Cortical High-Density Counterstream Architectures

By Nikola T. Markov, Mária Ercsey-Ravasz, D. V. Van Essen, Kenneth Knoblauch, Zoltan Toroczkai, Henry Kennedy. Science, 2013

557 citations recorded by the source corpus.

Background The cerebral cortex is divisible into many individual areas, each exhibiting distinct connectivity profiles, architecture, and physiological characteristics. Interactions among cortical areas underlie higher sensory, motor, and cognitive functions. Graph theory provides an important framework for understanding network properties of the interareal weighted and directed connectivity matrix reported in recent studies. Density and topology of the cortical graph. (Left) The 66% density of the cortical matrix (black triangle) is considerably greater than in previous reports (colored points) and is inconsistent with a small-world network. (Right) A bow-tie representation of the high-density cortical matrix. The high-efficiency cortical core has defined relations with the cortical periphery in the two fans. Advances We derive an exponential distance rule that predicts many binary and weighted features of the cortical network, including efficiency of information transfer, the high specificity of long-distance compared to short-distance connections, wire length minimization, and the existence of a highly interconnected cortical core. We propose a bow-tie representation of the cortex, which combines these features with hierarchical processing. Outlook The exponential distance rule has important implications for understanding scaling properties of the cortex and developing future large-scale dynamic models of the cortex. Small-world networks provide an appealing description of cortical architecture owing to their capacity for integration and segregation combined with an economy of connectivity. Previous reports of low-density interareal graphs and apparent small-world properties are challenged by data that reveal high-density cortical graphs in which economy of connections is achieved by weight heterogeneity and distance-weight correlations. These properties define a model that predicts many binary and weighted features of the cortical network including a core-periphery, a typical feature of self-organizing information processing systems. Feedback and feedforward pathways between areas exhibit a dual counterstream organization, and their integration into local circuits constrains cortical computation. Here, we propose a bow-tie representation of interareal architecture derived from the hierarchical laminar weights of pathways between the high-efficiency dense core and periphery.

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/b1a0f1faa0a93ad8ccac74c09b21d37868d16fbf

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In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Network neuroscience (2017, background): “Recent investigations have examined more complex organizational features such as hierarchical organization 51,52 , the role of geometry and spatial embedding in large-scale anatomical connectivity 53 , and the importance of considering relationships among multiple structural constraints when…”
LFP and oscillations—what do they tell us? (2015, background): “A key challenge for future work will be to understand not only the functional segregation between top-down and bottom-up signalling but also the functional integration of these streams, which may be subserved by laminar specific processing within the cortical microcircuit [3,13 ].”
Small-World Brain Networks Revisited (2016, methodology): “(And the WS model does not seem like a biologically plausible generative model for brain networks [10–12].)”
Breaking the curse of dimensionality in regression (2017, background): “Nevertheless, recent work in neuroscience has shown that the structural wiring of the brain doesn’t correspond to a sparse network (Markov et al., 2013), thus questioning the underlying assumption of sparsity often used to estimate brain network connectivity.”
The graphical brain: Belief propagation and active inference (2017, background): “Neuroanatomically, this may reﬂect the fact that laminar speciﬁcity is less pronounced in short-range extrinsic connections (Markov et al., 2013).”
Varieties of cognitive penetration in visual perception. (2014, unknown): “In fact, recent evidence suggests that the brain has much more long-range connections between distant brain areas than previously thought (Markov et al., 2013).”
A voxel-wise encoding model for early visual areas decodes mental images of remembered scenes (2014, unknown): “Anatomical studies have revealed that EVA receives feedback projections from higher-order visual areas (Rockland and Pandya, 1979; Felleman and Van Essen, 1991; Markov et al., 2014a).”
Signature of consciousness in the dynamics of resting-state brain activity (2015, result): “Recent anatomical observations concur with the present results in suggesting that the structural network of the macaque brain does not fit well with a SM architecture (34).”