Exploration of a Network-Based EEG Marker for Major Depressive Disorder.

Major depressive disorder (MDD) is a highly prevalent and debilitating disorder that impacts an individual's quality of life and ability to function due to persistent sadness, hopelessness, and lack of interest or pleasure in daily activities. Despite its high prevalence, many MDD patients are misdiagnosed, lack adequate treatment, and will never enter remission. We consider scalp EEG as a promising imaging modality for a computationally-driven biomarker.

Patient-Specific Electrical Stimulation to Effectively Suppress Seizures using a Data-Driven Dynamical Network Model.

Seizure suppression is critical for enhancing the quality of life for patients with epilepsy. However, nearly 15 million patients have medically refractory epilepsy, meaning their seizures cannot be suppressed with medication. Electrical stimulation therapy is an alternative therapy to suppress seizures.

Localizing the Seizure Onset Zone with Bayesian Learning During iEEG Monitoring.

A critical step in treating epilepsy patients is identifying the region where seizure activity originates: the seizure onset zone (SOZ). We present a novel approach to localizing the SOZ during ictal periods, i.e.

Scalp EEG Correlates of Anti-Seizure Medications in Adult Epilepsy.

An anti-seizure medication (ASM) regimen that relieves seizure burden for patients with epilepsy can take weeks to months to establish or become ineffective over time and require revision. There is no clear clinical algorithm for choosing one specific ASM regimen over another, and an ASM is often chosen based on a patient's individual characteristics and electroencephalogram (EEG). These approaches can vary from patient to patient and may be prohibitive in low-resource settings.

A resting-state fMRI network biomarker for autism spectrum disorder.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder impacting a person's social communication skills and behaviors. Due to its a wide range of symptoms and presentations, diagnosis is a subjective process reliant on clinician experience and symptom reports. Our pilot study aims to improve this process using a resting-state fMRI biomarker based on dynamic network modeling.