Francesco Carlomagno

This paper introduces BROAD-NESS to study auditory memory networks and finds dual-stream mechanisms and whole-brain coordination in predictive coding.

The paper investigates brain networks involved in predictive coding of temporal sequences using MEG, finding modality-specific and shared networks for auditory and visual processing.

This study examines how working memory (WM) relates to long-term memory (LTM) recognition of musical sequences, finding WM impacts novel sequences more in older adults than younger ones, and musical training improves memory performance.

This paper reviews and meta-analyzes neuroimaging studies on non-verbal auditory memory, identifying a frontal brain hub and exploring individual differences in auditory memory processes.

This study investigates how the brain generalises abstract musical concepts over time using MEG, finding that neural responses to novel sequences differ significantly at faster paces.

This study uses MEG to investigate hierarchical brain mechanisms during auditory memory recognition and predictive coding, showing feedforward and backward connections and distinct power dynamics in different brain regions.

This study examines how age and musical training affect auditory short-term, long-term, and working memory in young and older adults, finding age-related declines and training benefits in specific tasks.

This study uses MEG to show that older adults have distinct neural responses to musical sequences compared to young adults, challenging the idea that aging uniformly reduces brain function.

The paper examines the relationship between working memory abilities and brain activity during long-term recognition of auditory sequences, finding positive correlations in specific brain regions.

The paper investigates brain mechanisms for predicting and error processing in music, finding distinct neural regions for local acoustic and cognitive priors, with enhanced involvement in musicians.

The paper investigates rapid brain mechanisms for encoding and predicting complex temporal sound sequences using MEG data from 68 participants.

The paper investigates brain dynamics during musical pattern recognition using MEG, finding increased activity and connectivity in a widespread network over time.