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Huang H, Ojeda Valencia G, Gregg NM, Osman GM, Montoya MN, Worrell GA, Miller KJ, Hermes D. CARLA: Adjusted common average referencing for cortico-cortical evoked potential data. J Neurosci Methods 2024; 407:110153. [PMID: 38710234 PMCID: PMC11149384 DOI: 10.1016/j.jneumeth.2024.110153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 02/22/2024] [Accepted: 04/27/2024] [Indexed: 05/08/2024]
Abstract
Human brain connectivity can be mapped by single pulse electrical stimulation during intracranial EEG measurements. The raw cortico-cortical evoked potentials (CCEP) are often contaminated by noise. Common average referencing (CAR) removes common noise and preserves response shapes but can introduce bias from responsive channels. We address this issue with an adjusted, adaptive CAR algorithm termed "CAR by Least Anticorrelation (CARLA)". CARLA was tested on simulated CCEP data and real CCEP data collected from four human participants. In CARLA, the channels are ordered by increasing mean cross-trial covariance, and iteratively added to the common average until anticorrelation between any single channel and all re-referenced channels reaches a minimum, as a measure of shared noise. We simulated CCEP data with true responses in 0-45 of 50 total channels. We quantified CARLA's error and found that it erroneously included 0 (median) truly responsive channels in the common average with ≤42 responsive channels, and erroneously excluded ≤2.5 (median) unresponsive channels at all responsiveness levels. On real CCEP data, signal quality was quantified with the mean R2 between all pairs of channels, which represents inter-channel dependency and is low for well-referenced data. CARLA re-referencing produced significantly lower mean R2 than standard CAR, CAR using a fixed bottom quartile of channels by covariance, and no re-referencing. CARLA minimizes bias in re-referenced CCEP data by adaptively selecting the optimal subset of non-responsive channels. It showed high specificity and sensitivity on simulated CCEP data and lowered inter-channel dependency compared to CAR on real CCEP data.
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Affiliation(s)
- Harvey Huang
- Mayo Clinic Medical Scientist Training Program, Rochester, MN, USA.
| | | | | | - Gamaleldin M Osman
- Department of Neurology, Mayo Clinic, Rochester, MN, USA; Division of Child Neurology, Department of Pediatrics, McGovern Medical School at UTHealth, Houston, TX, USA
| | - Morgan N Montoya
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Gregory A Worrell
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA; Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Kai J Miller
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA; Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Dora Hermes
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA; Department of Neurology, Mayo Clinic, Rochester, MN, USA; Department of Radiology, Mayo Clinic, Rochester, MN 55901, USA.
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Boring MJ, Richardson RM, Ghuman AS. Interacting ventral temporal gradients of timescales and functional connectivity and their relationships to visual behavior. iScience 2024; 27:110003. [PMID: 38868193 PMCID: PMC11166696 DOI: 10.1016/j.isci.2024.110003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 04/02/2024] [Accepted: 05/14/2024] [Indexed: 06/14/2024] Open
Abstract
Cortical gradients in endogenous and stimulus-evoked neurodynamic timescales, and long-range cortical interactions, provide organizational constraints to the brain and influence neural populations' roles in cognition. It is unclear how these functional gradients interrelate and which influence behavior. Here, intracranial recordings from 4,090 electrode contacts in 35 individuals map gradients of neural timescales and functional connectivity to assess their interactions along category-selective ventral temporal cortex. Endogenous and stimulus-evoked information processing timescales were not significantly correlated with one another suggesting that local neural timescales are context dependent and may arise through distinct neurophysiological mechanisms. Endogenous neural timescales correlated with functional connectivity even after removing the effects of shared anatomical gradients. Neural timescales and functional connectivity correlated with how strongly a population's activity predicted behavior in a simple visual task. These results suggest both interrelated and distinct neurophysiological processes give rise to different functional connectivity and neural timescale gradients, which together influence behavior.
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Affiliation(s)
- Matthew J. Boring
- Center for Neuroscience at the University of Pittsburgh, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
| | - R. Mark Richardson
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Avniel Singh Ghuman
- Center for Neuroscience at the University of Pittsburgh, University of Pittsburgh, Pittsburgh, PA, USA
- Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University, Pittsburgh, PA, USA
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
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Olaru M, Cernera S, Hahn A, Wozny TA, Anso J, de Hemptinne C, Little S, Neumann WJ, Abbasi-Asl R, Starr PA. Motor network gamma oscillations in chronic home recordings predict dyskinesia in Parkinson's disease. Brain 2024; 147:2038-2052. [PMID: 38195196 PMCID: PMC11146421 DOI: 10.1093/brain/awae004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 12/17/2023] [Accepted: 12/21/2023] [Indexed: 01/11/2024] Open
Abstract
In Parkinson's disease, imbalances between 'antikinetic' and 'prokinetic' patterns of neuronal oscillatory activity are related to motor dysfunction. Invasive brain recordings from the motor network have suggested that medical or surgical therapy can promote a prokinetic state by inducing narrowband gamma rhythms (65-90 Hz). Excessive narrowband gamma in the motor cortex promotes dyskinesia in rodent models, but the relationship between narrowband gamma and dyskinesia in humans has not been well established. To assess this relationship, we used a sensing-enabled deep brain stimulator system, attached to both motor cortex and basal ganglia (subthalamic or pallidal) leads, paired with wearable devices that continuously tracked motor signs in the contralateral upper limbs. We recorded 984 h of multisite field potentials in 30 hemispheres of 16 subjects with Parkinson's disease (2/16 female, mean age 57 ± 12 years) while at home on usual antiparkinsonian medications. Recordings were done 2-4 weeks after implantation, prior to starting therapeutic stimulation. Narrowband gamma was detected in the precentral gyrus, subthalamic nucleus or both structures on at least one side of 92% of subjects with a clinical history of dyskinesia. Narrowband gamma was not detected in the globus pallidus. Narrowband gamma spectral power in both structures co-fluctuated similarly with contralateral wearable dyskinesia scores (mean correlation coefficient of ρ = 0.48 with a range of 0.12-0.82 for cortex, ρ = 0.53 with a range of 0.5-0.77 for subthalamic nucleus). Stratification analysis showed the correlations were not driven by outlier values, and narrowband gamma could distinguish 'on' periods with dyskinesia from 'on' periods without dyskinesia. Time lag comparisons confirmed that gamma oscillations herald dyskinesia onset without a time lag in either structure when using 2-min epochs. A linear model incorporating the three oscillatory bands (beta, theta/alpha and narrowband gamma) increased the predictive power of dyskinesia for several subject hemispheres. We further identified spectrally distinct oscillations in the low gamma range (40-60 Hz) in three subjects, but the relationship of low gamma oscillations to dyskinesia was variable. Our findings support the hypothesis that excessive oscillatory activity at 65-90 Hz in the motor network tracks with dyskinesia similarly across both structures, without a detectable time lag. This rhythm may serve as a promising control signal for closed-loop deep brain stimulation using either cortical or subthalamic detection.
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Affiliation(s)
- Maria Olaru
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Stephanie Cernera
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Amelia Hahn
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Thomas A Wozny
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Juan Anso
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Coralie de Hemptinne
- Department of Neurology, University of Florida Gainesville, Gainesville, FL 32611, USA
| | - Simon Little
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
| | - Wolf-Julian Neumann
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin 10117, Germany
| | - Reza Abbasi-Asl
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA 94143, USA
| | - Philip A Starr
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94143, USA
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
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Kremen V, Sladky V, Mivalt F, Gregg NM, Balzekas I, Marks V, Brinkmann BH, Lundstrom BN, Cui J, St Louis EK, Croarkin P, Alden EC, Fields J, Crockett K, Adolf J, Bilderbeek J, Hermes D, Messina S, Miller KJ, Van Gompel J, Denison T, Worrell GA. A platform for brain network sensing and stimulation with quantitative behavioral tracking: Application to limbic circuit epilepsy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.09.24302358. [PMID: 38370724 PMCID: PMC10871449 DOI: 10.1101/2024.02.09.24302358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Temporal lobe epilepsy is a common neurological disease characterized by recurrent seizures. These seizures often originate from limbic networks and people also experience chronic comorbidities related to memory, mood, and sleep (MMS). Deep brain stimulation targeting the anterior nucleus of the thalamus (ANT-DBS) is a proven therapy, but the optimal stimulation parameters remain unclear. We developed a neurotechnology platform for tracking seizures and MMS to enable data streaming between an investigational brain sensing-stimulation implant, mobile devices, and a cloud environment. Artificial Intelligence algorithms provided accurate catalogs of seizures, interictal epileptiform spikes, and wake-sleep brain states. Remotely administered memory and mood assessments were used to densely sample cognitive and behavioral response during ANT-DBS. We evaluated the efficacy of low-frequency versus high-frequency ANT-DBS. They both reduced seizures, but low-frequency ANT-DBS showed greater reductions and better sleep and memory. These results highlight the potential of synchronized brain sensing and behavioral tracking for optimizing neuromodulation therapy.
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Mandal AS, Wiener C, Assem M, Romero-Garcia R, Coelho P, McDonald A, Woodberry E, Morris RC, Price SJ, Duncan J, Santarius T, Suckling J, Hart MG, Erez Y. Tumour-infiltrated cortex participates in large-scale cognitive circuits. Cortex 2024; 173:1-15. [PMID: 38354669 PMCID: PMC10988771 DOI: 10.1016/j.cortex.2024.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 01/03/2024] [Accepted: 01/11/2024] [Indexed: 02/16/2024]
Abstract
The extent to which tumour-infiltrated brain tissue contributes to cognitive function remains unclear. We tested the hypothesis that cortical tissue infiltrated by diffuse gliomas participates in large-scale cognitive circuits using a unique combination of intracranial electrocorticography (ECoG) and resting-state functional magnetic resonance (fMRI) imaging in four patients. We also assessed the relationship between functional connectivity with tumour-infiltrated tissue and long-term cognitive outcomes in a larger, overlapping cohort of 17 patients. We observed significant task-related high gamma (70-250 Hz) power modulations in tumour-infiltrated cortex in response to increased cognitive effort (i.e., switch counting compared to simple counting), implying preserved functionality of neoplastic tissue for complex tasks probing executive function. We found that tumour locations corresponding to task-responsive electrodes exhibited functional connectivity patterns that significantly co-localised with canonical brain networks implicated in executive function. Specifically, we discovered that tumour-infiltrated cortex with larger task-related high gamma power modulations tended to be more functionally connected to the dorsal attention network (DAN). Finally, we demonstrated that tumour-DAN connectivity is evident across a larger cohort of patients with gliomas and that it relates to long-term postsurgical outcomes in goal-directed attention. Overall, this study contributes convergent fMRI-ECoG evidence that tumour-infiltrated cortex participates in large-scale neurocognitive circuits that support executive function in health. These findings underscore the potential clinical utility of mapping large-scale connectivity of tumour-infiltrated tissue in the care of patients with diffuse gliomas.
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Affiliation(s)
- Ayan S Mandal
- Brain-Gene Development Lab, Department of Psychiatry, Perelman School of Medicine at the University of Pennsylvania, USA; Brain Mapping Unit, Department of Psychiatry, University of Cambridge, UK.
| | - Chemda Wiener
- Faculty of Engineering, Bar-Ilan University, Ramat-Gan, Israel
| | - Moataz Assem
- Medical Research Council, Cognition and Brain Sciences Unit, University of Cambridge, UK
| | - Rafael Romero-Garcia
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, UK; Department of Medical Physiology and Biophysics, Instituto de Biomedicina de Sevilla (IBiS) HUVR/CSIC/Universidad de Sevilla/CIBERSAM, ISCIII, Sevilla, Spain
| | | | - Alexa McDonald
- Department of Neuropsychology, Cambridge University Hospitals NHS Foundation Trust, UK
| | - Emma Woodberry
- Department of Neuropsychology, Cambridge University Hospitals NHS Foundation Trust, UK
| | - Robert C Morris
- Department of Neuropsychology, Cambridge University Hospitals NHS Foundation Trust, UK
| | - Stephen J Price
- Department of Neurosurgery, Cambridge University Hospitals NHS Foundation Trust, UK
| | - John Duncan
- Medical Research Council, Cognition and Brain Sciences Unit, University of Cambridge, UK; Department of Experimental Psychology, University of Oxford, UK
| | - Thomas Santarius
- Department of Neurosurgery, Cambridge University Hospitals NHS Foundation Trust, UK; Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, UK; Department of Physiology, Development and Neuroscience, University of Cambridge, UK
| | - John Suckling
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, UK; Behavioural and Clinical Neuroscience Institute, University of Cambridge, UK; Cambridge and Peterborough NHS Foundation Trust, UK
| | - Michael G Hart
- St George's, University of London & St George's University Hospitals NHS Foundation Trust, Institute of Molecular and Clinical Sciences, Neurosciences Research Centre, Cranmer Terrace, London, UK
| | - Yaara Erez
- Faculty of Engineering, Bar-Ilan University, Ramat-Gan, Israel; Medical Research Council, Cognition and Brain Sciences Unit, University of Cambridge, UK; Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel.
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6
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Blenkmann AO, Leske SL, Llorens A, Lin JJ, Chang EF, Brunner P, Schalk G, Ivanovic J, Larsson PG, Knight RT, Endestad T, Solbakk AK. Anatomical registration of intracranial electrodes. Robust model-based localization and deformable smooth brain-shift compensation methods. J Neurosci Methods 2024; 404:110056. [PMID: 38224783 DOI: 10.1016/j.jneumeth.2024.110056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 11/27/2023] [Accepted: 01/03/2024] [Indexed: 01/17/2024]
Abstract
BACKGROUND Intracranial electrodes are typically localized from post-implantation CT artifacts. Automatic algorithms localizing low signal-to-noise ratio artifacts and high-density electrode arrays are missing. Additionally, implantation of grids/strips introduces brain deformations, resulting in registration errors when fusing post-implantation CT and pre-implantation MR images. Brain-shift compensation methods project electrode coordinates to cortex, but either fail to produce smooth solutions or do not account for brain deformations. NEW METHODS We first introduce GridFit, a model-based fitting approach that simultaneously localizes all electrodes' CT artifacts in grids, strips, or depth arrays. Second, we present CEPA, a brain-shift compensation algorithm combining orthogonal-based projections, spring-mesh models, and spatial regularization constraints. RESULTS We tested GridFit on ∼6000 simulated scenarios. The localization of CT artifacts showed robust performance under difficult scenarios, such as noise, overlaps, and high-density implants (<1 mm errors). Validation with data from 20 challenging patients showed 99% accurate localization of the electrodes (3160/3192). We tested CEPA brain-shift compensation with data from 15 patients. Projections accounted for simple mechanical deformation principles with < 0.4 mm errors. The inter-electrode distances smoothly changed across neighbor electrodes, while changes in inter-electrode distances linearly increased with projection distance. COMPARISON WITH EXISTING METHODS GridFit succeeded in difficult scenarios that challenged available methods and outperformed visual localization by preserving the inter-electrode distance. CEPA registration errors were smaller than those obtained for well-established alternatives. Additionally, modeling resting-state high-frequency activity in five patients further supported CEPA. CONCLUSION GridFit and CEPA are versatile tools for registering intracranial electrode coordinates, providing highly accurate results even in the most challenging implantation scenarios. The methods are implemented in the iElectrodes open-source toolbox.
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Affiliation(s)
- Alejandro Omar Blenkmann
- Department of Psychology, University of Oslo, Norway; RITMO Centre for Interdisciplinary Studies in Rhythm, Time, and Motion, University of Oslo, Norway.
| | - Sabine Liliana Leske
- Department of Musicology, University of Oslo, Norway; RITMO Centre for Interdisciplinary Studies in Rhythm, Time, and Motion, University of Oslo, Norway; Department of Neuropsychology, Helgeland Hospital, Mosjøen, Norway
| | - Anaïs Llorens
- Department of Psychology, University of Oslo, Norway; Department of Psychology and the Helen Wills Neuroscience Institute, University of California, Berkeley, USA; Université de Franche-Comté, SUPMICROTECH, CNRS, Institut FEMTO-ST, 25000 Besançon, France; Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, Team TURC, 75014 Paris, France
| | - Jack J Lin
- Department of Neurology and Center for Mind and Brain, University of California, Davis, USA
| | - Edward F Chang
- Department of Neurological Surgery, University of California, San Francisco, USA
| | - Peter Brunner
- Department of Neurology, Albany Medical College, Albany, NY, USA; National Center for Adaptive Neurotechnologies, Albany, NY, USA; Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, USA
| | - Gerwin Schalk
- Department of Neurology, Albany Medical College, Albany, NY, USA; National Center for Adaptive Neurotechnologies, Albany, NY, USA; Tianqiao and Chrissy Chen Institute, Chen Frontier Lab for Applied Neurotechnology, Shanghai, China; Fudan University/Huashan Hospital, Department of Neurosurgery, Shanghai, China
| | | | | | - Robert Thomas Knight
- Department of Psychology and the Helen Wills Neuroscience Institute, University of California, Berkeley, USA
| | - Tor Endestad
- Department of Psychology, University of Oslo, Norway; RITMO Centre for Interdisciplinary Studies in Rhythm, Time, and Motion, University of Oslo, Norway; Department of Neuropsychology, Helgeland Hospital, Mosjøen, Norway
| | - Anne-Kristin Solbakk
- Department of Psychology, University of Oslo, Norway; RITMO Centre for Interdisciplinary Studies in Rhythm, Time, and Motion, University of Oslo, Norway; Department of Neurosurgery, Oslo University Hospital, Norway; Department of Neuropsychology, Helgeland Hospital, Mosjøen, Norway
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Leinders S, Vansteensel MJ, Piantoni G, Branco MP, Freudenburg ZV, Gebbink TA, Pels EGM, Raemaekers MAH, Schippers A, Aarnoutse EJ, Ramsey NF. Using fMRI to localize target regions for implanted brain-computer interfaces in locked-in syndrome. Clin Neurophysiol 2023; 155:1-15. [PMID: 37657190 DOI: 10.1016/j.clinph.2023.08.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 06/29/2023] [Accepted: 08/09/2023] [Indexed: 09/03/2023]
Abstract
OBJECTIVE Electrocorticography (ECoG)-based brain-computer interface (BCI) systems have the potential to improve quality of life of people with locked-in syndrome (LIS) by restoring their ability to communicate independently. Before implantation of such a system, it is important to localize ECoG electrode target regions. Here, we assessed the predictive value of functional magnetic resonance imaging (fMRI) for the localization of suitable target regions on the sensorimotor cortex for ECoG-based BCI in people with locked-in syndrome. METHODS Three people with locked-in syndrome were implanted with a chronic, fully implantable ECoG-BCI system. We compared pre-surgical fMRI activity with post-implantation ECoG activity from areas known to be active and inactive during attempted hand movement (sensorimotor hand region and dorsolateral prefrontal cortex, respectively). RESULTS Results showed a spatial match between fMRI activity and changes in ECoG low and high frequency band power (10 - 30 and 65 - 95 Hz, respectively) during attempted movement. Also, we found that fMRI can be used to select a sub-set of electrodes that show strong task-related signal changes that are therefore likely to generate adequate BCI control. CONCLUSIONS Our findings indicate that fMRI is a useful non-invasive tool for the pre-surgical workup of BCI implant candidates. SIGNIFICANCE If these results are confirmed in more BCI studies, fMRI might be used for more efficient surgical BCI procedures with focused cortical coverage and lower participant burden.
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Affiliation(s)
- Sacha Leinders
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands
| | - Mariska J Vansteensel
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands
| | - Giovanni Piantoni
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands
| | - Mariana P Branco
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands
| | - Zac V Freudenburg
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands
| | - Tineke A Gebbink
- Department of Clinical Neurophysiology, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands
| | - Elmar G M Pels
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands
| | - Mathijs A H Raemaekers
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands
| | - Anouck Schippers
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands
| | - Erik J Aarnoutse
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands
| | - Nick F Ramsey
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands.
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Wang Z, Magnotti JF, Zhang X, Beauchamp MS. YAEL: Your Advanced Electrode Localizer. eNeuro 2023; 10:ENEURO.0328-23.2023. [PMID: 37857509 PMCID: PMC10591275 DOI: 10.1523/eneuro.0328-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 09/27/2023] [Accepted: 10/02/2023] [Indexed: 10/21/2023] Open
Abstract
Intracranial electroencephalography (iEEG) provides a unique opportunity to record and stimulate neuronal populations in the human brain. A key step in neuroscience inference from iEEG is localizing the electrodes relative to individual subject anatomy and identified regions in brain atlases. We describe a new software tool, Your Advanced Electrode Localizer (YAEL), that provides an integrated solution for every step of the electrode localization process. YAEL is compatible with all common data formats to provide an easy-to-use, drop-in replacement for problematic existing workflows that require users to grapple with multiple programs and interfaces. YAEL's automatic extrapolation and interpolation functions speed localization, especially important in patients with many implanted stereotactic (sEEG) electrode shafts. The graphical user interface is presented in a web browser for broad compatibility and includes an interactive 3D viewer for easier localization of nearby sEEG contacts. After localization is complete, users may enter or import data into YAEL's 3D viewer to create publication-ready visualizations of electrodes and brain anatomy, including identified brain areas from atlases; the response to experimental tasks measured with iEEG; and clinical measures such as epileptiform activity or the results of electrical stimulation mapping. YAEL is free and open source and does not depend on any commercial software. Installation instructions for Mac, Windows, and Linux are available at https://yael.wiki.
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Affiliation(s)
- Zhengjia Wang
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - John F Magnotti
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Xiang Zhang
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Michael S Beauchamp
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
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9
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Ojeda Valencia G, Gregg NM, Huang H, Lundstrom BN, Brinkmann BH, Pal Attia T, Van Gompel JJ, Bernstein MA, In MH, Huston J, Worrell GA, Miller KJ, Hermes D. Signatures of Electrical Stimulation Driven Network Interactions in the Human Limbic System. J Neurosci 2023; 43:6697-6711. [PMID: 37620159 PMCID: PMC10538586 DOI: 10.1523/jneurosci.2201-22.2023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 08/04/2023] [Accepted: 08/08/2023] [Indexed: 08/26/2023] Open
Abstract
Stimulation-evoked signals are starting to be used as biomarkers to indicate the state and health of brain networks. The human limbic network, often targeted for brain stimulation therapy, is involved in emotion and memory processing. Previous anatomic, neurophysiological, and functional studies suggest distinct subsystems within the limbic network (Rolls, 2015). Studies using intracranial electrical stimulation, however, have emphasized the similarities of the evoked waveforms across the limbic network. We test whether these subsystems have distinct stimulation-driven signatures. In eight patients (four male, four female) with drug-resistant epilepsy, we stimulated the limbic system with single-pulse electrical stimulation. Reliable corticocortical evoked potentials (CCEPs) were measured between hippocampus and the posterior cingulate cortex (PCC) and between the amygdala and the anterior cingulate cortex (ACC). However, the CCEP waveform in the PCC after hippocampal stimulation showed a unique and reliable morphology, which we term the "limbic Hippocampus-Anterior nucleus of the thalamus-Posterior cingulate, HAP-wave." This limbic HAP-wave was visually distinct and separately decoded from the CCEP waveform in ACC after amygdala stimulation. Diffusion MRI data show that the measured end points in the PCC overlap with the end points of the parolfactory cingulum bundle rather than the parahippocampal cingulum, suggesting that the limbic HAP-wave may travel through fornix, mammillary bodies, and the anterior nucleus of the thalamus (ANT). This was further confirmed by stimulating the ANT, which evoked the same limbic HAP-wave but with an earlier latency. Limbic subsystems have unique stimulation-evoked signatures that may be used in the future to help network pathology diagnosis.SIGNIFICANCE STATEMENT The limbic system is often compromised in diverse clinical conditions, such as epilepsy or Alzheimer's disease, and characterizing its typical circuit responses may provide diagnostic insight. Stimulation-evoked waveforms have been used in the motor system to diagnose circuit pathology. We translate this framework to limbic subsystems using human intracranial stereo EEG (sEEG) recordings that measure deeper brain areas. Our sEEG recordings describe a stimulation-evoked waveform characteristic to the memory and spatial subsystem of the limbic network that we term the "limbic HAP-wave." The limbic HAP-wave follows anatomic white matter pathways from hippocampus to thalamus to the posterior cingulum and shows promise as a distinct biomarker of signaling in the human brain memory and spatial limbic network.
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Affiliation(s)
- Gabriela Ojeda Valencia
- Department of Physiology and Biomedical Engineering, Mayo Clinic Rochester, Rochester, Minnesota 55902
| | - Nicholas M Gregg
- Department of Neurology, Mayo Clinic Rochester, Rochester, Minnesota 55902
| | - Harvey Huang
- Mayo Clinic Medical Scientist Training Program, Mayo Clinic Rochester, Rochester, Minnesota 55902
| | - Brian N Lundstrom
- Department of Neurology, Mayo Clinic Rochester, Rochester, Minnesota 55902
| | | | - Tal Pal Attia
- Department of Physiology and Biomedical Engineering, Mayo Clinic Rochester, Rochester, Minnesota 55902
| | - Jamie J Van Gompel
- Department of Neurologic Surgery, Mayo Clinic Rochester, Rochester, Minnesota 55902
| | - Matt A Bernstein
- Department of Radiology, Mayo Clinic Rochester, Rochester, Minnesota 55902
| | - Myung-Ho In
- Department of Radiology, Mayo Clinic Rochester, Rochester, Minnesota 55902
| | - John Huston
- Department of Radiology, Mayo Clinic Rochester, Rochester, Minnesota 55902
| | - Gregory A Worrell
- Department of Physiology and Biomedical Engineering, Mayo Clinic Rochester, Rochester, Minnesota 55902
- Department of Neurology, Mayo Clinic Rochester, Rochester, Minnesota 55902
| | - Kai J Miller
- Department of Physiology and Biomedical Engineering, Mayo Clinic Rochester, Rochester, Minnesota 55902
- Department of Neurologic Surgery, Mayo Clinic Rochester, Rochester, Minnesota 55902
| | - Dora Hermes
- Department of Physiology and Biomedical Engineering, Mayo Clinic Rochester, Rochester, Minnesota 55902
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10
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Mivalt F, Kremen V, Sladky V, Cui J, Gregg NM, Balzekas I, Marks V, St Louis EK, Croarkin P, Lundstrom BN, Nelson N, Kim J, Hermes D, Messina S, Worrell S, Richner T, Brinkmann BH, Denison T, Miller KJ, Van Gompel J, Stead M, Worrell GA. Impedance Rhythms in Human Limbic System. J Neurosci 2023; 43:6653-6666. [PMID: 37620157 PMCID: PMC10538585 DOI: 10.1523/jneurosci.0241-23.2023] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 08/09/2023] [Accepted: 08/14/2023] [Indexed: 08/26/2023] Open
Abstract
The impedance is a fundamental electrical property of brain tissue, playing a crucial role in shaping the characteristics of local field potentials, the extent of ephaptic coupling, and the volume of tissue activated by externally applied electrical brain stimulation. We tracked brain impedance, sleep-wake behavioral state, and epileptiform activity in five people with epilepsy living in their natural environment using an investigational device. The study identified impedance oscillations that span hours to weeks in the amygdala, hippocampus, and anterior nucleus thalamus. The impedance in these limbic brain regions exhibit multiscale cycles with ultradian (∼1.5-1.7 h), circadian (∼21.6-26.4 h), and infradian (∼20-33 d) periods. The ultradian and circadian period cycles are driven by sleep-wake state transitions between wakefulness, nonrapid eye movement (NREM) sleep, and rapid eye movement (REM) sleep. Limbic brain tissue impedance reaches a minimum value in NREM sleep, intermediate values in REM sleep, and rises through the day during wakefulness, reaching a maximum in the early evening before sleep onset. Infradian (∼20-33 d) impedance cycles were not associated with a distinct behavioral correlate. Brain tissue impedance is known to strongly depend on the extracellular space (ECS) volume, and the findings reported here are consistent with sleep-wake-dependent ECS volume changes recently observed in the rodent cortex related to the brain glymphatic system. We hypothesize that human limbic brain ECS changes during sleep-wake state transitions underlie the observed multiscale impedance cycles. Impedance is a simple electrophysiological biomarker that could prove useful for tracking ECS dynamics in human health, disease, and therapy.SIGNIFICANCE STATEMENT The electrical impedance in limbic brain structures (amygdala, hippocampus, anterior nucleus thalamus) is shown to exhibit oscillations over multiple timescales. We observe that impedance oscillations with ultradian and circadian periodicities are associated with transitions between wakefulness, NREM, and REM sleep states. There are also impedance oscillations spanning multiple weeks that do not have a clear behavioral correlate and whose origin remains unclear. These multiscale impedance oscillations will have an impact on extracellular ionic currents that give rise to local field potentials, ephaptic coupling, and the tissue activated by electrical brain stimulation. The approach for measuring tissue impedance using perturbational electrical currents is an established engineering technique that may be useful for tracking ECS volume.
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Affiliation(s)
- Filip Mivalt
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, 61600 Brno, Czech Republic
- International Clinical Research Center, St. Anne's University Hospital, 60200 Brno, Czech Republic
| | - Vaclav Kremen
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
- Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University, 16000 Prague, Czech Republic
| | - Vladimir Sladky
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
- International Clinical Research Center, St. Anne's University Hospital, 60200 Brno, Czech Republic
- Faculty of Biomedical Engineering, Czech Technical University, 16000 Prague, Czech Republic
| | - Jie Cui
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
| | - Nicholas M Gregg
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
| | - Irena Balzekas
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota 55905
| | - Victoria Marks
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota 55905
| | - Erik K St Louis
- Center for Sleep Medicine, Departments of Neurology and Medicine, Divisions of Sleep Neurology and Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, Minnesota 55905
| | | | - Brian Nils Lundstrom
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
| | - Noelle Nelson
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
| | - Jiwon Kim
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
| | - Dora Hermes
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota 55905
| | - Steven Messina
- Department of Radiology, Mayo Clinic Rochester, Minnesota 55905
| | - Samuel Worrell
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
| | - Thomas Richner
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
| | - Benjamin H Brinkmann
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota 55905
| | - Timothy Denison
- Department of Engineering Science, Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford OX3 7DQ, United Kingdom
| | - Kai J Miller
- Department of Neurologic Surgery, Mayo Clinic, Rochester, Minnesota 55905
| | - Jamie Van Gompel
- Department of Neurologic Surgery, Mayo Clinic, Rochester, Minnesota 55905
| | - Matthew Stead
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
| | - Gregory A Worrell
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota 55905
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota 55905
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11
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Berezutskaya J, Freudenburg ZV, Vansteensel MJ, Aarnoutse EJ, Ramsey NF, van Gerven MAJ. Direct speech reconstruction from sensorimotor brain activity with optimized deep learning models. J Neural Eng 2023; 20:056010. [PMID: 37467739 PMCID: PMC10510111 DOI: 10.1088/1741-2552/ace8be] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 07/12/2023] [Accepted: 07/19/2023] [Indexed: 07/21/2023]
Abstract
Objective.Development of brain-computer interface (BCI) technology is key for enabling communication in individuals who have lost the faculty of speech due to severe motor paralysis. A BCI control strategy that is gaining attention employs speech decoding from neural data. Recent studies have shown that a combination of direct neural recordings and advanced computational models can provide promising results. Understanding which decoding strategies deliver best and directly applicable results is crucial for advancing the field.Approach.In this paper, we optimized and validated a decoding approach based on speech reconstruction directly from high-density electrocorticography recordings from sensorimotor cortex during a speech production task.Main results.We show that (1) dedicated machine learning optimization of reconstruction models is key for achieving the best reconstruction performance; (2) individual word decoding in reconstructed speech achieves 92%-100% accuracy (chance level is 8%); (3) direct reconstruction from sensorimotor brain activity produces intelligible speech.Significance.These results underline the need for model optimization in achieving best speech decoding results and highlight the potential that reconstruction-based speech decoding from sensorimotor cortex can offer for development of next-generation BCI technology for communication.
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Affiliation(s)
- Julia Berezutskaya
- Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht 3584 CX, The Netherlands
- Donders Center for Brain, Cognition and Behaviour, Nijmegen 6525 GD, The Netherlands
| | - Zachary V Freudenburg
- Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht 3584 CX, The Netherlands
| | - Mariska J Vansteensel
- Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht 3584 CX, The Netherlands
| | - Erik J Aarnoutse
- Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht 3584 CX, The Netherlands
| | - Nick F Ramsey
- Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht 3584 CX, The Netherlands
| | - Marcel A J van Gerven
- Donders Center for Brain, Cognition and Behaviour, Nijmegen 6525 GD, The Netherlands
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12
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Oehrn CR, Cernera S, Hammer LH, Shcherbakova M, Yao J, Hahn A, Wang S, Ostrem JL, Little S, Starr PA. Personalized chronic adaptive deep brain stimulation outperforms conventional stimulation in Parkinson's disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.03.23293450. [PMID: 37649907 PMCID: PMC10463549 DOI: 10.1101/2023.08.03.23293450] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Deep brain stimulation is a widely used therapy for Parkinson's disease (PD) but currently lacks dynamic responsiveness to changing clinical and neural states. Feedback control has the potential to improve therapeutic effectiveness, but optimal control strategy and additional benefits of "adaptive" neurostimulation are unclear. We implemented adaptive subthalamic nucleus stimulation, controlled by subthalamic or cortical signals, in three PD patients (five hemispheres) during normal daily life. We identified neurophysiological biomarkers of residual motor fluctuations using data-driven analyses of field potentials over a wide frequency range and varying stimulation amplitudes. Narrowband gamma oscillations (65-70 Hz) at either site emerged as the best control signal for sensing during stimulation. A blinded, randomized trial demonstrated improved motor symptoms and quality of life compared to clinically optimized standard stimulation. Our approach highlights the promise of personalized adaptive neurostimulation based on data-driven selection of control signals and may be applied to other neurological disorders.
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Affiliation(s)
- Carina R Oehrn
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Stephanie Cernera
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Lauren H Hammer
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA
| | - Maria Shcherbakova
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Jiaang Yao
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- University of California, Berkeley - University of California, San Francisco Graduate Program in Bioengineering, Berkeley, CA, USA
| | - Amelia Hahn
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Sarah Wang
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Jill L Ostrem
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Simon Little
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA
- University of California, Berkeley - University of California, San Francisco Graduate Program in Bioengineering, Berkeley, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Philip A Starr
- Department of Neurological Surgery, University of California, San Francisco, San Francisco, CA, USA
- University of California, Berkeley - University of California, San Francisco Graduate Program in Bioengineering, Berkeley, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
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13
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Gruenwald J, Sieghartsleitner S, Kapeller C, Scharinger J, Kamada K, Brunner P, Guger C. Characterization of High-Gamma Activity in Electrocorticographic Signals. Front Neurosci 2023; 17:1206120. [PMID: 37609450 PMCID: PMC10440607 DOI: 10.3389/fnins.2023.1206120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 07/10/2023] [Indexed: 08/24/2023] Open
Abstract
Introduction Electrocorticographic (ECoG) high-gamma activity (HGA) is a widely recognized and robust neural correlate of cognition and behavior. However, fundamental signal properties of HGA, such as the high-gamma frequency band or temporal dynamics of HGA, have never been systematically characterized. As a result, HGA estimators are often poorly adjusted, such that they miss valuable physiological information. Methods To address these issues, we conducted a thorough qualitative and quantitative characterization of HGA in ECoG signals. Our study is based on ECoG signals recorded from 18 epilepsy patients while performing motor control, listening, and visual perception tasks. In this study, we first categorize HGA into HGA types based on the cognitive/behavioral task. For each HGA type, we then systematically quantify three fundamental signal properties of HGA: the high-gamma frequency band, the HGA bandwidth, and the temporal dynamics of HGA. Results The high-gamma frequency band strongly varies across subjects and across cognitive/behavioral tasks. In addition, HGA time courses have lowpass character, with transients limited to 10 Hz. The task-related rise time and duration of these HGA time courses depend on the individual subject and cognitive/behavioral task. Task-related HGA amplitudes are comparable across the investigated tasks. Discussion This study is of high practical relevance because it provides a systematic basis for optimizing experiment design, ECoG acquisition and processing, and HGA estimation. Our results reveal previously unknown characteristics of HGA, the physiological principles of which need to be investigated in further studies.
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Affiliation(s)
- Johannes Gruenwald
- g.tec medical engineering GmbH, Schiedlberg, Austria
- Institute of Computational Perception, Johannes Kepler University, Linz, Austria
| | - Sebastian Sieghartsleitner
- g.tec medical engineering GmbH, Schiedlberg, Austria
- Institute of Computational Perception, Johannes Kepler University, Linz, Austria
| | | | - Josef Scharinger
- Institute of Computational Perception, Johannes Kepler University, Linz, Austria
| | - Kyousuke Kamada
- Department for Neurosurgery, Asahikawa Medical University, Asahikawa, Japan
- Hokashin Group Megumino Hospital, Sapporo, Japan
| | - Peter Brunner
- National Center for Adaptive Neurotechnologies, Albany, NY, United States
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, United States
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14
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Alreja A, Ward MJ, Ma Q, Russ BE, Bickel S, Van Wouwe NC, González-Martínez JA, Neimat JS, Abel TJ, Bagić A, Parker LS, Richardson RM, Schroeder CE, Morency LP, Ghuman AS. A new paradigm for investigating real-world social behavior and its neural underpinnings. Behav Res Methods 2023; 55:2333-2352. [PMID: 35877024 PMCID: PMC10841340 DOI: 10.3758/s13428-022-01882-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/15/2022] [Indexed: 11/08/2022]
Abstract
Eye tracking and other behavioral measurements collected from patient-participants in their hospital rooms afford a unique opportunity to study natural behavior for basic and clinical translational research. We describe an immersive social and behavioral paradigm implemented in patients undergoing evaluation for surgical treatment of epilepsy, with electrodes implanted in the brain to determine the source of their seizures. Our studies entail collecting eye tracking with other behavioral and psychophysiological measurements from patient-participants during unscripted behavior, including social interactions with clinical staff, friends, and family in the hospital room. This approach affords a unique opportunity to study the neurobiology of natural social behavior, though it requires carefully addressing distinct logistical, technical, and ethical challenges. Collecting neurophysiological data synchronized to behavioral and psychophysiological measures helps us to study the relationship between behavior and physiology. Combining across these rich data sources while participants eat, read, converse with friends and family, etc., enables clinical-translational research aimed at understanding the participants' disorders and clinician-patient interactions, as well as basic research into natural, real-world behavior. We discuss data acquisition, quality control, annotation, and analysis pipelines that are required for our studies. We also discuss the clinical, logistical, and ethical and privacy considerations critical to working in the hospital setting.
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Affiliation(s)
- Arish Alreja
- Center for the Neural Basis of Cognition, Carnegie Mellon University and University of Pittsburgh, Pittsburgh, USA.
- Neuroscience Institute, Carnegie Mellon University, Pittsburgh, USA.
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, USA.
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, USA.
| | - Michael J Ward
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, USA
- David Geffen School of Medicine, University of California Los Angeles, Los Angeles, USA
| | - Qianli Ma
- Language Technologies Institute, Carnegie Mellon University, Pittsburgh, USA
| | - Brian E Russ
- Nathan Kline Institute for Psychiatric Research, Orangeburg, USA
| | - Stephan Bickel
- Department of Neurosurgery and Neurology, Northwell Health, The Feinstein Institutes for Medical Research, Manhasset, USA
| | - Nelleke C Van Wouwe
- Department of Neurological Surgery, University of Louisville, Louisville, USA
| | | | - Joseph S Neimat
- Department of Neurological Surgery, University of Louisville, Louisville, USA
| | - Taylor J Abel
- Center for the Neural Basis of Cognition, Carnegie Mellon University and University of Pittsburgh, Pittsburgh, USA
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, USA
- Brain Institute, University of Pittsburgh, Pittsburgh, USA
| | - Anto Bagić
- Department of Neurology, University of Pittsburgh, Pittsburgh, USA
| | - Lisa S Parker
- School of Public Health, University of Pittsburgh, Pittsburgh, USA
| | - R Mark Richardson
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, USA
- Department of Neurosurgery, Harvard Medical School and Massachusetts General Hospital, Boston, USA
| | - Charles E Schroeder
- Nathan Kline Institute for Psychiatric Research, Orangeburg, USA
- Departments of Neurosurgery and Psychiatry, Columbia University, New York, USA
| | | | - Avniel Singh Ghuman
- Center for the Neural Basis of Cognition, Carnegie Mellon University and University of Pittsburgh, Pittsburgh, USA
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, USA
- Brain Institute, University of Pittsburgh, Pittsburgh, USA
- Departments of Psychology, Neurobiology, and Psychiatry, University of Pittsburgh, Pittsburgh, USA
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15
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Yuasa K, Groen IIA, Piantoni G, Montenegro S, Flinker A, Devore S, Devinsky O, Doyle W, Dugan P, Friedman D, Ramsey N, Petridou N, Winawer J. Precise Spatial Tuning of Visually Driven Alpha Oscillations in Human Visual Cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.11.528137. [PMID: 36865223 PMCID: PMC9979988 DOI: 10.1101/2023.02.11.528137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/15/2023]
Abstract
Neuronal oscillations at about 10 Hz, called alpha oscillations, are often thought to arise from synchronous activity across occipital cortex, reflecting general cognitive states such as arousal and alertness. However, there is also evidence that modulation of alpha oscillations in visual cortex can be spatially specific. Here, we used intracranial electrodes in human patients to measure alpha oscillations in response to visual stimuli whose location varied systematically across the visual field. We separated the alpha oscillatory power from broadband power changes. The variation in alpha oscillatory power with stimulus position was then fit by a population receptive field (pRF) model. We find that the alpha pRFs have similar center locations to pRFs estimated from broadband power (70-180 Hz), but are several times larger. The results demonstrate that alpha suppression in human visual cortex can be precisely tuned. Finally, we show how the pattern of alpha responses can explain several features of exogenous visual attention. Significance Statement The alpha oscillation is the largest electrical signal generated by the human brain. An important question in systems neuroscience is the degree to which this oscillation reflects system-wide states and behaviors such as arousal, alertness, and attention, versus much more specific functions in the routing and processing of information. We examined alpha oscillations at high spatial precision in human patients with intracranial electrodes implanted over visual cortex. We discovered a surprisingly high spatial specificity of visually driven alpha oscillations, which we quantified with receptive field models. We further use our discoveries about properties of the alpha response to show a link between these oscillations and the spread of visual attention.
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16
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Soper DJ, Reich D, Ross A, Salami P, Cash SS, Basu I, Peled N, Paulk AC. Modular pipeline for reconstruction and localization of implanted intracranial ECoG and sEEG electrodes. PLoS One 2023; 18:e0287921. [PMID: 37418486 PMCID: PMC10328232 DOI: 10.1371/journal.pone.0287921] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 06/15/2023] [Indexed: 07/09/2023] Open
Abstract
Implantation of electrodes in the brain has been used as a clinical tool for decades to stimulate and record brain activity. As this method increasingly becomes the standard of care for several disorders and diseases, there is a growing need to quickly and accurately localize the electrodes once they are placed within the brain. We share here a protocol pipeline for localizing electrodes implanted in the brain, which we have applied to more than 260 patients, that is accessible to multiple skill levels and modular in execution. This pipeline uses multiple software packages to prioritize flexibility by permitting multiple different parallel outputs while minimizing the number of steps for each output. These outputs include co-registered imaging, electrode coordinates, 2D and 3D visualizations of the implants, automatic surface and volumetric localizations of the brain regions per electrode, and anonymization and data sharing tools. We demonstrate here some of the pipeline's visualizations and automatic localization algorithms which we have applied to determine appropriate stimulation targets, to conduct seizure dynamics analysis, and to localize neural activity from cognitive tasks in previous studies. Further, the output facilitates the extraction of information such as the probability of grey matter intersection or the nearest anatomic structure per electrode contact across all data sets that go through the pipeline. We expect that this pipeline will be a useful framework for researchers and clinicians alike to localize implanted electrodes in the human brain.
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Affiliation(s)
- Daniel J. Soper
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, United States of America
- Department of Neurology, Harvard Medical School, Boston, MA, United States of America
| | - Dustine Reich
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, United States of America
- Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Alex Ross
- Department of Neurosurgery, University of Cincinnati College of Medicine, Cincinnati, OH, United States of America
| | - Pariya Salami
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, United States of America
- Department of Neurology, Harvard Medical School, Boston, MA, United States of America
| | - Sydney S. Cash
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, United States of America
- Department of Neurology, Harvard Medical School, Boston, MA, United States of America
| | - Ishita Basu
- Department of Neurosurgery, University of Cincinnati College of Medicine, Cincinnati, OH, United States of America
| | - Noam Peled
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States of America
- Harvard Medical School, Boston, MA, United States of America
| | - Angelique C. Paulk
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Boston, MA, United States of America
- Department of Neurology, Harvard Medical School, Boston, MA, United States of America
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17
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Jensen MA, Huang H, Valencia GO, Klassen BT, van den Boom MA, Kaufmann TJ, Schalk G, Brunner P, Worrell GA, Hermes D, Miller KJ. A motor association area in the depths of the central sulcus. Nat Neurosci 2023; 26:1165-1169. [PMID: 37202552 PMCID: PMC10322697 DOI: 10.1038/s41593-023-01346-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 04/24/2023] [Indexed: 05/20/2023]
Abstract
Cells in the precentral gyrus directly send signals to the periphery to generate movement and are principally organized as a topological map of the body. We find that movement-induced electrophysiological responses from depth electrodes extend this map three-dimensionally throughout the gyrus. Unexpectedly, this organization is interrupted by a previously undescribed motor association area in the depths of the midlateral aspect of the central sulcus. This 'Rolandic motor association' (RMA) area is active during movements of different body parts from both sides of the body and may be important for coordinating complex behaviors.
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Affiliation(s)
- Michael A Jensen
- Medical Scientist Training Program, Mayo Clinic, Rochester, MN, USA.
- Neurosurgery, Mayo Clinic, Rochester, MN, USA.
| | - Harvey Huang
- Medical Scientist Training Program, Mayo Clinic, Rochester, MN, USA
| | | | | | - Max A van den Boom
- Neurosurgery, Mayo Clinic, Rochester, MN, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | | | - Gerwin Schalk
- Neurosurgery, Mayo Clinic, Rochester, MN, USA
- Chen Frontier Lab for Applied Neurotechnology, Tianqiao and Chrissy Chen Institute, Shanghai, China
- Neurosurgery, Fudan University/Huashan Hospital, Shanghai, China
| | - Peter Brunner
- Neurosurgery, Washington University School of Medicine, St Louis, MO, USA
| | - Gregory A Worrell
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
- Neurology, Mayo Clinic, Rochester, MN, USA
| | - Dora Hermes
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Kai J Miller
- Neurosurgery, Mayo Clinic, Rochester, MN, USA.
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA.
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18
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Blenkmann AO, Leske SL, Llorens A, Lin JJ, Chang E, Brunner P, Schalk G, Ivanovic J, Larsson PG, Knight RT, Endestad T, Solbakk AK. Anatomical registration of intracranial electrodes. Robust model-based localization and deformable smooth brain-shift compensation methods. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.08.539503. [PMID: 37214984 PMCID: PMC10197594 DOI: 10.1101/2023.05.08.539503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Precise electrode localization is important for maximizing the utility of intracranial EEG data. Electrodes are typically localized from post-implantation CT artifacts, but algorithms can fail due to low signal-to-noise ratio, unrelated artifacts, or high-density electrode arrays. Minimizing these errors usually requires time-consuming visual localization and can still result in inaccurate localizations. In addition, surgical implantation of grids and strips typically introduces non-linear brain deformations, which result in anatomical registration errors when post-implantation CT images are fused with the pre-implantation MRI images. Several projection methods are currently available, but they either fail to produce smooth solutions or do not account for brain deformations. To address these shortcomings, we propose two novel algorithms for the anatomical registration of intracranial electrodes that are almost fully automatic and provide highly accurate results. We first present GridFit, an algorithm that simultaneously localizes all contacts in grids, strips, or depth arrays by fitting flexible models to the electrodes' CT artifacts. We observed localization errors of less than one millimeter (below 8% relative to the inter-electrode distance) and robust performance under the presence of noise, unrelated artifacts, and high-density implants when we ran ~6000 simulated scenarios. Furthermore, we validated the method with real data from 20 intracranial patients. As a second registration step, we introduce CEPA, a brain-shift compensation algorithm that combines orthogonal-based projections, spring-mesh models, and spatial regularization constraints. When tested with real data from 15 patients, anatomical registration errors were smaller than those obtained for well-established alternatives. Additionally, CEPA accounted simultaneously for simple mechanical deformation principles, which is not possible with other available methods. Inter-electrode distances of projected coordinates smoothly changed across neighbor electrodes, while changes in inter-electrode distances linearly increased with projection distance. Moreover, in an additional validation procedure, we found that modeling resting-state high-frequency activity (75-145 Hz ) in five patients further supported our new algorithm. Together, GridFit and CEPA constitute a versatile set of tools for the registration of subdural grid, strip, and depth electrode coordinates that provide highly accurate results even in the most challenging implantation scenarios. The methods presented here are implemented in the iElectrodes open-source toolbox, making their use simple, accessible, and straightforward to integrate with other popular toolboxes used for analyzing electrophysiological data.
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Affiliation(s)
- Alejandro Omar Blenkmann
- Department of Psychology, University of Oslo, Norway
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time, and Motion, University of Oslo, Norway
| | - Sabine Liliana Leske
- Department of Musicology, University of Oslo, Norway
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time, and Motion, University of Oslo, Norway
| | - Anaïs Llorens
- Department of Psychology, University of Oslo, Norway
- Department of Psychology and the Helen Wills Neuroscience Institute, University of California, Berkeley, USA
| | - Jack J. Lin
- Department of Neurology and Center for Mind and Brain, University of California, Davis, USA
| | - Edward Chang
- Department of Neurological Surgery, University of California, San Francisco, USA
| | - Peter Brunner
- Department of Neurology, Albany Medical College, Albany, NY, USA
- National Center for Adaptive Neurotechnologies, Albany, NY, USA
| | - Gerwin Schalk
- Department of Neurology, Albany Medical College, Albany, NY, USA
- National Center for Adaptive Neurotechnologies, Albany, NY, USA
- Tianqiao and Chrissy Chen Institute, Chen Frontier Lab for Applied Neurotechnology, Shanghai, China
- Fudan University/Huashan Hospital, Department of Neurosurgery, Shanghai, China
| | | | | | - Robert Thomas Knight
- Department of Psychology and the Helen Wills Neuroscience Institute, University of California, Berkeley, USA
| | - Tor Endestad
- Department of Psychology, University of Oslo, Norway
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time, and Motion, University of Oslo, Norway
- Department of Neuropsychology, Helgeland Hospital, Mosjøen, Norway
| | - Anne-Kristin Solbakk
- Department of Psychology, University of Oslo, Norway
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time, and Motion, University of Oslo, Norway
- Department of Neurosurgery, Oslo University Hospital, Norway
- Department of Neuropsychology, Helgeland Hospital, Mosjøen, Norway
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19
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Developmental trajectory of transmission speed in the human brain. Nat Neurosci 2023; 26:537-541. [PMID: 36894655 PMCID: PMC10076215 DOI: 10.1038/s41593-023-01272-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 02/09/2023] [Indexed: 03/11/2023]
Abstract
The structure of the human connectome develops from childhood throughout adolescence to middle age, but how these structural changes affect the speed of neuronal signaling is not well described. In 74 subjects, we measured the latency of cortico-cortical evoked responses across association and U-fibers and calculated their corresponding transmission speeds. Decreases in conduction delays until at least 30 years show that the speed of neuronal communication develops well into adulthood.
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20
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Assem M, Hart MG, Coelho P, Romero-Garcia R, McDonald A, Woodberry E, Morris RC, Price SJ, Suckling J, Santarius T, Duncan J, Erez Y. High gamma activity distinguishes frontal cognitive control regions from adjacent cortical networks. Cortex 2023; 159:286-298. [PMID: 36645968 PMCID: PMC9946792 DOI: 10.1016/j.cortex.2022.12.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 12/14/2022] [Accepted: 12/15/2022] [Indexed: 12/28/2022]
Abstract
Though the lateral frontal cortex is broadly implicated in cognitive control, functional MRI (fMRI) studies suggest fine-grained distinctions within this region. To examine this question electrophysiologically, we placed electrodes on the lateral frontal cortex in patients undergoing awake craniotomy for tumor resection. Patients performed verbal tasks with a manipulation of attentional switching, a canonical control demand. Power in the high gamma range (70-250 Hz) distinguished electrodes based on their location within a high-resolution fMRI network parcellation of the frontal lobe. Electrodes within the canonical fronto-parietal control network showed increased power in the switching condition, a result absent in electrodes within default mode, language and somato-motor networks. High gamma results contrasted with spatially distributed power decreases in the beta range (12-30 Hz). These results confirm the importance of fine-scale functional distinctions within the human frontal lobe, and pave the way for increased precision of functional mapping in tumor surgeries.
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Affiliation(s)
- Moataz Assem
- Medical Research Council, Cognition and Brain Sciences Unit, University of Cambridge UK.
| | - Michael G Hart
- Department of Neurosurgery, Cambridge University Hospitals NHS Foundation Trust UK; St George's, University of London & St George's University Hospitals NHS Foundation Trust, Institute of Molecular and Clinical Sciences UK
| | | | - Rafael Romero-Garcia
- Department of Psychiatry, University of Cambridge UK; Department of Medical Physiology and Biophysics, Instituto de Biomedicina de Sevilla (IBiS) HUVR/CSIC/Universidad de Sevilla/CIBERSAM, ISCIII, Sevilla, Spain
| | - Alexa McDonald
- Department of Neuropsychology, Cambridge University Hospitals NHS Foundation Trust UK
| | - Emma Woodberry
- Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge UK
| | - Robert C Morris
- Department of Neurosurgery, Cambridge University Hospitals NHS Foundation Trust UK
| | - Stephen J Price
- Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge UK
| | - John Suckling
- Department of Psychiatry, University of Cambridge UK; Behavioural and Clinical Neuroscience Institute, University of Cambridge UK; Cambridge and Peterborough NHS Foundation Trust UK
| | - Thomas Santarius
- Department of Neurosurgery, Cambridge University Hospitals NHS Foundation Trust UK; Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge UK; Department of Physiology, Development and Neuroscience, University of Cambridge UK
| | - John Duncan
- Medical Research Council, Cognition and Brain Sciences Unit, University of Cambridge UK; Department of Experimental Psychology, University of Oxford UK
| | - Yaara Erez
- Faculty of Engineering, Bar-Ilan University, Ramat-Gan, Israel; Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel; Medical Research Council, Cognition and Brain Sciences Unit, University of Cambridge UK.
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21
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Lottner T, Reiss S, Rieger SB, Schuettler M, Fischer J, Bielak L, Özen AC, Bock M. Radio-frequency induced heating of intra-cranial EEG electrodes: The more the colder? Neuroimage 2022; 264:119691. [PMID: 36375783 DOI: 10.1016/j.neuroimage.2022.119691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 09/20/2022] [Accepted: 10/16/2022] [Indexed: 11/13/2022] Open
Abstract
Many neurological disorders are analyzed and treated with implantable electrodes. Many patients with such electrodes have to undergo MRI examinations - often unrelated to their implant - at the risk of radio-frequency induced heating. The number of electrode contact sites of these implants keeps increasing due to improvements in manufacturing and computational algorithms. Electrode grids with multiple receive channels couple to the RF fields present in MRI, but, due to their proximity, a combination of leads has a coupling response which is not a superposition of the individual leads' response. To investigate the problem of RF-induced heating of coupled multi-lead implants, temperature mapping was performed on a set of intra-cranial electroencephalogram (icEEG) electrode grid prototypes with increasing number of contact sites (1-16). Additionally, electric field measurements were used to investigate the radio-frequency heating characteristics of the implants in different media combinations, simulating the device being partially immersed inside the patient. MR measurements show RF-induced heating up to 19.6 K for the single electrode, reducing monotonically with larger number of contact sites to a minimum of 0.9 K for the largest grid. The SAR calculated from temperature measurements agrees well with electric field mapping: The same trend is visible for different insertion lengths, however, the energy dissipated by the whole implant varies with the grid size and insertion length. Thus, in the tested circumstances, a larger electrode number either reduced or had a similar risk of RF induced heating, indicating, that the size of electrode grids is a design parameter, which can be used to change an implants RF response and in turn to reduce the risk of RF induced heating and improve the safety of patient with neuro-implants undergoing MRI examinations.
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Affiliation(s)
- Thomas Lottner
- Division of Medical Physics, Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Simon Reiss
- Division of Medical Physics, Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | | | | | - Johannes Fischer
- Division of Medical Physics, Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Lars Bielak
- Division of Medical Physics, Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Ali C Özen
- Division of Medical Physics, Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Michael Bock
- Division of Medical Physics, Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
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22
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Vansteensel MJ, Branco MP, Leinders S, Freudenburg ZF, Schippers A, Geukes SH, Gaytant MA, Gosselaar PH, Aarnoutse EJ, Ramsey NF. Methodological Recommendations for Studies on the Daily Life Implementation of Implantable Communication-Brain-Computer Interfaces for Individuals With Locked-in Syndrome. Neurorehabil Neural Repair 2022; 36:666-677. [PMID: 36124975 DOI: 10.1177/15459683221125788] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Implantable brain-computer interfaces (BCIs) promise to be a viable means to restore communication in individuals with locked-in syndrome (LIS). In 2016, we presented the world-first fully implantable BCI system that uses subdural electrocorticography electrodes to record brain signals and a subcutaneous amplifier to transmit the signals to the outside world, and that enabled an individual with LIS to communicate via a tablet computer by selecting icons in spelling software. For future clinical implementation of implantable communication-BCIs, however, much work is still needed, for example, to validate these systems in daily life settings with more participants, and to improve the speed of communication. We believe the design and execution of future studies on these and other topics may benefit from the experience we have gained. Therefore, based on relevant literature and our own experiences, we here provide an overview of procedures, as well as recommendations, for recruitment, screening, inclusion, imaging, hospital admission, implantation, training, and support of participants with LIS, for studies on daily life implementation of implantable communication-BCIs. With this article, we not only aim to inform the BCI community about important topics of concern, but also hope to contribute to improved methodological standardization of implantable BCI research.
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Affiliation(s)
- Mariska J Vansteensel
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Mariana P Branco
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Sacha Leinders
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Zac F Freudenburg
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Anouck Schippers
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Simon H Geukes
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Michael A Gaytant
- Department of Pulmonary Diseases/Home Mechanical Ventilation, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Peter H Gosselaar
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Erik J Aarnoutse
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Nick F Ramsey
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
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23
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Patient-specific solution of the electrocorticography forward problem in deforming brain. Neuroimage 2022; 263:119649. [PMID: 36167268 DOI: 10.1016/j.neuroimage.2022.119649] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 08/25/2022] [Accepted: 09/23/2022] [Indexed: 11/22/2022] Open
Abstract
Invasive intracranial electroencephalography (iEEG), or electrocorticography (ECoG), measures electric potential directly on the surface of the brain and can be used to inform treatment planning for epilepsy surgery. Combined with numerical modeling it can further improve accuracy of epilepsy surgery planning. Accurate solution of the iEEG forward problem, which is a crucial prerequisite for solving the iEEG inverse problemin epilepsy seizure onset zone localization, requires accurate representation of the patient's brain geometry and tissue electrical conductivity after implantation of electrodes. However, implantation of subdural grid electrodes causes the brain to deform, which invalidates preoperatively acquired image data. Moreover, postoperative magnetic resonance imaging (MRI) is incompatible with implanted electrodes and computed tomography (CT) has insufficient range of soft tissue contrast, which precludes both MRI and CT from being used to obtain the deformed postoperative geometry. In this paper, we present a biomechanics-based image warping procedure using preoperative MRI for tissue classification and postoperative CT for locating implanted electrodes to perform non-rigid registration of the preoperative image data to the postoperative configuration. We solve the iEEG forward problem on the predicted postoperative geometry using the finite element method (FEM) which accounts for patient-specific inhomogeneity and anisotropy of tissue conductivity. Results for the simulation of a current source in the brain show large differences in electric potential predicted by the models based on the original images and the deformed images corresponding to the brain geometry deformed by placement of invasive electrodes. Computation of the lead field matrix (useful for solution of the iEEG inverse problem) also showed significant differences between the different models. The results suggest that rapid and accurate solution of the forward problem in a deformed brain for a given patient is achievable.
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24
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Woodington BJ, Coles L, Rochford AE, Freeman P, Sawiak S, O'Neill SJK, Scherman OA, Barone DG, Proctor CM, Malliaras GG. X-Ray Markers for Thin Film Implants. Adv Healthc Mater 2022; 11:e2200739. [PMID: 35871265 DOI: 10.1002/adhm.202200739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 06/29/2022] [Indexed: 01/27/2023]
Abstract
Implantable electronic medical devices are used in functional mapping of the brain before surgery and to deliver neuromodulation for the treatment of neurological and neuropsychiatric disorders. Their electrode arrays are assembled by hand, and this leads to bulky form factors with limited flexibility and low electrode counts. Thin film implants, made using microfabrication techniques, are emerging as an attractive alternative, as they offer dramatically improved conformability and enable high density recording and stimulation. A major limitation of these devices, however, is that they are invisible to fluoroscopy, the most common method used to monitor the insertion of implantable electrodes. Here, the development of mechanically flexible X-ray markers using bismuth- and barium-infused elastomers is reported. Their X-ray attenuation properties in human cadavers are explored and it is shown that they are biocompatible in cell cultures. It is further shown that they do not distort magnetic resonance imaging images and their integration with thin film implants is demonstrated. This work removes a key barrier for the adoption of thin film implants in brain mapping and in neuromodulation.
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Affiliation(s)
- Ben J Woodington
- Electrical Engineering Division, Department of Engineering, University of Cambridge, Cambridge, CB3 0FA, UK
| | - Lawrence Coles
- Electrical Engineering Division, Department of Engineering, University of Cambridge, Cambridge, CB3 0FA, UK
| | - Amy E Rochford
- Electrical Engineering Division, Department of Engineering, University of Cambridge, Cambridge, CB3 0FA, UK
| | - Paul Freeman
- Department of Veterinary Medicine, University of Cambridge, Cambridge, CB3 0ES, UK
| | - Stephen Sawiak
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Stephen J K O'Neill
- Melville Laboratory for Polymer Synthesis, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
| | - Oren A Scherman
- Melville Laboratory for Polymer Synthesis, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
| | - Damiano G Barone
- Electrical Engineering Division, Department of Engineering, University of Cambridge, Cambridge, CB3 0FA, UK.,Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Christopher M Proctor
- Electrical Engineering Division, Department of Engineering, University of Cambridge, Cambridge, CB3 0FA, UK
| | - George G Malliaras
- Electrical Engineering Division, Department of Engineering, University of Cambridge, Cambridge, CB3 0FA, UK
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25
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van Dijk JA, de Jong MC, Piantoni G, Fracasso A, Vansteensel MJ, Groen IIA, Petridou N, Dumoulin SO. Intracranial recordings show evidence of numerosity tuning in human parietal cortex. PLoS One 2022; 17:e0272087. [PMID: 35921261 PMCID: PMC9348694 DOI: 10.1371/journal.pone.0272087] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 07/12/2022] [Indexed: 11/25/2022] Open
Abstract
Numerosity is the set size of a group of items. Numerosity perception is a trait shared across numerous species. Numerosity-selective neural populations are thought to underlie numerosity perception. These neurons have been identified primarily using electrical recordings in animal models and blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) in humans. Here we use electrical intracranial recordings to investigate numerosity tuning in humans, focusing on high-frequency transient activations. These recordings combine a high spatial and temporal resolution and can bridge the gap between animal models and human recordings. In line with previous studies, we find numerosity-tuned responses at parietal sites in two out of three participants. Neuronal populations at these locations did not respond to other visual stimuli, i.e. faces, houses, and letters, in contrast to several occipital sites. Our findings further corroborate the specificity of numerosity tuning of in parietal cortex, and further link fMRI results and electrophysiological recordings.
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Affiliation(s)
- Jelle A. van Dijk
- Spinoza Centre for Neuroimaging, Amsterdam, The Netherlands
- Experimental Psychology, Utrecht University, Utrecht, The Netherlands
| | - Maartje C. de Jong
- Spinoza Centre for Neuroimaging, Amsterdam, The Netherlands
- Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
- Amsterdam Brain and Cognition (ABC), University of Amsterdam, Amsterdam, The Netherlands
| | - Gio Piantoni
- Radiology Department, Imaging Division, Center for Image Sciences, University Medical Center Utrecht, The Netherlands
| | - Alessio Fracasso
- Spinoza Centre for Neuroimaging, Amsterdam, The Netherlands
- Radiology Department, Imaging Division, Center for Image Sciences, University Medical Center Utrecht, The Netherlands
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom
| | - Mariska J. Vansteensel
- UMC Utrecht Brain Center, Department Neurology and Neurosurgery, UMC Utrecht, Utrecht, The Netherlands
| | - Iris. I. A. Groen
- Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands
- Department of Psychology, New York University, New York, United States of America
| | - Natalia Petridou
- Radiology Department, Imaging Division, Center for Image Sciences, University Medical Center Utrecht, The Netherlands
| | - Serge O. Dumoulin
- Spinoza Centre for Neuroimaging, Amsterdam, The Netherlands
- Experimental Psychology, Utrecht University, Utrecht, The Netherlands
- Experimental and Applied Psychology, VU University, Amsterdam, The Netherlands
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26
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Mercier MR, Dubarry AS, Tadel F, Avanzini P, Axmacher N, Cellier D, Vecchio MD, Hamilton LS, Hermes D, Kahana MJ, Knight RT, Llorens A, Megevand P, Melloni L, Miller KJ, Piai V, Puce A, Ramsey NF, Schwiedrzik CM, Smith SE, Stolk A, Swann NC, Vansteensel MJ, Voytek B, Wang L, Lachaux JP, Oostenveld R. Advances in human intracranial electroencephalography research, guidelines and good practices. Neuroimage 2022; 260:119438. [PMID: 35792291 DOI: 10.1016/j.neuroimage.2022.119438] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 05/23/2022] [Accepted: 06/30/2022] [Indexed: 12/11/2022] Open
Abstract
Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.
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27
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Tan KM, Daitch AL, Pinheiro-Chagas P, Fox KCR, Parvizi J, Lieberman MD. Electrocorticographic evidence of a common neurocognitive sequence for mentalizing about the self and others. Nat Commun 2022; 13:1919. [PMID: 35395826 PMCID: PMC8993891 DOI: 10.1038/s41467-022-29510-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 03/11/2022] [Indexed: 01/12/2023] Open
Abstract
Neuroimaging studies of mentalizing (i.e., theory of mind) consistently implicate the default mode network (DMN). Nevertheless, the social cognitive functions of individual DMN regions remain unclear, perhaps due to limited spatiotemporal resolution in neuroimaging. Here we use electrocorticography (ECoG) to directly record neuronal population activity while 16 human participants judge the psychological traits of themselves and others. Self- and other-mentalizing recruit near-identical cortical sites in a common spatiotemporal sequence. Activations begin in the visual cortex, followed by temporoparietal DMN regions, then finally in medial prefrontal regions. Moreover, regions with later activations exhibit stronger functional specificity for mentalizing, stronger associations with behavioral responses, and stronger self/other differentiation. Specifically, other-mentalizing evokes slower and longer activations than self-mentalizing across successive DMN regions, implying lengthier processing at higher levels of representation. Our results suggest a common neurocognitive pathway for self- and other-mentalizing that follows a complex spatiotemporal gradient of functional specialization across DMN and beyond.
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Affiliation(s)
- Kevin M. Tan
- grid.19006.3e0000 0000 9632 6718Social Cognitive Neuroscience Laboratory, Department of Psychology, University of California, Los Angeles, CA USA
| | - Amy L. Daitch
- grid.168010.e0000000419368956Laboratory of Behavioral and Cognitive Neuroscience, Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA USA
| | - Pedro Pinheiro-Chagas
- grid.168010.e0000000419368956Laboratory of Behavioral and Cognitive Neuroscience, Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA USA
| | - Kieran C. R. Fox
- grid.168010.e0000000419368956Laboratory of Behavioral and Cognitive Neuroscience, Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA USA ,grid.168010.e0000000419368956School of Medicine, Stanford University, Stanford, CA USA
| | - Josef Parvizi
- grid.168010.e0000000419368956Laboratory of Behavioral and Cognitive Neuroscience, Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA USA ,grid.168010.e0000000419368956School of Medicine, Stanford University, Stanford, CA USA
| | - Matthew D. Lieberman
- grid.19006.3e0000 0000 9632 6718Social Cognitive Neuroscience Laboratory, Department of Psychology, University of California, Los Angeles, CA USA
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28
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Parvizi J, Veit MJ, Barbosa DA, Kucyi A, Perry C, Parker JJ, Shivacharan RS, Chen F, Yih J, Gross JJ, Fisher R, McNab JA, Falco-Walter J, Halpern CH. Complex negative emotions induced by electrical stimulation of the human hypothalamus. Brain Stimul 2022; 15:615-623. [DOI: 10.1016/j.brs.2022.04.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 03/21/2022] [Accepted: 04/05/2022] [Indexed: 11/02/2022] Open
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29
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Berezutskaya J, Vansteensel MJ, Aarnoutse EJ, Freudenburg ZV, Piantoni G, Branco MP, Ramsey NF. Open multimodal iEEG-fMRI dataset from naturalistic stimulation with a short audiovisual film. Sci Data 2022; 9:91. [PMID: 35314718 PMCID: PMC8938409 DOI: 10.1038/s41597-022-01173-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 01/24/2022] [Indexed: 12/19/2022] Open
Abstract
Intracranial human recordings are a valuable and rare resource of information about the brain. Making such data publicly available not only helps tackle reproducibility issues in science, it helps make more use of these valuable data. This is especially true for data collected using naturalistic tasks. Here, we describe a dataset collected from a large group of human subjects while they watched a short audiovisual film. The dataset has several unique features. First, it includes a large amount of intracranial electroencephalography (iEEG) data (51 participants, age range of 5-55 years, who all performed the same task). Second, it includes functional magnetic resonance imaging (fMRI) recordings (30 participants, age range of 7-47) during the same task. Eighteen participants performed both iEEG and fMRI versions of the task, non-simultaneously. Third, the data were acquired using a rich audiovisual stimulus, for which we provide detailed speech and video annotations. This dataset can be used to study neural mechanisms of multimodal perception and language comprehension, and similarity of neural signals across brain recording modalities.
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Affiliation(s)
- Julia Berezutskaya
- Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, the Netherlands.
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.
| | - Mariska J Vansteensel
- Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Erik J Aarnoutse
- Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Zachary V Freudenburg
- Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Giovanni Piantoni
- Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Mariana P Branco
- Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Nick F Ramsey
- Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, the Netherlands
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30
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Demuru M, van Blooijs D, Zweiphenning W, Hermes D, Leijten F, Zijlmans M. A Practical Workflow for Organizing Clinical Intraoperative and Long-term iEEG Data in BIDS. Neuroinformatics 2022; 20:727-736. [PMID: 35244855 PMCID: PMC9440951 DOI: 10.1007/s12021-022-09567-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/19/2022] [Indexed: 12/31/2022]
Abstract
The neuroscience community increasingly uses the Brain Imaging Data Structure (BIDS) to organize data, extending from MRI to electrophysiology data. While automated tools and workflows are developed that help organize MRI data from the scanner to BIDS, these workflows are lacking for clinical intracranial EEG (iEEG data). We present a practical workflow on how to organize full clinical iEEG epilepsy data into BIDS. We present electrophysiological datasets recorded from twelve subjects who underwent intracranial monitoring followed by resective epilepsy surgery at the University Medical Center Utrecht, the Netherlands, and became seizure-free after surgery. These data include intraoperative electrocorticography recordings from six patients, long-term electrocorticography recordings from three patients and stereo-encephalography recordings from three patients. We describe the 6 steps in the pipeline that are essential to structure the data from these clinical iEEG recordings into BIDS and the challenges during this process. These proposed workflow enable centers performing clinical iEEG recordings to structure their data to improve accessibility, reusability and interoperability of clinical data.
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Affiliation(s)
- Matteo Demuru
- Dept. of Neurology & Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands ,Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, The Netherlands
| | - Dorien van Blooijs
- Dept. of Neurology & Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Willemiek Zweiphenning
- Dept. of Neurology & Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Dora Hermes
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN USA
| | - Frans Leijten
- Dept. of Neurology & Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Maeike Zijlmans
- Dept. of Neurology & Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands ,Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, The Netherlands
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31
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Davis TS, Caston RM, Philip B, Charlebois CM, Anderson DN, Weaver KE, Smith EH, Rolston JD. LeGUI: A Fast and Accurate Graphical User Interface for Automated Detection and Anatomical Localization of Intracranial Electrodes. Front Neurosci 2021; 15:769872. [PMID: 34955721 PMCID: PMC8695687 DOI: 10.3389/fnins.2021.769872] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 11/18/2021] [Indexed: 11/24/2022] Open
Abstract
Accurate anatomical localization of intracranial electrodes is important for identifying the seizure foci in patients with epilepsy and for interpreting effects from cognitive studies employing intracranial electroencephalography. Localization is typically performed by coregistering postimplant computed tomography (CT) with preoperative magnetic resonance imaging (MRI). Electrodes are then detected in the CT, and the corresponding brain region is identified using the MRI. Many existing software packages for electrode localization chain together separate preexisting programs or rely on command line instructions to perform the various localization steps, making them difficult to install and operate for a typical user. Further, many packages provide solutions for some, but not all, of the steps needed for confident localization. We have developed software, Locate electrodes Graphical User Interface (LeGUI), that consists of a single interface to perform all steps needed to localize both surface and depth/penetrating intracranial electrodes, including coregistration of the CT to MRI, normalization of the MRI to the Montreal Neurological Institute template, automated electrode detection for multiple types of electrodes, electrode spacing correction and projection to the brain surface, electrode labeling, and anatomical targeting. The software is written in MATLAB, core image processing is performed using the Statistical Parametric Mapping toolbox, and standalone executable binaries are available for Windows, Mac, and Linux platforms. LeGUI was tested and validated on 51 datasets from two universities. The total user and computational time required to process a single dataset was approximately 1 h. Automatic electrode detection correctly identified 4362 of 4695 surface and depth electrodes with only 71 false positives. Anatomical targeting was verified by comparing electrode locations from LeGUI to locations that were assigned by an experienced neuroanatomist. LeGUI showed a 94% match with the 482 neuroanatomist-assigned locations. LeGUI combines all the features needed for fast and accurate anatomical localization of intracranial electrodes into a single interface, making it a valuable tool for intracranial electrophysiology research.
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Affiliation(s)
- Tyler S Davis
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, United States
| | - Rose M Caston
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States
| | - Brian Philip
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States
| | - Chantel M Charlebois
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States
| | - Daria Nesterovich Anderson
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, United States.,Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT, United States
| | - Kurt E Weaver
- Department of Radiology, University of Washington, Seattle, WA, United States.,Department of Biological Structure, University of Washington, Seattle, WA, United States
| | - Elliot H Smith
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, United States
| | - John D Rolston
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, United States.,Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States
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32
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Anderson DN, Charlebois CM, Smith EH, Arain AM, Davis TS, Rolston JD. Probabilistic comparison of gray and white matter coverage between depth and surface intracranial electrodes in epilepsy. Sci Rep 2021; 11:24155. [PMID: 34921176 PMCID: PMC8683494 DOI: 10.1038/s41598-021-03414-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 11/23/2021] [Indexed: 11/20/2022] Open
Abstract
In this study, we quantified the coverage of gray and white matter during intracranial electroencephalography in a cohort of epilepsy patients with surface and depth electrodes. We included 65 patients with strip electrodes (n = 12), strip and grid electrodes (n = 24), strip, grid, and depth electrodes (n = 7), or depth electrodes only (n = 22). Patient-specific imaging was used to generate probabilistic gray and white matter maps and atlas segmentations. Gray and white matter coverage was quantified using spherical volumes centered on electrode centroids, with radii ranging from 1 to 15 mm, along with detailed finite element models of local electric fields. Gray matter coverage was highly dependent on the chosen radius of influence (RoI). Using a 2.5 mm RoI, depth electrodes covered more gray matter than surface electrodes; however, surface electrodes covered more gray matter at RoI larger than 4 mm. White matter coverage and amygdala and hippocampal coverage was greatest for depth electrodes at all RoIs. This study provides the first probabilistic analysis to quantify coverage for different intracranial recording configurations. Depth electrodes offer increased coverage of gray matter over other recording strategies if the desired signals are local, while subdural grids and strips sample more gray matter if the desired signals are diffuse.
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Affiliation(s)
- Daria Nesterovich Anderson
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, USA. .,Department of Pharmacology and Toxicology, University of Utah, Salt Lake City, UT, USA.
| | - Chantel M Charlebois
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
| | - Elliot H Smith
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, USA
| | - Amir M Arain
- Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Tyler S Davis
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, USA
| | - John D Rolston
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, USA. .,Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA.
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33
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Piantoni G, Hermes D, Ramsey N, Petridou N. Size of the spatial correlation between ECoG and fMRI activity. Neuroimage 2021; 242:118459. [PMID: 34371189 PMCID: PMC10627020 DOI: 10.1016/j.neuroimage.2021.118459] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 07/13/2021] [Accepted: 08/04/2021] [Indexed: 10/20/2022] Open
Abstract
Electrocorticography (ECoG) is typically employed to accurately identify the seizure focus as well as the location of brain functions to be spared during surgical resection in participants with drug-resistant epilepsy. Increasingly, this technique has become a powerful tool to map cognitive functions onto brain regions. Cortical mapping is more commonly investigated with functional MRI (fMRI), which measures blood-oxygen level dependent (BOLD) changes induced by neuronal activity. The multimodal integration between typical 3T fMRI activity maps and ECoG measurements can provide unique insight into the spatiotemporal aspects of cognition. However, the optimal integration of fMRI and ECoG requires fundamental insight into the spatial smoothness of the BOLD signal under each electrode. Here we use ECoG as ground truth for the extent of activity, as each electrode is thought to record from the cortical tissue directly underneath the contact, to estimate the spatial smoothness of the associated BOLD response at 3T fMRI. We compared the high-frequency broadband (HFB) activity recorded with ECoG while participants performed a motor task. Activity maps were obtained with fMRI at 3T for the same task in the same participant prior to surgery. We then correlated HFB power with the fMRI BOLD signal change in the area around each electrode. This latter measure was quantified by applying a 3D Gaussian kernel of varying width (sigma between 1 mm and 20 mm) to the fMRI maps including only gray-matter. We found that the correlation between HFB and BOLD activity increased sharply up to the point when the kernel width was set to 4 mm, which we defined as the kernel width of maximal spatial specificity. After this point, as the kernel width increased, the highest level of explained variance was reached at a kernel width of 9 mm for most participants. Intriguingly, maximal specificity was also limited to 4 mm for low-frequency bands, such as alpha and beta, but the kernel width with the highest explained variance was less spatially limited than the HFB. In summary, spatial specificity is limited to a kernel width of 4 mm but explained variance keeps on increasing as you average over more and more voxels containing the relatively noisy BOLD signal. Future multimodal studies should choose the kernel width based on their research goal. For maximal spatial specificity, ECoG electrodes are best compared to 3T fMRI with a kernel width of 4 mm. When optimizing the correlation between modalities, highest explained variance can be obtained at larger kernel widths of 9 mm, at the expense of spatial specificity. Finally, we release the complete pipeline so that researchers can estimate the most appropriate kernel width from their multimodal datasets.
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Affiliation(s)
- Giovanni Piantoni
- Dept Neurology & Neurosurgery, UMC Utrecht, Heidelberglaan 100, Utrecht 3584 CX, the Netherlands.
| | - Dora Hermes
- Dept Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN, United States; Dept Neurology, Mayo Clinic, Rochester, MN, United States; Dept Radiology, Mayo Clinic, Rochester, MN, United States.
| | - Nick Ramsey
- Dept Neurology & Neurosurgery, UMC Utrecht, Heidelberglaan 100, Utrecht 3584 CX, the Netherlands.
| | - Natalia Petridou
- Dept Radiology, UMC Utrecht, Heidelberglaan 100, Utrecht, the Netherlands.
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34
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Huang H, Valencia GO, Hermes D, Miller KJ. A canonical visualization tool for SEEG electrodes. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:6175-6178. [PMID: 34892526 PMCID: PMC9035306 DOI: 10.1109/embc46164.2021.9630724] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Stereoencephalographic (SEEG) electrodes are clinically implanted into the brains of patients with refractory epilepsy to locate foci of seizure onset. They are increasingly used in neurophysiology research to determine focal human brain activity in response to tasks or stimuli. Clear visualization of SEEG electrode location with respect to patient anatomy on magnetic resonance image (MRI) scan is vital to neuroscientific understanding. An intuitive way to accomplish this is to plot brain activity and labels at electrode locations on closest MRI slices along the canonical axial, coronal, and sagittal planes. Therefore, we've developed an open-source software tool in Matlab for visualizing SEEG electrode positions, determined from computed tomography (CT), onto canonical planes of resliced brain MRI. The code and graphical user interface are available at: https://github.com/MultimodalNeuroimagingLab/mnl_seegviewClinical Relevance- This tool enables precise communication of SEEG electrode activity and location by visualization on slices of MRI in canonical axial, coronal, and sagittal planes.
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35
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Miller KJ, Müller KR, Hermes D. Basis profile curve identification to understand electrical stimulation effects in human brain networks. PLoS Comput Biol 2021; 17:e1008710. [PMID: 34473701 PMCID: PMC8412306 DOI: 10.1371/journal.pcbi.1008710] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 07/21/2021] [Indexed: 11/21/2022] Open
Abstract
Brain networks can be explored by delivering brief pulses of electrical current in one area while measuring voltage responses in other areas. We propose a convergent paradigm to study brain dynamics, focusing on a single brain site to observe the average effect of stimulating each of many other brain sites. Viewed in this manner, visually-apparent motifs in the temporal response shape emerge from adjacent stimulation sites. This work constructs and illustrates a data-driven approach to determine characteristic spatiotemporal structure in these response shapes, summarized by a set of unique "basis profile curves" (BPCs). Each BPC may be mapped back to underlying anatomy in a natural way, quantifying projection strength from each stimulation site using simple metrics. Our technique is demonstrated for an array of implanted brain surface electrodes in a human patient. This framework enables straightforward interpretation of single-pulse brain stimulation data, and can be applied generically to explore the diverse milieu of interactions that comprise the connectome.
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Affiliation(s)
- Kai J. Miller
- Department of Neurological Surgery, Mayo Clinic, Rochester, Minnesota, United States of America
- Department of Biomedical Engineering & Physiology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Klaus-Robert Müller
- Google Research, Brain Team, Berlin, Germany
- Machine Learning Group, Department of Computer Science, Berlin Institute of Technology, Berlin, Germany
- Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea
- Max Planck Institute for Informatics, Saarbrücken, Germany
| | - Dora Hermes
- Department of Biomedical Engineering & Physiology, Mayo Clinic, Rochester, Minnesota, United States of America
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36
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Charlebois CM, Caldwell DJ, Rampersad SM, Janson AP, Ojemann JG, Brooks DH, MacLeod RS, Butson CR, Dorval AD. Validating Patient-Specific Finite Element Models of Direct Electrocortical Stimulation. Front Neurosci 2021; 15:691701. [PMID: 34408621 PMCID: PMC8365306 DOI: 10.3389/fnins.2021.691701] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 07/12/2021] [Indexed: 11/13/2022] Open
Abstract
Direct electrocortical stimulation (DECS) with electrocorticography electrodes is an established therapy for epilepsy and an emerging application for stroke rehabilitation and brain-computer interfaces. However, the electrophysiological mechanisms that result in a therapeutic effect remain unclear. Patient-specific computational models are promising tools to predict the voltages in the brain and better understand the neural and clinical response to DECS, but the accuracy of such models has not been directly validated in humans. A key hurdle to modeling DECS is accurately locating the electrodes on the cortical surface due to brain shift after electrode implantation. Despite the inherent uncertainty introduced by brain shift, the effects of electrode localization parameters have not been investigated. The goal of this study was to validate patient-specific computational models of DECS against in vivo voltage recordings obtained during DECS and quantify the effects of electrode localization parameters on simulated voltages on the cortical surface. We measured intracranial voltages in six epilepsy patients during DECS and investigated the following electrode localization parameters: principal axis, Hermes, and Dykstra electrode projection methods combined with 0, 1, and 2 mm of cerebral spinal fluid (CSF) below the electrodes. Greater CSF depth between the electrode and cortical surface increased model errors and decreased predicted voltage accuracy. The electrode localization parameters that best estimated the recorded voltages across six patients with varying amounts of brain shift were the Hermes projection method and a CSF depth of 0 mm (r = 0.92 and linear regression slope = 1.21). These results are the first to quantify the effects of electrode localization parameters with in vivo intracranial recordings and may serve as the basis for future studies investigating the neuronal and clinical effects of DECS for epilepsy, stroke, and other emerging closed-loop applications.
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Affiliation(s)
- Chantel M Charlebois
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States.,Scientific Computing and Imaging (SCI) Institute, University of Utah, Salt Lake City, UT, United States
| | - David J Caldwell
- Department of Bioengineering, University of Washington, Seattle, WA, United States.,Center for Neurotechnology, University of Washington, Seattle, WA, United States.,Medical Scientist Training Program, University of Washington, Seattle, WA, United States
| | - Sumientra M Rampersad
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States
| | - Andrew P Janson
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States.,Scientific Computing and Imaging (SCI) Institute, University of Utah, Salt Lake City, UT, United States
| | - Jeffrey G Ojemann
- Department of Neurological Surgery, University of Washington, Seattle, WA, United States
| | - Dana H Brooks
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States
| | - Rob S MacLeod
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States.,Scientific Computing and Imaging (SCI) Institute, University of Utah, Salt Lake City, UT, United States
| | - Christopher R Butson
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States.,Scientific Computing and Imaging (SCI) Institute, University of Utah, Salt Lake City, UT, United States.,Department of Neurology, Neurosurgery and Psychiatry, University of Utah, Salt Lake City, UT, United States
| | - Alan D Dorval
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States
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37
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Boring MJ, Silson EH, Ward MJ, Richardson RM, Fiez JA, Baker CI, Ghuman AS. Multiple Adjoining Word- and Face-Selective Regions in Ventral Temporal Cortex Exhibit Distinct Dynamics. J Neurosci 2021; 41:6314-6327. [PMID: 34099511 PMCID: PMC8287994 DOI: 10.1523/jneurosci.3234-20.2021] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 05/26/2021] [Accepted: 06/01/2021] [Indexed: 11/21/2022] Open
Abstract
The map of category-selectivity in human ventral temporal cortex (VTC) provides organizational constraints to models of object recognition. One important principle is lateral-medial response biases to stimuli that are typically viewed in the center or periphery of the visual field. However, little is known about the relative temporal dynamics and location of regions that respond preferentially to stimulus classes that are centrally viewed, such as the face- and word-processing networks. Here, word- and face-selective regions within VTC were mapped using intracranial recordings from 36 patients. Partially overlapping, but also anatomically dissociable patches of face- and word-selectivity, were found in VTC. In addition to canonical word-selective regions along the left posterior occipitotemporal sulcus, selectivity was also located medial and anterior to face-selective regions on the fusiform gyrus at the group level and within individual male and female subjects. These regions were replicated using 7 Tesla fMRI in healthy subjects. Left hemisphere word-selective regions preceded right hemisphere responses by 125 ms, potentially reflecting the left hemisphere bias for language, with no hemispheric difference in face-selective response latency. Word-selective regions along the posterior fusiform responded first, then spread medially and laterally, then anteriorally. Face-selective responses were first seen in posterior fusiform regions bilaterally, then proceeded anteriorally from there. For both words and faces, the relative delay between regions was longer than would be predicted by purely feedforward models of visual processing. The distinct time courses of responses across these regions, and between hemispheres, suggest that a complex and dynamic functional circuit supports face and word perception.SIGNIFICANCE STATEMENT Representations of visual objects in the human brain have been shown to be organized by several principles, including whether those objects tend to be viewed centrally or peripherally in the visual field. However, it remains unclear how regions that process objects that are viewed centrally, such as words and faces, are organized relative to one another. Here, invasive and noninvasive neuroimaging suggests that there is a mosaic of regions in ventral temporal cortex that respond selectively to either words or faces. These regions display differences in the strength and timing of their responses, both within and between brain hemispheres, suggesting that they play different roles in perception. These results illuminate extended, bilateral, and dynamic brain pathways that support face perception and reading.
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Affiliation(s)
- Matthew J Boring
- Center for Neuroscience at the University of Pittsburgh, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
- Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania 15213
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania 15213
| | - Edward H Silson
- National Institute of Mental Health, National Institutes of Health, Magnuson Clinical Center, Bethesda, Maryland 20814
- School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, EH8 9JZ, United Kingdom
| | - Michael J Ward
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania 15213
| | - R Mark Richardson
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania 15213
- Department of Neurosurgery, Massachusetts General Hospital, Boston, Massachusetts 02144
- Harvard Medical School, Boston, Massachusetts 02115
| | - Julie A Fiez
- Center for Neuroscience at the University of Pittsburgh, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
- Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania 15213
- Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
| | - Chris I Baker
- National Institute of Mental Health, National Institutes of Health, Magnuson Clinical Center, Bethesda, Maryland 20814
| | - Avniel Singh Ghuman
- Center for Neuroscience at the University of Pittsburgh, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
- Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania 15213
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania 15213
- Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260
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38
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Li W, Li J, Cao D, Luo N, Jiang T. Neural Mechanism of Noise Affecting Face Recognition. Neuroscience 2021; 468:211-219. [PMID: 34147562 DOI: 10.1016/j.neuroscience.2021.06.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 06/09/2021] [Accepted: 06/11/2021] [Indexed: 10/21/2022]
Abstract
Face recognition is one of the most important cognitive functions for humans in social activities. The ability will be negatively affected when the face images deteriorate. However, the neural process of extracting facial information under challenging conditions is still poorly understood. Therefore, it is necessary to further understand the neurophysiological relevance of this effect. We examined patients with multiple subdural electrodes (ECoG) monitored for clinical purposes. During the experimental task, the patients were presented with face and house images with different noise levels and were asked to recognize the faces. We found a striking increase in high gamma band power (HGP; 60-160 Hz) when face images were shown. We localized the face-specific electrodes to the fusiform gyrus (FG) and surrounding cortices. For each subject, the behavioral performance and magnitudes of the HGP for the face-specific sites significantly both fit a sigmoid function and showed similar changes. Additionally, the curve profile of the average HGP magnitude across the face-specific sites was almost equal to the average behavior curve; the former could precisely track the behavioral performance. In general, these results suggest that the HGP in the FG is closely related to the performance of face image recognition.
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Affiliation(s)
- Wenlu Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jin Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Dan Cao
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Na Luo
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; Key Laboratory for Neuro Information of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China; Queensland Brain Institute, University of Queensland, 4072 Brisbane, Australia.
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39
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van den Boom M, Miller KJ, Gregg NM, Ojeda Valencia G, Lee KH, Richner TJ, Ramsey NF, Worrell GA, Hermes D. Typical somatomotor physiology of the hand is preserved in a patient with an amputated arm: An ECoG case study. Neuroimage Clin 2021; 31:102728. [PMID: 34182408 PMCID: PMC8253998 DOI: 10.1016/j.nicl.2021.102728] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 04/17/2021] [Accepted: 05/10/2021] [Indexed: 12/03/2022]
Abstract
Electrophysiological signals in the human motor system may change in different ways after deafferentation, with some studies emphasizing reorganization while others propose retained physiology. Understanding whether motor electrophysiology is retained over longer periods of time can be invaluable for patients with paralysis (e.g. ALS or brainstem stroke) when signals from sensorimotor areas may be used for communication or control over neural prosthetic devices. In addition, a maintained electrophysiology can potentially benefit the treatment of phantom limb pains through prolonged use of these signals in a brain-machine interface (BCI). Here, we were presented with the unique opportunity to investigate the physiology of the sensorimotor cortex in a patient with an amputated arm using electrocorticographic (ECoG) measurements. While implanted with an ECoG grid for clinical evaluation of electrical stimulation for phantom limb pain, the patient performed attempted finger movements with the contralateral (lost) hand and executed finger movements with the ipsilateral (healthy) hand. The electrophysiology of the sensorimotor cortex contralateral to the amputated hand remained very similar to that of hand movement in healthy people, with a spatially focused increase of high-frequency band (65-175 Hz; HFB) power over the hand region and a distributed decrease in low-frequency band (15-28 Hz; LFB) power. The representation of the three different fingers (thumb, index and little) remained intact and HFB patterns could be decoded using support vector learning at single-trial classification accuracies of >90%, based on the first 1-3 s of the HFB response. These results indicate that hand representations are largely retained in the motor cortex. The intact physiological response of the amputated hand, the high distinguishability of the fingers and fast temporal peak are encouraging for neural prosthetic devices that target the sensorimotor cortex.
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Affiliation(s)
- Max van den Boom
- Department of Physiology and Biomedical Engineering, Mayo Clinic Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA; Department of Neurology & Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands.
| | - Kai J Miller
- Department of Neurosurgery, Mayo Clinic Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Nicholas M Gregg
- Department of Neurology, Mayo Clinic Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Gabriela Ojeda Valencia
- Department of Physiology and Biomedical Engineering, Mayo Clinic Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Kendall H Lee
- Department of Neurosurgery, Mayo Clinic Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Thomas J Richner
- Department of Neurosurgery, Mayo Clinic Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Nick F Ramsey
- Department of Neurology & Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Greg A Worrell
- Department of Neurology, Mayo Clinic Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Dora Hermes
- Department of Physiology and Biomedical Engineering, Mayo Clinic Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
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MRI and CT Fusion in Stereotactic Electroencephalography: A Literature Review. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11125524] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Epilepsy is a common neurological disease characterized by spontaneous recurrent seizures. Resection of the epileptogenic tissue may be needed in approximately 25% of all cases due to ineffective treatment with anti-epileptic drugs. The surgical intervention depends on the correct detection of epileptogenic zones. The detection relies on invasive diagnostic techniques such as Stereotactic Electroencephalography (SEEG), which uses multi-modal fusion to aid localizing electrodes, using pre-surgical magnetic resonance and intra-surgical computer tomography as the input images. Moreover, it is essential to know how to measure the performance of fusion methods in the presence of external objects, such as electrodes. In this paper, a literature review is presented, applying the methodology proposed by Kitchenham to determine the main techniques of multi-modal brain image fusion, the most relevant performance metrics, and the main fusion tools. The search was conducted using the databases and search engines of Scopus, IEEE, PubMed, Springer, and Google Scholar, resulting in 15 primary source articles. The literature review found that rigid registration was the most used technique when electrode localization in SEEG is required, which was the proposed method in nine of the found articles. However, there is a lack of standard validation metrics, which makes the performance measurement difficult when external objects are presented, caused primarily by the absence of a gold-standard dataset for comparison.
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Erez Y, Assem M, Coelho P, Romero-Garcia R, Owen M, McDonald A, Woodberry E, Morris RC, Price SJ, Suckling J, Duncan J, Hart MG, Santarius T. Intraoperative mapping of executive function using electrocorticography for patients with low-grade gliomas. Acta Neurochir (Wien) 2021; 163:1299-1309. [PMID: 33222010 PMCID: PMC8053659 DOI: 10.1007/s00701-020-04646-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 11/09/2020] [Indexed: 01/03/2023]
Abstract
Background Intraoperative functional mapping with direct electrical stimulation during awake surgery for patients with diffuse low-grade glioma has been used in recent years to optimize the balance between surgical resection and quality of life following surgery. Mapping of executive functions is particularly challenging because of their complex nature, with only a handful of reports published so far. Here, we propose the recording of neural activity directly from the surface of the brain using electrocorticography to map executive functions and demonstrate its feasibility and potential utility. Methods To track a neural signature of executive function, we recorded neural activity using electrocorticography during awake surgery from the frontal cortex of three patients judged to have an appearance of diffuse low-grade glioma. Based on existing functional magnetic resonance imaging (fMRI) evidence from healthy participants for the recruitment of areas associated with executive function with increased task demands, we employed a task difficulty manipulation in two counting tasks performed intraoperatively. Following surgery, the data were extracted and analyzed offline to identify increases in broadband high-gamma power with increased task difficulty, equivalent to fMRI findings, as a signature of activity related to executive function. Results All three patients performed the tasks well. Data were recorded from five electrode strips, resulting in data from 15 channels overall. Eleven out of the 15 channels (73.3%) showed significant increases in high-gamma power with increased task difficulty, 26.6% of the channels (4/15) showed no change in power, and none of the channels showed power decrease. High-gamma power increases with increased task difficulty were more likely in areas that are within the canonical frontoparietal network template. Conclusions These results are the first step toward developing electrocorticography as a tool for mapping of executive function complementarily to direct electrical stimulation to guide resection. Further studies are required to establish this approach for clinical use.
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Centracchio J, Sarno A, Esposito D, Andreozzi E, Pavone L, Di Gennaro G, Bartolo M, Esposito V, Morace R, Casciato S, Bifulco P. Efficient automated localization of ECoG electrodes in CT images via shape analysis. Int J Comput Assist Radiol Surg 2021; 16:543-554. [PMID: 33687667 PMCID: PMC8052236 DOI: 10.1007/s11548-021-02325-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 02/15/2021] [Indexed: 11/30/2022]
Abstract
Purpose People with drug-refractory epilepsy are potential candidates for surgery. In many cases, epileptogenic zone localization requires intracranial investigations, e.g., via ElectroCorticoGraphy (ECoG), which uses subdural electrodes to map eloquent areas of large cortical regions. Precise electrodes localization on cortical surface is mandatory to delineate the seizure onset zone. Simple thresholding operations performed on patients’ computed tomography (CT) volumes recognize electrodes but also other metal objects (e.g., wires, stitches), which need to be manually removed. A new automated method based on shape analysis is proposed, which provides substantially improved performances in ECoG electrodes recognition. Methods The proposed method was retrospectively tested on 24 CT volumes of subjects with drug-refractory focal epilepsy, presenting a large number (> 1700) of round platinum electrodes. After CT volume thresholding, six geometric features of voxel clusters (volume, symmetry axes lengths, circularity and cylinder similarity) were used to recognize the actual electrodes among all metal objects via a Gaussian support vector machine (G-SVM). The proposed method was further tested on seven CT volumes from a public repository. Simultaneous recognition of depth and ECoG electrodes was also investigated on three additional CT volumes, containing penetrating depth electrodes. Results The G-SVM provided a 99.74% mean classification accuracy across all 24 single-patient datasets, as well as on the combined dataset. High accuracies were obtained also on the CT volumes from public repository (98.27% across all patients, 99.68% on combined dataset). An overall accuracy of 99.34% was achieved for the recognition of depth and ECoG electrodes. Conclusions The proposed method accomplishes automated ECoG electrodes localization with unprecedented accuracy and can be easily implemented into existing software for preoperative analysis process. The preliminary yet surprisingly good results achieved for the simultaneous depth and ECoG electrodes recognition are encouraging. Ethical approval n°NCT04479410 by “IRCCS Neuromed” (Pozzilli, Italy), 30th July 2020. Supplementary Information The online version contains supplementary material available at 10.1007/s11548-021-02325-0.
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Affiliation(s)
- Jessica Centracchio
- Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples Federico II, Naples, Italy
| | - Antonio Sarno
- National Institute for Nuclear Physics (INFN), Naples, Italy
| | - Daniele Esposito
- Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples Federico II, Naples, Italy
- Department of Neurorehabilitation, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy
| | - Emilio Andreozzi
- Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples Federico II, Naples, Italy
- Department of Neurorehabilitation, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy
| | | | | | | | - Vincenzo Esposito
- IRCCS Neuromed, Pozzilli, Italy
- Department of Human Neurosciences, Sapienza University, Rome, Italy
| | | | | | - Paolo Bifulco
- Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples Federico II, Naples, Italy
- Department of Neurorehabilitation, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy
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Berezutskaya J, Baratin C, Freudenburg ZV, Ramsey NF. High-density intracranial recordings reveal a distinct site in anterior dorsal precentral cortex that tracks perceived speech. Hum Brain Mapp 2020; 41:4587-4609. [PMID: 32744403 PMCID: PMC7555065 DOI: 10.1002/hbm.25144] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 06/23/2020] [Accepted: 07/06/2020] [Indexed: 01/15/2023] Open
Abstract
Various brain regions are implicated in speech processing, and the specific function of some of them is better understood than others. In particular, involvement of the dorsal precentral cortex (dPCC) in speech perception remains debated, and attribution of the function of this region is more or less restricted to motor processing. In this study, we investigated high-density intracranial responses to speech fragments of a feature film, aiming to determine whether dPCC is engaged in perception of continuous speech. Our findings show that dPCC exhibited preference to speech over other tested sounds. Moreover, the identified area was involved in tracking of speech auditory properties including speech spectral envelope, its rhythmic phrasal pattern and pitch contour. DPCC also showed the ability to filter out noise from the perceived speech. Comparing these results to data from motor experiments showed that the identified region had a distinct location in dPCC, anterior to the hand motor area and superior to the mouth articulator region. The present findings uncovered with high-density intracranial recordings help elucidate the functional specialization of PCC and demonstrate the unique role of its anterior dorsal region in continuous speech perception.
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Affiliation(s)
- Julia Berezutskaya
- Brain Center, Department of Neurology and NeurosurgeryUniversity Medical Center UtrechtUtrechtThe Netherlands
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenThe Netherlands
| | - Clarissa Baratin
- Brain Center, Department of Neurology and NeurosurgeryUniversity Medical Center UtrechtUtrechtThe Netherlands
- Université Grenoble AlpesGrenoble Institut des NeurosciencesGrenobleFrance
| | - Zachary V. Freudenburg
- Brain Center, Department of Neurology and NeurosurgeryUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Nicolas F. Ramsey
- Brain Center, Department of Neurology and NeurosurgeryUniversity Medical Center UtrechtUtrechtThe Netherlands
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44
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Li Y, Richardson RM, Ghuman AS. Posterior Fusiform and Midfusiform Contribute to Distinct Stages of Facial Expression Processing. Cereb Cortex 2020; 29:3209-3219. [PMID: 30124788 DOI: 10.1093/cercor/bhy186] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2018] [Revised: 07/15/2018] [Accepted: 07/19/2018] [Indexed: 11/12/2022] Open
Abstract
Though the fusiform is well-established as a key node in the face perception network, its role in facial expression processing remains unclear, due to competing models and discrepant findings. To help resolve this debate, we recorded from 17 subjects with intracranial electrodes implanted in face sensitive patches of the fusiform. Multivariate classification analysis showed that facial expression information is represented in fusiform activity and in the same regions that represent identity, though with a smaller effect size. Examination of the spatiotemporal dynamics revealed a functional distinction between posterior fusiform and midfusiform expression coding, with posterior fusiform showing an early peak of facial expression sensitivity at around 180 ms after subjects viewed a face and midfusiform showing a later and extended peak between 230 and 460 ms. These results support the hypothesis that the fusiform plays a role in facial expression perception and highlight a qualitative functional distinction between processing in posterior fusiform and midfusiform, with each contributing to temporally segregated stages of expression perception.
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Affiliation(s)
- Yuanning Li
- Center for the Neural Basis of Cognition, Carnegie Mellon University and University of Pittsburgh, Pittsburgh, PA, USA.,Program in Neural Computation and Machine Learning, Carnegie Mellon University, Pittsburgh, PA, USA.,Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
| | - R Mark Richardson
- Center for the Neural Basis of Cognition, Carnegie Mellon University and University of Pittsburgh, Pittsburgh, PA, USA.,Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
| | - Avniel Singh Ghuman
- Center for the Neural Basis of Cognition, Carnegie Mellon University and University of Pittsburgh, Pittsburgh, PA, USA.,Program in Neural Computation and Machine Learning, Carnegie Mellon University, Pittsburgh, PA, USA.,Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
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45
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Erhardt JB, Lottner T, Pasluosta CF, Gessner I, Mathur S, Schuettler M, Bock M, Stieglitz T. Fabrication and validation of reference structures for the localization of subdural standard- and micro-electrodes in MRI. J Neural Eng 2020; 17:046044. [PMID: 32764195 DOI: 10.1088/1741-2552/abad7a] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Report simple reference structure fabrication and validate the precise localization of subdural micro- and standard electrodes in magnetic resonance imaging (MRI) in phantom experiments. APPROACH Electrode contacts with diameters of 0.3 mm and 4 mm are localized in 1.5 T MRI using reference structures made of silicone and iron oxide nanoparticle doping. The precision of the localization procedure was assessed for several standard MRI sequences and implant orientations in phantom experiments and compared to common clinical localization procedures. MAIN RESULTS A localization precision of 0.41 ± 0.20 mm could be achieved for both electrode diameters compared to 1.46 ± 0.69 mm that was achieved for 4 mm standard electrode contacts localized using a common clinical standard method. The new reference structures are intrinsically bio-compatible, and they can be detected with currently available feature detection software so that a clinical implementation of this technology should be feasible. SIGNIFICANCE Neuropathologies are increasingly diagnosed and treated with subdural electrodes, where the exact localization of the electrode contacts with respect to the patient's cortical anatomy is a prerequisite for the procedure. Post-implantation electrode localization using MRI may be advantageous compared to the common alternative of CT-MRI image co-registration, as it avoids systematic localization errors associated with the co-registration itself, as well as brain shift and implant movement. Additionally, MRI provides superior soft tissue contrast for the identification of brain lesions without exposing the patient to ionizing radiation. Recent studies show that smaller electrodes and high-density electrode grids are ideal for clinical and research purposes, but the localization of these devices in MRI has not been demonstrated.
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Affiliation(s)
- Johannes B Erhardt
- Department of Microsystems Engineering-IMTEK, University of Freiburg, Freiburg, Germany. BrainLinks-BrainTools, Freiburg, Germany
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46
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Weaver KE, Caldwell DJ, Cronin JA, Kuo CH, Kogan M, Houston B, Sanchez V, Martinez V, Ojemann JG, Rane S, Ko AL. Concurrent Deep Brain Stimulation Reduces the Direct Cortical Stimulation Necessary for Motor Output. Mov Disord 2020; 35:2348-2353. [PMID: 32914888 DOI: 10.1002/mds.28255] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 07/09/2020] [Accepted: 08/03/2020] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Converging literatures suggest that deep brain stimulation (DBS) in Parkinson's disease affects multiple circuit mechanisms. One proposed mechanism is the normalization of primary motor cortex (M1) pathophysiology via effects on the hyperdirect pathway. OBJECTIVES We hypothesized that DBS would reduce the current intensity necessary to modulate motor-evoked potentials from focally applied direct cortical stimulation (DCS). METHODS Intraoperative subthalamic DBS, DCS, and preoperative diffusion tensor imaging data were acquired in 8 patients with Parkinson's disease. RESULTS In 7 of 8 patients, DBS significantly reduced the M1 DCS current intensity required to elicit motor-evoked potentials. This neuromodulation was specific to select DBS bipolar configurations. In addition, the volume of activated tissue models of these configurations were significantly associated with overlap of the hyperdirect pathway. CONCLUSIONS DBS reduces the current necessary to elicit a motor-evoked potential using DCS. This supports a circuit mechanism of DBS effectiveness, potentially involving the hyperdirect pathway that speculatively may underlie reductions in hypokinetic abnormalities in Parkinson's disease. © 2020 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Kurt E Weaver
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington, USA.,Graduate Program in Neuroscience, University of Washington School of Medicine, Seattle, Washington, USA.,Center for NeuroTechnologies, University of Washington School of Medicine, Seattle, Washington, USA
| | - David J Caldwell
- Graduate Program in Neuroscience, University of Washington School of Medicine, Seattle, Washington, USA.,Department of BioEngineering, University of Washington School of Medicine, Seattle, Washington, USA
| | - Jeneva A Cronin
- Graduate Program in Neuroscience, University of Washington School of Medicine, Seattle, Washington, USA.,Department of BioEngineering, University of Washington School of Medicine, Seattle, Washington, USA
| | - Chao-Hung Kuo
- Department of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan.,School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Michael Kogan
- Department of Neurosurgery, University of Buffalo, Buffalo, New York, USA
| | - Brady Houston
- Dept of Electrical Engineering, University of Washington School of Medicine, Seattle, Washington, USA
| | - Victor Sanchez
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington, USA
| | - Vicente Martinez
- Department of Rehabilitative Medicine, University of Washington School of Medicine, Seattle, Washington, USA
| | - Jeffrey G Ojemann
- Graduate Program in Neuroscience, University of Washington School of Medicine, Seattle, Washington, USA.,Center for NeuroTechnologies, University of Washington School of Medicine, Seattle, Washington, USA.,Department of Neurological Surgery, University of Washington School of Medicine, Seattle, Washington, USA
| | - Swati Rane
- Department of Radiology, University of Washington School of Medicine, Seattle, Washington, USA
| | - Andrew L Ko
- Graduate Program in Neuroscience, University of Washington School of Medicine, Seattle, Washington, USA.,Center for NeuroTechnologies, University of Washington School of Medicine, Seattle, Washington, USA.,Department of Neurological Surgery, University of Washington School of Medicine, Seattle, Washington, USA
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Gaglianese A, Branco MP, Groen IIA, Benson NC, Vansteensel MJ, Murray MM, Petridou N, Ramsey NF. Electrocorticography Evidence of Tactile Responses in Visual Cortices. Brain Topogr 2020; 33:559-570. [PMID: 32661933 PMCID: PMC7429547 DOI: 10.1007/s10548-020-00783-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2019] [Accepted: 06/28/2020] [Indexed: 01/30/2023]
Abstract
There is ongoing debate regarding the extent to which human cortices are specialized for processing a given sensory input versus a given type of information, independently of the sensory source. Many neuroimaging and electrophysiological studies have reported that primary and extrastriate visual cortices respond to tactile and auditory stimulation, in addition to visual inputs, suggesting these cortices are intrinsically multisensory. In particular for tactile responses, few studies have proven neuronal processes in visual cortex in humans. Here, we assessed tactile responses in both low-level and extrastriate visual cortices using electrocorticography recordings in a human participant. Specifically, we observed significant spectral power increases in the high frequency band (30-100 Hz) in response to tactile stimuli, reportedly associated with spiking neuronal activity, in both low-level visual cortex (i.e. V2) and in the anterior part of the lateral occipital-temporal cortex. These sites were both involved in processing tactile information and responsive to visual stimulation. More generally, the present results add to a mounting literature in support of task-sensitive and sensory-independent mechanisms underlying functions like spatial, motion, and self-processing in the brain and extending from higher-level as well as to low-level cortices.
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Affiliation(s)
- Anna Gaglianese
- The Laboratory for Investigative Neurophysiology (The LINE), Department of Radiology, University Hospital Center, University of Lausanne, Rue Centrale 7, Lausanne, 1003, Switzerland.
- Department of Neurosurgery and Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.
- Department of Radiology, Center for Image Sciences, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.
| | - Mariana P Branco
- Department of Neurosurgery and Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Iris I A Groen
- Department of Psychology, New York University, Washington Place 6, New York, 10003, NY, USA
| | - Noah C Benson
- Department of Psychology, New York University, Washington Place 6, New York, 10003, NY, USA
- eScience Institute, University of Washington, 15th Ave NE, Seattle, 98195, WA, USA
| | - Mariska J Vansteensel
- Department of Neurosurgery and Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Micah M Murray
- The Laboratory for Investigative Neurophysiology (The LINE), Department of Radiology, University Hospital Center, University of Lausanne, Rue Centrale 7, Lausanne, 1003, Switzerland
- Sensory, Perceptual and Cognitive Neuroscience Section, Center for Biomedical Imaging (CIBM), Station 6, Lausanne, 1015, Switzerland
- Ophthalmology Service, Fondation Asile des aveugles and University of Lausanne, Avenue de France 15, Lausanne, 1004, Switzerland
- Department of Hearing and Speech Sciences, Vanderbilt University, 21st Avenue South 1215, Nashville, 37232, TN, USA
| | - Natalia Petridou
- Department of Radiology, Center for Image Sciences, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Nick F Ramsey
- Department of Neurosurgery and Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
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Li Y, Ward MJ, Richardson RM, G'Sell M, Ghuman AS. Endogenous activity modulates stimulus and circuit-specific neural tuning and predicts perceptual behavior. Nat Commun 2020; 11:4014. [PMID: 32782303 PMCID: PMC7419548 DOI: 10.1038/s41467-020-17729-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Accepted: 07/07/2020] [Indexed: 11/08/2022] Open
Abstract
Perception reflects not only sensory inputs, but also the endogenous state when these inputs enter the brain. Prior studies show that endogenous neural states influence stimulus processing through non-specific, global mechanisms, such as spontaneous fluctuations of arousal. It is unclear if endogenous activity influences circuit and stimulus-specific processing and behavior as well. Here we use intracranial recordings from 30 pre-surgical epilepsy patients to show that patterns of endogenous activity are related to the strength of trial-by-trial neural tuning in different visual category-selective neural circuits. The same aspects of the endogenous activity that relate to tuning in a particular neural circuit also correlate to behavioral reaction times only for stimuli from the category that circuit is selective for. These results suggest that endogenous activity can modulate neural tuning and influence behavior in a circuit- and stimulus-specific manner, reflecting a potential mechanism by which endogenous neural states facilitate and bias perception.
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Affiliation(s)
- Yuanning Li
- Center for the Neural Basis of Cognition, Carnegie Mellon University and University of Pittsburgh, Pittsburgh, PA, USA.
- Program in Neural Computation and Machine Learning, Carnegie Mellon University and University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Neurological Surgery, University of California, San Francisco, CA, USA.
| | - Michael J Ward
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
| | - R Mark Richardson
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Max G'Sell
- Department of Statistics, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Avniel Singh Ghuman
- Center for the Neural Basis of Cognition, Carnegie Mellon University and University of Pittsburgh, Pittsburgh, PA, USA
- Program in Neural Computation and Machine Learning, Carnegie Mellon University and University of Pittsburgh, Pittsburgh, PA, USA
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
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49
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Foldes ST, Munter BT, Appavu BL, Kerrigan JF, Adelson PD. Shift in electrocorticography electrode locations after surgical implantation in children. Epilepsy Res 2020; 167:106410. [PMID: 32758670 DOI: 10.1016/j.eplepsyres.2020.106410] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 06/05/2020] [Accepted: 06/27/2020] [Indexed: 10/24/2022]
Abstract
Interpreting electrocorticography (ECoG) in the context of neuroimaging requires that multimodal information be integrated accurately. However, the implantation of ECoG electrodes can shift the brain impacting the spatial interpretation of electrode locations in the context of pre-implant imaging. We characterized the amount of shift in ECoG electrode locations immediately after implant in a pediatric population. Electrode-shift was quantified as the difference in the electrode locations immediately after surgery (via post-operation CT) compared to the brain surface before the operation (pre-implant T1 MRI). A total of 1140 ECoG contracts were assessed across 18 patients ranging from 3 to 19 (12.1 ± 4.8) years of age who underwent intracranial monitoring in preparation for epilepsy resection surgery. Patients had an average of 63 channels assessed with an average of 5.64 ± 3.27 mm shift from the pre-implant brain surface within 24 h of implant. This shift significantly increased with estimated intracranial volume, but not age. Shift also varied significantly depending of the lobe the contact was over; where contacts on the temporal and frontal lobe had less shift than the parietal. Furthermore, contacts on strips had significantly less shift than those on grids. The shift in the brain surface due to ECoG implantation could lead to a misinterpretation of contact location particularly in patients with larger intracranial volume and for grid contacts over the parietal lobes.
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Affiliation(s)
- Stephen T Foldes
- Barrow Neurological Institute, Phoenix Children's Hospital, Phoenix, AZ, United States; Department of Child Health, University of Arizona - College of Medicine, Phoenix, AZ, United States; School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, United States.
| | - Bryce T Munter
- Department of Child Health, University of Arizona - College of Medicine, Phoenix, AZ, United States
| | - Brian L Appavu
- Barrow Neurological Institute, Phoenix Children's Hospital, Phoenix, AZ, United States; Department of Child Health, University of Arizona - College of Medicine, Phoenix, AZ, United States; School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, United States
| | - John F Kerrigan
- Barrow Neurological Institute, Phoenix Children's Hospital, Phoenix, AZ, United States; Department of Child Health, University of Arizona - College of Medicine, Phoenix, AZ, United States
| | - P David Adelson
- Barrow Neurological Institute, Phoenix Children's Hospital, Phoenix, AZ, United States; Department of Child Health, University of Arizona - College of Medicine, Phoenix, AZ, United States; School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, United States
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Berezutskaya J, Freudenburg ZV, Ambrogioni L, Güçlü U, van Gerven MAJ, Ramsey NF. Cortical network responses map onto data-driven features that capture visual semantics of movie fragments. Sci Rep 2020; 10:12077. [PMID: 32694561 PMCID: PMC7374611 DOI: 10.1038/s41598-020-68853-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 06/30/2020] [Indexed: 11/08/2022] Open
Abstract
Research on how the human brain extracts meaning from sensory input relies in principle on methodological reductionism. In the present study, we adopt a more holistic approach by modeling the cortical responses to semantic information that was extracted from the visual stream of a feature film, employing artificial neural network models. Advances in both computer vision and natural language processing were utilized to extract the semantic representations from the film by combining perceptual and linguistic information. We tested whether these representations were useful in studying the human brain data. To this end, we collected electrocorticography responses to a short movie from 37 subjects and fitted their cortical patterns across multiple regions using the semantic components extracted from film frames. We found that individual semantic components reflected fundamental semantic distinctions in the visual input, such as presence or absence of people, human movement, landscape scenes, human faces, etc. Moreover, each semantic component mapped onto a distinct functional cortical network involving high-level cognitive regions in occipitotemporal, frontal and parietal cortices. The present work demonstrates the potential of the data-driven methods from information processing fields to explain patterns of cortical responses, and contributes to the overall discussion about the encoding of high-level perceptual information in the human brain.
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Affiliation(s)
- Julia Berezutskaya
- Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands.
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Montessorilaan 3, 6525 HR, Nijmegen, The Netherlands.
| | - Zachary V Freudenburg
- Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Luca Ambrogioni
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Montessorilaan 3, 6525 HR, Nijmegen, The Netherlands
| | - Umut Güçlü
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Montessorilaan 3, 6525 HR, Nijmegen, The Netherlands
| | - Marcel A J van Gerven
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Montessorilaan 3, 6525 HR, Nijmegen, The Netherlands
| | - Nick F Ramsey
- Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
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