Adaptive Neuromodulation for Parkinson's Disease
Developing closed-loop cortical and deep brain stimulation strategies that use neural signals to personalize therapy in Parkinson's disease.
Neurologist-Scientist in neuromodulation
Neurologist and aspiring clinician-scientist studying how neural signals can be used to build better, personalized brain stimulation therapies for neurological disorders.
My central focus: decode the neural activity underlying clinical states and use those biomarkers to drive adaptive brain stimulation that improves gait, sleep, severe movement symptoms, and neuropsychiatric dysfunction.

About

I am a neurologist specializing in movement disorders and a PhD candidate in neuromodulation under Drs. Alfonso Fasano and George Ibrahim, focused on leveraging intracranial electrophysiology and neural signal processing to advance neuromodulation interventions. The goal is to translate these biomarkers into actionable adaptive interventions that improve real clinical outcomes.

PhD, Clinician-Scientist Training Program • 2024–
Adult Neurology Residency • 2019–2024
Doctor of Medicine • 2015–2019

Movement Disorders Fellow • 2024–
Research
My research is organized around two translational questions. First, can we understand the neural activity underlying clinical states? Second, how can we convert intracranial neural signals into actionable biomarkers that guide personalized neuromodulation treatment? Key areas include understanding and treating gait dysfunction using cortical BCIs, sleep-state decoding from DBS, and neural biomarkers for understudied movement disorders such as status dystonicus and pediatric dystonia.
Developing closed-loop cortical and deep brain stimulation strategies that use neural signals to personalize therapy in Parkinson's disease.
Building brain-machine interface and neuromodulation interventions to address gait dysfunction and freezing episodes that remain difficult to treat.
Characterizing circuit-level biomarkers, including pallidal beta-band signatures, to better define and treat poorly understood pediatric movement disorders.
Using machine learning with chronic deep brain recordings to decode sleep states and other clinically relevant brain states.
Applying machine learning to clinical and biometric signals using smartphone accelerometry and voice data to improve diagnosis, phenotyping, and automated-detection in various disorders including tremor and post-stroke dysphagia.
Studying surgical safety, complications, and clinical outcomes across pediatric deep brain stimulation targets and indications to inform evidence-based care.
Projects
TORONTO WESTERN HOSPITAL
Co-Primary Investigator on a Michael J. Fox Foundation-funded study developing an electrocorticography-based brain-machine interface and adaptive cortical neuromodulation strategies for freezing of gait in Parkinson's disease, with the goal of enabling personalized symptom-responsive therapy.
THE HOSPITAL FOR SICK CHILDREN
Analyzing longitudinal multi-year pallidal DBS recordings in pediatric status dystonicus and generalized dystonia to identify biomarkers linked to clinical severity, including excessive beta-band activity and functional connectivity signatures, to improve biomarker-guided diagnosis, monitoring, and treatment targeting.
TORONTO WESTERN HOSPITAL
Built machine-learning pipelines to detect sleep states using deep brain stimulation recordings in movement disorders, supporting closed-loop therapeutic development and objective sleep-state monitoring in clinical care.
THE HOSPITAL FOR SICK CHILDREN
Analyzed multicenter CHILD-DBS registry data to quantify surgical complications and safety outcomes across pediatric DBS targets and indications to guide evidence-based care in children.
SUNNYBROOK HEALTH SCIENCES CENTRE
Worked with the Stroke Innovation (& Machine learning) Lab [SiLab] as co-Investigator on a Sunnybrook innovation grant applying clinical ML tools to improve post-stroke dysphagia screening in real-world care settings, employing neural networks trained using voice audio recordings alone to support scalable non-invasive screening pathways.
TORONTO WESTERN HOSPITAL
Developed computational approaches using smartphone accelerometry to classify tremor syndromes and support the development of automated tools for earlier diagnosis.
Publications
Nature Reviews Bioengineering · 2026 · Accepted
Balachandar A, Sorrento G, Moraud E, Bonizzato M, Fasano A.
Movement Disorders · 2026
Balachandar A, Boogers A, Naghdlou S, Lozano A, Kalia S, Tinkhauser G, Fasano A.
Nature Communications · 2025
Balachandar A, Vogt L, Mithani K, Coleman S, Ebden M, Fasano A, Gorodestky C, Ibrahim G.
CV
Recent Highlights
Selected recent grants and honors. Full details are available in the downloadable CV.