We use data-driven analysis to resolve oscillatory network dynamics and their underlying mechanisms. In particular, our goal is to uncover complex systems-level dynamics governing the emergence of oscillations and their network interactions using computational approaches focusing on brain network analysis in the context of brain oscillations and brain criticality frameworks (Palva & Palva 2012, Palva & Palva 2017, Palva & Palva 2018). Even in healthy adults, there is large variability in the oscillatory synchronization levels. Our recent study shows how this variability could be explained in the brain criticality framework (Fusca et al., 2023). Our research research has for instance also resolved new network-level coupling mechanisms in the human brain including synchronization of high-frequency oscillations (Arnulfo et al., 2020) and cross-frequency network interactions (Siebenhühner et al., 2020) that may serve as unique computational functions in brain hierarchical computations. We further have investigated how network oscillations are influenced by genetics (Simola et al., 2017) and neurotransmitter distribution (Siebenhühner et al., 2024).
Oscillations are thought to be fundamental for cognitive functions by providing a temporal clocking mechanism while their interactions reflect routing of information in neuronal circuits. Our research aims to understand computational principles carried out by network oscillations in visual cognition. We focus on understanding how network oscillations could implement top-down control and representation of sensory information in support of visual working memory (Honkanen et al., 2015) and visual attention (Lobier et al., 2018; Cruz et al ., 2024) and their cross-frequency coupling (Siebenhühner et al., 2016). We have also demonstrated that individual capacity of visual working memory (Palva et al., 2010; Sattelberger et al., 2024) and visual attention (Rouhinen et al., 2020) is explained by strength of network oscillations.
We investigate whether deviances of network dynamics from the normative range could lead to brain disorder symptoms. Our overarching goal is to develop novel diagnostic biomarkers for brain disorder subtypes and treatment outcome prediction based on multi-modal brain imaging. We have ongoing work on neurodegenerative diseases funded by EU-H2020-SC1-DTH-07-2018, on depression funded by the Wellcome LEAP MPYCH program and Business Finland and Sigrid Juselius foundation. Accurate treatment outcome prediction would allow personalized treatment selection and lead to increased treatment efficacy.