Pilar Garcés

This study shows that including zero-phase-delay connectivity improves the reliability and prognostic ability of functional connectivity metrics in neural populations.

The paper examines EEG abnormalities in ASD and controls, finding reduced fE/I in both groups with abnormalities, suggesting inhibition-dominated networks in sensory regions.

This study uses machine learning to analyze EEG data and shows that both brain and peripheral signals contribute to predicting age and sex, but brain signals are more effective for CNS-specific biomarkers.

The paper identifies patterns of neural variability in autism using fMRI across tasks, finding greater global connectivity deviations and similar spatial patterns in autistic individuals compared to controls.

The paper identifies a multimodal brain pattern associated with autism by integrating gray and white matter morphology data and linking it to clinical symptoms.

The paper identifies atypical brain asymmetry in autism as a potential neurophenotype for stratification, showing individualized patterns linked to symptom severity and language delay.

This study identifies reproducible functional connectivity alterations in ASD using rs-fMRI across four large cohorts, linking them to clinical symptoms and brain regions.