A visually-induced optogenetically-engineered system enables autonomous glucose homeostasis in mice

Area of Science:

• Neuroscience
• Biomedical Engineering
• Computer Science

Background:

• Rising global population and aging demographics increase diabetes prevalence.
• Peripheral neuropathies, often linked to diabetes, necessitate innovative therapeutic solutions.
• Brain-computer interfaces (BCIs) offer a novel approach for direct brain-device communication.

Purpose of the Study:

• To develop an autonomous diabetes therapeutic system using BCIs.
• To enable precise control of insulin expression via visual stimuli.
• To restore blood glucose homeostasis in a preclinical model.

Main Methods:

• Utilized visually-induced electroencephalography (EEG) to capture brain signals.
• Employed machine learning to decode event-related potentials from EEG data.
• Developed the VISITER system for optogenetically-controlled therapeutic expression regulation.

Main Results:

• VISITER system successfully decoded visual cues into control commands.
• Precisely modulated therapeutic protein (insulin) expression in mammalian cells.
• Achieved rapid restoration of blood glucose homeostasis in diabetic mice.

Conclusions:

• The VISITER system demonstrates effective autonomous blood glucose control.
• Highlights the potential of BCIs for self-directed diabetes management.
• Suggests future applications for managing insulin levels through visual focus.