scholarly journals Corrigendum to “Resting-state fMRI correlations: From link-wise unreliability to whole brain stability” [NeuroImage 157 (2017 Aug 15) 250–262]

NeuroImage ◽  
2018 ◽  
Vol 174 ◽  
pp. 599-604 ◽  
Author(s):  
M. Pannunzi ◽  
R. Hindriks ◽  
R.G. Bettinardi ◽  
E. Wenger ◽  
N. Lisofsky ◽  
...  
Author(s):  
Zhen-Zhen Ma ◽  
Jia-Jia Wu ◽  
Xu-Yun Hua ◽  
Mou-Xiong Zheng ◽  
Xiang-Xin Xing ◽  
...  

NeuroImage ◽  
2021 ◽  
Vol 231 ◽  
pp. 117844
Author(s):  
Behzad Iravani ◽  
Artin Arshamian ◽  
Peter Fransson ◽  
Neda Kaboodvand

PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
pp. e82715 ◽  
Author(s):  
Guihua Jiang ◽  
Xue Wen ◽  
Yingwei Qiu ◽  
Ruibin Zhang ◽  
Junjing Wang ◽  
...  

2018 ◽  
Author(s):  
Amrit Kashyap ◽  
Shella Keilholz

AbstractBrain Network Models have become a promising theoretical framework in simulating signals that are representative of whole brain activity such as resting state fMRI. However, it has been difficult to compare the complex brain activity between simulated and empirical data. Previous studies have used simple metrics that surmise coordination between regions such as functional connectivity, and we extend on this by using various different dynamical analysis tools that are currently used to understand resting state fMRI. We show that certain properties correspond to the structural connectivity input that is shared between the models, and certain dynamic properties relate more to the mathematical description of the Brain Network Model. We conclude that the dynamic properties that gauge more temporal structure rather than spatial coordination in the rs-fMRI signal seem to provide the largest contrasts between different BNMs and the unknown empirical dynamical system. Our results will be useful in constraining and developing more realistic simulations of whole brain activity.


2018 ◽  
Vol 18 ◽  
pp. 518-526 ◽  
Author(s):  
Nathan W. Churchill ◽  
Michael G. Hutchison ◽  
Simon J. Graham ◽  
Tom A. Schweizer

NeuroImage ◽  
2020 ◽  
Vol 208 ◽  
pp. 116367 ◽  
Author(s):  
Giulia Prando ◽  
Mattia Zorzi ◽  
Alessandra Bertoldo ◽  
Maurizio Corbetta ◽  
Marco Zorzi ◽  
...  

2016 ◽  
Vol 6 (6) ◽  
pp. 435-447 ◽  
Author(s):  
Garth J. Thompson ◽  
Valentin Riedl ◽  
Timo Grimmer ◽  
Alexander Drzezga ◽  
Peter Herman ◽  
...  

2021 ◽  
Author(s):  
Georgia Mary Cotter ◽  
Mohamed Salah Khlif ◽  
Laura Bird ◽  
Mark E Howard ◽  
Amy Brodtmann ◽  
...  

Background and Purpose. Fatigue is associated with poor functional outcomes and increased mortality following stroke. Survivors identify fatigue as one of their key unmet needs. Despite the growing body of research into post-stroke fatigue, the specific neural mechanisms remain largely unknown. Methods. This observational study included 63 stroke survivors (22 women; age 30-89 years; mean 67.5 years) from the Cognition And Neocortical Volume After Stroke (CANVAS) study, a cohort study examining cognition, mood, and brain volume in stroke survivors following ischaemic stroke. Participants underwent brain imaging 3 months post-stroke, including a 7-minute resting state fMRI echoplanar sequence. We calculated the fractional amplitude of low-frequency fluctuations, a measure of resting state brain activity at the whole-brain level. Results. Forty-five participants reported experiencing post-stroke fatigue as measured by an item on the Patient Health Questionnaire-9. A generalised linear regression model analysis with age, sex, and stroke severity covariates was conducted to compare resting state brain activity in the 0.01-0.08 Hz range, as well as its subcomponents - slow-5 (0.01-0.027 Hz), and slow-4 (0.027-0.073 Hz) frequency bands between fatigued and non-fatigued participants. We found no significant associations between post-stroke fatigue and ischaemic stroke lesion location or stroke volume. However, in the overall 0.01-0.08 Hz band, participants with post-stroke fatigue demonstrated significantly lower resting-state activity in the calcarine cortex (p<0.001, cluster-corrected pFDR=0.009, k=63) and lingual gyrus (p<0.001, cluster-corrected pFDR=0.025, k=42) and significantly higher activity in the medial prefrontal cortex (p<0.001, cluster-corrected pFDR=0.03, k=45), attributed to slow-4 and slow-5 oscillations, respectively. Conclusions. Post-stroke fatigue is associated with posterior hypoactivity and prefrontal hyperactivity, reflecting dysfunction within large-scale brain systems such as fronto-striatal-thalamic and frontal-occipital networks. These systems in turn might reflect a relationship between post-stroke fatigue and abnormalities in executive and visual functioning. This first whole-brain resting-state study provides new targets for further investigation of post-stroke fatigue beyond the lesion approach.


NeuroImage ◽  
2017 ◽  
Vol 163 ◽  
pp. 81-92 ◽  
Author(s):  
Rüdiger Stirnberg ◽  
Willem Huijbers ◽  
Daniel Brenner ◽  
Benedikt A. Poser ◽  
Monique Breteler ◽  
...  

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