Head centred meridian effect on auditory spatial attention orienting

Six experiments examined the issue of whether one single system or separate systems underlie visual and auditory orienting of spatial attention. When auditory targets were used, reaction times were slower on trials in which cued and target locations were at opposite sides of the vertical head-centred meridian than on trials in which cued and target locations were at opposite sides of the vertical visual meridian or were not separated by any meridian. The head- centred meridian effect for auditory stimuli was apparent when targets were cued by either visual (Experiments 2, 3, and 6) or auditory cues (Experiment 5). Also, the head- centred meridian effect was found when targets were delivered either through headphones (Experiments 2, 3, and 5) or external loudspeakers (Experiment 6). Conversely, participants showed a visual meridian effect when they were required to respond to visual targets (Experiment 4). These results strongly suggest that auditory and visual spatial attention systems are indeed separate, as far as endogenous orienting is concerned

Micromagnetic simulation of electrochemically deposited Co nanowire arrays for wideband microwave applications

We study the magnetic properties of arrays of Co nanowires which exhibit zero bias-field ferromagnetic resonance absorptions in a 0-30 GHz range. Columnar arrays of Co nanowires with lengths of 8-15 mu m were electrochemically grown using similar to 20 mu m thick anodic alumina membranes with 50 nm pore diameters. Microstructural, static magnetic, and microwave properties of five different nanowire arrays were characterized. The studied Co nanowires present different crystal structure textures and magnetic properties. The static magnetic loop shapes and the ferromagnetic resonance frequencies of the nanowire arrays were correctly reproduced using the Mumax3 micromagnetic software. For each sample input parameters dependent on the x-ray diffraction and microstructural data, were fine-tuned to allow the best fit of the experimental hysteresis loops and the related microwave spectra. Using this method, it was possible to analyze the rather complex interplay between geometry and magneto-structural features of the different arrays, defining which parameters play a key role in the development of nano-systems with specific microwave properties

Differential Effects of Brain Disorders on Structural and Functional Connectivity

Different measures of brain connectivity can be defined based on neuroimaging read-outs, including structural and functional connectivity. Neurological and psychiatric conditions are often associated with abnormal connectivity, but comparing the effects of the disease on different types of connectivity remains a challenge. In this paper, we address the problem of quantifying the relative effects of brain disease on structural and functional connectivity at a group level. Within the framework of a graph representation of connectivity, we introduce a kernel two-sample test as an effective method to assess the difference between the patients and control group. Moreover, we propose a common representation space for structural and functional connectivity networks, and a novel test statistics to quantitatively assess differential effects of the disease on different types of connectivity. We apply this approach to a dataset from BTBR mice, a murine model of Agenesis of the Corpus Callosum (ACC), a congenital disorder characterized by the absence of the main bundle of fibers connecting the two hemispheres. We used normo-callosal mice (B6) as a comparator. The application of the proposed methods to this data-set shows that the two types of connectivity can be successfully used to discriminate between BTBR and B6, meaning that both types of connectivity are affected by ACC. However, our novel test statistics shows that structural connectivity is significantly more affected than functional connectivity, consistent with the idea that functional connectivity has a robust topology that can tolerate substantial alterations in its structural connectivity substrate

Automated multi-subject fiber clustering of mouse brain using dominant sets

Mapping of structural and functional connectivity may provide deeper understanding of brain function and disfunction. Diffusion Magnetic Resonance Imaging (DMRI) is a powerful technique to non-invasively delineate white matter (WM) tracts and to obtain a three-dimensional description of the structural architecture of the brain. However, DMRI tractography methods produce highly multi-dimensional datasets whose interpretation requires advanced analytical tools. Indeed, manual identification of specific neuroanatomical tracts based on prior anatomical knowledge is time-consuming and prone to operator-induced bias. Here we propose an automatic multi-subject fiber clustering method that enables retrieval of group-wise WM fiber bundles. In order to account for variance across subjects, we developed a multi-subject approach based on a method known as Dominant Sets algorithm, via an intra- and cross-subject clustering. The intra-subject step allows us to reduce the complexity of the raw tractography data, thus obtaining homogeneous neuroanatomically-plausible bundles in each diffusion space. The cross-subject step, characterized by a proper space-invariant metric in the original diffusion space, enables the identification of the same WM bundles across multiple subjects without any prior neuroanatomical knowledge. Quantitative analysis was conducted comparing our algorithm with spectral clustering and affinity propagation methods on synthetic dataset. We also performed qualitative analysis on mouse brain tractography retrieving significant WM structures. The approach serves the final goal of detecting WM bundles at a population level, thus paving the way to the study of the WM organization across groups.Mapping of structural and functional connectivity may provide deeper understanding of brain function and disfunction. Diffusion Magnetic Resonance Imaging (DMRI) is a powerful technique to non-invasively delineate white matter (WM) tracts and to obtain a three-dimensional description of the structural architecture of the brain. However, DMRI tractography methods produce highly multi-dimensional datasets whose interpretation requires advanced analytical tools. Indeed, manual identification of specific neuroanatomical tracts based on prior anatomical knowledge is time-consuming and prone to operator-induced bias. Here we propose an automatic multi-subject fiber clustering method that enables retrieval of group-wise WM fiber bundles. In order to account for variance across subjects, we developed a multi-subject approach based on a method known as Dominant Sets algorithm, via an intra-and cross-subject clustering. The intra-subject step allows us to reduce the complexity of the raw tractography data, thus obtaining homogeneous neuroanatomically-plausible bundles in each diffusion space. The cross-subject step, characterized by a proper space-invariant metric in the original diffusion space, enables the identification of the same WM bundles across multiple subjects without any prior neuroanatomical knowledge. Quantitative analysis was conducted comparing our algorithm with spectral clustering and affinity propagation methods on synthetic dataset. We also performed qualitative analysis on mouse brain tractography retrieving significant WM structures. The approach serves the final goal of detecting WM bundles at a population level, thus paving the way to the study of the WM organization across groups

Mapping temporal expectancies for different rhytmical surfaces: The role of metric structure and phenomenal accents

This study explores the rules regulating the formation of temporal expectancies when we listen to a rhythmic sequence and extract regularities (or invariant temporal information) projecting them in the near future. Our ability to generate these expectancies is widely dependant on the metric structure suggested by the patterns we entrain to. In Experiment 1, we mapped temporal expectancies evoked by three different repeating patterns in which the phenomenal accents strength was manipulated keeping the metric structure constant in all three patterns. Results of the test tone timing evaluation reveal that expectancy waves are quite short (after the stimulus stops) and very dependent on phenomenal accent strength. In Experiment 2, we used four patterns with different metric structures and lengths: two patterns inducing isochronous meters, and a pattern inducing a Non-Isochronous structure. All the patterns were composed following a rhythm complexity evaluation algorithm. The timing evaluation judgment task after entraining to the patterns was identical to Exp. 1. Results confirm the crucial role of phenomenal accents time position and strength, and show that Isochronous meters generate strong and periodic expectancy waves, while Non-Isochronous meters tend to evoke periodicities of a different level. Our results are consistent with the recent oscillator models of attending. Discussion proposes an interpretation of the results with special attention devoted to the interpretation of N-I meters effects