To attack or to defend? Resolution os response competition by the basal ganglia
| Full text | |
| Author(s): |
Trambaiolli, Lucas R.
;
Biazoli, Jr., Claudinei E.
;
Balardin, Joana B.
;
Hoexter, Marcelo Q.
;
Sato, Joao R.
Total Authors: 5
|
| Document type: | Journal article |
| Source: | Journal of Affective Disorders; v. 222, p. 49-56, NOV 2017. |
| Web of Science Citations: | 3 |
| Abstract | |
Background: Magnetic resonance images (MRI) show detectable anatomical and functional differences between individuals with obsessive-compulsive disorder (OCD) and healthy subjects. Moreover, machine learning techniques have been proposed as tools to identify potential biomarkers and, ultimately, to support clinical diagnosis. However, few studies to date have investigated feature selection (FS) influences in OCD MRI-based classification. Methods: Volumes of cortical and subcortical structures, from MRI data of 38 OCD patients (split into two groups according symptoms severity) and 36 controls, were submitted to seven feature selection algorithms. FS aims to select the most relevant and less redundant features which discriminate between two classes. Then, a classification step was applied, from which the classification performances before and after different FS were compared. For the performance evaluation, leave-one-subject-out accuracies of Support Vector Machine classifiers were considered. Results: Using different FS algorithms, performance improvement was achieved for Controls vs. All OCD discrimination (19.08% of improvement reducing by 80% the amount of features), Controls vs. Low OCD (20.10%, 75%), Controls vs. High OCD (17.32%, 85%) and Low OCD vs. High OCD (10.53%, 75%). Furthermore, all algorithms pointed out classical cortico-striato-thalamo-cortical circuitry structures as relevant features for OCD classification. Limitations: Limitations include the sample size and using only filter approaches for FS. Conclusions: Our results suggest that FS positively impacts. OCD classification using machine-learning techniques. Complementarily, FS algorithms were able to select biologically plausible features automatically. (AU) | |
| FAPESP's process: | 13/10498-6 - Machine learning in neuroimaging: development of methods and clinical applications in psychiatric disorders |
| Grantee: | João Ricardo Sato |
| Support type: | Regular Research Grants |
| FAPESP's process: | 11/21357-9 - Research on neural circuits and biological markers involved in obsessive-compulsive disorder using behavioral paradigms of fear and anxiety |
| Grantee: | Eurípedes Constantino Miguel Filho |
| Support type: | Research Projects - Thematic Grants |
| FAPESP's process: | 15/17406-5 - Emotional decoding and neuromodulation of the prefrontal cortex with NIRS-EEG |
| Grantee: | Lucas Remoaldo Trambaiolli |
| Support type: | Scholarships in Brazil - Doctorate |