Quantifying Neurocognitive Decline: A Machine Learning Approach to Alzheimer’s Staging

Quantifying Neurocognitive Decline is designed as a Free and Open Source Software (FOSS) project to encourage collaboration, transparency, and innovation in healthcare technology. By making the platform open source, developers, researchers, and healthcare professionals can contribute to improving algorithms, enhancing cognitive assessment tools, and expanding the system’s capabilities.

The project allows the community to build new cognitive games, improve machine learning models, and integrate additional data sources such as speech analysis or wearable device data. Open-source development also ensures transparency in AI-driven healthcare systems, which is important for trust, validation, and ethical use in medical applications.

Through community contributions, this platform can evolve into a scalable and accessible tool for early detection and monitoring of neurocognitive disorders, helping researchers and clinicians worldwide develop better diagnostic and care strategies for Alzheimer’s disease.

Cognitive Assessment Games:

Cognitive Memory (Letter Recall)

This game evaluates short-term memory and recall ability.
Users observe a sequence of letters displayed one by one and must accurately reproduce the sequence after it disappears.
Performance is measured based on accuracy, response time, and sequence length.

Spatial Matrix

This game tests visual memory and attention.
A grid lights up in a specific sequence, and the user must recall and select the cells in the correct order.
Difficulty increases as the sequence becomes longer and more complex.

Dual N-Back

This game measures working memory and cognitive processing.
Users must track both the position of a highlighted grid cell and a letter shown at each step, and identify matches that occur N steps earlier.
The task evaluates attention, multitasking ability, and memory retention under cognitive load.