Elvira Brattico

This paper validates Spike-density Component Analysis (SCA) for isolating neural components in children's auditory ERPs, showing superior accuracy with SCA-HSAC over other methods.

The paper shows that musicianship can be predicted from brain structure MRI data using machine learning, achieving significant classification accuracy.

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

This paper introduces a theory of stochastic spike timing and a method called SCA to accurately isolate neural sources in MEG/EEG data, outperforming PCA and ICA in separating overlapping brain signals.

The paper investigates neural responses to melodic surprise in musicians and non-musicians using IDyOM and MEG, finding that sensory adaptation, not probabilistic prediction, drives the N1m component, while later components reflect auditory regularities and expectations.

The paper shows that predictive uncertainty reduces musical prediction error responses similarly in musicians and non-musicians, suggesting dissociable effects of expertise and stimulus uncertainty.

This study investigates the neural correlates of music-induced analgesia in fibromyalgia patients using resting-state fMRI and finds lateralized changes in functional connectivity associated with pain reduction.

The paper investigates how prediction error responses change in musical contexts with varying pitch uncertainty, finding reduced mismatch negativity in high-uncertainty conditions.

The paper compares whole-brain and regions-of-variance approaches to examine how personality traits relate to neural responses to emotional music.