Microscale multicircuit brain stimulation: Achieving real-time brain state control for novel applications
Neurological and psychiatric disorders typically result from dysfunction across multiple neural circuits. Most of these disorders lack a satisfactory neuromodulation treatment. However, deep brain stimulation (DBS) has been successful in a limited number of disorders; DBS typically targets one or tw...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | Current Research in Neurobiology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665945X22000444 |
| _version_ | 1797799939154116608 |
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| author | Yuri B. Saalmann Sima Mofakham Charles B. Mikell Petar M. Djuric |
| author_facet | Yuri B. Saalmann Sima Mofakham Charles B. Mikell Petar M. Djuric |
| author_sort | Yuri B. Saalmann |
| collection | DOAJ |
| description | Neurological and psychiatric disorders typically result from dysfunction across multiple neural circuits. Most of these disorders lack a satisfactory neuromodulation treatment. However, deep brain stimulation (DBS) has been successful in a limited number of disorders; DBS typically targets one or two brain areas with single contacts on relatively large electrodes, allowing for only coarse modulation of circuit function. Because of the dysfunction in distributed neural circuits – each requiring fine, tailored modulation – that characterizes most neuropsychiatric disorders, this approach holds limited promise. To develop the next generation of neuromodulation therapies, we will have to achieve fine-grained, closed-loop control over multiple neural circuits. Recent work has demonstrated spatial and frequency selectivity using microstimulation with many small, closely-spaced contacts, mimicking endogenous neural dynamics. Using custom electrode design and stimulation parameters, it should be possible to achieve bidirectional control over behavioral outcomes, such as increasing or decreasing arousal during central thalamic stimulation. Here, we discuss one possible approach, which we term microscale multicircuit brain stimulation (MMBS). We discuss how machine learning leverages behavioral and neural data to find optimal stimulation parameters across multiple contacts, to drive the brain towards desired states associated with behavioral goals. We expound a mathematical framework for MMBS, where behavioral and neural responses adjust the model in real-time, allowing us to adjust stimulation in real-time. These technologies will be critical to the development of the next generation of neurostimulation therapies, which will allow us to treat problems like disorders of consciousness and cognition. |
| first_indexed | 2024-03-13T04:26:40Z |
| format | Article |
| id | doaj.art-3037ba5c0b0d43d5bbeeb37508be82ca |
| institution | Directory Open Access Journal |
| issn | 2665-945X |
| language | English |
| last_indexed | 2024-03-13T04:26:40Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Current Research in Neurobiology |
| spelling | doaj.art-3037ba5c0b0d43d5bbeeb37508be82ca2023-06-20T04:20:43ZengElsevierCurrent Research in Neurobiology2665-945X2023-01-014100071Microscale multicircuit brain stimulation: Achieving real-time brain state control for novel applicationsYuri B. Saalmann0Sima Mofakham1Charles B. Mikell2Petar M. Djuric3Department of Psychology, University of Wisconsin-Madison, Madison, WI, USA; Wisconsin National Primate Research Center, University of Wisconsin-Madison, Madison, WI, USA; Corresponding author. Department of Psychology, University of Wisconsin-Madison, 1202 W Johnson St, Madison, WI, 53706, USA.Department of Neurological Surgery, Stony Brook University Hospital, Stony Brook, NY, USA; Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USADepartment of Neurological Surgery, Stony Brook University Hospital, Stony Brook, NY, USADepartment of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY, USANeurological and psychiatric disorders typically result from dysfunction across multiple neural circuits. Most of these disorders lack a satisfactory neuromodulation treatment. However, deep brain stimulation (DBS) has been successful in a limited number of disorders; DBS typically targets one or two brain areas with single contacts on relatively large electrodes, allowing for only coarse modulation of circuit function. Because of the dysfunction in distributed neural circuits – each requiring fine, tailored modulation – that characterizes most neuropsychiatric disorders, this approach holds limited promise. To develop the next generation of neuromodulation therapies, we will have to achieve fine-grained, closed-loop control over multiple neural circuits. Recent work has demonstrated spatial and frequency selectivity using microstimulation with many small, closely-spaced contacts, mimicking endogenous neural dynamics. Using custom electrode design and stimulation parameters, it should be possible to achieve bidirectional control over behavioral outcomes, such as increasing or decreasing arousal during central thalamic stimulation. Here, we discuss one possible approach, which we term microscale multicircuit brain stimulation (MMBS). We discuss how machine learning leverages behavioral and neural data to find optimal stimulation parameters across multiple contacts, to drive the brain towards desired states associated with behavioral goals. We expound a mathematical framework for MMBS, where behavioral and neural responses adjust the model in real-time, allowing us to adjust stimulation in real-time. These technologies will be critical to the development of the next generation of neurostimulation therapies, which will allow us to treat problems like disorders of consciousness and cognition.http://www.sciencedirect.com/science/article/pii/S2665945X22000444Deep brain stimulationMachine learningConsciousnessCognitive controlNeuromodulation |
| spellingShingle | Yuri B. Saalmann Sima Mofakham Charles B. Mikell Petar M. Djuric Microscale multicircuit brain stimulation: Achieving real-time brain state control for novel applications Current Research in Neurobiology Deep brain stimulation Machine learning Consciousness Cognitive control Neuromodulation |
| title | Microscale multicircuit brain stimulation: Achieving real-time brain state control for novel applications |
| title_full | Microscale multicircuit brain stimulation: Achieving real-time brain state control for novel applications |
| title_fullStr | Microscale multicircuit brain stimulation: Achieving real-time brain state control for novel applications |
| title_full_unstemmed | Microscale multicircuit brain stimulation: Achieving real-time brain state control for novel applications |
| title_short | Microscale multicircuit brain stimulation: Achieving real-time brain state control for novel applications |
| title_sort | microscale multicircuit brain stimulation achieving real time brain state control for novel applications |
| topic | Deep brain stimulation Machine learning Consciousness Cognitive control Neuromodulation |
| url | http://www.sciencedirect.com/science/article/pii/S2665945X22000444 |
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