We measure cosmological parameters using the three-dimensional power spectrum P (k) from over 200,000 galaxies in the Sloan Digital Sky Survey (SDSS) in combination with WMAP and other data. Our results are consistent with a "vanilla" flat adiabatic ΛCDM model without tilt (ns = 1), running tilt, tensor modes or massive neutrinos. Adding SDSS information more than halves the WMAP-only error bars on some parameters, tightening 1σ constraints on the Hubble parameter from h ≈ 0.74−0.03 , on the matter density from Ωm ≈ 0.25 ± 0.10 to Ωm ≈ 0.30 ± 0.04 (1σ) and on neutrino masses from < 11 eV to < 0.6 eV (95%). SDSS helps even more when dropping prior assumptions about curvature, neutrinos, tensor modes and the equation of state. Our results are in substantial agreement with the joint analysis of WMAP and the 2dF Galaxy Redshift Survey, which is an impressive consistency check with independent redshift survey data and analysis techniques. In this paper, we place particular emphasis on clarifying the physical origin of the constraints, i.e., what we do and do not know when using different data sets and prior assumptions. For instance, dropping the assumption that space is perfectly flat, the WMAP-only constraint on the measured age of the Universe tightens from t0 ≈ 16.3 +2.3 −1.8 Gyr to t0 ≈ 14.1Gyr by adding SDSS and SN Ia data. Including tensors, running tilt, neutrino mass and equation of state in the list of free parameters, many constraints are still quite weak, but future cosmological measurements from SDSS and other sources should allow these to be substantially tightened.

The capacity to identify the unique functional architecture of an individual’s brain is a critical step towards personalized medicine and understanding the neural basis of variations in human cognition and behavior. Here, we developed a novel cortical parcellation approach to accurately map functional organization at the individual level using resting-state fMRI. A population-based functional atlas and a map of inter-individual variability were employed to guide the iterative search for functional networks in individual subjects. Functional networks mapped by this approach were highly reproducible within subjects and effectively captured the variability across subjects, including individual differences in brain lateralization. The algorithm performed well across different subject populations and data types including task fMRI data. The approach was then validated by invasive cortical stimulation mapping in surgical patients, suggesting great potential for use in clinical applications.

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We perform a detailed analysis of the latest CMB measurements (including BOOMERaNG, DASI, Maxima and CBI), both alone and jointly with other cosmological data sets involving, e.g., galaxy clustering and the Lyman Alpha Forest. We first address the question of whether the CMB data are internally consistent once calibration and beam uncertainties are taken into account, performing a series of statistical tests. With a few minor caveats, our answer is yes, and we compress all data into a single set of 24 bandpowers with associated covariance matrix and window functions. We then compute joint constraints on the 11 parameters of the ``standard'' adiabatic inflationary cosmological model. Out best fit model passes a series of physical consistency checks and agrees with essentially all currently available cosmological data. In addition to sharp constraints on the cosmic matter budget in good agreement with those of the BOOMERaNG, DASI and Maxima teams, we obtain a heaviest neutrino mass range 0.04-4.2 eV and the sharpest constraints to date on gravity waves which (together with preference for a slight red-tilt) favors ``small-field'' inflation models.Comment: Replaced to match accepted PRD version. 14 pages, 12 figs. Tiny changes due to smaller DASI & Maxima calibration errors. Expanded neutrino and tensor discussion, added refs, typos fixed. Combined CMB data, window and covariance matrix at http://www.hep.upenn.edu/~max/consistent.html or from xiaomin@hep.upenn.ed