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Hussain I, Kwon C, Noh TS, Kim HC, Suh MW, Ku Y. An interpretable tinnitus prediction framework using gap-prepulse inhibition in auditory late response and electroencephalogram. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 255:108371. [PMID: 39173295 DOI: 10.1016/j.cmpb.2024.108371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 08/07/2024] [Accepted: 08/09/2024] [Indexed: 08/24/2024]
Abstract
BACKGROUND AND OBJECTIVE Tinnitus is a neuropathological condition that results in mild buzzing or ringing of the ears without an external sound source. Current tinnitus diagnostic methods often rely on subjective assessment and require intricate medical examinations. This study aimed to propose an interpretable tinnitus diagnostic framework using auditory late response (ALR) and electroencephalogram (EEG), inspired by the gap-prepulse inhibition (GPI) paradigm. METHODS We collected spontaneous EEG and ALR data from 44 patients with tinnitus and 47 hearing loss-matched controls using specialized hardware to capture responses to sound stimuli with embedded gaps. In this cohort study of tinnitus and control groups, we examined EEG spectral and ALR features of N-P complexes, comparing the responses to gap durations of 50 and 20 ms alongside no-gap conditions. To this end, we developed an interpretable tinnitus diagnostic model using ALR and EEG metrics, boosting machine learning architecture, and explainable feature attribution approaches. RESULTS Our proposed model achieved 90 % accuracy in identifying tinnitus, with an area under the performance curve of 0.89. The explainable artificial intelligence approaches have revealed gap-embedded ALR features such as the GPI ratio of N1-P2 and EEG spectral ratio, which can serve as diagnostic metrics for tinnitus. Our method successfully provides personalized prediction explanations for tinnitus diagnosis using gap-embedded auditory and neurological features. CONCLUSIONS Deficits in GPI alongside activity in the EEG alpha-beta ratio offer a promising screening tool for assessing tinnitus risk, aligning with current clinical insights from hearing research.
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Affiliation(s)
- Iqram Hussain
- Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University College of Medicine, Seoul 03080, Republic of Korea; Department of Anesthesiology, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA
| | - Chiheon Kwon
- Medical Device Research Center, Department of Biomedical Research Institute, Chungnam National University Hospital, Daejeon, Republic of Korea
| | - Tae-Soo Noh
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Hospital, Seoul 03080, Republic of Korea
| | - Hee Chan Kim
- Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University College of Medicine, Seoul 03080, Republic of Korea; Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul 03080, Republic of Korea; Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul 08826, Republic of Korea
| | - Myung-Whan Suh
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Hospital, Seoul 03080, Republic of Korea
| | - Yunseo Ku
- Medical Device Research Center, Department of Biomedical Research Institute, Chungnam National University Hospital, Daejeon, Republic of Korea; Department of Biomedical Engineering, College of Medicine, Chungnam National University, Daejeon 35015, Republic of Korea.
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Noda Y, Takano M, Wada M, Mimura Y, Nakajima S. Validation of the number of pulses required for TMS-EEG in the prefrontal cortex considering test feasibility. Neuroscience 2024; 554:63-71. [PMID: 39002755 DOI: 10.1016/j.neuroscience.2024.07.011] [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: 01/09/2024] [Revised: 06/27/2024] [Accepted: 07/08/2024] [Indexed: 07/15/2024]
Abstract
BACKGROUND Transcranial magnetic stimulation (TMS) combined with electroencephalography (EEG), TMS-EEG, is a useful neuroscientific tool for the assessment of neurophysiology in the human cerebral cortex. Theoretically, TMS-EEG data is expected to have a better data quality as the number of stimulation pulses increases. However, since TMS-EEG testing is a modality that is examined on human subjects, the burden on the subject and tolerability of the test must also be carefully considered. METHOD In this study, we aimed to determine the number of stimulation pulses that satisfy the reliability and validity of data quality in single-pulse TMS (spTMS) for the dorsolateral prefrontal cortex (DLPFC). TMS-EEG data for (1) 40-pulse, (2) 80-pulse, (3) 160-pulse, and (4) 240-pulse conditions were extracted from spTMS experimental data for the left DLPFC of 20 healthy subjects, and the similarities between TMS-evoked potentials (TEP) and oscillations across the conditions were evaluated. RESULTS As a result, (2) 80-pulse and (3) 160-pulse conditions showed highly equivalent to the benchmark condition of (4) 240-pulse condition. However, (1) 40-pulse condition showed only weak to moderate equivalence to the (4) 240-pulse condition. Thus, in the DLPFC TMS-EEG experiment, 80 pulses of stimulations was found to be a reasonable enough number of pulses to extract reliable TEPs, compared to 160 or 240 pulses. CONCLUSIONS This is the first substantial study to examine the appropriate number of stimulus pulses that are reasonable and feasible for TMS-EEG testing of the DLPFC.
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Affiliation(s)
- Yoshihiro Noda
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan.
| | - Mayuko Takano
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan; Teijin Pharma Ltd., Tokyo, Japan
| | - Masataka Wada
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Yu Mimura
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
| | - Shinichiro Nakajima
- Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
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3
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Bonetti L, Brattico E, Carlomagno F, Cabral J, Stevner A, Deco G, Whybrow PC, Pearce M, Pantazis D, Vuust P, Kringelbach ML. Spatiotemporal whole-brain activity and functional connectivity of melodies recognition. Cereb Cortex 2024; 34:bhae320. [PMID: 39110413 PMCID: PMC11304985 DOI: 10.1093/cercor/bhae320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Revised: 07/12/2024] [Accepted: 07/26/2024] [Indexed: 08/10/2024] Open
Abstract
Music is a non-verbal human language, built on logical, hierarchical structures, that offers excellent opportunities to explore how the brain processes complex spatiotemporal auditory sequences. Using the high temporal resolution of magnetoencephalography, we investigated the unfolding brain dynamics of 70 participants during the recognition of previously memorized musical sequences compared to novel sequences matched in terms of entropy and information content. Measures of both whole-brain activity and functional connectivity revealed a widespread brain network underlying the recognition of the memorized auditory sequences, which comprised primary auditory cortex, superior temporal gyrus, insula, frontal operculum, cingulate gyrus, orbitofrontal cortex, basal ganglia, thalamus, and hippocampus. Furthermore, while the auditory cortex responded mainly to the first tones of the sequences, the activity of higher-order brain areas such as the cingulate gyrus, frontal operculum, hippocampus, and orbitofrontal cortex largely increased over time during the recognition of the memorized versus novel musical sequences. In conclusion, using a wide range of analytical techniques spanning from decoding to functional connectivity and building on previous works, our study provided new insights into the spatiotemporal whole-brain mechanisms for conscious recognition of auditory sequences.
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Affiliation(s)
- Leonardo Bonetti
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, 8000 Aarhus/Aalborg, Denmark
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, OX39BX Oxford, United Kingdom
- Department of Psychiatry, University of Oxford, OX37JX Oxford, United Kingdom
| | - Elvira Brattico
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, 8000 Aarhus/Aalborg, Denmark
- Department of Education, Psychology, Communication, University of Bari Aldo Moro, 70121 Bari, Italy
| | - Francesco Carlomagno
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, 8000 Aarhus/Aalborg, Denmark
| | - Joana Cabral
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, 8000 Aarhus/Aalborg, Denmark
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, OX39BX Oxford, United Kingdom
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057 Braga, Portugal
| | - Angus Stevner
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, 8000 Aarhus/Aalborg, Denmark
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, OX39BX Oxford, United Kingdom
| | - Gustavo Deco
- Computational and Theoretical Neuroscience Group, Center for Brain and Cognition, Universitat Pompeu Fabra, 08018 Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona, Spain
| | - Peter C Whybrow
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, 90095 Los Angeles, CA, United States
| | - Marcus Pearce
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, 8000 Aarhus/Aalborg, Denmark
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology (MIT), 02139 Cambridge, MA, United States
| | - Peter Vuust
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, 8000 Aarhus/Aalborg, Denmark
| | - Morten L Kringelbach
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, 8000 Aarhus/Aalborg, Denmark
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, OX39BX Oxford, United Kingdom
- Department of Psychiatry, University of Oxford, OX37JX Oxford, United Kingdom
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Erdbrügger T, Höltershinken M, Radecke J, Buschermöhle Y, Wallois F, Pursiainen S, Gross J, Lencer R, Engwer C, Wolters C. CutFEM-based MEG forward modeling improves source separability and sensitivity to quasi-radial sources: A somatosensory group study. Hum Brain Mapp 2024; 45:e26810. [PMID: 39140847 PMCID: PMC11323619 DOI: 10.1002/hbm.26810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 06/21/2024] [Accepted: 07/20/2024] [Indexed: 08/15/2024] Open
Abstract
Source analysis of magnetoencephalography (MEG) data requires the computation of the magnetic fields induced by current sources in the brain. This so-called MEG forward problem includes an accurate estimation of the volume conduction effects in the human head. Here, we introduce the Cut finite element method (CutFEM) for the MEG forward problem. CutFEM's meshing process imposes fewer restrictions on tissue anatomy than tetrahedral meshes while being able to mesh curved geometries contrary to hexahedral meshing. To evaluate the new approach, we compare CutFEM with a boundary element method (BEM) that distinguishes three tissue compartments and a 6-compartment hexahedral FEM in an n = 19 group study of somatosensory evoked fields (SEF). The neural generators of the 20 ms post-stimulus SEF components (M20) are reconstructed using both an unregularized and a regularized inversion approach. Changing the forward model resulted in reconstruction differences of about 1 centimeter in location and considerable differences in orientation. The tested 6-compartment FEM approaches significantly increase the goodness of fit to the measured data compared with the 3-compartment BEM. They also demonstrate higher quasi-radial contributions for sources below the gyral crowns. Furthermore, CutFEM improves source separability compared with both other approaches. We conclude that head models with 6 compartments rather than 3 and the new CutFEM approach are valuable additions to MEG source reconstruction, in particular for sources that are predominantly radial.
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Affiliation(s)
- Tim Erdbrügger
- Institute for Biomagnetism and Biosignalanalysis, University of MünsterMünsterGermany
- Institute for Analysis and Numerics, University of MünsterMünsterGermany
| | - Malte Höltershinken
- Institute for Biomagnetism and Biosignalanalysis, University of MünsterMünsterGermany
- Institute for Analysis and Numerics, University of MünsterMünsterGermany
| | - Jan‐Ole Radecke
- Deptartment of Psychiatry and PsychotherapyUniversity of LübeckLübeckGermany
- Center for Brain, Behaviour and Metabolism (CBBM)University of LübeckLübeckGermany
| | - Yvonne Buschermöhle
- Institute for Biomagnetism and Biosignalanalysis, University of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of MünsterMünsterGermany
| | - Fabrice Wallois
- Institut National de la Santé et de la Recherche Médicale, University of Picardie Jules VerneAmiensFrance
| | - Sampsa Pursiainen
- Computing Sciences Unit, Faculty of Information Technology and Communication SciencesTampere UniversityTampereFinland
| | - Joachim Gross
- Institute for Biomagnetism and Biosignalanalysis, University of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of MünsterMünsterGermany
| | - Rebekka Lencer
- Deptartment of Psychiatry and PsychotherapyUniversity of LübeckLübeckGermany
- Center for Brain, Behaviour and Metabolism (CBBM)University of LübeckLübeckGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of MünsterMünsterGermany
- Institute for Translational Psychiatry, University of MünsterMünsterGermany
| | - Christian Engwer
- Institute for Analysis and Numerics, University of MünsterMünsterGermany
| | - Carsten Wolters
- Institute for Biomagnetism and Biosignalanalysis, University of MünsterMünsterGermany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of MünsterMünsterGermany
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Afnan J, Cai Z, Lina JM, Abdallah C, Delaire E, Avigdor T, Ros V, Hedrich T, von Ellenrieder N, Kobayashi E, Frauscher B, Gotman J, Grova C. EEG/MEG source imaging of deep brain activity within the maximum entropy on the mean framework: Simulations and validation in epilepsy. Hum Brain Mapp 2024; 45:e26720. [PMID: 38994740 PMCID: PMC11240147 DOI: 10.1002/hbm.26720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 04/16/2024] [Accepted: 05/06/2024] [Indexed: 07/13/2024] Open
Abstract
Electro/Magneto-EncephaloGraphy (EEG/MEG) source imaging (EMSI) of epileptic activity from deep generators is often challenging due to the higher sensitivity of EEG/MEG to superficial regions and to the spatial configuration of subcortical structures. We previously demonstrated the ability of the coherent Maximum Entropy on the Mean (cMEM) method to accurately localize the superficial cortical generators and their spatial extent. Here, we propose a depth-weighted adaptation of cMEM to localize deep generators more accurately. These methods were evaluated using realistic MEG/high-density EEG (HD-EEG) simulations of epileptic activity and actual MEG/HD-EEG recordings from patients with focal epilepsy. We incorporated depth-weighting within the MEM framework to compensate for its preference for superficial generators. We also included a mesh of both hippocampi, as an additional deep structure in the source model. We generated 5400 realistic simulations of interictal epileptic discharges for MEG and HD-EEG involving a wide range of spatial extents and signal-to-noise ratio (SNR) levels, before investigating EMSI on clinical HD-EEG in 16 patients and MEG in 14 patients. Clinical interictal epileptic discharges were marked by visual inspection. We applied three EMSI methods: cMEM, depth-weighted cMEM and depth-weighted minimum norm estimate (MNE). The ground truth was defined as the true simulated generator or as a drawn region based on clinical information available for patients. For deep sources, depth-weighted cMEM improved the localization when compared to cMEM and depth-weighted MNE, whereas depth-weighted cMEM did not deteriorate localization accuracy for superficial regions. For patients' data, we observed improvement in localization for deep sources, especially for the patients with mesial temporal epilepsy, for which cMEM failed to reconstruct the initial generator in the hippocampus. Depth weighting was more crucial for MEG (gradiometers) than for HD-EEG. Similar findings were found when considering depth weighting for the wavelet extension of MEM. In conclusion, depth-weighted cMEM improved the localization of deep sources without or with minimal deterioration of the localization of the superficial sources. This was demonstrated using extensive simulations with MEG and HD-EEG and clinical MEG and HD-EEG for epilepsy patients.
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Affiliation(s)
- Jawata Afnan
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, Canada
- Integrated Program in Neuroscience, McGill University, Montréal, Québec, Canada
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
| | - Zhengchen Cai
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
| | - Jean-Marc Lina
- Physnum Team, Centre De Recherches Mathématiques, Montréal, Québec, Canada
- Electrical Engineering Department, École De Technologie Supérieure, Montréal, Québec, Canada
- Center for Advanced Research in Sleep Medicine, Sacré-Coeur Hospital, Montréal, Québec, Canada
| | - Chifaou Abdallah
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, Canada
- Integrated Program in Neuroscience, McGill University, Montréal, Québec, Canada
- Analytical Neurophysiology Lab, Department of Neurology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Edouard Delaire
- Multimodal Functional Imaging Lab, Department of Physics and Concordia School of Health, Concordia University, Montréal, Québec, Canada
| | - Tamir Avigdor
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, Canada
- Integrated Program in Neuroscience, McGill University, Montréal, Québec, Canada
- Analytical Neurophysiology Lab, Department of Neurology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Victoria Ros
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
| | - Tanguy Hedrich
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, Canada
| | - Nicolas von Ellenrieder
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
| | - Eliane Kobayashi
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
| | - Birgit Frauscher
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Analytical Neurophysiology Lab, Department of Neurology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Jean Gotman
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
| | - Christophe Grova
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, Canada
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Physnum Team, Centre De Recherches Mathématiques, Montréal, Québec, Canada
- Multimodal Functional Imaging Lab, Department of Physics and Concordia School of Health, Concordia University, Montréal, Québec, Canada
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Herff SA, Bonetti L, Cecchetti G, Vuust P, Kringelbach ML, Rohrmeier MA. Hierarchical syntax model of music predicts theta power during music listening. Neuropsychologia 2024; 199:108905. [PMID: 38740179 DOI: 10.1016/j.neuropsychologia.2024.108905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 03/07/2024] [Accepted: 05/06/2024] [Indexed: 05/16/2024]
Abstract
Linguistic research showed that the depth of syntactic embedding is reflected in brain theta power. Here, we test whether this also extends to non-linguistic stimuli, specifically music. We used a hierarchical model of musical syntax to continuously quantify two types of expert-annotated harmonic dependencies throughout a piece of Western classical music: prolongation and preparation. Prolongations can roughly be understood as a musical analogue to linguistic coordination between constituents that share the same function (e.g., 'pizza' and 'pasta' in 'I ate pizza and pasta'). Preparation refers to the dependency between two harmonies whereby the first implies a resolution towards the second (e.g., dominant towards tonic; similar to how the adjective implies the presence of a noun in 'I like spicy … '). Source reconstructed MEG data of sixty-five participants listening to the musical piece was then analysed. We used Bayesian Mixed Effects models to predict theta envelope in the brain, using the number of open prolongation and preparation dependencies as predictors whilst controlling for audio envelope. We observed that prolongation and preparation both carry independent and distinguishable predictive value for theta band fluctuation in key linguistic areas such as the Angular, Superior Temporal, and Heschl's Gyri, or their right-lateralised homologues, with preparation showing additional predictive value for areas associated with the reward system and prediction. Musical expertise further mediated these effects in language-related brain areas. Results show that predictions of precisely formalised music-theoretical models are reflected in the brain activity of listeners which furthers our understanding of the perception and cognition of musical structure.
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Affiliation(s)
- Steffen A Herff
- Sydney Conservatorium of Music, University of Sydney, Sydney, Australia; The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Sydney, Australia; Digital and Cognitive Musicology Lab, College of Humanities, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
| | - Leonardo Bonetti
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, Aarhus/Aalborg, Denmark; Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, United Kingdom; Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Gabriele Cecchetti
- The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Sydney, Australia; Digital and Cognitive Musicology Lab, College of Humanities, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Peter Vuust
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, Aarhus/Aalborg, Denmark
| | - Morten L Kringelbach
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, Aarhus/Aalborg, Denmark; Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, United Kingdom; Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Martin A Rohrmeier
- Digital and Cognitive Musicology Lab, College of Humanities, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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Romeni S, Toni L, Artoni F, Micera S. Decoding electroencephalographic responses to visual stimuli compatible with electrical stimulation. APL Bioeng 2024; 8:026123. [PMID: 38894958 PMCID: PMC11184972 DOI: 10.1063/5.0195680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 05/20/2024] [Indexed: 06/21/2024] Open
Abstract
Electrical stimulation of the visual nervous system could improve the quality of life of patients affected by acquired blindness by restoring some visual sensations, but requires careful optimization of stimulation parameters to produce useful perceptions. Neural correlates of elicited perceptions could be used for fast automatic optimization, with electroencephalography as a natural choice as it can be acquired non-invasively. Nonetheless, its low signal-to-noise ratio may hinder discrimination of similar visual patterns, preventing its use in the optimization of electrical stimulation. Our work investigates for the first time the discriminability of the electroencephalographic responses to visual stimuli compatible with electrical stimulation, employing a newly acquired dataset whose stimuli encompass the concurrent variation of several features, while neuroscience research tends to study the neural correlates of single visual features. We then performed above-chance single-trial decoding of multiple features of our newly crafted visual stimuli using relatively simple machine learning algorithms. A decoding scheme employing the information from multiple stimulus presentations was implemented, substantially improving our decoding performance, suggesting that such methods should be used systematically in future applications. The significance of the present work relies in the determination of which visual features can be decoded from electroencephalographic responses to electrical stimulation-compatible stimuli and at which granularity they can be discriminated. Our methods pave the way to using electroencephalographic correlates to optimize electrical stimulation parameters, thus increasing the effectiveness of current visual neuroprostheses.
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Affiliation(s)
| | | | - Fiorenzo Artoni
- Department of Clinical Neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland
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Buschermöhle Y, Höltershinken MB, Erdbrügger T, Radecke JO, Sprenger A, Schneider TR, Lencer R, Gross J, Wolters CH. Comparing the performance of beamformer algorithms in estimating orientations of neural sources. iScience 2024; 27:109150. [PMID: 38420593 PMCID: PMC10901088 DOI: 10.1016/j.isci.2024.109150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 11/12/2023] [Accepted: 02/01/2024] [Indexed: 03/02/2024] Open
Abstract
The efficacy of transcranial electric stimulation (tES) to effectively modulate neuronal activity depends critically on the spatial orientation of the targeted neuronal population. Therefore, precise estimation of target orientation is of utmost importance. Different beamforming algorithms provide orientation estimates; however, a systematic analysis of their performance is still lacking. For fixed brain locations, EEG and MEG data from sources with randomized orientations were simulated. The orientation was then estimated (1) with an EEG and (2) with a combined EEG-MEG approach. Three commonly used beamformer algorithms were evaluated with respect to their abilities to estimate the correct orientation: Unit-Gain (UG), Unit-Noise-Gain (UNG), and Array-Gain (AG) beamformer. Performance depends on the signal-to-noise ratios for the modalities and on the chosen beamformer. Overall, the UNG and AG beamformers appear as the most reliable. With increasing noise, the UG estimate converges to a vector determined by the leadfield, thus leading to insufficient orientation estimates.
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Affiliation(s)
- Yvonne Buschermöhle
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, 48149 Münster, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, 48149 Münster, Germany
| | - Malte B Höltershinken
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, 48149 Münster, Germany
- Institute for Analysis and Numerics, University of Münster, 48149 Münster, Germany
| | - Tim Erdbrügger
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, 48149 Münster, Germany
- Institute for Analysis and Numerics, University of Münster, 48149 Münster, Germany
| | - Jan-Ole Radecke
- Department of Psychiatry and Psychotherapy, University of Lübeck, 23562 Lübeck, Germany
- Center of Brain, Behavior and Metabolism, University of Lübeck, 23562 Lübeck, Germany
| | - Andreas Sprenger
- Center of Brain, Behavior and Metabolism, University of Lübeck, 23562 Lübeck, Germany
- Department of Neurology, University of Lübeck, 23562 Lübeck, Germany
- Institute of Psychology II, University of Lübeck, 23562 Lübeck, Germany
| | - Till R Schneider
- Department of Neurophysiology and Pathophysiology, University Medical Center Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Rebekka Lencer
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, 48149 Münster, Germany
- Department of Psychiatry and Psychotherapy, University of Lübeck, 23562 Lübeck, Germany
- Center of Brain, Behavior and Metabolism, University of Lübeck, 23562 Lübeck, Germany
- Institute of Translational Psychiatry, University of Münster, 48149 Münster, Germany
| | - Joachim Gross
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, 48149 Münster, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, 48149 Münster, Germany
| | - Carsten H Wolters
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, 48149 Münster, Germany
- Otto Creutzfeldt Center for Cognitive and Behavioral Neuroscience, University of Münster, 48149 Münster, Germany
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Schofield H, Hill RM, Feys O, Holmes N, Osborne J, Doyle C, Bobela D, Corvilian P, Wens V, Rier L, Bowtell R, Ferez M, Mullinger KJ, Coleman S, Rhodes N, Rea M, Tanner Z, Boto E, de Tiège X, Shah V, Brookes MJ. A Novel, Robust, and Portable Platform for Magnetoencephalography using Optically Pumped Magnetometers. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.06.583313. [PMID: 38558964 PMCID: PMC10979878 DOI: 10.1101/2024.03.06.583313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Magnetoencephalography (MEG) measures brain function via assessment of magnetic fields generated by neural currents. Conventional MEG uses superconducting sensors, which place significant limitations on performance, practicality, and deployment; however, the field has been revolutionised in recent years by the introduction of optically-pumped-magnetometers (OPMs). OPMs enable measurement of the MEG signal without cryogenics, and consequently the conception of 'OPM-MEG' systems which ostensibly allow increased sensitivity and resolution, lifespan compliance, free subject movement, and lower cost. However, OPM-MEG remains in its infancy with limitations on both sensor and system design. Here, we report a new OPM-MEG design with miniaturised and integrated electronic control, a high level of portability, and improved sensor dynamic range (arguably the biggest limitation of existing instrumentation). We show that this system produces equivalent measures when compared to an established instrument; specifically, when measuring task-induced beta-band, gamma-band and evoked neuro-electrical responses, source localisations from the two systems were highly comparable and temporal correlation was >0.7 at the individual level and >0.9 for groups. Using an electromagnetic phantom, we demonstrate improved dynamic range by running the system in background fields up to 8 nT. We show that the system is effective in gathering data during free movement (including a sitting-to-standing paradigm) and that it is compatible with simultaneous electroencephalography (EEG - the clinical standard). Finally, we demonstrate portability by moving the system between two laboratories. Overall, our new system is shown to be a significant step forward for OPM-MEG technology and offers an attractive platform for next generation functional medical imaging.
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Affiliation(s)
- Holly Schofield
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
- Cerca Magnetics Limited, 2 Castlebridge Office Village, Kirtley Drive, Nottingham, NG7 1LD, Nottingham, UK
| | - Ryan M. Hill
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
- Cerca Magnetics Limited, 2 Castlebridge Office Village, Kirtley Drive, Nottingham, NG7 1LD, Nottingham, UK
| | - Odile Feys
- Université libre de Bruxelles, ULB Neuroscience Institute, Laboratoire de neuroanatomie et neuroimagerie translationelles, Brussels, Belgium
- Université libre de Bruxelles, Hôpital Universitaire de Bruxelles, Hôpital Erasme, Department of neurology, Brussels, Belgium
| | - Niall Holmes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
- Cerca Magnetics Limited, 2 Castlebridge Office Village, Kirtley Drive, Nottingham, NG7 1LD, Nottingham, UK
| | - James Osborne
- QuSpin Inc. 331 South 104 Street, Suite 130, Louisville, Colorado, 80027, USA
| | - Cody Doyle
- QuSpin Inc. 331 South 104 Street, Suite 130, Louisville, Colorado, 80027, USA
| | - David Bobela
- QuSpin Inc. 331 South 104 Street, Suite 130, Louisville, Colorado, 80027, USA
| | - Pierre Corvilian
- Université libre de Bruxelles, ULB Neuroscience Institute, Laboratoire de neuroanatomie et neuroimagerie translationelles, Brussels, Belgium
| | - Vincent Wens
- Université libre de Bruxelles, ULB Neuroscience Institute, Laboratoire de neuroanatomie et neuroimagerie translationelles, Brussels, Belgium
- Université libre de Bruxelles, Hôpital Universitaire de Bruxelles, Hôpital Erasme, Department of translational neuroimaging, Brussels, Belgium
| | - Lukas Rier
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Richard Bowtell
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Maxime Ferez
- Université libre de Bruxelles, ULB Neuroscience Institute, Laboratoire de neuroanatomie et neuroimagerie translationelles, Brussels, Belgium
| | - Karen J. Mullinger
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
- Centre for Human Brain Health, School of Psychology, University of Birmingham, UK
| | - Sebastian Coleman
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Natalie Rhodes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
| | - Molly Rea
- Cerca Magnetics Limited, 2 Castlebridge Office Village, Kirtley Drive, Nottingham, NG7 1LD, Nottingham, UK
| | - Zoe Tanner
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
- Cerca Magnetics Limited, 2 Castlebridge Office Village, Kirtley Drive, Nottingham, NG7 1LD, Nottingham, UK
| | - Elena Boto
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
- Cerca Magnetics Limited, 2 Castlebridge Office Village, Kirtley Drive, Nottingham, NG7 1LD, Nottingham, UK
| | - Xavier de Tiège
- Université libre de Bruxelles, ULB Neuroscience Institute, Laboratoire de neuroanatomie et neuroimagerie translationelles, Brussels, Belgium
- Université libre de Bruxelles, Hôpital Universitaire de Bruxelles, Hôpital Erasme, Department of translational neuroimaging, Brussels, Belgium
| | - Vishal Shah
- QuSpin Inc. 331 South 104 Street, Suite 130, Louisville, Colorado, 80027, USA
| | - Matthew J. Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK
- Cerca Magnetics Limited, 2 Castlebridge Office Village, Kirtley Drive, Nottingham, NG7 1LD, Nottingham, UK
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10
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Feys O, De Tiège X. From cryogenic to on-scalp magnetoencephalography for the evaluation of paediatric epilepsy. Dev Med Child Neurol 2024; 66:298-306. [PMID: 37421175 DOI: 10.1111/dmcn.15689] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 05/28/2023] [Accepted: 06/02/2023] [Indexed: 07/09/2023]
Abstract
Magnetoencephalography (MEG) is a neurophysiological technique based on the detection of brain magnetic fields. Whole-head MEG systems typically house a few hundred sensors requiring cryogenic cooling in a rigid one-size-fits-all (commonly adult-sized) helmet to keep a thermal insulation space. This leads to an increased brain-to-sensor distance in children, because of their smaller head circumference, and decreased signal-to-noise ratio. MEG allows detection and localization of interictal and ictal epileptiform discharges, and pathological high frequency oscillations, as a part of the presurgical assessment of children with refractory focal epilepsy, where electroencephalography is not contributive. MEG can also map the eloquent cortex before surgical resection. MEG also provides insights into the physiopathology of both generalized and focal epilepsy. On-scalp recordings based on cryogenic-free sensors have demonstrated their use in the field of childhood focal epilepsy and should become a reference technique for diagnosing epilepsy in the paediatric population. WHAT THIS PAPER ADDS: Magnetoencephalography (MEG) contributes to the diagnosis and understanding of paediatric epilepsy. On-scalp MEG recordings demonstrate some advantages over cryogenic MEG.
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Affiliation(s)
- Odile Feys
- Department of Neurology, Université libre de Bruxelles, Hôpital Universitaire de Bruxelles, Hôpital Erasme, Bruxelles, Belgium
- Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles, Université libre de Bruxelles, ULB Neuroscience Institute, Bruxelles, Belgium
| | - Xavier De Tiège
- Laboratoire de Neuroanatomie et Neuroimagerie Translationnelles, Université libre de Bruxelles, ULB Neuroscience Institute, Bruxelles, Belgium
- Department of Translational Neuroimaging, Université libre de Bruxelles, Hôpital Universitaire de Bruxelles, Hôpital Erasme, Bruxelles, Belgium
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11
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Das D, Shaw ME, Hämäläinen MS, Dykstra AR, Doll L, Gutschalk A. A role for retro-splenial cortex in the task-related P3 network. Clin Neurophysiol 2024; 157:96-109. [PMID: 38091872 DOI: 10.1016/j.clinph.2023.11.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 10/12/2023] [Accepted: 11/19/2023] [Indexed: 12/26/2023]
Abstract
OBJECTIVE The P3 is an event-related response observed in relation to task-relevant sensory events. Despite its ubiquitous presence, the neural generators of the P3 are controversial and not well identified. METHODS We compared source analysis of combined magneto- and electroencephalography (M/EEG) data with functional magnetic resonance imaging (fMRI) and simulation studies to better understand the sources of the P3 in an auditory oddball paradigm. RESULTS Our results suggest that the dominant source of the classical, postero-central P3 lies in the retro-splenial cortex of the ventral cingulate gyrus. A second P3 source in the anterior insular cortex contributes little to the postero-central maximum. Multiple other sources in the auditory, somatosensory, and anterior midcingulate cortex are active in an overlapping time window but can be functionally dissociated based on their activation time courses. CONCLUSIONS The retro-splenial cortex is a dominant source of the parietal P3 maximum in EEG. SIGNIFICANCE These results provide a new perspective for the interpretation of the extensive research based on the P3 response.
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Affiliation(s)
- Diptyajit Das
- Department of Neurology, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
| | - Marnie E Shaw
- College of Engineering & Computer Science, Australian National University, Canberra, Australia
| | - Matti S Hämäläinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, USA; Harvard, MIT Division of Health Science and Technology, USA; Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Finland
| | - Andrew R Dykstra
- Department of Biomedical Engineering, University of Miami, Coral Gables, USA
| | - Laura Doll
- Department of Neurology, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
| | - Alexander Gutschalk
- Department of Neurology, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany.
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12
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Reisinger L, Demarchi G, Weisz N. Eavesdropping on Tinnitus Using MEG: Lessons Learned and Future Perspectives. J Assoc Res Otolaryngol 2023; 24:531-547. [PMID: 38015287 PMCID: PMC10752863 DOI: 10.1007/s10162-023-00916-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 11/06/2023] [Indexed: 11/29/2023] Open
Abstract
Tinnitus has been widely investigated in order to draw conclusions about the underlying causes and altered neural activity in various brain regions. Existing studies have based their work on different tinnitus frameworks, ranging from a more local perspective on the auditory cortex to the inclusion of broader networks and various approaches towards tinnitus perception and distress. Magnetoencephalography (MEG) provides a powerful tool for efficiently investigating tinnitus and aberrant neural activity both spatially and temporally. However, results are inconclusive, and studies are rarely mapped to theoretical frameworks. The purpose of this review was to firstly introduce MEG to interested researchers and secondly provide a synopsis of the current state. We divided recent tinnitus research in MEG into study designs using resting state measurements and studies implementing tone stimulation paradigms. The studies were categorized based on their theoretical foundation, and we outlined shortcomings as well as inconsistencies within the different approaches. Finally, we provided future perspectives on how to benefit more efficiently from the enormous potential of MEG. We suggested novel approaches from a theoretical, conceptual, and methodological point of view to allow future research to obtain a more comprehensive understanding of tinnitus and its underlying processes.
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Affiliation(s)
- Lisa Reisinger
- Centre for Cognitive Neuroscience and Department of Psychology, Paris-Lodron-University Salzburg, Salzburg, Austria.
| | - Gianpaolo Demarchi
- Centre for Cognitive Neuroscience and Department of Psychology, Paris-Lodron-University Salzburg, Salzburg, Austria
| | - Nathan Weisz
- Centre for Cognitive Neuroscience and Department of Psychology, Paris-Lodron-University Salzburg, Salzburg, Austria
- Neuroscience Institute, Christian Doppler University Hospital, Paracelsus Medical University, Salzburg, Austria
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13
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Das D, Shaw ME, Hämäläinen MS, Dykstra AR, Doll L, Gutschalk A. A role for retro-splenial cortex in the task-related P3 network. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.03.530970. [PMID: 36945516 PMCID: PMC10028840 DOI: 10.1101/2023.03.03.530970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Objective The P3 is an event-related response observed in relation to task-relevant sensory events. Despite its ubiquitous presence, the neural generators of the P3 are controversial and not well identified. Methods We compared source analysis of combined magneto- and electroencephalography (M/EEG) data with functional magnetic resonance imaging (fMRI) and simulation studies to better understand the sources of the P3 in an auditory oddball paradigm. Results Our results suggest that the dominant source of the classical, postero-central P3 lies in the retro-splenial cortex of the ventral cingulate gyrus. A second P3 source in the anterior insular cortex contributes little to the postero-central maximum. Multiple other sources in the auditory, somatosensory, and anterior midcingulate cortex are active in an overlapping time window but can be functionally dissociated based on their activation time courses. Conclusion The retro-splenial cortex is a dominant source of the parietal P3 maximum in EEG. Significance These results provide a new perspective for the interpretation of the extensive research based on the P3 response.
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Affiliation(s)
- Diptyajit Das
- Department of Neurology, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
| | - Marnie E. Shaw
- College of Engineering & Computer Science, Australian National University, Canberra, Australia
| | - Matti S. Hämäläinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, USA
- Harvard, MIT Division of Health Science and Technology, USA
- Department of Neuroscience and Biomedical Engineering, Aalto University school of Science, Finland
| | - Andrew R. Dykstra
- Department of Biomedical Engineering, University of Miami, Coral Gables, USA
| | - Laura Doll
- Department of Neurology, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
| | - Alexander Gutschalk
- Department of Neurology, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
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14
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Lerud KD, Hancock R, Skoe E. A high-density EEG and structural MRI source analysis of the frequency following response to missing fundamental stimuli reveals subcortical and cortical activation to low and high frequency stimuli. Neuroimage 2023; 279:120330. [PMID: 37598815 DOI: 10.1016/j.neuroimage.2023.120330] [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: 04/02/2023] [Revised: 07/29/2023] [Accepted: 08/14/2023] [Indexed: 08/22/2023] Open
Abstract
Pitch is a perceptual rather than physical phenomenon, important for spoken language use, musical communication, and other aspects of everyday life. Auditory stimuli can be designed to probe the relationship between perception and physiological responses to pitch-evoking stimuli. One technique for measuring physiological responses to pitch-evoking stimuli is the frequency following response (FFR). The FFR is an electroencephalographic (EEG) response to periodic auditory stimuli. The FFR contains nonlinearities not present in the stimuli, including correlates of the amplitude envelope of the stimulus; however, these nonlinearities remain undercharacterized. The FFR is a composite response reflecting multiple neural and peripheral generators, and their contributions to the scalp-recorded FFR vary in ill-understood ways depending on the electrode montage, stimulus, and imaging technique. The FFR is typically assumed to be generated in the auditory brainstem; there is also evidence both for and against a cortical contribution to the FFR. Here a methodology is used to examine the FFR correlates of pitch and the generators of the FFR to stimuli with different pitches. Stimuli were designed to tease apart biological correlates of pitch and amplitude envelope. FFRs were recorded with 256-electrode EEG nets, in contrast to a typical FFR setup which only contains a single active electrode. Structural MRI scans were obtained for each participant to co-register with the electrode locations and constrain a source localization algorithm. The results of this localization shed light on the generating mechanisms of the FFR, including providing evidence for both cortical and subcortical auditory sources.
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Affiliation(s)
- Karl D Lerud
- University of Maryland College Park, Institute for Systems Research, 20742, United States of America.
| | - Roeland Hancock
- Yale University, Wu Tsai Institute, 06510, United States of America
| | - Erika Skoe
- University of Connecticut, Department of Speech, Language, and Hearing Sciences, Cognitive Sciences Program, 06269, United States of America
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15
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Shafiei G, Fulcher BD, Voytek B, Satterthwaite TD, Baillet S, Misic B. Neurophysiological signatures of cortical micro-architecture. Nat Commun 2023; 14:6000. [PMID: 37752115 PMCID: PMC10522715 DOI: 10.1038/s41467-023-41689-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 09/11/2023] [Indexed: 09/28/2023] Open
Abstract
Systematic spatial variation in micro-architecture is observed across the cortex. These micro-architectural gradients are reflected in neural activity, which can be captured by neurophysiological time-series. How spontaneous neurophysiological dynamics are organized across the cortex and how they arise from heterogeneous cortical micro-architecture remains unknown. Here we extensively profile regional neurophysiological dynamics across the human brain by estimating over 6800 time-series features from the resting state magnetoencephalography (MEG) signal. We then map regional time-series profiles to a comprehensive multi-modal, multi-scale atlas of cortical micro-architecture, including microstructure, metabolism, neurotransmitter receptors, cell types and laminar differentiation. We find that the dominant axis of neurophysiological dynamics reflects characteristics of power spectrum density and linear correlation structure of the signal, emphasizing the importance of conventional features of electromagnetic dynamics while identifying additional informative features that have traditionally received less attention. Moreover, spatial variation in neurophysiological dynamics is co-localized with multiple micro-architectural features, including gene expression gradients, intracortical myelin, neurotransmitter receptors and transporters, and oxygen and glucose metabolism. Collectively, this work opens new avenues for studying the anatomical basis of neural activity.
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Affiliation(s)
- Golia Shafiei
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ben D Fulcher
- School of Physics, The University of Sydney, Camperdown, NSW, 2006, Australia
| | - Bradley Voytek
- Department of Cognitive Science, Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada.
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16
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Hussain I, Jany R, Boyer R, Azad AKM, Alyami SA, Park SJ, Hasan MM, Hossain MA. An Explainable EEG-Based Human Activity Recognition Model Using Machine-Learning Approach and LIME. SENSORS (BASEL, SWITZERLAND) 2023; 23:7452. [PMID: 37687908 PMCID: PMC10490625 DOI: 10.3390/s23177452] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/06/2023] [Accepted: 08/24/2023] [Indexed: 09/10/2023]
Abstract
Electroencephalography (EEG) is a non-invasive method employed to discern human behaviors by monitoring the neurological responses during cognitive and motor tasks. Machine learning (ML) represents a promising tool for the recognition of human activities (HAR), and eXplainable artificial intelligence (XAI) can elucidate the role of EEG features in ML-based HAR models. The primary objective of this investigation is to investigate the feasibility of an EEG-based ML model for categorizing everyday activities, such as resting, motor, and cognitive tasks, and interpreting models clinically through XAI techniques to explicate the EEG features that contribute the most to different HAR states. The study involved an examination of 75 healthy individuals with no prior diagnosis of neurological disorders. EEG recordings were obtained during the resting state, as well as two motor control states (walking and working tasks), and a cognition state (reading task). Electrodes were placed in specific regions of the brain, including the frontal, central, temporal, and occipital lobes (Fz, C1, C2, T7, T8, Oz). Several ML models were trained using EEG data for activity recognition and LIME (Local Interpretable Model-Agnostic Explanations) was employed for interpreting clinically the most influential EEG spectral features in HAR models. The classification results of the HAR models, particularly the Random Forest and Gradient Boosting models, demonstrated outstanding performances in distinguishing the analyzed human activities. The ML models exhibited alignment with EEG spectral bands in the recognition of human activity, a finding supported by the XAI explanations. To sum up, incorporating eXplainable Artificial Intelligence (XAI) into Human Activity Recognition (HAR) studies may improve activity monitoring for patient recovery, motor imagery, the healthcare metaverse, and clinical virtual reality settings.
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Affiliation(s)
- Iqram Hussain
- Department of Anesthesiology, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA
| | - Rafsan Jany
- Department of Computer Science and Engineering, Islamic University and Technology (IUT), Gazipur 1704, Bangladesh; (R.J.); (M.A.H.)
| | - Richard Boyer
- Department of Anesthesiology, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA
| | - AKM Azad
- Department of Mathematics and Statistics, Al-Imam Muhammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia; (A.A.); (S.A.A.)
| | - Salem A. Alyami
- Department of Mathematics and Statistics, Al-Imam Muhammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia; (A.A.); (S.A.A.)
| | - Se Jin Park
- Sewon Intelligence Ltd., Seoul 04512, Republic of Korea;
| | - Md Mehedi Hasan
- Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka 1000, Bangladesh;
| | - Md Azam Hossain
- Department of Computer Science and Engineering, Islamic University and Technology (IUT), Gazipur 1704, Bangladesh; (R.J.); (M.A.H.)
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17
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Gupta A, Daniel R, Rao A, Roy PP, Chandra S, Kim BG. Raw Electroencephalogram-Based Cognitive Workload Classification Using Directed and Nondirected Functional Connectivity Analysis and Deep Learning. BIG DATA 2023; 11:307-319. [PMID: 36848586 DOI: 10.1089/big.2021.0204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
With the phenomenal rise in internet-of-things devices, the use of electroencephalogram (EEG) based brain-computer interfaces (BCIs) can empower individuals to control equipment with thoughts. These allow BCI to be used and pave the way for pro-active health management and the development of internet-of-medical-things architecture. However, EEG-based BCIs have low fidelity, high variance, and EEG signals are very noisy. These challenges compel researchers to design algorithms that can process big data in real-time while being robust to temporal variations and other variations in the data. Another issue in designing a passive BCI is the regular change in user's cognitive state (measured through cognitive workload). Though considerable amount of research has been conducted on this front, methods that could withstand high variability in EEG data and still reflect the neuronal dynamics of cognitive state variations are lacking and much needed in literature. In this research, we evaluate the efficacy of a combination of functional connectivity algorithms and state-of-the-art deep learning algorithms for the classification of three different levels of cognitive workload. We acquire 64-channel EEG data from 23 participants executing the n-back task at three different levels; 1-back (low-workload condition), 2-back (medium-workload condition), and 3-back (high-workload condition). We compared two different functional connectivity algorithms, namely phase transfer entropy (PTE) and mutual information (MI). PTE is a directed functional connectivity algorithm, whereas MI is non-directed. Both methods are suitable for extracting functional connectivity matrices in real-time, which could eventually be used for rapid, robust, and efficient classification. For classification, we use the recently proposed BrainNetCNN deep learning model, designed specifically to classify functional connectivity matrices. Results reveal a classification accuracy of 92.81% with MI and BrainNetCNN and a staggering 99.50% with PTE and BrainNetCNN on test data. PTE can yield a higher classification accuracy due to its robustness to linear mixing of the data and its ability to detect functional connectivity across a range of analysis lags.
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Affiliation(s)
- Anmol Gupta
- Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India
| | - Ronnie Daniel
- Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India
| | - Akash Rao
- School of Computing and Electrical Engineering, Applied Cognitive Science Laboratory, Indian Institute of Technology Mandi, Mandi, India
| | - Partha Pratim Roy
- Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India
| | - Sushil Chandra
- Department of Biomedical Engineering, INMAS Defence Research and Development Organization, New Delhi, India
| | - Byung-Gyu Kim
- Division of Artificial Intelligence Engineering, Sookmyung Women's University, Seoul, South Korea
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18
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Pan R, Yang C, Li Z, Ren J, Duan Y. Magnetoencephalography-based approaches to epilepsy classification. Front Neurosci 2023; 17:1183391. [PMID: 37502686 PMCID: PMC10368885 DOI: 10.3389/fnins.2023.1183391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 06/12/2023] [Indexed: 07/29/2023] Open
Abstract
Epilepsy is a chronic central nervous system disorder characterized by recurrent seizures. Not only does epilepsy severely affect the daily life of the patient, but the risk of premature death in patients with epilepsy is three times higher than that of the normal population. Magnetoencephalography (MEG) is a non-invasive, high temporal and spatial resolution electrophysiological data that provides a valid basis for epilepsy diagnosis, and used in clinical practice to locate epileptic foci in patients with epilepsy. It has been shown that MEG helps to identify MRI-negative epilepsy, contributes to clinical decision-making in recurrent seizures after previous epilepsy surgery, that interictal MEG can provide additional localization information than scalp EEG, and complete excision of the stimulation area defined by the MEG has prognostic significance for postoperative seizure control. However, due to the complexity of the MEG signal, it is often difficult to identify subtle but critical changes in MEG through visual inspection, opening up an important area of research for biomedical engineers to investigate and implement intelligent algorithms for epilepsy recognition. At the same time, the use of manual markers requires significant time and labor costs, necessitating the development and use of computer-aided diagnosis (CAD) systems that use classifiers to automatically identify abnormal activity. In this review, we discuss in detail the results of applying various different feature extraction methods on MEG signals with different classifiers for epilepsy detection, subtype determination, and laterality classification. Finally, we also briefly look at the prospects of using MEG for epilepsy-assisted localization (spike detection, high-frequency oscillation detection) due to the unique advantages of MEG for functional area localization in epilepsy, and discuss the limitation of current research status and suggestions for future research. Overall, it is hoped that our review will facilitate the reader to quickly gain a general understanding of the problem of MEG-based epilepsy classification and provide ideas and directions for subsequent research.
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Affiliation(s)
- Ruoyao Pan
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Chunlan Yang
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Zhimei Li
- Department of Internal Neurology, Tiantan Hospital, Beijing, China
| | - Jiechuan Ren
- Department of Internal Neurology, Tiantan Hospital, Beijing, China
| | - Ying Duan
- Beijing Universal Medical Imaging Diagnostic Center, Beijing, China
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19
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Craik A, González-España JJ, Alamir A, Edquilang D, Wong S, Sánchez Rodríguez L, Feng J, Francisco GE, Contreras-Vidal JL. Design and Validation of a Low-Cost Mobile EEG-Based Brain-Computer Interface. SENSORS (BASEL, SWITZERLAND) 2023; 23:5930. [PMID: 37447780 DOI: 10.3390/s23135930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 06/09/2023] [Accepted: 06/20/2023] [Indexed: 07/15/2023]
Abstract
Objective: We designed and validated a wireless, low-cost, easy-to-use, mobile, dry-electrode headset for scalp electroencephalography (EEG) recordings for closed-loop brain-computer (BCI) interface and internet-of-things (IoT) applications. Approach: The EEG-based BCI headset was designed from commercial off-the-shelf (COTS) components using a multi-pronged approach that balanced interoperability, cost, portability, usability, form factor, reliability, and closed-loop operation. Main Results: The adjustable headset was designed to accommodate 90% of the population. A patent-pending self-positioning dry electrode bracket allowed for vertical self-positioning while parting the user's hair to ensure contact of the electrode with the scalp. In the current prototype, five EEG electrodes were incorporated in the electrode bracket spanning the sensorimotor cortices bilaterally, and three skin sensors were included to measure eye movement and blinks. An inertial measurement unit (IMU) provides monitoring of head movements. The EEG amplifier operates with 24-bit resolution up to 500 Hz sampling frequency and can communicate with other devices using 802.11 b/g/n WiFi. It has high signal-to-noise ratio (SNR) and common-mode rejection ratio (CMRR) (121 dB and 110 dB, respectively) and low input noise. In closed-loop BCI mode, the system can operate at 40 Hz, including real-time adaptive noise cancellation and 512 MB of processor memory. It supports LabVIEW as a backend coding language and JavaScript (JS), Cascading Style Sheets (CSS), and HyperText Markup Language (HTML) as front-end coding languages and includes training and optimization of support vector machine (SVM) neural classifiers. Extensive bench testing supports the technical specifications and human-subject pilot testing of a closed-loop BCI application to support upper-limb rehabilitation and provides proof-of-concept validation for the device's use at both the clinic and at home. Significance: The usability, interoperability, portability, reliability, and programmability of the proposed wireless closed-loop BCI system provides a low-cost solution for BCI and neurorehabilitation research and IoT applications.
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Affiliation(s)
- Alexander Craik
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77004, USA
- Noninvasive Brain-Machine Interface Systems Laboratory, NSF Industry-University Cooperative Research Center for Building Reliable Advances and Innovations in Neurotechnology (IUCRC BRAIN) Center, University of Houston, Houston, TX 77004, USA
| | - Juan José González-España
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77004, USA
- Noninvasive Brain-Machine Interface Systems Laboratory, NSF Industry-University Cooperative Research Center for Building Reliable Advances and Innovations in Neurotechnology (IUCRC BRAIN) Center, University of Houston, Houston, TX 77004, USA
| | - Ayman Alamir
- Noninvasive Brain-Machine Interface Systems Laboratory, NSF Industry-University Cooperative Research Center for Building Reliable Advances and Innovations in Neurotechnology (IUCRC BRAIN) Center, University of Houston, Houston, TX 77004, USA
- Department of Biomedical Engineering, University of Houston, Houston, TX 77004, USA
- Department of Electrical Engineering, Jazan University, Jazan 45142, Saudi Arabia
| | - David Edquilang
- Department of Industrial Design, University of Houston, Houston, TX 77004, USA
| | - Sarah Wong
- Noninvasive Brain-Machine Interface Systems Laboratory, NSF Industry-University Cooperative Research Center for Building Reliable Advances and Innovations in Neurotechnology (IUCRC BRAIN) Center, University of Houston, Houston, TX 77004, USA
- Department of Industrial Design, University of Houston, Houston, TX 77004, USA
| | - Lianne Sánchez Rodríguez
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77004, USA
- Noninvasive Brain-Machine Interface Systems Laboratory, NSF Industry-University Cooperative Research Center for Building Reliable Advances and Innovations in Neurotechnology (IUCRC BRAIN) Center, University of Houston, Houston, TX 77004, USA
| | - Jeff Feng
- Noninvasive Brain-Machine Interface Systems Laboratory, NSF Industry-University Cooperative Research Center for Building Reliable Advances and Innovations in Neurotechnology (IUCRC BRAIN) Center, University of Houston, Houston, TX 77004, USA
- Department of Industrial Design, University of Houston, Houston, TX 77004, USA
| | - Gerard E Francisco
- Department of Physical Medicine & Rehabilitation, University of Texas Health McGovern Medical School, Houston, TX 77030, USA
- The Institute for Rehabilitation and Research (TIRR) Memorial Hermann Hospital, Houston, TX 77030, USA
| | - Jose L Contreras-Vidal
- Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77004, USA
- Noninvasive Brain-Machine Interface Systems Laboratory, NSF Industry-University Cooperative Research Center for Building Reliable Advances and Innovations in Neurotechnology (IUCRC BRAIN) Center, University of Houston, Houston, TX 77004, USA
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20
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Duan T, Wang Z, Liu S, Yin Y, Srihari SN. UNCER: A Framework for Uncertainty Estimation and Reduction in Neural Decoding of EEG Signals. Neurocomputing 2023. [DOI: 10.1016/j.neucom.2023.03.071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
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21
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Okamura A, Iida K, Hashizume A, Kagawa K, Seyama G, Horie N. Magnetoencephalographic spikes with small spikes on simultaneous electroencephalography have high spatial clustering in temporal lobe epilepsy. Epilepsy Res 2023; 192:107127. [PMID: 36963303 DOI: 10.1016/j.eplepsyres.2023.107127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 03/06/2023] [Accepted: 03/16/2023] [Indexed: 03/26/2023]
Abstract
OBJECTIVE To classify magnetoencephalographic (MEG) spikes according to the findings of simultaneous scalp electroencephalography (EEG) to study dipole estimation in patients with temporal lobe epilepsy. METHODS We analyzed MEG and simultaneous scalp EEG in 27 patients with intractable temporal lobe epilepsy. We classified MEG spikes into three groups (H-EM-spikes, L-EM-spikes, M-spikes) based on the amplitude of simultaneous EEG (50 μV or higher, lower than 50 μV, no spike morphology on EEG, respectively). We calculated parameters of the dipoles, such as goodness of fit (GOF), current moment, and location. RESULTS We detected 707 MEG spikes, consisting of 175 H-EM-spikes, 245 L-EM-spikes, and 287 M-spikes. Dipoles of H-EM-spikes showed the highest current moment among the three spike groups. Dipoles of L-EM-spikes showed the highest GOF, a moderate current moment, the highest density to cluster, and the highest proportion of being located in the temporal lobe among the three groups. Dipoles of M-spikes showed the lowest GOF and current moment among the three groups. CONCLUSIONS The characteristics of the dipoles of the MEG spikes differ depending on the simultaneous scalp EEG findings, though most of the MEG spikes were located in the temporal lobe. MEG spikes with concurrent small spikes on simultaneous scalp EEG may have higher spatial clustering in temporal lobe epilepsy.
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Affiliation(s)
- Akitake Okamura
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan; Epilepsy Center, Hiroshima University Hospital, Hiroshima, Japan
| | - Koji Iida
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan; Epilepsy Center, Hiroshima University Hospital, Hiroshima, Japan.
| | - Akira Hashizume
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan; Epilepsy Center, Hiroshima University Hospital, Hiroshima, Japan
| | - Kota Kagawa
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan; Epilepsy Center, Hiroshima University Hospital, Hiroshima, Japan
| | - Go Seyama
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan; Epilepsy Center, Hiroshima University Hospital, Hiroshima, Japan
| | - Nobutaka Horie
- Department of Neurosurgery, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
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22
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Lahtinen J, Moura F, Samavaki M, Siltanen S, Pursiainen S. In silicostudy of the effects of cerebral circulation on source localization using a dynamical anatomical atlas of the human head. J Neural Eng 2023; 20. [PMID: 36808911 DOI: 10.1088/1741-2552/acbdc1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 02/21/2023] [Indexed: 02/23/2023]
Abstract
Objective.This study focuses on the effects of dynamical vascular modeling on source localization errors in electroencephalography (EEG). Our aim of thisin silicostudy is to (a) find out the effects of cerebral circulation on the accuracy of EEG source localization estimates, and (b) evaluate its relevance with respect to measurement noise and interpatient variation.Approach.We employ a four-dimensional (3D + T) statistical atlas of the electrical properties of the human head with a cerebral circulation model to generate virtual patients with different cerebral circulatory conditions for EEG source localization analysis. As source reconstruction techniques, we use the linearly constraint minimum variance (LCMV) beamformer, standardized low-resolution brain electromagnetic tomography (sLORETA), and the dipole scan (DS).Main results.Results indicate that arterial blood flow affects source localization at different depths and with varying significance. The average flow rate plays an important role in source localization performance, while the pulsatility effects are very small. In cases where a personalized model of the head is available, blood circulation mismodeling causes localization errors, especially in the deep structures of the brain where the main cerebral arteries are located. When interpatient variations are considered, the results show differences up to 15 mm for sLORETA and LCMV beamformer and 10 mm for DS in the brainstem and entorhinal cortices regions. In regions far from the main arteries vessels, the discrepancies are smaller than 3 mm. When measurement noise is added and interpatient differences are considered in a deep dipolar source, the results indicate that the effects of conductivity mismatch are detectable even for moderate measurement noise. The signal-to-noise ratio limit for sLORETA and LCMV beamformer is 15 dB, while the limit is under 30 dB for DS.Significance.Localization of the brain activity via EEG constitutes an ill-posed inverse problem, where any modeling uncertainty, e.g. a slight amount of noise in the data or material parameter discrepancies, can lead to a significant deviation of the estimated activity, especially in the deep structures of the brain. Proper modeling of the conductivity distribution is necessary in order to obtain an appropriate source localization. In this study, we show that the conductivity of the deep brain structures is particularly impacted by blood flow-induced changes in conductivity because large arteries and veins access the brain through that region.
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Affiliation(s)
- Joonas Lahtinen
- Computing Sciences Unit, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland
| | - Fernando Moura
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland.,Engineering, Modelling and Applied Social Sciences Center, Federal University of ABC, São Bernardo do Campo, São Paulo, Brazil
| | - Maryam Samavaki
- Computing Sciences Unit, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland
| | - Samuli Siltanen
- Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Sampsa Pursiainen
- Computing Sciences Unit, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland
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23
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Hernandez-Pavon JC, Veniero D, Bergmann TO, Belardinelli P, Bortoletto M, Casarotto S, Casula EP, Farzan F, Fecchio M, Julkunen P, Kallioniemi E, Lioumis P, Metsomaa J, Miniussi C, Mutanen TP, Rocchi L, Rogasch NC, Shafi MM, Siebner HR, Thut G, Zrenner C, Ziemann U, Ilmoniemi RJ. TMS combined with EEG: Recommendations and open issues for data collection and analysis. Brain Stimul 2023; 16:567-593. [PMID: 36828303 DOI: 10.1016/j.brs.2023.02.009] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 02/10/2023] [Accepted: 02/19/2023] [Indexed: 02/25/2023] Open
Abstract
Transcranial magnetic stimulation (TMS) evokes neuronal activity in the targeted cortex and connected brain regions. The evoked brain response can be measured with electroencephalography (EEG). TMS combined with simultaneous EEG (TMS-EEG) is widely used for studying cortical reactivity and connectivity at high spatiotemporal resolution. Methodologically, the combination of TMS with EEG is challenging, and there are many open questions in the field. Different TMS-EEG equipment and approaches for data collection and analysis are used. The lack of standardization may affect reproducibility and limit the comparability of results produced in different research laboratories. In addition, there is controversy about the extent to which auditory and somatosensory inputs contribute to transcranially evoked EEG. This review provides a guide for researchers who wish to use TMS-EEG to study the reactivity of the human cortex. A worldwide panel of experts working on TMS-EEG covered all aspects that should be considered in TMS-EEG experiments, providing methodological recommendations (when possible) for effective TMS-EEG recordings and analysis. The panel identified and discussed the challenges of the technique, particularly regarding recording procedures, artifact correction, analysis, and interpretation of the transcranial evoked potentials (TEPs). Therefore, this work offers an extensive overview of TMS-EEG methodology and thus may promote standardization of experimental and computational procedures across groups.
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Affiliation(s)
- Julio C Hernandez-Pavon
- Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Legs + Walking Lab, Shirley Ryan AbilityLab, Chicago, IL, USA; Center for Brain Stimulation, Shirley Ryan AbilityLab, Chicago, IL, USA.
| | | | - Til Ole Bergmann
- Neuroimaging Center (NIC), Focus Program Translational Neuroscience (FTN), Johannes Gutenberg University Medical Center, Germany; Leibniz Institute for Resilience Research (LIR), Mainz, Germany
| | - Paolo Belardinelli
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, TN, Italy; Department of Neurology & Stroke, University of Tübingen, Tübingen, Germany
| | - Marta Bortoletto
- Neurophysiology Lab, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Silvia Casarotto
- Department of Biomedical and Clinical Sciences, University of Milan, Milan, Italy; IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan, Italy
| | - Elias P Casula
- Department of Systems Medicine, University of Tor Vergata, Rome, Italy
| | - Faranak Farzan
- Simon Fraser University, School of Mechatronic Systems Engineering, Surrey, British Columbia, Canada
| | - Matteo Fecchio
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Petro Julkunen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland; Department of Clinical Neurophysiology, Kuopio University Hospital, Kuopio, Finland
| | - Elisa Kallioniemi
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | - Pantelis Lioumis
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, Helsinki University and Aalto University School of Science, Helsinki, Finland
| | - Johanna Metsomaa
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, Helsinki University and Aalto University School of Science, Helsinki, Finland
| | - Carlo Miniussi
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, TN, Italy
| | - Tuomas P Mutanen
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, Helsinki University and Aalto University School of Science, Helsinki, Finland
| | - Lorenzo Rocchi
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
| | - Nigel C Rogasch
- University of Adelaide, Adelaide, Australia; South Australian Health and Medical Research Institute, Adelaide, Australia; Monash University, Melbourne, Australia
| | - Mouhsin M Shafi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Hartwig R Siebner
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark; Department of Neurology, Copenhagen University Hospital Bispebjerg and Frederiksberg, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Gregor Thut
- School of Psychology and Neuroscience, University of Glasgow, United Kingdom
| | - Christoph Zrenner
- Department of Neurology & Stroke, University of Tübingen, Tübingen, Germany; Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, Canada; Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany; Department of Psychiatry, University of Toronto, Toronto, Canada
| | - Ulf Ziemann
- Department of Neurology & Stroke, University of Tübingen, Tübingen, Germany; Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Risto J Ilmoniemi
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Hospital, Helsinki University and Aalto University School of Science, Helsinki, Finland
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24
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Lee S, Wu S, Tao JX, Rose S, Warnke PC, Issa NP, van Drongelen W. Manifestation of Hippocampal Interictal Discharges on Clinical Scalp EEG Recordings. J Clin Neurophysiol 2023; 40:144-150. [PMID: 34010227 PMCID: PMC8590709 DOI: 10.1097/wnp.0000000000000867] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
PURPOSE Epileptiform activity limited to deep sources such as the hippocampus currently lacks reliable scalp correlates. Recent studies, however, have found that a subset of hippocampal interictal discharges may be associated with visible scalp signals, suggesting that some types of hippocampal activity may be monitored noninvasively. The purpose of this study is to characterize the relationship between these scalp waveforms and the underlying intracranial activity. METHODS Paired intracranial and scalp EEG recordings obtained from 16 patients were used to identify hippocampal interictal discharges. Discharges were grouped by waveform shape, and spike-triggered averages of the intracranial and scalp signals were calculated for each group. Cross-correlation of intracranial and scalp spike-triggered averages was used to determine their temporal relationship, and topographic maps of the scalp were generated for each group. RESULTS Cross-correlation of intracranial and scalp correlates resulted in two classes of scalp waveforms-those with and without time delays from the associated hippocampal discharges. Scalp signals with no delay showed topographies with a broad field with higher amplitudes on the side ipsilateral to the discharges and a left-right flip in polarity-observations consistent with the volume conduction of a single unilateral deep source. In contrast, scalp correlates with time lags showed rotational dynamics, suggesting synaptic propagation mechanisms. CONCLUSIONS The temporal relationship between the intracranial and scalp signals suggests that both volume conduction and synaptic propagation contribute to these scalp manifestations. Furthermore, the topographic evolution of these scalp waveforms may be used to distinguish spikes that are limited to the hippocampus from those that travel to or engage other brain areas.
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Affiliation(s)
- Somin Lee
- Department of Pediatrics, The University of Chicago, Chicago, IL, 60607, USA
- Committee on Neurobiology, The University of Chicago, Chicago, IL, 60607, USA
| | - Shasha Wu
- Department of Neurology, The University of Chicago, Chicago, IL, 60607, USA
| | - James X. Tao
- Department of Neurology, The University of Chicago, Chicago, IL, 60607, USA
| | - Sandra Rose
- Department of Neurology, The University of Chicago, Chicago, IL, 60607, USA
| | - Peter C. Warnke
- Department of Surgery, The University of Chicago, Chicago, IL, 60607, USA
| | - Naoum P. Issa
- Department of Neurology, The University of Chicago, Chicago, IL, 60607, USA
| | - Wim van Drongelen
- Department of Pediatrics, The University of Chicago, Chicago, IL, 60607, USA
- Committee on Neurobiology, The University of Chicago, Chicago, IL, 60607, USA
- Department of Neurology, The University of Chicago, Chicago, IL, 60607, USA
- Committee on Computational Neuroscience, The University of Chicago, Chicago, IL, 60607, USA
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25
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Shafiei G, Fulcher BD, Voytek B, Satterthwaite TD, Baillet S, Misic B. Neurophysiological signatures of cortical micro-architecture. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.23.525101. [PMID: 36747831 PMCID: PMC9900796 DOI: 10.1101/2023.01.23.525101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Systematic spatial variation in micro-architecture is observed across the cortex. These micro-architectural gradients are reflected in neural activity, which can be captured by neurophysiological time-series. How spontaneous neurophysiological dynamics are organized across the cortex and how they arise from heterogeneous cortical micro-architecture remains unknown. Here we extensively profile regional neurophysiological dynamics across the human brain by estimating over 6 800 timeseries features from the resting state magnetoencephalography (MEG) signal. We then map regional time-series profiles to a comprehensive multi-modal, multi-scale atlas of cortical micro-architecture, including microstructure, metabolism, neurotransmitter receptors, cell types and laminar differentiation. We find that the dominant axis of neurophysiological dynamics reflects characteristics of power spectrum density and linear correlation structure of the signal, emphasizing the importance of conventional features of electromagnetic dynamics while identifying additional informative features that have traditionally received less attention. Moreover, spatial variation in neurophysiological dynamics is colocalized with multiple micro-architectural features, including genomic gradients, intracortical myelin, neurotransmitter receptors and transporters, and oxygen and glucose metabolism. Collectively, this work opens new avenues for studying the anatomical basis of neural activity.
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Affiliation(s)
- Golia Shafiei
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ben D. Fulcher
- School of Physics, The University of Sydney, NSW 2006, Australia
| | - Bradley Voytek
- Department of Cognitive Science, Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, USA
| | - Theodore D. Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
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26
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Li Y, Chen J, Sun J, Jiang P, Xiang J, Chen Q, Hu Z, Wang X. Changes in functional connectivity in newly diagnosed self-limited epilepsy with centrotemporal spikes and cognitive impairment: An MEG study. Brain Behav 2022; 12:e2830. [PMID: 36408856 PMCID: PMC9759146 DOI: 10.1002/brb3.2830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 09/23/2022] [Accepted: 11/03/2022] [Indexed: 11/22/2022] Open
Abstract
PURPOSE Our purpose was to explore the relationship between cognitive impairment and neural network changes in patients newly diagnosed with self-limited epilepsy with centrotemporal spikes (SeLECTS). METHODS The Wechsler Intelligence Scale for Children, fourth edition was used to divide all SeLECTS patients into two groups: patients with full-scale intelligence quotient (FSIQ) below 80 that corresponded to cognitive impairment, and patients with FSIQ above 80 that corresponded to a normal cognitive function. The data on the resting state were recorded using magnetoencephalography. The properties of the networks were analyzed using graph theory (GT) analysis. RESULTS The functional connectivity (FC) of the frontal cortex in patients with FSIQ < 80 was reduced in the 12-30 Hz frequency band, and the FC of the posterior cingulate cortex was reduced in the 80-250 and 250-500 Hz frequency bands. The GT analysis showed that patients in the FSIQ < 80 group had higher strength in the 8-12 and 12-30 Hz frequency bands than those in the healthy control and FSIQ > 80 group. However, the path length was reduced in the 80-250 Hz band, and the clustering coefficient was reduced in the 12-30, 80-250, and 250-500 Hz frequency bands. Moreover, the receiver operator characteristic analysis showed that the clustering coefficient in the 12-30 and 80-250 Hz frequency bands, as well as the path length in the 80-250 Hz frequency band possessed a good discriminative ability in distinguishing the FSIQ > 80 group. CONCLUSIONS SeLECTS patients with cognitive impairment in the early stage of the disease developed disordered networks in cognitive-related brain regions. The clustering coefficient in the 12-30 and 80-250 Hz frequency bands as well as the path length in the 80-250 Hz frequency band might be good indicators to distinguish the cognitive impairment of SeLECTS patients at the early stage.
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Affiliation(s)
- Yihan Li
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Jinan Chen
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Jintao Sun
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Ping Jiang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Jing Xiang
- MEG Center, Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Qiqi Chen
- MEG Center, Nanjing Brain Hospital, Nanjing, Jiangsu, China
| | - Zheng Hu
- Department of Neurology, Nanjing Children's Hospital, Nanjing, Jiangsu, China
| | - Xiaoshan Wang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
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27
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Blohm G, Cheyne DO, Crawford JD. Parietofrontal oscillations show hand-specific interactions with top-down movement plans. J Neurophysiol 2022; 128:1518-1533. [PMID: 36321728 DOI: 10.1152/jn.00240.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
To generate a hand-specific reach plan, the brain must integrate hand-specific signals with the desired movement strategy. Although various neurophysiology/imaging studies have investigated hand-target interactions in simple reach-to-target tasks, the whole brain timing and distribution of this process remain unclear, especially for more complex, instruction-dependent motor strategies. Previously, we showed that a pro/anti pointing instruction influences magnetoencephalographic (MEG) signals in frontal cortex that then propagate recurrently through parietal cortex (Blohm G, Alikhanian H, Gaetz W, Goltz HC, DeSouza JF, Cheyne DO, Crawford JD. NeuroImage 197: 306-319, 2019). Here, we contrasted left versus right hand pointing in the same task to investigate 1) which cortical regions of interest show hand specificity and 2) which of those areas interact with the instructed motor plan. Eight bilateral areas, the parietooccipital junction (POJ), superior parietooccipital cortex (SPOC), supramarginal gyrus (SMG), medial/anterior interparietal sulcus (mIPS/aIPS), primary somatosensory/motor cortex (S1/M1), and dorsal premotor cortex (PMd), showed hand-specific changes in beta band power, with four of these (M1, S1, SMG, aIPS) showing robust activation before movement onset. M1, SMG, SPOC, and aIPS showed significant interactions between contralateral hand specificity and the instructed motor plan but not with bottom-up target signals. Separate hand/motor signals emerged relatively early and lasted through execution, whereas hand-motor interactions only occurred close to movement onset. Taken together with our previous results, these findings show that instruction-dependent motor plans emerge in frontal cortex and interact recurrently with hand-specific parietofrontal signals before movement onset to produce hand-specific motor behaviors.NEW & NOTEWORTHY The brain must generate different motor signals depending on which hand is used. The distribution and timing of hand use/instructed motor plan integration are not understood at the whole brain level. Using MEG we show that different action planning subnetworks code for hand usage and integrating hand use into a hand-specific motor plan. The timing indicates that frontal cortex first creates a general motor plan and then integrates hand specificity to produce a hand-specific motor plan.
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Affiliation(s)
- Gunnar Blohm
- Centre of Neuroscience Studies, Departments of Biomedical & Molecular Sciences, Mathematics & Statistics, and Psychology and School of Computing, Queen's University, Kingston, Ontario, Canada.,Centre for Vision Research, York University, Toronto, Ontario, Canada.,Canadian Action and Perception Network (CAPnet), Montreal, Quebec, Canada.,Vision: Science to Applications (VISTA) program, Departments of Psychology, Biology, and Kinesiology and Health Sciences and Neuroscience Graduate Diploma Program, York University, Toronto, Ontario, Canada
| | - Douglas O Cheyne
- Program in Neurosciences and Mental Health, The Hospital for Sick Children Research Institute, Toronto, Ontario, Canada
| | - J Douglas Crawford
- Centre for Vision Research, York University, Toronto, Ontario, Canada.,Canadian Action and Perception Network (CAPnet), Montreal, Quebec, Canada.,Vision: Science to Applications (VISTA) program, Departments of Psychology, Biology, and Kinesiology and Health Sciences and Neuroscience Graduate Diploma Program, York University, Toronto, Ontario, Canada
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28
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Syntax through the looking glass: A review on two-word linguistic processing across behavioral, neuroimaging and neurostimulation studies. Neurosci Biobehav Rev 2022; 142:104881. [DOI: 10.1016/j.neubiorev.2022.104881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 09/13/2022] [Accepted: 09/15/2022] [Indexed: 11/23/2022]
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29
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Numan T, Breedt LC, Maciel BDAPC, Kulik SD, Derks J, Schoonheim MM, Klein M, de Witt Hamer PC, Miller JJ, Gerstner ER, Stufflebeam SM, Hillebrand A, Stam CJ, Geurts JJG, Reijneveld JC, Douw L. Regional healthy brain activity, glioma occurrence and symptomatology. Brain 2022; 145:3654-3665. [PMID: 36130310 PMCID: PMC9586543 DOI: 10.1093/brain/awac180] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 04/22/2022] [Accepted: 05/04/2022] [Indexed: 11/24/2022] Open
Abstract
It is unclear why exactly gliomas show preferential occurrence in certain brain areas. Increased spiking activity around gliomas leads to faster tumour growth in animal models, while higher non-invasively measured brain activity is related to shorter survival in patients. However, it is unknown how regional intrinsic brain activity, as measured in healthy controls, relates to glioma occurrence. We first investigated whether gliomas occur more frequently in regions with intrinsically higher brain activity. Second, we explored whether intrinsic cortical activity at individual patients’ tumour locations relates to tumour and patient characteristics. Across three cross-sectional cohorts, 413 patients were included. Individual tumour masks were created. Intrinsic regional brain activity was assessed through resting-state magnetoencephalography acquired in healthy controls and source-localized to 210 cortical brain regions. Brain activity was operationalized as: (i) broadband power; and (ii) offset of the aperiodic component of the power spectrum, which both reflect neuronal spiking of the underlying neuronal population. We additionally assessed (iii) the slope of the aperiodic component of the power spectrum, which is thought to reflect the neuronal excitation/inhibition ratio. First, correlation coefficients were calculated between group-level regional glioma occurrence, as obtained by concatenating tumour masks across patients, and group-averaged regional intrinsic brain activity. Second, intrinsic brain activity at specific tumour locations was calculated by overlaying patients’ individual tumour masks with regional intrinsic brain activity of the controls and was associated with tumour and patient characteristics. As proposed, glioma preferentially occurred in brain regions characterized by higher intrinsic brain activity in controls as reflected by higher offset. Second, intrinsic brain activity at patients’ individual tumour locations differed according to glioma subtype and performance status: the most malignant isocitrate dehydrogenase-wild-type glioblastoma patients had the lowest excitation/inhibition ratio at their individual tumour locations as compared to isocitrate dehydrogenase-mutant, 1p/19q-codeleted glioma patients, while a lower excitation/inhibition ratio related to poorer Karnofsky Performance Status, particularly in codeleted glioma patients. In conclusion, gliomas more frequently occur in cortical brain regions with intrinsically higher activity levels, suggesting that more active regions are more vulnerable to glioma development. Moreover, indices of healthy, intrinsic excitation/inhibition ratio at patients’ individual tumour locations may capture both tumour biology and patients’ performance status. These findings contribute to our understanding of the complex and bidirectional relationship between normal brain functioning and glioma growth, which is at the core of the relatively new field of ‘cancer neuroscience’.
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Affiliation(s)
- Tianne Numan
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands.,Cancer Center Amsterdam, Imaging and Biomarkers, Brain Tumor Center Amsterdam, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Systems and Network Neuroscience, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Brain Imaging, Amsterdam 1081 HV, The Netherlands
| | - Lucas C Breedt
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands.,Cancer Center Amsterdam, Imaging and Biomarkers, Brain Tumor Center Amsterdam, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Systems and Network Neuroscience, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Brain Imaging, Amsterdam 1081 HV, The Netherlands
| | - Bernardo de A P C Maciel
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands.,Cancer Center Amsterdam, Imaging and Biomarkers, Brain Tumor Center Amsterdam, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Systems and Network Neuroscience, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Brain Imaging, Amsterdam 1081 HV, The Netherlands
| | - Shanna D Kulik
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands.,Cancer Center Amsterdam, Imaging and Biomarkers, Brain Tumor Center Amsterdam, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Systems and Network Neuroscience, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Brain Imaging, Amsterdam 1081 HV, The Netherlands
| | - Jolanda Derks
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands.,Cancer Center Amsterdam, Imaging and Biomarkers, Brain Tumor Center Amsterdam, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Systems and Network Neuroscience, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Brain Imaging, Amsterdam 1081 HV, The Netherlands
| | - Menno M Schoonheim
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Systems and Network Neuroscience, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Brain Imaging, Amsterdam 1081 HV, The Netherlands
| | - Martin Klein
- Cancer Center Amsterdam, Imaging and Biomarkers, Brain Tumor Center Amsterdam, Amsterdam 1081 HV, The Netherlands.,Department of Medical Psychology, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
| | - Philip C de Witt Hamer
- Cancer Center Amsterdam, Imaging and Biomarkers, Brain Tumor Center Amsterdam, Amsterdam 1081 HV, The Netherlands.,Department of Neurosurgery, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
| | - Julie J Miller
- Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Elizabeth R Gerstner
- Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Steven M Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
| | - Jaap C Reijneveld
- Cancer Center Amsterdam, Imaging and Biomarkers, Brain Tumor Center Amsterdam, Amsterdam 1081 HV, The Netherlands.,Department of Neurology, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands.,Department of Neurology, Stichting Epilepsie Instellingen Nederland, Heemstede 2103 SW, The Netherlands
| | - Linda Douw
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands.,Cancer Center Amsterdam, Imaging and Biomarkers, Brain Tumor Center Amsterdam, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Systems and Network Neuroscience, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Brain Imaging, Amsterdam 1081 HV, The Netherlands.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
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30
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Kim SG. On the encoding of natural music in computational models and human brains. Front Neurosci 2022; 16:928841. [PMID: 36203808 PMCID: PMC9531138 DOI: 10.3389/fnins.2022.928841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 08/15/2022] [Indexed: 11/13/2022] Open
Abstract
This article discusses recent developments and advances in the neuroscience of music to understand the nature of musical emotion. In particular, it highlights how system identification techniques and computational models of music have advanced our understanding of how the human brain processes the textures and structures of music and how the processed information evokes emotions. Musical models relate physical properties of stimuli to internal representations called features, and predictive models relate features to neural or behavioral responses and test their predictions against independent unseen data. The new frameworks do not require orthogonalized stimuli in controlled experiments to establish reproducible knowledge, which has opened up a new wave of naturalistic neuroscience. The current review focuses on how this trend has transformed the domain of the neuroscience of music.
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31
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Shafiei G, Baillet S, Misic B. Human electromagnetic and haemodynamic networks systematically converge in unimodal cortex and diverge in transmodal cortex. PLoS Biol 2022; 20:e3001735. [PMID: 35914002 PMCID: PMC9371256 DOI: 10.1371/journal.pbio.3001735] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 08/11/2022] [Accepted: 06/30/2022] [Indexed: 11/21/2022] Open
Abstract
Whole-brain neural communication is typically estimated from statistical associations among electromagnetic or haemodynamic time-series. The relationship between functional network architectures recovered from these 2 types of neural activity remains unknown. Here, we map electromagnetic networks (measured using magnetoencephalography (MEG)) to haemodynamic networks (measured using functional magnetic resonance imaging (fMRI)). We find that the relationship between the 2 modalities is regionally heterogeneous and systematically follows the cortical hierarchy, with close correspondence in unimodal cortex and poor correspondence in transmodal cortex. Comparison with the BigBrain histological atlas reveals that electromagnetic-haemodynamic coupling is driven by laminar differentiation and neuron density, suggesting that the mapping between the 2 modalities can be explained by cytoarchitectural variation. Importantly, haemodynamic connectivity cannot be explained by electromagnetic activity in a single frequency band, but rather arises from the mixing of multiple neurophysiological rhythms. Correspondence between the two is largely driven by MEG functional connectivity at the beta (15 to 29 Hz) frequency band. Collectively, these findings demonstrate highly organized but only partly overlapping patterns of connectivity in MEG and fMRI functional networks, opening fundamentally new avenues for studying the relationship between cortical microarchitecture and multimodal connectivity patterns.
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Affiliation(s)
- Golia Shafiei
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
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32
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Hauk O, Stenroos M, Treder MS. Towards an objective evaluation of EEG/MEG source estimation methods - The linear approach. Neuroimage 2022; 255:119177. [PMID: 35390459 DOI: 10.1016/j.neuroimage.2022.119177] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 03/30/2022] [Accepted: 03/31/2022] [Indexed: 11/18/2022] Open
Abstract
The spatial resolution of EEG/MEG source estimates, often described in terms of source leakage in the context of the inverse problem, poses constraints on the inferences that can be drawn from EEG/MEG source estimation results. Software packages for EEG/MEG data analysis offer a large choice of source estimation methods but few tools to experimental researchers for methods evaluation and comparison. Here, we describe a framework and tools for objective and intuitive resolution analysis of EEG/MEG source estimation based on linear systems analysis, and apply those to the most widely used distributed source estimation methods such as L2-minimum-norm estimation (L2-MNE) and linearly constrained minimum variance (LCMV) beamformers. Within this framework it is possible to define resolution metrics that define meaningful aspects of source estimation results (such as localization accuracy in terms of peak localization error, PLE, and spatial extent in terms of spatial deviation, SD) that are relevant to the task at hand and can easily be visualized. At the core of this framework is the resolution matrix, which describes the potential leakage from and into point sources (point-spread and cross-talk functions, or PSFs and CTFs, respectively). Importantly, for linear methods these functions allow generalizations to multiple sources or complex source distributions. This paper provides a tutorial-style introduction into linear EEG/MEG source estimation and resolution analysis aimed at experimental (rather than methods-oriented) researchers. We used this framework to demonstrate how L2-MNE-type as well as LCMV beamforming methods can be evaluated in practice using software tools that have only recently become available for routine use. Our novel methods comparison includes PLE and SD for a larger number of methods than in similar previous studies, such as unweighted, depth-weighted and normalized L2-MNE methods (including dSPM, sLORETA, eLORETA) and two LCMV beamformers. The results demonstrate that some methods can achieve low and even zero PLE for PSFs. However, their SD as well as both PLE and SD for CTFs are far less optimal for all methods, in particular for deep cortical areas. We hope that our paper will encourage EEG/MEG researchers to apply this approach to their own tasks at hand.
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Affiliation(s)
- Olaf Hauk
- MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Road, Cambridge CB2 7EF, UK.
| | - Matti Stenroos
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Matthias S Treder
- School of Computer Sciences and Informatics, Cardiff University, Cardiff, UK
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33
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Huang X, Liang S, Li Z, Lai CYY, Choi KS. EEG-based vibrotactile evoked brain-computer interfaces system: A systematic review. PLoS One 2022; 17:e0269001. [PMID: 35657949 PMCID: PMC9165854 DOI: 10.1371/journal.pone.0269001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 05/12/2022] [Indexed: 11/18/2022] Open
Abstract
Recently, a novel electroencephalogram-based brain-computer interface (EVE-BCI) using the vibrotactile stimulus shows great potential for an alternative to other typical motor imagery and visual-based ones. (i) Objective: in this review, crucial aspects of EVE-BCI are extracted from the literature to summarize its key factors, investigate the synthetic evidence of feasibility, and generate recommendations for further studies. (ii) Method: five major databases were searched for relevant publications. Multiple key concepts of EVE-BCI, including data collection, stimulation paradigm, vibrotactile control, EEG signal processing, and reported performance, were derived from each eligible article. We then analyzed these concepts to reach our objective. (iii) Results: (a) seventy-nine studies are eligible for inclusion; (b) EEG data are mostly collected among healthy people with an embodiment of EEG cap in EVE-BCI development; (c) P300 and Steady-State Somatosensory Evoked Potential are the two most popular paradigms; (d) only locations of vibration are heavily explored by previous researchers, while other vibrating factors draw little interest. (e) temporal features of EEG signal are usually extracted and used as the input to linear predictive models for EVE-BCI setup; (f) subject-dependent and offline evaluations remain popular assessments of EVE-BCI performance; (g) accuracies of EVE-BCI are significantly higher than chance levels among different populations. (iv) Significance: we summarize trends and gaps in the current EVE-BCI by identifying influential factors. A comprehensive overview of EVE-BCI can be quickly gained by reading this review. We also provide recommendations for the EVE-BCI design and formulate a checklist for a clear presentation of the research work. They are useful references for researchers to develop a more sophisticated and practical EVE-BCI in future studies.
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Affiliation(s)
- Xiuyu Huang
- Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Shuang Liang
- School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing, China
| | - Zengguang Li
- School of Computer Science and Technology, Nanjing Tech University, Nanjing, China
| | - Cynthia Yuen Yi Lai
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Kup-Sze Choi
- Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong Kong SAR, China
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34
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Tait L, Zhang J. +microstate: A MATLAB toolbox for brain microstate analysis in sensor and cortical EEG/MEG. Neuroimage 2022; 258:119346. [PMID: 35660463 DOI: 10.1016/j.neuroimage.2022.119346] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 04/13/2022] [Accepted: 05/29/2022] [Indexed: 01/14/2023] Open
Abstract
+microstate is a MATLAB toolbox for brain functional microstate analysis. It builds upon previous EEG microstate literature and toolboxes by including algorithms for source-space microstate analysis. +microstate includes codes for performing individual- and group-level brain microstate analysis in resting-state and task-based data including event-related potentials/fields. Functions are included to visualise and perform statistical analysis of microstate sequences, including novel advanced statistical approaches such as statistical testing for associated functional connectivity patterns, cluster-permutation topographic ANOVAs, and χ2 analysis of microstate probabilities in response to stimuli. Additionally, codes for simulating microstate sequences and their associated M/EEG data are included in the toolbox, which can be used to generate artificial data with ground truth microstates and to validate the methodology. +microstate integrates with widely used toolboxes for M/EEG processing including Fieldtrip, SPM, LORETA/sLORETA, EEGLAB, and Brainstorm to aid with accessibility, and includes wrappers for pre-existing toolboxes for brain-state estimation such as Hidden Markov modelling (HMM-MAR) and independent component analysis (FastICA) to aid with direct comparison with these techniques. In this paper, we first introduce +microstate before subsequently performing example analyses using open access datasets to demonstrate and validate the methodology. MATLAB live scripts for each of these analyses are included in +microstate, to act as a tutorial and to aid with reproduction of the results presented in this manuscript.
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Affiliation(s)
- Luke Tait
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom; Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, UK.
| | - Jiaxiang Zhang
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, UK
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35
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Tait L, Zhang J. MEG cortical microstates: Spatiotemporal characteristics, dynamic functional connectivity and stimulus-evoked responses. Neuroimage 2022; 251:119006. [PMID: 35181551 PMCID: PMC8961001 DOI: 10.1016/j.neuroimage.2022.119006] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 01/29/2022] [Accepted: 02/14/2022] [Indexed: 12/12/2022] Open
Abstract
EEG microstate analysis is an approach to study brain states and their fast transitions in healthy cognition and disease. A key limitation of conventional microstate analysis is that it must be performed at the sensor level, and therefore gives limited anatomical insight. Here, we generalise the microstate methodology to be applicable to source-reconstructed electrophysiological data. Using simulations of a neural-mass network model, we first established the validity and robustness of the proposed method. Using MEG resting-state data, we uncovered ten microstates with distinct spatial distributions of cortical activation. Multivariate pattern analysis demonstrated that source-level microstates were associated with distinct functional connectivity patterns. We further demonstrated that the occurrence probability of MEG microstates were altered by auditory stimuli, exhibiting a hyperactivity of the microstate including the auditory cortex. Our results support the use of source-level microstates as a method for investigating brain dynamic activity and connectivity at the millisecond scale.
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Affiliation(s)
- Luke Tait
- Centre for Systems Modelling & Quantitative Biomedicine (SMQB), University of Birmingham, Birmingham, UK; Cardiff University Brain Research Imaging Centre, Cardiff, UK.
| | - Jiaxiang Zhang
- Cardiff University Brain Research Imaging Centre, Cardiff, UK
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36
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Woolnough O, Forseth KJ, Rollo PS, Roccaforte ZJ, Tandon N. Event-Related Phase Synchronization Propagates Rapidly across Human Ventral Visual Cortex. Neuroimage 2022; 256:119262. [PMID: 35504563 PMCID: PMC9382906 DOI: 10.1016/j.neuroimage.2022.119262] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 03/31/2022] [Accepted: 04/27/2022] [Indexed: 11/01/2022] Open
Abstract
Visual inputs to early visual cortex integrate with semantic, linguistic and memory inputs in higher visual cortex, in a manner that is rapid and accurate, and enables complex computations such as face recognition and word reading. This implies the existence of fundamental organizational principles that enable such efficiency. To elaborate on this, we performed intracranial recordings in 82 individuals while they performed tasks of varying visual and cognitive complexity. We discovered that visual inputs induce highly organized posterior-to-anterior propagating patterns of phase modulation across the ventral occipitotemporal cortex. At individual electrodes there was a stereotyped temporal pattern of phase progression following both stimulus onset and offset, consistent across trials and tasks. The phase of low frequency activity in anterior regions was predicted by the prior phase in posterior cortical regions. This spatiotemporal propagation of phase likely serves as a feed-forward organizational influence enabling the integration of information across the ventral visual stream. This phase modulation manifests as the early components of the event related potential; one of the most commonly used measures in human electrophysiology. These findings illuminate fundamental organizational principles of the higher order visual system that enable the rapid recognition and characterization of a variety of inputs.
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Affiliation(s)
- Oscar Woolnough
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School at UT Health Houston, Houston, TX, 77030, United States of America; Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX, 77030, United States of America
| | - Kiefer J Forseth
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School at UT Health Houston, Houston, TX, 77030, United States of America; Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX, 77030, United States of America
| | - Patrick S Rollo
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School at UT Health Houston, Houston, TX, 77030, United States of America; Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX, 77030, United States of America
| | - Zachary J Roccaforte
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School at UT Health Houston, Houston, TX, 77030, United States of America; Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX, 77030, United States of America
| | - Nitin Tandon
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School at UT Health Houston, Houston, TX, 77030, United States of America; Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX, 77030, United States of America; Memorial Hermann Hospital, Texas Medical Center, Houston, TX, 77030, United States of America.
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37
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Tenzer ML, Lisinski JM, LaConte SM. Decoding the Brain's Surface to Track Deeper Activity. FRONTIERS IN NEUROIMAGING 2022; 1:815778. [PMID: 37555135 PMCID: PMC10406232 DOI: 10.3389/fnimg.2022.815778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 02/14/2022] [Indexed: 08/10/2023]
Abstract
Neural activity can be readily and non-invasively recorded from the scalp using electromagnetic and optical signals, but unfortunately all scalp-based techniques have depth-dependent sensitivities. We hypothesize, though, that the cortex's connectivity with the rest of the brain could serve to construct proxy signals of deeper brain activity. For example, functional magnetic resonance imaging (fMRI)-derived models that link surface connectivity to deeper regions could subsequently extend the depth capabilities of other modalities. Thus, as a first step toward this goal, this study examines whether or not surface-limited support vector regression of resting-state fMRI can indeed track deeper regions and distributed networks in independent data. Our results demonstrate that depth-limited fMRI signals can in fact be calibrated to report ongoing activity of deeper brain structures. Although much future work remains to be done, the present study suggests that scalp recordings have the potential to ultimately overcome their intrinsic physical limitations by utilizing the multivariate information exchanged between the surface and the rest of the brain.
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Affiliation(s)
- Mark L. Tenzer
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, United States
| | - Jonathan M. Lisinski
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, United States
| | - Stephen M. LaConte
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, United States
- Department of Biomedical Engineering and Mechanics, Virginia Tech, Blacksburg, VA, United States
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38
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Millán AP, van Straaten ECW, Stam CJ, Nissen IA, Idema S, Baayen JC, Van Mieghem P, Hillebrand A. Epidemic models characterize seizure propagation and the effects of epilepsy surgery in individualized brain networks based on MEG and invasive EEG recordings. Sci Rep 2022; 12:4086. [PMID: 35260657 PMCID: PMC8904850 DOI: 10.1038/s41598-022-07730-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 02/24/2022] [Indexed: 11/08/2022] Open
Abstract
Epilepsy surgery is the treatment of choice for drug-resistant epilepsy patients. However, seizure-freedom is currently achieved in only 2/3 of the patients after surgery. In this study we have developed an individualized computational model based on MEG brain networks to explore seizure propagation and the efficacy of different virtual resections. Eventually, the goal is to obtain individualized models to optimize resection strategy and outcome. We have modelled seizure propagation as an epidemic process using the susceptible-infected (SI) model on individual brain networks derived from presurgical MEG. We included 10 patients who had received epilepsy surgery and for whom the surgery outcome at least one year after surgery was known. The model parameters were tuned in in order to reproduce the patient-specific seizure propagation patterns as recorded with invasive EEG. We defined a personalized search algorithm that combined structural and dynamical information to find resections that maximally decreased seizure propagation for a given resection size. The optimal resection for each patient was defined as the smallest resection leading to at least a 90% reduction in seizure propagation. The individualized model reproduced the basic aspects of seizure propagation for 9 out of 10 patients when using the resection area as the origin of epidemic spreading, and for 10 out of 10 patients with an alternative definition of the seed region. We found that, for 7 patients, the optimal resection was smaller than the resection area, and for 4 patients we also found that a resection smaller than the resection area could lead to a 100% decrease in propagation. Moreover, for two cases these alternative resections included nodes outside the resection area. Epidemic spreading models fitted with patient specific data can capture the fundamental aspects of clinically observed seizure propagation, and can be used to test virtual resections in silico. Combined with optimization algorithms, smaller or alternative resection strategies, that are individually targeted for each patient, can be determined with the ultimate goal to improve surgery outcome. MEG-based networks can provide a good approximation of structural connectivity for computational models of seizure propagation, and facilitate their clinical use.
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Affiliation(s)
- Ana P Millán
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
| | - Elisabeth C W van Straaten
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Ida A Nissen
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Sander Idema
- Department of Neurosurgery, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Johannes C Baayen
- Department of Neurosurgery, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Piet Van Mieghem
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
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Neurophysiological and Brain Structural Markers of Cognitive Frailty Differ from Alzheimer's Disease. J Neurosci 2022; 42:1362-1373. [PMID: 35012965 PMCID: PMC8883844 DOI: 10.1523/jneurosci.0697-21.2021] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 09/29/2021] [Accepted: 11/03/2021] [Indexed: 02/02/2023] Open
Abstract
With increasing life span and prevalence of dementia, it is important to understand the mechanisms of cognitive aging. Here, we focus on a subgroup of the population we term "cognitively frail," defined by reduced cognitive function in the absence of subjective memory complaints, or a clinical diagnosis of dementia. Cognitive frailty is distinct from cognitive impairment caused by physical frailty. It has been proposed to be a precursor to Alzheimer's disease, but may alternatively represent one end of a nonpathologic spectrum of cognitive aging. We test these hypotheses in humans of both sexes, by comparing the structural and neurophysiological properties of a community-based cohort of cognitive frail adults, to people presenting clinically with diagnoses of Alzheimer's disease or mild cognitive impairment, and community-based cognitively typical older adults. Cognitive performance of the cognitively frail was similar to those with mild cognitive impairment. We used a novel cross-modal paired-associates task that presented images followed by sounds, to induce physiological responses of novelty and associative mismatch, recorded by EEG/MEG. Both controls and cognitively frail showed stronger mismatch responses and larger temporal gray matter volume, compared with people with mild cognitive impairment and Alzheimer's disease. Our results suggest that community-based cognitively frail represents a spectrum of normal aging rather than incipient Alzheimer's disease, despite similar cognitive function. Lower lifelong cognitive reserve, hearing impairment, and cardiovascular comorbidities might contribute to the etiology of the cognitive frailty. Critically, community-based cohorts of older adults with low cognitive performance should not be interpreted as representing undiagnosed Alzheimer's disease.SIGNIFICANCE STATEMENT The current study investigates the neural signatures of cognitive frailty in relation to healthy aging and Alzheimer's disease. We focus on the cognitive aspect of frailty and show that, despite performing similarly to the patients with mild cognitive impairment, a cohort of community-based adults with poor cognitive performance do not show structural atrophy or neurophysiological signatures of Alzheimer's disease. Our results call for caution before assuming that cognitive frailty represents latent Alzheimer's disease. Instead, the cognitive underperformance of cognitively frail adults could result in cumulative effects of multiple psychosocial risk factors over the lifespan, and medical comorbidities.
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40
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Nenonen J, Helle L, Jaiswal A, Bock E, Ille N, Bornfleth H. Sensitivity of a 29-Channel MEG Source Montage. Brain Sci 2022; 12:brainsci12010105. [PMID: 35053848 PMCID: PMC8773883 DOI: 10.3390/brainsci12010105] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 01/08/2022] [Accepted: 01/11/2022] [Indexed: 12/04/2022] Open
Abstract
In this paper, we study the performance of a source montage corresponding to 29 brain regions reconstructed from whole-head magnetoencephalographic (MEG) recordings, with the aim of facilitating the review of MEG data containing epileptiform discharges. Test data were obtained by superposing simulated signals from 100-nAm dipolar sources to a resting state MEG recording from a healthy subject. Simulated sources were placed systematically to different cortical locations for defining the optimal regularization for the source montage reconstruction and for assessing the detectability of the source activity from the 29-channel MEG source montage. The signal-to-noise ratio (SNR), computed for each source from the sensor-level and source-montage signals, was used as the evaluation parameter. Without regularization, the SNR from the simulated sources was larger in the sensor-level signals than in the source montage reconstructions. Setting the regularization to 2% increased the source montage SNR to the same level as the sensor-level SNR, improving the detectability of the simulated events from the source montage reconstruction. Sources producing a SNR of at least 15 dB were visually detectable from the source-montage signals. Such sources are located closer than about 75 mm from the MEG sensors, in practice covering all areas in the grey matter. The 29-channel source montage creates more focal signals compared to the sensor space and can significantly shorten the detection time of epileptiform MEG discharges for focus localization.
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Affiliation(s)
- Jukka Nenonen
- Megin Oy, Keilasatama 5, FI-02150 Espoo, Finland; (L.H.); (A.J.); (E.B.)
- Correspondence: ; Tel.: +358-9-756-2400
| | - Liisa Helle
- Megin Oy, Keilasatama 5, FI-02150 Espoo, Finland; (L.H.); (A.J.); (E.B.)
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, FI-00076 Aalto, Finland
| | - Amit Jaiswal
- Megin Oy, Keilasatama 5, FI-02150 Espoo, Finland; (L.H.); (A.J.); (E.B.)
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, FI-00076 Aalto, Finland
| | - Elizabeth Bock
- Megin Oy, Keilasatama 5, FI-02150 Espoo, Finland; (L.H.); (A.J.); (E.B.)
| | - Nicole Ille
- BESA GmbH, 82166 Gräfelfing, Germany; (N.I.); (H.B.)
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41
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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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42
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Moradi N, LeVan P, Akin B, Goodyear BG, Sotero RC. Holo-Hilbert spectral-based noise removal method for EEG high-frequency bands. J Neurosci Methods 2021; 368:109470. [PMID: 34973273 DOI: 10.1016/j.jneumeth.2021.109470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 12/23/2021] [Accepted: 12/26/2021] [Indexed: 11/16/2022]
Abstract
Simultaneous EEG-fMRI is a growing and promising field, as it has great potential to further our understanding of the spatiotemporal dynamics of brain function in health and disease. In particular, there is much interest in understanding the fMRI correlates of brain activity in the gamma band (> 30 Hz), as these frequencies are thought to be associated with cognitive processes involving perception, attention, and memory, as well as with disorders such as schizophrenia and autism. However, progress in this area has been limited due to issues such as MR-induced artifacts in EEG recordings, which seem to be more problematic for gamma frequencies. This paper presents a noise removal method for the gamma band of EEG that is based on the Holo-Hilbert spectral analysis (HHSA), but with a new implementation strategy. HHSA uses a nested empirical mode decomposition (EMD) to identify amplitude and frequency modulations (AM and FM, respectively) by averaging over frequencies with high and significant powers. Our method examines gamma band by applying two layers of EMD to the FM and AM components, removing components with very low power based on the power-instantaneous frequency spectrum, and subsequently reconstructs the denoised gamma-band signal from the remaining components. Simulations demonstrate that our proposed method efficiently reduces artifacts while preserving the original gamma signal which is especially critical for simultaneous EEG/fMRI studies.
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Affiliation(s)
- Narges Moradi
- Biomedical Engineering Graduate Program, University of Calgary, Calgary, AB, Canada; Department of Radiology and Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.
| | - Pierre LeVan
- Department of Radiology and Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada; Alberta Children's Hospital Research Institute and Departments of Paediatrics, University of Calgary, Calgary, Canada; Department of Radiology, Medical Physics, Medical Center, University of Freiburg, Faculty of Medicine, Germany
| | - Burak Akin
- Department of Radiology, Medical Physics, Medical Center, University of Freiburg, Faculty of Medicine, Germany; Section on Functional Imaging Methods, NIMH, NIH, Bethesda, MD, USA
| | - Bradley G Goodyear
- Department of Radiology and Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Roberto C Sotero
- Department of Radiology and Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.
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43
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Hasanzadeh F, Mohebbi M, Rostami R. A Nonlinear Effective Connectivity Measure Based on Granger Causality and Volterra Series. IEEE J Biomed Health Inform 2021; 26:2299-2307. [PMID: 34951858 DOI: 10.1109/jbhi.2021.3138199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Estimating effective connectivity, especially in brain networks, is an important topic to find out the brain functions. Various effective connectivity measures are presented, but they have drawbacks, including bivariate structure, the problem in detecting nonlinear interactions, and high computational cost. In this paper, we have proposed a novel multivariate effective connectivity measure based on a hierarchical realization of the Volterra series model and Granger causality concept, namely hierarchical Volterra Granger causality (HVGC). HVGC is a multivariate connectivity measure that can detect linear and nonlinear causal effects. The performance of HVGC is compared with Granger causality index (GCI), conditional Granger causality index (CGCI), transfer entropy (TE), phase transfer entropy (Phase TE), and partial transfer entropy (Partial TE) in simulated and physiological datasets. In addition to accuracy, specificity, and sensitivity, the Matthews correlation coefficient (MCC) is used to evaluate the connectivity estimation in simulated datasets. Furthermore influence of different SNRs is investigated on the estimated connectivity. The obtained results show that HVGC with a minimum MCC of 0.76 performs well in the detection of both linear and nonlinear interactions in simulated data. HVGC is also applied to a physiological dataset that was cardiorespiratory interaction signals recorded during sleep from a patient suffering from sleep apnea. The results of this dataset also demonstrate the capability of the proposed method in the detection of causal interactions. Applying HVGC on the simulated fMRI dataset led to a high MCC of 0.78. Moreover, the results indicate that HVGC has slight changes in different SNRs. The results indicate that HVGC can estimate the causal effects of a linear and nonlinear system with a low computational cost and it is slightly affected by noise.
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44
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Hauk O, Rice GE, Volfart A, Magnabosco F, Ralph MAL, Rossion B. Face-selective responses in combined EEG/MEG recordings with fast periodic visual stimulation (FPVS). Neuroimage 2021; 242:118460. [PMID: 34363957 PMCID: PMC8463833 DOI: 10.1016/j.neuroimage.2021.118460] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 07/14/2021] [Accepted: 08/04/2021] [Indexed: 11/29/2022] Open
Abstract
Fast periodic visual stimulation (FPVS) allows the recording of objective brain responses of human face categorization (i.e., generalizable face-selective responses) with high signal-to-noise ratio. This approach has been successfully employed in a number of scalp electroencephalography (EEG) studies but has not been used with magnetoencephalography (MEG) yet, let alone with combined MEG/EEG recordings and distributed source estimation. Here, we presented various natural images of faces periodically (1.2 Hz) among natural images of objects (base frequency 6 Hz) whilst recording simultaneous EEG and MEG in 15 participants. Both measurement modalities showed face-selective responses at 1.2 Hz and harmonics across participants, with high and comparable signal-to-noise ratio (SNR) in about 3 min of stimulation. The correlation of face categorization responses between EEG and two MEG sensor types was lower than between the two MEG sensor types, indicating that the two sensor modalities provide independent information about the sources of face-selective responses. Face-selective EEG responses were right-lateralized as reported previously, and were numerically but non-significantly right-lateralized in MEG data. Distributed source estimation based on combined EEG/MEG signals confirmed a more bilateral face-selective response in visual brain regions located anteriorly to the common response to all stimuli at 6 Hz and harmonics. Conventional sensor and source space analyses of evoked responses in the time domain further corroborated this result. Our results demonstrate that FPVS in combination with simultaneously recorded EEG and MEG may serve as an efficient localizer paradigm for human face categorization.
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Affiliation(s)
- O Hauk
- MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Road, Cambridge CB2 7EF, UK.
| | - G E Rice
- MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Road, Cambridge CB2 7EF, UK
| | - A Volfart
- Université de Lorraine, CNRS, CRAN UMR 7039, Nancy F-54000, France; Research Institute for Psychological Science, University of Louvain, Louvain-la-Neuve, Belgium
| | - F Magnabosco
- MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Road, Cambridge CB2 7EF, UK
| | - M A Lambon Ralph
- MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Road, Cambridge CB2 7EF, UK
| | - B Rossion
- Université de Lorraine, CNRS, CRAN UMR 7039, Nancy F-54000, France; Université de Lorraine, CHRU-Nancy, Service de Neurologie, Nancy F-54000, France
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45
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Bonaiuto JJ, Little S, Neymotin SA, Jones SR, Barnes GR, Bestmann S. Laminar dynamics of high amplitude beta bursts in human motor cortex. Neuroimage 2021; 242:118479. [PMID: 34407440 PMCID: PMC8463839 DOI: 10.1016/j.neuroimage.2021.118479] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 08/12/2021] [Accepted: 08/14/2021] [Indexed: 12/28/2022] Open
Abstract
Motor cortical activity in the beta frequency range is one of the strongest and most studied movement-related neural signals. At the single trial level, beta band activity is often characterized by transient, high amplitude, bursting events rather than slowly modulating oscillations. The timing of these bursting events is tightly linked to behavior, suggesting a more dynamic functional role for beta activity than previously believed. However, the neural mechanisms underlying beta bursts in sensorimotor circuits are poorly understood. To address this, we here leverage and extend recent developments in high precision MEG for temporally resolved laminar analysis of burst activity, combined with a neocortical circuit model that simulates the biophysical generators of the electrical currents which drive beta bursts. This approach pinpoints the generation of beta bursts in human motor cortex to distinct excitatory synaptic inputs to deep and superficial cortical layers, which drive current flow in opposite directions. These laminar dynamics of beta bursts in motor cortex align with prior invasive animal recordings within the somatosensory cortex, and suggest a conserved mechanism for somatosensory and motor cortical beta bursts. More generally, we demonstrate the ability for uncovering the laminar dynamics of event-related neural signals in human non-invasive recordings. This provides important constraints to theories about the functional role of burst activity for movement control in health and disease, and crucial links between macro-scale phenomena measured in humans and micro-circuit activity recorded from animal models.
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Affiliation(s)
- James J Bonaiuto
- Institut des Sciences Cognitives Marc Jeannerod, CNRS UMR 5229, Bron, France; Université Claude Bernard Lyon 1, Université de Lyon, France; Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK; Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK.
| | - Simon Little
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK; Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Samuel A Neymotin
- Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Department of Neuroscience, Brown University, Providence, RI, USA; Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, USA
| | - Stephanie R Jones
- Department of Neuroscience, Brown University, Providence, RI, USA; Center for Neurorestoration and Neurotechnology, Providence VAMC, Providence, RI, USA
| | - Gareth R Barnes
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK
| | - Sven Bestmann
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK; Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK
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46
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Huang X, Zhou N, Choi KS. A Generalizable and Discriminative Learning Method for Deep EEG-Based Motor Imagery Classification. Front Neurosci 2021; 15:760979. [PMID: 34744622 PMCID: PMC8570040 DOI: 10.3389/fnins.2021.760979] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 09/06/2021] [Indexed: 11/29/2022] Open
Abstract
Convolutional neural networks (CNNs) have been widely applied to the motor imagery (MI) classification field, significantly improving the state-of-the-art (SoA) performance in terms of classification accuracy. Although innovative model structures are thoroughly explored, little attention was drawn toward the objective function. In most of the available CNNs in the MI area, the standard cross-entropy loss is usually performed as the objective function, which only ensures deep feature separability. Corresponding to the limitation of current objective functions, a new loss function with a combination of smoothed cross-entropy (with label smoothing) and center loss is proposed as the supervision signal for the model in the MI recognition task. Specifically, the smoothed cross-entropy is calculated by the entropy between the predicted labels and the one-hot hard labels regularized by a noise of uniform distribution. The center loss learns a deep feature center for each class and minimizes the distance between deep features and their corresponding centers. The proposed loss tries to optimize the model in two learning objectives, preventing overconfident predictions and increasing deep feature discriminative capacity (interclass separability and intraclass invariant), which guarantee the effectiveness of MI recognition models. We conduct extensive experiments on two well-known benchmarks (BCI competition IV-2a and IV-2b) to evaluate our method. The result indicates that the proposed approach achieves better performance than other SoA models on both datasets. The proposed learning scheme offers a more robust optimization for the CNN model in the MI classification task, simultaneously decreasing the risk of overfitting and increasing the discriminative power of deeply learned features.
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Affiliation(s)
- Xiuyu Huang
- Centre for Smart Health, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Nan Zhou
- College of Control Engineering, Chengdu University of Information Technology, Chengdu, China.,Centre for Smart Health, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
| | - Kup-Sze Choi
- Centre for Smart Health, The Hong Kong Polytechnic University, Hong Kong, Hong Kong SAR, China
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47
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Li Z, Liu C, Wang Q, Liang K, Han C, Qiao H, Zhang J, Meng F. Abnormal Functional Brain Network in Parkinson's Disease and the Effect of Acute Deep Brain Stimulation. Front Neurol 2021; 12:715455. [PMID: 34721258 PMCID: PMC8551554 DOI: 10.3389/fneur.2021.715455] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 08/26/2021] [Indexed: 01/21/2023] Open
Abstract
Objective: The objective of this study was to use functional connectivity and graphic indicators to investigate the abnormal brain network topological characteristics caused by Parkinson's disease (PD) and the effect of acute deep brain stimulation (DBS) on those characteristics in patients with PD. Methods: We recorded high-density EEG (256 channels) data from 21 healthy controls (HC) and 20 patients with PD who were in the DBS-OFF state and DBS-ON state during the resting state with eyes closed. A high-density EEG source connectivity method was used to identify functional brain networks. Power spectral density (PSD) analysis was compared between the groups. Functional connectivity was calculated for 68 brain regions in the theta (4-8 Hz), alpha (8-13 Hz), beta1 (13-20 Hz), and beta2 (20-30 Hz) frequency bands. Network estimates were measured at both the global (network topology) and local (inter-regional connection) levels. Results: Compared with HC, PSD was significantly increased in the theta (p = 0.003) frequency band and was decreased in the beta1 (p = 0.009) and beta2 (p = 0.04) frequency bands in patients with PD. However, there were no differences in any frequency bands between patients with PD with DBS-OFF and DBS-ON. The clustering coefficient and local efficiency of patients with PD showed a significant decrease in the alpha, beta1, and beta2 frequency bands (p < 0.001). In addition, edgewise statistics showed a significant difference between the HC and patients with PD in all analyzed frequency bands (p < 0.005). However, there were no significant differences between the DBS-OFF state and DBS-ON state in the brain network, except for the functional connectivity in the beta2 frequency band (p < 0.05). Conclusion: Compared with HC, patients with PD showed the following characteristics: slowed EEG background activity, decreased clustering coefficient and local efficiency of the brain network, as well as both increased and decreased functional connectivity between different brain areas. Acute DBS induces a local response of the brain network in patients with PD, mainly showing decreased functional connectivity in a few brain regions in the beta2 frequency band.
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Affiliation(s)
- Zhibao Li
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Chong Liu
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Qiao Wang
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Kun Liang
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Chunlei Han
- Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Neurostimulation, Beijing, China
| | - Hui Qiao
- Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Jianguo Zhang
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Neurostimulation, Beijing, China
| | - Fangang Meng
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Neurostimulation, Beijing, China.,Chinese Institute for Brain Research, Beijing (CIBR), Beijing, China
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48
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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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49
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Grent-'t-Jong T, Gajwani R, Gross J, Gumley AI, Krishnadas R, Lawrie SM, Schwannauer M, Schultze-Lutter F, Uhlhaas PJ. 40-Hz Auditory Steady-State Responses Characterize Circuit Dysfunctions and Predict Clinical Outcomes in Clinical High-Risk for Psychosis Participants: A Magnetoencephalography Study. Biol Psychiatry 2021; 90:419-429. [PMID: 34116790 DOI: 10.1016/j.biopsych.2021.03.018] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 03/02/2021] [Accepted: 03/17/2021] [Indexed: 01/11/2023]
Abstract
BACKGROUND This study aimed to examine whether 40-Hz auditory steady-state responses (ASSRs) are impaired in participants at clinical high-risk for psychosis (CHR-P) and predict clinical outcomes. METHODS Magnetoencephalography data were collected during a 40-Hz ASSR paradigm for a group of 116 CHR-P participants, 33 patients with first-episode psychosis (15 antipsychotic-naïve), a psychosis risk-negative group (n = 38), and 49 healthy control subjects. Analysis of group differences of 40-Hz intertrial phase coherence and 40-Hz amplitude focused on right Heschl's gyrus, superior temporal gyrus, hippocampus, and thalamus after establishing significant activations during 40-Hz ASSR stimulation. Linear regression and linear discriminant analyses were used to predict clinical outcomes in CHR-P participants, including transition to psychosis and persistence of attenuated psychotic symptoms (APSs). RESULTS CHR-P participants and patients with first-episode psychosis were impaired in 40-Hz amplitude in the right thalamus and hippocampus. In addition, patients with first-episode psychosis were impaired in 40-Hz amplitude in the right Heschl's gyrus, and CHR-P participants in 40-Hz intertrial phase coherence in the right Heschl's gyrus. The 40-Hz ASSR deficits were pronounced in CHR-P participants who later transitioned to psychosis (n = 13) or showed persistent APSs (n = 34). Importantly, both APS persistence and transition to psychosis were predicted by 40-Hz ASSR impairments, with ASSR activity in the right hippocampus, superior temporal gyrus, and middle temporal gyrus correctly classifying 69.2% individuals with nonpersistent APSs and 73.5% individuals with persistent APSs (area under the curve = 0.842), and right thalamus 40-Hz activity correctly classifying 76.9% transitioned and 53.6% nontransitioned CHR-P participants (area under the curve = 0.695). CONCLUSIONS Our data indicate that deficits in gamma-band entrainment in the primary auditory cortex and subcortical areas constitute a potential biomarker for predicting clinical outcomes in CHR-P participants.
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Affiliation(s)
- Tineke Grent-'t-Jong
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom; Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin, Berlin, Germany
| | - Ruchika Gajwani
- Mental Health and Wellbeing, Institute of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
| | - Joachim Gross
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom; Institute for Biomagnetism and Biosignalanalysis, University of Muenster, Muenster, Germany
| | - Andrew I Gumley
- Mental Health and Wellbeing, Institute of Health and Wellbeing, University of Glasgow, Glasgow, United Kingdom
| | - Rajeev Krishnadas
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom
| | - Stephen M Lawrie
- Department of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Matthias Schwannauer
- Department of Clinical Psychology, University of Edinburgh, Edinburgh, United Kingdom
| | - Frauke Schultze-Lutter
- Department of Psychiatry and Psychotherapy, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Department of Psychology and Mental Health, Faculty of Psychology, Airlangga University, Airlangga, Indonesia; University Hospital of Child and Adolescent Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Peter J Uhlhaas
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom; Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin, Berlin, Germany.
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Chaumon M, Puce A, George N. Statistical power: Implications for planning MEG studies. Neuroimage 2021; 233:117894. [PMID: 33737245 PMCID: PMC8148377 DOI: 10.1016/j.neuroimage.2021.117894] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 01/22/2021] [Accepted: 02/16/2021] [Indexed: 11/24/2022] Open
Abstract
Statistical power is key for robust, replicable science. Here, we systematically explored how numbers of trials and subjects affect statistical power in MEG sensor-level data. More specifically, we simulated "experiments" using the MEG resting-state dataset of the Human Connectome Project (HCP). We divided the data in two conditions, injected a dipolar source at a known anatomical location in the "signal condition", but not in the "noise condition", and detected significant differences at sensor level with classical paired t-tests across subjects, using amplitude, squared amplitude, and global field power (GFP) measures. Group-level detectability of these simulated effects varied drastically with anatomical origin. We thus examined in detail which spatial properties of the sources affected detectability, looking specifically at the distance from closest sensor and orientation of the source, and at the variability of these parameters across subjects. In line with previous single-subject studies, we found that the most detectable effects originate from source locations that are closest to the sensors and oriented tangentially with respect to the head surface. In addition, cross-subject variability in orientation also affected group-level detectability, boosting detection in regions where this variability was small and hindering detection in regions where it was large. Incidentally, we observed a considerable covariation of source position, orientation, and their cross-subject variability in individual brain anatomical space, making it difficult to assess the impact of each of these variables independently of one another. We thus also performed simulations where we controlled spatial properties independently of individual anatomy. These additional simulations confirmed the strong impact of distance and orientation and further showed that orientation variability across subjects affects detectability, whereas position variability does not. Importantly, our study indicates that strict unequivocal recommendations as to the ideal number of trials and subjects for any experiment cannot be realistically provided for neurophysiological studies and should be adapted according to the brain regions under study.
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Affiliation(s)
- Maximilien Chaumon
- Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Centre MEG-EEG, Centre de NeuroImagerie Recherche (CENIR), 47 Boulevard de l'hôpital, 75013 Paris, France.
| | - Aina Puce
- Department of Psychological & Brain Sciences, Programs in Neuroscience, Cognitive Science, Indiana University, 1101 East 10th St, Bloomington, IN 47405, United States
| | - Nathalie George
- Institut du Cerveau, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Centre MEG-EEG, Centre de NeuroImagerie Recherche (CENIR), 47 Boulevard de l'hôpital, 75013 Paris, France
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