scholarly journals Prediction and classification of sleep quality based on phase synchronization related whole-brain dynamic connectivity using resting state fMRI

NeuroImage ◽  
2020 ◽  
Vol 221 ◽  
pp. 117190 ◽  
Author(s):  
Zhongxing Zhou ◽  
Biao Cai ◽  
Gemeng Zhang ◽  
Aiying Zhang ◽  
Vince D. Calhoun ◽  
...  
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

2021 ◽  
Author(s):  
Victor Nozais ◽  
Philippe Boutinaud ◽  
Violaine Verrecchia ◽  
Marie-Fateye Gueye ◽  
Pierre-Yves Hervé ◽  
...  

NeuroImage ◽  
2018 ◽  
Vol 174 ◽  
pp. 599-604 ◽  
Author(s):  
M. Pannunzi ◽  
R. Hindriks ◽  
R.G. Bettinardi ◽  
E. Wenger ◽  
N. Lisofsky ◽  
...  

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.


2021 ◽  
Author(s):  
Jafar Zamani ◽  
Ali Sadr ◽  
Amir-Homayoun Javadi

AbstractsIdentifying individuals with early mild cognitive impairment (EMCI) can be an effective strategy for early diagnosis and delay the progression of Alzheimer’s disease (AD). Many approaches have been devised to discriminate those with EMCI from healthy control (HC) individuals. Selection of the most effective parameters has been one of the challenging aspects of these approaches. In this study we suggest an optimization method based on five evolutionary algorithms that can be used in optimization of neuroimaging data with a large number of parameters. Resting-state functional magnetic resonance imaging (rs-fMRI) measures, which measure functional connectivity, have been shown to be useful in prediction of cognitive decline. Analysis of functional connectivity data using graph measures is a common practice that results in a great number of parameters. Using graph measures we calculated 1155 parameters from the functional connectivity data of HC (n=36) and EMCI (n=34) extracted from the publicly available database of the Alzheimer’s disease neuroimaging initiative database (ADNI). These parameters were fed into the evolutionary algorithms to select a subset of parameters for classification of the data into two categories of EMCI and HC using a two-layer artificial neural network. All algorithms achieved classification accuracy of 94.55%, which is extremely high considering single-modality input and low number of data participants. These results highlight potential application of rs-fMRI and efficiency of such optimization methods in classification of images into HC and EMCI. This is of particular importance considering that MRI images of EMCI individuals cannot be easily identified by experts.


Sign in / Sign up

Export Citation Format

Share Document