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Updated: Jun 15, 2025

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons

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Published on: July 7, 2023

Transforming Digital Phenotyping Raw Data Into Actionable Biomarkers, Quality Metrics, and Data Visualizations Using

James Burns¹, Kelly Chen¹, Matthew Flathers¹

¹Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States.

Journal of Medical Internet Research

|August 23, 2024

Summary

This summary is machine-generated.

Cortex is an open-source pipeline that streamlines digital phenotyping data analysis. It helps researchers assess data quality, derive clinical features, and create visualizations for reproducible research.

Keywords:

Cortex app apps clinical data analysis data processing data set data visualization digital phenotyping mental health methodology mindLAMP mobile phone open-source real world smartphone smartphones

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Area of Science:

Digital Health
Computational Psychiatry
Data Science

Background:

Digital phenotyping, using data from consumer devices, is increasingly vital for research.
Processing this data and deriving reproducible features is a significant challenge.
Existing tools lack comprehensive solutions for data quality, feature extraction, and visualization.

Purpose of the Study:

Introduce Cortex, an open-source data processing pipeline for digital phenotyping.
Provide a practical guide to using Cortex for data analysis, quality assessment, and visualization.
Demonstrate the accessibility and rigor of digital phenotyping research through a user-friendly tool.

Main Methods:

Developed Cortex, an open-source Python package optimized for the mindLAMP platform.
Illustrated data handling, quality assessment, feature derivation, and visualization techniques.
Included example code for creating correlation matrices and real-world clinical applications.

Main Results:

Cortex enables real-time data quality assessment.
The pipeline facilitates the derivation of replicable clinical features from digital phenotyping data.
Cortex supports the creation of easily shareable data visualizations.

Conclusions:

Cortex enhances the accessibility and methodological rigor of digital phenotyping research.
The tool empowers research teams to efficiently analyze diverse digital phenotyping datasets.
Cortex promotes reproducible research practices in the growing field of digital health.