An MRI-Based, Data-Driven Model of Cortical Laminar Connectivity

Shamir, Ittai; Assaf, Yaniv

doi:10.1007/s12021-020-09491-7

Cited by 15 publications

(16 citation statements)

References 43 publications

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“…After selecting the best fitting routine for standard connectivity analysis, white and grey matter datasets were integrated through a novel model of cortical laminar connectivity (Shamir and Assaf 2021). We used the Circular-Connectome toolbox (available at: github.com/ittais/Circular-Connectome) to visualize both the model input datasets as well as the resulting model output in circular graph formats.…”

Section: Discussionmentioning

confidence: 99%

Modelling cortical laminar connectivity in the macaque brain

Shamir

Assaf

2020

Preprint

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In 1991, Felleman and Van Essen published their seminal study regarding hierarchical processing in the primate cerebral cortex. Their work encompassed a widescale analysis of connections reported through tracing between 35 regions in the macaque visual cortex, extending from cortical regions to the laminar level. Since then, great strides have been made in the field of MRI neuroimaging of white matter connectivity, also known as connectomics, as well as grey matter laminar composition. In this work, we revisit laminar-level connectivity in the macaque brain using a whole-brain MRI-based approach. We use multi-modal ex-vivo MRI imaging of both white and grey matter, which are then integrated via our simple model of laminar connectivity. This model uses a granularity-based approach to define a set of rules that expands cortical connections to the laminar level. The resulting whole-brain network of macaque cortical laminar connectivity is then validated in the visual cortex by comparison to results from the 1991 study by Felleman and Van Essen. By using an unbiased definition of the cortex that addresses its heterogenous laminar composition, we are able to explore a new avenue of connectomics on the laminar level.

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Section: Discussionmentioning

confidence: 99%

Modelling cortical laminar connectivity in the macaque brain

Shamir

Assaf

2020

Preprint

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“…This study presents an innovative exploration of a network model of the healthy human connectome on the laminar level. This model embodies a simplified set of laminar-level rules of connections, based on a systematic review of histological tract tracing studies (Shamir and Assaf 2021a). While this model has been corroborated ex-vivo in the macaque brain (Felleman and Van Essen 1991; Shamir and Assaf 2021b), corroborating the results in-vivo in the human brain is a more nuanced task.…”

Section: Statements and Declarations Fundingmentioning

confidence: 99%

“…The integration of macrostructural data regarding white matter connectomics and microstructural data regarding grey matter laminar composition poses a promising development in the field of connectomics (Johansen-Berg 2013). A recently published study offers a simplified granularity-based model of cortical laminar connectivity based on published tract tracing and histological findings, with the purpose of Modelling the laminar connectome of the human brain 3 integrating white and grey matter datasets derived from multimodal MRI imaging (Shamir and Assaf, 2021a). The model is then further explored in the macaque brain and corroborated in its visual cortex by comparison to published studies (Shamir and Assaf 2021b).…”

Section: Introductionmentioning

confidence: 99%

Modelling the laminar connectome of the human brain

Shamir

Tomer

Krupnik

et al. 2021

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The human connectome is the complete structural description of the network of connections and elements that form the wiring diagram of the brain. Because of the current scarcity of information regarding laminar end points of white matter tracts inside cortical grey matter, tractography remains focused on cortical partitioning into regions, while ignoring radial partitioning into laminar components. To overcome this biased representation of the cortex as a single homogenous unit, we use a recent data-derived model of cortical laminar connectivity, which has been further explored and corroborated in the macaque brain by comparison to published studies. The model integrates multimodal MRI imaging datasets regarding both white matter connectivity and grey matter laminar composition into a laminar-level connectome. In this study we model the laminar connectome of healthy human brains (N=20) and explore them via a set of neurobiologically meaningful complex network measures. Our analysis demonstrates a subdivision of network hubs that appear in the standard connectome into each individual component of the laminar connectome, giving a fresh look into the role of laminar components in cortical connectivity and offering new prospects in the fields of both structural and functional connectivity.

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“…Even with the advent of high-resolution functional neuroimaging, multilayer connectomics have mostly focused on anatomical networks derived from structural MRI and DTI [57, 58] due to their direct relationship to cortical architecture. For example, DTI and histological samples identified that cortical areas with similar laminar structure were more likely to be connected [59].…”

Section: Introductionmentioning

confidence: 99%

Multilayer Network Analysis across Cortical Depths in Resting-State 7T fMRI

Kotlarz,

Lankinen,

Hakonen

et al. 2023

Preprint

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In graph theory, “multilayer networks” represent systems involving several interconnected topological levels. A neuroscience example is the hierarchy of connections between different cortical depths or “lamina”. This hierarchy is becoming non-invasively accessible in humans using ultra-high-resolution functional MRI (fMRI). Here, we applied multilayer graph theory to examine functional connectivity across different cortical depths in humans, using 7T fMRI (1-mm3voxels; 30 participants). Blood oxygenation level dependent (BOLD) signals were derived from five depths between the white matter and pial surface. We then compared networks where the inter-regional connections were limited to a single cortical depth only (“layer-by-layer matrices”) to those considering all possible connections between regions and cortical depths (“multilayer matrix”). We utilized global and local graph theory features that quantitatively characterize network attributes such as network composition, nodal centrality, path-based measures, and hub segregation. Detecting functional differences between cortical depths was improved using multilayer connectomics compared to the layer-by-layer versions. Superficial aspects of the cortex dominated information transfer and deeper aspects clustering. These differences were largest in frontotemporal and limbic brain regions. fMRI functional connectivity across different cortical depths may contain neurophysiologically relevant information. Multilayer connectomics could provide a methodological framework for studies on how information flows across this hierarchy.

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An MRI-Based, Data-Driven Model of Cortical Laminar Connectivity

Cited by 15 publications

References 43 publications

Modelling cortical laminar connectivity in the macaque brain

Modelling cortical laminar connectivity in the macaque brain

Modelling the laminar connectome of the human brain

Multilayer Network Analysis across Cortical Depths in Resting-State 7T fMRI

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