Hannes Ruge

Background: Anorexia Nervosa (AN) has been linked to altered self‐regulatory control processes and maladaptive habitual avoidance behavior. Both goal‐directed and habit systems may be relevant to AN, in which restrictive eating behavior can appear highly deliberate and increasingly automatic over time. A previous fMRI study identified behavioral and neural markers hinting at heightened self‐regulatory control (‘overcontrol’) during an avoidance habit task in acutely underweight adolescents and young women with AN. Extending this research to recovered individuals with a history of AN (recAN), we aimed to clarify whether these findings persist and can be seen as trait vulnerabilities. Methods: A sample of 40 recAN and 40 pairwise age‐matched healthy control (HC) participants carried out a three‐phase experimental paradigm including (1) training of goal‐directed response‐outcome behavior, (2) training of avoidance stimulus–response pairings, and (3) a test of habitual response interference. Our fMRI analysis strategy focused on capturing training‐related changes in neural activity related to reinforcement learning processes. Results: Analyses of main effects of task confirmed expected behavioral and fMRI results. However, unlike previous findings of greater activation in frontoparietal control regions in young acutely underweight patients with AN during the establishment of avoidance behavior and of higher task performance, all behavioral task measures, region‐of‐interest fMRI, and follow‐up whole‐brain analyses yielded no group differences between recAN and HC. Conclusions: Our findings of intact avoidance habit learning after recovery from AN suggest that correlates of overcontrol previously observed in acutely underweight individuals with AN may reflect state‐dependent rather than persistent trait markers of the disorder.

The thalamus is essential for learning, dynamically engaging with other subcortical and cerebral cortex regions throughout the learning process. Here, the thalamus serves as a critical connector hub and synchroniser within the thalamocortical system of the brain. However, whilst higher order thalamic nuclei are known to be particularly important for this process, the exact contributions of individual higher order and first order thalamic nuclei, alongside their individual involvement with cortical networks and subcortical regions, remains unexplored within the initial phase of learning. In light of this, we analysed fMRI data obtained within a paradigm which is designed to examine initial learning processes within feedback-driven stimulus-response learning, in order to explore thalamic contributions. We investigated dynamic learning-related functional connectivity alterations between various thalamic nuclei with other subcortical regions and cortical networks. Our results show that the initial phase of learning was associated with: (1) decreasing functional connectivity between thalamic nuclei and frontoparietal and cingulo-opercular networks, (2) increasing functional connectivity between thalamic nuclei with default mode and salience networks, (3) decreasing functional connectivity between thalamic nuclei and the putamen, and (4) decreasing functional connectivity amongst higher order thalamic nuclei. Furthermore (5) these dynamic alterations were associated primarily by mediodorsal thalamus. Altogether, these results indicate that higher order thalamic nuclei play a crucial role within initial learning and in the generation of novel goal-directed behaviour. This was demonstrated through enhanced functional connectivity with selected cortical networks which drive goal-directed behaviour, alongside decreased functional connectivity with striatal regions which drive motor selectivity.

Human behavior arises from the continuous transformation of sensory input into goal-directed actions. While existing analytical methods often break time into discrete events, the stages and underlying representations involved in stimulus-response (S-R) transformations within time-continuous, complex environments remain incompletely understood. Encoding models, combined with deep neural networks (DNNs) for feature generation, offer a promising framework for capturing these neural processes. While DNNs continue to improve in performance, it remains unclear whether these advances translate into closer alignment with human cognitive mechanisms. To address this, we collected fMRI data from participants (N = 23) as they played arcad video games and used DNN-based encoding models to predict human brain activity. We compared the prediction accuracy of features from three DNNs at different stages of development within our encoding model. The results show that the most advanced DNN provides the most predictive feature space for neural responses, while also revealing a closer hierarchical alignment between its internal representations and the brain’s functional organization. These results enable a more fine-grained characterization of time-continuous S-R transformations in high-dimensional visuomotor tasks, progressing along the dorsal visual stream and extending into motor-related regions. This approach highlights the potential of machine learning to advance cognitive neuroscience by enhancing the investigation of ecological valid experimental tasks.

Previous research suggests that habit formation is associated with a decreasing control by the goal-directed system and increasing control of the habit-related brain systems. However, the causal contribution of these systems in human habit formation and expression remains unclear. In the current study, we applied 1-Hz inhibitory repetitive transcranial magnetic stimulation (rTMS) targeting the inferior parietal cortex (IPL) and the dorsal premotor cortex (PMC) to interfere with goal- and habit-related brain systems, respectively. Two groups of participants (IPL/PMC) received real or sham-rTMS on separate days prior to habit training. We found that TMS did not induce significant learning-related changes during habit learning. Importantly, we found a significantly increased habit expression during goal-habit competition following IPL stimulation. In contrast, while numerically, habit expression decreased following PMC stimulation, this effect did not reach statistical significance. Thereby, the current study provides direct causal evidence suggesting that habit expression is significantly influenced by the release of control from the goal-directed system, while evidence regarding the influence of the habit-related system remains less conclusive.

The thalamus is connected to the cerebral cortex and subcortical regions, serving as a node within cognitive networks. It is a heterogeneous structure formed of functionally distinct nuclei with unique connectivity patterns. However, their contributions to cognitive functioning within networks is poorly understood. Recent animal research suggests that thalamic nuclei such as the mediodorsal nucleus play critical roles in goal‐directed behaviour. Our aim was to investigate how functional integration of thalamic nuclei within cortical and subcortical networks changes whilst transitioning from more controlled goal‐directed behaviour towards more automatic or habitual behaviour in humans. We analysed functional magnetic resonance imaging (fMRI) data from a stimulus–response learning study to investigate functional connectivity (FC) changes across learning between thalamic nuclei with cortical networks and subcortical structures in 52 healthy subjects. We also defined additional regions‐of‐interest (ROIs) individually in native space, segmenting the thalamus into 47 nuclei and segmenting 38 subregions within the basal ganglia and hippocampus. Additionally, we defined 12 cerebral cortex ROIs via maximum‐probability network templates. Associative S‐R learning‐related connectivity changes were examined via ROI‐to‐ROI functional network analysis. Our results showed that learning was associated with: (1) decreasing FC between the frontoparietal network and higher order thalamic nuclei; (2) increasing FC between the cingulo‐opercular network and pulvinar nuclei; (3) decreasing FC between the default mode network (DMN) and right mediodorsal nuclei; (4) increasing FC between the DMN and left mediodorsal nuclei; (5) changes in functional connectivity between thalamic nuclei and putamen subregions, and (6) increasing intrathalamic FC. Together, this suggests that several thalamic nuclei are involved in the learning‐related transition from controlled to more automatic behaviour.

Human behavior arises from the continuous transformation of sensory input into goal-directed actions. While existing analytical methods often break time into discrete events, the stages and underlying representations involved in stimulus-response (S-R) transformations within time-continuous, complex environments remain only partially understood. Encoding models, combined with deep neural networks (DNNs) for feature generation, offer a promising framework for capturing these neural processes. While DNNs continue to improve in performance, it remains unclear whether these advances translate into more accurate models of brain activity. To address this, we collected fMRI data from participants (N = 23) as they played arcade-style video games and applied DNN-based encoding models to predict voxel-level brain activity. We compared the prediction accuracy of features from three DNNs at different stages of development within our encoding model. We show that the most advanced DNN provides the most predictive feature space for neural responses, while also exhibiting a closer hierarchical alignment between its internal representations and the brain’s functional organization. These results enable a more fine-grained characterization of time-continuous S-R transformations in high-dimensional visuomotor tasks, progressing along the dorsal visual stream and extending into motor-related regions. This approach highlights the potential of machine learning to advance cognitive neuroscience by enhancing the ecological validity of experimental tasks.

Background: Anorexia nervosa (AN), often with an onset in adolescence, is a complex eating disorder characterized by distorted body image, fear of weight gain, and extreme food restriction, leading to severe underweight. Excessive goal pursuit and avoidance behaviors have been proposed as key factors in AN, which over time may become over‐trained into habits. Methods: This study investigated the behavioral and neural correlates of habit learning in AN with an experiment consisting of three consecutive phases: (1) training goal‐directed behavior, (2) avoidance learning, and (3) a habit test. Forty‐five acutely underweight adolescent female patients with AN and 45 age‐matched healthy control participants underwent an fMRI scan. Results: No behavioral group differences were evident either during learning of avoidance habits or when testing habit strength. Importantly, however, the AN group showed both generally superior task performance and increased involvement of the frontoparietal brain regions during habit learning. Conclusions: Collectively, our findings provide novel evidence suggesting that excessive goal pursuit may predominate in young AN in an avoidance learning context. Future research should examine if this tendency develops into habit learning over time and investigate the speed and strength of avoidance habit formation in adults with a longer history of AN to further elucidate the intricate dynamic between goal‐directed and habitual processes in the disorder.

This paper extends a recent study on the neural mechanisms underlying initial learning through instruction, trial-and-error, and observation of stimulus–response associations. Adopting a network perspective, we examine the functional connectivity patterns during the early stages of learning, demonstrating that the brain undergoes extensive network reorganization, regardless of the acquisition method. Our findings reveal a general segregation of task-positive networks from the default mode network, which is paralleled by and may facilitate the integration within and between task-positive networks. This segregation-integration pattern likely reflects a balance between internal and external task-related processes, modulated by learning progression and task difficulty across different acquisition modes. Differences between learning conditions, as well as brain connectivity–behavior associations between rule learning and rule implementation, point to varying cognitive demands: more efficient learning in instruction-based learning, inhibitory processes in observation-based learning, and the integration of reward, valence, and somatomotor processes in trial-and-error learning. We conclude that while extensive neural reorganization occurs during the initial learning trials, irrespective of response implementation or acquisition mode, this reorganization also exhibits distinct features that support the unique demands of each learning method.

Goal-directed behavior is thought to rely critically on the anticipation of potential future outcomes of an action. In this study, we used an fMRI instrumental learning paradigm with selective outcome devaluation in a sample of 59 participants to identify regions of the brain's goal-directed system showing anticipatory neural representations of action outcomes. Using multivariate pattern analysis, we could show that an area in the left dorsolateral prefrontal cortex significantly encoded action outcomes in an anticipatory manner. Critically, anticipatory outcome encoding strength in a subset of voxels in this dorsolateral prefrontal cluster significantly predicted behavioral sensitivity to outcome devaluation, hence behavioral goal-directedness. This finding is important because anticipatory outcome encoding in dlPFC has never been directly linked to behavioral goal-directedness during outcome devaluation in previous research. However, future research is needed to investigate the specific role of different regions along the lateral prefrontal cortex in this context and to investigate whether the effect reported here can explain impairments in goal-directed behavior under specific conditions such as, for example, the experience of acute stress.

The thalamus is connected to cerebral cortex and subcortical regions, serving as a node within cognitive networks. It is a heterogeneous structure formed of functionally distinct nuclei with unique connectivity patterns. However, their contributions to cognitive functioning within networks is poorly understood. Recent animal research suggests that thalamic nuclei such as the mediodorsal nucleus play critical roles in goal-directed behaviour. Our aim was to investigate how functional integration of thalamic nuclei within cortical and subcortical networks changes whilst transitioning from more controlled goal-directed behaviour towards more automatic or habitual behaviour in humans. We analysed functional magnetic resonance imaging (fMRI) data from a stimulus-response learning study to investigate functional connectivity (FC) changes across learning between thalamic nuclei with cortical networks and subcortical structures in healthy subjects. We defined subcortical regions-of-interest (ROIs) individually in native space, segmenting the thalamus into 47 nuclei and segmenting 38 subregions within the basal ganglia and hippocampus. Additionally, we defined 12 cerebral cortex ROIs via maximum-probability network templates. Learning-related connectivity changes were examined via ROI-to-ROI functional network analysis. Our results showed that learning was associated with: 1) decreasing FC between the frontoparietal network and higher order thalamic nuclei; 2) increasing FC between the cingulo-opercular network and pulvinar nuclei, 3) decreasing FC between the default mode network (DMN) and right mediodorsal nuclei; 4) increasing FC between the DMN and left mediodorsal nuclei; 5) altered functional connectivity between thalamic nuclei and putamen subregions, and 6) increasing intrathalamic FC. Together, this suggests that several thalamic nuclei are involved in the learning-related transition from controlled to more automatic behaviour.