Daniele Marinazzo

Assessing whole-cortex excitability from electromagnetic brain signals

This study evaluates ten excitability metrics from MEG data to show cortical excitability is multidimensional with distinct spatial and functional patterns.

Structural integrity moderates the cognitive bearing of dopamine clearance in aging

The paper develops ADAM, a model linking dopamine clearance and structural connectivity to cognitive aging, finding elevated clearance timescales and a structure-age-dependent association with cognitive performance.

A spectral partial information decomposition framework for quantifying information about cognitive variables in oscillatory brain networks

This paper introduces NeOPID, a framework to quantify information in brain oscillations, showing phase and amplitude co-modulations act as complementary coding channels with redundant and synergistic components.

From periodic/aperiodic to static/dynamic: rethinking the decomposition of neural power spectra

This paper challenges the standard decomposition of neural power spectra into periodic and aperiodic components, arguing that the distinction is not as clear-cut and that temporal envelope information is lost in static power spectra.

Bridging Higher-Order Information Theory and Neuroimaging: A Voxel-Wise O-Information Framework

This paper introduces a voxel-wise O-information framework to analyze high-order functional interactions in neuroimaging data, revealing age-related changes in default mode network redundancy.

Higher-order and distributed synergistic functional interactions encode information gain in goal-directed learning

This study shows that information gain in goal-directed learning is encoded through synergistic and higher-order functional interactions across cortical circuits.

Brain structural modules associated to functional high-order interactions in the human brain

This study investigates the coupling between structural connectivity and high-order functional interactions in the human brain, finding that structural networks are associated with both redundant and synergistic functional interactions.

The influence of the head model on magnetoencephalography-derived functional connectivity fingerprinting

This study shows that the head model significantly affects the accuracy of subject identification and differentiation in MEG-derived functional connectivity fingerprinting.

Intra-V1 functional networks predict observed stimuli

The paper uses fMRI data to predict Navon letter stimuli from intra-V1 functional networks, showing inter-individual stability but intra-individual variability in shape prediction.

Assessing cortical excitability with electroencephalography: a pilot study with EEG-iTBS.

This study evaluates EEG-based measures of cortical excitability before and after iTBS in healthy subjects, finding that gamma band phase consistency correlates with excitability changes in M1.

The impact of ROI extraction method for MEG connectivity estimation: practical recommendations for the study of resting state data.

This paper evaluates ROI extraction methods for MEG connectivity analysis and recommends strategies based on sensitivity and specificity trade-offs.

Aperiodic component of EEG power spectrum and cognitive performance in aging: the role of education

The paper examines how education modulates the relationship between the aperiodic component of EEG power spectrum and cognitive performance in aging.

Task matters: individual MEG signatures from naturalistic and neurophysiological brain states

This study shows that MEG signatures for individual identification improve during task-related behavior compared to resting-state, with controlled tasks showing the most significant improvements.

Dynamic causal modelling shows a prominent role of local inhibition in alpha power modulation in higher visual cortex

The paper uses dynamic causal modelling to show that intrinsic disinhibition in higher visual cortex contributes to alpha power differences between eyes-open and eyes-closed states.

Investigation on How Dynamic Effective Connectivity Patterns Encode the Fluctuating Pain Intensity in Chronic Migraine

This study investigates how dynamic effective connectivity patterns in EEG data relate to fluctuating pain intensity in chronic migraine patients using Bayesian models.

Theta-phase connectivity between medial prefrontal and posterior areas underlies novel instructions implementation

The paper shows that theta-phase connectivity between medial prefrontal and posterior brain areas increases during the implementation of novel instructions compared to their maintenance, supporting a role for theta oscillations in task execution.

How hot is the hot zone? Computational modelling clarifies the role of parietal and frontoparietal connectivity during anaesthetic-induced loss of consciousness

This paper uses computational modeling to show that changes in posterior hot zone and frontoparietal connectivity during propofol anaesthesia correlate with loss of consciousness.

Modeling brain dynamics after tumor resection using The Virtual Brain

This study evaluates brain network models to predict brain dynamics after tumor resection by comparing parameter stability in patients and healthy controls.

Disambiguating the role of blood flow and global signal with Partial Information Decomposition

The paper evaluates the impact of global signal regression on functional connectivity and finds that blood flow and global signal contribute differently across brain networks.

Objects seen as scenes: neural circuitry for attending whole or parts

The paper identifies cortical regions specialized for processing global and local shape levels in visual objects and shows they form distinct functional pathways within the ventral-temporal-cortex.

The effect of global signal regression on DCM estimates of noise and effective connectivity from resting state fMRI

This study evaluates the impact of global signal regression on DCM estimates of noise and effective connectivity in resting state fMRI, finding minimal effects on connectivity but caution in interpreting noise parameters.

Information decomposition of multichannel EMG to map functional interactions in the distributed motor system

This study uses information decomposition of EMG data to analyze functional interactions between muscles during postural tasks, revealing task-dependent changes in neural networks.

Functional cortico-subcortical reorganization after complete hemispheric disconnection for intractable epilepsy

This study examines functional reorganization in children after hemispherotomy, finding altered perfusion and connectivity patterns in the remaining hemisphere.

Modulation of the spontaneous hemodynamic response function across levels of consciousness

The paper investigates how spontaneous hemodynamic response changes with different levels of consciousness using resting state fMRI, finding common dysfunction in precuneus and posterior cingulate across conditions.

Evaluating the performance of 3-tissue constrained spherical deconvolution pipelines for within-tumor tractography

This study evaluates 3-tissue constrained spherical deconvolution pipelines for within-tumor tractography, finding that single-shell 3-tissue CSD enables tractography in infiltrating gliomas without increasing dMRI acquisition requirements.

Deletion of autism risk gene Shank3 disrupts prefrontal connectivity

This study shows that deleting the autism risk gene Shank3 in mice disrupts prefrontal connectivity and is linked to social communication deficits.

Effect of head volume conduction on directed connectivity estimated between reconstructed EEG sources

This study evaluates how head volume conduction affects directional connectivity estimates from EEG source reconstruction using simulations and multiple connectivity measures.

Dynamic causal modelling of fluctuating connectivity in resting-state EEG

This study uses dynamic causal modelling to analyze fluctuations in resting-state EEG connectivity, finding both subject-specific and common components in network connectivity patterns.

Connectome preprocessing by Consensus Clustering increases separability in group neuroimaging studies

The paper proposes using consensus clustering on brain connectivity matrices to improve subgroup separation in neuroimaging studies by reducing intra-group variability.

Predicting functional networks from region connectivity profiles in task-based versus resting-state fMRI data

The paper uses machine learning to compare brain network configurations in task-based and resting-state fMRI data, finding that task-related areas show consistent connectivity patterns while others differ.