1
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Gilmore N, Tseng CEJ, Maffei C, Tromly SL, Deary KB, McKinney IR, Kelemen JN, Healy BC, Hu CG, Ramos-Llordén G, Masood M, Cali RJ, Guo J, Belanger HG, Yao EF, Baxter T, Fischl B, Foulkes AS, Polimeni JR, Rosen BR, Perl DP, Hooker JM, Zürcher NR, Huang SY, Kimberly WT, Greve DN, Mac Donald CL, Dams-O’Connor K, Bodien YG, Edlow BL. Impact of repeated blast exposure on active-duty United States Special Operations Forces. Proc Natl Acad Sci U S A 2024; 121:e2313568121. [PMID: 38648470 PMCID: PMC11087753 DOI: 10.1073/pnas.2313568121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 03/22/2024] [Indexed: 04/25/2024] Open
Abstract
United States (US) Special Operations Forces (SOF) are frequently exposed to explosive blasts in training and combat, but the effects of repeated blast exposure (RBE) on SOF brain health are incompletely understood. Furthermore, there is no diagnostic test to detect brain injury from RBE. As a result, SOF personnel may experience cognitive, physical, and psychological symptoms for which the cause is never identified, and they may return to training or combat during a period of brain vulnerability. In 30 active-duty US SOF, we assessed the relationship between cumulative blast exposure and cognitive performance, psychological health, physical symptoms, blood proteomics, and neuroimaging measures (Connectome structural and diffusion MRI, 7 Tesla functional MRI, [11C]PBR28 translocator protein [TSPO] positron emission tomography [PET]-MRI, and [18F]MK6240 tau PET-MRI), adjusting for age, combat exposure, and blunt head trauma. Higher blast exposure was associated with increased cortical thickness in the left rostral anterior cingulate cortex (rACC), a finding that remained significant after multiple comparison correction. In uncorrected analyses, higher blast exposure was associated with worse health-related quality of life, decreased functional connectivity in the executive control network, decreased TSPO signal in the right rACC, and increased cortical thickness in the right rACC, right insula, and right medial orbitofrontal cortex-nodes of the executive control, salience, and default mode networks. These observations suggest that the rACC may be susceptible to blast overpressure and that a multimodal, network-based diagnostic approach has the potential to detect brain injury associated with RBE in active-duty SOF.
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Affiliation(s)
- Natalie Gilmore
- Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA02114
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA02114
| | - Chieh-En J. Tseng
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA02129
| | - Chiara Maffei
- Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA02114
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA02114
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA02129
| | - Samantha L. Tromly
- Institute of Applied Engineering, University of South Florida, Tampa, FL33612
| | | | - Isabella R. McKinney
- Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA02114
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA02114
| | - Jessica N. Kelemen
- Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA02114
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA02114
| | - Brian C. Healy
- Harvard T.H. Chan School of Public Health, Boston, MA02115
| | - Collin G. Hu
- United States Army Special Operations Aviation Command, Fort Liberty, NC28307
- Department of Family Medicine, F. Edward Hebert School of Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD20814
| | - Gabriel Ramos-Llordén
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA02129
| | - Maryam Masood
- Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA02114
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA02114
| | - Ryan J. Cali
- Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA02114
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA02114
| | - Jennifer Guo
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA02114
| | - Heather G. Belanger
- Department of Psychiatry and Behavioral Neurosciences, University of South Florida, Tampa, FL33613
| | - Eveline F. Yao
- Office of the Air Force Surgeon General, Falls Church, VA22042
| | - Timothy Baxter
- Institute of Applied Engineering, University of South Florida, Tampa, FL33612
| | - Bruce Fischl
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA02129
| | | | - Jonathan R. Polimeni
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA02129
| | - Bruce R. Rosen
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA02129
| | - Daniel P. Perl
- Department of Pathology, F. Edward Hébert School of Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD20814
| | - Jacob M. Hooker
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA02129
| | - Nicole R. Zürcher
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA02129
| | - Susie Y. Huang
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA02129
| | - W. Taylor Kimberly
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA02114
| | - Douglas N. Greve
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA02129
| | | | - Kristen Dams-O’Connor
- Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY10029
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY10029
| | - Yelena G. Bodien
- Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA02114
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA02114
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital and Harvard Medical School, Charlestown, MA02129
| | - Brian L. Edlow
- Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA02114
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA02114
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA02129
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2
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Verzhbinsky IA, Rubin DB, Kajfez S, Bu Y, Kelemen JN, Kapitonava A, Williams ZM, Hochberg LR, Cash SS, Halgren E. Co-occurring ripple oscillations facilitate neuronal interactions between cortical locations in humans. Proc Natl Acad Sci U S A 2024; 121:e2312204121. [PMID: 38157452 PMCID: PMC10769862 DOI: 10.1073/pnas.2312204121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 11/05/2023] [Indexed: 01/03/2024] Open
Abstract
How the human cortex integrates ("binds") information encoded by spatially distributed neurons remains largely unknown. One hypothesis suggests that synchronous bursts of high-frequency oscillations ("ripples") contribute to binding by facilitating integration of neuronal firing across different cortical locations. While studies have demonstrated that ripples modulate local activity in the cortex, it is not known whether their co-occurrence coordinates neural firing across larger distances. We tested this hypothesis using local field-potentials and single-unit firing from four 96-channel microelectrode arrays in the supragranular cortex of 3 patients. Neurons in co-rippling locations showed increased short-latency co-firing, prediction of each other's firing, and co-participation in neural assemblies. Effects were similar for putative pyramidal and interneurons, during non-rapid eye movement sleep and waking, in temporal and Rolandic cortices, and at distances up to 16 mm (the longest tested). Increased co-prediction during co-ripples was maintained when firing-rate changes were equated, indicating that it was not secondary to non-oscillatory activation. Co-rippling enhanced prediction was strongly modulated by ripple phase, supporting the most common posited mechanism for binding-by-synchrony. Co-ripple enhanced prediction is reciprocal, synergistic with local upstates, and further enhanced when multiple sites co-ripple, supporting re-entrant facilitation. Together, these results support the hypothesis that trans-cortical co-occurring ripples increase the integration of neuronal firing of neurons in different cortical locations and do so in part through phase-modulation rather than unstructured activation.
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Affiliation(s)
- Ilya A. Verzhbinsky
- Neurosciences Graduate Program, University of California San Diego, La Jolla, CA92093
- Medical Scientist Training Program, University of California San Diego, La Jolla, CA92093
| | - Daniel B. Rubin
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA02114
| | - Sophie Kajfez
- Department of Radiology, University of California San Diego, La Jolla, CA92093
| | - Yiting Bu
- Department of Neurosciences, University of California San Diego, La Jolla, CA92093
| | - Jessica N. Kelemen
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA02114
| | - Anastasia Kapitonava
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA02114
| | - Ziv M. Williams
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA02114
| | - Leigh R. Hochberg
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA02114
- Center for Neurorestoration and Neurotechnology, Department of Veterans Affairs, Providence, RI02908
- Carney Institute for Brain Science and School of Engineering, Brown University, Providence, RI02912
| | - Sydney S. Cash
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA02114
| | - Eric Halgren
- Department of Radiology, University of California San Diego, La Jolla, CA92093
- Department of Neurosciences, University of California San Diego, La Jolla, CA92093
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3
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Edlow BL, Gilmore N, Tromly SL, Deary KB, McKinney IR, Hu CG, Kelemen JN, Maffei C, Tseng CEJ, Llorden GR, Healy BC, Masood M, Cali RJ, Baxter T, Yao EF, Belanger HG, Benjamini D, Basser PJ, Priemer DS, Kimberly WT, Polimeni JR, Rosen BR, Fischl B, Zurcher NR, Greve DN, Hooker JM, Huang SY, Caruso A, Smith GA, Szymanski TG, Perl DP, Dams-O'Connor K, Mac Donald CL, Bodien YG. Optimizing Brain Health of United States Special Operations Forces. J Spec Oper Med 2023; 23:47-56. [PMID: 37851859 DOI: 10.55460/99qw-k0hg] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 12/01/2023] [Indexed: 10/20/2023]
Abstract
United States Special Operations Forces (SOF) personnel are frequently exposed to explosive blasts in training and combat. However, the effects of repeated blast exposure on the human brain are incompletely understood. Moreover, there is currently no diagnostic test to detect repeated blast brain injury (rBBI). In this "Human Performance Optimization" article, we discuss how the development and implementation of a reliable diagnostic test for rBBI has the potential to promote SOF brain health, combat readiness, and quality of life.
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4
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Verzhbinsky IA, Rubin DB, Kajfez S, Bu Y, Kelemen JN, Kapitonava A, Williams ZM, Hochberg LR, Cash SS, Halgren E. Co-occurring ripple oscillations facilitate neuronal interactions between cortical locations in humans. bioRxiv 2023:2023.05.20.541588. [PMID: 37292943 PMCID: PMC10245779 DOI: 10.1101/2023.05.20.541588] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Synchronous bursts of high frequency oscillations ('ripples') are hypothesized to contribute to binding by facilitating integration of neuronal firing across cortical locations. We tested this hypothesis using local field-potentials and single-unit firing from four 96-channel microelectrode arrays in supragranular cortex of 3 patients. Neurons in co-rippling locations showed increased short-latency co-firing, prediction of each-other's firing, and co-participation in neural assemblies. Effects were similar for putative pyramidal and interneurons, during NREM sleep and waking, in temporal and Rolandic cortices, and at distances up to 16mm. Increased co-prediction during co-ripples was maintained when firing-rate changes were equated, and were strongly modulated by ripple phase. Co-ripple enhanced prediction is reciprocal, synergistic with local upstates, and further enhanced when multiple sites co-ripple. Together, these results support the hypothesis that trans-cortical co-ripples increase the integration of neuronal firing of neurons in different cortical locations, and do so in part through phase-modulation rather than unstructured activation.
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Affiliation(s)
- Ilya A. Verzhbinsky
- Neurosciences Graduate Program, University of California San Diego, La Jolla, CA 92093, USA
- Medical Scientist Training Program, University of California San Diego, La Jolla, CA 92093, USA
| | - Daniel B. Rubin
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02114, USA
| | - Sophie Kajfez
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA
| | - Yiting Bu
- Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Jessica N. Kelemen
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Anastasia Kapitonava
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Ziv M. Williams
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114
- Program in Neuroscience, Harvard-MIT Program in Health Sciences and Technology, Harvard Medical School, Boston, MA 02115
| | - Leigh R. Hochberg
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02114, USA
- Center for Neurorestoration and Neurotechnology, Department of Veterans Affairs, Providence, RI 02908, USA
- Carney Institute for Brain Science and School of Engineering, Brown University, Providence, RI 02912, USA
| | - Sydney S. Cash
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02114, USA
| | - Eric Halgren
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, USA
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5
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Fecchio M, Cambareri MK, Kelemen JN, Marujo RM, Masood M, Sanders WR, Lawrence PK, Meydan A, Bodien YG, Edlow BL. Electroencephalographic responses to transcranial magnetic stimulation are sensitive to fluctuations in level of consciousness in patients with severe brain injuries. Brain Stimul 2023. [DOI: 10.1016/j.brs.2023.01.742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023] Open
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6
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Rubin DB, Hosman T, Kelemen JN, Kapitonava A, Willett FR, Coughlin BF, Halgren E, Kimchi EY, Williams ZM, Simeral JD, Hochberg LR, Cash SS. Learned Motor Patterns Are Replayed in Human Motor Cortex during Sleep. J Neurosci 2022; 42:5007-5020. [PMID: 35589391 PMCID: PMC9233445 DOI: 10.1523/jneurosci.2074-21.2022] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 02/04/2022] [Accepted: 02/28/2022] [Indexed: 11/30/2022] Open
Abstract
Consolidation of memory is believed to involve offline replay of neural activity. While amply demonstrated in rodents, evidence for replay in humans, particularly regarding motor memory, is less compelling. To determine whether replay occurs after motor learning, we sought to record from motor cortex during a novel motor task and subsequent overnight sleep. A 36-year-old man with tetraplegia secondary to cervical spinal cord injury enrolled in the ongoing BrainGate brain-computer interface pilot clinical trial had two 96-channel intracortical microelectrode arrays placed chronically into left precentral gyrus. Single- and multi-unit activity was recorded while he played a color/sound sequence matching memory game. Intended movements were decoded from motor cortical neuronal activity by a real-time steady-state Kalman filter that allowed the participant to control a neurally driven cursor on the screen. Intracortical neural activity from precentral gyrus and 2-lead scalp EEG were recorded overnight as he slept. When decoded using the same steady-state Kalman filter parameters, intracortical neural signals recorded overnight replayed the target sequence from the memory game at intervals throughout at a frequency significantly greater than expected by chance. Replay events occurred at speeds ranging from 1 to 4 times as fast as initial task execution and were most frequently observed during slow-wave sleep. These results demonstrate that recent visuomotor skill acquisition in humans may be accompanied by replay of the corresponding motor cortex neural activity during sleep.SIGNIFICANCE STATEMENT Within cortex, the acquisition of information is often followed by the offline recapitulation of specific sequences of neural firing. Replay of recent activity is enriched during sleep and may support the consolidation of learning and memory. Using an intracortical brain-computer interface, we recorded and decoded activity from motor cortex as a human research participant performed a novel motor task. By decoding neural activity throughout subsequent sleep, we find that neural sequences underlying the recently practiced motor task are repeated throughout the night, providing direct evidence of replay in human motor cortex during sleep. This approach, using an optimized brain-computer interface decoder to characterize neural activity during sleep, provides a framework for future studies exploring replay, learning, and memory.
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Affiliation(s)
- Daniel B Rubin
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts 02114
- Harvard Medical School, Boston, Massachusetts 02114
| | - Tommy Hosman
- Center for Neurorestoration and Neurotechnology, Department of Veterans Affairs, Providence, Rhode Island 02908
- Carney Institute for Brain Science and School of Engineering, Brown University, Providence, Rhode Island 02912
| | - Jessica N Kelemen
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts 02114
| | - Anastasia Kapitonava
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts 02114
| | - Francis R Willett
- Hughes Medical Institute at Stanford University, Palo Alto, California 94305
| | - Brian F Coughlin
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts 02114
| | - Eric Halgren
- Departments of Neurosciences and Radiology, University of California at San Diego, La Jolla, California 92093
| | - Eyal Y Kimchi
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts 02114
- Harvard Medical School, Boston, Massachusetts 02114
| | - Ziv M Williams
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts 02114
- Program in Neuroscience, Harvard-MIT Program in Health Sciences and Technology, Harvard Medical School, Boston, Massachusetts 02115
| | - John D Simeral
- Center for Neurorestoration and Neurotechnology, Department of Veterans Affairs, Providence, Rhode Island 02908
- Carney Institute for Brain Science and School of Engineering, Brown University, Providence, Rhode Island 02912
| | - Leigh R Hochberg
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts 02114
- Harvard Medical School, Boston, Massachusetts 02114
- Center for Neurorestoration and Neurotechnology, Department of Veterans Affairs, Providence, Rhode Island 02908
- Carney Institute for Brain Science and School of Engineering, Brown University, Providence, Rhode Island 02912
| | - Sydney S Cash
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts 02114
- Harvard Medical School, Boston, Massachusetts 02114
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7
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Edlow BL, Bodien YG, Baxter T, Belanger H, Cali R, Deary K, Fischl B, Foulkes AS, Gilmore N, Greve DN, Hooker JM, Huang SY, Kelemen JN, Kimberly WT, Maffei C, Masood M, Perl D, Polimeni JR, Rosen BR, Tromly S, Tseng CEJ, Yao EF, Zurcher NR, Mac Donald CL, Dams-O'Connor K. Long-Term Effects of Repeated Blast Exposure in United States Special Operations Forces Personnel: A Pilot Study Protocol. J Neurotrauma 2022; 39:1391-1407. [PMID: 35620901 PMCID: PMC9529318 DOI: 10.1089/neu.2022.0030] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Emerging evidence suggests that repeated blast exposure (RBE) is associated with brain injury in military personnel. United States (U.S.) Special Operations Forces (SOF) personnel experience high rates of blast exposure during training and combat, but the effects of low-level RBE on brain structure and function in SOF have not been comprehensively characterized. Further, the pathophysiological link between RBE-related brain injuries and cognitive, behavioral, and physical symptoms has not been fully elucidated. We present a protocol for an observational pilot study, Long-Term Effects of Repeated Blast Exposure in U.S. SOF Personnel (ReBlast). In this exploratory study, 30 active-duty SOF personnel with RBE will participate in a comprehensive evaluation of: 1) brain network structure and function using Connectome magnetic resonance imaging (MRI) and 7 Tesla MRI; 2) neuroinflammation and tau deposition using positron emission tomography; 3) blood proteomics and metabolomics; 4) behavioral and physical symptoms using self-report measures; and 5) cognition using a battery of conventional and digitized assessments designed to detect subtle deficits in otherwise high-performing individuals. We will identify clinical, neuroimaging, and blood-based phenotypes that are associated with level of RBE, as measured by the Generalized Blast Exposure Value. Candidate biomarkers of RBE-related brain injury will inform the design of a subsequent study that will test a diagnostic assessment battery for detecting RBE-related brain injury. Ultimately, we anticipate that the ReBlast study will facilitate the development of interventions to optimize the brain health, quality of life, and battle readiness of U.S. SOF personnel.
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Affiliation(s)
- Brian L Edlow
- Harvard Medical School, 1811, 175 Cambridge Street - Suite 300, Boston, Massachusetts, United States, 02115.,Massachusetts General Hospital, 2348, Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, United States;
| | - Yelena G Bodien
- Massachusetts General Hospital, 2348, Department of Neurology, 101 Merrimac, Boston, Massachusetts, United States, 02114;
| | - Timothy Baxter
- University of South Florida, 7831, Institute for Applied Engineering, Tampa, Florida, United States;
| | - Heather Belanger
- University of South Florida, 7831, Department of Psychiatry and Behavioral Neurosciences, Tampa, Florida, United States;
| | - Ryan Cali
- Massachusetts General Hospital, 2348, Boston, Massachusetts, United States;
| | - Katryna Deary
- Navy SEAL Foundation, Virginia Beach, Virginia, United States;
| | - Bruce Fischl
- Massachusetts General Hospital, 2348, Athinoula A. Martinos Center for Biomedical Imaging, Room 2301, 149 13th Street, Charlestown, Massachusetts, United States, 02129-2020.,Massachusetts General Hospital;
| | - Andrea S Foulkes
- Massachusetts General Hospital, 2348, Boston, Massachusetts, United States;
| | - Natalie Gilmore
- Massachusetts General Hospital, 2348, Boston, Massachusetts, United States;
| | - Douglas N Greve
- Massachusetts General Hospital, 2348, Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, United States;
| | - Jacob M Hooker
- Massachusetts General Hospital, 2348, Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, United States;
| | - Susie Y Huang
- Massachusetts General Hospital, 2348, Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, United States;
| | - Jessica N Kelemen
- Massachusetts General Hospital, 2348, Boston, Massachusetts, United States;
| | - W Taylor Kimberly
- Massachusetts General Hospital, 2348, Boston, Massachusetts, United States;
| | - Chiara Maffei
- Massachusetts General Hospital, 2348, Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, United States;
| | - Maryam Masood
- Massachusetts General Hospital, 2348, Boston, Massachusetts, United States;
| | - Daniel Perl
- Uniformed Services University of the Health Sciences, 1685, Pathology, 4301 Jones Bridge Road, Room B3138, Bethesda, Maryland, United States, 20814;
| | - Jonathan R Polimeni
- Massachusetts General Hospital, 2348, Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, United States;
| | - Bruce R Rosen
- Massachusetts General Hospital, 2348, Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts, United States;
| | - Samantha Tromly
- University of South Florida, 7831, Institute for Applied Engineering, Tampa, Florida, United States;
| | - Chieh-En J Tseng
- Massachusetts General Hospital, 2348, Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, United States;
| | - Eveline F Yao
- United States Special Operations Command, Office of the Surgeon General, MacDill Air Force Base, United States;
| | - Nicole R Zurcher
- Massachusetts General Hospital, 2348, Athinoula A. Martinos Center for Biomedical Imaging, Boston, Massachusetts, United States;
| | - Christine L Mac Donald
- University of Washington, 7284, Department of Neurological Surgery, Seattle, Washington, United States;
| | - Kristen Dams-O'Connor
- Icahn School of Medicine at Mount Sinai, 5925, Rehabilitation Medicine, One Gustave Levy Place, Box 1163, New York, New York, United States, 10029; kristen.dams-o'
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8
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Wilson GH, Stavisky SD, Willett FR, Avansino DT, Kelemen JN, Hochberg LR, Henderson JM, Druckmann S, Shenoy KV. Decoding spoken English from intracortical electrode arrays in dorsal precentral gyrus. J Neural Eng 2020; 17:066007. [PMID: 33236720 PMCID: PMC8293867 DOI: 10.1088/1741-2552/abbfef] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
OBJECTIVE To evaluate the potential of intracortical electrode array signals for brain-computer interfaces (BCIs) to restore lost speech, we measured the performance of decoders trained to discriminate a comprehensive basis set of 39 English phonemes and to synthesize speech sounds via a neural pattern matching method. We decoded neural correlates of spoken-out-loud words in the 'hand knob' area of precentral gyrus, a step toward the eventual goal of decoding attempted speech from ventral speech areas in patients who are unable to speak. APPROACH Neural and audio data were recorded while two BrainGate2 pilot clinical trial participants, each with two chronically-implanted 96-electrode arrays, spoke 420 different words that broadly sampled English phonemes. Phoneme onsets were identified from audio recordings, and their identities were then classified from neural features consisting of each electrode's binned action potential counts or high-frequency local field potential power. Speech synthesis was performed using the 'Brain-to-Speech' pattern matching method. We also examined two potential confounds specific to decoding overt speech: acoustic contamination of neural signals and systematic differences in labeling different phonemes' onset times. MAIN RESULTS A linear decoder achieved up to 29.3% classification accuracy (chance = 6%) across 39 phonemes, while an RNN classifier achieved 33.9% accuracy. Parameter sweeps indicated that performance did not saturate when adding more electrodes or more training data, and that accuracy improved when utilizing time-varying structure in the data. Microphonic contamination and phoneme onset differences modestly increased decoding accuracy, but could be mitigated by acoustic artifact subtraction and using a neural speech onset marker, respectively. Speech synthesis achieved r = 0.523 correlation between true and reconstructed audio. SIGNIFICANCE The ability to decode speech using intracortical electrode array signals from a nontraditional speech area suggests that placing electrode arrays in ventral speech areas is a promising direction for speech BCIs.
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Affiliation(s)
- Guy H Wilson
- Neurosciences Graduate Program, Stanford University, Stanford, CA, United States of America
| | - Sergey D Stavisky
- Department of Neurosurgery, Stanford University, Stanford, CA, United States of America
- Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, United States of America
- Department of Electrical Engineering, Stanford University, Stanford, CA, United States of America
| | - Francis R Willett
- Department of Neurosurgery, Stanford University, Stanford, CA, United States of America
- Department of Electrical Engineering, Stanford University, Stanford, CA, United States of America
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, United States of America
| | - Donald T Avansino
- Department of Neurosurgery, Stanford University, Stanford, CA, United States of America
| | - Jessica N Kelemen
- Department of Neurology, Harvard Medical School, Boston, MA, United States of America
| | - Leigh R Hochberg
- Department of Neurology, Harvard Medical School, Boston, MA, United States of America
- Center for Neurotechnology and Neurorecovery, Dept. of Neurology, Massachusetts General Hospital, Boston, MA, United States of America
- VA RR&D Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Providence VA Medical Center, Providence, RI, United States of America
- Carney Institute for Brain Science and School of Engineering, Brown University, Providence, RI, United States of America
| | - Jaimie M Henderson
- Department of Neurosurgery, Stanford University, Stanford, CA, United States of America
- Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, United States of America
| | - Shaul Druckmann
- Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, United States of America
- Department of Neurobiology, Stanford University, Stanford, CA, United States of America
| | - Krishna V Shenoy
- Wu Tsai Neurosciences Institute and Bio-X Institute, Stanford University, Stanford, CA, United States of America
- Department of Electrical Engineering, Stanford University, Stanford, CA, United States of America
- Howard Hughes Medical Institute at Stanford University, Stanford, CA, United States of America
- Department of Neurobiology, Stanford University, Stanford, CA, United States of America
- Department of Bioengineering, Stanford University, Stanford, CA, United States of America
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