An MRI-Derived Definition of MCI-to-AD Conversion for Long-Term, Automati c Prognosis of MCI Patients

Alzheimer's disease (AD) and mild cognitive impairment (MCI), continue to be widely studied. While there is no consensus on whether MCIs actually "convert" to AD, the more important question is not whether MCIs convert, but what is the best such definition. We focus on automatic prognostication, nominally using only a baseline image brain scan, of whether an MCI individual will convert to AD within a multi-year period following the initial clinical visit. This is in fact not a traditional supervised learning problem since, in ADNI, there are no definitive labeled examples of MCI conversion. Prior works have defined MCI subclasses based on whether or not clinical/cognitive scores such as CDR significantly change from baseline. There are concerns with these definitions, however, since e.g. most MCIs (and ADs) do not change from a baseline CDR=0.5, even while physiological changes may be occurring. These works ignore rich phenotypical information in an MCI patient's brain scan and labeled AD and Control examples, in defining conversion. We propose an innovative conversion definition, wherein an MCI patient is declared to be a converter if any of the patient's brain scans (at follow-up visits) are classified "AD" by an (accurately-designed) Control-AD classifier. This novel definition bootstraps the design of a second classifier, specifically trained to predict whether or not MCIs will convert. This second classifier thus predicts whether an AD-Control classifier will predict that a patient has AD. Our results demonstrate this new definition leads not only to much higher prognostic accuracy than by-CDR conversion, but also to subpopulations much more consistent with known AD brain region biomarkers. We also identify key prognostic region biomarkers, essential for accurately discriminating the converter and nonconverter groups

On the black-box complexity of Sperner's Lemma

We present several results on the complexity of various forms of Sperner's Lemma in the black-box model of computing. We give a deterministic algorithm for Sperner problems over pseudo-manifolds of arbitrary dimension. The query complexity of our algorithm is linear in the separation number of the skeleton graph of the manifold and the size of its boundary. As a corollary we get an $O(\sqrt{n})$ deterministic query algorithm for the black-box version of the problem {\bf 2D-SPERNER}, a well studied member of Papadimitriou's complexity class PPAD. This upper bound matches the $\Omega(\sqrt{n})$ deterministic lower bound of Crescenzi and Silvestri. The tightness of this bound was not known before. In another result we prove for the same problem an $\Omega(\sqrt[4]{n})$ lower bound for its probabilistic, and an $\Omega(\sqrt[8]{n})$ lower bound for its quantum query complexity, showing that all these measures are polynomially related.Comment: 16 pages with 1 figur

Statistical Network Analysis for Functional MRI: Summary Networks and Group Comparisons

Comparing weighted networks in neuroscience is hard, because the topological properties of a given network are necessarily dependent on the number of edges of that network. This problem arises in the analysis of both weighted and unweighted networks. The term density is often used in this context, in order to refer to the mean edge weight of a weighted network, or to the number of edges in an unweighted one. Comparing families of networks is therefore statistically difficult because differences in topology are necessarily associated with differences in density. In this review paper, we consider this problem from two different perspectives, which include (i) the construction of summary networks, such as how to compute and visualize the mean network from a sample of network-valued data points; and (ii) how to test for topological differences, when two families of networks also exhibit significant differences in density. In the first instance, we show that the issue of summarizing a family of networks can be conducted by adopting a mass-univariate approach, which produces a statistical parametric network (SPN). In the second part of this review, we then highlight the inherent problems associated with the comparison of topological functions of families of networks that differ in density. In particular, we show that a wide range of topological summaries, such as global efficiency and network modularity are highly sensitive to differences in density. Moreover, these problems are not restricted to unweighted metrics, as we demonstrate that the same issues remain present when considering the weighted versions of these metrics. We conclude by encouraging caution, when reporting such statistical comparisons, and by emphasizing the importance of constructing summary networks.Comment: 16 pages, 5 figure

Accurate classification of 29 objects detected in the 39 months Palermo Swift/BAT hard X-ray catalogue

Through an optical campaign performed at 4 telescopes located in the northern and the southern hemispheres, plus archival data from two on-line sky surveys, we have obtained optical spectroscopy for 29 counterparts of unclassified or poorly studied hard X-ray emitting objects detected with Swift/BAT and listed in the 39 months Palermo catalogue. All these objects have also observations taken with Swift/XRT or XMM-EPIC which not only allow us to pinpoint their optical counterpart, but also to study their X-ray spectral properties (column density, power law photon index and F2-10 keV flux). We find that 28 sources in our sample are AGN; 7 are classified as type 1 while 21 are of type 2; the remaining object is a galactic cataclysmic variable. Among our type 1 AGN, we find 5 objects of intermediate Seyfert type (1.2-1.9) and one Narrow Line Seyfert 1 galaxy; for 4 out of 7 sources, we have been able to estimate the central black hole mass. Three of the type 2 AGN of our sample display optical features typical of the LINER class and one is a likely Compton thick AGN. All galaxies classified in this work are relatively nearby objects since their redshifts lie in the range 0.008-0.075; the only galactic object found lies at an estimated distance of 90 pc. We have also investigated the optical versus X-ray emission ratio of the galaxies of our sample to test the AGN unified model. For them, we have also compared the X-ray absorption (due to gas) with the optical reddening (due to dust): we find that for most of our sources, specifically those of type 1.9-2.0 the former is higher than the latter confirming early results by Maiolino et al. (2001); this is possibly due to the properties of dust in the circumnuclear obscuring torus of the AGN.Comment: 15 pages, 4 figures, 8 tables, accepted for publication on Astronomy and Astrophysic