Abstract
Background: Bipolar disorder (BD) broadly affects brain structure, in particular areas involved in emotion processing and cognition. In the last years, the psychiatric field's interest in machine learning approaches has been steadily growing, thanks to the potentiality of automatically discriminating patients from healthy controls. Methods: In this work, we employed cortical thickness of 58 regions of interest obtained from magnetic resonance imaging scans of 41 BD patients and 34 healthy controls, to automatically identify the regions which are mostly involved with the disease. We used a semi-supervised method, addressing the criticisms on supervised methods, related to the fact that the diagnosis is not unaffected by uncertainty. Results: Our results confirm findings in previous studies, with a classification accuracy of about 75% when mean thickness and skewness of up to five regions are considered. We obtained that the parietal lobe and some areas in the temporal sulcus were the regions which were the most involved with BD. Limitations: The major limitation of our work is the limited size or our dataset, but in line with other recent machine learning works in the field. Moreover, we considered chronic patients, whose brain characteristics may thus be affected. Conclusions: The automatic selection of the brain regions most involved in BD may be of great importance when dealing with the pathogenesis of the disorder. Our method selected regions which are known to be involved with BD, indicating that damage to the identified areas can be considered as a marker of disease.
| Original language | English |
|---|---|
| Pages (from-to) | 416-423 |
| Number of pages | 8 |
| Journal | Journal of Affective Disorders |
| Volume | 256 |
| DOIs | |
| Publication status | Published - Sep 1 2019 |
Fingerprint
Keywords
- Bipolar disorder
- Classification
- Machine learning
- Magnetic resonance imaging
- MRI pattern recognition
- Neuroimaging
- Semi-supervised learning
ASJC Scopus subject areas
- Clinical Psychology
- Psychiatry and Mental health
Cite this
Automated cortical thickness and skewness feature selection in bipolar disorder using a semi-supervised learning method. / Squarcina, L.; Dagnew, T. M.; Rivolta, M. W.; Bellani, M.; Sassi, R.; Brambilla, P.
In: Journal of Affective Disorders, Vol. 256, 01.09.2019, p. 416-423.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Automated cortical thickness and skewness feature selection in bipolar disorder using a semi-supervised learning method
AU - Squarcina, L.
AU - Dagnew, T. M.
AU - Rivolta, M. W.
AU - Bellani, M.
AU - Sassi, R.
AU - Brambilla, P.
PY - 2019/9/1
Y1 - 2019/9/1
N2 - Background: Bipolar disorder (BD) broadly affects brain structure, in particular areas involved in emotion processing and cognition. In the last years, the psychiatric field's interest in machine learning approaches has been steadily growing, thanks to the potentiality of automatically discriminating patients from healthy controls. Methods: In this work, we employed cortical thickness of 58 regions of interest obtained from magnetic resonance imaging scans of 41 BD patients and 34 healthy controls, to automatically identify the regions which are mostly involved with the disease. We used a semi-supervised method, addressing the criticisms on supervised methods, related to the fact that the diagnosis is not unaffected by uncertainty. Results: Our results confirm findings in previous studies, with a classification accuracy of about 75% when mean thickness and skewness of up to five regions are considered. We obtained that the parietal lobe and some areas in the temporal sulcus were the regions which were the most involved with BD. Limitations: The major limitation of our work is the limited size or our dataset, but in line with other recent machine learning works in the field. Moreover, we considered chronic patients, whose brain characteristics may thus be affected. Conclusions: The automatic selection of the brain regions most involved in BD may be of great importance when dealing with the pathogenesis of the disorder. Our method selected regions which are known to be involved with BD, indicating that damage to the identified areas can be considered as a marker of disease.
AB - Background: Bipolar disorder (BD) broadly affects brain structure, in particular areas involved in emotion processing and cognition. In the last years, the psychiatric field's interest in machine learning approaches has been steadily growing, thanks to the potentiality of automatically discriminating patients from healthy controls. Methods: In this work, we employed cortical thickness of 58 regions of interest obtained from magnetic resonance imaging scans of 41 BD patients and 34 healthy controls, to automatically identify the regions which are mostly involved with the disease. We used a semi-supervised method, addressing the criticisms on supervised methods, related to the fact that the diagnosis is not unaffected by uncertainty. Results: Our results confirm findings in previous studies, with a classification accuracy of about 75% when mean thickness and skewness of up to five regions are considered. We obtained that the parietal lobe and some areas in the temporal sulcus were the regions which were the most involved with BD. Limitations: The major limitation of our work is the limited size or our dataset, but in line with other recent machine learning works in the field. Moreover, we considered chronic patients, whose brain characteristics may thus be affected. Conclusions: The automatic selection of the brain regions most involved in BD may be of great importance when dealing with the pathogenesis of the disorder. Our method selected regions which are known to be involved with BD, indicating that damage to the identified areas can be considered as a marker of disease.
KW - Bipolar disorder
KW - Classification
KW - Machine learning
KW - Magnetic resonance imaging
KW - MRI pattern recognition
KW - Neuroimaging
KW - Semi-supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85067568930&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85067568930&partnerID=8YFLogxK
U2 - 10.1016/j.jad.2019.06.019
DO - 10.1016/j.jad.2019.06.019
M3 - Article
AN - SCOPUS:85067568930
VL - 256
SP - 416
EP - 423
JO - Journal of Affective Disorders
JF - Journal of Affective Disorders
SN - 0165-0327
ER -