CanICA: Model-based extraction of reproducible group-level ICA patterns from fMRI time series

Spatial Independent Component Analysis (ICA) is an increasingly used data-driven method to analyze functional Magnetic Resonance Imaging (fMRI) data. To date, it has been used to extract meaningful patterns without prior information. However, ICA is not robust to mild data variation and remains a parameter-sensitive algorithm. The validity of the extracted patterns is hard to establish, as well as the significance of differences between patterns extracted from different groups of subjects. We start from a generative model of the fMRI group data to introduce a probabilistic ICA pattern-extraction algorithm, called CanICA (Canonical ICA). Thanks to an explicit noise model and canonical correlation analysis, our method is auto-calibrated and identifies the group-reproducible data subspace before performing ICA. We compare our method to state-of-the-art multi-subject fMRI ICA methods and show that the features extracted are more reproducible

A group model for stable multi-subject ICA on fMRI datasets

Spatial Independent Component Analysis (ICA) is an increasingly used data-driven method to analyze functional Magnetic Resonance Imaging (fMRI) data. To date, it has been used to extract sets of mutually correlated brain regions without prior information on the time course of these regions. Some of these sets of regions, interpreted as functional networks, have recently been used to provide markers of brain diseases and open the road to paradigm-free population comparisons. Such group studies raise the question of modeling subject variability within ICA: how can the patterns representative of a group be modeled and estimated via ICA for reliable inter-group comparisons? In this paper, we propose a hierarchical model for patterns in multi-subject fMRI datasets, akin to mixed-effect group models used in linear-model-based analysis. We introduce an estimation procedure, CanICA (Canonical ICA), based on i) probabilistic dimension reduction of the individual data, ii) canonical correlation analysis to identify a data subspace common to the group iii) ICA-based pattern extraction. In addition, we introduce a procedure based on cross-validation to quantify the stability of ICA patterns at the level of the group. We compare our method with state-of-the-art multi-subject fMRI ICA methods and show that the features extracted using our procedure are more reproducible at the group level on two datasets of 12 healthy controls: a resting-state and a functional localizer study

Analysing the bioactive makeup of demineralised dentine matrix on bone marrow mesenchymal stem cells for enhanced bone repair

Dentine matrix has proposed roles for directing mineralised tissue repair in dentine and bone; however, the range of bioactive components in dentine and specific biological effects on bone-derived mesenchymal stem cells (MSCs) in humans are less well understood. The aims of this study were to further elucidate the biological response of MSCs to demineralised dentine matrix (DDM) in enhancing wound repair responses and ascertain key contributing components. Dentine was obtained from human teeth and DDM proteins solubilised with ethylenediaminetetraacetic acid (EDTA). Bone marrow derived MSCs were commercially obtained. Cells with a more immature phenotype were then selected by preferential fibronectin adhesion (FN-BMMSCs) for use in subsequent in vitro assays. DDM at 10 μg/mL reduced cell expansion, attenuated apoptosis and was the minimal concentration capable of inducing osteoblastic differentiation. Enzyme-linked immunosorbent assay (ELISA) quantification of growth factors indicated physiological levels produced the above responses; transforming growth factor β (TGF-β1) was predominant (15.6 ng/mg DDM), with relatively lower concentrations of BMP-2, FGF, VEGF and PDGF (6.2-4.7 ng/mg DDM). Fractionation of growth factors from other DDM components by heparin affinity chromatography diminished osteogenic responses. Depletion of biglycan from DDM also attenuated osteogenic potency, which was partially rescued by the isolated biglycan. Decorin depletion from DDM had no influence on osteogenic potency. Collectively, these results demonstrate the potential of DDM for the delivery of physiological levels of growth factors for bone repair processes, and substantiate a role for biglycan as an additional adjuvant for driving osteogenic pathways

Development and Uses of Upper-division Conceptual Assessment

The use of validated conceptual assessments alongside more standard course exams has become standard practice for the introductory courses in many physics departments. These assessments provide a more standard measure of certain learning goals, allowing for comparisons of student learning across instructors, semesters, and institutions. Researchers at the University of Colorado Boulder have developed several similar assessments designed to target the more advanced physics content of upper-division classical mechanics, electrostatics, quantum mechanics, and electrodynamics. Here, we synthesize the existing research on our upper-division assessments and discuss some of the barriers and challenges associated with developing, validating, and implementing these assessments as well as some of the strategies we have used to overcome these barriers.Comment: 12 pages, 5 figures, submitted to the Phys. Rev. ST - PER Focused collection on Upper-division PE

A review on recent advances on knowledge management implementations

Knowledge management plays an essential role on developing efficient systems in educational systems. However, there are different factors influencing the success of knowledge management. In this paper, we review recent advances on implementation of knowledge management (KM) in different areas and discuss why some of KM implementations fail and how they could turn to a successful one. The review focus more on recently published papers in different perspective from the implementation of KM in educational units to KM implementation on project management field

A study on the effects of intellectual capital efficiency on economic performance

Intellectual capital plays essential role in corporate performance and this paper examines the impact of intellectual capital and its components on the ratio of corporate operating profit on sales as an indicator of economic performance. The study was accomplished among 1035 companies listed on Tehran Stock Exchange and by using the Pulic-2004 model over the period 2005-2012. The results indicate that intellectual value added coefficient, as an indicator of intellectual capital efficiency, preserves a positive effect on sales and efficiency of structural capital and capital employed maintains a positive and meaningful effects on different financial ratios

Growth factor liberation and DPSCs response following dentine conditioning

Liberation of the sequestrated bioactive molecules from dentine by the action of applied dental materials has been proposed as an important mechanism in inducing a dentinogenic response in teeth with viable pulps. Although adhesive restorations and dentine-bonding procedures are routinely practiced, clinical protocols to improve pulp protection and dentine regeneration are not currently driven by biological knowledge. This study investigated the effect of dentine (powder and slice) conditioning by etchants/conditioners relevant to adhesive restorative systems on growth factor solubilization and odontoblast-like cell differentiation of human dental pulp progenitor cells (DPSCs). The agents included ethylenediaminetetraacetic acid (EDTA; 10%, pH 7.2), phosphoric acid (37%, pH <1), citric acid (10%, pH 1.5), and polyacrylic acid (25%, pH 3.9). Growth factors were detected in dentine matrix extracts drawn by EDTA, phosphoric acid, and citric acid from powdered dentine. The dentine matrix extracts were shown to be bioactive, capable of stimulating odontogenic/osteogenic differentiation as observed by gene expression and phenotypic changes in DPSCs cultured in monolayer on plastic. Polyacrylic acid failed to solubilize proteins from powdered dentine and was therefore considered ineffective in triggering a growth factor–mediated response in cells. The study went on to investigate the effect of conditioning dentine slices on growth factor liberation and DPSC behavior. Conditioning by EDTA, phosphoric acid, and citric acid exposed growth factors on dentine and triggered an upregulation in genes associated with mineralized differentiation, osteopontin, and alkaline phosphatase in DPSCs cultured on dentine. The cells demonstrated odontoblast-like appearances with elongated bodies and long extracellular processes extending on dentine surface. However, phosphoric acid–treated dentine appeared strikingly less populated with cells, suggesting a detrimental impact on cell attachment and growth when conditioning by this agent. These findings take crucial steps in informing clinical practice on dentine-conditioning protocols as far as treatment of operatively exposed dentine in teeth with vital pulps is concerned

The Relation of Ongoing Brain Activity, Evoked Neural Responses, and Cognition

Ongoing brain activity has been observed since the earliest neurophysiological recordings and is found over a wide range of temporal and spatial scales. It is characterized by remarkably large spontaneous modulations. Here, we review evidence for the functional role of these ongoing activity fluctuations and argue that they constitute an essential property of the neural architecture underlying cognition. The role of spontaneous activity fluctuations is probably best understood when considering both their spatiotemporal structure and their functional impact on cognition. We first briefly argue against a “segregationist” view on ongoing activity, both in time and space, which would selectively associate certain frequency bands or levels of spatial organization with specific functional roles. Instead, we emphasize the functional importance of the full range, from differentiation to integration, of intrinsic activity within a hierarchical spatiotemporal structure. We then highlight the flexibility and context-sensitivity of intrinsic functional connectivity that suggest its involvement in functionally relevant information processing. This role in information processing is pursued by reviewing how ongoing brain activity interacts with afferent and efferent information exchange of the brain with its environment. We focus on the relationship between the variability of ongoing and evoked brain activity, and review recent reports that tie ongoing brain activity fluctuations to variability in human perception and behavior. Finally, these observations are discussed within the framework of the free-energy principle which – applied to human brain function – provides a theoretical account for a non-random, coordinated interaction of ongoing and evoked activity in perception and behavior

Markov models for fMRI correlation structure: is brain functional connectivity small world, or decomposable into networks?

Correlations in the signal observed via functional Magnetic Resonance Imaging (fMRI), are expected to reveal the interactions in the underlying neural populations through hemodynamic response. In particular, they highlight distributed set of mutually correlated regions that correspond to brain networks related to different cognitive functions. Yet graph-theoretical studies of neural connections give a different picture: that of a highly integrated system with small-world properties: local clustering but with short pathways across the complete structure. We examine the conditional independence properties of the fMRI signal, i.e. its Markov structure, to find realistic assumptions on the connectivity structure that are required to explain the observed functional connectivity. In particular we seek a decomposition of the Markov structure into segregated functional networks using decomposable graphs: a set of strongly-connected and partially overlapping cliques. We introduce a new method to efficiently extract such cliques on a large, strongly-connected graph. We compare methods learning different graph structures from functional connectivity by testing the goodness of fit of the model they learn on new data. We find that summarizing the structure as strongly-connected networks can give a good description only for very large and overlapping networks. These results highlight that Markov models are good tools to identify the structure of brain connectivity from fMRI signals, but for this purpose they must reflect the small-world properties of the underlying neural systems