scholarly journals Modular slowing of resting-state dynamic Functional Connectivity as a marker of cognitive dysfunction induced by sleep deprivation

2020 ◽  
Author(s):  
Diego Lombardo ◽  
Catherine Cassé-Perrot ◽  
Jean-Philippe Ranjeva ◽  
Arnaud Le Troter ◽  
Maxime Guye ◽  
...  
Keyword(s):  
Random Walk ◽  
Functional Connectivity ◽  
Sleep Deprivation ◽  
Cognitive Performance ◽  
Cognitive Dysfunction ◽  
Resting State ◽  
List Type ◽  
Dynamic Functional Connectivity ◽  
Whole Brain ◽  
Brain Level

AbstractDynamic Functional Connectivity (dFC) in the resting state (rs) is considered as a correlate of cognitive processing. Describing dFC as a flow across morphing connectivity configurations, our notion of dFC speed quantifies the rate at which FC networks evolve in time. Here we probe the hypothesis that variations of rs dFC speed and cognitive performance are selectively interrelated within specific functional subnetworks.In particular, we focus on Sleep Deprivation (SD) as a reversible model of cognitive dysfunction. We found that whole-brain level (global) dFC speed significantly slows down after 24h of SD. However, the reduction in global dFC speed does not correlate with variations of cognitive performance in individual tasks, which are subtle and highly heterogeneous. On the contrary, we found strong correlations between performance variations in individual tasks –including Rapid Visual Processing (RVP, assessing sustained visual attention)– and dFC speed quantified at the level of functional subnetworks of interest. Providing a compromise between classic static FC (no time) and global dFC (no space), modular dFC speed analyses allow quantifying a different speed of dFC reconfiguration independently for sub-networks overseeing different tasks. Importantly, we found that RVP performance robustly correlates with the modular dFC speed of a characteristic frontoparietal module.HighlightsSleep Deprivation (SD) slows down the random walk in FC space implemented by Dynamic Functional Connectivity (dFC) at rest.Whole-brain level slowing of dFC speed does not selectively correlate with fine and taskspecific changes in performanceWe quantify dFC speed separately for different link-based modules coordinated by distinct regional “meta-hubs”Modular dFC speed variations capture subtle and task-specific variations of cognitive performance induced by SD.Author summaryWe interpreted dynamic Functional Connectivity (dFC) as a random walk in the space of possible FC networks performed with a quantifiable “speed”.Here, we analyze a fMRI dataset in which subjects are scanned and cognitively tested both before and after Sleep Deprivation (SD), used as a reversible model of cognitive dysfunction. While global dFC speed slows down after a sleepless night, it is not a sufficiently sensitive metric to correlate with fine and specific cognitive performance changes. To boost the capacity of dFC speed analyses to account for fine and specific cognitive decline, we introduce the notion of modular dFC speed. Capitalizing on an edge-centric measure of functional connectivity, which we call Meta-Connectivity, we isolate subgraphs of FC describing relatively independent random walks (dFC modules) and controlled by distinct “puppet masters” (meta-hubs). We then find that variations of the random walk speed of distinct dFC modules now selectively correlate with SD-induced variations of performance in the different tasks. This is in agreement with the fact that different subsystems – distributed but functionally distinct– oversee different tasks.The high sensitivity of modular dFC analyses bear promise of future applications to the early detection and longitudinal characterization of pathologies such as Alzheimer’s disease.

scholarly journals Modular slowing of resting-state dynamic functional connectivity as a marker of cognitive dysfunction induced by sleep deprivation

NeuroImage ◽  
2020 ◽  
Vol 222 ◽  
pp. 117155 ◽  
Author(s):  
Diego Lombardo ◽  
Catherine Cassé-Perrot ◽  
Jean-Philippe Ranjeva ◽  
Arnaud Le Troter ◽  
Maxime Guye ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Sleep Deprivation ◽  
Cognitive Dysfunction ◽  
Resting State ◽  
Dynamic Functional Connectivity ◽  
State Dynamic

scholarly journals Impact of anaesthesia on static and dynamic functional connectivity in mice

2021 ◽  
Author(s):  
Tomokazu Tsurugizawa ◽  
Daisuke Yoshimaru
Keyword(s):  
Functional Connectivity ◽  
Functional Mri ◽  
Resting State ◽  
Low Frequency ◽  
Resting State Fmri ◽  
Brain Network ◽  
List Type ◽  
Thalamic Nuclei ◽  
Dynamic Functional Connectivity ◽  
Subcortical Regions

AbstractA few studies have compared the static functional connectivity between awake and anaesthetized states in rodents by resting-state fMRI. However, impact of anaesthesia on static and dynamic fluctuations in functional connectivity has not been fully understood. Here, we developed a resting-state fMRI protocol to perform awake and anaesthetized functional MRI in the same mice. Static functional connectivity showed a widespread decrease under anaesthesia, such as when under isoflurane or a mixture of isoflurane and medetomidine. Several interhemispheric connections were key connections for anaesthetized condition from awake. Dynamic functional connectivity demonstrates the shift from frequent broad connections across the cortex, the hypothalamus, and the auditory-visual cortex to frequent local connections within the cortex only. Fractional amplitude of low frequency fluctuation in the thalamic nuclei decreased under both anaesthesia. These results indicate that typical anaesthetics for functional MRI alters the spatiotemporal profile of the dynamic brain network in subcortical regions, including the thalamic nuclei and limbic system.HighlightsResting-state fMRI was compared between awake and anaesthetized in the same mice.Anaesthesia induced a widespread decrease of static functional connectivity.Anaesthesia strengthened local connections within the cortex.fALFF in the thalamus was decreased by anaesthesia.

Impact of 36 h of total sleep deprivation on resting-state dynamic functional connectivity

2018 ◽  
Vol 1688 ◽  
pp. 22-32 ◽  
Author(s):  
Huaze Xu ◽  
Hui Shen ◽  
Lubin Wang ◽  
Qi Zhong ◽  
Yu Lei ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Sleep Deprivation ◽  
Resting State ◽  
Dynamic Functional Connectivity ◽  
Total Sleep Deprivation ◽  
State Dynamic

scholarly journals Dynamic Functional Connectivity between Order and Randomness and its Evolution across the Human Adult Lifespan

2017 ◽  
Author(s):  
Demian Battaglia ◽  
Thomas Boudou ◽  
Enrique C. A. Hansen ◽  
Diego Lombardo ◽  
Sabrina Chettouf ◽  
...  
Keyword(s):  
Random Walk ◽  
Functional Connectivity ◽  
Cognitive Task ◽  
Complex Structure ◽  
List Type ◽  
Dynamic Functional Connectivity ◽  
Adult Lifespan ◽  
Healthy Elderly ◽  
Age Related ◽  
Task Conditions

AbstractFunctional Connectivity (FC) during resting-state or task conditions is not fixed but inherently dynamic. Yet, there is no consensus on whether fluctuations in FC may resemble isolated transitions between discrete FC states rather than continuous changes. This quarrel hampers advancing the study of dynamic FC. This is unfortunate as the structure of fluctuations in FC can certainly provide more information about developmental changes, aging, and progression of pathologies. We merge the two perspectives and consider dynamic FC as an ongoing network reconfiguration, including a stochastic exploration of the space of possible steady FC states. The statistical properties of this random walk deviate both from a purely “order-driven” dynamics, in which the mean FC is preserved, and from a purely “randomness-driven” scenario, in which fluctuations of FC remain uncorrelated over time. Instead, dynamic FC has a complex structure endowed with long-range sequential correlations that give rise to transient slowing and acceleration epochs in the continuous flow of reconfiguration. Our analysis for fMRI data in healthy elderly revealed that dynamic FC tends to slow down and becomes less complex as well as more random with increasing age. These effects appear to be strongly associated with age-related changes in behavioural and cognitive performance.HighlightsDynamic Functional Connectivity (dFC) at rest and during cognitive task performs a “complex” (anomalous) random walk.Speed of dFC slows down with aging.Resting dFC replaces complexity by randomness with aging.Task performance correlates with the speed and complexity of dFC.

scholarly journals fMRI Informed Montage Selection for Transcranial Electrical Stimulation: Frontoparietal Synchronization for Drug Cue Reactivity

2021 ◽  
Author(s):  
Ghazaleh Soleimani ◽  
Rayus Kupliki ◽  
Jerzy Bodurka ◽  
Martin Paulus ◽  
Hamed Ekhtiari
Keyword(s):  
Electrical Stimulation ◽  
Functional Connectivity ◽  
Resting State ◽  
Cue Reactivity ◽  
Transcranial Electrical Stimulation ◽  
List Type ◽  
Whole Brain ◽  
Resting State Connectivity ◽  
Dual Site ◽  
Drug Cue

AbstractBackgroundFrontoparietal network (FPN) with multiple cortical nodes is involved in executive functions. Transcranial electrical stimulation (tES) can potentially modulate interactions between these nodes using frontoparietal synchronization (FPS). Here we used fMRI and computational head models (CHMs) to inform electrode montage and dosage selection in FPS.MethodsSixty methamphetamine users completed an fMRI drug cue-reactivity task. Two sets of 4×1 HD electrodes with anode over F3 and F4 were simulated and spheres around maximum electric field in each hemisphere were defined as frontal seeds. Using frontal seeds, a task-based functional connectivity analysis was conducted based on a seed-to-whole brain generalized psychophysiological interaction (gPPI). Electrode placement for parietal sites was selected based on gPPI results. Task-based and resting-state connectivity were compared between fMRI-informed and classic F3-P3/F4-P4 montages.ResultsWhole-brain gPPI showed two significant clusters (left: 506 voxels P=0.006, right: 455 voxels P=0.016), located in the inferior parietal lobule under the CP5 and CP6 electrode location. Pair-wise ROI-based gPPI comparing informed (F3-CP5/F4-CP6) and classic (F3-P3/F4-P4) montages showed significant increased PPI and resting-state connectivity only in the informed montage. Cue-induced craving score was also correlated with left (F3-CP5) frontoparietal connectivity in the fMRI-informed montage.ConclusionThis study proposes an analytic pipeline to select electrode montage and dosage in dual site tES using CHMs and task-based connectivity. Stimulating F3-F4 can tap into both FPN and saliency network (SN) based on the montage selection. Using CHM and fMRI will be essential to navigating ample parameter space in the stimulation protocols for future tES studies.HighlightsWe demonstrated a methodology for montage selection in network-based tESTask-based functional connectivity can inform dual-site tES montage selectionHead models can help to induce balance tES dose in targeted brain regionsTargeting DLPFC with tES can tap into both saliency and frontoparietal networksLower resting-state frontoparietal connectivity before cue exposure followed by a greater craving

scholarly journals 0143 Vigilance Declines Following Sleep Deprivation are Associated with Two Previously Identified Dynamic Connectivity States

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A56-A57
Author(s):  
J Teng ◽  
J Ong ◽  
A Patanaik ◽  
J Zhou ◽  
M Chee ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Sleep Deprivation ◽  
Sustained Attention ◽  
Resting State ◽  
Resting State Fmri ◽  
Fmri Data ◽  
Dynamic Functional Connectivity ◽  
Arousal State ◽  
Total Sleep Deprivation ◽  
Dynamic Connectivity

Abstract Introduction Dynamic functional connectivity (DFC) analysis of resting-state fMRI data has been successfully used to track fluctuations in arousal in the human brain. Changes in DFC have also been reported with acute sleep deprivation. Here, we demonstrate that dynamic connectivity states (DCS) previously related to arousal are reproducible, and are associated with individual differences in sustained attention declines after one night of total sleep deprivation. Methods 32 participants underwent two counterbalanced resting-state fMRI scans: during rested wakefulness (RW) and following total sleep deprivation (SD). They also completed the Psychomotor Vigilance Test (PVT), a sustained attention task that is highly sensitive to the effects of sleep loss. SD vulnerability was computed as the decrease in response speed (∆RS) and increase in lapses (∆lapse) in SD compared with RW. Dynamic functional connectivity analysis was conducted on rs-fMRI data. Connectivity matrices were clustered to obtain 5 prototypical DCS. We calculated the proportion of time participants spent in each of these DCS, as well as how often participants transitioned between DCSs. Relationships between SD vulnerability and connectivity metrics were then correlated. Results We recovered two DCS that were highly similar (ρ = .89-.91) to arousal-related DCS observed in previous work (high arousal state (HAS); low arousal state (LAS)). After sleep deprivation, the proportion of time spent in the LAS increased significantly (t29=3.16, p=.0039), while there was no significant change in HAS (t29=-1.43, p=.16). We observed significantly more state transitions in RW compared with SD. Change in LAS and HAS across sleep conditions correlated significantly with SD vulnerability (ΔLASxΔRS: r=-0.64, p<.0001; ΔLASxΔlapse: r=0.43, p=.018; ΔHASxΔRS; r=0.43, p=.019; ΔHASxΔlapse; r=-0.39, p=.033). Finally, Δ%transitions was correlated with ΔRS but not Δlapse. Conclusion This study adds to the evidence that two specific reproducible DCS are robust markers of arousal and attention, and may be useful indicators of SD vulnerability. Support This work was supported by the National Medical Research Council, Singapore (STaR/0015/2013), and the National Research Foundation Science of Learning (NRF2016-SOL002-001).

scholarly journals Flexible annotation atlas of the mouse brain: combining and dividing brain structures of the Allen Brain Atlas while maintaining anatomical hierarchy

2020 ◽  
Author(s):  
Norio Takata ◽  
Nobuhiko Sato ◽  
Yuji Komaki ◽  
Hideyuki Okano ◽  
Kenji F. Tanaka
Keyword(s):  
Functional Connectivity ◽  
Mouse Brain ◽  
Resting State ◽  
Brain Structures ◽  
Resting State Fmri ◽  
Brain Atlas ◽  
List Type ◽  
Resting State Functional Connectivity ◽  
Whole Brain ◽  
Link Type

AbstractA brain atlas is necessary for analyzing structure and function in neuroimaging research. Although various annotation volumes (AVs) for the mouse brain have been proposed, it is common in magnetic resonance imaging (MRI) of the mouse brain that regions-of-interest (ROIs) for brain structures (nodes) are created arbitrarily according to each researcher’s necessity, leading to inconsistent ROIs among studies. One reason for such a situation is the fact that earlier AVs were fixed, i.e. combination and division of nodes were not implemented. This report presents a pipeline for constructing a flexible annotation atlas (FAA) of the mouse brain by leveraging public resources of the Allen Institute for Brain Science on brain structure, gene expression, and axonal projection. A mere two-step procedure with user-specified, text-based information and Python codes constructs FAA with nodes which can be combined or divided objectively while maintaining anatomical hierarchy of brain structures. Four FAAs with total node count of 4, 101, 866, and 1,381 were demonstrated. Unique characteristics of FAA realized analysis of resting-state functional connectivity (FC) across the anatomical hierarchy and among cortical layers, which were thin but large brain structures. FAA can improve the consistency of whole brain ROI definition among laboratories by fulfilling various requests from researchers with its flexibility and reproducibility.Highlights–A flexible annotation atlas (FAA) for the mouse brain is proposed.–FAA is expected to improve whole brain ROI-definition consistency among laboratories.–The ROI can be combined or divided objectively while maintaining anatomical hierarchy.–FAA realizes functional connectivity analysis across the anatomical hierarchy.–Codes for FAA reconstruction is available at https://github.com/ntakata/flexible-annotation-atlas–Datasets for resting-state fMRI in awake mice are available at https://openneuro.org/datasets/ds002551

scholarly journals Exploring Anti-correlated Resting State BOLD Signals Through Dynamic Functional Connectivity and Whole-brain Computational Modeling

2016 ◽  
Author(s):  
Murat Demirtaş ◽  
Matthieu Gilson ◽  
John D. Murray ◽  
Dina Popovic ◽  
Eduard Vieta ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Computational Modeling ◽  
Resting State ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Hemodynamic Response Function ◽  
Dynamic Functional Connectivity ◽  
Whole Brain ◽  
Global Average ◽  
Functional Connections

AbstractResting-state functional magnetic resonance imaging and diffusion weight imaging became a conventional tool to study brain connectivity in healthy and diseased individuals. However, both techniques provide indirect measures of brain connectivity leading to controversies on their interpretation. Among these controversies, interpretation of anti-correlated functional connections and global average signal is a major challenge for the field. In this paper, we used dynamic functional connectivity to calculate the probability of anti-correlations between brain regions. The brain regions forming task-positive and task-negative networks showed high anti-correlation probabilities. The fluctuations in anti-correlation probabilities were significantly correlated with those in global average signal and functional connectivity. We investigated the mechanisms behind these fluctuations using whole-brain computational modeling approach. We found that the underlying effective connectivity and intrinsic noise reflect the static spatiotemporal patterns, whereas the hemodynamic response function is the key factor defining the fluctuations in functional connectivity and anti-correlations. Furthermore, we illustrated the clinical implications of these findings on a group of bipolar disorder patients suffering a depressive relapse (BPD).

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