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Biomarkers of Eating Disorders Using Support Vector Machine Analysis of Structural Neuroimaging Data: Preliminary Results.

Cerasa A, Castiglioni I, Salvatore C, Funaro A, Martino I, Alfano S, Donzuso G, Perrotta P, Gioia MC, Gilardi MC, Quattrone A - Behav Neurol (2015)

Bottom Line: Machine learning allowed individual diagnosis of ED versus HC with an Accuracy ≥ 0.80.Voxel-based pattern recognition analysis demonstrated that voxels influencing the classification Accuracy involved the occipital cortex, the posterior cerebellar lobule, precuneus, sensorimotor/premotor cortices, and the medial prefrontal cortex, all critical regions known to be strongly involved in the pathophysiological mechanisms of ED.Although these findings should be considered preliminary given the small size investigated, SVM analysis highlights the role of well-known brain regions as possible biomarkers to distinguish ED from HC at an individual level, thus encouraging the translational implementation of this new multivariate approach in the clinical practice.

View Article: PubMed Central - PubMed

Affiliation: IBFM-CNR, 88100 Catanzaro, Italy.

ABSTRACT
Presently, there are no valid biomarkers to identify individuals with eating disorders (ED). The aim of this work was to assess the feasibility of a machine learning method for extracting reliable neuroimaging features allowing individual categorization of patients with ED. Support Vector Machine (SVM) technique, combined with a pattern recognition method, was employed utilizing structural magnetic resonance images. Seventeen females with ED (six with diagnosis of anorexia nervosa and 11 with bulimia nervosa) were compared against 17 body mass index-matched healthy controls (HC). Machine learning allowed individual diagnosis of ED versus HC with an Accuracy ≥ 0.80. Voxel-based pattern recognition analysis demonstrated that voxels influencing the classification Accuracy involved the occipital cortex, the posterior cerebellar lobule, precuneus, sensorimotor/premotor cortices, and the medial prefrontal cortex, all critical regions known to be strongly involved in the pathophysiological mechanisms of ED. Although these findings should be considered preliminary given the small size investigated, SVM analysis highlights the role of well-known brain regions as possible biomarkers to distinguish ED from HC at an individual level, thus encouraging the translational implementation of this new multivariate approach in the clinical practice.

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Related in: MedlinePlus

Plot of the PCA coefficients that showed the highest FDR (a) and joint plot of the PCA coefficients before (triangles) and after (circles) FDR ranking (b) for the ED versus HC group discrimination (1st and 2nd components). Data from a single round of CV are shown as a representative example.
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fig1: Plot of the PCA coefficients that showed the highest FDR (a) and joint plot of the PCA coefficients before (triangles) and after (circles) FDR ranking (b) for the ED versus HC group discrimination (1st and 2nd components). Data from a single round of CV are shown as a representative example.

Mentions: Among MR images acquired for this study, no images were excluded from the subsequent analysis due to problems with image quality or problems occurred during preprocessing. As a representative example, 1st and 2nd extracted PCA coefficients that showed the highest FDR are plotted in Figure 1(a) for the ED versus HC group discrimination (data from a single round of CV). In this case, the number of subject involved was equal to 31 (16 ED, 15 HC). The total number of extracted PCA coefficients was equal to 30. The analysis of variance for the ED versus HC group discrimination showed that the percentage of variance retained by the first principal component was equal to 27.0%, while the number of extracted principal components accounting for 50% and 95% of the whole variance was 6 and 27, respectively.


Biomarkers of Eating Disorders Using Support Vector Machine Analysis of Structural Neuroimaging Data: Preliminary Results.

Cerasa A, Castiglioni I, Salvatore C, Funaro A, Martino I, Alfano S, Donzuso G, Perrotta P, Gioia MC, Gilardi MC, Quattrone A - Behav Neurol (2015)

Plot of the PCA coefficients that showed the highest FDR (a) and joint plot of the PCA coefficients before (triangles) and after (circles) FDR ranking (b) for the ED versus HC group discrimination (1st and 2nd components). Data from a single round of CV are shown as a representative example.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4663371&req=5

fig1: Plot of the PCA coefficients that showed the highest FDR (a) and joint plot of the PCA coefficients before (triangles) and after (circles) FDR ranking (b) for the ED versus HC group discrimination (1st and 2nd components). Data from a single round of CV are shown as a representative example.
Mentions: Among MR images acquired for this study, no images were excluded from the subsequent analysis due to problems with image quality or problems occurred during preprocessing. As a representative example, 1st and 2nd extracted PCA coefficients that showed the highest FDR are plotted in Figure 1(a) for the ED versus HC group discrimination (data from a single round of CV). In this case, the number of subject involved was equal to 31 (16 ED, 15 HC). The total number of extracted PCA coefficients was equal to 30. The analysis of variance for the ED versus HC group discrimination showed that the percentage of variance retained by the first principal component was equal to 27.0%, while the number of extracted principal components accounting for 50% and 95% of the whole variance was 6 and 27, respectively.

Bottom Line: Machine learning allowed individual diagnosis of ED versus HC with an Accuracy ≥ 0.80.Voxel-based pattern recognition analysis demonstrated that voxels influencing the classification Accuracy involved the occipital cortex, the posterior cerebellar lobule, precuneus, sensorimotor/premotor cortices, and the medial prefrontal cortex, all critical regions known to be strongly involved in the pathophysiological mechanisms of ED.Although these findings should be considered preliminary given the small size investigated, SVM analysis highlights the role of well-known brain regions as possible biomarkers to distinguish ED from HC at an individual level, thus encouraging the translational implementation of this new multivariate approach in the clinical practice.

View Article: PubMed Central - PubMed

Affiliation: IBFM-CNR, 88100 Catanzaro, Italy.

ABSTRACT
Presently, there are no valid biomarkers to identify individuals with eating disorders (ED). The aim of this work was to assess the feasibility of a machine learning method for extracting reliable neuroimaging features allowing individual categorization of patients with ED. Support Vector Machine (SVM) technique, combined with a pattern recognition method, was employed utilizing structural magnetic resonance images. Seventeen females with ED (six with diagnosis of anorexia nervosa and 11 with bulimia nervosa) were compared against 17 body mass index-matched healthy controls (HC). Machine learning allowed individual diagnosis of ED versus HC with an Accuracy ≥ 0.80. Voxel-based pattern recognition analysis demonstrated that voxels influencing the classification Accuracy involved the occipital cortex, the posterior cerebellar lobule, precuneus, sensorimotor/premotor cortices, and the medial prefrontal cortex, all critical regions known to be strongly involved in the pathophysiological mechanisms of ED. Although these findings should be considered preliminary given the small size investigated, SVM analysis highlights the role of well-known brain regions as possible biomarkers to distinguish ED from HC at an individual level, thus encouraging the translational implementation of this new multivariate approach in the clinical practice.

Show MeSH
Related in: MedlinePlus