Spatiotemporal brain hierarchies of auditory memory recognition and predictive coding

Bonetti, Leonardo; Fernández-Rubio, Gemma; Carlomagno, Francesco; Pantazis, Dimitrios; Vuust, Peter; Kringelbach, Morten L.

doi:10.1101/2022.11.19.517195

Cited by 6 publications

(12 citation statements)

References 159 publications

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“…However, in this case, the recruitment was bilateral, and it showed hierarchical dynamics from lower-to higher-order brain areas in different frequency bands (e.g. 0.1-1 Hz and 2-8 Hz) 26 and in relation to the recognition of previously memorised or varied musical sequences [27][28][29][30] .…”

Section: Introductionmentioning

confidence: 68%

Challenging age-related decline in brain function: Evidence from fast neuroimaging of musical sequence recognition

Bonetti

Fernández-Rubio

Lumaca

et al. 2023

Preprint

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Aging is often associated with decline in brain processing power and neural predictive capabilities. To challenge this notion, we used the excellent temporal resolution of magnetoencephalography (MEG) to record the whole-brain activity of 39 older adults (over 60 years old) and 37 young adults (aged 18-25 years) during recognition of previously memorised and novel musical sequences. Our results demonstrate that independent of behavioural measures, older compared to young adults showed increased rapid auditory cortex responses (around 100 and 250 ms after each tone of the sequence) and decreased later responses (around 250 and 350 ms) in hippocampus, ventromedial prefrontal cortex and inferior frontal gyrus. Working memory abilities were associated with stronger brain activity for both young and older adults. Our findings unpick the complexity of the healthy aging brain, showing age-related neural transformations in predictive and memory processes and challenging simplistic notions that non-pathological aging merely diminishes neural predictive capabilities.

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Section: Introductionmentioning

confidence: 68%

Challenging age-related decline in brain function: Evidence from fast neuroimaging of musical sequence recognition

Bonetti

Fernández-Rubio

Lumaca

et al. 2023

Preprint

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“…The pre-processed neuroimaging data generated in this study have been deposited in the Zenodo database under accession code [https:// doi.org/10.5281/zenodo.10715160 96 ] and are publicly available. The raw neuroimaging data are protected and are not available due to data privacy laws.…”

Section: Reporting Summarymentioning

confidence: 99%

“…In-house-built code and functions used in this study are part of the LBPD repository which is available at the following link: https://doi.org/10.5281/ zenodo.10701724 97 . The full analysis pipeline used in this study and the code employed for delivering the stimuli are available at the following link: https://doi.org/10.5281/zenodo.11072410 98 .…”

Section: Reporting Summarymentioning

confidence: 99%

Spatiotemporal brain hierarchies of auditory memory recognition and predictive coding

Bonetti,

Fernández-Rubio,

Carlomagno

et al. 2024

Nat Commun

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Our brain is constantly extracting, predicting, and recognising key spatiotemporal features of the physical world in order to survive. While neural processing of visuospatial patterns has been extensively studied, the hierarchical brain mechanisms underlying conscious recognition of auditory sequences and the associated prediction errors remain elusive. Using magnetoencephalography (MEG), we describe the brain functioning of 83 participants during recognition of previously memorised musical sequences and systematic variations. The results show feedforward connections originating from auditory cortices, and extending to the hippocampus, anterior cingulate gyrus, and medial cingulate gyrus. Simultaneously, we observe backward connections operating in the opposite direction. Throughout the sequences, the hippocampus and cingulate gyrus maintain the same hierarchical level, except for the final tone, where the cingulate gyrus assumes the top position within the hierarchy. The evoked responses of memorised sequences and variations engage the same hierarchical brain network but systematically differ in terms of temporal dynamics, strength, and polarity. Furthermore, induced-response analysis shows that alpha and beta power is stronger for the variations, while gamma power is enhanced for the memorised sequences. This study expands on the predictive coding theory by providing quantitative evidence of hierarchical brain mechanisms during conscious memory and predictive processing of auditory sequences.

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“…Finally, we deemed the original clusters to be significant if their sizes were larger than 99.99% of the clusters in the reference distribution. Further details on this MCS procedure can be read in our previous studies (Bonetti et al, 2020(Bonetti et al, , 2021a(Bonetti et al, , 2022a(Bonetti et al, , 2022cFernandez-Rubio et al, 2022a.…”

Section: Neural Sources Of the Brain Activity Underlying Processing O...mentioning

confidence: 99%

“…Recently, we were able to explore, through detailed analysis of ERP topographical and temporal expression, the spatio-temporal dynamics underpinning long-term, global recognition of musical sequences. This revealed a large number of hierarchically organised brain regions which were responsible for generating predictions of the upcoming sounds of the melodies (Bonetti et al, 2020, 2021a, 2022a, 2022c; Fernandez-Rubio et al, 2022a, 2022b). These studies provided solid evidence of the neural mechanisms underlying predictive processes during long-term recognition of musical sounds.…”

Section: Introductionmentioning

confidence: 99%

Neurophysiological correlates of short-term recognition of sounds: Insights from magnetoencephalography

Serra,

Lumaca,

Brattico

et al. 2023

Preprint

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Understanding the brain’s dynamic retrieval and updating of encoded information is a key focus in memory research. This study employed a same versus different auditory paradigm to investigate short-term auditory recognition within a predictive coding (PC) framework, concerning the perceptual interplay between experience-informed predictions and incoming sensory information. Using magnetoencephalography (MEG), we captured the neurophysiological correlates associated with a single-sound, short-term memory task. Twenty-six healthy participants were tasked with recognizing whether presented sounds were same or different compared to strings of standard stimuli. To prompt conscious memory retention, a white noise interlude separated these sounds from the standards. MEG sensor-level results revealed that recognition of same sounds elicited two significantly stronger negative components of the event-related field compared to different sounds. The first one was the N1, peaking 100ms post-sound onset, while the second one corresponded to a slower negative component arising between 300 and 600ms after sound onset. This effect was observed in several significant clusters of MEG sensors, especially temporal and parietal regions of the scalp. Conversely, different sounds produced scattered and smaller clusters of stronger activity than same sounds, peaking later than 600ms after sound onset. Source reconstruction using beamforming algorithms revealed the involvement of auditory cortices, hippocampus, and cingulate gyrus in both conditions. Overall, the results are coherent with PC principles and previous results on the brain mechanisms underlying auditory recognition, highlighting the relevance of early and later negative brain responses for successful prediction of previously listened sounds in the context of conscious short-term memory.

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Spatiotemporal brain hierarchies of auditory memory recognition and predictive coding

Cited by 6 publications

References 159 publications

Challenging age-related decline in brain function: Evidence from fast neuroimaging of musical sequence recognition

Challenging age-related decline in brain function: Evidence from fast neuroimaging of musical sequence recognition

Spatiotemporal brain hierarchies of auditory memory recognition and predictive coding

Neurophysiological correlates of short-term recognition of sounds: Insights from magnetoencephalography

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