Simultaneous BOLD fMRI and fiber-optic calcium recording in rat neocortex

Functional magnetic resonance imaging (fMRI) based on blood oxygen level-dependent (BOLD) contrast is widely used for probing brain activity, but its relationship to underlying neural activity remains elusive. Here, we combined fMRI with fiber-optic recordings of fluorescent calcium indicator signals to investigate this relationship in rat somatosensory cortex. Electrical forepaw stimulation (1-10 Hz) evoked fast calcium signals of neuronal origin that showed frequency-dependent adaptation. Additionally, slower calcium signals occurred in astrocyte networks, as verified by astrocyte-specific staining and two-photon microscopy. Without apparent glia activation, we could predict BOLD responses well from simultaneously recorded fiber-optic signals, assuming an impulse response function and taking into account neuronal adaptation. In cases with glia activation, we uncovered additional prolonged BOLD signal components. Our findings highlight the complexity of fMRI BOLD signals, involving both neuronal and glial activity. Combined fMRI and fiber-optic recordings should help to clarify cellular mechanisms underlying BOLD signals.     

One picture is worth at least a million neurons

How many neurons participate in the representation of a single visual image? Answering this question is critical for constraining biologically inspired models of object recognition, which vary greatly in their assumptions from few "grandmother cells" to numerous neurons in widely distributed networks. Functional imaging techniques, such as fMRI, provide an opportunity to explore this issue, since they allow the simultaneous detection of the entire neuronal population responding to each stimulus. Several studies have shown that fMRI BOLD signal is approximately proportional to neuronal activity. However, since it provides an indirect measure of this activity, obtaining a realistic estimate of the number of activated neurons requires several intervening steps. Here, we used the extensive knowledge of primate V1 to yield a conservative estimate of the ratio between hemodynamic response and neuronal firing. This ratio was then used, in addition to several cautious assumptions, to assess the number of neurons responding to a single-object image in the entire visual cortex and particularly in object-related areas. Our results show that at least a million neurons in object-related cortex and about two hundred million neurons in the entire visual cortex are involved in the representation of a single-object image.     

What does fMRI tell us about neuronal activity?

In recent years, cognitive neuroscientists have taken great advantage of functional magnetic resonance imaging (fMRI) as a non-invasive method of measuring neuronal activity in the human brain. But what exactly does fMRI tell us? We know that its signals arise from changes in local haemodynamics that, in turn, result from alterations in neuronal activity, but exactly how neuronal activity, haemodynamics and fMRI signals are related is unclear. It has been assumed that the fMRI signal is proportional to the local average neuronal activity, but many factors can influence the relationship between the two. A clearer understanding of how neuronal activity influences the fMRI signal is needed if we are correctly to interpret functional imaging data.     

Emotion-perception interplay in the visual cortex: "the eyes follow the heart"

Emotive aspects of stimuli have been shown to modulate perceptual thresholds. Lately, studies using functional Magnetic Resonance Imaging (fMRI) showed that emotive aspects of visual stimuli activated not only canonical limbic regions, but also sensory areas in the cerebral cortex. However, it is still arguable to what extent such emotive, related activation in sensory areas of the cortex are affected by physical characteristic or attribute difference of stimuli. To manipulate valence of stimuli while keeping visual features largely unchanged, we took advantage of the Expressional Transfiguration (ET) of faces. In addition, to explore the sensitivity of high level visual regions, we compared repeated with unrepeated (i.e. different) stimuli presentations (fMR adaptation). Thus, the dynamics of brain responses was determined according to the relative signal reduction during repeated relative to different presentations (adaptation ratio). Our results showed, for the first time, that emotional valence produced significant differences in fMR adaptation, but not in overall levels of activation of lateral occipital complex (LOC). We then asked whether this emotion modulation on sensory cortex could be related to previous personal experience that attached negative attributes of stimuli. To clarify this, we investigated Posttraumatic Stress Disorder (PTSD) and non-PTSD veterans. PTSD is characterized by recurrent revival of trauma-related sensations. Such phenomena have been attributed to a disturbed processing of trauma-related stimuli, either at the perceptual level or at the cognitive level. We assumed that PTSD veterans would differ from non-PTSD veterans (who have similar combat experience) in their high order visual cortex responses to combat-related visual stimuli that are associated with their traumatic experience. An fMRI study measured the cerebral activation of subjects while viewing pictures with and without combat content, in repeated or different presentation conditions. The emotive effect on the visual cortex was found, again, only in the fMR-adaptation paradigm. Visual cortical regions showed significant differences between PTSD and non-PTSD veterans only in repeated presentations of trauma-related stimuli (i.e. combat). In these regions, PTSD veterans showed less decrease in signal with repeated presentations of the same combat-related stimuli. This finding points to the possibility that traumatic experience modulates brain activity at the level of sensory cortex itself.     

Bayesian comparison of neurovascular coupling models using EEG-fMRI

Functional magnetic resonance imaging (fMRI), with blood oxygenation level-dependent (BOLD) contrast, is a widely used technique for studying the human brain. However, it is an indirect measure of underlying neuronal activity and the processes that link this activity to BOLD signals are still a topic of much debate. In order to relate findings from fMRI research to other measures of neuronal activity it is vital to understand the underlying neurovascular coupling mechanism. Currently, there is no consensus on the relative roles of synaptic and spiking activity in the generation of the BOLD response. Here we designed a modelling framework to investigate different neurovascular coupling mechanisms. We use Electroencephalographic (EEG) and fMRI data from a visual stimulation task together with biophysically informed mathematical models describing how neuronal activity generates the BOLD signals. These models allow us to non-invasively infer the degree of local synaptic and spiking activity in the healthy human brain. In addition, we use Bayesian model comparison to decide between neurovascular coupling mechanisms. We show that the BOLD signal is dependent upon both the synaptic and spiking activity but that the relative contributions of these two inputs are dependent upon the underlying neuronal firing rate. When the underlying neuronal firing is low then the BOLD response is best explained by synaptic activity. However, when the neuronal firing rate is high then both synaptic and spiking activity are required to explain the BOLD signal.     

The neural basis of the blood-oxygen-level-dependent functional magnetic resonance imaging signal

Magnetic resonance imaging (MRI) has rapidly become an important tool in clinical medicine and biological research. Its functional variant (functional magnetic resonance imaging; fMRI) is currently the most widely used method for brain mapping and studying the neural basis of human cognition. While the method is widespread, there is insufficient knowledge of the physiological basis of the fMRI signal to interpret the data confidently with respect to neural activity. This paper reviews the basic principles of MRI and fMRI, and subsequently discusses in some detail the relationship between the blood-oxygen-level-dependent (BOLD) fMRI signal and the neural activity elicited during sensory stimulation. To examine this relationship, we conducted the first simultaneous intracortical recordings of neural signals and BOLD responses. Depending on the temporal characteristics of the stimulus, a moderate to strong correlation was found between the neural activity measured with microelectrodes and the BOLD signal averaged over a small area around the microelectrode tips. However, the BOLD signal had significantly higher variability than the neural activity, indicating that human fMRI combined with traditional statistical methods underestimates the reliability of the neuronal activity. To understand the relative contribution of several types of neuronal signals to the haemodynamic response, we compared local field potentials (LFPs), single- and multi-unit activity (MUA) with high spatio-temporal fMRI responses recorded simultaneously in monkey visual cortex. At recording sites characterized by transient responses, only the LFP signal was significantly correlated with the haemodynamic response. Furthermore, the LFPs had the largest magnitude signal and linear systems analysis showed that the LFPs were better than the MUAs at predicting the fMRI responses. These findings, together with an analysis of the neural signals, indicate that the BOLD signal primarily measures the input and processing of neuronal information within a region and not the output signal transmitted to other brain regions.     

Distributed neural systems for the generation of visual images

Visual perception of houses, faces, and chairs evoke differential responses in ventral temporal cortex. Using fMRI, we compared activations evoked by perception and imagery of these object categories. We found content-related activation during imagery in extrastriate cortex, but this activity was restricted to small subsets of the regions that showed category-related activation during perception. Within ventral temporal cortex, activation during imagery evoked stronger responses on the left whereas perception evoked stronger responses on the right. Additionally, visual imagery evoked activity in parietal and frontal cortex, but this activity was not content related. These results suggest that content-related activation during imagery in visual extrastriate cortex may be implemented by "top-down" mechanisms in parietal and frontal cortex that mediate the retrieval of face and object representations from long-term memory and their maintenance through visual imagery.     

Selectivity of neuronal adaptation does not match response selectivity: a single-cell study of the FMRI adaptation paradigm

fMRI-based adaptation paradigms (fMR-A) have been used to infer neuronal stimulus selectivities in humans. Inferring neuronal selectivities from fMR-A, however, requires an understanding of the relationship between the stimulus selectivity of neuronal adaptation and responses. We studied this relationship by recording single cells in macaque inferior temporal (IT) cortex, an area that shows fMRI adaptation. Repetition of identical object images reduced the responsiveness of single IT neurons. Presentation of an image to which the neuron was unresponsive did not alter the response to a subsequent image that activated the neuron. Successive presentation of two different images to which the neuron responded similarly produced adaptation, but less so than the repeated presentation of an image. The neuronal adaptation at the single-cell level showed a greater degree of stimulus selectivity than the responses. This complicates the interpretation of fMR-A paradigms when inferring neuronal selectivity.     

Sparse representation of sounds in the unanesthetized auditory cortex

How do neuronal populations in the auditory cortex represent acoustic stimuli? Although sound-evoked neural responses in the anesthetized auditory cortex are mainly transient, recent experiments in the unanesthetized preparation have emphasized subpopulations with other response properties. To quantify the relative contributions of these different subpopulations in the awake preparation, we have estimated the representation of sounds across the neuronal population using a representative ensemble of stimuli. We used cell-attached recording with a glass electrode, a method for which single-unit isolation does not depend on neuronal activity, to quantify the fraction of neurons engaged by acoustic stimuli (tones, frequency modulated sweeps, white-noise bursts, and natural stimuli) in the primary auditory cortex of awake head-fixed rats. We find that the population response is sparse, with stimuli typically eliciting high firing rates (>20 spikes/second) in less than 5% of neurons at any instant. Some neurons had very low spontaneous firing rates (<0.01 spikes/second). At the other extreme, some neurons had driven rates in excess of 50 spikes/second. Interestingly, the overall population response was well described by a lognormal distribution, rather than the exponential distribution that is often reported. Our results represent, to our knowledge, the first quantitative evidence for sparse representations of sounds in the unanesthetized auditory cortex. Our results are compatible with a model in which most neurons are silent much of the time, and in which representations are composed of small dynamic subsets of highly active neurons.     

Dynamic Adjustment of Stimuli in Real Time Functional Magnetic Resonance Imaging

The conventional fMRI image analysis approach to associating stimuli to brain activation is performed by carrying out a massive number of parallel univariate regression analyses. fMRI blood-oxygen-level dependent (BOLD) signal, the basis of these analyses, is known for its low signal-noise-ratio and high spatial and temporal signal correlation. In order to ensure accurate localization of brain activity, stimulus administration in an fMRI session is often lengthy and repetitive. Real-time fMRI BOLD signal analysis is carried out as the signal is observed. This method allows for dynamic, real-time adjustment of stimuli through sequential experimental designs. We have developed a voxel-wise sequential probability ratio test (SPRT) approach for dynamically determining localization, as well as decision rules for stopping stimulus administration. SPRT methods and general linear model (GLM) approaches are combined to identify brain regions that are activated by specific elements of stimuli. Stimulus administration is dynamically stopped when sufficient statistical evidence is collected to determine activation status across regions of interest, following predetermined statistical error thresholds. Simulation experiments and an example based on real fMRI data show that scan volumes can be substantially reduced when compared with pre-determined, fixed designs while achieving similar or better accuracy in detecting activated voxels. Moreover, the proposed approach is also able to accurately detect differentially activated areas, and other comparisons between task-related GLM parameters that can be formulated in a hypothesis-testing framework. Finally, we give a demonstration of SPRT being employed in conjunction with a halving algorithm to dynamically adjust stimuli.     

Long-range, pattern-dependent contextual effects in early human visual cortex

The standard view of neurons in early visual cortex is that they behave like localized feature detectors. Here we demonstrate that processing in early visual areas goes beyond feature detection by showing that neural responses are greater when a feature deviates from its context compared to when it does not deviate from its context. Using psychophysics, fMRI, and electroencephalography methodologies, we measured neural responses to an oriented Gabor ("target") embedded in various visual patterns as defined by the relative orientation of flanking stimuli. We first show using psychophysical contrast adaptation and fMRI that a target that differs from its context results in more neural activity compared to a target that is contained within an alternating sequence, suggesting that neurons in early visual cortex are sensitive to large-scale orientation patterns. Next, we use event-related potentials to show that orientation deviations affect the earliest sensory components of the target response. Finally, we use forced-choice classification of "noise" stimuli to show that we are more likely to "see" orientations that deviate from the context. Our results suggest that early visual cortex is sensitive to global patterns in images in a way that is markedly different from the predictions of standard models of cortical visual processing.     

Adaptation of oriented and unoriented color-selective neurons in human visual areas

Primary visual cortex contains at least two distinct populations of color-selective cells: neurons in one have circularly symmetric receptive fields and respond best to reddish and greenish light, while neurons in another have oriented receptive fields and a variety of color preferences. The relative prevalence and perceptual roles of the two kinds of neurons remain controversial, however. We used fMRI and a selective adaptation technique to measure responses attributable to these two populations. The technique revealed evidence of adaptation in both populations and indicated that they each produced strong signals in V1 and other human visual areas. The activity of both sets of neurons was also reflected in color appearance measurements made with the same stimuli. Thus, both oriented and unoriented color-selective cells in V1 are important components of the neural pathways that underlie perception of color.     

Nonmonotonic noise tuning of BOLD fMRI signal to natural images in the visual cortex of the anesthetized monkey

BACKGROUND: The perceptual ability of humans and monkeys to identify objects in the presence of noise varies systematically and monotonically as a function of how much noise is introduced to the visual display. That is, it becomes more and more difficult to identify an object with increasing noise. Here we examine whether the blood oxygen level-dependent functional magnetic resonance imaging (BOLD fMRI) signal in anesthetized monkeys also shows such monotonic tuning. We employed parametric stimulus sets containing natural images and noise patterns matched for spatial frequency and intensity as well as intermediate images generated by interpolation between natural images and noise patterns. Anesthetized monkeys provide us with the unique opportunity to examine visual processing largely in the absence of top-down cognitive modulations and can thus provide an important baseline against which work with awake monkeys and humans can be compared. RESULTS: We measured BOLD activity in occipital visual cortical areas as natural images and noise patterns, as well as intermediate interpolated patterns at three interpolation levels (25%, 50%, and 75%) were presented to anesthetized monkeys in a block paradigm. We observed reliable visual activity in occipital visual areas including V1, V2, V3, V3A, and V4 as well as the fundus and anterior bank of the superior temporal sulcus (STS). Natural images consistently elicited higher BOLD levels than noise patterns. For intermediate images, however, we did not observe monotonic tuning. Instead, we observed a characteristic V-shaped noise-tuning function in primary and extrastriate visual areas. BOLD signals initially decreased as noise was added to the stimulus but then increased again as the pure noise pattern was approached. We present a simple model based on the number of activated neurons and the strength of activation per neuron that can account for these results. CONCLUSIONS: We show that, for our parametric stimulus set, BOLD activity varied nonmonotonically as a function of how much noise was added to the visual stimuli, unlike the perceptual ability of humans and monkeys to identify such stimuli. This raises important caveats for interpreting fMRI data and demonstrates the importance of assessing not only which neural populations are activated by contrasting conditions during an fMRI study, but also the strength of this activation. This becomes particularly important when using the BOLD signal to make inferences about the relationship between neural activity and behavior.     

Adaptation to left-right reversed vision rapidly activates ipsilateral visual cortex in humans.

The brain mechanisms of adaptation to visual transposition are of increasing interest, not only for research on sensory-motor coordination, but also for neuropsychological rehabilitation. Sugita [Nature 380 (1996) 523] found that after adaptation to left-right reversed vision for one and a half months, monkey V1 neurons responded to stimuli presented not only in the contralateral visual field, but also in the ipsilateral visual field. To identify the underlying neuronal mechanisms of adaptation to visual transposition, we conducted fMRI and behavioral experiments for which four adult human subjects wore left-right reversing goggles for 35/39 days, and investigated: (1) whether ipsilateral V1 activation can be induced in human adult subjects; (2) if yes, when the ipsilateral activity starts, and what kind of behavioral/psychological changes occur accompanying the ipsilateral activity; (3) whether other visual cortices also show an ipsilateral activity change. The results of behavioral experiments showed that visuomotor coordinative function and internal representation of peripersonal space rapidly adapted to the left-right reversed vision within the first or second week. Accompanying these behavioral changes, we found that both primary (V1) and extrastriate (MT/MST) visual cortex in human adults responded to visual stimuli presented in the ipsilateral visual field. In addition, the ipsilateral activity started much sooner than the one and a half months, which had been expected from the monkey neurophysiological study. The results of the present study serve as physiological evidence of large-scale, cross-hemisphere, cerebral plasticity that exists even in adult human brain.     

Selective stabilization of neuronal representations by resonance between spontaneous prerepresentations of the cerebral network and percepts evoked by interaction with the outside world

Changeux et al. (Changeux, Heidmann and Patte, in "The Biology of Learning" Dahlem Conference, 1984, pp. 115-133, Springer Verlag) have recently discussed a model of "learning by selection" in which the storage of patterns of activity--or prerepresentations--within a network of neurons, results from the coincidence or "resonance" between a spontaneous activity of the neurons and external signals applied to the network--for instance sensory stimuli. In this Note, a mathematical formulation of the model is presented, based on that proposed by Little and Shaw (Little, Math. Biosci., 19, 1974, pp. 101-120; Little and Shaw, Math. Biosci., 39, 1978, pp. 281-290) for the statistical analysis of neuronal activity within a network, and on a rule for modulation of synaptic efficacies derived from that proposed by Hebb (Hebb, The Organisation of Behaviour, 1949, Wiley). The effect of an external signal sigma on the probability P(beta) of occurrence of a given prerepresentation beta under stationary conditions has been analytically derived [cf. equation (16) in text]. Taking into account that the system spontaneously fluctuates between various prerepresentations, it is shown that P(beta) is increased by the external signal sigma when (1) beta is close to sigma--namely the external signal significantly modifies the probabilities of those prerepresentations that resemble sigma--, and (2) when the external signal sigma sets the neurons precisely in the state that they would have more probably reached at the moment when the external signal was applied. Namely there should exist a "resonance" between sigma and the prerepresentation of the network when sigma is applied.     

Differential processing of objects under various viewing conditions in the human lateral occipital complex

The invariant properties of human cortical neurons cannot be studied directly by fMRI due to its limited spatial resolution. Here, we circumvented this limitation by using fMR adaptation, namely, reduction of the fMR signal due to repeated presentation of identical images. Object-selective regions (lateral occipital complex [LOC]) showed a monotonic signal decrease as repetition frequency increased. The invariant properties of fMR adaptation were studied by presenting the same object in different viewing conditions. LOC exhibited stronger fMR adaptation to changes in size and position (more invariance) compared to illumination and viewpoint. The effect revealed two putative subdivisions within LOC: caudal-dorsal (LO), which exhibited substantial recovery from adaptation under all transformations, and posterior fusiform (PF/LOa), which displayed stronger adaptation. This study demonstrates the utility of fMR adaptation for revealing functional characteristics of neurons in fMRI studies.     

Regional variation in neurovascular coupling and why we still lack a Rosetta Stone

Functional magnetic resonance imaging (fMRI) is the dominant tool in cognitive neuroscience although its relation to underlying neural activity, particularly in the human brain, remains largely unknown. A major research goal, therefore, has been to uncover a ‘Rosetta Stone’ providing direct translation between the blood oxygen level-dependent (BOLD) signal, the local field potential and single-neuron activity. Here, I evaluate the proposal that BOLD signal changes equate to changes in gamma-band activity, which in turn may partially relate to the spiking activity of neurons. While there is some support for this idea in sensory cortices, findings in deeper brain structures like the hippocampus instead suggest both regional and frequency-wise differences. Relatedly, I consider four important factors in linking fMRI to neural activity: interpretation of correlations between these signals, regional variability in local vasculature, distributed neural coding schemes and varying fMRI signal quality. Novel analytic fMRI techniques, such as multivariate pattern analysis (MVPA), employ the distributed patterns of voxels across a brain region to make inferences about information content rather than whether a small number of voxels go up or down relative to baseline in response to a stimulus. Although unlikely to provide a Rosetta Stone, MVPA, therefore, may represent one possible means forward for better linking BOLD signal changes to the information coded by underlying neural activity.This article is part of the theme issue ‘Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity’.     

Large Ventral Lateral Neurons Determine the Phase of Evening Activity Peak across Photoperiods in Drosophila melanogaster

The dual-oscillator model, originally proposed as a mechanism for how vertebrates adapt to seasonal changes, has been invoked to explain circadian entrainment in Drosophila melanogaster. Distinct subsets of neurons have been designated as "morning" and "evening" oscillators that function as regulators of rhythmic activity/rest behavior. Some studies have led to a model in which a subset of 8 "morning" cells (4 bilaterally located small ventral lateral neurons) and another subset of approximately 130 "evening" cells exert different levels of dominance within the circadian circuit in different seasons. However, many studies propose a more integrative neuronal network, with the whole network orchestrating activity/rest rhythms in different seasons, as opposed to hierarchical dominance among neurons. Within the circadian network, our understanding of the role of the large ventral lateral neurons (l-LN(v)) has thus far been limited to conveying light information to the clocks and as light-activated neurons mediating arousal. In support of the framework of a more distributed model, we report an important circadian clock-related role for the l-LN(v) in electrical activity-dependent phasing of the evening peak across a range of photoperiods. Further, we propose a model in which l-LN(v) enable adaptation to seasonal changes by regulating the phase of the evening peak. Additionally, we demonstrate a hitherto unknown role for the small ventral lateral neurons (s-LN(v)) in the arousal circuit, thus showing that neuronal function is flexible such that certain neurons can play more than one role in distinct circuits.     

Thematic Minireview Series: Molecular Mechanisms of Synaptic Plasticity

The human brain contains ∼86 billion neurons, which are precisely organized in specific brain regions and nuclei. High fidelity synaptic communication between subsets of neurons in specific circuits is required for most human behaviors, and is often disrupted in neuropsychiatric disorders. The presynaptic axon terminals of one neuron release neurotransmitters that activate receptors on multiple postsynaptic neuron targets to induce electrical and chemical responses. Typically, postsynaptic neurons integrate signals from multiple presynaptic neurons at thousands of synaptic inputs to control downstream communication to the next neuron in the circuit. Importantly, the strength (or efficiency) of signal transmission at each synapse can be modulated on time scales ranging up to the lifetime of the organism. This “synaptic plasticity” leads to changes in overall neuronal circuit activity, resulting in behavioral modifications. This series of minireviews will focus on recent advances in our understanding of the molecular and cellular mechanisms that control synaptic plasticity.