The heavy tail of the human brain

Roberts, James A.; Boonstra, Tjeerd W.; Breakspear, Michael

doi:10.1016/j.conb.2014.10.014

Cited by 85 publications

(79 citation statements)

References 72 publications

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“…If many neighbors of a node enter into a particular oscillatory regime, they act as an external synchronizing force on that node. The resulting transient, coherent mode could act much like a balanced branching process (Beggs and Plenz, 2003), thus leading to an oscillatory cascade (Roberts et al, 2015).…”

Section: Resultsmentioning

confidence: 99%

Cooperative and Competitive Spreading Dynamics on the Human Connectome

Mišić

Betzel

Nematzadeh

et al. 2015

Neuron

344

373

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Increasingly detailed data on the network topology of neural circuits create a need for theoretical principles that explain how these networks shape neural communication. Here we use a model of cascade spreading to reveal architectural features of human brain networks that facilitate spreading. Using an anatomical brain network derived from high-resolution diffusion spectrum imaging (DSI), we investigate scenarios where perturbations initiated at seed nodes result in global cascades that interact either cooperatively or competitively. We find that hub regions and a backbone of pathways facilitate early spreading, while the shortest path structure of the connectome enables cooperative effects, accelerating the spread of cascades. Finally, competing cascades become integrated by converging on polysensory associative areas. These findings show that the organizational principles of brain networks shape global communication and facilitate integrative function.

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Section: Resultsmentioning

confidence: 99%

Cooperative and Competitive Spreading Dynamics on the Human Connectome

Mišić

Betzel

Nematzadeh

et al. 2015

Neuron

344

373

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“…As brain-rhythms are not strictly timelocked to external events, they cannot be accessed by averaging. Instead, they are commonly represented by the signal power in shorter or longer time windows and often give rise to log-linear models (Buzsáki and Mizuseki, 2014;Roberts et al, 2015). A consequence of such non-linearities is that it cannot be readily captured by a linear model.…”

Section: State-of-the Art Approaches To Predict From M/eeg Observationsmentioning

confidence: 99%

Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states

Sabbagh

Ablin

Varoquaux

et al. 2019

Preprint

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Predicting biomedical outcomes from Magnetoencephalography and Electroencephalography (M/EEG) is central to applications like decoding, brain-computer-interfaces (BCI) or biomarker development and is facilitated by supervised machine learning. Yet most of the literature is concerned with within-subject classification. Here, we focus on predicting continuous outcomes from M/EEG signal power across subjects. Considering different generative mechanisms for M/EEG signals and the biomedical outcome, we propose statistically-consistent predictive models that avoid sourcereconstruction based on the covariance as representation. Our mathematical analysis and ground truth simulations demonstrated that consistent parameter estimation can be obtained with Source Power Comodulation (SPoC) supervised spatial filtering or by embedding with Riemannian geometry. Additional simulations revealed that Riemannian methods were more robust to model violations, in particular geometric distortions induced by individual anatomy. To estimate the relative contribution of brain dynamics and anatomy to prediction performance, we propose a novel model inspection procedure based on biophysical forward modeling. Applied to cross-subject prediction, the analysis revealed that the Riemannian model better exploited anatomical information while sensitivity to brain dynamics was similar across methods. We then probed the robustness of the models across different data cleaning options. Environmental denoising was globally important but Riemannian models were strikingly robust and continued performing even without preprocessing. Our results suggest each method has its niche: SPoC is practical for within-subject prediction while the Riemannian model may enable simple end-to-end learning.

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“…Similarly, we need to elucidate what is the integration of a signal from a higher area with a long time constant on a neuron with a short time constant in a lower area. Differences in time scales should be contrasted to the idea of scale-free neural dynamics [42,43•,44,45]. Insofar as scale-free dynamics is defined by the lack of a characteristic timescale, does it imply a homogeneity of (however complex) neural dynamics regardless of the cortical location?…”

Section: From Structure To Dynamics: a Hierarchy Of Timescalesmentioning

confidence: 99%

Brain structure and dynamics across scales: in search of rules

Wang

Kennedy

2016

Current Opinion in Neurobiology

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Louis Henry Sullivan, the father of skyscrapers, famously stated “Form ever follows function”. In this short review, we will focus on the relationship between form (structure) and function (dynamics) in the brain. We summarize recent advances on the quantification of directed- and weighted-mesoscopic connectivity of mammalian cortex, the exponential distance rule for mesoscopic and microscopic circuit wiring, a spatially-embedded random model of inter-areal cortical networks, and a large-scale dynamical circuit model of money’s cortex that gives rise to a hierarchy of timescales. These findings demonstrate that inter-areal cortical networks are dense (hence such concepts as “small-world” need to be refined when applied to the brain), spatially dependent (therefore purely topological approach of graph theory has limited applicability) and heterogeneous (consequently cortical areas cannot be treated as identical “nodes”).

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The heavy tail of the human brain

Cited by 85 publications

References 72 publications

Cooperative and Competitive Spreading Dynamics on the Human Connectome

Cooperative and Competitive Spreading Dynamics on the Human Connectome

Predictive regression modeling with MEG/EEG: from source power to signals and cognitive states

Brain structure and dynamics across scales: in search of rules

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