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Attention Score in Context
| Title |
Verbal working memory and functional large-scale networks in schizophrenia
|
|---|---|
| Published in |
Psychiatry Research: Neuroimaging Section, October 2017
|
| DOI | 10.1016/j.pscychresns.2017.10.004 |
| Pubmed ID | |
| Authors |
Maria R. Dauvermann, Thomas WJ Moorhead, Andrew R. Watson, Barbara Duff, Liana Romaniuk, Jeremy Hall, Neil Roberts, Graham L. Lee, Zoë A. Hughes, Nicholas J. Brandon, Brandon Whitcher, Douglas HR Blackwood, Andrew M. McIntosh, Stephen M. Lawrie |
| Abstract |
The aim of this study was to test whether bilinear and nonlinear effective connectivity (EC) measures of working memory fMRI data can differentiate between patients with schizophrenia (SZ) and healthy controls (HC). We applied bilinear and nonlinear Dynamic Causal Modeling (DCM) for the analysis of verbal working memory in 16 SZ and 21 HC. The connection strengths with nonlinear modulation between the dorsolateral prefrontal cortex (DLPFC) and the ventral tegmental area/substantia nigra (VTA/SN) were evaluated. We used Bayesian Model Selection at the group and family levels to compare the optimal bilinear and nonlinear models. Bayesian Model Averaging was used to assess the connection strengths with nonlinear modulation. The DCM analyses revealed that SZ and HC used different bilinear networks despite comparable behavioral performance. In addition, the connection strengths with nonlinear modulation between the DLPFC and the VTA/SN area showed differences between SZ and HC. The adoption of different functional networks in SZ and HC indicated neurobiological alterations underlying working memory performance, including different connection strengths with nonlinear modulation between the DLPFC and the VTA/SN area. These novel findings may increase our understanding of connectivity in working memory in schizophrenia. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 62 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 14 | 23% |
| Student > Bachelor | 9 | 15% |
| Researcher | 9 | 15% |
| Student > Ph. D. Student | 8 | 13% |
| Professor | 3 | 5% |
| Other | 6 | 10% |
| Unknown | 13 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 16 | 26% |
| Neuroscience | 12 | 19% |
| Medicine and Dentistry | 4 | 6% |
| Biochemistry, Genetics and Molecular Biology | 2 | 3% |
| Computer Science | 2 | 3% |
| Other | 7 | 11% |
| Unknown | 19 | 31% |
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 08 November 2017.
All research outputs
#20,663,600
of 25,382,440 outputs
Outputs from Psychiatry Research: Neuroimaging Section
#470
of 816 outputs
Outputs of similar age
#262,307
of 338,323 outputs
Outputs of similar age from Psychiatry Research: Neuroimaging Section
#22
of 43 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 816 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one is in the 32nd percentile – i.e., 32% of its peers scored the same or lower than it.
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 338,323 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 43 others from the same source and published within six weeks on either side of this one. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.