Testing of several distributed file-systems (HDFS, Ceph and GlusterFS) for supporting the HEP experiments analysis

The activity of testing new storage solution is of great importance in order to provide both features and performance evaluation and give few hints to small-medium sites that are interested in exploiting new storage technologies. In particular this work will cover storage solutions that provide both standard POSIX storage access and cloud technologies; we focused our attention and our test on HDFS, Ceph, and GlusterFS

Explainable Deep Learning for Personalized Age Prediction with Brain Morphology

Predicting brain age has become one of the most attractive challenges in computational neuroscience due to the role of the predicted age as an effective biomarker for different brain diseases and conditions. A great variety of machine learning (ML) approaches and deep learning (DL) techniques have been proposed to predict age from brain magnetic resonance imaging scans. If on one hand, DL models could improve performance and reduce model bias compared to other less complex ML methods, on the other hand, they are typically black boxes as do not provide an in-depth understanding of the underlying mechanisms. Explainable Artificial Intelligence (XAI) methods have been recently introduced to provide interpretable decisions of ML and DL algorithms both at local and global level. In this work, we present an explainable DL framework to predict the age of a healthy cohort of subjects from ABIDE I database by using the morphological features extracted from their MRI scans. We embed the two local XAI methods SHAP and LIME to explain the outcomes of the DL models, determine the contribution of each brain morphological descriptor to the final predicted age of each subject and investigate the reliability of the two methods. Our findings indicate that the SHAP method can provide more reliable explanations for the morphological aging mechanisms and be exploited to identify personalized age-related imaging biomarker. (c) Copyright (c) 2021 Lombardi, Diacono, Amoroso, Monaco, Tavares, Bellotti and Tangaro

Deep Learning and Multiplex Networks for Accurate Modeling of Brain Age

Recent works have extensively investigated the possibility to predict brain aging from T1-weighted MRI brain scans. The main purposes of these studies are the investigation of subject-specific aging mechanisms and the development of accurate models for age prediction. Deviations between predicted and chronological age are known to occur in several neurodegenerative diseases; as a consequence, reaching higher levels of age prediction accuracy is of paramount importance to develop diagnostic tools. In this work, we propose a novel complex network model for brain based on segmenting T1-weighted MRI scans in rectangular boxes, called patches, and measuring pairwise similarities using Pearson's correlation to define a subject-specific network. We fed a deep neural network with nodal metrics, evaluating both the intensity and the uniformity of connections, to predict subjects' ages. Our model reaches high accuracies which compare favorably with state-of-the-art approaches. We observe that the complex relationships involved in this brain description cannot be accurately modeled with standard machine learning approaches, such as Ridge and Lasso regression, Random Forest, and Support Vector Machines, instead a deep neural network has to be used

Towards the extraction of the crystal cell parameters from pair distribution function profiles

The approach based on atomic pair distribution function (PDF) has revolutionized structural investigations by X-ray/electron diffraction of nano or quasi-amorphous materials, opening up the possibility of exploring short-range order. However, the ab initio crystal structural solution by the PDF is far from being achieved due to the difficulty in determining the crystallographic properties of the unit cell. A method for estimating the crystal cell parameters directly from a PDF profile is presented, which is composed of two steps: first, the type of crystal cell is inferred using machine-learning approaches applied to the PDF profile; second, the crystal cell parameters are extracted by means of multivariate analysis combined with vector superposition techniques. The procedure has been validated on a large number of PDF profiles calculated from known crystal structures and on a small number of measured PDF profiles. The lattice determination step has been benchmarked by a comprehensive exploration of different classifiers and different input data. The highest performance is obtained using the k-nearest neighbours classifier applied to whole PDF profiles. Descriptors calculated from the PDF profiles by recurrence quantitative analysis produce results that can be interpreted in terms of PDF properties, and the significance of each descriptor in determining the prediction is evaluated. The cell parameter extraction step depends on the cell metric rather than its type. Monometric, dimetric and trimetric cells have top-1 estimates that are correct 40, 20 and 5% of the time, respectively. Promising results were obtained when analysing real nanocrystals, where unit cells close to the true ones are found within the top-1 ranked solution in the case of monometric cells and within the top-6 ranked solutions in the case of dimetric cells, even in the presence of a crystalline impurity with a weight fraction up to 40%

CrystalMELA: a new crystallographic machine learning platform for crystal system determination

Determination of the crystal system and space group is the first step of crystal structure analysis. Often this turns out to be a bottleneck in the material characterization workflow for polycrystalline compounds, thus requiring manual interventions. This work proposes a new machine-learning (ML)-based web platform, CrystalMELA (Crystallography MachinE LeArning), for crystal systems classification. Two different ML models, random forest and convolutional neural network, are available through the platform, as well as the extremely randomized trees algorithm, available from the literature. The ML models learned from simulated powder X-ray diffraction patterns of more than 280 000 published crystal structures from organic, inorganic and metal-organic compounds and minerals which were collected from the POW_COD database. A crystal system classification accuracy of 70%, which improved to more than 90% when considering the Top-2 classification accuracy, was obtained in tenfold cross-validation. The validity of the trained models has also been tested against independent experimental data of published compounds. The classification options in the CrystalMELA platform are powerful, easy to use and supported by a user-friendly graphic interface. They can be extended over time with contributions from the community. The tool is freely available at https:// www.ba.ic.cnr.it/softwareic/crystalmela/ following registration

Salient networks: A novel application to study brain connectivity

Extracting meaningful structures and data, thus unveiling the underlying base of knowledge, is a common challenging task in social, physical and life sciences. In this paper we apply a novel complex network approach based on the detection of salient links to reveal the effect of atrophy on brain connectivity. Starting from structural Magnetic Resonance Imaging (MRI) data, we firstly define a complex network model of brain connectivity, then we show how salient networks extracted from the original ones can emphasize the presence of the disease significantly reducing data complexity and computational requirements. As a proof of concept, we discuss the experimental results on a mixed cohort of 29 normal controls (NC) and 38 Alzheimer disease (AD) patients from the Alzheimer Disease Neuroimaging Initiative (ADNI). In particular, the proposed framework can reach state-of-the-art classification performances with an area under the curve AUC = 0.93 ± 0.01 for the NC-AD classification

Multidimensional neuroimaging processing in ReCaS datacenter

In the last decade, a large amount of neuroimaging datasets became publicly available on different archives, so there is an increasing need to manage heterogeneous data, aggregate and process them by means of large-scale computational resources. ReCaS datacenter offers the most important features to manage big datasets, process them, store results in efficient manner and make all the pipeline steps available for reproducible data analysis. Here, we present a scientific computing environment in ReCaS datacenter to deal with common problems of large-scale neuroimaging processing. We show the general architecture of the datacenter and the main steps to perform multidimensional neuroimaging processing

Communicability Characterization of Structural DWI Subcortical Networks in Alzheimer’s Disease

In this paper, we investigate the connectivity alterations of the subcortical brain network due to Alzheimer’s disease (AD). Mostly, the literature investigated AD connectivity abnormalities at the whole brain level or at the cortex level, while very few studies focused on the sub-network composed only by the subcortical regions, especially using diffusion-weighted imaging (DWI) data. In this work, we examine a mixed cohort including 46 healthy controls (HC) and 40 AD patients from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data set. We reconstruct the brain connectome through the use of state of the art tractography algorithms and we propose a method based on graph communicability to enhance the information content of subcortical brain regions in discriminating AD. We develop a classification framework, achieving 77% of area under the receiver operating characteristic (ROC) curve in the binary discrimination AD vs. HC only using a 12 × 12 subcortical features matrix. We find some interesting AD-related connectivity patterns highlighting that subcortical regions tend to increase their communicability through cortical regions to compensate the physical connectivity reduction between them due to AD. This study also suggests that AD connectivity alterations mostly regard the inter-connectivity between subcortical and cortical regions rather than the intra-subcortical connectivity

Salient networks: a novel application to study Alzheimer disease

Abstract Background Extracting fundamental information from data, thus underlining hidden structures or removing noisy information, is one of the most important aims in different scientific fields especially in biological and medical sciences. In this article, we propose an innovative complex network application able to identify salient links for detecting the effect of Alzheimer’s disease on brain connectivity. We first build a network model of brain connectivity from structural Magnetic Resonance Imaging (MRI) data, then we study salient networks retrieved from the original ones. Results Investigating informative power of the salient skeleton features in combination with those of the original networks we obtain an accuracy of $$0.91 \pm 0.01$$ 0.91±0.01 for the distinction of Alzheimer disease (AD) patients from normal controls (NC). This performance significantly overcomes accuracy of the original network features. Moreover salient networks are able to correctly discriminate normal controls (NC) from AD patients and NC from subjects with mild cognitive impairment that will convert to AD (cMCI). These evaluations, performed on an independent dataset, give an accuracy of $$0.79 \pm 0.01$$ 0.79±0.01 and $$0.76 \pm 0.01$$ 0.76±0.01 respectively for NC-AD and NC-cMCI classifications. Therefore, most of the informative content of the original networks is kept after the 92 $$\%$$ % and 82 $$\%$$ % reduction respectively in the number of nodes and links. In addition, the present approach, applied to a publicly available MRI dataset from the Alzheimer Disease Neuroimaging Initiative (ADNI), brings out also some interesting aspects related to the topologies and hubs of the networks. Conclusions The experimental results demonstrate how salient networks can highlight important brain network characteristics and structural pathological changes, while reducing considerably data complexity and computational requirements

Association between Structural Connectivity and Generalized Cognitive Spectrum in Alzheimer’s Disease

Modeling disease progression through the cognitive scores has become an attractive challenge in the field of computational neuroscience due to its importance for early diagnosis of Alzheimer’s disease (AD). Several scores such as Alzheimer’s Disease Assessment Scale cognitive total score, Mini Mental State Exam score and Rey Auditory Verbal Learning Test provide a quantitative assessment of the cognitive conditions of the patients and are commonly used as objective criteria for clinical diagnosis of dementia and mild cognitive impairment (MCI). On the other hand, connectivity patterns extracted from diffusion tensor imaging (DTI) have been successfully used to classify AD and MCI subjects with machine learning algorithms proving their potential application in the clinical setting. In this work, we carried out a pilot study to investigate the strength of association between DTI structural connectivity of a mixed ADNI cohort and cognitive spectrum in AD. We developed a machine learning framework to find a generalized cognitive score that summarizes the different functional domains reflected by each cognitive clinical index and to identify the connectivity biomarkers more significantly associated with the score. The results indicate that the efficiency and the centrality of some regions can effectively track cognitive impairment in AD showing a significant correlation with the generalized cognitive score (R = 0.7)