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Xie T, Foutz TJ, Adamek M, Swift JR, Inman CS, Manns JR, Leuthardt EC, Willie JT, Brunner P. Single-pulse electrical stimulation artifact removal using the novel matching pursuit-based artifact reconstruction and removal method (MPARRM). J Neural Eng 2023; 20:066036. [PMID: 38063368 PMCID: PMC10751949 DOI: 10.1088/1741-2552/ad1385] [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: 07/07/2023] [Revised: 11/02/2023] [Accepted: 12/07/2023] [Indexed: 12/28/2023]
Abstract
Objective.Single-pulse electrical stimulation (SPES) has been widely used to probe effective connectivity. However, analysis of the neural response is often confounded by stimulation artifacts. We developed a novel matching pursuit-based artifact reconstruction and removal method (MPARRM) capable of removing artifacts from stimulation-artifact-affected electrophysiological signals.Approach.To validate MPARRM across a wide range of potential stimulation artifact types, we performed a bench-top experiment in which we suspended electrodes in a saline solution to generate 110 types of real-world stimulation artifacts. We then added the generated stimulation artifacts to ground truth signals (stereoelectroencephalography signals from nine human subjects recorded during a receptive speech task), applied MPARRM to the combined signal, and compared the resultant denoised signal with the ground truth signal. We further applied MPARRM to artifact-affected neural signals recorded from the hippocampus while performing SPES on the ipsilateral basolateral amygdala in nine human subjects.Main results.MPARRM could remove stimulation artifacts without introducing spectral leakage or temporal spread. It accommodated variable stimulation parameters and recovered the early response to SPES within a wide range of frequency bands. Specifically, in the early response period (5-10 ms following stimulation onset), we found that the broadband gamma power (70-170 Hz) of the denoised signal was highly correlated with the ground truth signal (R=0.98±0.02, Pearson), and the broadband gamma activity of the denoised signal faithfully revealed the responses to the auditory stimuli within the ground truth signal with94%±1.47%sensitivity and99%±1.01%specificity. We further found that MPARRM could reveal the expected temporal progression of broadband gamma activity along the anterior-posterior axis of the hippocampus in response to the ipsilateral amygdala stimulation.Significance.MPARRM could faithfully remove SPES artifacts without confounding the electrophysiological signal components, especially during the early-response period. This method can facilitate the understanding of the neural response mechanisms of SPES.
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Affiliation(s)
- Tao Xie
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, United States of America
- National Center for Adaptive Neurotechnologies, St. Louis, MO, United States of America
| | - Thomas J Foutz
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States of America
| | - Markus Adamek
- National Center for Adaptive Neurotechnologies, St. Louis, MO, United States of America
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO, United States of America
| | - James R Swift
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, United States of America
- National Center for Adaptive Neurotechnologies, St. Louis, MO, United States of America
| | - Cory S Inman
- Department of Psychology, University of Utah, Salt Lake City, UT, United States of America
| | - Joseph R Manns
- Department of Psychology, Emory University, Atlanta, GA, United States of America
| | - Eric C Leuthardt
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, United States of America
| | - Jon T Willie
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, United States of America
- National Center for Adaptive Neurotechnologies, St. Louis, MO, United States of America
| | - Peter Brunner
- Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO, United States of America
- National Center for Adaptive Neurotechnologies, St. Louis, MO, United States of America
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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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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: 20] [Impact Index Per Article: 20.0] [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.
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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
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Cornblath EJ, Lucas A, Armstrong C, Greenblatt AS, Stein JM, Hadar PN, Raghupathi R, Marsh E, Litt B, Davis KA, Conrad EC. Quantifying trial-by-trial variability during cortico-cortical evoked potential mapping of epileptogenic tissue. Epilepsia 2023; 64:1021-1034. [PMID: 36728906 PMCID: PMC10480141 DOI: 10.1111/epi.17528] [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: 09/18/2022] [Revised: 01/31/2023] [Accepted: 02/01/2023] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Measuring cortico-cortical evoked potentials (CCEPs) is a promising tool for mapping epileptic networks, but it is not known how variability in brain state and stimulation technique might impact the use of CCEPs for epilepsy localization. We test the hypotheses that (1) CCEPs demonstrate systematic variability across trials and (2) CCEP amplitudes depend on the timing of stimulation with respect to endogenous, low-frequency oscillations. METHODS We studied 11 patients who underwent CCEP mapping after stereo-electroencephalography electrode implantation for surgical evaluation of drug-resistant epilepsy. Evoked potentials were measured from all electrodes after each pulse of a 30 s, 1 Hz bipolar stimulation train. We quantified monotonic trends, phase dependence, and standard deviation (SD) of N1 (15-50 ms post-stimulation) and N2 (50-300 ms post-stimulation) amplitudes across the 30 stimulation trials for each patient. We used linear regression to quantify the relationship between measures of CCEP variability and the clinical seizure-onset zone (SOZ) or interictal spike rates. RESULTS We found that N1 and N2 waveforms exhibited both positive and negative monotonic trends in amplitude across trials. SOZ electrodes and electrodes with high interictal spike rates had lower N1 and N2 amplitudes with higher SD across trials. Monotonic trends of N1 and N2 amplitude were more positive when stimulating from an area with higher interictal spike rate. We also found intermittent synchronization of trial-level N1 amplitude with low-frequency phase in the hippocampus, which did not localize the SOZ. SIGNIFICANCE These findings suggest that standard approaches for CCEP mapping, which involve computing a trial-averaged response over a .2-1 Hz stimulation train, may be masking inter-trial variability that localizes to epileptogenic tissue. We also found that CCEP N1 amplitudes synchronize with ongoing low-frequency oscillations in the hippocampus. Further targeted experiments are needed to determine whether phase-locked stimulation could have a role in localizing epileptogenic tissue.
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Affiliation(s)
- Eli J. Cornblath
- Department of Neurology, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Alfredo Lucas
- Department of Neurology, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Department of Bioengineering, School of Engineering & Applied Science, Philadelphia, Pennsylvania, USA
| | - Caren Armstrong
- Pediatric Epilepsy Program, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Adam S. Greenblatt
- Department of Neurology, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Joel M. Stein
- Department of Radiology, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Peter N. Hadar
- Department of Neurology, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Ramya Raghupathi
- Department of Neurology, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Eric Marsh
- Department of Neurology, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Pediatric Epilepsy Program, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Pediatrics, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Brian Litt
- Department of Neurology, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Kathryn A. Davis
- Department of Neurology, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Erin C. Conrad
- Department of Neurology, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
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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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Seguin C, Jedynak M, David O, Mansour S, Sporns O, Zalesky A. Communication dynamics in the human connectome shape the cortex-wide propagation of direct electrical stimulation. Neuron 2023; 111:1391-1401.e5. [PMID: 36889313 DOI: 10.1016/j.neuron.2023.01.027] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 11/28/2022] [Accepted: 01/30/2023] [Indexed: 03/09/2023]
Abstract
Communication between gray matter regions underpins all facets of brain function. We study inter-areal communication in the human brain using intracranial EEG recordings, acquired following 29,055 single-pulse direct electrical stimulations in a total of 550 individuals across 20 medical centers (average of 87 ± 37 electrode contacts per subject). We found that network communication models-computed on structural connectivity inferred from diffusion MRI-can explain the causal propagation of focal stimuli, measured at millisecond timescales. Building on this finding, we show that a parsimonious statistical model comprising structural, functional, and spatial factors can accurately and robustly predict cortex-wide effects of brain stimulation (R2=46% in data from held-out medical centers). Our work contributes toward the biological validation of concepts in network neuroscience and provides insight into how connectome topology shapes polysynaptic inter-areal signaling. We anticipate that our findings will have implications for research on neural communication and the design of brain stimulation paradigms.
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Affiliation(s)
- Caio Seguin
- Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.
| | - Maciej Jedynak
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Olivier David
- Aix-Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
| | - Sina Mansour
- Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia; Department of Biomedical Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA; Cognitive Science Program, Indiana University, Bloomington, IN, USA; Program in Neuroscience, Indiana University, Bloomington, IN, USA; Network Science Institute, Indiana University, Bloomington, IN, USA
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, VIC, Australia; Department of Biomedical Engineering, Melbourne School of Engineering, The University of Melbourne, Melbourne, VIC, Australia
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Jedynak M, Boyer A, Chanteloup-Forêt B, Bhattacharjee M, Saubat C, Tadel F, Kahane P, David O. Variability of Single Pulse Electrical Stimulation Responses Recorded with Intracranial Electroencephalography in Epileptic Patients. Brain Topogr 2023; 36:119-127. [PMID: 36520342 PMCID: PMC9834344 DOI: 10.1007/s10548-022-00928-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 11/09/2022] [Indexed: 12/23/2022]
Abstract
Cohort studies of brain stimulations performed with stereo-electroencephalographic (SEEG) electrodes in epileptic patients allow to derive large scale functional connectivity. It is known, however, that brain responses to electrical or magnetic stimulation techniques are not always reproducible. Here, we study variability of responses to single pulse SEEG electrical stimulation. We introduce a second-order probability analysis, i.e. we extend estimation of connection probabilities, defined as the proportion of responses trespassing a statistical threshold (determined in terms of Z-score with respect to spontaneous neuronal activity before stimulation) over all responses and derived from a number of individual measurements, to an analysis of pairs of measurements.Data from 445 patients were processed. We found that variability between two equivalent measurements is substantial in particular conditions. For long ( > ~ 90 mm) distances between stimulating and recording sites, and threshold value Z = 3, correlation between measurements drops almost to zero. In general, it remains below 0.5 when the threshold is smaller than Z = 4 or the stimulating current intensity is 1 mA. It grows with an increase of either of these factors. Variability is independent of interictal spiking rates in the stimulating and recording sites.We conclude that responses to SEEG stimulation in the human brain are variable, i.e. in a subject at rest, two stimulation trains performed at the same electrode contacts and with the same protocol can give discrepant results. Our findings highlight an advantage of probabilistic interpretation of such results even in the context of a single individual.
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Affiliation(s)
- Maciej Jedynak
- Grenoble Institut Neurosciences, Université Grenoble Alpes, Inserm, U1216, 38000, Grenoble, France.
- Aix Marseille Université, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France.
| | - Anthony Boyer
- Grenoble Institut Neurosciences, Université Grenoble Alpes, Inserm, U1216, 38000, Grenoble, France
- Aix Marseille Université, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | | | - Manik Bhattacharjee
- Grenoble Institut Neurosciences, Université Grenoble Alpes, Inserm, U1216, 38000, Grenoble, France
- Aix Marseille Université, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Carole Saubat
- Grenoble Institut Neurosciences, Université Grenoble Alpes, Inserm, U1216, 38000, Grenoble, France
| | - François Tadel
- Grenoble Institut Neurosciences, Université Grenoble Alpes, Inserm, U1216, 38000, Grenoble, France
- Signal and Image Processing Institute, University of Southern California, Los Angeles, USA
| | - Philippe Kahane
- Grenoble Institut Neurosciences, Université Grenoble Alpes, Inserm, U1216, 38000, Grenoble, France
- Neurology Department, CHU Grenoble Alpes, Grenoble, France
| | - Olivier David
- Grenoble Institut Neurosciences, Université Grenoble Alpes, Inserm, U1216, 38000, Grenoble, France
- Aix Marseille Université, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
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Machado MMP, Voda A, Besançon G, Becq G, David O, Kahane P. Electrode–brain interface fractional order modelling for brain tissue classification in SEEG. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
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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 PMCID: PMC10190110 DOI: 10.1016/j.neuroimage.2022.119438] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [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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Affiliation(s)
- Manuel R Mercier
- INSERM, INS, Institut de Neurosciences des Systèmes, Aix-Marseille University, Marseille, France.
| | | | - François Tadel
- Signal & Image Processing Institute, University of Southern California, Los Angeles, CA United States of America
| | - Pietro Avanzini
- Institute of Neuroscience, National Research Council of Italy, Parma, Italy
| | - Nikolai Axmacher
- Department of Neuropsychology, Faculty of Psychology, Institute of Cognitive Neuroscience, Ruhr University Bochum, Universitätsstraße 150, Bochum 44801, Germany; State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, 19 Xinjiekou Outer St, Beijing 100875, China
| | - Dillan Cellier
- Department of Cognitive Science, University of California, La Jolla, San Diego, United States of America
| | - Maria Del Vecchio
- Institute of Neuroscience, National Research Council of Italy, Parma, Italy
| | - Liberty S Hamilton
- Department of Neurology, Dell Medical School, The University of Texas at Austin, Austin, TX, United States of America; Institute for Neuroscience, The University of Texas at Austin, Austin, TX, United States of America; Department of Speech, Language, and Hearing Sciences, Moody College of Communication, The University of Texas at Austin, Austin, TX, United States of America
| | - Dora Hermes
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States of America
| | - Michael J Kahana
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Robert T Knight
- Department of Psychology and the Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720, United States of America
| | - Anais Llorens
- Helen Wills Neuroscience Institute, University of California, Berkeley, United States of America
| | - Pierre Megevand
- Department of Clinical neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Lucia Melloni
- Department of Neuroscience, Max Planck Institute for Empirical Aesthetics, Grüneburgweg 14, Frankfurt am Main 60322, Germany; Department of Neurology, NYU Grossman School of Medicine, 145 East 32nd Street, Room 828, New York, NY 10016, United States of America
| | - Kai J Miller
- Department of Neurosurgery, Mayo Clinic, Rochester, MN 55905, USA
| | - Vitória Piai
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands; Department of Medical Psychology, Radboudumc, Donders Centre for Medical Neuroscience, Nijmegen, the Netherlands
| | - Aina Puce
- Department of Psychological & Brain Sciences, Programs in Neuroscience, Cognitive Science, Indiana University, Bloomington, IN, United States of America
| | - Nick F Ramsey
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, UMC Utrecht, the Netherlands
| | - Caspar M Schwiedrzik
- Neural Circuits and Cognition Lab, European Neuroscience Institute Göttingen - A Joint Initiative of the University Medical Center Göttingen and the Max Planck Society, Göttingen, Germany; Perception and Plasticity Group, German Primate Center, Leibniz Institute for Primate Research, Göttingen, Germany
| | - Sydney E Smith
- Neurosciences Graduate Program, University of California, La Jolla, San Diego, United States of America
| | - Arjen Stolk
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands; Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States of America
| | - Nicole C Swann
- University of Oregon in the Department of Human Physiology, United States of America
| | - Mariska J Vansteensel
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, UMC Utrecht, the Netherlands
| | - Bradley Voytek
- Department of Cognitive Science, University of California, La Jolla, San Diego, United States of America; Neurosciences Graduate Program, University of California, La Jolla, San Diego, United States of America; Halıcıoğlu Data Science Institute, University of California, La Jolla, San Diego, United States of America; Kavli Institute for Brain and Mind, University of California, La Jolla, San Diego, United States of America
| | - Liang Wang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Jean-Philippe Lachaux
- Lyon Neuroscience Research Center, EDUWELL Team, INSERM UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon, Lyon F-69000, France
| | - Robert Oostenveld
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands; NatMEG, Karolinska Institutet, Stockholm, Sweden
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10
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Parmigiani S, Mikulan EP, Russo S, Sarasso S, Zauli FM, Rubino A, Cattani A, Fecchio M, Giampiccolo D, Lanzone J, D'Orio P, Del Vecchio M, Avanzini P, Nobili L, Sartori I, Massimini M, Pigorini A. Simultaneous stereo-EEG and high-density scalp EEG recordings to study the effects of intracerebral stimulation parameters. Brain Stimul 2022; 15:664-675. [PMID: 35421585 DOI: 10.1016/j.brs.2022.04.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 04/06/2022] [Accepted: 04/06/2022] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Cortico-cortical evoked potentials (CCEPs) recorded by stereo-electroencephalography (SEEG) are a valuable tool to investigate brain reactivity and effective connectivity. However, invasive recordings are spatially sparse since they depend on clinical needs. This sparsity hampers systematic comparisons across-subjects, the detection of the whole-brain effects of intracortical stimulation, as well as their relationships to the EEG responses evoked by non-invasive stimuli. OBJECTIVE To demonstrate that CCEPs recorded by high-density electroencephalography (hd-EEG) provide additional information with respect SEEG alone and to provide an open, curated dataset to allow for further exploration of their potential. METHODS The dataset encompasses SEEG and hd-EEG recordings simultaneously acquired during Single Pulse Electrical Stimulation (SPES) in drug-resistant epileptic patients (N = 36) in whom stimulations were delivered with different physical, geometrical, and topological parameters. Differences in CCEPs were assessed by amplitude, latency, and spectral measures. RESULTS While invasively and non-invasively recorded CCEPs were generally correlated, differences in pulse duration, angle and stimulated cortical area were better captured by hd-EEG. Further, intracranial stimulation evoked site-specific hd-EEG responses that reproduced the spectral features of EEG responses to transcranial magnetic stimulation (TMS). Notably, SPES, albeit unperceived by subjects, elicited scalp responses that were up to one order of magnitude larger than the responses typically evoked by sensory stimulation in awake humans. CONCLUSIONS CCEPs can be simultaneously recorded with SEEG and hd-EEG and the latter provides a reliable descriptor of the effects of SPES as well as a common reference to compare the whole-brain effects of intracortical stimulation to those of non-invasive transcranial or sensory stimulations in humans.
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Affiliation(s)
- S Parmigiani
- Department of Biomedical and Clinical Sciences "L. Sacco" Università degli Studi di Milano, Milan, Italy
| | - E P Mikulan
- Department of Biomedical and Clinical Sciences "L. Sacco" Università degli Studi di Milano, Milan, Italy
| | - S Russo
- Department of Biomedical and Clinical Sciences "L. Sacco" Università degli Studi di Milano, Milan, Italy; Department of Philosophy "Piero Martinetti", Università degli Studi di Milano, Milan, Italy
| | - S Sarasso
- Department of Biomedical and Clinical Sciences "L. Sacco" Università degli Studi di Milano, Milan, Italy
| | - F M Zauli
- Department of Biomedical and Clinical Sciences "L. Sacco" Università degli Studi di Milano, Milan, Italy; Department of Philosophy "Piero Martinetti", Università degli Studi di Milano, Milan, Italy
| | - A Rubino
- "C. Munari" Epilepsy Surgery Centre, Department of Neuroscience, Niguarda Hospital, Milan, Italy
| | - A Cattani
- Department of Mathematics & Statistics, Boston University, Boston, MA, USA
| | - M Fecchio
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - D Giampiccolo
- Department of Neurosurgery, Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK; Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, UK; Institute of Neurosciences, Cleveland Clinic London, London, UK
| | - J Lanzone
- Department of Systems Medicine, Neuroscience, University of Rome Tor Vergata, Rome, Italy; Istituti Clinici Scientifici Maugeri, IRCCS, Neurorehabilitation Department of Milano Institute, Milan, Italy
| | - P D'Orio
- "C. Munari" Epilepsy Surgery Centre, Department of Neuroscience, Niguarda Hospital, Milan, Italy; Istituto di Neuroscienze, Consiglio Nazionale delle Ricerche, Parma, Italy
| | - M Del Vecchio
- Istituto di Neuroscienze, Consiglio Nazionale delle Ricerche, Parma, Italy
| | - P Avanzini
- Istituto di Neuroscienze, Consiglio Nazionale delle Ricerche, Parma, Italy
| | - L Nobili
- Child Neuropsychiatry, IRCCS Istituto G. Gaslini, Genova, Italy
| | - I Sartori
- "C. Munari" Epilepsy Surgery Centre, Department of Neuroscience, Niguarda Hospital, Milan, Italy
| | - M Massimini
- Department of Biomedical and Clinical Sciences "L. Sacco" Università degli Studi di Milano, Milan, Italy; Istituto Di Ricovero e Cura a Carattere Scientifico, Fondazione Don Carlo Gnocchi, Milan, Italy; Azrieli Program in Brain, Mind and Consciousness, Canadian Institute for Advanced Research, Toronto, Canada
| | - A Pigorini
- Department of Biomedical and Clinical Sciences "L. Sacco" Università degli Studi di Milano, Milan, Italy; Department of Biomedical, V, Università degli Studi di Milano, Milan, Italy.
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11
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Jaroszynski C, Job A, Jedynak M, David O, Delon-Martin C. Tinnitus Perception in Light of a Parietal Operculo-Insular Involvement: A Review. Brain Sci 2022; 12:334. [PMID: 35326290 PMCID: PMC8946618 DOI: 10.3390/brainsci12030334] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 02/21/2022] [Accepted: 02/24/2022] [Indexed: 12/07/2022] Open
Abstract
In tinnitus literature, researchers have increasingly been advocating for a clearer distinction between tinnitus perception and tinnitus-related distress. In non-bothersome tinnitus, the perception itself can be more specifically investigated: this has provided a body of evidence, based on resting-state and activation fMRI protocols, highlighting the involvement of regions outside the conventional auditory areas, such as the right parietal operculum. Here, we aim to conduct a review of available investigations of the human parietal operculo-insular subregions conducted at the microscopic, mesoscopic, and macroscopic scales arguing in favor of an auditory-somatosensory cross-talk. Both the previous literature and new results on functional connectivity derived from cortico-cortical evoked potentials show that these subregions present a dense tissue of interconnections and a strong connectivity with auditory and somatosensory areas in the healthy brain. Disrupted integration processes between these modalities may thus result in erroneous perceptions, such as tinnitus. More precisely, we highlight the role of a subregion of the right parietal operculum, known as OP3 according to the Jülich atlas, in the integration of auditory and somatosensory representation of the orofacial muscles in the healthy population. We further discuss how a dysfunction of these muscles could induce hyperactivity in the OP3. The evidence of direct electrical stimulation of this area eliciting auditory hallucinations further suggests its involvement in tinnitus perception. Finally, a small number of neuroimaging studies of therapeutic interventions for tinnitus provide additional evidence of right parietal operculum involvement.
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Affiliation(s)
- Chloé Jaroszynski
- University Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France; (C.J.); (M.J.); (O.D.)
| | - Agnès Job
- Institut de Recherche Biomédicale des Armées, IRBA, 91220 Brétigny-sur-Orge, France;
| | - Maciej Jedynak
- University Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France; (C.J.); (M.J.); (O.D.)
- Aix Marseille University, Inserm, INS, Inst Neurosci Syst, 13005 Marseille, France
| | - Olivier David
- University Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France; (C.J.); (M.J.); (O.D.)
- Aix Marseille University, Inserm, INS, Inst Neurosci Syst, 13005 Marseille, France
| | - Chantal Delon-Martin
- University Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France; (C.J.); (M.J.); (O.D.)
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12
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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.
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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
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13
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Barborica A, Oane I, Donos C, Daneasa A, Mihai F, Pistol C, Dabu A, Roceanu A, Mindruta I. Imaging the effective networks associated with cortical function through intracranial high-frequency stimulation. Hum Brain Mapp 2021; 43:1657-1675. [PMID: 34904772 PMCID: PMC8886668 DOI: 10.1002/hbm.25749] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 12/01/2021] [Accepted: 12/02/2021] [Indexed: 01/23/2023] Open
Abstract
Direct electrical stimulation (DES) is considered to be the gold standard for mapping cortical function. A careful mapping of the eloquent cortex is key to successful resective or ablative surgeries, with a minimal postoperative deficit, for treatment of drug‐resistant epilepsy. There is accumulating evidence suggesting that not only local, but also remote activations play an equally important role in evoking clinical effects. By introducing a new intracranial stimulation paradigm and signal analysis methodology allowing to disambiguate EEG responses from stimulation artifacts we highlight the spatial extent of the networks associated with clinical effects. Our study includes 26 patients that underwent stereoelectroencephalographic investigations for drug‐resistant epilepsy, having 337 depth electrodes with 4,351 contacts sampling most brain structures. The routine high‐frequency electrical stimulation protocol for eloquent cortex mapping was altered in a subtle way, by alternating the polarity of the biphasic pulses in a train, causing the splitting the spectral lines of the artifactual components, exposing the underlying tissue response. By performing a frequency‐domain analysis of the EEG responses during DES we were able to capture remote activations and highlight the effect's network. By using standard intersubject averaging and a fine granularity HCP‐MMP parcellation, we were able to create local and distant connectivity maps for 614 stimulations evoking specific clinical effects. The clinical value of such maps is not only for a better understanding of the extent of the effects' networks guiding the invasive exploration, but also for understanding the spatial patterns of seizure propagation given the timeline of the seizure semiology.
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Affiliation(s)
- Andrei Barborica
- Physics Department, University of Bucharest, Bucharest, Romania.,FHC Inc., Bowdoin, Maine, USA
| | - Irina Oane
- Epilepsy Monitoring Unit, Neurology Department, Emergency University Hospital Bucharest, Bucharest, Romania
| | - Cristian Donos
- Physics Department, University of Bucharest, Bucharest, Romania
| | - Andrei Daneasa
- Epilepsy Monitoring Unit, Neurology Department, Emergency University Hospital Bucharest, Bucharest, Romania
| | - Felicia Mihai
- Physics Department, University of Bucharest, Bucharest, Romania
| | | | - Aurelia Dabu
- Epilepsy Monitoring Unit, Neurology Department, Emergency University Hospital Bucharest, Bucharest, Romania
| | - Adina Roceanu
- Epilepsy Monitoring Unit, Neurology Department, Emergency University Hospital Bucharest, Bucharest, Romania
| | - Ioana Mindruta
- Epilepsy Monitoring Unit, Neurology Department, Emergency University Hospital Bucharest, Bucharest, Romania.,Neurology Department, Faculty of Medicine, Carol Davila University of Medicine and Pharmacy Bucharest, Bucharest, Romania
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14
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Perez-Prieto N, Rodriguez-Vazquez A, Alvarez-Dolado M, Delgado-Restituto M. A 32-Channel Time-Multiplexed Artifact-Aware Neural Recording System. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2021; 15:960-977. [PMID: 34460384 DOI: 10.1109/tbcas.2021.3108725] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
This paper presents a low-power, low-noise microsystem for the recording of neural local field potentials or intracranial electroencephalographic signals. It features 32 time-multiplexed channels at the electrode interface and offers the possibility to spatially delta encode data to take advantage of the large correlation of signals captured from nearby channels. The circuit also implements a mixed-signal voltage-triggered auto-ranging algorithm which allows to attenuate large interferers in digital domain while preserving neural information. This effectively increases the system dynamic range and avoids the onset of saturation. A prototype, fabricated in a standard 180 nm CMOS process, has been experimentally verified in-vitro with cellular cultures of primary cortical neurons from mice. The system shows an integrated input-referred noise in the 0.5-200 Hz band of 1.4 μVrms for a spot noise of about 85 nV /√{Hz}. The system draws 1.5 μW per channel from 1.2 V supply and obtains 71 dB + 26 dB dynamic range when the artifact-aware auto-ranging mechanism is enabled, without penalising other critical specifications such as crosstalk between channels or common-mode and power supply rejection ratios.
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15
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Ranta R, Le Cam S, Chaudet B, Tyvaert L, Maillard L, Colnat-Coulbois S, Louis-Dorr V. Approximate Canonical Correlation Analysis for common/specific subspace decompositions. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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16
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Cho J, Seong G, Chang Y, Kim C. Energy-Efficient Integrated Circuit Solutions Toward Miniaturized Closed-Loop Neural Interface Systems. Front Neurosci 2021; 15:667447. [PMID: 34135727 PMCID: PMC8200530 DOI: 10.3389/fnins.2021.667447] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Accepted: 04/13/2021] [Indexed: 11/29/2022] Open
Abstract
Miniaturized implantable devices play a crucial role in neural interfaces by monitoring and modulating neural activities on the peripheral and central nervous systems. Research efforts toward a compact wireless closed-loop system stimulating the nerve automatically according to the user's condition have been maintained. These systems have several advantages over open-loop stimulation systems such as reduction in both power consumption and side effects of continuous stimulation. Furthermore, a compact and wireless device consuming low energy alleviates foreign body reactions and risk of frequent surgical operations. Unfortunately, however, the miniaturized closed-loop neural interface system induces several hardware design challenges such as neural activity recording with severe stimulation artifact, real-time stimulation artifact removal, and energy-efficient wireless power delivery. Here, we will review recent approaches toward the miniaturized closed-loop neural interface system with integrated circuit (IC) techniques.
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Affiliation(s)
- Jaeouk Cho
- Biomedical Energy-Efficient Electronics Laboratory, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Geunchang Seong
- Biomedical Energy-Efficient Electronics Laboratory, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Yonghee Chang
- Biomedical Energy-Efficient Electronics Laboratory, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea
| | - Chul Kim
- Biomedical Energy-Efficient Electronics Laboratory, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.,KAIST Institute for Health Science and Technology, Daejeon, South Korea
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17
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Gronlier E, Vendramini E, Volle J, Wozniak-Kwasniewska A, Antón Santos N, Coizet V, Duveau V, David O. Single-pulse electrical stimulation methodology in freely moving rat. J Neurosci Methods 2021; 353:109092. [PMID: 33549638 DOI: 10.1016/j.jneumeth.2021.109092] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 01/15/2021] [Accepted: 02/01/2021] [Indexed: 11/30/2022]
Abstract
BACKGROUND Cortico-cortical evoked potentials (CCEP) are becoming popular to infer brain connectivity and cortical excitability in implanted refractory epilepsy patients. Our goal was to transfer this methodology to the freely moving rodent. NEW METHOD CCEP were recorded on freely moving Sprague-Dawley rats, from cortical and subcortical areas using depth electrodes. Electrical stimulation was applied using 1 ms biphasic current pulse, cathodic first, at a frequency of 0.5 Hz, with intensities ranging from 0.2 to 0.8 mA. Data were then processed in a similar fashion to human clinical studies, which included epoch selection, artefact correction and smart averaging. RESULTS For a large range of tested intensities, we recorded CCEPs with very good signal to noise ratio and reproducibility between animals, without any behavioral modification. The CCEP were composed of different components according to recorded and stimulated sites, similarly to human recordings. COMPARISON WITH EXISTING METHODS We minimally adapted a clinically-motivated methodology to a freely moving rodent model to achieve high translational relevance of future preclinical studies. CONCLUSIONS Our results indicate that the CCEP methodology can be applied to freely moving rodents and transferred to preclinical research. This will be of interest to address various neuroscientific questions, in physiological and pathological conditions.
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Affiliation(s)
- Eloïse Gronlier
- SynapCell SAS, Saint-Ismier, France; Univ. Grenoble Alpes, Inserm, GIN, Grenoble Institut des Neurosciences, Grenoble, France.
| | - Estelle Vendramini
- Univ. Grenoble Alpes, Inserm, GIN, Grenoble Institut des Neurosciences, Grenoble, France
| | | | | | - Noelia Antón Santos
- Univ. Grenoble Alpes, Inserm, GIN, Grenoble Institut des Neurosciences, Grenoble, France
| | - Véronique Coizet
- Univ. Grenoble Alpes, Inserm, GIN, Grenoble Institut des Neurosciences, Grenoble, France
| | | | - Olivier David
- Univ. Grenoble Alpes, Inserm, GIN, Grenoble Institut des Neurosciences, Grenoble, France; Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
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18
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Sadeghi Najafabadi M, Chen L, Dutta K, Norris A, Feng B, Schnupp JWH, Rosskothen-Kuhl N, Read HL, Escabí MA. Optimal Multichannel Artifact Prediction and Removal for Neural Stimulation and Brain Machine Interfaces. Front Neurosci 2020; 14:709. [PMID: 32765212 PMCID: PMC7379342 DOI: 10.3389/fnins.2020.00709] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Accepted: 06/11/2020] [Indexed: 11/13/2022] Open
Abstract
Neural implants that deliver multi-site electrical stimulation to the nervous systems are no longer the last resort but routine treatment options for various neurological disorders. Multi-site electrical stimulation is also widely used to study nervous system function and neural circuit transformations. These technologies increasingly demand dynamic electrical stimulation and closed-loop feedback control for real-time assessment of neural function, which is technically challenging since stimulus-evoked artifacts overwhelm the small neural signals of interest. We report a novel and versatile artifact removal method that can be applied in a variety of settings, from single- to multi-site stimulation and recording and for current waveforms of arbitrary shape and size. The method capitalizes on linear electrical coupling between stimulating currents and recording artifacts, which allows us to estimate a multi-channel linear Wiener filter to predict and subsequently remove artifacts via subtraction. We confirm and verify the linearity assumption and demonstrate feasibility in a variety of recording modalities, including in vitro sciatic nerve stimulation, bilateral cochlear implant stimulation, and multi-channel stimulation and recording between the auditory midbrain and cortex. We demonstrate a vast enhancement in the recording quality with a typical artifact reduction of 25-40 dB. The method is efficient and can be scaled to arbitrary number of stimulus and recording sites, making it ideal for applications in large-scale arrays, closed-loop implants, and high-resolution multi-channel brain-machine interfaces.
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Affiliation(s)
- Mina Sadeghi Najafabadi
- Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, United States
| | - Longtu Chen
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
| | - Kelsey Dutta
- Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, United States
| | - Ashley Norris
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
| | - Bin Feng
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
| | - Jan W. H. Schnupp
- Department of Biomedical Sciences, City University of Hong Kong, Kowloon, Hong Kong
- Shenzhen Research Institute, City University of Hong Kong, Shenzhen, China
| | - Nicole Rosskothen-Kuhl
- Department of Biomedical Sciences, City University of Hong Kong, Kowloon, Hong Kong
- Neurobiological Research Laboratory, Section for Clinical and Experimental Otology, University Medical Center Freiburg, Freiburg im Breisgau, Germany
| | - Heather L. Read
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
- Department of Psychology, University of Connecticut, Storrs, CT, United States
- The Connecticut Institute for the Brain and Cognitive Sciences, University of Connecticut, Storrs, CT, United States
| | - Monty A. Escabí
- Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, United States
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
- Department of Psychology, University of Connecticut, Storrs, CT, United States
- The Connecticut Institute for the Brain and Cognitive Sciences, University of Connecticut, Storrs, CT, United States
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19
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Caldwell DJ, Cronin JA, Rao RPN, Collins KL, Weaver KE, Ko AL, Ojemann JG, Kutz JN, Brunton BW. Signal recovery from stimulation artifacts in intracranial recordings with dictionary learning. J Neural Eng 2020; 17:026023. [PMID: 32103828 PMCID: PMC7333778 DOI: 10.1088/1741-2552/ab7a4f] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
OBJECTIVE Electrical stimulation of the human brain is commonly used for eliciting and inhibiting neural activity for clinical diagnostics, modifying abnormal neural circuit function for therapeutics, and interrogating cortical connectivity. However, recording electrical signals with concurrent stimulation results in dominant electrical artifacts that mask the neural signals of interest. Here we develop a method to reproducibly and robustly recover neural activity during concurrent stimulation. We concentrate on signal recovery across an array of electrodes without channel-wise fine-tuning of the algorithm. Our goal includes signal recovery with trains of stimulation pulses, since repeated, high-frequency pulses are often required to induce desired effects in both therapeutic and research domains. We have made all of our code and data publicly available. APPROACH We developed an algorithm that automatically detects templates of artifacts across many channels of recording, creating a dictionary of learned templates using unsupervised clustering. The artifact template that best matches each individual artifact pulse is subtracted to recover the underlying activity. To assess the success of our method, we focus on whether it extracts physiologically interpretable signals from real recordings. MAIN RESULTS We demonstrate our signal recovery approach on invasive electrophysiologic recordings from human subjects during stimulation. We show the recovery of meaningful neural signatures in both electrocorticographic (ECoG) arrays and deep brain stimulation (DBS) recordings. In addition, we compared cortical responses induced by the stimulation of primary somatosensory (S1) by natural peripheral touch, as well as motor cortex activity with and without concurrent S1 stimulation. SIGNIFICANCE Our work will enable future advances in neural engineering with simultaneous stimulation and recording.
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Affiliation(s)
- D J Caldwell
- Department of Bioengineering, University of Washington, Seattle, WA, United States of America. Medical Scientist Training Program, University of Washington, Seattle, WA, United States of America. Center for Neurotechnology, Seattle, WA, United States of America. University of Washington Institute for Neuroengineering, Seattle, WA, United States of America. Author to whom any correspondence should be addressed
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Prime D, Woolfe M, O'Keefe S, Rowlands D, Dionisio S. Quantifying volume conducted potential using stimulation artefact in cortico-cortical evoked potentials. J Neurosci Methods 2020; 337:108639. [PMID: 32156547 DOI: 10.1016/j.jneumeth.2020.108639] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Revised: 02/16/2020] [Accepted: 02/18/2020] [Indexed: 01/27/2023]
Abstract
BACKGROUND Cortico-cortical evoked potentials (CCEP) are a technique using low frequency stimulation to infer regions of cortical connectivity in patients undergoing Stereo-electroencephalographic (SEEG) monitoring for refractory epilepsy. Little attention has been given to volume conducted components of CCEP responses, and how they may inflate CCEP connectivity. NEW METHOD Using data from 37 SEEG-CCEPs patients, a novel method was developed to quantify stimulation artefact by measuring the peak-to-peak voltage difference in the first 10 ms after CCEP stimulation. Early responses to CCEP stimulation were also quantified by calculating the root mean square of the 10-100 ms period after each stimulation pulse. Both the early CCEP responses and amplitude of stimulation artefact were regressed by physical distance, stimulation waveform, stimulation intensity and tissue type to identify conduction related properties. RESULTS Both stimulation artefact and early responses were correlated strongly with the inverse square of the distance from the stimulating electrode. Once corrected for the inverse square distance from the electrode, stimulation artefact and CCEP responses showed a linear relationship, indicating a volume conducted component. COMPARISON WITH EXISTING METHODS This is the first study to use stimulation artefact to quantify volume conducted potentials, and is the first to quantify volume conducted potentials in SEEG. A single prior study utilizing electrocorticography has shown that parts of early CCEP responses are due to volume conduction. CONCLUSIONS The linear relationship between stimulation artefact amplitude and CCEP early responses, once corrected for distance, suggests that stimulation artefact can be used as a measure to quantify the volume conducted components.
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Affiliation(s)
- David Prime
- Griffith University School of Engineering, Nathan, QLD, Australia; Mater Advanced Epilepsy Unit, Brisbane, QLD, Australia.
| | - Matthew Woolfe
- Griffith University School of Engineering, Nathan, QLD, Australia; Mater Advanced Epilepsy Unit, Brisbane, QLD, Australia
| | - Steven O'Keefe
- Griffith University School of Engineering, Nathan, QLD, Australia
| | - David Rowlands
- Griffith University School of Engineering, Nathan, QLD, Australia
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Li Y, Chen J, Yang Y. A Method for Suppressing Electrical Stimulation Artifacts from Electromyography. Int J Neural Syst 2019; 29:1850054. [DOI: 10.1142/s0129065718500545] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
When surface electromyography (EMG) signal is used in a real-time functional electrical stimulation (FES) system for feedback control, the artifact from electrical stimulation is a key challenge for EMG signal processing. To address this challenge, this study proposes a novel method to suppress stimulation artifacts in the EMG-driven closed-loop FES system. The proposed method is inspired by an experimental study that compares artifacts generated by electrical stimulations with different current intensities. It is found that (1) spikes of stimulation artifacts are susceptible to the current intensity and (2) tailing components are similar under different current intensities. Based on these observations, the proposed method combines the blanking and template subtracting strategies for suppressing stimulation artifact. The length of blanking window for suppressing the stimulation spike is adaptively determined by a spike detection algorithm and the first-order derivative analysis of signal. An autoregressive model is used to estimate the tailing part of stimulation artifact, which is an adaptive template for subtracting the artifact. The proposed method is evaluated on both semi-synthetic and experimental datasets. Verified on the semi-synthetic dataset, the proposed method achieves better performance than the classic blanking method. Validated on the experimental dataset, the proposed method substantially decreases the power of stimulation artifact in the EMG. These results indicate that the proposed method can effectively suppress the stimulation artifact while retains the useful EMG signal for an EMG-driven FES system.
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Affiliation(s)
- Yurong Li
- College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, Fujian 350116, P. R. China
- Fujian Key Lab of Medical Instrumentation and Pharmaceutical Technology, Fuzhou, Fujian 350116, P. R. China
| | - Jun Chen
- College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, Fujian 350116, P. R. China
- Fujian Key Lab of Medical Instrumentation and Pharmaceutical Technology, Fuzhou, Fujian 350116, P. R. China
| | - Yuan Yang
- College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, Fujian 350116, P. R. China
- Fujian Key Lab of Medical Instrumentation and Pharmaceutical Technology, Fuzhou, Fujian 350116, P. R. China
- Department of Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
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22
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Zhao C, Liang Y, Li C, Gao R, Wei J, Zuo R, Zhong Y, Ren Z, Geng X, Zhang G, Zhang X. Localization of Epileptogenic Zone Based on Cortico-Cortical Evoked Potential (CCEP): A Feature Extraction and Graph Theory Approach. Front Neuroinform 2019; 13:31. [PMID: 31068798 PMCID: PMC6491865 DOI: 10.3389/fninf.2019.00031] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Accepted: 04/03/2019] [Indexed: 01/01/2023] Open
Abstract
Objective Epilepsy is a chronic brain disease, which is prone to relapse and affects individuals of all ages worldwide, particularly the very young and elderly. Up to one-third of these patients are medically intractable and require resection surgery. However, the outcomes of epilepsy surgery rely upon the clear identification of epileptogenic zone (EZ). The combination of cortico-cortical evoked potential (CCEP) and electrocorticography (ECoG) provides an opportunity to observe the connectivity of human brain network and more comprehensive information that may help the clinicians localize the epileptogenic focus more precisely. However, there is no standard analysis method in the clinical application of CCEPs, especially for the quantitative analysis of abnormal connectivity of epileptic networks. The aim of this paper was to present an approach on the batch processing of CCEPs and provide information relating to the localization of EZ for clinical study. Methods Eight medically intractable epilepsy patients were included in this study. Each patient was implanted with subdural grid electrodes and electrical stimulations were applied directly to their cortex to induce CCEPs. After signal preprocessing, we constructed three effective brain networks at different spatial scales for each patient, regarding the amplitudes of CCEPs as the connection weights. Graph theory was then applied to analyze the brain network topology of epileptic patients, and the topological metrics of EZ and non-EZ (NEZ) were compared. Results The effective connectivity network reconstructed from CCEPs was asymmetric, both the number and the amplitudes of effective CCEPs decreased with increasing distance between stimulating and recording sites. Besides, the distribution of CCEP responses was associated with the locations of EZ which tended to have higher degree centrality (DC) and nodal shortest path length (NLP) than NEZ. Conclusion Our results indicated that the brain networks of epileptics were asymmetric and mainly composed of short-distance connections. The DC and NLP were highly consistent to the distribution of the EZ, and these topological parameters have great potential to be readily applied to the clinical localization of the EZ.
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Affiliation(s)
- Cui Zhao
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China.,School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Ying Liang
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China.,School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Chunlin Li
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China.,School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Runshi Gao
- Department of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Jing Wei
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China.,School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Rui Zuo
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China.,School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Yihua Zhong
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China.,School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Zhaohui Ren
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China.,School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Xinling Geng
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China.,School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Guojun Zhang
- Department of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Xu Zhang
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China.,School of Biomedical Engineering, Capital Medical University, Beijing, China
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Iturrate I, Pereira M, Millán JDR. Closed-loop electrical neurostimulation: Challenges and opportunities. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2018. [DOI: 10.1016/j.cobme.2018.09.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
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Alagapan S, Shin HW, Fröhlich F, Wu HT. Diffusion geometry approach to efficiently remove electrical stimulation artifacts in intracranial electroencephalography. J Neural Eng 2018; 16:036010. [PMID: 30523899 DOI: 10.1088/1741-2552/aaf2ba] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Cortical oscillations, electrophysiological activity patterns, associated with cognitive functions and impaired in many psychiatric disorders can be observed in intracranial electroencephalography (iEEG). Direct cortical stimulation (DCS) may directly target these oscillations and may serve as therapeutic approaches to restore functional impairments. However, the presence of electrical stimulation artifacts in neurophysiological data limits the analysis of the effects of stimulation. Currently available methods suffer in performance in the presence of nonstationarity inherent in biological data. APPROACH Our algorithm, shape adaptive nonlocal artifact removal (SANAR) is based on unsupervised manifold learning. By estimating the Euclidean median of k-nearest neighbors of each artifact in a nonlocal fashion, we obtain a faithful representation of the artifact which is then subtracted. This approach overcomes the challenges presented by nonstationarity. MAIN RESULTS SANAR is effective in removing stimulation artifacts in the time domain while preserving the spectral content of the endogenous neurophysiological signal. We demonstrate the performance in a simulated dataset as well as in human iEEG data. Using two quantitative measures, that capture how much of information from endogenous activity is retained, we demonstrate that SANAR's performance exceeds that of one of the widely used approaches, independent component analysis, in the time domain as well as the frequency domain. SIGNIFICANCE This approach allows for the analysis of iEEG data, single channel or multiple channels, during DCS, a crucial step in advancing our understanding of the effects of periodic stimulation and developing new therapies.
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Affiliation(s)
- Sankaraleengam Alagapan
- Carolina Center for Neurostimulation, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America. Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States of America
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Bhattacharyya A, Ranta R, Le Cam S, Louis-Dorr V, Tyvaert L, Colnat-Coulbois S, Maillard L, Pachori RB. A multi-channel approach for cortical stimulation artefact suppression in depth EEG signals using time-frequency and spatial filtering. IEEE Trans Biomed Eng 2018; 66:1915-1926. [PMID: 30418880 DOI: 10.1109/tbme.2018.2881051] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE The stereo electroencephalogram (SEEG) recordings are the sate of the art tool used in pre-surgical evaluation of drug-unresponsive epileptic patients. Coupled with SEEG, electrical cortical stimulation (CS) offer a complementary tool to investigate the lesioned/healthy brain regions and to identify the epileptic zones with precision. However, the propagation of this stimulation inside the brain masks the cerebral activity recorded by nearby multi-contact SEEG electrodes. The objective of this paper is to propose a novel filtering approach for suppressing the CS artifact in SEEG signals using time, frequency as well as spatial information. METHODS The method combines spatial filtering with tunable-Q wavelet transform (TQWT). SEEG signals are spatially filtered to isolate the CS artifacts within a few number of sources/components. The artifacted components are then decomposed into oscillatory background and sharp varying transient signals using tunable-Q wavelet transform (TQWT). The CS artifact is assumed to lie in the transient part of the signal. Using prior known time-frequency information of the CS artifacts, we selectively mask the wavelet coefficients of the transient signal and extract out any remaining significant electrophysiological activity. RESULTS We have applied our proposed method of CS artifact suppression on simulated and real SEEG signals with convincing performance. The experimental results indicate the effectiveness of the proposed approach. CONCLUSION The proposed method suppresses CS artifacts without affecting the background SEEG signal. SIGNIFICANCE The proposed method can be applied for suppressing both low and high frequency CS artifacts and outperforms current methods from the literature.
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Yoshimoto T, Maruichi K, Itoh Y, Takamiya S, Kaneko T. Monitoring Corticocortical Evoked Potentials During Intracranial Vascular Surgery. World Neurosurg 2018; 122:e947-e954. [PMID: 30408608 DOI: 10.1016/j.wneu.2018.10.179] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 10/23/2018] [Accepted: 10/26/2018] [Indexed: 11/17/2022]
Abstract
BACKGROUND Monitoring of corticocortical evoked potentials (CCEPs) during brain tumor surgery of patients under anesthesia was recently reported to be effective in assisting in preservation of speech function. The aim of this study was to investigate whether CCEPs can be reproducibly measured between the frontal and temporal lobes during standard intracranial vascular surgery under general anesthesia; whether dynamic changes in CCEPs caused by reduced focal cerebral blood flow can be measured; and whether CCEPs can be used to monitor speech function, particularly associated with the left side of the brain. METHODS We monitored CCEPs during 58 vascular surgeries (42 clipping procedures; 15 bypasses, 1 of which overlapped with clipping; and 2 hematoma removals from the left frontal and temporal lobe) at Kashiwaba Neurosurgical Hospital from October 2016 to January 2018. RESULTS CCEPs could be reproducibly and routinely monitored in bilateral vascular surgeries. None of the patients experienced any postoperative symptoms or showed any ischemic lesions on postoperative magnetic resonance imaging; however, 5 patients temporarily demonstrated reduced CCEPs intraoperatively that were caused by transient obstructions of blood flow. Motor evoked potentials and somatosensory evoked potentials were simultaneously monitored intraoperatively and did not show any changes. CONCLUSIONS The results of our pilot study show that CCEPs can be routinely monitored during bilateral intracranial vascular surgery and that they are sensitive to ischemia. CCEPs on the left side could serve as unique intraoperative monitoring of speech function under anesthesia.
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Affiliation(s)
- Tetsuyuki Yoshimoto
- Department of Neurosurgery, Kashiwaba Neurosurgical Hospital, Sapporo, Japan.
| | - Katsuhiko Maruichi
- Department of Neurosurgery, Kashiwaba Neurosurgical Hospital, Sapporo, Japan
| | - Yasuhiro Itoh
- Department of Neurosurgery, Hokkaido University School of Medicine, Sapporo, Japan
| | - Soichiro Takamiya
- Department of Neurosurgery, Kashiwaba Neurosurgical Hospital, Sapporo, Japan
| | - Tetsuya Kaneko
- Department of Neurophysiology, Kashiwaba Neurosurgical Hospital, Sapporo, Japan
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27
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A quantitative method for evaluating cortical responses to electrical stimulation. J Neurosci Methods 2018; 311:67-75. [PMID: 30292823 DOI: 10.1016/j.jneumeth.2018.09.034] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Revised: 09/05/2018] [Accepted: 09/06/2018] [Indexed: 12/27/2022]
Abstract
BACKGROUND Electrical stimulation of the cortex using subdurally implanted electrodes can causally reveal structural connectivity by eliciting cortico-cortical evoked potentials (CCEPs). While many studies have demonstrated the potential value of CCEPs, the methods to evaluate them were often relatively subjective, did not consider potential artifacts, and did not lend themselves to systematic scientific investigations. NEW METHOD We developed an automated and quantitative method called SIGNI (Stimulation-Induced Gamma-based Network Identification) to evaluate cortical population-level responses to electrical stimulation that minimizes the impact of electrical artifacts. We applied SIGNI to electrocorticographic (ECoG) data from eight human subjects who were implanted with a total of 978 subdural electrodes. Across the eight subjects, we delivered 92 trains of approximately 200 discrete electrical stimuli each (amplitude 4-15 mA) to a total of 64 electrode pairs. RESULTS We verified SIGNI's efficacy by demonstrating a relationship between the magnitude of evoked cortical activity and stimulation amplitude, as well as between the latency of evoked cortical activity and the distance from the stimulated locations. CONCLUSIONS SIGNI reveals the timing and amplitude of cortical responses to electrical stimulation as well as the structural connectivity supporting these responses. With these properties, it enables exploration of new and important questions about the neurophysiology of cortical communication and may also be useful for pre-surgical planning.
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28
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Trebaul L, Deman P, Tuyisenge V, Jedynak M, Hugues E, Rudrauf D, Bhattacharjee M, Tadel F, Chanteloup-Foret B, Saubat C, Reyes Mejia GC, Adam C, Nica A, Pail M, Dubeau F, Rheims S, Trébuchon A, Wang H, Liu S, Blauwblomme T, Garcés M, De Palma L, Valentin A, Metsähonkala EL, Petrescu AM, Landré E, Szurhaj W, Hirsch E, Valton L, Rocamora R, Schulze-Bonhage A, Mindruta I, Francione S, Maillard L, Taussig D, Kahane P, David O. Probabilistic functional tractography of the human cortex revisited. Neuroimage 2018; 181:414-429. [PMID: 30025851 PMCID: PMC6150949 DOI: 10.1016/j.neuroimage.2018.07.039] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Revised: 06/21/2018] [Accepted: 07/15/2018] [Indexed: 12/13/2022] Open
Abstract
In patients with pharmaco-resistant focal epilepsies investigated with intracranial electroencephalography (iEEG), direct electrical stimulations of a cortical region induce cortico-cortical evoked potentials (CCEP) in distant cerebral cortex, which properties can be used to infer large scale brain connectivity. In 2013, we proposed a new probabilistic functional tractography methodology to study human brain connectivity. We have now been revisiting this method in the F-TRACT project (f-tract.eu) by developing a large multicenter CCEP database of several thousand stimulation runs performed in several hundred patients, and associated processing tools to create a probabilistic atlas of human cortico-cortical connections. Here, we wish to present a snapshot of the methods and data of F-TRACT using a pool of 213 epilepsy patients, all studied by stereo-encephalography with intracerebral depth electrodes. The CCEPs were processed using an automated pipeline with the following consecutive steps: detection of each stimulation run from stimulation artifacts in raw intracranial EEG (iEEG) files, bad channels detection with a machine learning approach, model-based stimulation artifact correction, robust averaging over stimulation pulses. Effective connectivity between the stimulated and recording areas is then inferred from the properties of the first CCEP component, i.e. onset and peak latency, amplitude, duration and integral of the significant part. Finally, group statistics of CCEP features are implemented for each brain parcel explored by iEEG electrodes. The localization (coordinates, white/gray matter relative positioning) of electrode contacts were obtained from imaging data (anatomical MRI or CT scans before and after electrodes implantation). The iEEG contacts were repositioned in different brain parcellations from the segmentation of patients' anatomical MRI or from templates in the MNI coordinate system. The F-TRACT database using the first pool of 213 patients provided connectivity probability values for 95% of possible intrahemispheric and 56% of interhemispheric connections and CCEP features for 78% of intrahemisheric and 14% of interhemispheric connections. In this report, we show some examples of anatomo-functional connectivity matrices, and associated directional maps. We also indicate how CCEP features, especially latencies, are related to spatial distances, and allow estimating the velocity distribution of neuronal signals at a large scale. Finally, we describe the impact on the estimated connectivity of the stimulation charge and of the contact localization according to the white or gray matter. The most relevant maps for the scientific community are available for download on f-tract. eu (David et al., 2017) and will be regularly updated during the following months with the addition of more data in the F-TRACT database. This will provide an unprecedented knowledge on the dynamical properties of large fiber tracts in human.
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Affiliation(s)
- Lena Trebaul
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - Pierre Deman
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - Viateur Tuyisenge
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - Maciej Jedynak
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - Etienne Hugues
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - David Rudrauf
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - Manik Bhattacharjee
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - François Tadel
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - Blandine Chanteloup-Foret
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - Carole Saubat
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - Gina Catalina Reyes Mejia
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - Claude Adam
- Epilepsy Unit, Dept of Neurology, Pitié-Salpêtrière Hospital, APHP, Paris, France
| | - Anca Nica
- Neurology Department, CHU, Rennes, France
| | - Martin Pail
- Brno Epilepsy Center, Department of Neurology, St. Anne's University Hospital and Medical Faculty of Masaryk University, Brno, Czech Republic
| | - François Dubeau
- Montreal Neurological Institute and Hospital, Montreal, Canada
| | - Sylvain Rheims
- Department of Functional Neurology and Epileptology, Hospices Civils de Lyon and University of Lyon, Lyon, France
| | - Agnès Trébuchon
- Service de Neurophysiologie Clinique, APHM, Hôpitaux de la Timone, Marseille, France
| | - Haixiang Wang
- Yuquan Hospital Epilepsy Center, Tsinghua University, Beijing, China
| | - Sinclair Liu
- Canton Sanjiu Brain Hospital Epilepsy Center, Jinan University, Guangzhou, China
| | - Thomas Blauwblomme
- Department of Pediatric Neurosurgery, Hôpital Necker-Enfants Malades, Université Paris V Descartes, Sorbonne Paris Cité, Paris, France
| | - Mercedes Garcés
- Multidisciplinary Epilepsy Unit, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - Luca De Palma
- Department of Neuroscience, Bambino Gesù Children's Hospital, IRRCS, Rome, Italy
| | - Antonio Valentin
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), London, UK
| | | | | | | | - William Szurhaj
- Epilepsy Unit, Department of Clinical Neurophysiology, Lille University Medical Center, Lille, France
| | - Edouard Hirsch
- University Hospital, Department of Neurology, Strasbourg, France
| | - Luc Valton
- University Hospital, Department of Neurology, Toulouse, France
| | - Rodrigo Rocamora
- Epilepsy Monitoring Unit, Department of Neurology, Hospital del Mar-IMIM, Barcelona, Spain
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Ioana Mindruta
- Neurology Department, University Emergency Hospital, Bucharest, Romania
| | | | - Louis Maillard
- Centre Hospitalier Universitaire de Nancy, Nancy, France
| | - Delphine Taussig
- Service de neurochirurgie pédiatrique, Fondation Rothschild, Paris, France
| | - Philippe Kahane
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France; CHU Grenoble Alpes, Neurology Department, Grenoble, France
| | - Olivier David
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France.
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Jung H, Kim J, Nam Y. Recovery of early neural spikes from stimulation electrodes using a DC-coupled low gain high resolution data acquisition system. J Neurosci Methods 2018; 304:118-125. [PMID: 29709657 DOI: 10.1016/j.jneumeth.2018.04.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2018] [Revised: 04/16/2018] [Accepted: 04/18/2018] [Indexed: 11/29/2022]
Abstract
BACKGROUND Neural responses to electrical stimulation provide valuable information to probe and study the network function. Especially, recording neural responses from the stimulated site provides improved neural interfacing method. However, it is difficult to measure short-delayed responses at the stimulated electrode due to the saturation of the amplifier after stimulation which is called "stimulus artifact". Despite the advances in handling stimulation artifacts, it is still very challenging to deal with the artifacts if one tries to stimulate and record from the same electrode. NEW METHOD In this paper, we developed a system consisting of 24 bit ADC and low gain DC-amplifier which allows us to record the entire responses including saturation-free stimulus artifact and neural responses with excellent resolution. RESULTS Our approach showed saturation-free recording after stimulation, which makes it possible to recover neural spike as early as in 2 ms at the stimulating electrode with digital elimination methods. COMPARISON WITH EXISTING METHODS With our system we could record neural signals after stimulation that was difficult with high gain and high pass filtered recording system due to amplifier saturation. CONCLUSIONS Our new system can enhance interface performance with its higher robustness and with simple system configuration.
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Affiliation(s)
- Hyunjun Jung
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Jintae Kim
- Department of Electronics Engineering, Konkuk University, Seoul, Republic of Korea
| | - Yoonkey Nam
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
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Zhou A, Johnson BC, Muller R. Toward true closed-loop neuromodulation: artifact-free recording during stimulation. Curr Opin Neurobiol 2018; 50:119-127. [DOI: 10.1016/j.conb.2018.01.012] [Citation(s) in RCA: 61] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Revised: 01/12/2018] [Accepted: 01/17/2018] [Indexed: 11/29/2022]
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Prime D, Rowlands D, O'Keefe S, Dionisio S. Considerations in performing and analyzing the responses of cortico-cortical evoked potentials in stereo-EEG. Epilepsia 2017; 59:16-26. [DOI: 10.1111/epi.13939] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/05/2017] [Indexed: 12/14/2022]
Affiliation(s)
- David Prime
- Griffith University School of Engineering; Brisbane Qld Australia
- Mater Advanced Epilepsy Unit; Mater Hospital; Brisbane Qld Australia
| | - David Rowlands
- Griffith University School of Engineering; Brisbane Qld Australia
| | - Steven O'Keefe
- Griffith University School of Engineering; Brisbane Qld Australia
| | - Sasha Dionisio
- Mater Advanced Epilepsy Unit; Mater Hospital; Brisbane Qld Australia
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Discrimination of a medial functional module within the temporal lobe using an effective connectivity model: A CCEP study. Neuroimage 2017; 161:219-231. [PMID: 28774647 DOI: 10.1016/j.neuroimage.2017.07.061] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2016] [Revised: 06/14/2017] [Accepted: 07/28/2017] [Indexed: 11/21/2022] Open
Abstract
The temporal lobe is classically divided in two functional systems: the ventral visual pathway and the medial temporal memory system. However, their functional separation has been challenged by studies suggesting that the medial temporal lobe could be best understood as an extension of the hierarchically organized ventral visual pathway. Our purpose was to investigate (i) whether cerebral regions within the temporal lobe could be grouped into distinct functional assemblies, and (ii) which regions were central within these functional assemblies. We studied low intensity and low frequency electrical stimulations (0.5 mA, 1 Hz, 4 ms) performed during sixteen pre-surgical intracerebral EEG investigations in patients with medically intractable temporal or temporo-occipital lobe epilepsies. Eleven regions of interest were delineated per anatomical landmarks such as gyri and sulci. Effective connectivity based on electrophysiological feature (amplitude) of cortico-cortical evoked potentials (CCEPs) was evaluated and subjected to graph metrics. The amplitudes discriminated one medial module where the hippocampus could act as a signal amplifier. Mean amplitudes of CCEPs in regions of the temporal lobe showed a generalized Pareto distribution of probability suggesting neural synchronies to be self-organized critically. Our description of effective interactions within the temporal lobe provides a regional electrophysiological model of effective connectivity which is discussed in the context of the current hypothesis of pattern completion.
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