scholarly journals Efficient Coding in the Economics of Human Brain Connectomics

2021 ◽  
pp. 1-65
Author(s):  
Dale Zhou ◽  
Christopher W. Lynn ◽  
Zaixu Cui ◽  
Rastko Ciric ◽  
Graham L. Baum ◽  
...  
Keyword(s):  
Random Walk ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Lossy Compression ◽  
Hierarchical Organization ◽  
Network Efficiency ◽  
Compression Efficiency ◽  
Efficient Coding ◽  
Trade Offs ◽  
Structural Networks

Abstract In systems neuroscience, most models posit that brain regions communicate information under constraints of efficiency. Yet, evidence for efficient communication in structural brain networks remains sparse. The principle of efficient coding proposes that the brain transmits maximal information in a metabolically economical or compressed form to improve future behavior. To determine how structural connectivity supports efficient coding, we develop a theory specifying minimum rates of message transmission between brain regions to achieve an expected fidelity, and we test five predictions from the theory based on random walk communication dynamics. In doing so, we introduce the metric of compression efficiency, which quantifies the trade-off between lossy compression and transmission fidelity in structural networks. In a large sample of youth (n = 1,042; age 8–23 years), we analyze structural networks derived from diffusion weighted imaging and metabolic expenditure operationalized using cerebral blood flow. We show that structural networks strike compression efficiency trade-offs consistent with theoretical predictions. We find that compression efficiency prioritizes fidelity with development, heightens when metabolic resources and myelination guide communication, explains advantages of hierarchical organization, links higher input fidelity to disproportionate areal expansion, and shows that hubs integrate information by lossy compression. Lastly, compression efficiency is predictive of behavior—beyond the conventional network efficiency metric—for cognitive domains including executive function, memory, complex reasoning, and social cognition. Our findings elucidate how macroscale connectivity supports efficient coding, and serve to foreground communication processes that utilize random walk dynamics constrained by network connectivity.

scholarly journals Efficient Coding in the Economics of Human Brain Connectomics

2020 ◽  
Author(s):  
Dale Zhou ◽  
Christopher W. Lynn ◽  
Zaixu Cui ◽  
Rastko Ciric ◽  
Graham L. Baum ◽  
...  
Keyword(s):  
Information Transfer ◽  
Information Diffusion ◽  
Brain Regions ◽  
Compression Efficiency ◽  
Rich Club ◽  
Trade Offs ◽  
Neural Communication ◽  
Structural Path ◽  
Structural Networks ◽  
Speed Accuracy

AbstractIn systems neuroscience, most models posit that brain regions communicate information under constraints of efficiency. Yet, metabolic and information transfer efficiency across structural networks are not understood. In a large cohort of youth, we find metabolic costs associated with structural path strengths supporting information diffusion. Metabolism is balanced with the coupling of structures supporting diffusion and network modularity. To understand efficient network communication, we develop a theory specifying minimum rates of message diffusion that brain regions should transmit for an expected fidelity, and we test five predictions from the theory. We introduce compression efficiency, which quantifies differing trade-offs between lossy compression and communication fidelity in structural networks. Compression efficiency evolves with development, heightens when metabolic gradients guide diffusion, constrains network complexity, explains how rich-club hubs integrate information, and correlates with cortical areal scaling, myelination, and speed-accuracy trade-offs. Our findings elucidate how network structures and metabolic resources support efficient neural communication.

scholarly journals Integrative Structural Brain Network Analysis In Diffusion Tensor Imaging

2017 ◽  
Author(s):  
Moo K. Chung ◽  
Jamie L. Hanson ◽  
Nagesh Adluru ◽  
Andrew L. Alexander ◽  
Richard J. Davidson ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Node Degree ◽  
Fiber Tracts ◽  
White Matter Fiber Tracts ◽  
Structural Brain Network ◽  
Structural Networks

AbstractIn diffusion tensor imaging, structural connectivity between brain regions is often measured by the number of white matter fiber tracts connecting them. Other features such as the length of tracts or fractional anisotropy (FA) are also used in measuring the strength of connectivity. In this study, we investigated the effects of incorporating the number of tracts, the tract length and FA-values into the connectivity model. Using various node-degree based graph theory features, the three connectivity models are compared. The methods are applied in characterizing structural networks between normal controls and maltreated children, who experienced maltreatment while living in post-institutional settings before being adopted by families in the US.

scholarly journals Signal propagation via cortical hierarchies

2020 ◽  
Vol 4 (4) ◽  
pp. 1072-1090 ◽  
Author(s):  
Bertha Vézquez-Rodríguez ◽  
Zhen-Qi Liu ◽  
Patric Hagmann ◽  
Bratislav Misic
Keyword(s):  
Shortest Paths ◽  
Signal Propagation ◽  
Brain Regions ◽  
Hierarchical Organization ◽  
Attention Networks ◽  
Diffusion Spectrum Imaging ◽  
Cortical Hierarchy ◽  
Spectrum Imaging ◽  
Structural Networks ◽  
The Brain

The wiring of the brain is organized around a putative unimodal-transmodal hierarchy. Here we investigate how this intrinsic hierarchical organization of the brain shapes the transmission of information among regions. The hierarchical positioning of individual regions was quantified by applying diffusion map embedding to resting-state functional MRI networks. Structural networks were reconstructed from diffusion spectrum imaging and topological shortest paths among all brain regions were computed. Sequences of nodes encountered along a path were then labeled by their hierarchical position, tracing out path motifs. We find that the cortical hierarchy guides communication in the network. Specifically, nodes are more likely to forward signals to nodes closer in the hierarchy and cover a range of unimodal and transmodal regions, potentially enriching or diversifying signals en route. We also find evidence of systematic detours, particularly in attention networks, where communication is rerouted. Altogether, the present work highlights how the cortical hierarchy shapes signal exchange and imparts behaviorally relevant communication patterns in brain networks.

scholarly journals Disrupted structural connectivity in ArcAβ mouse model of Aβ amyloidosis

2020 ◽  
Author(s):  
Md. Mamun Al-Amin ◽  
Joanes Grandjean ◽  
Jan Klohs ◽  
Jungsu Kim
Keyword(s):  
White Matter ◽  
Transgenic Mice ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Mice Model ◽  
Network Efficiency ◽  
Fiber Density ◽  
Neurofilament Light ◽  
Diffusion Weighted Images ◽  
Network Properties

AbstractAlthough amyloid beta (Aβ) deposition is one of the major causes of white matter (WM) alterations in Alzheimer’s disease (AD), little is known about the underlying basis of WM damage and its association with global structural connectivity and network topology. We aimed to dissect the contributions of WM microstructure to structural connectivity and network properties in the ArcAβ mice model of Aβ amyloidosis.We acquired diffusion-weighted images (DWI) of wild type (WT) and ArcAβ transgenic (TG) mice using a 9.4 T MRI scanner. Fixel-based analysis (FBA) was performed to measure fiber tract-specific properties. We also performed three complementary experiments; to identify the global differences in structural connectivity, to compute network properties and to measure cellular basis of white matter alterations.Transgenic mice displayed disrupted structural connectivity centered to the entorhinal cortex (EC) and a lower fiber density and fiber bundle cross-section. In addition, there was a reduced network efficiency and degree centrality in weighted structural connectivity in the transgenic mice. To further examine the underlying neuronal basis of connectivity and network deficits, we performed histology experiments. We found no alteration in myelination and an increased level of neurofilament light (NFL) in the brain regions with disrupted connectivity in the TG mice. Furthermore, TG mice had a reduced number of perineuronal nets (PNN) in the EC.The observed FDC reductions may indicate a decrease in axonal diameter or axon count which would explain the basis of connectivity deficits and reduced network efficiency in TG mice. The increase in NFL suggests a breakdown of axonal integrity, which would reduce WM fiber health. Considering the pivotal role of the EC in AD, Aβ deposition may primarily increase NFL release, damaging PNN in the entorhinal pathway, resulting in disrupted structural connectivity.

scholarly journals Signal propagation via cortical hierarchies

2020 ◽  
Author(s):  
Bertha Vázquez-Rodríguez ◽  
Zhen-Qi Liu ◽  
Patric Hagmann ◽  
Bratislav Mišić
Keyword(s):  
Shortest Paths ◽  
Signal Propagation ◽  
Brain Regions ◽  
Hierarchical Organization ◽  
Attention Networks ◽  
Diffusion Spectrum Imaging ◽  
Cortical Hierarchy ◽  
Spectrum Imaging ◽  
Structural Networks ◽  
The Brain

The wiring of the brain is organized around a putative unimodal-transmodal hierarchy. Here we investigate how this intrinsic hierarchical organization of the brain shapes the transmission of information among regions. The hierarchical positioning of individual regions was quantified by applying diffusion map embedding to resting state functional MRI networks. Structural networks were reconstructed from diffusion spectrum imaging and topological shortest paths among all brain regions were computed. Sequences of nodes encountered along a path were labelled by their hierarchical position, tracing out path motifs. We find that the cortical hierarchy guides communication in the network. Specifically, nodes are more likely to forward signals to nodes closer in the hierarchy and cover a range of unimodal and transmodal regions, potentially enriching or diversifying signals en route. We also find evidence of systematic detours, particularly in attention networks, where communication is re-routed. Altogether, the present work highlights how the cortical hierarchy shapes signal exchange and imparts behaviourally-relevant communication patterns in brain networks.

scholarly journals Network Curvature as a Hallmark of Brain Structural Connectivity

2017 ◽  
Author(s):  
Hamza Farooq ◽  
Yongxin Chen ◽  
Tryphon T. Georgiou ◽  
Allen Tannenbaum ◽  
Christophe Lenglet
Keyword(s):  
Ricci Curvature ◽  
Biological Networks ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Or Efficiency ◽  
Healthy Development ◽  
Structural Networks

AbstractStudies show that while brain networks are remarkably robust to a variety of adverse events, such as injuries and lesions due to accidents or disease, they may be fragile when the disturbance takes place in specific locations. This seems to be the case for diseases in which accumulated changes in network topology dramatically affect certain sensitive areas. To this end, previous attempts have been made to quantify robustness and fragility of brain functionality in two broadly defined ways: (i) utilizing model-based techniques to predict lesion effects, and (ii) studying empirical effects from brain lesions due to injury or disease. Both directions aim at assessing functional connectivity changes resulting from structural network variations. In the present work, we follow a more geometric viewpoint that is based on a notion of curvature of networks, the so-called Ollivier-Ricci curvature. A similar approach has been used in recent studies to quantify financial market robustness as well as to differentiate biological networks corresponding to cancer cells from normal cells. The same notion of curvature, defined at the node level for brain networks obtained from MRI data, may help identify and characterize the effects of diseases on specific brain regions. In the present paper, we apply the Ollivier-Ricci curvature to brain structural networks to: i) Demonstrate its unique ability to identify robust (or fragile) brain regions in healthy subjects. We compare our results to previously published work which identified a unique set of regions (called structural core) of the human cerebral cortex. This novel characterization of brain networks, complementary to measures such as degree, strength, clustering or efficiency, may be particularly useful to detect and monitor candidate areas for targeting by surgery (e.g. deep brain stimulation) or pharmaco-therapeutic agents; ii) Illustrate the power our curvature-derived measures to track changes in brain connectivity with healthy development/aging and; iii) Detect changes in brain structural connectivity in people with Autism Spectrum Disorders (ASD) which are in agreement with previous morphometric MRI studies.

scholarly journals Network curvature as a hallmark of brain structural connectivity

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Hamza Farooq ◽  
Yongxin Chen ◽  
Tryphon T. Georgiou ◽  
Allen Tannenbaum ◽  
Christophe Lenglet
Keyword(s):  
Financial Market ◽  
Biological Networks ◽  
Healthy Subjects ◽  
Structural Changes ◽  
Brain Connectivity ◽  
Empirical Studies ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Structural Networks

Abstract Although brain functionality is often remarkably robust to lesions and other insults, it may be fragile when these take place in specific locations. Previous attempts to quantify robustness and fragility sought to understand how the functional connectivity of brain networks is affected by structural changes, using either model-based predictions or empirical studies of the effects of lesions. We advance a geometric viewpoint relying on a notion of network curvature, the so-called Ollivier-Ricci curvature. This approach has been proposed to assess financial market robustness and to differentiate biological networks of cancer cells from healthy ones. Here, we apply curvature-based measures to brain structural networks to identify robust and fragile brain regions in healthy subjects. We show that curvature can also be used to track changes in brain connectivity related to age and autism spectrum disorder (ASD), and we obtain results that are in agreement with previous MRI studies.

scholarly journals An architectonic type principle in the development of laminar patterns of cortico-cortical connections

2021 ◽  
Author(s):  
Sarah F. Beul ◽  
Alexandros Goulas ◽  
Claus C. Hilgetag
Keyword(s):  
Structural Connectivity ◽  
Macaque Monkey ◽  
Brain Regions ◽  
Adult Brain ◽  
Mammalian Brain ◽  
Cortical Connections ◽  
Cortical Projections ◽  
Structural Connections ◽  
High Degree ◽  
The Brain

AbstractStructural connections between cortical areas form an intricate network with a high degree of specificity. Many aspects of this complex network organization in the adult mammalian cortex are captured by an architectonic type principle, which relates structural connections to the architectonic differentiation of brain regions. In particular, the laminar patterns of projection origins are a prominent feature of structural connections that varies in a graded manner with the relative architectonic differentiation of connected areas in the adult brain. Here we show that the architectonic type principle is already apparent for the laminar origins of cortico-cortical projections in the immature cortex of the macaque monkey. We find that prenatal and neonatal laminar patterns correlate with cortical architectonic differentiation, and that the relation of laminar patterns to architectonic differences between connected areas is not substantially altered by the complete loss of visual input. Moreover, we find that the degree of change in laminar patterns that projections undergo during development varies in proportion to the relative architectonic differentiation of the connected areas. Hence, it appears that initial biases in laminar projection patterns become progressively strengthened by later developmental processes. These findings suggest that early neurogenetic processes during the formation of the brain are sufficient to establish the characteristic laminar projection patterns. This conclusion is in line with previously suggested mechanistic explanations underlying the emergence of the architectonic type principle and provides further constraints for exploring the fundamental factors that shape structural connectivity in the mammalian brain.

scholarly journals Brain Metabolic Correlates of Persistent Olfactory Dysfunction after SARS-Cov2 Infection

Biomedicines ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 287
Author(s):  
Maria Isabella Donegani ◽  
Alberto Miceli ◽  
Matteo Pardini ◽  
Matteo Bauckneht ◽  
Silvia Chiola ◽  
...  
Keyword(s):  
Fdg Pet ◽  
Structural Connectivity ◽  
Statistical Parametric Mapping ◽  
Brain Regions ◽  
Olfactory Dysfunction ◽  
Whole Body ◽  
Mapping Software ◽  
18F Fdg ◽  
Left Insula

We aimed to evaluate the brain hypometabolic signature of persistent isolated olfactory dysfunction after SARS-CoV-2 infection. Twenty-two patients underwent whole-body [18F]-FDG PET, including a dedicated brain acquisition at our institution between May and December 2020 following their recovery after SARS-Cov2 infection. Fourteen of these patients presented isolated persistent hyposmia (smell diskettes olfaction test was used). A voxel-wise analysis (using Statistical Parametric Mapping software version 8 (SPM8)) was performed to identify brain regions of relative hypometabolism in patients with hyposmia with respect to controls. Structural connectivity of these regions was assessed (BCB toolkit). Relative hypometabolism was demonstrated in bilateral parahippocampal and fusiform gyri and in left insula in patients with respect to controls. Structural connectivity maps highlighted the involvement of bilateral longitudinal fasciculi. This study provides evidence of cortical hypometabolism in patients with isolated persistent hyposmia after SARS-Cov2 infection. [18F]-FDG PET may play a role in the identification of long-term brain functional sequelae of COVID-19.

scholarly journals Pairwise maximum entropy model explains the role of white matter structure in shaping emergent co-activation states

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Arian Ashourvan ◽  
Preya Shah ◽  
Adam Pines ◽  
Shi Gu ◽  
Christopher W. Lynn ◽  
...  
Keyword(s):  
White Matter ◽  
Maximum Entropy ◽  
Large Scale ◽  
Structural Connectivity ◽  
Quantitative Relationship ◽  
Brain Regions ◽  
Maximum Entropy Model ◽  
Entropy Model ◽  
Activation Patterns ◽  
Co Activation

AbstractA major challenge in neuroscience is determining a quantitative relationship between the brain’s white matter structural connectivity and emergent activity. We seek to uncover the intrinsic relationship among brain regions fundamental to their functional activity by constructing a pairwise maximum entropy model (MEM) of the inter-ictal activation patterns of five patients with medically refractory epilepsy over an average of ~14 hours of band-passed intracranial EEG (iEEG) recordings per patient. We find that the pairwise MEM accurately predicts iEEG electrodes’ activation patterns’ probability and their pairwise correlations. We demonstrate that the estimated pairwise MEM’s interaction weights predict structural connectivity and its strength over several frequencies significantly beyond what is expected based solely on sampled regions’ distance in most patients. Together, the pairwise MEM offers a framework for explaining iEEG functional connectivity and provides insight into how the brain’s structural connectome gives rise to large-scale activation patterns by promoting co-activation between connected structures.

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