Gregory A. Fonzo

This study identifies early functional connectivity changes in MDD patients treated with antidepressants or placebo, revealing common and distinct treatment effects.

This study uses machine learning and transcriptomic integration to identify brain network and molecular signatures associated with amyloid-beta and tau in Alzheimer's disease.

This paper introduces a wearable patch for non-invasive ultrasound neuromodulation and sleep recording, showing increased REM sleep duration and reduced latency in human subjects.

The paper uses deep learning to identify brain-behavior dimensions and biotypes in youth with ADHD and anxiety disorders, revealing distinct symptom profiles and brain development patterns.

This study identifies a functional connectivity signature, ARC, that distinguishes superagers from AD patients and predicts cognitive decline and amyloid-beta accumulation.

This paper identifies two neurophysiology-informed symptom dimensions in MDD using contrastive functional connectivity analysis of fMRI data.

This study shows that tFUS targeting the amygdala enhances both emotional and neutral memory, suggesting a role for the amygdala in filtering non-salient stimuli rather than amplifying salient ones.

This study identifies two transdiagnostic subtypes in ADHD and ASD using contrastive graph machine learning on brain connectivity patterns from resting-state fMRI.

The paper identifies dementia subtypes based on neuropsychiatric symptom-linked brain connectivity patterns using neuroimaging data.

The paper identifies two EEG connectivity dimensions linked to ASD symptoms using contrastive learning and sparse canonical correlation analysis in 392 ASD samples.