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Zauli FM, Del Vecchio M, Pigorini A, Russo S, Massimini M, Sartori I, Cardinale F, d'Orio P, Mikulan E. Localizing hidden Interictal Epileptiform Discharges with simultaneous intracerebral and scalp high-density EEG recordings. J Neurosci Methods 2024; 409:110193. [PMID: 38871302 DOI: 10.1016/j.jneumeth.2024.110193] [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: 12/31/2023] [Revised: 05/02/2024] [Accepted: 06/08/2024] [Indexed: 06/15/2024]
Abstract
BACKGROUND Scalp EEG is one of the main tools in the clinical evaluation of epilepsy. In some cases intracranial Interictal Epileptiform Discharges (IEDs) are not visible from the scalp. Recent studies have shown the feasibility of revealing them in the EEG if their timings are extracted from simultaneous intracranial recordings, but their potential for the localization of the epileptogenic zone is not yet well defined. NEW METHOD We recorded simultaneous high-density EEG (HD-EEG) and stereo-electroencephalography (SEEG) during interictal periods in 8 patients affected by drug-resistant focal epilepsy. We identified IEDs in the SEEG and systematically analyzed the time-locked signals on the EEG by means of evoked potentials, topographical analysis and Electrical Source Imaging (ESI). The dataset has been standardized and is being publicly shared. RESULTS Our results showed that IEDs that were not clearly visible at single-trials could be uncovered by averaging, in line with previous reports. They also showed that their topographical voltage distributions matched the position of the SEEG electrode where IEDs had been identified, and that ESI techniques can reconstruct it with an accuracy of ∼2 cm. Finally, the present dataset provides a reference to test the accuracy of different methods and parameters. COMPARISON WITH EXISTING METHODS Our study is the first to systematically compare ESI methods on simultaneously recorded IEDs, and to share a public resource with in-vivo data for their evaluation. CONCLUSIONS Simultaneous HD-EEG and SEEG recordings can unveil hidden IEDs whose origins can be reconstructed using topographical and ESI analyses, but results depend on the selected methods and parameters.
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Affiliation(s)
- Flavia Maria Zauli
- Department of Philosophy "P. Martinetti", Università degli Studi di Milano, Milan, Italy; Department of Biomedical and Clinical Sciences "L. Sacco", Università degli Studi di Milano, Milan, Italy; ASST GOM Niguarda, Piazza dell'Ospedale Maggiore 3, Milan, Italy
| | - Maria Del Vecchio
- Institute of Neuroscience, Consiglio Nazionale delle Ricerche, Parma, Italy
| | - Andrea Pigorini
- Department of Biomedical, Surgical and Dental Sciences, Università degli Studi di Milano, Milan, Italy; UOC Maxillo-facial Surgery and dentistry, Fondazione IRCCS Cà Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Simone Russo
- Department of Biomedical and Clinical Sciences "L. Sacco", Università degli Studi di Milano, Milan, Italy; Wallace H Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Marcello Massimini
- Department of Biomedical and Clinical Sciences "L. Sacco", Università degli Studi di Milano, Milan, Italy; Istituto Di Ricovero e Cura a Carattere Scientifico, Fondazione Don Carlo Gnocchi, Milan, Italy
| | - Ivana Sartori
- ASST GOM Niguarda, Piazza dell'Ospedale Maggiore 3, Milan, Italy
| | - Francesco Cardinale
- ASST GOM Niguarda, Piazza dell'Ospedale Maggiore 3, Milan, Italy; Institute of Neuroscience, Consiglio Nazionale delle Ricerche, Parma, Italy; Department of Medicine and Surgery, Unit of Neuroscience, Università degli Studi di Parma, Parma, Italy
| | - Piergiorgio d'Orio
- ASST GOM Niguarda, Piazza dell'Ospedale Maggiore 3, Milan, Italy; Institute of Neuroscience, Consiglio Nazionale delle Ricerche, Parma, Italy; Department of Medicine and Surgery, Unit of Neuroscience, Università degli Studi di Parma, Parma, Italy
| | - Ezequiel Mikulan
- Department of Health Sciences, Università degli Studi di Milano, Milan, Italy.
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Meziane HB, Jabès A, Klencklen G, Banta Lavenex P, Lavenex P. EEG markers of successful allocentric spatial working memory maintenance in humans. Eur J Neurosci 2024. [PMID: 38863237 DOI: 10.1111/ejn.16446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 06/02/2024] [Accepted: 06/04/2024] [Indexed: 06/13/2024]
Abstract
Several brain regions in the frontal, occipital and medial temporal lobes are known to contribute to spatial information processing. In contrast, the oscillatory patterns contributing to allocentric spatial working memory maintenance are poorly understood, especially in humans. Here, we tested twenty-three 21- to 32-year-old and twenty-two 64- to 76-year-old healthy right-handed adults in a real-world, spatial working memory task and recorded electroencephalographic (EEG) activity during the maintenance period. We established criteria for designating recall trials as perfect (no errors) or failed (errors and random search) and identified 8 young and 13 older adults who had at least 1 perfect and 1 failed trial amongst 10 recall trials. Individual alpha frequency-based analyses were used to identify oscillatory patterns during the maintenance period of perfect and failed trials. Spectral scalp topographies showed that individual theta frequency band relative power was stronger in perfect than in failed trials in the frontal midline and posterior regions. Similarly, gamma band (30-40 Hz) relative power was stronger in perfect than in failed trials over the right motor cortex. Exact low-resolution brain electromagnetic tomography in the frequency domain identified greater theta power in perfect than in failed trials in the secondary visual area (BA19) and greater gamma power in perfect than in failed trials in the right supplementary motor area. The findings of this exploratory study suggest that theta oscillations in the occipital lobe and gamma oscillations in the secondary motor cortex (BA6) play a particular role in successful allocentric spatial working memory maintenance.
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Affiliation(s)
- Hadj Boumediene Meziane
- Faculty of Psychology, Swiss Distance University Institute, Brig, Switzerland
- Institute of Psychology, University of Lausanne, Lausanne, Switzerland
| | - Adeline Jabès
- Institute of Psychology, University of Lausanne, Lausanne, Switzerland
| | - Giuliana Klencklen
- Faculty of Psychology, Swiss Distance University Institute, Brig, Switzerland
- Institute of Psychology, University of Lausanne, Lausanne, Switzerland
| | - Pamela Banta Lavenex
- Faculty of Psychology, Swiss Distance University Institute, Brig, Switzerland
- Institute of Psychology, University of Lausanne, Lausanne, Switzerland
| | - Pierre Lavenex
- Institute of Psychology, University of Lausanne, Lausanne, Switzerland
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Zhang W, Jiang M, Teo KAC, Bhuvanakantham R, Fong L, Sim WKJ, Guo Z, Foo CHV, Chua RHJ, Padmanabhan P, Leong V, Lu J, Gulyás B, Guan C. Revealing the spatiotemporal brain dynamics of covert speech compared with overt speech: A simultaneous EEG-fMRI study. Neuroimage 2024; 293:120629. [PMID: 38697588 DOI: 10.1016/j.neuroimage.2024.120629] [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: 12/05/2023] [Revised: 04/17/2024] [Accepted: 04/29/2024] [Indexed: 05/05/2024] Open
Abstract
Covert speech (CS) refers to speaking internally to oneself without producing any sound or movement. CS is involved in multiple cognitive functions and disorders. Reconstructing CS content by brain-computer interface (BCI) is also an emerging technique. However, it is still controversial whether CS is a truncated neural process of overt speech (OS) or involves independent patterns. Here, we performed a word-speaking experiment with simultaneous EEG-fMRI. It involved 32 participants, who generated words both overtly and covertly. By integrating spatial constraints from fMRI into EEG source localization, we precisely estimated the spatiotemporal dynamics of neural activity. During CS, EEG source activity was localized in three regions: the left precentral gyrus, the left supplementary motor area, and the left putamen. Although OS involved more brain regions with stronger activations, CS was characterized by an earlier event-locked activation in the left putamen (peak at 262 ms versus 1170 ms). The left putamen was also identified as the only hub node within the functional connectivity (FC) networks of both OS and CS, while showing weaker FC strength towards speech-related regions in the dominant hemisphere during CS. Path analysis revealed significant multivariate associations, indicating an indirect association between the earlier activation in the left putamen and CS, which was mediated by reduced FC towards speech-related regions. These findings revealed the specific spatiotemporal dynamics of CS, offering insights into CS mechanisms that are potentially relevant for future treatment of self-regulation deficits, speech disorders, and development of BCI speech applications.
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Affiliation(s)
- Wei Zhang
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Muyun Jiang
- School of Computer Science and Engineering, Nanyang Technological University, Singapore
| | - Kok Ann Colin Teo
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; IGP-Neuroscience, Interdisciplinary Graduate Programme, Nanyang Technological University, Singapore; Division of Neurosurgery, National University Health System, Singapore
| | - Raghavan Bhuvanakantham
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - LaiGuan Fong
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore
| | - Wei Khang Jeremy Sim
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore; IGP-Neuroscience, Interdisciplinary Graduate Programme, Nanyang Technological University, Singapore
| | - Zhiwei Guo
- School of Computer Science and Engineering, Nanyang Technological University, Singapore
| | | | | | - Parasuraman Padmanabhan
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Victoria Leong
- Division of Psychology, Nanyang Technological University, Singapore; Department of Pediatrics, University of Cambridge, United Kingdom
| | - Jia Lu
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore; DSO National Laboratories, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Balázs Gulyás
- Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
| | - Cuntai Guan
- School of Computer Science and Engineering, Nanyang Technological University, Singapore.
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4
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Vitali D, Olugbade T, Eccleston C, Keogh E, Bianchi-Berthouze N, de C Williams AC. Sensing behavior change in chronic pain: a scoping review of sensor technology for use in daily life. Pain 2024; 165:1348-1360. [PMID: 38258888 DOI: 10.1097/j.pain.0000000000003134] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 09/26/2023] [Indexed: 01/24/2024]
Abstract
ABSTRACT Technology offers possibilities for quantification of behaviors and physiological changes of relevance to chronic pain, using wearable sensors and devices suitable for data collection in daily life contexts. We conducted a scoping review of wearable and passive sensor technologies that sample data of psychological interest in chronic pain, including in social situations. Sixty articles met our criteria from the 2783 citations retrieved from searching. Three-quarters of recruited people were with chronic pain, mostly musculoskeletal, and the remainder with acute or episodic pain; those with chronic pain had a mean age of 43 (few studies sampled adolescents or children) and 60% were women. Thirty-seven studies were performed in laboratory or clinical settings and the remainder in daily life settings. Most used only 1 type of technology, with 76 sensor types overall. The commonest was accelerometry (mainly used in daily life contexts), followed by motion capture (mainly in laboratory settings), with a smaller number collecting autonomic activity, vocal signals, or brain activity. Subjective self-report provided "ground truth" for pain, mood, and other variables, but often at a different timescale from the automatically collected data, and many studies reported weak relationships between technological data and relevant psychological constructs, for instance, between fear of movement and muscle activity. There was relatively little discussion of practical issues: frequency of sampling, missing data for human or technological reasons, and the users' experience, particularly when users did not receive data in any form. We conclude the review with some suggestions for content and process of future studies in this field.
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Affiliation(s)
- Diego Vitali
- Research Department of Clinical, Educational & Health Psychology, University College London, London, United Kingdom
| | - Temitayo Olugbade
- School of Engineering and Informatics, University of Sussex, Brighton, United Kingdom
- Interaction Centre, University College London, London, United Kingdom
| | - Christoper Eccleston
- Centre for Pain Research, The University of Bath, Bath, United Kingdom
- Department of Experimental, Clinical and Health Psychology, Ghent University, Ghent, Belgium
- Department of Psychology, The University of Helsinki, Helsinki, Finland
| | - Edmund Keogh
- Centre for Pain Research, The University of Bath, Bath, United Kingdom
| | | | - Amanda C de C Williams
- Research Department of Clinical, Educational & Health Psychology, University College London, London, United Kingdom
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5
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Ataei A, Amini A, Ghazizadeh A. Robust memory of face moral values is encoded in the human caudate tail: a simultaneous EEG-fMRI study. Sci Rep 2024; 14:12629. [PMID: 38824168 PMCID: PMC11144224 DOI: 10.1038/s41598-024-63085-w] [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: 12/10/2023] [Accepted: 05/24/2024] [Indexed: 06/03/2024] Open
Abstract
Moral judgements about people based on their actions is a key component that guides social decision making. It is currently unknown how positive or negative moral judgments associated with a person's face are processed and stored in the brain for a long time. Here, we investigate the long-term memory of moral values associated with human faces using simultaneous EEG-fMRI data acquisition. Results show that only a few exposures to morally charged stories of people are enough to form long-term memories a day later for a relatively large number of new faces. Event related potentials (ERPs) showed a significant differentiation of remembered good vs bad faces over centerofrontal electrode sites (value ERP). EEG-informed fMRI analysis revealed a subcortical cluster centered on the left caudate tail (CDt) as a correlate of the face value ERP. Importantly neither this analysis nor a conventional whole-brain analysis revealed any significant coding of face values in cortical areas, in particular the fusiform face area (FFA). Conversely an fMRI-informed EEG source localization using accurate subject-specific EEG head models also revealed activation in the left caudate tail. Nevertheless, the detected caudate tail region was found to be functionally connected to the FFA, suggesting FFA to be the source of face-specific information to CDt. A further psycho-physiological interaction analysis also revealed task-dependent coupling between CDt and dorsomedial prefrontal cortex (dmPFC), a region previously identified as retaining emotional working memories. These results identify CDt as a main site for encoding the long-term value memories of faces in humans suggesting that moral value of faces activates the same subcortical basal ganglia circuitry involved in processing reward value memory for objects in primates.
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Affiliation(s)
- Ali Ataei
- EE Department, Sharif University of Technology, Azadi Avenue, Tehran, 1458889694, Iran
- Sharif Brain Center, Sharif University of Technology, Tehran, Iran
| | - Arash Amini
- EE Department, Sharif University of Technology, Azadi Avenue, Tehran, 1458889694, Iran
| | - Ali Ghazizadeh
- EE Department, Sharif University of Technology, Azadi Avenue, Tehran, 1458889694, Iran.
- Sharif Brain Center, Sharif University of Technology, Tehran, Iran.
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences, Tehran, Iran.
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6
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Nagy P, Tóth B, Winkler I, Boncz Á. The effects of spatial leakage correction on the reliability of EEG-based functional connectivity networks. Hum Brain Mapp 2024; 45:e26747. [PMID: 38825981 PMCID: PMC11144954 DOI: 10.1002/hbm.26747] [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: 07/04/2023] [Revised: 03/28/2024] [Accepted: 05/16/2024] [Indexed: 06/04/2024] Open
Abstract
Electroencephalography (EEG) functional connectivity (FC) estimates are confounded by the volume conduction problem. This effect can be greatly reduced by applying FC measures insensitive to instantaneous, zero-lag dependencies (corrected measures). However, numerous studies showed that FC measures sensitive to volume conduction (uncorrected measures) exhibit higher reliability and higher subject-level identifiability. We tested how source reconstruction contributed to the reliability difference of EEG FC measures on a large (n = 201) resting-state data set testing eight FC measures (including corrected and uncorrected measures). We showed that the high reliability of uncorrected FC measures in resting state partly stems from source reconstruction: idiosyncratic noise patterns define a baseline resting-state functional network that explains a significant portion of the reliability of uncorrected FC measures. This effect remained valid for template head model-based, as well as individual head model-based source reconstruction. Based on our findings we made suggestions how to best use spatial leakage corrected and uncorrected FC measures depending on the main goals of the study.
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Affiliation(s)
- Péter Nagy
- HUN‐REN Research Centre for Natural SciencesBudapestHungary
- Faculty of Electrical Engineering and Informatics, Department of Measurement and Information SystemsBudapest University of Technology and EconomicsBudapestHungary
| | - Brigitta Tóth
- HUN‐REN Research Centre for Natural SciencesBudapestHungary
| | - István Winkler
- HUN‐REN Research Centre for Natural SciencesBudapestHungary
| | - Ádám Boncz
- HUN‐REN Research Centre for Natural SciencesBudapestHungary
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7
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Zhang G, Garrett DR, Luck SJ. Optimal filters for ERP research I: A general approach for selecting filter settings. Psychophysiology 2024; 61:e14531. [PMID: 38297978 PMCID: PMC11096084 DOI: 10.1111/psyp.14531] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 11/15/2023] [Accepted: 01/09/2024] [Indexed: 02/02/2024]
Abstract
Filtering plays an essential role in event-related potential (ERP) research, but filter settings are usually chosen on the basis of historical precedent, lab lore, or informal analyses. This reflects, in part, the lack of a well-reasoned, easily implemented method for identifying the optimal filter settings for a given type of ERP data. To fill this gap, we developed an approach that involves finding the filter settings that maximize the signal-to-noise ratio for a specific amplitude score (or minimizes the noise for a latency score) while minimizing waveform distortion. The signal is estimated by obtaining the amplitude score from the grand average ERP waveform (usually a difference waveform). The noise is estimated using the standardized measurement error of the single-subject scores. Waveform distortion is estimated by passing noise-free simulated data through the filters. This approach allows researchers to determine the most appropriate filter settings for their specific scoring methods, experimental designs, subject populations, recording setups, and scientific questions. We have provided a set of tools in ERPLAB Toolbox to make it easy for researchers to implement this approach with their own data.
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Affiliation(s)
- Guanghui Zhang
- Center for Mind & Brain, University of California-Davis, Davis, California, USA
| | - David R Garrett
- Center for Mind & Brain, University of California-Davis, Davis, California, USA
| | - Steven J Luck
- Center for Mind & Brain, University of California-Davis, Davis, California, USA
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8
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Haigh A, Buckby B. Rhythmic Attention and ADHD: A Narrative and Systematic Review. Appl Psychophysiol Biofeedback 2024; 49:185-204. [PMID: 38198019 DOI: 10.1007/s10484-023-09618-x] [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] [Accepted: 12/24/2023] [Indexed: 01/11/2024]
Abstract
In recent decades, a growing body of evidence has confirmed the existence of rhythmic fluctuations in attention, but the effect of inter-individual variations in these attentional rhythms has yet to be investigated. The aim of this review is to identify trends in the attention deficit/hyperactivity disorder (ADHD) literature that could be indicative of between-subject differences in rhythmic attention. A narrative review of the rhythmic attention and electrophysiological ADHD research literature was conducted, and the commonly-reported difference in slow-wave power between ADHD subjects and controls was found to have the most relevance to an understanding of rhythmic attention. A systematic review of the literature examining electrophysiological power differences in ADHD was then conducted to identify studies with conditions similar to those utilised in the rhythmic attention research literature. Fifteen relevant studies were identified and reviewed. The most consistent finding in the studies reviewed was for no spectral power differences between ADHD subjects and controls. However, the strongest trend in the studies reporting power differences was for higher power in the delta and theta frequency bands and lower power in the alpha band. In the context of rhythmic attention, this trend is suggestive of a slowing in the frequency and/or increase in the amplitude of the attentional oscillation in a subgroup of ADHD subjects. It is suggested that this characteristic electrophysiological modulation could be indicative of a global slowing of the attentional rhythm and/or an increase in the rhythmic recruitment of neurons in frontal attention networks in individuals with ADHD.
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Affiliation(s)
- Andrew Haigh
- Department of Psychology, James Cook University, Townsville, Australia.
| | - Beryl Buckby
- Department of Psychology, James Cook University, Townsville, Australia
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Deroche MLD, Wolfe J, Neumann S, Manning J, Hanna L, Towler W, Wilson C, Bien AG, Miller S, Schafer E, Gemignani J, Alemi R, Muthuraman M, Koirala N, Gracco VL. Cross-modal plasticity in children with cochlear implant: converging evidence from EEG and functional near-infrared spectroscopy. Brain Commun 2024; 6:fcae175. [PMID: 38846536 PMCID: PMC11154148 DOI: 10.1093/braincomms/fcae175] [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: 10/18/2023] [Revised: 04/02/2024] [Accepted: 05/17/2024] [Indexed: 06/09/2024] Open
Abstract
Over the first years of life, the brain undergoes substantial organization in response to environmental stimulation. In a silent world, it may promote vision by (i) recruiting resources from the auditory cortex and (ii) making the visual cortex more efficient. It is unclear when such changes occur and how adaptive they are, questions that children with cochlear implants can help address. Here, we examined 7-18 years old children: 50 had cochlear implants, with delayed or age-appropriate language abilities, and 25 had typical hearing and language. High-density electroencephalography and functional near-infrared spectroscopy were used to evaluate cortical responses to a low-level visual task. Evidence for a 'weaker visual cortex response' and 'less synchronized or less inhibitory activity of auditory association areas' in the implanted children with language delays suggests that cross-modal reorganization can be maladaptive and does not necessarily strengthen the dominant visual sense.
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Affiliation(s)
- Mickael L D Deroche
- Department of Psychology, Concordia University, Montreal, Quebec, Canada, H4B 1R6
| | - Jace Wolfe
- Hearts for Hearing Foundation, Oklahoma City, OK 73120, USA
| | - Sara Neumann
- Hearts for Hearing Foundation, Oklahoma City, OK 73120, USA
| | - Jacy Manning
- Hearts for Hearing Foundation, Oklahoma City, OK 73120, USA
| | - Lindsay Hanna
- Hearts for Hearing Foundation, Oklahoma City, OK 73120, USA
| | - Will Towler
- Hearts for Hearing Foundation, Oklahoma City, OK 73120, USA
| | - Caleb Wilson
- Department of Otolaryngology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Alexander G Bien
- Department of Otolaryngology, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Sharon Miller
- Department of Audiology & Speech-Language Pathology, University of North Texas, Denton, TX 76201, USA
| | - Erin Schafer
- Department of Audiology & Speech-Language Pathology, University of North Texas, Denton, TX 76201, USA
| | - Jessica Gemignani
- Department of Developmental and Social Psychology, University of Padova, 35131 Padua, Italy
| | - Razieh Alemi
- Department of Psychology, Concordia University, Montreal, Quebec, Canada, H4B 1R6
| | - Muthuraman Muthuraman
- Section of Neural Engineering with Signal Analytics and Artificial Intelligence, Department of Neurology, University Hospital Würzburg, 97080 Würzburg, Germany
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Kouti M, Ansari-Asl K, Namjoo E. EEG dynamic source imaging using a regularized optimization with spatio-temporal constraints. Med Biol Eng Comput 2024:10.1007/s11517-024-03125-9. [PMID: 38771431 DOI: 10.1007/s11517-024-03125-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 05/11/2024] [Indexed: 05/22/2024]
Abstract
One of the most important needs in neuroimaging is brain dynamic source imaging with high spatial and temporal resolution. EEG source imaging estimates the underlying sources from EEG recordings, which provides enhanced spatial resolution with intrinsically high temporal resolution. To ensure identifiability in the underdetermined source reconstruction problem, constraints on EEG sources are essential. This paper introduces a novel method for estimating source activities based on spatio-temporal constraints and a dynamic source imaging algorithm. The method enhances time resolution by incorporating temporal evolution of neural activity into a regularization function. Additionally, two spatial regularization constraints based on L 1 and L 2 norms are applied in the transformed domain to address both focal and spread neural activities, achieved through spatial gradient and Laplacian transform. Performance evaluation, conducted quantitatively using synthetic datasets, discusses the influence of parameters such as source extent, number of sources, correlation level, and SNR level on temporal and spatial metrics. Results demonstrate that the proposed method provides superior spatial and temporal reconstructions compared to state-of-the-art inverse solutions including STRAPS, sLORETA, SBL, dSPM, and MxNE. This improvement is attributed to the simultaneous integration of transformed spatial and temporal constraints. When applied to a real auditory ERP dataset, our algorithm accurately reconstructs brain source time series and locations, effectively identifying the origins of auditory evoked potentials. In conclusion, our proposed method with spatio-temporal constraints outperforms the state-of-the-art algorithms in estimating source distribution and time courses.
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Affiliation(s)
- Mayadeh Kouti
- Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
- Department of Electrical Engineering, Shohadaye Hoveizeh Campus of Technology, Shahid Chamran University of Ahvaz, Ahvaz, Iran
| | - Karim Ansari-Asl
- Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
| | - Ehsan Namjoo
- Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
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11
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Tamburro G, Bruña R, Fiedler P, De Fano A, Raeisi K, Khazaei M, Zappasodi F, Comani S. An Analytical Approach for Naturalistic Cooperative and Competitive EEG-Hyperscanning Data: A Proof-of-Concept Study. SENSORS (BASEL, SWITZERLAND) 2024; 24:2995. [PMID: 38793851 PMCID: PMC11125252 DOI: 10.3390/s24102995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 04/03/2024] [Accepted: 05/02/2024] [Indexed: 05/26/2024]
Abstract
Investigating the neural mechanisms underlying both cooperative and competitive joint actions may have a wide impact in many social contexts of human daily life. An effective pipeline of analysis for hyperscanning data recorded in a naturalistic context with a cooperative and competitive motor task has been missing. We propose an analytical pipeline for this type of joint action data, which was validated on electroencephalographic (EEG) signals recorded in a proof-of-concept study on two dyads playing cooperative and competitive table tennis. Functional connectivity maps were reconstructed using the corrected imaginary part of the phase locking value (ciPLV), an algorithm suitable in case of EEG signals recorded during turn-based competitive joint actions. Hyperbrain, within-, and between-brain functional connectivity maps were calculated in three frequency bands (i.e., theta, alpha, and beta) relevant during complex motor task execution and were characterized with graph theoretical measures and a clustering approach. The results of the proof-of-concept study are in line with recent findings on the main features of the functional networks sustaining cooperation and competition, hence demonstrating that the proposed pipeline is promising tool for the analysis of joint action EEG data recorded during cooperation and competition using a turn-based motor task.
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Affiliation(s)
- Gabriella Tamburro
- Behavioral Imaging and Neural Dynamics Center, G. d’Annunzio University of Chieti-Pescara, 66100 Chieti, Italy; (A.D.F.); (F.Z.); (S.C.)
- Department of Neuroscience, Imaging and Clinical Sciences, G. d’Annunzio University of Chieti–Pescara, 66100 Chieti, Italy; (K.R.); (M.K.)
| | - Ricardo Bruña
- Center for Cognitive and Computational Neuroscience (C3N), Universidad Complutense de Madrid, 28040 Madrid, Spain;
- Department of Radiology, Rehabilitation and Physiotherapy, School of Medicine, Universidad Complutense de Madrid, IdISSC, 28040 Madrid, Spain
| | - Patrique Fiedler
- Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, 98693 Ilmenau, Germany
| | - Antonio De Fano
- Behavioral Imaging and Neural Dynamics Center, G. d’Annunzio University of Chieti-Pescara, 66100 Chieti, Italy; (A.D.F.); (F.Z.); (S.C.)
- Department of Neuroscience, Imaging and Clinical Sciences, G. d’Annunzio University of Chieti–Pescara, 66100 Chieti, Italy; (K.R.); (M.K.)
| | - Khadijeh Raeisi
- Department of Neuroscience, Imaging and Clinical Sciences, G. d’Annunzio University of Chieti–Pescara, 66100 Chieti, Italy; (K.R.); (M.K.)
| | - Mohammad Khazaei
- Department of Neuroscience, Imaging and Clinical Sciences, G. d’Annunzio University of Chieti–Pescara, 66100 Chieti, Italy; (K.R.); (M.K.)
| | - Filippo Zappasodi
- Behavioral Imaging and Neural Dynamics Center, G. d’Annunzio University of Chieti-Pescara, 66100 Chieti, Italy; (A.D.F.); (F.Z.); (S.C.)
- Department of Neuroscience, Imaging and Clinical Sciences, G. d’Annunzio University of Chieti–Pescara, 66100 Chieti, Italy; (K.R.); (M.K.)
- Institute for Advanced Biomedical Technologies, University “Gabriele d’Annunzio” of Chieti–Pescara, 66100 Chieti, Italy
| | - Silvia Comani
- Behavioral Imaging and Neural Dynamics Center, G. d’Annunzio University of Chieti-Pescara, 66100 Chieti, Italy; (A.D.F.); (F.Z.); (S.C.)
- Department of Neuroscience, Imaging and Clinical Sciences, G. d’Annunzio University of Chieti–Pescara, 66100 Chieti, Italy; (K.R.); (M.K.)
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12
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Soler A, Giraldo E, Molinas M. EEG source imaging of hand movement-related areas: an evaluation of the reconstruction and classification accuracy with optimized channels. Brain Inform 2024; 11:11. [PMID: 38703311 PMCID: PMC11069493 DOI: 10.1186/s40708-024-00224-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 04/04/2024] [Indexed: 05/06/2024] Open
Abstract
The hand motor activity can be identified and converted into commands for controlling machines through a brain-computer interface (BCI) system. Electroencephalography (EEG) based BCI systems employ electrodes to measure the electrical brain activity projected at the scalp and discern patterns. However, the volume conduction problem attenuates the electric potential from the brain to the scalp and introduces spatial mixing to the signals. EEG source imaging (ESI) techniques can be applied to alleviate these issues and enhance the spatial segregation of information. Despite this potential solution, the use of ESI has not been extensively applied in BCI systems, largely due to accuracy concerns over reconstruction accuracy when using low-density EEG (ldEEG), which is commonly used in BCIs. To overcome these accuracy issues in low channel counts, recent studies have proposed reducing the number of EEG channels based on optimized channel selection. This work presents an evaluation of the spatial and temporal accuracy of ESI when applying optimized channel selection towards ldEEG number of channels. For this, a simulation study of source activity related to hand movement has been performed using as a starting point an EEG system with 339 channels. The results obtained after optimization show that the activity in the concerned areas can be retrieved with a spatial accuracy of 3.99, 10.69, and 14.29 mm (localization error) when using 32, 16, and 8 channel counts respectively. In addition, the use of optimally selected electrodes has been validated in a motor imagery classification task, obtaining a higher classification performance when using 16 optimally selected channels than 32 typical electrode distributions under 10-10 system, and obtaining higher classification performance when combining ESI methods with the optimal selected channels.
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Affiliation(s)
- Andres Soler
- Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, Norway.
| | - Eduardo Giraldo
- Department of Electrical Engineering, Universidad Tecnológica de Pereira, Pereira, Colombia
| | - Marta Molinas
- Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, Norway
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13
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Krethlow G, Fargier R, Atanasova T, Ménétré E, Laganaro M. Asynchronous behavioral and neurophysiological changes in word production in the adult lifespan. Cereb Cortex 2024; 34:bhae187. [PMID: 38715409 PMCID: PMC11077060 DOI: 10.1093/cercor/bhae187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 04/05/2024] [Accepted: 04/18/2024] [Indexed: 05/12/2024] Open
Abstract
Behavioral and brain-related changes in word production have been claimed to predominantly occur after 70 years of age. Most studies investigating age-related changes in adulthood only compared young to older adults, failing to determine whether neural processes underlying word production change at an earlier age than observed in behavior. This study aims to fill this gap by investigating whether changes in neurophysiological processes underlying word production are aligned with behavioral changes. Behavior and the electrophysiological event-related potential patterns of word production were assessed during a picture naming task in 95 participants across five adult lifespan age groups (ranging from 16 to 80 years old). While behavioral performance decreased starting from 70 years of age, significant neurophysiological changes were present at the age of 40 years old, in a time window (between 150 and 220 ms) likely associated with lexical-semantic processes underlying referential word production. These results show that neurophysiological modifications precede the behavioral changes in language production; they can be interpreted in line with the suggestion that the lexical-semantic reorganization in mid-adulthood influences the maintenance of language skills longer than for other cognitive functions.
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Affiliation(s)
- Giulia Krethlow
- Faculty of Psychology and Educational Sciences, University of Geneva, Bd du Pont d’Arve 40, 1205, Geneva, Switzerland
| | | | - Tanja Atanasova
- Faculty of Psychology and Educational Sciences, University of Geneva, Bd du Pont d’Arve 40, 1205, Geneva, Switzerland
| | - Eric Ménétré
- Faculty of Psychology and Educational Sciences, University of Geneva, Bd du Pont d’Arve 40, 1205, Geneva, Switzerland
| | - Marina Laganaro
- Faculty of Psychology and Educational Sciences, University of Geneva, Bd du Pont d’Arve 40, 1205, Geneva, Switzerland
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14
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Latrèche C, Mancini V, Rochas V, Maeder J, Cantonas LM, Férat V, Schneider M, Michel CM, Eliez S. Using transcranial alternating current stimulation to enhance working memory skills in youths with 22q11.2 deletion syndrome: A randomized double-blind sham-controlled study. Psychiatry Res 2024; 335:115835. [PMID: 38460352 DOI: 10.1016/j.psychres.2024.115835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 02/29/2024] [Accepted: 03/02/2024] [Indexed: 03/11/2024]
Abstract
Abnormal cognitive development, particularly working memory (WM) deficits, is among the first apparent manifestations of psychosis. Yet, cognitive impairment only shows limited response to current pharmacological treatment. Alternative interventions to target cognition are highly needed in individuals at high risk for psychosis, like carriers of 22q11.2 deletion syndrome (22q11.2DS). Here we applied theta-tuned transcranial alternating current stimulation (tACS) between frontal and temporal regions during a visual WM task in 34 deletion carriers. We conducted a double-blind sham-controlled study over three consecutive days. The stimulation parameters were derived from individual structural MRI scan and HD-EEG data acquired at baseline (Day 1) to model current intensity and individual preferential theta peak. Participants were randomized to either sham or tACS (Days 2 and 3) and then completed a visual WM task and a control task. Our findings reveal that tACS was safe and well-tolerated among participants. We found a significantly increased accuracy in the visual WM but not the control task following tACS. Moreover, this enhancement in WM accuracy was greater after tACS than during tACS, indicating stronger offline effects than online effects. Our study therefore supports the application of repeated sessions of brain stimulation in 22q11.2DS.
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Affiliation(s)
- Caren Latrèche
- Developmental Imaging and Psychopathology Lab, Department of Psychiatry, University of Geneva School of Medicine, Switzerland.
| | - Valentina Mancini
- Developmental Imaging and Psychopathology Lab, Department of Psychiatry, University of Geneva School of Medicine, Switzerland
| | - Vincent Rochas
- Functional Brain Mapping Laboratory, Department of Basic Neurosciences, University of Geneva, Switzerland; Human Neuroscience Platform, Fondation Campus Biotech Geneva, Geneva, Switzerland
| | - Johanna Maeder
- Developmental Imaging and Psychopathology Lab, Department of Psychiatry, University of Geneva School of Medicine, Switzerland
| | - Lucia M Cantonas
- Autism Brain and Behavior Laboratory, Department of Psychiatry, University of Geneva School of Medicine, Geneva, Switzerland
| | - Victor Férat
- Functional Brain Mapping Laboratory, Department of Basic Neurosciences, University of Geneva, Switzerland
| | - Maude Schneider
- Clinical Psychology Unit for Developmental and Intellectual Disabilities, Faculty of Psychology and Educational Sciences, University of Geneva, Switzerland
| | - Christoph M Michel
- Functional Brain Mapping Laboratory, Department of Basic Neurosciences, University of Geneva, Switzerland
| | - Stephan Eliez
- Developmental Imaging and Psychopathology Lab, Department of Psychiatry, University of Geneva School of Medicine, Switzerland; Department of Genetic Medicine and Development, University of Geneva School of Medicine, Switzerland
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15
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Shin M, Seo M, Lee K, Yoon K. Super-resolution techniques for biomedical applications and challenges. Biomed Eng Lett 2024; 14:465-496. [PMID: 38645589 PMCID: PMC11026337 DOI: 10.1007/s13534-024-00365-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 02/12/2024] [Accepted: 02/18/2024] [Indexed: 04/23/2024] Open
Abstract
Super-resolution (SR) techniques have revolutionized the field of biomedical applications by detailing the structures at resolutions beyond the limits of imaging or measuring tools. These techniques have been applied in various biomedical applications, including microscopy, magnetic resonance imaging (MRI), computed tomography (CT), X-ray, electroencephalogram (EEG), ultrasound, etc. SR methods are categorized into two main types: traditional non-learning-based methods and modern learning-based approaches. In both applications, SR methodologies have been effectively utilized on biomedical images, enhancing the visualization of complex biological structures. Additionally, these methods have been employed on biomedical data, leading to improvements in computational precision and efficiency for biomedical simulations. The use of SR techniques has resulted in more detailed and accurate analyses in diagnostics and research, essential for early disease detection and treatment planning. However, challenges such as computational demands, data interpretation complexities, and the lack of unified high-quality data persist. The article emphasizes these issues, underscoring the need for ongoing development in SR technologies to further improve biomedical research and patient care outcomes.
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Affiliation(s)
- Minwoo Shin
- School of Mathematics and Computing (Computational Science and Engineering), Yonsei University, 50 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722 Republic of Korea
| | - Minjee Seo
- School of Mathematics and Computing (Computational Science and Engineering), Yonsei University, 50 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722 Republic of Korea
| | - Kyunghyun Lee
- School of Mathematics and Computing (Computational Science and Engineering), Yonsei University, 50 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722 Republic of Korea
| | - Kyungho Yoon
- School of Mathematics and Computing (Computational Science and Engineering), Yonsei University, 50 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722 Republic of Korea
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16
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Berchio C, Kumar SS, Micali N. EEG Spatial-temporal Dynamics of Resting-state Activity in Young Women with Anorexia Nervosa: Preliminary Evidence. Brain Topogr 2024; 37:447-460. [PMID: 37615798 DOI: 10.1007/s10548-023-01001-7] [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: 05/12/2023] [Accepted: 08/14/2023] [Indexed: 08/25/2023]
Abstract
The aim of this study was to provide preliminary evidence on temporal dynamics of resting-state brain networks in youth with anorexia nervosa (AN) using electroencephalography (EEG). Resting-state EEG data were collected in 18 young women with AN and 18 healthy controls (HC). Between-group differences in brain networks were assessed using microstates analyses. Five microstates were identified across all subjects (A, B, C, D, E). Using a single set of maps representative of the whole dataset, group differences were identified for microstates A, C, and E. A common-for-all template revealed a relatively high degree of consistency in results for reduced time coverage of microstate C, but also an increased presence of microstate class E. AN and HC had different microstate transition probabilities, largely involving microstate A. Using LORETA, for microstate D, we found that those with AN had augmented activations in the left frontal inferior operculum, left insula, and bilateral paracentral lobule, compared with HC. For microstate E, AN had augmented activations in the para-hippocampal gyrus, caudate, pallidum, cerebellum, and cerebellar vermis. Our findings suggest altered microstates in young women with AN associated with integration of sensory and bodily signals, monitoring of internal/external mental states, and self-referential processes. Future research should examine how EEG-derived microstates could be applied to develop diagnostic and prognostic models of AN.
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Affiliation(s)
- Cristina Berchio
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Piazza Giulio Cesare, 11, 70121, Bari, Italy.
| | - Samika S Kumar
- Department of Psychology, University of Cambridge, Cambridge, UK
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA
| | - Nadia Micali
- Great Ormond Street Institute of Child Health, University College London, London, UK
- Mental Health Services in the Capital Region of Denmark, Eating Disorders Research Unit, Psychiatric Centre Ballerup, Ballerup, Denmark
- Institute of biological Psychiatry, Psykiatrisk Center Sct. Hans, Region Hovedstaden, Denmark
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17
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Takarae Y, Zanesco A, Erickson CA, Pedapati EV. EEG Microstates as Markers for Cognitive Impairments in Fragile X Syndrome. Brain Topogr 2024; 37:432-446. [PMID: 37751055 DOI: 10.1007/s10548-023-01009-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 09/12/2023] [Indexed: 09/27/2023]
Abstract
Fragile X syndrome (FXS) is one of the most common inherited causes of intellectual disabilities. While there is currently no cure for FXS, EEG is considered an important method to investigate the pathophysiology and evaluate behavioral and cognitive treatments. We conducted EEG microstate analysis to investigate resting brain dynamics in FXS participants. Resting-state recordings from 70 FXS participants and 71 chronological age-matched typically developing control (TDC) participants were used to derive microstates via modified k-means clustering. The occurrence, mean global field power (GFP), and global explained variance (GEV) of microstate C were significantly higher in the FXS group compared to the TDC group. The mean GFP was significantly negatively correlated with non-verbal IQ (NVIQ) in the FXS group, where lower NVIQ scores were associated with greater GFP. In addition, the occurrence, mean duration, mean GFP, and GEV of microstate D were significantly greater in the FXS group than the TDC group. The mean GFP and occurrence of microstate D were also correlated with individual alpha frequencies in the FXS group, where lower IAF frequencies accompanied greater microstate GFP and occurrence. Alterations in microstates C and D may be related to the two well-established cognitive characteristics of FXS, intellectual disabilities and attention impairments, suggesting that microstate parameters could serve as markers to study cognitive impairments and evaluate treatment outcomes in this population. Slowing of the alpha peak frequency and its correlation to microstate D parameters may suggest changes in thalamocortical dynamics in FXS, which could be specifically related to attention control. (250 words).
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Affiliation(s)
- Yukari Takarae
- Department of Psychiatry and Behavioral Sciences, University of California, Davis, Sacramento, CA, USA.
- M.I.N.D. Institute, University of California, Davis, Sacramento, CA, USA.
| | - Anthony Zanesco
- Department of Psychology, University of Miami, Coral Gables, FL, USA
| | - Craig A Erickson
- Division of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Psychiatry, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Ernest V Pedapati
- Division of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Psychiatry, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
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18
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Cambi S, Solcà M, Micali N, Berchio C. Cardiac interoception in Anorexia Nervosa: A resting-state heartbeat-evoked potential study. EUROPEAN EATING DISORDERS REVIEW 2024; 32:417-430. [PMID: 38009624 DOI: 10.1002/erv.3049] [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: 08/01/2023] [Revised: 11/01/2023] [Accepted: 11/03/2023] [Indexed: 11/29/2023]
Abstract
OBJECTIVE A deficit in interoception - the ability to perceive, interpret and integrate afferent signals about the physiological state of the body - has been shown in Anorexia Nervosa (AN), and linked to altered hunger sensations, body dysmorphia, and abnormal emotional awareness. The present high-density electroencephalography (hdEEG) study aims to assess cardiac interoception in AN and to investigate its neural correlates, using an objective neurophysiological measure. METHOD Heartbeat-evoked potentials (HEPs) were computed from 5 min of resting-state EEG and electrocardiogram (ECG) data and compared between individuals with AN (N = 22) and healthy controls (HC) (N = 19) with waveform, topographic, and source imaging analyses. RESULTS Differences in the cortical representation of heartbeats were present between AN and HC at a time window of 332-348 ms after the ECG R-peak. Source imaging analyses revealed a right-sided hypoactivation in AN of brain regions linked to interoceptive processing, such as the anterior cingulate and orbitofrontal areas. CONCLUSIONS To the best of our knowledge, this is the first study using hdEEG to localise the underlying sources of HEPs in AN. Results point to altered interoceptive processing during resting-state in AN. As our participants had a short duration of illness, this might not be the consequence of prolonged starvation. Interventions targeted at interoception could provide an additional tool to facilitate recovery.
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Affiliation(s)
- Susanne Cambi
- Department of Psychiatry, Geneva University Hospital, Geneva, Switzerland
| | - Marco Solcà
- Department of Psychiatry, Geneva University Hospital, Geneva, Switzerland
| | - Nadia Micali
- Mental Health Services of the Capital Region of Denmark, Center for Eating and Feeding Disorders Research, Psychiatric Centre Ballerup, Ballerup, Denmark
- University College London, Great Ormond Street Institute of Child Health, London, UK
| | - Cristina Berchio
- Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
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19
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Murphy M, Carrión RE, Rubio J, Malhotra AK. Peak alpha frequency and electroencephalographic microstates are correlated with aggression in schizophrenia. J Psychiatr Res 2024; 175:60-67. [PMID: 38704982 DOI: 10.1016/j.jpsychires.2024.04.051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 03/28/2024] [Accepted: 04/25/2024] [Indexed: 05/07/2024]
Abstract
Large scale retrospective studies have shown an association between schizophrenia and risk of violence. Overall, this increase in risk is small and does not justify or support stigmatizing public perceptions or media depictions of people with schizophrenia. Nonetheless, in some situations, some symptoms of schizophrenia can increase the risk of violent behavior. Prediction of this behavior would allow high impact preventive interventions. However, to date the neurobiological correlates of violent behavior in schizophrenia are not well understood, precluding the development of prognostic biomarkers. We used electroencephalography to measure alpha activity and microstates from 31 patients with schizophrenia and 18 age matched controls. Participants also completed multiple assessments of current aggressive tendencies and their lifetime history of aggressive acts. We found that individual alpha peak frequency was negatively correlated with aggression scores in both patients and controls (largest Spearman's r = -0.45). Furthermore, this result could be replicated in data taken from a single frontal channel suggesting that this may be possible to obtain in routine clinical settings (largest Spearman's r = -0.40). We also found that transitions between microstates corresponding to auditory and visual networks were inversely correlated with aggression scores. Finally, we found that, within patients, aggression was correlated with the degree of randomness between microstate transitions. This suggests that aggression is related to inappropriate switching between large scale brain networks and subsequent failure to appropriately integrate complicated environmental and internal stimuli. By elucidating some of the electrophysiological correlates of aggression, these data facilitate the development of prognostic biomarkers.
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Affiliation(s)
- Michael Murphy
- McLean Hospital, Belmont, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Ricardo E Carrión
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA; Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA; Northwell, New Hyde Park, NY, USA
| | - Jose Rubio
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA; Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA; Northwell, New Hyde Park, NY, USA
| | - Anil K Malhotra
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA; Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA; Northwell, New Hyde Park, NY, USA
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20
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Lee DA, Jang T, Kang J, Park S, Park KM. Functional Connectivity Alterations in Patients with Post-stroke Epilepsy Based on Source-level EEG and Graph Theory. Brain Topogr 2024:10.1007/s10548-024-01048-0. [PMID: 38625521 DOI: 10.1007/s10548-024-01048-0] [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: 01/03/2023] [Accepted: 03/28/2024] [Indexed: 04/17/2024]
Abstract
We investigated the differences in functional connectivity based on the source-level electroencephalography (EEG) analysis between stroke patients with and without post-stroke epilepsy (PSE). Thirty stroke patients with PSE and 35 stroke patients without PSE were enrolled. EEG was conducted during a resting state period. We used a Brainstorm program for source estimation and the connectivity matrix. Data were processed according to EEG frequency bands. We used a BRAPH program to apply a graph theoretical analysis. In the beta band, radius and diameter were increased in patients with PSE than in those without PSE (2.699 vs. 2.579, adjusted p = 0.03; 2.261 vs. 2.171, adjusted p = 0.03). In the low gamma band, radius was increased in patients with PSE than in those without PSE (2.808 vs. 2.617, adjusted p = 0.03). In the high gamma band, the radius, diameter, average eccentricity, and characteristic path length were increased (1.828 vs. 1.559, adjusted p < 0.01; 2.653 vs. 2.306, adjusted p = 0.01; 2.212 vs. 1.913, adjusted p < 0.01; 1.425 vs. 1.286, adjusted p = 0.01), whereas average strength, mean clustering coefficient, and transitivity were decreased in patients with PSE than in those without PSE (49.955 vs. 55.055, adjusted p < 0.01; 0.727 vs. 0.810, adjusted p < 0.01; 1.091 vs. 1.215, adjusted p < 0.01). However, in the delta, theta, and alpha bands, none of the functional connectivity measures were different between groups. We demonstrated significant alterations of functional connectivity in patients with PSE, who have decreased segregation and integration in brain network, compared to those without PSE.
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Affiliation(s)
- Dong Ah Lee
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan, 48108, Korea
| | - Taeik Jang
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan, 48108, Korea
| | - Jaeho Kang
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan, 48108, Korea
| | - Seongho Park
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan, 48108, Korea
| | - Kang Min Park
- Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Haeundae-ro 875, Haeundae-gu, Busan, 48108, Korea.
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21
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Frank LR, Galinsky VL, Krigolson O, Tapert SF, Bickel S, Martinez A. Imaging of brain electric field networks. RESEARCH SQUARE 2024:rs.3.rs-2432269. [PMID: 38659785 PMCID: PMC11042417 DOI: 10.21203/rs.3.rs-2432269/v2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
We present a method for direct imaging of the electric field networks in the human brain from electroencephalography (EEG) data with much higher temporal and spatial resolution than functional MRI (fMRI), without the concomitant distortions. The method is validated using simultaneous EEG/fMRI data in healthy subjects, intracranial EEG data in epilepsy patients, and in a direct comparison with standard EEG analysis in a well-established attention paradigm. The method is then demonstrated on a very large cohort of subjects performing a standard gambling task designed to activate the brain's 'reward circuit'. The technique uses the output from standard EEG systems and thus has potential for immediate benefit to a broad range of important basic scientific and clinical questions concerning brain electrical activity, but also provides an inexpensive and portable alternative to function MRI (fMRI).
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Affiliation(s)
- Lawrence R. Frank
- Center for Scientific Computation in Imaging, UC San Diego, La Jolla, CA, USA
- 7Center for Functional MRI, UC San Diego, La Jolla, CA, USA
| | - Vitaly L. Galinsky
- Center for Scientific Computation in Imaging, UC San Diego, La Jolla, CA, USA
| | - Olave Krigolson
- Centre for Biomedical Research, University of Victoria, Victoria, BC, Canada
| | | | - Stephan Bickel
- Nathan Kline Institute, Orangeburg, NY, USA
- The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
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22
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Proverbio AM, Cesati F. Neural correlates of recalled sadness, joy, and fear states: a source reconstruction EEG study. Front Psychiatry 2024; 15:1357770. [PMID: 38638416 PMCID: PMC11024723 DOI: 10.3389/fpsyt.2024.1357770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 03/18/2024] [Indexed: 04/20/2024] Open
Abstract
Introduction The capacity to understand the others' emotional states, particularly if negative (e.g. sadness or fear), underpins the empathic and social brain. Patients who cannot express their emotional states experience social isolation and loneliness, exacerbating distress. We investigated the feasibility of detecting non-invasive scalp-recorded electrophysiological signals that correspond to recalled emotional states of sadness, fear, and joy for potential classification. Methods The neural activation patterns of 20 healthy and right-handed participants were studied using an electrophysiological technique. Analyses were focused on the N400 component of Event-related potentials (ERPs) recorded during silent recall of subjective emotional states; Standardized weighted Low-resolution Electro-magnetic Tomography (swLORETA) was employed for source reconstruction. The study classified individual patterns of brain activation linked to the recollection of three distinct emotional states into seven regions of interest (ROIs). Results Statistical analysis (ANOVA) of the individual magnitude values revealed the existence of a common emotional circuit, as well as distinct brain areas that were specifically active during recalled sad, happy and fearful states. In particular, the right temporal and left superior frontal areas were more active for sadness, the left limbic region for fear, and the right orbitofrontal cortex for happy affective states. Discussion In conclusion, this study successfully demonstrated the feasibility of detecting scalp-recorded electrophysiological signals corresponding to internal and subjective affective states. These findings contribute to our understanding of the emotional brain, and have potential applications for future BCI classification and identification of emotional states in LIS patients who may be unable to express their emotions, thus helping to alleviate social isolation and sense of loneliness.
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Affiliation(s)
- Alice Mado Proverbio
- Cognitive Electrophysiology Lab, Department of Psychology, University of Milano-Bicocca, Milan, Italy
- NEURO-MI Milan Center for Neuroscience, Milan, Italy
| | - Federico Cesati
- Cognitive Electrophysiology Lab, Department of Psychology, University of Milano-Bicocca, Milan, Italy
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23
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Wang YP, Li L, Jin H, Chen Y, Jiang Y, Liu WX, Xue YX, Huang L, Wang DJ. Relative band power in assessing temporary neurological dysfunction post- type A aortic dissection surgery: a prospective study. Sci Rep 2024; 14:7845. [PMID: 38570622 PMCID: PMC10991486 DOI: 10.1038/s41598-024-58557-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: 07/22/2023] [Accepted: 04/01/2024] [Indexed: 04/05/2024] Open
Abstract
Temporary neurological dysfunction (TND), a common complication following surgical repair of Type A Aortic Dissection (TAAD), is closely associated with increased mortality and long-term cognitive impairment. Currently, effective treatment options for TND remain elusive. Therefore, we sought to investigate the potential of postoperative relative band power (RBP) in predicting the occurrence of postoperative TND, with the aim of identifying high-risk patients prior to the onset of TND. We conducted a prospective observational study between February and December 2022, involving 165 patients who underwent surgical repair for TAAD at our institution. Bedside Quantitative electroencephalography (QEEG) was utilized to monitor the post-operative brain electrical activity of each participant, recording changes in RBP (RBP Delta, RBP Theta, RBP Beta and RBP Alpha), and analyzing their correlation with TND. Univariate and multivariate analyses were employed to identify independent risk factors for TND. Subsequently, line graphs were generated to estimate the incidence of TND. The primary outcome of interest was the development of TND, while secondary outcomes included intensive care unit (ICU) admission and length of hospital stay. A total of 165 patients were included in the study, among whom 68 (41.2%) experienced TND. To further investigate the independent risk factors for postoperative TND, we conducted both univariate and multivariate logistic regression analyses on all variables. In the univariate regression analysis, we identified age (Odds Ratio [OR], 1.025; 95% CI, 1.002-1.049), age ≥ 60 years (OR, 2.588; 95% CI, 1.250-5.475), hemopericardium (OR, 2.767; 95% CI, 1.150-7.009), cardiopulmonary bypass (CPB) (OR, 1.007; 95% CI, 1.001-1.014), RBP Delta (OR, 1.047; 95% CI, 1.020-1.077), RBP Alpha (OR, 0.853; 95% CI, 0.794-0.907), and Beta (OR, 0.755; 95% CI, 0.649-0.855) as independent risk factors for postoperative TND. Further multivariate regression analyses, we discovered that CPB time ≥ 180 min (OR, 1.021; 95% CI, 1.011-1.032), RBP Delta (OR, 1.168; 95% CI, 1.105-1.245), and RBP Theta (OR, 1.227; 95% CI, 1.135-1.342) emerged as independent risk factors. TND patients had significantly longer ICU stays (p < 0.001), and hospital stays (p = 0.002). We obtained the simplest predictive model for TND, consisting of three variables (CPB time ≥ 180 min, RBP Delta, RBP Theta, upon which we constructed column charts. The areas under the receiver operating characteristic (AUROC) were 0.821 (0.755, 0.887). Our study demonstrates that postoperative RBP monitoring can detect changes in brain function in patients with TAAD during the perioperative period, providing clinicians with an effective predictive method that can help improve postoperative TND in TAAD patients. These findings have important implications for improving clinical care in this population.Trial registration ChiCTR2200055980. Registered 30th Jan. 2022. This trial was registered before the first participant was enrolled.
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Affiliation(s)
- Ya-Peng Wang
- Department of Cardio-Thoracic Surgery, Nanjing Drum Tower Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Li Li
- Department of Cardio-Thoracic Surgery, Nanjing Drum Tower Hospital, The Afliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Hua Jin
- Department of Cardio-Thoracic Surgery, Nanjing Drum Tower Hospital, The Afliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Yang Chen
- Department of Cardio-Thoracic Surgery, Nanjing Drum Tower Hospital, The Afliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Yi Jiang
- Department of Cardio-Thoracic Surgery, Nanjing Drum Tower Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Wen-Xue Liu
- Department of Cardio-Thoracic Surgery, Nanjing Drum Tower Hospital, The Afliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Yun-Xing Xue
- Department of Cardio-Thoracic Surgery, Nanjing Drum Tower Hospital, The Afliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Li Huang
- Department of Critical Care Medicine, Xiangya Hospital of Central South University, Changsha, 410008, Hunan, People's Republic of China.
| | - Dong-Jin Wang
- Department of Cardio-Thoracic Surgery, Nanjing Drum Tower Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
- Department of Cardio-Thoracic Surgery, Nanjing Drum Tower Hospital, The Afliated Hospital of Nanjing University Medical School, Nanjing, China.
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24
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Silva Alves A, Rigoni I, Mégevand P, Lagarde S, Picard F, Seeck M, Vulliémoz S, Roehri N. High-density electroencephalographic functional networks in genetic generalized epilepsy: Preserved whole-brain topology hides local reorganization. Epilepsia 2024; 65:961-973. [PMID: 38306118 DOI: 10.1111/epi.17903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 01/15/2024] [Accepted: 01/16/2024] [Indexed: 02/03/2024]
Abstract
OBJECTIVE Genetic generalized epilepsy (GGE) accounts for approximately 20% of adult epilepsy cases and is considered a disorder of large brain networks, involving both hemispheres. Most studies have not shown any difference in functional whole-brain network topology when compared to healthy controls. Our objective was to examine whether this preserved global network topology could hide local reorganizations that balance out at the global network level. METHODS We recorded high-density electroencephalograms from 20 patients and 20 controls, and reconstructed the activity of 118 regions. We computed functional connectivity in windows free of interictal epileptiform discharges in broad, delta, theta, alpha, and beta frequency bands, characterized the network topology, and used the Hub Disruption Index (HDI) to quantify the topological reorganization. We examined the generalizability of our results by reproducing a 25-electrode clinical system. RESULTS Our study did not reveal any significant change in whole-brain network topology among GGE patients. However, the HDI was significantly different between patients and controls in all frequency bands except alpha (p < .01, false discovery rate [FDR] corrected, d < -1), and accompanied by an increase in connectivity in the prefrontal regions and default mode network. This reorganization suggests that regions that are important in transferring the information in controls were less so in patients. Inversely, the crucial regions in patients are less so in controls. These findings were also found in delta and theta frequency bands when using 25 electrodes (p < .001, FDR corrected, d < -1). SIGNIFICANCE In GGE patients, the overall network topology is similar to that of healthy controls but presents a balanced local topological reorganization. This reorganization causes the prefrontal areas and default mode network to be more integrated and segregated, which may explain executive impairment associated with GGE. Additionally, the reorganization distinguishes patients from controls even when using 25 electrodes, suggesting its potential use as a diagnostic tool.
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Affiliation(s)
- André Silva Alves
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Isotta Rigoni
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Pierre Mégevand
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Stanislas Lagarde
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Aix Marseille University, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Fabienne Picard
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Margitta Seeck
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Serge Vulliémoz
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Nicolas Roehri
- EEG and Epilepsy Unit, University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
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25
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Rué‐Queralt J, Fluhr H, Tourbier S, Aleman‐Gómez Y, Pascucci D, Yerly J, Glomb K, Plomp G, Hagmann P. Connectome spectrum electromagnetic tomography: A method to reconstruct electrical brain source networks at high-spatial resolution. Hum Brain Mapp 2024; 45:e26638. [PMID: 38520365 PMCID: PMC10960556 DOI: 10.1002/hbm.26638] [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: 03/24/2023] [Revised: 02/01/2024] [Accepted: 02/13/2024] [Indexed: 03/25/2024] Open
Abstract
Connectome spectrum electromagnetic tomography (CSET) combines diffusion MRI-derived structural connectivity data with well-established graph signal processing tools to solve the M/EEG inverse problem. Using simulated EEG signals from fMRI responses, and two EEG datasets on visual-evoked potentials, we provide evidence supporting that (i) CSET captures realistic neurophysiological patterns with better accuracy than state-of-the-art methods, (ii) CSET can reconstruct brain responses more accurately and with more robustness to intrinsic noise in the EEG signal. These results demonstrate that CSET offers high spatio-temporal accuracy, enabling neuroscientists to extend their research beyond the current limitations of low sampling frequency in functional MRI and the poor spatial resolution of M/EEG.
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Affiliation(s)
- Joan Rué‐Queralt
- Department of RadiologyLausanne University Hospital and University of Lausanne (CHUV‐UNIL)LausanneSwitzerland
- Department of PsychologyUniversity of FribourgFribourgSwitzerland
- Center for ImagingEPFLLausanneSwitzerland
| | - Hugo Fluhr
- Department of RadiologyLausanne University Hospital and University of Lausanne (CHUV‐UNIL)LausanneSwitzerland
| | - Sebastien Tourbier
- Department of RadiologyLausanne University Hospital and University of Lausanne (CHUV‐UNIL)LausanneSwitzerland
| | - Yasser Aleman‐Gómez
- Department of RadiologyLausanne University Hospital and University of Lausanne (CHUV‐UNIL)LausanneSwitzerland
- Department of PsychiatryLausanne University HospitalLausanneSwitzerland
| | | | - Jérôme Yerly
- Department of Diagnostic and Interventional RadiologyLausanne University HospitalLausanneSwitzerland
- Center for Biomedical ImagingEPFLLausanneSwitzerland
| | - Katharina Glomb
- Department of NeurologyCharité University Medicine Berlin and Berlin Institute of HealthBerlinGermany
| | - Gijs Plomp
- Department of PsychologyUniversity of FribourgFribourgSwitzerland
| | - Patric Hagmann
- Department of RadiologyLausanne University Hospital and University of Lausanne (CHUV‐UNIL)LausanneSwitzerland
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26
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Schreiner L, Jordan M, Sieghartsleitner S, Kapeller C, Pretl H, Kamada K, Asman P, Ince NF, Miller KJ, Guger C. Mapping of the central sulcus using non-invasive ultra-high-density brain recordings. Sci Rep 2024; 14:6527. [PMID: 38499709 PMCID: PMC10948849 DOI: 10.1038/s41598-024-57167-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: 12/06/2023] [Accepted: 03/14/2024] [Indexed: 03/20/2024] Open
Abstract
Brain mapping is vital in understanding the brain's functional organization. Electroencephalography (EEG) is one of the most widely used brain mapping approaches, primarily because it is non-invasive, inexpensive, straightforward, and effective. Increasing the electrode density in EEG systems provides more neural information and can thereby enable more detailed and nuanced mapping procedures. Here, we show that the central sulcus can be clearly delineated using a novel ultra-high-density EEG system (uHD EEG) and somatosensory evoked potentials (SSEPs). This uHD EEG records from 256 channels with an inter-electrode distance of 8.6 mm and an electrode diameter of 5.9 mm. Reconstructed head models were generated from T1-weighted MRI scans, and electrode positions were co-registered to these models to create topographical plots of brain activity. EEG data were first analyzed with peak detection methods and then classified using unsupervised spectral clustering. Our topography plots of the spatial distribution from the SSEPs clearly delineate a division between channels above the somatosensory and motor cortex, thereby localizing the central sulcus. Individual EEG channels could be correctly classified as anterior or posterior to the central sulcus with 95.2% accuracy, which is comparable to accuracies from invasive intracranial recordings. Our findings demonstrate that uHD EEG can resolve the electrophysiological signatures of functional representation in the brain at a level previously only seen from surgically implanted electrodes. This novel approach could benefit numerous applications, including research, neurosurgical mapping, clinical monitoring, detection of conscious function, brain-computer interfacing (BCI), rehabilitation, and mental health.
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Affiliation(s)
- Leonhard Schreiner
- g.Tec Medical Engineering GmbH, Schiedlberg, Austria.
- Institute for Integrated Circuits, Johannes Kepler University, Linz, Austria.
| | | | - Sebastian Sieghartsleitner
- g.Tec Medical Engineering GmbH, Schiedlberg, Austria
- Institute of Computational Perception, Johannes Kepler University, Linz, Austria
| | | | - Harald Pretl
- Institute for Integrated Circuits, Johannes Kepler University, Linz, Austria
| | | | - Priscella Asman
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA
| | - Nuri F Ince
- Department of Biomedical Engineering, University of Houston, Houston, TX, USA
| | - Kai J Miller
- Department of Neurosurgery, Mayo Clinic, Rochester, USA
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27
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Yang S, Jiao M, Xiang J, Fotedar N, Sun H, Liu F. Rejuvenating classical brain electrophysiology source localization methods with spatial graph Fourier filters for source extents estimation. Brain Inform 2024; 11:8. [PMID: 38472438 PMCID: PMC10933195 DOI: 10.1186/s40708-024-00221-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 02/25/2024] [Indexed: 03/14/2024] Open
Abstract
EEG/MEG source imaging (ESI) aims to find the underlying brain sources to explain the observed EEG or MEG measurement. Multiple classical approaches have been proposed to solve the ESI problem based on different neurophysiological assumptions. To support clinical decision-making, it is important to estimate not only the exact location of the source signal but also the extended source activation regions. Existing methods may render over-diffuse or sparse solutions, which limit the source extent estimation accuracy. In this work, we leverage the graph structures defined in the 3D mesh of the brain and the spatial graph Fourier transform (GFT) to decompose the spatial graph structure into sub-spaces of low-, medium-, and high-frequency basis. We propose to use the low-frequency basis of spatial graph filters to approximate the extended areas of brain activation and embed the GFT into the classical ESI methods. We validated the classical source localization methods with the corresponding improved version using GFT in both synthetic data and real data. We found the proposed method can effectively reconstruct focal source patterns and significantly improve the performance compared to the classical algorithms.
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Affiliation(s)
- Shihao Yang
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, 07030, USA
| | - Meng Jiao
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, 07030, USA
| | - Jing Xiang
- MEG Center, Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
| | - Neel Fotedar
- Epilepsy Center, Neurological Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USA
- Department of Neurology, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA
| | - Hai Sun
- Department of Neurosurgery, Rutgers Robert Wood Johnson Medical School of Rutgers University, Brunswick, NJ, 08901, USA
| | - Feng Liu
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, 07030, USA.
- Semcer Center for Healthcare Innovation, Stevens Institute of Technology, Hoboken, NJ, 07030, USA.
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28
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Filosa M, De Rossi E, Carbone GA, Farina B, Massullo C, Panno A, Adenzato M, Ardito RB, Imperatori C. Altered connectivity between the central executive network and the salience network in delusion-prone individuals: A resting state eLORETA report. Neurosci Lett 2024; 825:137686. [PMID: 38364996 DOI: 10.1016/j.neulet.2024.137686] [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: 10/30/2023] [Revised: 01/30/2024] [Accepted: 02/09/2024] [Indexed: 02/18/2024]
Abstract
Although the Triple Network (TN) model has been proposed as a valid neurophysiological framework for conceptualizing delusion-like experiences, the neurodynamics of TN in relation to delusion proneness have been relatively understudied in nonclinical samples so far. Therefore, the main aim of the current study was to investigate the functional connectivity of resting state electroencephalography (EEG) in subjects with high levels of delusion proneness. Twenty-one delusion-prone (DP) individuals and thirty-seven non-delusion prone (N-DP) individuals were included in the study. The exact Low-Resolution Electromagnetic Tomography (eLORETA) software was used for all EEG analyses. Compared to N-DP participants, DP individuals showed an increas of theta connectivity (T = 3.618; p = 0.045) between the Salience Network (i.e., the left anterior insula) and the Central Executive Network (i.e., the left posterior parietal cortex). Increased theta connectivity was also positively correlated with the frequency of delusional experiences (rho = 0.317; p = 0.015). Our results suggest that increased theta connectivity between the Salience Network and the Central Executive Network may underline brain correlates of altered resting state salience detection, information processing, and cognitive control processes typical of delusional thinking.
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Affiliation(s)
- Margherita Filosa
- Experimental and Applied Psychology Laboratory, Department of Human Sciences, European University of Rome, Italy
| | - Elena De Rossi
- Experimental and Applied Psychology Laboratory, Department of Human Sciences, European University of Rome, Italy
| | | | - Benedetto Farina
- Experimental and Applied Psychology Laboratory, Department of Human Sciences, European University of Rome, Italy
| | - Chiara Massullo
- Experimental Psychology Laboratory, Department of Education, Roma Tre University, Italy
| | - Angelo Panno
- Experimental and Applied Psychology Laboratory, Department of Human Sciences, European University of Rome, Italy
| | | | - Rita B Ardito
- Department of Psychology, University of Turin, Italy
| | - Claudio Imperatori
- Experimental and Applied Psychology Laboratory, Department of Human Sciences, European University of Rome, Italy
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29
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Bagdasarov A, Brunet D, Michel CM, Gaffrey MS. Microstate Analysis of Continuous Infant EEG: Tutorial and Reliability. Brain Topogr 2024:10.1007/s10548-024-01043-5. [PMID: 38430283 DOI: 10.1007/s10548-024-01043-5] [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: 07/12/2023] [Accepted: 02/16/2024] [Indexed: 03/03/2024]
Abstract
Microstate analysis of resting-state EEG is a unique data-driven method for identifying patterns of scalp potential topographies, or microstates, that reflect stable but transient periods of synchronized neural activity evolving dynamically over time. During infancy - a critical period of rapid brain development and plasticity - microstate analysis offers a unique opportunity for characterizing the spatial and temporal dynamics of brain activity. However, whether measurements derived from this approach (e.g., temporal properties, transition probabilities, neural sources) show strong psychometric properties (i.e., reliability) during infancy is unknown and key information for advancing our understanding of how microstates are shaped by early life experiences and whether they relate to individual differences in infant abilities. A lack of methodological resources for performing microstate analysis of infant EEG has further hindered adoption of this cutting-edge approach by infant researchers. As a result, in the current study, we systematically addressed these knowledge gaps and report that most microstate-based measurements of brain organization and functioning except for transition probabilities were stable with four minutes of video-watching resting-state data and highly internally consistent with just one minute. In addition to these results, we provide a step-by-step tutorial, accompanying website, and open-access data for performing microstate analysis using a free, user-friendly software called Cartool. Taken together, the current study supports the reliability and feasibility of using EEG microstate analysis to study infant brain development and increases the accessibility of this approach for the field of developmental neuroscience.
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Affiliation(s)
- Armen Bagdasarov
- Department of Psychology & Neuroscience, Duke University, Reuben-Cooke Building, 417 Chapel Drive, Durham, NC, 27708, USA.
| | - Denis Brunet
- Department of Basic Neurosciences, University of Geneva, Campus Biotech, 9 Chemin des Mines, Geneva, 1202, Switzerland
- Center for Biomedical Imaging (CIBM) Lausanne, EPFL AVP CP CIBM Station 6, Lausanne, 1015, Switzerland
| | - Christoph M Michel
- Department of Basic Neurosciences, University of Geneva, Campus Biotech, 9 Chemin des Mines, Geneva, 1202, Switzerland
- Center for Biomedical Imaging (CIBM) Lausanne, EPFL AVP CP CIBM Station 6, Lausanne, 1015, Switzerland
| | - Michael S Gaffrey
- Department of Psychology & Neuroscience, Duke University, Reuben-Cooke Building, 417 Chapel Drive, Durham, NC, 27708, USA
- Children's Wisconsin, 9000 W. Wisconsin Avenue, Milwaukee, WI, 53226, USA
- Medical College of Wisconsin, Division of Pediatric Psychology and Developmental Medicine, Department of Pediatrics, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA
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30
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Chang YJ, Chen YI, Yeh HC, Santacruz SR. Neurobiologically realistic neural network enables cross-scale modeling of neural dynamics. Sci Rep 2024; 14:5145. [PMID: 38429297 PMCID: PMC10907713 DOI: 10.1038/s41598-024-54593-w] [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: 08/02/2023] [Accepted: 02/14/2024] [Indexed: 03/03/2024] Open
Abstract
Fundamental principles underlying computation in multi-scale brain networks illustrate how multiple brain areas and their coordinated activity give rise to complex cognitive functions. Whereas brain activity has been studied at the micro- to meso-scale to reveal the connections between the dynamical patterns and the behaviors, investigations of neural population dynamics are mainly limited to single-scale analysis. Our goal is to develop a cross-scale dynamical model for the collective activity of neuronal populations. Here we introduce a bio-inspired deep learning approach, termed NeuroBondGraph Network (NBGNet), to capture cross-scale dynamics that can infer and map the neural data from multiple scales. Our model not only exhibits more than an 11-fold improvement in reconstruction accuracy, but also predicts synchronous neural activity and preserves correlated low-dimensional latent dynamics. We also show that the NBGNet robustly predicts held-out data across a long time scale (2 weeks) without retraining. We further validate the effective connectivity defined from our model by demonstrating that neural connectivity during motor behaviour agrees with the established neuroanatomical hierarchy of motor control in the literature. The NBGNet approach opens the door to revealing a comprehensive understanding of brain computation, where network mechanisms of multi-scale activity are critical.
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Affiliation(s)
- Yin-Jui Chang
- Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Yuan-I Chen
- Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
| | - Hsin-Chih Yeh
- Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA
- Texas Materials Institute, The University of Texas at Austin, Austin, TX, USA
| | - Samantha R Santacruz
- Biomedical Engineering, The University of Texas at Austin, Austin, TX, USA.
- Institute for Neuroscience, The University of Texas at Austin, Austin, TX, USA.
- Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA.
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31
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Hirata A, Niitsu M, Phang CR, Kodera S, Kida T, Rashed EA, Fukunaga M, Sadato N, Wasaka T. High-resolution EEG source localization in personalized segmentation-free head model with multi-dipole fitting. Phys Med Biol 2024; 69:055013. [PMID: 38306964 DOI: 10.1088/1361-6560/ad25c3] [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: 08/30/2023] [Accepted: 02/02/2024] [Indexed: 02/04/2024]
Abstract
Objective. Electroencephalograms (EEGs) are often used to monitor brain activity. Several source localization methods have been proposed to estimate the location of brain activity corresponding to EEG readings. However, only a few studies evaluated source localization accuracy from measured EEG using personalized head models in a millimeter resolution. In this study, based on a volume conductor analysis of a high-resolution personalized human head model constructed from magnetic resonance images, a finite difference method was used to solve the forward problem and to reconstruct the field distribution.Approach. We used a personalized segmentation-free head model developed using machine learning techniques, in which the abrupt change of electrical conductivity occurred at the tissue interface is suppressed. Using this model, a smooth field distribution was obtained to address the forward problem. Next, multi-dipole fitting was conducted using EEG measurements for each subject (N= 10 male subjects, age: 22.5 ± 0.5), and the source location and electric field distribution were estimated.Main results.For measured somatosensory evoked potential for electrostimulation to the wrist, a multi-dipole model with lead field matrix computed with the volume conductor model was found to be superior than a single dipole model when using personalized segmentation-free models (6/10). The correlation coefficient between measured and estimated scalp potentials was 0.89 for segmentation-free head models and 0.71 for conventional segmented models. The proposed method is straightforward model development and comparable localization difference of the maximum electric field from the target wrist reported using fMR (i.e. 16.4 ± 5.2 mm) in previous study. For comparison, DUNEuro based on sLORETA was (EEG: 17.0 ± 4.0 mm). In addition, somatosensory evoked magnetic fields obtained by Magnetoencephalography was 25.3 ± 8.5 mm using three-layer sphere and sLORETA.Significance. For measured EEG signals, our procedures using personalized head models demonstrated that effective localization of the somatosensory cortex, which is located in a non-shallower cortex region. This method may be potentially applied for imaging brain activity located in other non-shallow regions.
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Affiliation(s)
- Akimasa Hirata
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
- Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya 466-8555, Japan
| | - Masamune Niitsu
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
| | - Chun Ren Phang
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
- Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya 466-8555, Japan
| | - Sachiko Kodera
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
- Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya 466-8555, Japan
| | - Tetsuo Kida
- Department of Functioning and Disability, Institute for Developmental Research, Aichi Developmental Disability Center, Kasugai 480-0392, Japan
| | - Essam A Rashed
- Graduate School of Information Science, University of Hyogo, Kobe 650-0047, Japan
| | - Masaki Fukunaga
- Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, Aichi 444-8585, Japan
| | - Norihiro Sadato
- Division of Cerebral Integration, National Institute for Physiological Sciences, Okazaki, Aichi 444-8585, Japan
| | - Toshiaki Wasaka
- Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Nagoya 466-8555, Japan
- Center of Biomedical Physics and Information Technology, Nagoya Institute of Technology, Nagoya 466-8555, Japan
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32
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Czarnetzki C, Spinelli L, Huppertz HJ, Schaller K, Momjian S, Lobrinus J, Vargas MI, Garibotto V, Vulliemoz S, Seeck M. Yield of non-invasive imaging in MRI-negative focal epilepsy. J Neurol 2024; 271:995-1003. [PMID: 37907727 PMCID: PMC10827933 DOI: 10.1007/s00415-023-11987-6] [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: 06/05/2023] [Revised: 09/02/2023] [Accepted: 09/04/2023] [Indexed: 11/02/2023]
Abstract
OBJECTIVE The absence of MRI-lesion reduces considerably the probability of having an excellent outcome (International League Against Epilepsies [ILAE] class I-II) after epilepsy surgery. Surgical success in magnetic-resonance imaging (MRI)-negative cases relies therefore mainly on non-invasive techniques such as positron-emission tomography (PET), subtraction ictal/inter-ictal single-photon-emission-computed-tomography co-registered to MRI (SISCOM), electric source imaging (ESI) and morphometric MRI analysis (MAP). We were interested in identifying the optimal imaging technique or combination to achieve post-operative class I-II in patients with MRI-negative focal epilepsy. METHODS We identified 168 epileptic patients without MRI lesion. Thirty-three (19.6%) were diagnosed with unifocal epilepsy, underwent surgical resection and follow-up ⩾ 2 years. Sensitivity, specificity, predictive values, and diagnostic odds ratio (OR) were calculated for each technique individually and in combination (after co-registration). RESULTS 23/33 (70%) were free of disabling seizures (75.0% with temporal and 61.5% extratemporal lobe epilepsy). None of the individual modalities presented an OR > 1.5, except ESI if only patients with interictal epileptiform discharges (IEDs) were considered (OR 3.2). On a dual combination, SISCOM with ESI presented the highest outcome (OR = 6). MAP contributed to detecting indistinguishable focal cortical dysplasia in particular in extratemporal epilepsies with a sensitivity of 75%. Concordance of PET, ESI on interictal epileptic discharges, and SISCOM was associated with the highest chance for post-operative seizure control (OR = 11). CONCLUSION If MRI is negative, the chances to benefit from epilepsy surgery are almost as high as in lesional epilepsy, provided that multiple established non-invasive imaging tools are rigorously applied and co-registered together.
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Affiliation(s)
- Christian Czarnetzki
- EEG & Epilepsy Unit, Department of Clinical Neurosciences, University Hospital and Faculty of Medicine, University of Geneva, 4, Rue Gabrielle-Perret-Gentil, 1211, Geneva, Switzerland.
| | - Laurent Spinelli
- EEG & Epilepsy Unit, Department of Clinical Neurosciences, University Hospital and Faculty of Medicine, University of Geneva, 4, Rue Gabrielle-Perret-Gentil, 1211, Geneva, Switzerland
| | | | - Karl Schaller
- Department of Clinical Neurosciences, Neurosurgery Clinic, University Hospital of Geneva, Geneva, Switzerland
| | - Shahan Momjian
- Department of Clinical Neurosciences, Neurosurgery Clinic, University Hospital of Geneva, Geneva, Switzerland
| | - Johannes Lobrinus
- Department of Clinical Pathology, Faculty of Medicine, University Hospital of Geneva, Geneva, Switzerland
| | - Maria-Isabel Vargas
- Department of Radiology, Faculty of Medicine, University Hospital of Geneva, Geneva, Switzerland
| | - Valentina Garibotto
- Department of Radiology, Faculty of Medicine, University Hospital of Geneva, Geneva, Switzerland
| | - Serge Vulliemoz
- EEG & Epilepsy Unit, Department of Clinical Neurosciences, University Hospital and Faculty of Medicine, University of Geneva, 4, Rue Gabrielle-Perret-Gentil, 1211, Geneva, Switzerland
| | - Margitta Seeck
- EEG & Epilepsy Unit, Department of Clinical Neurosciences, University Hospital and Faculty of Medicine, University of Geneva, 4, Rue Gabrielle-Perret-Gentil, 1211, Geneva, Switzerland.
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Zhang W, Kappenman ES. Maximizing signal-to-noise ratio and statistical power in ERP measurement: Single sites versus multi-site average clusters. Psychophysiology 2024; 61:e14440. [PMID: 37973199 DOI: 10.1111/psyp.14440] [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: 08/28/2022] [Revised: 05/14/2023] [Accepted: 08/18/2023] [Indexed: 11/19/2023]
Abstract
One important decision in every event-related potential (ERP) experiment is which electrode site(s) to use in quantifying the ERP component of interest. A common approach is to measure the ERP from a single electrode site, typically the site where the ERP component is largest. Alternatively, two or more electrode sites in a given spatial region are averaged together, and the ERP is measured from the resulting multi-site cluster. The goal of the present study was to systematically compare these two measurement approaches across a range of outcome measures and ERP components to determine whether measuring from a single electrode site or an average of multiple sites yields consistently better results. We examined seven common ERP components from the open-source ERP CORE dataset that span a range of neurocognitive processes: N170, mismatch negativity (MMN), N2pc, N400, P3, lateralized readiness potential (LRP), and error-related negativity (ERN). For each component, we compared ERP amplitude, noise level, signal-to-noise ratio, and effect size at two single electrode sites and four multi-site clusters. We also used a Monte Carlo approach to simulate within-participant and between-groups experiments with known effect magnitudes to compare statistical power at single sites and multi-site clusters. Overall, measuring from a multi-site cluster produced results that were as good as or better than measuring from a single electrode site across analyses and components, indicating that the cluster-based measurement approach may be beneficial in quantifying ERPs from a range of neurocognitive domains.
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Affiliation(s)
- Wendy Zhang
- Department of Psychology, San Diego State University, San Diego, California, USA
- San Diego Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California, San Diego, California, USA
| | - Emily S Kappenman
- Department of Psychology, San Diego State University, San Diego, California, USA
- San Diego Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California, San Diego, California, USA
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Avigdor T, Abdallah C, Afnan J, Cai Z, Rammal S, Grova C, Frauscher B. Consistency of electrical source imaging in presurgical evaluation of epilepsy across different vigilance states. Ann Clin Transl Neurol 2024; 11:389-403. [PMID: 38217279 PMCID: PMC10863930 DOI: 10.1002/acn3.51959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 10/24/2023] [Accepted: 11/18/2023] [Indexed: 01/15/2024] Open
Abstract
OBJECTIVE The use of electrical source imaging (ESI) in assessing the source of interictal epileptic discharges (IEDs) is gaining increasing popularity in presurgical work-up of patients with drug-resistant focal epilepsy. While vigilance affects the ability to locate IEDs and identify the epileptogenic zone, we know little about its impact on ESI. METHODS We studied overnight high-density electroencephalography recordings in focal drug-resistant epilepsy. IEDs were marked visually in each vigilance state, and examined in the sensor and source space. ESIs were calculated and compared between all vigilance states and the clinical ground truth. Two conditions were considered within each vigilance state, an unequalized and an equalized number of IEDs. RESULTS The number, amplitude, and duration of IEDs were affected by the vigilance state, with N3 sleep presenting the highest number, amplitude, and duration for both conditions (P < 0.001), while signal-to-noise ratio only differed in the unequalized condition (P < 0.001). The vigilance state did not affect channel involvement (P > 0.05). ESI maps showed no differences in distance, quality, extent, or maxima distances compared to the clinical ground truth for both conditions (P > 0.05). Only when an absolute reference (wakefulness) was used, the channel involvement (P < 0.05) and ESI source extent (P < 0.01) were impacted during rapid-eye-movement (REM) sleep. Clustering of amplitude-sensitive and -insensitive ESI maps pointed to amplitude rather than the spatial profile as the driver (P < 0.05). INTERPRETATION IED ESI results are stable across vigilance states, including REM sleep, if controlled for amplitude and IED number. ESI is thus stable and invariant to the vigilance state.
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Affiliation(s)
- Tamir Avigdor
- Analytical Neurophysiology LabMontreal Neurological Institute and Hospital, McGill UniversityMontrealQuebecCanada
- Multimodal Functional Imaging Lab, Biomedical Engineering DepartmentMcGill UniversityMontrealCanada
| | - Chifaou Abdallah
- Analytical Neurophysiology LabMontreal Neurological Institute and Hospital, McGill UniversityMontrealQuebecCanada
- Multimodal Functional Imaging Lab, Biomedical Engineering DepartmentMcGill UniversityMontrealCanada
| | - Jawata Afnan
- Multimodal Functional Imaging Lab, Biomedical Engineering DepartmentMcGill UniversityMontrealCanada
| | - Zhengchen Cai
- Montreal Neurological Institute and Hospital, McGill UniversityMontrealQuebecCanada
| | - Saba Rammal
- Analytical Neurophysiology LabMontreal Neurological Institute and Hospital, McGill UniversityMontrealQuebecCanada
| | - Christophe Grova
- Multimodal Functional Imaging Lab, Biomedical Engineering DepartmentMcGill UniversityMontrealCanada
- Multimodal Functional Imaging Lab, PERFORM Centre, Department of PhysicsConcordia UniversityMontrealQuebecCanada
| | - Birgit Frauscher
- Analytical Neurophysiology LabMontreal Neurological Institute and Hospital, McGill UniversityMontrealQuebecCanada
- Department of NeurologyDuke University Medical CenterDurhamNorth CarolinaUSA
- Department of Biomedical EngineeringDuke Pratt School of EngineeringDurhamNorth CarolinaUSA
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Silva Pereira S, Özer EE, Sebastian-Galles N. Complexity of STG signals and linguistic rhythm: a methodological study for EEG data. Cereb Cortex 2024; 34:bhad549. [PMID: 38236741 DOI: 10.1093/cercor/bhad549] [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: 08/01/2023] [Revised: 12/29/2023] [Accepted: 12/30/2023] [Indexed: 02/06/2024] Open
Abstract
The superior temporal and the Heschl's gyri of the human brain play a fundamental role in speech processing. Neurons synchronize their activity to the amplitude envelope of the speech signal to extract acoustic and linguistic features, a process known as neural tracking/entrainment. Electroencephalography has been extensively used in language-related research due to its high temporal resolution and reduced cost, but it does not allow for a precise source localization. Motivated by the lack of a unified methodology for the interpretation of source reconstructed signals, we propose a method based on modularity and signal complexity. The procedure was tested on data from an experiment in which we investigated the impact of native language on tracking to linguistic rhythms in two groups: English natives and Spanish natives. In the experiment, we found no effect of native language but an effect of language rhythm. Here, we compare source projected signals in the auditory areas of both hemispheres for the different conditions using nonparametric permutation tests, modularity, and a dynamical complexity measure. We found increasing values of complexity for decreased regularity in the stimuli, giving us the possibility to conclude that languages with less complex rhythms are easier to track by the auditory cortex.
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Affiliation(s)
- Silvana Silva Pereira
- Center for Brain and Cognition, Department of Information and Communications Technologies, Universitat Pompeu Fabra, 08005 Barcelona, Spain
| | - Ege Ekin Özer
- Center for Brain and Cognition, Department of Information and Communications Technologies, Universitat Pompeu Fabra, 08005 Barcelona, Spain
| | - Nuria Sebastian-Galles
- Center for Brain and Cognition, Department of Information and Communications Technologies, Universitat Pompeu Fabra, 08005 Barcelona, Spain
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Erickson B, Rich R, Shankar S, Kim B, Driscoll N, Mentzelopoulos G, Fernandez-Nuñez G, Vitale F, Medaglia JD. Evaluating and benchmarking the EEG signal quality of high-density, dry MXene-based electrode arrays against gelled Ag/AgCl electrodes. J Neural Eng 2024; 21:016005. [PMID: 38081060 PMCID: PMC10788783 DOI: 10.1088/1741-2552/ad141e] [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: 06/14/2023] [Revised: 09/17/2023] [Accepted: 12/11/2023] [Indexed: 01/13/2024]
Abstract
Objective.To evaluate the signal quality of dry MXene-based electrode arrays (also termed 'MXtrodes') for electroencephalographic (EEG) recordings where gelled Ag/AgCl electrodes are a standard.Approach.We placed 4 × 4 MXtrode arrays and gelled Ag/AgCl electrodes on different scalp locations. The scalp was cleaned with alcohol and rewetted with saline before application. We recorded from both electrode types simultaneously while participants performed a vigilance task.Main results.The root mean squared amplitude of MXtrodes was slightly higher than that of Ag/AgCl electrodes (.24-1.94 uV). Most MXtrode pairs had slightly lower broadband spectral coherence (.05 to .1 dB) and Delta- and Theta-band timeseries correlation (.05 to .1 units) compared to the Ag/AgCl pair (p< .001). However, the magnitude of correlation and coherence was high across both electrode types. Beta-band timeseries correlation and spectral coherence were higher between neighboring MXtrodes in the array (.81 to .84 units) than between any other pair (.70 to .75 units). This result suggests the close spacing of the nearest MXtrodes (3 mm) more densely sampled high spatial-frequency topographies. Event-related potentials were more similar between MXtrodes (ρ⩾ .95) than equally spaced Ag/AgCl electrodes (ρ⩽ .77,p< .001). Dry MXtrode impedance (x̄= 5.15 KΩ cm2) was higher and more variable than gelled Ag/AgCl electrodes (x̄= 1.21 KΩ cm2,p< .001). EEG was also recorded on the scalp across diverse hair types.Significance.Dry MXene-based electrodes record EEG at a quality comparable to conventional gelled Ag/AgCl while requiring minimal scalp preparation and no gel. MXtrodes can record independent signals at a spatial density four times higher than conventional electrodes, including through hair, thus opening novel opportunities for research and clinical applications that could benefit from dry and higher-density configurations.
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Affiliation(s)
- Brian Erickson
- Applied Cognitive and Brain Sciences, Department of Psychology, Drexel University, Philadelphia, PA 19104, United States of America
| | - Ryan Rich
- Applied Cognitive and Brain Sciences, Department of Psychology, Drexel University, Philadelphia, PA 19104, United States of America
| | - Sneha Shankar
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA 19104, United States of America
| | - Brian Kim
- Applied Cognitive and Brain Sciences, Department of Psychology, Drexel University, Philadelphia, PA 19104, United States of America
| | - Nicolette Driscoll
- Laboratory of Electronics Research, Massachusetts Institute of Technology, Cambridge, MA 02139, United States of America
| | - Georgios Mentzelopoulos
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neurotrauma, Neurodegeneration, and Restoration, Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA 19104, United States of America
| | - Guadalupe Fernandez-Nuñez
- Applied Cognitive and Brain Sciences, Department of Psychology, Drexel University, Philadelphia, PA 19104, United States of America
| | - Flavia Vitale
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Physical Medicine and Rehabilitation, University of Pennsylvania, Philadelphia, PA 19104, United States of America
| | - John D Medaglia
- Applied Cognitive and Brain Sciences, Department of Psychology, Drexel University, Philadelphia, PA 19104, United States of America
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, United States of America
- Department of Neurology, Drexel University, Philadelphia, PA 19104, United States of America
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Walia P, Fu Y, Norfleet J, Schwaitzberg SD, Intes X, De S, Cavuoto L, Dutta A. Brain-behavior analysis of transcranial direct current stimulation effects on a complex surgical motor task. FRONTIERS IN NEUROERGONOMICS 2024; 4:1135729. [PMID: 38234492 PMCID: PMC10790853 DOI: 10.3389/fnrgo.2023.1135729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Accepted: 12/11/2023] [Indexed: 01/19/2024]
Abstract
Transcranial Direct Current Stimulation (tDCS) has demonstrated its potential in enhancing surgical training and performance compared to sham tDCS. However, optimizing its efficacy requires the selection of appropriate brain targets informed by neuroimaging and mechanistic understanding. Previous studies have established the feasibility of using portable brain imaging, combining functional near-infrared spectroscopy (fNIRS) with tDCS during Fundamentals of Laparoscopic Surgery (FLS) tasks. This allows concurrent monitoring of cortical activations. Building on these foundations, our study aimed to explore the multi-modal imaging of the brain response using fNIRS and electroencephalogram (EEG) to tDCS targeting the right cerebellar (CER) and left ventrolateral prefrontal cortex (PFC) during a challenging FLS suturing with intracorporeal knot tying task. Involving twelve novices with a medical/premedical background (age: 22-28 years, two males, 10 females with one female with left-hand dominance), our investigation sought mechanistic insights into tDCS effects on brain areas related to error-based learning, a fundamental skill acquisition mechanism. The results revealed that right CER tDCS applied to the posterior lobe elicited a statistically significant (q < 0.05) brain response in bilateral prefrontal areas at the onset of the FLS task, surpassing the response seen with sham tDCS. Additionally, right CER tDCS led to a significant (p < 0.05) improvement in FLS scores compared to sham tDCS. Conversely, the left PFC tDCS did not yield a statistically significant brain response or improvement in FLS performance. In conclusion, right CER tDCS demonstrated the activation of bilateral prefrontal brain areas, providing valuable mechanistic insights into the effects of CER tDCS on FLS peformance. These insights motivate future investigations into the effects of CER tDCS on error-related perception-action coupling through directed functional connectivity studies.
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Affiliation(s)
- Pushpinder Walia
- Department of Biomedical Engineering, University at Buffalo, Buffalo, NY, United States
| | - Yaoyu Fu
- Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, NY, United States
| | - Jack Norfleet
- U.S. Army Futures Command, Combat Capabilities Development Command Soldier Center STTC, Orlando, FL, United States
| | - Steven D. Schwaitzberg
- University at Buffalo School of Medicine and Biomedical Sciences, Buffalo, NY, United States
| | - Xavier Intes
- Center for Modeling, Simulation, and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, NY, United States
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, United States
| | - Suvranu De
- Center for Modeling, Simulation, and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, NY, United States
| | - Lora Cavuoto
- Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, NY, United States
| | - Anirban Dutta
- Department of Biomedical Engineering, University at Buffalo, Buffalo, NY, United States
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Karittevlis C, Papadopoulos M, Lima V, Orphanides GA, Tiwari S, Antonakakis M, Papadopoulou Lesta V, Ioannides AA. First activity and interactions in thalamus and cortex using raw single-trial EEG and MEG elicited by somatosensory stimulation. Front Syst Neurosci 2024; 17:1305022. [PMID: 38250330 PMCID: PMC10797085 DOI: 10.3389/fnsys.2023.1305022] [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: 09/30/2023] [Accepted: 12/06/2023] [Indexed: 01/23/2024] Open
Abstract
Introduction One of the primary motivations for studying the human brain is to comprehend how external sensory input is processed and ultimately perceived by the brain. A good understanding of these processes can promote the identification of biomarkers for the diagnosis of various neurological disorders; it can also provide ways of evaluating therapeutic techniques. In this work, we seek the minimal requirements for identifying key stages of activity in the brain elicited by median nerve stimulation. Methods We have used a priori knowledge and applied a simple, linear, spatial filter on the electroencephalography and magnetoencephalography signals to identify the early responses in the thalamus and cortex evoked by short electrical stimulation of the median nerve at the wrist. The spatial filter is defined first from the average EEG and MEG signals and then refined using consistency selection rules across ST. The refined spatial filter is then applied to extract the timecourses of each ST in each targeted generator. These ST timecourses are studied through clustering to quantify the ST variability. The nature of ST connectivity between thalamic and cortical generators is then studied within each identified cluster using linear and non-linear algorithms with time delays to extract linked and directional activities. A novel combination of linear and non-linear methods provides in addition discrimination of influences as excitatory or inhibitory. Results Our method identifies two key aspects of the evoked response. Firstly, the early onset of activity in the thalamus and the somatosensory cortex, known as the P14 and P20 in EEG and the second M20 for MEG. Secondly, good estimates are obtained for the early timecourse of activity from these two areas. The results confirm the existence of variability in ST brain activations and reveal distinct and novel patterns of connectivity in different clusters. Discussion It has been demonstrated that we can extract new insights into stimulus processing without the use of computationally costly source reconstruction techniques which require assumptions and detailed modeling of the brain. Our methodology, thanks to its simplicity and minimal computational requirements, has the potential for real-time applications such as in neurofeedback systems and brain-computer interfaces.
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Affiliation(s)
- Christodoulos Karittevlis
- AAI Scientific Cultural Services Ltd., Nicosia, Cyprus
- Department of Computer Science, European University Cyprus, Nicosia, Cyprus
| | | | - Vinicius Lima
- Aix Marseille Université, INSERM, Institut de Neurosciences des Systèmes, Marseille, France
| | - Gregoris A. Orphanides
- AAI Scientific Cultural Services Ltd., Nicosia, Cyprus
- Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Shubham Tiwari
- Department of Geography, Durham University, Durham, United Kingdom
| | - Marios Antonakakis
- School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece
- Institute for Biomagnetism and Biosignal Analysis, Medicine Faculty, University of Münster, Münster, Germany
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Brinkmann BH. Technical Considerations in EEG Source Imaging. J Clin Neurophysiol 2024; 41:2-7. [PMID: 38181382 DOI: 10.1097/wnp.0000000000001029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2024] Open
Abstract
SUMMARY EEG source imaging is an established technique for identifying the origin of interictal and ictal epileptiform discharges in patients with epilepsy, and it is an important tool in neurophysiology research. Accurate and reliable EEG source imaging requires appropriate choices of how the head, skull, and scalp are modeled, and understanding of the different approaches to modeling is important to guide these choices. Similarly, numerous different approaches to modeling the electrical sources within the brain exist, and appropriate understanding of the strengths and limitations of each are essential to obtaining accurate, reliable, and interpretable solutions. This review aims to describe the essential theoretical basis for these head and source models while also discussing the practical implications of each in clinical or research applications.
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Affiliation(s)
- Benjamin H Brinkmann
- Departments of Neurology and Physiology and Biomedical Engineering, Mayo Clinic, Alfred 9-441C, SMH; 200 First Street SW, Rochester, Minnesota, U.S.A
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Mora-Gonzalez J, Esteban-Cornejo I, Solis-Urra P, Rodriguez-Ayllon M, Cadenas-Sanchez C, Hillman CH, Kramer AF, Catena A, Ortega FB. The effects of an exercise intervention on neuroelectric activity and executive function in children with overweight/obesity: The ActiveBrains randomized controlled trial. Scand J Med Sci Sports 2024; 34:e14486. [PMID: 37691352 DOI: 10.1111/sms.14486] [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: 04/21/2023] [Revised: 07/11/2023] [Accepted: 08/21/2023] [Indexed: 09/12/2023]
Abstract
OBJECTIVE To investigate whether a 20-week aerobic and resistance exercise program induces changes in brain current density underlying working memory and inhibitory control in children with overweight/obesity. METHODS A total of 67 children (10.00 ± 1.10 years) were randomized into an exercise or control group. Electroencephalography (EEG)-based current density (μA/mm2 ) was estimated using standardized low-resolution brain electromagnetic tomography (sLORETA) during a working memory task (Delayed non-matched-to-sample task, DNMS) and inhibitory control task (Modified flanker task, MFT). In DNMS, participants had to memorize four stimuli (Pokemons) and then select between two of them, one of which had not been previously shown. In MFT, participants had to indicate whether the centered cow (i.e., target) of five faced the right or left. RESULTS The exercise group had significantly greater increases in brain activation in comparison with the control group during the encoding phase of DNMS, particularly during retention of second stimuli in temporal and frontal areas (peak t = from 3.4 to 3.8, cluster size [k] = from 11 to 39), during the retention of the third stimuli in frontal areas (peak t = from 3.7 to 3.9, k = from 15 to 26), and during the retention of the fourth stimuli in temporal and occipital areas (peak t = from 2.7 to 4.3, k = from 13 to 101). In MFT, the exercise group presented a lower current density change in the middle frontal gyrus (peak t = -4.1, k = 5). No significant change was observed between groups for behavioral performance (p ≥ 0.05). CONCLUSION A 20-week exercise program modulates brain activity which might provide a positive influence on working memory and inhibitory control in children with overweight/obesity.
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Affiliation(s)
- Jose Mora-Gonzalez
- Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain
| | - Irene Esteban-Cornejo
- Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Madrid, Spain
| | - Patricio Solis-Urra
- Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain
- Faculty of Education and Social Sciences, Universidad Andres Bello, Viña del Mar, Chile
| | - María Rodriguez-Ayllon
- Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Cristina Cadenas-Sanchez
- Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Madrid, Spain
| | - Charles H Hillman
- Department of Psychology, Northeastern University, Boston, Massachusetts, USA
- Department of Physical Therapy, Movement & Rehabilitation Sciences, Northeastern University, Boston, Massachusetts, USA
| | - Arthur F Kramer
- Department of Psychology, Northeastern University, Boston, Massachusetts, USA
- Beckman Institute, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
| | - Andrés Catena
- School of Psychology, University of Granada, Granada, Spain
| | - Francisco B Ortega
- Department of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain
- Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, Madrid, Spain
- Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
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Bagdasarov A, Roberts K, Brunet D, Michel CM, Gaffrey MS. Exploring the Association Between EEG Microstates During Resting-State and Error-Related Activity in Young Children. Brain Topogr 2023:10.1007/s10548-023-01030-2. [PMID: 38141125 DOI: 10.1007/s10548-023-01030-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023]
Abstract
The error-related negativity (ERN) is a negative deflection in the electroencephalography (EEG) waveform at frontal-central scalp sites that occurs after error commission. The relationship between the ERN and broader patterns of brain activity measured across the entire scalp that support error processing during early childhood is unclear. We examined the relationship between the ERN and EEG microstates - whole-brain patterns of dynamically evolving scalp potential topographies that reflect periods of synchronized neural activity - during both a go/no-go task and resting-state in 90, 4-8-year-old children. The mean amplitude of the ERN was quantified during the -64 to 108 millisecond (ms) period of time relative to error commission, which was determined by data-driven microstate segmentation of error-related activity. We found that greater magnitude of the ERN associated with greater global explained variance (GEV; i.e., the percentage of total variance in the data explained by a given microstate) of an error-related microstate observed during the same -64 to 108 ms period (i.e., error-related microstate 3), and to greater anxiety risk as measured by parent-reported behavioral inhibition. During resting-state, six data-driven microstates were identified. Both greater magnitude of the ERN and greater GEV values of error-related microstate 3 associated with greater GEV values of resting-state microstate 4, which showed a frontal-central scalp topography. Source localization results revealed overlap between the underlying neural generators of error-related microstate 3 and resting-state microstate 4 and canonical brain networks (e.g., ventral attention) known to support the higher-order cognitive processes involved in error processing. Taken together, our results clarify how individual differences in error-related and intrinsic brain activity are related and enhance our understanding of developing brain network function and organization supporting error processing during early childhood.
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Affiliation(s)
- Armen Bagdasarov
- Department of Psychology & Neuroscience, Duke University, Reuben-Cooke Building, 417 Chapel Drive, Durham, NC, 27708, USA.
| | - Kenneth Roberts
- Duke Institute for Brain Sciences, Duke University, 308 Research Drive, Durham, NC, USA
| | - Denis Brunet
- Department of Basic Neurosciences, University of Geneva, Campus Biotech, 9 Chemin des Mines, Geneva, 1202, Switzerland
- Center for Biomedical Imaging (CIBM) Lausanne, EPFL AVP CP CIBM Station 6, Lausanne, 1015, Switzerland
| | - Christoph M Michel
- Department of Basic Neurosciences, University of Geneva, Campus Biotech, 9 Chemin des Mines, Geneva, 1202, Switzerland
- Center for Biomedical Imaging (CIBM) Lausanne, EPFL AVP CP CIBM Station 6, Lausanne, 1015, Switzerland
| | - Michael S Gaffrey
- Department of Psychology & Neuroscience, Duke University, Reuben-Cooke Building, 417 Chapel Drive, Durham, NC, 27708, USA
- Children's Wisconsin, 9000 W. Wisconsin Avenue, Milwaukee, WI, 53226, USA
- Medical College of Wisconsin, Division of Pediatric Psychology and Developmental Medicine, Department of Pediatrics, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA
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Everitt A, Richards H, Song Y, Smith J, Kobylarz E, Lukovits T, Halter R, Murphy E. EEG electrode localization with 3D iPhone scanning using point-cloud electrode selection (PC-ES). J Neural Eng 2023; 20:066033. [PMID: 38055968 DOI: 10.1088/1741-2552/ad12db] [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: 06/05/2023] [Accepted: 12/06/2023] [Indexed: 12/08/2023]
Abstract
Objective.Electroencephalography source imaging (ESI) is a valuable tool in clinical evaluation for epilepsy patients but is underutilized in part due to sensitivity to anatomical modeling errors. Accurate localization of scalp electrodes is instrumental to ESI, but existing localization devices are expensive and not portable. As a result, electrode localization challenges further impede access to ESI, particularly in inpatient and intensive care settings.Approach.To address this challenge, we present a portable and affordable electrode digitization method using the 3D scanning feature in modern iPhone models. This technique combines iPhone scanning with semi-automated image processing using point-cloud electrode selection (PC-ES), a custom MATLAB desktop application. We compare iPhone electrode localization to state-of-the-art photogrammetry technology in a human study with over 6000 electrodes labeled using each method. We also characterize the performance of PC-ES with respect to head location and examine the relative impact of different algorithm parameters.Main Results.The median electrode position variation across reviewers was 1.50 mm for PC-ES scanning and 0.53 mm for photogrammetry, and the average median distance between PC-ES and photogrammetry electrodes was 3.4 mm. These metrics demonstrate comparable performance of iPhone/PC-ES scanning to currently available technology and sufficient accuracy for ESI.Significance.Low cost, portable electrode localization using iPhone scanning removes barriers to ESI in inpatient, outpatient, and remote care settings. While PC-ES has current limitations in user bias and processing time, we anticipate these will improve with software automation techniques as well as future developments in iPhone 3D scanning technology.
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Affiliation(s)
- Alicia Everitt
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America
| | - Haley Richards
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America
| | - Yinchen Song
- Department of Neurology, Dartmouth-Hitchcock Medical Center, Lebanon, NH 03756, United States of America
- Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, United States of America
| | - Joel Smith
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America
| | - Erik Kobylarz
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America
- Department of Neurology, Dartmouth-Hitchcock Medical Center, Lebanon, NH 03756, United States of America
- Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, United States of America
| | - Timothy Lukovits
- Department of Neurology, Dartmouth-Hitchcock Medical Center, Lebanon, NH 03756, United States of America
| | - Ryan Halter
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America
- Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, United States of America
| | - Ethan Murphy
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, United States of America
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Manabe T, Rahul F, Fu Y, Intes X, Schwaitzberg SD, De S, Cavuoto L, Dutta A. Distinguishing Laparoscopic Surgery Experts from Novices Using EEG Topographic Features. Brain Sci 2023; 13:1706. [PMID: 38137154 PMCID: PMC10742221 DOI: 10.3390/brainsci13121706] [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: 11/02/2023] [Revised: 11/27/2023] [Accepted: 12/01/2023] [Indexed: 12/24/2023] Open
Abstract
The study aimed to differentiate experts from novices in laparoscopic surgery tasks using electroencephalogram (EEG) topographic features. A microstate-based common spatial pattern (CSP) analysis with linear discriminant analysis (LDA) was compared to a topography-preserving convolutional neural network (CNN) approach. Expert surgeons (N = 10) and novice medical residents (N = 13) performed laparoscopic suturing tasks, and EEG data from 8 experts and 13 novices were analysed. Microstate-based CSP with LDA revealed distinct spatial patterns in the frontal and parietal cortices for experts, while novices showed frontal cortex involvement. The 3D CNN model (ESNet) demonstrated a superior classification performance (accuracy > 98%, sensitivity 99.30%, specificity 99.70%, F1 score 98.51%, MCC 97.56%) compared to the microstate based CSP analysis with LDA (accuracy ~90%). Combining spatial and temporal information in the 3D CNN model enhanced classifier accuracy and highlighted the importance of the parietal-temporal-occipital association region in differentiating experts and novices.
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Affiliation(s)
- Takahiro Manabe
- School of Engineering, University of Lincoln, Lincoln LN6 7TS, UK;
| | - F.N.U. Rahul
- Centre for Modeling, Simulation, and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, MI 12180, USA; (F.R.); (X.I.)
| | - Yaoyu Fu
- Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, NY 14260, USA; (Y.F.); (L.C.)
| | - Xavier Intes
- Centre for Modeling, Simulation, and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, MI 12180, USA; (F.R.); (X.I.)
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, MI 12180, USA
| | - Steven D. Schwaitzberg
- School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, USA;
| | - Suvranu De
- College of Engineering, Florida A&M University-Florida State University, Tallahassee, FL 32310, USA;
| | - Lora Cavuoto
- Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, NY 14260, USA; (Y.F.); (L.C.)
| | - Anirban Dutta
- School of Engineering, University of Lincoln, Lincoln LN6 7TS, UK;
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Artoni F, Cometa A, Dalise S, Azzollini V, Micera S, Chisari C. Cortico-muscular connectivity is modulated by passive and active Lokomat-assisted Gait. Sci Rep 2023; 13:21618. [PMID: 38062035 PMCID: PMC10703891 DOI: 10.1038/s41598-023-48072-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 11/22/2023] [Indexed: 12/18/2023] Open
Abstract
The effects of robotic-assisted gait (RAG) training, besides conventional therapy, on neuroplasticity mechanisms and cortical integration in locomotion are still uncertain. To advance our knowledge on the matter, we determined the involvement of motor cortical areas in the control of muscle activity in healthy subjects, during RAG with Lokomat, both with maximal guidance force (100 GF-passive RAG) and without guidance force (0 GF-active RAG) as customary in rehabilitation treatments. We applied a novel cortico-muscular connectivity estimation procedure, based on Partial Directed Coherence, to jointly study source localized EEG and EMG activity during rest (standing) and active/passive RAG. We found greater cortico-cortical connectivity, with higher path length and tendency toward segregation during rest than in both RAG conditions, for all frequency bands except for delta. We also found higher cortico-muscular connectivity in distal muscles during swing (0 GF), and stance (100 GF), highlighting the importance of direct supraspinal control to maintain balance, even when gait is supported by a robotic exoskeleton. Source-localized connectivity shows that this control is driven mainly by the parietal and frontal lobes. The involvement of many cortical areas also in passive RAG (100 GF) justifies the use of the 100 GF RAG training for neurorehabilitation, with the aim of enhancing cortical-muscle connections and driving neural plasticity in neurological patients.
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Affiliation(s)
- Fiorenzo Artoni
- Department of Clinical Neurosciences, University of Genève, Faculty of Medicine, 1211, Geneva, Switzerland.
- Ago Neurotechnologies Sàrl, 1201, Geneva, Switzerland.
| | - Andrea Cometa
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio 34, 56025, Pontedera, Italy
- University School for Advanced Studies IUSS Pavia, 27100, Pavia, Italy
| | - Stefania Dalise
- Unit of Neurorehabilitation, Pisa University Hospital, Pisa, Italy
| | - Valentina Azzollini
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Silvestro Micera
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Viale Rinaldo Piaggio 34, 56025, Pontedera, Italy
- Translational Neural Engineering Laboratory (TNE), École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Carmelo Chisari
- Unit of Neurorehabilitation, Pisa University Hospital, Pisa, Italy
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
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Pitetzis D, Frantzidis C, Psoma E, Ketseridou SN, Deretzi G, Kalogera-Fountzila A, Bamidis PD, Spilioti M. The Pre-Interictal Network State in Idiopathic Generalized Epilepsies. Brain Sci 2023; 13:1671. [PMID: 38137119 PMCID: PMC10741409 DOI: 10.3390/brainsci13121671] [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: 10/29/2023] [Revised: 11/24/2023] [Accepted: 11/30/2023] [Indexed: 12/24/2023] Open
Abstract
Generalized spike wave discharges (GSWDs) are the typical electroencephalographic findings of Idiopathic Generalized Epilepsies (IGEs). These discharges are either interictal or ictal and recent evidence suggests differences in their pathogenesis. The aim of this study is to investigate, through functional connectivity analysis, the pre-interictal network state in IGEs, which precedes the formation of the interictal GSWDs. A high-density electroencephalogram (HD-EEG) was recorded in twenty-one patients with IGEs, and cortical connectivity was analyzed based on lagged coherence and individual anatomy. Graph theory analysis was used to estimate network features, assessed using the characteristic path length and clustering coefficient. The functional connectivity analysis identified two distinct networks during the pre-interictal state. These networks exhibited reversed connectivity attributes, reflecting synchronized activity at 3-4 Hz (delta2), and desynchronized activity at 8-10.5 Hz (alpha1). The delta2 network exhibited a statistically significant (p < 0.001) decrease in characteristic path length and an increase in the mean clustering coefficient. In contrast, the alpha1 network showed opposite trends in these features. The nodes influencing this state were primarily localized in the default mode network (DMN), dorsal attention network (DAN), visual network (VIS), and thalami. In conclusion, the coupling of two networks defined the pre-interictal state in IGEs. This state might be considered as a favorable condition for the generation of interictal GSWDs.
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Affiliation(s)
- Dimitrios Pitetzis
- Department of Neurology, Papageorgiou General Hospital, 56403 Thessaloniki, Greece;
- Lab of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (C.F.); (S.N.K.); (P.D.B.)
| | - Christos Frantzidis
- Lab of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (C.F.); (S.N.K.); (P.D.B.)
- School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK
| | - Elizabeth Psoma
- Department of Radiology, AHEPA General Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece; (E.P.); (A.K.-F.)
| | - Smaranda Nafsika Ketseridou
- Lab of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (C.F.); (S.N.K.); (P.D.B.)
| | - Georgia Deretzi
- Department of Neurology, Papageorgiou General Hospital, 56403 Thessaloniki, Greece;
| | - Anna Kalogera-Fountzila
- Department of Radiology, AHEPA General Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece; (E.P.); (A.K.-F.)
| | - Panagiotis D. Bamidis
- Lab of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (C.F.); (S.N.K.); (P.D.B.)
| | - Martha Spilioti
- 1st Department of Neurology, AHEPA General Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece;
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46
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Paitel ER, Nielson KA. Cerebellar EEG source localization reveals age-related compensatory activity moderated by genetic risk for Alzheimer's disease. Psychophysiology 2023; 60:e14395. [PMID: 37493042 DOI: 10.1111/psyp.14395] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 06/24/2023] [Accepted: 07/03/2023] [Indexed: 07/27/2023]
Abstract
The apolipoprotein-E (APOE) ε4 allele is the greatest genetic risk factor for late-onset Alzheimer's disease (AD), but alone it is not sufficiently predictive. Because neuropathological changes associated with AD begin decades before cognitive symptoms, neuroimaging of healthy, cognitively intact ε4 carriers (ε4+) may enable early characterization of patterns associated with risk for future decline. Research in the cerebral cortex highlights a period of compensatory recruitment in elders and ε4+, which serves to maintain cognitive functioning. Yet, AD-related changes may occur even earlier in the cerebellum. Advances in electroencephalography (EEG) source localization now allow effective modeling of cerebellar activity. Importantly, healthy aging and AD are associated with declines in both cerebellar functions and executive functioning (EF). However, it is not known whether cerebellar activity can detect pre-symptomatic AD risk. Thus, the current study analyzed cerebellar EEG source localization during an EF-dependent stop-signal task (i.e., inhibitory control) in healthy, intact older adults (Mage = 80 years; 20 ε4+, 25 ε4-). Task performance was comparable between groups. Older age predicted greater activity in left crus II and lobule VIIb during the P300 window (i.e., performance evaluation), consistent with age-related compensation. Age*ε4 moderations specifically showed that compensatory patterns were evident only in ε4-, suggesting that cerebellar compensatory resources may already be depleted in healthy ε4+ elders. Thus, the posterolateral cerebellum is sensitive to AD-related neural deficits in healthy elders. Characterization of these patterns may be essential for the earliest possible detection of AD risk, which would enable critical early intervention prior to symptom onset.
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Affiliation(s)
- Elizabeth R Paitel
- Department of Psychology, Marquette University, Milwaukee, Wisconsin, USA
| | - Kristy A Nielson
- Department of Psychology, Marquette University, Milwaukee, Wisconsin, USA
- Department of Neurology, Center for Imaging Research, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
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47
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Ortiz O, Kuruganti U, Chester V, Wilson A, Blustein D. Changes in EEG alpha-band power during prehension indicates neural motor drive inhibition. J Neurophysiol 2023; 130:1588-1601. [PMID: 37910541 DOI: 10.1152/jn.00506.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 10/24/2023] [Accepted: 10/30/2023] [Indexed: 11/03/2023] Open
Abstract
Changes in alpha band activity (8-12 Hz) indicate the downregulation of brain regions during cognitive tasks, reflecting real-time cognitive load. Despite this, its feasibility to be used in a more dynamic environment with ongoing motor corrections has not been studied. This research used electroencephalography (EEG) to explore how different brain regions are engaged during a simple grasp and lift task where unexpected changes to the object's weight or surface friction are introduced. The results suggest that alpha activity changes related to motor error correction occur only in motor-related areas (i.e. central areas) but not in error processing areas (i.e., frontoparietal network) during unexpected weight changes. This suggests that oscillations over motor areas reflect the reduction of motor drive related to motor error correction, thus, being a potential cortical electrophysiological biomarker for the process and not solely as a proxy for cognitive demands. This observation is particularly relevant in scenarios where these signals are used to evaluate high cognitive demands co-occurring with high levels of motor errors and corrections, such as prosthesis use. The establishment of electrophysiological biomarkers of mental resource allocation during movement and cognition can help identify indicators of mental workload and motor drive, which may be useful for improving brain-machine interfaces.NEW & NOTEWORTHY We demonstrated that alpha suppression, an EEG phenomenon with high temporal resolution, occurs over the primary sensorimotor area during error correction during lift movements. Interpretations of alpha activity are often attributed to high cognitive demands, thus recognizing that it is also influenced by motor processes is important in situations where cognitive demands are paired with movement errors. This could further have application as a biomarker for error correction in human-machine interfaces, such as neuroprostheses.
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Affiliation(s)
- Oscar Ortiz
- Andrew and Marjorie McCain Human Performance Laboratory, Faculty of Kinesiology, University of New Brunswick Fredericton, New Brunswick, Canada
- International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, British Columbia, Canada
| | - Usha Kuruganti
- Andrew and Marjorie McCain Human Performance Laboratory, Faculty of Kinesiology, University of New Brunswick Fredericton, New Brunswick, Canada
| | - Victoria Chester
- Andrew and Marjorie McCain Human Performance Laboratory, Faculty of Kinesiology, University of New Brunswick Fredericton, New Brunswick, Canada
| | - Adam Wilson
- Department of Electrical and Computer Engineering, Faculty of Engineering, University of New Brunswick, Fredericton, New Brunswick, Canada
| | - Daniel Blustein
- Department of Psychology, Acadia University, Wolfville, Nova Scotia, Canada
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48
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Chowdhury NS, Chiang AKI, Millard SK, Skippen P, Chang WJ, Seminowicz DA, Schabrun SM. Combined transcranial magnetic stimulation and electroencephalography reveals alterations in cortical excitability during pain. eLife 2023; 12:RP88567. [PMID: 37966464 PMCID: PMC10651174 DOI: 10.7554/elife.88567] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2023] Open
Abstract
Transcranial magnetic stimulation (TMS) has been used to examine inhibitory and facilitatory circuits during experimental pain and in chronic pain populations. However, current applications of TMS to pain have been restricted to measurements of motor evoked potentials (MEPs) from peripheral muscles. Here, TMS was combined with electroencephalography (EEG) to determine whether experimental pain could induce alterations in cortical inhibitory/facilitatory activity observed in TMS-evoked potentials (TEPs). In Experiment 1 (n=29), multiple sustained thermal stimuli were administered to the forearm, with the first, second, and third block of thermal stimuli consisting of warm but non-painful (pre-pain block), painful (pain block) and warm but non-painful (post-pain block) temperatures, respectively. During each stimulus, TMS pulses were delivered while EEG (64 channels) was simultaneously recorded. Verbal pain ratings were collected between TMS pulses. Relative to pre-pain warm stimuli, painful stimuli led to an increase in the amplitude of the frontocentral negative peak ~45 ms post-TMS (N45), with a larger increase associated with higher pain ratings. Experiments 2 and 3 (n=10 in each) showed that the increase in the N45 in response to pain was not due to changes in sensory potentials associated with TMS, or a result of stronger reafferent muscle feedback during pain. This is the first study to use combined TMS-EEG to examine alterations in cortical excitability in response to pain. These results suggest that the N45 TEP peak, which indexes GABAergic neurotransmission, is implicated in pain perception and is a potential marker of individual differences in pain sensitivity.
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Affiliation(s)
- Nahian Shahmat Chowdhury
- Center for Pain IMPACT, Neuroscience Research AustraliaSydneyAustralia
- University of New South WalesSydneyAustralia
| | - Alan KI Chiang
- Center for Pain IMPACT, Neuroscience Research AustraliaSydneyAustralia
- University of New South WalesSydneyAustralia
| | - Samantha K Millard
- Center for Pain IMPACT, Neuroscience Research AustraliaSydneyAustralia
- University of New South WalesSydneyAustralia
| | - Patrick Skippen
- Center for Pain IMPACT, Neuroscience Research AustraliaSydneyAustralia
| | - Wei-Ju Chang
- Center for Pain IMPACT, Neuroscience Research AustraliaSydneyAustralia
- School of Health Sciences, College of Health, Medicine and Wellbeing, The University of NewcastleCallaghanAustralia
| | - David A Seminowicz
- Center for Pain IMPACT, Neuroscience Research AustraliaSydneyAustralia
- Department of Medical Biophysics, Schulich School of Medicine & Dentistry, University of Western OntarioLondonCanada
| | - Siobhan M Schabrun
- Center for Pain IMPACT, Neuroscience Research AustraliaSydneyAustralia
- The Gray Centre for Mobility and Activity, University of Western OntarioLondonCanada
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49
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Li X, Kang Q, Gu H. A comprehensive review for machine learning on neuroimaging in obsessive-compulsive disorder. Front Hum Neurosci 2023; 17:1280512. [PMID: 38021236 PMCID: PMC10646310 DOI: 10.3389/fnhum.2023.1280512] [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: 08/20/2023] [Accepted: 10/18/2023] [Indexed: 12/01/2023] Open
Abstract
Obsessive-compulsive disorder (OCD) is a common mental disease, which can exist as a separate disease or become one of the symptoms of other mental diseases. With the development of society, statistically, the incidence rate of obsessive-compulsive disorder has been increasing year by year. At present, in the diagnosis and treatment of OCD, The clinical performance of patients measured by scales is no longer the only quantitative indicator. Clinical workers and researchers are committed to using neuroimaging to explore the relationship between changes in patient neurological function and obsessive-compulsive disorder. Through machine learning and artificial learning, medical information in neuroimaging can be better displayed. In this article, we discuss recent advancements in artificial intelligence related to neuroimaging in the context of Obsessive-Compulsive Disorder.
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Affiliation(s)
- Xuanyi Li
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Qiang Kang
- Department of Radiology, Xing’an League People’s Hospital of Inner Mongolia, Mongolia, China
| | - Hanxing Gu
- Department of Geriatric Psychiatry, Qingdao Mental Health Center, Qingdao, Shandong, China
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50
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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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