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Atypical Learning in Autism Spectrum Disorders: A Functional Magnetic Resonance Imaging Study of Transitive Inference.

ABSTRACT: To investigate the neural mechanisms underlying impairments in generalizing learning shown by adolescents with autism spectrum disorder (ASD).A total of 21 high-functioning individuals with ASD aged 12 to 18 years, and 23 gender-, IQ-, and age-matched adolescents with typical development (TYP), completed a transitive inference (TI) task implemented using rapid event-related functional magnetic resonance imaging (fMRI). Participants were trained on overlapping pairs in a stimulus hierarchy of colored ovals where A>B>C>D>E>F and then tested on generalizing this training to new stimulus pairings (AF, BD, BE) in a "Big Game." Whole-brain univariate, region of interest, and functional connectivity analyses were used.During training, the TYP group exhibited increased recruitment of the prefrontal cortex (PFC), whereas the group with ASD showed greater functional connectivity between the PFC and the anterior cingulate cortex (ACC). Both groups recruited the hippocampus and caudate comparably; however, functional connectivity between these regions was positively associated with TI performance for only the group with ASD. During the Big Game, the TYP group showed greater recruitment of the PFC, parietal cortex, and the ACC. Recruitment of these regions increased with age in the group with ASD.During TI, TYP individuals recruited cognitive control-related brain regions implicated in mature problem solving/reasoning including the PFC, parietal cortex, and ACC, whereas the group with ASD showed functional connectivity of the hippocampus and the caudate that was associated with task performance. Failure to reliably engage cognitive control-related brain regions may produce less integrated flexible learning in individuals with ASD unless they are provided with task support that, in essence, provides them with cognitive control; however, this pattern may normalize with age.

SUBMITTER: Solomon M

PROVIDER: S-EPMC4624100 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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Atypical Learning in Autism Spectrum Disorders: A Functional Magnetic Resonance Imaging Study of Transitive Inference.

Solomon Marjorie M Ragland J Daniel JD Niendam Tara A TA Lesh Tyler A TA Beck Jonathan S JS Matter John C JC Frank Michael J MJ Carter Cameron S CS

Journal of the American Academy of Child and Adolescent Psychiatry 20150903 11

<h4>Objective</h4>To investigate the neural mechanisms underlying impairments in generalizing learning shown by adolescents with autism spectrum disorder (ASD).<h4>Method</h4>A total of 21 high-functioning individuals with ASD aged 12 to 18 years, and 23 gender-, IQ-, and age-matched adolescents with typical development (TYP), completed a transitive inference (TI) task implemented using rapid event-related functional magnetic resonance imaging (fMRI). Participants were trained on overlapping pai ...[more]

PMID: 26506585

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Project description:BackgroundIn the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, their application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder (ASD). However, given their complexity and potential clinical implications, there is an ongoing need for further research on their accuracy.ObjectiveThis study aimed to perform a systematic review and meta-analysis to summarize the available evidence for the accuracy of machine learning algorithms in diagnosing ASD.MethodsThe following databases were searched on November 28, 2018: MEDLINE, EMBASE, CINAHL Complete (with Open Dissertations), PsycINFO, and Institute of Electrical and Electronics Engineers Xplore Digital Library. Studies that used a machine learning algorithm partially or fully for distinguishing individuals with ASD from control subjects and provided accuracy measures were included in our analysis. The bivariate random effects model was applied to the pooled data in a meta-analysis. A subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false-negative, and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw Summary Receiver Operating Characteristics curves, and obtain the area under the curve (AUC) and partial AUC (pAUC).ResultsA total of 43 studies were included for the final analysis, of which a meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural magnetic resonance imaging (sMRI) subgroup meta-analysis (12 samples with 1776 participants) showed a sensitivity of 0.83 (95% CI 0.76-0.89), a specificity of 0.84 (95% CI 0.74-0.91), and AUC/pAUC of 0.90/0.83. A functional magnetic resonance imaging/deep neural network subgroup meta-analysis (5 samples with 1345 participants) showed a sensitivity of 0.69 (95% CI 0.62-0.75), specificity of 0.66 (95% CI 0.61-0.70), and AUC/pAUC of 0.71/0.67.ConclusionsThe accuracy of machine learning algorithms for diagnosis of ASD was considered acceptable by few accuracy measures only in cases of sMRI use; however, given the many limitations indicated in our study, further well-designed studies are warranted to extend the potential use of machine learning algorithms to clinical settings.Trial registrationPROSPERO CRD42018117779; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=117779.

Project description: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 × 10(-7)). In subjects <20 years of age, the classifier performed at 89% accuracy (P = 5.4 × 10(-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 <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, particularly for long connections (Euclidean distance >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.

Project description:OBJECTIVE:Succinate dehydrogenase-deficient leukoencephalopathy is a complex II-related mitochondrial disorder for which the clinical phenotype, neuroimaging pattern, and genetic findings have not been comprehensively reviewed. METHODS:Nineteen individuals with succinate dehydrogenase deficiency-related leukoencephalopathy were reviewed for neuroradiological, clinical, and genetic findings as part of institutional review board-approved studies at Children's National Health System (Washington, DC) and VU University Medical Center (Amsterdam, the Netherlands). RESULTS:All individuals had signal abnormalities in the central corticospinal tracts and spinal cord where imaging was available. Other typical findings were involvement of the cerebral hemispheric white matter with sparing of the U fibers, the corpus callosum with sparing of the outer blades, the basis pontis, middle cerebellar peduncles, and cerebellar white matter, and elevated succinate on magnetic resonance spectroscopy (MRS). The thalamus was involved in most studies, with a predilection for the anterior nucleus, pulvinar, and geniculate bodies. Clinically, infantile onset neurological regression with partial recovery and subsequent stabilization was typical. All individuals had mutations in SDHA, SDHB, or SDHAF1, or proven biochemical defect. INTERPRETATION:Succinate dehydrogenase deficiency is a rare leukoencephalopathy, for which improved recognition by magnetic resonance imaging (MRI) in combination with advanced sequencing technologies allows noninvasive diagnostic confirmation. The MRI pattern is characterized by cerebral hemispheric white matter abnormalities with sparing of the U fibers, corpus callosum involvement with sparing of the outer blades, and involvement of corticospinal tracts, thalami, and spinal cord. In individuals with infantile regression and this pattern of MRI abnormalities, the differential diagnosis should include succinate dehydrogenase deficiency, in particular if MRS shows elevated succinate.

Dataset Information

Atypical Learning in Autism Spectrum Disorders: A Functional Magnetic Resonance Imaging Study of Transitive Inference.

Publications

Atypical Learning in Autism Spectrum Disorders: A Functional Magnetic Resonance Imaging Study of Transitive Inference.

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