PROFUMO

PROFUMO models subject variability in functional MRI (fMRI) data by extending the probabilistic functional modes (PFMs) framework (Harrison et al., 2015) to capture cross-subject spatial and temporal characteristics of functional modes.

Key Features:

• Cross-Subject Variability Modeling: Captures variability in spatial maps, mode-to-mode coupling, and amplitudes across subjects.
• Probabilistic Framework: Implements a probabilistic model extending PFMs to model inherent variability in fMRI spatial and functional representations.
• Scalability for Large Data Sets: New implementation supports analysis of large-scale datasets, including the Human Connectome Project.
• Comparison with Established Methods: Compared with independent component analysis with dual regression (ICA-DR), PROFUMO attributes more cross-subject variability to spatial maps under its assumptions.

Scientific Applications:

• Resting-state and Task-related Variability: Characterizes how resting-state and task-related fMRI activity vary across individuals.
• Functional Connectivity Analysis: Enhances interpretation of cross-sectional functional connectivity studies by providing detailed spatio-temporal descriptions.
• Cognitive Neuroscience of Individual Differences: Supports investigations of individual differences in cognitive neuroscience by modeling subject-level variability in fMRI.

Methodology:

Extends the PFM probabilistic framework and uses data simulation, analysis of resting-state data from large cohorts (e.g., 1000 subjects from the Human Connectome Project), and examination of task-related states in smaller groups (e.g., 14 subjects).