<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:contributor>Plomp, Gijs</dc:contributor>
  <dc:creator>Pagnotta, Mattia</dc:creator>
  <dc:date>2020</dc:date>
  <dc:description xmlns:ns0="xml" ns0:lang="en">Visual selective attention prioritizes the processing of behaviorally relevant  over irrelevant information, to optimize the use  of limited cognitive resources in the brain. These selective mechanisms  preferentially route relevant neuronal  representations through a network of distributed brain regions. Previous  studies suggested that selective information  routing in the attention network may be mediated by brain activity  modulations in the alpha (α, 7–14 Hz) and beta-band (β,  15–30 Hz). However, the precise temporal dynamics of the cortical network  interactions that support selective attention remain unclear. The investigation of these dynamic network  interactions, given their fast and flexible nature,  requires time-varying connectivity methods that can correctly estimate  rapidly changing patterns of large-scale directed  interactions between brain regions. While several such methods have been  proposed, there is a lack of unbiased and  systematic assessments of their performance and estimation accuracy. The  objective of this thesis was twofold: first, to  critically assess and compare the performance and estimation accuracy of  time-varying directed connectivity methods;  second, to provide a comprehensive characterization of the dynamic  reorganization of cortical network interactions during visual selective attention, under different task demands. To systematically  compare currently available time-varying  directed connectivity methods, I used a combination of numerical  simulations and real benchmark data recorded from rats  during unilateral whisker stimulations. I showed advantages and  shortcomings of the two main classes of methods, which  rely on either multivariate autoregressive modeling or spectral  decomposition of the recorded signals. The results served  as starting point to develop innovative methods with improved performance  and estimation accuracy. I employed these  novel methods together with electroencephalography (EEG) source- imaging, to investigate how cortical network  interactions mediate selective attention dynamically, while healthy  participants discriminated the perceived motion or  orientation direction of briefly presented stimuli. The results characterized  the temporal dynamics of selective attention,  unveiling how attention involves both local and network changes in different  frequency bands, and how these modulations further depend on the specific task demands. The results provided first  evidence of the role of taskspecific coupling  mechanisms in supporting the selective anticipation and processing of task- relevant stimuli, through different low-frequency  carriers in the β and α-band, respectively. These findings integrated into a  dynamical framework existing theories of how  brain rhythms establish inter-areal communication and local computation in  the attention network.</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>https://folia.unifr.ch/global/documents/308700</dc:identifier>
  <dc:identifier>https://folia.unifr.ch/documents/308700/files/PagnottaM.pdf</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/semantics/altIdentifier/urn/urn:nbn:ch:rero-002-120154</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>License undefined</dc:rights>
  <dc:subject>Cortex cérébral</dc:subject>
  <dc:subject>Réseaux cérébraux</dc:subject>
  <dc:subject>Attention</dc:subject>
  <dc:subject>Perception visuelle</dc:subject>
  <dc:subject>info:eu-repo/classification/udc/159.9</dc:subject>
  <dc:title xmlns:ns1="xml" ns1:lang="en">The Dynamic Reorganization of Cortical Network Interactions during Visual Selective Attention</dc:title>
  <dc:type>http://purl.org/coar/resource_type/c_db06</dc:type>
</oai_dc:dc>
