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.

Breast lesion detection through MammoWave device: Empirical detection capability assessment of 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 this paper, 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. 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 malignant. 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 82% when considering dense breasts only.

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.