Longitudinal markers of cognitive procedural learning in fronto-striatal circuits and putative effects of a BDNF plasticity-related variant
Procedural learning and automatization have widely been studied in behavioral psychology and typically involves a rapid improvement, followed by a plateau in performance throughout repeated training. More recently, brain imaging studies have implicated frontal-striatal brain circuits in skill learni...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
28 November 2024
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| In: |
npj science of learning
Year: 2024, Volume: 9, Issue: 1, Pages: 1-13 |
| ISSN: | 2056-7936 |
| DOI: | 10.1038/s41539-024-00282-2 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41539-024-00282-2 Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41539-024-00282-2 
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| Author Notes: | Lena S. Geiger, Torsten Wüstenberg, Zhenxiang Zang, Mirjam Melzer, Stephanie H. Witt, Marcella Rietschel, Markus M. Nöthen, Stefan Herms, Franziska Degenhardt, Andreas Meyer-Lindenberg & Carolin Moessnang |
| Summary: | Procedural learning and automatization have widely been studied in behavioral psychology and typically involves a rapid improvement, followed by a plateau in performance throughout repeated training. More recently, brain imaging studies have implicated frontal-striatal brain circuits in skill learning. However, it is largely unknown whether frontal-striatal activation during skill learning and behavioral changes follow a similar learning curve pattern. To address this gap in knowledge, we performed a longitudinal brain imaging study using a procedural working memory (pWM) task with repeated measurements across two weeks to map the temporal dynamics of skill learning. We additionally explored the effect of the BDNF Val66Met polymorphism, a common genetic polymorphism impacting neural plasticity, to further inform the relevance of the identified neural markers. We used linear and exponential modeling to characterize procedural learning by means of learning curves on the behavioral and brain functional level. We show that repeated training led to an exponential decay in a distributed set of brain regions including fronto-striatal circuits, which paralleled the exponential improvement in task performance. In addition, we show that both behavioral and neurofunctional readouts were sensitive to the BDNF Val66Met polymorphism, suggesting less efficient learning in 66Met-allele carriers along with protracted signal decay in frontal and striatal brain regions. Our results extend existing literature by showing the temporal relationship between procedural learning and frontal-striatal brain function and suggest a role of BDNF in mediating neural plasticity for establishing automatized behavior. |
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| Item Description: | Gesehen am 21.05.2025 |
| Physical Description: | Online Resource |
| ISSN: | 2056-7936 |
| DOI: | 10.1038/s41539-024-00282-2 |