scholarly journals A Novel 5D Brain Parcellation Approach Based on Spatio-temporal Encoding of Resting fMRI Data From Deep Residual Learning

2022 ◽  
pp. 109478
Author(s):  
Behnam Kazemivash ◽  
Vince D. Calhoun
Keyword(s):  
Fmri Data ◽  
Residual Learning ◽  
Temporal Encoding ◽  
Spatio Temporal

Overcoming limitations in diffusion-weighted MRI of breast by spatio-temporal encoding

2014 ◽  
Vol 73 (6) ◽  
pp. 2163-2173 ◽  
Author(s):  
Eddy Solomon ◽  
Noam Nissan ◽  
Edna Furman-Haran ◽  
Amir Seginer ◽  
Myra Shapiro-Feinberg ◽  
...  
Keyword(s):  
Diffusion Weighted Mri ◽  
Diffusion Weighted ◽  
Temporal Encoding ◽  
Spatio Temporal

scholarly journals Deep Transformation Method for Discriminant Analysis of Multi-Channel Resting State fMRI

2019 ◽  
Vol 33 ◽  
pp. 2556-2563 ◽  
Author(s):  
Abhay M S Aradhya ◽  
Aditya Joglekar ◽  
Sundaram Suresh ◽  
M. Pratama
Keyword(s):  
Resting State ◽  
Cognitive Disorders ◽  
Feature Space ◽  
Resting State Fmri ◽  
Transformation Method ◽  
Brain Network ◽  
Fmri Data ◽  
Functional Activities ◽  
Temporal Correlations ◽  
Spatio Temporal

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.

Localization of Latent Epileptic Activities Using Spatio-Temporal Independent Component Analysis of fMRI Data

2006 ◽  
Vol 19 (1-2) ◽  
pp. 21-28 ◽  
Author(s):  
Huafu Chen ◽  
Dezhong Yao ◽  
Guangming Lu ◽  
Zhiqiang Zhang ◽  
Qiaoli Hu
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Fmri Data ◽  
Spatio Temporal

scholarly journals Localization of Latent Epileptic Activities Using Spatio-Temporal Independent Component Analysis of fMRI Data

2007 ◽  
Vol 19 (4) ◽  
pp. 223-223
Author(s):  
Huafu Chen ◽  
Dezhong Yao ◽  
Guangming Lu ◽  
Zhiqiang Zhang ◽  
Qiaoli Hu
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Fmri Data ◽  
Spatio Temporal

Classification of Spatio-Temporal fMRI Data in the Spiking Neural Network

2018 ◽  
Vol 8 (6) ◽  
pp. 2670 ◽  
Author(s):  
Shaznoor Shakira Saharuddin ◽  
Norhanifah Murli ◽  
Muhammad Azani Hasibuan
Keyword(s):  
Neural Network ◽  
Fmri Data ◽  
Spiking Neural Network ◽  
Spatio Temporal

scholarly journals Investigations into resting-state connectivity using independent component analysis

2005 ◽  
Vol 360 (1457) ◽  
pp. 1001-1013 ◽  
Author(s):  
Christian F Beckmann ◽  
Marilena DeLuca ◽  
Joseph T Devlin ◽  
Stephen M Smith
Keyword(s):  
Independent Component Analysis ◽  
Resting State ◽  
Temporal Structure ◽  
Low Frequency ◽  
Component Analysis ◽  
Independent Component ◽  
Fmri Data ◽  
Analytical Technique ◽  
Spatio Temporal ◽  
Resting State Connectivity

Inferring resting-state connectivity patterns from functional magnetic resonance imaging (fMRI) data is a challenging task for any analytical technique. In this paper, we review a probabilistic independent component analysis (PICA) approach, optimized for the analysis of fMRI data, and discuss the role which this exploratory technique can take in scientific investigations into the structure of these effects. We apply PICA to fMRI data acquired at rest, in order to characterize the spatio-temporal structure of such data, and demonstrate that this is an effective and robust tool for the identification of low-frequency resting-state patterns from data acquired at various different spatial and temporal resolutions. We show that these networks exhibit high spatial consistency across subjects and closely resemble discrete cortical functional networks such as visual cortical areas or sensory–motor cortex.

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