Elsevier

NeuroImage

Volume 62, Issue 2, 15 August 2012, Pages 1121-1130
NeuroImage

Review
Which “neural activity” do you mean? fMRI, MEG, oscillations and neurotransmitters

https://doi.org/10.1016/j.neuroimage.2012.01.028Get rights and content

Abstract

Over the last 20 years, BOLD-FMRI has proved itself to be a powerful and versatile tool for the study of the neural substrate underpinning many of our cognitive and perceptual functions. However, exactly how it is coupled to the underlying neurophysiology, and how this coupling varies across the brain, across tasks and across individuals is still unclear. The story is further complicated by the fact that within the same cortical region, multiple evoked and induced oscillatory effects may be modulated during task execution, supporting different cognitive roles, and any or all of these may have metabolic demands that then drive the BOLD response. In this paper I shall concentrate on one experimental approach to shedding light on this problem i.e. the execution of the same experimental tasks using MEG and fMRI in order to reveal which electrophysiological responses best match the BOLD response spatially, temporally and functionally. The results demonstrate a rich and complex story that does not fit with a simplistic view of BOLD reflecting “neural activity” and suggests that we could consider the coupling between BOLD and the various parameters of neural function as an ill-posed inverse problem. Finally, I describe recent work linking individual variability in both cortical oscillations and the BOLD-fMRI response to variability in endogenous GABA concentration.

Introduction

There is no doubt that functional MRI, using the endogenous BOLD contrast, has become an incredibly popular and useful tool for neuroscience that has created a remarkable body of work in just 20 years. Given the indirect, and largely unknown, coupling of the BOLD signal to the underlying neural substrate, its usefulness is even more remarkable.

The popularity of BOLD-fMRI is at least partly driven by its surprising spatial specificity. Early in the history of the technique, it soon became clear that BOLD had exquisite spatial resolution, allowing us to generate high-resolution maps of the borders between human visual areas (Engel et al., 1994, Sereno et al., 1995) in an individual. The fact that these human retinotopic maps revealed exactly the structures and organisation we expected to see from animal neurophysiology studies was a major step forward for the field. In addition, this amazing spatial specificity of the brain's haemodynamics appears to allow us to map structures right down to the columnar level of the visual cortex (Yacoub et al., 2008). Almost magically, our ability to extract spatial information may go beyond the fundamental resolution limit of the images, as small biases in the response properties of cells in each voxel may allow us to decode what information the brain is representing/processing (Kamitani and Tong, 2005).

In my opinion, although I may be biased, fMRI has been most successful in studies of human visual cortex—precisely because these studies are designed, and their results interpreted, with direct reference to previous animal neurophysiology studies. BOLD-fMRI studies can, of course, be well designed and executed without reference to previous neurophysiological research and can reveal subtle distinctions between experimental paradigms and participant groups, but the interpretation of any finding should be necessarily limited—it should always be remembered that BOLD is a measure of haemodynamic changes in the brain and these are critically dependent on the nature of the coupling between neurons and haemodynamics. Presumably, the BOLD response is related to the energy demands of modulating various aspects of neural function, including action potentials, neurotransmitter cycling and excitatory and inhibitory post-synaptic potentials, but this still a subject of much active investigation and debate (Attwell and Iadecola, 2002, Attwell and Laughlin, 2001, Mangia et al., 2009, Shulman and Rothman, 1998). In addition, it is known that hemodynamic coupling changes across the brain, across individuals, when challenged with drugs such as caffeine, with age, with disease and with subtle changes in respiration. Many of these effects can be controlled for with appropriate physiological monitoring and calibration (Iannetti and Wise, 2007).

Given all of the above, it is a shame that so many recent fMRI-BOLD studies insist on describing their measured effects as “neural activity”. Of course, we hope these effects are in some sense correlated with neural function, but we can't be sure that this is true in all cases—this is why I emphasised the link with previous animal neurophysiological work in the visual domain as it gives at least indirect evidence that our measured BOLD-fMRI findings truly reflect neural function.

As many people have pointed out, and as I emphasise in this article, the very phrase neural activity is in itself a rather poorly specified and ultimately meaningless term. In most people's minds the term is probably a surrogate for the firing of action potentials. However, within the cortex there are multiple neural signals, at different oscillatory frequencies, that might all contribute to the metabolic demand that then drives the BOLD signal. Furthermore, it's not clear which of these neural signatures are most relevant to each aspect of perception and cognition. This complexity is outlined in Fig. 1. However, all is not lost—we have several tools at our disposal that allow us to investigate which aspects of neural function contribute to the BOLD response and, with appropriate links to behavioural paradigms, which signal is most relevant to each function.

Section snippets

Firing rates, perception and oscillations

Until recently, when people thought about the neural signatures underpinning perception and cognition, there was an implicit assumption that the key measure is the firing rates of neurons. This view arose from the seminal observations that individual neurons in visual cortex were exquisitely tuned to fundamental properties of the visual scene, such as retinotopic location and stimulus orientation (Hubel and Wiesel, 1962). Surprisingly, individual firing rates can also demonstrate specificity to

The relationship between BOLD and oscillatory activity in the cortex

There are many experimental approaches that we can adopt to try and understand which aspects of neural function drive the haemodynamic response in BOLD-fMRI. These include simultaneous invasive electrode recordings and BOLD-fMRI in animals such as rat (Boorman et al., 2010) and macaque (Logothetis et al., 2001) and studies comparing electrode recordings in implanted human epilepsy patients with BOLD-fMRI in healthy human participants performing the same tasks (Mukamel et al., 2005). The results

MEG and fMRI

An alternative, non-invasive, experimental approach is to perform the same experiments in human using BOLD-fMRI and techniques such as EEG and MEG and see which electrophysiological signals seem to match the BOLD response. To a certain extent, this can be done using simultaneous EEG–fMRI, but due to the MR environment, it can be difficult to measure the full spectrum of oscillatory responses and true source-localisation, as opposed to simple temporal correlation with the BOLD signal, can be

Primary visual cortex gamma

In the 1980s and 90s investigations of the local-field potential in cat primary visual cortex revealed that presentation of simple static stimuli induced an oscillation in the visual gamma band (30–90 Hz) that was sustained throughout the presentation of the stimulus (Gray and Singer, 1989, Kayser et al., 2003). Note that the stimulus itself is not temporally varying—the oscillation, and its properties such as frequency and bandwidth, are intrinsic properties of the cortex itself and arise from

Individual variability: induced gamma as a biomarker of inhibition?

Another way of investigating the coupling between function, neural signals and the BOLD response is to study the variability of each parameter across participants. Individual variability and repeatability of the BOLD response have been studied several times (See McGonigle, in press) but relatively little work has been done on the variability/repeatability of cortical oscillatory signals. Recently, our group and others (Hoogenboom et al., 2006, Muthukumaraswamy et al., 2010) have shown that

Conclusion: The BOLD “inverse problem”

In this article, I've attempted to show how adopting a multi-modal imaging approach, using MEG, fMRI and MRS can start to help us understand some of the complexity underpinning the tri-partite relationship between human cognition, neural responses and the BOLD-fMRI signal. These non-invasive approaches are complementary to animal studies attempting to shed light on the same problems.

In some ways, the use of fMRI in cognitive and clinical neuroscience has raced ahead of our understanding of

Acknowledgments

In this paper I have reviewed several of the studies I have been involved in over the last 10 years or so. Obviously many people have contributed to these endeavours and my thanks go to all of my collaborators, in particular those at Cardiff, Aston and Nottingham Universities in the UK. The data shown in Fig. 2 was part of long-standing collaborations with Arjan Hillebrand, Paul Furlong, Ian Holliday and Gareth Barnes when we worked together at Aston University and Peter Morris and Matt Brookes

References (98)

  • P. Fries

    The model- and the data-gamma

    Neuron

    (2009)
  • P. Fries et al.

    The gamma cycle

    Trends Neurosci.

    (2007)
  • W. Gaetz et al.

    Relating MEG measured motor cortical oscillations to resting γ-aminobutyric acid (GABA) concentration

    Neuroimage

    (2011)
  • W.C. Gaetz et al.

    Localization of human somatosensory cortex using spatially filtered magnetoencephalography

    Neurosci. Lett.

    (2003)
  • S.D. Hall et al.

    The missing link: analogous human and primate cortical gamma oscillations

    Neuroimage

    (2005)
  • S.D. Hall et al.

    GABA(A) alpha-1 subunit mediated desynchronization of elevated low frequency oscillations alleviates specific dysfunction in stroke—a case report

    Clin. Neurophysiol.

    (2010)
  • N. Hoogenboom et al.

    Localizing human visual gamma-band activity in frequency, time and space

    Neuroimage

    (2006)
  • G.D. Iannetti et al.

    BOLD functional MRI in disease and pharmacological studies: room for improvement?

    Magn. Reson. Imaging

    (2007)
  • O. Jensen et al.

    Cross-frequency coupling between neuronal oscillations

    Trends Cogn Sci (Regul Ed)

    (2007)
  • F.A. Maratos et al.

    The spatial distribution and temporal dynamics of brain regions activated during the perception of object and non-object patterns

    Neuroimage

    (2007)
  • F. McNab et al.

    Semantic and phonological task-set priming and stimulus processing investigated using magnetoencephalography (MEG)

    Neuropsychologia

    (2007)
  • F. Moradi et al.

    Consistent and precise localization of brain activity in human primary visual cortex by MEG and fMRI

    Neuroimage

    (2003)
  • R.J. Moran et al.

    An in vivo assay of synaptic function mediating human cognition

    Curr. Biol.

    (2011)
  • S.D. Muthukumaraswamy et al.

    Spatiotemporal frequency tuning of BOLD and gamma band MEG responses compared in primary visual cortex

    Neuroimage

    (2008)
  • S.D. Muthukumaraswamy et al.

    Visual gamma oscillations and evoked responses: variability, repeatability and structural MRI correlates

    Neuroimage

    (2010)
  • S. Palva et al.

    New vistas for alpha-frequency band oscillations

    Trends Neurosci.

    (2007)
  • K. Pammer et al.

    Visual word recognition: the first half second

    Neuroimage

    (2004)
  • G. Pfurtscheller et al.

    Event-related EEG/MEG synchronization and desynchronization: basic principles

    Clin. Neurophysiol.

    (1999)
  • N.A.J. Puts et al.

    In vivo magnetic resonance spectroscopy of GABA: A methodological review

    Progress in Nuclear Magnetic Resonance Spectroscopy

    (2012)
  • S. Ray et al.

    Differences in gamma frequencies across visual cortex restrict their possible use in computation

    Neuron

    (2010)
  • K.D. Singh et al.

    Group imaging of task-related changes in cortical synchronisation using nonparametric permutation testing

    Neuroimage

    (2003)
  • K.D. Singh et al.

    Task-related changes in cortical synchronization are spatially coincident with the hemodynamic response

    Neuroimage

    (2002)
  • C.J. Stagg et al.

    The role of GABA in human motor learning

    Curr. Biol.

    (2011)
  • M.A. Tagamets et al.

    Interpreting PET and fMRI measures of functional neural activity: the effects of synaptic inhibition on cortical activation in human imaging studies

    Brain Res. Bull.

    (2001)
  • J. Vrba et al.

    Signal processing in magnetoencephalography

    Methods

    (2001)
  • J.M. Zumer et al.

    Relating BOLD fMRI and neural oscillations through convolution and optimal linear weighting

    Neuroimage

    (2010)
  • P. Adjamian et al.

    Induced Gamma activity in primary visual cortex is related to luminance and not color contrast: an MEG study

    J. Vis.

    (2008)
  • P. Adjamian et al.

    Induced visual illusions and gamma oscillations in human primary visual cortex

    Eur. J. Neurosci.

    (2004)
  • S.P. Ahlfors et al.

    Spatiotemporal activity of a cortical network for processing visual motion revealed by MEG and fMRI

    J. Neurophysiol.

    (1999)
  • D. Attwell et al.

    An energy budget for signaling in the grey matter of the brain

    J. Cereb. Blood Flow Metab.

    (2001)
  • H. Berger

    On the electroencephalogram of man

    Electroencephalogr. Clin. Neurophysiol.

    (1969)
  • L. Boorman et al.

    Negative blood oxygen level dependence in the rat: a model for investigating the role of suppression in neurovascular coupling

    J. Neurosci.

    (2010)
  • N. Brunel et al.

    What determines the frequency of fast network oscillations with irregular neural discharges?

    I. Synaptic dynamics and excitation-inhibition balance. Journal of Neurophysiology

    (2003)
  • M. Castelo-Branco et al.

    Synchronization of visual responses between the cortex, lateral geniculate nucleus, and retina in the anesthetized cat

    J. Neurosci.

    (1998)
  • Z. Chen et al.

    Elevated endogenous GABA level correlates with decreased fMRI signals in the rat brain during acute inhibition of GABA transaminase

    J. Neurosci. Res.

    (2005)
  • L.L. Colgin et al.

    Frequency of gamma oscillations routes flow of information in the hippocampus

    Nature

    (2009)
  • F.P. de Lange et al.

    Interactions between posterior gamma and frontal alpha/beta oscillations during imagined actions

    Front. Hum. Neurosci.

    (2008)
  • Dunkley, B.T., Freeman, T.C., Muthukumaraswamy, S.D., Singh, K.D., in press. Cortical oscillatory changes in human...
  • R.A.E. Edden et al.

    Spatial effects in the detection of gamma-aminobutyric acid: improved sensitivity at high fields using inner volume saturation

    Magn. Reson. Med.

    (2007)
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