Reconstructing Large-Scale Brain Resting-State Networks from High-Resolution EEG: Spatial and Temporal Comparisons with fMRI

2016 ◽  
Vol 6 (2) ◽  
pp. 122-135 ◽  
Author(s):  
Han Yuan ◽  
Lei Ding ◽  
Min Zhu ◽  
Vadim Zotev ◽  
Raquel Phillips ◽  
...  
Keyword(s):  
High Resolution ◽  
Resting State ◽  
Large Scale ◽  
Resting State Networks ◽  
High Resolution Eeg

scholarly journals Association Between Childhood Anhedonia and Alterations in Large-scale Resting-State Networks and Task-Evoked Activation

2019 ◽  
Vol 76 (6) ◽  
pp. 624 ◽  
Author(s):  
Narun Pornpattananangkul ◽  
Ellen Leibenluft ◽  
Daniel S. Pine ◽  
Argyris Stringaris
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Resting State Networks ◽  
And Task

scholarly journals Resting-state fMRI data of awake dogs (Canis familiaris) via group-level independent component analysis reveal multiple, spatially distributed resting-state networks

2018 ◽  
Author(s):  
Dόra Szabό ◽  
Kálmán Czeibert ◽  
Ádám Kettinger ◽  
Márta Gácsi ◽  
Attila Andics ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Resting State ◽  
Large Scale ◽  
Functional Organization ◽  
Component Analysis ◽  
Independent Component ◽  
Group Level ◽  
Resting State Networks ◽  
Wide Range ◽  
Spatially Distributed

ABSTRACTResting-state networks are spatially distributed, functionally connected brain regions. Studying these networks gives us information about the large-scale functional organization of the brain and alternations in these networks are considered to play a role in a wide range of neurological conditions and aging. To describe resting-state networks in dogs, we measured 22 awake, unrestrained animals of either sex and carried out group-level spatial independent component analysis to explore whole-brain connectivity patterns. Using resting-state functional magnetic resonance imaging (rs-fMRI), in this exploratory study we found multiple resting-state networks in dogs, which resemble the pattern described in humans. We report the following dog resting-state networks: default mode network (DMN), visual network (VIS), sensorimotor network (SMN), combined auditory (AUD)-saliency (SAL) network and cerebellar network (CER). The DMN, similarly to Primates, but unlike previous studies in dogs, showed antero-posterior connectedness with involvement of hippocampal and lateral temporal regions. The results give us insight into the resting-state networks of awake animals from a taxon beyond rodents through a non-invasive method.

scholarly journals Whole brain high-resolution functional imaging at ultra high magnetic fields: An application to the analysis of resting state networks

NeuroImage ◽  
2011 ◽  
Vol 57 (3) ◽  
pp. 1031-1044 ◽  
Author(s):  
Federico De Martino ◽  
Fabrizio Esposito ◽  
Pierre-Francois van de Moortele ◽  
Noam Harel ◽  
Elia Formisano ◽  
...  
Keyword(s):  
High Resolution ◽  
Magnetic Fields ◽  
Functional Imaging ◽  
Resting State ◽  
High Magnetic Fields ◽  
Resting State Networks ◽  
Whole Brain

Activity flow over resting-state networks shapes cognitive task activations

2016 ◽  
Author(s):  
Michael W. Cole ◽  
Takuya Ito ◽  
Danielle S. Bassett ◽  
Douglas H. Schultz
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Cognitive Task ◽  
Brain Regions ◽  
Resting State Networks ◽  
Resting State Functional Connectivity ◽  
Activation Patterns ◽  
Distributed Processes ◽  
Evoked Activity ◽  
Task Activation

AbstractResting-state functional connectivity (FC) has helped reveal the intrinsic network organization of the human brain, yet its relevance to cognitive task activations has been unclear. Uncertainty remains despite evidence that resting-state FC patterns are highly similar to cognitive task activation patterns. Identifying the distributed processes that shape localized cognitive task activations may help reveal why resting-stateFC is so strongly related to cognitive task activations. We found that estimating task-evoked activity flow (the spread of activation amplitudes) over resting-state FC networks allows prediction of cognitive task activations in a large-scale neural network model. Applying this insight to empirical functional MRI data, we found that cognitive task activations can be predicted in held-out brain regions (and held-out individuals via estimated activity flow over resting-state FC networks. This suggests that task-evoked activity flow over intrinsic networks is a large-scale mechanism explaining the relevance of resting-state FC to cognitive task activations.

The neural correlates of falls: Alterations in large-scale resting-state networks in elderly fallers

2020 ◽  
Vol 80 ◽  
pp. 56-61
Author(s):  
Inbal Maidan ◽  
Amgad Droby ◽  
Yael Jacob ◽  
Nir Giladi ◽  
Jeffrey M Hausdorff ◽  
...  
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Neural Correlates ◽  
Resting State Networks

scholarly journals Quantifying fluctuations of resting state networks using arterial spin labeling perfusion MRI

2015 ◽  
Vol 36 (3) ◽  
pp. 463-473 ◽  
Author(s):  
Weiying Dai ◽  
Gopal Varma ◽  
Rachel Scheidegger ◽  
David C Alsop
Keyword(s):  
Resting State ◽  
Arterial Spin Labeling ◽  
Large Scale ◽  
Low Frequency ◽  
Spin Labeling ◽  
Blood Oxygen Level Dependent ◽  
Resting State Networks ◽  
Residual Noise ◽  
Quantitative Measurements ◽  
Smoothing Kernel

Blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) has been widely used to investigate spontaneous low-frequency signal fluctuations across brain resting state networks. However, BOLD only provides relative measures of signal fluctuations. Arterial Spin Labeling (ASL) MRI holds great potential for quantitative measurements of resting state network fluctuations. This study systematically quantified signal fluctuations of the large-scale resting state networks using ASL data from 20 healthy volunteers by separating them from global signal fluctuations and fluctuations caused by residual noise. Global ASL signal fluctuation was 7.59% ± 1.47% relative to the ASL baseline perfusion. Fluctuations of seven detected resting state networks vary from 2.96% ± 0.93% to 6.71% ± 2.35%. Fluctuations of networks and residual noise were 6.05% ± 1.18% and 6.78% ± 1.16% using 4-mm resolution ASL data applied with Gaussian smoothing kernel of 6mm. However, network fluctuations were reduced by 7.77% ± 1.56% while residual noise fluctuation was markedly reduced by 39.75% ± 2.90% when smoothing kernel of 12 mm was applied to the ASL data. Therefore, global and network fluctuations are the dominant structured noise sources in ASL data. Quantitative measurements of resting state networks may enable improved noise reduction and provide insights into the function of healthy and diseased brain.

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