Predicting the effect of micro-stimulation on macaque prefrontal activity based on spontaneous circuit dynamics

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Targeted manipulation of neural activity requires identifying effective stimulation sites.
• Current methods rely on labor-intensive and potentially harmful trial-and-error approaches.

Purpose of the Study:

• To predict the effects of electrical stimulation on neural activity using only spontaneous activity.
• To develop a non-invasive method for identifying optimal perturbation sites in neural circuits.

Main Methods:

• Inferred ensemble causal flow from directed functional interactions during spontaneous periods using convergent cross-mapping.
• Validated computational features with recurrent neural network models.
• Compared convergent cross-mapping with information theory-based methods.

Main Results:

• Spontaneous activity-based causal flow successfully predicted the spatiotemporal effects of micro-stimulation.
• Convergent cross-mapping uncovered a causal hierarchy between recording electrodes.
• The causal flow inference method proved robust to noise and common inputs.

Conclusions:

• Neural stimulation effects can be predicted from spontaneous activity, enabling targeted circuit manipulation.
• This approach facilitates the design of effective intervention protocols for brain-machine interfaces.
• Convergent cross-mapping offers advantages over information theory for predicting perturbation effects.