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Neuroanatomical features and its usefulness in classification of patients with PANDAS

Published online by Cambridge University Press:  15 November 2018

Brenda Cabrera ,
César Romero-Rebollar ,
Luis Jiménez-Ángeles ,
Alma D. Genis-Mendoza ,
Julio Flores ,
Nuria Lanzagorta ,
María Arroyo ,
Camilo de la Fuente-Sandoval ,
Daniel Santana  and
Verónica Medina-Bañuelos
...Show all authors
Brenda Cabrera
Affiliation:
Genomics of Psychiatric and Neurodegenerative Diseases Laboratory, National Institute of Genomic Medicine (INMEGEN), Mexico City, Mexico
César Romero-Rebollar
Affiliation:
Neuroimaging Laboratory, Department of Electrical Engineering, Autonomous Metropolitan University, Mexico City, Mexico
Luis Jiménez-Ángeles
Affiliation:
Departament of Biomedical Systems, Engineering Faculty, National Autonomous University of Mexico, Mexico City, Mexico
Alma D. Genis-Mendoza
Affiliation:
Genomics of Psychiatric and Neurodegenerative Diseases Laboratory, National Institute of Genomic Medicine (INMEGEN), Mexico City, Mexico Psychiatric Care Services, Child Psychiatric Hospital Dr Juan N Navarro, Mexico City, Mexico
Julio Flores
Affiliation:
Genomics of Psychiatric and Neurodegenerative Diseases Laboratory, National Institute of Genomic Medicine (INMEGEN), Mexico City, Mexico
Nuria Lanzagorta
Affiliation:
Carracci Medical Group, Mexico City, Mexico
María Arroyo
Affiliation:
Genomics of Psychiatric and Neurodegenerative Diseases Laboratory, National Institute of Genomic Medicine (INMEGEN), Mexico City, Mexico
Camilo de la Fuente-Sandoval
Affiliation:
National Institute of Neurology and Neurosurgery “Manuel Velasco Suárez”, Mexico City, Mexico
Daniel Santana
Affiliation:
Carracci Medical Group, Mexico City, Mexico
Verónica Medina-Bañuelos
Affiliation:
Neuroimaging Laboratory, Department of Electrical Engineering, Autonomous Metropolitan University, Mexico City, Mexico
Emilio Sacristán
Affiliation:
Neuroimaging Laboratory, Department of Electrical Engineering, Autonomous Metropolitan University, Mexico City, Mexico
Humberto Nicolini*
Affiliation:
Genomics of Psychiatric and Neurodegenerative Diseases Laboratory, National Institute of Genomic Medicine (INMEGEN), Mexico City, Mexico Carracci Medical Group, Mexico City, Mexico
*
*Address for correspondence: Humberto Nicolini, Periférico Sur 4809, Tlalpan, Arenal Tepepan, 14610, Mexico City, Mexico. (Email: hnicolini@inmegen.gob.mx)
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Abstract

Objective

An obsessive-compulsive disorder (OCD) subtype has been associated with streptococcal infections and is called pediatric autoimmune neuropsychiatric disorders associated with streptococci (PANDAS). The neuroanatomical characterization of subjects with this disorder is crucial for the better understanding of its pathophysiology; also, evaluation of these features as classifiers between patients and controls is relevant to determine potential biomarkers and useful in clinical diagnosis. This was the first multivariate pattern analysis (MVPA) study on an early-onset OCD subtype.

Methods

Fourteen pediatric patients with PANDAS were paired with 14 healthy subjects and were scanned to obtain structural magnetic resonance images (MRI). We identified neuroanatomical differences between subjects with PANDAS and healthy controls using voxel-based morphometry, diffusion tensor imaging (DTI), and surface analysis. We investigated the usefulness of these neuroanatomical differences to classify patients with PANDAS using MVPA.

Results

The pattern for the gray and white matter was significantly different between subjects with PANDAS and controls. Alterations emerged in the cortex, subcortex, and cerebellum. There were no significant group differences in DTI measures (fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity) or cortical features (thickness, sulci, volume, curvature, and gyrification). The overall accuracy of 75% was achieved using the gray matter features to classify patients with PANDAS and healthy controls.

Conclusion

The results of this integrative study allow a better understanding of the neural substrates in this OCD subtype, suggesting that the anatomical gray matter characteristics could have an immune origin that might be helpful in patient classification.

Keywords

PANDASearly-onset OCDgray mattermachine learningMRImultivariate pattern analysisgaussian process classifiers
Type
Original Research
Information
CNS Spectrums , Volume 24 , Issue 5 , October 2019 , pp. 533 - 543
Copyright
© Cambridge University Press 2018 

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Footnotes

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

This project and all procedures were approved by the ethics committee of Carracci Medical Group Clinic in Mexico City.

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