Download On the nature of the BOLD fMRI contrast mechanism

Magnetic Resonance Imaging 22 (2004) 1517 – 1531
On the nature of the BOLD f MRI contrast mechanism
Nikos K. Logothetis*, Josef Pfeuffer
Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Spemannstrasse 38, 72076 Tübingen, Germany
Received 17 September 2004; accepted 15 October 2004
Abstract
Since its development about 15 years ago, functional magnetic resonance imaging (fMRI) has become the leading research tool for
mapping brain activity. The technique works by detecting the levels of oxygen in the blood, point by point, throughout the brain. In other
words, it relies on a surrogate signal, resulting from changes in oxygenation, blood volume and flow, and does not directly measure neural
activity. Although a relationship between changes in brain activity and blood flow has long been speculated, indirectly examined and
suggested and surely anticipated and expected, the neural basis of the fMRI signal was only recently demonstrated directly in experiments
using combined imaging and intracortical recordings. In the present paper, we discuss the results obtained from such combined experiments.
We also discuss our current knowledge of the extracellularly measured signals of the neural processes that they represent and of the structural
and functional neurovascular coupling, which links such processes with the hemodynamic changes that offer the surrogate signal that we use
to map brain activity. We conclude by considering applications of invasive MRI, including injections of paramagnetic tracers for the study of
connectivity in the living animal and simultaneous imaging and electrical microstimulation.
D 2004 Elsevier Inc. All rights reserved.
Keywords: Functional magnetic resonance imaging; Monkey brain; Local field potentials; Multiple-unit activity; Synaptic activity
1. Introduction
Our view of brain function has improved impressively in
recent years as a result of intense efforts to understand the
neural mechanisms underlying perception in humans and
nonhuman primates. A large body of evidence regarding the
processes through which sensory information at the biochemical, electrophysiological and systems levels contributes to the conscious experience of a stimulus has accrued.
Our efforts to understand the organization and function of
the sensory and perceptual systems have been greatly aided
by the development of new techniques including novel and
powerful methods of molecular biology, the refinement of
recordings from single and multiple cells for short or long
periods and noninvasive neuroimaging techniques allowing
us to localize and study activity within the human brain
while subjects perform a variety of cognitive tasks.
The contribution of neuroimaging cannot possibly be
overemphasized. All our mental capacities, ranging from
sensory representation and perception to reasoning and
* Corresponding author. Tel.: +49 7071 601 651; fax: +49 7071 601
652.
E-mail address: [email protected] (N.K. Logothetis).
0730-725X/$ – see front matter D 2004 Elsevier Inc. All rights reserved.
doi:10.1016/j.mri.2004.10.018
planning, rely on distributed, synergistic activities of large
neural populations; therefore, understanding these not only
requires a comprehension of the physiological workings of
individual neurons and glia cells but also demands a detailed
map of the brain’s functional architecture, a description of
the connections between populations of neurons and
insights into the operations performed by the neural
networks involved in the task at hand.
The present review deals with spatiotemporally resolved
functional magnetic resonance imaging (fMRI) in monkeys
and its combination with other invasive neuroscientific
techniques. Emphasis will be placed on simultaneous
imaging and electrophysiology experiments aiming to
elucidate the neural basis of the blood oxygen leveldependent (BOLD) signal. We first review the basic
organizational principles of the cortical system. Although
many examples are drawn from the visual system, generality
is hardly sacrificed as evidence over the last decades suggest
a similar organization in any other sensory system studied.
Our intention is not to provide the reader with an exhaustive
review on sensory or perceptual processing; instead, we
summarize examples of current work showing the increasing need of neuroimaging and integrative approaches in
addressing many of the interesting questions raised by
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systems neuroscience. A brief discussion on the principles
of energy metabolism and neurovascular coupling follows,
subsequent to which the neural and BOLD signals are
examined in some detail.
1.1. Maps and modular organization
Sensory systems are characterized by topographical
organization in the sense that receptors in the peripheral
receptive sheet project to central neurons in an orderly
manner preserving neighborhood relationships. Within each
modality, there are multiple such maps representing the
sensory surface. Examples of maps (considering columns as
spatial units) are the primary cortical areas of the visual,
auditory and somatosensory modalities. Topographical
maps, among others, imply local processing. In the case
of the visual system, images from the retina at the back of
each eye are channeled first to a subdivision of the cerebral
hemispheres deep in the brain called the lateral geniculate
nucleus (LGN) and, from then on, to the primary visual
cortex. In the primary visual cortex (V1), neurons were
thought to analyze local spatial information within their
small receptive fields (RF), basically ignoring processing
carried out elsewhere.
It is notable that in the past few years this concept was
radically modified by studies showing that V1 cells can
actually integrate information over a much larger part of
visual space than originally believed and may be an
important part of the network underlying perceptual
organization. Because their responses are not solely
determined by the optimal stimulus with their classical RF
[1], they can rather be considerably modulated by perceived
brightness, perceptual bpop-outQ or grouping of line segments, figure-ground segregation based on a variety of
visual cues [2– 4] and the attentional requirement of the task
(for a review, see Ref. [5]).
What was previously thought of as a static RF with fixed
physiological properties is now conceived as a contextdependent dynamic entity that may undergo shifts in
preferred orientation [6], position or size [7] or may
modulate activity as a function of viewing distance [8].
Modulation of contextual effects, possibly through the
feedback connections, may also be the mechanism by
which attention affects the response of V1 cells [9]. Many
of these effects are, at least in part, attributable to horizontal
or feedback connections. Disentangling the relative contributions of feedforward, lateral and feedback connections is
of obvious importance for understanding both sensory and
perceptual processing and is likely to instigate a great deal
of future research; it is also likely to require an increasingly
greater involvement of neuroimaging in a typical neuroscience laboratory.
The influence of context on cell responsiveness is one
prominent example of dynamic neural behavior; plasticity of
maps, another. Neurons in various cortical sensory areas,
even in the earliest ones, are far more flexible and mutable
in their functional properties than previously thought. Maps
are not fixed but are instead continuously modified by
experience [10,11] and are likely involved in perceptual
learning [10 –12].
Finally, modular organization is a principle shared by
most sensory systems. In the visual system, modularity was
already well established in the 1970s with the discovery of
multiple areas that are functionally specialized to undertake
different tasks and have certain hierarchical relationships
with each other (for a review, see Refs. [13,14]). From a
physiological viewpoint, the notion of hierarchical organization emerged from the pioneering experiments of David
Hubel and Torsten Wiesel on the structure of RFs in visual
cortex. Evidence from anatomy came through the observation that different types of corticocortical connections [15],
initially called rostral and caudal and subsequently termed
feedforward and feedback, can be distinguished by their
neurons of origin as well as by the highly specific laminar
distribution of their efferent terminal arborizations.
Further detailed physiological and anatomical studies
yielded an elaborate route map describing the functional
organization of the visual system [16,17]. In addition, an
influential proposal that two anatomically distinct and
functionally specialized cortical streams of visual processing
emanating from the primary visual cortex exist — a dorsal,
occipitoparietal stream stretching through the visual association areas of the parietal lobe, which processes spatial
information, and a ventral, occipitotemporal stream through
the visual areas of the temporal lobe, which is involved in
the representation of visual objects — was made based on
anatomical, physiological and lesion studies [18,19].
Recently, a series of long-awaited tract-tracing, electrophysiological and lesion experiments in monkeys as well as
imaging experiments in humans revealed a similar hierarchical organization in the primate auditory system (see, for a
review, Ref. [20]). The reported similarity in the organization of the visual and auditory systems is in good agreement
with the parallel nature of the visual and auditory perceptual
requirements; namely, the localization and identification of
patterns. While the former system integrates information
across space and time, the latter does so over frequency and
time dimensions.
1.2. Neurons, networks and perception
Perceptual organization, selective or attentive information processing, decision making and categorization are only
a few of the cognitive capacities intensively investigated in
today’s neuroscience, which seeks to understand the
relationship between the mind and the brain. All these
capacities rely on distributed, synergistic activities of large
neural populations; therefore, understanding these not only
requires a comprehension of the physiological workings of
individual neurons and glia cells but also demands a detailed
map of the brain’s functional architecture, a description of
the connections between populations of neurons and
insights into the operations performed by the neural
networks involved in the task at hand.
N.K. Logothetis, J. Pfeuffer / Magnetic Resonance Imaging 22 (2004) 1517–1531
Yet, until very recently, systems neuroscience almost
exclusively has been relying on physiological studies
utilizing the much celebrated single microelectrode technique, reporting the number of action potentials produced
by an isolated neuron within a unit of time. Although it
proved very useful in characterizing the response properties
of different structures, the method clearly falls short of
providing information on spatiotemporal cooperativeness
and on the global, associational operations performed by
neural networks.
Understanding perception or cognition will ultimately
depend on the development and application of integrative
approaches. Single cell recordings, large electrode or
tetrode-array recordings, monitoring of action potentials
and slow waves must be employed in combination with
neuroimaging using calibrated BOLD signals, cerebral
blood flow (CBF), volume (CBV) and MR spectroscopy
(MRS) of cerebral metabolites and neurotransmitters to
obtain the information required for studying the brain’s
capacity to generate various behaviors. The recent development of high-field MRI and functional CBF imaging as
well as MR spectroscopic imaging [chemical shift imaging
(CSI)] in our laboratory [see papers in this volume by
Pfeuffer et al. [21,22] (MRI and MRS at 7 T) and Juchem
et al. [23] (CSI at 7 T)] directly reflects our conviction that
such integrative approaches can and must be applied in
systems neuroscience.
Integrative approaches, however, require the interdisciplinary education of researchers and a thorough understanding of, at least, the basics of closely associated
research fields. Invasive neuroimaging in a typical neuroscience laboratory, for instance, requires acquaintance —to
a certain extent — with the organization of the brain’s
metabolism and vascular system in addition to fathoming
into the workings of the nerve and glia cells. The next
paragraphs attempt to briefly review the essentials of these
fields, introducing the commonly measured neural signals
and continuing with the very basics of metabolism and
hemodynamics, to tap the issue of structural and functional
neurovascular coupling.
2. The neural signals
2.1. The compound neural signal
The signal measured by an electrode placed at a neural
site represents the mean extracellular field potential (mEFP)
from the weighted sum of all sinks and sources along
multiple cells (Fig. 1). If a microelectrode with a small tip is
placed close to the soma or axon of a neuron, then the
measured mEFP directly reports the spike traffic of that
neuron and frequently that of its immediate neighbors as
well. Recent studies in rats, for instance, show that tetrodes
placed close (within 50 Am) to pyramidal neurons in the
hippocampus provide accurate information on a number of
their parameters such as latency, amplitude and shape of
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action potentials because they are simultaneously measured
by intracellular recordings [24,25].
The firing rate of such well-isolated neurons has been the
critical measure for comparing neural activity with sensory
processing or behavior ever since the early development of
microelectrodes. A great deal has been learned since then,
and the single-electrode single-unit recording technique still
remains the method of choice in many behavioral experiments with conscious animals. However, it also has the
drawback of providing information mainly on single RFs,
with no access to subthreshold integrative processes or to
the associational operations taking place at a given site.
Moreover, it suffers from an element of bias toward certain
cell types (cf. Ref. [26]) and sizes [27]. The size bias, which
is partially responsible for the cell-type bias as well, is
considerable. For equivalent transmembrane action potentials, the discharge of a large neuron generates a substantially greater flow of membrane current and a larger
extracellular spike than a small cell, and the resulting
extracellular field remains above recording noise levels over
a greater distance. Larger neurons (cells with 20 –30 Am of
diameter or greater) are estimated to generate a potential of
100 AV or more within a 100-Am-diameter sphere with the
electrode tip at its center [28]. The amplitude of this
potential decreases rapidly with increasing distance from the
electrode tip because of the aforementioned low-pass
properties of the extracellular medium.
For distances larger than ca. 140 Am, spikes become
indistinguishable from background noise [25]. Spikes
generated by large neurons will thus remain above the
noise level over a greater distance from the cell than spikes
from small neurons, so microelectrodes are likely to sample
their somas or axons preferentially, a prediction supported
by experimental work [27,29]. It follows that the commonly
measured spikes, especially those reported in alert trained
animals, most likely represent only very small neural
populations of large cells, which in the cortex are by and
large the principal cells (e.g., pyramidal cells in the cerebral
cortex and Purkinje neurons in the cerebellar cortex).
If the impedance of the microelectrode is sufficiently
low and its exposed tip is a bit farther from the spikegenerating sources so that action potentials do not
predominate the neural signal, then the electrode can
monitor the totality of the potentials in that region. The
EFPs recorded under these conditions (see the seminal
studies of Refs. [30,31] are related both to integrative
processes (dendritic events) and to spikes generated by
several hundreds of neurons. This kind of comprehensive
signal (Fig. 2A) can be analyzed in different ways to obtain
information originating from different cellular subdivisions
and processes. When time resolution is not a limiting
factor, the signal can be analyzed with time-dependent
frequency analysis (spectrograms with a Hamming window
of a few hundreds of milliseconds; Fig. 2B). Each
frequency band can then be examined for stimulus-related
activity. Alternatively, the signal can be separated in band
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Fig. 1. Top, A trace from extracellular recordings. The signal represents the changes in the mEFP generated by the sum of current sinks and sources in the
volume conductor. It consists of time-varying spatial distributions of action potentials that are riding slower waves generated by population postsynaptic
potentials, voltage-gated membrane oscillations, dendritic spikes and afterpotentials. Middle, The shaded region of the top trace expanded to partially reveal the
actual spikes and demonstrate better the low-frequency signal changes originating from perisynaptic activity. Bottom, The shaded region of the middle trace is
further expanded to reveal the large spikes (red ellipses) originating from projection neurons and the smaller spikes (green ellipses) presumably reflecting
interneuron activity. The slow-changing baseline can be further analyzed using time-dependent power-spectrum analysis.
by using conventional band-pass filtering. A high-pass
filter cutoff of approximately 400 Hz is used in most
recordings to obtain a signal, which has been shown to
reflect multiple-unit spiking activity (MUA); a low-pass
filter cutoff of ca. 300 Hz, to obtain the so-called local field
potentials (LFPs), which reflect perisynaptic events. A large
number of experiments have presented data indicating that
such a band separation does indeed underlie different
neural events.
2.2. Multiple-unit spiking activity
The magnitude of EFPs in the MUA range was shown
to be a function of cell and axon sizes. Combined
physiology and histology experiments demonstrated that
the magnitude of MUA is site- [32] and, thus, cell sizespecific [33], varying considerably from one brain region to
another but remaining relatively constant for any particular
site (e.g., neocortex vs. hippocampus). Homogeneous
populations of large cells were found to systematically
occur at sites of large-amplitude fast activity and vice versa
[34]. Similarly, the magnitude of axonal spikes is directly
correlated with the size of the transmitting axon [35,36].
All of these experiments show that MUA-range activity
reflects the variations in the magnitude of extracellular
spike potentials. In other words, large-amplitude signal
variations in the MUA range reflect large-amplitude
extracellular potentials and small-amplitude fast activities
are correlated with small ones.
The summation range for the fast MUA has also been
studied by a number of investigators. Electrodes with
exposed tips of approximately 100 Am (impedance,
40–120 kV), for example, were estimated to record from
a sphere with a radius of 50–350 Am [34,37,38], whereby
the activity from each point within the sphere is weighted by
a factor depending on the distance of the point from the tip
of the electrode [39]. All in all, depending on the recording
site and the electrode properties, the MUA most likely
represents a weighted sum of the extracellular action
potentials of all neurons within a sphere of approximately
140–300 Am radius, with the electrode at its center. Spikes
produced by the synchronous firings of many cells can, in
principle, be enhanced by summation and thus detected over
a larger distance [40,41].
2.3. Local field potentials
The low-frequency range of the mEFP signal, the LFPs,
represents mostly slow events reflecting cooperative activity
in neural populations. Until recently, these signals were
N.K. Logothetis, J. Pfeuffer / Magnetic Resonance Imaging 22 (2004) 1517–1531
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Fig. 2. Time-dependent frequency analysis of the comprehensive neural signal. (A) The neural signal, recorded during neuroimaging experiments, after
removing the gradient interference. (B) Spectrogram of the first 40 s of the neural response. Each time course is expressed in units of the S.D. of the prestimulus
period. Color coding indicates the reliability of signal change for each frequency. Red, green and blue lines show three frequency bands, the averages of which
are respectively illustrated with the red, green and blue time courses at the bottom right. (C) An alternative to the typical spectrogram that suffers from reduced
temporal resolution is band separation through band-pass filtering. Traditionally, two bands are examined in more detail: LFP in the range of 10 – 90 Hz,
including slower waves and gamma activity, and multiple unit activity (400 – 3000 Hz) representing spiking of large pyramidal cells and some inhibitory
interneurons. The band-pass signals can be rectified and low-pass filtered to obtain the benvelopQ of the signal or they can be Hilbert transformed to obtain the
exact bmodulatingQ function.
thought to represent exclusively synaptic events. Evidence
for this came from combined electroencephalographic
[electroencephalography (EEG)] and intracortical recordings showing that the slow wave activity in the EEG is
largely independent of neuronal spiking [42 – 45]. These
studies showed that, unlike the multiple-unit activity, the
magnitude of the slow field fluctuations is not correlated
with cell size but instead reflects the extent and geometry of
dendrites in each recording site. Cells in the so-called open
field geometrical arrangement, in which dendrites face one
direction and somata another, like the cortical pyramidal
cells of cortex, produce strong dendrite-to-soma dipoles
when they are activated by synchronous synaptic input.
Evidence concerning the origin of LFPs can also be
gathered from current-source density (CSD) analysis and
combined field potential and intracellular recordings (for a
review of CSD as well as of other types of ensemble
recordings, see Refs. [46,47]. Mitzdorf [48] has suggested
that LFPs actually reflect a weighted average of synchronized dendrosomatic components of the synaptic signals of
a neural population within 0.5–3 mm of the electrode tip
[49]. The upper limits of the spatial extent of LFP
summation were indirectly calculated by computing the
phase coherence of LFPs as a function of interelectrode
distance in experiments with simultaneous multiple-electrode recordings [50].
As mentioned above, LFPs were initially attributed
exclusively to population excitatory or inhibitory postsyn-
aptic potentials that are considerably slower than the spiking
activity. Later studies, however, provided evidence of the
existence of other types of slow activity unrelated to
synaptic events such as voltage-dependent membrane
oscillations (e.g., Ref. [51]) and spike afterpotentials. To
be more specific, the dendrosomatic spikes in the neurons
of the central nervous system are generally followed by
afterpotentials, a brief delayed depolarization, afterdepolarization and a longer-lasting afterhyperpolarization, which
are thought to play an important role in the control
of excitation-to-frequency transduction (e.g., Refs.
[52–54]). Afterpotentials, which were shown to be generated by calcium-activated potassium currents (e.g., Refs.
[53,55 –58] ), have a duration in the order of tens of
milliseconds and most likely contribute to the generation
of the LFP signals, as has been first suggested by Buzsaki
[59] and Buzsaki and Gage [60]. In summary, LFPs
represent slow waveforms including synaptic potentials,
afterpotentials of dendrosomatic spikes and voltage-gated
membrane oscillations that reflect the input of a given cortical area as well as its local intracortical processing, including the activity of excitatory and inhibitory interneurons.
3. Neural and hemodynamic responses
Functional neuroimaging techniques are divided into two
fundamentally different approaches: (a) electromagnetic
approaches including EEG and magnetoencephalography,
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both providing a high temporal resolution but poor spatial
information, and (b) hemodynamic–metabolic approaches
based on the fact that neuronal activity is coupled to energy
metabolism and the subsequent changes in CBF and
volume. The functional MRI techniques discussed in this
paper rely on hemodynamic–metabolic changes during
neural activation and its thorough understanding requires
some knowledge of (a) the basics of the brain’s energy
metabolism, (b) the anatomical neurovascular coupling, (c)
the type of neural activity and cells generating it, (d) the link
between activity and energy demands and (e) the processes
coupling the supply and demand of energy to the brain.
3.1. Basics of brain energy metabolism
The brain’s demand for substrate requires adequate
delivery of oxygen and glucose via elaborate mechanisms
regulating CBF. Not surprisingly, these mechanisms are
closely coupled with regional neural activity. Experimental
evidence for such coupling was provided more than 100
years ago in laboratory animals [61]. The seminal study of
Roy and Sherrington [61] was followed by the systematic
investigations of Kety and Schmidt [62], who introduced
the nitrous oxide technique, a global flow measurement
method that initially seemed to disprove the notion of a
local coupling of cerebral flow and neural activity [63]. But
the regional coupling of metabolic rate and neural activity
was soon verified with methods allowing local cerebral
flow measurements. Although such methods had been used
in conscious laboratory animals since the early 1960s [64],
a precise quantitative assessment of the relationship
between neural activity and regional blood flow was only
possible after the introduction of the deoxyglucose autoradiographic technique that enabled spatially resolved measurements of glucose metabolism in laboratory animals [65].
The results of a large number of experiments with the [14C]
deoxyglucose method have in fact revealed a clear
relationship between local cerebral activation and glucose
consumption [66].
3.2. Structural neurovascular coupling
The blood supply of several cerebral structures including
the cortex and deep grey matter has been studied in great
detail. Many studies involve intravascular injections outlining the lumen of the vessels. Complete casts of the
cortical vascular network can be obtained by infusing lowviscosity resins into the vasculature and allowing the resin
to polymerize. Dissolving away the surrounding tissue with
alkali leaves a model of the three-dimensional distribution
of vessels in that tissue, which can then be sectioned and
studied with scanning electron microscopy. The picture that
emerged from these studies is of a vascular network whose
density largely correlates with the average activity of any
given region.
Interestingly, the spatial correlations reported from a
large number of investigators have been mainly between
vascular density and the number of synapses rather than of
neurons [67–72]. In a careful and detailed study, Duvernoy
et al. [72] demonstrated that, based on its density, the human
cortical vascular network can be subdivided into four layers
parallel to the surface that overlap in a systematic manner
with certain portions of the cytoarchitectonically defined
Brodmann laminae [73]. Notably, the first Duvernoy layer,
consisting of vessels with orientation approximately parallel
to neural fibers, is entirely within the lower part of the
molecular layer (Layer I), which in the rodent has the lowest
concentration of cell bodies and highest density of synapses
[74]. Similarly, in the primate, this layer has the lowest
concentration of neurons, the highest concentration of
astrocytes and a high density of synapses [75]. Duvernoy’s
second layer coincides with the Lamina II and the upper
portion of Lamina III, while the third layer with the highest
vascular density covers the lower portion of Layer III, the
granular layer (IV) and the upper part of Layer V. Finally,
the vascular layer (VI) corresponds to the remaining deep
neuronal layers (see Figure 52 of Ref. [72]). In the same
study, vascular density differences were found not only in
the tangential but also in the vertical planes, with the latter
suggesting a column-like densification of the cortical
capillary network that is perpendicular to the surface.
The spatial correlations reported for the human brain
were later confirmed in animal studies. Zheng et al. [76]
from Duke University observed anatomical neurovascular
correlations on the finest scales, such as those represented
by the smallest capillaries, in the striate cortex of the squirrel
monkey. They demonstrated that high microvessel density —measured as the total length of vessels visible in a
given slice —is in close correspondence with areas stained
by cytochrome oxidase, an enzyme involved in oxidative
metabolism and used for the study of the cortical modular
organization [77,78]. Functional subdivisions such as the
blobs of V1 and the stripes of area V2 showed considerable
overlap with areas of dense vascularization. Microvessels
also reflected laminar and areal boundaries. Lamina I had
the lowest vascularization and IVc had the highest
vascularization, with a IVc/I ratio of 3.3:1 (averaged across
animals). Interestingly, the IVc/I ratio of synaptic density in
the striate cortex of macaque is 2.43:1, that of astrocytes is
1.2:1 and that of neurons is 78.8:1. Assuming some
similarity in the distribution of neurons, synapses and
astrocytes between squirrel monkeys and macaques, the
recent data would also support the notion of the vascular
density being correlated with perisynaptic elements rather
than with the density neuronal somata.
Similar results were obtained in different cortical areas
of rodents. For instance, endovascular casts revealed capillary densities resembling the whisker barrel pattern characterizing the somatosensory cortex of rats [79,80] and the
spatial patterns of stimulus-induced activation in the audi
tory cortex of chinchilla [81]. In the latter study, microscopy
of corrosion casts was combined with optical imaging of
intrinsic signals [82,83] to examine the covariation of
vascular density and hemodynamic response induced by
N.K. Logothetis, J. Pfeuffer / Magnetic Resonance Imaging 22 (2004) 1517–1531
noise stimuli. Similar to previous studies, overlaps were
observed between areas in which intrinsic signals were
detected, vascularization and distribution of myogenic
valves that are thought to control the blood flow to the
capillary networks.
3.3. Functional neurovascular coupling
The cerebral metabolic rate (CMR) is commonly
expressed in terms of oxygen consumption (CMRO2)
because glucose metabolism is about 90% aerobic and
therefore parallels oxygen consumption [84,85]. The
CMRO2 is likely to vary with neuronal firing properties,
shape and size, whereby large projection neurons, which
maintain energy-consuming processes such as ion pumping over a large membrane surface, may have larger
energy requirements (for detailed references, see Ref.
[86]). Moreover, neurons are not the only elements contributing to the energy metabolism of the brain; glia and
vascular endothelial cells do so as well. In fact, research
suggests a tightly regulated glucose metabolism in all
brain cell types. An interesting case is the glia cell known
as astrocyte. The structural and functional characteristics
of astrocytes make them ideal bridges between the
neuropil and the intraparenchymal capillaries. They are
indeed massively connected with both neurons and the
brain’s vasculature and express receptors and uptake sites
with which the neurotransmitters released during synaptic
activity can interact.
It has been suggested that for each synaptically released
glutamate molecule taken up with two to three Na+ ions by an
astrocyte, one glucose molecule enters the same astrocyte,
two ATP molecules are produced through glycolysis and two
lactate molecules are released and consumed by neurons to
yield 18 ATPs through oxidative phosphorylation. Neuronal
signals of some sort can therefore trigger receptor-mediated
glycogenolysis in astrocytes in a manner similar to peripheral
hormones in their target cells. Such signals can be ions or
molecules that transiently accumulate in the extracellular
space after neuronal activity and/or in fast neurotransmitters
eliciting both hemodynamic and metabolic responses in
anticipation of or at least in parallel with the regional
activation. Such findings, suggesting that presynaptic activity
may be a major consumer of energy, are in agreement with
microstimulation experiments in which the increase in
glucose utilization is assessed during orthodromic and
antidromic stimulation, the former activating both presynaptic and postsynaptic terminals and the latter activating only
postsynaptic terminals. Increases were only observed during
orthodromic stimulation [87–89] (for a review, see Ref. [90]).
Others, however, have challenged the notion that
presynaptic activity is the major energy consumer in the
brain [91]. Based on computations of the number of vesicles
released per action potential, the number of postsynaptic
receptors activated per vesicle released, the metabolic
consequences of activating a single receptor and changing
ion fluxes and neurotransmitter recycling, these investiga-
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tors concluded that the greater part of energy expenditure is
attributable to the postsynaptic effects of glutamate (about
34% of the energy in rodents and 74% of that in humans are
attributable to excitatory postsynaptic currents).
An interesting alternative was recently proposed, that is,
that the hemodynamic responses are probably driven by
neurotransmitter-related signaling rather than the direct local
energy needs of the brain [92]. There is indeed evidence that
blood flow in a number of brain structures including the
neocortex, cerebellum and hippocampus may be controlled
directly by glutamate and GABA. In the cerebellar cortex,
for example, the activation of parallel fibers releases
glutamate and leads to the depolarization of Purkinje cells
and interneurons. These cells, in turn, release GABA.
Notably, the increased blood flow that typically follows
the activation of parallel fibers is blocked by inhibitors of
non-NMDA glutamate receptors, nitric oxide synthase
(NOS) and adenosine receptors [93], while microinjections
of glutamate have vascular effects similar to those observed
during stimulation of the parallel fibers [94]. In the
neocortex and hippocampus, microinjection of neurotransmitters dilates pial arterioles and/or precapillary microvessels — an effect attenuated by inhibitors of NOS [95,96].
According to these latter findings, CBF may be driven by
neurotransmitter-related signaling being correlated with but
not triggered by the utilization of energy.
All in all, the results from a large number of experiments
together with literature-based estimations of energy budget
suggest that brain energy consumption is due to perisynaptic
activity, that is, neurotransmitter cycling as well as
restoration of gradients, following changes in voltage due
to input signals and to changes of intrinsic conductances. As
will become evident in the next paragraphs, such great
energy-requiring postsynaptic gradient restorations are
necessary whether or not the integration over the large
dendritic sites leads to the production of a typical Na+/K+
action potential at the axon hillock of the neurons.
4. Magnetic resonance imaging of the monkey brain
The application of neuroimaging for both noninvasive and
invasive research in a nonhuman primate requires high
spatiotemporal resolution and good SNR. Only then can the
fMRI signals be compared with signals obtained from optical
or electrical recordings. To meet these requirements, dedicated vertical, large-bore monkey MR systems were used at
high magnetic field (4,7 T) in combination with extensive
optimization of both the hardware (gradient performance, RF
coils) and the acquisition (automated shimming, zoomed and
inversion-prepared EPI, navigators) (see the work of Logothetis et al. [97] and Pfeuffer et al. [21] in this issue. High
magnetic fields are known to increase the signal-to-noise and
contrast-to-noise ratios as well as the spatial resolution and
specificity for functional imaging and to improve the spectral
dispersion and quality for MRS and CSI using 1H, 13C, 17O
and other nuclei [98,99].
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4.1. Structural imaging
High-resolution anatomical scans of the monkey brain
are of paramount importance in precisely determining
recording sites, in particular when recordings are attempted
from different areas simultaneously. Images of high signal
and contrast-to-noise ratios were obtained. Fig. 3 shows a
typical anatomical scan obtained with a volume coil. The
images were generated with T1-weighted, high-resolution
(256256 or 512512 matrix; 0.5-mm slice thickness)
scans by using the 3D-modified driven equilibrium Fourier
transform (MDEFT) [100] pulse sequence, with an echo
time of 4 ms, repetition time of 14.9 ms, flip angle of 208,
inversion time (H ) of 800 ms and 8 segments. The 3DMDEFT is largely insensitive to B1 inhomogeneities that
are present at high fields due to dielectric resonances,
achieving relatively uniform contrast for different brain
regions such as cortex, thalamus and basal ganglia. Fig. 4,
shows typical functional data obtained using the BOLD
contrast mechanism.
4.2. The BOLD contrast mechanism
For a detailed account of the BOLD mechanism, see Ref.
[101]. Here, briefly, the BOLD contrast mechanism was first
described in rat studies [102,103]. BOLD contrast is
basically produced by field inhomogeneities induced by
deoxyhemoglobin (dHb), which is confined in the intracellular space of the red blood cells that in turn are restricted to
the blood vessels. Magnetic susceptibility differences
between the dHb-containing compartments and the surrounding space generate magnetic field gradients across and
near the compartment boundaries. Pulse sequences designed
to be sensitive to such susceptibility differences generate
signal alterations whenever the concentration of dHb
changes. Upon neural activation, any increase in dHb would
be expected to enhance the field inhomogeneities and
reduce the BOLD signal. Yet, a few seconds after the onset
of stimulation, the BOLD signal actually increases. This
enhancement reflects an increase in CBF that overcompensates for the increase in oxygen and ultimately delivers an
oversupply of oxygenated blood [104,105].
The seminal studies of Ogawa et al. excited great interest
in applying BOLD fMRI to humans. In 1992, three groups
simultaneously and independently obtained results in
humans with the BOLD mechanism [106 –108], setting off
the flood of fMRI publications that have been appearing in
scientific journals ever since. Research over the last decade
has established that BOLD contrast depends not only on
blood oxygenation but also on CBF and volume, a complex
response controlled by several parameters. Despite this
complexity, much progress has been made toward quantitatively elucidating various aspects of the BOLD signal and
the way it relates to the hemodynamic–metabolic changes
occurring in response to elevated neuronal activity (for
detailed reading and references, see Refs. [109 –112].
4.3. BOLD MRI in monkeys
The left top of Fig. 4 shows a diagrammatic view of the
primate visual system to facilitate the interpretation of the
functional scans shown in the same figure. The images were
obtained with a 4.7-T scanner of 40 cm diameter (see Refs.
[113,114]). Resonators, primate chairs and special transport
Fig. 3. High-resolution anatomical scan of the monkey brain. The anatomical images were generated with T1-weighted, high-resolution (256256 or 512512
matrix; 0.5-mm thickness) scans by using the 3D-MDEFT.
N.K. Logothetis, J. Pfeuffer / Magnetic Resonance Imaging 22 (2004) 1517–1531
1525
Fig. 4. Activation of the retinogeniculostriate pathway of the monkey. On the left top, a diagrammatic representation of the visual system is shown. Visual
information processing starts in the retina in the back of the eye. The axons of the retinal ganglion cells, forming the optic nerve and tract, project to the
thalamic nuclei called the lateral geniculate bodies and then via the optic radiations onto the primary visual cortex, also called the striated cortex or area V1. The
left lower panel shows a thresholded z-score map rendered on an anatomical 3D-MDEFT T1-weighted scan. The activation was elicited by a polar-transformed
checkerboard pattern rotating in alternating directions. The functional resolution in this case was 1 mm2 in plane with 2-mm slice thickness. Activated are
the geniculate bodies and the striate cortex. On the right half, 500-A slices are shown, demonstrating the specificity of activation in both the thalamus and the
cortex. The LGN of the monkey is only about 6 mm across in the rostrocaudal and approximately 5 mm in the dorsoventral and mediolateral directions. The
precise anatomical localization of its activation is good evidence for the spatial specificity of fMRI in high magnetic fields.
systems were designed and built to position the anesthetized
or alert monkey inside the magnet (e.g., Ref. [115]). The
bottom left of Fig. 4 shows the functional results from an
anesthetized animal [115]. Thresholded z-score maps
showing brain activation are color coded and superimposed
on anatomical scans. The activation was elicited by a polartransformed checkerboard pattern rotating in alternating
directions. Robust BOLD signals in the LGNs and the
striate cortex were routinely obtained. On the right panel of
Fig. 4, multiple parasagittal slices are shown cutting through
the lateral geniculate body and the striate cortex. Anatomical and functional images of much higher resolution can be
obtained with small, tissue-compatible, intraosteally implantable radio frequency coils [97]. Tiny voxel sizes can be
obtained with good signal and contrast-to-noise ratios
revealing both structural and functional cortical architecture
in great detail.
4.4. Neural events underlying the BOLD contrast
One way to determine which cellular events contribute to
the generation of the hemodynamic response measured in
neuroimaging is to examine the correlation of LFPs, MUA
and single neuron activity with the hemodynamic response
in combined imaging and physiology experiments [116]. At
first sight, all these signals seemed to be correlated with the
BOLD signal. However, in all experiments, increases in the
LFP range were greater in both spectral power and
reliability. Furthermore, correlation analysis showed that
LFPs are better predictors of the BOLD response than
multiple-unit spiking. In fact, it was demonstrated that spike
rate is nothing but a bfortuitousQ predictor of the BOLD
signal, simply because the firing of neurons itself usually
happens to correlate with the LFPs.
Important were those cases in which a dissociation
between LFP and spiking activity was observed. In all these
cases, BOLD was predicted only by the LFPs. In sites
exhibiting strong multiple-unit response adaptation, for
example, MUA returned to the baseline approximately 2.5
s after stimulus onset, while the activity underlying the LFPs
remained elevated for the entire duration of the visual
stimulus, being the only neural signal to be associated with
the BOLD response.
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N.K. Logothetis, J. Pfeuffer / Magnetic Resonance Imaging 22 (2004) 1517–1531
Similarly, results from ongoing research in our laboratory involving the injection of various neurotransmitters
show that selective blocking of MUA has minimal effects
on the BOLD responses averaged over regions coextensive
with the spread of the injected substance. Finally, a
dissociation between spikes and CBF has been also
demonstrated in microstimulation studies in the cerebellar
cortex [117].
Taken together, these results suggest that changes in the
LFPs are more closely related to the evolution of the
BOLD signal than changes in the spiking activity of single
or multiple neurons. In other words, the BOLD signal
mainly reflects the incoming specific or association inputs
to an area and the processing of this input information by
the local cortical circuitry (including excitatory and
inhibitory interneurons). Of course, the incoming subcortical or cortical input to an area will often generate the kind
of output activity typically measured in intracortical
recordings. In this case, the spike rate will indeed be
correlated to the measured BOLD signal. If the activity of
large projection neurons, however, is shunted by concurrent
modulatory input, the incoming afferent signals and the
ongoing intracortical activity will still elicit strong hemodynamic responses. In such cases, spiking activity measured with microelectrodes will be a poor predictor of the
BOLD response.
5. The study of networks with MRI
5.1. Connectivity studies with paramagnetic tracers
Neuroanatomical corticocortical and corticosubcortical
connections have been examined mainly by means of
degeneration methods and anterograde and retrograde tracer
techniques (e.g., Refs. [118,119]). Although such studies
have demonstrated the value of the information gained from
the investigation of the topographic connections between
different brain areas, they do require fixed, processed tissue
for data analysis and therefore cannot be applied to an
animal participating in longitudinal studies, where consecutive studies examining an entire circuit could be carried out
in the same subjects.
MRI visible tracers that are infused into a specific brain
region and are transported anterogradely or retrogradely
along the axon may therefore enable us to study connectivity in the living animal. Such paramagnetic tracer studies
may also be used to validate and further develop noninvasive fiber-tracking techniques such as diffusion tensor
MRI that permit the study of connectivity even in the
human brain.
Manganese (Mn2+) is an interesting example of an MRIvisible contrast agent. The axonal transport of its radioactive isotope (54Mn2+) was first studied using histological
methods [120,121]. Although these studies were carried out
Fig. 5. Modeling BOLD from neural responses. The next slide shows the application of correlation analysis between neural and hemodynamic signals. The
positive lags of the cross-covariance function of the neural and BOLD responses represent the impulse response that is shown in the next slide. (A) Impulse
response. Note the initial dip. (B) Neural response, convolution: estimated BOLD response. (C) Note that the system is not time invariant. The impulse
response does not predict the response of the system for arbitrary input shifts in time.
N.K. Logothetis, J. Pfeuffer / Magnetic Resonance Imaging 22 (2004) 1517–1531
with the goal of understanding the regional specificity
of Mn2+ distribution, they indicated the usefulness of
Mn2+ as an anterograde neuronal tract tracer. Mn2+
distribution and transport have been also studied with
MRI in rats and mice [122,123]. Injection of MnCl2 in the
naris or the eye yields a clear signal enhancement in the
olfactory and visual pathways [122,123]. Furthermore,
the possibility that the transport of manganese may pass
across synapses was suggested by a number of studies
[121,122]. Pautler et al. [122] indicated that Mn2+ must
have traversed a synapse to explain the enhancements
detected in the olfactory cortex of the mouse following
the injection of its olfactory bulb. In contrast, Watanabe
et al. [123] reported that the signal enhancement they
1527
observed in their rat study was confined to regions known
to receive direct projections from the retina and concluded that it did not constitute evidence for transsynaptic
crossing of Mn2+.
An example of local manganese injection is shown in
Fig. 5. Injections were made into the striatum [124]. Its
projections were confirmed histologically in the same
animals by injecting WGA-HRP at the same sites where
MnCl2 had been injected. The size and location of the
projection foci in the striatal targets were comparable
with those found in both the MR and histology images.
By injecting WGA-HRP at the same sites as MnCl2, we
also confirmed for each animal the absence of a direct
connection from the injection sites to various brain
Fig. 6. Manganese-enhanced MRI. (A) Schematic lateral views of the monkey brain showing the injection sites in the rostral part of the right putamen and the
head of the left caudate nucleus. (B) Coronal histology sections (left) and MR coronal slices (right) showing the WGA-HRP labeling and the Mn2+ signal in the
GPe and GPi (arrow heads) 24.5 h after right caudate and left putamen injections. cd indicates caudate nucleus; cs, cingulate sulcus; GPe, globus pallidus
external segment; GPi, globus pallidus internal segment; ls, lateral sulcus; pu, putamen; sts, superior temporal sulcus. (C) Mn2+ signal changes in thalamic
nuclei over long periods (4 h–18 d) after MnCl2 injection into the right caudate and left putamen. (A) Coronal MR images showing the Mn2+ signal in the
habenular thalamic nuclei (Hb, bright discrete regions; arrowheads). As seen in the figure, in addition to the signal intensity increases observed in the globus
pallidus and the SN, we also found significant increases in several thalamic nuclei not known to receive direct projections from the caudate or putamen: (a) the
ventral anterior and ventral lateral nuclei (VA/VL) and (b) the habenular nucleus (Hb). The distribution of signal in the Hb was discrete and more prominent
than in the VA/VL complex. The selective distribution of the Mn2+ signal in areas not receiving direct projections from the injection sites demonstrates
convincingly the transsynaptic transfer of the manganese.
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N.K. Logothetis, J. Pfeuffer / Magnetic Resonance Imaging 22 (2004) 1517–1531
structures (e.g., thalamic nuclei). In this study, manganese was actually found in a number of structures
receiving no direct projections from the injected sites
(Fig. 6).
5.2. MR imaging and electrical microstimulation
Our knowledge of connectivity and functional organization could profit a great deal from the combination of
MRI with electrical microstimulation. The latter is established as an important neurobiological tool for the study of
areal representation and the functional properties of CNS
output structures. A new method that combines this
technique with fMRI for the detailed study of neural
connectivity in the alive animal was recently developed.
Specially constructed microelectrodes were used to directly
stimulate a selected subcortical or cortical area while
simultaneously measuring changes in brain activity, which
was indexed by the BOLD signal [125]. The exact location
of the stimulation site was determined by means of
anatomical scans as well as by the study of the physiological properties of neurons. Electrical stimulation was
delivered using a biphasic pulse generator attached to a
constant-current stimulus isolation unit. The compensation
circuit, designed to minimize interference generated by the
switching gradients during recording, was always active,
minimizing the gradient-induced currents in the range of
the stimulation current. Local microstimulation of striate
cortex yielded both local BOLD signals and activation of
areas V2, V3 and MT. Microstimulation of dLGN resulted
in the activation of both striate cortex and areas V2, V3 and
MT. The findings show that microstimulation combined
with fMRI can be an exquisite tool for finding and studying
target areas of electrophysiological interest.
6. Conclusions
The suitability of MRI for functional brain mapping is
firmly established. BOLD fMRI has been successfully
implemented in awake human subjects as well as in
animals such as rats, cats and monkeys. The use of high
magnetic fields increases functional signal changes and
improves both signal specificity and spatial resolution. MRI
studies, in which small voxels of microliter volumes may
contain as few as 600–800 cortical neurons, can help us
understand how neural networks are organized and how
small cell assemblies contribute to the activation patterns
revealed in fMRI. The combination of this technique with
electrophysiology has fully confirmed the longstanding
assumption that the regional activations measured in MR
neuroimaging do indeed reflect local increases in neural
activity. In addition, it has been demonstrated that fMRI
responses mostly reflect the input of a given cortical area
and its local intracortical processing, including the activity
of excitatory and inhibitory interneurons. Finally, MRI
visible tracers and microstimulation appear to be ideal for
the study of connectivity in living animals.
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