Cortical High-Density Counterstream Architectures

Markov, Nikola; Ercsey-Ravasz, Mária; Essen, David C. Van; Knoblauch, Kenneth; Toroczkai, Zoltán; Kennedy, Henry

doi:10.1126/science.1238406

Cited by 531 publications

(622 citation statements)

References 130 publications

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“…2N; β-value = −1.82; R 2 = 0.75; P < 0.02): the more similar to structure a brain state is and therefore the more likely it is to dominate in the sedated brain, the lower its network capacity. 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). Crucially, our results show that, in the wake condition only, the constantly fluctuating functional networks that ride on top of this fixed architecture constitute a better approximation of this optimal communication structure (3, 33).…”

Section: Resultssupporting

confidence: 82%

Signature of consciousness in the dynamics of resting-state brain activity

Barttfeld

Uhrig

Sitt

et al. 2015

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

618

696

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Section: Resultssupporting

confidence: 82%

Signature of consciousness in the dynamics of resting-state brain activity

Barttfeld

Uhrig

Sitt

et al. 2015

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

618

696

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“…Another interplay involves cortex and thamalus (Vukadinovic 2011). More broadly, new approaches applied to the study of the connectivity properties of large-scale brain systems are exciting developments (Sporns 2009, Bullmore and Sporns 2009, Markov et al 2013) with important implications for psychiatric disorders , Rubinov and Bullmore 2013, Yang et al 2014). …”

Section: Looking Forward: Building a New Cross-disciplinary Fieldmentioning

confidence: 99%

Computational Psychiatry

2016

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Psychiatric disorders such as autism and schizophrenia arise from abnormalities in brain systems that underlie cognitive, emotional and social functions. The brain is enormously complex and its abundant feedback loops on multiple scales preclude intuitive explication of circuit functions. In close interplay with experiments, theory and computational modeling are essential for understanding how, precisely, neural circuits generate flexible behaviors and their impairments give rise to psychiatric symptoms. This Perspective highlights recent progress in applying computational neuroscience to the study of mental disorders. We outline basic approaches, including identification of core deficits that cut across disease categories, biologically-realistic modeling bridging cellular and synaptic mechanisms with behavior, model-aided diagnosis. The need for new research strategies in psychiatry is urgent. Computational psychiatry potentially provides powerful tools for elucidating pathophysiology that may inform both diagnosis and treatment. To achieve this promise will require investment in cross-disciplinary training and research in this nascent field. © 2014 Elsevier Inc. All rights reserved. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.Neither Dr. Wang nor Dr. Krystal has any financial interests relevant to the advancement of computational psychiatry. to and engage with psychiatric research. Second, it will be important to develop a cadre of young psychiatrists who learn computational modeling, lest computational psychiatry.

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“…These distributed networks have certain organizational properties that depart from evolutionarily conserved unimodal sensory and motor zones, where connectional topography between areas or fields is often densest between neighboring locations (12). Higher-order cortical regions also possess local connections, but are distinguished by the relative prevalence of long-range connections (13)(14)(15). An open question is how the molecular architecture underlying these different cortical classes supports their distinct connectivity patterns.…”

mentioning

confidence: 99%

Transcriptional profiles of supragranular-enriched genes associate with corticocortical network architecture in the human brain

Krienen

Yeo

et al. 2016

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

214

203

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The human brain is patterned with disproportionately large, distributed cerebral networks that connect multiple association zones in the frontal, temporal, and parietal lobes. The expansion of the cortical surface, along with the emergence of long-range connectivity networks, may be reflected in changes to the underlying molecular architecture. Using the Allen Institute's human brain transcriptional atlas, we demonstrate that genes particularly enriched in supragranular layers of the human cerebral cortex relative to mouse distinguish major cortical classes. The topography of transcriptional expression reflects large-scale brain network organization consistent with estimates from functional connectivity MRI and anatomical tracing in nonhuman primates. Microarray expression data for genes preferentially expressed in human upper layers (II/III), but enriched only in lower layers (V/VI) of mouse, were cross-correlated to identify molecular profiles across the cerebral cortex of postmortem human brains (n = 6). Unimodal sensory and motor zones have similar molecular profiles, despite being distributed across the cortical mantle. Sensory/motor profiles were anticorrelated with paralimbic and certain distributed association network profiles. Tests of alternative gene sets did not consistently distinguish sensory and motor regions from paralimbic and association regions: (i) genes enriched in supragranular layers in both humans and mice, (ii) genes cortically enriched in humans relative to nonhuman primates, (iii) genes related to connectivity in rodents, (iv) genes associated with human and mouse connectivity, and (v) 1,454 gene sets curated from known gene ontologies. Molecular innovations of upper cortical layers may be an important component in the evolution of long-range corticocortical projections.corticocortical connectivity | human transcriptome | association cortex | supragranular | brain evolution P atterns of gene expression in the cerebral cortex are generally conserved across species, reflecting strong constraints in the development and evolution of cortical architecture (1-6). Previous work examining transcriptional variation in nonhuman primates and rodents indicate that molecular similarities between cortical regions in the adult brain are best explained by spatial proximity (7, 8). Molecular variation often takes the form of graded expression along a principal axis, in many cases appearing as rostrocaudal gradients across the cortex (8). The strong tendency for transcriptional variation to follow spatial proximity in the adult cortex likely reflects both functional gradients, as well as their developmental origins in terms of physical and temporal adjacency during neurogenesis of cells destined for neighboring locations in the cortex (at least for cells derived from the ventricular proliferative pool) (7).Spatial proximity likely captures the major features governing how molecular profiles vary across the cortex in all species, including humans. However, the expansion of the cerebral cortex in primates...

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Cortical High-Density Counterstream Architectures

Cited by 531 publications

References 130 publications

Signature of consciousness in the dynamics of resting-state brain activity

Signature of consciousness in the dynamics of resting-state brain activity

Computational Psychiatry

Transcriptional profiles of supragranular-enriched genes associate with corticocortical network architecture in the human brain

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