The role of memory and restricted context in repeated visual search

Previous studies have shown that the efficiency of visual search does not improve when participants search through the same unchanging display for hundreds of trials (repeated search), even though the participants have a clear memory of the search display. In this article, we ask two important questions. First, why do participants not use memory to help search the repeated display? Second, can context be introduced so that participants are able to guide their attention to the relevant repeated items? Experiments 1–4 show that participants choose not to use a memory strategy because, under these conditions, repeated memory search is actually less efficient than repeated visual search, even though the latter task is in itself relatively inefficient. However, when the visual search task is given context, so that only a subset of the items are ever pertinent, participants can learn to restrict their attention to the relevant stimuli (Experiments 5 and 6)

Diary reports of concerns in mothers of infant siblings of children with autism across the first year of life

We examined the home-based concerns reported by mothers of infant siblings of children with autism across the first year of life. At all three ages measured, mothers of high-risk infants were significantly more likely than mothers of low-risk infants to report language, social communication, and restricted and repetitive behavior concerns but were not more likely to report general, medically based concerns. At 6 and 9 months of age, maternal concerns were poorly related to infant or family variables. At 12 months of age, there were moderate correlations between maternal concerns and infant behavior, and concerns were associated with the proband's autism symptoms and mothers' concurrent depressive symptoms. These findings highlight the need to examine high-risk infants' development in the family context.R21 DC 08637 - NIDCD NIH HHS; R01-DC010290 - NIDCD NIH HHS; AS1323 - Autism Speaks; R21 DC008637 - NIDCD NIH HHS; R01 DC010290 - NIDCD NIH HH

Maternal vocal feedback to 9-month-old infant siblings of children with ASD

Infant siblings of children with autism spectrum disorder display differences in early language and social communication skills beginning as early as the first year of life. While environmental influences on early language development are well documented in other infant populations, they have received relatively little attention inside of the infant sibling context. In this study, we analyzed home video diaries collected prospectively as part of a longitudinal study of infant siblings. Infant vowel and consonant-vowel vocalizations and maternal language-promoting and non-promoting verbal responses were scored for 30 infant siblings and 30 low risk control infants at 9 months of age. Analyses evaluated whether infant siblings or their mothers exhibited differences from low risk dyads in vocalization frequency or distribution, and whether mothers' responses were associated with other features of the high risk context. Analyses were conducted with respect to both initial risk group and preliminary outcome classification. Overall, we found no differences in infants' consonant-vowel vocalizations, the frequency of overall maternal utterances, or the distribution of mothers' response types. Both groups of infants produced more vowel than consonant-vowel vocalizations, and both groups of mothers responded to consonant-vowel vocalizations with more language-promoting than non-promoting responses. These results indicate that as a group, mothers of high risk infants provide equally high quality linguistic input to their infants in the first year of life and suggest that impoverished maternal linguistic input does not contribute to high risk infants' initial language difficulties. Implications for intervention strategies are also discussed.R21 DC 08637 - NIDCD NIH HHS; T32 MH073124 - NIMH NIH HHS; R01-DC010290 - NIDCD NIH HHS; R21 DC008637 - NIDCD NIH HHS; R01 DC010290 - NIDCD NIH HHS; U54 HD090255 - NICHD NIH HH

Sylvian fissure and parietal anatomy in children with autism spectrum disorder

Autism spectrum disorder (ASD) is characterized by deficits in social functioning and language and communication, with restricted interests or stereotyped behaviors. Anatomical differences have been found in the parietal cortex in children with ASD, but parietal subregions and associations between Sylvian fissure (SF) and parietal anatomy have not been explored. In this study, SF length and anterior and posterior parietal volumes were measured on MRI in 30 right-handed boys with ASD and 30 right-handed typically developing boys (7–14 years), matched on age and non-verbal IQ. There was leftward SF and anterior parietal asymmetry, and rightward posterior parietal asymmetry, across groups. There were associations between SF and parietal asymmetries, with slight group differences. Typical SF asymmetry was associated with typical anterior and posterior parietal asymmetry, in both groups. In the atypical SF asymmetry group, controls had atypical parietal asymmetry, whereas in ASD there were more equal numbers of individuals with typical as atypical anterior parietal asymmetry. We did not find significant anatomical-behavioral associations. Our findings of more individuals in the ASD group having a dissociation between cortical asymmetries warrants further investigation of these subgroups and emphasizes the importance of investigating anatomical relationships in addition to group differences in individual regions.This study was supported by a program project grant from the National Institute on Deafness and Other Communication Disorders (U19 DC 03610), which is part of the NICHD/NIDCD funded Collaborative Programs on Excellence in Autism, as well as funding for the GCRC at Boston University School of Medicine (M01-RR0533). We thank all of our research assistants for help in collecting the data and Andrew Silver, Melanee Schuring, Danielle Delosh, and Jeremy Siegal for completing the total hemisphere measurements. We also extend our sincere gratitude to the children and families who participated in this study. (U19 DC 03610 - National Institute on Deafness and Other Communication Disorders; NICHD/NIDCD; M01-RR0533 - Boston University School of Medicine)Published versio

EEG analytics for early detection of autism spectrum disorder: a data-driven approach

Autism spectrum disorder (ASD) is a complex and heterogeneous disorder, diagnosed on the basis of behavioral symptoms during the second year of life or later. Finding scalable biomarkers for early detection is challenging because of the variability in presentation of the disorder and the need for simple measurements that could be implemented routinely during well-baby checkups. EEG is a relatively easy-to-use, low cost brain measurement tool that is being increasingly explored as a potential clinical tool for monitoring atypical brain development. EEG measurements were collected from 99 infants with an older sibling diagnosed with ASD, and 89 low risk controls, beginning at 3 months of age and continuing until 36 months of age. Nonlinear features were computed from EEG signals and used as input to statistical learning methods. Prediction of the clinical diagnostic outcome of ASD or not ASD was highly accurate when using EEG measurements from as early as 3 months of age. Specificity, sensitivity and PPV were high, exceeding 95% at some ages. Prediction of ADOS calibrated severity scores for all infants in the study using only EEG data taken as early as 3 months of age was strongly correlated with the actual measured scores. This suggests that useful digital biomarkers might be extracted from EEG measurements.This research was supported by National Institute of Mental Health (NIMH) grant R21 MH 093753 (to WJB), National Institute on Deafness and Other Communication Disorders (NIDCD) grant R21 DC08647 (to HTF), NIDCD grant R01 DC 10290 (to HTF and CAN) and a grant from the Simons Foundation (to CAN, HTF, and WJB). We are especially grateful to the staff and students who worked on the study and to the families who participated. (R21 MH 093753 - National Institute of Mental Health (NIMH); R21 DC08647 - National Institute on Deafness and Other Communication Disorders (NIDCD); R01 DC 10290 - NIDCD; Simons Foundation)Published versio

Time to guide: evidence for delayed attentional guidance in contextual cueing

Contextual cueing experiments show that, when displays are repeated, reaction times (RTs) to find a target decrease over time even when the observers are not aware of the repetition. Recent evidence suggests that this benefit in standard contextual cueing tasks is not likely to be due to an improvement in attentional guidance (Kunar, Flusberg, Horowitz, & Wolfe, 2007). Nevertheless, we ask whether guidance can help participants find the target in a repeated display, if they are given sufficient time to encode the display. In Experiment 1 we increased the display complexity so that it took participants longer to find the target. Here we found a larger effect of guidance than in a condition with shorter RTs. Experiment 2 gave participants prior exposure to the display context. The data again showed that with more time participants could implement guidance to help find the target, provided that there was something in the search stimuli locations to guide attention to. The data suggest that, although the benefit in a standard contextual cueing task is unlikely to be a result of guidance, guidance can play a role if it is given time to develop

Eye-tracking measurements of language processing: developmental differences in children at high risk for ASD

To explore how being at high risk for autism spectrum disorder (ASD), based on having an older sibling diagnosed with ASD, affects word comprehension and language processing speed, 18-, 24- and 36-month-old children, at high and low risk for ASD were tested in a cross- sectional study, on an eye gaze measure of receptive language that measured how accurately and rapidly the children looked at named target images. There were no significant differences between the high risk ASD group and the low risk control group of 18- and 24-month-olds. However, 36-month-olds in the high risk for ASD group performed significantly worse on the accuracy measure, but not on the speed measure. We propose that the language processing efficiency of the high risk group is not compromised, but other vocabulary acquisition factors might have lead to the high risk 36-month-olds to comprehend significantly fewer nouns on our measure.K01 DC013306 - NIDCD NIH HHS; R01 DC010290 - NIDCD NIH HHS; K01DC013306 - NIDCD NIH HHS; R01 DC 10290 - NIDCD NIH HH

Atypical hemispheric specialization for faces in infants at risk for autism spectrum disorder

Among the many experimental findings that tend to distinguish those with and without autism spectrum disorder (ASD) are face processing deficits, reduced hemispheric specialization, and atypical neurostructural and functional connectivity. To investigate the earliest manifestations of these features, we examined lateralization of event-related gamma-band coherence to faces during the first year of life in infants at high risk for autism (HRA; defined as having an older sibling with ASD) who were compared with low-risk comparison (LRC) infants, defined as having no family history of ASD. Participants included 49 HRA and 46 LRC infants who contributed a total of 127 data sets at 6 and 12 months. Electroencephalography was recorded while infants viewed images of familiar/unfamiliar faces. Event-related gamma-band (30-50 Hz) phase coherence between anterior-posterior electrode pairs for left and right hemispheres was computed. Developmental trajectories for lateralization of intra-hemispheric coherence were significantly different in HRA and LRC infants: by 12 months, HRA infants showed significantly greater leftward lateralization compared with LRC infants who showed rightward lateralization. Preliminary results indicate that infants who later met criteria for ASD were those that showed the greatest leftward lateralization. HRA infants demonstrate an aberrant pattern of leftward lateralization of intra-hemispheric coherence by the end of the first year of life, suggesting that the network specialized for face processing may develop atypically. Further, infants with the greatest leftward asymmetry at 12 months where those that later met criteria for ASD, providing support to the growing body of evidence that atypical hemispheric specialization may be an early neurobiological marker for ASD.R01 DC010290 - NIDCD NIH HHS; R01-DC010290 - NIDCD NIH HH

Longitudinal EEG power in the first postnatal year differentiates autism outcomes

An aim of autism spectrum disorder (ASD) research is to identify early biomarkers that inform ASD pathophysiology and expedite detection. Brain oscillations captured in electroencephalography (EEG) are thought to be disrupted as core ASD pathophysiology. We leverage longitudinal EEG power measurements from 3 to 36 months of age in infants at low- and high-risk for ASD to test how and when power distinguishes ASD risk and diagnosis by age 3-years. Power trajectories across the first year, second year, or first three years postnatally were submitted to data-driven modeling to differentiate ASD outcomes. Power dynamics during the first postnatal year best differentiate ASD diagnoses. Delta and gamma frequency power trajectories consistently distinguish infants with ASD diagnoses from others. There is also a developmental shift across timescales towards including higher-frequency power to differentiate outcomes. These findings reveal the importance of developmental timing and trajectory in understanding pathophysiology and classifying ASD outcomes.R01 DC010290 - NIDCD NIH HHS; T32 MH112510 - NIMH NIH HHS; U54 HD090255 - NICHD NIH HHSPublished versio

EEG complexity as a biomarker for autism spectrum disorder risk

Background: Complex neurodevelopmental disorders may be characterized by subtle brain function signatures early in life before behavioral symptoms are apparent. Such endophenotypes may be measurable biomarkers for later cognitive impairments. The nonlinear complexity of electroencephalography (EEG) signals is believed to contain information about the architecture of the neural networks in the brain on many scales. Early detection of abnormalities in EEG signals may be an early biomarker for developmental cognitive disorders. The goal of this paper is to demonstrate that the modified multiscale entropy (mMSE) computed on the basis of resting state EEG data can be used as a biomarker of normal brain development and distinguish typically developing children from a group of infants at high risk for autism spectrum disorder (ASD), defined on the basis of an older sibling with ASD. Methods: Using mMSE as a feature vector, a multiclass support vector machine algorithm was used to classify typically developing and high-risk groups. Classification was computed separately within each age group from 6 to 24 months. Results: Multiscale entropy appears to go through a different developmental trajectory in infants at high risk for autism (HRA) than it does in typically developing controls. Differences appear to be greatest at ages 9 to 12 months. Using several machine learning algorithms with mMSE as a feature vector, infants were classified with over 80% accuracy into control and HRA groups at age 9 months. Classification accuracy for boys was close to 100% at age 9 months and remains high (70% to 90%) at ages 12 and 18 months. For girls, classification accuracy was highest at age 6 months, but declines thereafter. Conclusions: This proof-of-principle study suggests that mMSE computed from resting state EEG signals may be a useful biomarker for early detection of risk for ASD and abnormalities in cognitive development in infants. To our knowledge, this is the first demonstration of an information theoretic analysis of EEG data for biomarkers in infants at risk for a complex neurodevelopmental disorder