Infant Visual Brain Development and Inherited Genetic Liability in Autism

3D CNN for neuropsychiatry: Predicting Autism with interpretable Deep Learning applied to minimally preprocessed structural MRI data

Predictive modeling approaches are enabling progress toward robust and reproducible brain-based markers of neuropsychiatric conditions by leveraging the power of multivariate analyses of large datasets. While deep learning (DL) offers another promising avenue to further advance progress, there are challenges related to implementation in 3D (best for MRI) and interpretability. Here, we address these challenges and describe an interpretable predictive pipeline for inferring Autism diagnosis using 3D DL applied to minimally processed structural MRI scans. We trained 3D DL models to predict Autism diagnosis using the openly available ABIDE I and II datasets (n = 1329, split into training, validation, and test sets). Importantly, we did not perform transformation to template space, to reduce bias and maximize sensitivity to structural alterations associated with Autism. Our models attained predictive accuracies equivalent to those of previous machine learning (ML) studies, while side-stepping the time- and resource-demanding requirement to first normalize data to a template. Our interpretation step, which identified brain regions that contributed most to accurate inference, revealed regional Autism-related alterations that were highly consistent with the literature, encompassing a left-lateralized network of regions supporting language processing. We have openly shared our code and models to enable further progress towards remaining challenges, such as the clinical heterogeneity of Autism and site effects, and to enable the extension of our method to other neuropsychiatric conditions.

Multi-site EEG studies in early infancy: Methods to enhance data quality

Brain differences linked to autism spectrum disorder (ASD) can manifest before observable symptoms. Studying these early neural precursors in larger and more diverse cohorts is crucial for advancing our understanding of developmental pathways and potentially facilitating earlier identification. EEG is an ideal tool for investigating early neural differences in ASD, given its scalability and high tolerability in infant populations. In this context, we integrated EEG into an existing multi-site MRI study of infants with a higher familial likelihood of developing ASD. This paper describes the comprehensive protocol established to collect longitudinal, high-density EEG data from infants across five sites as part of the Infant Brain Imaging Study (IBIS) Network and reports interim feasibility and data quality results. We evaluated feasibility by measuring the percentage of infants from whom we successfully collected each EEG paradigm. The quality of task-free data was assessed based on the duration of EEG recordings remaining after artifact removal. Preliminary analyses revealed low data loss, with average in-session loss rates at 4.16 % and quality control loss rates at 11.66 %. Overall, the task-free data retention rate, accounting for both in-session issues and quality control, was 84.16 %, with high consistency across sites. The insights gained from this preliminary analysis highlight key sources of data attrition and provide practical considerations to guide similar research endeavors.

Using the Infant Sibling-Design to Explore Associations Between Autism and ADHD Traits in Probands and Temperament in the Younger Siblings

The purpose of the current study was to use the infant sibling design to explore whether proband traits of autism and ADHD could provide information about their infant sibling's temperament. This could help us to gain information about the extent to which infant temperament traits are differentially associated with autism and ADHD traits. We used parent-ratings of autistic traits and ADHD traits (CRS-3) in older siblings diagnosed with autism (age range 4 to 19 years), and their infant siblings' temperament traits (IBQ) at 9 months of age in 216 sibling pairs from two sites (BASIS, UK, and EASE, Sweden) to examine associations across siblings. We found specific, but modest, associations across siblings after controlling for sex, age, developmental level and site. Proband autistic traits were specifically related to low levels of approach in the infant siblings, with infant developmental level explaining part of the variance in infant approach. Proband ADHD traits were specifically related to high levels of infant activity even after controlling for covariates. Our findings suggest that proband traits of autism and ADHD carry information for infant sibling's temperament, indicating that inherited liability may influence early emerging behaviours in infant siblings. The impact of sex, age, developmental level and site are discussed.

Neural Correlates of Novelty-Evoked Distress in 4-Month-Old Infants: A Synthetic Cohort Study

BACKGROUND

Observational assessments of infant temperament have provided unparalleled insight into prediction of risk for social anxiety. However, it is challenging to administer and score these assessments alongside high-quality infant neuroimaging data. In the current study, we aimed to identify infant resting-state functional connectivity associated with both parent report and observed behavioral estimates of infant novelty-evoked distress.

METHODS

Using data from the OIT (Origins of Infant Temperament) study, which includes deep phenotyping of infant temperament, we identified parent-report measures that were associated with observed novelty-evoked distress. These parent-report measures were then summarized into a composite score used for imaging analysis. Our infant magnetic resonance imaging sample was a synthetic cohort, harmonizing data from 2 functional magnetic resonance imaging studies of 4-month-old infants (OIT and BCP [Baby Connectome Project]; n = 101), both of which included measures of parent-reported temperament. Brain-behavior associations were evaluated using enrichment, a statistical approach that quantifies the clustering of brain-behavior associations within network pairs.

RESULTS

Results demonstrated that parent-report composites of novelty-evoked distress were significantly associated with 3 network pairs: dorsal attention-salience/ventral attention, dorsal attention-default mode, and dorsal attention-control. These network pairs demonstrated negative associations with novelty-evoked distress, indicating that less connectivity between these network pairs was associated with greater novelty-evoked distress. Additional analyses demonstrated that dorsal attention-control network connectivity was associated with observed novelty-evoked distress in the OIT sample (n = 38).

CONCLUSIONS

Overall, this work is broadly consistent with existing work and implicates dorsal attention network connectivity in novelty-evoked distress. This study provides novel data on the neural basis of infant novelty-evoked distress.

Mapping neural correlates of biological motion perception in autistic children using high-density diffuse optical tomography

BACKGROUND

Autism spectrum disorder (ASD), a neurodevelopmental disorder defined by social communication deficits plus repetitive behaviors and restricted interests, currently affects 1/36 children in the general population. Recent advances in functional brain imaging show promise to provide useful biomarkers of ASD diagnostic likelihood, behavioral trait severity, and even response to therapeutic intervention. However, current gold-standard neuroimaging methods (e.g., functional magnetic resonance imaging, fMRI) are limited in naturalistic studies of brain function underlying ASD-associated behaviors due to the constrained imaging environment. Compared to fMRI, high-density diffuse optical tomography (HD-DOT), a non-invasive and minimally constraining optical neuroimaging modality, can overcome these limitations. Herein, we aimed to establish HD-DOT to evaluate brain function in autistic and non-autistic school-age children as they performed a biological motion perception task previously shown to yield results related to both ASD diagnosis and behavioral traits.

METHODS

We used HD-DOT to image brain function in 46 ASD school-age participants and 49 non-autistic individuals (NAI) as they viewed dynamic point-light displays of coherent biological and scrambled motion. We assessed group-level cortical brain function with statistical parametric mapping. Additionally, we tested for brain-behavior associations with dimensional metrics of autism traits, as measured with the Social Responsiveness Scale-2, with hierarchical regression models.

RESULTS

We found that NAI participants presented stronger brain activity contrast (coherent > scrambled) than ASD children in cortical regions related to visual, motor, and social processing. Additionally, regression models revealed multiple cortical regions in autistic participants where brain function is significantly associated with dimensional measures of ASD traits.

LIMITATIONS

Optical imaging methods are limited in depth sensitivity and so cannot measure brain activity within deep subcortical regions. However, the field of view of this HD-DOT system includes multiple brain regions previously implicated in both task-based and task-free studies on autism.

CONCLUSIONS

This study demonstrates that HD-DOT is sensitive to brain function that both differentiates between NAI and ASD groups and correlates with dimensional measures of ASD traits. These findings establish HD-DOT as an effective tool for investigating brain function in autistic and non-autistic children. Moreover, this study established neural correlates related to biological motion perception and its association with dimensional measures of ASD traits.

Atypical functional connectivity between the amygdala and visual, salience regions in infants with genetic liability for autism

The amygdala undergoes a period of overgrowth in the first year of life, resulting in enlarged volume by 12 months in infants later diagnosed with ASD. The overgrowth of the amygdala may have functional consequences during infancy. We investigated whether amygdala connectivity differs in 12-month-olds at high likelihood (HL) for ASD (defined by having an older sibling with autism), compared to those at low likelihood (LL). We examined seed-based connectivity of left and right amygdalae, hypothesizing that the HL and LL groups would differ in amygdala connectivity, especially with the visual cortex, based on our prior reports demonstrating that components of visual circuitry develop atypically and are linked to genetic liability for autism. We found that HL infants exhibited weaker connectivity between the right amygdala and the left visual cortex, as well as between the left amygdala and the right anterior cingulate, with evidence that these patterns occur in distinct subgroups of the HL sample. Amygdala connectivity strength with the visual cortex was related to motor and communication abilities among HL infants. Findings indicate that aberrant functional connectivity between the amygdala and visual regions is apparent in infants with genetic liability for ASD and may have implications for early differences in adaptive behaviors.

Transcriptional cartography integrates multiscale biology of the human cortex

The cerebral cortex underlies many of our unique strengths and vulnerabilities, but efforts to understand human cortical organization are challenged by reliance on incompatible measurement methods at different spatial scales. Macroscale features such as cortical folding and functional activation are accessed through spatially dense neuroimaging maps, whereas microscale cellular and molecular features are typically measured with sparse postmortem sampling. Here, we integrate these distinct windows on brain organization by building upon existing postmortem data to impute, validate, and analyze a library of spatially dense neuroimaging-like maps of human cortical gene expression. These maps allow spatially unbiased discovery of cortical zones with extreme transcriptional profiles or unusually rapid transcriptional change which index distinct microstructure and predict neuroimaging measures of cortical folding and functional activation. Modules of spatially coexpressed genes define a family of canonical expression maps that integrate diverse spatial scales and temporal epochs of human brain organization - ranging from protein-protein interactions to large-scale systems for cognitive processing. These module maps also parse neuropsychiatric risk genes into subsets which tag distinct cyto-laminar features and differentially predict the location of altered cortical anatomy and gene expression in patients. Taken together, the methods, resources, and findings described here advance our understanding of human cortical organization and offer flexible bridges to connect scientific fields operating at different spatial scales of human brain research.

Associations between early trajectories of amygdala development and later school-age anxiety in two longitudinal samples

Amygdala function is implicated in the pathogenesis of autism spectrum disorder (ASD) and anxiety. We investigated associations between early trajectories of amygdala growth and anxiety and ASD outcomes at school age in two longitudinal studies: high- and low-familial likelihood for ASD, Infant Brain Imaging Study (IBIS, n = 257) and typically developing (TD) community sample, Early Brain Development Study (EBDS, n = 158). Infants underwent MRI scanning at up to 3 timepoints from neonate to 24 months. Anxiety was assessed at 6-12 years. Linear multilevel modeling tested whether amygdala volume growth was associated with anxiety symptoms at school age. In the IBIS sample, children with higher anxiety showed accelerated amygdala growth from 6 to 24 months. ASD diagnosis and ASD familial likelihood were not significant predictors. In the EBDS sample, amygdala growth from birth to 24 months was associated with anxiety. More anxious children had smaller amygdala volume and slower rates of amygdala growth. We explore reasons for the contrasting results between high-familial likelihood for ASD and TD samples, grounding results in the broader literature of variable associations between early amygdala volume and later anxiety. Results have the potential to identify mechanisms linking early amygdala growth to later anxiety in certain groups.

A Potential Biomarker of Brain Activity in Autism Spectrum Disorders: A Pilot fNIRS Study in Female Preschoolers

Autism spectrum disorder (ASD) refers to a neurodevelopmental condition whose detection still remains challenging in young females due to the heterogeneity of the behavioral phenotype and the capacity of camouflage. The availability of quantitative biomarkers to assess brain function may support in the assessment of ASD. Functional Near-infrared Spectroscopy (fNIRS) is a non-invasive and flexible tool that quantifies cortical hemodynamic responses (HDR) that can be easily employed to describe brain activity. Since the study of the visual phenotype is a paradigmatic model to evaluate cerebral processing in many neurodevelopmental conditions, we hypothesized that visually-evoked HDR (vHDR) might represent a potential biomarker in ASD females. We performed a case-control study comparing vHDR in a cohort of high-functioning preschooler females with ASD (fASD) and sex/age matched peers. We demonstrated the feasibility of visual fNIRS measurements in fASD, and the possibility to discriminate between fASD and typical subjects using different signal features, such as the amplitude and lateralization of vHDR. Moreover, the level of response lateralization was correlated to the severity of autistic traits. These results corroborate the cruciality of sensory symptoms in ASD, paving the way for the validation of the fNIRS analytical tool for diagnosis and treatment outcome monitoring in the ASD population.

Heterogeneity thwarts autism explanatory power: A proposal for endophenotypes

Many researchers now believe that autism heterogeneity is likely to include many disorders, but most research is based on samples defined by the DSM-5 Autism Spectrum Disorder (ASD) criteria. However, individuals diagnosed with autism have complex and varied biological causes for their symptoms. Therefore, autism is not a unitary biological entity. And although autism is significantly different from typical development, autism is not a unitary clinical disorder because diagnosed individuals vary in symptom patterns, comorbidities, biomarkers, and gene variants. The DSM-5 ASD criteria were designed to reduce heterogeneity, and there have been many other efforts to reduce autism heterogeneity including using more stringent clinical criteria, dividing autism into low and high functioning groups, creating subgroups, and by studying larger samples. However, to date these efforts have not been successful. Heterogeneity is extensive and remains unexplained, and no autism pathophysiology has been discovered. Most importantly, heterogeneity has hindered the explanatory power of the autism diagnosis to discover drug regimens and effective behavioral treatments. The paper proposes that possible transdiagnostic endophenotypes may reduce autism heterogeneity. Searching for transdiagnostic endophenotypes requires exploring autism symptoms outside of the framework of the DSM-5 autism diagnosis. This paper proposes that researchers relax diagnostic criteria to increase the range of phenotypes to support the search for transdiagnostic endophenotypes. The paper proposes possible candidates for transdiagnostic endophenotypes. These candidates are taken from DSM-5 ASD criteria, from concepts that have resulted from researched theories, and from symptoms that are the result of subtyping. The paper then sketches a possible basis for a future transdiagnostic endophenotypes screening tool that includes symptoms of autism and other neurodevelopmental disorders.