working memory performance, rather than to higher general intelligence. This bears the important implication that emotionally labile children (high level of neuroticism) should be supported to regulate their negative emotions, intrusive thoughts and anxiety as early as possible to eliminate progressive learning disadvantages. One approach to do so is by specific working memory training targeting the improvement of emotional regulation skills.

Elucidating the Functional Relationship Between Working Memory Capacity and Psychometric Intelligence: A Fixed-Links Modeling Approach for Experimental Repeated-Measures Designs

Numerous studies reported a strong link between working memory capacity (WMC) and fluid intelligence (Gf), although views differ in respect to how close these two constructs are related to each other. In the present study, we used a WMC task with five levels of task demands to assess the relationship between WMC and Gf by means of a new methodological approach referred to as fixed-links modeling. Fixed-links models belong to the family of confirmatory factor analysis (CFA) and are of particular interest for experimental, repeated-measures designs. With this technique, processes systematically varying across task conditions can be disentangled from processes unaffected by the experimental manipulation. Proceeding from the assumption that experimental manipulation in a WMC task leads to increasing demands on WMC, the processes systematically varying across task conditions can be assumed to be WMC-specific. Processes not varying across task conditions, on the other hand, are probably independent of WMC. Fixed-links models allow for representing these two kinds of processes by two independent latent variables. In contrast to traditional CFA where a common latent variable is derived from the different task conditions, fixed-links models facilitate a more precise or purified representation of the WMC-related processes of interest. By using fixed-links modeling to analyze data of 200 participants, we identified a non-experimental latent variable, representing processes that remained constant irrespective of the WMC task conditions, and an experimental latent variable which reflected processes that varied as a function of experimental manipulation. This latter variable represents the increasing demands on WMC and, hence, was considered a purified measure of WMC controlled for the constant processes. Fixed-links modeling showed that both the purified measure of WMC (β = .48) as well as the constant processes involved in the task (β = .45) were related to Gf. Taken together, these two latent variables explained the same portion of variance of Gf as a single latent variable obtained by traditional CFA (β = .65) indicating that traditional CFA causes an overestimation of the effective relationship between WMC and Gf. Thus, fixed-links modeling provides a feasible method for a more valid investigation of the functional relationship between specific constructs.

Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.

Topology of the ambient boundary and the convergence of causal curves

We discuss the topological nature of the boundary spacetime, the conformal infinity of the ambient cosmological metric. Due to the existence of a homothetic group, the bounding spacetime must be equipped not with the usual Euclidean metric topology but with the Zeeman fine topology. This then places severe constraints to the convergence of a sequence of causal curves and the extraction of a limit curve, and also to our ability to formulate conditions for singularity formation.

Intelligence and the psychological refractory period: A lateralized readiness potential study

The psychological refractory period (PRP) refers to a delay of response times (RT) to the second of two stimuli when these stimuli are presented in rapid succession. If this limitation of rapidly processing the second stimulus contributes to the well-known differences in speed of information processing between individuals with higher and lower mental ability, individuals with lower mental ability should exhibit a more pronounced PRP effect than individuals with higher mental ability. Previous studies on this question, however, yielded inconsistent results. In the present study, we assessed mental ability-related differences in the PRP by measuring lateralized readiness potentials (LRPs) to separate premotor and motor aspects of speed of information processing in 95 individuals with higher and 95 individuals with lower mental ability. Although individuals with higher mental ability processed information faster than individuals with lower mental ability as indicated by shorter RTs and shorter premotor LRP latencies, the PRP effect was equally pronounced in both groups. These findings suggest that the processes underlying the PRP effect do not contribute to mental ability-related differences in speed of information processing. Rather, these differences seem to occur at an earlier stage of information processing such as stimulus encoding, stimulus analysis, or stimulus evaluation.

Elucidating underlying processes of psychometric intelligence: A comparison of behavioral response latency and P3 latency in a modified Hick reaction time paradigm

The mental speed approach explains individual differences in intelligence by faster information processing in individuals with higher compared to lower intelligence - especially in elementary cognitive tasks (ECTs). One of the most examined ECTs is the Hick paradigm. The present study aimed to contrast reaction time (RT) and P3 latency in a Hick task as predictors of intelligence. Although both, RT and P3 latency, are commonly used as indicators of mental speed, it is also known that they measure different aspects of information processing. Participants were 113 female students. RT and P3 latency were measured while participants completed the Hick task with four levels of complexity. Intelligence was assessed with Cattell's Culture Fair Test. A RT factor and a P3 factor were extracted by employing a PCA across complexity levels. There was no significant correlation between the factors. Commonality analysis was used to determine the proportions of unique and shared variance in intelligence explained by the RT and P3 latency factors. RT and P3 latency explained 5.5% and 5% of unique variance in intelligence. However, the two speed factors did not explain a significant portion of shared variance. This result suggests that RT and P3 latency in the Hick paradigm are measuring different aspects of information processing that explain different parts of variance in intelligence.

Real-time analysis of δ13C- and δD-CH4 in ambient air with laser spectroscopy: method development and first intercomparison results

In situ and simultaneous measurement of the three most abundant isotopologues of methane using mid-infrared laser absorption spectroscopy is demonstrated. A field-deployable, autonomous platform is realized by coupling a compact quantum cascade laser absorption spectrometer (QCLAS) to a preconcentration unit, called trace gas extractor (TREX). This unit enhances CH4 mole fractions by a factor of up to 500 above ambient levels and quantitatively separates interfering trace gases such as N2O and CO2. The analytical precision of the QCLAS isotope measurement on the preconcentrated (750 ppm, parts-per-million, µmole mole−1) methane is 0.1 and 0.5 ‰ for δ13C- and δD-CH4 at 10 min averaging time. Based on repeated measurements of compressed air during a 2-week intercomparison campaign, the repeatability of the TREX–QCLAS was determined to be 0.19 and 1.9 ‰ for δ13C and δD-CH4, respectively. In this intercomparison campaign the new in situ technique is compared to isotope-ratio mass spectrometry (IRMS) based on glass flask and bag sampling and real time CH4 isotope analysis by two commercially available laser spectrometers. Both laser-based analyzers were limited to methane mole fraction and δ13C-CH4 analysis, and only one of them, a cavity ring down spectrometer, was capable to deliver meaningful data for the isotopic composition. After correcting for scale offsets, the average difference between TREX–QCLAS data and bag/flask sampling–IRMS values are within the extended WMO compatibility goals of 0.2 and 5 ‰ for δ13C- and δD-CH4, respectively. This also displays the potential to improve the interlaboratory compatibility based on the analysis of a reference air sample with accurately determined isotopic composition.

Worry activation impairs intelligence test performace only under ego depletion

Detrimental effects of anxiety on cognitive performance have been explained by the activation of worry, detracting attention away from the task at hand. However, recent research has shown that anxiety is only related to performance when self-control capacity is low (i.e., ego depletion). The aim of the present work has been to extend these findings by showing that activation of worry will interfere with cognitive performance more strongly when self-control capacity is momentarily depleted compared to intact. After manipulations of self-control capacity and worry activation, 70 undergraduates completed a standardized intelligence test. As expected, activation of worry was associated with lower performance when self-control capacity was depleted, but had no effect when self-control capacity was intact. The findings implicate that worry may play a causal role in the anxiety–performance relationship, but only when its regulation by self-control is momentarily hindered.