Neurobiological support to the diagnosis of ADHD in stimulant-naive adults: pattern recognition analyses of MRI data

Neurobiological support to the diagnosis of ADHD in stimulant-naive adults: pattern recognition analyses of MRI data

Author Chaim-Avancini, T. M. Google Scholar
Doshi, J. Google Scholar
Zanetti, M. V. Google Scholar
Erus, G. Google Scholar
Silva, M. A. Google Scholar
Duran, F. L. S. Google Scholar
Cavallet, M. Google Scholar
Serpa, M. H. Google Scholar
Caetano, S. C. Autor UNIFESP Google Scholar
Louza, M. R. Google Scholar
Davatzikos, C. Google Scholar
Busatto, G. F. Google Scholar
Abstract Objective: In adulthood, the diagnosis of attention-deficit/hyperactivity disorder (ADHD) has been subject of recent controversy. We searched for a neuroanatomical signature associated with ADHD spectrum symptoms in adults by applying, for the first time, machine learning-based pattern classification methods to structural MRI and diffusion tensor imaging (DTI) data obtained from stimulant-naive adults with childhood-onset ADHD and healthy controls (HC). Method: Sixty-seven ADHD patients and 66 HC underwent high-resolution T1-weighted and DTI acquisitions. A support vector machine (SVM) classifier with a non-linear kernel was applied on multimodal image features extracted on regions of interest placed across the whole brain. Results: The discrimination between a mixed-gender ADHD subgroup and individually matched HC (n = 58 each) yielded area-under-the-curve (AUC) and diagnostic accuracy (DA) values of up to 0.71% and 66% (P = 0.003) respectively. AUC and DA values increased to 0.74% and 74% (P = 0.0001) when analyses were restricted to males (52 ADHD vs. 44 HC). Conclusion: Introvert personality traits showed independent risk effects on suicidality regardless of diagnosis status. Among high risk individuals with suicidal thoughts, higher neuroticism tendency is further associated with increased risk of suicide attempt.
Keywords attention-deficit
hyperactivity disorder
adults
structural MRI
diffusion tensor imaging
machine learning-based methods
xmlui.dri2xhtml.METS-1.0.item-coverage Hoboken
Language English
Sponsor NARSAD Independent Investigator Award (NARSAD: The Brain and Behavior Research Fund)
CNPq-Brazil
FAPESP, Brazil
Grant number FAPESP: 2013/03905-4
Date 2017
Published in Acta Psychiatrica Scandinavica. Hoboken, v. 136, n. 6, p. 623-636, 2017.
ISSN 0001-690X (Sherpa/Romeo, impact factor)
Publisher Wiley
Extent 623-636
Origin http://dx.doi.org/10.1111/acps.12824
Access rights Closed access
Type Article
Web of Science ID WOS:000414594900010
URI https://repositorio.unifesp.br/handle/11600/58142

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