1
|
Levinson LH, Sun S, Paschall CJ, Perks KM, Weaver KE, Perlmutter SI, Ko AL, Ojemann JG, Herron JA. Data processing techniques impact quantification of cortico-cortical evoked potentials. J Neurosci Methods 2024; 408:110130. [PMID: 38653381 DOI: 10.1016/j.jneumeth.2024.110130] [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: 07/30/2023] [Revised: 01/16/2024] [Accepted: 04/03/2024] [Indexed: 04/25/2024]
Abstract
BACKGROUND Cortico-cortical evoked potentials (CCEPs) are a common tool for probing effective connectivity in intracranial human electrophysiology. As with all human electrophysiology data, CCEP data are highly susceptible to noise. To address noise, filters and re-referencing are often applied to CCEP data, but different processing strategies are used from study to study. NEW METHOD We systematically compare how common average re-referencing and filtering CCEP data impacts quantification. RESULTS We show that common average re-referencing and filters, particularly filters that cut out more frequencies, can significantly impact the quantification of CCEP magnitude and morphology. We identify that high cutoff high pass filters (> 0.5 Hz), low cutoff low pass filters (< 200 Hz), and common average re-referencing impact quantification across subjects. However, we also demonstrate that the presence of noise may impact CCEP quantification, and preprocessing is necessary to mitigate this. We show that filtering is more effective than re-referencing or averaging across trials for reducing most common types of noise. COMPARISON WITH EXISTING METHODS These results suggest that existing CCEP processing methods must be applied with care to maximize noise reduction and minimize changes to the data. We do not test every available processing strategy; rather we demonstrate that processing can influence the results of CCEP studies. We emphasize the importance of reporting all processing methods, particularly re-referencing methods. CONCLUSIONS We propose a general framework for choosing an appropriate processing pipeline for CCEP data, taking into consideration the noise levels of a specific dataset. We suggest that minimal gentle filtering is preferable.
Collapse
Affiliation(s)
- L H Levinson
- University of Washington Graduate Program in Neuroscience, 1959 NE Pacific Street, T-47, Seattle, WA 98195-7270, United States.
| | - S Sun
- University of Washington Department of Bioengineering, Box 355061, Seattle, WA 98195-5061, United States
| | - C J Paschall
- University of Washington Department of Bioengineering, Box 355061, Seattle, WA 98195-5061, United States
| | - K M Perks
- University of Washington Graduate Program in Neuroscience, 1959 NE Pacific Street, T-47, Seattle, WA 98195-7270, United States
| | - K E Weaver
- University of Washington Department of Radiology, 1959 NE Pacific Street, Seattle, WA 98195, United States
| | - S I Perlmutter
- University of Washington Department of Physiology and Biophysics, 1705 NE Pacific Street, HSB Room G424, Box 357290, Seattle, WA 98195-7290, United States
| | - A L Ko
- University of Washington Department of Neurological Surgery, Box 356470, 1959 NE Pacific St, Seattle, WA 98195-6470, United States
| | - J G Ojemann
- University of Washington Department of Neurological Surgery, Box 356470, 1959 NE Pacific St, Seattle, WA 98195-6470, United States
| | - J A Herron
- University of Washington Department of Neurological Surgery, Box 356470, 1959 NE Pacific St, Seattle, WA 98195-6470, United States
| |
Collapse
|
2
|
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.
Collapse
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.
| |
Collapse
|
3
|
Lyu D, Stiger J, Lusk Z, Buch V, Parvizi J. Causal Cortical and Thalamic Connections in the Human Brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.22.600166. [PMID: 38979261 PMCID: PMC11230252 DOI: 10.1101/2024.06.22.600166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
The brain's functional architecture is intricately shaped by causal connections between its cortical and subcortical structures. Here, we studied 27 participants with 4864 electrodes implanted across the anterior, mediodorsal, and pulvinar thalamic regions, and the cortex. Using data from electrical stimulation procedures and a data-driven approach informed by neurophysiological standards, we dissociated three unique spectral patterns generated by the perturbation of a given brain area. Among these, a novel waveform emerged, marked by delayed-onset slow oscillations in both ipsilateral and contralateral cortices following thalamic stimulations, suggesting a mechanism by which a thalamic site can influence bilateral cortical activity. Moreover, cortical stimulations evoked earlier signals in the thalamus than in other connected cortical areas suggesting that the thalamus receives a copy of signals before they are exchanged across the cortex. Our causal connectivity data can be used to inform biologically-inspired computational models of the functional architecture of the brain.
Collapse
Affiliation(s)
- Dian Lyu
- Laboratory of Behavioral and Cognitive Neuroscience, Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California USA
| | - James Stiger
- Laboratory of Behavioral and Cognitive Neuroscience, Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California USA
| | - Zoe Lusk
- Laboratory of Behavioral and Cognitive Neuroscience, Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California USA
| | - Vivek Buch
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, California USA
| | - Josef Parvizi
- Laboratory of Behavioral and Cognitive Neuroscience, Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California USA
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, California USA
| |
Collapse
|
4
|
Parvizi J, Lyu D, Stieger J, Lusk Z, Buch V. Causal Cortical and Thalamic Connections in the Human Brain. RESEARCH SQUARE 2024:rs.3.rs-4366486. [PMID: 38853954 PMCID: PMC11160924 DOI: 10.21203/rs.3.rs-4366486/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
The brain's functional architecture is intricately shaped by causal connections between its cortical and subcortical structures. Here, we studied 27 participants with 4864 electrodes implanted across the anterior, mediodorsal, and pulvinar thalamic regions, and the cortex. Using data from electrical stimulation procedures and a data-driven approach informed by neurophysiological standards, we dissociated three unique spectral patterns generated by the perturbation of a given brain area. Among these, a novel waveform emerged, marked by delayed-onset slow oscillations in both ipsilateral and contralateral cortices following thalamic stimulations, suggesting a mechanism by which a thalamic site can influence bilateral cortical activity. Moreover, cortical stimulations evoked earlier signals in the thalamus than in other connected cortical areas suggesting that the thalamus receives a copy of signals before they are exchanged across the cortex. Our causal connectivity data can be used to inform biologically-inspired computational models of the functional architecture of the brain.
Collapse
|
5
|
van den Boom MA, Gregg NM, Valencia GO, Lundstrom BN, Miller KJ, van Blooijs D, Huiskamp GJ, Leijten FS, Worrell GA, Hermes D. ER-detect: a pipeline for robust detection of early evoked responses in BIDS-iEEG electrical stimulation data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.09.574915. [PMID: 38260687 PMCID: PMC10802406 DOI: 10.1101/2024.01.09.574915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Human brain connectivity can be measured in different ways. Intracranial EEG (iEEG) measurements during single pulse electrical stimulation provide a unique way to assess the spread of electrical information with millisecond precision. To provide a robust workflow to process these cortico-cortical evoked potential (CCEP) data and detect early evoked responses in a fully automated and reproducible fashion, we developed Early Response (ER)-detect. ER-detect is an open-source Python package and Docker application to preprocess BIDS structured iEEG data and detect early evoked CCEP responses. ER-detect can use three response detection methods, which were validated against 14-manually annotated CCEP datasets from two different sites by four independent raters. Results showed that ER-detect's automated detection performed on par with the inter-rater reliability (Cohen's Kappa of ~0.6). Moreover, ER-detect was optimized for processing large CCEP datasets, to be used in conjunction with other connectomic investigations. ER-detect provides a highly efficient standardized workflow such that iEEG-BIDS data can be processed in a consistent manner and enhance the reproducibility of CCEP based connectivity results.
Collapse
Affiliation(s)
- Max A. van den Boom
- Department of Physiology and Biomedical Engineering, Mayo Clinic; Rochester, MN, USA
- Department of Neurosurgery, Mayo Clinic; Rochester, MN, USA
| | | | | | | | - Kai J. Miller
- Department of Neurosurgery, Mayo Clinic; Rochester, MN, USA
| | - Dorien van Blooijs
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht; Utrecht, NL
- Stichting Epilepsie Instellingen Nederland (SEIN); Zwolle, The Netherlands
| | - Geertjan J.M. Huiskamp
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht; Utrecht, NL
| | - Frans S.S. Leijten
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht; Utrecht, NL
| | - Gregory A. Worrell
- Department of Physiology and Biomedical Engineering, Mayo Clinic; Rochester, MN, USA
- Department of Neurology, Mayo Clinic, Rochester, MN; USA
| | - Dora Hermes
- Department of Physiology and Biomedical Engineering, Mayo Clinic; Rochester, MN, USA
| |
Collapse
|
6
|
Rossel O, Schlosser-Perrin F, Duffau H, Matsumoto R, Mandonnet E, Bonnetblanc F. Short-range axono-cortical evoked-potentials in brain tumor surgery: Waveform characteristics as markers of direct connectivity. Clin Neurophysiol 2023; 153:189-201. [PMID: 37353389 DOI: 10.1016/j.clinph.2023.05.011] [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: 11/14/2022] [Revised: 04/20/2023] [Accepted: 05/24/2023] [Indexed: 06/25/2023]
Abstract
OBJECTIVE Intraoperative measurement of axono-cortical evoked potentials (ACEP) has emerged as a promising tool for studying neural connectivity. However, it is often difficult to determine if the activity recorded by cortical grids is generated by stimulated tracts or by spurious phenomena. This study aimed to identify criteria that would indicate a direct neurophysiological connection between a recording contact and a stimulated pathway. METHODS Electrical stimulation was applied to white matter fascicles within the resection cavity, while the evoked response was recorded at the cortical level in seven patients. RESULTS By analyzing the ACEP recordings, we identified a main epicenter characterized by a very early positive (or negative) evoked response occurring just after the stimulation artifact (<5 ms, |Amplitude| > 100 µV) followed by an early and large negative (or positive) monophasic evoked response (<40 ms; |Amplitude| > 300 µV). The neighboring activity had a different waveform and was attenuated compared to the hot-spot activity. CONCLUSIONS It is possible to distinguish the hotspot with direct connectivity to the stimulated site from neighboring activity using the identified criteria. SIGNIFICANCE The electrogenesis of the ACEP at the hotspot and neighboring activity is discussed.
Collapse
Affiliation(s)
| | | | - Hugues Duffau
- Département de Neurochirurgie, Centre Hospitalier Universitaire de Montpellier Gui de Chauliac, Montpellier, France
| | - Riki Matsumoto
- Division of Neurology, Kobe University Graduate School of Medicine, Japan
| | - Emmanuel Mandonnet
- Département de Neurochirurgie, Centre Hospitalier Universitaire, Hôpital Lariboisière, Paris, France
| | | |
Collapse
|
7
|
Barbosa DAN, Gattas S, Salgado JS, Kuijper FM, Wang AR, Huang Y, Kakusa B, Leuze C, Luczak A, Rapp P, Malenka RC, Hermes D, Miller KJ, Heifets BD, Bohon C, McNab JA, Halpern CH. An orexigenic subnetwork within the human hippocampus. Nature 2023; 621:381-388. [PMID: 37648849 PMCID: PMC10499606 DOI: 10.1038/s41586-023-06459-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 07/20/2023] [Indexed: 09/01/2023]
Abstract
Only recently have more specific circuit-probing techniques become available to inform previous reports implicating the rodent hippocampus in orexigenic appetitive processing1-4. This function has been reported to be mediated at least in part by lateral hypothalamic inputs, including those involving orexigenic lateral hypothalamic neuropeptides, such as melanin-concentrating hormone5,6. This circuit, however, remains elusive in humans. Here we combine tractography, intracranial electrophysiology, cortico-subcortical evoked potentials, and brain-clearing 3D histology to identify an orexigenic circuit involving the lateral hypothalamus and converging in a hippocampal subregion. We found that low-frequency power is modulated by sweet-fat food cues, and this modulation was specific to the dorsolateral hippocampus. Structural and functional analyses of this circuit in a human cohort exhibiting dysregulated eating behaviour revealed connectivity that was inversely related to body mass index. Collectively, this multimodal approach describes an orexigenic subnetwork within the human hippocampus implicated in obesity and related eating disorders.
Collapse
Affiliation(s)
- Daniel A N Barbosa
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sandra Gattas
- Department of Electrical Engineering and Computer Science, University of California, Irvine, Irvine, CA, USA
| | - Juliana S Salgado
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Fiene Marie Kuijper
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
- Université Paris Cité, Paris, France
- Assistance Publique des Hôpitaux de Paris, Paris, France
| | - Allan R Wang
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Yuhao Huang
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Bina Kakusa
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Christoph Leuze
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Artur Luczak
- Canadian Centre for Behavioral Neuroscience, University of Lethbridge, Lethbridge, Alberta, Canada
| | - Paul Rapp
- Department of Military & Emergency Medicine, Uniformed Services University, Bethesda, MD, USA
| | - Robert C Malenka
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Nancy Pritzker Laboratory, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Dora Hermes
- Department of Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Kai J Miller
- Department of Neurosurgery, Mayo Clinic, Rochester, MN, USA
| | - Boris D Heifets
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Cara Bohon
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
| | - Jennifer A McNab
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Casey H Halpern
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Surgery, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA.
| |
Collapse
|
8
|
Bernabei JM, Li A, Revell AY, Smith RJ, Gunnarsdottir KM, Ong IZ, Davis KA, Sinha N, Sarma S, Litt B. Quantitative approaches to guide epilepsy surgery from intracranial EEG. Brain 2023; 146:2248-2258. [PMID: 36623936 PMCID: PMC10232272 DOI: 10.1093/brain/awad007] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 12/11/2022] [Accepted: 12/28/2022] [Indexed: 01/11/2023] Open
Abstract
Over the past 10 years, the drive to improve outcomes from epilepsy surgery has stimulated widespread interest in methods to quantitatively guide epilepsy surgery from intracranial EEG (iEEG). Many patients fail to achieve seizure freedom, in part due to the challenges in subjective iEEG interpretation. To address this clinical need, quantitative iEEG analytics have been developed using a variety of approaches, spanning studies of seizures, interictal periods, and their transitions, and encompass a range of techniques including electrographic signal analysis, dynamical systems modeling, machine learning and graph theory. Unfortunately, many methods fail to generalize to new data and are sensitive to differences in pathology and electrode placement. Here, we critically review selected literature on computational methods of identifying the epileptogenic zone from iEEG. We highlight shared methodological challenges common to many studies in this field and propose ways that they can be addressed. One fundamental common pitfall is a lack of open-source, high-quality data, which we specifically address by sharing a centralized high-quality, well-annotated, multicentre dataset consisting of >100 patients to support larger and more rigorous studies. Ultimately, we provide a road map to help these tools reach clinical trials and hope to improve the lives of future patients.
Collapse
Affiliation(s)
- John M Bernabei
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Adam Li
- Department of Computer Science, Columbia University, New York, NY 10027, USA
| | - Andrew Y Revell
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Rachel J Smith
- Department of Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, AL 35294, USA
- Neuroengineering Program, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Kristin M Gunnarsdottir
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Ian Z Ong
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kathryn A Davis
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Nishant Sinha
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sridevi Sarma
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Brian Litt
- Department of Bioengineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering & Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| |
Collapse
|
9
|
Miller KJ, Müller KR, Valencia GO, Huang H, Gregg NM, Worrell GA, Hermes D. Canonical Response Parameterization: Quantifying the structure of responses to single-pulse intracranial electrical brain stimulation. PLoS Comput Biol 2023; 19:e1011105. [PMID: 37228169 DOI: 10.1371/journal.pcbi.1011105] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 04/14/2023] [Indexed: 05/27/2023] Open
Abstract
Single-pulse electrical stimulation in the nervous system, often called cortico-cortical evoked potential (CCEP) measurement, is an important technique to understand how brain regions interact with one another. Voltages are measured from implanted electrodes in one brain area while stimulating another with brief current impulses separated by several seconds. Historically, researchers have tried to understand the significance of evoked voltage polyphasic deflections by visual inspection, but no general-purpose tool has emerged to understand their shapes or describe them mathematically. We describe and illustrate a new technique to parameterize brain stimulation data, where voltage response traces are projected into one another using a semi-normalized dot product. The length of timepoints from stimulation included in the dot product is varied to obtain a temporal profile of structural significance, and the peak of the profile uniquely identifies the duration of the response. Using linear kernel PCA, a canonical response shape is obtained over this duration, and then single-trial traces are parameterized as a projection of this canonical shape with a residual term. Such parameterization allows for dissimilar trace shapes from different brain areas to be directly compared by quantifying cross-projection magnitudes, response duration, canonical shape projection amplitudes, signal-to-noise ratios, explained variance, and statistical significance. Artifactual trials are automatically identified by outliers in sub-distributions of cross-projection magnitude, and rejected. This technique, which we call "Canonical Response Parameterization" (CRP) dramatically simplifies the study of CCEP shapes, and may also be applied in a wide range of other settings involving event-triggered data.
Collapse
Affiliation(s)
- Kai J Miller
- Dept of Neurological Surgery, Mayo Clinic, Rochester, Minnesota, United States of America
- Dept 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
- Dept of Artificial Intelligence, Korea University, Seoul, Republic of Korea
- Max Planck Institute for Informatics, Saarbrücken, Germany
| | - Gabriela Ojeda Valencia
- Dept of Biomedical Engineering & Physiology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Harvey Huang
- Medical Scientist Training Program, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Nicholas M Gregg
- Dept of Neurology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Gregory A Worrell
- Dept of Biomedical Engineering & Physiology, Mayo Clinic, Rochester, Minnesota, United States of America
- Dept of Neurology, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Dora Hermes
- Dept of Biomedical Engineering & Physiology, Mayo Clinic, Rochester, Minnesota, United States of America
| |
Collapse
|
10
|
Campbell JM, Davis TS, Anderson DN, Arain A, Inman CS, Smith EH, Rolston JD. Subsets of cortico-cortical evoked potentials propagate as traveling waves. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.27.534002. [PMID: 37034691 PMCID: PMC10081214 DOI: 10.1101/2023.03.27.534002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Emerging evidence suggests that the temporal dynamics of cortico-cortical evoked potentials (CCEPs) may be used to characterize the patterns of information flow between and within brain networks. At present, however, the spatiotemporal dynamics of CCEP propagation cortically and subcortically are incompletely understood. We hypothesized that CCEPs propagate as an evoked traveling wave emanating from the site of stimulation. To elicit CCEPs, we applied single-pulse stimulation to stereoelectroencephalography (SEEG) electrodes implanted in 21 adult patients with intractable epilepsy. For each robust CCEP, we measured the timing of the maximal descent in evoked local field potentials and broadband high-gamma power (70-150 Hz) envelopes relative to the distance between the recording and stimulation contacts using three different metrics (i.e., Euclidean distance, path length, geodesic distance), representing direct, subcortical, and transcortical propagation, respectively. Many evoked responses to single-pulse electrical stimulation appear to propagate as traveling waves (~17-30%), even in the sparsely sampled, three-dimensional SEEG space. These results provide new insights into the spatiotemporal dynamics of CCEP propagation.
Collapse
Affiliation(s)
- Justin M. Campbell
- MD-PhD Program, School of Medicine, University of Utah, Salt Lake City, UT, USA
- Interdepartmental Program in Neuroscience, University of Utah, Salt Lake City, UT, USA
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, USA
| | - Tyler S. Davis
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, USA
| | - Daria Nesterovich Anderson
- School of Biomedical Engineering, Faculty of Engineering, University of Sydney, Sydney, New South Wales, Australia
| | - Amir Arain
- Department of Neurology, University of Utah, Salt Lake City, UT, USA
| | - Cory S. Inman
- Interdepartmental Program in Neuroscience, University of Utah, Salt Lake City, UT, USA
- Department of Psychology, University of Utah, Salt Lake City, UT, USA
| | - Elliot H. Smith
- Interdepartmental Program in Neuroscience, University of Utah, Salt Lake City, UT, USA
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, USA
| | - John D. Rolston
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
- Department of Neurosurgery, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA
| |
Collapse
|
11
|
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.
Collapse
|
12
|
Desai M, Field AM, Hamilton LS. Dataset size considerations for robust acoustic and phonetic speech encoding models in EEG. Front Hum Neurosci 2023; 16:1001171. [PMID: 36741776 PMCID: PMC9895838 DOI: 10.3389/fnhum.2022.1001171] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 12/22/2022] [Indexed: 01/21/2023] Open
Abstract
In many experiments that investigate auditory and speech processing in the brain using electroencephalography (EEG), the experimental paradigm is often lengthy and tedious. Typically, the experimenter errs on the side of including more data, more trials, and therefore conducting a longer task to ensure that the data are robust and effects are measurable. Recent studies used naturalistic stimuli to investigate the brain's response to individual or a combination of multiple speech features using system identification techniques, such as multivariate temporal receptive field (mTRF) analyses. The neural data collected from such experiments must be divided into a training set and a test set to fit and validate the mTRF weights. While a good strategy is clearly to collect as much data as is feasible, it is unclear how much data are needed to achieve stable results. Furthermore, it is unclear whether the specific stimulus used for mTRF fitting and the choice of feature representation affects how much data would be required for robust and generalizable results. Here, we used previously collected EEG data from our lab using sentence stimuli and movie stimuli as well as EEG data from an open-source dataset using audiobook stimuli to better understand how much data needs to be collected for naturalistic speech experiments measuring acoustic and phonetic tuning. We found that the EEG receptive field structure tested here stabilizes after collecting a training dataset of approximately 200 s of TIMIT sentences, around 600 s of movie trailers training set data, and approximately 460 s of audiobook training set data. Thus, we provide suggestions on the minimum amount of data that would be necessary for fitting mTRFs from naturalistic listening data. Our findings are motivated by highly practical concerns when working with children, patient populations, or others who may not tolerate long study sessions. These findings will aid future researchers who wish to study naturalistic speech processing in healthy and clinical populations while minimizing participant fatigue and retaining signal quality.
Collapse
Affiliation(s)
- Maansi Desai
- Department of Speech, Language, and Hearing Sciences, Moody College of Communication, The University of Texas at Austin, Austin, TX, United States
| | - Alyssa M. Field
- Department of Speech, Language, and Hearing Sciences, Moody College of Communication, The University of Texas at Austin, Austin, TX, United States
| | - Liberty S. Hamilton
- Department of Speech, Language, and Hearing Sciences, Moody College of Communication, The University of Texas at Austin, Austin, TX, United States,Department of Neurology, Dell Medical School, The University of Texas at Austin, Austin, TX, United States,*Correspondence: Liberty S. Hamilton ✉
| |
Collapse
|
13
|
Wong JK, Deuschl G, Wolke R, Bergman H, Muthuraman M, Groppa S, Sheth SA, Bronte-Stewart HM, Wilkins KB, Petrucci MN, Lambert E, Kehnemouyi Y, Starr PA, Little S, Anso J, Gilron R, Poree L, Kalamangalam GP, Worrell GA, Miller KJ, Schiff ND, Butson CR, Henderson JM, Judy JW, Ramirez-Zamora A, Foote KD, Silburn PA, Li L, Oyama G, Kamo H, Sekimoto S, Hattori N, Giordano JJ, DiEuliis D, Shook JR, Doughtery DD, Widge AS, Mayberg HS, Cha J, Choi K, Heisig S, Obatusin M, Opri E, Kaufman SB, Shirvalkar P, Rozell CJ, Alagapan S, Raike RS, Bokil H, Green D, Okun MS. Proceedings of the Ninth Annual Deep Brain Stimulation Think Tank: Advances in Cutting Edge Technologies, Artificial Intelligence, Neuromodulation, Neuroethics, Pain, Interventional Psychiatry, Epilepsy, and Traumatic Brain Injury. Front Hum Neurosci 2022; 16:813387. [PMID: 35308605 PMCID: PMC8931265 DOI: 10.3389/fnhum.2022.813387] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 01/11/2022] [Indexed: 01/09/2023] Open
Abstract
DBS Think Tank IX was held on August 25-27, 2021 in Orlando FL with US based participants largely in person and overseas participants joining by video conferencing technology. The DBS Think Tank was founded in 2012 and provides an open platform where clinicians, engineers and researchers (from industry and academia) can freely discuss current and emerging deep brain stimulation (DBS) technologies as well as the logistical and ethical issues facing the field. The consensus among the DBS Think Tank IX speakers was that DBS expanded in its scope and has been applied to multiple brain disorders in an effort to modulate neural circuitry. After collectively sharing our experiences, it was estimated that globally more than 230,000 DBS devices have been implanted for neurological and neuropsychiatric disorders. As such, this year's meeting was focused on advances in the following areas: neuromodulation in Europe, Asia and Australia; cutting-edge technologies, neuroethics, interventional psychiatry, adaptive DBS, neuromodulation for pain, network neuromodulation for epilepsy and neuromodulation for traumatic brain injury.
Collapse
Affiliation(s)
- Joshua K. Wong
- Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| | - Günther Deuschl
- Department of Neurology, Christian-Albrechts-University, Kiel, Germany
| | - Robin Wolke
- Department of Neurology, Christian-Albrechts-University, Kiel, Germany
| | - Hagai Bergman
- Department of Medical Neurobiology (Physiology), Institute of Medical Research Israel-Canada, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Muthuraman Muthuraman
- Biomedical Statistics and Multimodal Signal Processing Unit, Section of Movement Disorders and Neurostimulation, Focus Program Translational Neuroscience, Department of Neurology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Sergiu Groppa
- Biomedical Statistics and Multimodal Signal Processing Unit, Section of Movement Disorders and Neurostimulation, Focus Program Translational Neuroscience, Department of Neurology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Sameer A. Sheth
- Department of Neurological Surgery, Baylor College of Medicine, Houston, TX, United States
| | - Helen M. Bronte-Stewart
- The Human Motor Control and Neuromodulation Laboratory, Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA, United States
| | - Kevin B. Wilkins
- The Human Motor Control and Neuromodulation Laboratory, Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA, United States
| | - Matthew N. Petrucci
- The Human Motor Control and Neuromodulation Laboratory, Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA, United States
| | - Emilia Lambert
- The Human Motor Control and Neuromodulation Laboratory, Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA, United States
| | - Yasmine Kehnemouyi
- The Human Motor Control and Neuromodulation Laboratory, Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA, United States
| | - Philip A. Starr
- Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Simon Little
- Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Juan Anso
- Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Ro’ee Gilron
- Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Lawrence Poree
- Department of Anesthesia, University of California, San Francisco, San Francisco, CA, United States
| | - Giridhar P. Kalamangalam
- Department of Neurology, Wilder Center for Epilepsy Research, University of Florida, Gainesville, FL, United States
| | | | - Kai J. Miller
- Department of Neurosurgery, Mayo Clinic, Rochester, NY, United States
| | - Nicholas D. Schiff
- Department of Neurology, Weill Cornell Brain and Spine Institute, Weill Cornell Medicine, New York, NY, United States
| | - Christopher R. Butson
- Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| | - Jaimie M. Henderson
- Department of Neurosurgery, Stanford University, Stanford, CA, United States
| | - Jack W. Judy
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, United States
| | - Adolfo Ramirez-Zamora
- Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| | - Kelly D. Foote
- Department of Neurosurgery, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| | - Peter A. Silburn
- Queensland Brain Institute, University of Queensland and Saint Andrews War Memorial Hospital, Brisbane, QLD, Australia
| | - Luming Li
- National Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China
| | - Genko Oyama
- Department of Neurology, Faculty of Medicine, Juntendo University, Tokyo, Japan
| | - Hikaru Kamo
- Department of Neurology, Faculty of Medicine, Juntendo University, Tokyo, Japan
| | - Satoko Sekimoto
- Department of Neurology, Faculty of Medicine, Juntendo University, Tokyo, Japan
| | - Nobutaka Hattori
- Department of Neurology, Faculty of Medicine, Juntendo University, Tokyo, Japan
| | - James J. Giordano
- Neuroethics Studies Program, Department of Neurology, Georgetown University Medical Center, Washington, DC, United States
| | - Diane DiEuliis
- US Department of Defense Fort Lesley J. McNair, National Defense University, Washington, DC, United States
| | - John R. Shook
- Department of Philosophy and Science Education, University of Buffalo, Buffalo, NY, United States
| | - Darin D. Doughtery
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Alik S. Widge
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, United States
| | - Helen S. Mayberg
- Department of Neurology and Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Jungho Cha
- Department of Neurology and Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Kisueng Choi
- Department of Neurology and Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Stephen Heisig
- Department of Neurology and Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Mosadolu Obatusin
- Department of Neurology and Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Enrico Opri
- Department of Neurology, Emory University, Atlanta, GA, United States
| | - Scott B. Kaufman
- Department of Psychology, Columbia University, New York, NY, United States
| | - Prasad Shirvalkar
- The Human Motor Control and Neuromodulation Laboratory, Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford University, Stanford, CA, United States
- Department of Anesthesiology (Pain Management) and Neurology, University of California, San Francisco, San Francisco, CA, United States
| | - Christopher J. Rozell
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Sankaraleengam Alagapan
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Robert S. Raike
- Restorative Therapies Group Implantables, Research and Core Technology, Medtronic Inc., Minneapolis, MN, United States
| | - Hemant Bokil
- Boston Scientific Neuromodulation Corporation, Valencia, CA, United States
| | - David Green
- NeuroPace, Inc., Mountain View, CA, United States
| | - Michael S. Okun
- Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, United States
| |
Collapse
|
14
|
Paulk AC, Zelmann R, Crocker B, Widge AS, Dougherty DD, Eskandar EN, Weisholtz DS, Richardson RM, Cosgrove GR, Williams ZM, Cash SS. Local and distant cortical responses to single pulse intracranial stimulation in the human brain are differentially modulated by specific stimulation parameters. Brain Stimul 2022; 15:491-508. [PMID: 35247646 PMCID: PMC8985164 DOI: 10.1016/j.brs.2022.02.017] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 02/23/2022] [Accepted: 02/24/2022] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND Electrical neuromodulation via direct electrical stimulation (DES) is an increasingly common therapy for a wide variety of neuropsychiatric diseases. Unfortunately, therapeutic efficacy is inconsistent, likely due to our limited understanding of the relationship between the massive stimulation parameter space and brain tissue responses. OBJECTIVE To better understand how different parameters induce varied neural responses, we systematically examined single pulse-induced cortico-cortico evoked potentials (CCEP) as a function of stimulation amplitude, duration, brain region, and whether grey or white matter was stimulated. METHODS We measured voltage peak amplitudes and area under the curve (AUC) of intracranially recorded stimulation responses as a function of distance from the stimulation site, pulse width, current injected, location relative to grey and white matter, and brain region stimulated (N = 52, n = 719 stimulation sites). RESULTS Increasing stimulation pulse width increased responses near the stimulation location. Increasing stimulation amplitude (current) increased both evoked amplitudes and AUC nonlinearly. Locally (<15 mm), stimulation at the boundary between grey and white matter induced larger responses. In contrast, for distant sites (>15 mm), white matter stimulation consistently produced larger responses than stimulation in or near grey matter. The stimulation location-response curves followed different trends for cingulate, lateral frontal, and lateral temporal cortical stimulation. CONCLUSION These results demonstrate that a stronger local response may require stimulation in the grey-white boundary while stimulation in the white matter could be needed for network activation. Thus, stimulation parameters tailored for a specific anatomical-functional outcome may be key to advancing neuromodulatory therapy.
Collapse
Affiliation(s)
- Angelique C Paulk
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
| | - Rina Zelmann
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Britni Crocker
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Harvard-MIT Health Sciences and Technology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Alik S Widge
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, 02129, USA
| | - Darin D Dougherty
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, 02129, USA
| | - Emad N Eskandar
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Daniel S Weisholtz
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, 02114, USA
| | - R Mark Richardson
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - G Rees Cosgrove
- Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, 02114, USA
| | - Ziv M Williams
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA; Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| |
Collapse
|