CLoSES: A platform for closed-loop intracranial stimulation in humans

Abstract

Targeted interrogation of brain networks through invasive brain stimulation has become an increasingly important research tool as well as a therapeutic modality. The majority of work with this emerging capability has been focused on open-loop approaches. Closed-loop techniques, however, could improve neuromodulatory therapies and research investigations by optimizing stimulation approaches using neurally informed, personalized targets. Specifically, closed-loop direct electrical stimulation tests in humans performed during semi-chronic electrode implantation in patients with refractory epilepsy could help deepen our understanding of basic research questions as well as the mechanisms and treatment solutions for many neuropsychiatric diseases. However, implementing closed-loop systems is challenging. In particular, during intracranial epilepsy monitoring, electrodes are implanted exclusively for clinical reasons. Thus, detection and stimulation sites must be participant- and task-specific. In addition, the system must run in parallel with clinical systems, integrate seamlessly with existing setups, and ensure safety features. A robust, yet flexible platform is required to perform different tests in a single participant and to comply with clinical settings. In order to investigate closed-loop stimulation for research and therapeutic use, we developed a Closed-Loop System for Electrical Stimulation (CLoSES) that computes neural features which are then used in a decision algorithm to trigger stimulation in near real-time. To summarize CLoSES, intracranial EEG signals are acquired, band-pass filtered, and local and network features are continuously computed. If target features are detected (e.g. above a preset threshold for certain duration), stimulation is triggered. An added benefit is the flexibility of CLoSES. Not only could the system trigger stimulation while detecting real-time neural features, but we incorporated a pipeline wherein we used an encoder/decoder model to estimate a hidden cognitive state from the neural features. Other features include randomly timed stimulation, which percentage of biomarker detections produce stimulation, and safety refractory periods. CLoSES has been successfully used in twelve patients with implanted depth electrodes in the epilepsy monitoring unit during cognitive tasks, spindle detection during sleep, and epileptic activity detection. CLoSES provides a flexible platform to implement a variety of closed-loop experimental paradigms in humans. We anticipate that probing neural dynamics and interaction between brain states and stimulation responses with CLoSES will lead to novel insights into the mechanism of normal and pathological brain activity, the discovery and evaluation of potential electrographic biomarkers of neurological and psychiatric disorders, and the development and testing of patient-specific stimulation targets and control signals before implanting a therapeutic device.

Effect of Closed-Loop Direct Electrical Stimulation during Sleep Spindles in Humans

“…Our results demonstrate the ability to successfully detect and stimulate during spindles using our novel closed-loop setup [15]. Spindle occurrence was assessed at local and global scales, using voltage and power.…”

“…We used a system developed in our lab to perform closedloop stimulation [15]. Briefly, iEEG data was acquired, bandpass filtered, features were computed and fed into a decision algorithm.…”

Closed loop enhancement and neural decoding of human cognitive control

“…Finally, decoding might perform better on network-level measures rather than the LFP power features we used, based on recent studies linking connectivity metrics to cognitive control 18,[26][27][28] . At present, connectivity is difficult to estimate in real time or on a therapeutic device, although approaches are emerging 45 .…”

“…Pre-processed and anonymized neural data will be available through OpenNeuro. The closed-loop neurostimulation system has been released as opensource code with a corresponding preprint 45 , and the neural decoding and state-space modeling engines have similarly been released for open download (https://github.com/TRANSFORM-DBS/Encoder-Decoder-Paper and https://github.com/Eden-Kramer-Lab/COMPASS ).…”