Neuroimaging-based methods for autism identification: A possible translational application?

Functional Neurology

Volumn 29, Issue 4, 2014, Pages 231-239

  Retico, Alessandra ^a   Tosetti, Michela ^b   Muratori, Filippo ^b,c   Calderoni, Sara ^b  

^a INFN SEZIONE DI PISA   (Italy)

^b DEPARTMENT OF DEVELOPMENTAL NEUROSCIENCE   (Italy)

^c UNIVERSITY OF PISA   (Italy)

Author keywords

Autism spectrum disorders; Brain alterations; Machine learning; Magnetic resonance imaging; Translational research

Indexed keywords

ARTICLE; AUTISM; COMPUTER ANALYSIS; DISEASE SEVERITY; HUMAN; MATHEMATICAL PARAMETERS; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; TRANSLATIONAL RESEARCH; TREATMENT RESPONSE; VOXEL BASED MORPHOMETRY; ARTIFICIAL INTELLIGENCE; BRAIN; CHILD DEVELOPMENT DISORDERS, PERVASIVE; PATHOLOGY; PROCEDURES;

ARTIFICIAL INTELLIGENCE; BRAIN; CHILD DEVELOPMENT DISORDERS, PERVASIVE; HUMANS; MAGNETIC RESONANCE IMAGING;

EID: 84924853647     PISSN: 03935264     EISSN: 19713274     Source Type: Journal    
DOI: 10.11138/fneur/2014.29.4.231     Document Type: Article

References (69)

1. Akshoomoff N, Lord C, Lincoln AJ, et al (2004). Outcome classification of preschool children with autism spectrum disorders using MRI brain measures. J Am Acad Child Adolesc Psychiatry 43: 349-357.
2. American Psychiatric Association (2013). Diagnostic and Statistical Manual of Mental Disorders (DSM-5) (5th ed.).Washington DC, American Psychiatric Publishing.
3. Ashburner J (2007). A fast diffeomorphic image registration algorithm. Neuroimage 38: 95-113.
4. Ashburner J, Friston KJ (2000). Voxel-based morphometry—the methods Neuroimage 11: 805-821.
5. Boddaert N, Zilbovicius M, Philipe A, et al (2009). MRI findings in 77 children with non-syndromic autistic disorder. PLoS One 4: e4415.
6. Calderoni S, Retico A, Biagi L, et al (2012). Female children with autism spectrum disorder: an insight from mass-univariate and pattern classification analyses. Neuroimage 59:1013-1022.
7. Carper RA, Moses P, Tigue ZD, et al (2002). Cerebral lobes in autism: early hyperplasia and abnormal age effects. Neuroimage 16: 1038-1051.
8. Castellani U, Rossato E, Murino V, et al (2012). Classification of schizophrenia using feature-based morphometry. J Neural Transm 119: 395-404.
9. Centers for Disease Control and Prevention (2014). Prevalence of autism spectrum disorder among children aged 8 years - Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2010. Morbidity and Mortality Weekly Report (MMWR) Surveillance Summaries 63: 1-22.
10. Chawarska K, Klin A, Paul R, et al (2007). Autism spectrum disorder in the second year: stability and change in syndrome expression. J Child Psychol Psychiatry 48: 128-138.
11. Chen R, Jiao Y, Herskovits EH (2011). Structural MRI in autism spectrum disorder. Pediatr Res 69: 63R-68R.
12. Chincarini A, Bosco P, Calvini P, et al (2011). Local MRI analysis approach in the diagnosis of early and prodromal Alzheimer’s disease. Neuroimage 58: 469-480.
13. Cho Y, Seong JK, Jeong Y, et al (2012). Individual subject classification for Alzheimer’s disease based on incremental learning using a spatial frequency representation of cortical thickness data. Neuroimage 59: 2217-2230.
14. Costafreda SG, Chu C, Ashburner J, et al (2009). Prognostic and diagnostic potential of the structural neuroanatomy of depression. PLoS One 4: e6353.
15. Courchesne E, Karns CM, Davis HR, et al (2001). Unusual brain growth patterns in early life in patients with autistic disorder: an MRI study. Neurology 57: 245-254.
16. Dale AM, Fischl B, Sereno MI (1999). Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9: 179-194.
17. Dawson G, Jones EJ, Merkle K, et al (2012). Early behavioral intervention is associated with normalized brain activity in young children with autism. J Am Acad Child Adolesc Psychiatry 51: 1150-1159.
18. Deshpande G, Libero LE, Sreenivasan KR, et al (2013). Identification of neural connectivity signatures of autism using machine learning. Front Hum Neurosci 7: 670.
19. Dinstein I, Pierce K, Eyler L, et al (2011). Disrupted neural synchronization in toddlers with autism. Neuron 70: 1218-1225.
20. Ecker C, Murphy D (2014). Neuroimaging in autism-from basic science to translational research. Nat Rev Neurol 10: 82-91.
21. Ecker C, Marquand A, Mourão-Miranda J, et al (2010a). Describing the brain in autism in five dimensions—magnetic resonance imaging-assisted diagnosis of autism spectrum disorder using a multiparameter classification approach. J Neurosci 30: 10612-10623.
22. Ecker C, Rocha-Rego V, Johnston P, et al (2010b). Investigating the predictive value of whole-brain structural MR scans in autism: a pattern classification approach. Neuroimage 49: 44-56.
23. Erbetta A, Bulgheroni S, Contarino V, et al (2013). Neuroimaging findings in 41 low-functioning children with autism spectrum disorder: a single-center experience. J Child Neurol doi:10.1177/0883073813511856.
24. Fan Y, Batmanghelich N, Clark CM, et al (2008). Spatial patterns of brain atrophy in MCI patients, identified via highdimensional pattern classification, predict subsequent cognitive decline. Neuroimage 39: 1731-1743.
25. Fein D, Barton M, Eigsti IM, et al (2013). Optimal outcome in individuals with a history of autism. J Child Psychol Psychiatry 54: 195-205.
26. Filipek PA, Accardo PJ, Ashwal S, et al (2000). Practice parameter: screening and diagnosis of autism: report of the Quality Standards Subcommittee of the American Academy of Neurology and the Child Neurology Society. Neurology 55: 468-479.
27. Fischl B (2012). FreeSurfer. Neuroimage 62: 774-781.
28. Ganz ML (2006). The costs of autism. In: Moldin SO, Rubenstein JLR (Eds) Understanding Autism: From Basic Neuroscience to Treatment. Boca Raton, FL, Taylor and Francis Group.
29. Grzadzinski R, Huerta M, Lord C (2013). DSM-5 and autism spectrum disorders (ASDs): an opportunity for identifying ASD subtypes. Mol Autism 4: 12.
30. Hanley JA, McNeil BJ (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143: 29-36.
31. Hazlett HC, Poe MD, Gerig G, et al (2011). Early brain overgrowth in autism associated with an increase in cortical surface area before age 2 years. Arch Gen Psychiatry 68: 467-476.
32. Herbert MR, Ziegler DA, Deutsch CK, et al (2003). Dissociations of cerebral cortex, subcortical and cerebral white matter volumes in autistic boys. Brain 126: 1182-1192.
33. Hinrichs C, Singh V, Xu G, et al (2011). Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population. Neuroimage 55: 574-589.
34. Ingalhalikar M, Parker D, Bloy L, et al (2011). Diffusion based abnormality markers of pathology: toward learned diagnostic prediction of ASD. Neuroimage 57: 918-927.
35. Jenkinson M, Beckmann CF, Behrens TE, et al (2012). FSL. Neuroimage 62: 782-790.
36. Jeste SS, Geschwind DH (2014). Disentangling the heterogeneity of autism spectrum disorder through genetic findings. Nat Rev Neurol 10: 74-81.
37. Jiao Y, Chen R, Ke X, et al (2010). Predictive models of autism spectrum disorder based on brain regional cortical thickness. Neuroimage 50: 589-599.
38. King M, Bearman P (2009). Diagnostic change and the increased prevalence of autism. Int J Epidemiol 38: 1224-1234.
39. Klöppel S, Stonnington CM, Chu C, et al (2008). Automatic classification of MR scans in Alzheimer’s disease. Brain 131: 681-689.
40. Lemm S, Blankertz B, Dickhaus T et al (2011). Introduction to machine learning for brain imaging. Neuroimage 56: 387-399.
41. Liu KY, King M, Bearman PS (2010). Social influence and the autism epidemic. AJS 115: 1387-1434.
42. Lord C, Risi S, Lambrecht L, et al (2000). The autism diagnostic observation schedule-generic: a standard measure of social and communication deficits associated with the spectrum of autism. J Autism Dev Disord 30: 205-223.
43. Lord C, Rutter M, Le Couteur A (1994). Autism Diagnostic Interview-Revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. J Autism Dev Disord 24:659-685.
44. Metz CE (2006). Receiver operating characteristic analysis: a tool for the quantitative evaluation of observer performance and imaging systems. J Am Coll Radiol 3: 413-422.
45. Mourão-Miranda J, Bokde AL, Born C, et al (2005). Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data. Neuroimage 28: 980-995.
46. Neeley ES, Bigler ED, Krasny L, et al (2007). Quantitative temporal lobe differences: autism distinguished from controls using classification and regression tree analysis. Brain Dev 29: 389-399.
47. Nordahl CW, Lange N, Li DD, et al (2011). Brain enlargement is associated with regression in preschool-age boys with autism spectrum disorders. Proc Natl Acad Sci U S A 108: 20195-20200.
48. Nordahl CW, Simon TJ, Zierhut C, et al (2008). Brief report: methods for acquiring structural MRI data in very young children with autism without the use of sedation. J Autism Dev Disord 38: 1581-1590.
49. Orrù G, Pettersson-Yeo W, Marquand AF, et al (2012). Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci Biobehav Rev 36: 1140-1152.
50. Palmen SJ, Hulshoff Pol HE, Kemner C, et al (2005). Increased gray-matter volume in medication-naive high-functioning children with autism spectrum disorder. Psychol Med 35: 561-570.
51. Pardini M, Elia M, Garaci FG, et al (2012). Long-term cognitive and behavioral therapies, combined with augmentative communication, are related to uncinate fasciculus integrity in autism. J Autism Dev Disord 42: 585-592.
52. Pierce K (2011). Early functional brain development in autism and the promise of sleep fMRI. Brain Res 1380: 162-174.
53. Redcay E, Kennedy DP, Courchesne E (2007). fMRI during natural sleep as a method to study brain function during early childhood. Neuroimage 38: 696-707.
54. Redolfi A, Bosco P, Manset D, et al (2013). Brain investigation and brain conceptualization. Funct Neurol 28: 175-190.
55. Redolfi A, McClatchey R, Anjum A, et al (2009). Grid infrastructures for computational neuroscience: the neuGRID example. Future Neurology 4: 703-722.
56. Retico A, Bosco P, Cerello P, et al (2014). Predictive models based on Support Vector Machines: whole-brain versus regional analysis of structural MRI in the Alzheimer’s disease. J Neuroimaging doi: 10.1111/jon.12163
57. Sato JR, Hoexter MQ, Oliveira PP Jr, et al (2013). Inter-regional cortical thickness correlations are associated with autistic symptoms: a machine-learning approach. J Psychiatr Res 47: 453-459.
58. Schumann CM, Bloss CS, Barnes CC, et al (2010). Longitudinal magnetic resonance imaging study of cortical development through early childhood in autism. J Neurosci 30:4419-4427.
59. Singh V, Mukherjee L, Chung MK (2008). Cortical surface thickness as a classifier: boosting for autism classification. Med Image Comput Comput Assist Interv 11: 999-1007.
60. Sparks BF, Friedman SD, Friedman SD, et al (2002). Brain structural abnormalities in young children with autism spectrum disorder. Neurology 59: 184-192.
61. Swets JA, Pickett RM (1982). Evaluation of Diagnostic systems: Methods from Signal Detection Theory. New York, Academic Press.
62. Uddin LQ, Menon V, Young CB, et al (2011). Multivariate searchlight classification of structural magnetic resonance imaging in children and adolescents with autism. Biol Psychiatry 70: 833-841.
63. Vapnik V (1995). The Nature of Statistical Learning Theory. Berlin, Springer-Verlag.
64. Ventola PE, Kleinman J, Pandey J, et al (2006). Agreement among four diagnostic instruments for autism spectrum disorders in toddlers. J Autism Dev Disord 36: 839-847.
65. Vismara LA, Rogers SJ (2010). Behavioral treatments in autism spectrum disorder: what do we know? Annu Rev Clin Psychol 6: 447-468.
66. Voos AC, Pelphrey KA, Tirrell J, et al (2013). Neural mechanisms of improvements in social motivation after pivotal response treatment: two case studies. J Autism Dev Disord 43: 1-10.
67. Walsh P, Elsabbagh M, Bolton P, et al (2011). In search of biomarkers for autism: scientific, social and ethical challenges. Nat Rev Neurosci 12: 603-612.
68. Weiner MW, Veitch DP, Aisen PS, et al (2012). The Alzheimer’s Disease Neuroimaging Initiative: a review of papers published since its inception. Alzheimers Dement 8 (1): S1-68.
69. Wolff JJ, Gu H, Gerig G, et al (2012). Differences in white matter fiber tract development present from 6 to 24 months in infants with autism. Am J Psychiatry 169: 589-600.