Structural brain imaging of attention-deficit/hyperactivity disorder.
Many investigators have hypothesized that attention-deficit/hyperactivity disorder (ADHD) involves structural and functional brain abnormalities in frontal-striatal circuitry. Although our review suggests that there is substantial support for this hypothesis, a growing literature demonstrates widespread abnormalities affecting other cortical regions and the cerebellum. Because there is only one report studying adults with ADHD, this summary is based on children. A key limitation of the literature is that most of the studies until recently have been underpowered, using samples of fewer than 20 subjects per group. Nevertheless, these studies are largely consistent with the most comprehensive and definitive study (Castellanos et al 2002). Moreover, studies differ in the degree to which they address the influence of medications, comorbidities, or gender, and most have not addressed potentially important sources of heterogeneity such as family history of ADHD, subtype, or perinatal complications. Despite these limitations, a relatively consistent picture has emerged. The most replicated alterations in ADHD in childhood include significantly smaller volumes in the dorsolateral prefrontal cortex, caudate, pallidum, corpus callosum, and cerebellum. These results suggest that the brain is altered in a more widespread manner than has been previously hypothesized. Developmental studies are needed to address the evolution of this brain disorder into adulthood.
Seidman
Larry J
LJ
Pediatric Psychopharmacology Unit and Psychiatric Neuroscience Program, Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA. larry_seidman@hms.harvard.edu
Valera
Eve M
EM
Makris
Nikos
N
D000328:Adult; D001289:Attention Deficit Disorder with Hyperactivity / Q000473:pathology*; D001921:Brain / Q000002:abnormalities / Q000473:pathology*; D001931:Brain Mapping; D002648:Child; D007839:Functional Laterality; D006801:Humans; D008279:Magnetic Resonance Imaging; D016571:Neural Networks, Computer; D012727:Sex Characteristics