EPFL researchers have developed LogSync, an implantable neural chip designed to monitor communication between brain regions and support adaptive deep brain stimulation. Existing systems typically deliver stimulation according to preset settings or respond to activity in one brain area, but psychiatric conditions may involve disrupted coordination across wider networks. LogSync records neural signals, calculates biomarkers of brain-wave strength and synchrony across regions, and uses machine learning to classify cognitive and emotional states and produce stimulation triggers when specified patterns appear.
The team tested the system on human recordings from experiments measuring mental-task engagement and emotional arousal; its algorithm distinguished the states from neural activity. Rat experiments also showed that the implant could track brain oscillations and trigger stimulation at selected phases. The chip is about 4 by 2 millimeters and processes network-level signals using less than 200 microwatts, enabled in part by hardware and algorithms designed together and by logarithmic computations that simplify analysis. The work, published in the IEEE Journal of Solid-State Circuits, is an early validation rather than a demonstrated psychiatric treatment. Its network-based approach could inform future adaptive therapies for mood and anxiety disorders, as well as applications in movement disorders and other forms of neural stimulation.
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