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Bastola S, Jahromi S, Chikara R, Stufflebeam SM, Ottensmeyer MP, De Novi G, Papadelis C, Alexandrakis G. Improved Dipole Source Localization from Simultaneous MEG-EEG Data by Combining a Global Optimization Algorithm with a Local Parameter Search: A Brain Phantom Study. Bioengineering (Basel) 2024; 11:897. [PMID: 39329639 PMCID: PMC11428344 DOI: 10.3390/bioengineering11090897] [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: 08/18/2024] [Revised: 09/03/2024] [Accepted: 09/04/2024] [Indexed: 09/28/2024] Open
Abstract
Dipole localization, a fundamental challenge in electromagnetic source imaging, inherently constitutes an optimization problem aimed at solving the inverse problem of electric current source estimation within the human brain. The accuracy of dipole localization algorithms is contingent upon the complexity of the forward model, often referred to as the head model, and the signal-to-noise ratio (SNR) of measurements. In scenarios characterized by low SNR, often corresponding to deep-seated sources, existing optimization techniques struggle to converge to global minima, thereby leading to the localization of dipoles at erroneous positions, far from their true locations. This study presents a novel hybrid algorithm that combines simulated annealing with the traditional quasi-Newton optimization method, tailored to address the inherent limitations of dipole localization under low-SNR conditions. Using a realistic head model for both electroencephalography (EEG) and magnetoencephalography (MEG), it is demonstrated that this novel hybrid algorithm enables significant improvements of up to 45% in dipole localization accuracy compared to the often-used dipole scanning and gradient descent techniques. Localization improvements are not only found for single dipoles but also in two-dipole-source scenarios, where sources are proximal to each other. The novel methodology presented in this work could be useful in various applications of clinical neuroimaging, particularly in cases where recordings are noisy or sources are located deep within the brain.
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Affiliation(s)
- Subrat Bastola
- Bioengineering Department, The University of Texas at Arlington, Arlington, TX 76019, USA; (S.J.); (R.C.); (C.P.); (G.A.)
| | - Saeed Jahromi
- Bioengineering Department, The University of Texas at Arlington, Arlington, TX 76019, USA; (S.J.); (R.C.); (C.P.); (G.A.)
- Neuroscience Research Center, Jane and John Justin Institute for Mind Health, Cook Children’s Health Care System, Fort Worth, TX 76104, USA
| | - Rupesh Chikara
- Bioengineering Department, The University of Texas at Arlington, Arlington, TX 76019, USA; (S.J.); (R.C.); (C.P.); (G.A.)
- Neuroscience Research Center, Jane and John Justin Institute for Mind Health, Cook Children’s Health Care System, Fort Worth, TX 76104, USA
| | - Steven M. Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02129, USA;
| | - Mark P. Ottensmeyer
- Medical Device & Simulation Laboratory, Massachusetts General Hospital, Harvard Medical School, Cambridge, MA 02139, USA; (M.P.O.); (G.D.N.)
| | - Gianluca De Novi
- Medical Device & Simulation Laboratory, Massachusetts General Hospital, Harvard Medical School, Cambridge, MA 02139, USA; (M.P.O.); (G.D.N.)
| | - Christos Papadelis
- Bioengineering Department, The University of Texas at Arlington, Arlington, TX 76019, USA; (S.J.); (R.C.); (C.P.); (G.A.)
- Neuroscience Research Center, Jane and John Justin Institute for Mind Health, Cook Children’s Health Care System, Fort Worth, TX 76104, USA
| | - George Alexandrakis
- Bioengineering Department, The University of Texas at Arlington, Arlington, TX 76019, USA; (S.J.); (R.C.); (C.P.); (G.A.)
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Akaiwa M, Matsuda Y, Kurokawa R, Sugawara Y, Kosuge R, Saito H, Shibata E, Sasaki T, Sugawara K, Kozuka N. Does 20 Hz Transcranial Alternating Current Stimulation over the Human Primary Motor Cortex Modulate Beta Rebound Following Voluntary Movement? Brain Sci 2024; 14:74. [PMID: 38248289 PMCID: PMC10813667 DOI: 10.3390/brainsci14010074] [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: 01/04/2024] [Accepted: 01/09/2024] [Indexed: 01/23/2024] Open
Abstract
Beta frequency oscillations originating from the primary motor cortex increase in amplitude following the initiation of voluntary movement, a process termed beta rebound. The strength of beta rebound has been reported to predict the recovery of motor function following stroke, suggesting therapeutic applications of beta rebound modulation. The present study examined the effect of 20 Hz transcranial alternating current stimulation (tACS) on the beta rebound induced by self-paced voluntary movement. Electroencephalograms (EEGs) and electromyograms (EMGs) were recorded from 16 healthy adults during voluntary movements performed before and after active or sham tACS. There was no significant change in average beta rebound after active tACS. However, the beta rebound amplitude was significantly enhanced in a subset of participants, and the magnitude of the increase across all participants was negatively correlated with the difference between individual peak beta frequency and tACS frequency. Thus, matching the stimulus frequency of tACS with individual beta frequency may facilitate therapeutic enhancement for motor rehabilitation.
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Affiliation(s)
- Mayu Akaiwa
- Graduate School of Health Sciences, Sapporo Medical University, Sapporo 060-8556, Japan
| | - Yuya Matsuda
- Graduate School of Health Sciences, Sapporo Medical University, Sapporo 060-8556, Japan
| | - Ryo Kurokawa
- Graduate School of Health Sciences, Sapporo Medical University, Sapporo 060-8556, Japan
| | - Yasushi Sugawara
- Graduate School of Health Sciences, Sapporo Medical University, Sapporo 060-8556, Japan
| | - Rin Kosuge
- Graduate School of Health Sciences, Sapporo Medical University, Sapporo 060-8556, Japan
| | - Hidekazu Saito
- Department of Occupational Therapy, School of Health Sciences, Sapporo Medical University, Sapporo 060-8556, Japan
| | - Eriko Shibata
- Major of Physical Therapy, Department of Rehabilitation, Faculty of Healthcare and Science, Hokkaido Bunkyo University, Eniwa 061-1449, Japan;
| | - Takeshi Sasaki
- Department of Physical Therapy, School of Health Sciences, Sapporo Medical University, Sapporo 060-8556, Japan; (T.S.); (K.S.); (N.K.)
| | - Kazuhiro Sugawara
- Department of Physical Therapy, School of Health Sciences, Sapporo Medical University, Sapporo 060-8556, Japan; (T.S.); (K.S.); (N.K.)
| | - Naoki Kozuka
- Department of Physical Therapy, School of Health Sciences, Sapporo Medical University, Sapporo 060-8556, Japan; (T.S.); (K.S.); (N.K.)
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Westin K, Beniczky S, Pfeiffer C, Hämäläinen M, Lundqvist D. On the clinical utility of on-scalp MEG: A modeling study of epileptic activity source estimation. Clin Neurophysiol 2023; 156:143-155. [PMID: 37951041 DOI: 10.1016/j.clinph.2023.10.006] [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: 10/12/2022] [Revised: 10/06/2023] [Accepted: 10/21/2023] [Indexed: 11/13/2023]
Abstract
OBJECTIVE Epilepsy surgery requires localization of the seizure onset zone (SOZ). Today this can only be achieved by intracranial electroencephalography (iEEG). The iEEG electrode placement is guided by findings from non-invasive modalities that cannot themselves detect SOZ-generated initial seizure activity. On scalp magnetoencephalography (osMEG), with sensors placed on the scalp, demonstrates higher sensitivity than conventional MEG (convMEG) and could potentially detect early seizure activity. Here, we modeled EEG, convMEG and osMEG to compare the modalities' ability to localize SOZ activity and to detect epileptic spikes. METHODS We modeled seizure propagation within ten epileptic networks located in the mesial and lateral temporal lobe; basal, dorsal, central and frontopolar frontal lobe; parietal and occipital lobe as well as insula and cingulum. The networks included brain regions often involved in focal epilepsy. 128-channel osMEG, convMEG, EEG and combined osMEG + EEG and convMEG + EEG were modeled, and the SOZ source estimation accuracy was quantified and compared using Student's t-test. RESULTS OsMEG was significantly (p-value <0.01) better than both convMEG and EEG at detecting the earliest SOZ-generated seizure activity and epileptic spikes, and better at localizing seizure activity from all epileptic networks (p < 0.01). CONCLUSIONS Our modeling results clearly show that osMEG has an unsurpassed potential to detect both epileptic spikes and seizure activity from all simulated anatomical sites. SIGNIFICANCE No clinically available non-invasive technique can detect SOZ activity from all brain regions. Our study indicates that osMEG has the potential to become an important clinical tool, improving both non-invasive SOZ localization and iEEG electrode placement accuracy.
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Affiliation(s)
- Karin Westin
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden; Clinical Neurophysiology, Karolinska University Hospital, Stockholm, Sweden.
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Aarhus University Hospital, Denmark and Danish Epilepsy Centre, Dianalund, Denmark
| | - Christoph Pfeiffer
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Matti Hämäläinen
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland
| | - Daniel Lundqvist
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
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Valt C, Quarto T, Tavella A, Romanelli F, Fazio L, Arcara G, Altamura M, Barrasso G, Bellomo A, Blasi G, Brudaglio F, Carofiglio A, D'Ambrosio E, Padalino FA, Rampino A, Saponaro A, Semisa D, Suma D, Pergola G, Bertolino A. Reduced magnetic mismatch negativity: a shared deficit in psychosis and related risk. Psychol Med 2023; 53:6037-6045. [PMID: 36321391 DOI: 10.1017/s003329172200321x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
BACKGROUND Abnormal auditory processing of deviant stimuli, as reflected by mismatch negativity (MMN), is often reported in schizophrenia (SCZ). At present, it is still under debate whether this dysfunctional response is specific to the full-blown SCZ diagnosis or rather a marker of psychosis in general. The present study tested MMN in patients with SCZ, bipolar disorder (BD), first episode of psychosis (FEP), and in people at clinical high risk for psychosis (CHR). METHODS Source-based MEG activity evoked during a passive auditory oddball task was recorded from 135 patients grouped according to diagnosis (SCZ, BD, FEP, and CHR) and 135 healthy controls also divided into four subgroups, age- and gender-matched with diagnostic subgroups. The magnetic MMN (mMMN) was analyzed as event-related field (ERF), Theta power, and Theta inter-trial phase coherence (ITPC). RESULTS The clinical group as a whole showed reduced mMMN ERF amplitude, Theta power, and Theta ITPC, without any statistically significant interaction between diagnosis and mMMN reductions. The mMMN subgroup contrasts showed lower ERF amplitude in all the diagnostic subgroups. In the analysis of Theta frequency, SCZ showed significant power and ITPC reductions, while only indications of diminished ITPC were observed in CHR, but no significant decreases characterized BD and FEP. CONCLUSIONS Significant mMMN alterations in people experiencing psychosis, also for diagnoses other than SCZ, suggest that this neurophysiological response may be a feature shared across psychotic disorders. Additionally, reduced Theta ITPC may be associated with risk for psychosis.
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Affiliation(s)
- Christian Valt
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Tiziana Quarto
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
- Department of Humanities, University of Foggia, Foggia, Italy
| | | | | | - Leonardo Fazio
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
- Department of Medicine and Surgery, LUM University, Casamassima, Italy
| | | | - Mario Altamura
- Department of Clinical and Experimental Medicine, University of Foggia, Foggia, Italy
| | - Giuseppe Barrasso
- Department of Mental Health, ASL Barletta-Andria-Trani, Andria, Italy
| | - Antonello Bellomo
- Department of Clinical and Experimental Medicine, University of Foggia, Foggia, Italy
| | - Giuseppe Blasi
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
- Bari University Hospital, Bari, Italy
| | - Flora Brudaglio
- Department of Mental Health, ASL Barletta-Andria-Trani, Andria, Italy
| | | | - Enrico D'Ambrosio
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
- Bari University Hospital, Bari, Italy
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience - King's College London, London, UK
| | | | - Antonio Rampino
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
- Bari University Hospital, Bari, Italy
| | | | | | - Domenico Suma
- Department of Mental Health, ASL Brindisi, Brindisi, Italy
| | - Giulio Pergola
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Alessandro Bertolino
- Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
- Bari University Hospital, Bari, Italy
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Hillebrand A, Holmes N, Sijsma N, O'Neill GC, Tierney TM, Liberton N, Stam AH, van Klink N, Stam CJ, Bowtell R, Brookes MJ, Barnes GR. Non-invasive measurements of ictal and interictal epileptiform activity using optically pumped magnetometers. Sci Rep 2023; 13:4623. [PMID: 36944674 PMCID: PMC10030968 DOI: 10.1038/s41598-023-31111-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 03/06/2023] [Indexed: 03/23/2023] Open
Abstract
Magneto- and electroencephalography (MEG/EEG) are important techniques for the diagnosis and pre-surgical evaluation of epilepsy. Yet, in current cryogen-based MEG systems the sensors are offset from the scalp, which limits the signal-to-noise ratio (SNR) and thereby the sensitivity to activity from deep structures such as the hippocampus. This effect is amplified in children, for whom adult-sized fixed-helmet systems are typically too big. Moreover, ictal recordings with fixed-helmet systems are problematic because of limited movement tolerance and/or logistical considerations. Optically Pumped Magnetometers (OPMs) can be placed directly on the scalp, thereby improving SNR and enabling recordings during seizures. We aimed to demonstrate the performance of OPMs in a clinical population. Seven patients with challenging cases of epilepsy underwent MEG recordings using a 12-channel OPM-system and a 306-channel cryogen-based whole-head system: three adults with known deep or weak (low SNR) sources of interictal epileptiform discharges (IEDs), along with three children with focal epilepsy and one adult with frequent seizures. The consistency of the recorded IEDs across the two systems was assessed. In one patient the OPMs detected IEDs that were not found with the SQUID-system, and in two patients no IEDs were found with either system. For the other patients the OPM data were remarkably consistent with the data from the cryogenic system, noting that these were recorded in different sessions, with comparable SNRs and IED-yields overall. Importantly, the wearability of OPMs enabled the recording of seizure activity in a patient with hyperkinetic movements during the seizure. The observed ictal onset and semiology were in agreement with previous video- and stereo-EEG recordings. The relatively affordable technology, in combination with reduced running and maintenance costs, means that OPM-based MEG could be used more widely than current MEG systems, and may become an affordable alternative to scalp EEG, with the potential benefits of increased spatial accuracy, reduced sensitivity to volume conduction/field spread, and increased sensitivity to deep sources. Wearable MEG thus provides an unprecedented opportunity for epilepsy, and given its patient-friendliness, we envisage that it will not only be used for presurgical evaluation of epilepsy patients, but also for diagnosis after a first seizure.
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Affiliation(s)
- Arjan Hillebrand
- Department of Clinical Neurophysiology and Magnetoencephalography Center, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081HV, Amsterdam, The Netherlands.
- Brain Imaging, Amsterdam Neuroscience, Amsterdam, The Netherlands.
- Systems and Network Neurosciences, Amsterdam Neuroscience, Amsterdam, The Netherlands.
| | - Niall Holmes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Ndedi Sijsma
- Department of Clinical Neurophysiology and Magnetoencephalography Center, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081HV, Amsterdam, The Netherlands
| | - George C O'Neill
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3AR, UK
| | - Tim M Tierney
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3AR, UK
| | - Niels Liberton
- Department of Medical Technology, 3D Innovation Lab, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Anine H Stam
- Department of Clinical Neurophysiology and Magnetoencephalography Center, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081HV, Amsterdam, The Netherlands
| | - Nicole van Klink
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and Magnetoencephalography Center, Amsterdam UMC Location Vrije Universiteit Amsterdam, De Boelelaan 1117, 1081HV, Amsterdam, The Netherlands
- Brain Imaging, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Neurodegeneration, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Richard Bowtell
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Gareth R Barnes
- Wellcome Centre for Human Neuroimaging, Department of Imaging Neuroscience, UCL Queen Square Institute of Neurology, University College London, London, WC1N 3AR, UK
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Ahlfors SP, Graham S, Alho J, Joseph RM, McGuiggan NM, Nayal Z, Hämäläinen MS, Khan S, Kenet T. Magnetoencephalography and electroencephalography can both detect differences in cortical responses to vibrotactile stimuli in individuals on the autism spectrum. Front Psychiatry 2022; 13:902332. [PMID: 35990048 PMCID: PMC9388788 DOI: 10.3389/fpsyt.2022.902332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 07/18/2022] [Indexed: 11/28/2022] Open
Abstract
Autism Spectrum (AS) is defined primarily by differences in social interactions, with impairments in sensory processing also characterizing the condition. In the search for neurophysiological biomarkers associated with traits relevant to the condition, focusing on sensory processing offers a path that is likely to be translatable across populations with different degrees of ability, as well as into animal models and across imaging modalities. In a prior study, a somatosensory neurophysiological signature of AS was identified using magnetoencephalography (MEG). Specifically, source estimation results showed differences between AS and neurotypically developing (NTD) subjects in the brain response to 25-Hz vibrotactile stimulation of the right fingertips, with lower inter-trial coherence (ITC) observed in the AS group. Here, we examined whether these group differences can be detected without source estimation using scalp electroencephalography (EEG), which is more commonly available in clinical settings than MEG, and therefore offers a greater potential for clinical translation. To that end, we recorded simultaneous whole-head MEG and EEG in 14 AS and 10 NTD subjects (age 15-28 years) using the same vibrotactile paradigm. Based on the scalp topographies, small sets of left hemisphere MEG and EEG sensors showing the maximum overall ITC were selected for group comparisons. Significant differences between the AS and NTD groups in ITC at 25 Hz as well as at 50 Hz were recorded in both MEG and EEG sensor data. For each measure, the mean ITC was lower in the AS than in the NTD group. EEG ITC values correlated with behaviorally assessed somatosensory sensation avoiding scores. The results show that information about ITC from MEG and EEG signals have substantial overlap, and thus EEG sensor-based ITC measures of the AS somatosensory processing biomarker previously identified using source localized MEG data have a potential to be developed into clinical use in AS, thanks to the higher accessibility to EEG in clinical settings.
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Affiliation(s)
- Seppo P Ahlfors
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States.,Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Steven Graham
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Jussi Alho
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Robert M Joseph
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, United States
| | - Nicole M McGuiggan
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Zein Nayal
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Matti S Hämäläinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States.,Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Sheraz Khan
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States.,Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States.,Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Tal Kenet
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States.,Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
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Laohathai C, Ebersole JS, Mosher JC, Bagić AI, Sumida A, Von Allmen G, Funke ME. Practical Fundamentals of Clinical MEG Interpretation in Epilepsy. Front Neurol 2021; 12:722986. [PMID: 34721261 PMCID: PMC8551575 DOI: 10.3389/fneur.2021.722986] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 09/06/2021] [Indexed: 11/29/2022] Open
Abstract
Magnetoencephalography (MEG) is a neurophysiologic test that offers a functional localization of epileptic sources in patients considered for epilepsy surgery. The understanding of clinical MEG concepts, and the interpretation of these clinical studies, are very involving processes that demand both clinical and procedural expertise. One of the major obstacles in acquiring necessary proficiency is the scarcity of fundamental clinical literature. To fill this knowledge gap, this review aims to explain the basic practical concepts of clinical MEG relevant to epilepsy with an emphasis on single equivalent dipole (sECD), which is one the most clinically validated and ubiquitously used source localization method, and illustrate and explain the regional topology and source dynamics relevant for clinical interpretation of MEG-EEG.
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Affiliation(s)
- Christopher Laohathai
- Division of Child Neurology, Department of Pediatrics, McGovern Medical School at UTHealth, Houston, TX, United States
- Department of Neurology, Saint Louis University, Saint Louis, MO, United States
| | - John S. Ebersole
- Northeast Regional Epilepsy Group, Atlantic Health Neuroscience Institute, Summit, NJ, United States
| | - John C. Mosher
- Department of Neurology, McGovern Medical School at UTHealth, Houston, TX, United States
| | - Anto I. Bagić
- University of Pittsburgh Comprehensive Epilepsy Center (UPCEC), Department of Neurology, University of Pittsburgh Medical Center, Pittsburg, PA, United States
| | - Ai Sumida
- Department of Neurology, McGovern Medical School at UTHealth, Houston, TX, United States
| | - Gretchen Von Allmen
- Division of Child Neurology, Department of Pediatrics, McGovern Medical School at UTHealth, Houston, TX, United States
| | - Michael E. Funke
- Division of Child Neurology, Department of Pediatrics, McGovern Medical School at UTHealth, Houston, TX, United States
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8
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Beltrachini L, von Ellenrieder N, Eichardt R, Haueisen J. Optimal design of on-scalp electromagnetic sensor arrays for brain source localisation. Hum Brain Mapp 2021; 42:4869-4879. [PMID: 34245061 PMCID: PMC8449117 DOI: 10.1002/hbm.25586] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 06/09/2021] [Accepted: 06/25/2021] [Indexed: 11/11/2022] Open
Abstract
Optically pumped magnetometers (OPMs) are quickly widening the scopes of noninvasive neurophysiological imaging. The possibility of placing these magnetic field sensors on the scalp allows not only to acquire signals from people in movement, but also to reduce the distance between the sensors and the brain, with a consequent gain in the signal‐to‐noise ratio. These advantages make the technique particularly attractive to characterise sources of brain activity in demanding populations, such as children and patients with epilepsy. However, the technology is currently in an early stage, presenting new design challenges around the optimal sensor arrangement and their complementarity with other techniques as electroencephalography (EEG). In this article, we present an optimal array design strategy focussed on minimising the brain source localisation error. The methodology is based on the Cramér‐Rao bound, which provides lower error bounds on the estimation of source parameters regardless of the algorithm used. We utilise this framework to compare whole head OPM arrays with commercially available electro/magnetoencephalography (E/MEG) systems for localising brain signal generators. In addition, we study the complementarity between EEG and OPM‐based MEG, and design optimal whole head systems based on OPMs only and a combination of OPMs and EEG electrodes for characterising deep and superficial sources alike. Finally, we show the usefulness of the approach to find the nearly optimal sensor positions minimising the estimation error bound in a given cortical region when a limited number of OPMs are available. This is of special interest for maximising the performance of small scale systems to ad hoc neurophysiological experiments, a common situation arising in most OPM labs.
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Affiliation(s)
- Leandro Beltrachini
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff
| | | | - Roland Eichardt
- Institute of Biomedical Engineering and Informatics, Ilmenau University of Technology, Ilmenau, Germany
| | - Jens Haueisen
- Institute of Biomedical Engineering and Informatics, Ilmenau University of Technology, Ilmenau, Germany
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9
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Manting CL, Gulyas B, Ullén F, Lundqvist D. Auditory steady-state responses during and after a stimulus: Cortical sources, and the influence of attention and musicality. Neuroimage 2021; 233:117962. [PMID: 33744455 DOI: 10.1016/j.neuroimage.2021.117962] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 02/19/2021] [Accepted: 03/08/2021] [Indexed: 11/28/2022] Open
Abstract
The auditory steady-state response (ASSR) is an oscillatory brain response generated by periodic auditory stimuli and originates mainly from the temporal auditory cortices. Recent data show that while the auditory cortices are indeed strongly activated by the stimulus when it is present (ON ASSR), the anatomical distribution of ASSR sources involves also parietal and frontal cortices, indicating that the ASSR is a more complex phenomenon than previously believed. Furthermore, while the ASSR typically continues to oscillate even after the stimulus has stopped (OFF ASSR), very little is known about the characteristics of the OFF ASSR and how it compares to the ON ASSR. Here, we assessed whether the OFF and ON ASSR powers are modulated by the stimulus properties (i.e. volume and pitch), selective attention, as well as individual musical sophistication. We also investigated the cortical source distribution of the OFF ASSR using a melody tracking task, in which attention was directed between uniquely amplitude-modulated melody streams that differed in pitch. The ON and OFF ASSRs were recorded with magnetoencephalography (MEG) on a group of participants varying from low to high degree of musical sophistication. Our results show that the OFF ASSR is different from the ON ASSR in nearly every aspect. While the ON ASSR was modulated by the stimulus properties and selective attention, the OFF ASSR was not influenced by any of these factors. Furthermore, while the ON ASSR was generated primarily from temporal sources, the OFF ASSR originated mainly from the frontal cortex. These findings challenge the notion that the OFF ASSR is merely a continuation of the ON ASSR. Rather, they suggest that the OFF ASSR is an internally-driven signal that develops from an initial sensory processing state (ON ASSR), with both types of ASSRs clearly differing in cortical representation and character. Furthermore, our results show that the ON ASSR power was enhanced by selective attention at cortical sources within each of the bilateral frontal, temporal, parietal and insular lobes. Finally, the ON ASSR proved sensitive to musicality, demonstrating positive correlations between musical sophistication and ASSR power, as well as with the degree of attentional ASSR modulation at the left and right parietal cortices. Taken together, these results show new aspects of the ASSR response, and demonstrate its usefulness as an effective tool for analysing how selective attention interacts with individual abilities in music perception.
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Affiliation(s)
- Cassia Low Manting
- NatMEG, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden; Cognitive Neuroimaging Centre (CoNiC), Lee Kong Chien School of Medicine, Nanyang Technological University, Singapore.
| | - Balazs Gulyas
- Cognitive Neuroimaging Centre (CoNiC), Lee Kong Chien School of Medicine, Nanyang Technological University, Singapore; Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Fredrik Ullén
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Daniel Lundqvist
- NatMEG, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden; Cognitive Neuroimaging Centre (CoNiC), Lee Kong Chien School of Medicine, Nanyang Technological University, Singapore
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10
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Dinh C, Samuelsson JG, Hunold A, Hämäläinen MS, Khan S. Contextual MEG and EEG Source Estimates Using Spatiotemporal LSTM Networks. Front Neurosci 2021; 15:552666. [PMID: 33767606 PMCID: PMC7985163 DOI: 10.3389/fnins.2021.552666] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 01/25/2021] [Indexed: 11/13/2022] Open
Abstract
Most magneto- and electroencephalography (M/EEG) based source estimation techniques derive their estimates sample wise, independently across time. However, neuronal assemblies are intricately interconnected, constraining the temporal evolution of neural activity that is detected by MEG and EEG; the observed neural currents must thus be highly context dependent. Here, we use a network of Long Short-Term Memory (LSTM) cells where the input is a sequence of past source estimates and the output is a prediction of the following estimate. This prediction is then used to correct the estimate. In this study, we applied this technique on noise-normalized minimum norm estimates (MNE). Because the correction is found by using past activity (context), we call this implementation Contextual MNE (CMNE), although this technique can be used in conjunction with any source estimation method. We test CMNE on simulated epileptiform activity and recorded auditory steady state response (ASSR) data, showing that the CMNE estimates exhibit a higher degree of spatial fidelity than the unfiltered estimates in the tested cases.
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Affiliation(s)
- Christoph Dinh
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States.,Department of Radiology, Massachusetts General Hospital (MGH), Charlestown, MA, United States.,Institute for Medical Engineering, Research Campus STIMULATE, Otto-von-Guericke University, Magdeburg, Germany
| | - John G Samuelsson
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States.,Harvard Medical School, Boston, MA, United States.,Harvard-MIT Division of Health Sciences and Technology (HST), Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Alexander Hunold
- Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, Ilmenau, Germany
| | - Matti S Hämäläinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States.,Department of Radiology, Massachusetts General Hospital (MGH), Charlestown, MA, United States.,Harvard Medical School, Boston, MA, United States
| | - Sheraz Khan
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States.,Department of Radiology, Massachusetts General Hospital (MGH), Charlestown, MA, United States.,Harvard Medical School, Boston, MA, United States
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11
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Piastra MC, Nüßing A, Vorwerk J, Clerc M, Engwer C, Wolters CH. A comprehensive study on electroencephalography and magnetoencephalography sensitivity to cortical and subcortical sources. Hum Brain Mapp 2021; 42:978-992. [PMID: 33156569 PMCID: PMC7856654 DOI: 10.1002/hbm.25272] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 10/19/2020] [Accepted: 10/21/2020] [Indexed: 12/31/2022] Open
Abstract
Signal-to-noise ratio (SNR) maps are a good way to visualize electroencephalography (EEG) and magnetoencephalography (MEG) sensitivity. SNR maps extend the knowledge about the modulation of EEG and MEG signals by source locations and orientations and can therefore help to better understand and interpret measured signals as well as source reconstruction results thereof. Our work has two main objectives. First, we investigated the accuracy and reliability of EEG and MEG finite element method (FEM)-based sensitivity maps for three different head models, namely an isotropic three and four-compartment and an anisotropic six-compartment head model. As a result, we found that ignoring the cerebrospinal fluid leads to an overestimation of EEG SNR values. Second, we examined and compared EEG and MEG SNR mappings for both cortical and subcortical sources and their modulation by source location and orientation. Our results for cortical sources show that EEG sensitivity is higher for radial and deep sources and MEG for tangential ones, which are the majority of sources. As to the subcortical sources, we found that deep sources with sufficient tangential source orientation are recordable by the MEG. Our work, which represents the first comprehensive study where cortical and subcortical sources are considered in highly detailed FEM-based EEG and MEG SNR mappings, sheds a new light on the sensitivity of EEG and MEG and might influence the decision of brain researchers or clinicians in their choice of the best modality for their experiment or diagnostics, respectively.
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Affiliation(s)
- Maria Carla Piastra
- Institute for Biomagnetism and BiosignalanalysisUniversity of MünsterMünsterGermany
- Institute for Computational and Applied MathematicsUniversity of MünsterMünsterGermany
- Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical CenterNijmegenThe Netherlands
| | - Andreas Nüßing
- Institute for Biomagnetism and BiosignalanalysisUniversity of MünsterMünsterGermany
- Institute for Computational and Applied MathematicsUniversity of MünsterMünsterGermany
| | - Johannes Vorwerk
- Institute of Electrical and Biomedical Engineering, University for Health SciencesMedical Informatics and TechnologyHall in TirolAustria
| | - Maureen Clerc
- Inria Sophia Antipolis‐MediterranéeBiotFrance
- Université Côte d'AzurNiceFrance
| | - Christian Engwer
- Institute for Computational and Applied MathematicsUniversity of MünsterMünsterGermany
- Cluster of Excellence EXC 1003, Cells in Motion, CiM, University of MünsterMünsterGermany
| | - Carsten H. Wolters
- Institute for Biomagnetism and BiosignalanalysisUniversity of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of MünsterMünsterGermany
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12
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Beppi C, Ribeiro Violante I, Scott G, Sandrone S. EEG, MEG and neuromodulatory approaches to explore cognition: Current status and future directions. Brain Cogn 2021; 148:105677. [PMID: 33486194 DOI: 10.1016/j.bandc.2020.105677] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Revised: 12/26/2020] [Accepted: 12/27/2020] [Indexed: 01/04/2023]
Abstract
Neural oscillations and their association with brain states and cognitive functions have been object of extensive investigation over the last decades. Several electroencephalography (EEG) and magnetoencephalography (MEG) analysis approaches have been explored and oscillatory properties have been identified, in parallel with the technical and computational advancement. This review provides an up-to-date account of how EEG/MEG oscillations have contributed to the understanding of cognition. Methodological challenges, recent developments and translational potential, along with future research avenues, are discussed.
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Affiliation(s)
- Carolina Beppi
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland; Department of Neurology, University Hospital Zurich and University of Zurich, Zurich, Switzerland; Clinical Neuroscience Center, University Hospital Zurich and University of Zurich, Zurich, Switzerland.
| | - Inês Ribeiro Violante
- Computational, Cognitive and Clinical Neuroscience Laboratory (C3NL), Department of Brain Sciences, Imperial College London, London, United Kingdom; School of Psychology, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom.
| | - Gregory Scott
- Computational, Cognitive and Clinical Neuroscience Laboratory (C3NL), Department of Brain Sciences, Imperial College London, London, United Kingdom.
| | - Stefano Sandrone
- Computational, Cognitive and Clinical Neuroscience Laboratory (C3NL), Department of Brain Sciences, Imperial College London, London, United Kingdom.
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13
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Samuelsson JG, Peled N, Mamashli F, Ahveninen J, Hämäläinen MS. Spatial fidelity of MEG/EEG source estimates: A general evaluation approach. Neuroimage 2021; 224:117430. [PMID: 33038537 PMCID: PMC7793168 DOI: 10.1016/j.neuroimage.2020.117430] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 09/15/2020] [Accepted: 09/29/2020] [Indexed: 11/29/2022] Open
Abstract
Low spatial resolution is often cited as the most critical limitation of magneto- and electroencephalography (MEG and EEG), but a unifying framework for quantifying the spatial fidelity of M/EEG source estimates has yet to be established; previous studies have focused on linear estimation methods under ideal scenarios without noise. Here we present an approach that quantifies the spatial fidelity of M/EEG estimates from simulated patch activations over the entire neocortex superposed on measured resting-state data. This approach grants more generalizability in the evaluation process that allows for, e.g., comparing linear and non-linear estimates in the whole brain for different signal-to-noise ratios (SNR), number of active sources and activation waveforms. Using this framework, we evaluated the MNE, dSPM, sLORETA, eLORETA, and MxNE methods and found that the spatial fidelity varies significantly with SNR, following a largely sigmoidal curve whose shape varies depending on which aspect of spatial fidelity that is being quantified and the source estimation method. We believe that these methods and results will be useful when interpreting M/EEG source estimates as well as in methods development.
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Affiliation(s)
- John G Samuelsson
- Harvard-MIT Division of Health Sciences and Technology (HST), Massachusetts Institute of Technology (MIT), Cambridge, MA 02139, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Harvard Medical School, Boston, MA 02115, USA.
| | - Noam Peled
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Department of Radiology, Massachusetts General Hospital (MGH), Charlestown, MA 02129, USA; Harvard Medical School, Boston, MA 02115, USA
| | - Fahimeh Mamashli
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Department of Radiology, Massachusetts General Hospital (MGH), Charlestown, MA 02129, USA; Harvard Medical School, Boston, MA 02115, USA
| | - Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Department of Radiology, Massachusetts General Hospital (MGH), Charlestown, MA 02129, USA; Harvard Medical School, Boston, MA 02115, USA
| | - Matti S Hämäläinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Department of Radiology, Massachusetts General Hospital (MGH), Charlestown, MA 02129, USA; Harvard Medical School, Boston, MA 02115, USA
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14
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Head phantoms for bioelectromagnetic applications: a material study. Biomed Eng Online 2020; 19:87. [PMID: 33228687 PMCID: PMC7685571 DOI: 10.1186/s12938-020-00830-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 11/07/2020] [Indexed: 11/30/2022] Open
Abstract
Background Assessments of source reconstruction procedures in electroencephalography and computations of transcranial electrical stimulation profiles require verification and validation with the help of ground truth configurations as implemented by physical head phantoms. For these phantoms, synthetic materials are needed, which are mechanically and electrochemically stable and possess conductivity values similar to the modeled human head tissues. Three-compartment head models comprise a scalp layer with a conductivity range of 0.137 S/m to 2.1 S/m, a skull layer with conductivity values between 0.066 S/m and 0.00275 S/m, and an intracranial volume with an often-used average conductivity value of 0.33 S/m. To establish a realistically shaped physical head phantom with a well-defined volume conduction configuration, we here characterize the electrical conductivity of synthetic materials for modeling head compartments. We analyzed agarose hydrogel, gypsum, and sodium chloride (NaCl) solution as surrogate materials for scalp, skull, and intracranial volume. We measured the impedance of all materials when immersed in NaCl solution using a four-electrode setup. The measured impedance values were used to calculate the electrical conductivity values of each material. Further, the conductivities in the longitudinal and transverse directions of reed sticks immersed in NaCl solution were measured to test their suitability for mimicking the anisotropic conductivity of white matter tracts. Results We obtained conductivities of 0.314 S/m, 0.30 S/m, 0.311 S/m (2%, 3%, 4% agarose), 0.0425 S/m and 0.0017 S/m (gypsum with and without NaCl in the compound), and 0.332 S/m (0.17% NaCl solution). These values are within the range of the conductivity values used for EEG and TES modeling. The reed sticks showed anisotropic conductivity with a ratio of 1:2.8. Conclusion We conclude that agarose, gypsum, and NaCl solution can serve as stable representations of the three main conductivity compartments of the head, i.e., scalp, skull, and intracranial volume. An anisotropic conductivity structure such as a fiber track in white matter can be modeled using tailored reed sticks inside a volume conductor.![]()
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15
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Höller Y, Nardone R. Quantitative EEG biomarkers for epilepsy and their relation to chemical biomarkers. Adv Clin Chem 2020; 102:271-336. [PMID: 34044912 DOI: 10.1016/bs.acc.2020.08.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The electroencephalogram (EEG) is the most important method to diagnose epilepsy. In clinical settings, it is evaluated by experts who identify patterns visually. Quantitative EEG is the application of digital signal processing to clinical recordings in order to automatize diagnostic procedures, and to make patterns visible that are hidden to the human eye. The EEG is related to chemical biomarkers, as electrical activity is based on chemical signals. The most well-known chemical biomarkers are blood laboratory tests to identify seizures after they have happened. However, research on chemical biomarkers is much less extensive than research on quantitative EEG, and combined studies are rarely published, but highly warranted. Quantitative EEG is as old as the EEG itself, but still, the methods are not yet standard in clinical practice. The most evident application is an automation of manual work, but also a quantitative description and localization of interictal epileptiform events as well as seizures can reveal important hints for diagnosis and contribute to presurgical evaluation. In addition, the assessment of network characteristics and entropy measures were found to reveal important insights into epileptic brain activity. Application scenarios of quantitative EEG in epilepsy include seizure prediction, pharmaco-EEG, treatment monitoring, evaluation of cognition, and neurofeedback. The main challenges to quantitative EEG are poor reliability and poor generalizability of measures, as well as the need for individualization of procedures. A main hindrance for quantitative EEG to enter clinical routine is also that training is not yet part of standard curricula for clinical neurophysiologists.
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Affiliation(s)
- Yvonne Höller
- Faculty of Psychology, University of Akureyri, Akureyri, Iceland.
| | - Raffaele Nardone
- Department of Neurology, Franz Tappeiner Hospital, Merano, Italy; Spinal Cord Injury and Tissue Regeneration Center, Salzburg, Austria; Department of Neurology, Christian Doppler Klinik, Paracelsus Medical University, Salzburg, Austria
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16
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S. Rodrigues M, Fiedler P, Küchler N, P. Domingues R, Lopes C, Borges J, Haueisen J, Vaz F. Dry Electrodes for Surface Electromyography Based on Architectured Titanium Thin Films. MATERIALS 2020; 13:ma13092135. [PMID: 32380683 PMCID: PMC7254203 DOI: 10.3390/ma13092135] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2020] [Revised: 04/30/2020] [Accepted: 05/01/2020] [Indexed: 11/21/2022]
Abstract
Electrodes of silver/silver chloride (Ag/AgCl) are dominant in clinical settings for surface electromyography (sEMG) recordings. These electrodes need a conductive electrolyte gel to ensure proper performance, which dries during long-term measurements inhibiting the immediate electrode’s reuse and is often linked to skin irritation episodes. To overcome these drawbacks, a new type of dry electrodes based on architectured titanium (Ti) thin films were proposed in this work. The architectured microstructures were zigzags, obtained with different sputtering incidence angles (α), which have been shown to directly influence the films’ porosity and electrical conductivity. The electrodes were prepared using thermoplastic polyurethane (TPU) and stainless-steel (SS) substrates, and their performance was tested in male volunteers (athletes) by recording electromyography (EMG) signals, preceded by electrode-skin impedance measurements. In general, the results showed that both SS and TPU dry electrodes can be used for sEMG recordings. While SS electrodes almost match the signal quality parameters of reference electrodes of Ag/AgCl, the performance of electrodes based on TPU functionalized with a Ti thin film still requires further improvements. Noteworthy was the clear increase of the signal to noise ratios when the thin films’ microstructure evolved from normal growth towards zigzag microstructures, meaning that further tailoring of the thin film microstructure is a possible route to achieve optimized performances. Finally, the developed dry electrodes are reusable and allow for multiple EMG recordings without being replaced.
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Affiliation(s)
- Marco S. Rodrigues
- Centro de Física, Universidade do Minho, Campus de Gualtar, 4710-057 Braga, Portugal; (M.S.R.); (R.P.D.); (J.B.); (F.V.)
| | - Patrique Fiedler
- Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, 98693 Ilmenau, Germany; (P.F.); (N.K.); (J.H.)
- eemagine Medical Imaging Solutions GmbH, 10243 Berlin, Germany
| | - Nora Küchler
- Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, 98693 Ilmenau, Germany; (P.F.); (N.K.); (J.H.)
| | - Rui P. Domingues
- Centro de Física, Universidade do Minho, Campus de Gualtar, 4710-057 Braga, Portugal; (M.S.R.); (R.P.D.); (J.B.); (F.V.)
| | - Cláudia Lopes
- Centro de Física, Universidade do Minho, Campus de Gualtar, 4710-057 Braga, Portugal; (M.S.R.); (R.P.D.); (J.B.); (F.V.)
- Correspondence:
| | - Joel Borges
- Centro de Física, Universidade do Minho, Campus de Gualtar, 4710-057 Braga, Portugal; (M.S.R.); (R.P.D.); (J.B.); (F.V.)
| | - Jens Haueisen
- Institute of Biomedical Engineering and Informatics, Technische Universität Ilmenau, 98693 Ilmenau, Germany; (P.F.); (N.K.); (J.H.)
- Biomagnetic Center, Department of Neurology, University Hospital Jena, 07747 Jena, Germany
| | - Filipe Vaz
- Centro de Física, Universidade do Minho, Campus de Gualtar, 4710-057 Braga, Portugal; (M.S.R.); (R.P.D.); (J.B.); (F.V.)
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17
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Naskovska K, Lau S, Korobkov AA, Haueisen J, Haardt M. Coupled CP Decomposition of Simultaneous MEG-EEG Signals for Differentiating Oscillators During Photic Driving. Front Neurosci 2020; 14:261. [PMID: 32327966 PMCID: PMC7161426 DOI: 10.3389/fnins.2020.00261] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 03/09/2020] [Indexed: 11/13/2022] Open
Abstract
Magnetoencephalography (MEG) and electroencephalography (EEG) are contemporary methods to investigate the function and organization of the brain. Simultaneously acquired MEG-EEG data are inherently multi-dimensional and exhibit coupling. This study uses a coupled tensor decomposition to extract the signal sources from MEG-EEG during intermittent photic stimulation (IPS). We employ the Coupled Semi-Algebraic framework for approximate CP decomposition via SImultaneous matrix diagonalization (C-SECSI). After comparing its performance with alternative methods using simulated benchmark data, we apply it to MEG-EEG recordings of 12 participants during IPS with fractions of the individual alpha frequency between 0.4 and 1.3. In the benchmark tests, C-SECSI is more accurate than SECSI and alternative methods, especially in ill-conditioned scenarios, e.g., involving collinear factors or noise sources with different variances. The component field-maps allow us to separate physiologically meaningful oscillations of visually evoked brain activity from background signals. The frequency signatures of the components identify either an entrainment to the respective stimulation frequency or its first harmonic, or an oscillation in the individual alpha band or theta band. In the group analysis of both, MEG and EEG data, we observe a reciprocal relationship between alpha and theta band oscillations. The coupled tensor decomposition using C-SECSI is a robust, powerful method for the extraction of physiologically meaningful sources from multidimensional biomedical data. Unsupervised signal source extraction is an essential solution for rendering advanced multi-modal signal acquisition technology accessible to clinical diagnostics, pre-surgical planning, and brain computer interface applications.
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Affiliation(s)
- Kristina Naskovska
- Communications Research Laboratory, Ilmenau University of Technology, Ilmenau, Germany
| | - Stephan Lau
- Institute of Biomedical Engineering and Informatics, Ilmenau University of Technology, Ilmenau, Germany.,School of Computer Science, Australian Institute for Machine Learning, The University of Adelaide, Adelaide, SA, Australia
| | - Alexey A Korobkov
- Communications Research Laboratory, Ilmenau University of Technology, Ilmenau, Germany.,Institute for Radio-Electronics and Telecommunications, Department for Radio-Electronic and Telecommunication Systems, Kazan National Research Technical University named after A.N Tupolev-KAI, Kazan, Russia
| | - Jens Haueisen
- Institute of Biomedical Engineering and Informatics, Ilmenau University of Technology, Ilmenau, Germany
| | - Martin Haardt
- Communications Research Laboratory, Ilmenau University of Technology, Ilmenau, Germany
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18
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Comparing MEG and EEG in detecting the ~20-Hz rhythm modulation to tactile and proprioceptive stimulation. Neuroimage 2020; 215:116804. [PMID: 32276061 DOI: 10.1016/j.neuroimage.2020.116804] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 03/06/2020] [Accepted: 03/31/2020] [Indexed: 12/13/2022] Open
Abstract
Modulation of the ~20-Hz brain rhythm has been used to evaluate the functional state of the sensorimotor cortex both in healthy subjects and patients, such as stroke patients. The ~20-Hz brain rhythm can be detected by both magnetoencephalography (MEG) and electroencephalography (EEG), but the comparability of these methods has not been evaluated. Here, we compare these two methods in the evaluating of ~20-Hz activity modulation to somatosensory stimuli. Rhythmic ~20-Hz activity during separate tactile and proprioceptive stimulation of the right and left index finger was recorded simultaneously with MEG and EEG in twenty-four healthy participants. Both tactile and proprioceptive stimulus produced a clear suppression at 300-350 ms followed by a subsequent rebound at 700-900 ms after stimulus onset, detected at similar latencies both with MEG and EEG. The relative amplitudes of suppression and rebound correlated strongly between MEG and EEG recordings. However, the relative strength of suppression and rebound in the contralateral hemisphere (with respect to the stimulated hand) was significantly stronger in MEG than in EEG recordings. Our results indicate that MEG recordings produced signals with higher signal-to-noise ratio than EEG, favoring MEG as an optimal tool for studies evaluating sensorimotor cortical functions. However, the strong correlation between MEG and EEG results encourages the use of EEG when translating studies to clinical practice. The clear advantage of EEG is the availability of the method in hospitals and bed-side measurements at the acute phase.
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19
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Hannan S, Faulkner M, Aristovich K, Avery J, Walker MC, Holder DS. In vivo imaging of deep neural activity from the cortical surface during hippocampal epileptiform events in the rat brain using electrical impedance tomography. Neuroimage 2020; 209:116525. [DOI: 10.1016/j.neuroimage.2020.116525] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 12/12/2019] [Accepted: 01/06/2020] [Indexed: 02/07/2023] Open
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20
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van Mierlo P, Höller Y, Focke NK, Vulliemoz S. Network Perspectives on Epilepsy Using EEG/MEG Source Connectivity. Front Neurol 2019; 10:721. [PMID: 31379703 PMCID: PMC6651209 DOI: 10.3389/fneur.2019.00721] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 06/18/2019] [Indexed: 12/17/2022] Open
Abstract
The evolution of EEG/MEG source connectivity is both, a promising, and controversial advance in the characterization of epileptic brain activity. In this narrative review we elucidate the potential of this technology to provide an intuitive view of the epileptic network at its origin, the different brain regions involved in the epilepsy, without the limitation of electrodes at the scalp level. Several studies have confirmed the added value of using source connectivity to localize the seizure onset zone and irritative zone or to quantify the propagation of epileptic activity over time. It has been shown in pilot studies that source connectivity has the potential to obtain prognostic correlates, to assist in the diagnosis of the epilepsy type even in the absence of visually noticeable epileptic activity in the EEG/MEG, and to predict treatment outcome. Nevertheless, prospective validation studies in large and heterogeneous patient cohorts are still lacking and are needed to bring these techniques into clinical use. Moreover, the methodological approach is challenging, with several poorly examined parameters that most likely impact the resulting network patterns. These fundamental challenges affect all potential applications of EEG/MEG source connectivity analysis, be it in a resting, spiking, or ictal state, and also its application to cognitive activation of the eloquent area in presurgical evaluation. However, such method can allow unique insights into physiological and pathological brain functions and have great potential in (clinical) neuroscience.
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Affiliation(s)
- Pieter van Mierlo
- Medical Image and Signal Processing Group, Ghent University, Ghent, Belgium
| | - Yvonne Höller
- Faculty of Psychology, University of Akureyri, Akureyri, Iceland
| | - Niels K Focke
- Clinical Neurophysiology, University Medicine Göttingen, Göttingen, Germany
| | - Serge Vulliemoz
- EEG and Epilepsy Unit, University Hospital of Geneva, Geneva, Switzerland
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21
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Altered neocortical tactile but preserved auditory early change detection responses in Friedreich ataxia. Clin Neurophysiol 2019; 130:1299-1310. [PMID: 31176929 DOI: 10.1016/j.clinph.2019.05.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 04/16/2019] [Accepted: 05/01/2019] [Indexed: 12/17/2022]
Abstract
OBJECTIVE To study using magnetoencephalography (MEG) the spatio-temporal dynamics of neocortical responses involved in sensory processing and early change detection in Friedreich ataxia (FRDA). METHODS Tactile (TERs) and auditory (AERs) evoked responses, and early neocortical change detection responses indexed by the mismatch negativity (MMN) were recorded using tactile and auditory oddballs in sixteen FRDA patients and matched healthy subjects. Correlations between the maximal amplitude of each response, genotype and clinical parameters were investigated. RESULTS Evoked responses were detectable in all FRDA patients but one. In patients, TERs were delayed and reduced in amplitude, while AERs were only delayed. Only tactile MMN responses at the contralateral secondary somatosensory cortex were altered in FRDA patients. Maximal amplitudes of TERs, AERs and tactile MMN correlated with genotype, but did not correlate with clinical parameters. CONCLUSIONS In FRDA, theamplitude of tactile MMN responses at SII cortex are reduced and correlate with the genotype, whileauditory MMN responses are not altered. SIGNIFICANCE Somatosensory pathways and tactile early change detection are selectively impaired in FRDA.
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22
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Maestú F, Cuesta P, Hasan O, Fernandéz A, Funke M, Schulz PE. The Importance of the Validation of M/EEG With Current Biomarkers in Alzheimer's Disease. Front Hum Neurosci 2019; 13:17. [PMID: 30792632 PMCID: PMC6374629 DOI: 10.3389/fnhum.2019.00017] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Accepted: 01/15/2019] [Indexed: 12/22/2022] Open
Abstract
Current biomarkers used in research and in clinical practice in Alzheimer's Disease (AD) are the analysis of cerebral spinal fluid (CSF) to detect levels of Aβ42 and phosphorylated-tau, amyloid and FDG-PET, and MRI volumetry. Some of these procedures are still invasive for patients or expensive. Electroencephalography (EEG) and Magnetoencephalography (MEG) are two non-invasive techniques able to detect the early synaptic dysfunction and track the course of the disease. However, in spite of its added value they are not part of the standard of care in clinical practice in dementia. In this paper we review what these neurophysiological techniques can add to the early diagnosis of AD, whether results in both modalities are related to each other or not, as well as the need of its validation against current biomarkers. We discuss their potential implications for the better understanding of the pathophysiological mechanisms of the disease as well as the need of performing simultaneous M/EEG recordings to better understand discrepancies between these two techniques. Finally, more studies are needed studying M/EEG with amyloid and Tau biomarkers.
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Affiliation(s)
- Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain
- Magnetic Source Imaging Unit, Department of Pediatrics, McGovern Medical School, University of Texas Health Science Center, Houston, TX, United States
| | - Pablo Cuesta
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
- Electrical Engineering and Bioengineering Lab, Department of Industrial Engineering & IUNE Universidad de La Laguna, Tenerife, Spain
| | - Omar Hasan
- McGovern Medical School University of Texas Health Science Center, Houston, TX, United States
| | - Alberto Fernandéz
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
- Department of Legal Medicine, Psychiatry, and Pathology, Universidad Complutense de Madrid, Madrid, Spain
| | - Michael Funke
- Magnetic Source Imaging Unit, Department of Pediatrics, McGovern Medical School, University of Texas Health Science Center, Houston, TX, United States
| | - Paul E. Schulz
- McGovern Medical School University of Texas Health Science Center, Houston, TX, United States
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23
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Samuelsson JG, Khan S, Sundaram P, Peled N, Hämäläinen MS. Cortical Signal Suppression (CSS) for Detection of Subcortical Activity Using MEG and EEG. Brain Topogr 2019; 32:215-228. [PMID: 30604048 DOI: 10.1007/s10548-018-00694-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Accepted: 12/17/2018] [Indexed: 11/28/2022]
Abstract
Magnetoencephalography (MEG) and electroencephalography (EEG) use non-invasive sensors to detect neural currents. Since the contribution of superficial neural sources to the measured M/EEG signals are orders-of-magnitude stronger than the contribution of subcortical sources, most MEG and EEG studies have focused on cortical activity. Subcortical structures, however, are centrally involved in both healthy brain function as well as in many neurological disorders such as Alzheimer's disease and Parkinson's disease. In this paper, we present a method that can separate and suppress the cortical signals while preserving the subcortical contributions to the M/EEG data. The resulting signal subspace of the data mainly originates from subcortical structures. Our method works by utilizing short-baseline planar gradiometers with short-sighted sensitivity distributions as reference sensors for cortical activity. Since the method is completely data-driven, forward and inverse modeling are not required. In this study, we use simulations and auditory steady state response experiments in a human subject to demonstrate that the method can remove the cortical signals while sparing the subcortical signals. We also test our method on MEG data recorded in an essential tremor patient with a deep brain stimulation implant and show how it can be used to reduce the DBS artifact in the MEG data by ~ 99.9% without affecting low frequency brain rhythms.
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Affiliation(s)
- John G Samuelsson
- Harvard-MIT Division of Health Sciences and Technology (HST), Massachusetts Institute of Technology (MIT), Cambridge, MA, 02139, USA. .,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA. .,Harvard Medical School, Boston, MA, 02115, USA.
| | - Sheraz Khan
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA.,Harvard Medical School, Boston, MA, 02115, USA
| | - Padmavathi Sundaram
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA.,Harvard Medical School, Boston, MA, 02115, USA
| | - Noam Peled
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA.,Harvard Medical School, Boston, MA, 02115, USA
| | - Matti S Hämäläinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA.,Harvard Medical School, Boston, MA, 02115, USA
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24
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Faulkner M, Hannan S, Aristovich K, Avery J, Holder D. Feasibility of imaging evoked activity throughout the rat brain using electrical impedance tomography. Neuroimage 2018; 178:1-10. [DOI: 10.1016/j.neuroimage.2018.05.022] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Revised: 04/26/2018] [Accepted: 05/08/2018] [Indexed: 10/16/2022] Open
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25
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Kaltiainen H, Helle L, Liljeström M, Renvall H, Forss N. Theta-Band Oscillations as an Indicator of Mild Traumatic Brain Injury. Brain Topogr 2018; 31:1037-1046. [PMID: 30097835 PMCID: PMC6182433 DOI: 10.1007/s10548-018-0667-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2017] [Accepted: 07/28/2018] [Indexed: 11/30/2022]
Abstract
Mild traumatic brain injury (mTBI) patients continue to pose a diagnostic challenge due to their diverse symptoms without trauma-specific changes in structural imaging. We addressed here the possible early changes in spontaneous oscillatory brain activity after mTBI, and their feasibility as an indicator of injury in clinical evaluation. We recorded resting-state magnetoencephalography (MEG) data in both eyes-open and eyes-closed conditions from 26 patients (11 females and 15 males, aged 20–59) with mTBI 6 days–6 months after the injury, and compared their spontaneous oscillatory activity to corresponding data from 139 healthy controls. Twelve of the patients underwent a follow-up measurement at 6 months. Ten of all patients were without structural lesions in MRI. At single-subject level, aberrant 4–7 Hz (theta) band activity exceeding the + 2 SD limit of the healthy subjects was visible in 7 out of 26 patients; three out of the seven patients with abnormal theta activity were without any detectable lesions in MRI. Of the patients that participated in the follow-up measurements, five showed abnormal theta activity in the first recording, but only two in the second measurement. Our results suggest that aberrant theta-band oscillatory activity can provide an early objective sign of brain dysfunction after mTBI. In 3/7 patients, the slow-wave activity was transient and visible only in the first recording, urging prompt timing for the measurements in clinical settings.
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Affiliation(s)
- Hanna Kaltiainen
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, P.O. Box 12200, 00076, Aalto, Espoo, Finland.
- Aalto Neuroimaging, MEG Core, Aalto University, Espoo, Finland.
- Department of Neurology, Lohja District Hospital, Sairaalatie 8, 08200, Lohja, Finland.
- Clinical Neurosciences and Department of Neurology, University of Helsinki and Helsinki University Central Hospital, P.O. Box 340, 00029, HUS, Helsinki, Finland.
| | - Liisa Helle
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, P.O. Box 12200, 00076, Aalto, Espoo, Finland
- Aalto Neuroimaging, MEG Core, Aalto University, Espoo, Finland
- Elekta Oy, P.O. Box 34, 00531, Helsinki, Finland
| | - Mia Liljeström
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, P.O. Box 12200, 00076, Aalto, Espoo, Finland
- Aalto Neuroimaging, MEG Core, Aalto University, Espoo, Finland
| | - Hanna Renvall
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, P.O. Box 12200, 00076, Aalto, Espoo, Finland
- Aalto Neuroimaging, MEG Core, Aalto University, Espoo, Finland
- Clinical Neurosciences and Department of Neurology, University of Helsinki and Helsinki University Central Hospital, P.O. Box 340, 00029, HUS, Helsinki, Finland
| | - Nina Forss
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, P.O. Box 12200, 00076, Aalto, Espoo, Finland
- Aalto Neuroimaging, MEG Core, Aalto University, Espoo, Finland
- Clinical Neurosciences and Department of Neurology, University of Helsinki and Helsinki University Central Hospital, P.O. Box 340, 00029, HUS, Helsinki, Finland
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26
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Chowdhury RA, Pellegrino G, Aydin Ü, Lina JM, Dubeau F, Kobayashi E, Grova C. Reproducibility of EEG-MEG fusion source analysis of interictal spikes: Relevance in presurgical evaluation of epilepsy. Hum Brain Mapp 2017; 39:880-901. [PMID: 29164737 DOI: 10.1002/hbm.23889] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2017] [Revised: 11/03/2017] [Accepted: 11/07/2017] [Indexed: 11/06/2022] Open
Abstract
Fusion of electroencephalography (EEG) and magnetoencephalography (MEG) data using maximum entropy on the mean method (MEM-fusion) takes advantage of the complementarities between EEG and MEG to improve localization accuracy. Simulation studies demonstrated MEM-fusion to be robust especially in noisy conditions such as single spike source localizations (SSSL). Our objective was to assess the reliability of SSSL using MEM-fusion on clinical data. We proposed to cluster SSSL results to find the most reliable and consistent source map from the reconstructed sources, the so-called consensus map. Thirty-four types of interictal epileptic discharges (IEDs) were analyzed from 26 patients with well-defined epileptogenic focus. SSSLs were performed on EEG, MEG, and fusion data and consensus maps were estimated using hierarchical clustering. Qualitative (spike-to-spike reproducibility rate, SSR) and quantitative (localization error and spatial dispersion) assessments were performed using the epileptogenic focus as clinical reference. Fusion SSSL provided significantly better results than EEG or MEG alone. Fusion found at least one cluster concordant with the clinical reference in all cases. This concordant cluster was always the one involving the highest number of spikes. Fusion yielded highest reproducibility (SSR EEG = 55%, MEG = 71%, fusion = 90%) and lowest localization error. Also, using only few channels from either modality (21EEG + 272MEG or 54EEG + 25MEG) was sufficient to reach accurate fusion. MEM-fusion with consensus map approach provides an objective way of finding the most reliable and concordant generators of IEDs. We, therefore, suggest the pertinence of SSSL using MEM-fusion as a valuable clinical tool for presurgical evaluation of epilepsy.
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Affiliation(s)
- Rasheda Arman Chowdhury
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montreal, Québec, Canada
| | | | - Ümit Aydin
- Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montreal, Québec, Canada
| | - Jean-Marc Lina
- Ecole de Technologie Supérieure, Montréal, Québec, Canada.,Centre de Recherches Mathématiques, Université de Montréal, Montréal, Québec, Canada
| | - François Dubeau
- Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Eliane Kobayashi
- Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| | - Christophe Grova
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montreal, Québec, Canada.,Centre de Recherches Mathématiques, Université de Montréal, Montréal, Québec, Canada.,Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada.,Multimodal Functional Imaging Lab, Department of Physics and PERFORM Centre, Concordia University, Montreal, Québec, Canada
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27
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Neugebauer F, Möddel G, Rampp S, Burger M, Wolters CH. The Effect of Head Model Simplification on Beamformer Source Localization. Front Neurosci 2017; 11:625. [PMID: 29209157 PMCID: PMC5701642 DOI: 10.3389/fnins.2017.00625] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Accepted: 10/26/2017] [Indexed: 11/13/2022] Open
Abstract
Beamformers are a widely-used tool in brain analysis with magnetoencephalography (MEG) and electroencephalography (EEG). For the construction of the beamformer filters realistic head volume conductor modeling is necessary for accurately computing the EEG and MEG leadfields, i.e., for solving the EEG and MEG forward problem. In this work, we investigate the influence of including realistic head tissue compartments into a finite element method (FEM) model on the beamformer's localization ability. Specifically, we investigate the effect of including cerebrospinal fluid, gray matter, and white matter distinction, as well as segmenting the skull bone into compacta and spongiosa, and modeling white matter anisotropy. We simulate an interictal epileptic measurement with white sensor noise. Beamformer filters are constructed with unit gain, unit array gain, and unit noise gain constraint. Beamformer source positions are determined by evaluating power and excess sample kurtosis (g2) of the source-waveforms at all source space nodes. For both modalities, we see a strong effect of modeling the cerebrospinal fluid and white and gray matter. Depending on the source position, both effects can each be in the magnitude of centimeters, rendering their modeling necessary for successful localization. Precise skull modeling mainly effected the EEG up to a few millimeters, while both modalities could profit from modeling white matter anisotropy to a smaller extent of 5-10 mm. The unit noise gain or neural activity index beamformer behaves similarly to the array gain beamformer when noise strength is sufficiently high. Variance localization seems more robust against modeling errors than kurtosis.
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Affiliation(s)
- Frank Neugebauer
- Institute for Biomagnetism und Biosignalanalysis, University of Münster, Münster, Germany
| | - Gabriel Möddel
- Department of Sleep Medicine and Neuromuscular Disorders, Epilepsy Center Münster-Osnabrück, University of Münster, Münster, Germany
| | - Stefan Rampp
- Department of Neurosurgery, University Hospital Erlangen, Erlangen, Germany
| | - Martin Burger
- Institute for Computational and Applied Mathematics, University of Münster, Münster, Germany
| | - Carsten H. Wolters
- Institute for Biomagnetism und Biosignalanalysis, University of Münster, Münster, Germany
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28
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De Tiège X, Lundqvist D, Beniczky S, Seri S, Paetau R. Current clinical magnetoencephalography practice across Europe: Are we closer to use MEG as an established clinical tool? Seizure 2017. [PMID: 28623727 DOI: 10.1016/j.seizure.2017.06.002] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Affiliation(s)
- Xavier De Tiège
- Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles (ULB), Brussels, Belgium; Laboratoire de Cartographie fonctionnelle du Cerveau, ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.
| | - Daniel Lundqvist
- NatMEG, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund, Denmark; Department of Clinical Neurophysiology, Aarhus University, Aarhus, Denmark
| | - Stefano Seri
- School of Life and Health Sciences, Aston Brain Centre, Aston University, Birmingham, United Kingdom
| | - Ritva Paetau
- Departments of Paediatric Neurology and Clinical Neurophysiology, Helsinki University Central Hospital, Helsinki, Finland
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29
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Soriano MC, Niso G, Clements J, Ortín S, Carrasco S, Gudín M, Mirasso CR, Pereda E. Automated Detection of Epileptic Biomarkers in Resting-State Interictal MEG Data. Front Neuroinform 2017; 11:43. [PMID: 28713260 PMCID: PMC5491593 DOI: 10.3389/fninf.2017.00043] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Accepted: 06/13/2017] [Indexed: 11/13/2022] Open
Abstract
Certain differences between brain networks of healthy and epilectic subjects have been reported even during the interictal activity, in which no epileptic seizures occur. Here, magnetoencephalography (MEG) data recorded in the resting state is used to discriminate between healthy subjects and patients with either idiopathic generalized epilepsy or frontal focal epilepsy. Signal features extracted from interictal periods without any epileptiform activity are used to train a machine learning algorithm to draw a diagnosis. This is potentially relevant to patients without frequent or easily detectable spikes. To analyze the data, we use an up-to-date machine learning algorithm and explore the benefits of including different features obtained from the MEG data as inputs to the algorithm. We find that the relative power spectral density of the MEG time-series is sufficient to distinguish between healthy and epileptic subjects with a high prediction accuracy. We also find that a combination of features such as the phase-locked value and the relative power spectral density allow to discriminate generalized and focal epilepsy, when these features are calculated over a filtered version of the signals in certain frequency bands. Machine learning algorithms are currently being applied to the analysis and classification of brain signals. It is, however, less evident to identify the proper features of these signals that are prone to be used in such machine learning algorithms. Here, we evaluate the influence of the input feature selection on a clinical scenario to distinguish between healthy and epileptic subjects. Our results indicate that such distinction is possible with a high accuracy (86%), allowing the discrimination between idiopathic generalized and frontal focal epilepsy types.
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Affiliation(s)
- Miguel C Soriano
- Instituto de Física Interdisciplinar y Sistemas Complejos, Consejo Superior de Investigaciones Científicas (CSIC), Campus Universitat Illes BalearsPalma de Mallorca, Spain
| | - Guiomar Niso
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill UniversityMontreal, QC, Canada.,Laboratory of Cognitive and Computational Neuroscience, Center of Biomedical Technology, Politechnical University of MadridMadrid, Spain
| | - Jillian Clements
- Department of Electrical and Computer Engineering, Duke UniversityDurham, NC, United States
| | - Silvia Ortín
- Instituto de Física Interdisciplinar y Sistemas Complejos, Consejo Superior de Investigaciones Científicas (CSIC), Campus Universitat Illes BalearsPalma de Mallorca, Spain
| | - Sira Carrasco
- Teaching General Hospital of Ciudad RealCiudad Real, Spain
| | - María Gudín
- Teaching General Hospital of Ciudad RealCiudad Real, Spain
| | - Claudio R Mirasso
- Instituto de Física Interdisciplinar y Sistemas Complejos, Consejo Superior de Investigaciones Científicas (CSIC), Campus Universitat Illes BalearsPalma de Mallorca, Spain
| | - Ernesto Pereda
- Laboratory of Cognitive and Computational Neuroscience, Center of Biomedical Technology, Politechnical University of MadridMadrid, Spain.,Electrical Engineering and Bioengineering Group, Department of Industrial Engineering, Instituto Universitario de Neurociencia, Universidad de La LagunaTenerife, Spain
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30
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Ioannides AA, Liu L, Poghosyan V, Kostopoulos GK. Using MEG to Understand the Progression of Light Sleep and the Emergence and Functional Roles of Spindles and K-Complexes. Front Hum Neurosci 2017; 11:313. [PMID: 28670270 PMCID: PMC5472839 DOI: 10.3389/fnhum.2017.00313] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2017] [Accepted: 05/31/2017] [Indexed: 12/20/2022] Open
Abstract
We used tomographic analysis of MEG signals to characterize regional spectral changes in the brain at sleep onset and during light sleep. We identified two key processes that may causally link to loss of consciousness during the quiet or "core" periods of NREM1. First, active inhibition in the frontal lobe leads to delta and theta spectral power increases. Second, activation suppression leads to sharp drop of spectral power in alpha and higher frequencies in posterior parietal cortex. During NREM2 core periods, the changes identified in NREM1 become more widespread, but focal increases also emerge in alpha and low sigma band power in frontal midline cortical structures, suggesting reemergence of some monitoring of internal and external environment. Just before spindles and K-complexes (KCs), the hallmarks of NREM2, we identified focal spectral power changes in pre-frontal cortex, mid cingulate, and areas involved in environmental and internal monitoring, i.e., the rostral and sub-genual anterior cingulate. During both spindles and KCs, alpha and low sigma bands increases. Spindles emerge after further active inhibition (increase in delta power) of the frontal areas responsible for environmental monitoring, while in posterior parietal cortex, power increases in low and high sigma bands. KCs are correlated with increase in alpha power in the monitoring areas. These specific regional changes suggest strong and varied vigilance changes for KCs, but vigilance suppression and sharpening of cognitive processing for spindles. This is consistent with processes designed to ensure accurate and uncorrupted memory consolidation. The changes during KCs suggest a sentinel role: evaluation of the salience of provoking events to decide whether to increase processing and possibly wake up, or to actively inhibit further processing of intruding influences. The regional spectral patterns of NREM1, NREM2, and their dynamic changes just before spindles and KCs reveal an edge effect facilitating the emergence of spindles and KCs and defining the precise loci where they might emerge. In the time domain, the spindles are seen in widespread areas of the cortex just as reported from analysis of intracranial data, consistent with the emerging consensus of a differential topography that depends on the kind of memory stored.
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Affiliation(s)
- Andreas A. Ioannides
- Laboratory for Human Brain Dynamics, AAI Scientific Cultural Services Ltd.Nicosia, Cyprus
| | - Lichan Liu
- Laboratory for Human Brain Dynamics, AAI Scientific Cultural Services Ltd.Nicosia, Cyprus
| | - Vahe Poghosyan
- Laboratory for Human Brain Dynamics, AAI Scientific Cultural Services Ltd.Nicosia, Cyprus
- MEG Unit, Department of Neurophysiology, King Fahad Medical CityRiyadh, Saudi Arabia
| | - George K. Kostopoulos
- Neurophysiology Unit, Department of Physiology, Medical School, University of PatrasRion, Greece
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31
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Puce A, Hämäläinen MS. A Review of Issues Related to Data Acquisition and Analysis in EEG/MEG Studies. Brain Sci 2017; 7:E58. [PMID: 28561761 PMCID: PMC5483631 DOI: 10.3390/brainsci7060058] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Revised: 05/23/2017] [Accepted: 05/25/2017] [Indexed: 11/16/2022] Open
Abstract
Electroencephalography (EEG) and magnetoencephalography (MEG) are non-invasive electrophysiological methods, which record electric potentials and magnetic fields due to electric currents in synchronously-active neurons. With MEG being more sensitive to neural activity from tangential currents and EEG being able to detect both radial and tangential sources, the two methods are complementary. Over the years, neurophysiological studies have changed considerably: high-density recordings are becoming de rigueur; there is interest in both spontaneous and evoked activity; and sophisticated artifact detection and removal methods are available. Improved head models for source estimation have also increased the precision of the current estimates, particularly for EEG and combined EEG/MEG. Because of their complementarity, more investigators are beginning to perform simultaneous EEG/MEG studies to gain more complete information about neural activity. Given the increase in methodological complexity in EEG/MEG, it is important to gather data that are of high quality and that are as artifact free as possible. Here, we discuss some issues in data acquisition and analysis of EEG and MEG data. Practical considerations for different types of EEG and MEG studies are also discussed.
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Affiliation(s)
- Aina Puce
- Psychological & Brain Sciences, Indiana University, 1101 East 10th St, Bloomington, IN 47405, USA.
| | - Matti S Hämäläinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA.
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32
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Abstract
Magnetoencephalography (MEG) is a method to study electrical activity in the human brain by recording the neuromagnetic field outside the head. MEG, like electroencephalography (EEG), provides an excellent, millisecond-scale time resolution, and allows the estimation of the spatial distribution of the underlying activity, in favorable cases with a localization accuracy of a few millimeters. To detect the weak neuromagnetic signals, superconducting sensors, magnetically shielded rooms, and advanced signal processing techniques are used. The analysis and interpretation of MEG data typically involves comparisons between subject groups and experimental conditions using various spatial, temporal, and spectral measures of cortical activity and connectivity. The application of MEG to cognitive neuroscience studies is illustrated with studies of spoken language processing in subjects with normal and impaired reading ability. The mapping of spatiotemporal patterns of activity within networks of cortical areas can provide useful information about the functional architecture of the brain related to sensory and cognitive processing, including language, memory, attention, and perception.
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Affiliation(s)
- Seppo P Ahlfors
- MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 149 13th St., Mailcode 149-2301, Charlestown, MA 02129; U.S.A. Tel. +1-617-726-0663
| | - Maria Mody
- MGH/HST Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 149 13th St., Mailcode 149-2301, Charlestown, MA 02129; U.S.A. Tel. +1-617-726-0663
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