Biology subjects

Koppe, G.

Publications and source records attributed to Koppe, G..

4 recordsLinked to original sources

bioRxiv2025

A Data-Driven Closed-Loop Control Approach to Drive NeuralState Transitions for Mechanistic Insight

Repetitive negative thinking (RNT) is a transdiagnostic risk factor for mood disorders, consistently associated with altered biological substrates, including functional connectivity in key brain networks. As a stable cognitive feature linked to vulnerability across disorders, RNT presents a compelling target for intervention. However, leveraging RNT as a modifiable mechanism requires a deeper understanding of its causal neural dynamics and how targeted modulation can induce adaptive change. We introduce a data-driven framework that combines dynamical system reconstruction (DSR) with model predictive control (MPC) to infer optimal control policies for transitions between resting and sad mood brain states from functional magnetic resonance imaging (fMRI) data. Using nonlinear generative DSR models trained on individuals with remitted major depressive disorder (rMDD) and matched healthy controls (HCs), we derive region-specific, state-dependent control strategies. We find that small brain regions (e.g., sgACC, NAcc) exhibit higher controllability, requiring less energy to drive state transitions. Critically, rMDD participants require less control energy than HCs to move into sad mood from rest and - unexpectedly - also to move back to rest, though the latter effect is spatially restricted. Despite comparable target attainment, rMDD participants remain closer to the sad mood distribution when returning to rest, indicating a residual negative-affect bias. Our data-driven analysis reveals elevated effective coupling in rMDD, most prominently toward (but not away from) the DLPFC. Across regions, greater coupling is associated with reduced control energy, suggesting that enhanced network influence facilitates more efficient state transitions. These results suggest dynamics in rMDD that facilitate entry into negative affect and hinders full disengagement without sustained input, highlighting closed-loop control as a tool for mechanistic insight and potentially for designing targeted neuromodulatory interventions in the future.

neuroscience↗

bioRxiv2023

Neural Correlates of Self-Referential Belief Processes

BackgroundBelief processing as well as self-referential processing have both been consistently associated with cortical midline structures. In addition, seminal neuroimaging papers have implicated cortical regions such as the vmPFC in general belief processing. However, the neural correlates of self-referential belief are yet to be investigated in functional magnetic resonance imaging (fMRI). MethodsIn this fMRI study, we presented 120 statements with trait adjectives as target words to N=27 young healthy participants and asked them to judge whether they believed that these trait adjectives applied to themselves, a self-chosen close person, or a public person (the German chancellor at that time). Participants subsequently rated how certain (0-100%) they were in their judgment. ResultsAs expected, self-referential processing evoked a large cluster in the vmPFC, ACC and dmPFC. For belief, we found an activated cluster in the vmPFC during statement presentation, which partly overlapped with the cluster for self-referential processing. The cluster for self-belief vs. disbelief was similar in location and size to the cluster for general belief processing and distinct from the cluster for self-referential processing. We also found dmPFC activation for uncertainty in belief evaluations. DiscussionWe successfully replicated vmPFC involvement in belief processing and found a common neural correlate for belief and self-belief in the vmPFC. The activation clusters for self-belief versus self-referential processing were distinct, implying distinct neural processes. This insight will prove relevant for investigations in clinical populations with aberrant (self-)belief processing. Furthermore, we replicated the role of the dmPFC in uncertainty, supporting a dual neural process model of belief and certainty.

neuroscience↗

bioRxiv2022

Species-conserved mechanisms of cognitive flexibility in complex environments

Rapid learning in complex and changing environments is a hallmark of intelligent behavior. Humans achieve this in part through abstract concepts applicable to multiple, related situations. It is unclear, however, whether some of the underlying computational mechanisms also exist in other species. We combined behavioral, computational and electrophysiological analyses of a multidimensional rule-learning paradigm in rats and humans. We report that both species infer task rules by sequentially testing different hypotheses, rather than learning the correct action for all possible combinations of task-related cues. Neural substrates of hypothetical rules were detected in prefrontal network activity of both species. This species-conserved mechanism reduces task dimensionality and explains key experimental observations: sudden behavioral transitions and facilitated learning after prior experience. Our findings open the black box of hypothesis testing in rodents and provide a foundation for the translational investigation of impaired cognitive flexibility.

neuroscience↗

bioRxiv2022

Reconstructing Computational Dynamics from Neural Measurements with Recurrent Neural Networks

Mechanistic and computational models in neuroscience usually take the form of systems of differential or time-recursive equations. The spatio-temporal behavior of such systems is the subject of dynamical systems theory (DST). DST provides a powerful mathematical toolbox for describing and analyzing neurobiological processes at any level, from molecules to behavior, and has been a mainstay of computational neuroscience for decades. Recently, recurrent neural networks (RNNs) became a popular machine learning tool for studying the nonlinear dynamics underlying neural or behavioral observations. By training RNNs on the same behavioral tasks as employed for animal subjects and dissecting their inner workings, insights and hypotheses about the neuro-computational underpinnings of behavior could be generated. Alternatively, RNNs may be trained directly on the physiological and behavioral time series at hand. Ideally, the once trained RNN would then be able to generate data with the same temporal and geometrical properties as those observed. This is called dynamical systems reconstruction, a burgeoning field in machine learning and nonlinear dynamics. Through this more powerful approach the trained RNN becomes a surrogate for the experimentally probed system, as far as its dynamical and computational properties are concerned. The trained system can then be systematically analyzed, probed and simulated. Here we will review this highly exciting and rapidly expanding field, including recent trends in machine learning that may as yet be less well known in neuroscience. We will also discuss important validation tests, caveats, and requirements of RNN-based dynamical systems reconstruction. Concepts and applications will be illustrated with various examples from neuroscience.

neuroscience↗