Fast, scalable, Bayesian spike identification for multi-electrode arrays

We present an algorithm to identify individual neural spikes observed on high-density multi-electrode arrays (MEAs). Our method can distinguish large numbers of distinct neural units, even when spikes overlap, and accounts for intrinsic variability of spikes from each unit. As MEAs grow larger, it is important to find spike-identification methods that are scalable, that is, the computational cost of spike fitting should scale well with the number of units observed. Our algorithm accomplishes this goal, and is fast, because it exploits the spatial locality of each unit and the basic biophysics of extracellular signal propagation. Human intervention is minimized and streamlined via a graphical interface. We illustrate our method on data from a mammalian retina preparation and document its performance on simulated data consisting of spikes added to experimentally measured background noise. The algorithm is highly accurate

Identifying Clusters in Bayesian Disease Mapping

Disease mapping is the field of spatial epidemiology interested in estimating the spatial pattern in disease risk across $n$ areal units. One aim is to identify units exhibiting elevated disease risks, so that public health interventions can be made. Bayesian hierarchical models with a spatially smooth conditional autoregressive prior are used for this purpose, but they cannot identify the spatial extent of high-risk clusters. Therefore we propose a two stage solution to this problem, with the first stage being a spatially adjusted hierarchical agglomerative clustering algorithm. This algorithm is applied to data prior to the study period, and produces $n$ potential cluster structures for the disease data. The second stage fits a separate Poisson log-linear model to the study data for each cluster structure, which allows for step-changes in risk where two clusters meet. The most appropriate cluster structure is chosen by model comparison techniques, specifically by minimising the Deviance Information Criterion. The efficacy of the methodology is established by a simulation study, and is illustrated by a study of respiratory disease risk in Glasgow, Scotland

ROSAT All-Sky Survey observations of IRAS galaxies; I. Soft X-ray and far-infrared properties

The 120,000 X-ray sources detected in the RASS II processing of the ROSAT All-Sky Survey are correlated with the 14,315 IRAS galaxies selected from the IRAS Point Source Catalogue: 372 IRAS galaxies show X-ray emission within a distance of 100 arcsec from the infrared position. By inspecting the structure of the X-ray emission in overlays on optical images we quantify the likelihood that the X-rays originate from the IRAS galaxy. For 197 objects the soft X-ray emission is very likely associated with the IRAS galaxy. Their soft X-ray properties are determined and compared with their far-infrared emission. X-ray contour plots overlaid on Palomar Digitized Sky Survey images are given for each of the 372 potential identifications. All images and tables displayed here are also available in electronic form.Comment: accepted for publication in A&AS, complete version including all figures and tables available at http://www.rosat.mpe-garching.mpg.de/~bol/iras_rassI

Image patch analysis and clustering of sunspots: a dimensionality reduction approach

Sunspots, as seen in white light or continuum images, are associated with regions of high magnetic activity on the Sun, visible on magnetogram images. Their complexity is correlated with explosive solar activity and so classifying these active regions is useful for predicting future solar activity. Current classification of sunspot groups is visually based and suffers from bias. Supervised learning methods can reduce human bias but fail to optimally capitalize on the information present in sunspot images. This paper uses two image modalities (continuum and magnetogram) to characterize the spatial and modal interactions of sunspot and magnetic active region images and presents a new approach to cluster the images. Specifically, in the framework of image patch analysis, we estimate the number of intrinsic parameters required to describe the spatial and modal dependencies, the correlation between the two modalities and the corresponding spatial patterns, and examine the phenomena at different scales within the images. To do this, we use linear and nonlinear intrinsic dimension estimators, canonical correlation analysis, and multiresolution analysis of intrinsic dimension.Comment: 5 pages, 7 figures, accepted to ICIP 201

Imaging haemodynamic changes related to seizures: comparison of EEG-based general linear model, independent component analysis of fMRI and intracranial EEG

Background: Simultaneous EEG-fMRI can reveal haemodynamic changes associated with epileptic activity which may contribute to understanding seizure onset and propagation. Methods: Nine of 83 patients with focal epilepsy undergoing pre-surgical evaluation had seizures during EEG-fMRI and analysed using three approaches, two based on the general linear model (GLM) and one using independent component analysis (ICA): 1. EEGs were divided into up to three phases: early ictal EEG change, clinical seizure onset and late ictal EEG change and convolved with a canonical haemodynamic response function (HRF) (canonical GLM analysis). 2. Seizures lasting three scans or longer were additionally modelled using a Fourier basis set across the entire event (Fourier GLM analysis). 3. Independent component analysis (ICA) was applied to the fMRI data to identify ictal BOLD patterns without EEG. The results were compared with intracranial EEG. Results: The canonical GLM analysis revealed significant BOLD signal changes associated with seizures on EEG in 7/9 patients, concordant with the seizure onset zone in 4/7. The Fourier GLM analysis revealed changes in BOLD signal corresponding with the results of the canonical analysis in two patients. ICA revealed components spatially concordant with the seizure onset zone in all patients (8/9 confirmed by intracranial EEG). Conclusion: Ictal EEG-fMRI visualises plausible seizure related haemodynamic changes. The GLM approach to analysing EEG-fMRI data reveals localised BOLD changes concordant with the ictal onset zone when scalp EEG reflects seizure onset. ICA provides additional information when scalp EEG does not accurately reflect seizures and may give insight into ictal haemodynamics

Subaru and Gemini High Spatial Resolution Infrared 18 Micron Imaging Observations of Nearby Luminous Infrared Galaxies

We present the results of a ground-based, high spatial resolution infrared 18 micron imaging study of nearby luminous infrared galaxies (LIRGs), using the Subaru 8.2-m and Gemini South 8.1-m telescopes. The diffraction-limited images routinely achieved with these telescopes in the Q-band (17-23 micron) allow us to investigate the detailed spatial distribution of infrared emission in these LIRGs. We then investigate whether the emission surface brightnesses are modest, as observed in starbursts, or are so high that luminous active galactic nuclei (AGNs; high emission surface brightness energy sources) are indicated. The sample consists of 18 luminous buried AGN candidates and starburst-classified LIRGs identified in earlier infrared spectroscopy. We find that the infrared 18 micron emission from the buried AGN candidates is generally compact, and the estimated emission surface brightnesses are high, sometimes exceeding the maximum value observed in and theoretically predicted for a starburst phenomenon. The starburst-classified LIRGs usually display spatially extended 18 micron emission and the estimated emission surface brightnesses are modest, within the range sustained by a starburst phenomenon. The general agreement between infrared spectroscopic and imaging energy diagnostic methods suggests that both are useful tools for understanding the hidden energy sources of the dusty LIRG population.Comment: 17 pages, 3 figures, accepted for publication in AJ (No. 141, 2011 May issue). Higher resolution version is available at http://optik2.mtk.nao.ac.jp/~imanishi/Paper/20um/20um.pd

The enigma of GCIRS 3 - Constraining the properties of the mid-infrared reference star of the central parsec of the Milky Way with optical long baseline interferometry

GCIRS3 is the most prominent MIR source in the central pc of the Galaxy. NIR spectroscopy failed to solve the enigma of its nature. The properties of extreme individual objects of the central stellar cluster contribute to our knowledge of star and dust formation close to a supermassive black hole. We initiated an interferometric experiment to understand IRS3 and investigate its properties as spectroscopic and interferometric reference star at 10um. VISIR imaging separates a compact source from diffuse, surrounding emission. The VLTI/MIDI instrument was used to measure visibilities at 10mas resolution of that compact 10um source, still unresolved by a single VLT. Photometry data were added to enable simple SED- and full radiative transfer-models of the data. The luminosity and size estimates show that IRS3 is probably a cool carbon star enshrouded by a complex dust distribution. Dust temperatures were derived. The coinciding interpretation of multiple datasets confirm dust emission at several spatial scales. The IF data resolve the innermost area of dust formation. Despite observed deep silicate absorption towards IRS3 we favor a carbon rich chemistry of the circumstellar dust shell. The silicate absorption most probably takes place in the outer diffuse dust, which is mostly ignored by MIDI measurements. This indicates physically and chemically distinct conditions of the local dust, changing with the distance to IRS3. We have demonstrated that optical long baseline interferometry at infrared wavelengths is an indispensable tool to investigate sources at the Galactic Center. Our findings suggest further studies of the composition of interstellar dust and the shape of the 10um silicate feature at this outstanding region.Comment: accepted by A&A, now in press; 19 pages, 22 figures, 5 table

Structured count data regression

Overdispersion in count data regression is often caused by neglection or inappropriate modelling of individual heterogeneity, temporal or spatial correlation, and nonlinear covariate effects. In this paper, we develop and study semiparametric count data models which can deal with these issues by incorporating corresponding components in structured additive form into the predictor. The models are fully Bayesian and inference is carried out by computationally efficient MCMC techniques. In a simulation study, we investigate how well the different components can be identified with the data at hand. The approach is applied to a large data set of claim frequencies from car insurance

CARBayes: an R package for Bayesian spatial modeling with conditional autoregressive priors

Conditional autoregressive models are commonly used to represent spatial autocorrelation in data relating to a set of non-overlapping areal units, which arise in a wide variety of applications including agriculture, education, epidemiology and image analysis. Such models are typically specified in a hierarchical Bayesian framework, with inference based on Markov chain Monte Carlo (MCMC) simulation. The most widely used software to fit such models is WinBUGS or OpenBUGS, but in this paper we introduce the R package CARBayes. The main advantage of CARBayes compared with the BUGS software is its ease of use, because: (1) the spatial adjacency information is easy to specify as a binary neighbourhood matrix; and (2) given the neighbourhood matrix the models can be implemented by a single function call in R. This paper outlines the general class of Bayesian hierarchical models that can be implemented in the CARBayes software, describes their implementation via MCMC simulation techniques, and illustrates their use with two worked examples in the fields of house price analysis and disease mapping