Dynamic reconfiguration of cortical functional connectivity across brain states

Stitt, Iain; Hollensteiner, Karl J.; Galindo-Leon, Edgar; Pieper, Florian; Fiedler, Eva; Engler, Gerhard; Nolte, Guido; Engel, Andreas K.

doi:10.1038/s41598-017-08050-6

Cited by 36 publications

(66 citation statements)

References 54 publications

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“…The dynamics of the system can then be interrogated within this low-dimensional space, offering insights into the mechanisms that underlie the system's function. These approaches have successfully been used in the past to elucidate the functional brain circuitry that underlies the behavioral repertoires of a diverse range of organisms, including the nematode (Caenorhabditis elegans), the fruit fly (Drosophila melanogaster) and the ferret (Mustela putorius furo) [11][12][13][14] .…”

Section: Main Textmentioning

confidence: 99%

The dynamic basis of cognition: an integrative core under the control of the ascending neuromodulatory system

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Bell

et al. 2018

Preprint

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The human brain integrates diverse cognitive processes into a coherent whole, shifting fluidly as a function of changing environmental demands. Despite recent progress, the neurobiological mechanisms responsible for this dynamic system-level integration remain poorly understood. Here, we used multi-task fMRI data from the Human Connectome Project to examine the spatiotemporal architecture of cognition in the human brain. By investigating the spatial, dynamic and molecular signatures of system-wide neural activity across a range of cognitive tasks, we show that large-scale neuronal activity converges onto a low dimensional manifold that facilitates the dynamic execution of diverse task states. Flow within this attractor space is associated with dissociable cognitive functions, and with unique patterns of network-level topology and information processing complexity. The axes of the low-dimensional neurocognitive architecture align with regional differences in the density of neuromodulatory receptors, which in turn relate to distinct signatures of network controllability estimated from the structural connectome. These results advance our understanding of functional brain organization by emphasizing the interface between low dimensional neural activity, network topology, neuromodulatory systems and cognitive function.One Sentence SummaryA diverse set of neuromodulators facilitates the formation of a dynamic, low-dimensional integrative core in the brain that is recruited by diverse cognitive demands

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Section: Main Textmentioning

confidence: 99%

The dynamic basis of cognition: an integrative core under the control of the ascending neuromodulatory system

Shine

Breakspear

Bell

et al. 2018

Preprint

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“…In rodents, ECoG approaches have been employed to investigate patterns of synchrony in visual cortex (Toda et al, 2018). We have recently used ECoG implants to investigate phase coupling during spontaneously occurring state changes in the ferret (Stitt et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

“…For studies chronically implanted animals, ECoG arrays are advantageous because of the long-term stability of the implant and the recorded signals (Rubehn et al, 2009; Bosman et al, 2012; Bastos et al, 2015; Stitt et al, 2017). Thus, they permit repetitive and long-term tracking of brain activity and the investigation of spatiotemporal neural patterns under a broad variety of experimental conditions.…”

Section: Discussionmentioning

confidence: 99%

Dynamic changes in large-scale functional connectivity prior to stimulation determine performance in a multisensory task

Hollensteiner

Galindo-Leon

Pieper

et al. 2019

Preprint

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AbstractComplex and variable behavior requires fast changes of functional connectivity in large-scale cortical networks. Here, we report on the cortical dynamics of functional coupling across visual, auditory and parietal areas during a lateralized detection task in the ferret. We hypothesized that fluctuations in coupling, indicative of dynamic variations in the network state, might predict the animals’ performance. While power for hit and miss trials showed significant differences only around stimulus and response onset, phase coupling already differed before stimulus onset. Principal component analysis of directed coupling at the single-trial level during this period revealed subnetworks that most strongly related to behavior. While higher global phase coupling of visual and auditory regions to parietal cortex was predictive of task performance, a second component showed that a reduction in coupling between subnetworks of sensory modalities was also necessary, probably to allow a better detection of the unimodal signals. Furthermore, we observed that long-range coupling became more predominant during the task period compared to the pre-stimulus baseline. Taken together, these results suggest that fluctuations in the network state, particular with respect to long-range connectivity, are key determinants of the animals’ behavior.

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“…Complex network dynamics are a topic of substantial interest 108 , particularly in computational and network neuroscience [109][110][111][112] . Network fluctuations co-occur with a variety of fluctuating cognitive processes within and across rs-fMRI sessions 113,114 To understand how basic dynamics between pairs of coupled systems scale up, we simulated networks dynamics on a structural connectome using connectivity data from Cocomac.…”

Section: Discussionmentioning

confidence: 99%

Putting the “dynamic” back into dynamic functional connectivity

Heitmann

Breakspear

2017

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The study of fluctuations in time-resolved functional connectivity is a topic of substantial current interest. As the term "dynamic functional connectivity" implies, such fluctuations are believed to arise from dynamics in the neuronal systems generating these signals. While considerable activity currently attends to methodological and statistical issues regarding dynamic functional connectivity, less attention has been paid toward its candidate causes. Here, we review candidate scenarios for dynamic (functional) connectivity that arise in dynamical systems with two or more subsystems; generalized synchronization, itinerancy (a form of metastability), and multistability. Each of these scenarios arise under different configurations of local dynamics and inter-system coupling: We show how they generate time series data with nonlinear and/or non-stationary multivariate statistics. The key issue is that time series generated by coupled nonlinear systems contain a richer temporal structure than matched multivariate (linear) stochastic processes. In turn, this temporal structure yields many of the phenomena proposed as important to large-scale communication and computation in the brain, such as phase-amplitude coupling, complexity and flexibility. The code for simulating these dynamics is available in a freeware software platform, the "Brain Dynamics Toolbox".

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Dynamic reconfiguration of cortical functional connectivity across brain states

Cited by 36 publications

References 54 publications

The dynamic basis of cognition: an integrative core under the control of the ascending neuromodulatory system

The dynamic basis of cognition: an integrative core under the control of the ascending neuromodulatory system

Dynamic changes in large-scale functional connectivity prior to stimulation determine performance in a multisensory task

Putting the “dynamic” back into dynamic functional connectivity

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