Cognitive flexibility describes the human ability to switch between modes of mental function to achieve goals. Mental switching is accompanied by transient changes in brain activity, which must occur atop an anatomical architecture that bridges disparate cortical and subcortical regions by underlying white matter tracts. However, an integrated perspective regarding how white matter networks might constrain brain dynamics during cognitive processes requiring flexibility has remained elusive. To address this challenge, we applied emerging tools from graph signal processing to examine whether BOLD signals measured at each point in time correspond to complex underlying anatomical networks in 28 individuals performing a perceptual task that probed cognitive flexibility. We found that the alignment between functional signals and the architecture of the underlying white matter network was associated with greater cognitive flexibility across subjects. By computing a concise measure using multi-modal neuroimaging data, we uncovered an integrated structure-function correlate of human behavior.

Modern neuroimaging techniques provide us with unique views on brain structure and function; i.e., how the brain is wired, and where and when activity takes place. Data acquired using these techniques can be analyzed in terms of its network structure to reveal organizing principles at the systems level. Graph representations are versatile models where nodes are associated to brain regions and edges to structural or functional connections. Structural graphs model neural pathways in white matter that are the anatomical backbone between regions. Functional graphs are built based on functional connectivity, which is a pairwise measure of statistical interdependency between activity traces of regions. Therefore, most research to date has focused on analyzing these graphs reflecting structure or function.Graph signal processing (GSP) is an emerging area of research where signals recorded at the nodes of the graph are studied atop the underlying graph structure. An increasing number of fundamental operations have been generalized to the graph setting, allowing to analyze the signals from a new viewpoint. Here, we review GSP for brain imaging data and discuss their potential to integrate brain structure, contained in the graph itself, with brain function, residing in the graph signals. We review how brain activity can be meaningfully filtered based on concepts of spectral modes derived from brain structure. We also derive other operations such as surrogate data generation or decompositions informed by cognitive systems. In sum, GSP offers a novel framework for the analysis of brain imaging data.

This paper presents methods to analyze functional brain networks and signals from graph spectral perspectives. The notion of frequency and filters traditionally defined for signals supported on regular domains such as discrete time and image grids has been recently generalized to irregular graph domains, and defines brain graph frequencies associated with different levels of spatial smoothness across the brain regions. Brain network frequency also enables the decomposition of brain signals into pieces corresponding to smooth or rapid variations. We relate graph frequency with principal component analysis when the networks of interest denote functional connectivity. The methods are utilized to analyze brain networks and signals as subjects master a simple motor skill. We observe that brain signals corresponding to different graph frequencies exhibit different levels of adaptability throughout learning. Further, we notice a strong association between graph spectral properties of brain networks and the level of exposure to tasks performed, and recognize the most contributing and important frequency signatures at different levels of task familiarity.

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The scientific concepts of human immunology are inherently complicated and extremely difficult to understand. Hence, this study reports on the development of an educational game entitled Humunology and examines the impact of using Humunology for learning how the body's defense system works. A total of 132 middle school students participated in this study and a quasi‐experimental approach with a two‐group pretest/posttest research design was used. A knowledge assessment including 19 items was developed, and a questionnaire instrument with seven dimensions, which focuses mainly on perceptions toward the use of Humunology, and the help‐seeking behaviors of the students, was employed. The results show that students who learned by playing Humunology significantly outperformed those who learned by using web‐based content on items that examined their understanding of procedural knowledge and higher level of cognitive process. Students in the experimental group also had a significantly higher level of satisfaction than their counterparts. In terms of predicting a student's learning achievement on the posttest, the three positive variables were the results of the pretest, perceived ease of use, peer learning and help‐seeking behaviors. The only negative one was perceived playfulness. The implications and suggestions for further research derived from these findings are discussed.

The surface tension of molten tin has been determined by the sessile drop method at temperatures ranging from 523 to 1033 K and in the oxygen partial pressure (P(O(2))) range from 2.85 x 10(-19) to 8.56 x 10(-6) MPa, and its dependence on temperature and oxygen partial pressure has been analyzed. At P(O(2))=2.85 x 10(-19) and 1.06 x 10(-15) MPa, the surface tension decreases linearly with the increase of temperature and its temperature coefficients are -0.151 and -0.094 mN m(-1) K(-1), respectively. However, at high P(O(2)) (3.17 x 10(-10), 8.56 x 10(-6) MPa), the surface tension increases with the temperature near the melting point (505 K) and decreases above 723 K. The surface tension decrease with increasing P(O(2)) is much larger near the melting point than at temperatures above 823 K. The contact angle between the molten tin and the alumina substrate is 158-173 degrees, and the wettability is poor.

Objective:To apply network-based statistics to diffusion-weighted imaging tractography data and detect Alzheimer disease vs non-Alzheimer degeneration in the context of corticobasal syndrome.Methods:In a cross-sectional design, pathology was confirmed by autopsy or a pathologically validated CSF total tau-to-β-amyloid ratio (T-tau/Aβ). Using structural MRI data, we identify association areas in fronto-temporo-parietal cortex with reduced gray matter density in corticobasal syndrome (n = 40) relative to age-matched controls (n = 40). Using these fronto-temporo-parietal regions of interest, we construct structural brain networks in clinically similar subgroups of individuals with Alzheimer disease (n = 21) or non-Alzheimer pathology (n = 19) by linking these regions by the number of white matter streamlines identified in a deterministic tractography analysis of diffusion tensor imaging data. We characterize these structural networks using 5 graph-based statistics, and assess their relative utility in classifying underlying pathology with leave-one-out cross-validation using a supervised support vector machine.Results:Gray matter density poorly discriminates between Alzheimer disease and non-Alzheimer pathology subgroups with low sensitivity (57%) and specificity (52%). In contrast, a statistic of local network efficiency demonstrates very good discriminatory power, with 85% sensitivity and 84% specificity.Conclusions:Our results indicate that the underlying pathologic sources of corticobasal syndrome can be classified more accurately using graph theoretical statistics derived from patterns of white matter network organization in association cortex than by regional gray matter density alone. These results highlight the importance of a multimodal neuroimaging approach to diagnostic analyses of corticobasal syndrome.

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