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Khambhati AN, Kahn AE, Costantini J, Ezzyat Y, Solomon EA, Gross RE, Jobst BC, Sheth SA, Zaghloul KA, Worrell G, Seger S, Lega BC, Weiss S, Sperling MR, Gorniak R, Das SR, Stein JM, Rizzuto DS, Kahana MJ, Lucas TH, Davis KA, Tracy JI, Bassett DS. Functional control of electrophysiological network architecture using direct neurostimulation in humans. Netw Neurosci 2019; 3:848-877. [PMID: 31410383 PMCID: PMC6663306 DOI: 10.1162/netn_a_00089] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Accepted: 04/14/2019] [Indexed: 01/30/2023] Open
Abstract
Chronically implantable neurostimulation devices are becoming a clinically viable option for treating patients with neurological disease and psychiatric disorders. Neurostimulation offers the ability to probe and manipulate distributed networks of interacting brain areas in dysfunctional circuits. Here, we use tools from network control theory to examine the dynamic reconfiguration of functionally interacting neuronal ensembles during targeted neurostimulation of cortical and subcortical brain structures. By integrating multimodal intracranial recordings and diffusion-weighted imaging from patients with drug-resistant epilepsy, we test hypothesized structural and functional rules that predict altered patterns of synchronized local field potentials. We demonstrate the ability to predictably reconfigure functional interactions depending on stimulation strength and location. Stimulation of areas with structurally weak connections largely modulates the functional hubness of downstream areas and concurrently propels the brain towards more difficult-to-reach dynamical states. By using focal perturbations to bridge large-scale structure, function, and markers of behavior, our findings suggest that stimulation may be tuned to influence different scales of network interactions driving cognition. Brain stimulation devices capable of perturbing the physiological state of neural systems are rapidly gaining popularity for their potential to treat neurological and psychiatric disease. A root problem is that underlying dysfunction spans a large-scale network of brain regions, requiring the ability to control the complex interactions between multiple brain areas. Here, we use tools from network control theory to examine the dynamic reconfiguration of functionally interacting neuronal ensembles during targeted neurostimulation of cortical and subcortical brain structures. We demonstrate the ability to predictably reconfigure patterns of interactions between functional brain areas by modulating the strength and location of stimulation. Our findings have high significance for designing stimulation protocols capable of modulating distributed neural circuits in the human brain.
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Affiliation(s)
- Ankit N Khambhati
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Ari E Kahn
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Julia Costantini
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Youssef Ezzyat
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Ethan A Solomon
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Robert E Gross
- Department of Neurosurgery, Emory University Hospital, Atlanta, GA, USA
| | - Barbara C Jobst
- Department of Neurology, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Kareem A Zaghloul
- Surgical Neurology Branch, National Institutes of Health, Bethesda, MD, USA
| | | | - Sarah Seger
- Department of Neurosurgery, University of Texas, Southwestern Medical Center, Dallas, TX, USA
| | - Bradley C Lega
- Department of Neurosurgery, University of Texas, Southwestern Medical Center, Dallas, TX, USA
| | - Shennan Weiss
- Department of Neurology, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Michael R Sperling
- Department of Neurology, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Richard Gorniak
- Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Sandhitsu R Das
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Joel M Stein
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel S Rizzuto
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael J Kahana
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Timothy H Lucas
- Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Kathryn A Davis
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Joseph I Tracy
- Department of Neurology, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
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Sandler RA, Geng K, Song D, Hampson RE, Witcher MR, Deadwyler SA, Berger TW, Marmarelis VZ. Designing Patient-Specific Optimal Neurostimulation Patterns for Seizure Suppression. Neural Comput 2018; 30:1180-1208. [PMID: 29566356 DOI: 10.1162/neco_a_01075] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Neurostimulation is a promising therapy for abating epileptic seizures. However, it is extremely difficult to identify optimal stimulation patterns experimentally. In this study, human recordings are used to develop a functional 24 neuron network statistical model of hippocampal connectivity and dynamics. Spontaneous seizure-like activity is induced in silico in this reconstructed neuronal network. The network is then used as a testbed to design and validate a wide range of neurostimulation patterns. Commonly used periodic trains were not able to permanently abate seizures at any frequency. A simulated annealing global optimization algorithm was then used to identify an optimal stimulation pattern, which successfully abated 92% of seizures. Finally, in a fully responsive, or closed-loop, neurostimulation paradigm, the optimal stimulation successfully prevented the network from entering the seizure state. We propose that the framework presented here for algorithmically identifying patient-specific neurostimulation patterns can greatly increase the efficacy of neurostimulation devices for seizures.
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Affiliation(s)
- Roman A Sandler
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, U.S.A.
| | - Kunling Geng
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, U.S.A.
| | - Dong Song
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, U.S.A.
| | - Robert E Hampson
- Department of Physiology and Pharmacology, Wake Forest University, Winston-Salem, NC 27109, U.S.A.
| | - Mark R Witcher
- Department of Neurosurgery, Wake Forest University, Winston-Salem, NC 27109, U.S.A.
| | - Sam A Deadwyler
- Department of Physiology and Pharmacology, Wake Forest University, Winston-Salem, NC 27109, U.S.A.
| | - Theodore W Berger
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, U.S.A.
| | - Vasilis Z Marmarelis
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, U.S.A.
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Bassett DS, Khambhati AN, Grafton ST. Emerging Frontiers of Neuroengineering: A Network Science of Brain Connectivity. Annu Rev Biomed Eng 2017; 19:327-352. [PMID: 28375650 PMCID: PMC6005206 DOI: 10.1146/annurev-bioeng-071516-044511] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Neuroengineering is faced with unique challenges in repairing or replacing complex neural systems that are composed of many interacting parts. These interactions form intricate patterns over large spatiotemporal scales and produce emergent behaviors that are difficult to predict from individual elements. Network science provides a particularly appropriate framework in which to study and intervene in such systems by treating neural elements (cells, volumes) as nodes in a graph and neural interactions (synapses, white matter tracts) as edges in that graph. Here, we review the emerging discipline of network neuroscience, which uses and develops tools from graph theory to better understand and manipulate neural systems from micro- to macroscales. We present examples of how human brain imaging data are being modeled with network analysis and underscore potential pitfalls. We then highlight current computational and theoretical frontiers and emphasize their utility in informing diagnosis and monitoring, brain-machine interfaces, and brain stimulation. A flexible and rapidly evolving enterprise, network neuroscience provides a set of powerful approaches and fundamental insights that are critical for the neuroengineer's tool kit.
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Affiliation(s)
- Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Ankit N Khambhati
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Scott T Grafton
- UCSB Brain Imaging Center and Department of Psychological and Brain Sciences, University of California, Santa Barbara, California 93106
- Institute for Collaborative Biotechnologies, University of California, Santa Barbara, California 93106
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Kafashan M, Ching S. Optimal stimulus scheduling for active estimation of evoked brain networks. J Neural Eng 2015; 12:066011. [PMID: 26448130 DOI: 10.1088/1741-2560/12/6/066011] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE We consider the problem of optimal probing to learn connections in an evoked dynamic network. Such a network, in which each edge measures an input-output relationship between sites in sensor/actuator-space, is relevant to emerging applications in neural mapping and neural connectivity estimation. APPROACH We show that the problem of scheduling nodes to a probe (i.e., stimulate) amounts to a problem of optimal sensor scheduling. MAIN RESULTS By formulating the evoked network in state-space, we show that the solution to the greedy probing strategy has a convenient form and, under certain conditions, is optimal over a finite horizon. We adopt an expectation maximization technique to update the state-space parameters in an online fashion and demonstrate the efficacy of the overall approach in a series of detailed numerical examples. SIGNIFICANCE The proposed method provides a principled means to actively probe time-varying connections in neuronal networks. The overall method can be implemented in real time and is particularly well-suited to applications in stimulation-based cortical mapping in which the underlying network dynamics are changing over time.
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Affiliation(s)
- MohammadMehdi Kafashan
- Department of Electrical and Systems Engineering, Washington University in St. Louis, MO 63130, USA
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Lepage KQ, Kramer MA, Ching S. An active method for tracking connectivity in temporally changing brain networks. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2013:4374-7. [PMID: 24110702 DOI: 10.1109/embc.2013.6610515] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The inference of connectivity in brain networks has typically been performed using passive measurements of ongoing activity across recording sites. Passive measures of connectivity are harder to interpret, however, in terms of causality - how evoked activity in one region might induce activity in another. To obviate this issue, recent work has proposed the use of active stimulation in conjunction with network estimation. By actively stimulating the network, more accurate information can be gleaned regarding evoked connectivity. The assumption in these previous works, however, was that the underlying networks were static and do not change in time. Such an assumption may be limiting in situations of clinical relevance, where the introduction of a drug or of brain pathology, might change the underlying networks structure. Here, an extension of the evoked connectivity paradigm is introduced that enables tracking networks that change in time.
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Sinha DB, Ledbetter NM, Barbour DL. Spike-timing computation properties of a feed-forward neural network model. Front Comput Neurosci 2014; 8:5. [PMID: 24478688 PMCID: PMC3904091 DOI: 10.3389/fncom.2014.00005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2013] [Accepted: 01/09/2014] [Indexed: 11/13/2022] Open
Abstract
Brain function is characterized by dynamical interactions among networks of neurons. These interactions are mediated by network topology at many scales ranging from microcircuits to brain areas. Understanding how networks operate can be aided by understanding how the transformation of inputs depends upon network connectivity patterns, e.g., serial and parallel pathways. To tractably determine how single synapses or groups of synapses in such pathways shape these transformations, we modeled feed-forward networks of 7–22 neurons in which synaptic strength changed according to a spike-timing dependent plasticity (STDP) rule. We investigated how activity varied when dynamics were perturbed by an activity-dependent electrical stimulation protocol (spike-triggered stimulation; STS) in networks of different topologies and background input correlations. STS can successfully reorganize functional brain networks in vivo, but with a variability in effectiveness that may derive partially from the underlying network topology. In a simulated network with a single disynaptic pathway driven by uncorrelated background activity, structured spike-timing relationships between polysynaptically connected neurons were not observed. When background activity was correlated or parallel disynaptic pathways were added, however, robust polysynaptic spike timing relationships were observed, and application of STS yielded predictable changes in synaptic strengths and spike-timing relationships. These observations suggest that precise input-related or topologically induced temporal relationships in network activity are necessary for polysynaptic signal propagation. Such constraints for polysynaptic computation suggest potential roles for higher-order topological structure in network organization, such as maintaining polysynaptic correlation in the face of relatively weak synapses.
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Affiliation(s)
- Drew B Sinha
- Laboratory of Sensory Neuroscience and Neuroengineering, Department of Biomedical Engineering, Washington University in St. Louis St. Louis, MO, USA
| | - Noah M Ledbetter
- Laboratory of Sensory Neuroscience and Neuroengineering, Department of Biomedical Engineering, Washington University in St. Louis St. Louis, MO, USA
| | - Dennis L Barbour
- Laboratory of Sensory Neuroscience and Neuroengineering, Department of Biomedical Engineering, Washington University in St. Louis St. Louis, MO, USA
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