Automated, high accuracy classification of Parkinsonian disorders: a pattern recognition approach

Progressive supranuclear palsy (PSP), multiple system atrophy (MSA) and idiopathic Parkinson’s disease (IPD) can be clinically indistinguishable, especially in the early stages, despite distinct patterns of molecular pathology. Structural neuroimaging holds promise for providing objective biomarkers for discriminating these diseases at the single subject level but all studies to date have reported incomplete separation of disease groups. In this study, we employed multi-class pattern recognition to assess the value of anatomical patterns derived from a widely available structural neuroimaging sequence for automated classification of these disorders. To achieve this, 17 patients with PSP, 14 with IPD and 19 with MSA were scanned using structural MRI along with 19 healthy controls (HCs). An advanced probabilistic pattern recognition approach was employed to evaluate the diagnostic value of several pre-defined anatomical patterns for discriminating the disorders, including: (i) a subcortical motor network; (ii) each of its component regions and (iii) the whole brain. All disease groups could be discriminated simultaneously with high accuracy using the subcortical motor network. The region providing the most accurate predictions overall was the midbrain/brainstem, which discriminated all disease groups from one another and from HCs. The subcortical network also produced more accurate predictions than the whole brain and all of its constituent regions. PSP was accurately predicted from the midbrain/brainstem, cerebellum and all basal ganglia compartments; MSA from the midbrain/brainstem and cerebellum and IPD from the midbrain/brainstem only. This study demonstrates that automated analysis of structural MRI can accurately predict diagnosis in individual patients with Parkinsonian disorders, and identifies distinct patterns of regional atrophy particularly useful for this process

Deep Herschel view of obscured star formation in the Bullet cluster

We use deep, five band (100–500 μm) data from the Herschel Lensing Survey (HLS) to fully constrain the obscured star formation rate, SFRFIR, of galaxies in the Bullet cluster (z = 0.296), and a smaller background system (z = 0.35) in the same field. Herschel detects 23 Bullet cluster members with a total SFRFIR = 144±14 yr-1. On average, the background system contains brighter far-infrared (FIR) galaxies, with ~50% higher SFRFIR (21 galaxies; 207± 9 yr-1). SFRs extrapolated from 24 μm flux via recent templates (SFR24 µm) agree well with SFRFIR for ~60% of the cluster galaxies. In the remaining ~40%, SFR24 µm underestimates SFRFIR due to a significant excess in observed S100/S24 (rest frame S75/S18) compared to templates of the same FIR luminosity

The Herschel Lensing Survey (HLS) : Overview

The Herschel Lensing Survey (HLS) will conduct deep PACS and SPIRE imaging of ~40 massive clusters of galaxies. The strong gravitational lensing power of these clusters will enable us to penetrate through the confusion noise, which sets the ultimate limit on our ability to probe the Universe with Herschel. Here we present an overview of our survey and a summary of the major results from our science demonstration phase (SDP) observations of the Bullet cluster (z = 0.297). The SDP data are rich and allow us to study not only the background high-redshift galaxies (e.g., strongly lensed and distorted galaxies at z = 2.8 and 3.2) but also the properties of cluster-member galaxies. Our preliminary analysis shows a great diversity of far-infrared/submillimeter spectral energy distributions (SEDs), indicating that we have much to learn with Herschel about the properties of galaxy SEDs. We have also detected the Sunyaev-Zel'dovich (SZ) effect increment with the SPIRE data. The success of this SDP program demonstrates the great potential of the Herschel Lensing Survey to produce exciting results in a variety of science areas