Back

Non-invasive neuromodulation of cerebello-hippocampal volume-behavior relationships

Magalhaes, T. N. C.; Maldonado, T.; Jackson, T. B.; Hicks, T. H.; Herrejon, I. A.; Rezende, T. J. R.; Symm, A. C.; Bernard, J. A.

2024-04-01 neuroscience
10.1101/2024.03.29.587400 bioRxiv
Show abstract

The study here explores the link between transcranial direct current stimulation (tDCS) and brain-behavior relationships. We propose that tDCS may indirectly influence the complex relationships between brain volume and behavior. We focused on the dynamics between the hippocampus (HPC) and cerebellum (CB) in cognitive processes, a relationship with significant implications for understanding memory and motor skills. Seventy-four young adults (mean age: 22{+/-}0.42 years, mean education: 14.7{+/-}0.25 years) were randomly assigned to receive either anodal, cathodal, or sham stimulation. Following stimulation, participants completed computerized tasks assessing working memory and sequence learning in a magnetic resonance imaging (MRI) environment. We investigated the statistical interaction between CB and HPC volumes. Our findings showed that individuals with larger cerebellar volumes had shorter reaction times (RT) on a high-load working memory task in the sham stimulation group. In contrast, the anodal stimulation group exhibited faster RTs during the low-load working memory condition. These RT differences were associated with the cortical volumetric interaction between CB-HPC. Literature suggests that anodal stimulation down-regulates the CB and here, those with larger volumes perform more quickly, suggesting the potential need for additional cognitive resources to compensate for cerebellar downregulation. This new insight suggests that tDCS can aid in revealing structure-function relationships, due to greater performance variability, especially in young adults. It may also reveal new targets of interest in the study of aging or in diseases where there is also greater behavioral variability.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.