Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection Procedure

We develop an LM test for Granger causality in high-dimensional VAR models based on penalized least squares estimations. To obtain a test retaining the appropriate size after the variable selection done by the lasso, we propose a post-double-selection procedure to partial out effects of nuisance variables and establish its uniform asymptotic validity. We conduct an extensive set of Monte-Carlo simulations that show our tests perform well under different data generating processes, even without sparsity. We apply our testing procedure to find networks of volatility spillovers and we find evidence that causal relationships become clearer in high-dimensional compared to standard low-dimensional VARs

Inference in high-dimensional time series models

Today’s world provides us with great potential in terms of data availability: “big data” is a term that very much circulates and many came across with. While having loads of data is a great opportunity to better understand the complexity of the real world, designing reliable statistical inference in such data-dense contexts requires careful modelling. Furthermore, when data such as time series is considered, the matter gets further complicated, given the inherent time dependency one needs to account for. This research develops statistical techniques aimed at both testing causal hypothesis and obtain forecasts in high-dimensional time series models. Applications of these techniques are provided in both finance, macroeconomics and climate econometrics, thus demonstrating the relevance of such tools across various sub-disciplines

Parameter clustering in Bayesian functional principal component analysis of neuroscientific data

The extraordinary advancements in neuroscientific technology for brain recordings over the last decades have led to increasingly complex spatiotemporal data sets. To reduce oversimplifications, new models have been developed to be able to identify meaningful patterns and new insights within a highly demanding data environment. To this extent, we propose a new model called parameter clustering functional principal component analysis (PCl-fPCA) that merges ideas from functional data analysis and Bayesian nonparametrics to obtain a flexible and computationally feasible signal reconstruction and exploration of spatiotemporal neuroscientific data. In particular, we use a Dirichlet process Gaussian mixture model to cluster functional principal component scores within the standard Bayesian functional PCA framework. This approach captures the spatial dependence structure among smoothed time series (curves) and its interaction with the time domain without imposing a prior spatial structure on the data. Moreover, by moving the mixture from data to functional principal component scores, we obtain a more general clustering procedure, thus allowing a higher level of intricate insight and understanding of the data. We present results from a simulation study showing improvements in curve and correlation reconstruction compared with different Bayesian and frequentist fPCA models and we apply our method to functional magnetic resonance imaging and electroencephalogram data analyses providing a rich exploration of the spatiotemporal dependence in brain time series.Publisher PDFPeer reviewe

Parameter clustering in Bayesian functional PCA of neuroscientific data

The extraordinary advancements in neuroscientific technology for brain recordings over the last decades have led to increasingly complex spatiotemporal data sets. To reduce oversimplifications, new models have been developed to be able to identify meaningful patterns and new insights within a highly demanding data environment. To this extent, we propose a new model called parameter clustering functional principal component analysis (PCl-fPCA) that merges ideas from functional data analysis and Bayesian nonparametrics to obtain a flexible and computationally feasible signal reconstruction and exploration of spatiotemporal neuroscientific data. In particular, we use a Dirichlet process Gaussian mixture model to cluster functional principal component scores within the standard Bayesian functional PCA framework. This approach captures the spatial dependence structure among smoothed time series (curves) and its interaction with the time domain without imposing a prior spatial structure on the data. Moreover, by moving the mixture from data to functional principal component scores, we obtain a more general clustering procedure, thus allowing a higher level of intricate insight and understanding of the data. We present results from a simulation study showing improvements in curve and correlation reconstruction compared with different Bayesian and frequentist fPCA models and we apply our method to functional magnetic resonance imaging and electroencephalogram data analyses providing a rich exploration of the spatiotemporal dependence in brain time series.Publisher PDFPeer reviewe

Sympathetic skin response in multiple sclerosis : a meta-analysis of case-control studies

The study was supported by a grant of the Italian Ministry of Health, Ricerca Corrente funding program 2014–2015 [RC2014].The usefulness of sympathetic skin responses (SSR) in multiple sclerosis (MS) has been advocated by several studies in the last 20 years; however, due to a great heterogeneity of findings, a comprehensive meta-analysis of case-control studies is in order to pinpoint consistencies and investigate the causes of discrepancies. We searched MEDLINE, EMBASE and Cochrane databases for case-control studies comparing SSR absence frequency and latency between patients with MS and healthy controls. Thirteen eligible studies including 415 MS patients and 331 healthy controls were identified. The pooled analysis showed that SSR can be always obtained in healthy controls while 34% of patients had absent SSRs in at least one limb (95% CI 22-47%; p < 0.0001) but with considerable heterogeneity across studies (I2 = 90.3%). Patients' age explained 22% of the overall variability and positive correlations were found with Expanded Disability Status Scale and disease duration. The pooled mean difference of SSR latency showed a significant increase in patients on both upper (193 ms; 95% CI 120-270 ms) and lower (350 ms; 95% CI 190-510 ms) extremities. We tested the discriminatory value of SSR latency thresholds defined as the 95% confidence interval (CI) upper bound of the healthy controls, and validated the results on a new dataset. The lower limb threshold of 1.964 s produces the best results in terms of sensitivity 0.86, specificity 0.67, positive predicted value 0.75 and negative predicted value 0.80. Despite a considerable heterogeneity of findings, there is evidence that SSR is a useful tool in MS.Publisher PDFPeer reviewe

Exploring the predictive value of the evoked potentials score in MS within an appropriate patient population : a hint for an early identification of benign MS?

This study was supported by the Italian Ministry of Health, Ricerca Corrente funding plan to the institutional research activity of the Scientific Institute S. Maria Nascente of the Don C. Gnocchi Foundation.Background: The prognostic value of evoked potentials (EPs) in multiple sclerosis (MS) has not been fully established. The correlations between the Expanded Disability Status Scale (EDSS) at First Neurological Evaluation (FNE) and the duration of the disease, as well as between EDSS and EPs, have influenced the outcome of most previous studies. To overcome this confounding relations, we propose to test the prognostic value of EPs within an appropriate patient population which should be based on patients with low EDSS at FNE and short disease duration. Methods: We retrospectively selected a sample of 143 early relapsing remitting MS (RRMS) patients with an EDSS < 3.5 from a larger database spanning 20 years. By means of bivariate logistic regressions, the best predictors of worsening were selected among several demographic and clinical variables. The best multivariate logistic model was statistically validated and prospectively applied to 50 patients examined during 2009-2011. Results: The Evoked Potentials score (EP score) and the Time to EDSS 2.0 (TT2) were the best predictors of worsening in our sample (Odds Ratio 1.10 and 0.82 respectively, p=0.001). Low EP score (below 15-20 points), short TT2 (lower than 3-5 years) and their interaction resulted to be the most useful for the identification of worsening patterns. Moreover, in patients with an EP score at FNE below 6 points and a TT2 greater than 3 years the probability of worsening was 10% after 4-5 years and rapidly decreased thereafter. Conclusions: In an appropriate population of early RRMS patients, the EP score at FNE is a good predictor of disability at low values as well as in combination with a rapid buildup of disability. Interestingly, an EP score at FNE under the median together with a clinical stability lasting more than 3 years turned out to be a protective pattern. This finding may contribute to an early identification of benign patients, well before the term required to diagnose Benign MS (BMS).Publisher PDFPeer reviewe

Relationship between herpes simplex virus-1-specific antibody titers and cortical brain damage in Alzheimer's disease and amnestic mild cognitive impairment

This work was supported by 2012–2014 Ricerca Corrente (Italian Ministry of Health).Alzheimer's disease (AD) is a multifactorial disease with a still barely understood etiology. Herpes simplex virus 1 (HSV-1) has long been suspected to play a role in the pathogenesis of AD because of its neurotropism, high rate of infection in the general population, and life-long persistence in neuronal cells, particularly in the same brain regions that are usually altered in AD. The goal of this study was to evaluate HSV-1-specific humoral immune responses in patients with a diagnosis of either AD or amnestic mild cognitive impairment (aMCI), and to verify the possible relation between HSV-1-specific antibody (Ab) titers and cortical damage; results were compared to those obtained in a group of healthy controls (HC). HSV-1 serum IgG titers were measured in 225 subjects (83 AD, 68 aMCI, and 74 HC). HSV-specific Ab avidity and cortical gray matter volumes analyzed by magnetic resonance imaging (MRI) were evaluated as well in a subgroup of these individuals (44 AD, 23 aMCI, and 26 HC). Results showed that, whereas HSV-1 seroprevalence and IgG avidity were comparable in the three groups, increased Ab titers (p < 0.001) were detected in AD and aMCI compared to HC. Positive significant correlations were detected in AD patients alone between HSV-1 IgG titers and cortical volumes in orbitofrontal (region of interest, ROI1 RSp0.56; p = 0.0001) and bilateral temporal cortices (ROI2 RSp0.57; p < 0.0001; ROI3 RSp0.48; p = 0.001); no correlations could be detected between IgG avidity and MRI parameters. Results herein suggest that a strong HSV-1-specific humoral response could be protective toward AD-associated cortical damage.Publisher PDFPeer reviewe

Suppression of host gene expression is associated with latent TB infection : a possible diagnostic biomarker

Funding:This work was Supported by the Wellcome Trust Institutional Strategic Support fund of the University of St Andrews, grant code 204821/Z/16/Z. Additional funding was obtained from Helse Nord Tuberculosis Initiative (HNTI), Pathology Department, Kamuzu University of Health Sciences and the Africa Centre for Public Health and Herbal Medicines (ACEPHEM), Kamuzu University of Health Sciences.The World Health Organization End TB strategy aims for a 90% reduction of tuberculosis (TB) incidence by 2035. Systematic testing and treatment of latent TB infection (LTBI) among contacts of active TB patients is recommended as one of the ways to curtail TB incidence. However, there is a shortage of tools to accurately diagnose LTBI. We assessed the appropriateness of whole blood host transcriptomic markers (TM) to diagnose LTBI among household contacts of bacteriologically confirmed index cases compared to HIV negative healthy controls (HC). QuantiFERON-TB Gold Plus Interferon gamma release assay (IGRA) and reverse-transcriptase quantitative PCR were used to determine LTBI and quantify TM expression respectively. Association between TM expression and LTBI was evaluated by logistic regression modelling. A total of 100 participants, 49 TB exposed (TBEx) household contacts and 51 HC, were enrolled. Twenty-five (51%) TBEx individuals tested positive by IGRA, and were denoted as LTBI individuals, and 37 (72.5%) HC were IGRA-negative. Expression of 11 evaluated TM was significantly suppressed among LTBI compared to HC. Out of the 11 TM, ZNF296 and KLF2 expression were strongly associated with LTBI and successfully differentiated LTBI from HC. Paradoxically, 21 (49%) TBEx participants who tested IGRA negative exhibited the same pattern of suppressed TM expression as IGRA positive (LTBI-confirmed individuals). Results suggest that suppression of gene expression underlies LTBI and may be a more sensitive diagnostic biomarker than standard-of-care IGRA.Peer reviewe