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Functional connectivity (FC) characterizes brain activity from a multivariate set of N brain signals by means of an NxN matrix A, whose elements estimate the dependence within each possible pair of signals. Such matrix can be used as a feature vector for (un)supervised subject classification. Yet if N is large, A is highly dimensional. Little is known on the effect that different strategies to reduce its dimensionality may have on its classification ability. Here, we apply different machine learning algorithms to classify 33 children (age [6-14 years]) into two groups (healthy controls and Attention Deficit Hyperactivity Disorder patients) using EEG FC patterns obtained from two phase synchronisation indices. We found that the classification is highly successful (around 95%) if the whole matrix A is taken into account, and the relevant features are selected using machine learning methods. However, if FC algorithms are applied instead to transform A into a lower dimensionality matrix, the classification rate drops to less than 80%. We conclude that, for the purpose of pattern classification, the relevant features should be selected among the elements of A by using appropriate machine learning algorithms.

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This study presents analyses of triple-antenna configurations and designs for microwave (MW) hepatic ablation using 3-D finite-element (FE) analyses verified by in vitro experiments. Treatment of hepatic cancer often requires removal or destruction of large volume lesions. Using multiple antennas offers a potential solution for creating ablation zones with larger dimensions, as well as varied geometrical shapes. We performed both 3-D FE analyses and in vitro experiments using three identical open-tip MW antennas simultaneously, placing them in three types of configurations-"linear array," "triangular," and "T-shaped" arrangements. We compared coagulation volumes created, as well as temperature distribution characteristics, from the three-antenna arrangements after power delivery of 50 W for 60 s. We also performed additional tests using nonidentical antennas (open tip, slot, and slot with insulating jacket) for the three configurations. The results illustrate that arranging antennas in the "T-shaped" pattern destroyed more unwanted tissues than those found when using "linear array" and "triangular" arrangements, with maximum coagulation width and depth of 46 and 81 mm, respectively, and coagulation volume of 30.7 cm(3) . In addition, using nonidentical triple antennas caused variations in coagulation zone characteristics, and thus, the technique could be applied to treatment situations where nonsymmetric coagulation zones are required.

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This work aims at assessing the maturational changes in the interdependence between the activities of different cortical areas in neonates during active sleep (AS) and quiet sleep (QS). Eight electroencephalography (EEG) channels were recorded in 3 groups of neonates of increasing postmenstrual age. The average linear (AVL) and average nonlinear (AVN) interdependencies of each electrode region with the remaining ones were calculated using the coherence function and a recently developed index of nonlinear coupling between 2 signals in their state spaces, respectively. In theta band, AVL increased with neonate's age for central and temporal regions during QS. In beta band, AVL increased for most cortical regions during QS and a parallel decrease of AVL with neonate's age was found during AS. For all regions, beta AVL was greater in AS than in QS in preterm neonates but the reverse happened in older term neonates. Contrarily to AVL, AVN decreased with age during QS for most cortical regions. Surrogate data test showed that the interdependencies were nonlinear in preterm and younger term neonates but in older term both linear and nonlinear interdependencies coexisted. It is concluded that neonatal maturation is associated with changes in the magnitude and character of the EEG interdependencies during sleep.