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Mendeley readers
Attention Score in Context
| Title |
Biclustered Independent Component Analysis for Complex Biomarker and Subtype Identification from Structural Magnetic Resonance Images in Schizophrenia
|
|---|---|
| Published in |
Frontiers in Psychiatry, September 2017
|
| DOI | 10.3389/fpsyt.2017.00179 |
| Pubmed ID | |
| Authors |
Cota Navin Gupta, Eduardo Castro, Srinivas Rachkonda, Theo G. M. van Erp, Steven Potkin, Judith M. Ford, Daniel Mathalon, Hyo Jong Lee, Bryon A. Mueller, Douglas N. Greve, Ole A. Andreassen, Ingrid Agartz, Andrew R. Mayer, Julia Stephen, Rex E. Jung, Juan Bustillo, Vince D. Calhoun, Jessica A. Turner |
| Abstract |
Clinical and cognitive symptoms domain-based subtyping in schizophrenia (Sz) has been critiqued due to the lack of neurobiological correlates and heterogeneity in symptom scores. We, therefore, present a novel data-driven framework using biclustered independent component analysis to detect subtypes from the reliable and stable gray matter concentration (GMC) of patients with Sz. The developed methodology consists of the following steps: source-based morphometry (SBM) decomposition, selection and sorting of two component loadings, subtype component reconstruction using group information-guided ICA (GIG-ICA). This framework was applied to the top two group discriminative components namely the insula/superior temporal gyrus/inferior frontal gyrus (I-STG-IFG component) and the superior frontal gyrus/middle frontal gyrus/medial frontal gyrus (SFG-MiFG-MFG component) from our previous SBM study, which showed diagnostic group difference and had the highest effect sizes. The aggregated multisite dataset consisted of 382 patients with Sz regressed of age, gender, and site voxelwise. We observed two subtypes (i.e., two different subsets of subjects) each heavily weighted on these two components, respectively. These subsets of subjects were characterized by significant differences in positive and negative syndrome scale (PANSS) positive clinical symptoms (p = 0.005). We also observed an overlapping subtype weighing heavily on both of these components. The PANSS general clinical symptom of this subtype was trend level correlated with the loading coefficients of the SFG-MiFG-MFG component (r = 0.25; p = 0.07). The reconstructed subtype-specific component using GIG-ICA showed variations in voxel regions, when compared to the group component. We observed deviations from mean GMC along with conjunction of features from two components characterizing each deciphered subtype. These inherent variations in GMC among patients with Sz could possibly indicate the need for personalized treatment and targeted drug development. |
X Demographics
The data shown below were collected from the profiles of 16 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 | 3 | 19% |
| Switzerland | 1 | 6% |
| Canada | 1 | 6% |
| Spain | 1 | 6% |
| China | 1 | 6% |
| Korea, Republic of | 1 | 6% |
| Germany | 1 | 6% |
| Unknown | 7 | 44% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 12 | 75% |
| Scientists | 3 | 19% |
| Practitioners (doctors, other healthcare professionals) | 1 | 6% |
Mendeley readers
The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 52 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 8 | 15% |
| Professor | 6 | 12% |
| Researcher | 6 | 12% |
| Professor > Associate Professor | 5 | 10% |
| Student > Ph. D. Student | 5 | 10% |
| Other | 6 | 12% |
| Unknown | 16 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 11 | 21% |
| Neuroscience | 7 | 13% |
| Computer Science | 3 | 6% |
| Medicine and Dentistry | 3 | 6% |
| Biochemistry, Genetics and Molecular Biology | 2 | 4% |
| Other | 5 | 10% |
| Unknown | 21 | 40% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 12 October 2017.
All research outputs
#4,744,829
of 25,382,250 outputs
Outputs from Frontiers in Psychiatry
#2,611
of 12,585 outputs
Outputs of similar age
#76,381
of 326,585 outputs
Outputs of similar age from Frontiers in Psychiatry
#30
of 86 outputs
Altmetric has tracked 25,382,250 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 12,585 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.4. This one has done well, scoring higher than 79% 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 326,585 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 76% of its contemporaries.
We're also able to compare this research output to 86 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 66% of its contemporaries.