About Us
NeuroHarmoniX Labs Pvt. Ltd. builds infrastructure for quantitative neuroimaging, enabling brain MRI data to be analyzed through structured, reproducible workflows.
The platform is informed by peer-reviewed neuroimaging research published in the academic literature. The founder of NeuroHarmoniX has contributed to several of these methodologies, which form part of the scientific foundation of NeuroHL.
NeuroHL focuses on translating validated neuroimaging methods into consistent, clinically usable workflows that support reliable analysis across clinical and research environments.
About Us
Why NeuroHL exists
NeuroHarmoniX Labs was founded to address a gap in neurological care: structural MRI is routinely performed, yet quantitative analysis, such as determining whether a hippocampus falls in the 6th percentile for a 65-year-old South Asian woman, or whether temporal lobe thinning is consistent with early Alzheimer’s disease, is rarely available in clinical workflows.
The methods to perform this analysis have existed for years within academic neuroimaging labs. What has been missing is a reliable, clinical-grade framework that applies these methods to real-world data, incorporates population-relevant context, and delivers structured outputs for clinical use.
That is what NeuroHL builds.
Every output is verified through an automated quality control pipeline that evaluates label integrity, brain volume consistency, bilateral symmetry, and spatial alignment before release.
Our Vision
Our Mission
Our Leadership
Prasanna Parvathaneni, PhD
Founder and Chief Architect
Sydulu Maheswarapu, PhD
Academic and Strategic Advisor
Mahender Bogi
Director - Operations & Delivery
Our Core Capabilities
Quantitative Structural MRI
Automated processing of 3D T1-weighted MRI scans for precise brain volumetrics, cortical thickness, and subcortical segmentation. Longitudinal tracking with normative Z-scores.
Diffusion MRI Analytics
Advanced diffusion MRI analysis quantifying microstructural integrity across white matter tracts, cortical gray matter, and deep nuclei. FA, MD, and tract-specific metrics.
Longitudinal Monitoring
Version-controlled processing with drift detection and trajectory modeling. Reliable progression measurement distinguishing biological change from scanner variability.
Cross-Scanner Harmonization
Scanner-bias correction enabling multi-site dataset pooling and research consortium readiness. Harmonized metrics across vendors and field strengths..
Interested in bringing advanced MRI neuroimaging analytics to your institution?
Let’s explore a pilot or collaboration.
20+
Experience
in the Medical Image Analysis & AI Systems