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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Effective artifact removal in resting state fMRI data improves detection of DMN functional connectivity alteration in Alzheimer's disease
|
|---|---|
| Published in |
Frontiers in Human Neuroscience, August 2015
|
| DOI | 10.3389/fnhum.2015.00449 |
| Pubmed ID | |
| Authors |
Ludovica Griffanti, Ottavia Dipasquale, Maria M. Laganà, Raffaello Nemni, Mario Clerici, Stephen M. Smith, Giuseppe Baselli, Francesca Baglio |
| Abstract |
Artifact removal from resting state fMRI data is an essential step for a better identification of the resting state networks and the evaluation of their functional connectivity (FC), especially in pathological conditions. There is growing interest in the development of cleaning procedures, especially those not requiring external recordings (data-driven), which are able to remove multiple sources of artifacts. It is important that only inter-subject variability due to the artifacts is removed, preserving the between-subject variability of interest-crucial in clinical applications using clinical scanners to discriminate different pathologies and monitor their staging. In Alzheimer's disease (AD) patients, decreased FC is usually observed in the posterior cingulate cortex within the default mode network (DMN), and this is becoming a possible biomarker for AD. The aim of this study was to compare four different data-driven cleaning procedures (regression of motion parameters; regression of motion parameters, mean white matter and cerebrospinal fluid signal; FMRIB's ICA-based Xnoiseifier-FIX-cleanup with soft and aggressive options) on data acquired at 1.5 T. The approaches were compared using data from 20 elderly healthy subjects and 21 AD patients in a mild stage, in terms of their impact on within-group consistency in FC and ability to detect the typical FC alteration of the DMN in AD patients. Despite an increased within-group consistency across subjects after applying any of the cleaning approaches, only after cleaning with FIX the expected DMN FC alteration in AD was detectable. Our study validates the efficacy of artifact removal even in a relatively small clinical population, and supports the importance of cleaning fMRI data for sensitive detection of FC alterations in a clinical environment. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 4 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 75% |
| Members of the public | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 110 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 3% |
| United Kingdom | 1 | <1% |
| Germany | 1 | <1% |
| Unknown | 105 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 23 | 21% |
| Researcher | 20 | 18% |
| Student > Ph. D. Student | 18 | 16% |
| Student > Bachelor | 7 | 6% |
| Student > Doctoral Student | 5 | 5% |
| Other | 19 | 17% |
| Unknown | 18 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 20 | 18% |
| Psychology | 18 | 16% |
| Engineering | 17 | 15% |
| Medicine and Dentistry | 10 | 9% |
| Computer Science | 5 | 5% |
| Other | 14 | 13% |
| Unknown | 26 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 27 March 2017.
All research outputs
#2,774,957
of 22,821,814 outputs
Outputs from Frontiers in Human Neuroscience
#1,400
of 7,150 outputs
Outputs of similar age
#37,288
of 264,425 outputs
Outputs of similar age from Frontiers in Human Neuroscience
#22
of 139 outputs
Altmetric has tracked 22,821,814 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,150 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.6. This one has done well, scoring higher than 80% 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 264,425 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 139 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.