Moran's I quantifies spatio-temporal pattern formation in neural imaging data.

Area of Science:

• Neuroscience
• Computational Biology
• Biophysics

Background:

• Neural activity forms complex spatio-temporal patterns.
• Existing measures like the global order parameter lack spatial correlation details.
• The suprachiasmatic nucleus (SCN) network exhibits collective cellular circadian clock activities.

Purpose of the Study:

• To propose and demonstrate Moran's I as a quantitative measure for spatial coherence in neural imaging.
• To capture dynamic signatures of spatial organization in neural activity.
• To apply the technique to collective cellular circadian clock activities in the SCN.

Main Methods:

• Utilized the spatial autocorrelation measure, Moran's I.
• Adapted Moran's I to account for the characteristic length scale of neural activity patterns.
• Applied the method to synthetic datasets and experimental SCN explant imaging time-series.

Main Results:

• Moran's I effectively quantifies the degree of spatial coherence in neural imaging data.
• The measure accounts for the spatial organization of interacting neural units.
• Moran's I and the Kuramoto order parameter R provide complementary descriptions of collective neural states.

Conclusions:

• Moran's I is a practical tool for analyzing spatial organization in neural activity.
• This method enhances the understanding of spatio-temporal dynamics beyond global order parameters.
• The technique offers statistical significance for observed spatial patterns in neural networks.