scholarly journals Noninvasive Ultrasonic Drug Uncaging Maps Whole-Brain Functional Networks

Neuron ◽  
2018 ◽  
Vol 100 (3) ◽  
pp. 728-738.e7 ◽  
Author(s):  
Jeffrey B. Wang ◽  
Muna Aryal ◽  
Qian Zhong ◽  
Daivik B. Vyas ◽  
Raag D. Airan
Keyword(s):  
Functional Networks ◽  
Whole Brain ◽  
Brain Functional Networks

scholarly journals Disrupted Topological Organization in Whole-Brain Functional Networks of Heroin-Dependent Individuals: A Resting-State fMRI Study

PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
pp. e82715 ◽  
Author(s):  
Guihua Jiang ◽  
Xue Wen ◽  
Yingwei Qiu ◽  
Ruibin Zhang ◽  
Junjing Wang ◽  
...  
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Functional Networks ◽  
Whole Brain ◽  
Brain Functional Networks ◽  
Fmri Study ◽  
Topological Organization

scholarly journals Altered resting-state whole-brain functional networks of neonates with intrauterine growth restriction

Cortex ◽  
2016 ◽  
Vol 77 ◽  
pp. 119-131 ◽  
Author(s):  
Dafnis Batalle ◽  
Emma Muñoz-Moreno ◽  
Cristian Tornador ◽  
Nuria Bargallo ◽  
Gustavo Deco ◽  
...  
Keyword(s):  
Resting State ◽  
Intrauterine Growth Restriction ◽  
Growth Restriction ◽  
Functional Networks ◽  
Whole Brain ◽  
Intrauterine Growth ◽  
Brain Functional Networks

scholarly journals Revealing the Relevant Spatiotemporal Scale Underlying Whole-Brain Dynamics

2021 ◽  
Vol 15 ◽  
Author(s):  
Xenia Kobeleva ◽  
Ane López-González ◽  
Morten L. Kringelbach ◽  
Gustavo Deco
Keyword(s):  
Spatial Scales ◽  
Brain Dynamics ◽  
Functional Networks ◽  
Whole Brain ◽  
Spatiotemporal Scale ◽  
Brain Functional Networks ◽  
New Information ◽  
Brain Modeling ◽  
Spatiotemporal Scales ◽  
The Brain

The brain rapidly processes and adapts to new information by dynamically transitioning between whole-brain functional networks. In this whole-brain modeling study we investigate the relevance of spatiotemporal scale in whole-brain functional networks. This is achieved through estimating brain parcellations at different spatial scales (100–900 regions) and time series at different temporal scales (from milliseconds to seconds) generated by a whole-brain model fitted to fMRI data. We quantify the richness of the dynamic repertoire at each spatiotemporal scale by computing the entropy of transitions between whole-brain functional networks. The results show that the optimal relevant spatial scale is around 300 regions and a temporal scale of around 150 ms. Overall, this study provides much needed evidence for the relevant spatiotemporal scales and recommendations for analyses of brain dynamics.

scholarly journals Small-world properties of the whole-brain functional networks in patients with obstructive sleep apnea‐hypopnea syndrome

2019 ◽  
Vol 62 ◽  
pp. 53-58 ◽  
Author(s):  
Yaqing Huang ◽  
Yuting Liu ◽  
Dadi Zhao ◽  
Bin Liu ◽  
Huixin Zhang ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Small World ◽  
Functional Networks ◽  
Whole Brain ◽  
Brain Functional Networks ◽  
Obstructive Sleep ◽  
Hypopnea Syndrome

scholarly journals A network clustering based feature selection strategy for classifying autism spectrum disorder

2019 ◽  
Vol 12 (S7) ◽  
Author(s):  
Lingkai Tang ◽  
Sakib Mostafa ◽  
Bo Liao ◽  
Fang-Xiang Wu
Keyword(s):  
Autism Spectrum Disorder ◽  
Feature Selection ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Selection Strategy ◽  
Functional Networks ◽  
Network Clustering ◽  
Whole Brain ◽  
Default Mode ◽  
Brain Functional Networks

Abstract Background Advanced non-invasive neuroimaging techniques offer new approaches to study functions and structures of human brains. Whole-brain functional networks obtained from resting state functional magnetic resonance imaging has been widely used to study brain diseases like autism spectrum disorder (ASD). Auto-classification of ASD has become an important issue. Existing classification methods for ASD are based on features extracted from the whole-brain functional networks, which may be not discriminant enough for good performance. Methods In this study, we propose a network clustering based feature selection strategy for classifying ASD. In our proposed method, we first apply symmetric non-negative matrix factorization to divide brain networks into four modules. Then we extract features from one of four modules called default mode network (DMN) and use them to train several classifiers for ASD classification. Results The computational experiments show that our proposed method achieves better performances than those trained with features extracted from the whole brain network. Conclusion It is a good strategy to train the classifiers for ASD based on features from the default mode subnetwork.

Abnormal whole-brain functional networks in homogeneous acute mild traumatic brain injury

Neurology ◽  
2012 ◽  
Vol 79 (2) ◽  
pp. 175-182 ◽  
Author(s):  
E. Shumskaya ◽  
T. M. J. C. Andriessen ◽  
D. G. Norris ◽  
P. E. Vos
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Mild Traumatic Brain Injury ◽  
Functional Networks ◽  
Whole Brain ◽  
Brain Functional Networks

Changes in whole-brain functional networks and memory performance in aging

2014 ◽  
Vol 35 (10) ◽  
pp. 2193-2202 ◽  
Author(s):  
Roser Sala-Llonch ◽  
Carme Junqué ◽  
Eider M. Arenaza-Urquijo ◽  
Dídac Vidal-Piñeiro ◽  
Cinta Valls-Pedret ◽  
...  
Keyword(s):  
Memory Performance ◽  
Functional Networks ◽  
Whole Brain ◽  
Brain Functional Networks
Sign in / Sign up

Export Citation Format

Share Document