An ICA Investigation into the Effect of Physiological Noise Correction on Dimensionality and Spatial Maps of Intrinsic Connectivity Networks

Author(s):  
Behnaz Jarrahi
2019 ◽  
Author(s):  
Nigel Colenbier ◽  
Frederik Van de Steen ◽  
Lucina Q. Uddin ◽  
Russell A. Poldrack ◽  
Vince D. Calhoun ◽  
...  

AbstractIn resting state functional magnetic resonance imaging (rs-fMRI) a common strategy to reduce the impact of physiological noise and other artifacts on the data is to regress out the global signal using global signal regression (GSR). Yet, GSR is one of the most controversial preprocessing techniques for rs-fMRI. It effectively removes non-neuronal artifacts, but at the same time it alters correlational patterns in unpredicted ways. Furthermore the global signal includes neural BOLD signal by construction, and is consequently related to neural and behavioral function. Performing GSR taking into account the underlying physiology (mainly the blood arrival time) has been proved to be beneficial. From these observations we aimed to: 1) characterize the effect of GSR on network-level functional connectivity in a large dataset; 2) assess the complementary role of global signal and vessels; and 3) use the framework of partial information decomposition to further look into the joint dynamics of the global signal and vessels, and their respective influence on the dynamics of cortical areas. We observe that GSR affects intrinsic connectivity networks in the connectome in a non-uniform way. Furthermore, by estimating the predictive information of blood flow and the global signal using partial information decomposition, we observe that both signals are present in different amounts across intrinsic connectivity networks. Simulations showed that differences in blood arrival time can largely explain this phenomenon. With these results we confirm network-specific effects of GSR and the importance of taking blood flow into account for improve denoising methods. Using GSR but not correcting for blood flow might selectively introduce physiological artifacts across intrinsic connectivity networks that distort the functional connectivity estimates.


2014 ◽  
Vol 153 ◽  
pp. S12-S13
Author(s):  
Vince Calhoun ◽  
Dan Mathalon ◽  
Theo van Erp ◽  
Sarah J. McEwen ◽  
Adrian Preda ◽  
...  

2021 ◽  
Author(s):  
Jonathan S Jones ◽  
Duncan Astle ◽  

Functional connectivity within and between Intrinsic Connectivity Networks (ICNs) transforms over development and supports high order cognitive functions. But how variable is this process, and does it diverge with altered cognitive developmental trajectories? We investigated age-related changes in integration and segregation within and between ICNs in neurodevelopmentally at-risk children, identified by practitioners as experiencing cognitive difficulties in attention, learning, language, or memory. In our analysis we used performance on a battery of 10 cognitive tasks, alongside resting-state functional Magnetic Resonance Imaging in 175 at-risk children and 62 comparison children aged 5-16. We observed significant age-by-group interactions in functional connectivity between two network pairs. Integration between the ventral attention and visual networks and segregation of the limbic and fronto-parietal networks increased with age in our comparison sample, relative to at-risk children. Furthermore, functional connectivity between the ventral attention and visual networks in comparison children significantly mediated age-related improvements in executive function, compared to at-risk children. We conclude that integration between ICNs show divergent neurodevelopmental trends in the broad population of children experiencing cognitive difficulties, and that these differences in functional brain organisation may partly explain the pervasive cognitive difficulties within this group over childhood and adolescence.


NeuroImage ◽  
2009 ◽  
Vol 47 ◽  
pp. S152 ◽  
Author(s):  
C Habas ◽  
N Kamdar ◽  
D Nguyen ◽  
C Keller ◽  
CF Beckmann ◽  
...  

2018 ◽  
Vol 44 (6) ◽  
pp. 1332-1340 ◽  
Author(s):  
Simon Anhøj ◽  
Mette Ødegaard Nielsen ◽  
Maria Høj Jensen ◽  
Kristin Ford ◽  
Birgitte Fagerlund ◽  
...  

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