Electrocorticographic Temporal Alteration Mapping: A Clinical Technique for Mapping the Motor Cortex with Movement-Related Cortical Potentials

Wu, Zehan; Xie, Tao; Yao, Lin; Zhang, Dingguo; Sheng, Xinjun; Farina, Dario; Chen, Liang; Mao, Ying; Zhu, Xiangyang

doi:10.3389/fnins.2017.00326

Cited by 6 publications

(4 citation statements)

References 52 publications

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“…11,13,[32][33][34] Given these landmarks, in clinical practice, performing ECoG mapping prior to ECS is reasonable and can serve to reduce the number of electrodes for ECS reconfirmation. Two aspects of our study are particularly new and noteworthy: (1) a very wide spectrum of intrinsic activities, including both very slow cortical components (MRCPs) recorded using a long time constant (10 seconds) and fast activities of ERS/ERD, were employed across the primary motor cortices; and (2) the M1 mapping using MRCP/ERS/ERD was directly compared with ECS, and a clear clinical application was specified.…”

Section: Discussionmentioning

confidence: 99%

“…The score accuracy of spatial concordance with ECS was as good as or better than those in previous studies. 11,13,[32][33][34] Given these landmarks, in clinical practice, performing ECoG mapping prior to ECS is reasonable and can serve to reduce the number of electrodes for ECS reconfirmation. This strategy is especially true for upper limb mapping rather than face mapping.…”

Section: Discussionmentioning

confidence: 99%

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A rational, multispectral mapping algorithm for primary motor cortex: A primary step before cortical stimulation

et al. 2019

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Summary Objective For future artificial intelligence–based brain mapping, development of a rational and safe scoring system for a brain motor mapping algorithm using electrocorticography (ECoG score), which contains various spectral, purely intrinsic brain activities, is necessary for either before or in the absence of electrical cortical stimulation (ECS). Methods We evaluated 1114 electrodes of 10 consecutive focal epilepsy patients who underwent subdural electrode implantation before epilepsy surgery at Kyoto University Hospital during 2011‐2017. Data from ECoG‐based mapping (bandpass filter of 0.016‐300/600 Hz) to define the primary motor area (M1) localization were used to create an ECoG score (range = 0‐4) by assigning 1 point each for the occurrence of ECoG components: very slow movement‐related cortical potentials (<0.5‐1.0 Hz), event‐related synchronization (76‐100 Hz or 100‐200 Hz), and event‐related desynchronization (8‐12 Hz or 12‐24 Hz). The ECoG score was assessed by calculating the sensitivity, specificity, and cutoff values of the score for localization concordance with M1 defined using only ECS as a reference. Results With an area under the receiver operating characteristic curve (AUC) of 0.76, cutoffs of scores of 4 and 1 showed high specificity (94%) and sensitivity (98%) in concordance with ECS‐based mapping, respectively. The ECoG score for mapping M1 of the upper limb achieved greater accuracy (AUC = 0.85) compared to that of the face (AUC = 0.64). Significance The ECoG score proposed in the present study is rational, simple, and useful to define M1, and it is spatially concordant with ECS. Although ECS is still widely employed for presurgical examination, our proposed application of the ECoG score may be suitable for future brain M1 mapping, and possibly beyond M1 mapping, independently of ECS.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A rational, multispectral mapping algorithm for primary motor cortex: A primary step before cortical stimulation

et al. 2019

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“…Over the past two decades, many studies have identified and confirmed HGA as a robust neural correlate of cognition and behavior. These studies encompass motor (Crone et al, 1998 ; Leuthardt et al, 2004 ; Canolty et al, 2006 ; Miller et al, 2007 , 2010 ; Li et al, 2017 ; Wu et al, 2017 ; Pan et al, 2018 ; Gruenwald et al, 2019 ; Thomas et al, 2019 ), somatosensory (Menon et al, 1996 ; Canolty et al, 2006 ; Genetti et al, 2015 ; Prueckl et al, 2015 ; Wahnoun et al, 2015 ), auditory (Crone et al, 2001 ; Ray et al, 2003 ; Towle et al, 2008 ; Gaona et al, 2011 ; Potes et al, 2012 ; Sturm et al, 2014 ; Tamura et al, 2016 ), language (Leuthardt et al, 2004 ; Sinai et al, 2005 ; Towle et al, 2008 ; Pei et al, 2011a , b ; Arya et al, 2017 , 2018 , 2019 ; Kambara et al, 2018 ; Williams Roberson et al, 2020 ), and visual (Ramot et al, 2012 ; Matsuzaki et al, 2013 ; Boucher et al, 2015 ; Miller et al, 2016 ; Rupp et al, 2017 ; Schalk et al, 2017 ; Kapeller et al, 2018a , b ; Nakai et al, 2018 ; Wittevrongel et al, 2020 ) functions, as well as various types of cognitive (e.g., memory) tasks (Sederberg et al, 2003 ; Canolty et al, 2006 ; Axmacher et al, 2007 ; Lachaux et al, 2007 ; Ray et al, 2008 ; Burke et al, 2014 ; Kunii et al, 2014 ; Serruya et al, 2014 ; Noy et al, 2015 ; Ueda et al, 2015 ). Further studies demonstrated the role of HGA during resting state and sleep (Hirai et al, 1999 ; Freeman et al, 2000 ; He et al, 2008 ; Geller et al, ...…”

Section: Introductionmentioning

confidence: 99%

“…The ability to precisely and robustly locate the cortical areas involved in cognitive and behavioral tasks from HGA has given rise to functional mapping applications that are now widely used in the presurgical evaluation of epilepsy and tumor patients. In these functional mapping applications, HGA identifies sensorimotor regions (Crone et al, 1998 ; Sinai et al, 2005 ; Leuthardt et al, 2007 ; Brunner et al, 2009 ; Hermes et al, 2010 ; Ruescher et al, 2013 ; Genetti et al, 2015 ; Prueckl et al, 2015 ; Wahnoun et al, 2015 ; Wu et al, 2017 ), expressive language regions and the auditory cortex (Sinai et al, 2005 ; Towle et al, 2008 ; Edwards et al, 2010 ; Roland et al, 2010 ; Pei et al, 2011b ; Babajani-Feremi et al, 2016 ; Arya et al, 2017 , 2018 , 2019 ; Kambara et al, 2018 ), visual regions (Matsuzaki et al, 2013 ; Kapeller et al, 2018b ; Nakai et al, 2018 ; Wittevrongel et al, 2020 ), and memory regions (Axmacher et al, 2007 ; Burke et al, 2014 ; Kunii et al, 2014 ). This identification of cortical regions is commonly realized by a statistical test that compares task-related changes in HGA to a resting-state condition.…”

Section: Introductionmentioning

confidence: 99%

Characterization of High-Gamma Activity in Electrocorticographic Signals

et al. 2023

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IntroductionElectrocorticographic (ECoG) high-gamma activity (HGA) is a widely recognized and robust neural correlate of cognition and behavior. However, fundamental signal properties of HGA, such as the high-gamma frequency band or temporal dynamics of HGA, have never been systematically characterized. As a result, HGA estimators are often poorly adjusted, such that they miss valuable physiological information.MethodsTo address these issues, we conducted a thorough qualitative and quantitative characterization of HGA in ECoG signals. Our study is based on ECoG signals recorded from 18 epilepsy patients while performing motor control, listening, and visual perception tasks. In this study, we first categorize HGA into HGA types based on the cognitive/behavioral task. For each HGA type, we then systematically quantify three fundamental signal properties of HGA: the high-gamma frequency band, the HGA bandwidth, and the temporal dynamics of HGA.ResultsThe high-gamma frequency band strongly varies across subjects and across cognitive/behavioral tasks. In addition, HGA time courses have lowpass character, with transients limited to 10 Hz. The task-related rise time and duration of these HGA time courses depend on the individual subject and cognitive/behavioral task. Task-related HGA amplitudes are comparable across the investigated tasks.DiscussionThis study is of high practical relevance because it provides a systematic basis for optimizing experiment design, ECoG acquisition and processing, and HGA estimation. Our results reveal previously unknown characteristics of HGA, the physiological principles of which need to be investigated in further studies.

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An update on tests used for intraoperative monitoring of cognition during awake craniotomy

de Zwart,

Ruis

2024

Acta Neurochir

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Purpose Mapping higher-order cognitive functions during awake brain surgery is important for cognitive preservation which is related to postoperative quality of life. A systematic review from 2018 about neuropsychological tests used during awake craniotomy made clear that until 2017 language was most often monitored and that the other cognitive domains were underexposed (Ruis, J Clin Exp Neuropsychol 40(10):1081–1104, 218). The field of awake craniotomy and cognitive monitoring is however developing rapidly. The aim of the current review is therefore, to investigate whether there is a change in the field towards incorporation of new tests and more complete mapping of (higher-order) cognitive functions. Methods We replicated the systematic search of the study from 2018 in PubMed and Embase from February 2017 to November 2023, yielding 5130 potentially relevant articles. We used the artificial machine learning tool ASReview for screening and included 272 papers that gave a detailed description of the neuropsychological tests used during awake craniotomy. Results Comparable to the previous study of 2018, the majority of studies (90.4%) reported tests for assessing language functions (Ruis, J Clin Exp Neuropsychol 40(10):1081–1104, 218). Nevertheless, an increasing number of studies now also describe tests for monitoring visuospatial functions, social cognition, and executive functions. Conclusions Language remains the most extensively tested cognitive domain. However, a broader range of tests are now implemented during awake craniotomy and there are (new developed) tests which received more attention. The rapid development in the field is reflected in the included studies in this review. Nevertheless, for some cognitive domains (e.g., executive functions and memory), there is still a need for developing tests that can be used during awake surgery.

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Electrocorticographic Temporal Alteration Mapping: A Clinical Technique for Mapping the Motor Cortex with Movement-Related Cortical Potentials

Cited by 6 publications

References 52 publications

A rational, multispectral mapping algorithm for primary motor cortex: A primary step before cortical stimulation

A rational, multispectral mapping algorithm for primary motor cortex: A primary step before cortical stimulation

Characterization of High-Gamma Activity in Electrocorticographic Signals

An update on tests used for intraoperative monitoring of cognition during awake craniotomy

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