Functional Connectivity Magnetic Resonance Imaging Classification of Autism

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Autor/inn/en	Anderson, Jeffrey S.; Nielsen, Jared A.; Froehlich, Alyson L.; DuBray, Molly B.; Druzgal, T. Jason; Cariello, Annahir N.; Cooperrider, Jason R.; Zielinski, Brandon A.; Ravichandran, Caitlin; Fletcher, P. Thomas; Alexander, Andrew L.; Bigler, Erin D.; Lange, Nicholas; Lainhart, Janet E.
Titel	Functional Connectivity Magnetic Resonance Imaging Classification of Autism
Quelle	In: Brain, 134 (2011) 12, S.3739-3751 (13 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0006-8950
DOI	10.1093/brain/awr263
Schlagwörter	Siblings; Autism; Intelligence Quotient; Young Adults; Classification; Brain; Diagnostic Tests; Males; Brain Hemisphere Functions; Adolescents; Comparative Analysis; Correlation; Autism Diagnostic Observation Schedule + Suchen Sie Ihr Suchwort? Sibling; Geschwister; Autismus; Intelligenzquotient; Young adult; Junger Erwachsener; Classification system; Klassifikation; Klassifikationssystem; Gehirn; Diagnostic test; Diagnostischer Test; Male; Männliches Geschlecht; Adolescent; Adolescence; Adoleszenz; Jugend; Jugendalter; Jugendlicher; Korrelation
Abstract	Group differences in resting state functional magnetic resonance imaging connectivity between individuals with autism and typically developing controls have been widely replicated for a small number of discrete brain regions, yet the whole-brain distribution of connectivity abnormalities in autism is not well characterized. It is also unclear whether functional connectivity is sufficiently robust to be used as a diagnostic or prognostic metric in individual patients with autism. We obtained pairwise functional connectivity measurements from a lattice of 7266 regions of interest covering the entire grey matter (26.4 million connections) in a well-characterized set of 40 male adolescents and young adults with autism and 40 age-, sex- and IQ-matched typically developing subjects. A single resting state blood oxygen level-dependent scan of 8 min was used for the classification in each subject. A leave-one-out classifier successfully distinguished autism from control subjects with 83% sensitivity and 75% specificity for a total accuracy of 79% (P = 1.1 x 10[superscript -7]). In subjects less than 20 years of age, the classifier performed at 89% accuracy (P = 5.4 x 10[superscript -7]). In a replication dataset consisting of 21 individuals from six families with both affected and unaffected siblings, the classifier performed at 71% accuracy (91% accuracy for subjects less than 20 years of age). Classification scores in subjects with autism were significantly correlated with the Social Responsiveness Scale (P = 0.05), verbal IQ (P = 0.02) and the Autism Diagnostic Observation Schedule-Generic's combined social and communication subscores (P = 0.05). An analysis of informative connections demonstrated that region of interest pairs with strongest correlation values were most abnormal in autism. Negatively correlated region of interest pairs showed higher correlation in autism (less anticorrelation), possibly representing weaker inhibitory connections, p (Euclidean distance greater than 10 cm). Brain regions showing greatest differences included regions of the default mode network, superior parietal lobule, fusiform gyrus and anterior insula. Overall, classification accuracy was better for younger subjects, with differences between autism and control subjects diminishing after 19 years of age. Classification scores of unaffected siblings of individuals with autism were more similar to those of the control subjects than to those of the subjects with autism. These findings indicate feasibility of a functional connectivity magnetic resonance imaging diagnostic assay for autism. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10