Weight Consistency Specifies Regularities of Macaque Cortical Networks

Markov, Nikola; Misery, Pierre; Falchier, Arnaud; Lamy, Camille; Vezoli, Julien; Quilodran, René; Gariel, Marie-Alice; Giroud, Pascale; Ercsey-Ravasz, Mária; Pilaz, Louis-Jan; Huissoud, Cyril; Barone, Pascal; Dehay, Colette; Toroczkai, Zoltán; Essen, David C. Van; Kennedy, Henry; Knoblauch, Kenneth

doi:10.1093/cercor/bhq201

Cited by 353 publications

(533 citation statements)

References 109 publications

(187 reference statements)

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“…Accordingly, previous studies demonstrated that orthodromic stimulation effects are many times stronger than antidromic effects even between areas with strong direct projections (62,70,71). The direct connectivity between V1 and V4 is relatively sparse (72), and the antidromic contribution to our findings was therefore presumably even smaller than in these previous studies.…”

Section: Discussioncontrasting

confidence: 37%

Alpha and gamma oscillations characterize feedback and feedforward processing in monkey visual cortex

Kerkoerle

Self

Dagnino

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

850

138

776

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This Feature Article is part of a series identified by the Editorial Board as reporting findings of exceptional significance.Edited by Terrence J. Sejnowski, Salk Institute for Biological Studies, La Jolla, CA, and approved August 8, 2014 (received for review February 22, 2014) Cognitive functions rely on the coordinated activity of neurons in many brain regions, but the interactions between cortical areas are not yet well understood. Here we investigated whether lowfrequency (α) and high-frequency (γ) oscillations characterize different directions of information flow in monkey visual cortex. We recorded from all layers of the primary visual cortex (V1) and found that γ-waves are initiated in input layer 4 and propagate to the deep and superficial layers of cortex, whereas α-waves propagate in the opposite direction. Simultaneous recordings from V1 and downstream area V4 confirmed that γ-and α-waves propagate in the feedforward and feedback direction, respectively. Microstimulation in V1 elicited γ-oscillations in V4, whereas microstimulation in V4 elicited α-oscillations in V1, thus providing causal evidence for the opposite propagation of these rhythms. Furthermore, blocking NMDA receptors, thought to be involved in feedback processing, suppressed α while boosting γ. These results provide new insights into the relation between brain rhythms and cognition.neuronal synchronization | attention | perceptual organization | phase coherence | Granger causality A reas of the visual cortex are arranged hierarchically, with low-level areas representing simple features and higher areas representing the more complex aspects of the visual world (1, 2). Neurons in many visual areas are coactive during the perception of a visual stimulus and it is difficult to disentangle the influences of lower areas onto higher areas from the effects that go in the opposite direction (3). Studies of visual cognition could benefit enormously from markers of cortical activity that distinguish between feedforward and feedback effects. One such putative marker is cortical oscillatory activity, because oscillations of different frequencies have been proposed to propagate either in feedforward or in the feedback direction (4, 5), but experimental evidence for this view is sparse (6).Low-frequency rhythms, like the α-rhythm-which is particularly pronounced in the visual cortex-have been proposed to characterize spontaneous activity (7,8) as the α-rhythm increases when the subject closes the eyes (9). More recent observations have also implicated α-oscillations in the active suppression of irrelevant, unattended information (10, 11). In contrast, the high-frequency γ-rhythm increases if visual stimuli are presented, and in particular if they are task-relevant (12, 13). One influential hypothesis has been that γ-oscillations play a role in feature binding (14), but later studies cast doubt on this proposal (15,16). A more recent hypothesis holds that γ-oscillations facilitate the communication between cortical areas (17), but both evidence in fa...

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Section: Discussioncontrasting

confidence: 37%

Alpha and gamma oscillations characterize feedback and feedforward processing in monkey visual cortex

Kerkoerle

Self

Dagnino

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

850

138

776

View full text Add to dashboard Cite

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“…Major progress on this front has come from a recent systematic effort from the laboratory of Henry Kennedy, revealing that connectivity profiles are more highly distributed and that connection strengths span a much wider range than previously realized (Knoblauch et al 2015). Using a 91-area cortical parcellation and retrograde tracers injected into 29 cortical areas, Markov et al (2012) determined that each cortical area receives on average inputs from 55 other areas out of a (minimum) 26 and (maximum) 87; when expressed as the fraction of retrogradely labeled neurons, these pathways vary over five orders of magnitude in connection strength (Markov et al 2011(Markov et al , 2012Knoblauch et al 2015). This translates to 1615 inter-areal pathways out of 2610 possible in a 29 Â 91 connectivity matrix.…”

Section: Distributed Cortical Connectivitymentioning

confidence: 99%

“…Each mm 2 patch of macaque cortex contains~10 5 neurons and may contain~10 9 synapses (but with significant regional variability, as noted above). An estimated 80 % of inputs come from local (intra-areal) circuits, within a radius of several mm of a given patch (Markov et al 2011). The remaining 20 % of long-distance within-hemisphere inputs are distributed across the 10,000-mm 2 surface area of each hemisphere.…”

Section: Distributed Cortical Connectivitymentioning

confidence: 99%

Parcellations and Connectivity Patterns in Human and Macaque Cerebral Cortex

Essen

Donahue

Dierker

et al. 2016

Research and Perspectives in Neurosciences

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To decipher brain function, it is vital to know how the brain is wired. This entails elucidation of brain circuits at multiple scales, including microscopic, mesoscopic, and macroscopic levels. Here, we review recent progress in mapping the macroscopic brain circuits and functional organization of the cerebral cortex in primates-humans and macaque monkeys, in particular. There are many similarities across species in terms of overall patterns of cortical gray matter myelination as well as functional areas that are presumed to be homologous. However, there are also many important species differences, including cortical convolutions that are much more complex and more variable in humans than in monkeys. Our ability to analyze structure and function has benefited from improved methods for intersubject registration that cope with this individual variability. To characterize long-distance connectivity, powerful but indirect methods are now available, including resting-state functional connectivity and diffusion imaging coupled with probabilistic tractography. We illustrate how connectivity inferred from diffusion imaging and tractography can be evaluated in relation to 'ground truth' based on anatomical tracers in the macaque. Interspecies registration between human and macaque cortex based on presumed interspecies homologies demonstrates an impressive degree of areal expansion in regions associated with higher cognitive function.

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“…An extensive set of studies carried out by Henry Kennedy and colleagues (Markov et al 2011(Markov et al , 2013a(Markov et al , b, 2014 have revealed the connectional anatomy of the macaque cerebral cortex in new detail. Injections of retrograde tracers in 29 cortical areas followed by rigorous quantification of label density across the entire cortex demonstrated a previously unknown degree of connectedness among areas.…”

Section: Macroscalementioning

confidence: 99%

“…Injections of retrograde tracers in 29 cortical areas followed by rigorous quantification of label density across the entire cortex demonstrated a previously unknown degree of connectedness among areas. Numerous new (and mostly relatively weak) projections were uncovered, and the overall connectivity profile for each area was best approximated by a lognormal distribution (Markov et al 2011), with a few strong projections and a large admixture of medium or weak pathways. Graph analysis provided evidence for a relatively high proportion of unidirectional links (Markov et al 2014), a strong contribution of long-distance projections towards areal specificity (Markov et al 2013a), significant distance-dependence of connection densities (Ercsey-Ravasz et al 2013), and hierarchical arrangement of areas into "counter-streams" (Markov et al 2013b).…”

Section: Macroscalementioning

confidence: 99%