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

The dark clump near Abell 1942: dark matter halo or statistical fluke?

Weak lensing surveys provide the possibility of identifying dark matter halos based on their total matter content rather than just the luminous matter content. On the basis of two sets of observations carried out with the CFHT, Erben et al. (2000) presented the first candidate dark clump, i.e. a dark matter concentration identified by its significant weak lensing signal without a corresponding galaxy overdensity or X-ray emission. We present a set of HST mosaic observations which confirms the presence of an alignment signal at the dark clump position. The signal strength, however, is weaker than in the ground-based data. It is therefore still unclear whether the signal is caused by a lensing mass or is just a chance alignment. We also present Chandra observations of the dark clump, which fail to reveal any significant extended emission. A comparison of the ellipticity measurements from the space-based HST data and the ground-based CFHT data shows a remarkable agreement on average, demonstrating that weak lensing studies from high-quality ground-based observations yield reliable results.Comment: 33 pages, 34 figures, submitted to A&A. Version with full resolution figures available at http://www.mpa-garching.mpg.de/~anja/aaclump.pd

Coordinated neuronal ensembles in primary auditory cortical columns.

The synchronous activity of groups of neurons is increasingly thought to be important in cortical information processing and transmission. However, most studies of processing in the primary auditory cortex (AI) have viewed neurons as independent filters; little is known about how coordinated AI neuronal activity is expressed throughout cortical columns and how it might enhance the processing of auditory information. To address this, we recorded from populations of neurons in AI cortical columns of anesthetized rats and, using dimensionality reduction techniques, identified multiple coordinated neuronal ensembles (cNEs), which are groups of neurons with reliable synchronous activity. We show that cNEs reflect local network configurations with enhanced information encoding properties that cannot be accounted for by stimulus-driven synchronization alone. Furthermore, similar cNEs were identified in both spontaneous and evoked activity, indicating that columnar cNEs are stable functional constructs that may represent principal units of information processing in AI

Strong Lensing Analysis of A1689 from Deep Advanced Camera Images

We analyse deep multi-colour Advanced Camera images of the largest known gravitational lens, A1689. Radial and tangential arcs delineate the critical curves in unprecedented detail and many small counter-images are found near the center of mass. We construct a flexible light deflection field to predict the appearance and positions of counter-images. The model is refined as new counter-images are identified and incorporated to improve the model, yielding a total of 106 images of 30 multiply lensed background galaxies, spanning a wide redshift range, 1.0$<$z$<$5.5. The resulting mass map is more circular in projection than the clumpy distribution of cluster galaxies and the light is more concentrated than the mass within $r<50kpc/h$. The projected mass profile flattens steadily towards the center with a shallow mean slope of $d\log\Sigma/d\log r \simeq -0.55\pm0.1$, over the observed range, r$<250kpc/h$, matching well an NFW profile, but with a relatively high concentration, $C_{vir}=8.2^{+2.1}_{-1.8}$. A softened isothermal profile ($r_{core}=20\pm2$\arcs) is not conclusively excluded, illustrating that lensing constrains only projected quantities. Regarding cosmology, we clearly detect the purely geometric increase of bend-angles with redshift. The dependence on the cosmological parameters is weak due to the proximity of A1689, $z=0.18$, constraining the locus, $\Omega_M+\Omega_{\Lambda} \leq 1.2$. This consistency with standard cosmology provides independent support for our model, because the redshift information is not required to derive an accurate mass map. Similarly, the relative fluxes of the multiple images are reproduced well by our best fitting lens model.Comment: Accepted by ApJ. For high quality figures see http://wise-obs.tau.ac.il/~kerens/A168

A Comparison of Weak Lensing Measurements From Ground- and Space-Based Facilities

We assess the relative merits of weak lensing surveys, using overlapping imaging data from the ground-based Subaru telescope and the Hubble Space Telescope (HST). Our tests complement similar studies undertaken with simulated data. From observations of 230,000 matched objects in the 2 square degree COSMOS field, we identify the limit at which faint galaxy shapes can be reliably measured from the ground. Our ground-based shear catalog achieves sub-percent calibration bias compared to high resolution space-based data, for galaxies brighter than i'~24.5 and with half-light radii larger than 1.8". This selection corresponds to a surface density of ~15 galaxies per sq arcmin compared to ~71 per sq arcmin from space. On the other hand the survey speed of current ground-based facilities is much faster than that of HST, although this gain is mitigated by the increased depth of space-based imaging desirable for tomographic (3D) analyses. As an independent experiment, we also reconstruct the projected mass distribution in the COSMOS field using both data sets, and compare the derived cluster catalogs with those from X-ray observations. The ground-based catalog achieves a reasonable degree of completeness, with minimal contamination and no detected bias, for massive clusters at redshifts 0.2<z<0.5. The space-based data provide improved precision and a greater sensitivity to clusters of lower mass or at higher redshift.Comment: 12 pages, 8 figures, submitted to ApJ, Higher resolution figures available at http://www.astro.caltech.edu/~mansi/GroundvsSpace.pd

Logistic Knowledge Tracing: A Constrained Framework for Learner Modeling

Adaptive learning technology solutions often use a learner model to trace learning and make pedagogical decisions. The present research introduces a formalized methodology for specifying learner models, Logistic Knowledge Tracing (LKT), that consolidates many extant learner modeling methods. The strength of LKT is the specification of a symbolic notation system for alternative logistic regression models that is powerful enough to specify many extant models in the literature and many new models. To demonstrate the generality of LKT, we fit 12 models, some variants of well-known models and some newly devised, to 6 learning technology datasets. The results indicated that no single learner model was best in all cases, further justifying a broad approach that considers multiple learner model features and the learning context. The models presented here avoid student-level fixed parameters to increase generalizability. We also introduce features to stand in for these intercepts. We argue that to be maximally applicable, a learner model needs to adapt to student differences, rather than needing to be pre-parameterized with the level of each student's ability