Tracking recurrence of correlation structure in neuronal recordings

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Correlated neuronal activity is crucial for brain information processing and dynamics.
• High-dimensional neural data presents challenges in studying temporal correlation structure changes.

Purpose of the Study:

• To introduce a novel multiscale method, Population Coordination (PCo), for assessing neural population structure.
• To enable direct visualization of temporal variations in neural correlation structure within high-dimensional datasets.

Main Methods:

• Population Coordination (PCo) method developed for multiunit single neuron ensemble and multi-site local field potential (LFP) recordings.
• PCo utilizes population correlation (PCorr) vectors and analyzes correlations between these vectors over time (PCo matrix).

Main Results:

• PCo successfully interpreted dynamics in both LFP and single-unit ensemble datasets.
• In an epilepsy model, PCo identified anomalous brain states where regions desynchronized.
• PCo visualized neuronal ensemble correlation changes linked to environmental shifts in rat hippocampal recordings.

Conclusions:

• PCo offers direct visualization of high-dimensional neural data, unlike traditional dimensionality reduction techniques.
• The method allows intuitive assessment of temporal recurrence in correlation structure at single sites.
• PCo facilitates the investigation of neural correlation structure across multiple scales reflecting dynamical recurrence without dimensionality reduction.