Rich-club in the brain’s macrostructure: Insights from graph theoretical analysis

Kim, Dae‐Jin; Min, Byoung-Kyong

doi:10.1016/j.csbj.2020.06.039

Cited by 55 publications

(46 citation statements)

References 143 publications

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“…Especially vulnerable (but not exclusive to scale-free networks) is the rich club (Zhou and Mondragón, 2004 ), a group of hubs with a high degree of interconnectivity between each other. While there is growing evidence for the presence of rich-club topology in the brain (Griffa and Van den Heuvel, 2018 ; Kim and Min, 2020 ) the presence of scale-free topology (Bonifazi et al, 2009 ) is still somewhat controversial, but it provides important insight in modeling studies of dynamics on network topology (Broido and Clauset, 2019 ).…”

Section: Criticality and Networkmentioning

confidence: 99%

Criticality, Connectivity, and Neural Disorder: A Multifaceted Approach to Neural Computation

Heiney

Ramstad

Fiskum

et al. 2021

Front. Comput. Neurosci.

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It has been hypothesized that the brain optimizes its capacity for computation by self-organizing to a critical point. The dynamical state of criticality is achieved by striking a balance such that activity can effectively spread through the network without overwhelming it and is commonly identified in neuronal networks by observing the behavior of cascades of network activity termed “neuronal avalanches.” The dynamic activity that occurs in neuronal networks is closely intertwined with how the elements of the network are connected and how they influence each other's functional activity. In this review, we highlight how studying criticality with a broad perspective that integrates concepts from physics, experimental and theoretical neuroscience, and computer science can provide a greater understanding of the mechanisms that drive networks to criticality and how their disruption may manifest in different disorders. First, integrating graph theory into experimental studies on criticality, as is becoming more common in theoretical and modeling studies, would provide insight into the kinds of network structures that support criticality in networks of biological neurons. Furthermore, plasticity mechanisms play a crucial role in shaping these neural structures, both in terms of homeostatic maintenance and learning. Both network structures and plasticity have been studied fairly extensively in theoretical models, but much work remains to bridge the gap between theoretical and experimental findings. Finally, information theoretical approaches can tie in more concrete evidence of a network's computational capabilities. Approaching neural dynamics with all these facets in mind has the potential to provide a greater understanding of what goes wrong in neural disorders. Criticality analysis therefore holds potential to identify disruptions to healthy dynamics, granted that robust methods and approaches are considered.

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Section: Criticality and Networkmentioning

confidence: 99%

Criticality, Connectivity, and Neural Disorder: A Multifaceted Approach to Neural Computation

Heiney

Ramstad

Fiskum

et al. 2021

Front. Comput. Neurosci.

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“…One of the most well-known neural topographic structures has been described as being a rich club , characterized by clusters in which every node is connected with every other node in that cluster. Recently, Kim and Min (2020) take a graph theoretical approach to describing the rich-club structure in the brain [ 44 ].…”

Section: Discussionmentioning

confidence: 99%

The 2-D Cluster Variation Method: Topography Illustrations and Their Enthalpy Parameter Correlations

Maren

2021

Entropy

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One of the biggest challenges in characterizing 2-D image topographies is finding a low-dimensional parameter set that can succinctly describe, not so much image patterns themselves, but the nature of these patterns. The 2-D cluster variation method (CVM), introduced by Kikuchi in 1951, can characterize very local image pattern distributions using configuration variables, identifying nearest-neighbor, next-nearest-neighbor, and triplet configurations. Using the 2-D CVM, we can characterize 2-D topographies using just two parameters; the activation enthalpy (ε0) and the interaction enthalpy (ε1). Two different initial topographies (“scale-free-like” and “extreme rich club-like”) were each computationally brought to a CVM free energy minimum, for the case where the activation enthalpy was zero and different values were used for the interaction enthalpy. The results are: (1) the computational configuration variable results differ significantly from the analytically-predicted values well before ε1 approaches the known divergence as ε1→0.881, (2) the range of potentially useful parameter values, favoring clustering of like-with-like units, is limited to the region where ε0<3 and ε1<0.25, and (3) the topographies in the systems that are brought to a free energy minimum show interesting visual features, such as extended “spider legs” connecting previously unconnected “islands,” and as well as evolution of “peninsulas” in what were previously solid masses.

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“…The rich-club of the brain network plays a critical role in global integration of neural information and is essential for efficient communication across multiple segregated and distant brain regions. 26,27 Therefore, network disruptions within the rich club have a direct impact on various behavioral and cognitive tasks. Connectivity-based neuroimaging studies have identified alterations in rich-club network in several neuropsychiatric disorders.…”

Section: Discussionmentioning

confidence: 99%

Microbiota alterations in proline metabolism impact on depression through GABA and ECM homeostasis

Mayneris‐Perxachs

Arnoriaga-Rodríguez

Martín

et al. 2021

Preprint

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The microbiota-gut-brain axis has emerged as a novel target in depression, a disorder with low treatment efficacy. However, the field is dominated by underpowered studies focusing on major depression not addressing microbiome functionality, compositional nature, or confounding factors. We applied a multi-omics approach combining pre-clinical models with three human cohorts including mild-depressed patients. Microbial functions and metabolites converging into glutamate/GABA metabolism, particularly proline, were linked to depression. Whole-brain dynamics revealed rich club network disruptions associated with depression and circulating proline. Proline supplementation in mice exacerbated depression along with microbial translocation. Human microbiota transplantation induced an emotional-impaired phenotype in mice and alterations in GABA-, proline-, and extracellular matrix-related pre-frontal cortex genes. Targeting the microbiome and dietary proline may open new windows for an efficient depression treatment.

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Rich-club in the brain’s macrostructure: Insights from graph theoretical analysis

Cited by 55 publications

References 143 publications

Criticality, Connectivity, and Neural Disorder: A Multifaceted Approach to Neural Computation

Criticality, Connectivity, and Neural Disorder: A Multifaceted Approach to Neural Computation

The 2-D Cluster Variation Method: Topography Illustrations and Their Enthalpy Parameter Correlations

Microbiota alterations in proline metabolism impact on depression through GABA and ECM homeostasis

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