Manuel Delfino

Manual validation of FreeSurfer's automated hippocampal segmentation in normal aging, mild cognitive impairment, and Alzheimer Disease subjects

Hippocampal volume is reduced in Alzheimer Disease (AD) and has been proposed as a possible surrogate biomarker to aid early diagnosis. Whilst automated methods to segment the hippocampus from magnetic resonance images are available, manual segmentation, in spite of being time-consuming and unsuitable for large samples, is still the standard. In order to study the validity of FreeSurfers automated method, we compared hippocampal automated measures with manual tracing in a sample composed of healthy elderly (N=41), Mild Cognitive Impairment (MCI) (N=23), and AD (N=25) subjects. Percent volume overlap, percent volume difference, correlations, and Bland-Altman plots were studied. Automated measures were slightly larger than hand tracing ones (mean difference 10%). Percent volume overlap showed good results, but was far from perfect (78%). Manual and automated volume correlations were approximately 0.84 and the Bland-Altman analysis showed acceptable interchangeability of methods. Within-group analysis demonstrated that patient samples obtained smaller values in validity indexes than controls. Globally, FreeSurfers automated hippocampal volumetry showed adequate validity when compared to manual tracing, with a tendency to overestimation. Nevertheless, the greater difference between automated and manual segmentation in atrophic brains suggests that studies in AD based on this software could be more likely to produce false negatives.

Pattern of regional cortical thinning associated with cognitive deterioration in Parkinson's disease.

Background Dementia is a frequent and devastating complication in Parkinson’s disease (PD). There is an intensive search for biomarkers that may predict the progression from normal cognition (PD-NC) to dementia (PDD) in PD. Mild cognitive impairment in PD (PD-MCI) seems to represent a transitional state between PD-NC and PDD. Few studies have explored the structural changes that differentiate PD-NC from PD-MCI and PDD patients. Objectives and Methods We aimed to analyze changes in cortical thickness on 3.0T Magnetic Resonance Imaging (MRI) across stages of cognitive decline in a prospective sample of PD-NC (n = 26), PD-MCI (n = 26) and PDD (n = 20) patients, compared to a group of healthy subjects (HC) (n = 18). Cortical thickness measurements were made using the automatic software Freesurfer. Results In a sample of 72 PD patients, a pattern of linear and progressive cortical thinning was observed between cognitive groups in cortical areas functionally specialized in declarative memory (entorhinal cortex, anterior temporal pole), semantic knowledge (parahippocampus, fusiform gyrus), and visuoperceptive integration (banks of the superior temporal sulcus, lingual gyrus, cuneus and precuneus). Positive correlation was observed between confrontation naming and thinning in the fusiform gyrus, parahippocampal gyrus and anterior temporal pole; clock copy with thinning of the precuneus, parahippocampal and lingual gyrus; and delayed memory with thinning of the bilateral anteromedial temporal cortex. Conclusions The pattern of regional decreased cortical thickness that relates to cognitive deterioration is present in PD-MCI patients, involving areas that play a central role in the storage of prior experiences, integration of external perceptions, and semantic processing.

Predicting dementia development in Parkinson's disease using Bayesian network classifiers

Parkinsons disease (PD) has broadly been associated with mild cognitive impairment (PDMCI) and dementia (PDD). Researchers have studied surrogate, neuroanatomic biomarkers provided by magnetic resonance imaging (MRI) that may help in the early diagnosis of this condition. In this article, four classification models (naïve Bayes, multivariate filter-based naïve Bayes, filter selective naïve Bayes and support vector machines, SVM) have been applied to evaluate their capacity to discriminate between cognitively intact patients with Parkinsons disease (PDCI), PDMCI and PDD. For this purpose, the MRI studies of 45 subjects (16 PDCI, 15 PDMCI and 14 PDD) were acquired and post-processed with Freesurfer, obtaining 112 variables (volumes of subcortical structures and thickness of cortical parcels) per subject. A multivariate filter-based naïve Bayes model was found to be the best classifier, having the highest cross-validated sensitivity, specificity and accuracy. Additionally, the most relevant variables related to dementia in PD, as predicted by our classifiers, were cerebral white matter, and volumes of the lateral ventricles and hippocampi.

Spectroscopic Changes Associated with Mild Cognitive Impairment and Dementia in Parkinson’s Disease

Frontal subcortical cognitive defects are predominant in Parkinson’s disease (PD). Temporal lobe dysfunction seems more relevant for progression to dementia. We aimed to study the relative importance of temporal lobe defects versus executive impairment in the progression to dementia in PD by using proton magnetic resonance spectroscopy (1H-MRS). The 1H-MRS features of PD patients with intact cognition (PD-CgInt; n = 16), mild cognitive impairment (MCI; n = 15) and dementia (PDD; n = 15) were compared, to delineate the metabolic alterations correlating with cognitive status. Metabolite concentrations were acquired from voxels localized to the hippocampus and dorsolateral prefrontal cortex (DL-PFC). Cognitive status was established following the Movement Disorder Society PDD criteria, administering the Clinical Dementia Rating Scale and Mattis Dementia Rating Scale. The Parkinson’s Disease Cognitive Rating Scale (PD-CRS) was used to correlate 1H-MRS with neuropsychology. N-acetylaspartate (NAA) concentrations in the right DL-PFC were decreased in PD-MCI compared with PD-CgInt patients (p = 0.002), and correlated with frontal subcortical tasks. Decreased NAA concentrations in the left hippocampus in PDD compared to PD-MCI (p = 0.03) correlated with confrontation naming. The present findings support that executive impairment is related to dorsolateral prefrontal dysfunction from the early stages, while progression to dementia is linked to the additional impairment of temporal lobe structures. The PD-CRS was able to capture the differential impairment of prefrontal versus temporal cortical areas.

Experience with the MAC data flow system

Abstract An overview of the data flow system used by the MAC detector at SLAC is given, with emphasis on three features of its environment: First, the tapeless data logging and automated raw data file fetch and filter job submission. Second, the interactive aspects of the offline analysis program, including the availability of the symbolic debugging; and third, the availability of a large amount of disk space (> 2 GB) for data files, which allows minimum use of magnetic tape for physics analysis. Figures on reliability and number of tape mounts are compared to those of typical detectors using more traditional environments.

The LHC Tier-1 at PIC: ten years of operations

This paper summarizes ten years of operational experience of the WLCG Tier-1 computer centre at Port d’Informacio Cientifica (PIC), which serves the ATLAS, CMS and LHCb experiments. The centre, located in Barcelona (Spain), has supported all of the commissioning activities be- fore the Large Hadron Collider (LHC) produced real collisions, it has continuously adapted to the new requirements, introducing new technologies as they became available, and it has grown significantly in resources offered to the LHC experiments, while maintaining top reliability levels as compared to other centres. Additional work has been done in the last years to reduce the power costs of the Tier-1 centre, and to prepare for the next challenges that are expected to come. Some thoughts on how the current WLCG system could evolve are also presented.

Lessons learned from the ATLAS performance studies of the Iberian Cloud for the first LHC running period

In this contribution we describe the performance of the Iberian (Spain and Portugal) ATLAS cloud during the first LHC running period (March 2010-January 2013) in the context of the GRID Computing and Data Distribution Model. The evolution of the resources for CPU, disk and tape in the Iberian Tier-1 and Tier-2s is summarized. The data distribution over all ATLAS destinations is shown, focusing on the number of files transferred and the size of the data. The status and distribution of simulation and analysis jobs within the cloud are discussed. The Distributed Analysis tools used to perform physics analysis are explained as well. Cloud performance in terms of the availability and reliability of its sites is discussed. The effect of the changes in the ATLAS Computing Model on the cloud is analyzed. Finally, the readiness of the Iberian Cloud towards the first Long Shutdown (LS1) is evaluated and an outline of the foreseen actions to take in the coming years is given. The shutdown will be a good opportunity to improve and evolve the ATLAS Distributed Computing system to prepare for the future challenges of the LHC operation.

Dimensioning storage and computing clusters for efficient high throughput computing

Scientific experiments are producing huge amounts of data, and the size of their datasets and total volume of data continues increasing. These data are then processed by researchers belonging to large scientific collaborations, with the Large Hadron Collider being a good example. The focal point of scientific data centers has shifted from efficiently coping with PetaByte scale storage to deliver quality data processing throughput. The dimensioning of the internal components in High Throughput Computing (HTC) data centers is of crucial importance to cope with all the activities demanded by the experiments, both the online (data acceptance) and the offline (data processing, simulation and user analysis). This requires a precise setup involving disk and tape storage services, a computing cluster and the internal networking to prevent bottlenecks, overloads and undesired slowness that lead to losses cpu cycles and batch jobs failures. In this paper we point out relevant features for running a successful data storage and processing service in an intensive HTC environment.

Experiences with http/WebDAV protocols for data access in high throughput computing

In the past, access to remote storage was considered to be at least one order of magnitude slower than local disk access. Improvement on network technologies provide the alternative of using remote disk. For those accesses one can today reach levels of throughput similar or exceeding those of local disks. Common choices as access protocols in the WLCG collaboration are RFIO, [GSI]DCAP, GRIDFTP, XROOTD and NFS. HTTP protocol shows a promising alternative as it is a simple, lightweight protocol. It also enables the use of standard technologies such as http caching or load balancing which can be used to improve service resilience and scalability or to boost performance for some use cases seen in HEP such as the hot files. WebDAV extensions allow writing data, giving it enough functionality to work as a remote access protocol. This paper will show our experiences with the WebDAV door for dCache, in terms of functionality and performance, applied to some of the HEP work flows in the LHC Tier1 at PIC.