Advanced neuroimaging methodologies to improve connectivity detection in normal and abnormal language brain networks

The language connectome was in-vivo investigated using multimodal non-invasive quantitative MRI. In PPA patients (n=18) recruited by the IRCCS ISNB, Bologna, cortical thickness measures showed a predominant reduction on the left hemisphere (p<0.005) with respect to matched healthy controls (HC) (n=18), and an accuracy of 86.1% in discrimination from Alzheimer’s disease patients (n=18). The left temporal and para-hippocampal gyri significantly correlated (p<0.01) with language fluency. In PPA patients (n=31) recruited by the Northwestern University Chicago, DTI measures were longitudinally evaluated (2-years follow-up) under the supervision of Prof. M. Catani, King’s College London. Significant differences with matched HC (n=27) were found, tract-localized at baseline and widespread in the follow-up. Language assessment scores correlated with arcuate (AF) and uncinate (UF) fasciculi DTI measures. In left-ischemic stroke patients (n=16) recruited by the NatBrainLab, King’s College London, language recovery was longitudinally evaluated (6-months follow-up). Using arterial spin labelling imaging a significant correlation (p<0.01) between language recovery and cerebral blood flow asymmetry, was found in the middle cerebral artery perfusion, towards the right. In HC (n=29) recruited by the DIBINEM Functional MR Unit, University of Bologna, an along-tract algorithm was developed suitable for different tractography methods, using the Laplacian operator. A higher left superior temporal gyrus and precentral operculum AF connectivity was found (Talozzi L et al., 2018), and lateralized UF projections towards the left dorsal orbital cortex. In HC (n=50) recruited in the Human Connectome Project, a new tractography-driven approach was developed for left association fibres, using a principal component analysis. The first component discriminated cortical areas typically connected by the AF, suggesting a good discrimination of cortical areas sharing a similar connectivity pattern. The evaluation of morphological, microstructural and metabolic measures could be used as in-vivo biomarkers to monitor language impairment related to neurodegeneration or as surrogate of cognitive rehabilitation/interventional treatment efficacy.

Models for the Study of Cortical Activity During Cognitive ans Motor Tasks

This thesis is mainly devoted to show how EEG data and related phenomena can be reproduced and analyzed using mathematical models of neural masses (NMM). The aim is to describe some of these phenomena, to show in which ways the design of the models architecture is influenced by such phenomena, point out the difficulties of tuning the dozens of parameters of the models in order to reproduce the activity recorded with EEG systems during different kinds of experiments, and suggest some strategies to cope with these problems. In particular the chapters are organized as follows: chapter I gives a brief overview of the aims and issues addressed in the thesis; in chapter II the main characteristics of the cortical column, of the EEG signal and of the neural mass models will be presented, in order to show the relationships that hold between these entities; chapter III describes a study in which a NMM from the literature has been used to assess brain connectivity changes in tetraplegic patients; in chapter IV a modified version of the NMM is presented, which has been developed to overcomes some of the previous version’s intrinsic limitations; chapter V describes a study in which the new NMM has been used to reproduce the electrical activity evoked in the cortex by the transcranial magnetic stimulation (TMS); chapter VI presents some preliminary results obtained in the simulation of the neural rhythms associated with memory recall; finally, some general conclusions are drawn in chapter VII.

Methods for the estimation of the cortical activity and connectivity during cognitive tasks in humans

The research activity characterizing the present thesis was mainly centered on the design, development and validation of methodologies for the estimation of stationary and time-varying connectivity between different regions of the human brain during specific complex cognitive tasks. Such activity involved two main aspects: i) the development of a stable, consistent and reproducible procedure for functional connectivity estimation with a high impact on neuroscience field and ii) its application to real data from healthy volunteers eliciting specific cognitive processes (attention and memory). In particular the methodological issues addressed in the present thesis consisted in finding out an approach to be applied in neuroscience field able to: i) include all the cerebral sources in connectivity estimation process; ii) to accurately describe the temporal evolution of connectivity networks; iii) to assess the significance of connectivity patterns; iv) to consistently describe relevant properties of brain networks. The advancement provided in this thesis allowed finding out quantifiable descriptors of cognitive processes during a high resolution EEG experiment involving subjects performing complex cognitive tasks.

A simple voltage/mass index increases the suspicion of amyloidotic cardiomyopathy: an electrocardiographic and echocardiographic study of 767 patients with increased left ventricular wall thickness due to different causes

Background-Amyloidotic cardiomyopathy (AC) can mimic true left ventricular hypertrophy (LVH), including hypertrophic cardiomyopathy (HCM) and hypertensive heart disease (HHD). We assessed the diagnostic value of combined electrocardiographic/echocardiographic indexes to identify AC among patients with increased echocardiographic LV wall thickness due to either different etiologies of amyloidosis or HCM or HHD. Method-First, we studied 469 consecutive patients: 262 with biopsy/genetically proven AC (with either AL or transthyretin (TTR)-related amyloidosis); 106 with HCM; 101 with HHD. We compared the diagnostic performance of: low QRS voltage, symmetric LVH, low QRS voltage plus interventricular septal thickness >1.98 cm, Sokolow index divided by the cross-sectional area of LV wall, Sokolow index divided by body surface area indexed LV mass (LVMI), Sokolow index divided by LV wall thickness, Sokolow index divided by (LV wall/height^2.7); peripheral QRS score divided by LVMI, Peripheral QRS score divided by LV wall thickness, Peripheral QRS score divided by LV wall thickness indexed to height^2.7, total QRS score divided by LVMI, total QRS score divided by LV wall thickness; total QRS score divided by (LV wall/height^2.7). We tested each criterion, separately in males and females, in the following settings: AC vs. HCM+HHD; AC vs. HCM; AL vs. HCM+HHD; AL vs. HCM; TTR vs. HCM+HHD; TTR vs. HCM. Results-Low QRS voltage showed high specificity but low sensitivity for the identification of AC. All the combined indexes had a higher diagnostic accuracy, being total QRS score divided by LV wall thickness or by LVMI associated with the best performances and the largest areas under the ROC curve. These results were validated in 298 consecutive patients with AC, HCM or HHD. Conclusions-In patients with increased LV wall thickness, a combined ECG/ echocardiogram analysis provides accurate indexes to non-invasively identify AC. Total QRS score divided by LVMI or LV wall thickness offers the best diagnostic performance.