PT  - JOURNAL ARTICLE
AU  - Eva Berlot
AU  - Nicola J. Popp
AU  - Scott T. Grafton
AU  - Jörn Diedrichsen
TI  - The role of primary motor cortex in sequence learning: resolving conflicting fMRI evidence from repetition suppression and pattern analysis
AID  - 10.1101/2020.08.21.261453
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.08.21.261453
4099  - http://biorxiv.org/content/early/2020/08/21/2020.08.21.261453.short
4100  - http://biorxiv.org/content/early/2020/08/21/2020.08.21.261453.full
AB  - How does the brain change during learning? Functional magnetic resonance imaging studies have used both pattern analysis and repetition suppression (RS) to detect changes in neuronal representations. In the context of motor sequence learning, the two techniques have provided discrepant findings. Specifically, pattern analysis showed that only premotor and parietal regions, but not primary motor cortex (M1), develop a representation of trained sequences. In contrast, RS suggested trained sequence representations in all these regions. Here we applied both analysis techniques to data from a 5-week finger sequence training study, in which participants executed each sequence twice before switching to a different sequence. While we replicated both previously reported findings in the same paradigm, a more fine-grained analysis revealed that the RS effect in M1 and parietal areas reflect fundamentally different processes. On the first execution, M1 represents especially the first finger of each sequence, which might reflect preparatory processes, and this effect dramatically reduces during the second execution. In contrast, parietal areas represent the identity of a sequence, and this representation stays relatively stable on the second execution, only reducing proportionally to the reduction in overall activity. These results suggest that the RS effect in M1 does not reflect trained sequence representation, but rather the altered communication with higher-order areas. More generally, our study demonstrates that RS can reflect different representational changes in the underlying neuronal population code across regions, emphasizing the importance of combining pattern analysis and RS techniques.Competing Interest StatementThe authors have declared no competing interest.