Multiscale Network Organization in the Human Brain

Bassett, Danielle S.; Siebenhühner, Felix

doi:10.1002/9783527671632.ch07

Cited by 31 publications

(27 citation statements)

References 178 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Self-similarity of small-worldness would be indexed by scale invariance of network path length and clustering parameters as the anatomical resolution “zooms in” from macro- to microscales. Although there is abundant evidence for scaling, fractal or self-similar statistics in many aspects of brain network topology ( Bassett and others 2010 ; Bullmore and others 2009 ; Klimm and others 2014 ), experimental data do not yet exist that could support a multiscale, macro-to-micro analysis of small-worldness (and other network properties) in the same (human or mammalian) nervous system ( Bassett and Siebenhuhner 2013 ).…”

Section: What Have We (Not) Learnt Since 2006?mentioning

confidence: 99%

Small-World Brain Networks Revisited

2016

Self Cite

View full text Add to dashboard Cite

It is nearly 20 years since the concept of a small-world network was first quantitatively defined, by a combination of high clustering and short path length; and about 10 years since this metric of complex network topology began to be widely applied to analysis of neuroimaging and other neuroscience data as part of the rapid growth of the new field of connectomics. Here, we review briefly the foundational concepts of graph theoretical estimation and generation of small-world networks. We take stock of some of the key developments in the field in the past decade and we consider in some detail the implications of recent studies using high-resolution tract-tracing methods to map the anatomical networks of the macaque and the mouse. In doing so, we draw attention to the important methodological distinction between topological analysis of binary or unweighted graphs, which have provided a popular but simple approach to brain network analysis in the past, and the topology of weighted graphs, which retain more biologically relevant information and are more appropriate to the increasingly sophisticated data on brain connectivity emerging from contemporary tract-tracing and other imaging studies. We conclude by highlighting some possible future trends in the further development of weighted small-worldness as part of a deeper and broader understanding of the topology and the functional value of the strong and weak links between areas of mammalian cortex.

show abstract

Section: What Have We (Not) Learnt Since 2006?mentioning

confidence: 99%

Small-World Brain Networks Revisited

2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…A tentative modeling of time perception processes necessarily points to a description of brain functioning based on the interplay of multi-scale brain networks (Fingelkurts et al, 2010 ; Bassett and Siebenhühner, 2013 ). The meaning of “scale” can vary according to the context: (i) a network's spatial scale, which refers to the resolution at which its connected regions of interest (nodes) and connections (edges) are defined, and can range from individual cells and synapses size (Jarrell et al, 2012 ; Shimono and Beggs, 2015 ; Lee et al, 2016 ), to brain regions and fiber tracts (Bullmore and Bassett, 2011 ) and (ii) temporal scales with precision ranging from sub-millisecond (Burns et al, 2014 ), to lifetime (Betzel et al, 2014 ; Gu et al, 2015 ).…”

Section: Multiscale Brain Networkmentioning

confidence: 99%

Metastable States of Multiscale Brain Networks Are Keys to Crack the Timing Problem

Gili¹,

Ciullo²,

Spalletta³

2018

Front. Comput. Neurosci.

View full text Add to dashboard Cite

The dynamics of the environment where we live in and the interaction with it, predicting events, provided strong evolutionary pressures for the brain functioning to process temporal information and generate timed responses. As a result, the human brain is able to process temporal information and generate temporal patterns. Despite the clear importance of temporal processing to cognition, learning, communication and sensory, motor and emotional processing, the basal mechanisms of how animals differentiate simple intervals or provide timed responses are still under debate. The lesson we learned from the last decade of research in neuroscience is that functional and structural brain connectivity matter. Specifically, it has been accepted that the organization of the brain in interacting segregated networks enables its function. In this paper we delineate the route to a promising approach for investigating timing mechanisms. We illustrate how novel insight into timing mechanisms can come by investigating brain functioning as a multi-layer dynamical network whose clustered dynamics is bound to report the presence of metastable states. We anticipate that metastable dynamics underlie the real-time coordination necessary for the brain's dynamic functioning associated to time perception. This new point of view will help further clarifying mechanisms of neuropsychiatric disorders.

show abstract

“…Among recent developments is the understanding that brain networks are fundamentally multi-scale entities (Bassett and Siebenhuhner, 2013). The meaning of “scale” can vary depending on context; here we focus on three possible definitions relevant to the study of brain networks.…”

Section: Introductionmentioning

confidence: 99%

Multi-scale brain networks

2017

Self Cite

View full text Add to dashboard Cite

The network architecture of the human brain has become a feature of increasing interest to the neuroscientific community, largely because of its potential to illuminate human cognition, its variation over development and aging, and its alteration in disease or injury. Traditional tools and approaches to study this architecture have largely focused on single scales—of topology, time, and space. Expanding beyond this narrow view, we focus this review on pertinent questions and novel methodological advances for the multi-scale brain. We separate our exposition into content related to multi-scale topological structure, multi-scale temporal structure, and multi-scale spatial structure. In each case, we recount empirical evidence for such structures, survey network-based methodological approaches to reveal these structures, and outline current frontiers and open questions. Although predominantly peppered with examples from human neuroimaging, we hope that this account will offer an accessible guide to any neuroscientist aiming to measure, characterize, and understand the full richness of the brain’s multiscale network structure—irrespective of species, imaging modality, or spatial resolution.

show abstract

Multiscale Network Organization in the Human Brain

Cited by 31 publications

References 178 publications

Small-World Brain Networks Revisited

Small-World Brain Networks Revisited

Metastable States of Multiscale Brain Networks Are Keys to Crack the Timing Problem

Multi-scale brain networks

Contact Info

Product

Resources

About