Back

Age-related brain mechanisms underlying short-term recognition of musical sequences: An EEG study

Costa, M.; Vuust, P.; Kringelbach, M. L.; Bonetti, L.

2023-03-13 neuroscience
10.1101/2023.03.12.532256 bioRxiv
Show abstract

Recognition is the ability to correctly identify previously learned information. It is an important part of declarative episodic memory and a vital cognitive function, which declines with ageing. Several studies investigated recognition of visual elements, complex images, spatial patterns, and musical melodies, focusing especially on automatic and long-term recognition. Here, we studied the impact of ageing on the event-related potentials using electroencephalography (EEG) associated with short-term recognition of auditory sequences. To this end, we recruited 54 participants, which were divided into two groups: (i) 29 young adults (20-30 years old), (ii) 25 older adults (60-80 years old). We presented two sequences with an interval of a few seconds. Participants were asked to state how similar the second sequence was with regards to the first one. The neural results indicated a stronger negative, widespread activity associated with the recognition of the same sequence compared to the sequences that were transposed or completely different. This difference was widely distributed across the EEG sensors and involved especially temporo-parietal areas of the scalp. Notably, we reported largely reduced neural responses for the older versus young adults, even when no behavioral differences were observed. In conclusion, our study suggests that the combination of auditory sequences, music, and fast-scale neurophysiology may represent a privileged solution to better understand short-term memory and the cognitive decline associated with ageing.

Matching journals

The top 9 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.