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Attention Score in Context
| Title |
Impact of Healthy Aging on Multifractal Hemodynamic Fluctuations in the Human Prefrontal Cortex
|
|---|---|
| Published in |
Frontiers in Physiology, August 2018
|
| DOI | 10.3389/fphys.2018.01072 |
| Pubmed ID | |
| Authors |
Peter Mukli, Zoltan Nagy, Frigyes S. Racz, Peter Herman, Andras Eke |
| Abstract |
Fluctuations in resting-state cerebral hemodynamics show scale-free behavior over two distinct scaling ranges. Changes in such bimodal (multi) fractal pattern give insight to altered cerebrovascular or neural function. Our main goal was to assess the distribution of local scale-free properties characterizing cerebral hemodynamics and to disentangle the influence of aging on these multifractal parameters. To this end, we obtained extended resting-state records (N = 214) of oxyhemoglobin (HbO), deoxyhemoglobin (HbR) and total hemoglobin (HbT) concentration time series with continuous-wave near-infrared spectroscopy technology from the brain cortex. 52 healthy volunteers were enrolled in this study: 24 young (30.6 ± 8.2 years), and 28 elderly (60.5 ± 12.0 years) subjects. Using screening tests on power-law, multifractal noise, and shuffled data sets we evaluated the presence of true multifractal hemodynamics reflecting long-range correlation (LRC). Subsequently, scaling-range adaptive bimodal signal summation conversion (SSC) was performed based on standard deviation (σ) of signal windows across a range of temporal scales (s). Building on moments of different order (q) of the measure, σ(s), multifractal SSC yielded generalized Hurst exponent function, H(q), and singularity spectrum, D(h) separately for a fast and slow component (the latter dominating the highest temporal scales). Parameters were calculated reflecting the estimated measure at s = N (focus), degree of LRC [Hurst exponent, H(2) and maximal Hölder exponent, hmax] and measuring strength of multifractality [full-width-half-maximum of D(h) and ΔH15 = H(-15)-H(15)]. Correlation-based signal improvement (CBSI) enhanced our signal in terms of interpreting changes due to neural activity or local/systemic hemodynamic influences. We characterized the HbO-HbR relationship with the aid of fractal scale-wise correlation coefficient, rσ(s) and SSC-based multifractal covariance analysis. In the majority of subjects, cerebral hemodynamic fluctuations proved bimodal multifractal. In case of slow component of raw HbT, hmax, and Ĥ(2) were lower in the young group explained by a significantly increased rσ(s) among elderly at high temporal scales. Regarding the fast component of CBSI-pretreated HbT and that of HbO-HbR covariance, hmax, and focus were decreased in the elderly group. These observations suggest an attenuation of neurovascular coupling reflected by a decreased autocorrelation of the neuronal component concomitant with an accompanying increased autocorrelation of the non-neuronal component in the elderly group. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 2 | 40% |
| Switzerland | 2 | 40% |
| Denmark | 1 | 20% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 60% |
| Scientists | 1 | 20% |
| Practitioners (doctors, other healthcare professionals) | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 31 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 26% |
| Student > Master | 4 | 13% |
| Other | 3 | 10% |
| Researcher | 3 | 10% |
| Professor | 2 | 6% |
| Other | 5 | 16% |
| Unknown | 6 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 4 | 13% |
| Medicine and Dentistry | 4 | 13% |
| Physics and Astronomy | 3 | 10% |
| Agricultural and Biological Sciences | 2 | 6% |
| Nursing and Health Professions | 2 | 6% |
| Other | 5 | 16% |
| Unknown | 11 | 35% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 16 September 2018.
All research outputs
#7,517,207
of 23,099,576 outputs
Outputs from Frontiers in Physiology
#3,688
of 13,847 outputs
Outputs of similar age
#128,913
of 331,118 outputs
Outputs of similar age from Frontiers in Physiology
#186
of 477 outputs
Altmetric has tracked 23,099,576 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 13,847 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one has gotten more attention than average, scoring higher than 72% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 331,118 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.
We're also able to compare this research output to 477 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.