Synthesis of high-complexity rhythmic signals for closed-loop electrical neuromodulation

• Published in: Neural networks 2013-06, Vol.42, p.62-73
• Main Authors: Zalay, Osbert C., Bardakjian, Berj L.
• Format: Article
• Language: English
• Subjects: Applied sciences; Artificial intelligence; Biological and medical sciences; Biomimetic; Biomimetics; Brain stimulation; Brain Waves - physiology; Central nervous system; Computer science; control theory; systems; Computer Simulation; Connectionism. Neural networks; Coupled oscillator; Electric Stimulation; Electrophysiology; Epilepsy; Epilepsy - physiopathology; Epilepsy - therapy; Exact sciences and technology; Feedback; Fundamental and applied biological sciences. Psychology; General aspects; Headache. Facial pains. Syncopes. Epilepsia. Intracranial hypertension. Brain oedema. Cerebral palsy; Humans; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Medical sciences; Models, Neurological; Nerve Net - physiology; Nervous system (semeiology, syndromes); Neural rhythm generation; Neurology; Neuromodulation; Neurons - physiology; Periodicity; Time Factors; Vertebrates: nervous system and sense organs
• ISSN: 0893-6080; 1879-2782
• DOI: 10.1016/j.neunet.2013.01.005

We propose an approach to synthesizing high-complexity rhythmic signals for closed-loop electrical neuromodulation using cognitive rhythm generator (CRG) networks, wherein the CRG is a hybrid oscillator comprised of (1) a bank of neuronal modes, (2) a ring device (clock), and (3) a static output nonlinearity (mapper). Networks of coupled CRGs have been previously implemented to simulate the electrical activity of biological neural networks, including in silico models of epilepsy, producing outputs of similar waveform and complexity to the biological system. This has enabled CRG network models to be used as platforms for testing seizure control strategies. Presently, we take the application one step further, envisioning therapeutic CRG networks as rhythmic signal generators creating neuromimetic signals for stimulation purposes, motivated by recent research indicating that stimulus complexity and waveform characteristics influence neuromodulation efficacy. To demonstrate this concept, an epileptiform CRG network generating spontaneous seizure-like events (SLEs) was coupled to a therapeutic CRG network, forming a closed-loop neuromodulation system. SLEs are associated with low-complexity dynamics and high phase coherence in the network. The tuned therapeutic network generated a high-complexity, multi-banded rhythmic stimulation signal with prominent theta and gamma-frequency power that suppressed SLEs and increased dynamic complexity in the epileptiform network, as measured by a relative increase in the maximum Lyapunov exponent and decrease in phase coherence. CRG-based neuromodulation outperformed both low and high-frequency periodic pulse stimulation, suggesting that neuromodulation using complex, biomimetic signals may provide an improvement over conventional electrical stimulation techniques for treating neurological disorders such as epilepsy.