Conference Agenda

Brain states emerge as recurring patterns of distributed neural activity, shaped by the brain’s structural and functional networks. These states are fundamental to cognition, reflecting individual differences and offering insight into the neurobiological mechanisms underlying behavior and pathology. Investigating their spatiotemporal dynamics, transitions, and interactions with various functional processes provides a valuable framework for understanding the brain’s functional organization and how it shapes individual behavior and thought.

This symposium brings together research that examines brain states in the context of cognitive engagement and behavioral outcomes. Our contributors employ diverse experimental paradigms to elicit specific brain states and use network-based approaches to analyze their properties. The symposium will highlight research exploring brain states arising from cognitive demands, their role in memory and decision-making, and their alterations across the lifespan. Additionally, we will discuss how cognitive training and interventions may shape these states, with implications for clinical neuroscience.

The symposium will conclude with a panel discussion on the conceptual and methodological frontiers of brain state research, addressing both the opportunities and challenges of studying dynamic brain activity through means of network neuroscience and machine learning.

Neuroticism, the tendency to experience negative emotions such as anxiety, irritability, or emotional instability, is a key risk factor for mental disorders. Therefore, identifying its neurobiological basis, particularly whether there exists a shared neural foundation among individuals, presents an important endeavor. While previous research often falls short in ecological validity due to its focus on brain activity during rest or well-standardized but artificial in-scanner tasks, this preregistered study considers brain activity during movie watching, suggested to more closely resemble individuals’ real-life experiences. Specifically, we examined whether participants’ similarity in neuroticism is reflected in the similarity of their brain activity. Further, we tested whether this brain-trait representational similarity is stronger during movie scenes that were rated as particularly relevant to neuroticism, generating insights into the trait-relevance assumption of contemporary personality conceptions.

To identify trait-relevant movie scenes, we first conducted an independent online study (N = 80). In the main study, Inter-Subject Representational Similarity Analysis was performed on a subsample of the Human Connectome Project (N = 184) for whom fMRI movie data and neuroticism scores (NEO-FFI) were available. Brain-trait representational similarity was assessed at whole-brain, network, and region-specific levels, while accounting for the influence of trait-relevant contexts. Additionally, statistical mapping to different theoretical models of trait similarity was explored.

By linking similarity in neuroticism to shared neural representations during movie watching, our study informs about the location and context-dependent manifestation of its neurobiological foundation, ultimately offering important implications for the diagnosis and treatment of mental health disorders associated with heightened neuroticism.

Network Control Theory (NCT) offers a powerful framework to explore how the brain’s structural architecture shapes its dynamic functional states. By modeling the brain as a controllable system, NCT enables us to quantify the energy required to transition between brain states, providing novel insights into structure-function relationships. In this talk, I will introduce the core concepts of NCT and highlight its relevance for cognitive neuroscience, particularly in understanding cognitive control. I will present recent findings from our group showing that the brain’s rich-club—a densely connected set of hub regions—is surprisingly inefficient in facilitating state transitions, challenging prevailing assumptions about its central role. Furthermore, I will discuss how NCT-derived metrics can capture individual differences in brain dynamics, offering potential biomarkers for behavior and cognition. Together, these insights position NCT as a key tool for deciphering brain states and their cognitive and behavioral impact.

In our study, we investigate the dynamic neural underpinnings of cognitive and emotional processes using the longitudinal fMRI data from the Midnight Scanning Club (MSC) dataset (Gordon et al., 2017). Building on two recent methodological advances in modelling dynamical systems from time-series data —the piecewise-linear recurrent neural network (PLRNN) framework for dynamical systems identification (Koppe et al., 2019) and hierarchical dynamical systems modeling (Brenner et al., 2025)— we analyze individual and group-level brain dynamics across three different fMRI tasks over multiple imaging sessions. We characterize the extracted dynamical systems using features that described the underlying energy landscape such as bifurcations or attractors and explore how individual differences in these features relate to cognitive performance and behavioral variability.

We find 1) task-specific dynamical patterns that differentiate between cognitive processes, with distinct stability profiles observed for working memory, social cognition, and emotion processing tasks, and 2) conserved dynamical features across individuals, suggesting a general organizational principle in brain dynamics during cognitive-emotional tasks.

Our findings highlight both task-specific dynamical patterns and conserved features across individuals, revealing a complex interplay between adaptive and fundamental organizational principles in brain dynamics. This study demonstrates the potential of advanced dynamical systems modeling to uncover the temporal structures of neural activity, bridging individual variability with general cognitive mechanisms.

The human brain undergoes continuous development, with childhood and adolescence being critical periods for cognitive refinement, yet there is also increased susceptibility to neuropsychological disorders (Baum et al., 2020; Larsen & Luna, 2018). During these stages, cortical development has been shown to follow a well-characterized sensory-to-association axis (Sydnor et al., 2023); however, cortical development does not occur in isolation. From the earliest stages, the thalamic subnuclei and cortex interact closely, forming a network essential for sensory perception, cognitive function (Hwang et al., 2022), and the shift between brain states (Müller et al., 2020). Disruptions in this thalamocortical network have been linked to neurodevelopmental disorders (Anticevic et al., 2015; Xia et al., 2012; Zhang et al., 2021).

Recent evidence suggests that the trajectory of structural thalamocortical connectivity also follows a sensory-to-association axis (Sydnor et al., 2025). However, how this organizational pattern emerges at the level of individual thalamic subnuclei remains unclear, as does the developmental trajectory of structure-function coupling between subnuclei and cortical regions.

In this talk, I will present our findings on the maturation of thalamocortical connectivity from childhood to young adulthood, focusing on individual thalamic subnuclei and their maturing structure-function relationships. Using diffusion-weighted and resting-state fMRI data from the Human Connectome Project Development (N = 626, ages 5–21), we computed structural and functional connectivity between thalamic nuclei and cortical parcels, modeling age effects with Generalized Additive Models.

Understanding these developmental processes is crucial for uncovering thalamocortical network contribution to functional maturation and may offer new perspectives on neurodevelopmental disorders.