Biology subjects

Stramaglia, S.

Publications and source records attributed to Stramaglia, S..

6 recordsLinked to original sources

bioRxiv2018

Hyperconnectivity of the default mode network in multiorgan dysfunction syndrome

Multiple Organ Dysfunction Syndrome (MODS) is a systemic physiological disorder affecting two or more body organs triggered after an insult complication. Beyond the systemic failure, patients who survive MODS present cognitive and neurological impairments that remain stable even several years after Intensive Care Unit (ICU) discharge. Here, we focus on the specific situation of MODS patients with no apparent brain damage (NABD), where the mechanisms driving cognitive impairment at long term are not well-understood. We recruit N1 = 13 MODS patients with NABD at 6 months after ICU discharge, together with N2 = 13 healthy controls (matched by age, sex and years of education), and acquire functional magnetic resonance imaging at rest to find that, as compared to control, MODS patients with NABD present an overall increase of the functional connectivity (FC) at rest. In particular, we find that the default mode network (DMN) hyperconnects (increasing the node strength of the FC matrix) to three classes of networks: primary sensory (such as auditory, sensory-motor and visual), multimodal integration (such as dorsal attention and salience) and higher order cognition networks (such as fronto-parietal, language and executive control). Therefore, although these patients do not have an apparent structural damage after MODS, at the functional level, we found brain network alterations coexisting with hyperconnectivity of the DMN, that similar to what happens at the onset of other pathologies, might indicate a possible mechanism for brain compensation occurring after MODS.

neuroscience↗

bioRxiv2018

Interaction information along lifespan of the resting brain dynamics reveals a major redundant role of the default mode network

Interaction Information generalizes the univariate Shannon Entropy to triplets of variables, allowing the detection of redundant or synergetic interactions in dynamical networks. Here, we calculated interaction information from functional magnetic resonance imaging and asked whether redundancy or synergy vary across brain regions and along lifespan. We found high overlapping between the pattern of high redundancy and the default mode network, and this occurred along lifespan. The pattern of high values of synergy, more heterogeneous and variable along lifespan, was overlapping with different cognitive domains, such as spatial and temporal memory, emotion processing and motor skills. Moreover, the amount of redundancy and synergy seem to be balanced along lifespan, suggesting informational compensatory mechanisms in brain networks.

neuroscience↗

bioRxiv2018

Connectome preprocessing by Consensus Clustering increases separability in group neuroimaging studies

One of the biggest challenges in preprocessing pipelines for neuroimaging data is to increase the signal-to-noise ratio of the data which will be used for subsequent analyses. In the same line, we suggest in the present work that the application of consensus clustering for brain connectivity matrices to find subgroups of subjects can be a valid additional\"connectome processing\" step helpful to reduce intra-group variability and therefore increase the separability of distinct classes. In addition, by partitioning the data before any group comparison, we demonstrate that unique regions within each cluster arise and bring new information that could be relevant from a clinical point of view.

neuroscience↗

bioRxiv2018

Predicting functional networks from region connectivity profiles in task-based versus resting-state fMRI data

Intrinsic Connectivity Networks, patterns of correlated activity emerging from \"resting-state\" Blood Oxygenation Level Dependent time series, are increasingly being associated to cognitive, clinical, and behavioral aspects, and compared with the pattern of activity elicited by specific tasks. We study the reconfiguration of the brain networks between task and resting-state conditions by a machine learning approach, to highlight the Intrinsic Connectivity Networks (ICNs) which are more affected by the change of network configurations in task vs. rest. We use a large cohort of publicly available data in both resting and task-based fMRI paradigms; by trying a battery of different supervised classifiers relying only on task-based measurements, we show that the highest accuracy is reached with a simple neural network of one hidden layer. In addition, when testing the fitted model on resting state measurements, such architecture yields a performance close to 90% for areas connected to the task performed, which mainly involve the visual and sensorimotor cortex, whilst a relevant decrease of the performance is observed in the other ICNs. On one hand, our results confirm the correspondence of ICNs in both paradigms (task and resting) thus opening a window for future clinical applications to subjects whose participation in a required task cannot be guaranteed. On the other hand it is shown that brain areas not involved in the task display different connectivity patterns in the two paradigms.

neuroscience↗

bioRxiv2017

What the structural-functional connectome reveals about brain aging: The key role of the fronto-striatal-thalamic circuit and the rejuvenating impact of physical activity

Physiological ageing affects brain structure and function impacting its morphology, connectivity and performance. However, at which extent brain-connectivity metrics reflect the age of an individual and whether treatments or lifestyle factors such as physical activity influence the age-connectivity match is still unclear. Here, we assessed the level of physical activity and collected brain images from healthy participants (N=155) ranging from 10 to 80 years to build functional (resting-state) and structural (tractography) connectivity matrices that were combined as connectivity descriptors. Connectivity descriptors were used to compute a maximum likelihood age estimator that was optimized by minimizing the mean absolute error. The connectivity-based estimated age, i.e. the brain-connectome age (BCA), was compared to the chronological age (ChA). Our results were threefold. First, we showed that ageing widely affects the structural-functional connectivity of multiple structures, such as the anterior part of the default mode network, basal ganglia, thalamus, insula, cingulum, hippocampus, parahippocampus, occipital cortex, fusiform, precuneus and temporal pole. Second, our analysis showed that the structure-function connectivity between basal ganglia and thalamus to orbitofrontal and frontal areas make a major contribution to age estimation. Third, we found that high levels of physical activity reduce BCA as compared to ChA, and vice versa, low levels increment it. In conclusion, the BCA model results highlight the impact of physical activity and the key role played by the connectivity between basal ganglia and thalamus to frontal areas on the process of healthy aging. Notably, the same methodology can be generally applied both to evaluate the impact of other factors and therapies on brain ageing, and to identify the structural-functional brain connectivity correlate of other biomarkers than ChA.

neuroscience↗

bioRxiv2017

Progressive alterations in brain connectivity patterns revealed by diffusion-tensor brain networks across severity stages in Alzheimer’s disease

Alzheimers disease (AD) is a chronically progressive neurodegenerative disease highly correlated to aging. Whether AD originates by targeting a localized brain area and propagates to the rest of the brain across disease-severity progression is a question with an unknown answer. Here, we aim to provide an answer to this question at the group-level by looking at differences in diffusion-tensor brain networks. In particular, making use of data from Alzheimer's Disease Neuroimaging Initiative (ADNI), four different groups were defined (all of them matched by age, sex and education level): G1 (N1=36, healthy control subjects, Control), G2 (N2=36, early mild cognitive impairment, EMCI), G3 (N3=36, late mild cognitive impairment, LMCI) and G4 (N4=36, AD). Diffusion-tensor brain networks were compared across three disease stages: stage I 3(Control vs EMCI), stage II (Control vs LMCI) and stage III (Control vs AD). The group comparison was performed using the multivariate distance matrix regression analysis, a technique that was born in genomics and was recently proposed to handle brain functional networks, but here applied to diffusion-tensor data. The results were three-fold: First, no significant differences were found in stage I. Second, significant differences were found in stage II in the connectivity pattern of a subnetwork strongly associated to memory function (including part of the hippocampus, amygdala, entorhinal cortex, fusiform gyrus, inferior and middle temporal gyrus, parahippocampal gyrus and temporal pole). Third, a widespread disconnection across the entire AD brain was found in stage III, affecting more strongly the same memory subnetwork appearing in stage II, plus the other new subnetworks,including the default mode network, medial visual network, frontoparietal regions and striatum. Our results are consistent with a scenario where progressive alterations of connectivity arise as the disease severity increases and provide the brain areas possibly involved in such a degenerative process. Further studies applying the same strategy to longitudinal data are needed to fully confirm this scenario.

neuroscience↗