Three-Dimensional Shape Representation in Monkey Cortex

Sereno, Margaret E.; Trinath, T; Augath, M; Logothetis, Nikos K.

doi:10.1016/s0896-6273(02)00598-6

Cited by 151 publications

(133 citation statements)

References 63 publications

(3 reference statements)

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“…S1). This finding contrasts with the fMRI data from anesthetized monkeys, which show no 3D-SFM sensitivity to random dot stimuli in V4 (13). Yet our human fMRI results matched the awake monkey fMRI data: A large region of cortex immediately posterior to hMT/V5ϩ was more activated during 3D than during 2D moving displays.…”

contrasting

confidence: 95%

“…In line with earlier reports (4)(5)(6)13), area MT/V5 was activated more by 3D than by 2D moving random-line displays. In addition, the area in the fundus of the superior temporal sulcus (FST) also exhibited significant 3D-SFM sensitivity (Fig.…”

supporting

confidence: 92%

“…To address these issues, we turned to recently developed fMRI techniques (8) in awake (9-12) rather than anesthetized (13)(14) monkeys. By performing human fMRI with virtually identical experimental procedures as in the awake monkey fMRI experiments, reliable interspecies comparisons could be made.…”

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confidence: 99%

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Extracting 3D from Motion: Differences in Human and Monkey Intraparietal Cortex

Vanduffel

Fize

Peuskens

et al. 2002

Science

307

277

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We compared three-dimensional structure-from-motion (3D-SFM) processing in awake monkeys and humans using functional magnetic resonance imaging. Occipital and midlevel extrastriate visual areas showed similar activation by 3D-SFM stimuli in both species. In contrast, intraparietal areas showed significant 3D-SFM activation in humans but not in monkeys. This suggests that human intraparietal cortex contains visuospatial processing areas that are not present in monkeys.To reconstruct the third dimension from a two-dimensional (2D) retinal image, our brain uses binocular as well as monocular cues such as shading, texture, and occlusion. Both humans and monkeys are also able to extract the 3D structure of an object from motion parallax cues that activate occipitoparietal areas in both species (1-3). Because neurons in the middle temporal area (MT/V5) are sensitive for speed gradients that reflect planes tilted in depth (4, see also 5), this area might play a crucial role in the extraction of depth from motion. Supporting evidence has been gleaned from a functional magnetic resonance imaging (fMRI) study showing 3D-SFM sensitivity in the human MT/V5 complex (hMT/V5ϩ) (6 ). These human fMRI results raise a first question: To what extent can they be generalized to the primate visual system? Furthermore, anatomical evidence suggests that there might be functional differences between human and monkey intraparietal cortex: The intraparietal sulcus separates area 5 from area 7 in the monkey, whereas in humans these two areas belong to the superior parietal lobe. In addition, in humans, the intraparietal lobe separates area 7 from areas 39 and 40, which have no clear counterpart in monkeys (7 ). Therefore, our second goal was to determine whether monkey intraparietal cortex is as important for motion-dependent depth processing as implied by human imaging (6 ).To address these issues, we turned to recently developed fMRI techniques (8) in awake (9-12) rather than anesthetized (13-14 ) monkeys. By performing human fMRI with virtually identical experimental procedures as in the awake monkey fMRI experiments, reliable interspecies comparisons could be made.The stimuli were displays of nine randomly connected lines, rotating in depth, that created a clear 3D percept (movie S1). Control stimuli consisted of the same displays that were either static or moving in one plane. We controlled for potential attentional differences between the 3D and 2D conditions by using a 1-back task in humans, as well as a demanding high-acuity fixation task (8, 9) in both species.In line with earlier reports (4-6, 13), area MT/V5 was activated more by 3D than by 2D moving random-line displays. In addition, the area in the fundus of the superior temporal sulcus (FST) also exhibited significant 3D-SFM sensitivity (Fig. 1A). Figure 2A shows the (3D -2D) pattern of activation for a single human subject, and the average activation for a group of eight subjects is shown in Fig. 2B. These results are similar to those obtained in an earlier study in which so...

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confidence: 95%

supporting

confidence: 92%

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Extracting 3D from Motion: Differences in Human and Monkey Intraparietal Cortex

Vanduffel

Fize

Peuskens

et al. 2002

Science

307

277

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“…Additionally, it has been shown that using remifentanil (an ultra-fast-acting -opioid receptor agonist) has no significant effect on the neurovascular and neural activity of brain areas that do not belong to the pain matrix (Lund et al, 1994;Logothetis et al, 1999Logothetis et al, , 2009Sereno et al, 2002;Zappe et al, 2008a,b). In particular, visual cortex does not bind remifentanil (Jones et al, 1991).…”

Section: Methodsmentioning

confidence: 99%

The Amplitude and Timing of the BOLD Signal Reflects the Relationship between Local Field Potential Power at Different Frequencies

et al. 2012

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There is growing evidence that several components of the mass neural activity contributing to the local field potential (LFP) can be partly separated by decomposing the LFP into nonoverlapping frequency bands. Although the blood oxygen level-dependent (BOLD) signal has been found to correlate preferentially with specific frequency bands of the LFP, it is still unclear whether the BOLD signal relates to the activity expressed by each LFP band independently of the others or if, instead, it also reflects specific relationships among different bands. We investigated these issues by recording, simultaneously and with high spatiotemporal resolution, BOLD signal and LFP during spontaneous activity in early visual cortices of anesthetized monkeys (Macaca mulatta). We used information theory to characterize the statistical dependency between BOLD and LFP. We found that the alpha (8 -12 Hz), beta (18 -30 Hz), and gamma (40 -100 Hz) LFP bands were informative about the BOLD signal. In agreement with previous studies, gamma was the most informative band. Both increases and decreases in BOLD signal reliably followed increases and decreases in gamma power. However, both alpha and beta power signals carried information about BOLD that was largely complementary to that carried by gamma power. In particular, the relationship between alpha and gamma power was reflected in the amplitude of the BOLD signal, while the relationship between beta and gamma bands was reflected in the latency of BOLD with respect to significant changes in gamma power. These results lay the basis for identifying contributions of different neural pathways to cortical processing using fMRI.

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“…Details on these applications can be found in various recent publications (Logothetis et al 1999;Rainer et al 2001;Tolias et al 2001;Brewer et al 2002;Sereno et al 2002).…”

Section: Mri Of the Monkey Brainmentioning

confidence: 99%

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

Logothetis

2002

Phil. Trans. R. Soc. Lond. B

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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-oxygenlevel-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.

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Three-Dimensional Shape Representation in Monkey Cortex

Cited by 151 publications

References 63 publications

Extracting 3D from Motion: Differences in Human and Monkey Intraparietal Cortex

Extracting 3D from Motion: Differences in Human and Monkey Intraparietal Cortex

The Amplitude and Timing of the BOLD Signal Reflects the Relationship between Local Field Potential Power at Different Frequencies

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

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