New insights on the subsidence of Lipari island (Aeolian islands, southern Italy) from the submerged Roman age pier at Marina Lunga

Lipari island belongs to the Aeolian archipelago, located in the Southern Tyrrhenian Sea (Italy), which is one of the most active volcanic areas of the Mediterranean basin. Although this region has been settled since prehistory, only during Roman times were coastal installations built in these islands. In this study, we present and discuss data on the relative sea level change estimated from a submerged pier of Roman age dated 2100 ± 100 BP. This structure, about 140 60 m, is located along the coast of Marina Lunga, corresponding to the present location of the main harbor of Lipari island. This pier, which was accidentally discovered in 2008 during preliminary excavations for the construction of a new pier, is a valuable indicator of relative sea level changes and vertical land movements. Its top surface is presently located at 9.1 ± 0.05 m, while the foundations at the outer end of the pier are at 11.6 ± 0.05 m, above a shoreline placed at 13.0 ± 0.05 m. We studied this site through direct archaeological investigations and ultra-high resolution multibeam bathymetry. The current submergence of this pier can be explained by the cumulative effect of the relative sea level changes caused by the regional glacio-hydro-isostatic signal, active since the end of the last glacial maximum, and the local volcano-tectonic land subsidence. From our investigations, a relative sea level change at 12.3 ± 0.7 m with a subsidence rate at 5.79 ± 0.01 mm y1 and an average value of volcanotectonic contribution at 5.17 ± 0.01 m y1 for the last 2100 ± 100 years BP, is estimated from comparison against the latest predicted sea level model for the Southern Tyrrhenian Sea. These rates of relative sea level change led to the disuse of the harbor after around the fourth century AD, in agreement with archaeological interpretations. Our results provide new insights on the recent evolution of this active volcanic area

Multivariate regression analysis of structural MRI connectivity matrices in Alzheimer's disease.

Alzheimer's disease (AD) is the most common form of dementia among older people and increasing longevity ensures its prevalence will rise even further. Whether AD originates by disconnecting a localized brain area and propagates to the rest of the brain across disease-severity progression is a question with an unknown answer. An important related challenge is to predict whether a given subject, with a mild cognitive impairment (MCI), will convert or not to AD. Here, our aim is to characterize the structural connectivity pattern of MCI and AD subjects using the multivariate distance matrix regression (MDMR) analysis, and to compare it to those of healthy subjects. MDMR is a technique developed in genomics that has been recently applied to functional brain network data, and here applied to identify brain nodes with different connectivity patterns, in controls and patients, because of brain atrophy. We address this issue at the macroscale by looking to differences in individual structural MRI brain networks, obtained from MR images according to a recently proposed definition of connectivity which measures the image similarity between patches at different locations in the brain. In particular, using data from ADNI, we selected four groups of subjects (all of them matched by age and sex): HC (healthy control participants), ncMCI (mild cognitive impairment not converting to AD), cMCI (mild cognitive impairment converting to AD) and AD. Next, we built structural MRI brain networks and performed group comparison for all the pairs of groups. Our results were three-fold: (i) considering the comparison of HC with the three other groups, the number of significant brain regions was 4 for ncMCI, 290 for cMCI and 74 for AD, out of a total of 549 regions; hence, in terms of the structural MRI connectivity here adopted, cMCI subjects have the maximal altered pattern w.r.t. healthy conditions. (ii) Eight and seven nodes were significant for the comparisons AD-ncMCI and AD-cMCI, respectively; six nodes, among them, were significant in both comparisons and these nodes form a connected brain region (corresponding to hippocampus, amygdala, Parahippocampal Gyrus, Planum Polare, Frontal Orbital Cortex, Temporal Pole and subcallosal cortex) showing reduced strength of connectivity in the MCI stages; (iii) The connectivity maps of cMCI and ncMCI subjects significantly differ from the connectome of healthy subjects in three regions all corresponding to Frontal Orbital Cortex

Ambulatory blood pressure monitoring, 2D-echo and clinical variables relating to cardiac events in ischaemic cardiomyopathy following cardioverter-defibrillator implantation

Aims Evaluation of ambulatory blood pressure monitoring (ABPM), two-dimensional (2D) echo and clinical variables in predicting cardiac death and acute decompensated heart failure in patients with ischaemic cardiomyopathy and receiving a cardioverter-defibrillator implantation. Methods and results We studied 180 consecutive patients (169 men) on an out-patient basis, with systolic dysfunction (ejection fraction<— 35%) and previous myocardial infarction. All received a cardioverter defibrillator (ICD) (116 dual chamber, 36 monocameral and 28 biventricular), for primary prevention of sudden death and standard medical therapy for heart failure. Mean follow-up was 11.7 months. Two-dimensional echo was performed just before ICD implantation, ABPM and haematological samples 2 weeks later. Age, ejection fraction, creatinine, haemoglobin concentration, mean 24-h systolic blood pressure, mean 24-h diastolic blood pressure, mean 24-h heart rate, brain natriuretic peptide, QRS duration, % paced beats, ventricular scar, biventricular pacing, sex and diabetes were considered. Cox proportional hazards regression analysis was used explore the relationship between events. ROC curves were built for each independent variable. Events occurred in 47 patients (26%); 7 deaths for refractory heart failure and 40 hospitalizations for acute decompensated heart failure. Low mean 24-h systolic blood pressure [hazard ratio 0.96, 95% confidence interval (CI) 0.93–0.99, PU0.02], high creatinine (hazard ratio 1.61,95%CI 1.06–2.47, PU0.01), low haemoglobin concentration (hazard ratio 0.81, 95% CI 0.65– 0.99, PU0.04) and older age (hazard ratio 1.04, 95%CI 1.01– 1.08, PU0.02) were independent predictors of events. Conclusions Ambulatory systolic blood pressure, haemoglobin, creatinine and age can stratify risk of death and acute decompensated heart failure in patients with ischaemic cardiomyopathy and ICD in whom 2D-echo ejection fraction is not predictive

Multivariate regression analysis of structural MRI connectivity matrices in Alzheimerâ\u80\u99s disease

Alzheimerâ\u80\u99s disease (AD) is the most common form of dementia among older people and increasing longevity ensures its prevalence will rise even further. Whether AD originates by disconnecting a localized brain area and propagates to the rest of the brain across disease-severity progression is a question with an unknown answer. An important related challenge is to predict whether a given subject, with a mild cognitive impairment (MCI), will convert or not to AD. Here, our aim is to characterize the structural connectivity pattern of MCI and AD subjects using the multivariate distance matrix regression (MDMR) analysis, and to compare it to those of healthy subjects. MDMR is a technique developed in genomics that has been recently applied to functional brain network data, and here applied to identify brain nodes with different connectivity patterns, in controls and patients, because of brain atrophy. We address this issue at the macroscale by looking to differences in individual structural MRI brain networks, obtained from MR images according to a recently proposed definition of connectivity which measures the image similarity between patches at different locations in the brain. In particular, using data from ADNI, we selected four groups of subjects (all of them matched by age and sex): HC (healthy control participants), ncMCI (mild cognitive impairment not converting to AD), cMCI (mild cognitive impairment converting to AD) and AD. Next, we built structural MRI brain networks and performed group comparison for all the pairs of groups. Our results were three-fold: (i) considering the comparison of HC with the three other groups, the number of significant brain regions was 4 for ncMCI, 290 for cMCI and 74 for AD, out of a total of 549 regions; hence, in terms of the structural MRI connectivity here adopted, cMCI subjects have the maximal altered pattern w.r.t. healthy conditions. (ii) Eight and seven nodes were significant for the comparisons AD-ncMCI and AD-cMCI, respectively; six nodes, among them, were significant in both comparisons and these nodes form a connected brain region (corresponding to hippocampus, amygdala, Parahippocampal Gyrus, Planum Polare, Frontal Orbital Cortex, Temporal Pole and subcallosal cortex) showing reduced strength of connectivity in the MCI stages; (iii) The connectivity maps of cMCI and ncMCI subjects significantly differ from the connectome of healthy subjects in three regions all corresponding to Frontal Orbital Cortex

The brain nodes significantly differing in MCI and AD.

<p>Their patches correspond to the following anatomical structures: (A) left hemisphere: Hippocampus, Amygdala, Planum Polare; (B) left hemisphere: Parahippocampal Gyrus; (C) right hemisphere: Parahippocampal Gyrus; (D) right hemisphere: Hippocampus, Amygdala, Planum Polare; (E) left hemisphere: Frontal Orbital Cortex, Temporal Pole; (F) Inter-hemispheric: Subcallosal Cortex. (G) right hemisphere: Frontal Orbital Cortex, Temporal Pole; (H) left hemisphere: Subcallosal Cortex. Green patches are significant for both ncMCI and cMCI, whilst red patches are significant only for ncMCI.</p

Galcanezumab treatment changes visual related EEG connectivity patterns in migraine patients

Background: Monoclonal antibodies against calcitonin gene-related peptides (CGRP) are innovative therapies for migraine treatment. Although they are clinically effective, how anti-CGRP treatment reduces migraine attacks still remains unclear.Objective: In this observational case-control study, we aimed to apply graph theory to EEG data from 20 migraine patients and 10 controls to investigate the effects of 3 months of galcanezumab on brain connectivity.Methods: We analyzed EEG rhythms during black-white pattern reversal stimulation with 0.5 cycle per degree spatial frequency before (T0) galcanezumab injection, as well as after 3 months (T2). EEG recordings made 1 hour after galcanezumab administration served as the control session (T1). Patients' connectivity patterns obtained at T0, T1 and T2 were compared with normal controls.Results: We found that galcanezumab increased network integration (with a 5% significance level corrected with the false discovery rate), changing the intensity of connections between the occipital through the frontal areas. At 3 months follow up, patients with persistent high headache intensity had a minor effect on the strength of connections (evaluated using Kendall's rank correlation test and p &lt; 0.05).Conclusions: The potent anti-nociceptive action that galcanezumab exerts at a peripheral level could restore cortical connections and possibly factors predisposing to attack onset

Multidimensional scaling from all brain regions.

<p>The same as the previous figure but all the brain regions have been used to provide the input to multidimensional scaling. It shows that also globally in AD the connectivity is closer to those of controls than MCI states, although to a lesser extent than the six regions of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0187281#pone.0187281.g001" target="_blank">Fig 1</a>. As we are dealing with distances between arrays of Pearson correlation coefficients, the units of both axis are dimensionless.</p

Multidimensional scaling from signficant MCI-AD nodes.

<p>In order to visualize the peculiar pattern of the region corresponding to the six nodes that are significant when AD patients are compared with MCI, the four groups are represented here, using multidimensional scaling applied to the average connectivity map of these brain regions, as explained in the text. The plot clearly shows that, in AD, the connectivity pattern of this region is similar to those corresponding to healthy conditions. As we are dealing with distances between arrays of Pearson correlation coefficients, the units of both axis are dimensionless.</p