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Rassoulou F, Steina A, Hartmann CJ, Vesper J, Butz M, Schnitzler A, Hirschmann J. Exploring the electrophysiology of Parkinson's disease with magnetoencephalography and deep brain recordings. Sci Data 2024; 11:889. [PMID: 39147788 PMCID: PMC11327342 DOI: 10.1038/s41597-024-03768-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Accepted: 08/08/2024] [Indexed: 08/17/2024] Open
Abstract
Aberrant information processing in the basal ganglia and connected cortical areas are key to many neurological movement disorders such as Parkinson's disease. Investigating the electrophysiology of this system is difficult in humans because non-invasive methods, such as electroencephalography or magnetoencephalography, have limited sensitivity to deep brain areas. Recordings from electrodes implanted for therapeutic deep brain stimulation, in contrast, provide clear deep brain signals but are not suited for studying cortical activity. Therefore, we combine magnetoencephalography and local field potential recordings from deep brain stimulation electrodes in individuals with Parkinson's disease. Here, we make these data available, inviting a broader scientific community to explore the dynamics of neural activity in the subthalamic nucleus and its functional connectivity to cortex. The dataset encompasses resting-state recordings, plus two motor tasks: static forearm extension and self-paced repetitive fist clenching. Most patients were recorded both in the medicated and the unmedicated state. Along with the raw data, we provide metadata on channels, events and scripts for pre-processing to help interested researchers get started.
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Affiliation(s)
- Fayed Rassoulou
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, 40225, Düsseldorf, Germany
| | - Alexandra Steina
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, 40225, Düsseldorf, Germany
| | - Christian J Hartmann
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, 40225, Düsseldorf, Germany
- Center for Movement Disorders and Neuromodulation, Department of Neurology, Medical Faculty, Heinrich Heine University, 40225, Düsseldorf, Germany
| | - Jan Vesper
- Department of Functional Neurosurgery and Stereotaxy, Medical Faculty, Heinrich Heine University, 40225, Düsseldorf, Germany
| | - Markus Butz
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, 40225, Düsseldorf, Germany
| | - Alfons Schnitzler
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, 40225, Düsseldorf, Germany
- Center for Movement Disorders and Neuromodulation, Department of Neurology, Medical Faculty, Heinrich Heine University, 40225, Düsseldorf, Germany
| | - Jan Hirschmann
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich Heine University, 40225, Düsseldorf, Germany.
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Huang Y, Zelmann R, Hadar P, Dezha-Peralta J, Richardson RM, Williams ZM, Cash SS, Keller CJ, Paulk AC. Theta-burst direct electrical stimulation remodels human brain networks. Nat Commun 2024; 15:6982. [PMID: 39143083 PMCID: PMC11324911 DOI: 10.1038/s41467-024-51443-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 08/07/2024] [Indexed: 08/16/2024] Open
Abstract
Theta-burst stimulation (TBS), a patterned brain stimulation technique that mimics rhythmic bursts of 3-8 Hz endogenous brain rhythms, has emerged as a promising therapeutic approach for treating a wide range of brain disorders, though the neural mechanism of TBS action remains poorly understood. We investigated the neural effects of TBS using intracranial EEG (iEEG) in 10 pre-surgical epilepsy participants undergoing intracranial monitoring. Here we show that individual bursts of direct electrical TBS at 29 frontal and temporal sites evoked strong neural responses spanning broad cortical regions. These responses exhibited dynamic local field potential voltage changes over the course of stimulation presentations, including either increasing or decreasing responses, suggestive of short-term plasticity. Stronger stimulation augmented the mean TBS response amplitude and spread with more recording sites demonstrating short-term plasticity. TBS responses were stimulation site-specific with stronger TBS responses observed in regions with strong baseline stimulation effective (cortico-cortical evoked potentials) and functional (low frequency phase locking) connectivity. Further, we could use these measures to predict stable and varying (e.g. short-term plasticity) TBS response locations. Future work may integrate pre-treatment connectivity alongside other biophysical factors to personalize stimulation parameters, thereby optimizing induction of neuroplasticity within disease-relevant brain networks.
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Affiliation(s)
- Yuhao Huang
- Department of Neurosurgery, Stanford University, Palo Alto, CA, USA
| | - Rina Zelmann
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Peter Hadar
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jaquelin Dezha-Peralta
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - R Mark Richardson
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Ziv M Williams
- Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Corey J Keller
- Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, USA.
- Wu Tsai Neurosciences Institute, Stanford University, Palo Alto, CA, USA.
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, USA.
| | - Angelique C Paulk
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
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Levinson LH, Sun S, Paschall CJ, Perks KM, Weaver KE, Perlmutter SI, Ko AL, Ojemann JG, Herron JA. Data processing techniques impact quantification of cortico-cortical evoked potentials. J Neurosci Methods 2024; 408:110130. [PMID: 38653381 DOI: 10.1016/j.jneumeth.2024.110130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 01/16/2024] [Accepted: 04/03/2024] [Indexed: 04/25/2024]
Abstract
BACKGROUND Cortico-cortical evoked potentials (CCEPs) are a common tool for probing effective connectivity in intracranial human electrophysiology. As with all human electrophysiology data, CCEP data are highly susceptible to noise. To address noise, filters and re-referencing are often applied to CCEP data, but different processing strategies are used from study to study. NEW METHOD We systematically compare how common average re-referencing and filtering CCEP data impacts quantification. RESULTS We show that common average re-referencing and filters, particularly filters that cut out more frequencies, can significantly impact the quantification of CCEP magnitude and morphology. We identify that high cutoff high pass filters (> 0.5 Hz), low cutoff low pass filters (< 200 Hz), and common average re-referencing impact quantification across subjects. However, we also demonstrate that the presence of noise may impact CCEP quantification, and preprocessing is necessary to mitigate this. We show that filtering is more effective than re-referencing or averaging across trials for reducing most common types of noise. COMPARISON WITH EXISTING METHODS These results suggest that existing CCEP processing methods must be applied with care to maximize noise reduction and minimize changes to the data. We do not test every available processing strategy; rather we demonstrate that processing can influence the results of CCEP studies. We emphasize the importance of reporting all processing methods, particularly re-referencing methods. CONCLUSIONS We propose a general framework for choosing an appropriate processing pipeline for CCEP data, taking into consideration the noise levels of a specific dataset. We suggest that minimal gentle filtering is preferable.
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Affiliation(s)
- L H Levinson
- University of Washington Graduate Program in Neuroscience, 1959 NE Pacific Street, T-47, Seattle, WA 98195-7270, United States.
| | - S Sun
- University of Washington Department of Bioengineering, Box 355061, Seattle, WA 98195-5061, United States
| | - C J Paschall
- University of Washington Department of Bioengineering, Box 355061, Seattle, WA 98195-5061, United States
| | - K M Perks
- University of Washington Graduate Program in Neuroscience, 1959 NE Pacific Street, T-47, Seattle, WA 98195-7270, United States
| | - K E Weaver
- University of Washington Department of Radiology, 1959 NE Pacific Street, Seattle, WA 98195, United States
| | - S I Perlmutter
- University of Washington Department of Physiology and Biophysics, 1705 NE Pacific Street, HSB Room G424, Box 357290, Seattle, WA 98195-7290, United States
| | - A L Ko
- University of Washington Department of Neurological Surgery, Box 356470, 1959 NE Pacific St, Seattle, WA 98195-6470, United States
| | - J G Ojemann
- University of Washington Department of Neurological Surgery, Box 356470, 1959 NE Pacific St, Seattle, WA 98195-6470, United States
| | - J A Herron
- University of Washington Department of Neurological Surgery, Box 356470, 1959 NE Pacific St, Seattle, WA 98195-6470, United States
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Chung RS, Martin Del Campo Vera R, Sundaram S, Cavaleri J, Gilbert ZD, Leonor A, Shao X, Zhang S, Kammen A, Mason X, Heck C, Liu CY, Kellis SS, Lee B. Beta-band power modulation in the human amygdala differentiates between go/no-go responses in an arm-reaching task. J Neural Eng 2024; 21:046019. [PMID: 38959877 DOI: 10.1088/1741-2552/ad5ebe] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 07/03/2024] [Indexed: 07/05/2024]
Abstract
Objective. Traditionally known for its involvement in emotional processing, the amygdala's involvement in motor control remains relatively unexplored, with sparse investigations into the neural mechanisms governing amygdaloid motor movement and inhibition. This study aimed to characterize the amygdaloid beta-band (13-30 Hz) power between 'Go' and 'No-go' trials of an arm-reaching task.Approach. Ten participants with drug-resistant epilepsy implanted with stereoelectroencephalographic (SEEG) electrodes in the amygdala were enrolled in this study. SEEG data was recorded throughout discrete phases of a direct reach Go/No-go task, during which participants reached a touchscreen monitor or withheld movement based on a colored cue. Multitaper power analysis along with Wilcoxon signed-rank and Yates-correctedZtests were used to assess significant modulations of beta power between the Response and fixation (baseline) phases in the 'Go' and 'No-go' conditions.Main results. In the 'Go' condition, nine out of the ten participants showed a significant decrease in relative beta-band power during the Response phase (p⩽ 0.0499). In the 'No-go' condition, eight out of the ten participants presented a statistically significant increase in relative beta-band power during the response phase (p⩽ 0.0494). Four out of the eight participants with electrodes in the contralateral hemisphere and seven out of the eight participants with electrodes in the ipsilateral hemisphere presented significant modulation in beta-band power in both the 'Go' and 'No-go' conditions. At the group level, no significant differences were found between the contralateral and ipsilateral sides or between genders.Significance.This study reports beta-band power modulation in the human amygdala during voluntary movement in the setting of motor execution and inhibition. This finding supplements prior research in various brain regions associating beta-band power with motor control. The distinct beta-power modulation observed between these response conditions suggests involvement of amygdaloid oscillations in differentiating between motor inhibition and execution.
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Affiliation(s)
- Ryan S Chung
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Roberto Martin Del Campo Vera
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Shivani Sundaram
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Jonathon Cavaleri
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Zachary D Gilbert
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Andrea Leonor
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Xiecheng Shao
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
| | - Selena Zhang
- Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
| | - Alexandra Kammen
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Xenos Mason
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, United States of America
- Department of Neurology, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Christi Heck
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, United States of America
- Department of Neurology, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Charles Y Liu
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, United States of America
- Department of Neurology, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
| | - Spencer S Kellis
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, United States of America
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
| | - Brian Lee
- Department of Neurological Surgery, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- USC Neurorestoration Center, Keck School of Medicine of USC, Los Angeles, CA, United States of America
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States of America
- Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States of America
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Huang H, Ojeda Valencia G, Gregg NM, Osman GM, Montoya MN, Worrell GA, Miller KJ, Hermes D. CARLA: Adjusted common average referencing for cortico-cortical evoked potential data. J Neurosci Methods 2024; 407:110153. [PMID: 38710234 PMCID: PMC11149384 DOI: 10.1016/j.jneumeth.2024.110153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 02/22/2024] [Accepted: 04/27/2024] [Indexed: 05/08/2024]
Abstract
Human brain connectivity can be mapped by single pulse electrical stimulation during intracranial EEG measurements. The raw cortico-cortical evoked potentials (CCEP) are often contaminated by noise. Common average referencing (CAR) removes common noise and preserves response shapes but can introduce bias from responsive channels. We address this issue with an adjusted, adaptive CAR algorithm termed "CAR by Least Anticorrelation (CARLA)". CARLA was tested on simulated CCEP data and real CCEP data collected from four human participants. In CARLA, the channels are ordered by increasing mean cross-trial covariance, and iteratively added to the common average until anticorrelation between any single channel and all re-referenced channels reaches a minimum, as a measure of shared noise. We simulated CCEP data with true responses in 0-45 of 50 total channels. We quantified CARLA's error and found that it erroneously included 0 (median) truly responsive channels in the common average with ≤42 responsive channels, and erroneously excluded ≤2.5 (median) unresponsive channels at all responsiveness levels. On real CCEP data, signal quality was quantified with the mean R2 between all pairs of channels, which represents inter-channel dependency and is low for well-referenced data. CARLA re-referencing produced significantly lower mean R2 than standard CAR, CAR using a fixed bottom quartile of channels by covariance, and no re-referencing. CARLA minimizes bias in re-referenced CCEP data by adaptively selecting the optimal subset of non-responsive channels. It showed high specificity and sensitivity on simulated CCEP data and lowered inter-channel dependency compared to CAR on real CCEP data.
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Affiliation(s)
- Harvey Huang
- Mayo Clinic Medical Scientist Training Program, Rochester, MN, USA.
| | | | | | - Gamaleldin M Osman
- Department of Neurology, Mayo Clinic, Rochester, MN, USA; Division of Child Neurology, Department of Pediatrics, McGovern Medical School at UTHealth, Houston, TX, USA
| | - Morgan N Montoya
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Gregory A Worrell
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA; Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Kai J Miller
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA; Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, USA
| | - Dora Hermes
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA; Department of Neurology, Mayo Clinic, Rochester, MN, USA; Department of Radiology, Mayo Clinic, Rochester, MN 55901, USA.
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Singh J, Miller JA, Lucas T, Yang J, Sollars C, Eliashiv DS, Bartolomei F. Anterior thalamic nucleus local field potentials during focal temporal lobe epileptic seizures. Front Neurol 2024; 15:1419835. [PMID: 38962474 PMCID: PMC11221306 DOI: 10.3389/fneur.2024.1419835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Accepted: 06/03/2024] [Indexed: 07/05/2024] Open
Abstract
Objective To analyze the local field potentials (LFPs) in patients with focal drug-resistant epilepsy (DRE) from the anterior nucleus of the thalamus (ANT) during inter-ictal state and seizure state. Method ANT stereotactic EEG (SEEG) recordings were studied in four patients with focal temporal lobe epilepsy. SEEG data was classified as inter-ictal and ictal state and sub-categorized into electrographic (ESz), focal aware seizure (FAS), focal with impaired awareness (FIA), or focal to bilateral tonic-clonic seizure (FBTC). LFP was analyzed at 4 Hz, 8 Hz, 16 Hz, 32 Hz, high gamma (100 Hz), and ripples (200 Hz) using spectrogram analysis and a statistical comparison of normalized power spectral density (PSD) averaged during seizures versus pre-ictal baseline segments. Result The LFP recordings were analyzed for 162 seizures (127 ESz, 23 FAS, 6 FIA, and 6 FBTC). Based on time-frequency data (spectrogram), a broad band of activity, occurring between 2 and 6 Hz and centered at 4 Hz, and thin-band activity occurring specifically at 8 Hz on the frequency spectrogram were observed during the inter-ictal state. Statistically significant changes in LFP-PSD were seen for FAS, FIA, and FBTC. We observed a significant gain in LFP at the lower frequency band during FAS at 4 Hz, FIA, and FBTC at 4, 8, and 16 Hz while also observing increases at higher frequencies during FBTC at 100 and 200 Hz and a decrease during FAS seizures at 32 Hz. In contrast, no significant change in LFP power was seen for electrographic seizures. Interpretation Our observations from a limited dataset indicate that all clinical seizure types, but not electrographic seizures, caused a change in ANT-LFP based on the magnitude of the associated power spectral density (PSD). Future work will be needed to validate the use of ANT-LFP at these frequencies as accurate measurements of seizure occurrence and severity. This work represents a first step toward understanding ANT thalamic LFP patterns during focal seizures and developing adaptive DBS strategies.
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Affiliation(s)
- Jaysingh Singh
- Department of Neurology, The Ohio State University Wexner Medical Center, Columbus, OH, United States
| | - Jacob A. Miller
- Department of Biomedical Engineering, The Ohio State University, Columbus, OH, United States
| | - Timothy Lucas
- Depart of Neurosurgery, The Ohio State University Wexner Medical Center, Columbus, OH, United States
| | - Jimmy Yang
- Depart of Neurosurgery, The Ohio State University Wexner Medical Center, Columbus, OH, United States
| | - Caleb Sollars
- Lead EEG Technician, Neurodiagnostic EEG Lab, The Ohio State University Wexner Medical Center, Columbus, OH, United States
| | - Dawn S. Eliashiv
- Department of Neurology, David Gefen School of Medicine, UCLA, Los Angeles, CA, United States
| | - Fabrice Bartolomei
- APHM, Timone Hospital, Epileptology and Cerebral Rhythmology Department, Marseille, France
- Univ Aix Marseille, INSERM, INS, Inst Neurosci Syst, Marseille, France
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Shi H, Pattnaik AR, Aguila C, Lucas A, Sinha N, Prager B, Mojena M, Gallagher R, Parashos A, Bonilha L, Gleichgerrcht E, Davis KA, Litt B, Conrad EC. Utility of intracranial EEG networks depends on re-referencing and connectivity choice. Brain Commun 2024; 6:fcae165. [PMID: 38799618 PMCID: PMC11126314 DOI: 10.1093/braincomms/fcae165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 04/02/2024] [Accepted: 05/09/2024] [Indexed: 05/29/2024] Open
Abstract
Studies of intracranial EEG networks have been used to reveal seizure generators in patients with drug-resistant epilepsy. Intracranial EEG is implanted to capture the epileptic network, the collection of brain tissue that forms a substrate for seizures to start and spread. Interictal intracranial EEG measures brain activity at baseline, and networks computed during this state can reveal aberrant brain tissue without requiring seizure recordings. Intracranial EEG network analyses require choosing a reference and applying statistical measures of functional connectivity. Approaches to these technical choices vary widely across studies, and the impact of these technical choices on downstream analyses is poorly understood. Our objective was to examine the effects of different re-referencing and connectivity approaches on connectivity results and on the ability to lateralize the seizure onset zone in patients with drug-resistant epilepsy. We applied 48 pre-processing pipelines to a cohort of 125 patients with drug-resistant epilepsy recorded with interictal intracranial EEG across two epilepsy centres to generate intracranial EEG functional connectivity networks. Twenty-four functional connectivity measures across time and frequency domains were applied in combination with common average re-referencing or bipolar re-referencing. We applied an unsupervised clustering algorithm to identify groups of pre-processing pipelines. We subjected each pre-processing approach to three quality tests: (i) the introduction of spurious correlations; (ii) robustness to incomplete spatial sampling; and (iii) the ability to lateralize the clinician-defined seizure onset zone. Three groups of similar pre-processing pipelines emerged: common average re-referencing pipelines, bipolar re-referencing pipelines and relative entropy-based connectivity pipelines. Relative entropy and common average re-referencing networks were more robust to incomplete electrode sampling than bipolar re-referencing and other connectivity methods (Friedman test, Dunn-Šidák test P < 0.0001). Bipolar re-referencing reduced spurious correlations at non-adjacent channels better than common average re-referencing (Δ mean from machine ref = -0.36 versus -0.22) and worse in adjacent channels (Δ mean from machine ref = -0.14 versus -0.40). Relative entropy-based network measures lateralized the seizure onset hemisphere better than other measures in patients with temporal lobe epilepsy (Benjamini-Hochberg-corrected P < 0.05, Cohen's d: 0.60-0.76). Finally, we present an interface where users can rapidly evaluate intracranial EEG pre-processing choices to select the optimal pre-processing methods tailored to specific research questions. The choice of pre-processing methods affects downstream network analyses. Choosing a single method among highly correlated approaches can reduce redundancy in processing. Relative entropy outperforms other connectivity methods in multiple quality tests. We present a method and interface for researchers to optimize their pre-processing methods for deriving intracranial EEG brain networks.
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Affiliation(s)
- Haoer Shi
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Akash Ranjan Pattnaik
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Carlos Aguila
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alfredo Lucas
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Nishant Sinha
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Brian Prager
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marissa Mojena
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ryan Gallagher
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alexandra Parashos
- Department of Neurology, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Leonardo Bonilha
- Department of Neurology, Emory University, Atlanta, GA 30325, USA
| | | | - Kathryn A Davis
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Brian Litt
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Erin C Conrad
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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8
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Huang Y, Gopal J, Kakusa B, Li AH, Huang W, Wang JB, Persad A, Ramayya A, Parvizi J, Buch VP, Keller C. Naturalistic acute pain states decoded from neural and facial dynamics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.10.593652. [PMID: 38766098 PMCID: PMC11100805 DOI: 10.1101/2024.05.10.593652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Pain is a complex experience that remains largely unexplored in naturalistic contexts, hindering our understanding of its neurobehavioral representation in ecologically valid settings. To address this, we employed a multimodal, data-driven approach integrating intracranial electroencephalography, pain self-reports, and facial expression quantification to characterize the neural and behavioral correlates of naturalistic acute pain in twelve epilepsy patients undergoing continuous monitoring with neural and audiovisual recordings. High self-reported pain states were associated with elevated blood pressure, increased pain medication use, and distinct facial muscle activations. Using machine learning, we successfully decoded individual participants' high versus low self-reported pain states from distributed neural activity patterns (mean AUC = 0.70), involving mesolimbic regions, striatum, and temporoparietal cortex. High self-reported pain states exhibited increased low-frequency activity in temporoparietal areas and decreased high-frequency activity in mesolimbic regions (hippocampus, cingulate, and orbitofrontal cortex) compared to low pain states. This neural pain representation remained stable for hours and was modulated by pain onset and relief. Objective facial expression changes also classified self-reported pain states, with results concordant with electrophysiological predictions. Importantly, we identified transient periods of momentary pain as a distinct naturalistic acute pain measure, which could be reliably differentiated from affect-neutral periods using intracranial and facial features, albeit with neural and facial patterns distinct from self-reported pain. These findings reveal reliable neurobehavioral markers of naturalistic acute pain across contexts and timescales, underscoring the potential for developing personalized pain interventions in real-world settings.
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Affiliation(s)
- Yuhao Huang
- Department of Neurosurgery, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Jay Gopal
- Brown University, Providence, RI, 02912, USA
| | - Bina Kakusa
- Department of Neurosurgery, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Alice H. Li
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Weichen Huang
- Department of Neurology, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Jeffrey B. Wang
- Department of Anesthesia and Critical Care Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Amit Persad
- Department of Neurosurgery, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Ashwin Ramayya
- Department of Neurosurgery, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Josef Parvizi
- Department of Neurology, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Vivek P. Buch
- Department of Neurosurgery, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Corey Keller
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto, CA, USA
- Wu Tsai Neuroscience Institute, Stanford University School of Medicine, Palo Alto, CA
- Veterans Affairs Palo Alto Healthcare System, and the Sierra Pacific Mental Illness, Research, Education, and Clinical Center (MIRECC), Palo Alto, CA, 94394, USA
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9
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Blanpain LT, Cole ER, Chen E, Park JK, Walelign MY, Gross RE, Cabaniss BT, Willie JT, Singer AC. Multisensory flicker modulates widespread brain networks and reduces interictal epileptiform discharges. Nat Commun 2024; 15:3156. [PMID: 38605017 PMCID: PMC11009358 DOI: 10.1038/s41467-024-47263-y] [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: 07/25/2023] [Accepted: 03/26/2024] [Indexed: 04/13/2024] Open
Abstract
Modulating brain oscillations has strong therapeutic potential. Interventions that both non-invasively modulate deep brain structures and are practical for chronic daily home use are desirable for a variety of therapeutic applications. Repetitive audio-visual stimulation, or sensory flicker, is an accessible approach that modulates hippocampus in mice, but its effects in humans are poorly defined. We therefore quantified the neurophysiological effects of flicker with high spatiotemporal resolution in patients with focal epilepsy who underwent intracranial seizure monitoring. In this interventional trial (NCT04188834) with a cross-over design, subjects underwent different frequencies of flicker stimulation in the same recording session with the effect of sensory flicker exposure on local field potential (LFP) power and interictal epileptiform discharges (IEDs) as primary and secondary outcomes, respectively. Flicker focally modulated local field potentials in expected canonical sensory cortices but also in the medial temporal lobe and prefrontal cortex, likely via resonance of stimulated long-range circuits. Moreover, flicker decreased interictal epileptiform discharges, a pathological biomarker of epilepsy and degenerative diseases, most strongly in regions where potentials were flicker-modulated, especially the visual cortex and medial temporal lobe. This trial met the scientific goal and is now closed. Our findings reveal how multi-sensory stimulation may modulate cortical structures to mitigate pathological activity in humans.
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Affiliation(s)
- Lou T Blanpain
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, USA
- Neuroscience Graduate Program, Graduate Division of Biological and Biomedical Sciences, Emory University, Atlanta, GA, USA
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, GA, USA
| | - Eric R Cole
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, USA
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, GA, USA
| | - Emily Chen
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, USA
| | - James K Park
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, USA
| | - Michael Y Walelign
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Robert E Gross
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, USA
- Departments of Neurosurgery and Neuroscience and Cell Biology, Rutgers Robert Wood Johnson Medical School, New Brunswick and New Jersey Medical School, Newark, NJ, USA
| | - Brian T Cabaniss
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Jon T Willie
- Departments of Neurological Surgery, Neurology, Psychiatry, and Biomedical Engineering, Washington University, St. Louis, MO, USA.
| | - Annabelle C Singer
- Neuroscience Graduate Program, Graduate Division of Biological and Biomedical Sciences, Emory University, Atlanta, GA, USA.
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, GA, USA.
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10
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Gattas S, Larson MS, Mnatsakanyan L, Sen-Gupta I, Vadera S, Swindlehurst AL, Rapp PE, Lin JJ, Yassa MA. Theta mediated dynamics of human hippocampal-neocortical learning systems in memory formation and retrieval. Nat Commun 2023; 14:8505. [PMID: 38129375 PMCID: PMC10739909 DOI: 10.1038/s41467-023-44011-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 11/23/2023] [Indexed: 12/23/2023] Open
Abstract
Episodic memory arises as a function of dynamic interactions between the hippocampus and the neocortex, yet the mechanisms have remained elusive. Here, using human intracranial recordings during a mnemonic discrimination task, we report that 4-5 Hz (theta) power is differentially recruited during discrimination vs. overgeneralization, and its phase supports hippocampal-neocortical when memories are being formed and correctly retrieved. Interactions were largely bidirectional, with small but significant net directional biases; a hippocampus-to-neocortex bias during acquisition of new information that was subsequently correctly discriminated, and a neocortex-to-hippocampus bias during accurate discrimination of new stimuli from similar previously learned stimuli. The 4-5 Hz rhythm may facilitate the initial stages of information acquisition by neocortex during learning and the recall of stored information from cortex during retrieval. Future work should further probe these dynamics across different types of tasks and stimuli and computational models may need to be expanded accordingly to accommodate these findings.
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Affiliation(s)
- Sandra Gattas
- Department of Electrical Engineering and Computer Science, School of Engineering, University of California, Irvine, CA, 92617, USA
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, 92697, USA
| | - Myra Sarai Larson
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, 92697, USA
- Department of Neurobiology and Behavior, School of Biological Sciences, University of California, Irvine, CA, 92697, USA
| | - Lilit Mnatsakanyan
- Department of Neurology, School of Medicine, University of California, Irvine, CA, 92697, USA
| | - Indranil Sen-Gupta
- Department of Neurology, School of Medicine, University of California, Irvine, CA, 92697, USA
| | - Sumeet Vadera
- Department of Neurological Surgery, School of Medicine, University of California, Irvine, CA, 92697, USA
| | - A Lee Swindlehurst
- Department of Electrical Engineering and Computer Science, School of Engineering, University of California, Irvine, CA, 92617, USA
| | - Paul E Rapp
- Department of Military & Emergency Medicine, Uniformed Services University, Bethesda, MD, 20814, USA
| | - Jack J Lin
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, 92697, USA
- Department of Neurology, School of Medicine, University of California, Irvine, CA, 92697, USA
| | - Michael A Yassa
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, CA, 92697, USA.
- Department of Neurobiology and Behavior, School of Biological Sciences, University of California, Irvine, CA, 92697, USA.
- Department of Neurology, School of Medicine, University of California, Irvine, CA, 92697, USA.
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11
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Dikeçligil GN, Yang AI, Sanghani N, Lucas T, Chen HI, Davis KA, Gottfried JA. Odor representations from the two nostrils are temporally segregated in human piriform cortex. Curr Biol 2023; 33:5275-5287.e5. [PMID: 37924807 DOI: 10.1016/j.cub.2023.10.021] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 10/12/2023] [Accepted: 10/12/2023] [Indexed: 11/06/2023]
Abstract
The human olfactory system has two discrete channels of sensory input, arising from olfactory epithelia housed in the left and right nostrils. Here, we asked whether the primary olfactory cortex (piriform cortex [PC]) encodes odor information arising from the two nostrils as integrated or distinct stimuli. We recorded intracranial electroencephalogram (iEEG) signals directly from PC while human subjects participated in an odor identification task where odors were delivered to the left, right, or both nostrils. We analyzed the time course of odor identity coding using machine-learning approaches and found that uni-nostril odor inputs to the ipsilateral nostril are encoded ∼480-ms faster than odor inputs to the contralateral nostril on average. During naturalistic bi-nostril odor sampling, odor information emerged in two temporally segregated epochs, with the first epoch corresponding to the ipsilateral and the second epoch corresponding to the contralateral odor representations. These findings reveal that PC maintains distinct representations of odor input from each nostril through temporal segregation, highlighting an olfactory coding scheme at the cortical level that can parse odor information across nostrils within the course of a single inhalation.
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Affiliation(s)
- Gülce Nazlı Dikeçligil
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Andrew I Yang
- Department of Neurosurgery, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Nisha Sanghani
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Timothy Lucas
- Department of Neurosurgery and Biomedical Engineering, Ohio State University, Columbus, OH 43210, USA
| | - H Isaac Chen
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kathryn A Davis
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jay A Gottfried
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
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12
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Peng G, Nourani M, Dave H, Harvey J. SEEG-based epileptic seizure network modeling and analysis for pre-surgery evaluation. Comput Biol Med 2023; 167:107692. [PMID: 37976827 DOI: 10.1016/j.compbiomed.2023.107692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 09/27/2023] [Accepted: 11/06/2023] [Indexed: 11/19/2023]
Abstract
Stereo-electroencephalography is a minimally invasive technique for patients with refractory epilepsy pursuing surgery to reduce or control seizures. Electrodes are implanted based on pre-surgery evaluations and can collect deep brain activities for surgery decisions. This paper presents a methodology to analyze stereo-electroencephalography and assist clinicians by recommending the optimal surgical option and target areas for focal epilepsy patients. A seizure network (graph) model is proposed to characterize the spatial distribution and temporal changes of ictal events. The network nodes and edges correspond to specific epileptogenic regions and propagation/impact pathways (weighted by directed transfer function), respectively. We then employ a K-means clustering strategy to group nodes into a few clusters, from which the target surgical areas can be identified. Ten patients with different types of focal seizures were thoroughly analyzed. Promising consistency between results of our method's recommendations, clinical decisions and surgery outcomes were observed.
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Affiliation(s)
- Genchang Peng
- Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX 75080, USA.
| | - Mehrdad Nourani
- Department of Electrical and Computer Engineering, The University of Texas at Dallas, Richardson, TX 75080, USA.
| | - Hina Dave
- Department of Neurology and Neurotherapeutics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
| | - Jay Harvey
- Department of Neurology, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
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13
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Shih WY, Yu HY, Lee CC, Chou CC, Chen C, Glimcher PW, Wu SW. Electrophysiological population dynamics reveal context dependencies during decision making in human frontal cortex. Nat Commun 2023; 14:7821. [PMID: 38016973 PMCID: PMC10684521 DOI: 10.1038/s41467-023-42092-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 09/28/2023] [Indexed: 11/30/2023] Open
Abstract
Evidence from monkeys and humans suggests that the orbitofrontal cortex (OFC) encodes the subjective value of options under consideration during choice. Data from non-human primates suggests that these value signals are context-dependent, representing subjective value in a way influenced by the decision makers' recent experience. Using electrodes distributed throughout cortical and subcortical structures, human epilepsy patients performed an auction task where they repeatedly reported the subjective values they placed on snack food items. High-gamma activity in many cortical and subcortical sites including the OFC positively correlated with subjective value. Other OFC sites showed signals contextually modulated by the subjective value of previously offered goods-a context dependency predicted by theory but not previously observed in humans. These results suggest that value and value-context signals are simultaneously present but separately represented in human frontal cortical activity.
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Affiliation(s)
- Wan-Yu Shih
- Institute of Neuroscience, College of Life Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC.
| | - Hsiang-Yu Yu
- College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
- Department of Epilepsy, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
| | - Cheng-Chia Lee
- Department of Epilepsy, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
- Department of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Chien-Chen Chou
- College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
- Department of Epilepsy, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
| | - Chien Chen
- College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
- Department of Epilepsy, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
| | - Paul W Glimcher
- Neuroscience Institute, NYU Grossman School of Medicine, New York, NY, USA.
- Department of Neuroscience and Physiology, NYU Grossman School of Medicine, New York, NY, USA.
| | - Shih-Wei Wu
- Institute of Neuroscience, College of Life Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC.
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC.
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14
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Kalina A, Jezdik P, Fabera P, Marusic P, Hammer J. Electrical Source Imaging of Somatosensory Evoked Potentials from Intracranial EEG Signals. Brain Topogr 2023; 36:835-853. [PMID: 37642729 DOI: 10.1007/s10548-023-00994-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 07/24/2023] [Indexed: 08/31/2023]
Abstract
Stereoelectroencephalography (SEEG) records electrical brain activity with intracerebral electrodes. However, it has an inherently limited spatial coverage. Electrical source imaging (ESI) infers the position of the neural generators from the recorded electric potentials, and thus, could overcome this spatial undersampling problem. Here, we aimed to quantify the accuracy of SEEG ESI under clinical conditions. We measured the somatosensory evoked potential (SEP) in SEEG and in high-density EEG (HD-EEG) in 20 epilepsy surgery patients. To localize the source of the SEP, we employed standardized low resolution brain electromagnetic tomography (sLORETA) and equivalent current dipole (ECD) algorithms. Both sLORETA and ECD converged to similar solutions. Reflecting the large differences in the SEEG implantations, the localization error also varied in a wide range from 0.4 to 10 cm. The SEEG ESI localization error was linearly correlated with the distance from the putative neural source to the most activated contact. We show that it is possible to obtain reliable source reconstructions from SEEG under realistic clinical conditions, provided that the high signal fidelity recording contacts are sufficiently close to the source of the brain activity.
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Affiliation(s)
- Adam Kalina
- Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital (Full Member of the ERN EpiCARE), V Uvalu 84, 150 06, Prague 5, Czechia.
| | - Petr Jezdik
- Department of Measurement, Faculty of Electrical Engineering, Czech Technical University in Prague, Technicka 2, 166 27, Prague 6, Czechia
| | - Petr Fabera
- Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital (Full Member of the ERN EpiCARE), V Uvalu 84, 150 06, Prague 5, Czechia
| | - Petr Marusic
- Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital (Full Member of the ERN EpiCARE), V Uvalu 84, 150 06, Prague 5, Czechia
| | - Jiri Hammer
- Department of Neurology, Second Faculty of Medicine, Charles University and Motol University Hospital (Full Member of the ERN EpiCARE), V Uvalu 84, 150 06, Prague 5, Czechia.
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15
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Dikecligil GN, Yang AI, Sanghani N, Lucas T, Chen HI, Davis KA, Gottfried JA. Odor representations from the two nostrils are temporally segregated in human piriform cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.14.528521. [PMID: 36824705 PMCID: PMC9948982 DOI: 10.1101/2023.02.14.528521] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
The human olfactory system has two discrete channels of sensory input, arising from olfactory epithelia housed in the left and right nostrils. Here, we asked whether primary olfactory cortex (piriform cortex, PC) encodes odor information arising from the two nostrils as integrated or distinct stimuli. We recorded intracranial EEG signals directly from PC while human subjects participated in an odor identification task where odors were delivered to the left, right, or both nostrils. We analyzed the time-course of odor-identity coding using machine learning approaches, and found that uni-nostril odor inputs to the ipsilateral nostril are encoded ~480 ms faster than odor inputs to the contralateral nostril on average. During naturalistic bi-nostril odor sampling, odor information emerged in two temporally segregated epochs with the first epoch corresponding to the ipsilateral and the second epoch corresponding to the contralateral odor representations. These findings reveal that PC maintains distinct representations of odor input from each nostril through temporal segregation, highlighting an olfactory coding scheme at the cortical level that can parse odor information across nostrils within the course of a single inhalation.
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16
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Gattas S, Larson MS, Mnatsakanyan L, Sen-Gupta I, Vadera S, Swindlehurst L, Rapp PE, Lin JJ, Yassa MA. Theta mediated dynamics of human hippocampal-neocortical learning systems in memory formation and retrieval. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.20.558688. [PMID: 37790541 PMCID: PMC10542525 DOI: 10.1101/2023.09.20.558688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Episodic memory arises as a function of dynamic interactions between the hippocampus and the neocortex, yet the mechanisms have remained elusive. Here, using human intracranial recordings during a mnemonic discrimination task, we report that 4-5 Hz (theta) power is differentially recruited during discrimination vs. overgeneralization, and its phase supports hippocampal-neocortical when memories are being formed and correctly retrieved. Interactions were largely bidirectional, with small but significant net directional biases; a hippocampus-to-neocortex bias during acquisition of new information that was subsequently correctly discriminated, and a neocortex-to-hippocampus bias during accurate discrimination of new stimuli from similar previously learned stimuli. The 4-5 Hz rhythm may facilitate the initial stages of information acquisition by neocortex during learning and the recall of stored information from cortex during retrieval. Future work should further probe these dynamics across different types of tasks and stimuli and computational models may need to be expanded accordingly to accommodate these findings.
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Affiliation(s)
- Sandra Gattas
- Department of Electrical Engineering and Computer Science, School of Engineering, University of California, Irvine, Irvine, CA, 92617, USA
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, California, 92697, USA
| | - Myra Sarai Larson
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, California, 92697, USA
- Department of Neurobiology and Behavior, School of Biological Sciences, University of California, Irvine, Irvine, CA, 92697, USA
| | - Lilit Mnatsakanyan
- Department of Neurology, School of Medicine, University of California, Irvine, CA, 92697, USA
| | - Indranil Sen-Gupta
- Department of Neurology, School of Medicine, University of California, Irvine, CA, 92697, USA
| | - Sumeet Vadera
- Department of Neurological Surgery, School of Medicine, University of California, Irvine, Irvine, CA, 92697, USA
| | - Lee Swindlehurst
- Department of Electrical Engineering and Computer Science, School of Engineering, University of California, Irvine, Irvine, CA, 92617, USA
| | - Paul E. Rapp
- Department of Military & Emergency Medicine, Uniformed Services University, Bethesda, MD, 20814, USA
| | - Jack J. Lin
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, California, 92697, USA
- Department of Neurology, School of Medicine, University of California, Irvine, CA, 92697, USA
| | - Michael A. Yassa
- Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, California, 92697, USA
- Department of Neurobiology and Behavior, School of Biological Sciences, University of California, Irvine, Irvine, CA, 92697, USA
- Department of Neurology, School of Medicine, University of California, Irvine, CA, 92697, USA
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17
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Zhang Y, Valsecchi M, Gegenfurtner KR, Chen J. Laplacian reference is optimal for steady-state visual-evoked potentials. J Neurophysiol 2023; 130:557-568. [PMID: 37492903 DOI: 10.1152/jn.00469.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 07/18/2023] [Accepted: 07/20/2023] [Indexed: 07/27/2023] Open
Abstract
Steady-state visual-evoked potentials (SSVEPs) are widely used in human neuroscience studies and applications such as brain-computer interfaces (BCIs). Surprisingly, no previous study has systematically evaluated different reference methods for SSVEP analysis, despite that signal reference is crucial for the proper assessment of neural activities. In the present study, using four datasets from our previous SSVEP studies (Chen J, Valsecchi M, Gegenfurtner KR. J Neurophysiol 118: 749-754, 2017; Chen J, Valsecchi M, Gegenfurtner KR. Neuropsychologia 102: 206-216, 2017; Chen J, McManus M, Valsecchi M, Harris LR, Gegenfurtner KR. J Vis 19: 8, 2019) and three public datasets from other studies (Baker DH, Vilidaite G, Wade AR. PLoS Comput Biol 17: e1009507, 2021; Lygo FA, Richard B, Wade AR, Morland AB, Baker DH. NeuroImage 230: 117780, 2021; Vilidaite G, Norcia AM, West RJH, Elliott CJH, Pei F, Wade AR, Baker DH. Proc R Soc B 285: 20182255, 2018), we compared four reference methods: monopolar reference, common average reference, averaged-mastoids reference, and Laplacian reference. The quality of the resulting SSVEP signals was compared in terms of both signal-to-noise ratios (SNRs) and reliability. The results showed that Laplacian reference, which uses signals at the maximally activated electrode after subtracting the average of the nearby electrodes to reduce common noise, gave rise to the highest SNRs. Furthermore, the Laplacian reference resulted in SSVEP signals that were highly reliable across recording sessions or trials. These results suggest that Laplacian reference is optimal for SSVEP studies and applications. Laplacian reference is especially advantageous for SSVEP experiments where short preparation time is preferred as it requires only data from the maximally activated electrode and a few surrounding electrodes.NEW & NOTEWORTHY The present study provides a comprehensive evaluation of the use of different reference methods for steady-state visual-evoked potentials (SSVEPs) and has found that Laplacian reference increases signal-to-noise ratios (SNRs) and enhances reliabilities of SSVEP signals. Thus, the results suggest that Laplacian reference is optimal for SSVEP analysis.
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Affiliation(s)
- Yuan Zhang
- School of Psychology, Shanghai University of Sport, Shanghai, China
| | - Matteo Valsecchi
- Dipartimento di Psicologia, Universitá di Bologna, Bologna, Italy
| | - Karl R Gegenfurtner
- Abteilung Allgemeine Psychologie, Justus-Liebig-Universität Gießen, Gießen, Germany
| | - Jing Chen
- School of Psychology, Shanghai University of Sport, Shanghai, China
- Research Center for Exercise and Brain Science, Shanghai University of Sport, Shanghai, China
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18
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Li G, Jiang S, Meng J, Wu Z, Jiang H, Fan Z, Hu J, Sheng X, Zhang D, Schalk G, Chen L, Zhu X. Spatio-temporal evolution of human neural activity during visually cued hand movements. Cereb Cortex 2023; 33:9764-9777. [PMID: 37464883 DOI: 10.1093/cercor/bhad242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 06/14/2023] [Accepted: 06/15/2023] [Indexed: 07/20/2023] Open
Abstract
Making hand movements in response to visual cues is common in daily life. It has been well known that this process activates multiple areas in the brain, but how these neural activations progress across space and time remains largely unknown. Taking advantage of intracranial electroencephalographic (iEEG) recordings using depth and subdural electrodes from 36 human subjects using the same task, we applied single-trial and cross-trial analyses to high-frequency iEEG activity. The results show that the neural activation was widely distributed across the human brain both within and on the surface of the brain, and focused specifically on certain areas in the parietal, frontal, and occipital lobes, where parietal lobes present significant left lateralization on the activation. We also demonstrate temporal differences across these brain regions. Finally, we evaluated the degree to which the timing of activity within these regions was related to sensory or motor function. The findings of this study promote the understanding of task-related neural processing of the human brain, and may provide important insights for translational applications.
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Affiliation(s)
- Guangye Li
- Institute of Robotics, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Shize Jiang
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jianjun Meng
- Institute of Robotics, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Zehan Wu
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Haiteng Jiang
- Department of Neurobiology, Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou 310013, China
- MOE Frontier Science Center for Brain Science & Brain-Machine Integration, Zhejiang University, Hangzhou 310058, China
| | - Zhen Fan
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Jie Hu
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Xinjun Sheng
- Institute of Robotics, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Dingguo Zhang
- Department of Electronic and Electrical Engineering, University of Bath, Bath BA2 7AY, United Kingdom
| | - Gerwin Schalk
- Chen Frontier Lab for Applied Neurotechnology, Tianqiao and Chrissy Chen Institute, Shanghai 200052, China
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Liang Chen
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Xiangyang Zhu
- Institute of Robotics, Shanghai Jiao Tong University, Shanghai 200240, China
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19
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Rijal S, Corona L, Perry MS, Tamilia E, Madsen JR, Stone SSD, Bolton J, Pearl PL, Papadelis C. Functional connectivity discriminates epileptogenic states and predicts surgical outcome in children with drug resistant epilepsy. Sci Rep 2023; 13:9622. [PMID: 37316544 PMCID: PMC10267141 DOI: 10.1038/s41598-023-36551-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 06/06/2023] [Indexed: 06/16/2023] Open
Abstract
Normal brain functioning emerges from a complex interplay among regions forming networks. In epilepsy, these networks are disrupted causing seizures. Highly connected nodes in these networks are epilepsy surgery targets. Here, we assess whether functional connectivity (FC) using intracranial electroencephalography can quantify brain regions epileptogenicity and predict surgical outcome in children with drug resistant epilepsy (DRE). We computed FC between electrodes on different states (i.e. interictal without spikes, interictal with spikes, pre-ictal, ictal, and post-ictal) and frequency bands. We then estimated the electrodes' nodal strength. We compared nodal strength between states, inside and outside resection for good- (n = 22, Engel I) and poor-outcome (n = 9, Engel II-IV) patients, respectively, and tested their utility to predict the epileptogenic zone and outcome. We observed a hierarchical epileptogenic organization among states for nodal strength: lower FC during interictal and pre-ictal states followed by higher FC during ictal and post-ictal states (p < 0.05). We further observed higher FC inside resection (p < 0.05) for good-outcome patients on different states and bands, and no differences for poor-outcome patients. Resection of nodes with high FC was predictive of outcome (positive and negative predictive values: 47-100%). Our findings suggest that FC can discriminate epileptogenic states and predict outcome in patients with DRE.
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Affiliation(s)
- Sakar Rijal
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children's Health Care System, 1500 Cooper St., Fort Worth, TX, 76104, USA
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, 76010, USA
| | - Ludovica Corona
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children's Health Care System, 1500 Cooper St., Fort Worth, TX, 76104, USA
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, 76010, USA
| | - M Scott Perry
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children's Health Care System, 1500 Cooper St., Fort Worth, TX, 76104, USA
| | - Eleonora Tamilia
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Joseph R Madsen
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Scellig S D Stone
- Division of Epilepsy Surgery, Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Jeffrey Bolton
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Phillip L Pearl
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Christos Papadelis
- Jane and John Justin Institute for Mind Health Neurosciences Center, Cook Children's Health Care System, 1500 Cooper St., Fort Worth, TX, 76104, USA.
- Department of Bioengineering, The University of Texas at Arlington, Arlington, TX, 76010, USA.
- School of Medicine, Texas Christian University, Fort Worth, TX, 76129, USA.
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20
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Huang Y, Wang JB, Parker JJ, Shivacharan R, Lal RA, Halpern CH. Spectro-spatial features in distributed human intracranial activity proactively encode peripheral metabolic activity. Nat Commun 2023; 14:2729. [PMID: 37169738 PMCID: PMC10174612 DOI: 10.1038/s41467-023-38253-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 04/17/2023] [Indexed: 05/13/2023] Open
Abstract
Mounting evidence demonstrates that the central nervous system (CNS) orchestrates glucose homeostasis by sensing glucose and modulating peripheral metabolism. Glucose responsive neuronal populations have been identified in the hypothalamus and several corticolimbic regions. However, how these CNS gluco-regulatory regions modulate peripheral glucose levels is not well understood. To better understand this process, we simultaneously measured interstitial glucose concentrations and local field potentials in 3 human subjects from cortical and subcortical regions, including the hypothalamus in one subject. Correlations between high frequency activity (HFA, 70-170 Hz) and peripheral glucose levels are found across multiple brain regions, notably in the hypothalamus, with correlation magnitude modulated by sleep-wake cycles, circadian coupling, and hypothalamic connectivity. Correlations are further present between non-circadian (ultradian) HFA and glucose levels which are higher during awake periods. Spectro-spatial features of neural activity enable decoding of peripheral glucose levels both in the present and up to hours in the future. Our findings demonstrate proactive encoding of homeostatic glucose dynamics by the CNS.
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Affiliation(s)
- Yuhao Huang
- Department of Neurosurgery, Stanford University Medical Center, Stanford, CA, 94305, USA
| | - Jeffrey B Wang
- Department of Neurosurgery, Stanford University Medical Center, Stanford, CA, 94305, USA
- Medical Scientist Training Program, Stanford School of Medicine, Stanford, CA, 94305, USA
| | - Jonathon J Parker
- Department of Neurosurgery, Stanford University Medical Center, Stanford, CA, 94305, USA
| | - Rajat Shivacharan
- Department of Neurosurgery, Stanford University Medical Center, Stanford, CA, 94305, USA
| | - Rayhan A Lal
- Department of Medicine (Endocrinology), Stanford University Medical Center, Stanford, CA, 94305, USA.
- Department of Pediatrics (Endocrinology), Stanford University Medical Center, Stanford, CA, 94305, USA.
| | - Casey H Halpern
- Department of Neurosurgery, Stanford University Medical Center, Stanford, CA, 94305, USA.
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21
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Soroush PZ, Herff C, Ries SK, Shih JJ, Schultz T, Krusienski DJ. The nested hierarchy of overt, mouthed, and imagined speech activity evident in intracranial recordings. Neuroimage 2023; 269:119913. [PMID: 36731812 DOI: 10.1016/j.neuroimage.2023.119913] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 01/05/2023] [Accepted: 01/29/2023] [Indexed: 02/01/2023] Open
Abstract
Recent studies have demonstrated that it is possible to decode and synthesize various aspects of acoustic speech directly from intracranial measurements of electrophysiological brain activity. In order to continue progressing toward the development of a practical speech neuroprosthesis for the individuals with speech impairments, better understanding and modeling of imagined speech processes are required. The present study uses intracranial brain recordings from participants that performed a speaking task with trials consisting of overt, mouthed, and imagined speech modes, representing various degrees of decreasing behavioral output. Speech activity detection models are constructed using spatial, spectral, and temporal brain activity features, and the features and model performances are characterized and compared across the three degrees of behavioral output. The results indicate the existence of a hierarchy in which the relevant channels for the lower behavioral output modes form nested subsets of the relevant channels from the higher behavioral output modes. This provides important insights for the elusive goal of developing more effective imagined speech decoding models with respect to the better-established overt speech decoding counterparts.
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22
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Blanpain LT, Chen E, Park J, Walelign MY, Gross RE, Cabaniss BT, Willie JT, Singer AC. Multisensory Flicker Modulates Widespread Brain Networks and Reduces Interictal Epileptiform Discharges in Humans. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.14.23286691. [PMID: 36993248 PMCID: PMC10055448 DOI: 10.1101/2023.03.14.23286691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Modulating brain oscillations has strong therapeutic potential. However, commonly used non-invasive interventions such as transcranial magnetic or direct current stimulation have limited effects on deeper cortical structures like the medial temporal lobe. Repetitive audio-visual stimulation, or sensory flicker, modulates such structures in mice but little is known about its effects in humans. Using high spatiotemporal resolution, we mapped and quantified the neurophysiological effects of sensory flicker in human subjects undergoing presurgical intracranial seizure monitoring. We found that flicker modulates both local field potential and single neurons in higher cognitive regions, including the medial temporal lobe and prefrontal cortex, and that local field potential modulation is likely mediated via resonance of involved circuits. We then assessed how flicker affects pathological neural activity, specifically interictal epileptiform discharges, a biomarker of epilepsy also implicated in Alzheimer's and other diseases. In our patient population with focal seizure onsets, sensory flicker decreased the rate interictal epileptiform discharges. Our findings support the use of sensory flicker to modulate deeper cortical structures and mitigate pathological activity in humans.
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Affiliation(s)
- Lou T. Blanpain
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, USA
- Neuroscience Graduate Program, Graduate Division of Biological and Biomedical Sciences, Emory University, Atlanta, GA, USA
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, GA, USA
| | - Emily. Chen
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, USA
| | - James Park
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, USA
| | - Michael Y. Walelign
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Robert E. Gross
- Department of Neurosurgery, Emory University School of Medicine, Atlanta, GA, USA
| | - Brian T. Cabaniss
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Jon T. Willie
- Department of Neurosurgery, Washington University, St. Louis, MO, USA
| | - Annabelle C. Singer
- Neuroscience Graduate Program, Graduate Division of Biological and Biomedical Sciences, Emory University, Atlanta, GA, USA
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, GA, USA
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23
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Zauli FM, Del Vecchio M, Russo S, Mariani V, Pelliccia V, d'Orio P, Sartori I, Avanzini P, Caruana F. The web of laughter: frontal and limbic projections of the anterior cingulate cortex revealed by cortico-cortical evoked potential from sites eliciting laughter. Philos Trans R Soc Lond B Biol Sci 2022; 377:20210180. [PMID: 36126672 PMCID: PMC9489285 DOI: 10.1098/rstb.2021.0180] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 02/02/2022] [Indexed: 12/20/2022] Open
Abstract
According to an evolutionist approach, laughter is a multifaceted behaviour affecting social, emotional, motor and speech functions. Albeit previous studies have suggested that high-frequency electrical stimulation (HF-ES) of the pregenual anterior cingulate cortex (pACC) may induce bursts of laughter-suggesting a crucial contribution of this region to the cortical control of this behaviour-the complex nature of laughter implies that outward connections from the pACC may reach and affect a complex network of frontal and limbic regions. Here, we studied the effective connectivity of the pACC by analysing the cortico-cortical evoked potentials elicited by single-pulse electrical stimulation of pACC sites whose HF-ES elicited laughter in 12 patients. Once these regions were identified, we studied their clinical response to HF-ES, to reveal the specific functional target of pACC representation of laughter. Results reveal that the neural representation of laughter in the pACC interacts with several frontal and limbic regions, including cingulate, orbitofrontal, medial prefrontal and anterior insular regions-involved in interoception, emotion, social reward and motor behaviour. These results offer neuroscientific support to the evolutionist approach to laughter, providing a possible mechanistic explanation of the interplay between this behaviour and emotion regulation, speech production and social interactions. This article is part of the theme issue 'Cracking the laugh code: laughter through the lens of biology, psychology and neuroscience'.
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Affiliation(s)
- F M Zauli
- Department of Biomedical and Clinical Sciences 'L. Sacco', University of Milan, Milan, Italy
- Department of Philosophy 'Piero Martinetti', University of Milan, Milan, Italy
- 'Claudio Munari' Epilepsy Surgery Center, ASST GOM Niguarda, Milan, Italy
| | - M Del Vecchio
- Institute of Neuroscience, National Research Council of Italy (CNR), Parma, Italy
| | - S Russo
- Department of Biomedical and Clinical Sciences 'L. Sacco', University of Milan, Milan, Italy
- Department of Philosophy 'Piero Martinetti', University of Milan, Milan, Italy
- Allen Institute, Seattle, WA
| | - V Mariani
- Neurology and Stroke Unit Division, Circolo Hospital ASST Settelaghi University of Insubria, Varese, Italy
| | - V Pelliccia
- 'Claudio Munari' Epilepsy Surgery Center, ASST GOM Niguarda, Milan, Italy
| | - P d'Orio
- 'Claudio Munari' Epilepsy Surgery Center, ASST GOM Niguarda, Milan, Italy
- Institute of Neuroscience, National Research Council of Italy (CNR), Parma, Italy
- Department of Medicine and Surgery, University of Parma, Parma
| | - I Sartori
- 'Claudio Munari' Epilepsy Surgery Center, ASST GOM Niguarda, Milan, Italy
| | - P Avanzini
- Institute of Neuroscience, National Research Council of Italy (CNR), Parma, Italy
| | - F Caruana
- Institute of Neuroscience, National Research Council of Italy (CNR), Parma, Italy
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24
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Berger JI, Johari K, Kovach CK, Greenlee JD. Speech artifact is also present in spike data. Neuroimage 2022; 263:119642. [PMID: 36150607 PMCID: PMC11321429 DOI: 10.1016/j.neuroimage.2022.119642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 07/25/2022] [Accepted: 09/20/2022] [Indexed: 11/23/2022] Open
Abstract
Bush et al. (2022) highlight that brain recordings examining speech production can be significantly affected by microphonic artifact, which would change the interpretation of these kinds of data. While these findings are vital in determining whether data are artifactual or physiological in origin, frequencies were only examined up to 250 Hz (i.e., local field potentials), which would imply that spike-related data (single or multi-neuron recordings) are unaffected. We highlight here that this type of contamination may also be present in unit recordings, with the same aim to understand genuine neural mechanisms rather than mis-interpreting artifactual data.
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Affiliation(s)
- Joel I Berger
- Department of Neurosurgery, University of Iowa, Iowa City, 52242 USA
| | - Karim Johari
- Human Neurophysiology & Neuromodulation Lab, Department of Communication Sciences &, Disorders. Louisiana State University, Baton Rouge, 70803 USA
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25
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Sabra Z, Alawieh A, Bonilha L, Naselaris T, AuYong N. Modulation of Spectral Representation and Connectivity Patterns in Response to Visual Narrative in the Human Brain. Front Hum Neurosci 2022; 16:886938. [PMID: 36277048 PMCID: PMC9582122 DOI: 10.3389/fnhum.2022.886938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 06/22/2022] [Indexed: 11/24/2022] Open
Abstract
The regional brain networks and the underlying neurophysiological mechanisms subserving the cognition of visual narrative in humans have largely been studied with non-invasive brain recording. In this study, we specifically investigated how regional and cross-regional cortical activities support visual narrative interpretation using intracranial stereotactic electroencephalograms recordings from thirteen human subjects (6 females, and 7 males). Widely distributed recording sites across the brain were sampled while subjects were explicitly instructed to observe images from fables presented in “sequential” order, and a set of images drawn from multiple fables presented in “scrambled” order. Broadband activity mainly within the frontal and temporal lobes were found to encode if a presented image is part of a visual narrative (sequential) or random image set (scrambled). Moreover, the temporal lobe exhibits strong activation in response to visual narratives while the frontal lobe is more engaged when contextually novel stimuli are presented. We also investigated the dynamics of interregional interactions between visual narratives and contextually novel series of images. Interestingly, the interregional connectivity is also altered between sequential and scrambled sequences. Together, these results suggest that both changes in regional neuronal activity and cross-regional interactions subserve visual narrative and contextual novelty processing.
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Affiliation(s)
- Zahraa Sabra
- Department of Neurosurgery, Emory University, Atlanta, GA, United States
| | - Ali Alawieh
- Department of Neurosurgery, Emory University, Atlanta, GA, United States
| | - Leonardo Bonilha
- Department of Neurology, Medical University of South Carolina, Charleston, SC, United States
| | - Thomas Naselaris
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, United States
| | - Nicholas AuYong
- Department of Neurosurgery, Emory University, Atlanta, GA, United States
- *Correspondence: Nicholas AuYong,
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26
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Jacques C, Jonas J, Colnat-Coulbois S, Maillard L, Rossion B. Low and high frequency intracranial neural signals match in the human associative cortex. eLife 2022; 11:76544. [PMID: 36074548 PMCID: PMC9457683 DOI: 10.7554/elife.76544] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 08/18/2022] [Indexed: 11/13/2022] Open
Abstract
In vivo intracranial recordings of neural activity offer a unique opportunity to understand human brain function. Intracranial electrophysiological (iEEG) activity related to sensory, cognitive or motor events manifests mostly in two types of signals: event-related local field potentials in lower frequency bands (<30 Hz, LF) and broadband activity in the higher end of the frequency spectrum (>30 Hz, High frequency, HF). While most current studies rely exclusively on HF, thought to be more focal and closely related to spiking activity, the relationship between HF and LF signals is unclear, especially in human associative cortex. Here, we provide a large-scale in-depth investigation of the spatial and functional relationship between these 2 signals based on intracranial recordings from 121 individual brains (8000 recording sites). We measure category-selective responses to complex ecologically salient visual stimuli - human faces - across a wide cortical territory in the ventral occipito-temporal cortex (VOTC), with a frequency-tagging method providing high signal-to-noise ratio (SNR) and the same objective quantification of signal and noise for the two frequency ranges. While LF face-selective activity has higher SNR across the VOTC, leading to a larger number of significant electrode contacts especially in the anterior temporal lobe, LF and HF display highly similar spatial, functional, and timing properties. Specifically, and contrary to a widespread assumption, our results point to nearly identical spatial distribution and local spatial extent of LF and HF activity at equal SNR. These observations go a long way towards clarifying the relationship between the two main iEEG signals and reestablish the informative value of LF iEEG to understand human brain function.
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Affiliation(s)
- Corentin Jacques
- Université de Lorraine, CNRS, CRAN, Nancy, France.,Psychological Sciences Research Institute (IPSY), Université Catholique de Louvain (UCLouvain), Louvain-la-Neuve, Belgium
| | - Jacques Jonas
- Université de Lorraine, CNRS, CRAN, Nancy, France.,Université de Lorraine, CHRU-Nancy, Service de Neurologie, Nancy, France
| | | | - Louis Maillard
- Université de Lorraine, CNRS, CRAN, Nancy, France.,Université de Lorraine, CHRU-Nancy, Service de Neurologie, Nancy, France
| | - Bruno Rossion
- Université de Lorraine, CNRS, CRAN, Nancy, France.,Université de Lorraine, CHRU-Nancy, Service de Neurologie, Nancy, France
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27
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Petito GT, Housekeeper J, Buroker J, Scholle C, Ervin B, Frink C, Greiner HM, Skoch J, Mangano FT, Dye TJ, Hogenesch JB, Glauser TA, Holland KD, Arya R. Diurnal rhythm of spontaneous intracranial high-frequency oscillations. Seizure 2022; 102:105-112. [DOI: 10.1016/j.seizure.2022.09.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 09/05/2022] [Accepted: 09/28/2022] [Indexed: 11/27/2022] Open
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28
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Hu M, Chen J, Jiang S, Ji W, Mei S, Chen L, Wang X. E2SGAN: EEG-to-SEEG translation with generative adversarial networks. Front Neurosci 2022; 16:971829. [PMID: 36117642 PMCID: PMC9477431 DOI: 10.3389/fnins.2022.971829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 08/04/2022] [Indexed: 11/19/2022] Open
Abstract
High-quality brain signal data recorded by Stereoelectroencephalography (SEEG) electrodes provide clinicians with clear guidance for presurgical assessments for epilepsy surgeries. SEEG, however, is limited to selected patients with epilepsy due to its invasive procedure. In this work, a brain signal synthesis framework is presented to synthesize SEEG signals from non-invasive EEG signals. First, a strategy to determine the matching relation between EEG and SEEG channels is presented by considering both signal correlation and spatial distance. Second, the EEG-to-SEEG generative adversarial network (E2SGAN) is proposed to precisely synthesize SEEG data from the simultaneous EEG data. Although the widely adopted magnitude spectra has proved to be informative in EEG tasks, it leaves much to be desired in the setting of signal synthesis. To this end, instantaneous frequency spectra is introduced to further represent the alignment of the signal. Correlative spectral attention (CSA) is proposed to enhance the discriminator of E2SGAN by capturing the correlation between each pair of EEG and SEEG frequencies. The weighted patch prediction (WPP) technique is devised to ensure robust temporal results. Comparison experiments on real-patient data demonstrate that E2SGAN outperforms baseline methods in both temporal and frequency domains. The perturbation experiment reveals that the synthesized results have the potential to capture abnormal discharges in epileptic patients before seizures.
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Affiliation(s)
- Mengqi Hu
- School of Computer Science and Technology, East China Normal University, Shanghai, China
| | - Jin Chen
- Institute for Biomedical Informatics, University of Kentucky Lexington, Lexington, KY, United States
| | - Shize Jiang
- Department of Neurosurgery, Huashan Hospital of Fudan University, Shanghai, China
| | - Wendi Ji
- School of Computer Science and Technology, East China Normal University, Shanghai, China
| | - Shuhao Mei
- Department of Neurosurgery, Huashan Hospital of Fudan University, Shanghai, China
| | - Liang Chen
- Department of Neurosurgery, Huashan Hospital of Fudan University, Shanghai, China
| | - Xiaoling Wang
- School of Computer Science and Technology, East China Normal University, Shanghai, China
- *Correspondence: Xiaoling Wang
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29
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Rupp K, Hect JL, Remick M, Ghuman A, Chandrasekaran B, Holt LL, Abel TJ. Neural responses in human superior temporal cortex support coding of voice representations. PLoS Biol 2022; 20:e3001675. [PMID: 35900975 PMCID: PMC9333263 DOI: 10.1371/journal.pbio.3001675] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 05/13/2022] [Indexed: 11/19/2022] Open
Abstract
The ability to recognize abstract features of voice during auditory perception is an intricate feat of human audition. For the listener, this occurs in near-automatic fashion to seamlessly extract complex cues from a highly variable auditory signal. Voice perception depends on specialized regions of auditory cortex, including superior temporal gyrus (STG) and superior temporal sulcus (STS). However, the nature of voice encoding at the cortical level remains poorly understood. We leverage intracerebral recordings across human auditory cortex during presentation of voice and nonvoice acoustic stimuli to examine voice encoding at the cortical level in 8 patient-participants undergoing epilepsy surgery evaluation. We show that voice selectivity increases along the auditory hierarchy from supratemporal plane (STP) to the STG and STS. Results show accurate decoding of vocalizations from human auditory cortical activity even in the complete absence of linguistic content. These findings show an early, less-selective temporal window of neural activity in the STG and STS followed by a sustained, strongly voice-selective window. Encoding models demonstrate divergence in the encoding of acoustic features along the auditory hierarchy, wherein STG/STS responses are best explained by voice category and acoustics, as opposed to acoustic features of voice stimuli alone. This is in contrast to neural activity recorded from STP, in which responses were accounted for by acoustic features. These findings support a model of voice perception that engages categorical encoding mechanisms within STG and STS to facilitate feature extraction. Voice perception occurs via specialized networks in higher order auditory cortex, but how voice features are encoded remains a central unanswered question. Using human intracerebral recordings of auditory cortex, this study provides evidence for categorical encoding of voice.
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Affiliation(s)
- Kyle Rupp
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Jasmine L. Hect
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Madison Remick
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Avniel Ghuman
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Bharath Chandrasekaran
- Department of Communication Science and Disorders, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Lori L. Holt
- Department of Psychology, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Taylor J. Abel
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- * E-mail:
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30
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Zheng B, Hsieh B, Rex N, Lauro PM, Collins SA, Blum AS, Roth JL, Ayub N, Asaad WF. A hierarchical anatomical framework and workflow for organizing stereotactic encephalography in epilepsy. Hum Brain Mapp 2022; 43:4852-4863. [PMID: 35851977 PMCID: PMC9582372 DOI: 10.1002/hbm.26017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 06/20/2022] [Accepted: 06/24/2022] [Indexed: 11/17/2022] Open
Abstract
Stereotactic electroencephalography (SEEG) is an increasingly utilized method for invasive monitoring in patients with medically intractable epilepsy. Yet, the lack of standardization for labeling electrodes hinders communication among clinicians. A rational clustering of contacts based on anatomy rather than arbitrary physical leads may help clinical neurophysiologists interpret seizure networks. We identified SEEG electrodes on post‐implant CTs and registered them to preoperative MRIs segmented according to an anatomical atlas. Individual contacts were automatically assigned to anatomical areas independent of lead. These contacts were then organized using a hierarchical anatomical schema for display and interpretation. Bipolar‐referenced signal cross‐correlations were used to compare the similarity of grouped signals within a conventional montage versus this anatomical montage. As a result, we developed a hierarchical organization for SEEG contacts using well‐accepted, free software that is based solely on their post‐implant anatomical location. When applied to three example SEEG cases for epilepsy, clusters of contacts that were anatomically related collapsed into standardized groups. Qualitatively, seizure events organized using this framework were better visually clustered compared to conventional schemes. Quantitatively, signals grouped by anatomical region were more similar to each other than electrode‐based groups as measured by Pearson correlation. Further, we uploaded visualizations of SEEG reconstructions into the electronic medical record, rendering them durably useful given the interpretable electrode labels. In conclusion, we demonstrate a standardized, anatomically grounded approach to the organization of SEEG neuroimaging and electrophysiology data that may enable improved communication among and across surgical epilepsy teams and promote a clearer view of individual seizure networks.
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Affiliation(s)
- Bryan Zheng
- Department of Neurosurgery Warren Alpert Medical School, Brown University Providence Rhode Island USA
| | - Ben Hsieh
- Department of Diagnostic Imaging Warren Alpert Medical School, Brown University Providence Rhode Island USA
| | - Nathaniel Rex
- Department of Diagnostic Imaging Warren Alpert Medical School, Brown University Providence Rhode Island USA
| | - Peter M. Lauro
- Department of Neurosurgery Warren Alpert Medical School, Brown University Providence Rhode Island USA
| | - Scott A. Collins
- Department of Diagnostic Imaging Warren Alpert Medical School, Brown University Providence Rhode Island USA
| | - Andrew S. Blum
- Department of Neurology Warren Alpert Medical School, Brown University Providence Rhode Island USA
| | - Julie L. Roth
- Department of Neurology Warren Alpert Medical School, Brown University Providence Rhode Island USA
| | - Neishay Ayub
- Department of Neurology Warren Alpert Medical School, Brown University Providence Rhode Island USA
| | - Wael F. Asaad
- Department of Neurosurgery Warren Alpert Medical School, Brown University Providence Rhode Island USA
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Dickey AS, Alwaki A, Kheder A, Willie JT, Drane DL, Pedersen NP. The Referential Montage Inadequately Localizes Corticocortical Evoked Potentials in Stereoelectroencephalography. J Clin Neurophysiol 2022; 39:412-418. [PMID: 33337663 PMCID: PMC10069706 DOI: 10.1097/wnp.0000000000000792] [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] [Indexed: 11/26/2022] Open
Abstract
PURPOSE Corticocortical evoked potentials (CCEPs) resulting from single pulse electrical stimulation are increasingly used to understand seizure networks, as well as normal brain connectivity. However, we observed that when using depth electrodes, traditional measures of CCEPs amplitude using a referential montage can be falsely localizing, often to white matter. METHODS We pooled 27 linear electrode arrays targeting the amygdala, hippocampus, or cingulate cortex from eight participants. Using postoperative imaging, we classified contacts as being in gray matter, white matter, or bordering each and measured the amplitude using the root-mean-squared deviation from baseline in a referential, common average, bipolar, or Laplacian montage. RESULTS Of 27 electrode contacts, 25 (93%) had a significantly higher mean amplitude when in gray matter than in white matter using a Laplacian montage, which was significantly more than the 12 of 27 electrodes (44%) when using a referential montage ( P = 0.0003, Fisher exact test). The area under the curve for a receiver operating characteristic classifying contacts as gray or white matter was significantly higher for either the Laplacian (0.79) or the bipolar (0.72) montage when compared with either the common average (0.56) or the referential (0.51) montage ( P ≤ 0.005, bootstrap). CONCLUSIONS Both the Laplacian and bipolar montages were superior to the common average or referential montage in localizing CCEPs to gray matter. These montages may be more appropriate for interpreting CCEPs when using depth electrodes than the referential montage, which has typically been used in prior studies of CCEPs with subdural grids.
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Affiliation(s)
- Adam S. Dickey
- Department of Neurology, Emory University and Emory Epilepsy Center, 101 Woodruff Circle, Atlanta, GA 30322, USA
| | - Abdulrahman Alwaki
- Department of Neurology, Emory University and Emory Epilepsy Center, 101 Woodruff Circle, Atlanta, GA 30322, USA
| | - Ammar Kheder
- Department of Neurology, Emory University and Emory Epilepsy Center, 101 Woodruff Circle, Atlanta, GA 30322, USA
- Children’s Pediatric Institute, Emory University and Children’s Healthcare of Atlanta
| | - Jon T. Willie
- Department of Neurology, Emory University and Emory Epilepsy Center, 101 Woodruff Circle, Atlanta, GA 30322, USA
- Department of Neurosurgery, Emory University, 101 Woodruff Circle, Atlanta, GA 30322, USA
- Emory Neuromodulation Technology Innovation Center, Emory University and Georgia Institute of Technology, 101 Woodruff Circle, Atlanta, GA 30322, USA
| | - Daniel L. Drane
- Department of Neurology, Emory University and Emory Epilepsy Center, 101 Woodruff Circle, Atlanta, GA 30322, USA
| | - Nigel P. Pedersen
- Department of Neurology, Emory University and Emory Epilepsy Center, 101 Woodruff Circle, Atlanta, GA 30322, USA
- Emory Neuromodulation Technology Innovation Center, Emory University and Georgia Institute of Technology, 101 Woodruff Circle, Atlanta, GA 30322, USA
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Soroush PZ, Herff C, Ries S, Shih JJ, Schultz T, Krusienski DJ. Contributions of Stereotactic EEG Electrodes in Grey and White Matter to Speech Activity Detection. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:4789-4792. [PMID: 36086071 DOI: 10.1109/embc48229.2022.9871464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Recent studies have shown it is possible to decode and synthesize speech directly using brain activity recorded from implanted electrodes. While this activity has been extensively examined using electrocorticographic (ECoG) recordings from cortical surface grey matter, stereotactic electroen-cephalography (sEEG) provides comparatively broader coverage and access to deeper brain structures including both grey and white matter. The present study examines the relative and joint contributions of grey and white matter electrodes for speech activity detection in a brain-computer interface.
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Paschall CJ, Rao RPN, Hauptmann J, Ojemann JG, Herron J. An Immersive Virtual Reality Platform Integrating Human ECOG & sEEG: Implementation & Noise Analysis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:3105-3110. [PMID: 36086622 DOI: 10.1109/embc48229.2022.9871754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Virtual reality (VR) offers a robust platform for human behavioral neuroscience, granting unprecedented experimental control over every aspect of an immersive and interactive visual environment. VR experiments have already integrated non-invasive neural recording modalities such as EEG and functional MRI to explore the neural correlates of human behavior and cognition. Integration with implanted electrodes would enable significant increase in spatial and temporal resolution of recorded neural signals and the option of direct brain stimulation for neurofeedback. In this paper, we discuss the first such implementation of a VR platform with implanted electrocorticography (ECoG) and stereo-electroencephalography ( sEEG) electrodes in human, in-patient subjects. Noise analyses were performed to evaluate the effect of the VR headset on neural data collected in two VR-naive subjects, one child and one adult, including both ECOG and sEEG electrodes. Results demonstrate an increase in line noise power (57-63Hz) while wearing the VR headset that is mitigated effectively by common average referencing (CAR), and no significant change in the noise floor bandpower (125-240Hz). To our knowledge, this study represents first demonstrations of VR immersion during invasive neural recording with in-patient human subjects. Clinical Relevance- Immersive virtual reality tasks were well-tolerated and the quality of clinical neural signals preserved during VR immersion with two in-patient invasive neural recording subjects.
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Wang Y, Yang Y, Cao G, Guo J, Wei P, Feng T, Dai Y, Huang J, Kang G, Zhao G. SEEG-Net: An explainable and deep learning-based cross-subject pathological activity detection method for drug-resistant epilepsy. Comput Biol Med 2022; 148:105703. [PMID: 35791972 DOI: 10.1016/j.compbiomed.2022.105703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 05/16/2022] [Accepted: 06/04/2022] [Indexed: 11/30/2022]
Abstract
OBJECTIVE Precise preoperative evaluation of drug-resistant epilepsy (DRE) requires accurate analysis of invasive stereoelectroencephalography (SEEG). With the tremendous breakthrough of Artificial intelligence (AI), previous studies can help clinical experts to identify pathological activities automatically. However, they still face limitations when applied in real-world clinical DRE scenarios, such as sample imbalance, cross-subject domain shift, and poor interpretability. Our objective is to propose a model that can address the above problems and realizes high-sensitivity SEEG pathological activity detection based on two real clinical datasets. METHODS Our proposed innovative and effective SEEG-Net introduces a multiscale convolutional neural network (MSCNN) to increase the receptive field of the model, and to learn SEEG multiple frequency domain features, local and global features. Moreover, we designed a novel focal domain generalization loss (FDG-loss) function to enhance the target sample weight and to learn domain consistency features. Furthermore, to enhance the interpretability and flexibility of SEEG-Net, we explain SEEG-Net from multiple perspectives, such as significantly different features, interpretable models, and model learning process interpretation by Grad-CAM++. RESULTS The performance of our proposed method is verified on a public benchmark multicenter SEEG dataset and a private clinical SEEG dataset for a robust comparison. The experimental results demonstrate that the SEEG-Net model achieves the highest sensitivity and is state-of-the-art on cross-subject (for different patients) evaluation, and well deal with the known problems. Besides, we provide an SEEG processing and database construction flow, by maintaining consistency with the real-world clinical scenarios. SIGNIFICANCE According to the results, SEEG-Net is constructed to increase the sensitivity of SEEG pathological activity detection. Simultaneously, we settled certain problems about AI assistance in clinical DRE, built a bridge between AI algorithm application and clinical practice.
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Affiliation(s)
- Yiping Wang
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China
| | - Yanfeng Yang
- Department of Neurosurgery, Xuan Wu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China
| | - Gongpeng Cao
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China
| | - Jinjie Guo
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China
| | - Penghu Wei
- Department of Neurosurgery, Xuan Wu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China
| | - Tao Feng
- Department of Neurosurgery, Xuan Wu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China
| | - Yang Dai
- Department of Neurosurgery, Xuan Wu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China
| | - Jinguo Huang
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China
| | - Guixia Kang
- Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, No. 10 Xitucheng Road, Haidian District, Beijing, 100876, China.
| | - Guoguang Zhao
- Department of Neurosurgery, Xuan Wu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China.
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Liu D, Xu X, Li D, Li J, Yu X, Ling Z, Hong B. Intracranial brain-computer interface spelling using localized visual motion response. Neuroimage 2022; 258:119363. [PMID: 35688315 DOI: 10.1016/j.neuroimage.2022.119363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 06/03/2022] [Accepted: 06/06/2022] [Indexed: 11/30/2022] Open
Abstract
Intracranial brain-computer interfaces (BCIs) can assist severely disabled persons in text communication and environmental control with high precision and speed. Nevertheless, sustainable BCI implants require minimal invasiveness. One of the implantation strategies is to adopt localized and robust cortical activities to drive BCI communication and to make a precise presurgical planning. The visual motion response is a good candidate for inclusion in this strategy because of its focal activity over the middle temporal visual area (MT). Here, we developed an intracranial BCI for spelling, utilizing only three electrodes over the MT area. The best recording electrodes were decided by preoperative functional magnetic resonance imaging (MRI) localization of the MT, and local neural activities were further enhanced by differential rereferencing of these electrodes. The BCI spelling system was validated both offline and online by five epilepsy patients, achieving the fastest speed of 62 bits/min, i.e., 12 characters/min. Moreover, the response patterns of dual-directional visual motion stimuli provided an additional dimension of BCI target encoding and paved the way for a higher information transfer rate of intracranial BCI spelling.
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Affiliation(s)
- Dingkun Liu
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, Beijing, 100084, China
| | - Xin Xu
- Department of Neurosurgery, Chinese PLA General Hospital, Beijing, Beijing, 100853, China
| | - Dongyang Li
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, Beijing, 100084, China
| | - Jie Li
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, Beijing, 100084, China
| | - Xinguang Yu
- Department of Neurosurgery, Chinese PLA General Hospital, Beijing, Beijing, 100853, China
| | - Zhipei Ling
- Department of Neurosurgery, Chinese PLA General Hospital, Beijing, Beijing, 100853, China
| | - Bo Hong
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, Beijing, 100084, China; McGovern Institute for Brain Research, Tsinghua University, Beijing, Beijing, 100084, China.
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Adkinson JA, Tsolaki E, Sheth SA, Metzger BA, Robinson ME, Oswalt D, McIntyre CC, Mathura RK, Waters AC, Allawala AB, Noecker AM, Malekmohammadi M, Chiu K, Mustakos R, Goodman W, Borton D, Pouratian N, Bijanki KR. Imaging versus electrographic connectivity in human mood-related fronto-temporal networks. Brain Stimul 2022; 15:554-565. [PMID: 35292403 PMCID: PMC9232982 DOI: 10.1016/j.brs.2022.03.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 02/09/2022] [Accepted: 03/09/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND The efficacy of psychiatric DBS is thought to be driven by the connectivity of stimulation targets with mood-relevant fronto-temporal networks, which is typically evaluated using diffusion-weighted tractography. OBJECTIVE Leverage intracranial electrophysiology recordings to better predict the circuit-wide effects of neuromodulation to white matter targets. We hypothesize strong convergence between tractography-predicted structural connectivity and stimulation-induced electrophysiological responses. METHODS Evoked potentials were elicited by single-pulse stimulation to two common DBS targets for treatment-resistant depression - the subcallosal cingulate (SCC) and ventral capsule/ventral striatum (VCVS) - in two patients undergoing DBS with stereo-electroencephalographic (sEEG) monitoring. Evoked potentials were compared with predicted structural connectivity between DBS leads and sEEG contacts using probabilistic, patient-specific diffusion-weighted tractography. RESULTS Evoked potentials and tractography showed strong convergence in both patients in orbitofrontal, ventromedial prefrontal, and lateral prefrontal cortices for both SCC and VCVS stimulation targets. Low convergence was found in anterior cingulate (ACC), where tractography predicted structural connectivity from SCC targets but produced no evoked potentials during SCC stimulation. Further, tractography predicted no connectivity to ACC from VCVS targets, but VCVS stimulation produced robust evoked potentials. CONCLUSION The two connectivity methods showed significant convergence, but important differences emerged with respect to the ability of tractography to predict electrophysiological connectivity between SCC and VCVS to regions of the mood-related network. This multimodal approach raises intriguing implications for the use of tractography in surgical targeting and provides new data to enhance our understanding of the network-wide effects of neuromodulation.
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Affiliation(s)
- Joshua A Adkinson
- Department of Neurosurgery, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
| | - Evangelia Tsolaki
- Department of Neurosurgery, David Geffen School of Medicine at UCLA, 300 Stein Plaza Suite 562, Los Angeles, CA, 90095, USA.
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
| | - Brian A Metzger
- Department of Neurosurgery, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
| | - Meghan E Robinson
- Department of Neurosurgery, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
| | - Denise Oswalt
- Department of Neurosurgery, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
| | - Cameron C McIntyre
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave., Cleveland, OH, 44106, USA.
| | - Raissa K Mathura
- Department of Neurosurgery, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
| | - Allison C Waters
- Department of Psychiatry, Mount Sinai School of Medicine, 1000 10th Ave., New York, NY, 10019, USA.
| | - Anusha B Allawala
- School of Engineering, Brown University, 182 Hope St., Providence, RI, 02912, USA.
| | - Angela M Noecker
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave., Cleveland, OH, 44106, USA.
| | - Mahsa Malekmohammadi
- Boston Scientific Neuromodulation, 25155 Rye Canyon Loop, Valencia, CA, 91355, USA.
| | - Kevin Chiu
- Brainlab, Inc., 5 Westbrook Corporate Center, Suite 1000, Westchester IL, 60154, USA.
| | - Richard Mustakos
- Boston Scientific Neuromodulation, 25155 Rye Canyon Loop, Valencia, CA, 91355, USA.
| | - Wayne Goodman
- Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, 1977 Butler Blvd., Houston, TX, 77030, USA.
| | - David Borton
- School of Engineering, Brown University, 182 Hope St., Providence, RI, 02912, USA; Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Department of Veterans Affairs, Providence, RI, 02912, USA.
| | - Nader Pouratian
- Department of Neurological Surgery, University of Texas Southwestern Medical Center, 8353 Harry Hines Blvd MC8855, Dallas, TX, 75239, USA.
| | - Kelly R Bijanki
- Department of Neurosurgery, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
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Ye H, Li G, Sheng X, Zhu X. Phase-amplitude coupling between low-frequency scalp EEG and high-frequency intracranial EEG during working memory task. J Neural Eng 2022; 19. [PMID: 35441594 DOI: 10.1088/1741-2552/ac63e9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 04/04/2022] [Indexed: 11/12/2022]
Abstract
Objective. Revealing the relationship between simultaneous scalp electroencephalography (EEG) and intracranial electroencephalography (iEEG) is of great importance for both neuroscientific research and translational applications. However, whether prominent iEEG features in the high-gamma band can be reflected by scalp EEG is largely unknown. To address this, we investigated the phase-amplitude coupling (PAC) phenomenon between the low-frequency band of scalp EEG and the high-gamma band of iEEG.Approach. We analyzed a simultaneous iEEG and scalp EEG dataset acquired under a verbal working memory paradigm from nine epilepsy subjects. The PAC values between pairs of scalp EEG channel and identified iEEG channel were explored. After identifying the frequency combinations and electrode locations that generated the most significant PAC values, we compared the PAC values of different task periods (encoding, maintenance, and retrieval) and memory loads.Main results. We demonstrated that the amplitude of high-gamma activities in the entorhinal cortex, hippocampus, and amygdala was correlated to the delta or theta phase at scalp locations such as Cz and Pz. In particular, the frequency bin that generated the maximum PAC value centered at 3.16-3.84 Hz for the phase and 50-85 Hz for the amplitude. Moreover, our results showed that PAC values for the retrieval period were significantly higher than those of the encoding and maintenance periods, and the PAC was also influenced by the memory load.Significance. This is the first human simultaneous iEEG and scalp EEG study demonstrating that the amplitude of iEEG high-gamma components is associated with the phase of low-frequency components in scalp EEG. These findings enhance our understanding of multiscale neural interactions during working memory, and meanwhile, provide a new perspective to estimate intracranial high-frequency features with non-invasive neural recordings.
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Affiliation(s)
- Huanpeng Ye
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Guangye Li
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Xinjun Sheng
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Xiangyang Zhu
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
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Wu X, Li G, Jiang S, Wellington S, Liu S, Wu Z, Metcalfe B, Chen L, Zhang D. Decoding Continuous Kinetic Information of Grasp from Stereo-electroencephalographic (SEEG) Recordings. J Neural Eng 2022; 19. [PMID: 35395645 DOI: 10.1088/1741-2552/ac65b1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 04/08/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Brain-computer interfaces (BCIs) have the potential to bypass damaged neural pathways and restore functionality lost due to injury or disease. Approaches to decoding kinematic information are well documented; however, the decoding of kinetic information has received less attention. Additionally, the possibility of using stereo-electroencephalography (SEEG) for kinetic decoding during hand grasping tasks is still largely unknown. Thus, the objective of this paper is to demonstrate kinetic parameter decoding using SEEG in patients performing a grasping task with two different force levels under two different ascending rates. APPROACH Temporal-spectral representations were studied to investigate frequency modulation under different force tasks. Then, force amplitude was decoded from SEEG recordings using multiple decoders, including a linear model, a partial least squares (PLS) model, an unscented Kalman filter, and three deep learning models (shallow convolutional neural network, deep convolutional neural network and the proposed CNN+RNN neural network). MAIN RESULTS The current study showed that: 1) for some channel, both low-frequency modulation (event-related desynchronization (ERD)) and high-frequency modulation (event-related synchronization (ERS)) were sustained during prolonged force holding periods; 2) continuously changing grasp force can be decoded from the SEEG signals; 3) the novel CNN+RNN deep learning model achieved the best decoding performance, with the predicted force magnitude closely aligned to the ground truth under different force amplitudes and changing rates. SIGNIFICANCE This work verified the possibility of decoding continuously changing grasp force using SEEG recordings. The result presented in this study demonstrated the potential of SEEG recordings for future BCI application.
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Affiliation(s)
- Xiaolong Wu
- Electric, Electronic and Engineering, University of Bath, Pulteney Court PD42.2,Pulteney Road,BA2 4HL, Bath, Bath, Somerset, BA2 4HL, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Guangye Li
- Shanghai Jiao Tong University, Shanghai JiaoTong university, Shanghai, China, Shanghai, 200240, CHINA
| | - Shize Jiang
- Fudan University Huashan Hospital Department of Neurosurgery, Fudan University Huanshan hospital, Shanghai, Shanghai, 201906, CHINA
| | - Scott Wellington
- University of Bath, University of Bath, Bath, UK, Bath, Bath and North East Somer, BA2 7AY, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Shengjie Liu
- Shanghai Jiao Tong University, Shanghai jIaotong University, Shanghai, China, Shanghai, 200240, CHINA
| | - Zehan Wu
- Huashan Hospital Fudan University, Huashan hospital, Shanghai, China, Shanghai, Shanghai, 200040, CHINA
| | - Benjamin Metcalfe
- University of Bath, University of Bath, UK, Bath, Bath and North East Somer, BA2 7AY, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Liang Chen
- Fudan University Huashan Hospital Department of Neurosurgery, Fudan University Huashan Hospital, Shanghai, China, Shanghai, Shanghai, 201906, CHINA
| | - Dingguo Zhang
- University of Bath, University of Bath, UK, Bath, Somerset, BA2 4HL, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
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Berezutskaya J, Vansteensel MJ, Aarnoutse EJ, Freudenburg ZV, Piantoni G, Branco MP, Ramsey NF. Open multimodal iEEG-fMRI dataset from naturalistic stimulation with a short audiovisual film. Sci Data 2022; 9:91. [PMID: 35314718 PMCID: PMC8938409 DOI: 10.1038/s41597-022-01173-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 01/24/2022] [Indexed: 12/19/2022] Open
Abstract
Intracranial human recordings are a valuable and rare resource of information about the brain. Making such data publicly available not only helps tackle reproducibility issues in science, it helps make more use of these valuable data. This is especially true for data collected using naturalistic tasks. Here, we describe a dataset collected from a large group of human subjects while they watched a short audiovisual film. The dataset has several unique features. First, it includes a large amount of intracranial electroencephalography (iEEG) data (51 participants, age range of 5-55 years, who all performed the same task). Second, it includes functional magnetic resonance imaging (fMRI) recordings (30 participants, age range of 7-47) during the same task. Eighteen participants performed both iEEG and fMRI versions of the task, non-simultaneously. Third, the data were acquired using a rich audiovisual stimulus, for which we provide detailed speech and video annotations. This dataset can be used to study neural mechanisms of multimodal perception and language comprehension, and similarity of neural signals across brain recording modalities.
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Affiliation(s)
- Julia Berezutskaya
- Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, the Netherlands.
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands.
| | - Mariska J Vansteensel
- Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Erik J Aarnoutse
- Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Zachary V Freudenburg
- Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Giovanni Piantoni
- Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Mariana P Branco
- Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Nick F Ramsey
- Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, the Netherlands
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40
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Ye H, Fan Z, Li G, Wu Z, Hu J, Sheng X, Chen L, Zhu X. Spontaneous State Detection Using Time-Frequency and Time-Domain Features Extracted From Stereo-Electroencephalography Traces. Front Neurosci 2022; 16:818214. [PMID: 35368269 PMCID: PMC8968069 DOI: 10.3389/fnins.2022.818214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 02/15/2022] [Indexed: 11/23/2022] Open
Abstract
As a minimally invasive recording technique, stereo-electroencephalography (SEEG) measures intracranial signals directly by inserting depth electrodes shafts into the human brain, and thus can capture neural activities in both cortical layers and subcortical structures. Despite gradually increasing SEEG-based brain-computer interface (BCI) studies, the features utilized were usually confined to the amplitude of the event-related potential (ERP) or band power, and the decoding capabilities of other time-frequency and time-domain features have not been demonstrated for SEEG recordings yet. In this study, we aimed to verify the validity of time-domain and time-frequency features of SEEG, where classification performances served as evaluating indicators. To do this, using SEEG signals under intermittent auditory stimuli, we extracted features including the average amplitude, root mean square, slope of linear regression, and line-length from the ERP trace and three traces of band power activities (high-gamma, beta, and alpha). These features were used to detect the active state (including activations to two types of names) against the idle state. Results suggested that valid time-domain and time-frequency features distributed across multiple regions, including the temporal lobe, parietal lobe, and deeper structures such as the insula. Among all feature types, the average amplitude, root mean square, and line-length extracted from high-gamma (60–140 Hz) power and the line-length extracted from ERP were the most informative. Using a hidden Markov model (HMM), we could precisely detect the onset and the end of the active state with a sensitivity of 95.7 ± 1.3% and a precision of 91.7 ± 1.6%. The valid features derived from high-gamma power and ERP in this work provided new insights into the feature selection procedure for further SEEG-based BCI applications.
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Affiliation(s)
- Huanpeng Ye
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zhen Fan
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China
| | - Guangye Li
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zehan Wu
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China
| | - Jie Hu
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China
| | - Xinjun Sheng
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Liang Chen
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China
- *Correspondence: Liang Chen
| | - Xiangyang Zhu
- State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China
- Xiangyang Zhu
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Neurophysiological Evidence for Cognitive Map Formation during Sequence Learning. eNeuro 2022; 9:ENEURO.0361-21.2022. [PMID: 35105662 PMCID: PMC8896554 DOI: 10.1523/eneuro.0361-21.2022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 12/03/2021] [Accepted: 01/03/2022] [Indexed: 12/29/2022] Open
Abstract
Humans deftly parse statistics from sequences. Some theories posit that humans learn these statistics by forming cognitive maps, or underlying representations of the latent space which links items in the sequence. Here, an item in the sequence is a node, and the probability of transitioning between two items is an edge. Sequences can then be generated from walks through the latent space, with different spaces giving rise to different sequence statistics. Individual or group differences in sequence learning can be modeled by changing the time scale over which estimates of transition probabilities are built, or in other words, by changing the amount of temporal discounting. Latent space models with temporal discounting bear a resemblance to models of navigation through Euclidean spaces. However, few explicit links have been made between predictions from Euclidean spatial navigation and neural activity during human sequence learning. Here, we use a combination of behavioral modeling and intracranial encephalography (iEEG) recordings to investigate how neural activity might support the formation of space-like cognitive maps through temporal discounting during sequence learning. Specifically, we acquire human reaction times from a sequential reaction time task, to which we fit a model that formulates the amount of temporal discounting as a single free parameter. From the parameter, we calculate each individual's estimate of the latent space. We find that neural activity reflects these estimates mostly in the temporal lobe, including areas involved in spatial navigation. Similar to spatial navigation, we find that low-dimensional representations of neural activity allow for easy separation of important features, such as modules, in the latent space. Lastly, we take advantage of the high temporal resolution of iEEG data to determine the time scale on which latent spaces are learned. We find that learning typically happens within the first 500 trials, and is modulated by the underlying latent space and the amount of temporal discounting characteristic of each participant. Ultimately, this work provides important links between behavioral models of sequence learning and neural activity during the same behavior, and contextualizes these results within a broader framework of domain general cognitive maps.
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McCarty MJ, Woolnough O, Mosher JC, Seymour J, Tandon N. The Listening Zone of Human Electrocorticographic Field Potential Recordings. eNeuro 2022; 9:ENEURO.0492-21.2022. [PMID: 35410871 PMCID: PMC9034754 DOI: 10.1523/eneuro.0492-21.2022] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 02/09/2022] [Accepted: 03/04/2022] [Indexed: 01/05/2023] Open
Abstract
Intracranial electroencephalographic (icEEG) recordings provide invaluable insights into neural dynamics in humans because of their unmatched spatiotemporal resolution. Yet, such recordings reflect the combined activity of multiple underlying generators, confounding the ability to resolve spatially distinct neural sources. To empirically quantify the listening zone of icEEG recordings, we computed correlations between signals as a function of distance (full width at half maximum; FWHM) between 8752 recording sites in 71 patients (33 female) implanted with either subdural electrodes (SDEs), stereo-encephalography electrodes (sEEG), or high-density sEEG electrodes. As expected, for both SDEs and sEEGs, higher frequency signals exhibited a sharper fall off relative to lower frequency signals. For broadband high γ (BHG) activity, the mean FWHM of SDEs (6.6 ± 2.5 mm) and sEEGs in gray matter (7.14 ± 1.7 mm) was not significantly different; however, FWHM for low frequencies recorded by sEEGs was 2.45 mm smaller than SDEs. White matter sEEGs showed much lower power for frequencies 17-200 Hz (q < 0.01) and a much broader decay (11.3 ± 3.2 mm) than gray matter electrodes (7.14 ± 1.7 mm). The use of a bipolar referencing scheme significantly lowered FWHM for sEEGs, relative to a white matter reference or a common average reference (CAR). These results outline the influence of array design, spectral bands, and referencing schema on local field potential recordings and source localization in icEEG recordings in humans. The metrics we derive have immediate relevance to the analysis and interpretation of both cognitive and epileptic data.
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Affiliation(s)
- Meredith J McCarty
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, University of Texas Health Houston, Houston, TX 77030
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX 77030
| | - Oscar Woolnough
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, University of Texas Health Houston, Houston, TX 77030
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX 77030
| | - John C Mosher
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX 77030
| | - John Seymour
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, University of Texas Health Houston, Houston, TX 77030
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX 77030
| | - Nitin Tandon
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, University of Texas Health Houston, Houston, TX 77030
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX 77030
- Memorial Hermann Hospital, Texas Medical Center, Houston, TX 77030
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Yu H, Jiang S, Huang Y, Li X, Wang X, Chen L, Chen J. Characterizing Brain Signals for Epileptic Pre-ictal Signal Classification. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2022; 2021:1215-1224. [PMID: 35308952 PMCID: PMC8861758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Epilepsy is a kind of neurological disorder characterized by recurrent epileptic seizures. While it is crucial to characterize pre-ictal brain electrical activities, the problem to this day still remains computationally challenging. Using brain signal acquisition and advances in deep learning technology, we aim to classify pre-ictal signals and characterize the brain waveforms of patients with epilepsy during the pre-ictal period. We develop a novel machine learning model called Pre-ictal Signal Classification (PiSC) for pre-ictal signal classification and for identifying brain waveform patterns critical for seizure onset early detection. In PiSC, a unique preprocessing procedure is developed to convert the stereo-electroencephalography (sEEG) signals to data blocks ready for pre-ictal signal classification. Also, a novel deep learning framework is developed to integrate deep neural networks and meta-learning to effectively mitigate patient-to-patient variances as well as fine-tuning a trained classification model for new patients. The unique network architecture ensures model stability and generalization in sEEG data modeling. The experimental results on a real-world patient dataset show that PiSC improved the accuracy and F1 score by 10% compared with the existing models. Two types of sEEG patterns were discovered to be associated with seizure development in nocturnal epileptic patients.
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Affiliation(s)
- Hao Yu
- Shanghai Key Lab of Trustworthy Computing, East China Normal University, Shanghai, China
| | - Shize Jiang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Yan Huang
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, TX
| | - Xiaojin Li
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, TX
| | - Xiaoling Wang
- Shanghai Key Lab of Trustworthy Computing, East China Normal University, Shanghai, China
| | - Liang Chen
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Jin Chen
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, TX
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44
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Li G, Jiang S, Meng J, Chai G, Wu Z, Fan Z, Hu J, Sheng X, Zhang D, Chen L, Zhu X. Assessing differential representation of hand movements in multiple domains using stereo-electroencephalographic recordings. Neuroimage 2022; 250:118969. [DOI: 10.1016/j.neuroimage.2022.118969] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 01/28/2022] [Accepted: 02/02/2022] [Indexed: 01/03/2023] Open
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Mulinari Pinheiro Machado M, Voda A, Besançon G, Becq G, Kahane P, David O. Brain tissue classification from stereoelectroencephalographic recordings. J Neurosci Methods 2022; 365:109375. [PMID: 34627927 DOI: 10.1016/j.jneumeth.2021.109375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 09/29/2021] [Accepted: 10/01/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND Stereoelectroencephalographic (SEEG) recordings can be performed before final resective surgery in some drug-resistant patients with focal epilepsies. For good SEEG signal interpretation, it is important to correctly identify the brain tissue in which each contact is inserted. Tissue classification is usually done with the coregistration of CT scan (with implanted SEEG electrodes) with preoperative MRI. NEW METHOD Brain tissue classification is done here directly from SEEG signals obtained at rest by a linear discriminant analysis (LDA) classifier using measured SEEG signals. The classification operates on features extracted from Bode plots obtained via non-parametric frequency domain transfer functions of adjacent contacts pairs. Classification results have been compared with classification from T1 MRI following the labelling procedure described in Deman et al. (2018), together with minor corrections by visual inspection by specialists. RESULTS With the data processed from 19 epileptic patients representing 1284 contact pairs, an accuracy of 72 ± 3% was obtained for homogeneous tissue separation. To our knowledge only one previous study conducted brain tissue classification using the power spectra of SEEG signals, and the distance between contacts on a shaft. The features proposed in our article performed better with the LDA classifier. However, the Bayesian classifier proposed in Greene et al. (2020) is more robust and could be used in a future study to enhance the classification performance. CONCLUSIONS AND SIGNIFICANCE Our findings suggest that careful analysis of the transfer function between adjacent contacts measuring resting activity via frequency domain identification, could allow improved interpretation of SEEG data and or their co-registration with subject's anatomy.
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Affiliation(s)
| | - Alina Voda
- Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab, 38000 Grenoble, France.
| | - Gildas Besançon
- Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab, 38000 Grenoble, France.
| | - Guillaume Becq
- Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab, 38000 Grenoble, France.
| | - Philippe Kahane
- Univ. Grenoble Alpes, CHU Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, 38000 Grenoble, France.
| | - Olivier David
- Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, 38000 Grenoble, France; Aix Marseille Univ, Inserm, INS, Institut de Neurosciences des Systémes, Marseille, France.
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47
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Liu S, Li G, Jiang S, Wu X, Hu J, Zhang D, Chen L. Investigating Data Cleaning Methods to Improve Performance of Brain-Computer Interfaces Based on Stereo-Electroencephalography. Front Neurosci 2021; 15:725384. [PMID: 34690673 PMCID: PMC8528199 DOI: 10.3389/fnins.2021.725384] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 09/01/2021] [Indexed: 11/13/2022] Open
Abstract
Stereo-electroencephalography (SEEG) utilizes localized and penetrating depth electrodes to directly measure electrophysiological brain activity. The implanted electrodes generally provide a sparse sampling of multiple brain regions, including both cortical and subcortical structures, making the SEEG neural recordings a potential source for the brain–computer interface (BCI) purpose in recent years. For SEEG signals, data cleaning is an essential preprocessing step in removing excessive noises for further analysis. However, little is known about what kinds of effect that different data cleaning methods may exert on BCI decoding performance and, moreover, what are the reasons causing the differentiated effects. To address these questions, we adopted five different data cleaning methods, including common average reference, gray–white matter reference, electrode shaft reference, bipolar reference, and Laplacian reference, to process the SEEG data and evaluated the effect of these methods on improving BCI decoding performance. Additionally, we also comparatively investigated the changes of SEEG signals induced by these different methods from multiple-domain (e.g., spatial, spectral, and temporal domain). The results showed that data cleaning methods could improve the accuracy of gesture decoding, where the Laplacian reference produced the best performance. Further analysis revealed that the superiority of the data cleaning method with excellent performance might be attributed to the increased distinguishability in the low-frequency band. The findings of this work highlighted the importance of applying proper data clean methods for SEEG signals and proposed the application of Laplacian reference for SEEG-based BCI.
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Affiliation(s)
- Shengjie Liu
- State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Guangye Li
- State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, China
| | - Shize Jiang
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China
| | - Xiaolong Wu
- Department of Electronic and Electrical Engineering, University of Bath, Bath, United Kingdom
| | - Jie Hu
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China
| | - Dingguo Zhang
- Department of Electronic and Electrical Engineering, University of Bath, Bath, United Kingdom
| | - Liang Chen
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, China
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Yang AI, Dikecligil GN, Jiang H, Das SR, Stein JM, Schuele SU, Rosenow JM, Davis KA, Lucas TH, Gottfried JA. The what and when of olfactory working memory in humans. Curr Biol 2021; 31:4499-4511.e8. [PMID: 34450088 DOI: 10.1016/j.cub.2021.08.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 06/15/2021] [Accepted: 08/02/2021] [Indexed: 12/31/2022]
Abstract
Encoding and retaining novel sequences of sensory stimuli in working memory is crucial for adaptive behavior. A fundamental challenge for the central nervous system is to maintain each sequence item in an active and discriminable state, while also preserving their temporal context. Nested neural oscillations have been postulated to disambiguate the "what" and "when" of sequences, but the mechanisms by which these multiple streams of information are coordinated in the human brain remain unclear. Drawing from foundational animal studies, we recorded local field potentials from the human piriform cortex and hippocampus during a working memory task in which subjects experienced sequences of three distinct odors. Our data revealed a unique organization of odor memories across multiple timescales of the theta rhythm. During encoding, odors elicited greater gamma at distinct theta phases in both regions, time stamping their positions in the sequence, whereby the robustness of this effect was predictive of temporal order memory. During maintenance, stimulus-driven patterns of theta-coupled gamma were spontaneously reinstated in piriform cortex, recapitulating the order of the initial sequence. Replay events were time compressed across contiguous theta cycles, coinciding with periods of enhanced piriform-hippocampal theta-phase synchrony, and their prevalence forecasted subsequent recall accuracy on a trial-by-trial basis. Our data provide a novel link between endogenous replay orchestrated by the theta rhythm and short-term retention of sequential memories in the human brain.
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Affiliation(s)
- Andrew I Yang
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Gulce N Dikecligil
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Heidi Jiang
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Sandhitsu R Das
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Joel M Stein
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Stephan U Schuele
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Joshua M Rosenow
- Department of Neurosurgery, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Kathryn A Davis
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Timothy H Lucas
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jay A Gottfried
- Department of Psychology, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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Leszczynski M, Chaieb L, Staudigl T, Enkirch SJ, Fell J, Schroeder CE. Neural activity in the human anterior thalamus during natural vision. Sci Rep 2021; 11:17480. [PMID: 34471183 PMCID: PMC8410783 DOI: 10.1038/s41598-021-96588-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Accepted: 08/11/2021] [Indexed: 12/23/2022] Open
Abstract
In natural vision humans and other primates explore environment by active sensing, using saccadic eye movements to relocate the fovea and sample different bits of information multiple times per second. Saccades induce a phase reset of ongoing neuronal oscillations in primary and higher-order visual cortices and in the medial temporal lobe. As a result, neuron ensembles are shifted to a common state at the time visual input propagates through the system (i.e., just after fixation). The extent of the brain’s circuitry that is modulated by saccades is not yet known. Here, we evaluate the possibility that saccadic phase reset impacts the anterior nuclei of the thalamus (ANT). Using recordings in the human thalamus of three surgical patients during natural vision, we found that saccades and visual stimulus onset both modulate neural activity, but with distinct field potential morphologies. Specifically, we found that fixation-locked field potentials had a component that preceded saccade onset. It was followed by an early negativity around 50 ms after fixation onset which is significantly faster than any response to visual stimulus presentation. The timing of these events suggests that the ANT is predictively modulated before the saccadic eye movement. We also found oscillatory phase concentration, peaking at 3–4 Hz, coincident with suppression of Broadband High-frequency Activity (BHA; 80–180 Hz), both locked to fixation onset supporting the idea that neural oscillations in these nuclei are reorganized to a low excitability state right after fixation onset. These findings show that during real-world natural visual exploration neural dynamics in the human ANT is influenced by visual and oculomotor events, which supports the idea that ANT, apart from their contribution to episodic memory, also play a role in natural vision.
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Affiliation(s)
- Marcin Leszczynski
- Department of Psychiatry, College of Physicians and Surgeons, Columbia University Medical Center, 1051 Riverside Drive Kolb Annex Rm 561, New York, NY, 10032, USA. .,Translational Neuroscience Division, Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA.
| | - Leila Chaieb
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | - Tobias Staudigl
- Department of Psychology, Ludwig-Maximilians-Universität München, Munich, Germany
| | | | - Juergen Fell
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | - Charles E Schroeder
- Department of Psychiatry, College of Physicians and Surgeons, Columbia University Medical Center, 1051 Riverside Drive Kolb Annex Rm 561, New York, NY, 10032, USA.,Translational Neuroscience Division, Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA
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50
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Li G, Jiang S, Paraskevopoulou SE, Chai G, Wei Z, Liu S, Wang M, Xu Y, Fan Z, Wu Z, Chen L, Zhang D, Zhu X. Detection of human white matter activation and evaluation of its function in movement decoding using stereo-electroencephalography (SEEG). J Neural Eng 2021; 18. [PMID: 34284361 DOI: 10.1088/1741-2552/ac160e] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 07/20/2021] [Indexed: 11/11/2022]
Abstract
Objective. White matter tissue takes up approximately 50% of the human brain volume and it is widely known as a messenger conducting information between areas of the central nervous system. However, the characteristics of white matter neural activity and whether white matter neural recordings can contribute to movement decoding are often ignored and still remain largely unknown. In this work, we make quantitative analyses to investigate these two important questions using invasive neural recordings.Approach. We recorded stereo-electroencephalography (SEEG) data from 32 human subjects during a visually-cued motor task, where SEEG recordings can tap into gray and white matter electrical activity simultaneously. Using the proximal tissue density method, we identified the location (i.e. gray or white matter) of each SEEG contact. Focusing on alpha oscillatory and high gamma activities, we compared the activation patterns between gray matter and white matter. Then, we evaluated the performance of such white matter activation in movement decoding.Main results. The results show that white matter also presents activation under the task, in a similar way with the gray matter but at a significantly lower amplitude. Additionally, this work also demonstrates that combing white matter neural activities together with that of gray matter significantly promotes the movement decoding accuracy than using gray matter signals only.Significance. Taking advantage of SEEG recordings from a large number of subjects, we reveal the response characteristics of white matter neural signals under the task and demonstrate its enhancing function in movement decoding. This study highlights the importance of taking white matter activities into consideration in further scientific research and translational applications.
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Affiliation(s)
- Guangye Li
- State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China.,These authors contributed to this paper equally and should be considered as co-first authors
| | - Shize Jiang
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, People's Republic of China.,These authors contributed to this paper equally and should be considered as co-first authors
| | - Sivylla E Paraskevopoulou
- National Center for Adaptive Neurotechnologies, Wadsworth Center, New York State Department of Health, Albany, NY, United States of America.,These authors contributed to this paper equally and should be considered as co-first authors
| | - Guohong Chai
- State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Zixuan Wei
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Shengjie Liu
- State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Meng Wang
- State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Yang Xu
- State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Zhen Fan
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Zehan Wu
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Liang Chen
- Department of Neurosurgery of Huashan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Dingguo Zhang
- Department of Electronic and Electrical Engineering, University of Bath, Bath, United Kingdom
| | - Xiangyang Zhu
- State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China
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