The main focus of research in my lab is on forebrain oscillations in rodent models of psychiatric diseases. These represent dimensional constructs relevant to human mental disorders. In particular, partial models of schizophrenia exhibit the animal equivalents of schizophrenia-related neurocognitive deficits and show characteristic abnormalities in the organization of cortical networks, reminiscent of those in schizophrenic patients. We use several neurodevelopmental and pharmacological models to examine how pathologic alterations in the neuronal circuitry lead to impaired oscillatory synchronization in the hippocampus and prefrontal cortex and how these, in turn, contribute to neurocognitive deficits.
Rhythmic synchronization between hippocampus and prefrontal cortex is thought to mediate key cognitive functions, such as working memory and goal-directed spatial navigation and disruption of this dynamic coupling may contribute to pathophysiology of psychiatric diseases. Prior studies focused on coupling mediated by hippocampal theta rhythm (the most prominent oscillatory signal in the rodent brain) but two-way coupling between these structures also requires a synchronizing signal generated by the prefrontal cortex transmitted down to hippocampal networks in specific situations (behaviors, tasks, drug effects). We recently demonstrated that a 2-5 Hz rhythm of prefrontal origin conveyed through the nucleus reuniens of the thalamus may play such role. Neural oscillations at different frequencies are organized in an evolutionarily well-preserved hierarchy. Slow rhythms (most importantly cortical delta and hippocampal theta, but also including for example the respiratory rhythm derived by the olfactory bulb from rhythmic nasal airflow) are transmitted with minor phase delay and thus by modulating local oscillations might establish gamma synchrony between distant structures. In our latest study, we are investigating the possible role of such mechanism in shaping the complex of schizophrenia-like syndromes.
We use advanced signal analysis techniques and are extensively collaborating with neuroscientists experienced in mathematics and bioengineering to further explore the potential of these methods.
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