Detection and analysis of spatiotemporal patterns in brain activity

Area of Science:

• Computational neuroscience
• Systems neuroscience
• Data analysis

Background:

• Population-level brain activity exhibits spatiotemporal wave patterns crucial for neural function.
• Analyzing these complex patterns is essential for understanding neural circuit mechanisms.

Purpose of the Study:

• To present a mathematical and computational framework for identifying and analyzing diverse wave patterns in neural population recordings.
• To introduce novel methods for characterizing spatiotemporal dynamics in neural data.

Main Methods:

• Adapted optical flow estimation from computer vision to calculate velocity vector fields for neural signal phase and amplitude.
• Introduced order parameters and critical point analysis to detect various wave patterns (planar, spiral, source, sink, saddle).
• Developed a vector field decomposition method for representing neural data by dominant spatiotemporal modes.

Main Results:

• Demonstrated the framework's capability with simulated data, local field potentials, and optical voltage recordings.
• Showed that neural pattern dynamics are non-random and influenced by visual stimuli.
• Validated the detection of diverse wave patterns, including spiral waves.

Conclusions:

• The developed framework effectively identifies and characterizes complex spatiotemporal wave patterns in neural activity.
• This approach offers an alternative to traditional dimensionality reduction techniques for neural data.
• The findings highlight the structured nature of neural population dynamics and their modulation by external stimuli.