Extensive evaluation of morphological statistical harmonization for brain age prediction

Characterizing both neurodevelopmental and aging brain structural trajectories is important for understanding normal biological processes and atypical patterns that are related to pathological phenomena. Initiatives to share open access morphological data contributed significantly to the advance in brain structure characterization. Indeed, such initiatives allow large brain morphology multi-site datasets to be shared, which increases the statistical sensitivity of the outcomes. However, using neuroimaging data from multi-site studies requires harmonizing data across the site to avoid bias. In this work we evaluated three different harmonization techniques on the Autism Brain Imaging Data Exchange (ABIDE) dataset for age prediction analysis in two groups of subjects (i.e., controls and autism spectrum disorder). We extracted the morphological features from T1-weighted images of a mixed cohort of 654 subjects acquired from 17 sites to predict the biological age of the subjects using three machine learning regression models. A machine learning framework was developed to quantify the effects of the different harmonization strategies on the final performance of the models and on the set of morphological features that are relevant to the age prediction problem in both the presence and absence of pathology. The results show that, even if two harmonization strategies exhibit similar accuracy of predictive models, a greater mismatch occurs between the sets of most age-related predictive regions for the Autism Spectrum Disorder (ASD) subjects. Thus, we propose to use a stability index to extract meaningful features for a robust clinical validation of the outcomes of multiple harmonization strategies

Individual topological analysis of synchronization-based brain connectivity

Functional connectivity analysis aims at assessing the strength of functional coupling between the signal responses in distinct brain areas. Usually, functional magnetic resonance imaging (fMRI) time series connections are estimated through zero-lag correlation metrics that quantify the statistical similarity between pairs of regions or spectral measures that assess synchronization at a frequency band of interest. Here, we explored the application of a new metric to assess the functional synchronization in phase space between fMRI time series in a resting state. We applied a complete topological analysis to the resulting connectivity matrix to uncover both the macro-scale organization of the brain and detect the most important nodes. The synchronization metric is also compared with Pearson's correlation coefficient and spectral coherence to highlight similarities and differences between the topologies of the three functional networks. We found that the individual topological organization of the resulting synchronization-based connectivity networks shows a finer modular organization than that identified with the other two metrics and a low overlap with the modular partitions of the other two networks suggesting that the derived topological information is not redundant and could be potentially integrated to provide a multi-scale description of functional connectivity

Hydrodynamics and growth laws in lamellar ordering

Ordering of lamellar phases described by a free-energy functional with short-range interactions is numerically investigated in two dimensions by means of a pseudo-spectral method. The ordering process is found to depend on the fluid viscosity: it is arrested for large viscosity values and proceeds as a power law for small ones, with a crossover regime for intermediate values. At varying the free energy parameters, strong evidence has been found that the ordering law, unlike binary mixtures, is not unique. Copyright c (c) EPLA, 2007

Machine learning and DWI brain communicability networks for Alzheimer's disease detection

Signal processing and machine learning techniques are changing the clinical practice based on medical imaging from many perspectives. A major topic is related to (i) the development of computer aided diagnosis systems to provide clinicians with novel, non-invasive and low-cost support-tools, and (ii) to the development of new methodologies for the analysis of biomedical data for finding new disease biomarkers. Advancements have been recently achieved in the context of Alzheimer's disease (AD) diagnosis through the use of diffusionweighted imaging (DWI) data. When combinedwith tractography algorithms, this imaging modality enables the reconstruction of the physical connections of the brain that can be subsequently investigated through a complex network-based approach. A graph metric particularly suited to describe the disruption of the brain connectivity due to AD is communicability. In this work, we develop a machine learning framework for the classification and feature importance analysis of AD based on communicability at the whole brain level. We fairly compare the performance of three state-of-the-art classification models, namely support vector machines, random forests and artificial neural networks, on the connectivity networks of a balanced cohort of healthy control subjects and AD patients from the ADNI database. Moreover, we clinically validate the information content of the communicabilitymetric by performing a feature importance analysis. Both performance comparison and feature importance analysis provide evidence of the robustness of the method. The results obtained confirm that the whole brain structural communicability alterations due to AD are a valuable biomarker for the characterization and investigation of pathological conditions

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)

Psychological counseling in the Italian academic context: Expected needs, activities, and target population in a large sample of students

University psychological counseling (UPC) is receiving growing attention as a means to promote mental health and academic success among young adults and prevent irregular attendance and dropout. However, thus far, little effort has been directed towards the implementation of services attuned to students' expectations and needs. This work intends to contribute to the existing literature on this topic, by exploring the perceptions of UPC among a population of 39,277 students attending one of the largest universities in the South of Italy. Almost half of the total population correctly identified the UPC target population as university students, and about one third correctly expected personal distress to be the main need that UPC should target. However, a large percentage did not have a clear idea about UPC target needs, activities, and population. When two specific student subsamples were analyzed using a person-centered analysis, namely (i) those who expressed their intention to use the counseling service but had not yet done so and (ii) those who had already used it, the first subsample clustered into two groups, characterized by an "emotional" and a "psychopathological" focus, respectively, while the second subsample clustered into three groups with a "clinical", "socioemotional", and "learning" focus, respectively. This result shows a somewhat more "superficial" and "common" representation of UPC in the first subsample and a more "articulated" and "flexible" vision in the second subsample. Taken together, these findings suggest that UPC services could adopt "student-centered" strategies to both identify and reach wider audiences and specific student subgroups. Recommended strategies include robust communication campaigns to help students develop a differentiated perception of the available and diverse academic services, and the involvement of active students to remove the barriers of embarrassment and shame often linked to the stigma of using mental health services

Levers and Obstacles of Effective Research and Innovation for Organic Food and Farming in Italy

The objectives of this paper are to present the dynamic of organic food and farming (OFF) research and innovation, to outline challenges in deploying programs and accessing funding, and to define key actions to foster the development of tailored quality research on organic farming in Italy. The baseline starts from the main outcomes that emerged during the World Caf\ue9 held in the frame of the Salone Internazionale del biologico e del naturale (SANA Expo) in 2018, where the Italian OFF research community met to build a convergence on scope and modus operandi in the research endeavor. These outcomes were examined in the light of the key features of the research and innovation projects funded in Italy in the last 10 years, respectively by the Italian Ministry of Agriculture and the regional administrations through the innovation support instruments in the Rural Development Plan programming periods. In the period 2009\u20132018, 70 research projects for a total funding of 21.081 million \ubf (<0.1% of the value of the sector) were launched, addressing nine dierent topic areas. Over a similar period (2007\u20132019), 53 regional innovation projects addressing organic farming were activated for a total budget of 14.299 million \ubf (<10% of the entire available funding). The implementation of interventions in the research and the innovation areas were often scattered in terms of the topics, disciplines, and types of supply chain/network addressed. The relatively high share of multi/interdisciplinary research and innovation projects aswell as the acknowledgement of the multi-actor approach as a fundamental step toward co-research and co-innovation were upshots that emerged from our analysis. The outcomes of this study can be used by competent national and the regional authorities to design their future research and innovation policies and interventions

MRI analysis for Hippocampus segmentation on a distributed infrastructure

Medical image computing raises new challenges due to the scale and the complexity of the required analyses. Medical image databases are currently available to supply clinical diagnosis. For instance, it is possible to provide diagnostic information based on an imaging biomarker comparing a single case to the reference group (controls or patients with disease). At the same time many sophisticated and computationally intensive algorithms have been implemented to extract useful information from medical images. Many applications would take great advantage by using scientific workflow technology due to its design, rapid implementation and reuse. However this technology requires a distributed computing infrastructure (such as Grid or Cloud) to be executed efficiently. One of the most used workflow manager for medical image processing is the LONI pipeline (LP), a graphical workbench developed by the Laboratory of Neuro Imaging (http://pipeline.loni.usc.edu). In this article we present a general approach to submit and monitor workflows on distributed infrastructures using LONI Pipeline, including European Grid Infrastructure (EGI) and Torque-based batch farm. In this paper we implemented a complete segmentation pipeline in brain magnetic resonance imaging (MRI). It requires time-consuming and data-intensive processing and for which reducing the computing time is crucial to meet clinical practice constraints. The developed approach is based on web services and can be used for any medical imaging application

Innovazioni di processo per la produzione di compost di qualità idonei alla conservazione del suolo e alla sostenibilità in agricoltura biologica

La tutela della risorsa suolo è tra gli aspetti fondamentali del metodo di produzione biologico. L’applicazione di compost di qualità coniuga la necessità del recupero di materia da scarti organici con l’esigenza di reintegrare il contenuto di sostanza organica dei suoli. Tali premesse sono la base di una ricerca finalizzata alla produzione di compost tramite un sistema innovativo, alla caratterizzazione del prodotto finito e alla realizzazione di prove sperimentali in ambiente confinato e in pieno campo, idonee ad individuare un codice di buona pratica agricola per l’utilizzo del compost in agricoltura biologica. Il protocollo sperimentale ha previsto la produzione di 4 tipi di compost (C1, C2, C3, C4) ottenuti da una miscela di partenza contenente: sansa umida denocciolata (sn), stallatico (st) e residui ligneocellulosici triturati (lc). I compost C1 (C/N=30) e C3 (C/N=45) sono stati ottenuti dalla miscelazione di sn :st: lc nel rapporto 7:1:5 (p/p) e 1:5:5 (p/p). C2 e C4 derivano rispettivamente da C1 e C3 per essiccazione all’aria in strato sottile alla fine della fase di biossidazione accelerata (BA). L’essiccazione è stata effettuata al fine di rallentare le attività microbiche ed i processi di evoluzione della sostanza organica ottenendo matrici a due stadi di maturazione. I parametri di processo monitorati sono stati: umidità, temperatura, pH, e solidi volatili. Ad inizio processo (T0), alla fine della fase di BA (T1) e alla fine della fase di curing (T2), sono stati prelevati campioni rappresentativi dai cumuli per la misurazione dell’indice respirometrico dinamico (IRD). I 4 compost, prodotti presso l’impianto di compostaggio sperimentale IAMB, sono stati applicati su una rotazione biennale farro - cece da granella e su una coltivazione di spinacio, entrambi condotti con metodo di produzione biologico. La fase di BA, della durata di 35 gg per C1 e C2 e 18 gg per C3 e C4, è stata condotta in cassone areato non movimentato. La fase di curing (86 gg per C1 e 65 gg per C3) è stata condotta in cumulo statico. L’umidità è stata controllata settimanalmente e corretta al fine di garantire valori di processo tra 50 e 60%, la temperatura massima raggiunta è stata di 72 °C per C1 e 76 °C per C3. L’IRD, partendo da valori compatibili con i dati di letteratura nella miscela iniziale (T0: 4.171 mgO2 gSV-1 h-1 per C1 e C2; 5.955 mgO2 gSV-1 h-1 per C3 e C4), ha raggiunto livelli di piena stabilità per tutti i materiali già alla fine della fase di BA (T1: 424 mgO2 gSV-1 h-1 per C1e C2 e 789 mgO2 gSV-1 h-1 per C3 e C4). I diversi rapporti C/N e i contributi della matrice sn nelle due miscelazioni hanno comportato differenze nei tempi di processo e negli andamenti dei picchi di temperatura giornalieri, risultando più brevi per C3-C4 rispetto a C1-C2. In attesa dei risultati finali relativi all’applicazione in pieno campo, si ipotizza che il prolungarsi del processo in C1-C3 rispetto a C2-C4 comporterà una diversa disponibilità di elementi nutritivi nelle relative tesi sperimentali per effetto del procedere dei processi di biossidazione ed evoluzione della sostanza organica