Longitudinal Effects of Brain Stimulation Techniques (ECT, TMS, VNS, taVNS) on Decision-Making and Reward Learning

Brain stimulation techniques are increasingly applied to treat mental disorders, especially when psychotherapy and psychopharmacotherapy fail (Conroy & Holtzheimer, 2021). The efficacy of treating treatment-resistant depression has been demonstrated using electroconvulsive therapy (ECT; Espinoza & Kellern, 2022; Subramanian et al., 2022), transcranial magnetic stimulation (TMS, Trapp et al., 2025), and vagus nerve stimulation (VNS, Kamel et al., 2022). With regard to transcutaneous auricular vagus nerve stimulation (taVNS), a motivation-enhancing effect has been demonstrated in individuals with depression (Ferstl et al., 2024).

It is unclear to what extent these stimulation techniques also affect cognitive processes, such as learning and decision-making behavior, and whether such changes can explain treatment success. Temporary cognitive side effects, such as memory impairments, are known to occur with ECT treatments (Landry et al., 2021). Initial evidence regarding learning and decision-making behavior is available for taVNS, in which the vagal auricular branch is noninvasively stimulated at the ear. Acute taVNS stimulation at the left ear showed a reduced learning rate particularly during punishment (Kühnel et al., 2020), as well as increased reward sensitivity (Weber et al., 2021). Additionally, TMS over the left dorsolateral prefrontal cortex was shown to alter the ratio of reward-to-punishment learning rates (Biernacki et al., 2023). However, these initial findings on learning and decision-making behavior and the underlying computational parameters relate to acute stimulation. Long-term effects of brain stimulation techniques on theses processes, as used in routine clinical practice (e.g., eight TMS sessions or daily stimulation of the vagus nerve) remain unclear to date. Likewise, the influence of fluctuations in mental state, such as mood, on learning and decision-making behavior over the course of a treatment, as well as potential changes in this influence after successful treatment, remain mostly unexplored. However, a recent study found, that fluctuations in metabolic state affected reward sensitivity and punishment learning rates in obesity (Kühnel et al., 2025). In the context of effort-based decision-making, it has been shown that fluctuations in motivation influence reward sensitivity (Hewitt et al., 2025).

It is also not yet possible to say to what extent stimulation-induced changes in clinical symptoms are reflected in possible changes in decision-making and reward learning. However, increasing evidence of altered computational mechanisms in patients points towards this potential connection. For example, higher punishment learning rates were observed in individuals with depression (Pike & Robinson, 2022) and anhedonia was linked to reduced reward sensitivity (Huys & Browning, 2025). Also, greater temporal discounting of future rewards was observed in individuals with depression compared to those without a diagnosis (Amlung et al., 2019). An open question remains, that is whether, as symptoms improve, changes in cognitive processes also diminish.

Based on the above, the following objectives have been established: First, the acute and mid-term effects of the mentioned neurostimulation therapies on computational processes of decision-making and reward learning will be investigated over the course of a clinical application. We expect, that brain stimulation lowers punishment learning rates (Hypothesis 1). Subsequently, the effects of the various stimulation methods on punishment learning rates will be compared (acutely after stimulation as well as over the course of the treatment). In addition, the influence of state fluctuations, such as mood, on computational parameters of decision-making and reward learning, such as learning rates, will be modeled. We expect, that fluctuations in mood, motivation, and metabolic state are associated with changes in punishment learning rates (Hypothesis 2). Next, we investigate the effect of brain stimulation on working memory. We expect, that ECT impairs working memory compared to the other treatments (Hypothesis 3). Finally, we expect that changes in punishment learning rates predict changes in symptoms of depression, general well-being, and somatic symptoms (Hypothesis 4), as well as cognitive side effects on working memory (Hypothesis 5).

Here, we use daily assessments of reinforcement learning with a gamified online task (Neuser et al., 2023) over the course of 8 weeks to track longitudinal effects of brain stimulation on decision-making and reward learning in patients with depression who receive neuromodulation treatment (N = 100). Daily assessments are accompanied by an ecological momentary assessment (EMA) of mood and metabolic states. At least five runs of the reinforcement learning task should be played before the first stimulation as a baseline. Additionally, working memory (backward digit span task), depressive symptoms (BDI-II), well-being (WHO-5), and somatic symptoms (PHQ-15) are measured online at the start of the study before the first treatment, after 4 weeks, and after 8 weeks at the end of the study. Patients will be recruited directly from the clinic after brain stimulation treatment is indicated for them..