Neurodegenerative disorders affect millions of people worldwide and often progress silently until noticeable cognitive or motor symptoms appear. However, structural and functional changes in the brain can emerge much earlier and may serve as valuable markers for early detection and monitoring.

In this competition, you will be working with a dataset of preprocessed fake brain images—such as MRI scans—paired with a quantitative indicator of disease severity or risk. Your objective is to develop a model that can predict this simulated indicator using only the imaging data.

The provided target is a continuous clinical score or biomarker associated with neurodegeneration (e.g., memory impairment score, cognitive decline index, or volumetric atrophy measure). High prediction accuracy could support early diagnosis, patient stratification, and improved disease tracking.

Key aspects of the competition:

Input: 2D brain images in a standardized format.

Output: A continuous-valued prediction of a neurodegeneration indicator between 0 and 1.

Goal: Minimize the error between predicted and true values across a holdout test set.