scholarly journals Multiscale network activity in resting state fMRI

Author(s):  
Shella D. Keilholz ◽  
Jacob C.W. Billings ◽  
Kai Wang ◽  
Anzar Abbas ◽  
Claudia Hafeneger ◽  
...  
2017 ◽  
Vol 29 (1) ◽  
pp. 95-113 ◽  
Author(s):  
Laurens Van Calster ◽  
Arnaud D'Argembeau ◽  
Eric Salmon ◽  
Frédéric Peters ◽  
Steve Majerus

Neuroimaging studies have revealed the recruitment of a range of neural networks during the resting state, which might reflect a variety of cognitive experiences and processes occurring in an individual's mind. In this study, we focused on the default mode network (DMN) and attentional networks and investigated their association with distinct mental states when participants are not performing an explicit task. To investigate the range of possible cognitive experiences more directly, this study proposes a novel method of resting-state fMRI experience sampling, informed by a phenomenological investigation of the fluctuation of mental states during the resting state. We hypothesized that DMN activity would increase as a function of internal mentation and that the activity of dorsal and ventral networks would indicate states of top–down versus bottom–up attention at rest. Results showed that dorsal attention network activity fluctuated as a function of subjective reports of attentional control, providing evidence that activity of this network reflects the perceived recruitment of controlled attentional processes during spontaneous cognition. Activity of the DMN increased when participants reported to be in a subjective state of internal mentation, but not when they reported to be in a state of perception. This study provides direct evidence for a link between fluctuations of resting-state neural activity and fluctuations in specific cognitive processes.


2021 ◽  
Author(s):  
Xiaodi Zhang ◽  
Eric Maltbie ◽  
Shella Keilholz

AbstractRecent resting-state fMRI studies have shown that brain activity exhibits temporal variations in functional connectivity by using various approaches including sliding window correlation, co-activation patterns, independent component analysis, quasi-periodic patterns, and hidden Markov models. These methods often model the brain activity as a discretized hopping among several brain states that are defined by the spatial configurations of network activity. However, the discretized states are merely a simplification of what is likely to be a continuous process, where each network evolves over time following its unique path. To model these characteristic spatiotemporal trajectories, we trained a variational autoencoder using rs-fMRI data and evaluated the spatiotemporal features of the latent variables obtained from the trained networks. Our results suggest that there are a relatively small number of approximately orthogonal whole-brain spatiotemporal patterns that capture the most prominent features of rs-fMRI data, which can serve as the building blocks to construct all possible spatiotemporal dynamics in resting state fMRI. These spatiotemporal patterns provide insight into how activity flows across the brain in concordance with known network structures and functional connectivity gradients.


2014 ◽  
Vol 40 ◽  
pp. e42 ◽  
Author(s):  
M. Lekander ◽  
B. Karshikoff ◽  
G. Nilsonne ◽  
M. Ingvar ◽  
E. Johansson ◽  
...  

NeuroImage ◽  
2010 ◽  
Vol 51 (1) ◽  
pp. 280-287 ◽  
Author(s):  
W. Koch ◽  
S. Teipel ◽  
S. Mueller ◽  
K. Buerger ◽  
A.L.W. Bokde ◽  
...  

2010 ◽  
Vol 117 (2-3) ◽  
pp. 355-356 ◽  
Author(s):  
Gianluca Mingoia ◽  
Gerd Wagner ◽  
Kerstin Langbein ◽  
Sigrid Scherpiet ◽  
Ralf Schloesser ◽  
...  

2013 ◽  
Vol 44 (S 01) ◽  
Author(s):  
C Dorfer ◽  
T Czech ◽  
G Kasprian ◽  
A Azizi ◽  
J Furtner ◽  
...  

2014 ◽  
Vol 45 (01) ◽  
Author(s):  
G Mingoia ◽  
K Langbein ◽  
M Dietzek ◽  
G Wagner ◽  
S Smesny ◽  
...  

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