Models of functional neuroimaging data.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Medical Imaging Analysis

Background:

• Functional neuroimaging data analysis relies on models of brain function.
• Current models range from anatomical to mathematical, including hemodynamic and neuronal responses.

Purpose of the Study:

• To review and interrelate key models used for analyzing functional neuroimaging data.
• To provide a framework for understanding inferences about brain function from neuroimaging.

Main Methods:

• Review of anatomical foundations of brain function models.
• Introduction to statistical models (e.g., general linear model) for inference.
• Discussion of biophysical and effective connectivity models for causal inference.
• Examination of neuronal mass models for electroencephalographic (EEG) data.

Main Results:

• Models are interrelated, building from anatomical to complex causal inference.
• Statistical models facilitate inferences about the location of neuronal responses.
• Biophysical and connectivity models address the causality of responses.
• Neuronal mass models offer mechanistic insights into evoked responses.

Conclusions:

• A comprehensive understanding of various models is crucial for accurate brain function inference.
• Models range in complexity, addressing different aspects from localization to causality.
• Advanced models like neuronal mass models enable mechanistic understanding of neural processes.