The Whiting Lab is a translational neurosurgery research group at Allegheny Health Network, led by Alexander C. Whiting, MD. Our work spans three areas:
Decoding the Brain's Reward and Decision-Making Systems
We use human intracranial EEG to study the neural dynamics of reward valuation, risk, and craving, and how these circuits are aberrantly co-opted in addiction, depression, anxiety, and related disorders. Stereo-EEG recordings sample mesial temporal, insular, orbitofrontal, and cingulate nodes with millisecond task synchronization, allowing direct measurement of cue reactivity, anticipatory craving, and risky choice in humans. The same recordings support broader questions in human network physiology, including the distributed effects of chronic thalamic stimulation. Combining these data with signal processing and machine learning approaches, including neural foundation models, we aim to identify state-specific biomarkers capable of guiding closed-loop neuromodulation for neuropsychiatric disorders.
Improving Epilepsy Surgery Outcomes and Developing Novel Procedures
We study the durability, safety, and broader impact of epilepsy surgery and neuromodulation through evidence synthesis and single- and multicenter cohort studies. Our work spans long-term seizure and neuropsychiatric outcomes, imaging and electrophysiologic predictors of surgical success, perioperative safety, and the structural and social factors that shape access to surgical care. We also develop novel surgical approaches, including first-reported techniques for invasive monitoring and ablation in patients with previously implanted neuromodulation systems, extending surgical evaluation and treatment to patients once considered ineligible.
Building and Translating the Future of Neurotechnology
We work closely with the Allegheny Neuro-Technology Innovation Center (ANTIC) and engineers at Carnegie Mellon University, using in-house rapid prototyping, additive manufacturing, and high-performance computing to develop translational neurotechnology spanning machine learning systems to implantable hardware. Recent and ongoing work includes novel neuromodulation devices, 3D-printed surgical guides and instrumentation evaluated in controlled bench studies, low-cost open-source simulation platforms for procedural training, and multimodal deep learning models that integrate medical imaging and clinical data to approximate expert surgical decision-making. Through ANTIC, early-stage concepts progress from design and prototyping through feasibility testing, intellectual property protection, and commercialization.