Background Clinically and biologically, ASD is heterogeneous. Unusual patterns of visual preference as indexed by eye-tracking are hallmarks, yet whether they can be used to define an early biomarker of ASD as a whole, or leveraged to define a subtype is unclear. To begin to examine this issue, large cohorts are required. Methods A sample of 334 toddlers from 6 distinct groups (115 ASD, 20 ASD-Features, 57 DD, 53 Other, 64 TD, and 25 Typ SIB) participated. Toddlers watched a movie containing both geometric and social images. Fixation duration and number of saccades within each AOI and validation statistics for this independent sample computed. Next, to maximize power, data from our previous study (N=110) was added totaling 444 subjects. A subset of toddlers repeated the eye-tracking procedure. Results As in the original study, a subset of toddlers with ASD fixated on geometric images greater than 69%. Using this cutoff, sensitivity for ASD was 21%, specificity 98%, and PPV 86%. Toddlers with ASD who strongly preferred geometric images had (a) worse cognitive, language, and social skills relative to toddlers with ASD who strongly preferred social images and (b) fewer saccades when viewing geometric images. Unaffected siblings of ASD probands did not show evidence of heightened preference for geometric images. Test-retest reliability was good. Examination of age effects suggest that this test may not be appropriate with children > 4 years. Conclusions Enhanced visual preference for geometric repetition may be an early developmental biomarker of an ASD subtype with more severe symptoms.
Summary Sleep deprivation has been shown to alter decision‐making abilities. The majority of research has utilized fairly complex tasks with the goal of emulating ’real‐life’ scenarios. Here, we use a Lottery Choice Task (LCT) which assesses risk and ambiguity preference for both decisions involving potential gains and those involving potential losses. We hypothesized that one night of sleep deprivation would make subjects more risk seeking in both gains and losses. Both a control group and an experimental group took the LCT on two consecutive days, with an intervening night of either sleep or sleep deprivation. The control group demonstrated that there was no effect of repeated administration of the LCT. For the experimental group, results showed significant interactions of night (normal sleep versus total sleep deprivation, TSD) by frame (gains versus losses), which demonstrate that following as little as 23 h of TSD, the prototypical response to decisions involving risk is altered. Following TSD, subjects were willing to take more risk than they ordinarily would when they were considering a gain, but less risk than they ordinarily would when they were considering a loss. For ambiguity preferences, there seems to be no direct effect of TSD. These findings suggest that, overall, risk preference is moderated by TSD, but whether an individual is willing to take more or less risk than when well‐rested depends on whether the decision is framed in terms of gains or losses.
Background Underage drinking is widely recognized as a leading public health and social problem for adolescents in the United States. Being able to identify at-risk children before they initiate heavy alcohol use could have immense clinical and public health implications; however, few investigations have explored individual-level precursors of adolescent substance use. This prospective investigation used machine learning with demographic, neurocognitive, and neuroimaging data in substance-naïve adolescents to predict alcohol use initiation by age 18. Materials and Methods Participants (N=137) were healthy substance-naïve adolescents (ages 12–14) who underwent neuropsychological testing and structural and functional magnetic resonance imaging (sMRI and fMRI), then were followed annually. By age 18, 70 youth (51%) initiated moderate-to-heavy alcohol use and 67 remained non-users. Random forests classification generated individual alcohol use outcome predictions based on demographic, neuropsychological, sMRI, and fMRI data. Results The final random forests model was 74% accurate, with good sensitivity (74%) and specificity (73%) and included 34 predictors contributing to alcohol use by age 18, including several demographic and behavioral factors (being male, higher socioeconomic status, early dating, more externalizing behaviors, positive alcohol expectancies), worse executive functioning, and thinner cortices and less brain activation in diffusely distributed regions of the brain. Inclusion of neuropsychological, sMRI, and fMRI data significantly increased the prediction accuracy of the model. Discussion Identification of at-risk youth is not validated for clinical use. Its value is for research to address brain mechanisms that predispose to early drinking.
Patients with OSA showed decreased brain activation compared with control subjects during an attention task. The association of arousal index (but not hypoxia) with slow reaction times and brain activation suggests that alertness and reaction times show greater correlations with measures of sleep disruption than with measures of hypoxia.
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