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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Dynamic connectivity states estimated from resting fMRI Identify differences among Schizophrenia, bipolar disorder, and healthy control subjects
|
|---|---|
| Published in |
Frontiers in Human Neuroscience, November 2014
|
| DOI | 10.3389/fnhum.2014.00897 |
| Pubmed ID | |
| Authors |
Barnaly Rashid, Eswar Damaraju, Godfrey D. Pearlson, Vince D. Calhoun |
| Abstract |
Schizophrenia (SZ) and bipolar disorder (BP) share significant overlap in clinical symptoms, brain characteristics, and risk genes, and both are associated with dysconnectivity among large-scale brain networks. Resting state functional magnetic resonance imaging (rsfMRI) data facilitates studying macroscopic connectivity among distant brain regions. Standard approaches to identifying such connectivity include seed-based correlation and data-driven clustering methods such as independent component analysis (ICA) but typically focus on average connectivity. In this study, we utilize ICA on rsfMRI data to obtain intrinsic connectivity networks (ICNs) in cohorts of healthy controls (HCs) and age matched SZ and BP patients. Subsequently, we investigated difference in functional network connectivity, defined as pairwise correlations among the timecourses of ICNs, between HCs and patients. We quantified differences in both static (average) and dynamic (windowed) connectivity during the entire scan duration. Disease-specific differences were identified in connectivity within different dynamic states. Notably, results suggest that patients make fewer transitions to some states (states 1, 2, and 4) compared to HCs, with most such differences confined to a single state. SZ patients showed more differences from healthy subjects than did bipolars, including both hyper and hypo connectivity in one common connectivity state (dynamic state 3). Also group differences between SZ and bipolar patients were identified in patterns (states) of connectivity involving the frontal (dynamic state 1) and frontal-parietal regions (dynamic state 3). Our results provide new information about these illnesses and strongly suggest that state-based analyses are critical to avoid averaging together important factors that can help distinguish these clinical groups. |
X Demographics
The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 11% |
| Japan | 1 | 11% |
| Finland | 1 | 11% |
| Germany | 1 | 11% |
| United Kingdom | 1 | 11% |
| Unknown | 4 | 44% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 33% |
| Scientists | 3 | 33% |
| Practitioners (doctors, other healthcare professionals) | 2 | 22% |
| Unknown | 1 | 11% |
Mendeley readers
The data shown below were compiled from readership statistics for 336 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 1% |
| Germany | 1 | <1% |
| France | 1 | <1% |
| Italy | 1 | <1% |
| United Kingdom | 1 | <1% |
| Switzerland | 1 | <1% |
| Singapore | 1 | <1% |
| Canada | 1 | <1% |
| Japan | 1 | <1% |
| Other | 1 | <1% |
| Unknown | 323 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 83 | 25% |
| Student > Master | 57 | 17% |
| Researcher | 51 | 15% |
| Student > Bachelor | 24 | 7% |
| Student > Doctoral Student | 19 | 6% |
| Other | 41 | 12% |
| Unknown | 61 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 79 | 24% |
| Psychology | 56 | 17% |
| Engineering | 32 | 10% |
| Medicine and Dentistry | 24 | 7% |
| Computer Science | 13 | 4% |
| Other | 41 | 12% |
| Unknown | 91 | 27% |
Attention Score in Context
This research output has an Altmetric Attention Score of 13. 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 23 August 2016.
All research outputs
#2,412,345
of 22,771,140 outputs
Outputs from Frontiers in Human Neuroscience
#1,216
of 7,141 outputs
Outputs of similar age
#30,311
of 262,839 outputs
Outputs of similar age from Frontiers in Human Neuroscience
#49
of 232 outputs
Altmetric has tracked 22,771,140 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,141 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.5. This one has done well, scoring higher than 82% 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 262,839 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 88% of its contemporaries.
We're also able to compare this research output to 232 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.