Inter-regional BOLD signal variability is an organizational feature of functional brain networks

Baracchini, Giulia; Mišić, Bratislav; Setton, Roni; Mwilambwe-Tshilobo, Laetitia; Girn, Manesh; Nomi, Jason S.; Uddin, Lucina Q.; Turner, Gary R.; Spreng, R. Nathan

doi:10.1016/j.neuroimage.2021.118149

Cited by 32 publications

(42 citation statements)

References 84 publications

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“…Furthermore, SA and TA are influenced by local circuit connectivity and properties such as intrinsic timescale 9,16,17 . The influence of confounding physiological processes on SA and TA must be better understood 52–54 , including the link between TA and signal variability 55 , especially related to aging 45 . The hemodynamic response function imposes not only temporal but also spatial filtering to the BOLD signal 56,57 , which could contribute to changes in SA and TA due to age 58 or serotonergic drugs 59 .…”

Section: Discussionmentioning

confidence: 99%

Spatial and temporal autocorrelation weave complexity in brain networks

Shinn

Turner

et al. 2021

Preprint

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Correlations are a basic object of analysis across neuroscience, but multivariate patterns of correlations can be difficult to interpret. For example, correlations are fundamental to understanding timeseries derived from resting-state functional magnetic resonance imaging (rs-fMRI), a proxy of brain activity. Networks constructed from regional correlations in rs-fMRI timeseries are often interpreted as brain connectivity, yet the links between brain networks and neurobiology have until now been largely speculative. Here, we show that the topology of rs-fMRI brain networks is caused by the spatial and temporal autocorrelation of the timeseries used to construct them. Spatial and temporal autocorrelation show high test-retest reliability, and are correlated with popular measures of network topology. A generative model of spatially and temporally autocorrelated timeseries exhibits similar network topology to brain networks, and when fit to individual subjects, it captures near the reliability limit of subject and regional variation. We demonstrate why spatial and temporal autocorrelation induce network structure, and highlight their ability to link graph properties to neurobiology during healthy aging. These results offer a reductionistic account of brain network complexity, explaining characteristic patterns in brain networks using timeseries statistics.

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Section: Discussionmentioning

confidence: 99%

Spatial and temporal autocorrelation weave complexity in brain networks

Shinn

Turner

et al. 2021

Preprint

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“…Although subsecond estimates of DA fluctuations have been achieved in Parkinson's patients undergoing deep brain stimulation 75 , this has not yet proven possible in healthy adults. Third, alternative neuroimaging modalities, data processing routines, and variability estimation methods can sometimes reveal spatially and statistically differential results (see 2,12,33,76,77 ). In particular, choice of artifact removal and normalization technique prior to estimating variability can dramatically shift results.…”

Section: Future Directionsmentioning

confidence: 99%

Dynamic regulation of neural variability during working memory reflects dopamine, functional integration, and decision-making

Garrett

Kloosterman

Epp

et al. 2022

Preprint

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The regulation of moment-to-moment neural variability may permit effective responses to changing cognitive demands. However, the mechanisms that support variability regulation are unknown. In the context of working memory, we leverage the largest available PET and fMRI dataset to jointly consider three lenses through which neural variability regulation could be understood: dopamine capacity, network-level functional integration, and flexible decision processes. We show that with greater working memory load, upregulation of variability was associated with elevated dopamine capacity and heightened functional integration, effects dominantly expressed in the striato-thalamic system rather than cortex. Strikingly, behavioral modeling revealed that working memory load evoked substantial decision biases during evidence accumulation, and those who jointly expressed a more optimal decision bias and higher dopamine capacity were most likely to upregulate striato-thalamic variability under load. We argue that the ability to align striato-thalamic variability to level of demand may be a hallmark of a well-functioning brain.

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“…Here we approach these challenges through the variability of neural activity, which has been hypothesized to enable the formation of the connectome 5,6,8,29,37 , and which is increasingly recognized to carry important functional links to age and behavior 30,31,38-40 . Using time-resolved complexity estimation of resting-state BOLD signals, we uncover the universal phenomenon of “complexity drops” that represent spontaneous episodes of increased pattern regularity in the brain.…”

Section: Mainmentioning

confidence: 99%

“…Given this unidirectionality of network construction (regional activity defines inter-regional connections, but not vice versa), the emergence of functional networks must ultimately be rooted in these signals themselves. To this effect, the intrinsic variability of neural activity fluctuations is thought to facilitate network formation 4,[23][24][25][26] , but how variability enables connectivity has not been shown. In this regard, recent work on temporal brain dynamics suggests that FC may arise from only a few decisive episodes in the blood oxygen level-dependent (BOLD) brain signals 16,[27][28][29] , yet the nature of such critical moments remains unclear.…”

mentioning

confidence: 99%

A spatiotemporal complexity architecture of human brain activity

Krohn

Schwanenflug

Waschke

et al. 2021

Preprint

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The human brain operates in large-scale functional networks, collectively subsumed as the functional connectome1-13. Recent work has begun to unravel the organization of the connectome, including the temporal dynamics of brain states14-20, the trade-off between segregation and integration9,15,21-23, and a functional hierarchy from lower-order unimodal to higher-order transmodal processing systems24-27. However, it remains unknown how these network properties are embedded in the brain and if they emerge from a common neural foundation. Here we apply time-resolved estimation of brain signal complexity to uncover a unifying principle of brain organization, linking the connectome to neural variability6,28-31. Using functional magnetic resonance imaging (fMRI), we show that neural activity is marked by spontaneous "complexity drops" that reflect episodes of increased pattern regularity in the brain, and that functional connections among brain regions are an expression of their simultaneous engagement in such episodes. Moreover, these complexity drops ubiquitously propagate along cortical hierarchies, suggesting that the brain intrinsically reiterates its own functional architecture. Globally, neural activity clusters into temporal complexity states that dynamically shape the coupling strength and configuration of the connectome, implementing a continuous re-negotiation between cost-efficient segregation and communication-enhancing integration9,15,21,23. Furthermore, complexity states resolve the recently discovered association between anatomical and functional network hierarchies comprehensively25-27,32. Finally, brain signal complexity is highly sensitive to age and reflects inter-individual differences in cognition and motor function. In sum, we identify a spatiotemporal complexity architecture of neural activity — a functional "complexome" that gives rise to the network organization of the human brain.

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Inter-regional BOLD signal variability is an organizational feature of functional brain networks

Cited by 32 publications

References 84 publications

Spatial and temporal autocorrelation weave complexity in brain networks

Spatial and temporal autocorrelation weave complexity in brain networks

Dynamic regulation of neural variability during working memory reflects dopamine, functional integration, and decision-making

A spatiotemporal complexity architecture of human brain activity

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