Brain Predictability toolbox

Brain Predictability toolbox applies machine learning to neuroimaging and tabulated human datasets to build predictive models relating brain-derived variables to psychiatric, behavioral, and physiological data.

Key Features:

• Python implementation: Implemented in Python and compatible with Python 3.6 and higher.
• Data modality support: Handles tabulated data and neuroimaging-specific datasets.
• Brain imaging types: Processes brain volumes and brain surface data.
• Machine learning integration: Integrates a variety of machine learning tools and algorithms for predictive modeling.
• Multimodal dataset support: Accommodates multimodal datasets combining brain-derived variables with psychiatric, behavioral, and physiological measures.
• Large-scale cohort handling: Designed to manage large-scale human datasets with numerous subjects.

Scientific Applications:

• Predictive modeling of brain-behavior relationships: Builds models that relate brain-derived measures to psychiatric, behavioral, and physiological outcomes.
• Multimodal neuroimaging analysis: Enables analyses that combine structural neuroimaging (volumes and surfaces) with tabulated subject-level variables.
• Large-scale population neuroscience: Supports investigations of predictive effects across large cohorts and multimodal datasets.

Methodology:

Implemented in Python, the toolbox integrates machine learning tools to process tabulated and neuroimaging data (including brain volumes and surfaces) for predictive modeling on multimodal, large-scale human datasets.