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Ahulló-Fuster MA, Sánchez-Sánchez ML, Varela-Donoso E, Ortiz T. Early attentional processing and cortical remapping strategies of tactile stimuli in adults with an early and late-onset visual impairment: A cross-sectional study. PLoS One 2024; 19:e0306478. [PMID: 38980866 PMCID: PMC11232978 DOI: 10.1371/journal.pone.0306478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 06/18/2024] [Indexed: 07/11/2024] Open
Abstract
Neuroplastic changes appear in people with visual impairment (VI) and they show greater tactile abilities. Improvements in performance could be associated with the development of enhanced early attentional processes based on neuroplasticity. Currently, the various early attentional and cortical remapping strategies that are utilized by people with early (EB) and late-onset blindness (LB) remain unclear. Thus, more research is required to develop effective rehabilitation programs and substitution devices. Our objective was to explore the differences in spatial tactile brain processing in adults with EB, LB and a sighted control group (CG). In this cross-sectional study 27 participants with VI were categorized into EB (n = 14) and LB (n = 13) groups. They were then compared with a CG (n = 15). A vibrotactile device and event-related potentials (ERPs) were utilized while participants performed a spatial tactile line recognition task. The P100 latency and cortical areas of maximal activity were analyzed during the task. The three groups had no statistical differences in P100 latency (p>0.05). All subjects showed significant activation in the right superior frontal areas. Only individuals with VI activated the left superior frontal regions. In EB subjects, a higher activation was found in the mid-frontal and occipital areas. A higher activation of the mid-frontal, anterior cingulate cortex and orbitofrontal zones was observed in LB participants. Compared to the CG, LB individuals showed greater activity in the left orbitofrontal zone, while EB exhibited greater activity in the right superior parietal cortex. The EB had greater activity in the left orbitofrontal region compared to the LB. People with VI may not have faster early attentional processing. EB subjects activate the occipital lobe and right superior parietal cortex during tactile stimulation because of an early lack of visual stimuli and a multimodal information processing. In individuals with LB and EB the orbitofrontal area is activated, suggesting greater emotional processing.
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Affiliation(s)
- Mónica-Alba Ahulló-Fuster
- Department of Radiology, Rehabilitation and Physiotherapy, Faculty of Nursing, Physiotherapy and Podiatry, Complutense University of Madrid, Madrid, Spain
| | - M. Luz Sánchez-Sánchez
- Physiotherapy in Motion, Multispeciality Research Group (PTinMOTION), Department of Physiotherapy, University of Valencia, Valencia, Spain
| | - Enrique Varela-Donoso
- Department of Radiology, Rehabilitation and Physiotherapy, Faculty of Nursing, Physiotherapy and Podiatry, Complutense University of Madrid, Madrid, Spain
| | - Tomás Ortiz
- Department of Legal Medicine, Psychiatry and Pathology, Faculty of Medicine, Complutense University of Madrid, Madrid, Spain
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2
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López-Caballero F, Auksztulewicz R, Howard Z, Rosch RE, Todd J, Salisbury DF. Computational Synaptic Modeling of Pitch and Duration Mismatch Negativity in First-Episode Psychosis Reveals Selective Dysfunction of the N-Methyl-D-Aspartate Receptor. Clin EEG Neurosci 2024:15500594241238294. [PMID: 38533562 DOI: 10.1177/15500594241238294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/28/2024]
Abstract
Mismatch negativity (MMN) to pitch (pMMN) and to duration (dMMN) deviant stimuli is significantly more attenuated in long-term psychotic illness compared to first-episode psychosis (FEP). It was recently shown that source-modeling of magnetically recorded MMN increases the detection of left auditory cortex MMN deficits in FEP, and that computational circuit modeling of electrically recorded MMN also reveals left-hemisphere auditory cortex abnormalities. Computational modeling using dynamic causal modeling (DCM) can also be used to infer synaptic activity from EEG-based scalp recordings. We measured pMMN and dMMN with EEG from 26 FEP and 26 matched healthy controls (HCs) and used a DCM conductance-based neural mass model including α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid, N-methyl-D-Aspartate (NMDA), and Gamma-aminobutyric acid receptors to identify any changes in effective connectivity and receptor rate constants in FEP. We modeled MMN sources in bilateral A1, superior temporal gyrus, and inferior frontal gyrus (IFG). No model parameters distinguished groups for pMMN. For dMMN, reduced NMDA receptor activity in right IFG in FEP was detected. This finding is in line with literature of prefrontal NMDA receptor hypofunction in chronic schizophrenia and suggests impaired NMDA-induced synaptic plasticity may be present at psychosis onset where scalp dMMN is only moderately reduced. To the best of our knowledge, this is the first report of impaired NMDA receptor activity in FEP found through computational modeling of dMMN and shows the potential of DCM to non-invasively reveal synaptic-level abnormalities that underly subtle functional auditory processing deficits in early psychosis.
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Affiliation(s)
- F López-Caballero
- Clinical Neurophysiology Research Laboratory, Western Psychiatric Hospital, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - R Auksztulewicz
- Department of Education and Psychology, Freie Universität Berlin, Berlin, Germany
| | - Z Howard
- School of Psychological Science, University of Western Australia, Perth, Australia
| | - R E Rosch
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK
| | - J Todd
- School of Psychological Sciences, University of Newcastle, Callaghan, Australia
| | - D F Salisbury
- Clinical Neurophysiology Research Laboratory, Western Psychiatric Hospital, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
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3
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Moradi N, Goodyear BG, Sotero RC. Deep EEG source localization via EMD-based fMRI high spatial frequency. PLoS One 2024; 19:e0299284. [PMID: 38427616 PMCID: PMC10906834 DOI: 10.1371/journal.pone.0299284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 02/07/2024] [Indexed: 03/03/2024] Open
Abstract
Brain imaging with a high-spatiotemporal resolution is crucial for accurate brain-function mapping. Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are two popular neuroimaging modalities with complementary features that record brain function with high temporal and spatial resolution, respectively. One popular non-invasive way to obtain data with both high spatial and temporal resolutions is to combine the fMRI activation map and EEG data to improve the spatial resolution of the EEG source localization. However, using the whole fMRI map may cause spurious results for the EEG source localization, especially for deep brain regions. Considering the head's conductivity, deep regions' sources with low activity are unlikely to be detected by the EEG electrodes at the scalp. In this study, we use fMRI's high spatial-frequency component to identify the local high-intensity activations that are most likely to be captured by the EEG. The 3D Empirical Mode Decomposition (3D-EMD), a data-driven method, is used to decompose the fMRI map into its spatial-frequency components. Different validation measurements for EEG source localization show improved performance for the EEG inverse-modeling informed by the fMRI's high-frequency spatial component compared to the fMRI-informed EEG source-localization methods. The level of improvement varies depending on the voxels' intensity and their distribution. Our experimental results also support this conclusion.
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Affiliation(s)
- Narges Moradi
- Biomedical Engineering Department, University of Calgary, Calgary, AB, Canada
- Department of Radiology, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Bradley G. Goodyear
- Department of Radiology, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Roberto C. Sotero
- Department of Radiology, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
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4
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Paz-Linares D, Gonzalez-Moreira E, Areces-Gonzalez A, Wang Y, Li M, Martinez-Montes E, Bosch-Bayard J, Bringas-Vega ML, Valdes-Sosa M, Valdes-Sosa PA. Identifying oscillatory brain networks with hidden Gaussian graphical spectral models of MEEG. Sci Rep 2023; 13:11466. [PMID: 37454235 PMCID: PMC10349891 DOI: 10.1038/s41598-023-38513-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 07/11/2023] [Indexed: 07/18/2023] Open
Abstract
Identifying the functional networks underpinning indirectly observed processes poses an inverse problem for neurosciences or other fields. A solution of such inverse problems estimates as a first step the activity emerging within functional networks from EEG or MEG data. These EEG or MEG estimates are a direct reflection of functional brain network activity with a temporal resolution that no other in vivo neuroimage may provide. A second step estimating functional connectivity from such activity pseudodata unveil the oscillatory brain networks that strongly correlate with all cognition and behavior. Simulations of such MEG or EEG inverse problem also reveal estimation errors of the functional connectivity determined by any of the state-of-the-art inverse solutions. We disclose a significant cause of estimation errors originating from misspecification of the functional network model incorporated into either inverse solution steps. We introduce the Bayesian identification of a Hidden Gaussian Graphical Spectral (HIGGS) model specifying such oscillatory brain networks model. In human EEG alpha rhythm simulations, the estimation errors measured as ROC performance do not surpass 2% in our HIGGS inverse solution and reach 20% in state-of-the-art methods. Macaque simultaneous EEG/ECoG recordings provide experimental confirmation for our results with 1/3 times larger congruence according to Riemannian distances than state-of-the-art methods.
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Affiliation(s)
- Deirel Paz-Linares
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neuroscience Center, Havana, Cuba
| | - Eduardo Gonzalez-Moreira
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- School of Electrical Engineering, Central University "Marta Abreu" of Las Villas, Santa Clara, Cuba
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Ariosky Areces-Gonzalez
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- School of Technical Sciences, University of Pinar del Río "Hermanos Saiz Montes de Oca", Pinar del Rio, Cuba
| | - Ying Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Min Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Jorge Bosch-Bayard
- Department of Neuroinformatics, Cuban Neuroscience Center, Havana, Cuba
- McGill Centre for Integrative Neurosciences MCIN, Ludmer Centre for Mental Health, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Maria L Bringas-Vega
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neuroscience Center, Havana, Cuba
| | - Mitchell Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Neuroinformatics, Cuban Neuroscience Center, Havana, Cuba
| | - Pedro A Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
- Department of Neuroinformatics, Cuban Neuroscience Center, Havana, Cuba.
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Prado P, Mejía JA, Sainz‐Ballesteros A, Birba A, Moguilner S, Herzog R, Otero M, Cuadros J, Z‐Rivera L, O'Byrne DF, Parra M, Ibáñez A. Harmonized multi-metric and multi-centric assessment of EEG source space connectivity for dementia characterization. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2023; 15:e12455. [PMID: 37424962 PMCID: PMC10329259 DOI: 10.1002/dad2.12455] [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] [Received: 09/01/2022] [Revised: 06/05/2023] [Accepted: 06/05/2023] [Indexed: 07/11/2023]
Abstract
Introduction Harmonization protocols that address batch effects and cross-site methodological differences in multi-center studies are critical for strengthening electroencephalography (EEG) signatures of functional connectivity (FC) as potential dementia biomarkers. Methods We implemented an automatic processing pipeline incorporating electrode layout integrations, patient-control normalizations, and multi-metric EEG source space connectomics analyses. Results Spline interpolations of EEG signals onto a head mesh model with 6067 virtual electrodes resulted in an effective method for integrating electrode layouts. Z-score transformations of EEG time series resulted in source space connectivity matrices with high bilateral symmetry, reinforced long-range connections, and diminished short-range functional interactions. A composite FC metric allowed for accurate multicentric classifications of Alzheimer's disease and behavioral variant frontotemporal dementia. Discussion Harmonized multi-metric analysis of EEG source space connectivity can address data heterogeneities in multi-centric studies, representing a powerful tool for accurately characterizing dementia.
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Affiliation(s)
- Pavel Prado
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo IbáñezSantiago de ChileChile
- Escuela de FonoaudiologíaFacultad de Odontología y Ciencias de la RehabilitaciónUniversidad San SebastiánSantiagoChile
| | - Jhony A. Mejía
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo IbáñezSantiago de ChileChile
- Departamento de Ingeniería BiomédicaUniversidad de Los AndesBogotáColombia
- Memory and Aging ClinicUniversity of CaliforniaSan FranciscoUnited States
| | - Agustín Sainz‐Ballesteros
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo IbáñezSantiago de ChileChile
- Cognitive Neuroscience Center (CNC)Universidad de San AndrésBuenos AiresArgentina
| | - Agustina Birba
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo IbáñezSantiago de ChileChile
- Cognitive Neuroscience Center (CNC)Universidad de San AndrésBuenos AiresArgentina
- Instituto Universitario de NeurocienciaUniversidad de La LagunaTenerifeSpain
- Facultad de PsicologíaUniversidad de La LagunaTenerifeSpain
| | - Sebastian Moguilner
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo IbáñezSantiago de ChileChile
- Cognitive Neuroscience Center (CNC)Universidad de San AndrésBuenos AiresArgentina
- Department of NeurologyMassachusetts General Hospital and Harvard Medical SchoolBostonUnited States
| | - Rubén Herzog
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo IbáñezSantiago de ChileChile
- Fundación para el Estudio de la Conciencia Humana (EcoH)Santiago de ChileChile
| | - Mónica Otero
- Facultad de Ingeniería, Arquitectura y DiseñoUniversidad San SebastiánSantiagoChile
- Centro BASAL Ciencia & Vida; Facultad de Ingeniería y TecnologíaUniversidad San SebastiánSantiago de ChileChile
| | - Jhosmary Cuadros
- Advanced Center for Electrical and Electronic Engineering (AC3E)Universidad Técnica Federico Santa MaríaValparaísoChile
| | - Lucía Z‐Rivera
- Advanced Center for Electrical and Electronic Engineering (AC3E)Universidad Técnica Federico Santa MaríaValparaísoChile
| | - Daniel Franco O'Byrne
- Center for Social and Cognitive Neuroscience (CSCN)School of PsychologyUniversidad Adolfo IbáñezSantiagoChile
| | - Mario Parra
- School of Psychological Sciences and HealthUniversity of StrathclydeGlasgowUK
| | - Agustín Ibáñez
- Latin American Brain Health Institute (BrainLat)Universidad Adolfo IbáñezSantiago de ChileChile
- Cognitive Neuroscience Center (CNC)Universidad de San AndrésBuenos AiresArgentina
- Center for Social and Cognitive Neuroscience (CSCN)School of PsychologyUniversidad Adolfo IbáñezSantiagoChile
- National Scientific and Technical Research Council (CONICET)Buenos AiresArgentina
- Global Brain Health Institute (GBHI)University of California San FranciscoCalifornia and Trinity College DublinDublinIreland
- Trinity College Dublin (TCD)DublinIreland
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6
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Riaz U, Razzaq FA, Areces-Gonzalez A, Piastra MC, Vega MLB, Paz-Linares D, Valdés-Sosa PA. Automatic Quality Control of the numerical accuracy of EEG Lead fields. Neuroimage 2023; 273:120091. [PMID: 37060935 DOI: 10.1016/j.neuroimage.2023.120091] [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: 09/22/2021] [Revised: 03/22/2023] [Accepted: 04/04/2023] [Indexed: 04/17/2023] Open
Abstract
Precise individualized EEG source localization is predicated on having accurate subject-specific Lead Fields (LFs) obtained from their Magnetic Resonance Images (MRI). LF calculation is a complex process involving several error-prone steps that start with obtaining a realistic head model from the MRI and finalizing with computationally expensive solvers such as the Boundary Element Method (BEM) or Finite Element Method (FEM). Current Big-Data applications require the calculation of batches of hundreds or thousands of LFs. LF. Quality Control is conventionally checked subjectively by experts, a procedure not feasible in practice for larger batches. To facilitate this step, we introduce the Lead Field Automatic-Quality Control Index (LF-AQI) that flags LF with potential errors. We base our LF-AQI on the assumption that LFs obtained from simpler head models, i.e., the homogeneous head model LF (HHM-LF) or spherical head model LF (SHM-LF), deviate only moderately from a "good" realistic test LF. Since these simpler LFs are easier to compute and check for errors, they may serve as "reference LF" to detect anomalous realistic test LF. We investigated this assumption by comparing correlation-based channel ρmin(ref,test)and source τmin(ref,test) similarity indices (SI) between "gold standards," i.e., very accurate FEM and BEM LFs, and the proposed references (HHM-LF and SHM-LF). Surprisingly we found that the most uncomplicated possible reference, HHM-LF had high SI values with the gold standards-leading us to explore further use of the channel ρmin(HHM-LF,test)and source τmin(HHM-LF,test)SI as a basis for our LF-AQI. Indeed, these SI successfully detected five simulated scenarios of LFs artifacts. This result encouraged us to evaluate the SI on a large dataset and thus define our LF-AQI. We thus computed the SI of 1251 LFs obtained from the Child Mind Institute (CMI) MRI dataset. When ρmin(HHM-LF,test)and source τmin(HHM-LF,test) were plotted for all test subjects on a 2D space, most were tightly clustered around the median of a high similarity centroid (HSC), except for a smaller proportion of outliers. We define the LF-AQI for a given LF as the log Euclidean distance between its SI and the HSC median. To automatically detect outliers, the threshold is at the 90th percentile of the CMI LF-AQIs (-0.9755). LF-AQI greater than this threshold flag individual LF to be checked. The robustness of this LF-AQI screening was checked by repeated out-of-sample validation. Strikingly, minor corrections in re-processing the flagged cases eliminated their status as outliers. Furthermore, the "doubtful" labels assigned by LF-AQI were validated by neuroscience students using a Likert scale questionnaire designed to manually check the LF's quality. Item Response Theory (IRT) analysis was applied to the questionnaire results to compute an optimized model and a latent variable θ for that model. A linear mixed model (LMM) between the θ and LF-AQI resulted in an effect with a Cohen's d value of 1.3 and a p-value <0.001, thus validating the correspondence of LF-AQI with the visual quality control. We provide an open-source pipeline to implement both LF calculation and its quality control to allow further evaluation of our index.
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Affiliation(s)
- Usama Riaz
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Fuleah A Razzaq
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Ariosky Areces-Gonzalez
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Science and Technology of China, Chengdu, China; Department of Informatics, University of Pinar del Rio Hermanos Saiz Montes de Oca, Cuba
| | | | - Maria L Bringas Vega
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Science and Technology of China, Chengdu, China; Cuban Neuroscience Center, Havana, Cuba
| | - Deirel Paz-Linares
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Pedro A Valdés-Sosa
- The Clinical Hospital of Chengdu Brain Sciences, University of Electronic Science and Technology of China, Chengdu, China; Cuban Neuroscience Center, Havana, Cuba.
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Johnsdorf M, Kisker J, Gruber T, Schöne B. Comparing encoding mechanisms in realistic virtual reality and conventional 2D laboratory settings: Event-related potentials in a repetition suppression paradigm. Front Psychol 2023; 14:1051938. [PMID: 36777234 PMCID: PMC9912617 DOI: 10.3389/fpsyg.2023.1051938] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 01/06/2023] [Indexed: 01/28/2023] Open
Abstract
Although the human brain is adapted to function within three-dimensional environments, conventional laboratory research commonly investigates cognitive mechanisms in a reductionist approach using two-dimensional stimuli. However, findings regarding mnemonic processes indicate that realistic experiences in Virtual Reality (VR) are stored in richer and more intertwined engrams than those obtained from the conventional laboratory. Our study aimed to further investigate the generalizability of laboratory findings and to differentiate whether the processes underlying memory formation differ between VR and the conventional laboratory already in early encoding stages. Therefore, we investigated the Repetition Suppression (RS) effect as a correlate of the earliest instance of mnemonic processes under conventional laboratory conditions and in a realistic virtual environment. Analyses of event-related potentials (ERPs) indicate that the ERP deflections at several electrode clusters were lower in VR compared to the PC condition. These results indicate an optimized distribution of cognitive resources in realistic contexts. The typical RS effect was replicated under both conditions at most electrode clusters for a late time window. Additionally, a specific RS effect was found in VR at anterior electrodes for a later time window, indicating more extensive encoding processes in VR compared to the laboratory. Specifically, electrotomographic results (VARETA) indicate multimodal integration involving a broad cortical network and higher cognitive processes during the encoding of realistic objects. Our data suggest that object perception under realistic conditions, in contrast to the conventional laboratory, requires multisensory integration involving an interconnected functional system, facilitating the formation of intertwined memory traces in realistic environments.
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Calvetti D, Pascarella A, Pitolli F, Somersalo E, Vantaggi B. The IAS-MEEG Package: A Flexible Inverse Source Reconstruction Platform for Reconstruction and Visualization of Brain Activity from M/EEG Data. Brain Topogr 2023; 36:10-22. [PMID: 36460892 PMCID: PMC9834133 DOI: 10.1007/s10548-022-00926-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 10/31/2022] [Indexed: 12/05/2022]
Abstract
We present a standalone Matlab software platform complete with visualization for the reconstruction of the neural activity in the brain from MEG or EEG data. The underlying inversion combines hierarchical Bayesian models and Krylov subspace iterative least squares solvers. The Bayesian framework of the underlying inversion algorithm allows to account for anatomical information and possible a priori belief about the focality of the reconstruction. The computational efficiency makes the software suitable for the reconstruction of lengthy time series on standard computing equipment. The algorithm requires minimal user provided input parameters, although the user can express the desired focality and accuracy of the solution. The code has been designed so as to favor the parallelization performed automatically by Matlab, according to the resources of the host computer. We demonstrate the flexibility of the platform by reconstructing activity patterns with supports of different sizes from MEG and EEG data. Moreover, we show that the software reconstructs well activity patches located either in the subcortical brain structures or on the cortex. The inverse solver and visualization modules can be used either individually or in combination. We also provide a version of the inverse solver that can be used within Brainstorm toolbox. All the software is available online by Github, including the Brainstorm plugin, with accompanying documentation and test data.
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Affiliation(s)
- Daniela Calvetti
- Department of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106 USA
| | - Annalisa Pascarella
- Istituto per le Applicazioni del Calcolo “M. Picone”, National Research Council, Via dei Taurini 19, 00185 Rome, Italy
| | - Francesca Pitolli
- Department of Basic and Applied Sciences for Engineering, University of Rome “La Sapienza”, Via Scarpa 16, 00161 Rome, Italy
| | - Erkki Somersalo
- Department of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106 USA
| | - Barbara Vantaggi
- Dipartimento Metodi e Modelli per l’Economia, il Territorio e la Finanza MEMOTEF, University of Rome “La Sapienza”, Via Castro Laurenziano 9, 00161 Rome, Italy
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9
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Hsu CT, Sato W, Kochiyama T, Nakai R, Asano K, Abe N, Yoshikawa S. Enhanced Mirror Neuron Network Activity and Effective Connectivity during Live Interaction Among Female Subjects. Neuroimage 2022; 263:119655. [PMID: 36182055 DOI: 10.1016/j.neuroimage.2022.119655] [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: 05/10/2022] [Revised: 09/26/2022] [Accepted: 09/27/2022] [Indexed: 11/24/2022] Open
Abstract
Facial expressions are indispensable in daily human communication. Previous neuroimaging studies investigating facial expression processing have presented pre-recorded stimuli and lacked live face-to-face interaction. Our paradigm alternated between presentations of real-time model performance and pre-recorded videos of dynamic facial expressions to participants. Simultaneous functional magnetic resonance imaging (fMRI) and facial electromyography activity recordings, as well as post-scan valence and arousal ratings were acquired from 44 female participants. Live facial expressions enhanced the subjective valence and arousal ratings as well as facial muscular responses. Live performances showed greater engagement of the right posterior superior temporal sulcus (pSTS), right inferior frontal gyrus (IFG), right amygdala and right fusiform gyrus, and modulated the effective connectivity within the right mirror neuron system (IFG, pSTS, and right inferior parietal lobule). A support vector machine algorithm could classify multivoxel activation patterns in brain regions involved in dynamic facial expression processing in the mentalizing networks (anterior and posterior cingulate cortex). These results indicate that live social interaction modulates the activity and connectivity of the right mirror neuron system and enhances spontaneous mimicry, further facilitating emotional contagion.
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Affiliation(s)
- Chun-Ting Hsu
- Psychological Process Research Team, Guardian Robot Project, RIKEN, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan..
| | - Wataru Sato
- Psychological Process Research Team, Guardian Robot Project, RIKEN, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan..
| | - Takanori Kochiyama
- Brain Activity Imaging Center, ATR- Promotions, Inc., 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0288, Japan
| | - Ryusuke Nakai
- Institute for the Future of Human Society, Kyoto University, 46 Yoshidashimoadachi-cho, Sakyo-ku, Kyoto, 606-8501 Japan
| | - Kohei Asano
- Institute for the Future of Human Society, Kyoto University, 46 Yoshidashimoadachi-cho, Sakyo-ku, Kyoto, 606-8501 Japan; Department of Children Education, Osaka University of Comprehensive Children Education, 6-chome-4-26 Yuzato, Higashisumiyoshi Ward, Osaka, 546-0013, Japan
| | - Nobuhito Abe
- Institute for the Future of Human Society, Kyoto University, 46 Yoshidashimoadachi-cho, Sakyo-ku, Kyoto, 606-8501 Japan
| | - Sakiko Yoshikawa
- Institute of Philosophy and Human Values, Kyoto University of the Arts, 2-116 Uryuyama Kitashirakawa, Sakyo, Kyoto, Kyoto 606-8271, Japan
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Huang J, Choo S, Pugh ZH, Nam CS. Evaluating Effective Connectivity of Trust in Human-Automation Interaction: A Dynamic Causal Modeling (DCM) Study. HUMAN FACTORS 2022; 64:1051-1069. [PMID: 33657902 DOI: 10.1177/0018720820987443] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
OBJECTIVE Using dynamic causal modeling (DCM), we examined how credibility and reliability affected the way brain regions exert causal influence over each other-effective connectivity (EC)-in the context of trust in automation. BACKGROUND Multiple brain regions of the central executive network (CEN) and default mode network (DMN) have been implicated in trust judgment. However, the neural correlates of trust judgment are still relatively unexplored in terms of the directed information flow between brain regions. METHOD Sixteen participants observed the performance of four computer algorithms, which differed in credibility and reliability, of the system monitoring subtask of the Air Force Multi-Attribute Task Battery (AF-MATB). Using six brain regions of the CEN and DMN commonly identified to be activated in human trust, a total of 30 (forward, backward, and lateral) connection models were developed. Bayesian model averaging (BMA) was used to quantify the connectivity strength among the brain regions. RESULTS Relative to the high trust condition, low trust showed unique presence of specific connections, greater connectivity strengths from the prefrontal cortex, and greater network complexity. High trust condition showed no backward connections. CONCLUSION Results indicated that trust and distrust can be two distinctive neural processes in human-automation interaction-distrust being a more complex network than trust, possibly due to the increased cognitive load. APPLICATION The causal architecture of distributed brain regions inferred using DCM can help not only in the design of a balanced human-automation interface design but also in the proper use of automation in real-life situations.
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Affiliation(s)
- Jiali Huang
- 6798 North Carolina State University, Raleigh, USA
| | | | | | - Chang S Nam
- 6798 North Carolina State University, Raleigh, USA
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11
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Birba A, Santamaría-García H, Prado P, Cruzat J, Ballesteros AS, Legaz A, Fittipaldi S, Duran-Aniotz C, Slachevsky A, Santibañez R, Sigman M, García AM, Whelan R, Moguilner S, Ibáñez A. Allostatic-Interoceptive Overload in Frontotemporal Dementia. Biol Psychiatry 2022; 92:54-67. [PMID: 35491275 PMCID: PMC11184918 DOI: 10.1016/j.biopsych.2022.02.955] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 01/28/2022] [Accepted: 02/16/2022] [Indexed: 12/22/2022]
Abstract
BACKGROUND The predictive coding theory of allostatic-interoceptive load states that brain networks mediating autonomic regulation and interoceptive-exteroceptive balance regulate the internal milieu to anticipate future needs and environmental demands. These functions seem to be distinctly compromised in behavioral variant frontotemporal dementia (bvFTD), including alterations of the allostatic-interoceptive network (AIN). Here, we hypothesize that bvFTD is typified by an allostatic-interoceptive overload. METHODS We assessed resting-state heartbeat evoked potential (rsHEP) modulation as well as its behavioral and multimodal neuroimaging correlates in patients with bvFTD relative to healthy control subjects and patients with Alzheimer's disease (N = 94). We measured 1) resting-state electroencephalography (to assess the rsHEP, prompted by visceral inputs and modulated by internal body sensing), 2) associations between rsHEP and its neural generators (source location), 3) cognitive disturbances (cognitive state, executive functions, facial emotion recognition), 4) brain atrophy, and 5) resting-state functional magnetic resonance imaging functional connectivity (AIN vs. control networks). RESULTS Relative to healthy control subjects and patients with Alzheimer's disease, patients with bvFTD presented more negative rsHEP amplitudes with sources in critical hubs of the AIN (insula, amygdala, somatosensory cortex, hippocampus, anterior cingulate cortex). This exacerbated rsHEP modulation selectively predicted the patients' cognitive profile (including cognitive decline, executive dysfunction, and emotional impairments). In addition, increased rsHEP modulation in bvFTD was associated with decreased brain volume and connectivity of the AIN. Machine learning results confirmed AIN specificity in predicting the bvFTD group. CONCLUSIONS Altogether, these results suggest that bvFTD may be characterized by an allostatic-interoceptive overload manifested in ongoing electrophysiological markers, brain atrophy, functional networks, and cognition.
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Affiliation(s)
- Agustina Birba
- Latin American Brain Health Institute, Universidad Adolfo Ibáñez, Santiago, Chile; National Scientific and Technical Research Council, Buenos Aires, Argentina; Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina
| | - Hernando Santamaría-García
- PhD Neuroscience Program, Physiology and Psychiatry Departments, Pontificia Universidad Javeriana, Bogotá, Colombia; Memory and Cognition Center Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia; Global Brain Health Institute, University of California San Francisco, San Francisco, California, and Trinity College Dublin, Dublin, Ireland
| | - Pavel Prado
- Latin American Brain Health Institute, Universidad Adolfo Ibáñez, Santiago, Chile
| | - Josefina Cruzat
- Latin American Brain Health Institute, Universidad Adolfo Ibáñez, Santiago, Chile
| | | | - Agustina Legaz
- National Scientific and Technical Research Council, Buenos Aires, Argentina; Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina
| | - Sol Fittipaldi
- National Scientific and Technical Research Council, Buenos Aires, Argentina; Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina
| | - Claudia Duran-Aniotz
- Latin American Brain Health Institute, Universidad Adolfo Ibáñez, Santiago, Chile; Center for Social and Cognitive Neuroscience, School of Psychology, Universidad Adolfo Ibáñez, Santiago, Chile
| | - Andrea Slachevsky
- Center for Geroscience, Brain Health and Metabolism, Santiago, Chile; Neuropsychology and Clinical Neuroscience Laboratory, Physiopathology Department, Institute of Biomedical Sciences, Santiago, Chile; Memory and Neuropsychiatric Clinic, Neurology Department, Hospital del Salvador and Faculty of Medicine, University of Chile, Santiago, Chile; Servicio de Neurología, Departamento de Medicina, Clínica Alemana-Universidad del Desarrollo, Santiago, Chile
| | - Rodrigo Santibañez
- Neurology Service, Hospital Dr. Sótero del Río, Santiago, Chile; Neurology Department, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Mariano Sigman
- National Scientific and Technical Research Council, Buenos Aires, Argentina; Laboratorio de Neurociencia, Universidad Torcuato Di Tella, Buenos Aires, Argentina; Facultad de Lenguas y Educación, Universidad Nebrija, Madrid, Spain
| | - Adolfo M García
- Departamento de Lingüística y Literatura, Facultad de Humanidades, Universidad de Santiago de Chile, Santiago, Chile; National Scientific and Technical Research Council, Buenos Aires, Argentina; Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina; Global Brain Health Institute, University of California San Francisco, San Francisco, California, and Trinity College Dublin, Dublin, Ireland
| | - Robert Whelan
- Global Brain Health Institute, University of California San Francisco, San Francisco, California, and Trinity College Dublin, Dublin, Ireland; Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Sebastián Moguilner
- Latin American Brain Health Institute, Universidad Adolfo Ibáñez, Santiago, Chile; National Scientific and Technical Research Council, Buenos Aires, Argentina; Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina; Global Brain Health Institute, University of California San Francisco, San Francisco, California, and Trinity College Dublin, Dublin, Ireland; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Agustín Ibáñez
- Latin American Brain Health Institute, Universidad Adolfo Ibáñez, Santiago, Chile; National Scientific and Technical Research Council, Buenos Aires, Argentina; Cognitive Neuroscience Center, Universidad de San Andrés, Buenos Aires, Argentina; Global Brain Health Institute, University of California San Francisco, San Francisco, California, and Trinity College Dublin, Dublin, Ireland; Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland.
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12
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Bosch-Bayard J, Razzaq FA, Lopez-Naranjo C, Wang Y, Li M, Galan-Garcia L, Calzada-Reyes A, Virues-Alba T, Rabinowitz AG, Suarez-Murias C, Guo Y, Sanchez-Castillo M, Rogers K, Gallagher A, Prichep L, Anderson SG, Michel CM, Evans AC, Bringas-Vega ML, Galler JR, Valdes-Sosa PA. Early protein energy malnutrition impacts life-long developmental trajectories of the sources of EEG rhythmic activity. Neuroimage 2022; 254:119144. [PMID: 35342003 DOI: 10.1016/j.neuroimage.2022.119144] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 03/20/2022] [Accepted: 03/23/2022] [Indexed: 02/07/2023] Open
Abstract
Protein Energy Malnutrition (PEM) has lifelong consequences on brain development and cognitive function. We studied the lifelong developmental trajectories of resting-state EEG source activity in 66 individuals with histories of Protein Energy Malnutrition (PEM) limited to the first year of life and in 83 matched classmate controls (CON) who are all participants of the 49 years longitudinal Barbados Nutrition Study (BNS). qEEGt source z-spectra measured deviation from normative values of EEG rhythmic activity sources at 5-11 years of age and 40 years later at 45-51 years of age. The PEM group showed qEEGt abnormalities in childhood, including a developmental delay in alpha rhythm maturation and an insufficient decrease in beta activity. These profiles may be correlated with accelerated cognitive decline.
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Affiliation(s)
- Jorge Bosch-Bayard
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; McGill Center for Integrative Neuroscience Center MCIN. Ludmer Center for Mental Health. Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Fuleah Abdul Razzaq
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.
| | - Carlos Lopez-Naranjo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Ying Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Min Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | | | | | | | - Arielle G Rabinowitz
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | | | - Yanbo Guo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | | | - Kassandra Rogers
- LION Lab, Sainte-Justine University Hospital Research Centre, University of Montreal, Montreal, QC, Canada
| | - Anne Gallagher
- LION Lab, Sainte-Justine University Hospital Research Centre, University of Montreal, Montreal, QC, Canada
| | | | - Simon G Anderson
- Caribbean Institute for Health Research, University of the West Indies, Barbados
| | | | - Alan C Evans
- McGill Center for Integrative Neuroscience Center MCIN. Ludmer Center for Mental Health. Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Maria L Bringas-Vega
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; Cuban Neuroscience Center, La Habana, Cuba
| | - Janina R Galler
- Division of Pediatric Gastroenterology and Nutrition, Mucosal Immunology and Biology Research Center, Mass General Hospital for Children, Boston, MA, USA
| | - Pedro A Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; McGill Center for Integrative Neuroscience Center MCIN. Ludmer Center for Mental Health. Montreal Neurological Institute, McGill University, Montreal, Canada; Cuban Neuroscience Center, La Habana, Cuba.
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13
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Bosch-Bayard J, Biscay RJ, Fernandez T, Otero GA, Ricardo-Garcell J, Aubert-Vazquez E, Evans AC, Harmony T. EEG effective connectivity during the first year of life mirrors brain synaptogenesis, myelination, and early right hemisphere predominance. Neuroimage 2022; 252:119035. [PMID: 35218932 DOI: 10.1016/j.neuroimage.2022.119035] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 11/25/2021] [Accepted: 02/22/2022] [Indexed: 10/19/2022] Open
Abstract
INTRODUCTION The maturation of electroencephalogram (EEG) effective connectivity in healthy infants during the first year of life is described. METHODS Participants: A cross-sectional sample of 125 healthy at-term infants, from 0 to 12 months of age, underwent EEG in a state of quiet sleep. PROCEDURES The EEG primary currents at the source were described with the sLoreta method. An unmixing algorithm was applied to reduce the leakage, and the isolated effective coherence, a direct and directed measurement of information flow, was calculated. RESULTS AND DISCUSSION Initially, the highest indices of connectivity are at the subcortical nuclei, continuing to the parietal lobe, predominantly the right hemisphere, then expanding to temporal, occipital, and finally the frontal areas, which is consistent with the myelination process. Age-related connectivity changes were mostly long-range and bilateral. Connections increased with age, mainly in the right hemisphere, while they mainly decreased in the left hemisphere. Increased connectivity from 20 to 30 Hz, mostly at the right hemisphere. These findings were consistent with right hemisphere predominance during the first three years of life. Theta and alpha connections showed the greatest changes with age. Strong connectivity was found between the parietal, temporal, and occipital regions to the frontal lobes, responsible for executive functions and consistent with behavioral development during the first year. The thalamus exchanges information bidirectionally with all cortical regions and frequency bands. CONCLUSIONS The maturation of EEG connectivity during the first year in healthy infants is very consistent with synaptogenesis, reductions in synaptogenesis, myelination, and functional and behavioral development.
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Affiliation(s)
- Jorge Bosch-Bayard
- McGill Center for Integrative Neuroscience (MCIN), Ludmer Center for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal H3A2B4, Canada
| | - Rolando J Biscay
- Centro de Investigación en Matemáticas, Guanajuato 36023, Mexico
| | - Thalia Fernandez
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Boulevard Juriquilla 3001, Querétaro 76230, Mexico
| | - Gloria A Otero
- Facultad de Medicina, Universidad Autónoma del Estado de México, Toluca de Lerdo 50180, Mexico
| | - Josefina Ricardo-Garcell
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Boulevard Juriquilla 3001, Querétaro 76230, Mexico
| | | | - Alan C Evans
- McGill Center for Integrative Neuroscience (MCIN), Ludmer Center for Neuroinformatics and Mental Health, Montreal Neurological Institute (MNI), McGill University, Montreal H3A2B4, Canada
| | - Thalia Harmony
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Boulevard Juriquilla 3001, Querétaro 76230, Mexico.
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14
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Cai Z, Machado A, Chowdhury RA, Spilkin A, Vincent T, Aydin Ü, Pellegrino G, Lina JM, Grova C. Diffuse optical reconstructions of functional near infrared spectroscopy data using maximum entropy on the mean. Sci Rep 2022; 12:2316. [PMID: 35145148 PMCID: PMC8831678 DOI: 10.1038/s41598-022-06082-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 01/24/2022] [Indexed: 02/07/2023] Open
Abstract
Functional near-infrared spectroscopy (fNIRS) measures the hemoglobin concentration changes associated with neuronal activity. Diffuse optical tomography (DOT) consists of reconstructing the optical density changes measured from scalp channels to the oxy-/deoxy-hemoglobin concentration changes within the cortical regions. In the present study, we adapted a nonlinear source localization method developed and validated in the context of Electro- and Magneto-Encephalography (EEG/MEG): the Maximum Entropy on the Mean (MEM), to solve the inverse problem of DOT reconstruction. We first introduced depth weighting strategy within the MEM framework for DOT reconstruction to avoid biasing the reconstruction results of DOT towards superficial regions. We also proposed a new initialization of the MEM model improving the temporal accuracy of the original MEM framework. To evaluate MEM performance and compare with widely used depth weighted Minimum Norm Estimate (MNE) inverse solution, we applied a realistic simulation scheme which contained 4000 simulations generated by 250 different seeds at different locations and 4 spatial extents ranging from 3 to 40[Formula: see text] along the cortical surface. Our results showed that overall MEM provided more accurate DOT reconstructions than MNE. Moreover, we found that MEM was remained particularly robust in low signal-to-noise ratio (SNR) conditions. The proposed method was further illustrated by comparing to functional Magnetic Resonance Imaging (fMRI) activation maps, on real data involving finger tapping tasks with two different montages. The results showed that MEM provided more accurate HbO and HbR reconstructions in spatial agreement with the main fMRI cluster, when compared to MNE.
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Affiliation(s)
- Zhengchen Cai
- Department of Physics and PERFORM Centre, Concordia University, Montreal, Canada.
| | - Alexis Machado
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montreal, Canada
| | - Rasheda Arman Chowdhury
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montreal, Canada
| | - Amanda Spilkin
- Department of Physics and PERFORM Centre, Concordia University, Montreal, Canada
| | - Thomas Vincent
- Department of Physics and PERFORM Centre, Concordia University, Montreal, Canada
- Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, Montreal, Canada
- Centre de médecine préventive et d'activité physique, Montréal Heart Institute, Montréal, Canada
| | - Ümit Aydin
- Department of Physics and PERFORM Centre, Concordia University, Montreal, Canada
- MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Giovanni Pellegrino
- Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Jean-Marc Lina
- École de technologie supérieure de l'Université du Québec, Montréal, Canada
- Centre de Recherches Mathématiques, Université de Montréal, Montréal, Canada
| | - Christophe Grova
- Department of Physics and PERFORM Centre, Concordia University, Montreal, Canada
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montreal, Canada
- Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, Montreal, Canada
- Centre de Recherches Mathématiques, Université de Montréal, Montréal, Canada
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15
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Optimizing EEG Source Reconstruction with Concurrent fMRI-Derived Spatial Priors. Brain Topogr 2022; 35:282-301. [PMID: 35142957 PMCID: PMC9098592 DOI: 10.1007/s10548-022-00891-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 01/31/2022] [Indexed: 02/01/2023]
Abstract
Reconstructing EEG sources involves a complex pipeline, with the inverse problem being the most challenging. Multiple inversion algorithms are being continuously developed, aiming to tackle the non-uniqueness of this problem, which has been shown to be partially circumvented by including prior information in the inverse models. Despite a few efforts, there are still current and persistent controversies regarding the inversion algorithm of choice and the optimal set of spatial priors to be included in the inversion models. The use of simultaneous EEG-fMRI data is one approach to tackle this problem. The spatial resolution of fMRI makes fMRI derived spatial priors very convenient for EEG reconstruction, however, only task activation maps and resting-state networks (RSNs) have been explored so far, overlooking the recent, but already accepted, notion that brain networks exhibit dynamic functional connectivity fluctuations. The lack of a systematic comparison between different source reconstruction algorithms, considering potentially more brain-informative priors such as fMRI, motivates the search for better reconstruction models. Using simultaneous EEG-fMRI data, here we compared four different inversion algorithms (minimum norm, MN; low resolution electromagnetic tomography, LORETA; empirical Bayes beamformer, EBB; and multiple sparse priors, MSP) under a Bayesian framework (as implemented in SPM), each with three different sets of priors consisting of: (1) those specific to the algorithm; (2) those specific to the algorithm plus fMRI task activation maps and RSNs; and (3) those specific to the algorithm plus fMRI task activation maps and RSNs and network modules of task-related dFC states estimated from the dFC fluctuations. The quality of the reconstructed EEG sources was quantified in terms of model-based metrics, namely the expectation of the posterior probability P(model|data) and variance explained of the inversion models, and the overlap/proportion of brain regions known to be involved in the visual perception tasks that the participants were submitted to, and RSN templates, with/within EEG source components. Model-based metrics suggested that model parsimony is preferred, with the combination MSP and priors specific to this algorithm exhibiting the best performance. However, optimal overlap/proportion values were found using EBB and priors specific to this algorithm and fMRI task activation maps and RSNs or MSP and considering all the priors (algorithm priors, fMRI task activation maps and RSNs and dFC state modules), respectively, indicating that fMRI spatial priors, including dFC state modules, might contain useful information to recover EEG source components reflecting neuronal activity of interest. Our main results show that providing fMRI spatial derived priors that reflect the dynamics of the brain might be useful to map neuronal activity more accurately from EEG-fMRI. Furthermore, this work paves the way towards a more informative selection of the optimal EEG source reconstruction approach, which may be critical in future studies.
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16
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Prado P, Birba A, Cruzat J, Santamaría-García H, Parra M, Moguilner S, Tagliazucchi E, Ibáñez A. Dementia ConnEEGtome: Towards multicentric harmonization of EEG connectivity in neurodegeneration. Int J Psychophysiol 2022; 172:24-38. [PMID: 34968581 PMCID: PMC9887537 DOI: 10.1016/j.ijpsycho.2021.12.008] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 11/26/2021] [Accepted: 12/19/2021] [Indexed: 02/02/2023]
Abstract
The proposal to use brain connectivity as a biomarker for dementia phenotyping can be potentiated by conducting large-scale multicentric studies using high-density electroencephalography (hd- EEG). Nevertheless, several barriers preclude the development of a systematic "ConnEEGtome" in dementia research. Here we review critical sources of variability in EEG connectivity studies, and provide general guidelines for multicentric protocol harmonization. We describe how results can be impacted by the choice for data acquisition, and signal processing workflows. The implementation of a particular processing pipeline is conditional upon assumptions made by researchers about the nature of EEG. Due to these assumptions, EEG connectivity metrics are typically applicable to restricted scenarios, e.g., to a particular neurocognitive disorder. "Ground truths" for the choice of processing workflow and connectivity analysis are impractical. Consequently, efforts should be directed to harmonizing experimental procedures, data acquisition, and the first steps of the preprocessing pipeline. Conducting multiple analyses of the same data and a proper integration of the results need to be considered in additional processing steps. Furthermore, instead of using a single connectivity measure, using a composite metric combining different connectivity measures brings a powerful strategy to scale up the replicability of multicentric EEG connectivity studies. These composite metrics can boost the predictive strength of diagnostic tools for dementia. Moreover, the implementation of multi-feature machine learning classification systems that include EEG-based connectivity analyses may help to exploit the potential of multicentric studies combining clinical-cognitive, molecular, genetics, and neuroimaging data towards a multi-dimensional characterization of the dementia.
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Affiliation(s)
- Pavel Prado
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile
| | - Agustina Birba
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile,Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina,National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
| | - Josefina Cruzat
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile
| | - Hernando Santamaría-García
- Pontificia Universidad Javeriana, Medical School, Physiology and Psychiatry Departments, Memory and Cognition Center Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia
| | - Mario Parra
- School of Psychological Sciences and Health, University of Strathclyde, Glasgow, United Kingdom
| | - Sebastian Moguilner
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile,Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina,Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), California, USA,Trinity College Dublin (TCD), Dublin, Ireland
| | - Enzo Tagliazucchi
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile,Departamento de Física, Universidad de Buenos Aires and Instituto de Fisica de Buenos Aires (IFIBA -CONICET), Buenos Aires, Argentina
| | - Agustín Ibáñez
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile,Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina,National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina,Global Brain Health Institute (GBHI), University of California San Francisco (UCSF), California, USA,Trinity College Dublin (TCD), Dublin, Ireland,Corresponding author at: Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago de Chile, Chile., (A. Ibáñez)
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17
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Cai Z, Uji M, Aydin Ü, Pellegrino G, Spilkin A, Delaire É, Abdallah C, Lina J, Grova C. Evaluation of a personalized functional near infra-red optical tomography workflow using maximum entropy on the mean. Hum Brain Mapp 2021; 42:4823-4843. [PMID: 34342073 PMCID: PMC8449120 DOI: 10.1002/hbm.25566] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 05/25/2021] [Accepted: 06/11/2021] [Indexed: 11/20/2022] Open
Abstract
In the present study, we proposed and evaluated a workflow of personalized near infra-red optical tomography (NIROT) using functional near-infrared spectroscopy (fNIRS) for spatiotemporal imaging of cortical hemodynamic fluctuations. The proposed workflow from fNIRS data acquisition to local 3D reconstruction consists of: (a) the personalized optimal montage maximizing fNIRS channel sensitivity to a predefined targeted brain region; (b) the optimized fNIRS data acquisition involving installation of optodes and digitalization of their positions using a neuronavigation system; and (c) the 3D local reconstruction using maximum entropy on the mean (MEM) to accurately estimate the location and spatial extent of fNIRS hemodynamic fluctuations along the cortical surface. The workflow was evaluated on finger-tapping fNIRS data acquired from 10 healthy subjects for whom we estimated the reconstructed NIROT spatiotemporal images and compared with functional magnetic resonance imaging (fMRI) results from the same individuals. Using the fMRI activation maps as our reference, we quantitatively compared the performance of two NIROT approaches, the MEM framework and the conventional minimum norm estimation (MNE) method. Quantitative comparisons were performed at both single subject and group-level. Overall, our results suggested that MEM provided better spatial accuracy than MNE, while both methods offered similar temporal accuracy when reconstructing oxygenated (HbO) and deoxygenated hemoglobin (HbR) concentration changes evoked by finger-tapping. Our proposed complete workflow was made available in the brainstorm fNIRS processing plugin-NIRSTORM, thus providing the opportunity for other researchers to further apply it to other tasks and on larger populations.
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Affiliation(s)
- Zhengchen Cai
- Multimodal Functional Imaging Lab, Department of Physics and PERFORM CentreConcordia UniversityMontréalQuébecCanada
| | - Makoto Uji
- Multimodal Functional Imaging Lab, Department of Physics and PERFORM CentreConcordia UniversityMontréalQuébecCanada
| | - Ümit Aydin
- Multimodal Functional Imaging Lab, Department of Physics and PERFORM CentreConcordia UniversityMontréalQuébecCanada
- Social, Genetic and Developmental Psychiatry CentreInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
| | - Giovanni Pellegrino
- Neurology and Neurosurgery Department, Montreal Neurological InstituteMcGill UniversityMontréalQuébecCanada
- Multimodal Functional Imaging Lab, Biomedical Engineering DepartmentMcGill UniversityMontréalQuébecCanada
| | - Amanda Spilkin
- Multimodal Functional Imaging Lab, Department of Physics and PERFORM CentreConcordia UniversityMontréalQuébecCanada
| | - Édouard Delaire
- Multimodal Functional Imaging Lab, Department of Physics and PERFORM CentreConcordia UniversityMontréalQuébecCanada
| | - Chifaou Abdallah
- Neurology and Neurosurgery Department, Montreal Neurological InstituteMcGill UniversityMontréalQuébecCanada
- Multimodal Functional Imaging Lab, Biomedical Engineering DepartmentMcGill UniversityMontréalQuébecCanada
| | - Jean‐Marc Lina
- Département de Génie ElectriqueÉcole de Technologie SupérieureMontréalQuébecCanada
- Centre De Recherches En MathématiquesMontréalQuébecCanada
| | - Christophe Grova
- Multimodal Functional Imaging Lab, Department of Physics and PERFORM CentreConcordia UniversityMontréalQuébecCanada
- Neurology and Neurosurgery Department, Montreal Neurological InstituteMcGill UniversityMontréalQuébecCanada
- Multimodal Functional Imaging Lab, Biomedical Engineering DepartmentMcGill UniversityMontréalQuébecCanada
- Centre De Recherches En MathématiquesMontréalQuébecCanada
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18
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Hashemi A, Cai C, Kutyniok G, Müller KR, Nagarajan SS, Haufe S. Unification of sparse Bayesian learning algorithms for electromagnetic brain imaging with the majorization minimization framework. Neuroimage 2021; 239:118309. [PMID: 34182100 PMCID: PMC8433122 DOI: 10.1016/j.neuroimage.2021.118309] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 05/17/2021] [Accepted: 06/23/2021] [Indexed: 11/23/2022] Open
Abstract
Methods for electro- or magnetoencephalography (EEG/MEG) based brain source imaging (BSI) using sparse Bayesian learning (SBL) have been demonstrated to achieve excellent performance in situations with low numbers of distinct active sources, such as event-related designs. This paper extends the theory and practice of SBL in three important ways. First, we reformulate three existing SBL algorithms under the majorization-minimization (MM) framework. This unification perspective not only provides a useful theoretical framework for comparing different algorithms in terms of their convergence behavior, but also provides a principled recipe for constructing novel algorithms with specific properties by designing appropriate bounds of the Bayesian marginal likelihood function. Second, building on the MM principle, we propose a novel method called LowSNR-BSI that achieves favorable source reconstruction performance in low signal-to-noise-ratio (SNR) settings. Third, precise knowledge of the noise level is a crucial requirement for accurate source reconstruction. Here we present a novel principled technique to accurately learn the noise variance from the data either jointly within the source reconstruction procedure or using one of two proposed cross-validation strategies. Empirically, we could show that the monotonous convergence behavior predicted from MM theory is confirmed in numerical experiments. Using simulations, we further demonstrate the advantage of LowSNR-BSI over conventional SBL in low-SNR regimes, and the advantage of learned noise levels over estimates derived from baseline data. To demonstrate the usefulness of our novel approach, we show neurophysiologically plausible source reconstructions on averaged auditory evoked potential data.
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Affiliation(s)
- Ali Hashemi
- Uncertainty, Inverse Modeling and Machine Learning Group, Technische Universität Berlin, Germany; Machine Learning Group, Technische Universität Berlin, Germany; Berlin Center for Advanced Neuroimaging (BCAN), Charité - Universitätsmedizin Berlin, Germany; Institut für Mathematik, Technische Universität Berlin, Germany.
| | - Chang Cai
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA; National Engineering Research Center for E-Learning, Central China Normal University, China
| | - Gitta Kutyniok
- Mathematisches Institut, Ludwig-Maximilians-Universität München, Germany; Department of Physics and Technology, University of Tromsø, Norway
| | - Klaus-Robert Müller
- Machine Learning Group, Technische Universität Berlin, Germany; BIFOLD - Berlin Institute for the Foundations of Learning and Data, Berlin, Germany; Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea; Max Planck Institute for Informatics, Saarbrücken, Germany.
| | - Srikantan S Nagarajan
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA.
| | - Stefan Haufe
- Uncertainty, Inverse Modeling and Machine Learning Group, Technische Universität Berlin, Germany; Berlin Center for Advanced Neuroimaging (BCAN), Charité - Universitätsmedizin Berlin, Germany; Mathematical Modelling and Data Analysis Department, Physikalisch-Technische Bundesanstalt Braunschweig und Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany.
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19
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Wang L, Noordanus E, van Opstal AJ. Estimating multiple latencies in the auditory system from auditory steady-state responses on a single EEG channel. Sci Rep 2021; 11:2150. [PMID: 33495484 PMCID: PMC7835249 DOI: 10.1038/s41598-021-81232-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 01/05/2021] [Indexed: 01/30/2023] Open
Abstract
The latency of the auditory steady-state response (ASSR) may provide valuable information regarding the integrity of the auditory system, as it could potentially reveal the presence of multiple intracerebral sources. To estimate multiple latencies from high-order ASSRs, we propose a novel two-stage procedure that consists of a nonparametric estimation method, called apparent latency from phase coherence (ALPC), followed by a heuristic sequential forward selection algorithm (SFS). Compared with existing methods, ALPC-SFS requires few prior assumptions, and is straightforward to implement for higher-order nonlinear responses to multi-cosine sound complexes with their initial phases set to zero. It systematically evaluates the nonlinear components of the ASSRs by estimating multiple latencies, automatically identifies involved ASSR components, and reports a latency consistency index. To verify the proposed method, we performed simulations for several scenarios: two nonlinear subsystems with different or overlapping outputs. We compared the results from our method with predictions from existing, parametric methods. We also recorded the EEG from ten normal-hearing adults by bilaterally presenting superimposed tones with four frequencies that evoke a unique set of ASSRs. From these ASSRs, two major latencies were found to be stable across subjects on repeated measurement days. The two latencies are dominated by low-frequency (LF) (near 40 Hz, at around 41-52 ms) and high-frequency (HF) (> 80 Hz, at around 21-27 ms) ASSR components. The frontal-central brain region showed longer latencies on LF components, but shorter latencies on HF components, when compared with temporal-lobe regions. In conclusion, the proposed nonparametric ALPC-SFS method, applied to zero-phase, multi-cosine sound complexes is more suitable for evaluating embedded nonlinear systems underlying ASSRs than existing methods. It may therefore be a promising objective measure for hearing performance and auditory cortex (dys)function.
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Affiliation(s)
- Lei Wang
- Department of Biophysics, Radboud University, Nijmegen, 6525 AJ, The Netherlands.
- Donders Centre for Neuroscience, Radboud University, Nijmegen, 6525 AJ, The Netherlands.
| | - Elisabeth Noordanus
- Department of Biophysics, Radboud University, Nijmegen, 6525 AJ, The Netherlands
- Donders Centre for Neuroscience, Radboud University, Nijmegen, 6525 AJ, The Netherlands
| | - A John van Opstal
- Department of Biophysics, Radboud University, Nijmegen, 6525 AJ, The Netherlands
- Donders Centre for Neuroscience, Radboud University, Nijmegen, 6525 AJ, The Netherlands
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20
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Olivares EI, Urraca AS, Lage-Castellanos A, Iglesias J. Different and common brain signals of altered neurocognitive mechanisms for unfamiliar face processing in acquired and developmental prosopagnosia. Cortex 2020; 134:92-113. [PMID: 33271437 DOI: 10.1016/j.cortex.2020.10.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Revised: 09/21/2020] [Accepted: 10/14/2020] [Indexed: 11/25/2022]
Abstract
Neuropsychological studies have shown that prosopagnosic individuals perceive face structure in an atypical way. This might preclude the formation of appropriate face representations and, consequently, hamper effective recognition. The present ERP study, in combination with Bayesian source reconstruction, investigates how information related to both external (E) and internal (I) features was processed by E.C. and I.P., suffering from acquired and developmental prosopagnosia, respectively. They carried out a face-feature matching task with new faces. E.C. showed poor performance and remarkable lack of early face-sensitive P1, N170 and P2 responses on right (damaged) posterior cortex. Although she presented the expected mismatch effect to target faces in the E-I sequence, it was of shorter duration than in Controls, and involved left parietal, right frontocentral and dorsofrontal regions, suggestive of reduced neural circuitry to process face configurations. In turn, I.P. performed efficiently but with a remarkable bias to give "match" responses. His face-sensitive potentials P1-N170 were comparable to those from Controls, however, he showed no subsequent P2 response and a mismatch effect only in the I-E sequence, reflecting activation confined to those regions that sustain typically the initial stages of face processing. Relevantly, neither of the prosopagnosics exhibited conspicuous P3 responses to features acting as primes, indicating that diagnostic information for constructing face representations could not be sufficiently attended nor deeply encoded. Our findings suggest a different locus for altered neurocognitive mechanisms in the face network in participants with different types of prosopagnosia, but common indicators of a deficient allocation of attentional resources for further recognition.
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Affiliation(s)
- Ela I Olivares
- Department of Biological and Health Psychology, Faculty of Psychology, Universidad Autónoma de Madrid, Spain.
| | - Ana S Urraca
- Centro Universitario Cardenal Cisneros, Alcalá de Henares, Madrid, Spain
| | - Agustín Lage-Castellanos
- Department of Neuroinformatics, Cuban Center for Neuroscience, Havana, Cuba; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Jaime Iglesias
- Department of Biological and Health Psychology, Faculty of Psychology, Universidad Autónoma de Madrid, Spain
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21
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Pellegrino G, Hedrich T, Porras-Bettancourt M, Lina JM, Aydin Ü, Hall J, Grova C, Kobayashi E. Accuracy and spatial properties of distributed magnetic source imaging techniques in the investigation of focal epilepsy patients. Hum Brain Mapp 2020; 41:3019-3033. [PMID: 32386115 PMCID: PMC7336148 DOI: 10.1002/hbm.24994] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Revised: 02/18/2020] [Accepted: 03/11/2020] [Indexed: 02/03/2023] Open
Abstract
Source localization of interictal epileptiform discharges (IEDs) is clinically useful in the presurgical workup of epilepsy patients. We aimed to compare the performance of four different distributed magnetic source imaging (dMSI) approaches: Minimum norm estimate (MNE), dynamic statistical parametric mapping (dSPM), standardized low-resolution electromagnetic tomography (sLORETA), and coherent maximum entropy on the mean (cMEM). We also evaluated whether a simple average of maps obtained from multiple inverse solutions (Ave) can improve localization accuracy. We analyzed dMSI of 206 IEDs derived from magnetoencephalography recordings in 28 focal epilepsy patients who had a well-defined focus determined through intracranial EEG (iEEG), epileptogenic MRI lesions or surgical resection. dMSI accuracy and spatial properties were quantitatively estimated as: (a) distance from the epilepsy focus, (b) reproducibility, (c) spatial dispersion (SD), (d) map extension, and (e) effect of thresholding on map properties. Clinical performance was excellent for all methods (median distance from the focus MNE = 2.4 mm; sLORETA = 3.5 mm; cMEM = 3.5 mm; dSPM = 6.8 mm, Ave = 0 mm). Ave showed the lowest distance between the map maximum and epilepsy focus (Dmin lower than cMEM, MNE, and dSPM, p = .021, p = .008, p < .001, respectively). cMEM showed the best spatial features, with lowest SD outside the focus (SD lower than all other methods, p < .001 consistently) and high contrast between the generator and surrounding regions. The average map Ave provided the best localization accuracy, whereas cMEM exhibited the lowest amount of spurious distant activity. dMSI techniques have the potential to significantly improve identification of iEEG targets and to guide surgical planning, especially when multiple methods are combined.
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Affiliation(s)
- Giovanni Pellegrino
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.,IRCCS Fondazione San Camillo Hospital, Venice, Italy.,Department of Multimodal Functional Imaging Lab, Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - Tanguy Hedrich
- Department of Multimodal Functional Imaging Lab, Biomedical Engineering, McGill University, Montreal, Quebec, Canada
| | - Manuel Porras-Bettancourt
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Jean-Marc Lina
- Departement de Genie Electrique, Ecole de Technologie Superieure, Montreal, Quebec, Canada.,Centre de Recherches Mathematiques, Montréal, Quebec, Canada
| | - Ümit Aydin
- Physics Department and PERFORM Centre, Concordia University, Montreal, Quebec, Canada
| | - Jeffery Hall
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Christophe Grova
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.,Department of Multimodal Functional Imaging Lab, Biomedical Engineering, McGill University, Montreal, Quebec, Canada.,Centre de Recherches Mathematiques, Montréal, Quebec, Canada.,Physics Department and PERFORM Centre, Concordia University, Montreal, Quebec, Canada
| | - Eliane Kobayashi
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
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22
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Ortiz T, Ortiz-Teran L, Turrero A, Poch-Broto J, de Erausquin GA. A N400 ERP Study in letter recognition after passive tactile stimulation training in blind children and sighted controls. Restor Neurol Neurosci 2020; 37:197-206. [PMID: 31227674 DOI: 10.3233/rnn-180838] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND We previously demonstrated that using a sensory substitution device (SSD) for one week, tactile stimulation results in faster activation of lateral occipital complex in blind children than in seeing controls. OBJECTIVE We used long-term haptic tactile stimulation training with an SSD to test if it results in stable cross-modal reassignment of visual pathways after six months, to provide high level processing of tactile semantic content. METHODS We enrolled 12 blind and 12 sighted children. The SSD transforms images to a stimulation matrix in contact with the dominant hand. Subjects underwent twice-daily training sessions, 5 days/week for six months. Children were asked to describe line orientation, name letters, and read words. ERP sessions were performed at baseline and 6 months to analyze the N400 ERP component and reaction times (RT). N400 sources were estimated with Low Resolution Electromagnetic Tomography (LORETA). SPM8 was used to make population-level inferences. RESULTS We found no group differences in RTs, accuracy of identifications, N400 latencies or distributions with the line task at 1 week or at 6 months. RTs on the letter recognition task were also similar. After 6 months, behavioral training increased accurate letter identification in both seeing and blind children (Chi 2 = 11906.934, p = 0.000), but the increase was larger in blind children (Chi 2 = 8.272, p = 0.004). Behavioral training shifted peak N400 amplitude to left occipital and bilateral parietal cortices in blind children, but to left precentral and postcentral and bilateral occipital cortices in sighted controls. CONCLUSIONS Blind children learn to recognize SSD-delivered letters better than seeing controls and had greater N400 amplitude in the occipital region. To the best of our knowledge, our results provide the first published example of standard letter recognition (not Braille) by children with blindness using a tactile delivery system.
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Affiliation(s)
- Tomas Ortiz
- Department of Psychiatry, Faculty of Medicine Universidad Complutense, Madrid, Spain
| | - Laura Ortiz-Teran
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital Harvard University, Boston, USA
| | - Agustin Turrero
- Department of Biostatistics, Faculty of Medicine Universidad Complutense, Madrid, Spain
| | - Joaquin Poch-Broto
- Department of Ear, Nose and Throat, Hospital Clínico Universitario San Carlos, Madrid, Spain
| | - Gabriel A de Erausquin
- Department of Psychiatry and Neurology, Institute of Neuroscience, University of Texas Rio Grande Valley School of Medicine, Harlingen, USA
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23
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Rikir E, Maillard LG, Abdallah C, Gavaret M, Bartolomei F, Vignal JP, Colnat-Coulbois S, Koessler L. Respective Contribution of Ictal and Inter-ictal Electrical Source Imaging to Epileptogenic Zone Localization. Brain Topogr 2020; 33:384-402. [DOI: 10.1007/s10548-020-00768-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 04/07/2020] [Indexed: 10/24/2022]
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24
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Cai C, Diwakar M, Chen D, Sekihara K, Nagarajan SS. Robust Empirical Bayesian Reconstruction of Distributed Sources for Electromagnetic Brain Imaging. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:567-577. [PMID: 31380750 PMCID: PMC7446954 DOI: 10.1109/tmi.2019.2932290] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Electromagnetic brain imaging is the reconstruction of brain activity from non-invasive recordings of the magnetic fields and electric potentials. An enduring challenge in this imaging modality is estimating the number, location, and time course of sources, especially for the reconstruction of distributed brain sources with complex spatial extent. Here, we introduce a novel robust empirical Bayesian algorithm that enables better reconstruction of distributed brain source activity with two key ideas: kernel smoothing and hyperparameter tiling. Since the proposed algorithm builds upon many of the performance features of the sparse source reconstruction algorithm - Champagne and we refer to this algorithm as Smooth Champagne. Smooth Champagne is robust to the effects of high levels of noise, interference, and highly correlated brain source activity. Simulations demonstrate excellent performance of Smooth Champagne when compared to benchmark algorithms in accurately determining the spatial extent of distributed source activity. Smooth Champagne also accurately reconstructs real MEG and EEG data.
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25
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Performance of source imaging techniques of spatially extended generators of uterine activity. INFORMATICS IN MEDICINE UNLOCKED 2019. [DOI: 10.1016/j.imu.2019.100167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
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26
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Rimpiläinen V, Koulouri A, Lucka F, Kaipio JP, Wolters CH. Improved EEG source localization with Bayesian uncertainty modelling of unknown skull conductivity. Neuroimage 2018; 188:252-260. [PMID: 30529398 DOI: 10.1016/j.neuroimage.2018.11.058] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Accepted: 11/30/2018] [Indexed: 10/27/2022] Open
Abstract
Electroencephalography (EEG) source imaging is an ill-posed inverse problem that requires accurate conductivity modelling of the head tissues, especially the skull. Unfortunately, the conductivity values are difficult to determine in vivo. In this paper, we show that the exact knowledge of the skull conductivity is not always necessary when the Bayesian approximation error (BAE) approach is exploited. In BAE, we first postulate a probability distribution for the skull conductivity that describes our (lack of) knowledge on its value, and model the effects of this uncertainty on EEG recordings with the help of an additive error term in the observation model. Before the Bayesian inference, the likelihood is marginalized over this error term. Thus, in the inversion we estimate only our primary unknown, the source distribution. We quantified the improvements in the source localization when the proposed Bayesian modelling was used in the presence of different skull conductivity errors and levels of measurement noise. Based on the results, BAE was able to improve the source localization accuracy, particularly when the unknown (true) skull conductivity was much lower than the expected standard conductivity value. The source locations that gained the highest improvements were shallow and originally exhibited the largest localization errors. In our case study, the benefits of BAE became negligible when the signal-to-noise ratio dropped to 20 dB.
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Affiliation(s)
- Ville Rimpiläinen
- Department of Physics, University of Bath, Claverton Down, Bath, BA2 7AY, United Kingdom; Institute for Biomagnetism and Biosignalanalysis, University of Münster, Malmedyweg 15, D-48149, Münster, Germany.
| | - Alexandra Koulouri
- Laboratory of Mathematics, Tampere University of Technology, P. O. Box 692, 33101, Tampere, Finland; Department of Physics, Aristotle University of Thessaloniki, Thessaloniki, 541 24, Greece
| | - Felix Lucka
- Computational Imaging, Centrum Wiskunde & Informatica, Science Park 123, 1098 XG, Amsterdam, the Netherlands; Centre for Medical Image Computing, University College London, Gower Street, London, WC1E 6BT, United Kingdom
| | - Jari P Kaipio
- Department of Mathematics, University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand; Department of Applied Physics, University of Eastern Finland, FI-90211, Kuopio, Finland
| | - Carsten H Wolters
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Malmedyweg 15, D-48149, Münster, Germany
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27
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Brain Activity Mapping from MEG Data via a Hierarchical Bayesian Algorithm with Automatic Depth Weighting. Brain Topogr 2018; 32:363-393. [PMID: 30121834 DOI: 10.1007/s10548-018-0670-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Accepted: 08/03/2018] [Indexed: 10/28/2022]
Abstract
A recently proposed iterated alternating sequential (IAS) MEG inverse solver algorithm, based on the coupling of a hierarchical Bayesian model with computationally efficient Krylov subspace linear solver, has been shown to perform well for both superficial and deep brain sources. However, a systematic study of its ability to correctly identify active brain regions is still missing. We propose novel statistical protocols to quantify the performance of MEG inverse solvers, focusing in particular on how their accuracy and precision at identifying active brain regions. We use these protocols for a systematic study of the performance of the IAS MEG inverse solver, comparing it with three standard inversion methods, wMNE, dSPM, and sLORETA. To avoid the bias of anecdotal tests towards a particular algorithm, the proposed protocols are Monte Carlo sampling based, generating an ensemble of activity patches in each brain region identified in a given atlas. The performance in correctly identifying the active areas is measured by how much, on average, the reconstructed activity is concentrated in the brain region of the simulated active patch. The analysis is based on Bayes factors, interpreting the estimated current activity as data for testing the hypothesis that the active brain region is correctly identified, versus the hypothesis of any erroneous attribution. The methodology allows the presence of a single or several simultaneous activity regions, without assuming that the number of active regions is known. The testing protocols suggest that the IAS solver performs well with both with cortical and subcortical activity estimation.
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28
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Tremblay J, Martínez-Montes E, Vannasing P, Nguyen DK, Sawan M, Lepore F, Gallagher A. Comparison of source localization techniques in diffuse optical tomography for fNIRS application using a realistic head model. BIOMEDICAL OPTICS EXPRESS 2018; 9:2994-3016. [PMID: 30619642 PMCID: PMC6033567 DOI: 10.1364/boe.9.002994] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Revised: 05/18/2018] [Accepted: 05/26/2018] [Indexed: 05/24/2023]
Abstract
Functional near-infrared spectroscopy (fNIRS) is a non-invasive imaging technique that elicits growing interest for research and clinical applications. In the last decade, efforts have been made to develop a mathematical framework in order to image the effective sources of hemoglobin variations in brain tissues. Different approaches can be used to impose additional information or constraints when reconstructing the cerebral images of an ill-posed problem. The goal of this study is to compare the performance and limitations of several source localization techniques in the context of fNIRS tomography using individual anatomical magnetic resonance imaging (MRI) to model light propagation. The forward problem is solved using a Monte Carlo simulation of light propagation in the tissues. The inverse problem has been linearized using the Rytov approximation. Then, Tikhonov regularization applied to least squares, truncated singular value decomposition, back-projection, L1-norm regularization, minimum norm estimates, low resolution electromagnetic tomography and Bayesian model averaging techniques are compared using a receiver operating characteristic analysis, blurring and localization error measures. Using realistic simulations (n = 450) and data acquired from a human participant, this study depicts how these source localization techniques behave in a human head fNIRS tomography. When compared to other methods, Bayesian model averaging is proposed as a promising method in DOT and shows great potential to improve specificity, accuracy, as well as to reduce blurring and localization error even in presence of noise and deep sources. Classical reconstruction methods, such as regularized least squares, offer better sensitivity but higher blurring; while more novel L1-based method provides sparse solutions with small blurring and high specificity but lower sensitivity. The application of these methods is also demonstrated experimentally using visual fNIRS experiment with adult participant.
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Affiliation(s)
- Julie Tremblay
- LIONLAB, Centre de recherche du CHU Sainte-Justine, Université de Montréal, Montréal, Canada
| | | | - Phetsamone Vannasing
- LIONLAB, Centre de recherche du CHU Sainte-Justine, Université de Montréal, Montréal, Canada
| | - Dang K Nguyen
- Neurology Division, Centre hospitalier de l'Université de Montréal (CHUM), Hôpital Notre-Dame, Montréal, Canada
| | - Mohamad Sawan
- Polystim Neurotech Lab, Polytechnique Montréal, Montréal, Canada
| | - Franco Lepore
- Centre de recherche en neuropsychologie et cognition (CERNEC), Département de psychologie, Université de Montréal, Montréal, Canada
| | - Anne Gallagher
- LIONLAB, Centre de recherche du CHU Sainte-Justine, Université de Montréal, Montréal, Canada
- Centre de recherche en neuropsychologie et cognition (CERNEC), Département de psychologie, Université de Montréal, Montréal, Canada
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29
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Abstract
Brain activity and connectivity are distributed in the three-dimensional space and evolve in time. It is important to image brain dynamics with high spatial and temporal resolution. Electroencephalography (EEG) and magnetoencephalography (MEG) are noninvasive measurements associated with complex neural activations and interactions that encode brain functions. Electrophysiological source imaging estimates the underlying brain electrical sources from EEG and MEG measurements. It offers increasingly improved spatial resolution and intrinsically high temporal resolution for imaging large-scale brain activity and connectivity on a wide range of timescales. Integration of electrophysiological source imaging and functional magnetic resonance imaging could further enhance spatiotemporal resolution and specificity to an extent that is not attainable with either technique alone. We review methodological developments in electrophysiological source imaging over the past three decades and envision its future advancement into a powerful functional neuroimaging technology for basic and clinical neuroscience applications.
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Affiliation(s)
- Bin He
- Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA;
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - Abbas Sohrabpour
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, Minnesota 55455, USA
| | - Emery Brown
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Zhongming Liu
- Weldon School of Biomedical Engineering, School of Electrical and Computer Engineering, and Purdue Institute of Integrative Neuroscience, Purdue University, West Lafayette, Indiana 47906, USA
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30
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Hu S, Yao D, Valdes-Sosa PA. Unified Bayesian Estimator of EEG Reference at Infinity: rREST (Regularized Reference Electrode Standardization Technique). Front Neurosci 2018; 12:297. [PMID: 29780302 PMCID: PMC5946034 DOI: 10.3389/fnins.2018.00297] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2017] [Accepted: 04/17/2018] [Indexed: 12/02/2022] Open
Abstract
The choice of reference for the electroencephalogram (EEG) is a long-lasting unsolved issue resulting in inconsistent usages and endless debates. Currently, both the average reference (AR) and the reference electrode standardization technique (REST) are two primary, apparently irreconcilable contenders. We propose a theoretical framework to resolve this reference issue by formulating both (a) estimation of potentials at infinity, and (b) determination of the reference, as a unified Bayesian linear inverse problem, which can be solved by maximum a posterior estimation. We find that AR and REST are very particular cases of this unified framework: AR results from biophysically non-informative prior; while REST utilizes the prior based on the EEG generative model. To allow for simultaneous denoising and reference estimation, we develop the regularized versions of AR and REST, named rAR and rREST, respectively. Both depend on a regularization parameter that is the noise to signal variance ratio. Traditional and new estimators are evaluated with this framework, by both simulations and analysis of real resting EEGs. Toward this end, we leverage the MRI and EEG data from 89 subjects which participated in the Cuban Human Brain Mapping Project. Generated artificial EEGs—with a known ground truth, show that relative error in estimating the EEG potentials at infinity is lowest for rREST. It also reveals that realistic volume conductor models improve the performances of REST and rREST. Importantly, for practical applications, it is shown that an average lead field gives the results comparable to the individual lead field. Finally, it is shown that the selection of the regularization parameter with Generalized Cross-Validation (GCV) is close to the “oracle” choice based on the ground truth. When evaluated with the real 89 resting state EEGs, rREST consistently yields the lowest GCV. This study provides a novel perspective to the EEG reference problem by means of a unified inverse solution framework. It may allow additional principled theoretical formulations and numerical evaluation of performance.
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Affiliation(s)
- Shiang Hu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Pedro A Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China.,Cuban Neuroscience Center, Havana, Cuba
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31
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Ojeda A, Kreutz-Delgado K, Mullen T. Fast and robust Block-Sparse Bayesian learning for EEG source imaging. Neuroimage 2018; 174:449-462. [PMID: 29596978 DOI: 10.1016/j.neuroimage.2018.03.048] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2017] [Revised: 03/10/2018] [Accepted: 03/21/2018] [Indexed: 10/17/2022] Open
Abstract
We propose a new Sparse Bayesian Learning (SBL) algorithm that can deliver fast, block-sparse, and robust solutions to the EEG source imaging (ESI) problem in the presence of noisy measurements. Current implementations of the SBL framework are computationally expensive and typically handle fluctuations in the measurement noise using different heuristics that are unsuitable for real-time imaging applications. We address these shortcomings by decoupling the estimation of the sensor noise covariance and the sparsity profile of the sources, thereby yielding an efficient two-stage algorithm. In the first stage, we optimize a simplified non-sparse generative model to get an estimate of the sensor noise covariance and a good initialization of the group-sparsity profile of the sources. Sources obtained at this stage are equivalent to those estimated with the popular inverse method LORETA. In the second stage, we apply a fast SBL algorithm with the noise covariance fixed to the value obtained in the first stage to efficiently shrink to zero groups of sources that are irrelevant for explaining the EEG measurements. In addition, we derive an initialization to the first stage of the algorithm that is optimal in the least squares sense, which prevents delays due to suboptimal initial conditions. We validate our method on both simulated and real EEG data. Simulations show that the method is robust to measurement noise and performs well in real-time, with faster performance than two state of the art SBL solvers. On real error-related negativity EEG data, we obtain source images in agreement with the experimental literature. The method shows promise for real-time neuroimaging and brain-machine interface applications.
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Affiliation(s)
- Alejandro Ojeda
- Intheon Labs, San Diego, CA, USA; Electrical & Computer Engineering Department, University of California San Diego, La Jolla, CA, USA.
| | - Kenneth Kreutz-Delgado
- Electrical & Computer Engineering Department, University of California San Diego, La Jolla, CA, USA
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32
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Olivares EI, Lage-Castellanos A, Bobes MA, Iglesias J. Source Reconstruction of Brain Potentials Using Bayesian Model Averaging to Analyze Face Intra-Domain vs. Face-Occupation Cross-Domain Processing. Front Integr Neurosci 2018; 12:12. [PMID: 29628877 PMCID: PMC5876247 DOI: 10.3389/fnint.2018.00012] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2017] [Accepted: 03/06/2018] [Indexed: 01/01/2023] Open
Abstract
We investigated the neural correlates of the access to and retrieval of face structure information in contrast to those concerning the access to and retrieval of person-related verbal information, triggered by faces. We experimentally induced stimulus familiarity via a systematic learning procedure including faces with and without associated verbal information. Then, we recorded event-related potentials (ERPs) in both intra-domain (face-feature) and cross-domain (face-occupation) matching tasks while N400-like responses were elicited by incorrect eyes-eyebrows completions and occupations, respectively. A novel Bayesian source reconstruction approach plus conjunction analysis of group effects revealed that in both cases the generated N170s were of similar amplitude but had different neural origin. Thus, whereas the N170 of faces was associated predominantly to right fusiform and occipital regions (the so-called “Fusiform Face Area”, “FFA” and “Occipital Face Area”, “OFA”, respectively), the N170 of occupations was associated to a bilateral very posterior activity, suggestive of basic perceptual processes. Importantly, the right-sided perceptual P200 and the face-related N250 were evoked exclusively in the intra-domain task, with sources in OFA and extensively in the fusiform region, respectively. Regarding later latencies, the intra-domain N400 seemed to be generated in right posterior brain regions encompassing mainly OFA and, to some extent, the FFA, likely reflecting neural operations triggered by structural incongruities. In turn, the cross-domain N400 was related to more anterior left-sided fusiform and temporal inferior sources, paralleling those described previously for the classic verbal N400. These results support the existence of differentiated neural streams for face structure and person-related verbal processing triggered by faces, which can be activated differentially according to specific task demands.
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Affiliation(s)
- Ela I Olivares
- Department of Biological and Health Psychology, Faculty of Psychology, Universidad Autónoma de Madrid, Madrid, Spain
| | - Agustín Lage-Castellanos
- Cognitive Neuroscience Department, Cuban Neuroscience Center, Havana, Cuba.,Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands.,Maastricht Brain Imaging Center, Maastricht University, Maastricht, Netherlands
| | - María A Bobes
- Cognitive Neuroscience Department, Cuban Neuroscience Center, Havana, Cuba.,The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Chinese University of Electronic Science and Technology, Chengdu, China
| | - Jaime Iglesias
- Department of Biological and Health Psychology, Faculty of Psychology, Universidad Autónoma de Madrid, Madrid, Spain
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33
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ERP Source Analysis Guided by fMRI During Familiar Face Processing. Brain Topogr 2018; 32:720-740. [DOI: 10.1007/s10548-018-0619-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2017] [Accepted: 01/12/2018] [Indexed: 10/18/2022]
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34
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Penny W, Iglesias-Fuster J, Quiroz YT, Lopera FJ, Bobes MA. Dynamic Causal Modeling of Preclinical Autosomal-Dominant Alzheimer's Disease. J Alzheimers Dis 2018; 65:697-711. [PMID: 29562504 PMCID: PMC6923812 DOI: 10.3233/jad-170405] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/06/2017] [Indexed: 01/13/2023]
Abstract
Dynamic causal modeling (DCM) is a framework for making inferences about changes in brain connectivity using neuroimaging data. We fitted DCMs to high-density EEG data from subjects performing a semantic picture matching task. The subjects are carriers of the PSEN1 mutation, which leads to early onset Alzheimer's disease, but at the time of EEG acquisition in 1999, these subjects were cognitively unimpaired. We asked 1) what is the optimal model architecture for explaining the event-related potentials in this population, 2) which connections are different between this Presymptomatic Carrier (PreC) group and a Non-Carrier (NonC) group performing the same task, and 3) which network connections are predictive of subsequent Mini-Mental State Exam (MMSE) trajectories. We found 1) a model with hierarchical rather than lateral connections between hemispheres to be optimal, 2) that a pathway from right inferotemporal cortex (IT) to left medial temporal lobe (MTL) was preferentially activated by incongruent items for subjects in the PreC group but not the NonC group, and 3) that increased effective connectivity among left MTL, right IT, and right MTL was predictive of subsequent MMSE scores.
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Affiliation(s)
- Will Penny
- School of Psychology, University of East Anglia, Norwich, UK
- Wellcome Trust Centre for Neuroimaging, University College, London, UK
| | | | - Yakeel T. Quiroz
- Massachusetts General Hospital, Boston, MA, USA
- Group of Neurosciences, Medical School, Universidad de Antioquia, Medellin, Colombia
| | | | - Maria A. Bobes
- Department of Cognitive Neuroscience Cuban Neuroscience Center, Havana, Cuba
- Key Laboratory for Neuroinformation of Ministry of Education, Center for Information in Medicine, University of Electronic Science and Technology of China
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35
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Cebolla AM, Palmero-Soler E, Leroy A, Cheron G. EEG Spectral Generators Involved in Motor Imagery: A swLORETA Study. Front Psychol 2017; 8:2133. [PMID: 29312028 PMCID: PMC5733067 DOI: 10.3389/fpsyg.2017.02133] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Accepted: 11/22/2017] [Indexed: 01/26/2023] Open
Abstract
In order to characterize the neural generators of the brain oscillations related to motor imagery (MI), we investigated the cortical, subcortical, and cerebellar localizations of their respective electroencephalogram (EEG) spectral power and phase locking modulations. The MI task consisted in throwing a ball with the dominant upper limb while in a standing posture, within an ecological virtual reality (VR) environment (tennis court). The MI was triggered by the visual cues common to the control condition, during which the participant remained mentally passive. As previously developed, our paradigm considers the confounding problem that the reference condition allows two complementary analyses: one which uses the baseline before the occurrence of the visual cues in the MI and control resting conditions respectively; and the other which compares the analog periods between the MI and the control resting-state conditions. We demonstrate that MI activates specific, complex brain networks for the power and phase modulations of the EEG oscillations. An early (225 ms) delta phase-locking related to MI was generated in the thalamus and cerebellum and was followed (480 ms) by phase-locking in theta and alpha oscillations, generated in specific cortical areas and the cerebellum. Phase-locking preceded the power modulations (mainly alpha-beta ERD), whose cortical generators were situated in the frontal BA45, BA11, BA10, central BA6, lateral BA13, and posterior cortex BA2. Cerebellar-thalamic involvement through phase-locking is discussed as an underlying mechanism for recruiting at later stages the cortical areas involved in a cognitive role during MI.
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Affiliation(s)
- Ana-Maria Cebolla
- Laboratory of Neurophysiology and Movement Biomechanics, Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium
| | - Ernesto Palmero-Soler
- Laboratory of Neurophysiology and Movement Biomechanics, Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium
| | - Axelle Leroy
- Laboratory of Neurophysiology and Movement Biomechanics, Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium
| | - Guy Cheron
- Laboratory of Neurophysiology and Movement Biomechanics, Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium.,Laboratory of Electrophysiology, Université de Mons, Mons, Belgium
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36
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Krishnaswamy P, Obregon-Henao G, Ahveninen J, Khan S, Babadi B, Iglesias JE, Hämäläinen MS, Purdon PL. Sparsity enables estimation of both subcortical and cortical activity from MEG and EEG. Proc Natl Acad Sci U S A 2017; 114:E10465-E10474. [PMID: 29138310 PMCID: PMC5715738 DOI: 10.1073/pnas.1705414114] [Citation(s) in RCA: 70] [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] [Indexed: 11/18/2022] Open
Abstract
Subcortical structures play a critical role in brain function. However, options for assessing electrophysiological activity in these structures are limited. Electromagnetic fields generated by neuronal activity in subcortical structures can be recorded noninvasively, using magnetoencephalography (MEG) and electroencephalography (EEG). However, these subcortical signals are much weaker than those generated by cortical activity. In addition, we show here that it is difficult to resolve subcortical sources because distributed cortical activity can explain the MEG and EEG patterns generated by deep sources. We then demonstrate that if the cortical activity is spatially sparse, both cortical and subcortical sources can be resolved with M/EEG. Building on this insight, we develop a hierarchical sparse inverse solution for M/EEG. We assess the performance of this algorithm on realistic simulations and auditory evoked response data, and show that thalamic and brainstem sources can be correctly estimated in the presence of cortical activity. Our work provides alternative perspectives and tools for characterizing electrophysiological activity in subcortical structures in the human brain.
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Affiliation(s)
- Pavitra Krishnaswamy
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129
- Harvard-Massachusetts Institute of Technology Division of Health Sciences and Technology, Cambridge, MA 02139
- Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), Singapore 138632, Singapore
| | - Gabriel Obregon-Henao
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114
| | - Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129
- Harvard Medical School, Boston, MA 02115
| | - Sheraz Khan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129
- Harvard Medical School, Boston, MA 02115
- Department of Neurology, Massachusetts General Hospital, Charlestown, MA 02129
| | - Behtash Babadi
- Department of Electrical & Computer Engineering, University of Maryland, College Park, MD 20742
| | - Juan Eugenio Iglesias
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129
| | - Matti S Hämäläinen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129;
- Harvard Medical School, Boston, MA 02115
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo 02150, Finland
- The Swedish National Facility for Magnetoencephalography (NatMEG), Department of Clinical Neuroscience, Karolinska Institute, Stockholm 17177, Sweden
| | - Patrick L Purdon
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, MA 02114;
- Harvard Medical School, Boston, MA 02115
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37
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Paz-Linares D, Vega-Hernández M, Rojas-López PA, Valdés-Hernández PA, Martínez-Montes E, Valdés-Sosa PA. Spatio Temporal EEG Source Imaging with the Hierarchical Bayesian Elastic Net and Elitist Lasso Models. Front Neurosci 2017; 11:635. [PMID: 29200994 PMCID: PMC5696363 DOI: 10.3389/fnins.2017.00635] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2017] [Accepted: 11/01/2017] [Indexed: 11/13/2022] Open
Abstract
The estimation of EEG generating sources constitutes an Inverse Problem (IP) in Neuroscience. This is an ill-posed problem due to the non-uniqueness of the solution and regularization or prior information is needed to undertake Electrophysiology Source Imaging. Structured Sparsity priors can be attained through combinations of (L1 norm-based) and (L2 norm-based) constraints such as the Elastic Net (ENET) and Elitist Lasso (ELASSO) models. The former model is used to find solutions with a small number of smooth nonzero patches, while the latter imposes different degrees of sparsity simultaneously along different dimensions of the spatio-temporal matrix solutions. Both models have been addressed within the penalized regression approach, where the regularization parameters are selected heuristically, leading usually to non-optimal and computationally expensive solutions. The existing Bayesian formulation of ENET allows hyperparameter learning, but using the computationally intensive Monte Carlo/Expectation Maximization methods, which makes impractical its application to the EEG IP. While the ELASSO have not been considered before into the Bayesian context. In this work, we attempt to solve the EEG IP using a Bayesian framework for ENET and ELASSO models. We propose a Structured Sparse Bayesian Learning algorithm based on combining the Empirical Bayes and the iterative coordinate descent procedures to estimate both the parameters and hyperparameters. Using realistic simulations and avoiding the inverse crime we illustrate that our methods are able to recover complicated source setups more accurately and with a more robust estimation of the hyperparameters and behavior under different sparsity scenarios than classical LORETA, ENET and LASSO Fusion solutions. We also solve the EEG IP using data from a visual attention experiment, finding more interpretable neurophysiological patterns with our methods. The Matlab codes used in this work, including Simulations, Methods, Quality Measures and Visualization Routines are freely available in a public website.
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Affiliation(s)
- Deirel Paz-Linares
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba
| | | | - Pedro A Rojas-López
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba
| | - Pedro A Valdés-Hernández
- Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba.,Department of Biomedical Engineering, Florida International University, Miami, FL, United States
| | - Eduardo Martínez-Montes
- Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba.,Politecnico di Torino, Turin, Italy
| | - Pedro A Valdés-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,Neuroinformatics Department, Cuban Neuroscience Center, Havana, Cuba
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38
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Barzegaran E, Knyazeva MG. Functional connectivity analysis in EEG source space: The choice of method. PLoS One 2017; 12:e0181105. [PMID: 28727750 PMCID: PMC5519059 DOI: 10.1371/journal.pone.0181105] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2017] [Accepted: 06/25/2017] [Indexed: 11/18/2022] Open
Abstract
Functional connectivity (FC) is among the most informative features derived from EEG. However, the most straightforward sensor-space analysis of FC is unreliable owing to volume conductance effects. An alternative-source-space analysis of FC-is optimal for high- and mid-density EEG (hdEEG, mdEEG); however, it is questionable for widely used low-density EEG (ldEEG) because of inadequate surface sampling. Here, using simulations, we investigate the performance of the two source FC methods, the inverse-based source FC (ISFC) and the cortical partial coherence (CPC). To examine the effects of localization errors of the inverse method on the FC estimation, we simulated an oscillatory source with varying locations and SNRs. To compare the FC estimations by the two methods, we simulated two synchronized sources with varying between-source distance and SNR. The simulations were implemented for hdEEG, mdEEG, and ldEEG. We showed that the performance of both methods deteriorates for deep sources owing to their inaccurate localization and smoothing. The accuracy of both methods improves with the increasing between-source distance. The best ISFC performance was achieved using hd/mdEEG, while the best CPC performance was observed with ldEEG. In conclusion, with hdEEG, ISFC outperforms CPC and therefore should be the preferred method. In the studies based on ldEEG, the CPC is a method of choice.
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Affiliation(s)
- Elham Barzegaran
- Laboratoire de recherche en neuroimagerie (LREN), Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
- * E-mail:
| | - Maria G. Knyazeva
- Laboratoire de recherche en neuroimagerie (LREN), Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
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39
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Courellis H, Mullen T, Poizner H, Cauwenberghs G, Iversen JR. EEG-Based Quantification of Cortical Current Density and Dynamic Causal Connectivity Generalized across Subjects Performing BCI-Monitored Cognitive Tasks. Front Neurosci 2017; 11:180. [PMID: 28566997 PMCID: PMC5434743 DOI: 10.3389/fnins.2017.00180] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2017] [Accepted: 03/20/2017] [Indexed: 11/13/2022] Open
Abstract
Quantification of dynamic causal interactions among brain regions constitutes an important component of conducting research and developing applications in experimental and translational neuroscience. Furthermore, cortical networks with dynamic causal connectivity in brain-computer interface (BCI) applications offer a more comprehensive view of brain states implicated in behavior than do individual brain regions. However, models of cortical network dynamics are difficult to generalize across subjects because current electroencephalography (EEG) signal analysis techniques are limited in their ability to reliably localize sources across subjects. We propose an algorithmic and computational framework for identifying cortical networks across subjects in which dynamic causal connectivity is modeled among user-selected cortical regions of interest (ROIs). We demonstrate the strength of the proposed framework using a "reach/saccade to spatial target" cognitive task performed by 10 right-handed individuals. Modeling of causal cortical interactions was accomplished through measurement of cortical activity using (EEG), application of independent component clustering to identify cortical ROIs as network nodes, estimation of cortical current density using cortically constrained low resolution electromagnetic brain tomography (cLORETA), multivariate autoregressive (MVAR) modeling of representative cortical activity signals from each ROI, and quantification of the dynamic causal interaction among the identified ROIs using the Short-time direct Directed Transfer function (SdDTF). The resulting cortical network and the computed causal dynamics among its nodes exhibited physiologically plausible behavior, consistent with past results reported in the literature. This physiological plausibility of the results strengthens the framework's applicability in reliably capturing complex brain functionality, which is required by applications, such as diagnostics and BCI.
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Affiliation(s)
- Hristos Courellis
- Swartz Center for Computational Neuroscience, University of California, San DiegoSan Diego, CA, United States.,Department of Bioengineering, University of California, San DiegoSan Diego, CA, United States
| | - Tim Mullen
- Swartz Center for Computational Neuroscience, University of California, San DiegoSan Diego, CA, United States
| | - Howard Poizner
- Institute for Neural Computation, University of California, San DiegoSan Diego, CA, United States
| | - Gert Cauwenberghs
- Department of Bioengineering, University of California, San DiegoSan Diego, CA, United States.,Institute for Neural Computation, University of California, San DiegoSan Diego, CA, United States
| | - John R Iversen
- Swartz Center for Computational Neuroscience, University of California, San DiegoSan Diego, CA, United States
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40
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Hincapié AS, Kujala J, Mattout J, Pascarella A, Daligault S, Delpuech C, Mery D, Cosmelli D, Jerbi K. The impact of MEG source reconstruction method on source-space connectivity estimation: A comparison between minimum-norm solution and beamforming. Neuroimage 2017; 156:29-42. [PMID: 28479475 DOI: 10.1016/j.neuroimage.2017.04.038] [Citation(s) in RCA: 45] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Revised: 04/01/2017] [Accepted: 04/15/2017] [Indexed: 01/11/2023] Open
Abstract
Despite numerous important contributions, the investigation of brain connectivity with magnetoencephalography (MEG) still faces multiple challenges. One critical aspect of source-level connectivity, largely overlooked in the literature, is the putative effect of the choice of the inverse method on the subsequent cortico-cortical coupling analysis. We set out to investigate the impact of three inverse methods on source coherence detection using simulated MEG data. To this end, thousands of randomly located pairs of sources were created. Several parameters were manipulated, including inter- and intra-source correlation strength, source size and spatial configuration. The simulated pairs of sources were then used to generate sensor-level MEG measurements at varying signal-to-noise ratios (SNR). Next, the source level power and coherence maps were calculated using three methods (a) L2-Minimum-Norm Estimate (MNE), (b) Linearly Constrained Minimum Variance (LCMV) beamforming, and (c) Dynamic Imaging of Coherent Sources (DICS) beamforming. The performances of the methods were evaluated using Receiver Operating Characteristic (ROC) curves. The results indicate that beamformers perform better than MNE for coherence reconstructions if the interacting cortical sources consist of point-like sources. On the other hand, MNE provides better connectivity estimation than beamformers, if the interacting sources are simulated as extended cortical patches, where each patch consists of dipoles with identical time series (high intra-patch coherence). However, the performance of the beamformers for interacting patches improves substantially if each patch of active cortex is simulated with only partly coherent time series (partial intra-patch coherence). These results demonstrate that the choice of the inverse method impacts the results of MEG source-space coherence analysis, and that the optimal choice of the inverse solution depends on the spatial and synchronization profile of the interacting cortical sources. The insights revealed here can guide method selection and help improve data interpretation regarding MEG connectivity estimation.
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Affiliation(s)
- Ana-Sofía Hincapié
- Psychology Department, University of Montreal, Quebec, Canada; Lyon Neuroscience Research Center, CRNL, INSERM, U1028 - CNRS - UMR5292, University Lyon 1, Brain Dynamics and Cognition Team, Lyon, France; Department of Computer Science, Pontificia Universidad Católica de Chile, Santiago de Chile, Chile; Escuela de Psicología, Pontificia Universidad Católica de Chile and Interdisciplinary Center for Neurosciences, Pontificia Universidad Católica de Chile, Santiago de Chile, Chile.
| | - Jan Kujala
- Lyon Neuroscience Research Center, CRNL, INSERM, U1028 - CNRS - UMR5292, University Lyon 1, Brain Dynamics and Cognition Team, Lyon, France; Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland.
| | - Jérémie Mattout
- Lyon Neuroscience Research Center, CRNL, INSERM, U1028 - CNRS - UMR5292, University Lyon 1, Brain Dynamics and Cognition Team, Lyon, France.
| | - Annalisa Pascarella
- Consiglio Nazionale delle Ricerche (CNR - National Research Council), Rome, Italy.
| | | | - Claude Delpuech
- Lyon Neuroscience Research Center, CRNL, INSERM, U1028 - CNRS - UMR5292, University Lyon 1, Brain Dynamics and Cognition Team, Lyon, France; MEG Center, CERMEP, Lyon, France.
| | - Domingo Mery
- Department of Computer Science, Pontificia Universidad Católica de Chile, Santiago de Chile, Chile.
| | - Diego Cosmelli
- Escuela de Psicología, Pontificia Universidad Católica de Chile and Interdisciplinary Center for Neurosciences, Pontificia Universidad Católica de Chile, Santiago de Chile, Chile.
| | - Karim Jerbi
- Psychology Department, University of Montreal, Quebec, Canada; Lyon Neuroscience Research Center, CRNL, INSERM, U1028 - CNRS - UMR5292, University Lyon 1, Brain Dynamics and Cognition Team, Lyon, France.
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41
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Bonhage CE, Meyer L, Gruber T, Friederici AD, Mueller JL. Oscillatory EEG dynamics underlying automatic chunking during sentence processing. Neuroimage 2017; 152:647-657. [PMID: 28288909 DOI: 10.1016/j.neuroimage.2017.03.018] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2016] [Revised: 03/02/2017] [Accepted: 03/09/2017] [Indexed: 10/20/2022] Open
Abstract
Sentences are easier to remember than random word sequences, likely because linguistic regularities facilitate chunking of words into meaningful groups. The present electroencephalography study investigated the neural oscillations modulated by this so-called sentence superiority effect during the encoding and maintenance of sentence fragments versus word lists. We hypothesized a chunking-related modulation of neural processing during the encoding and retention of sentences (i.e., sentence fragments) as compared to word lists. Time-frequency analysis revealed a two-fold oscillatory pattern for the memorization of sentences: Sentence encoding was accompanied by higher delta amplitude (4Hz), originating both from regions processing syntax as well as semantics (bilateral superior/middle temporal regions and fusiform gyrus). Subsequent sentence retention was reflected in decreased theta (6Hz) and beta/gamma (27-32Hz) amplitude instead. Notably, whether participants simply read or properly memorized the sentences did not impact chunking-related activity during encoding. Therefore, we argue that the sentence superiority effect is grounded in highly automatized language processing mechanisms, which generate meaningful memory chunks irrespective of task demands.
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Affiliation(s)
- Corinna E Bonhage
- Max Planck Institute for Human Cognitive and Brain Sciences, Neuropsychology Department, Leipzig, Germany; Max Planck Institute for Empirical Aesthetics, Neuroscience Department, Frankfurt a. M., Germany.
| | - Lars Meyer
- Max Planck Institute for Human Cognitive and Brain Sciences, Neuropsychology Department, Leipzig, Germany
| | - Thomas Gruber
- Institute of Psychology, Osnabrueck University, Osnabrueck, Germany
| | - Angela D Friederici
- Max Planck Institute for Human Cognitive and Brain Sciences, Neuropsychology Department, Leipzig, Germany
| | - Jutta L Mueller
- Max Planck Institute for Human Cognitive and Brain Sciences, Neuropsychology Department, Leipzig, Germany; Institute of Cognitive Science, Osnabrueck University, Osnabrueck, Germany
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42
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The Role of the Auditory Brainstem in Regularity Encoding and Deviance Detection. THE FREQUENCY-FOLLOWING RESPONSE 2017. [DOI: 10.1007/978-3-319-47944-6_5] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2022]
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43
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Complex patterns of spatially extended generators of epileptic activity: Comparison of source localization methods cMEM and 4-ExSo-MUSIC on high resolution EEG and MEG data. Neuroimage 2016; 143:175-195. [DOI: 10.1016/j.neuroimage.2016.08.044] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2016] [Revised: 08/18/2016] [Accepted: 08/20/2016] [Indexed: 11/23/2022] Open
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Pion-Tonachini L, Hsu SH, Makeig S, Jung TP, Cauwenberghs G. Real-time EEG Source-mapping Toolbox (REST): Online ICA and source localization. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2015:4114-7. [PMID: 26737199 DOI: 10.1109/embc.2015.7319299] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The Electroencephalogram (EEG) is a noninvasive functional brain activity recording method that shows promise for becoming a 3-D cortical imaging modality with high temporal resolution. Currently, most of the tools developed for EEG analysis focus mainly on offline processing. This study introduces and demonstrates the Real-time EEG Source-mapping Toolbox (REST), an extension to the widely distributed EEGLAB environment. REST allows blind source separation of EEG data in real-time using Online Recursive Independent Component Analysis (ORICA), plus near real-time localization of separated sources. Two source localization methods are available to fit equivalent current dipoles or estimate spatial source distributions of selected sources. Selected measures of raw EEG data or component activations (e.g. time series of the data, spectral changes over time, equivalent current dipoles, etc.) can be visualized in near real-time. Finally, this study demonstrates the accuracy and functionality of REST with data from two experiments and discusses some relevant applications.
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Attentional Bias Predicts Increased Reward Salience and Risk Taking in Bipolar Disorder. Biol Psychiatry 2016; 79:311-9. [PMID: 25863360 DOI: 10.1016/j.biopsych.2015.03.014] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2014] [Revised: 03/07/2015] [Accepted: 03/11/2015] [Indexed: 01/20/2023]
Abstract
BACKGROUND There is amassing evidence that risky decision-making in bipolar disorder is related to reward-based differences in frontostriatal regions. However, the roles of early attentional and later cognitive processes remain unclear, limiting theoretical understanding and development of targeted interventions. METHODS Twenty euthymic bipolar disorder and 19 matched control participants played a Roulette task in which they won and lost money. Event-related potentials and source analysis were used to quantify predominantly sensory-attentional (N1), motivational salience (feedback-related negativities [FRN]), and cognitive appraisal (P300) stages of processing. We predicted that the bipolar disorder group would show increased N1, consistent with increased attentional orienting, and reduced FRN, consistent with a bias to perceive outcomes more favorably. RESULTS As predicted, the bipolar disorder group showed increased N1 and reduced FRN but no differences in P300. N1 amplitude was additionally associated with real-life risk taking, and N1 source activity was reduced in visual cortex but increased activity in precuneus, frontopolar, and premotor cortex, compared to those of controls. CONCLUSIONS These findings demonstrate an early attentional bias to reward that potentially drives risk taking by priming approach behavior and elevating reward salience in the frontostriatal pathway. Although later cognitive appraisals of these inputs may be relatively intact in remission, interventions targeting attention orienting may also be effective in long-term reduction of relapse.
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Strobbe G, Carrette E, López JD, Montes Restrepo V, Van Roost D, Meurs A, Vonck K, Boon P, Vandenberghe S, van Mierlo P. Electrical source imaging of interictal spikes using multiple sparse volumetric priors for presurgical epileptogenic focus localization. NEUROIMAGE-CLINICAL 2016; 11:252-263. [PMID: 26958464 PMCID: PMC4773507 DOI: 10.1016/j.nicl.2016.01.017] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2015] [Revised: 10/09/2015] [Accepted: 01/17/2016] [Indexed: 11/07/2022]
Abstract
Electrical source imaging of interictal spikes observed in EEG recordings of patients with refractory epilepsy provides useful information to localize the epileptogenic focus during the presurgical evaluation. However, the selection of the time points or time epochs of the spikes in order to estimate the origin of the activity remains a challenge. In this study, we consider a Bayesian EEG source imaging technique for distributed sources, i.e. the multiple volumetric sparse priors (MSVP) approach. The approach allows to estimate the time courses of the intensity of the sources corresponding with a specific time epoch of the spike. Based on presurgical averaged interictal spikes in six patients who were successfully treated with surgery, we estimated the time courses of the source intensities for three different time epochs: (i) an epoch starting 50 ms before the spike peak and ending at 50% of the spike peak during the rising phase of the spike, (ii) an epoch starting 50 ms before the spike peak and ending at the spike peak and (iii) an epoch containing the full spike time period starting 50 ms before the spike peak and ending 230 ms after the spike peak. To identify the primary source of the spike activity, the source with the maximum energy from 50 ms before the spike peak till 50% of the spike peak was subsequently selected for each of the time windows. For comparison, the activity at the spike peaks and at 50% of the peaks was localized using the LORETA inversion technique and an ECD approach. Both patient-specific spherical forward models and patient-specific 5-layered finite difference models were considered to evaluate the influence of the forward model. Based on the resected zones in each of the patients, extracted from post-operative MR images, we compared the distances to the resection border of the estimated activity. Using the spherical models, the distances to the resection border for the MSVP approach and each of the different time epochs were in the same range as the LORETA and ECD techniques. We found distances smaller than 23 mm, with robust results for all the patients. For the finite difference models, we found that the distances to the resection border for the MSVP inversions of the full spike time epochs were generally smaller compared to the MSVP inversions of the time epochs before the spike peak. The results also suggest that the inversions using the finite difference models resulted in slightly smaller distances to the resection border compared to the spherical models. The results we obtained are promising because the MSVP approach allows to study the network of the estimated source-intensities and allows to characterize the spatial extent of the underlying sources. A Bayesian ESI technique is evaluated to localize interictal spike activity. Averaged spikes in six patients were used that were seizure free after surgery. We compared the technique with the LORETA an ECD technique. We evaluated both spherical and 5-layered finite difference forward models. Our approach is potentially useful to delineate the irritative zone.
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Affiliation(s)
- Gregor Strobbe
- Ghent University, Department of Electronics and Information Systems, MEDISIP, De Pintelaan 185, Building BB Floor 5, 9000 Ghent, Belgium; iMinds Medical IT Department, Belgium.
| | - Evelien Carrette
- Laboratory for Clinical and Experimental Neurophysiology, Ghent University Hospital, Ghent, Belgium.
| | - José David López
- SISTEMIC, Department of Electronic Engineering, Universidad de Antioquia UDEA, Calle 70 No. 52-21,Medellín, Colombia.
| | - Victoria Montes Restrepo
- Ghent University, Department of Electronics and Information Systems, MEDISIP, De Pintelaan 185, Building BB Floor 5, 9000 Ghent, Belgium; iMinds Medical IT Department, Belgium
| | - Dirk Van Roost
- Department of Neurosurgery, Ghent University Hospital, Ghent, Belgium.
| | - Alfred Meurs
- Laboratory for Clinical and Experimental Neurophysiology, Ghent University Hospital, Ghent, Belgium.
| | - Kristl Vonck
- Laboratory for Clinical and Experimental Neurophysiology, Ghent University Hospital, Ghent, Belgium.
| | - Paul Boon
- Laboratory for Clinical and Experimental Neurophysiology, Ghent University Hospital, Ghent, Belgium.
| | - Stefaan Vandenberghe
- Ghent University, Department of Electronics and Information Systems, MEDISIP, De Pintelaan 185, Building BB Floor 5, 9000 Ghent, Belgium; iMinds Medical IT Department, Belgium.
| | - Pieter van Mierlo
- Ghent University, Department of Electronics and Information Systems, MEDISIP, De Pintelaan 185, Building BB Floor 5, 9000 Ghent, Belgium; iMinds Medical IT Department, Belgium.
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Akdeniz G. Electrical source localization by LORETA in patients with epilepsy: Confirmation by postoperative MRI. Ann Indian Acad Neurol 2016; 19:37-43. [PMID: 27011626 PMCID: PMC4782550 DOI: 10.4103/0972-2327.168632] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2015] [Revised: 05/01/2015] [Accepted: 06/05/2015] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Few studies have been conducted that have compared electrical source localization (ESL) results obtained by analyzing ictal patterns in scalp electroencephalogram (EEG) with the brain areas that are found to be responsible for seizures using other brain imaging techniques. Additionally, adequate studies have not been performed to confirm the accuracy of ESL methods. MATERIALS AND METHODS In this study, ESL was conducted using LORETA (Low Resolution Brain Electromagnetic Tomography) in 9 patients with lesions apparent on magnetic resonance imaging (MRI) and in 6 patients who did not exhibit lesions on their MRIs. EEGs of patients who underwent surgery for epilepsy and had follow-ups for at least 1 year after operations were analyzed for ictal spike, rhythmic, paroxysmal fast, and obscured EEG activities. Epileptogenic zones identified in postoperative MRIs were then compared with localizations obtained by LORETA model we employed. RESULTS We found that brain areas determined via ESL were in concordance with resected brain areas for 13 of the 15 patients evaluated, and those 13 patients were post-operatively determined as being seizure-free. CONCLUSION ESL, which is a noninvasive technique, may contribute to the correct delineation of epileptogenic zones in patients who will eventually undergo surgery to treat epilepsy, (regardless of neuroimaging status). Moreover, ESL may aid in deciding on the number and localization of intracranial electrodes to be used in patients who are candidates for invasive recording.
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Affiliation(s)
- Gülsüm Akdeniz
- Department of Biophysics, Ankara Atatürk Training and Research Hospital, Yıldırım Beyazıt University, Faculty of Medicine, Ankara, Turkey
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Hsu SH, Mullen TR, Jung TP, Cauwenberghs G. Real-Time Adaptive EEG Source Separation Using Online Recursive Independent Component Analysis. IEEE Trans Neural Syst Rehabil Eng 2015; 24:309-19. [PMID: 26685257 DOI: 10.1109/tnsre.2015.2508759] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Independent component analysis (ICA) has been widely applied to electroencephalographic (EEG) biosignal processing and brain-computer interfaces. The practical use of ICA, however, is limited by its computational complexity, data requirements for convergence, and assumption of data stationarity, especially for high-density data. Here we study and validate an optimized online recursive ICA algorithm (ORICA) with online recursive least squares (RLS) whitening for blind source separation of high-density EEG data, which offers instantaneous incremental convergence upon presentation of new data. Empirical results of this study demonstrate the algorithm's: 1) suitability for accurate and efficient source identification in high-density (64-channel) realistically-simulated EEG data; 2) capability to detect and adapt to nonstationarity in 64-ch simulated EEG data; and 3) utility for rapidly extracting principal brain and artifact sources in real 61-channel EEG data recorded by a dry and wearable EEG system in a cognitive experiment. ORICA was implemented as functions in BCILAB and EEGLAB and was integrated in an open-source Real-time EEG Source-mapping Toolbox (REST), supporting applications in ICA-based online artifact rejection, feature extraction for real-time biosignal monitoring in clinical environments, and adaptable classifications in brain-computer interfaces.
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Friston KJ, Litvak V, Oswal A, Razi A, Stephan KE, van Wijk BCM, Ziegler G, Zeidman P. Bayesian model reduction and empirical Bayes for group (DCM) studies. Neuroimage 2015; 128:413-431. [PMID: 26569570 PMCID: PMC4767224 DOI: 10.1016/j.neuroimage.2015.11.015] [Citation(s) in RCA: 359] [Impact Index Per Article: 39.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2015] [Revised: 11/05/2015] [Accepted: 11/06/2015] [Indexed: 11/16/2022] Open
Abstract
This technical note describes some Bayesian procedures for the analysis of group studies that use nonlinear models at the first (within-subject) level - e.g., dynamic causal models - and linear models at subsequent (between-subject) levels. Its focus is on using Bayesian model reduction to finesse the inversion of multiple models of a single dataset or a single (hierarchical or empirical Bayes) model of multiple datasets. These applications of Bayesian model reduction allow one to consider parametric random effects and make inferences about group effects very efficiently (in a few seconds). We provide the relatively straightforward theoretical background to these procedures and illustrate their application using a worked example. This example uses a simulated mismatch negativity study of schizophrenia. We illustrate the robustness of Bayesian model reduction to violations of the (commonly used) Laplace assumption in dynamic causal modelling and show how its recursive application can facilitate both classical and Bayesian inference about group differences. Finally, we consider the application of these empirical Bayesian procedures to classification and prediction.
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Affiliation(s)
- Karl J Friston
- The Wellcome Trust Centre for Neuroimaging, UCL, 12 Queen Square, London, UK
| | - Vladimir Litvak
- The Wellcome Trust Centre for Neuroimaging, UCL, 12 Queen Square, London, UK
| | - Ashwini Oswal
- The Wellcome Trust Centre for Neuroimaging, UCL, 12 Queen Square, London, UK
| | - Adeel Razi
- The Wellcome Trust Centre for Neuroimaging, UCL, 12 Queen Square, London, UK; Department of Electronic Engineering, NED University of Engineering & Technology, Karachi, Pakistan
| | - Klaas E Stephan
- The Wellcome Trust Centre for Neuroimaging, UCL, 12 Queen Square, London, UK; Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Switzerland
| | | | - Gabriel Ziegler
- The Wellcome Trust Centre for Neuroimaging, UCL, 12 Queen Square, London, UK
| | - Peter Zeidman
- The Wellcome Trust Centre for Neuroimaging, UCL, 12 Queen Square, London, UK.
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Bendixen A, Schwartze M, Kotz SA. Temporal dynamics of contingency extraction from tonal and verbal auditory sequences. BRAIN AND LANGUAGE 2015; 148:64-73. [PMID: 25512177 DOI: 10.1016/j.bandl.2014.11.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2014] [Revised: 11/12/2014] [Accepted: 11/15/2014] [Indexed: 06/04/2023]
Abstract
Consecutive sound events are often to some degree predictive of each other. Here we investigated the brain's capacity to detect contingencies between consecutive sounds by means of electroencephalography (EEG) during passive listening. Contingencies were embedded either within tonal or verbal stimuli. Contingency extraction was measured indirectly via the elicitation of the mismatch negativity (MMN) component of the event-related potential (ERP) by contingency violations. MMN results indicate that structurally identical forms of predictability can be extracted from both tonal and verbal stimuli. We also found similar generators to underlie the processing of contingency violations across stimulus types, as well as similar performance in an active-listening follow-up test. However, the process of passive contingency extraction was considerably slower (twice as many rule exemplars were needed) for verbal than for tonal stimuli These results suggest caution in transferring findings on complex predictive regularity processing obtained with tonal stimuli directly to the speech domain.
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Affiliation(s)
- Alexandra Bendixen
- Auditory Psychophysiology Lab, Department of Psychology, Cluster of Excellence "Hearing4all", European Medical School, Carl von Ossietzky University of Oldenburg, D-26111 Oldenburg, Germany; Institute of Psychology, University of Leipzig, D-04103 Leipzig, Germany.
| | - Michael Schwartze
- School of Psychological Sciences, University of Manchester, M13 9PL Manchester, UK; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, D-04103 Leipzig, Germany.
| | - Sonja A Kotz
- School of Psychological Sciences, University of Manchester, M13 9PL Manchester, UK; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, D-04103 Leipzig, Germany.
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