| Title |
Multiple time scale complexity analysis of resting state FMRI
|
|---|---|
| Published in |
Brain Imaging and Behavior, November 2013
|
| DOI | 10.1007/s11682-013-9276-6 |
| Pubmed ID | |
| Authors |
Robert X. Smith, Lirong Yan, Danny J. J. Wang |
| Abstract |
The present study explored multi-scale entropy (MSE) analysis to investigate the entropy of resting state fMRI signals across multiple time scales. MSE analysis was developed to distinguish random noise from complex signals since the entropy of the former decreases with longer time scales while the latter signal maintains its entropy due to a "self-resemblance" across time scales. A long resting state BOLD fMRI (rs-fMRI) scan with 1000 data points was performed on five healthy young volunteers to investigate the spatial and temporal characteristics of entropy across multiple time scales. A shorter rs-fMRI scan with 240 data points was performed on a cohort of subjects consisting of healthy young (age 23 ± 2 years, n = 8) and aged volunteers (age 66 ± 3 years, n = 8) to investigate the effect of healthy aging on the entropy of rs-fMRI. The results showed that MSE of gray matter, rather than white matter, resembles closely that of f (-1) noise over multiple time scales. By filtering out high frequency random fluctuations, MSE analysis is able to reveal enhanced contrast in entropy between gray and white matter, as well as between age groups at longer time scales. Our data support the use of MSE analysis as a validation metric for quantifying the complexity of rs-fMRI signals. |
Mendeley readers
The data shown below were compiled from readership statistics for 77 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 4% |
| China | 1 | 1% |
| Unknown | 73 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 27 | 35% |
| Researcher | 8 | 10% |
| Student > Master | 8 | 10% |
| Student > Doctoral Student | 6 | 8% |
| Student > Bachelor | 4 | 5% |
| Other | 13 | 17% |
| Unknown | 11 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 16 | 21% |
| Psychology | 11 | 14% |
| Engineering | 8 | 10% |
| Medicine and Dentistry | 8 | 10% |
| Computer Science | 4 | 5% |
| Other | 13 | 17% |
| Unknown | 17 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 March 2015.
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#20,265,770
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Outputs from Brain Imaging and Behavior
#1,008
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Outputs of similar age
#163,088
of 187,963 outputs
Outputs of similar age from Brain Imaging and Behavior
#24
of 25 outputs
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