The community structure of functional brain networks exhibits scale-specific patterns of variability across individuals and time

Betzel, Richard F.; Bertolero, Maxwell A.; Gordon, Evan M.; Gratton, Caterina; Dosenbach, Nico U.F.; Bassett, Danielle S.

doi:10.1101/413278

“…Because of modules' near-autonomy from one another, they are thought to represent groups of nodes that perform the same or similar brain function and are believed to engender specialized information processing. In general, modules are not restricted to a single topological scale and can be arranged hierarchically, with deeper levels of the hierarchy reflecting increasing functional specialization (Betzel & Bassett, 2017b;Betzel, Bertolero, Gordon, et al, 2018;Betzel et al, 2013).…”

Section: Modular Organization Of Spontaneous Fcmentioning

confidence: 99%

Organizing principles of whole-brain functional connectivity in zebrafish larvae

Betzel

¹

2020

Network Neuroscience

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Network science has begun to reveal the fundamental principles by which large-scale brain networks are organized, including geometric constraints, a balance between segregative and integrative features, and functionally flexible brain areas. However, it remains unknown whether whole-brain networks imaged at the cellular level are organized according to similar principles. Here, we analyze whole-brain functional networks reconstructed from calcium imaging data recorded in larval zebrafish. Our analyses reveal that functional connections are distance-dependent and that networks exhibit hierarchical modular structure and hubs that span module boundaries. We go on to show that spontaneous network structure places constraints on stimulus-evoked reconfigurations of connections and that networks are highly consistent across individuals. Our analyses reveal basic organizing principles of whole-brain functional brain networks at the mesoscale. Our overarching methodological framework provides a blueprint for studying correlated activity at the cellular level using a low-dimensional network representation. Our work forms a conceptual bridge between macro- and mesoscale network neuroscience and opens myriad paths for future studies to investigate network structure of nervous systems at the cellular level.

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“…Here, we used a recently-developed procedure to obtain estimates of community structure with the values of {γ, ω} sampled from a restricted parameter space [63]. This procedure involved first estimating the boundaries of a restricted parameter space wherein any {γ, ω} pair would result in community structure where the number of communities is > 1 and < N × T (where T is the total number of layers; T = 6, in this case), and where community structure is neither uniform across layers (flexibility of exactly 0) nor is it maximally dissimilar (flexibility of exactly 1).…”

Section: Module Detectionmentioning

confidence: 99%

Stability of spontaneous, correlated activity in mouse auditory cortex

Betzel

¹

,

Wood

²

,

Angeloni

³

et al. 2019

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Neural systems can be modeled as complex networks in which neural elements are represented as nodes linked to one another through structural or functional connections. The resulting network can be analyzed using mathematical tools from network science and graph theory to quantify the system's topological organization and to better understand its function. While the network-based approach has become common in the analysis of large-scale neural systems probed by non-invasive neuroimaging, few studies have used network science to study the organization of biological neuronal networks reconstructed at the cellular level, and thus many very basic and fundamental questions remain unanswered. Here, we used two-photon calcium imaging to record spontaneous activity from the same set of cells in mouse auditory cortex over the course of several weeks. We reconstruct functional networks in which cells are linked to one another by edges weighted according to the maximum lagged correlation of their fluorescence traces. We show that the networks exhibit modular structure across multiple topological scales and that these multi-scale modules unfold as part of a hierarchy. We also show that, on average, network architecture becomes increasingly dissimilar over time, with similarity decaying monotonically with the distance (in time) between sessions. Finally, we show that a small fraction of cells maintain strongly-correlated activity over multiple days, forming a stable temporal core surrounded by a fluctuating and variable periphery. Our work provides a careful methodological blueprint for future studies of spontaneous activity measured by two-photon calcium imaging using cutting-edge computational methods and machine learning algorithms informed by explicit graphical models from network science. The methods are flexible and easily extended to additional datasets, opening the possibility of studying cellular level network organization of neural systems and how that organization is modulated by stimuli or altered in models of disease.

show abstract

“…Here, we used a recently-developed procedure to obtain estimates of community structure with the values of {γ, ω} sampled from a restricted parameter space [63]. This procedure involved first estimating the boundaries of a restricted parameter space wherein any {γ, ω} pair would result in community structure where the number of communities is > and < N × T (where T is the total number of layers; T = 6, in this case), and where community structure is neither uniform across layers (flexibility of exactly 0) nor is it maximally dissimilar (flexibility of exactly 1).…”

Section: Module Detectionmentioning

confidence: 99%

Stability of spontaneous, correlated activity in mouse auditory cortex

Betzel

¹

,

Wood

²

,

Angeloni

³

et al. 2018

Preprint

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Neural systems can be modeled as complex networks in which neural elements are represented as nodes linked to one another through structural or functional connections. The resulting network can be analyzed using mathematical tools from network science and graph theory to quantify the system's topological organization and to better understand its function. While the network-based approach has become common in the analysis of large-scale neural systems probed by non-invasive neuroimaging, few studies have used network science to study the organization of biological neuronal networks reconstructed at the cellular level, and thus many very basic and fundamental questions remain unanswered. Here, we used two-photon calcium imaging to record spontaneous activity from the same set of cells in mouse auditory cortex over the course of several weeks. We reconstruct functional networks in which cells are linked to one another by edges weighted according to the maximum lagged correlation of their fluorescence traces. We show that the networks exhibit modular structure across multiple topological scales and that these multi-scale modules unfold as part of a hierarchy. We also show that, on average, network architecture becomes increasingly dissimilar over time, with similarity decaying monotonically with the distance (in time) between sessions. Finally, we show that a small fraction of cells maintain strongly-correlated activity over multiple days, forming a stable temporal core surrounded by a fluctuating and variable periphery. Our work provides a careful methodological blueprint for future studies of spontaneous activity measured by two-photon calcium imaging using cutting-edge computational methods and machine learning algorithms informed by explicit graphical models from network science. The methods are flexible and easily extended to additional datasets, opening the possibility of studying cellular level network organization of neural systems and how that organization is modulated by stimuli or altered in models of disease.

show abstract

The community structure of functional brain networks exhibits scale-specific patterns of variability across individuals and time

Cited by 8 publications

References 81 publications

Organizing principles of whole-brain functional connectivity in zebrafish larvae

Organizing principles of whole-brain functional connectivity in zebrafish larvae

Stability of spontaneous, correlated activity in mouse auditory cortex

Stability of spontaneous, correlated activity in mouse auditory cortex

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