FiNN

FiNN performs quantitative analysis of electrophysiological data to estimate phase-amplitude coupling (PAC) and characterize neural communication across spatial and temporal scales.

Key Features:

• Phase-amplitude coupling (PAC) analysis: Provides measures to estimate PAC from electrophysiological recordings.
• Direct Modulation Index (dMI): Implements the direct modulation index (dMI) as a PAC metric.
• Bounded dMI range: dMI yields estimates constrained to the fixed interval from 0 to +1 for consistent interpretation.
• Noise resistance: dMI is designed to provide PAC estimates that are robust against noise.
• Data efficiency: dMI remains reliable when applied to datasets of limited size.
• Other PAC measures: Includes implementations and comparisons with classical modulation index, mean vector length, and phase-locking value.
• Focus on neural communication: Targets analysis of neural communication across spatial and temporal scales relevant to cognitive and motor functions.
• Implementation: Implemented in Python for computational analysis of electrophysiological data.

Scientific Applications:

• Cognitive and motor neuroscience: Estimation of PAC to investigate mechanisms underlying cognitive and motor functions.
• Clinical and basic research: Use in clinical and basic neuroscience studies to characterize neurophysiological network interactions.
• Functional brain architecture: Quantification of PAC to inform analyses of functional brain architecture and neural underpinnings of behavior and cognition.
• Noisy or small datasets: Application of robust PAC metrics like dMI to obtain reliable estimates from noisy or limited-size electrophysiological datasets.

Methodology:

Implemented in Python; validation and comparative analysis using simulated data comparing dMI against the classical modulation index, mean vector length, and phase-locking value.