Christopher W. Tyler

Clare Howarth

Ekstrom

Heeger

Logothetis

Lopez da Silva

Lora T. Likova

Rauch

Rees

Viswanathan

Frontiers in Neuroscience

Christopher W Tyler

Clare eHowarth

Lora T Likova

Directory of Open Access Journals

Editorial: Neural signal estimation in the human brain

Tyler, C.W.

Howarth, C.

Likova, L.T.

White Rose Research Online

EDITORIALpublished: 29 April 2016doi: 10.3389/fnins.2016.00185Frontiers in Neuroscience | www.frontiersin.org 1 April 2016 | Volume 10 | Article 185Edited by:Russell A. Poldrack,Stanford University, USAReviewed by:Kamil Uludag,Maastricht University, Netherlands*Correspondence:Christopher W. Tylercwt@ski.orgSpecialty section:This article was submitted toBrain Imaging Methods,a section of the journalFrontiers in NeuroscienceReceived: 19 January 2016Accepted: 11 April 2016Published: 29 April 2016Citation:Tyler CW, Howarth C and Likova LT(2016) Editorial: Neural SignalEstimation in the Human Brain.Front. Neurosci. 10:185.doi: 10.3389/fnins.2016.00185Editorial: Neural Signal Estimation inthe Human BrainChristopher W. Tyler 1, 2*, Clare Howarth 3 and Lora T. Likova 11Division of Optometry and Vision Sciences, Centre for Applied Vision Research, City University London, London, UK,2 Smith-Kettlewell Eye Research Institute, San Francisco, CA, USA, 3Department of Psychology, University of Sheffield,Sheffield, UKKeywords: human brain, neural signal estimation, fMRI, MEG, EEGThe Editorial on the Research TopicNeural Signal Estimation in the Human BrainThe ultimate goal of functional brain imaging is to estimate the neural signals that flow through thebrain, mediating behavior, and conscious experience during the spectrum of activities controlledby the nervous system. Although, various brain imaging techniques are in routine use, determiningthe underlying neural activity remains a challenge (Lopez da Silva, 2010). Despite its impressivespatial resolution, functional Magnetic Resonance Imaging (fMRI) measures a blood-oxygenation-level-dependent (BOLD) signal and, hence, only indirectly reflects the nearby neural activity. Theinterpretation of these signals is further complicated as it is sometimes unclear what aspects of“neural activity” BOLD represents. “Neural activity” could refer to spiking activity, subthresholdactivation, or synaptic currents, each of both excitatory and inhibitory neurons, to name a few.Although, early findings suggested that BOLD was directly proportional to average neuronal firingrates (Heeger et al., 2000; Rees et al., 2000), in the cortex BOLD fMRI signals are marginallybetter correlated with LFPs (reflecting slow waveforms of neural activity) than with MUAs(reflecting spiking; Logothetis et al., 2001), suggesting that they may preferentially reflect inputsand intracortical processing (Viswanathan and Freeman, 2007; Rauch et al., 2008). Further workis needed to better understand what aspect of neural activity is reflected by BOLD signals in caseswhere there is dissociation between LFPs and the BOLD signal, as occurs in the hippocampus (asdiscussed in Ekstrom, 2010).The vast majority of fMRI studies describe only the properties of BOLD signals and makeonly qualitative inferences of their implications for the underlying neural activity. Conversely, inelectrical and magnetic forms of non-invasive brain imaging the recorded signal derives directlyfrom the functional activity of neurons (though with varying degrees of transmission from theneural origin to the scalp recording sites), but the ability to localize these signals with any degreeof accuracy remains remarkably elusive as the complexity of brain activation for even the simplestof tasks tends to confound attempts to resolve the local neural components contributing to therecorded scalp responses. To improve our estimates of neural signals using non-invasive brainimaging techniques, this Frontiers Research Topic invited empirical and theoretical contributionsfocusing on the explicit relationship of brain imaging signals to causative neural activity.The submitted contributions responded to the challenge of neural signal estimation in a varietyof ways including: advanced analyses of the neural implications of magnetoencephalographic(MEG) and electroencephalographic (EEG) signals, derivations of the pathway for BOLD signalgeneration from the underlying neural activation signals through animal recording, human BOLDmodeling studies, detailed assessment of local BOLD response components and resting-stateactivation, and interpretation of the new field of functional diffusion tensor imaging in terms ofneural activation.Tyler et al. Human Neural Signal EstimationAlthough the EEG and MEG commonly used to measurehuman neural activity have high temporal resolution, spatiallocalization of the signal source is difficult to achieve. Cicmilet al. highlighted the limits on localizing MEG signal sourcesby testing the ability of several reconstruction approaches tolocalize the source of retinotopic MEG signals in the humanbrain and found that none of the approaches for assessingangular position were suitable for resolving annular stimulispanning different retinal eccentricities (unless restricted inangular position). A second contribution to such electricalsignal analysis is the time-frequency approach to the sourcelocalization and functional connectivity from simultaneousMEG/EEG signals proposed by Zerouali et al. Although, thisanalysis specifically targeted sleep spindles, the work has broaderimplications for the functional integration of MEG and EEGsignals and their source localization within the brain. Thisanalysis revealed that functional connectivity across the cortexevolved during the spindles from short-range intra-hemisphericconnections to longer range inter-hemispheric connections,suggesting an integrative role for these dynamic features of neuralactivity.Several contributions focused on estimating the properties ofthe underlying neural sources that generate BOLD fMRI signals.Martin reviews the need for accurate neurovascular modelsof the coupling between neural activity and the local BOLDsignal from animal studies. Animal studies have the strikingadvantage of allowing a wide variety of technical approachesto the analysis of neurovascular coupling. Martin evaluates 16of these, from single-neuron electrophysiology to tissue oxygenvoltammetry, considering both their advantages and limitationsand highlighting the key areas in which our understandingof fMRI signals has been improved through the use ofanimal models. Howarth takes up the issue of whether corticalastrocytes (glial cells), and calcium transients within them, areinvolved in the vascular response to neuronal activity basedon the recent debate regarding whether evoked glial calciumsignals occur quickly enough to account for the dynamicsof neurovascular coupling. Indeed, the exact mechanisms bywhich astrocytes respond to changes in neuronal activity andtrigger the intracellular events regulating the resulting vascularresponse underlying the fMRI BOLD signal remain unclear.To take an analytic approach to this question, Tyler et al.evaluate four models for the neurovascular coupling betweenlocal field potentials recorded in cortex and BOLD signalsrecorded simultaneously in an adjacent location, for a range ofstimulus durations. The results imply that the BOLD responseis most closely coupled with metabolic demand derived from theneuronal input waveform, suggesting that the astrocytic signalingis responsive to the neurotransmitter metabolism of the dendriticarborization rather than to the neuron’s spiking activity.Further studies focus on contributions to the positive andnegative components of the neurovascular relationships. Buxtonet al. assess the coupling ratio of blood flow and oxygenmetabolism to different kinds of neural activation, findingthat blood flow variations are more closely coupled withstimulus-driven variations than with endogenous variations inneural activity (e.g., those driven by attention, adaptation, andgeneralized excitability). Variations in oxygen metabolism, onthe other hand, are more closely coupled with endogenousneural variations. The authors suggest that these differences incoupling ratio reflect differential proportions of excitatory andinhibitory contributions of the neural signal to cortical BOLDsignals, and hence provide a new window into the assessment ofneural activity. A related topic is addressed by Chen, who usesstimulus-driven manipulations of activation and suppression toassess the excitatory and inhibitory contributions to the evokedBOLD signal. The stimuli were designed to have invariantlocal effects, but differential long-range interactions were foundaccording to configural relationships of local orientations, whichshould produce no differences in BOLD signal in the absence ofneural interactions. One component of the BOLD suppressionwas dependent on the orientation-specific inhibitory effect ofthe long-range interactions, while a second appeared to be ageneral negative BOLD response to adjacent contrast stimulationindependent of the stimulus configuration. Thus, BOLD responseproperties can be used to identify targeted aspects of theunderlying neural organization.Three papers focus on advanced methods of decomposingthe neural connectivity and reorganization in the brain fromthe distribution of BOLD signals. Gonzalez-Castillo et al. takethe novel approach of analyzing the time-course of resting-state BOLD signals across the cortex to assess the stability ofneural connectivity. The most stable connections were betweenhomologous (symmetric) interhemispheric local regions, withstability persisting for several minutes. The more variableconnections were found to correspond primarily to occipito-frontal connections across the traditional resting-state networks,which can be interpreted as corresponding to transient visualimagery. Gravel et al. take resting-state analysis a step furtherto develop the concept of local cortical connective fields. Theseare neural organizations analogous to neuronal receptive fields,but defined in terms of connectivity among cortical regions,rather than connectivity of the neuron to a sensory surface. Incombination with the population receptive mapping developedby this group for the analysis of the visual cortex, resting-state BOLD connectivity can be interpreted in visual space.This approach allows visuotopic maps to be reconstructed usingresting state data recorded in the visual cortex, enabling theseauthors to show that the local resting-state connectivity fromvisual area V1 to both V2 and V3 was invariant with eccentricitywith a scale of ∼2mm, substantially smaller than the populationreceptive fields for visual input in these cortical areas. This worksuggests that it is possible to obtain some neural properties fromresting-state fMRI data.Concentrating on the example of motor learning, Yang et al.extend the analysis of BOLD activation maps. Learning maygenerate not only changes in the strength of activation inpredefined regions of interest, but also changes in the spatialdistribution of the activation across the cortex. To address thisissue, the authors measure the changes in spatial distribution ofactivation following a simple motor learning task. Dimensionreduction via singular-value decomposition was able to captureaspects of the neural reorganization produced by this formof motor learning. These findings validate the capability ofFrontiers in Neuroscience | www.frontiersin.org 2 April 2016 | Volume 10 | Article 185Tyler et al. Human Neural Signal Estimationcomputational modeling to determine properties of neuralconnectivity and reorganization from BOLD signal analysis.The final two papers are concerned with a new functional formof Diffusion Tensor Imaging (DTI). DTI is a well-establishedtechnique for assessing the anatomical organization of the fiberpathways in vivo from the local anisotropy of the diffusiondirections of water molecules within brain tissue. FunctionalDTI, on the other hand, assesses changes in this kind ofanisotropy as a result of some functional manipulation of thestate of the brain. Autio and Roberts raise concerns aboutcontamination of this form of functional analysis by leakageof BOLD signal activation from adjacent gray matter into thevoxels designated as fiber pathways. Mandl et al., whose previouspaper on functional changes in fractional anisotropy in the opticradiations during visual stimulation was the subject of the Autioand Roberts critique, argue that such partial voluming would onlyoccur at the ends of fiber tracts where they meet with the corticalregions that they are connecting, whereas the reported changes infractional anisotropy occurred throughout the tracts.In summary, functional imaging techniques are increasinglyused to infer neural activity within the human brain. This specialissue improves our ability to estimate these neural signals non-invasively and points us in the direction of the remaining issuesthat must be addressed before we can fully understand functionalimaging signals.AUTHOR CONTRIBUTIONSAll authors listed, have made substantial, direct, andintellectual contribution to the work, and approved it forpublication.FUNDINGCH was a Vice Chancellor’s Advanced Fellow at the University ofSheffield and currently holds a Sir Henry Dale Fellowship jointlyfunded by the Wellcome Trust and the Royal Society (grantnumber:105586/Z/14/Z).REFERENCESEkstrom, A. (2010). How and when the fMRI BOLD signal relates to underlyingneural activity: the danger in dissociation. Brain Res. Rev. 62, 233–244. doi:10.1016/j.brainresrev.2009.12.004Heeger, D. J., Huk, A. C., Geisler, W. S., and Albrecht, D. G. (2000). Spikes versusBOLD: what does neuroimaging tell us about neuronal activity? Nat. Neurosci.3, 631–633. doi: 10.1038/76572Logothetis, N. K., Pauls, J., Augath, M., Trinath, T., and Oeltermann, A. (2001).Neurophysiological investigation of the basis of the fMRI signal. Nature 412,150–157. doi: 10.1038/35084005Lopez da Silva, F. H. (2010). “EEG origin and measurement,” in EEG - fMRI:Physiological Basis, Technique, and Applications, eds C. Mulert and L. Lemieux(Berlin; Heidelberg:Springer Verlag), 19–38.Rauch, A., Rainer, G., and Logothetis, N. K. (2008). The effect of aserotonin-induced dissociation between spiking and perisynaptic activity onBOLD functional MRI. Proc. Natl. Acad. Sci. U.S.A. 105, 6759–6764. doi:10.1073/pnas.0800312105Rees, G., Friston, K., and Koch, C. (2000). A direct quantitative relationshipbetween the functional properties of human and macaque V5. Nat. Neurosci.3, 716–723. doi: 10.1038/76673Viswanathan, A., and Freeman, R. D. (2007). Neurometabolic coupling incerebral cortex reflects synaptic more than spiking activity. Nat. Neurosci. 10,1308–1312. doi: 10.1038/nn1977Conflict of Interest Statement: The authors declare that the research wasconducted in the absence of any commercial or financial relationships that couldbe construed as a potential conflict of interest.Copyright © 2016 Tyler, Howarth and Likova. This is an open-access articledistributed under the terms of the Creative Commons Attribution License (CCBY). The use, distribution or reproduction in other forums is permitted, providedthe original author(s) or licensor are credited and that the original publicationin this journal is cited, in accordance with accepted academic practice. Nouse, distribution or reproduction is permitted which does not comply with theseterms.Frontiers in Neuroscience | www.frontiersin.org 3 April 2016 | Volume 10 | Article 185

Editorial: Neural Signal Estimation in the Human Brain

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Frontiers - Publisher Connector

EDITORIAL
published: 29 April 2016
doi: 10.3389/fnins.2016.00185
Frontiers in Neuroscience | www.frontiersin.org 1 April 2016 | Volume 10 | Article 185
Edited by:
Russell A. Poldrack,
Stanford University, USA
Reviewed by:
Kamil Uludag,
Maastricht University, Netherlands
*Correspondence:
Christopher W. Tyler
cwt@ski.org
Specialty section:
This article was submitted to
Brain Imaging Methods,
a section of the journal
Frontiers in Neuroscience
Received: 19 January 2016
Accepted: 11 April 2016
Published: 29 April 2016
Citation:
Tyler CW, Howarth C and Likova LT
(2016) Editorial: Neural Signal
Estimation in the Human Brain.
Front. Neurosci. 10:185.
doi: 10.3389/fnins.2016.00185
Editorial: Neural Signal Estimation in
the Human Brain
Christopher W. Tyler 1, 2*, Clare Howarth 3 and Lora T. Likova 1
1Division of Optometry and Vision Sciences, Centre for Applied Vision Research, City University London, London, UK,
2 Smith-Kettlewell Eye Research Institute, San Francisco, CA, USA, 3Department of Psychology, University of Sheffield,
Sheffield, UK
Keywords: human brain, neural signal estimation, fMRI, MEG, EEG
The Editorial on the Research Topic
Neural Signal Estimation in the Human Brain
The ultimate goal of functional brain imaging is to estimate the neural signals that flow through the
brain, mediating behavior, and conscious experience during the spectrum of activities controlled
by the nervous system. Although, various brain imaging techniques are in routine use, determining
the underlying neural activity remains a challenge (Lopez da Silva, 2010). Despite its impressive
spatial resolution, functional Magnetic Resonance Imaging (fMRI) measures a blood-oxygenation-
level-dependent (BOLD) signal and, hence, only indirectly reflects the nearby neural activity. The
interpretation of these signals is further complicated as it is sometimes unclear what aspects of
“neural activity” BOLD represents. “Neural activity” could refer to spiking activity, subthreshold
activation, or synaptic currents, each of both excitatory and inhibitory neurons, to name a few.
Although, early findings suggested that BOLD was directly proportional to average neuronal firing
rates (Heeger et al., 2000; Rees et al., 2000), in the cortex BOLD fMRI signals are marginally
better correlated with LFPs (reflecting slow waveforms of neural activity) than with MUAs
(reflecting spiking; Logothetis et al., 2001), suggesting that they may preferentially reflect inputs
and intracortical processing (Viswanathan and Freeman, 2007; Rauch et al., 2008). Further work
is needed to better understand what aspect of neural activity is reflected by BOLD signals in cases
where there is dissociation between LFPs and the BOLD signal, as occurs in the hippocampus (as
discussed in Ekstrom, 2010).
The vast majority of fMRI studies describe only the properties of BOLD signals and make
only qualitative inferences of their implications for the underlying neural activity. Conversely, in
electrical and magnetic forms of non-invasive brain imaging the recorded signal derives directly
from the functional activity of neurons (though with varying degrees of transmission from the
neural origin to the scalp recording sites), but the ability to localize these signals with any degree
of accuracy remains remarkably elusive as the complexity of brain activation for even the simplest
of tasks tends to confound attempts to resolve the local neural components contributing to the
recorded scalp responses. To improve our estimates of neural signals using non-invasive brain
imaging techniques, this Frontiers Research Topic invited empirical and theoretical contributions
focusing on the explicit relationship of brain imaging signals to causative neural activity.
The submitted contributions responded to the challenge of neural signal estimation in a variety
of ways including: advanced analyses of the neural implications of magnetoencephalographic
(MEG) and electroencephalographic (EEG) signals, derivations of the pathway for BOLD signal
generation from the underlying neural activation signals through animal recording, human BOLD
modeling studies, detailed assessment of local BOLD response components and resting-state
activation, and interpretation of the new field of functional diffusion tensor imaging in terms of
neural activation.
Tyler et al. Human Neural Signal Estimation
Although the EEG and MEG commonly used to measure
human neural activity have high temporal resolution, spatial
localization of the signal source is difficult to achieve. Cicmil
et al. highlighted the limits on localizing MEG signal sources
by testing the ability of several reconstruction approaches to
localize the source of retinotopic MEG signals in the human
brain and found that none of the approaches for assessing
angular position were suitable for resolving annular stimuli
spanning different retinal eccentricities (unless restricted in
angular position). A second contribution to such electrical
signal analysis is the time-frequency approach to the source
localization and functional connectivity from simultaneous
MEG/EEG signals proposed by Zerouali et al. Although, this
analysis specifically targeted sleep spindles, the work has broader
implications for the functional integration of MEG and EEG
signals and their source localization within the brain. This
analysis revealed that functional connectivity across the cortex
evolved during the spindles from short-range intra-hemispheric
connections to longer range inter-hemispheric connections,
suggesting an integrative role for these dynamic features of neural
activity.
Several contributions focused on estimating the properties of
the underlying neural sources that generate BOLD fMRI signals.
Martin reviews the need for accurate neurovascular models
of the coupling between neural activity and the local BOLD
signal from animal studies. Animal studies have the striking
advantage of allowing a wide variety of technical approaches
to the analysis of neurovascular coupling. Martin evaluates 16
of these, from single-neuron electrophysiology to tissue oxygen
voltammetry, considering both their advantages and limitations
and highlighting the key areas in which our understanding
of fMRI signals has been improved through the use of
animal models. Howarth takes up the issue of whether cortical
astrocytes (glial cells), and calcium transients within them, are
involved in the vascular response to neuronal activity based
on the recent debate regarding whether evoked glial calcium
signals occur quickly enough to account for the dynamics
of neurovascular coupling. Indeed, the exact mechanisms by
which astrocytes respond to changes in neuronal activity and
trigger the intracellular events regulating the resulting vascular
response underlying the fMRI BOLD signal remain unclear.
To take an analytic approach to this question, Tyler et al.
evaluate four models for the neurovascular coupling between
local field potentials recorded in cortex and BOLD signals
recorded simultaneously in an adjacent location, for a range of
stimulus durations. The results imply that the BOLD response
is most closely coupled with metabolic demand derived from the
neuronal input waveform, suggesting that the astrocytic signaling
is responsive to the neurotransmitter metabolism of the dendritic
arborization rather than to the neuron’s spiking activity.
Further studies focus on contributions to the positive and
negative components of the neurovascular relationships. Buxton
et al. assess the coupling ratio of blood flow and oxygen
metabolism to different kinds of neural activation, finding
that blood flow variations are more closely coupled with
stimulus-driven variations than with endogenous variations in
neural activity (e.g., those driven by attention, adaptation, and
generalized excitability). Variations in oxygen metabolism, on
the other hand, are more closely coupled with endogenous
neural variations. The authors suggest that these differences in
coupling ratio reflect differential proportions of excitatory and
inhibitory contributions of the neural signal to cortical BOLD
signals, and hence provide a new window into the assessment of
neural activity. A related topic is addressed by Chen, who uses
stimulus-driven manipulations of activation and suppression to
assess the excitatory and inhibitory contributions to the evoked
BOLD signal. The stimuli were designed to have invariant
local effects, but differential long-range interactions were found
according to configural relationships of local orientations, which
should produce no differences in BOLD signal in the absence of
neural interactions. One component of the BOLD suppression
was dependent on the orientation-specific inhibitory effect of
the long-range interactions, while a second appeared to be a
general negative BOLD response to adjacent contrast stimulation
independent of the stimulus configuration. Thus, BOLD response
properties can be used to identify targeted aspects of the
underlying neural organization.
Three papers focus on advanced methods of decomposing
the neural connectivity and reorganization in the brain from
the distribution of BOLD signals. Gonzalez-Castillo et al. take
the novel approach of analyzing the time-course of resting-
state BOLD signals across the cortex to assess the stability of
neural connectivity. The most stable connections were between
homologous (symmetric) interhemispheric local regions, with
stability persisting for several minutes. The more variable
connections were found to correspond primarily to occipito-
frontal connections across the traditional resting-state networks,
which can be interpreted as corresponding to transient visual
imagery. Gravel et al. take resting-state analysis a step further
to develop the concept of local cortical connective fields. These
are neural organizations analogous to neuronal receptive fields,
but defined in terms of connectivity among cortical regions,
rather than connectivity of the neuron to a sensory surface. In
combination with the population receptive mapping developed
by this group for the analysis of the visual cortex, resting-
state BOLD connectivity can be interpreted in visual space.
This approach allows visuotopic maps to be reconstructed using
resting state data recorded in the visual cortex, enabling these
authors to show that the local resting-state connectivity from
visual area V1 to both V2 and V3 was invariant with eccentricity
with a scale of ∼2mm, substantially smaller than the population
receptive fields for visual input in these cortical areas. This work
suggests that it is possible to obtain some neural properties from
resting-state fMRI data.
Concentrating on the example of motor learning, Yang et al.
extend the analysis of BOLD activation maps. Learning may
generate not only changes in the strength of activation in
predefined regions of interest, but also changes in the spatial
distribution of the activation across the cortex. To address this
issue, the authors measure the changes in spatial distribution of
activation following a simple motor learning task. Dimension
reduction via singular-value decomposition was able to capture
aspects of the neural reorganization produced by this form
of motor learning. These findings validate the capability of
Frontiers in Neuroscience | www.frontiersin.org 2 April 2016 | Volume 10 | Article 185
Tyler et al. Human Neural Signal Estimation
computational modeling to determine properties of neural
connectivity and reorganization from BOLD signal analysis.
The final two papers are concerned with a new functional form
of Diffusion Tensor Imaging (DTI). DTI is a well-established
technique for assessing the anatomical organization of the fiber
pathways in vivo from the local anisotropy of the diffusion
directions of water molecules within brain tissue. Functional
DTI, on the other hand, assesses changes in this kind of
anisotropy as a result of some functional manipulation of the
state of the brain. Autio and Roberts raise concerns about
contamination of this form of functional analysis by leakage
of BOLD signal activation from adjacent gray matter into the
voxels designated as fiber pathways. Mandl et al., whose previous
paper on functional changes in fractional anisotropy in the optic
radiations during visual stimulation was the subject of the Autio
and Roberts critique, argue that such partial voluming would only
occur at the ends of fiber tracts where they meet with the cortical
regions that they are connecting, whereas the reported changes in
fractional anisotropy occurred throughout the tracts.
In summary, functional imaging techniques are increasingly
used to infer neural activity within the human brain. This special
issue improves our ability to estimate these neural signals non-
invasively and points us in the direction of the remaining issues
that must be addressed before we can fully understand functional
imaging signals.
AUTHOR CONTRIBUTIONS
All authors listed, have made substantial, direct, and
intellectual contribution to the work, and approved it for
publication.
FUNDING
CH was a Vice Chancellor’s Advanced Fellow at the University of
Sheffield and currently holds a Sir Henry Dale Fellowship jointly
funded by the Wellcome Trust and the Royal Society (grant
number:105586/Z/14/Z).
REFERENCES
Ekstrom, A. (2010). How and when the fMRI BOLD signal relates to underlying
neural activity: the danger in dissociation. Brain Res. Rev. 62, 233–244. doi:
10.1016/j.brainresrev.2009.12.004
Heeger, D. J., Huk, A. C., Geisler, W. S., and Albrecht, D. G. (2000). Spikes versus
BOLD: what does neuroimaging tell us about neuronal activity? Nat. Neurosci.
3, 631–633. doi: 10.1038/76572
Logothetis, N. K., Pauls, J., Augath, M., Trinath, T., and Oeltermann, A. (2001).
Neurophysiological investigation of the basis of the fMRI signal. Nature 412,
150–157. doi: 10.1038/35084005
Lopez da Silva, F. H. (2010). “EEG origin and measurement,” in EEG - fMRI:
Physiological Basis, Technique, and Applications, eds C. Mulert and L. Lemieux
(Berlin; Heidelberg:Springer Verlag), 19–38.
Rauch, A., Rainer, G., and Logothetis, N. K. (2008). The effect of a
serotonin-induced dissociation between spiking and perisynaptic activity on
BOLD functional MRI. Proc. Natl. Acad. Sci. U.S.A. 105, 6759–6764. doi:
10.1073/pnas.0800312105
Rees, G., Friston, K., and Koch, C. (2000). A direct quantitative relationship
between the functional properties of human and macaque V5. Nat. Neurosci.
3, 716–723. doi: 10.1038/76673
Viswanathan, A., and Freeman, R. D. (2007). Neurometabolic coupling in
cerebral cortex reflects synaptic more than spiking activity. Nat. Neurosci. 10,
1308–1312. doi: 10.1038/nn1977
Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Copyright © 2016 Tyler, Howarth and Likova. This is an open-access article
distributed under the terms of the Creative Commons Attribution License (CC
BY). The use, distribution or reproduction in other forums is permitted, provided
the original author(s) or licensor are credited and that the original publication
in this journal is cited, in accordance with accepted academic practice. No
use, distribution or reproduction is permitted which does not comply with these
terms.
Frontiers in Neuroscience | www.frontiersin.org 3 April 2016 | Volume 10 | Article 185


Tyler, C. W.

Likova, L. T.

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