Annalisa Pascarella

This study investigates how LSD alters brain dynamics using MEG and machine learning, finding increased peak frequencies, reduced oscillatory power, and changes in spectral complexity.

This study uses MEG and connectome harmonics to show LSD reduces structure-function coupling in low-frequency brain activity, linking this to ego dissolution and altered network dynamics.

MEEGNet is a Python library for applying convolutional neural networks to MEG data, achieving high accuracy in classification tasks while offering interpretability tools.

This study investigates how brain rhythms track speech fundamental frequency (F0) across different speech rates using magnetoencephalography, finding coupling in auditory and somatomotor regions.

This study examines how meditation affects neural oscillations, complexity, and critical dynamics using MEG in expert monks, finding increased complexity and reduced gamma-band long-range correlations during meditation.

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

This study finds that the optimal regularization parameter for MEG connectivity estimation is smaller than that for source imaging and provides open-source code for analysis.

This paper evaluates the impact of class imbalance on ML performance in neuroscience, showing that Accuracy is misleading and recommending Balanced Accuracy and Random Forest with stratified cross-validation.

The paper compares ten ESI methods using in-vivo EEG data to evaluate their accuracy and parameter sensitivity in localizing focal brain sources.

This study examines altered criticality properties in schizophrenia patients using MEG data, finding differences in self-similarity and multifractality gradients compared to healthy controls.

The paper investigates how neural oscillations track natural versus artificial fast speech using MEG, finding that natural fast speech elicits stronger cortico-acoustic coupling than artificially accelerated speech.

NeuroPycon provides a Python toolkit for reproducible brain connectivity analysis by integrating neuroimaging tools into a unified framework with parameter files for replication.