Hostname: page-component-8448b6f56d-cfpbc Total loading time: 0 Render date: 2024-04-23T06:13:08.366Z Has data issue: false hasContentIssue false

Neuroanatomical features and its usefulness in classification of patients with PANDAS

Published online by Cambridge University Press:  15 November 2018

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)

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.

Type
Original Research
Copyright
© Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

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.

References

American Psychiatric Association Task Force on DSM-IV. Diagnostic and statistical manual of mental disorders: DSM-IV-TR. 2000.Google Scholar
Hu, X, Liu, Q, Li, B, et al. Multivariate pattern analysis of obsessive-compulsive disorder using structural neuroanatomy. Eur Neuropsychopharmcol. 2016;26(2):246254.CrossRefGoogle ScholarPubMed
Mataix-Cols, D, Rosario-Campos, MC, Leckman, JF. A multidimensional model of obsessive-compulsive disorder. Am J Psychiatry. 2005;162(2):228238.CrossRefGoogle ScholarPubMed
Leckman, JF, Bloch, MH, King, RA. Symptom dimensions and subtypes of obsessive-compulsive disorder: a developmental perspective. Dialogues Clin Neurosci. 2009;11(1):2133.Google ScholarPubMed
Swedo, SE, Leonard, HL, Garvey, M, et al. Pediatric Autoimmune neuropsychiatric disorders associated with streptococcal infections: clinical description of the first 50 cases. Am J Psychiatry. 1998;155(2):264271.Google Scholar
Swedo, SE, Leckman, JF, Rose, NR. From research subgroup to clinical syndrome: modifying the PANDAS criteria to describe PANS (Pediatric Acute-onset Neuropsychiatric Syndrome). Pediatr Therapeut. 2012;2(2):113.CrossRefGoogle Scholar
Nicolini, H, López, Y, Genis-Mendoza, AD, et al. Detection of anti-streptococcal, antienolase, and anti-neural antibodies in subjects with early-onset psychiatric disorders. Actas Esp Psiquiatr. 2015;43(2):3541.Google ScholarPubMed
Cox, CJ, Zuccolo, AJ, Edwards, EV, et al. Antineuronal antibodies in a heterogeneous group of youth and young adults with tics and obsessive-compulsive disorder. J Child Adolesc Psychopharmacol. 2015;25(1):7685.CrossRefGoogle Scholar
Kirvan, CA, Swedo, SE, Kurahara, D, Cunningham, MW. Streptococcal mimicry and antibody-mediated cell signaling in the pathogenesis of Sydenham’s chorea. Autoimmunity. 2006;39(1):2129.CrossRefGoogle ScholarPubMed
Radua, J, Mataix-Cols, D. Voxel-wise meta-analysis of grey matter changes in obsessive-compulsive disorder. B J Psychiatry. 2009;195(5):393402.CrossRefGoogle ScholarPubMed
Rotge, JY Langbour, N, Guehl, D, et al. Gray matter alterations in obsessive-compulsive disorder: an anatomic likelihood estimation meta-analysis. Neuropsychopharmacology. 2010; 35 (3):686691.CrossRefGoogle ScholarPubMed
Insel, TR. Toward a neuroanatomy of obsessive-compulsive disorder. Arch Gen Psychiatry. 1992;49(9):739744.CrossRefGoogle Scholar
Parmar, A, Sarkar, S. Neuroimaging studies in obsessive compulsive disorder: a narrative review. Indian J Psychol Med. 2016;38(5):386394.CrossRefGoogle ScholarPubMed
Rauch, SL, Baxter, LR Jr. Neuroimaging in obsessive-compulsive disorder and related disorders. In Jenicke MA, Baer L, Minichiello WE, eds. Obsessive-Compulsive Disorders: Practical Management, 3rd ed. St. Louis: Mosby; 1998.Google Scholar
Stein, DJ, Goodman, WK, Rauch, SL. The cognitive-affective neuroscience of obsessive-compulsive disorder. Curr Psychiatry Rep. 2000;2(4):341346.CrossRefGoogle ScholarPubMed
Giedd, JN, Rapoport, JL, Garvey, MA, Perlmutter, S, Swedo, SE. MRI assessment of children with obsessive-compulsive disorder or tics associated with streptococcal infection. Am J Psychiatry. 2000;157(2):281283.CrossRefGoogle ScholarPubMed
Elia, J, Dell, ML, Friedman, DF, et al. PANDAS with catatonia: a case report. Therapeutic response to lorazepam and plasmapheresis. J Am Acad Child Adolesc Psychiatry. 2005;44 (11):11451150.CrossRefGoogle ScholarPubMed
Citak, EC, Gücüyener, K, Karabacak, NI, Serdaroglu, A, Okuyaz, C, Aydin, K. Functional brain imaging in Sydenham’s chorea and streptococcal tic disorders. J Child Neurol. 2004;19(5):387390.CrossRefGoogle ScholarPubMed
Valente, AA Jr, Miguel, EC, Castro, CC, et al. Regional gray matter abnormalities in obsessive-compulsive disorder: a voxel-based morphometry study. Biol Psychiatry. 2005;58(6):479487.CrossRefGoogle ScholarPubMed
Pujol, J, Soriano-Mas, C, Alonso, P, et al. Mapping structural brain alterations in obsessive-compulsive disorder. Arch Gen Psychiatry. 2004;61(7):720730.CrossRefGoogle ScholarPubMed
Christian, CJ, Lencz, T, Robinson, DG, et al. Gray matter structural alterations in obsessive-compulsive disorder: relationship to neuropsychological functions. Psychiatry Res. 2008;164 (2):123131.CrossRefGoogle ScholarPubMed
Lázaro, L, Bargalló, N, Castro-Fornieles, J, et al. Brain changes in children and adolescents with obsessive-compulsive disorder before and after treatment: a voxel-based morphometric MRI study. Psychiatry Res. 2009;172(2):140146.CrossRefGoogle ScholarPubMed
Yoo, SY, Roh, MS, Choi, JS, et al. Voxel-based morphometry study of gray matter abnormalities in obsessive-compulsive disorder. J Korean Med Sci. 2008;23(1):2430.CrossRefGoogle ScholarPubMed
Trambaiolli, LR, Biazoli, CE Jr, Balardin, JB, Hoexter, MQ, Sato, JR. The relevance of feature selection methods to the classification of obsessive-compulsive disorder based on volumetric measures. J Affect Disord. 2017;222:4956.CrossRefGoogle ScholarPubMed
Pereira, F, Mitchell, T, Botvinick, M. Machine learning classifiers and fMRI: a tutorial overview. Neuroimage. 2009;45(1)(suppl):S199S209.CrossRefGoogle ScholarPubMed
Sheehan, DV, Lecrubier, Y, Sheehan, KH, et al. The Mini-International Neuropsychiatric Interview (M.I.N.I): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry. 1998;59 (suppl): 20:2233.Google Scholar
ICD-10 Classifications of Mental and Behavioural Disorder: Clinical Descriptions and Diagnostic Guidelines. Geneva, Italy: World Health Organization; 1992.Google Scholar
Nicolini, H, Herrera, K, Páez, F, et al. Traducción y confiabilidad de la escala Yale-Brown en español para trastorno obsesivo-compulsivo [Study of translation and reliability of the Spanish version of Yale-Brown obsessive-compulsive rating scale]. Salud Ment. 1996;19(suppl 3):1316. Spanish.Google Scholar
Ulloa, RE, de la Peña, F, Higeura, F, Palacios, L, Nicolini, H, Ávila, JM. Estudio de validez y confiabilidad de la versión en español de la escala Yale-Brown del trastorno obsesivo-compulsivo para niños y adolescentes [Validity and reliability of the Spanish version of Yale-Brown obsessive-compulsive rating scale for children and adolescents]. Actas Esp Psiquiatr. 2004;32(4):1621. Spanish.Google Scholar
Ashburner, J. A fast diffeomorphic image registration algorithm. Neuroimage. 2007;38(1):95113.CrossRefGoogle ScholarPubMed
Lancaster, JL, Woldorff, MG, Parsons, LM, et al. Automated Talairach atlas labels for functional brain mapping. Hum Brain Mapp. 2000;10(3):120131.3.0.CO;2-8>CrossRefGoogle ScholarPubMed
Lancaster, JL, Rainey, LH, Summerlin, JL, et al. Automated labeling of the human brain: a preliminary report on the development and evaluation of a forward-transform method. Hum Brain Mapp.1997;5(4):238242.3.0.CO;2-4>CrossRefGoogle ScholarPubMed
Segonne, F, Dale, AM, Busa, E, et al. A hybrid approach to the skull stripping problem in MRI. Neuroimage. 2004;22(3):10601075.CrossRefGoogle ScholarPubMed
Sled, JG, Zijdenbos, AP, Evans, AC. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging. 1998;17(1):8797.CrossRefGoogle ScholarPubMed
Reuter, M, Schmansky, NJ, Rosas, HD, Fischl, B. Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage. 2012;61(4):14021418.CrossRefGoogle ScholarPubMed
Desikan, RS, Ségonne, F, Fischl, B, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006;31(3):968980.CrossRefGoogle ScholarPubMed
Fischl, B, Sereno, MI, Tootell, RBH, Dale, AM. High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum Brain Mapp. 1999;8(4):272284.3.0.CO;2-4>CrossRefGoogle Scholar
Smith, SM, Jenkinson, M, Johansen-Berg, H, et al. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage. 2006;31(4):14871505.CrossRefGoogle ScholarPubMed
Smith, SM, Jenkinson, M, Woolrich, MW, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 2004;23(suppl 1):S208S219.CrossRefGoogle ScholarPubMed
Smith, SM. Fast robust automated brain extraction. Hum Brain Mapp. 2002;17(3):143155.CrossRefGoogle ScholarPubMed
Winkler, AM, Ridgway, GR, Webster, MA, Smith, SM, Nichols, TE. Permutation inference for the general linear model. Neuroimage. 2014;92(100):381397.CrossRefGoogle ScholarPubMed
Nichols, TE, Holmes, AP. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp. 2002;15(1):125.CrossRefGoogle ScholarPubMed
Rasmussen, CE, Williams, CKI. Gaussian processes for machine learning. Massachusetts: The MIT press; 2006.Google Scholar
Bishop, C. Pattern Recognition and Machine Learning (Information Science and Statistics). Secaucus, NJ: Springer-Verlag New York, Inc; 2006.Google Scholar
Rocha-Rego, V, Jogia, J, Marquand, AF, Mourao-Miranda, J, Simmons, A, Frangou, S. Examination of the predictive value of structural magnetic resonance scans in bipolar disorder: a pattern classification approach. Psychol Med. 2014;44(3):519532.CrossRefGoogle ScholarPubMed
Attwells, S, Setiawan, E, Wilson, AA, et al. Inflammation in the Neurocircuitry of Obsessive-Compulsive Disorder. JAMA Psychiatry. 2017;74(8):833840.CrossRefGoogle ScholarPubMed
Gruner, P, Vo, A, Ikuta, T, et al. White matter abnormalities in pediatric obsessive-compulsive disorder. Neuropsychopharmacology. 2012;37(12):27302739.CrossRefGoogle ScholarPubMed
Rosso, IM, Olson, EA, Britton, JC, et al. Brain white matter integrity and association with age at onset in pediatric obsessive-compulsive disorder. Biol Mood Anxiety Disord. 2014;4(1):13.CrossRefGoogle ScholarPubMed
Fallucca, E, MacMaster, FP, Haddad, J, et al. Distinguishing between major depressive disorder and obsessive-compulsive disorder in children by measuring regional cortical thickness. Arch Gen Psychiatr. 2011;68(5):527533.CrossRefGoogle ScholarPubMed
Peng, Z, Lui, SS, Cheung, EF, et al. Brain structural abnormalities in obsessive-compulsive disorder: converging evidence from white matter and grey matter. Asian J Psychiatr. 2012;5(4):290296.CrossRefGoogle ScholarPubMed
Lázaro, L, Ortiz, AG, Calvo, A, et al. White matter structural alterations in pediatric obsessive-compulsive disorder: relation to symptom dimensions. Prog Neuropsychopharmacol Biol Psychiatry. 2014;54:249258.CrossRefGoogle ScholarPubMed
Li, B., Mody, M. Cortico-striato-thalamo-cortical circuitry, working memory, and obsessive-compulsive disorder. Front Psychiatry. 2016;7:78.CrossRefGoogle ScholarPubMed
Jenkins, IH, Brooks, DJ, Nixon, PD, Frackowiak, RS, Passingham, RE. Motor sequence learning: a study with positron emission tomography. J Neurosci. 1994;14(6):37753790.CrossRefGoogle ScholarPubMed
Marchand, WR, Lee, JN, Thatcher, JW, et al. Putamen coactivation during motor task execution. Neuroreport. 2008;19(9):957960.CrossRefGoogle ScholarPubMed
Bari, A, Robbins, TW. Inhibition and impulsivity: behavioral and neural basis of response control. Prog Neurobiol. 2013;108:4479.CrossRefGoogle ScholarPubMed
Schmahmann, JD. Disorders of the cerebellum: ataxia, dysmetria of thought, and the cerebellar cognitive affective syndrome. J Neuropsychiatry Clin Neurosci. 2004.16(3):367378.CrossRefGoogle ScholarPubMed
Schmahmann, JD, Weilburg, JB, Sherman, JC. The neuropsychiatry of the cerebellum-insights from the clinic. Cerebellum. 2007;6(3): 254267.CrossRefGoogle ScholarPubMed
Stoodley, CJ, Schmahmann, JD. Evidence for topographic organization in the cerebellum of motor control versus cognitive and affective processing. Cortex. 2010;46(7):831844.CrossRefGoogle ScholarPubMed
Aron, AR. The neural basis of inhibition in cognitive control. Neuroscientist. 2007;13(3):214228.CrossRefGoogle ScholarPubMed
Posner, MI, Rothbart, MK. Attention, self-regulation and consciousness. Philos Trans R Soc Biol Sci. 1998;353(1377): 19151927.Google ScholarPubMed
Schmahmann, JD. Disorders of the cerebellum: ataxia, dysmetria of thought, and the cerebellar cognitive affective syndrome. J Neuropsychiatry Clin Neurosci. 2004;16(3):367378.CrossRefGoogle ScholarPubMed
Wolfers, T, Buitelaar, JK, Beckmann, CF, Franke, B, Marquand, AF. From estimating activation locality to predicting disorder: A review of pattern recognition for neuroimaging-based psychiatric diagnostics. Neurosci Biobehav Rev. 2015;57:328349.CrossRefGoogle ScholarPubMed
Supplementary material: File

Cabrera et al. supplementary material

Cabrera et al. supplementary material 1

Download Cabrera et al. supplementary material(File)
File 90.2 KB