Topic: intracranial EEG

In most brains across the animal kingdom, brain dynamics can enter pathological states that are recognisable as epileptic seizures. Yet usually, brain operate within certain constraints given through neuronal function and synaptic coupling, that will prevent epileptic seizure dynamics from emerging. In this talk, I will bring together different approaches to identifying how networks in the broadest sense shape brain dynamics. Using illustrative examples from intracranial EEG recordings, disorders characterised by molecular disruption of a single neurotransmitter receptor type, to single-cell r

The development of circuit-based therapeutics to treat neurological and neuropsychiatric diseases require detailed localization and understanding of electrophysiological signals in the human brain. Electrodes can record and stimulate circuits in many ways, and we often rely on non-invasive imaging methods to predict the location to implant electrodes. However, electrophysiological and imaging signals measure the underlying tissue in a fundamentally different manner. To integrate multimodal data and benefit from these complementary measurements, I will describe an approach that considers how di

Cognitive-relevant information is processed by different brain areas that cooperate to eventually produce a response. The relationship between local activity and global brain states during such processes, however, remains for the most part unexplored. To address this question, we designed a simple face-recognition task performed in drug-resistant epileptic patients with intracranial EEG. Based on our observations, we developed an analytical framework (named “local-global” framework) to statistically correlate the brain activity in every recorded gray-matter region with the widespread connecti

We learn different rules that can switch depending on context. When the rule depends on context, the context is important to learn to differentiate the rewarded choice in different contexts. While it has previously been shown that the reinstatement of context guides decisions for reward, it is less clear how or whether context-based reactivation guides the efficient learning of reward and context associations. We hypothesize that representations of the context are used to guide learning of associations between context and reward. We test whether this context is retrieved from memory circuits p