Microscale multicircuit brain stimulation: Achieving real-time brain state control for novel applications.

Area of Science:

• Neuroscience
• Biomedical Engineering
• Computational Psychiatry

Background:

• Neurological and psychiatric disorders stem from complex, distributed neural circuit dysfunction.
• Current neuromodulation, like deep brain stimulation (DBS), offers limited precision for these widespread issues.
• Next-generation therapies require fine-grained, closed-loop control over multiple neural circuits.

Purpose of the Study:

• To introduce microscale multicircuit brain stimulation (MMBS) as a novel neuromodulation approach.
• To outline a framework for achieving bidirectional control over neural circuits and behavior.
• To enable the development of advanced neurostimulation therapies for cognitive and consciousness disorders.

Main Methods:

• Utilizing microstimulation with numerous small, closely-spaced contacts for spatial and frequency selectivity.
• Employing machine learning to optimize stimulation parameters based on real-time behavioral and neural data.
• Developing a mathematical framework for adaptive, real-time adjustment of stimulation.

Main Results:

• Demonstrated potential for spatial and frequency selectivity in neural microstimulation.
• Proposed a method for achieving bidirectional behavioral control via central thalamic stimulation.
• Established a framework for real-time, adaptive neuromodulation using machine learning.

Conclusions:

• MMBS offers a pathway to precisely modulate multiple neural circuits for complex brain disorders.
• Machine learning integration enables adaptive, goal-directed neurostimulation.
• This approach is critical for advancing treatments for disorders of consciousness and cognition.