Neural Mechanisms of Understudied Neurological Disorders
Using intracranial recordings to elucidate pathophysiologic biomarkers across gait dysfunction, sleep disorders, status dystonicus, pediatric dystonia, and related high-burden conditions.
Neurologist-Scientist in neuromodulation
Arjun Balachandar, MD, FRCPC, PhD-Candidate
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.
Neuromodulation
Deep Brain Stimulation
Brain-Computer Interfaces
Motor Neurophysiology
Movement Disorders
Computational Clinical Biomarkers
About
From Neural Biomarkers to Personalized Neuromodulation
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
Core research themes
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? These themes focus on: understanding neural mechanisms in understudied disorders such as freezing of gait and status dystonicus; building biomarker-guided adaptive interventions for gait and sleep disorders; and developing translational neurotechnology that integrates neural recordings biometric data, and machine learning for real-world diagnosis, monitoring, and treatment optimization.
Click each theme to view a representative publication.
Biomarker-Guided Adaptive Neuromodulation
Decoding clinical-state neural activity to build personalized, adaptive brain stimulation strategies for movement and neuropsychiatric disorders.
Translational Neurotechnology and Clinical AI
Integrating neural recordings, wearables, biometric data and machine learning to produce deployable tools for diagnosis, monitoring, and treatment optimization in real-world neurology care.
Projects
Current and emerging initiatives
TORONTO WESTERN HOSPITAL
Adaptive Cortical Neuromodulation for Freezing of Gait
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
Intracranial Biomarkers of Status Dystonicus & Generalized Dystonia
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
Automated Sleep Detection from DBS Recordings
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
Pediatric DBS Safety Across Targets and Indications
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
Machine Learning Assisted Swallowing Assessment (MASA)
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
Smartphone Accelerometer & ML-based Neurologic Phenotyping
Developed computational approaches using smartphone accelerometry to classify tremor syndromes and support the development of automated tools for earlier diagnosis.
Publications
Selected recent work
Nature Reviews Bioengineering · 2026
Neuromodulation for Gait Disorders
Balachandar A, Sorrento G, Moraud E, Bonizzato M, Fasano A.
Movement Disorders · 2026
Beta-band and aperiodic activity across new and chronic STN DBS in Parkinson's disease
Balachandar A, Boogers A, Naghdlou S, Lozano A, Kalia S, Tinkhauser G, Fasano A.
Nature Communications · 2025
Status dystonicus is a distinct state characterized by pallidal beta-band activity
Balachandar A, Vogt L, Mithani K, Coleman S, Ebden M, Fasano A, Gorodestky C, Ibrahim G.
CV
Curriculum Vitae
Recent Highlights
Selected recent grants and honors. Full details are available in the downloadable CV.
Recent Grants
- 2026 • American Brain Foundation Next Generation Research Grant in Parkinson's Disease ($150,000 USD / 2 years)
- 2024 • Parkinson Canada Clinician-Scientist Research Fellowship ($150,000 CAD / 2 years)
- 2024 • Michael J. Fox Foundation grant (Co-Primary Investigator, $1,668,130.61 CAD)
- 2023 • Sunnybrook AFP Innovation Grant (Co-Investigator, $53,161.50 CAD)
Recent Awards
- 2025 • International Congress Travel Grant (MDS Congress)
- 2024 • PGME Research Award - Joseph M. West Family Memorial Fund and Starr Medal ($13,750 CAD)
- 2024 • Department of Medicine Outstanding Resident Researcher Award
- 2024 • Resident Scholarship to the AAN Annual Meeting