Breast lesion detection through MammoWave device: microwave images’ parameters

MammoWave is a microwave imaging device for breast lesions detection, which operates using two (azimuthally rotating) antennas without any matching liquid. Images, subsequently obtained by resorting to Huygens Principle, are intensity maps, representing the homogeneity of tissues’ dielectric properties. In the paper: “Breast lesion detection through MammoWave device: empirical detection capability assessment of microwave images’ parameters”, we propose to generate, for each breast, a set of conductivity weighted microwave images by using different values of conductivity in the Huygens Principle imaging algorithm. Next, microwave images’ parameters, i.e. features, are introduced to quantify the non-homogenous behaviour of the image. This data set contains such features. We empirically verify on 103 breasts that a selection of these features may allow distinction between breasts with no radiological finding (NF ) and breasts with radiological findings (WF), i.e. with lesions which may be benign or malign. Statistical significance was set at p <0.05. We obtained single features Area Under the receiver operating characteristic Curves (AUCs) spanning from 0.65 to 0.69. In addition, an empirical rule-of-thumb allowing breast assessment is introduced using a binary score S operating on an appropriate combination of features. Performances of such rule-of-thumb are evaluated empirically, obtaining a sensitivity of 74%, which increases to 84% when considering dense breasts only

Analysis and modeling of the EEG activity and connectivity in post-stroke conditions

The overall objective of the PhD project has been to establish a methodology for the definition and analysis of neurophysiological indices that can provide a measure of changes in brain activity and organization that can be quantifiable (i.e. measurable in real world conditions), is reliable (sensitive, specific, and consistent over time), widely applicable and modifiable (amenable to improvement using existing approaches). The aim is to describe the specific properties of the general brain organization to be correlated with the outcome of the rehabilitation intervention, with possible prognostic/decision support value. The objective is to support the diagnosis of motor and cognitive disabilities, to provide a neurophysiological description of the changes in brain activity and organization that underlie functional recover and to allow the evaluation of the effects of rehabilitative treatments (conventional and innovative) in terms of brain reorganization (measures of neurophysiological outcome of a treatment). Major attention has been given to the analysis of EEG recorded during the resting state. EEG measures of resting brain function provide insights into basal differences in brain state, therefore useful to predict the capacity of an individual brain, transcending inter-individual heterogeneity, to undergo plasticity and thus respond to therapeutic intervention. To this purpose, the research activity has focused on developing an approach for the extraction of neurophysiological indices from a non-invasive estimate of brain activity and connectivity based on electroencephalographic measurements

Resting state effective connectivity in stroke patients. An EEG study

Scientific evidences suggest the possibility of obtaining significant information about the state and the cognitive performances of the brain by using only EEG activity during resting state. In this study graph theory was applied to functional brain networks in order to describe the topographic reorganization of the brain connectivity network related to the resting state condition in a population of 42 stroke patients, with the aim to evaluate deviation from healthy conditions and characterize patients on the basis of their clinical features. Brain connectivity was estimated by means of the spectral estimator Partial Directed Coherence and synthetic graph indices were extracted from the estimated networks. Results showed significant differences between the properties of resting state brain networks of stroke patients and those of healthy subjects. A significant effect of the lesion side on the reorganization after the stroke event was also shown

Effective Interhemispheric Connectivity and Corticospinal Tract Integrity interdependency after unilateral stroke

In the healthy brain, activation of the motor cortex determines inhibition of the contralateral homologous region through the phenomenon of interhemispheric inhibition. Such motor intherhemispheric balance is altered in unilateral brain lesions such as stroke. The aim of this study was to explore whether the EEG-derived sensorimotor interhemispheric connectivity (IHC) at rest could vary in relation to the corticospinal tract integrity and excitability in subacute stroke patients

An EEG index of sensorimotor interhemispheric coupling after unilateral stroke: clinical and neurophysiological study

Brain connectivity has been employed to investigate on post-stroke recovery mechanisms and assess the effect of specific rehabilitation interventions. Changes in interhemispheric coupling after stroke have been related to the extent of damage in the corticospinal tract (CST) and thus, to motor impairment. In this study, we aimed at defining an index of interhemispheric connectivity derived from electroencephalography (EEG), correlated with CST integrity and clinical impairment. Thirty sub-acute stroke patients underwent clinical and neurophysiological evaluation: CST integrity was assessed by Transcranial Magnetic Stimulation and high-density EEG was recorded at rest. Connectivity was assessed by means of Partial Directed Coherence and the normalized Inter-Hemispheric Strength (nIHS) was calculated for each patient and frequency band on the whole network and in three sub-networks relative to the frontal, central (sensorimotor) and occipital areas. Interhemipheric coupling as expressed by nIHS on the whole network was significantly higher in patients with preserved CST integrity in beta and gamma bands. The same index estimated for the three sub-networks showed significant differences only in the sensorimotor area in lower beta, with higher values in patients with preserved CST integrity. The sensorimotor lower beta nIHS showed a significant positive correlation with clinical impairment. We propose an EEG-based connectivity index which is a measure of the interhemispheric cross-talking and correlates with functional motor impairment in subacute stroke patients. Such index could be employed to evaluate the effects of training aimed at re-establishing interhemispheric balance and eventually drive the design of future connectivity-driven rehabilitation interventions

Estimating brain connectivity when few data points are available: Perspectives and limitations

Methods based on the use of multivariate autoregressive modeling (MVAR) have proved to be an accurate and flexible tool for the estimation of brain functional connectivity. The multivariate approach, however, implies the use of a model whose complexity (in terms of number of parameters) increases quadratically with the number of signals included in the problem. This can often lead to an underdetermined problem and to the condition of multicollinearity. The aim of this paper is to introduce and test an approach based on Ridge Regression combined with a modified version of the statistics usually adopted for these methods, to broaden the estimation of brain connectivity to those conditions in which current methods fail, due to the lack of enough data points. We tested the performances of this new approach, in comparison with the classical approach based on ordinary least squares (OLS), by means of a simulation study implementing different ground-truth networks, under different network sizes and different levels of data points. Simulation results showed that the new approach provides better performances, in terms of accuracy of the parameters estimation and false positives/false negatives rates, in all conditions related to a low data points/model dimension ratio, and may thus be exploited to estimate and validate estimated patterns at single-trial level or when short time data segments are available

Measuring the agreement between brain connectivity networks

Investigating the level of similarity between two brain networks, resulting from measures of effective connectivity in the brain, can be of interest from many respects. In this study, we propose and test the idea to borrow measures of association used in machine learning to provide a measure of similarity between the structure of (un-weighted) brain connectivity networks. The measures here explored are the accuracy, Cohen's Kappa (K) and Area Under Curve (AUC). We implemented two simulation studies, reproducing two contexts of application that can be particularly interesting for practical applications, namely: i) in methodological studies, performed on surrogate data, aiming at comparing the estimated network with the corresponding ground-truth network; ii) in applications to real data, when it is necessary to compare the structure of a network obtained in a specific subject with a reference (e.g. a baseline condition or normative data). In the simulations, the level of similarity between two networks was manipulated through different factors. We then investigated the effect of such manipulations on the measures of association. Results showed how the three parameters modulated their values according to the level of similarity between the two networks. In particular, the AUC provided the better performances in terms of its capability to synthetize the similarity between two networks, showing high dynamic and sensitivity

Effect of inter-trials variability on the estimation of cortical connectivity by Partial Directed Coherence

Partial Directed Coherence (PDC) is a powerful estimator of effective connectivity. In neuroscience it is used in different applications with the aim to investigate the communication between brain regions during the execution of different motor or cognitive tasks. When multiple trials are available, PDC can be computed over multiple realizations, provided that the assumption of stationarity across trials is verified. This allows to improve the amount of data, which is an important constraint for the estimation accuracy. However, the stationarity of the data across trials is not always guaranteed, especially when dealing with patients. In this study we investigated how the inter-trials variability of an EEG dataset affects the PDC accuracy. Effects of density variations and of changes of connectivity values across trials were first investigated with a simulation study and then tested on real EEG data collected from two post-stroke patients during a motor imagery task and characterized by different inter-trials variability. Results showed the effect of different factors on the PDC accuracy and the robustness of such estimator in a range of conditions met in practical applications

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