Electroencephalography (EEG); fMRI; Visual processing; Information transer
Coito Ana, Genetti Melanie, Pittau Francesca, Iannotti Giannina R., Thomschewski Aljoscha, Höller Yvonne, Trinka Eugen, Wiest Roland, Seeck Margitta, Michel Christoph M., Plomp Gijs, Vulliemoz Serge (2016), Altered directed functional connectivity in temporal lobe epilepsy in the absence of interictal spikes: A high density EEG study, in
Epilepsia, 57(3), 402-411.
Plomp Gijs, Hervais-Adelman Alexis, Astolfi Laura, Michel Christoph M. (2015), Early recurrence and ongoing parietal driving during elementary visual processing, in
Sci. Rep., 5, 18733-18733.
Plomp Gijs, Astolfi Laura, Coito Ana, Michel Christoph M. (2015), Spectrally weighted Granger-causal modeling: Motivation and applications to data from animal models and epileptic patients, in
Conf Proc IEEE Eng Med Biol Soc, Institute of Electrical {&} Electronics Engineers ({IEEE}), ?.
Plomp Gijs, Quairiaux Charles, Kiss Jozsef Z, Astolfi Laura, Michel Christoph M (2014), Dynamic connectivity among cortical layers in local and large-scale sensory processing., in
The European journal of neuroscience, 40, 3215-3223.
Plomp Gijs, Quairiaux Charles, Michel Christoph M., Astolfi Laura (2014), The physiological plausibility of time-varying Granger-causal modeling: Normalization and weighting by spectral power, in
NEUROIMAGE, 97, 206-216.
Coito Ana, Plomp Gijs, Genetti Mélanie, Abela Eugenio, Wiest Roland, Seeck Margitta, Michel Christoph, Vulliemoz Serge, Dynamic directed interictal connectivity in left and right temporal lobe epilepsy, in
Epilepsia.
Visual processing is traditionally thought of as a hierarchical, feed-forward process in which the analysis of simple features in primary visual areas is followed by that of more complex ones in higher-level areas. Much recent research suggests, however, that visual processing involves top-down influences from higher-level visual areas onto lower ones in the hierarchy, as well as extensive parallel processing. Even the processing of a simple visual image recruits a network of low- and high-level areas, and gives rise to intricate interactions between them. Several important questions remain unanswered about how visual areas work together and about the direction of information flow between them. What brain areas exercise top-down influence, and at what latencies after the image onset? What are the most influential areas in the functional network, and how does this change with time? Several higher-level areas show increased activity for highly specific tasks, like for example motion processing. Do these areas only collect and process motion information, or do they actively influence other areas so as to stabilize the perceived image?To better understand the interactions between brain areas, a network approach is required that determines for each area what information it receives from others, and how this evolves in time. I will study the information flow in large-scale cerebral networks by combining functional magnetic resonance imaging (fMRI) and electrical source imaging based on high density EEG (ESI). From the source activity I will estimate the information flow between brain areas using multivariate Granger causality measures. This will distinguish the areas that predominantly drive activity in others from those that predominantly receive information, with a high temporal resolution.The project will focus on visual processing in healthy subjects but the methodology will also be systematically validated in animal models and in recordings from epileptic patients with implanted electrodes. Animal models display more simple cortical dynamics than humans and allow for more direct recordings of them. Patients with intracranial electrodes allow to test the validity of the information transfer methods in circumscribed brain areas.The functional network approach will provide a new view on the brain dynamics underlying visual perception by showing how information is routed. This will shed light on fundamental aspects of visual perception, like the relative contribution of top-down and bottom-up information flow and the nature of functional specialization in higher-level areas. The outcome of this project will open the door to a similar understanding of higher-level cognitive functioning and, eventually, of abnormal information flow in pathological states.