Transitions between human functional brain networks reveal complex, cost-efficient and behaviorally-relevant temporal paths

Ramírez-Mahaluf, Juan Pablo; Medel, Vicente; Tepper, Ángeles; Alliende, Luz María; Sato, João Ricardo; Ossandón, Tomás; Crossley, Nicolás

doi:10.1016/j.neuroimage.2020.117027

Cited by 24 publications

(42 citation statements)

References 45 publications

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“…These findings show that complexity states of neural activity shape the coupling strength and topology of the connectome in a moment-by-moment fashion. Notably, modularity and efficiency represent two complimentary network characteristics, where the former describes a segregated configuration thought to reduce biological cost and the latter implies an integrated configuration that enhances communication within the network 9,21,22,61 . The brain must balance both, however, yielding a cost-efficiency trade-off that is continuously re-negotiated 21 .…”

Section: Mainmentioning

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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Section: Mainmentioning

confidence: 99%

A spatiotemporal complexity architecture of human brain activity

Krohn

Schwanenflug

Waschke

et al. 2021

Preprint

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“…Let us focus on graph metrics computed from brain state transition networks. These networks are computed by first applying a SWC approach, and then defining a network based on the transitions between successive FC states, as for example proposed in Ramirez-Mahaluf et al (2020).…”

Section: Example 2: Graph Metricsmentioning

confidence: 99%

Interpreting Null Models of Resting-State Functional MRI

Liégeois

Yeo

Ville

2021

Preprint

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Null models are necessary for assessing whether a dataset exhibits non-trivial statistical properties. These models have recently gained interest in the neuroimaging community as means to explore dynamic properties of functional Magnetic Resonance Imaging (fMRI) time series. Interpretation of null-model testing in this context may not be straightforward because (i) null hypotheses associated to different null models are sometimes unclear and (ii) fMRI metrics might be `trivial', i.e. preserved under the null hypothesis, and still be useful in neuroimaging applications. In this commentary, we review several commonly used null models of fMRI time series and discuss the interpretation of the corresponding tests. We argue that, while null-model testing allows for a better characterization of the statistical properties of fMRI time series and associated metrics, it should not be considered as a mandatory validation step to assess their relevance in neuroimaging applications.

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“…Ramirez-Mahaluf et al [16] recently introduced the concept of transition networks to investigate the trajectory of traversing functional states (from hereon also referred to as meta-states following [16]).…”

Section: Introductionmentioning

confidence: 99%

“…Similar to other biological systems, [17] transition networks show properties of complex networks (i.e., heavy-tailed degree distribution, high local efficiency, and modularity) indicating an organized, cost-efficient, non-random temporal trajectory of brain states. [16] Furthermore, transition network characteristics have been related to motor function and cognitive performance in healthy controls indicating behavioral relevance. [16] Here, we aimed to specify alterations of the spatiotemporal trajectory of state transitions and its relation to disease severity in NMDAR encephalitis.…”

Section: Introductionmentioning

confidence: 99%

“…[16] Furthermore, transition network characteristics have been related to motor function and cognitive performance in healthy controls indicating behavioral relevance. [16] Here, we aimed to specify alterations of the spatiotemporal trajectory of state transitions and its relation to disease severity in NMDAR encephalitis. Therefore, we constructed transition networks for a large sample of patients and age-and sex-matched healthy controls.…”

Section: Introductionmentioning

confidence: 99%

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Reduced resilience of brain state transitions in anti-N-Methyl-D-Aspartate receptor encephalitis

Schwanenflug

Ramírez-Mahaluf

Krohn

et al. 2022

Preprint

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Background: Patients with anti-NMDA receptor (NMDAR) encephalitis suffer from a severe neuropsychiatric syndrome, yet most patients show no abnormalities in routine magnetic resonance imaging (MRI). Advanced neuroimaging studies have consistently shown disrupted functional connectivity in this patient population, with recent work suggesting increased volatility of functional state dynamics. Here, we investigate these network dynamics through the spatiotemporal trajectory of meta-state transitions, yielding a time-resolved account of brain state exploration in NMDAR encephalitis. Methods: Resting-state functional MRI data were acquired in 73 patients with NMDAR encephalitis and 73 age- and sex-matched healthy controls. Time-resolved functional connectivity was clustered into brain meta-states, giving rise to a time-resolved transition network graph with states as nodes and transitions between brain meta-states as weighted, directed edges. Network topology, robustness, and transition cost of these transition networks were compared between groups. Results: Transition networks of patients showed significantly lower local efficiency (t = -2.43, p = 0.016), lower robustness (t = -2.03, p = 0.044) and higher leap size (t = 2.21, p = 0.029) compared to controls. Furthermore, the ratio of within-to-between module transitions and state similarity was significantly lower in patients. Importantly, these metrics were correlated with disease severity and disease duration. Conclusions: These findings reveal systematic alterations of transition networks in patients, suggesting that NMDAR encephalitis is characterized by reduced stability of brain state transitions and that this reduced resilience of transition networks plays a clinically relevant role in the manifestation of the disease.

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Transitions between human functional brain networks reveal complex, cost-efficient and behaviorally-relevant temporal paths

Cited by 24 publications

References 45 publications

A spatiotemporal complexity architecture of human brain activity

A spatiotemporal complexity architecture of human brain activity

Interpreting Null Models of Resting-State Functional MRI

Reduced resilience of brain state transitions in anti-N-Methyl-D-Aspartate receptor encephalitis

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