Anatomically compliant modes of variations: New tools for brain connectivity.

Anatomical complexity and data dimensionality present major issues when analysing brain connectivity data. The functional and anatomical aspects of the connections taking place in the brain are in fact equally relevant and strongly intertwined. However, due to theoretical challenges and computational issues, their relationship is often overlooked in neuroscience and clinical research. In this work, we propose to tackle this problem through Smooth Functional Principal Component Analysis, which enables to perform dimensional reduction and exploration of the variability in functional connectivity maps, complying with the formidably complicated anatomy of the grey matter volume. In particular, we analyse a population that includes controls and subjects affected by schizophrenia, starting from fMRI data acquired at rest and during a task-switching paradigm. For both sessions, we first identify the common modes of variation in the entire population. We hence explore whether the subjects' expressions along these common modes of variation differ between controls and pathological subjects. In each session, we find principal components that are significantly differently expressed in the healthy vs pathological subjects (with p-values < 0.001), highlighting clearly interpretable differences in the connectivity in the two subpopulations. For instance, the second and third principal components for the rest session capture the imbalance between the Default Mode and Executive Networks characterizing schizophrenia patients.

Analyzing data in complicated 3D domains: Smoothing, semiparametric regression, and functional principal component analysis.

In this work, we introduce a family of methods for the analysis of data observed at locations scattered in three-dimensional (3D) domains, with possibly complicated shapes. The proposed family of methods includes smoothing, regression, and functional principal component analysis for functional signals defined over (possibly nonconvex) 3D domains, appropriately complying with the nontrivial shape of the domain. This constitutes an important advance with respect to the literature, because the available methods to analyze data observed in 3D domains rely on Euclidean distances, which are inappropriate when the shape of the domain influences the phenomenon under study. The common building block of the proposed methods is a nonparametric regression model with differential regularization. We derive the asymptotic properties of the methods and show, through simulation studies, that they are superior to the available alternatives for the analysis of data in 3D domains, even when considering domains with simple shapes. We finally illustrate an application to a neurosciences study, with neuroimaging signals from functional magnetic resonance imaging, measuring neural activity in the gray matter, a nonconvex volume with a highly complicated structure.

A PDE-regularized smoothing method for space-time data over manifolds with application to medical data.

We propose an innovative statistical-numerical method to model spatio-temporal data, observed over a generic two-dimensional Riemanian manifold. The proposed approach consists of a regression model completed with a regularizing term based on the heat equation. The model is discretized through a finite element scheme set on the manifold, and solved by resorting to a fixed point-based iterative algorithm. This choice leads to a procedure which is highly efficient when compared with a monolithic approach, and which allows us to deal with massive datasets. After a preliminary assessment on simulation study cases, we investigate the performance of the new estimation tool in practical contexts, by dealing with neuroimaging and hemodynamic data.

A look at the spatio-temporal mortality patterns in Italy during the COVID-19 pandemic through the lens of mortality densities.

With the tools and perspective of Object Oriented Spatial Statistics, we analyze official daily data on mortality from all causes in the provinces and municipalities of Italy for the year 2020, the first of the COVID-19 pandemic. By comparison with mortality data from 2011 to 2019, we assess the local impact of the pandemic as perturbation factor of the natural spatio-temporal death process. For each Italian province and year, mortality data are represented by the densities of time of death during the calendar year. Densities are regarded as functional data belonging to the Bayes space B 2 . In this space, we use functional-on-functional linear models to predict the expected mortality in 2020, based on mortality in previous years, and we compare predictions with actual observations, to assess the impact of the pandemic. Through spatial downscaling of the provincial data down to the municipality level, we identify spatial clusters characterized by mortality densities anomalous with respect to the surroundings. The proposed analysis pipeline could be extended to indexes different from death counts, measured at a granular spatio-temporal scale, and used as proxies for quantifying the local disruption generated by the pandemic.

A Functional Data Analysis Approach to Left Ventricular Remodeling Assessment.

Left ventricular remodeling is a mechanism common to various cardiovascular diseases affecting myocardial morphology. It can be often overlooked in clinical practice since the parameters routinely employed in the diagnostic process (e.g., the ejection fraction) mainly focus on evaluating volumetric aspects. Nevertheless, the integration of a quantitative assessment of structural modifications can be pivotal in the early individuation of this pathology. In this work, we propose an approach based on functional data analysis to evaluate myocardial contractility. A functional representation of ventricular shape is introduced, and functional principal component analysis and depth measures are used to discriminate healthy subjects from those affected by left ventricular hypertrophy. Our approach enables the integration of higher informative content compared to the traditional clinical parameters, allowing for a synthetic representation of morphological changes in the myocardium, which could be further explored and considered for future clinical practice implementation.

FunChIP: an R/Bioconductor package for functional classification of ChIP-seq shapes.

SUMMARY: Chromatin Immunoprecipitation followed by sequencing (ChIP-seq) generates local accumulations of sequencing reads on the genome ("peaks"), which correspond to specific protein-DNA interactions or chromatin modifications. Peaks are detected by considering their total area above a background signal, usually neglecting their shapes, which instead may convey additional biological information. We present FunChIP, an R/Bioconductor package for clustering peaks according to a functional representation of their shapes: after approximating their profiles with cubic B-splines, FunChIP minimizes their functional distance and classifies the peaks applying a k-mean alignment and clustering algorithm. The whole pipeline is user-friendly and provides visualization functions for a quick inspection of the results. An application to the transcription factor Myc in 3T9 murine fibroblasts shows that clusters of peaks with different shapes are associated with different genomic locations and different transcriptional regulatory activity. AVAILABILITY AND IMPLEMENTATION: The package is implemented in R and is available under Artistic Licence 2.0 from the Bioconductor website (http://bioconductor.org/packages/FunChIP). CONTACT: marco.morelli@iit.it. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Peak shape clustering reveals biological insights.

BACKGROUND: ChIP-seq experiments are widely used to detect and study DNA-protein interactions, such as transcription factor binding and chromatin modifications. However, downstream analysis of ChIP-seq data is currently restricted to the evaluation of signal intensity and the detection of enriched regions (peaks) in the genome. Other features of peak shape are almost always neglected, despite the remarkable differences shown by ChIP-seq for different proteins, as well as by distinct regions in a single experiment. RESULTS: We hypothesize that statistically significant differences in peak shape might have a functional role and a biological meaning. Thus, we design five indices able to summarize peak shapes and we employ multivariate clustering techniques to divide peaks into groups according to both their complexity and the intensity of their coverage function. In addition, our novel analysis pipeline employs a range of statistical and bioinformatics techniques to relate the obtained peak shapes to several independent genomic datasets, including other genome-wide protein-DNA maps and gene expression experiments. To clarify the meaning of peak shape, we apply our methodology to the study of the erythroid transcription factor GATA-1 in K562 cell line and in megakaryocytes. CONCLUSIONS: Our study demonstrates that ChIP-seq profiles include information regarding the binding of other proteins beside the one used for precipitation. In particular, peak shape provides new insights into cooperative transcriptional regulation and is correlated to gene expression.

Object Oriented Data Analysis: A few methodological challenges.

This is a discussion of the paper "Overview of object oriented data analysis" by J. Steve Marron and Andrés M. Alonso.

An introduction with medical applications to functional data analysis.

Functional data are data that can be represented by suitable functions, such as curves (potentially multi-dimensional) or surfaces. This paper gives an introduction to some basic but important techniques for the analysis of such data, and we apply the techniques to two datasets from biomedicine. One dataset is about white matter structures in the brain in multiple sclerosis patients; the other dataset is about three-dimensional vascular geometries collected for the study of cerebral aneurysms. The techniques described are smoothing, alignment, principal component analysis, and regression.

Identification and predictive value of interleukin-6+ interleukin-10+ and interleukin-6- interleukin-10+ cytokine patterns in ST-elevation acute myocardial infarction.

RATIONALE: At the onset of ST-elevation acute myocardial infarction (STEMI), patients can present with very high circulating interleukin-6 (IL-6(+)) levels or very low-IL-6(-) levels. OBJECTIVE: We compared these 2 groups of patients to understand whether it is possible to define specific STEMI phenotypes associated with outcome based on the cytokine response. METHODS AND RESULTS: We compared 109 patients with STEMI in the top IL-6 level (median, 15.6 pg/mL; IL-6(+) STEMI) with 96 in the bottom IL-6 level (median, 1.7 pg/mL; IL-6(-) STEMI) and 103 matched controls extracted from the multiethnic First Acute Myocardial Infarction study. We found minimal clinical differences between IL-6(+) STEMI and IL-6(-) STEMI. We assessed the inflammatory profiles of the 2 STEMI groups and the controls by measuring 18 cytokines in blood samples. We exploited clustering analysis algorithms to infer the functional modules of interacting cytokines. IL-6(+) STEMI patients were characterized by the activation of 2 modules of interacting signals comprising IL-10, IL-8, macrophage inflammatory protein-1α, and C-reactive protein, and monocyte chemoattractant protein-1, macrophage inflammatory protein-1ß, and monokine induced by interferon-γ. IL-10 was increased both in IL-6(+) STEMI and IL-6(-) STEMI patients compared with controls. IL-6(+)IL-10(+) STEMI patients had an increased risk of systolic dysfunction at discharge and an increased risk of death at 6 months in comparison with IL-6(-)IL-10(+) STEMI patients. We combined IL-10 and monokine induced by interferon-γ (derived from the 2 identified cytokine modules) with IL-6 in a formula yielding a risk index that outperformed any single cytokine in the prediction of systolic dysfunction and death. CONCLUSIONS: We have identified a characteristic circulating inflammatory cytokine pattern in STEMI patients, which is not related to the extent of myocardial damage. The simultaneous elevation of IL-6 and IL-10 levels distinguishes STEMI patients with worse clinical outcomes from other STEMI patients. These observations could have potential implications for risk-oriented patient stratification and immune-modulating therapies.

Invariant NKT cell reconstitution in pediatric leukemia patients given HLA-haploidentical stem cell transplantation defines distinct CD4+ and CD4- subset dynamics and correlates with remission state.

Immune reconstitution plays a crucial role on the outcome of patients given T cell-depleted HLA-haploidentical hematopoietic stem cell transplantation (hHSCT) for hematological malignancies. CD1d-restricted invariant NKT (iNKT) cells are innate-like, lipid-reactive T lymphocytes controlling infections, cancer, and autoimmunity. Adult mature iNKT cells are divided in two functionally distinct CD4(+) and CD4(-) subsets that express the NK receptor CD161 and derive from thymic CD4(+)CD161(-) precursors. We investigated iNKT cell reconstitution dynamics in 33 pediatric patients given hHSCT for hematological malignancies, with a follow-up reaching 6 y posttransplantation, and correlated their emergence with disease relapse. iNKT cells fully reconstitute and rapidly convert into IFN-γ-expressing effectors in the 25 patients maintaining remission. CD4(+) cells emerge earlier than the CD4(-) ones, both displaying CD161(-) immature phenotypes. CD4(-) cells expand more slowly than CD4(+) cells, though they mature with significantly faster kinetics, reaching full maturation by 18 mo post-hHSCT. Between 4 and 6 y post-hHSCT, mature CD4(-) iNKT cells undergo a substantial expansion burst, resulting in a CD4(+)