Graph Laplacian learning based Fourier Transform for brain network analysis with resting state fMRI

Author(s):  
Junqi Wang ◽  
Julia M. Stephen ◽  
Tony W. Wilson ◽  
Vince D. Calhoun ◽  
Yu-Ping Wang
2017 ◽  
Vol 14 (4) ◽  
pp. 046002 ◽  
Author(s):  
Bin Hu ◽  
Qunxi Dong ◽  
Yanrong Hao ◽  
Qinglin Zhao ◽  
Jian Shen ◽  
...  

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.


2016 ◽  
Vol 11 ◽  
pp. 302-315 ◽  
Author(s):  
Tingting Xu ◽  
Kathryn R. Cullen ◽  
Bryon Mueller ◽  
Mindy W. Schreiner ◽  
Kelvin O. Lim ◽  
...  

2020 ◽  
Vol 14 (6) ◽  
pp. 2771-2784 ◽  
Author(s):  
Chuan Wang ◽  
Sensen Song ◽  
Federico d’Oleire Uquillas ◽  
Anna Zilverstand ◽  
Hongwen Song ◽  
...  

Author(s):  
Abhay M S Aradhya ◽  
Aditya Joglekar ◽  
Sundaram Suresh ◽  
M. Pratama

Analysis of resting state - functional Magnetic Resonance Imaging (rs-fMRI) data has been a challenging problem due to a high homogeneity, large intra-class variability, limited samples and difference in acquisition technologies/techniques. These issues are predominant in the case of Attention Deficit Hyperactivity Disorder (ADHD). In this paper, we propose a new Deep Transformation Method (DTM) that extracts the discriminant latent feature space from rsfMRI and projects it in the subsequent layer for classification of rs-fMRI data. The hidden transformation layer in DTM projects the original rs-fMRI data into a new space using the learning policy and extracts the spatio-temporal correlations of the functional activities as a latent feature space. The subsequent convolution and decision layers transform the latent feature space into high-level features and provide accurate classification. The performance of DTM has been evaluated using the ADHD200 rs-fMRI benchmark data with crossvalidation. The results show that the proposed DTM achieves a mean classification accuracy of 70.36% and an improvement of 8.25% on the state of the art methodologies was observed. The improvement is due to concurrent analysis of the spatio-temporal correlations between the different regions of the brain and can be easily extended to study other cognitive disorders using rs-fMRI. Further, brain network analysis has been studied to identify the difference in functional activities and the corresponding regions behind cognitive symptoms in ADHD.


2016 ◽  
Vol 45 (1) ◽  
pp. 177-186 ◽  
Author(s):  
Ying-wei Qiu ◽  
Huan-Huan Su ◽  
Xiao-fei Lv ◽  
Xiao-fen Ma ◽  
Gui-hua Jiang ◽  
...  

2015 ◽  
Vol 36 (9) ◽  
pp. 3677-3686 ◽  
Author(s):  
Xueling Suo ◽  
Du Lei ◽  
Kaiming Li ◽  
Fuqin Chen ◽  
Fei Li ◽  
...  

2021 ◽  
Author(s):  
Tomokazu Tsurugizawa ◽  
Daisuke Yoshimaru

AbstractA few studies have compared the static functional connectivity between awake and anaesthetized states in rodents by resting-state fMRI. However, impact of anaesthesia on static and dynamic fluctuations in functional connectivity has not been fully understood. Here, we developed a resting-state fMRI protocol to perform awake and anaesthetized functional MRI in the same mice. Static functional connectivity showed a widespread decrease under anaesthesia, such as when under isoflurane or a mixture of isoflurane and medetomidine. Several interhemispheric connections were key connections for anaesthetized condition from awake. Dynamic functional connectivity demonstrates the shift from frequent broad connections across the cortex, the hypothalamus, and the auditory-visual cortex to frequent local connections within the cortex only. Fractional amplitude of low frequency fluctuation in the thalamic nuclei decreased under both anaesthesia. These results indicate that typical anaesthetics for functional MRI alters the spatiotemporal profile of the dynamic brain network in subcortical regions, including the thalamic nuclei and limbic system.HighlightsResting-state fMRI was compared between awake and anaesthetized in the same mice.Anaesthesia induced a widespread decrease of static functional connectivity.Anaesthesia strengthened local connections within the cortex.fALFF in the thalamus was decreased by anaesthesia.


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