Andrea Bizzego

The paper examines neural synchrony in dyads during a cooperative task and finds it varies with relationship type and performance metrics.

This paper shows that physiological group synchrony can be analyzed as a network topology, revealing how synchronization is distributed across individuals and relationships, rather than just as an aggregate of pairwise alignments.

This study evaluates wavelet transform coherence (WTC) in fNIRS hyperscanning, showing it reliably detects temporal correspondence but is context-sensitive, particularly influenced by social co-presence.

This study finds that higher maternal neuroticism is linked to stronger interpersonal neural synchrony with children during passive and free interactions, but not during structured tasks.

This study examines how interpersonal closeness and social interactivity affect neural synchrony using hyperscanning, finding that true dyads show higher synchrony than surrogates, with variations by dyad type and interaction condition.

This study investigates how emotional and linguistic features influence prefrontal functional connectivity during real-life dialogues using fNIRS hyperscanning and computational analysis.

This study finds that emotional content in dialogues predicts prefrontal neural synchrony better than syntactic/semantic features, with emotional content showing stronger overall associations.

The paper examines inter-subject synchronisation in father-child dyads during co-viewing of animation videos, finding greater synchrony in true dyads compared to control dyads, especially in emotionally arousing scenes.

This study finds a gene-environment interaction where oxytocin receptor genotype and nanny experience influence face categorization reaction times in adults.

The paper examines how maternal anxious attachment style affects mother-child brain-to-brain synchrony during shared media viewing.

The paper introduces a framework combining deep learning and radiomics for cancer prognosis using CT and PET imaging, showing improved accuracy over single-method approaches.

This study evaluates the reproducibility of AI algorithms in digital pathology using the DAPPER framework, analyzing accuracy and feature stability across multiple deep learning models and datasets.

This paper introduces and validates a pipeline for heartbeat detection and HRV analysis in wearable devices, showing improved accuracy over existing methods.