| Title |
Differences Between Schizophrenic and Normal Subjects Using Network Properties from fMRI
|
|---|---|
| Published in |
Journal of Digital Imaging, September 2017
|
| DOI | 10.1007/s10278-017-0020-4 |
| Pubmed ID | |
| Authors |
Youngoh Bae, Kunaraj Kumarasamy, Issa M. Ali, Panagiotis Korfiatis, Zeynettin Akkus, Bradley J. Erickson |
| Abstract |
Schizophrenia has been proposed to result from impairment of functional connectivity. We aimed to use machine learning to distinguish schizophrenic subjects from normal controls using a publicly available functional MRI (fMRI) data set. Global and local parameters of functional connectivity were extracted for classification. We found decreased global and local network connectivity in subjects with schizophrenia, particularly in the anterior right cingulate cortex, the superior right temporal region, and the inferior left parietal region as compared to healthy subjects. Using support vector machine and 10-fold cross-validation, nine features reached 92.1% prediction accuracy, respectively. Our results suggest that there are significant differences between control and schizophrenic subjects based on regional brain activity detected with fMRI. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 51 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 18% |
| Student > Master | 7 | 14% |
| Student > Ph. D. Student | 5 | 10% |
| Student > Bachelor | 4 | 8% |
| Student > Postgraduate | 4 | 8% |
| Other | 8 | 16% |
| Unknown | 14 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 13 | 25% |
| Computer Science | 6 | 12% |
| Engineering | 4 | 8% |
| Neuroscience | 4 | 8% |
| Physics and Astronomy | 2 | 4% |
| Other | 5 | 10% |
| Unknown | 17 | 33% |
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 23 August 2019.
All research outputs
#18,601,965
of 23,041,514 outputs
Outputs from Journal of Digital Imaging
#872
of 1,064 outputs
Outputs of similar age
#244,263
of 318,364 outputs
Outputs of similar age from Journal of Digital Imaging
#19
of 29 outputs
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