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Cited by 297EEG and Brain-Computer InterfacesNeural dynamics and brain functionOptical Imaging and Spectroscopy Techniques

Electrophysiological source imaging: A noninvasive window to brain dynamics

Open access
He, Bin, Sohrabpour, Abbas, Brown, Emery, Liu, Zhongming·2018·Annual Review of Biomedical Engineering
DOI 10.1146/annurev-bioeng-062117-120853PMC PMC7941524
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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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Crossref · 10.1146/annurev-bioeng-062117-120853

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Claims the paper makes

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  • Premisecitation

    Understanding human brain dynamics at macroscopic scales depends on noninvasive measurements such as EEG and MEG.

    “Understanding of human brain dynamics at macroscopic scales, however, relies on noninvasive measurements such as EEG and MEG (3, 4).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [3] Nunez PL, Srinivasan R. 2006. Electric fields of the brain: the neurophysics of EEG. Oxford University Press, USA source

      Not checked in the free run.

    • cites [4] He B, Ding L. 2013. Electrophysiological mapping and neuroimaging. In Neural Engineering, pp. 499– 543. Springer source

      Not checked in the free run.

  • Premisereasoning

    The number of current sources is significantly greater than the number of measurements, despite high-density EEG/MEG.

    “The number of current sources is significantly greater than the number of measurements, despite high-density EEG/MEG.”
    Quote in the paperSubstantial claim
  • Interpretationreasoning

    Among existing neuroimaging modalities, EEG/MEG offers uniquely high temporal resolution.

    “Among existing neuroimaging modalities, EEG/MEG offers uniquely high temporal resolution.”
    Quote in the paperSubstantial claim
  • Premisecitation

    Higher spatial sampling helps improve the precision of electrophysiological source imaging.

    “Studies indicate that higher spatial sampling helps improve the precision of ESI (18, 19).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [18] Lantz G, de Peralta RG, Spinelli L, Seeck M, Michel CM. 2003. Epileptic source localization with high density EEG: how many electrodes are needed? Clin. Neurophysiol 114(1): 63– 69 12495765 source

      Not checked in the free run.

    • cites [19] Sohrabpour A, Lu Y, Kankirawatana P, Blount J, Kim H, He B. 2015. Effect of EEG electrode number on epileptic source localization in pediatric patients. Clin. Neurophysiol 126(3): 472– 480 25088733 PMC4289666 source

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  • Premisecitation

    sLORETA has shown improved spatial accuracy.

    “Using this strategy, standardized LORETA (sLORETA) (29) has shown improved spatial accuracy.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [29] Pascual-Marqui RD. 2002. Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find Exp Clin Pharmacol. 24(Suppl D): 5– 12 12575463 source

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  • May combine two claims. 44
  • Classified as probably not a substantial claim. 27

  • May combine two claims.

    Interpretationreasoning

    Electrophysiological source imaging provides improved spatial resolution and intrinsically high temporal resolution for imaging large-scale brain activity and connectivity across a wide range of timescales.

    “It offers increasingly improved spatial resolution and intrinsically high temporal resolution for imaging large-scale brain activity and connectivity in a wide range of timescales.”
    • Also stated in: § INTRODUCTION · plain
      “resulting in high temporal and good spatial resolution that places EEG/MEG among the most widely used tools in neuroscience.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    Integrating electrophysiological source imaging with functional magnetic resonance imaging has the potential to further enhance spatiotemporal resolution and specificity beyond what either technique can achieve alone.

    “Integration of electrophysiological source imaging and functional magnetic resonance imaging holds the potential to further enhance the spatiotemporal resolution and specificity that are unattainable with either technique alone.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    EEG/MEG signals primarily arise from post-synaptic currents, while currents that drive action potentials are generally negligible in the scale of EEG/MEG.

    “EEG/MEG signals primarily arise from post-synaptic currents. Currents that drive action potentials are generally negligible in the scale of EEG/MEG (9).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [9] Lopes da Silva FH, Van Rotterdam AB, others. 2011. Biophysical aspects of EEG and magnetoencephalogram generation. In Niedermyer’s Electroencephalography: Basic Principles, Clinical Applications, and Related Fields (6th Edition), pp. 91– 110. Lippincott Williams & Wilkins

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    Interpretationnone given

    FEM allows for more accurate head modeling than BEM in principle, but accurate tissue conductivity information is not readily available.

    “In principle, FEM allows for more accurate head modeling than BEM; however, accurate information about tissue conductivity is not readily available.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The minimum norm approach was the earliest solution to the bio-electromagnetic inverse problem with distributed source models.

    “The minimum norm approach was the earliest solution to the bio-electromagnetic inverse problem with distributed source models (23).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [23] Hämäläinen MS, Ilmoniemi RJ. 1994. Interpreting magnetic fields of the brain: minimum norm estimates. Med. Biol. Eng. Comput 32(1): 35– 42 8182960 source

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    Premisecitation

    For realistic-geometry head models, numeric solutions are attainable with the boundary element method assuming isotropic conductivity or the finite element method assuming anisotropic conductivity.

    “For realistic-geometry head models, numeric solutions are attainable with either the boundary element method (BEM) by assuming isotropic conductivity (13, 14) or the finite element method (FEM) models by assuming anisotropic conductivity (15).”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [13] He B, Musha T, Okamoto Y, Homma S, Nakajima Y, Sato T. 1987. Electric dipole tracing in the brain by means of the boundary element method and its accuracy. IEEE Trans. Biomed. Eng, pp. 406– 414 3610187 source

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    • cites [14] Hamalainen MS, Sarvas J. 1989. Realistic conductivity geometry model of the human head for interpretation of neuromagnetic data. Biomed. Eng. IEEE Trans. On 36(2): 165– 171 2917762 source

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    • cites [15] Yan Y, Nunez PL, Hart RT. 1991. Finite-element model of the human head: scalp potentials due to dipole sources. Med. Biol. Eng. Comput 29(5): 475– 481 1817208 source

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  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    A recent guideline of the International Federation of Clinical Neurophysiology suggests at least 64 channels of EEG should be used for electrophysiological source imaging.

    “A recent guideline of the International Federation of Clinical Neurophysiology suggests at least 64 channels of EEG should be used for ESI (20).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [20] Seeck M, Koessler L, Bast T, Leijten F, Michel C, 2017. The standardized EEG electrode array of the IFCN. Clin. Neurophysiol 28778476 source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    Dipole source localization approaches are effective only if the number of unknown parameters is smaller than the number of sensors, source activity can be modeled as a single or few dipoles, and the number of dipoles is known a priori.

    “All these approaches are effective in different situations, if 1) the number of unknown parameters is smaller than the number of sensors, 2) source activity can be properly modeled as a single or few dipoles, and 3) the number of dipoles is known a priori.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    Estimating one or two equivalent current dipoles is an over-determined inverse problem with a unique solution because there are six parameters per dipole while clinical EEG normally uses at least 19 electrodes.

    “Estimating one or two ECDs is an over-determined inverse problem that has a unique solution, since there are six parameters for each dipole while clinical EEG recordings normally use at least 19 electrodes.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

    Interpretationnone given

    Significant efforts over the past three decades have greatly advanced electrophysiological source imaging based on EEG/MEG, resulting in high temporal and good spatial resolution that places EEG/MEG among the most widely used tools in neuroscience.

    “Significant efforts over the past three decades have greatly advanced electrophysiological source imaging based on EEG/MEG, resulting in high temporal and good spatial resolution that places EEG/MEG among the most widely used tools in neuroscience.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationnone given

    The assumption of focal sources is not always valid, and knowing the number of dipoles a priori is not feasible for many applications.

    “The assumption of focal sources is not always valid and knowing the number of dipoles a priori is also not feasible for many applications.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Interpretationnone given

    Solving the electrophysiological source imaging inverse problem encounters severe challenges if it is treated only mathematically.

    “Solving this ill-posed problem encounters severe challenges if it is only treated mathematically.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationnone given

    The minimum norm approach has established characteristics in statistics and mathematics, but its physiological plausibility is not fully understood despite wide use in EEG/MEG source imaging.

    “This approach has established characteristics in statistics and mathematics; however, its physiological plausibility is not fully understood despite its wide use in EEG/MEG source imaging.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    These limitations restrict the scope of equivalent current dipole localization, yet it is still used in clinical neuroscience, especially in MEG source localization.

    “These all limit the scope of application for ECD localization, yet it is still being used in clinical neuroscience applications especially in MEG source localization.”
    Quote in the paperNot a substantial claim

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    Premisecitation

    The cortical current density model is preferable because EEG/MEG sources are primarily currents through pyramidal cells aligned perpendicular to the cortical surface.

    “The latter is more preferable since the sources of EEG/MEG are primarily currents through pyramidal cells aligned perpendicular to the cortical surface, as proposed in the cortical current density (CCD) model (22).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [22] Dale AM, Sereno MI. 1993. Improved localizadon of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: a linear approach. J. Cogn. Neurosci 5(2): 162– 176 23972151 source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    In distributed source models, since dipole locations are fixed, the inverse problem of estimating dipole moments is linear but under-determined.

    “Since the dipole locations are fixed, the inverse problem of estimating dipole moments from EEG/MEG is linear but under-determined.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    L1-norm regularization favors sparse source distribution and assumes that sources follow a Laplace prior distribution in Bayesian inference.

    “L1-norm regularization favors sparse source distribution (57–60); it assumes that the sources follow a Laplace prior distribution in Bayesian inference.”
    Quote in the paperNot a substantial claim0 of 4 sources checked
    • cites [57] Matsuura K, Okabe Y. 1995. Selective minimum-norm solution of the biomagnetic inverse problem. IEEE Trans. Biomed. Eng 42(6): 608– 615 7790017 source

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    • cites [58] Uutela K, Hämäläinen M, Somersalo E. 1999. Visualization of magnetoencephalographic data using minimum current estimates. NeuroImage. 10(2): 173– 180 10417249 source

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    • cites [59] Wagner M, Wischmann H-A, Fuchs M, Köhler T, Drenckhahn R. 2000. Current density reconstructions using the L1 norm. In Biomag 96, pp. 393– 396. Springer source

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    • cites [60] Fuchs M, Wagner M, Köhler T, Wischmann H-A. 1999. Linear and nonlinear current density reconstructions. J. Clin. Neurophysiol 16(3): 267– 295 10426408 source

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    Premisecitation

    Beamforming is increasingly used for electrophysiological source imaging.

    “Alternatively, beamforming, originally established for radar and sonar applications (31), is increasingly used for ESI (32).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [31] Van Veen BD, Buckley KM. 1988. Beamforming: a versatile approach to spatial filtering. IEEE ASSP Mag. 5(2): 4– 24 source

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    • cites [32] Van Veen BD, Van Drongelen W, Yuchtman M, Suzuki A. 1997. Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. IEEE Trans. Biomed. Eng 44(9): 867– 880 9282479 source

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  • Classified as probably not a substantial claim.

    Interpretationreasoning

    The balanced temporal and spatial resolution of EEG/MEG makes them indispensable among existing tools for studying spatiotemporal dynamics of large brain networks in humans.

    “The balanced temporal and spatial resolution of EEG/MEG earns themselves indispensable positions among existing tools for studying the spatiotemporal dynamics of large brain networks in humans.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Electrophysiological source imaging has been used to map spatiotemporal responses underlying sensory processing, object recognition, disrupted visual processing in autistic patients, attention and consciousness, visual rivalry, visuomotor coordination, and speech recognition.

    “To name a few, ESI has been used to map the spatiotemporal responses underlying sensory processing, object recognition (64), disrupted visual processing in autistic patients (65), attention and consciousness (66), visual rivalry (67–69), visuomotor coordination (11), and speech recognition (70).”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [64] Bar M, Kassam KS, Ghuman AS, Boshyan J, Schmid AM, 2006. Top-down facilitation of visual recognition. Proc. Natl. Acad. Sci. U. S. A 103(2): 449– 454 16407167 PMC1326160 source

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    • cites [65] Khan S, Gramfort A, Shetty NR, Kitzbichler MG, Ganesan S, 2013. Local and long-range functional connectivity is reduced in concert in autism spectrum disorders. Proc. Natl. Acad. Sci 110(8): 3107– 12 23319621 PMC3581984 source

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    • cites [66] Sergent C, Baillet S, Dehaene S. 2005. Timing of the brain events underlying access to consciousness during the attentional blink. Nat. Neurosci 8(10): 1391– 1400 16158062 source

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  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationnone given

    FOCUSS solutions tend to be overly focused.

    “However, the resulting solutions tend to be overly focused.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Interpretationnone given

    Electrophysiological source imaging further enhances the spatial resolution and specificity towards comprehensive spatiotemporal imaging of the brain in action.

    “ESI further enhances the spatial resolution and specificity towards comprehensive spatiotemporal imaging of the brain in action.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationnone given

    Seizures are more difficult to localize due to patients' head movement and consequently low signal-to-noise ratio.

    “Seizures are more difficult to localize due to patients’ head movement and consequently low signal-to-noise ratio.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    Sparsity is often assumed to avoid over-fitting in inverse problems and has increasing applications in signal and image processing.

    “Sparsity is often assumed to avoid over-fitting in inverse problems, and has increasing applications in signal and image processing (43, 50–54).”
    Quote in the paperNot a substantial claim0 of 6 sources checked
    • cites [43] Friston K, Harrison L, Daunizeau J, Kiebel S, Phillips C, 2008. Multiple sparse priors for the M/EEG inverse problem. NeuroImage. 39(3): 1104– 1120 17997111 source

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    • cites [50] Gorodnitsky IF, George JS, Rao BD. 1995. Neuromagnetic source imaging with FOCUSS: a recursive weighted minimum norm algorithm. Electroencephalogr. Clin. Neurophysiol 95(4): 231– 51 8529554 source

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    • cites [51] Ding L, He B. 2008. Sparse source imaging in electroencephalography with accurate field modeling. Hum. Brain Mapp 29(9): 1053– 1067 17894400 PMC2612127 source

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    • cites [52] Haufe S, Nikulin VV, Ziehe A, Müller K-R, Nolte G. 2008. Combining sparsity and rotational invariance in EEG/MEG source reconstruction. NeuroImage. 42(2): 726– 738 18583157 source

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    • cites [53] Gramfort A, Strohmeier D, Haueisen J, Hämäläinen MS, Kowalski M. 2013. Time-frequency mixed-norm estimates: Sparse M/EEG imaging with non-stationary source activations. NeuroImage. 70: 410– 422 23291276 PMC3615257 source

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    • cites [54] Sohrabpour A, Lu Y, Worrell G, He B. 2016. Imaging brain source extent from EEG/MEG by means of an iteratively reweighted edge sparsity minimization (IRES) strategy. NeuroImage. 142: 27– 42 27241482 PMC5124544 source

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  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationnone given

    Selecting and parameterizing the prior distribution is not straightforward.

    “Selecting and parameterizing the prior distribution is not straightforward, bringing both challenges and opportunities.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    FOCUSS is an early example of using sparsity for electrophysiological source imaging.

    “FOCal Underdetermined System Solution (FOCUSS) (50), is an early example of using sparsity for ESI.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [50] Gorodnitsky IF, George JS, Rao BD. 1995. Neuromagnetic source imaging with FOCUSS: a recursive weighted minimum norm algorithm. Electroencephalogr. Clin. Neurophysiol 95(4): 231– 51 8529554 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Interpretationnone given

    Bayesian algorithms are more efficient if the prior distribution can be described with fewer hyper-parameters, for example by assuming independent sources.

    “Bayesian algorithms are more efficient if the prior distribution can be described with fewer hyper-parameters, e.g. by assuming independent sources (or a diagonal covariance matrix).”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationreasoning

    Beamforming is more widely used for MEG than EEG because the forward problem is relatively simpler for MEG.

    “Perhaps for this reason, beamforming is more widely used for MEG than EEG, since the forward problem is relatively simpler for MEG.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Interpretationreasoning

    Model-based connectivity methods bring higher computational demand but may lead to valuable insights if the model and parameters are appropriately selected.

    “This brings higher computational demand but may lead to valuable insights if the model and parameters can be appropriately selected.”
    Quote in the paperNot a substantial claim

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    Premisecitation

    Two notions are essential to the brain's functional organization: functional segregation and integration.

    “Two notions are essential to the brain’s functional organization: functional segregation and integration (91).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [91] Friston KJ. 2011. Functional and effective connectivity: a review. Brain Connect. 1(1): 13– 36 22432952 source

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  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given

    The aforementioned methods for directional connectivity are all data-driven.

    “The aforementioned methods for directional connectivity are all data-driven.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    The origin of fMRI is complex and incompletely understood.

    “The origin of fMRI is rather complex and incompletely understood (118).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [118] Kim S-G, Ogawa S. 2012. Biophysical and Physiological Origins of Blood Oxygenation Level-Dependent fMRI Signals. J. Cereb. Blood Flow Metab 32(7): 1188– 1206 22395207 PMC3390806 source

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    Premisecitation

    MEG is an effective tool for studying resting network functional connectivity and has been shown to correlate well with fMRI studies with the added value of high temporal resolution.

    “MEG is also an effective tool for studying resting network functional connectivity and has been shown to correlate well with fMRI studies with the added value of high temporal resolution (107).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [107] Brookes MJ, Hale JR, Zumer JM, Stevenson CM, Francis ST, 2011. Measuring functional connectivity using MEG: Methodology and comparison with fcMRI. NeuroImage. 56(3): 1082– 1104 21352925 PMC3224862 source

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    Premisecitation

    Model-based methods depend on the choice of the model and its parameters, so many different parameters need to be tested to ensure unbiased results.

    “Model-based methods depend on the choice of the model and its parameters; thus many different parameters need to be tested to ensure unbiased results (112).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [112] Daunizeau J, David O, Stephan KE. 2011. Dynamic causal modelling: a critical review of the biophysical and statistical foundations. Neuroimage. 58(2): 312– 322 19961941 source

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    Premisecitation

    Effective connectivity measures the causal relationship between regions, for example Granger causality.

    “This sets functional connectivity apart from directional connectivity or effective connectivity (94), which measures the causal relationship between A and B, e.g. Granger causality (95).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [94] Friston KJ. 2009. Modalities, modes, and models in functional neuroimaging. Science. 326(5951): 399– 403 19833961 source

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    • cites [95] Granger CW. 1969. Investigating causal relations by econometric models and cross-spectral methods. Econom. J. Econom. Soc, pp. 424– 438 source

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    Premisecitation

    Model-based connectivity measures such as dynamic causal modeling have drawn much attention.

    “Model-based connectivity measures such as the dynamic causal modeling (DCM) has also drawn much attention (110).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [110] Friston KJ, Harrison L, Penny W. 2003. Dynamic causal modelling. Neuroimage. 19(4): 1273– 1302 12948688 source

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    Premisecitation

    EEG/MEG can resolve neural events with high temporal resolution but limited spatial resolution; fMRI localizes brain activity with millimeter precision but cannot probe rapidly changing neuronal dynamics.

    “EEG and MEG can resolve neural events with high temporal resolution but limited spatial resolution (113); fMRI localizes brain activity with millimeter precision but cannot probe the rapidly changing neuronal dynamics (114).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [113] Baillet S 2017. Magnetoencephalography for brain electrophysiology and imaging. Nat. Neurosci 20(3): 327– 339 28230841 source

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    • cites [114] Logothetis NK. 2008. What we can do and what we cannot do with fMRI. Nature. 453(7197): 869– 878 18548064 source

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    Interpretationreasoning

    The feasibility of concurrent EEG and fMRI acquisition makes EEG more preferable than MEG, especially for functional imaging in task-free or self-paced states that are hard to precisely replicate in separate experiments.

    “The feasibility of concurrent EEG and fMRI acquisition makes EEG more preferable than MEG, especially for functional imaging in task-free or self-paced states that are hard to precisely replicate in separate experiments.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    The eConnectome approach has stirred interest in studying pathological networks such as epilepsy, where determining the source that drives seizures is important.

    “The eConnectome approach (104, 105) has stirred interest in studying pathological networks such as epilepsy, where determining the source that drives seizures is of particular importance (106).”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [104] He B, Dai Y, Astolfi L, Babiloni F, Yuan H, Yang L. 2011. eConnectome: A MATLAB toolbox for mapping and imaging of brain functional connectivity. J. Neurosci. Methods 195(2): 261– 269 21130115 PMC3244474 source

      Not checked in the free run.

    • cites [105] Sohrabpour A, Ye S, Worrell GA, Zhang W, He B. 2016. Noninvasive electromagnetic source imaging and granger causality analysis: An electrophysiological Connectome (eConnectome) approach. IEEE Trans. Biomed. Eng 63(12): 2474– 2487 27740473 PMC5152676 source

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    • cites [106] Lu Y, Yang L, Worrell GA, He B. 2012. Seizure source imaging by means of FINE spatio-temporal dipole localization and directed transfer function in partial epilepsy patients. Clin. Neurophysiol 123(7): 1275– 1283 22172768 PMC3327757 source

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  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Causality or directionality is often inferred from time series: if past activity in A predicts current or future activity in B, A causes or drives B through a connectivity pointing from A to B.

    “Causality or directionality is often inferred from time series: if the past activity in A predicts the current or future activity in B, A causes or drives B through a connectivity pointing from A to B (96).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [96] Geweke J 1982. Measurement of linear dependence and feedback between multiple time series. J. Am. Stat. Assoc 77(378): 304– 313 source

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  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    The most widely used fMRI signal is blood oxygenation level dependent and is observable with rapid pulse sequences sensitized to T2*-weighted contrast.

    “The most widely used fMRI signal is blood oxygenation level dependent (BOLD) (119) and observable with rapid pulse sequences sensitized to the T2*-weighted contrast (120–122).”
    Quote in the paperNot a substantial claim0 of 4 sources checked
    • cites [119] Ogawa S, Lee TM, Kay AR, Tank DW. 1990. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc. Natl. Acad. Sci 87(24): 9868– 72 2124706 PMC55275 source

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    • cites [120] Bandettini PA, Wong EC, Hinks RS, Tikofsky RS, Hyde JS. 1992. Time course EPI of human brain function during task activation. Magn. Reson. Med 25(2): 390– 97 1614324 source

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    • cites [121] Kwong KK, Belliveau JW, Chesler DA, Goldberg IE, Weisskoff RM, 1992. Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc. Natl. Acad. Sci 89(12): 5675– 79 1608978 PMC49355 source

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    • cites [122] Ogawa S, Tank DW, Menon R, Ellermann JM, Kim SG, 1992. Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proc. Natl. Acad. Sci 89(13): 5951– 55 1631079 PMC402116 source

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    Interpretationcitation

    Where EEG and MEG fall short in neural imaging is opposite to where fMRI excels, and vice versa.

    “Where EEG and MEG fall short in neural imaging is opposite to where fMRI excels, and vice versa.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    As neural activity elevates, local oxygen consumption and demand increases, triggering vasodilation of arterioles and capillaries to increase cerebral blood flow.

    “As neural activity elevates, local oxygen consumption and demand increases, triggering vasodilation of arterioles and capillaries to increase cerebral blood flow (124), which supplies oxygenated hemoglobin in excess of the metabolic rate of oxygen consumption (125).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [124] Attwell D, Buchan AM, Charpak S, Lauritzen M, MacVicar BA, Newman EA. 2010. Glial and neuronal control of brain blood flow. Nature. 468(7321): 232– 43 21068832 PMC3206737 source

      Not checked in the free run.

    • cites [125] Fox PT, Raichle ME. 1986. Focal physiological uncoupling of cerebral blood flow and oxidative metabolism during somatosensory stimulation in human subjects. Proc. Natl. Acad. Sci 83(4): 1140– 44 3485282 PMC323027 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    The mechanism of neurovascular coupling is not fully understood, but understanding it is essential for interpreting fMRI.

    “The mechanism of neurovascular coupling is incompletely understood but essential to interpretation of fMRI (114, 118).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [114] Logothetis NK. 2008. What we can do and what we cannot do with fMRI. Nature. 453(7197): 869– 878 18548064 source

      Not checked in the free run.

    • cites [118] Kim S-G, Ogawa S. 2012. Biophysical and Physiological Origins of Blood Oxygenation Level-Dependent fMRI Signals. J. Cereb. Blood Flow Metab 32(7): 1188– 1206 22395207 PMC3390806 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    The LFP-BOLD correlation spans a broadband of frequencies, not limited to a single frequency, though it is most notable in gamma.

    “Although it is perhaps most notable in the gamma range, the LFP-BOLD correlation is not limited to any single frequency, but spans a broadband (135).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [135] Goense JB, Logothetis NK. 2008. Neurophysiology of the BOLD fMRI signal in awake monkeys. Curr. Biol 18(9): 631– 640 18439825 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    The model does not account for negative BOLD responses or changes in spontaneous activity.

    “However, this model fails to account for negative BOLD responses (153), or changes in spontaneous activity (154).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [153] Shmuel A, Augath M, Oeltermann A, Logothetis NK. 2006. Negative functional MRI response correlates with decreases in neuronal activity in monkey visual area V1. Nat. Neurosci 9(4): 569– 577 16547508 source

      Not checked in the free run.

    • cites [154] Makeig S, Westerfield M, Jung T-P, Enghoff S, Townsend J, 2002. Dynamic brain sources of visual evoked responses. Science. 295(5555): 690– 694 11809976 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    As a result, the concentration of deoxygenated hemoglobin decreases in capillary and venous vessels, reducing the paramagnetic susceptibility effect and thus increasing the T2*-weighted signal.

    “As a result, the concentration of deoxygenated hemoglobin decreases in capillary and venous vessels, reducing the paramagnetic susceptibility effect and thus increasing the T2*-weighted signal (123).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [123] Thulborn KR, Waterton JC, Matthews PM, Radda GK. 1982. Oxygenation dependence of the transverse relaxation time of water protons in whole blood at high field. Biochim. Biophys. Acta BBA - Gen. Subj 714(2): 265– 70 6275909 source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    EEG/MEG arises from electromagnetic effects of gray-matter synaptic activity through head volume conduction, with severe loss of spatial specificity.

    “the electromagnetic effect gives rise to EEG/MEG through head volume conduction with a severe loss in spatial specificity.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    After neuronal input and output are dissociated, the BOLD response remains correlated with LFP but not MUA.

    “After dissociating neuronal input and output, the BOLD response is still correlated with LFP but not with MUA (133, 134).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [133] Thomsen K, Offenhauser N, Lauritzen M. 2004. Principal neuron spiking: neither necessary nor sufficient for cerebral blood flow in rat cerebellum. J. Physiol 560(1): 181– 189 15272036 PMC1665203 source

      Not checked in the free run.

    • cites [134] Rauch A, Rainer G, Logothetis NK. 2008. The effect of a serotonin-induced dissociation between spiking and perisynaptic activity on BOLD functional MRI. Proc. Natl. Acad. Sci 105(18): 6759– 6764 18456837 PMC2373337 source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    fMRI and EEG signals largely share a common origin in cortical synaptic activity, but reflect its distribution and dynamics at different spatial and temporal scales with distinct sensitivity, resolution, and specificity.

    “Given these findings, it is reasonable to state that fMRI and EEG signals, to a large extent, share a common origin of cortical synaptic activity, but reflect its distribution and dynamics in highly different spatial and temporal scales with distinctive sensitivity, resolution, and specificity (141).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [141] Nunez PL, Silberstein RB. 2000. On the relationship of synaptic activity to macroscopic measurements: does co-registration of EEG with fMRI make sense? Brain Topogr 13(2): 79– 96 11154104 source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    fMRI arises from metabolic and vascular effects of gray-matter synaptic activity through neurovascular coupling, with severe loss of temporal specificity.

    “the metabolic and vascular effects give rise to fMRI through neurovascular coupling with a severe loss in temporal specificity”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Premisecitation

    The cascade of metabolic and vascular events known as neurovascular coupling involves complex signaling among neurons, astrocytes, and the local vasculature and remains a topic of active research.

    “Such a cascade of metabolic and vascular events, known as the neurovascular coupling, involves complex signaling among neurons, astrocytes, and the local vasculature (124, 126, 127), and remains a topic of active research enabled by emerging tools (128).”
    Quote in the paperNot a substantial claim0 of 4 sources checked
    • cites [124] Attwell D, Buchan AM, Charpak S, Lauritzen M, MacVicar BA, Newman EA. 2010. Glial and neuronal control of brain blood flow. Nature. 468(7321): 232– 43 21068832 PMC3206737 source

      Not checked in the free run.

    • cites [126] Petzold GC, Murthy VN. 2011. Role of Astrocytes in Neurovascular Coupling. Neuron. 71(5): 782– 97 21903073 source

      Not checked in the free run.

    • cites [127] Devor A, Dunn AK, Andermann ML, Ulbert I, Boas DA, Dale AM. 2003. Coupling of Total Hemoglobin Concentration, Oxygenation, and Neural Activity in Rat Somatosensory Cortex. Neuron. 39(2): 353– 59 12873390 source

      Not checked in the free run.

    • cites [128] Otsu Y, Couchman K, Lyons DG, Collot M, Agarwal A, 2015. Calcium dynamics in astrocyte processes during neurovascular coupling. Nat. Neurosci 18(2): 210– 218 25531572 PMC4651918 source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    This relationship provides a quantitative model linking fMRI to EEG/MEG and allows a more principled use of fMRI to constrain the EEG/MEG inverse solution for stimulus-evoked responses.

    “This relationship yields a quantitative model to relate fMRI to EEG/MEG, enabling a more principled way for using fMRI to constrain the EEG/MEG inverse solution for stimulus-evoked responses (150–152).”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [150] Liu Z, He B. 2008. fMRI–EEG integrated cortical source imaging by use of time-variant spatial constraints. NeuroImage. 39(3): 1198– 1214 18036833 PMC2291020 source

      Not checked in the free run.

    • cites [151] Liu Z, Rios C, Zhang N, Yang L, Chen W, He B. 2010. Linear and nonlinear relationships between visual stimuli, EEG and BOLD fMRI signals. Neuroimage. 50(3): 1054– 1066 20079854 PMC2841568 source

      Not checked in the free run.

    • cites [152] Liu Z, Zhang N, Chen W, He B. 2009. Mapping the bilateral visual integration by EEG and fMRI. NeuroImage. 46(4): 989– 997 19306933 PMC2702152 source

      Not checked in the free run.

  • May combine two claims.

    Interpretationreasoning

    The thalamic EEG-fMRI correlation shows the benefit of combining modalities, because the thalamus is too deep for reliable EEG and deep sources are hard to localize with EEG alone.

    “The EEG-fMRI correlation in the thalamus demonstrates the synergistic merit of fMRI-EEG, since the thalamus is too far away to generate reliable EEG and localizing such deep sources with EEG alone is challenging.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    The proposed directionality of oscillations is consistent with laminar fMRI profiles, LFP-based phase relationships, and neuromodulation effects.

    “Such findings are consistent with the observable laminar profile of fMRI (187), LFP-based phase relationship (184), and effects of neuromodulation (188).”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites [187] Scheeringa R, Koopmans PJ, van Mourik T, Jensen O, Norris DG. 2016. The relationship between oscillatory EEG activity and the laminar-specific BOLD signal. Proc. Natl. Acad. Sci 113(24): 6761– 6766 27247416 PMC4914170 source

      Not checked in the free run.

    • cites [184] Van Kerkoerle T, Self MW, Dagnino B, Gariel-Mathis M-A, Poort J, 2014. Alpha and gamma oscillations characterize feedback and feedforward processing in monkey visual cortex. Proc. Natl. Acad. Sci 111(40): 14332– 14341 25205811 PMC4210002 source

      Not checked in the free run.

    • cites [188] Klink PC, Dagnino B, Gariel-Mathis M-A, Roelfsema PR. 2017. Distinct Feedforward and Feedback Effects of Microstimulation in Visual Cortex Reveal Neural Mechanisms of Texture Segregation. Neuron 28625487 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Oscillation power or amplitude fluctuations occur in a time range similar to the fMRI signal, though the oscillation itself is too fast for fMRI to follow.

    “Although the oscillation itself is too fast for fMRI to follow, its power or amplitude fluctuation falls in a similar time range as the fMRI signal (162).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [162] Leopold DA, Murayama Y, Logothetis NK. 2003. Very slow activity fluctuations in monkey visual cortex: implications for functional brain imaging. Cereb. Cortex 13(4): 422– 433 12631571 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    For event-like tasks or stimuli, EEG features derived from single-trial potential variations correlate with fMRI and expose the timing of information processing in task-related networks.

    “Moreover, for event-like tasks or stimuli, EEG features may also be extracted from temporal variations in single-trial potentials, and their correlations with fMRI reveal rich temporal dynamics of information processing in task-evoked neural networks (176, 177).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites [176] Eichele T, Specht K, Moosmann M, Jongsma ML, Quiroga RQ, 2005. Assessing the spatiotemporal evolution of neuronal activation with single-trial event-related potentials and functional MRI. Proc. Natl. Acad. Sci. U. S. A 102(49): 17798– 17803 16314575 PMC1295592 source

      Not checked in the free run.

    • cites [177] Debener S, Ullsperger M, Siegel M, Engel AK. 2006. Single-trial EEG–fMRI reveals the dynamics of cognitive function. Trends Cogn. Sci 10(12): 558– 563 17074530 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Artifacts are more concerning for EEG than fMRI and tend to worsen at higher fields.

    “The artifacts are more of concern for EEG than for fMRI, and tend to deteriorate in higher fields (179).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [179] Neuner I, Arrubla J, Felder J, Shah NJ. 2014. Simultaneous EEG–fMRI acquisition at low, high and ultra-high magnetic fields up to 9.4 T: Perspectives and challenges. Neuroimage. 102: 71– 79 23796544 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Recent work indicates that broadband non-rhythmic activity and narrowband rhythmic activity explain global and modular patterns of resting-state fMRI connectivity, respectively.

    “recent findings suggest that (broadband) arrhythmic and (narrowband) rhythmic processes account for global and modular patterns of functional connectivity observed with resting state fMRI, respectively (170).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [170] Wen H, Liu Z. 2016. Broadband electrophysiological dynamics contribute to global resting-state fMRI signal. J. Neurosci 36(22): 6030– 6040 27251624 PMC4887567 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    ESI separates sensor-level data to expose the dynamics of regional activity or inter-regional connectivity, providing insights into healthy and diseased brain function.

    “ESI also disentangles the sensor-level signals to reveal electrophysiological dynamics in regional activity or inter-regional connectivity, yielding new insights to brain functions in health (193) and disease (194–196).”
    Quote in the paperSubstantial claim0 of 4 sources checked
    • cites [193] Murphy M, Riedner BA, Huber R, Massimini M, Ferrarelli F, Tononi G. 2009. Source modeling sleep slow waves. Proc. Natl. Acad. Sci 106(5): 1608– 13 19164756 PMC2635823 source

      Not checked in the free run.

    • cites [194] Edelman BJ, Johnson N, Sohrabpour A, Tong S, Thakor N, He B. 2015. Systems Neuroengineering: Understanding and Interacting with the Brain. Engineering. 1(3): 292– 308 PMC8323844 34336364 source

      Not checked in the free run.

    • cites [195] Foxe JJ, Murray MM, Javitt DC. 2005. Filling-in in schizophrenia: a high-density electrical mapping and source-analysis investigation of illusory contour processing. Cereb. Cortex 15(12): 1914– 1927 15772373 source

      Not checked in the free run.

    • cites [196] Zhang HC, Sohrabpour A, Lu Y, He B. 2016. Spectral and spatial changes of brain rhythmic activity in response to the sustained thermal pain stimulation. Hum. Brain Mapp PMC4945371 27167709

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    EEG and its underlying neural activity almost never consist of a single-frequency rhythm; they include a mixture of rhythms and an arrhythmic component with a 1/f power-law distribution over a broad band.

    “This is because EEG, or its underlying neural activity, nearly never manifests itself as a single-frequency rhythm; instead, it always contains a mixture of rhythms, and an arrhythmic component that follows a power-law (1/f) distribution across a broadband (168).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [168] He BJ, Zempel JM, Snyder AZ, Raichle ME. 2010. The temporal structures and functional significance of scale-free brain activity. Neuron. 66(3): 353– 369 20471349 PMC2878725 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Frontal theta rhythm shows negative coupling with default-mode network.

    “e.g. showing the negative coupling between frontal theta rhythm and default-mode network (167).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [167] Scheeringa R, Bastiaansen MC, Petersson KM, Oostenveld R, Norris DG, Hagoort P. 2008. Frontal theta EEG activity correlates negatively with the default mode network in resting state. Int. J. Psychophysiol 67(3): 242– 251 17707538 source

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Recent studies suggest that low-frequency oscillations (e.g., alpha) reflect feedback and high-frequency oscillations (e.g., gamma) reflect feedforward network interactions.

    “Recent studies suggest that the frequency of neural oscillations marks the directionality of network interactions: low-frequency oscillations (e.g. alpha) reflect feedback processes and high-frequency oscillations (e.g. gamma) reflect feedforward processes (184–186).”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites [184] Van Kerkoerle T, Self MW, Dagnino B, Gariel-Mathis M-A, Poort J, 2014. Alpha and gamma oscillations characterize feedback and feedforward processing in monkey visual cortex. Proc. Natl. Acad. Sci 111(40): 14332– 14341 25205811 PMC4210002 source

      Not checked in the free run.

    • cites [185] Bastos AM, Vezoli J, Bosman CA, Schoffelen J-M, Oostenveld R, 2015. Visual areas exert feedforward and feedback influences through distinct frequency channels. Neuron. 85(2): 390– 401 25556836 source

      Not checked in the free run.

    • cites [186] Michalareas G, Vezoli J, Van Pelt S, Schoffelen J-M, Kennedy H, Fries P. 2016. Alpha-beta and gamma rhythms subserve feedback and feedforward influences among human visual cortical areas. Neuron. 89(2): 384– 397 26777277 PMC4871751 source

      Not checked in the free run.

63 of 63

  • The paper relies on this without support.

    Little is known about how huge numbers of neurons and synapses work collectively to give rise to macroscopic brain signals and human behaviors.

    “Although, a great deal is known about neurons in a microscopic scale, little is known as to how these huge number of neurons (and synapses) work collectively to give rise to macroscopic brain signals and human behaviors.”

  • The paper relies on this without support.

    Human brain functions are carried out by complex neural activations and interactions that elevate electromagnetic signal changes accompanied by hemodynamic and metabolic changes.

    “Human brain functions are carried out by complex neural activations and interactions, which elevate electromagnetic signal changes (the primary effects) accompanied by hemodynamic and metabolic changes (the secondary effects).”

  • The paper relies on this without support.

    Activation of single or multiple neurons does not lead to detectable EEG/MEG; synchronous excitation of a large number of neurons is required to generate detectable EEG/MEG.

    “An important notion is that activation of single or multiple neurons does not lead to detectable EEG/MEG. Instead it requires the synchronous excitation of a large number of neurons to generate detectable EEG/MEG.”

  • The paper relies on this without support.

    EEG/MEG has limited spatial resolution.

    “The challenge however is the limited spatial resolution of EEG/MEG.”

  • The paper relies on this without support.

    External events trigger rapid brain responses.

    “External events trigger rapid brain responses.”

  • The paper relies on this without support.

    Most combined fMRI-EEG/MEG imaging methods are developed in the context of two inverse problems: spatial localization of temporally resolved EEG/MEG and temporal decomposition of spatially resolved fMRI.

    “Most fMRI-EEG/MEG combined imaging methods are being developed in the context of two inverse problems.”
6 of 6

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • ESI uses the electrical properties and geometry of the head to reduce volume conduction and estimate cortical activity.

    “Electrical source imaging (ESI) is one such approach that uses the electrical properties and geometry of the head to mitigate the effects of volume conduction and estimate cortical activity (26).”

    From Noninvasive neuroimaging enhances continuous neural tracking for robotic device control (2019) · cites this paper as [26]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Directional methods are classified as nondirectional connectivity measures

    Minor

    The paper defines functional connectivity as unable to report directionality, then lists directed transfer function methods as ways to estimate functional connectivity. It later describes those methods as directional connectivity methods.

1 of 1

The citation lists haven’t been fetched yet

References 219 · Cited by 297

Research works printed but not linked: 8

  1. Lopes da Silva FH, 2011, Niedermeyer's Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, 91
  2. Fuchs M, 1994, ISBET Newsl, 5, 8
  3. Pascual-Marqui RD, 2002, Methods Find. Exp. Clin. Pharmacol., 24, 5
  4. Robinson SE, 1998, Recent Advances in Biomagnetism, 302
  5. Sadaghiani S, 2010, Front. Syst. Neurosci., 4, 20
  6. Liu Y, 2014, Cereb. Cortex, 26, 517
  7. Zhang HC, 2016, Hum. Brain Mapp., 37, 2796
  8. Dalal SS, 2004, Neurol. Clin. Neurophysiol., 2004, 52

Authors
He, B., Sohrabpour, A., Brown, E., & Liu, Z.
Affiliations
  • University of Minnesota (US)
  • Massachusetts Institute of Technology (US)
  • Purdue University West Lafayette (US)
Corresponding author
He, Bin
ORCID
Venue
Annual Review of Biomedical Engineering
Publisher
Annual Reviews
Published
2018-06-04
Volume
20
Issue
1
Pages
171-196
ISSN
  • 1523-9829 (ISSN-L)
  • 1545-4274
DOI
10.1146/annurev-bioeng-062117-120853
PMC
PMC7941524
OpenAlex
W2790994475
Semantic Scholar
132eb4add69f5562a75e3f983f85772661c9ce80
Cited by
297
References
219
Citations by year
2026: 41 · 2025: 43 · 2024: 44 · 2023: 48 · 2022: 43 · 2021: 24 · 2020: 30 · 2019: 20 · 2018: 4
FWCI
2.57
Type
journal article
Language
en
Open access
Locations
  • Annual Review of Biomedical Engineering (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
Funders
  • National Science Foundation (MH114233, 1450956, 1069104)
  • National Institutes of Health (CBET-1450956, DGE-1069104)
  • National Heart, Lung, and Blood Institute (U01HL117664)
  • National Institute of Mental Health (RF1MH114233, R01MH104402)
  • National Eye Institute (R01EY023101)
  • National Institute of Neurological Disorders and Stroke (R01NS096761)
  • National Institute of Biomedical Imaging and Bioengineering (R01EB021027, T32EB008389)
  • Division of Chemical, Bioengineering, Environmental, and Transport Systems (CBET-1450956, 1450956)
  • National Center for Complementary and Integrative Health (R01AT009263)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Optical Imaging and Spectroscopy Techniques
Keywords
EEG source localization, MEG source localization, brain dynamics, brain connectivity, spatiotemporal resolution, multimodal neuroimaging, non-invasive brain imaging
MeSH terms
Algorithms, Animals, Bayes Theorem, Brain, Electroencephalography, Electrophysiology, Humans, Magnetic Resonance Imaging, Neurosciences, Signal Processing, Computer-Assisted, Software, Magnetoencephalography

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-06.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-06.

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