Global motion processing in infants' visual cortex and the emergence of autism.

Autism is a heritable and common neurodevelopmental condition, with behavioural symptoms typically emerging around age 2 to 3 years. Differences in basic perceptual processes have been documented in autistic children and adults. Specifically, data from many experiments suggest links between autism and alterations in global visual motion processing (i.e., when individual motion information is integrated to perceive an overall coherent pattern). Yet, no study has investigated whether a distinctive organization of global motion processing precede the emergence of autistic symptoms in early childhood. Here, using a validated infant electroencephalography (EEG) experimental paradigm, we first establish the normative activation profiles for global form, global motion, local form, and local motion in the visual cortex based on data from two samples of 5-month-old infants (total n = 473). Further, in a sample of 5-month-olds at elevated likelihood of autism (n = 52), we show that a different topographical organization of global motion processing is associated with autistic symptoms in toddlerhood. These findings advance the understanding of neural organization of infants' basic visual processing, and its role in the development of autism.

The Babytwins Study Sweden (BATSS): A Multi-Method Infant Twin Study of Genetic and Environmental Factors Influencing Infant Brain and Behavioral Development.

Twin studies can help us understand the relative contributions of genes and environment to phenotypic trait variation, including attentional and brain activation measures. In terms of applying methodologies such as electroencephalography (EEG) and eye tracking, which are key methods in developmental neuroscience, infant twin studies are almost nonexistent. Here, we describe the Babytwins Study Sweden (BATSS), a multi-method longitudinal twin study of 177 MZ and 134 DZ twin pairs (i.e., 622 individual infants) covering the 5-36 month time period. The study includes EEG, eye tracking and genetics, together with more traditional measures based on in-person testing, direct observation and questionnaires. The results show that interest in participation in research among twin parents is high, despite the comprehensive protocol. DNA analysis from saliva samples was possible in virtually all participants, allowing for both zygosity confirmation and polygenic score analyses. Combining a longitudinal twin design with advanced technologies in developmental cognitive neuroscience and genomics, BATSS represents a new approach in infancy research, which we hope to have impact across multiple disciplines in the coming years.

Determining Zygosity in Infant Twins - Revisiting the Questionnaire Approach.

Accurate zygosity determination is a fundamental step in twin research. Although DNA-based testing is the gold standard for determining zygosity, collecting biological samples is not feasible in all research settings or all families. Previous work has demonstrated the feasibility of zygosity estimation based on questionnaire (physical similarity) data in older twins, but the extent to which this is also a reliable approach in infancy is less well established. Here, we report the accuracy of different questionnaire-based zygosity determination approaches (traditional and machine learning) in 5.5 month-old twins. The participant cohort comprised 284 infant twin pairs (128 dizygotic and 156 monozygotic) who participated in the Babytwins Study Sweden (BATSS). Manual scoring based on an established technique validated in older twins accurately predicted 90.49% of the zygosities with a sensitivity of 91.65% and specificity of 89.06%. The machine learning approach improved the prediction accuracy to 93.10%, with a sensitivity of 91.30% and specificity of 94.29%. Additionally, we quantified the systematic impact of zygosity misclassification on estimates of genetic and environmental influences using simulation-based sensitivity analysis on a separate data set to show the implication of our machine learning accuracy gain. In conclusion, our study demonstrates the feasibility of determining zygosity in very young infant twins using a questionnaire with four items and builds a scalable machine learning model with better metrics, thus a viable alternative to DNA tests in large-scale infant twin studies.

Single-Trial EEG Responses Classified Using Latency Features.

Covert attention has been repeatedly shown to impact on EEG responses after single and repeated practice sessions. Machine learning techniques are increasingly adopted to classify single-trial EEG responses thereby primarily relying on amplitude-based features instead of latency-based features. In this study, we investigated changes in EEG response signatures of nine healthy older subjects when performing 10 sessions of covert attention training. We show that, when we trained classifiers to distinguish recorded EEG patterns between the two experimental conditions (a target stimulus is "present" or "not present"), latency-based classifiers outperform the amplitude-based ones and that classification accuracy improved along with behavioral accuracy, providing supportive evidence of brain plasticity.