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Identifying neuroanatomical signatures of anorexia nervosa: a multivariate machine learning approach

Published online by Cambridge University Press:  20 May 2015

L. Lavagnino ,
F. Amianto ,
B. Mwangi ,
F. D'Agata ,
A. Spalatro ,
G. B. Zunta-Soares ,
G. Abbate Daga ,
P. Mortara ,
S. Fassino  and
J. C. Soares
L. Lavagnino*
Affiliation:
UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA
F. Amianto
Affiliation:
Department of Neuroscience, AOU San Giovanni Battista, Turin, Italy
B. Mwangi
Affiliation:
UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA
F. D'Agata
Affiliation:
Department of Neuroscience, AOU San Giovanni Battista, Turin, Italy
A. Spalatro
Affiliation:
Department of Neuroscience, AOU San Giovanni Battista, Turin, Italy
G. B. Zunta-Soares
Affiliation:
UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA
G. Abbate Daga
Affiliation:
Department of Neuroscience, AOU San Giovanni Battista, Turin, Italy
P. Mortara
Affiliation:
Department of Neuroscience, AOU San Giovanni Battista, Turin, Italy
S. Fassino
Affiliation:
Department of Neuroscience, AOU San Giovanni Battista, Turin, Italy
J. C. Soares
Affiliation:
UT Center of Excellence on Mood Disorders, Department of Psychiatry and Behavioral Sciences, UT Houston Medical School, Houston, TX, USA
*
* Address for correspondence: L. Lavagnino, Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at Houston, 1941 East Road, Houston, TX 77054, USA. (Email: luca.lavagnino@uth.tmc.edu)
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Abstract

Background

There are currently no neuroanatomical biomarkers of anorexia nervosa (AN) available to make clinical inferences at an individual subject level. We present results of a multivariate machine learning (ML) approach utilizing structural neuroanatomical scan data to differentiate AN patients from matched healthy controls at an individual subject level.

Method

Structural neuroimaging scans were acquired from 15 female patients with AN (age = 20, s.d. = 4 years) and 15 demographically matched female controls (age = 22, s.d. = 3 years). Neuroanatomical volumes were extracted using the FreeSurfer software and input into the Least Absolute Shrinkage and Selection Operator (LASSO) multivariate ML algorithm. LASSO was ‘trained’ to identify ‘novel’ individual subjects as either AN patients or healthy controls. Furthermore, the model estimated the probability that an individual subject belonged to the AN group based on an individual scan.

Results

The model correctly predicted 25 out of 30 subjects, translating into 83.3% accuracy (sensitivity 86.7%, specificity 80.0%) (p < 0.001; χ2 test). Six neuroanatomical regions (cerebellum white matter, choroid plexus, putamen, accumbens, the diencephalon and the third ventricle) were found to be relevant in distinguishing individual AN patients from healthy controls. The predicted probabilities showed a linear relationship with drive for thinness clinical scores (r = 0.52, p < 0.005) and with body mass index (BMI) (r = −0.45, p = 0.01).

Conclusions

The model achieved a good predictive accuracy and drive for thinness showed a strong neuroanatomical signature. These results indicate that neuroimaging scans coupled with ML techniques have the potential to provide information at an individual subject level that might be relevant to clinical outcomes.

Keywords

Anorexiadrive for thinnessmachine learningmagnetic resonance imaging

Information

Type
Original Articles
Information
Psychological Medicine , Volume 45 , Issue 13 , October 2015 , pp. 2805 - 2812
Copyright
Copyright © Cambridge University Press 2015 

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