Biology subjects

Lissek, S.

Publications and source records attributed to Lissek, S..

3 recordsLinked to original sources

bioRxiv2026

The first step is not always the hardest: A change-point analysis of predictive learning

The progress of learning is usually quantified by averaging responses across participants and/or multiple trials within a block. However, such approaches obscure the trial-by-trial progress of learning, which has been shown recently to express a rich variety of dynamics. An alternative approach which does not suffer from this problem is the detection and analysis of points of behavioral change, i.e., change-point analysis. Using change-point analysis, we reanalyzed data from human participants in different predictive learning tasks in which learned contingencies underwent reversal. We find that responses of individual participants were more accurately characterized by behavioral change points than the average learning curve. Importantly, change points significantly shifted to later trials during reversal learning indicating that reversal learning is more difficult than the initial learning. In a computational model based on deep reinforcement learning, we show that the change point shift required the replay of previous experiences, which in turn depends on the hippocampus. This finding is consistent with studies showing that lesions of the hippocampus yield faster reversal learning. In summary, we reaffirm the importance of the analysis of single participant responses, show that phenomenological learning rates are slower during reversal learning, and provide a theoretical account for this difference.

neuroscience↗

bioRxiv2025

Volumetric Differences of Thalamic Nuclei are Associated with Post-Trauma Psychopathology

Previous investigations of whole thalamus and thalamic nuclei volumes in post-trauma psychopathology have been sparse, limited in scope, and yielded inconsistent results. To address this, volumetric estimates of whole thalamus and thalamic nuclei were obtained from structural brain MRI scans from 2,058 participants across 20 worldwide sites in the ENIGMA PTSD working group. Thalamic volumes were compared between trauma-exposed participants with posttraumatic stress disorder (PTSD) (n=238), major depressive disorder (MDD) (n=184), comorbid PTSD+MDD (n=618), and trauma-exposed control participants (n=1,018). PTSD and MDD symptom severity, PTSD symptom clusters, and childhood trauma were similarly examined for associations with thalamic volume. Participants with PTSD only compared to controls had smaller thalamic nuclei volumes in sensorimotor nuclei, including the parafascicular (Pf), ventral anterior magnocellular (VAmc), medial pulvinar (PuM), and anterior pulvinar (PuA) nuclei of the thalamus. MDD only and comorbid PTSD+MDD participants exhibited smaller mediodorsal thalamus volumes compared to controls. Overall PTSD and MDD symptom severity negatively correlated with the volume of the mediodorsal thalamus. A significant interaction between PTSD and MDD severity was found, such that MDD severity was positively associated with thalamic volume only among individuals with high PTSD severity. Avoidance and hyperarousal symptoms of PTSD were positively associated with thalamic volume, while re- experiencing and negative mood/cognition symptoms were negatively associated with thalamic volume. Childhood physical and emotional abuse were positively and negatively associated with thalamic volume, respectively. Whole thalamus volume and volumes of the sensorimotor and limbic thalamus may play an important role in the development of PTSD and MDD in the aftermath of trauma exposure. The interaction between PTSD and MDD symptoms and contrasting effects across PTSD symptom clusters and types of childhood adversity suggests multiple neurobiological mechanisms are involved in shaping thalamic volume post-trauma.

neuroscience↗

bioRxiv2025

Predicting individual differences of fear and cognitive learning and extinction

The abilities to acquire new information and to modify previously learned knowledge are critical in an ever-changing world. However, the efficacy of learning is notably variable among individuals, with extinction learning being the epitome of such variability. Abundant studies have identified a core network of brain regions including amygdala, hippocampus, dorsal anterior cingulate cortex (ACC), ventromedial prefrontal cortex (PFC) and, more recently, the cerebellum, as key players in learning and extinction. Yet, the precise interactions within this network and their relationship to individual learning abilities and extinction have remained largely unexplored. In the present study, we examined how functional (FC), effective (EC), and structural (SC) connectivity patterns in the core learning network allow predicting individual differences in the efficacy of learning, extinction, and renewal. Analysing a large dataset of over 500 participants across a multitude of paradigms, our results revealed that FC predicted better acquisition, with a central role of ACC and hippocampus, whereas SC, involving ACC and amygdala, predicted higher levels of extinction learning. EC results suggested a predominantly inhibitory coupling among core learning network nodes, with paradigm-specific EC connectivity patterns predicting learning. Our predictions not only generalised between fear and cognitive predictive learning paradigms but were also successful in predicting learning from task-related FC and simulated data. Together, these results describe the multimodal neural determinants of learning, extinction, and renewal, and may inform individualised interventions for affective disorders based on neural connectivity patterns.

neuroscience↗