Ludovico Coletta

This study shows that endogenous oxytocin alters brain network dynamics, increasing salience-related network states and decreasing sensory-dominated states in mice.

The paper shows that cortical excitability inversely affects fMRI connectivity, with low-frequency neuronal coupling being the key driver of these changes.

The paper develops a whole-cortex model of resting-state fMRI activity in mice, showing that attractor dynamics explain the emergence and structure of co-activation patterns (CAPs) through anatomical connectivity.

This study shows that a transient developmental excitation-inhibition imbalance causes lasting social brain circuit dysfunction in mice, with behavioral and transcriptional consequences.

This paper evaluates transfer learning for adapting a deep learning model trained on healthy data to segment white matter bundles in glioma patients, finding performance drops due to domain shift and partial success of transfer learning.

The paper examines the relationship between structural and effective connectivity in the mouse brain, finding that effective connectivity aligns with a unimodal-transmodal hierarchy while structural connectivity shows mismatches in higher-order networks.

This study identifies bursty dynamics and modular subgraphs in mouse and human brain networks, showing cross-species consistency in co-fluctuation patterns and anatomical connectivity.

This paper introduces a method to weight anatomical brain connectivity data using multi-modal MRI data and evaluates its performance on the Human Connectome Project dataset.

The paper shows that silencing the prefrontal cortex paradoxically increases resting-state fMRI connectivity with its thalamo-cortical terminals.

This study investigates spatiotemporal dynamics of spontaneous brain activity in awake mice using resting-state fMRI, finding unique patterns distinct from anesthetized states.

This paper provides a voxel-level description of the directed mouse connectome, revealing hierarchical structure and functional organization.

This study identifies the anatomical composition and connectivity patterns of the mouse default mode network at the regional, layer, and cell-class levels.

This study identifies reproducible resting-state networks in the mouse brain across multiple centers using standardized rsfMRI analysis.