| Title |
Distinguishing bipolar and major depressive disorders by brain structural morphometry: a pilot study
|
|---|---|
| Published in |
BMC Psychiatry, November 2015
|
| DOI | 10.1186/s12888-015-0685-5 |
| Pubmed ID | |
| Authors |
Germaine Fung, Yi Deng, Qing Zhao, Zhi Li, Miao Qu, Ke Li, Ya-wei Zeng, Zhen Jin, Yan-tao Ma, Xin Yu, Zhi-ren Wang, David H. K. Shum, Raymond C. K. Chan |
| Abstract |
The clinical presentation of common symptoms during depressive episodes in bipolar disorder (BD) and major depressive disorder (MDD) poses challenges for accurate diagnosis. Disorder-specific neuroanatomical features may aid the development of reliable discrimination between these two clinical conditions. For our sample of 16 BD patients, 19 MDD patients and 29 healthy volunteers, we adopted vertex-wise cortical based brain imaging techniques to examine cortical thickness and surface area, two components of cortical volume with distinct genetic determinants. Based on specific characteristics of neuroanatomical features, we then used support vector machine (SVM) algorithm to discriminate between patients with BD and MDD. Compared to MDD patients, BD patients showed significantly larger cortical surface area in the left bankssts, precuneus, precentral, inferior parietal, superior parietal and the right middle temporal gyri. In addition, larger volumes of subcortical regions were found in BD patients. In SVM discriminative analyses, the overall accuracy was 74.3 %, with a sensitivity of 62.5 % and a specificity of 84.2 % (p = 0.028). Compared to controls, larger surface area in the temporo-parietal regions were observed in BD patients, and thinner cortices in fronto-temporal regions were observed in MDD patients, especially in the medial orbito-frontal area. These findings have demonstrated distinct spatially distributed variations in cortical thickness and surface area in patients with BD and MDD, suggesting potentially varying etiological and neuropathological processes in these two conditions. The employment of multimodal classification on disorder-specific biological features has shed light to the development of potential classification tools that could aid diagnostic decisions. |
X Demographics
The data shown below were collected from the profiles of 13 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 Kingdom | 3 | 23% |
| Japan | 2 | 15% |
| Greece | 1 | 8% |
| Venezuela, Bolivarian Republic of | 1 | 8% |
| Unknown | 6 | 46% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 9 | 69% |
| Practitioners (doctors, other healthcare professionals) | 3 | 23% |
| Science communicators (journalists, bloggers, editors) | 1 | 8% |
Mendeley readers
The data shown below were compiled from readership statistics for 136 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 136 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 22 | 16% |
| Student > Master | 18 | 13% |
| Student > Ph. D. Student | 17 | 13% |
| Researcher | 15 | 11% |
| Student > Doctoral Student | 6 | 4% |
| Other | 19 | 14% |
| Unknown | 39 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 28 | 21% |
| Neuroscience | 16 | 12% |
| Medicine and Dentistry | 16 | 12% |
| Nursing and Health Professions | 6 | 4% |
| Computer Science | 5 | 4% |
| Other | 19 | 14% |
| Unknown | 46 | 34% |
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 21 October 2023.
All research outputs
#4,663,607
of 25,765,370 outputs
Outputs from BMC Psychiatry
#1,854
of 5,511 outputs
Outputs of similar age
#68,692
of 395,054 outputs
Outputs of similar age from BMC Psychiatry
#22
of 80 outputs
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