Shaping the Default Activity Pattern of the Cortical Network

Sanchez-Vives, Maria V.; Massimini, Marcello; Mattia, Maurizio

doi:10.1016/j.neuron.2017.05.015

Cited by 144 publications

(153 citation statements)

References 81 publications

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“…Slow wave state has been proposed to constitute the default activity pattern of the cortex 21 ; This bimodal activity pattern -periods of quiescence interrupted by short bursts of activity -generates waves that can propagate across the cortex 24,59 . We recently showed that these waves generate a cortex-wide increase in BOLD activity 11 which may be responsible for the cortex-wide increase in functional connectivity in slow wave state.…”

Section: Population Down-up Transitions Drive Functional Connectivitymentioning

confidence: 99%

“…In persistent state, which can occur during but is not limited to awake periods, neurons are rather depolarized, sparsely active, leading to temporally dynamic, modality specific, network configurations 18 . In contrast to the persistent state, stands the bimodal activity pattern of slow oscillations, or slow wave state which has been extensively described 5,11,[19][20][21][22][23][24][25] , but only most recently in the framework of BOLD fMRI 11,26,27 . The corresponding low-frequency component ranges at 0.2-1 Hz, reflecting alternating activity patterns: active ("up") periods in which cells are depolarized and fire action potentials in temporally restricted periods, and silence ("down") periods with rather hyperpolarized membrane potentials and an almost complete absence of neuronal activity 28,29 .…”

Section: Introductionmentioning

confidence: 99%

“…The corresponding low-frequency component ranges at 0.2-1 Hz, reflecting alternating activity patterns: active ("up") periods in which cells are depolarized and fire action potentials in temporally restricted periods, and silence ("down") periods with rather hyperpolarized membrane potentials and an almost complete absence of neuronal activity 28,29 . Slow waves can occur rather locally 30 or spread over the entire cortex 11,19 , allowing activity to propagate 21 . In this work we found that independent component analysis in persistent state revealed a canonical resting state associated networks, such as the default mode 11,31 or sensory networks 32,33 .…”

Section: Introductionmentioning

confidence: 99%

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Brain states govern the spatio-temporal dynamics of resting state functional connectivity

Aedo-Jury

Schwalm

Hamzehpour

et al. 2019

Preprint

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Previously, using simultaneous task-free fMRI and optic-fiber-based neuronal calcium recordings in the anesthetized rat, we identified BOLD responses directly related to slow calcium waves, revealing a cortex-wide and spatially organized BOLD correlate (Schwalm et al. 2017). Here, with these bimodal recordings, we reveal two distinct brain states: persistent state, in which compartmentalized network activity was observed, including defined subsets such as the default mode network; and slow wave state, dominated by a cortex-wide component, suggesting a strong functional coupling of brain activity. In slow wave state we find a correlation between slow wave events and the strength of functional connectivity. These findings suggest that indeed down-up transitions of neuronal excitability drive cortex-wide functional connectivity. This study provides strong evidence that previously reported changes in functional connectivity are highly dependent on the brain's current state and directly linked with cortical excitability and slow waves generation.

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Section: Population Down-up Transitions Drive Functional Connectivitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Brain states govern the spatio-temporal dynamics of resting state functional connectivity

Aedo-Jury

Schwalm

Hamzehpour

et al. 2019

Preprint

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“…Low frequency fluctuations of the blood-oxygenation level-dependent fMRI signal putatively arise from the spontaneous neural activity of microcircuits and reflect the functional integrity of the brain (Sanchez-Vives, Massimini, & Mattia, 2017). These fluctuations are sensitive to changes in brain development (Smyser et al, 2013;Wu et al, 2016), aging (Biswal et al, 2010), cognitive deficits (Z.…”

mentioning

confidence: 99%

A machine learning investigation of volumetric and functional MRI abnormalities in adults born preterm

Shang

Fisher

Bäuml

et al. 2019

Human Brain Mapping

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Imaging studies have characterized functional and structural brain abnormalities in adults after premature birth, but these investigations have mostly used univariate methods that do not account for hypothesized interdependencies between brain regions or quantify accuracy in identifying individuals. To overcome these limitations, we used multivariate machine learning to identify gray matter volume (GMV) and amplitude of low frequency fluctuations (ALFF) brain patterns that best classify young adults born very preterm/very low birth weight (VP/VLBW; n = 94) from those born full‐term (FT; n = 92). We then compared the spatial maps of the structural and functional brain signatures and validated them by assessing associations with clinical birth history and basic cognitive variables. Premature birth could be predicted with a balanced accuracy of 80.7% using GMV and 77.4% using ALFF. GMV predictions were mediated by a pattern of subcortical and middle temporal reductions and volumetric increases of the lateral prefrontal, medial prefrontal, and superior temporal gyrus regions. ALFF predictions were characterized by a pattern including increases in the thalamus, pre‐ and post‐central gyri, and parietal lobes, in addition to decreases in the superior temporal gyri bilaterally. Decision scores from each classification, assessing the degree to which an individual was classified as a VP/VLBW case, were predicted by the number of days in neonatal hospitalization and birth weight. ALFF decision scores also contributed to the prediction of general IQ, which highlighted their potential clinical significance. Combined, the results clarified previous research and suggested that primary subcortical and temporal damage may be accompanied by disrupted neurodevelopment of the cortex.

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“…Slow waves have been hypothesized to represent the default mode of cortical network activity (Sanchez‐Vives, Massimini, & Mattia, ). During UP states, there is synchronization in beta and gamma frequencies, synaptic gain modulation, modulation of replay and memory formation, and some cortical features might inform about transitions between unconsciousness and consciousness [reviewed in Sanchez‐Vives et al, ]. An intriguing paradox exists in that astrocytes induce a synchronized state, but also mediate cholinergic and noradrenergic neuromodulations, which are characteristically associated with asynchronous, high‐rate activity that facilitates sensory processing (Lee & Dan, ).…”

Section: Computational Lessons Learned From Systems‐like Studies In Amentioning

confidence: 99%

A roadmap to integrate astrocytes into Systems Neuroscience

Kastanenka

Moreno-Bote

Pittà

et al. 2019

Glia

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Systems neuroscience is still mainly a neuronal field, despite the plethora of evidence supporting the fact that astrocytes modulate local neural circuits, networks, and complex behaviors. In this article, we sought to identify which types of studies are necessary to establish whether astrocytes, beyond their well‐documented homeostatic and metabolic functions, perform computations implementing mathematical algorithms that sub‐serve coding and higher‐brain functions. First, we reviewed Systems‐like studies that include astrocytes in order to identify computational operations that these cells may perform, using Ca2+ transients as their encoding language. The analysis suggests that astrocytes may carry out canonical computations in a time scale of subseconds to seconds in sensory processing, neuromodulation, brain state, memory formation, fear, and complex homeostatic reflexes. Next, we propose a list of actions to gain insight into the outstanding question of which variables are encoded by such computations. The application of statistical analyses based on machine learning, such as dimensionality reduction and decoding in the context of complex behaviors, combined with connectomics of astrocyte–neuronal circuits, is, in our view, fundamental undertakings. We also discuss technical and analytical approaches to study neuronal and astrocytic populations simultaneously, and the inclusion of astrocytes in advanced modeling of neural circuits, as well as in theories currently under exploration such as predictive coding and energy‐efficient coding. Clarifying the relationship between astrocytic Ca2+ and brain coding may represent a leap forward toward novel approaches in the study of astrocytes in health and disease.

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Shaping the Default Activity Pattern of the Cortical Network

Cited by 144 publications

References 81 publications

Brain states govern the spatio-temporal dynamics of resting state functional connectivity

Brain states govern the spatio-temporal dynamics of resting state functional connectivity

A machine learning investigation of volumetric and functional MRI abnormalities in adults born preterm

A roadmap to integrate astrocytes into Systems Neuroscience

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