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CURRENT ISSUEMINERVA PSICHIATRICA

A Journal on Psychiatry, Psychology and Psychopharmacology


Official Journal of the Italian Society of Social Psychiatry
Indexed/Abstracted in: EMBASE, e-psyche, PsycINFO, Scopus


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  ADHD: FROM CHILDHOOD TO ADULT DISEASE


Minerva Psichiatrica 2013 December;54(4):267-79

language: English

Neuroimaging of attention-deficit hyperactivity disorder

Cortese S. 1, 2, Angriman M. 3

1 Child Neuropsychiatry Unit, G. B. Rossi Hospital Department of Life Science and Reproduction Verona University, Verona, Italy;
2 Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience Child Study Center of the NYU Langone Medical Center, New York, NY, USA;
3 Child Neurology and Neurorehabilitation Unit Department of Pediatrics Central Hospital of Bolzano, Bolzano, Italy


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The aim of this paper was to review the neuroimaging literature on attention-deficit/hyperactivity disorder (ADHD), with a focus on structural and functional magnetic resonance imaging (MRI) studies. We surveyed in particular studies of structural voxel based morphometry MRI and diffusion tensor imaging, as well as of task-based functional and resting state MRI. The overall evidence points to dysfunctions in frontoparietal, striatal, thalamic, and cerebellar networks in ADHD, but also to dysfunctional interactions among the default network and top-down regulatory networks, and visual and sensorimotor cortex. Therefore, the classic model of ADHD pathophysiology focused on fronto-striatal circuits should be expanded to include a broader set of dysfunctional interactions within and among brain networks. Available neuroimaging studies also show that ADHD pharmacological treatments and, possibly, non-pharmacological approaches, tend to normalize structural and functional brain abnormalities. To date, results from neuroimaging studies do not have a direct application in the day-to-day clinical practice at the single patient level; however, it is hoped that the introduction of machine learning techniques, in particular support vector machine, may contribute to the use of neuroimaging in the clinical practice in terms of prediction of diagnosis for challenging cases and, perhaps more importantly, prediction of outcome and response to treatment.

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