Neural signatures of autism

Neural circuit disruptions of eye gaze processing in autism spectrum disorder and schizophrenia: An activation likelihood estimation meta-analysis

BACKGROUND

Impairment in social cognition, particularly eye gaze processing, is a shared feature common to autism spectrum disorder (ASD) and schizophrenia. However, it is unclear if a convergent neural mechanism also underlies gaze dysfunction in these conditions. The present study examined whether this shared eye gaze phenotype is reflected in a profile of convergent neurobiological dysfunction in ASD and schizophrenia.

METHODS

Activation likelihood estimation (ALE) meta-analyses were conducted on peak voxel coordinates across the whole brain to identify spatial convergence. Functional coactivation with regions emerging as significant was assessed using meta-analytic connectivity modeling. Functional decoding was also conducted.

RESULTS

Fifty-six experiments (n = 30 with schizophrenia and n = 26 with ASD) from 36 articles met inclusion criteria, which comprised 354 participants with ASD, 275 with schizophrenia and 613 healthy controls (1242 participants in total). In ASD, aberrant activation was found in the left amygdala relative to unaffected controls during gaze processing. In schizophrenia, aberrant activation was found in the right inferior frontal gyrus and supplementary motor area. Across ASD and schizophrenia, aberrant activation was found in the right inferior frontal gyrus and right fusiform gyrus during gaze processing. Functional decoding mapped the left amygdala to domains related to emotion processing and cognition, the right inferior frontal gyrus to cognition and perception, and the right fusiform gyrus to visual perception, spatial cognition, and emotion perception. These regions also showed meta-analytic connectivity to frontoparietal and frontotemporal circuitry.

CONCLUSION

Alterations in frontoparietal and frontotemporal circuitry emerged as neural markers of gaze impairments in ASD and schizophrenia. These findings have implications for advancing transdiagnostic biomarkers to inform targeted treatments for ASD and schizophrenia.

Endophenotype trait domains for advancing gene discovery in autism spectrum disorder

Autism spectrum disorder (ASD) is associated with a diverse range of etiological processes, including both genetic and non-genetic causes. For a plurality of individuals with ASD, it is likely that the primary causes involve multiple common inherited variants that individually account for only small levels of variation in phenotypic outcomes. This genetic landscape creates a major challenge for detecting small but important pathogenic effects associated with ASD. To address similar challenges, separate fields of medicine have identified endophenotypes, or discrete, quantitative traits that reflect genetic likelihood for a particular clinical condition and leveraged the study of these traits to map polygenic mechanisms and advance more personalized therapeutic strategies for complex diseases. Endophenotypes represent a distinct class of biomarkers useful for understanding genetic contributions to psychiatric and developmental disorders because they are embedded within the causal chain between genotype and clinical phenotype, and they are more proximal to the action of the gene(s) than behavioral traits. Despite their demonstrated power for guiding new understanding of complex genetic structures of clinical conditions, few endophenotypes associated with ASD have been identified and integrated into family genetic studies. In this review, we argue that advancing knowledge of the complex pathogenic processes that contribute to ASD can be accelerated by refocusing attention toward identifying endophenotypic traits reflective of inherited mechanisms. This pivot requires renewed emphasis on study designs with measurement of familial co-variation including infant sibling studies, family trio and quad designs, and analysis of monozygotic and dizygotic twin concordance for select trait dimensions. We also emphasize that clarification of endophenotypic traits necessarily will involve integration of transdiagnostic approaches as candidate traits likely reflect liability for multiple clinical conditions and often are agnostic to diagnostic boundaries. Multiple candidate endophenotypes associated with ASD likelihood are described, and we propose a new focus on the analysis of "endophenotype trait domains" (ETDs), or traits measured across multiple levels (e.g., molecular, cellular, neural system, neuropsychological) along the causal pathway from genes to behavior. To inform our central argument for research efforts toward ETD discovery, we first provide a brief review of the concept of endophenotypes and their application to psychiatry. Next, we highlight key criteria for determining the value of candidate endophenotypes, including unique considerations for the study of ASD. Descriptions of different study designs for assessing endophenotypes in ASD research then are offered, including analysis of how select patterns of results may help prioritize candidate traits in future research. We also present multiple candidate ETDs that collectively cover a breadth of clinical phenomena associated with ASD, including social, language/communication, cognitive control, and sensorimotor processes. These ETDs are described because they represent promising targets for gene discovery related to clinical autistic traits, and they serve as models for analysis of separate candidate domains that may inform understanding of inherited etiological processes associated with ASD as well as overlapping neurodevelopmental disorders.

Detection of ASD Children through Deep-Learning Application of fMRI

Autism spectrum disorder (ASD) necessitates prompt diagnostic scrutiny to enable immediate, targeted interventions. This study unveils an advanced convolutional-neural-network (CNN) algorithm that was meticulously engineered to examine resting-state functional magnetic resonance imaging (fMRI) for early ASD detection in pediatric cohorts. The CNN architecture amalgamates convolutional, pooling, batch-normalization, dropout, and fully connected layers, optimized for high-dimensional data interpretation. Rigorous preprocessing yielded 22,176 two-dimensional echo planar samples from 126 subjects (56 ASD, 70 controls) who were sourced from the Autism Brain Imaging Data Exchange (ABIDE I) repository. The model, trained on 17,740 samples across 50 epochs, demonstrated unparalleled diagnostic metrics-accuracy of 99.39%, recall of 98.80%, precision of 99.85%, and an F1 score of 99.32%-and thereby eclipsed extant computational methodologies. Feature map analyses substantiated the model's hierarchical feature extraction capabilities. This research elucidates a deep learning framework for computer-assisted ASD screening via fMRI, with transformative implications for early diagnosis and intervention.

Copy Number Variation Informs fMRI-based Prediction of Autism Spectrum Disorder

The multifactorial etiology of autism spectrum disorder (ASD) suggests that its study would benefit greatly from multimodal approaches that combine data from widely varying platforms, e.g., neuroimaging, genetics, and clinical characterization. Prior neuroimaging-genetic analyses often apply naive feature concatenation approaches in data-driven work or use the findings from one modality to guide posthoc analysis of another, missing the opportunity to analyze the paired multimodal data in a truly unified approach. In this paper, we develop a more integrative model for combining genetic, demographic, and neuroimaging data. Inspired by the influence of genotype on phenotype, we propose using an attention-based approach where the genetic data guides attention to neuroimaging features of importance for model prediction. The genetic data is derived from copy number variation parameters, while the neuroimaging data is from functional magnetic resonance imaging. We evaluate the proposed approach on ASD classification and severity prediction tasks, using a sex-balanced dataset of 228 ASD and typically developing subjects in a 10-fold cross-validation framework. We demonstrate that our attention-based model combining genetic information, demographic data, and functional magnetic resonance imaging results in superior prediction performance compared to other multimodal approaches.

PLSNet: Position-aware GCN-based autism spectrum disorder diagnosis via FC learning and ROIs sifting

Brain function connectivity, derived from functional magnetic resonance imaging (fMRI), has enjoyed high popularity in the studies of Autism Spectrum Disorder (ASD) diagnosis. Albeit rapid progress has been made, most studies still suffer from several knotty issues: (1) the hardship of modeling the sophisticated brain neuronal connectivity; (2) the mismatch of identically graph node setup to the variations of different brain regions; (3) the dimensionality explosion resulted from excessive voxels in each fMRI sample; (4) the poor interpretability giving rise to unpersuasive diagnosis. To ameliorate these issues, we propose a position-aware graph-convolution-network-based model, namely PLSNet, with superior accuracy and compelling built-in interpretability for ASD diagnosis. Specifically, a time-series encoder is designed for context-rich feature extraction, followed by a function connectivity generator to model the correlation with long range dependencies. In addition, to discriminate the brain nodes with different locations, the position embedding technique is adopted, giving a unique identity to each graph region. We then embed a rarefying method to sift the salient nodes during message diffusion, which would also benefit the reduction of the dimensionality complexity. Extensive experiments conducted on Autism Brain Imaging Data Exchange demonstrate that our PLSNet achieves state-of-the-art performance. Notably, on CC200 atlas, PLSNet reaches an accuracy of 76.4% and a specificity of 78.6%, overwhelming the previous state-of-the-art with 2.5% and 6.5% under five-fold cross-validation policy. Moreover, the most salient brain regions predicted by PLSNet are closely consistent with the theoretical knowledge in the medical domain, providing potential biomarkers for ASD clinical diagnosis. Our code is available at https://github.com/CodeGoat24/PLSNet.

Efficacy of a Parent-Implemented Pivotal Response Treatment for Children with Autism Spectrum Disorder

PURPOSE

Pivotal response treatment (PRT) is a well-established intervention addressing core symptoms of autism spectrum disorder (ASD), with parent involvement as a key component. The current study aimed to examine the effects of PRT parent training on parent fidelity and provide descriptive analyses of parent-child interactions before and after parent training. It also probed parental acceptance and satisfaction with the program.

METHODS

A concurrent multiple baseline design across participants was used to evaluate the effectiveness of the parent training program. Four parents (range 32-47 years old) and their children with ASD participated in the study. The intervention comprised 12 one-on-one parent training sessions over six consecutive weeks. The visual analysis and effect size calculation (Tau-U) were used to evaluate functional relationship between independent and dependent variables. The descriptive analysis was used to analyze parent-child interaction data.

RESULTS

The findings reveal that all parents learned and maintained PRT with a high level of fidelity, they also enhanced awareness to create more opportunities for interactions during free play, and the parents were very satisfied with the program. The results also indicate that the six-week parent training program is effective in teaching parents to implement PRT with their children.

CONCLUSION

These results suggest that the parent training program may be a promising treatment model that is effective, efficient, and cost-effective. Implications for future research and practice are then discussed.

Association of Sex With Neurobehavioral Markers of Executive Function in 2-Year-Olds at High and Low Likelihood of Autism

Importance

Children with autism and their siblings exhibit executive function (EF) deficits early in development, but associations between EF and biological sex or early brain alterations in this population are largely unexplored.

Objective

To investigate the interaction of sex, autism likelihood group, and structural magnetic resonance imaging alterations on EF in 2-year-old children at high familial likelihood (HL) and low familial likelihood (LL) of autism, based on having an older sibling with autism or no family history of autism in first-degree relatives.

Design, Setting, and Participants

This prospective cohort study assessed 165 toddlers at HL (n = 110) and LL (n = 55) of autism at 4 university-based research centers. Data were collected from January 1, 2007, to December 31, 2013, and analyzed between August 2021 and June 2022 as part of the Infant Brain Imaging Study.

Main Outcomes and Measures

Direct assessments of EF and acquired structural magnetic resonance imaging were performed to determine frontal lobe, parietal lobe, and total cerebral brain volume.

Results

A total of 165 toddlers (mean [SD] age, 24.61 [0.95] months; 90 [54%] male, 137 [83%] White) at HL for autism (n = 110; 17 diagnosed with ASD) and LL for autism (n = 55) were studied. The toddlers at HL for autism scored lower than the toddlers at LL for autism on EF tests regardless of sex (mean [SE] B = -8.77 [4.21]; 95% CI, -17.09 to -0.45; η2p = 0.03). With the exclusion of toddlers with autism, no group (HL vs LL) difference in EF was found in boys (mean [SE] difference, -7.18 [4.26]; 95% CI, 1.24-15.59), but EF was lower in HL girls than LL girls (mean [SE] difference, -9.75 [4.34]; 95% CI, -18.32 to -1.18). Brain-behavior associations were examined, controlling for overall cerebral volume and developmental level. Sex differences in EF-frontal (B [SE] = 16.51 [7.43]; 95% CI, 1.36-31.67; η2p = 0.14) and EF-parietal (B [SE] = 17.68 [6.99]; 95% CI, 3.43-31.94; η2p = 0.17) associations were found in the LL group but not the HL group (EF-frontal: B [SE] = -1.36 [3.87]; 95% CI, -9.07 to 6.35; η2p = 0.00; EF-parietal: B [SE] = -2.81 [4.09]; 95% CI, -10.96 to 5.34; η2p = 0.01). Autism likelihood group differences in EF-frontal (B [SE] = -9.93 [4.88]; 95% CI, -19.73 to -0.12; η2p = 0.08) and EF-parietal (B [SE] = -15.44 [5.18]; 95% CI, -25.86 to -5.02; η2p = 0.16) associations were found in girls not boys (EF-frontal: B [SE] = 6.51 [5.88]; 95% CI, -5.26 to 18.27; η2p = 0.02; EF-parietal: B [SE] = 4.18 [5.48]; 95% CI, -6.78 to 15.15; η2p = 0.01).

Conclusions and Relevance

This cohort study of toddlers at HL and LL of autism suggests that there is an association between sex and EF and that brain-behavior associations in EF may be altered in children at HL of autism. Furthermore, EF deficits may aggregate in families, particularly in girls.

Neuroanatomical markers of familial risk in adolescents with conduct disorder and their unaffected relatives

BACKGROUND

Previous studies have reported brain structure abnormalities in conduct disorder (CD), but it is unclear whether these neuroanatomical alterations mediate the effects of familial (genetic and environmental) risk for CD. We investigated brain structure in adolescents with CD and their unaffected relatives (URs) to identify neuroanatomical markers of familial risk for CD.

METHODS

Forty-one adolescents with CD, 24 URs of CD probands, and 38 healthy controls (aged 12-18), underwent structural magnetic resonance imaging. We performed surface-based morphometry analyses, testing for group differences in cortical volume, thickness, surface area, and folding. We also assessed the volume of key subcortical structures.

RESULTS

The CD and UR groups both displayed structural alterations (lower surface area and folding) in left inferior parietal cortex compared with controls. In contrast, CD participants showed lower insula and pars opercularis volume than controls, and lower surface area and folding in these regions than controls and URs. The URs showed greater folding in rostral anterior cingulate and inferior temporal cortex than controls and greater medial orbitofrontal folding than CD participants. The surface area and volume differences were not significant when controlling for attention-deficit/hyperactivity disorder comorbidity. There were no group differences in subcortical volumes.

CONCLUSIONS

These findings suggest that alterations in inferior parietal cortical structure partly mediate the effects of familial risk for CD. These structural changes merit investigation as candidate endophenotypes for CD. Neuroanatomical changes in medial orbitofrontal and anterior cingulate cortex differentiated between URs and the other groups, potentially reflecting neural mechanisms of resilience to CD.

Overactivation of posterior insular, postcentral and temporal regions during preserved experience of envy in autism

Social emotions are critical to successfully navigate in a complex social world because they promote self-regulation of behaviour. Difficulties in social behaviour are at the core of autism spectrum disorder (ASD). However, social emotions and their neural correlates have been scarcely investigated in this population. In particular, the experience of envy has not been addressed in ASD despite involving neurocognitive processes crucially compromised in this condition. Here, we used an fMRI adapted version of a well-validated task to investigate the subjective experience of envy and its neural correlates in adults with ASD (n = 30) in comparison with neurotypical controls (n = 28). Results revealed that both groups reported similarly intense experience of envy in association with canonical activation in the anterior cingulate cortex and the anterior insula, among other regions. However, in participants with ASD, the experience of envy was accompanied by overactivation of the posterior insula, the postcentral gyrus and the posterior superior temporal gyrus, regions subserving the processing of painful experiences and mentalizing. This pattern of results suggests that individuals with ASD may use compensatory strategies based on the embodied amplification of pain and additional mentalizing efforts to shape their subjective experience of envy. Results have relevant implications to better understand the heterogeneity of this condition and to develop new intervention targets.

Life is in motion (through a chick's eye)

Cognitive scientists, social psychologists, computer scientists, neuroscientists, ethologists and many others have all wondered how brains detect and interpret the motion of living organisms. It appears that specific cues, incorporated into our brains by natural selection, serve to signal the presence of living organisms. A simple geometric figure such as a triangle put in motion with specific kinematic rules can look alive, and it can even seem to have intentions and goals. In this article, we survey decades of parallel investigations on the motion cues that drive animacy perception-the sensation that something is alive-in non-human animals, especially in precocial species, such as the domestic chick, to identify inborn biological predispositions. At the same time, we highlight the relevance of these studies for an understanding of human typical and atypical cognitive development.

Lack of effects of four-week theta burst stimulation on white matter macro/microstructure in children and adolescents with autism

Following the published behavioral and cognitive results of this single-blind parallel sham-controlled randomized clinical trial, the current study aimed to explore the impact of intermittent theta burst stimulation (iTBS), a variant of excitatory transcranial magnetic stimulation, over the bilateral posterior superior temporal sulci (pSTS) on white matter macro/microstructure in intellectually able children and adolescents with autism. Participants were randomized and blindly received active or sham iTBS for 4 weeks (the single-blind sham-controlled phase). Then, all participants continued to receive active iTBS for another 4 weeks (the open-label phase). The clinical results were published elsewhere. Here, we present diffusion magnetic resonance imaging data on potential changes in white matter measures after iTBS. Twenty-two participants in Active-Active group and 27 participants in Sham-Active group underwent multi-shell high angular resolution diffusion imaging (64-direction for b = 2000 & 1000 s/mm², respectively) at baseline, week 4, and week 8. With longitudinal fixel-based analysis, we found no white matter changes following iTBS from baseline to week 4 (a null treatment by time interaction and a null within-group paired comparison in the Active-Active group), nor from baseline to week 8 (null within-group paired comparisons in both Active-Active and Sham-Active groups). As for the brain-symptoms relationship, we did not find baseline white matter metrics associated with symptom changes at week 4 in either group. Our results raise the question of what the minimal cumulative stimulation dose required to induce the white matter plasticity is.

Can the "female protective effect" liability threshold model explain sex differences in autism spectrum disorder?

Male sex is a strong risk factor for autism spectrum disorder (ASD). The leading theory for a "female protective effect" (FPE) envisions males and females have "differing thresholds" under a "liability threshold model" (DT-LTM). Specifically, this model posits that females require either a greater number or larger magnitude of risk factors (i.e., greater liability) to manifest ASD, which is supported by the finding that a greater proportion of females with ASD have highly penetrant genetic mutations. Herein, we derive testable hypotheses from the DT-LTM for ASD, investigating heritability, familial recurrence, correlation between ASD penetrance and sex ratio, population traits, clinical features, the stability of the sex ratio across diagnostic changes, and highlight other key prerequisites. Our findings reveal that several key predictions of the DT-LTM are not supported by current data, requiring us to establish a different conceptual framework for evaluating alternate models that explain sex differences in ASD.

Differences in white matter segments in autistic males, non-autistic siblings, and non-autistic participants: An intermediate phenotype approach

LAY ABSTRACT

White matter is the neural pathway that connects neurons in different brain regions. Although research has shown white matter differences between autistic and non-autistic people, little is known about the properties of white matter in non-autistic siblings. In addition, past studies often focused on the whole neural tracts; it is unclear where differences exist in specific segments of the tracts. This study identified neural segments that differed between autistic people, their non-autistic siblings, and the age- and non-autistic people. We found altered segments within the tracts connected to anterior brain regions corresponding to several higher cognitive functions (e.g. executive functions) in autistic people and non-autistic siblings. Segments connecting to regions for social cognition and Theory of Mind were altered only in autistic people, explaining a large portion of autistic traits and may serve as neuroimaging markers. Segments within the tracts associated with fewer autistic traits or connecting brain regions for diverse highly integrated functions showed compensatory increases in the microstructural properties in non-autistic siblings. Our findings suggest that differential white matter segments that are shared between autistic people and non-autistic siblings may serve as potential "intermediate phenotypes"-biological or neuropsychological characteristics in the causal link between genetics and symptoms-of autism. These findings shed light on a promising neuroimaging model to refine the intermediate phenotype of autism which may facilitate further identification of the genetic and biological bases of autism. Future research exploring links between compensatory segments and neurocognitive strengths in non-autistic siblings may help understand brain adaptation to autism.

Slower Peak Pupillary Response to Emotional Faces in Parents of Autistic Individuals

Background

Atypical autonomic arousal has been consistently documented in autism spectrum disorder (ASD) and is thought to contribute to the social-communication phenotype of ASD. Some evidence suggests that clinically unaffected first-degree relatives of autistic individuals may also show subtle differences in indices of autonomic arousal, potentially implicating heritable pathophysiological mechanisms in ASD. This study examined pupillary responses in parents of autistic individuals to investigate evidence that atypical autonomic arousal might constitute a subclinical physiological marker of ASD heritability within families of autistic individuals.

Methods

Pupillary responses to emotional faces were measured in 47 ASD parents and 20 age-matched parent controls. Macro-level pupillary responses (e.g., mean, peak, latency to peak) and dynamic pupillary responses over the course of the stimulus presentation were compared between groups, and in relationship to subclinical ASD-related features in ASD parents. A small ASD group (n = 20) and controls (n = 17) were also included for exploratory analyses of parent–child correlations in pupillary response.

Results

Parents of autistic individuals differed in the time course of pupillary response, exhibiting a later primary peak response than controls. In ASD parents, slower peak response was associated with poorer pragmatic language and larger peak response was associated with poorer social cognition. Exploratory analyses revealed correlations between peak pupillary responses in ASD parents and mean and peak pupillary responses in their autistic children.

Conclusion

Differences in pupillary responses in clinically unaffected parents, together with significant correlations with ASD-related features and significant parent–child associations, suggest that pupillary responses to emotional faces may constitute an objective physiological marker of ASD genetic liability, with potential to inform the mechanistic underpinnings of ASD symptomatology.

Ties between reading faces, bodies, eyes, and autistic traits

While reading covered with masks faces during the COVID-19 pandemic, for efficient social interaction, we need to combine information from different sources such as the eyes (without faces hidden by masks) and bodies. This may be challenging for individuals with neuropsychiatric conditions, in particular, autism spectrum disorders. Here we examined whether reading of dynamic faces, bodies, and eyes are tied in a gender-specific way, and how these capabilities are related to autistic traits expression. Females and males accomplished a task with point-light faces along with a task with point-light body locomotion portraying different emotional expressions. They had to infer emotional content of displays. In addition, participants were administered the Reading the Mind in the Eyes Test, modified and Autism Spectrum Quotient questionnaire. The findings show that only in females, inferring emotions from dynamic bodies and faces are firmly linked, whereas in males, reading in the eyes is knotted with face reading. Strikingly, in neurotypical males only, accuracy of face, body, and eyes reading was negatively tied with autistic traits. The outcome points to gender-specific modes in social cognition: females rely upon merely dynamic cues while reading faces and bodies, whereas males most likely trust configural information. The findings are of value for examination of face and body language reading in neuropsychiatric conditions, in particular, autism, most of which are gender/sex-specific. This work suggests that if male individuals with autistic traits experience difficulties in reading covered with masks faces, these deficits may be unlikely compensated by reading (even dynamic) bodies and faces. By contrast, in females, reading covered faces as well as reading language of dynamic bodies and faces are not compulsorily connected to autistic traits preventing them from paying high costs for maladaptive social interaction.

Internal noise measures in coarse and fine motion direction discrimination tasks and the correlation with autism traits

Motion perception is essential for visual guidance of behavior and is known to be limited by both internal additive noise (i.e., a constant level of random fluctuations in neural activity independent of the stimulus) and motion pooling (global integration of local motion signals across space). People with autism spectrum disorder (ASD) display abnormalities in motion processing, which have been linked to both elevated noise and abnormal pooling. However, to date, the impact of a third limit—induced internal noise (internal noise that scales up with increases in external stimulus noise)—has not been investigated in motion perception of any group. Here, we describe an extension on the double-pass paradigm to quantify additive noise and induced noise in a motion paradigm. We also introduce a new way to experimentally estimate motion pooling. We measured the impact of induced noise on direction discrimination, which we ascribe to fluctuations in decision-related variables. Our results are suggestive of higher internal noise in individuals with high ASD traits only on coarse but not fine motion direction discrimination tasks. However, we report no significant correlations between autism traits and additive noise, induced noise, or motion pooling in either task. We conclude that, under some conditions, the internal noise may be higher in individuals with pronounced ASD traits and that the assessment of induced internal noise is a useful way of exploring decision-related limits on motion perception, irrespective of ASD traits.

Lawrence K, Tachtsidis I, Tong Y, Torricelli A, Urner T, Wabnitz H, Wolf M, Wolf U, Xu S, Yang C, Yodh AG, Yücel MA, Zhou W. Optical imaging and spectroscopy for the study of the human brain: status report

This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions.

A two-stage framework for neural processing of biological motion

It remains to be understood how biological motion is hierarchically computed, from discrimination of local biological motion animacy to global dynamic body perception. Here, we addressed this functional separation of the correlates of the perception of local biological motion from perception of global motion of a body. We hypothesized that local biological motion processing can be isolated, by using a single dot motion perceptual decision paradigm featuring the biomechanical details of local realistic motion of a single joint. To ensure that we were indeed tackling processing of biological motion properties we used a discrimination instead of detection task. We discovered using representational similarity analysis that two key early dorsal and two ventral stream regions (visual motion selective hMT+ and V3A, extrastriate body area EBA and a region within fusiform gyrus FFG) showed robust and separable signals related to encoding of local biological motion and global motion-mediated shape. These signals reflected two independent processing stages, as revealed by representational similarity analysis and deconvolution of fMRI responses to each motion pattern. This study showed that higher level pSTS encodes both classes of biological motion in a similar way, revealing a higher-level integrative stage, reflecting scale independent biological motion perception. Our results reveal a two-stage framework for neural computation of biological motion, with an independent contribution of dorsal and ventral regions for the initial stage.

Neural Correlates of Enhancing Question Asking and Initiations in Children with Autism Spectrum Disorders: A Randomized Clinical Trial

Children diagnosed with autism spectrum disorder (ASD) demonstrate challenges in various areas of social communication. The aim of this study was to examine the effect of Pivotal Response Treatment (PRT) targeting question-asking on brain activity in twenty 6-12-year-old autistic boys, using a Randomized Clinical Trial (RCT) design. Verbal children, diagnosed with autism, who lacked question asking in their communication were matched based on age and mean length utterance (MLU) and were randomly placed in either PRT intervention or treatment as usual (TAU) groups. Sessions were individually administered, lasting for 60 minutes three days a week for a two-month period. All children were tested before and after intervention to assess behavioral areas (questions, general communicative skills, and MLU) and both groups underwent electroencephalography for 10 minutes in open and closed eye resting-state conditions to assess neural correlates. Data were analyzed using covariance analysis and post-hoc using Mann-Whitney and Wilcoxon methods. Significant behavioral improvements in the PRT group were observed after intervention that correlated with changes in Electroencephalography (EEG) oscillations at several brain regions compared to the TAU group. The results of this study support other studies suggesting collateral neural changes following the PRT.

A functional MRI facial emotion-processing study of autism in individuals with special educational needs

This study aimed to investigate the functional imaging associations of autism in individuals with special educational needs and demonstrate the feasibility of such research. The study included 18 individuals (3 female,15 male; mean age 24.3; mean IQ 69.7) with special educational needs (SEN), of whom 9 met criteria for autism. The task examined the Blood-oxygen-level dependant response to fearful and neutral faces. Individuals in the autism group had 2 clusters of significantly reduced activity centred on the left superior frontal gyrus and left angular gyrus compared to those with SEN alone in response to the fearful faces. In the response to neutral faces, individuals in the autism group also had a cluster of significantly greater activity centred on the right precentral gyrus compared to those with SEN alone. We suggest that autistic characteristics in individuals with SEN are associated with changes in fearful facial emotion processing analogous to those previously reported in autistic individuals without SEN, and who are of average or above average cognitive ability. The finding of enhanced response to neutral facial stimuli needs further investigation, although we speculate this may relate to reports of the experience of 'hyper-mentalisation' in social situations as reported by some autistic individuals.

Identifying and Predicting Autism Spectrum Disorder Based on Multi-Site Structural MRI With Machine Learning

Although emerging evidence has implicated structural/functional abnormalities of patients with Autism Spectrum Disorder(ASD), definitive neuroimaging markers remain obscured due to inconsistent or incompatible findings, especially for structural imaging. Furthermore, brain differences defined by statistical analysis are difficult to implement individual prediction. The present study has employed the machine learning techniques under the unified framework in neuroimaging to identify the neuroimaging markers of patients with ASD and distinguish them from typically developing controls(TDC). To enhance the interpretability of the machine learning model, the study has processed three levels of assessments including model-level assessment, feature-level assessment, and biology-level assessment. According to these three levels assessment, the study has identified neuroimaging markers of ASD including the opercular part of bilateral inferior frontal gyrus, the orbital part of right inferior frontal gyrus, right rolandic operculum, right olfactory cortex, right gyrus rectus, right insula, left inferior parietal gyrus, bilateral supramarginal gyrus, bilateral angular gyrus, bilateral superior temporal gyrus, bilateral middle temporal gyrus, and left inferior temporal gyrus. In addition, negative correlations between the communication skill score in the Autism Diagnostic Observation Schedule (ADOS_G) and regional gray matter (GM) volume in the gyrus rectus, left middle temporal gyrus, and inferior temporal gyrus have been detected. A significant negative correlation has been found between the communication skill score in ADOS_G and the orbital part of the left inferior frontal gyrus. A negative correlation between verbal skill score and right angular gyrus and a significant negative correlation between non-verbal communication skill and right angular gyrus have been found. These findings in the study have suggested the GM alteration of ASD and correlated with the clinical severity of ASD disease symptoms. The interpretable machine learning framework gives sight to the pathophysiological mechanism of ASD but can also be extended to other diseases.

The mirror neuron system compensates for amygdala dysfunction - associated social deficits in individuals with higher autistic traits

The amygdala is a core node in the social brain which exhibits structural and functional abnormalities in Autism spectrum disorder and there is evidence that the mirror neuron system (MNS) can functionally compensate for impaired emotion processing following amygdala lesions. In the current study, we employed an fMRI paradigm in 241 subjects investigating MNS and amygdala responses to observation, imagination and imitation of dynamic facial expressions and whether these differed in individuals with higher (n = 77) as opposed to lower (n = 79) autistic traits. Results indicated that individuals with higher compared to lower autistic traits showed worse recognition memory for fearful faces, smaller real-life social networks, and decreased left basolateral amygdala (BLA) responses to imitation. Additionally, functional connectivity between the left BLA and the left inferior frontal gyrus (IFG) as well as some other MNS regions was increased in individuals with higher autistic traits, especially during imitation of fearful expressions. The left BLA-IFG connectivity significantly moderated the autistic group differences on recognition memory for fearful faces, indicating that increased amygdala-MNS connectivity could diminish the social behavioral differences between higher and lower autistic trait groups. Overall, findings demonstrate decreased imitation-related amygdala activity in individuals with higher autistic traits in the context of increased amygdala-MNS connectivity which may functionally compensate for amygdala dysfunction and social deficits. Training targeting the MNS may capitalize on this compensatory mechanism for therapeutic benefits in Autism spectrum disorder.

A Preliminary Study of Abnormal Centrality of Cortical Regions and Subsystems in Whole Brain Functional Connectivity of Autism Spectrum Disorder Boys

The abnormal cortices of autism spectrum disorder (ASD) brains are uncertain. However, the pathological alterations of ASD brains are distributed throughout interconnected cortical systems. Functional connections (FCs) methodology identifies cooperation and separation characteristics of information process in macroscopic cortical activity patterns under the context of network neuroscience. Embracing the graph theory concepts, this paper introduces eigenvector centrality index (EC score) ground on the FCs, and further develops a new framework for researching the dysfunctional cortex of ASD in holism significance. The important process is to uncover noticeable regions and subsystems endowed with antagonistic stance in EC-scores of 26 ASD boys and 28 matched healthy controls (HCs). For whole brain regional EC scores of ASD boys, orbitofrontal superior medial cortex, insula R, posterior cingulate gyrus L, and cerebellum 9 L are endowed with different EC scores significantly. In the brain subsystems level, EC scores of DMN, prefrontal lobe, and cerebellum are aberrant in the ASD boys. Generally, the EC scores display widespread distribution of diseased regions in ASD brains. Meanwhile, the discovered regions and subsystems, such as MPFC, AMYG, INS, prefrontal lobe, and DMN, are engaged in social processing. Meanwhile, the CBCL externalizing problem scores are associated with EC scores.

BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis

Understanding which brain regions are related to a specific neurological disorder or cognitive stimuli has been an important area of neuroimaging research. We propose BrainGNN, a graph neural network (GNN) framework to analyze functional magnetic resonance images (fMRI) and discover neurological biomarkers. Considering the special property of brain graphs, we design novel ROI-aware graph convolutional (Ra-GConv) layers that leverage the topological and functional information of fMRI. Motivated by the need for transparency in medical image analysis, our BrainGNN contains ROI-selection pooling layers (R-pool) that highlight salient ROIs (nodes in the graph), so that we can infer which ROIs are important for prediction. Furthermore, we propose regularization terms-unit loss, topK pooling (TPK) loss and group-level consistency (GLC) loss-on pooling results to encourage reasonable ROI-selection and provide flexibility to encourage either fully individual- or patterns that agree with group-level data. We apply the BrainGNN framework on two independent fMRI datasets: an Autism Spectrum Disorder (ASD) fMRI dataset and data from the Human Connectome Project (HCP) 900 Subject Release. We investigate different choices of the hyper-parameters and show that BrainGNN outperforms the alternative fMRI image analysis methods in terms of four different evaluation metrics. The obtained community clustering and salient ROI detection results show a high correspondence with the previous neuroimaging-derived evidence of biomarkers for ASD and specific task states decoded for HCP. Our code is available at https://github.com/xxlya/BrainGNN_Pytorch.

Looking Back at the Next 40 Years of ASD Neuroscience Research

During the last 40 years, neuroscience has become one of the most central and most productive approaches to investigating autism. In this commentary, we assemble a group of established investigators and trainees to review key advances and anticipated developments in neuroscience research across five modalities most commonly employed in autism research: magnetic resonance imaging, functional near infrared spectroscopy, positron emission tomography, electroencephalography, and transcranial magnetic stimulation. Broadly, neuroscience research has provided important insights into brain systems involved in autism but not yet mechanistic understanding. Methodological advancements are expected to proffer deeper understanding of neural circuitry associated with function and dysfunction during the next 40 years.

Involvement of the habenula in the pathophysiology of autism spectrum disorder

The habenula is a small epithalamic structure with widespread connections to multiple cortical, subcortical and brainstem regions. It has been identified as the central structure modulating the reward value of social interactions, behavioral adaptation, sensory integration and circadian rhythm. Autism spectrum disorder (ASD) is characterized by social communication deficits, restricted interests, repetitive behaviors, and is frequently associated with altered sensory perception and mood and sleep disorders. The habenula is implicated in all these behaviors and results of preclinical studies suggest a possible involvement of the habenula in the pathophysiology of this disorder. Using anatomical magnetic resonance imaging and automated segmentation we show that the habenula is significantly enlarged in ASD subjects compared to controls across the entire age range studied (6-30 years). No differences were observed between sexes. Furthermore, support-vector machine modeling classified ASD with 85% accuracy (model using habenula volume, age and sex) and 64% accuracy in cross validation. The Social Responsiveness Scale (SRS) significantly differed between groups, however, it was not related to individual habenula volume. The present study is the first to provide evidence in human subjects of an involvement of the habenula in the pathophysiology of ASD.

Hippocampal contributions to social and cognitive deficits in autism spectrum disorder

Autism spectrum disorder (ASD) is characterized by hallmark impairments in social functioning. Nevertheless, nonsocial cognition, including hippocampus-dependent spatial reasoning and episodic memory, is also commonly impaired in ASD. ASD symptoms typically emerge between 12 and 24 months of age, a time window associated with critical developmental events in the hippocampus. Despite this temporal overlap and evidence of hippocampal structural abnormalities in ASD individuals, relatively few human studies have focused on hippocampal function in ASD. Herein, we review the existing evidence for the involvement of the hippocampus in ASD and highlight the hippocampus as a promising area of interest for future research in ASD.

Social cognitive skills groups increase medial prefrontal cortex activity in children with autism spectrum disorder

Few studies have examined the neural mechanisms of change following social skills interventions for children with autism spectrum disorder (ASD). This study examined the neural effects of social cognitive skills groups during functional MRI (fMRI) tasks of irony comprehension and eye gaze processing in school-aged children with ASD. Verbally fluent children (ages 8-11) were randomized to social cognitive skills groups or facilitated play comparison groups. Behavioral assessments and fMRI scans were obtained at baseline and endpoint (12 weeks). During fMRI, children completed two separate tasks to engage social cognition circuitry: comprehension of potentially ironic scenarios (n = 34) and viewing emotionally expressive faces with direct or averted gaze (n = 24). Whole-brain analyses were conducted to examine neural changes following treatment. Regression analyses were also conducted to explore the relationship between neural and behavioral changes. When comparing the two groups directly, the social cognitive skills group showed greater increases in activity in the medial prefrontal cortex (mPFC), implicated in theory of mind, relative to the comparison group for both irony comprehension and gaze processing tasks. Increased mPFC activity during the irony task was associated with improvement in social functioning on the Social Responsiveness Scale across both groups. Findings indicate that social cognitive skills interventions may increase activity in regions associated with social cognition and mentalizing abilities. LAY SUMMARY: Social skills groups are a common intervention for school-aged children with ASD. However, few studies have examined the neural response to social skills groups in school-aged children with ASD. Here, we report on a study evaluating neural outcomes from an empirically supported social cognitive skills training curriculum using fMRI. This study seeks to understand the effects of targeting emotion recognition and theory of mind on the brain circuitry involved in social cognition in verbally fluent children ages 8-11. Results indicate increased neural activity in the mPFC, a region considered to be a central hub of the "social brain," in children randomized to social cognitive skills groups relative to a comparison group that received a high-quality, child-directed play approach. In addition, increased activation in the mPFC during an irony comprehension task was associated with gains in social functioning across both groups from pre- to post-treatment. This is the first fMRI study of social skills treatment outcomes following a randomized trial with an active treatment condition in school-aged children with ASD.

DarkASDNet: Classification of ASD on Functional MRI Using Deep Neural Network

Non-invasive whole-brain scans aid the diagnosis of neuropsychiatric disorder diseases such as autism, dementia, and brain cancer. The assessable analysis for autism spectrum disorders (ASD) is rationally challenging due to the limitations of publicly available datasets. For diagnostic or prognostic tools, functional Magnetic Resonance Imaging (fMRI) exposed affirmation to the biomarkers in neuroimaging research because of fMRI pickup inherent connectivity between the brain and regions. There are profound studies in ASD with introducing machine learning or deep learning methods that have manifested advanced steps for ASD predictions based on fMRI data. However, utmost antecedent models have an inadequacy in their capacity to manipulate performance metrics such as accuracy, precision, recall, and F1-score. To overcome these problems, we proposed an avant-garde DarkASDNet, which has the competence to extract features from a lower level to a higher level and bring out promising results. In this work, we considered 3D fMRI data to predict binary classification between ASD and typical control (TC). Firstly, we pre-processed the 3D fMRI data by adopting proper slice time correction and normalization. Then, we introduced a novel DarkASDNet which surpassed the benchmark accuracy for the classification of ASD. Our model's outcomes unveil that our proposed method established state-of-the-art accuracy of 94.70% to classify ASD vs. TC in ABIDE-I, NYU dataset. Finally, we contemplated our model by performing evaluation metrics including precision, recall, F1-score, ROC curve, and AUC score, and legitimize by distinguishing with recent literature descriptions to vindicate our outcomes. The proposed DarkASDNet architecture provides a novel benchmark approach for ASD classification using fMRI processed data.

Impact of autism genetic risk on brain connectivity: a mechanism for the female protective effect

The biological mechanisms underlying the greater prevalence of autism spectrum disorder in males than females remain poorly understood. One hypothesis posits that this female protective effect arises from genetic load for autism spectrum disorder differentially impacting male and female brains. To test this hypothesis, we investigated the impact of cumulative genetic risk for autism spectrum disorder on functional brain connectivity in a balanced sample of boys and girls with autism spectrum disorder and typically developing boys and girls (127 youth, ages 8-17). Brain connectivity analyses focused on the salience network, a core intrinsic functional connectivity network which has previously been implicated in autism spectrum disorder. The effects of polygenic risk on salience network functional connectivity were significantly modulated by participant sex, with genetic load for autism spectrum disorder influencing functional connectivity in boys with and without autism spectrum disorder but not girls. These findings support the hypothesis that autism spectrum disorder risk genes interact with sex differential processes, thereby contributing to the male bias in autism prevalence and proposing an underlying neurobiological mechanism for the female protective effect.

Difficulties in Recognising Dynamic but not Static Emotional Body Movements in Autism Spectrum Disorder

In this study, we investigated whether the difficulties in body motion (BM) perception may led to deficit in emotion recognition in Autism spectrum disorder (ASD). To this aim, individuals with high-functioning ASD were asked to recognise fearful, happy, and neutral BM depicted as static images or dynamic point-light and full-light displays. Results showed slower response times in participants with ASD only in recognising dynamic stimuli, but no group differences in accuracy. This suggests that i) a deficit in action chaining mechanism in ASD may prevent the recognition of dynamic BM automatically and rapidly, ii) individuals with ASD and high cognitive resources can develop alternative—but equally successful—strategies to recognise emotional body expressions. Implications for treatment are discussed

A neurogenetic analysis of female autism

Females versus males are less frequently diagnosed with autism spectrum disorder (ASD), and while understanding sex differences is critical to delineating the systems biology of the condition, female ASD is understudied. We integrated functional MRI and genetic data in a sex-balanced sample of ASD and typically developing youth (8–17 years old) to characterize female-specific pathways of ASD risk. Our primary objectives were to: (i) characterize female ASD (n = 45) brain response to human motion, relative to matched typically developing female youth (n = 45); and (ii) evaluate whether genetic data could provide further insight into the potential relevance of these brain functional differences. For our first objective we found that ASD females showed markedly reduced response versus typically developing females, particularly in sensorimotor, striatal, and frontal regions. This difference between ASD and typically developing females does not resemble differences between ASD (n = 47) and typically developing males (n = 47), even though neural response did not significantly differ between female and male ASD. For our second objective, we found that ASD females (n = 61), versus males (n = 66), showed larger median size of rare copy number variants containing gene(s) expressed in early life (10 postconceptual weeks to 2 years) in regions implicated by the typically developing female > female functional MRI contrast. Post hoc analyses suggested this difference was primarily driven by copy number variants containing gene(s) expressed in striatum. This striatal finding was reproducible among n = 2075 probands (291 female) from an independent cohort. Together, our findings suggest that striatal impacts may contribute to pathways of risk in female ASD and advocate caution in drawing conclusions regarding female ASD based on male-predominant cohorts.

Signaling Pathways and Sex Differential Processes in Autism Spectrum Disorder

Autism spectrum disorders (ASDs) are a group of neurodevelopmental disorders associated with deficits in social communication and restrictive, repetitive patterns of behavior, that affect up to 1 in 54 children. ASDs clearly demonstrate a male bias, occurring ~4 times more frequently in males than females, though the basis for this male predominance is not well-understood. In recent years, ASD risk gene discovery has accelerated, with many whole-exome sequencing studies identifying genes that converge on common pathways, such as neuronal communication and regulation of gene expression. ASD genetics studies have suggested that there may be a "female protective effect," such that females may have a higher threshold for ASD risk, yet its etiology is not well-understood. Here, we review common biological pathways implicated by ASD genetics studies as well as recent analyses of sex differential processes in ASD using imaging genomics, transcriptomics, and animal models. Additionally, we discuss recent investigations of ASD risk genes that have suggested a potential role for estrogens as modulators of biological pathways in ASD, and highlight relevant molecular and cellular pathways downstream of estrogen signaling as potential avenues for further investigation.

A Unifying Theory for Autism: The Pathogenetic Triad as a Theoretical Framework

This paper presents a unifying theory for autism by applying the framework of a pathogenetic triad to the scientific literature. It proposes a deconstruction of autism into three contributing features (an autistic personality dimension, cognitive compensation, and neuropathological risk factors), and delineates how they interact to cause a maladaptive behavioral phenotype that may require a clinical diagnosis. The autistic personality represents a common core condition, which induces a set of behavioral issues when pronounced. These issues are compensated for by cognitive mechanisms, allowing the individual to remain adaptive and functional. Risk factors, both exogenous and endogenous ones, show pathophysiological convergence through their negative effects on neurodevelopment. This secondarily affects cognitive compensation, which disinhibits a maladaptive behavioral phenotype. The triad is operationalized and methods for quantification are presented. With respect to the breadth of findings in the literature that it can incorporate, it is the most comprehensive model yet for autism. Its main implications are that (1) it presents the broader autism phenotype as a non-pathological core personality domain, which is shared across the population and uncoupled from associated features such as low cognitive ability and immune dysfunction, (2) it proposes that common genetic variants underly the personality domain, and that rare variants act as risk factors through negative effects on neurodevelopment, (3) it outlines a common pathophysiological mechanism, through inhibition of neurodevelopment and cognitive dysfunction, by which a wide range of endogenous and exogenous risk factors lead to autism, and (4) it suggests that contributing risk factors, and findings of immune and autonomic dysfunction are clinically ascertained rather than part of the core autism construct.

The effects of the functional interplay between the Default Mode and Executive Control Resting State Networks on cognitive outcome in preterm born infants at 6 months of age

Preterm birth can affect cognitive functions, such as attention or more generally executive control mechanisms, with severity in impairments proportional to prematurity. The functional cross-talk between the Default Mode (DMN) and Executive Control (ECN) networks mirrors the integrity of cognitive processing and is directly related to brain development. In this study, a cohort of 20 preterm-born infants was investigated using rs-fMRI. First, we addressed biological maturity of the DMN per se and its interplay with the ECN in terms of patterns of increased functional connectivity. Second, we assessed the impact of the degree of prematurity on the DMN-ECN functional interplay development in relation to cognitive outcome at six months. Our results highlighted the emergence of DMN in preterm neonates, with connectivity strength and synchronization between the anterior DMN hub and frontal areas increasing as a function of biological maturity. Further, cognitive scores at 6 months were predicted by mPFC-ECN connectivity strength with degree of prematurity impacting on mPFC-ECN connectivity and triggering differential patterns of functional maturation of the ECN for very early/early and moderate/late preterm neonates. Our findings suggest that the prematurity window allows to observe precursors of functional plasticity that may underlie different developmental trajectories in preterm children.

Examining the value of body gestures in social reward contexts

Brain regions associated with the processing of tangible rewards (such as money, food, or sex) are also involved in anticipating social rewards and avoiding social punishment. To date, studies investigating the neural underpinnings of social reward have presented feedback via static or dynamic displays of faces to participants. However, research demonstrates that participants find another type of social stimulus, namely, biological motion, rewarding as well, and exert effort to engage with this type of stimulus. Here we examine whether feedback presented via body gestures in the absence of facial cues also acts as a rewarding stimulus and recruits reward-related brain regions. To achieve this, we investigated the neural underpinnings of anticipating social reward and avoiding social disapproval presented via gestures alone, using a social incentive delay task. As predicted, the anticipation of social reward and avoidance of social disapproval engaged reward-related brain regions, including the nucleus accumbens, in a manner similar to previous studies' reports of feedback presented via faces and money. This study provides the first evidence that human body motion alone engages brain regions associated with reward processing in a similar manner to other social (i.e. faces) and non-social (i.e. money) rewards. The findings advance our understanding of social motivation in human perception and behavior.

Oxytocin treatment attenuates amygdala activity in autism: a treatment-mechanism study with long-term follow-up

Intranasal administration of the neuropeptide oxytocin (IN-OT) is increasingly considered as a potential treatment for targeting the core symptoms of autism spectrum disorder (ASD), but the effects of continual use on neural substrates are fairly unexplored and long-term effects are unknown. In this double-blind, randomized, placebo-controlled study, we investigated the effects of single-dose and multiple-dose IN-OT treatment (4 weeks of daily (24 IU) administrations) on brain activity related to processing emotional states. Thirty-eight adult men with ASD (aged between 18 and 35 years) underwent functional magnetic resonance imaging of the posterior superior temporal gyrus (pSTS) and amygdala regions while processing emotional states from point-light biological motion. In line with prior research, a single dose of IN-OT induced a reliable increase in pSTS brain activity during the processing of point-light biological motion, but no consistent long-term changes in pSTS activity were induced after the multiple-dose treatment. In terms of bilateral amygdala, the multiple-dose treatment induced a consistent attenuation in brain activity, which outlasted the period of actual administrations until four weeks and one year post-treatment. Critically, participants with stronger attenuations in amygdala-activity showed greater behavioral improvements, particularly in terms of self-reported feelings of avoidant attachment and social functioning. Together, these observations provide initial insights into the long-lasting neural consequences of chronic IN-OT use on amygdala functioning and provide first indications that the acute versus chronic effects of IN-OT administration may be qualitatively different. Larger studies are however warranted to further elucidate the long-term impact of IN-OT treatment on human neural substrates and its behavioral consequences.

Face individual identity recognition: a potential endophenotype in autism

BACKGROUND

Face individual identity recognition skill is heritable and independent of intellectual ability. Difficulties in face individual identity recognition are present in autistic individuals and their family members and are possibly linked to oxytocin polymorphisms in families with an autistic child. While it is reported that developmental prosopagnosia (i.e., impaired face identity recognition) occurs in 2-3% of the general population, no prosopagnosia prevalence estimate is available for autism. Furthermore, an autism within-group approach has not been reported towards characterizing impaired face memory and to investigate its possible links to social and communication difficulties.

METHODS

The present study estimated the prevalence of prosopagnosia in 80 autistic adults with no intellectual disability, investigated its cognitive characteristics and links to autism symptoms' severity, personality traits, and mental state understanding from the eye region by using standardized tests and questionnaires.

RESULTS

More than one third of autistic participants showed prosopagnosia. Their face memory skill was not associated with their symptom's severity, empathy, alexithymia, or general intelligence. Face identity recognition was instead linked to mental state recognition from the eye region only in autistic individuals who had prosopagnosia, and this relationship did not depend on participants' basic face perception skills. Importantly, we found that autistic participants were not aware of their face memory skills.

LIMITATIONS

We did not test an epidemiological sample, and additional work is necessary to establish whether these results generalize to the entire autism spectrum.

CONCLUSIONS

Impaired face individual identity recognition meets the criteria to be a potential endophenotype in autism. In the future, testing for face memory could be used to stratify autistic individuals into genetically meaningful subgroups and be translatable to autism animal models.

Brain function distinguishes female carriers and non-carriers of familial risk for autism

Background

Autism spectrum disorder (ASD) is characterized by high population-level heritability and a three-to-one male-to-female ratio that occurs independent of sex linkage. Prior research in a mixed-sex pediatric sample identified neural signatures of familial risk elicited by passive viewing of point light motion displays, suggesting the possibility that both resilience and risk of autism might be associated with brain responses to biological motion. To confirm a relationship between these signatures and inherited risk of autism, we tested them in families enriched for genetic loading through undiagnosed (“carrier”) females.

Methods

Using functional magnetic resonance imaging, we examined brain responses to passive viewing of point light displays—depicting biological versus non-biological motion—in a sample of undiagnosed adult females enriched for inherited susceptibility to ASD on the basis of affectation in their respective family pedigrees. Brain responses in carrier females were compared to responses in age-, SRS-, and IQ-matched non-carrier-females—i.e., females unrelated to individuals with ASD. We conducted a hypothesis-driven analysis focused on previously published regions of interest as well as exploratory, brain-wide analyses designed to characterize more fully the rich responses to this paradigm.

Results

We observed robust responses to biological motion. Notwithstanding, the 12 regions implicated by prior research did not exhibit the hypothesized interaction between group (carriers vs. controls) and point light displays (biological vs. non-biological motion). Exploratory, brain-wide analyses identified this interaction in three novel regions. Post hoc analyses additionally revealed significant variations in the time course of brain activation in 20 regions spanning occipital and temporal cortex, indicating group differences in response to point light displays (irrespective of the nature of motion) for exploration in future studies.

Limitations

We were unable to successfully eye-track all participants, which prevented us from being able to control for potential differences in eye gaze position.

Conclusions

These methods confirmed pronounced neural signatures that differentiate brain responses to biological and scrambled motion. Our sample of undiagnosed females enriched for family genetic loading enabled discovery of numerous contrasts between carriers and non-carriers of risk of ASD that may index variations in visual attention and motion processing related to genetic susceptibility and inform our understanding of mechanisms incurred by inherited liability for ASD.

Pooling Regularized Graph Neural Network for fMRI Biomarker Analysis

Understanding how certain brain regions relate to a specific neurological disorder has been an important area of neuroimaging research. A promising approach to identify the salient regions is using Graph Neural Networks (GNNs), which can be used to analyze graph structured data, e.g. brain networks constructed by functional magnetic resonance imaging (fMRI). We propose an interpretable GNN framework with a novel salient region selection mechanism to determine neurological brain biomarkers associated with disorders. Specifically, we design novel regularized pooling layers that highlight salient regions of interests (ROIs) so that we can infer which ROIs are important to identify a certain disease based on the node pooling scores calculated by the pooling layers. Our proposed framework, Pooling Regularized-GNN (PR-GNN), encourages reasonable ROI-selection and provides flexibility to preserve either individual- or group-level patterns. We apply the PR-GNN framework on a Biopoint Autism Spectral Disorder (ASD) fMRI dataset. We investigate different choices of the hyperparameters and show that PR-GNN outperforms baseline methods in terms of classification accuracy. The salient ROI detection results show high correspondence with the previous neuroimaging-derived biomarkers for ASD.

Demographic-Guided Attention in Recurrent Neural Networks for Modeling Neuropathophysiological Heterogeneity

Heterogeneous presentation of a neurological disorder suggests potential differences in the underlying pathophysiological changes that occur in the brain. We propose to model heterogeneous patterns of functional network differences using a demographic-guided attention (DGA) mechanism for recurrent neural network models for prediction from functional magnetic resonance imaging (fMRI) time-series data. The context computed from the DGA head is used to help focus on the appropriate functional networks based on individual demographic information. We demonstrate improved classification on 3 subsets of the ABIDE I dataset used in published studies that have previously produced state-of-the-art results, evaluating performance under a leave-one-site-out cross-validation framework for better generalizeability to new data. Finally, we provide examples of interpreting functional network differences based on individual demographic variables.

A preliminary study of atypical cortical change ability of dynamic whole-brain functional connectivity in autism spectrum disorder

OBJECTIVES

Designing new objectively diagnostic methods of autism spectrum disorder (ASD) are burning questions. Dynamic functional connectivity (DFC) methodology based on fMRI data are an effective lever to investigate changeability evolution of signal synchronization in macroscopic neural activity patterns.

METHODS

Embracing the network dynamics concepts, this paper introduces changeability index (C-score)which is focused on time-varying aspects of FCs, and develops a new framework for researching the roots of ASD brains at resting states in holism significance. The important process is to uncover noticeable regions and subsystems endowed with antagonistic stance in C-scores of between atypical and typical DFCs of 30 healthy controls (HCs) and 48 ASD patients.

RESULTS

The abnormities of edge C-scores are found across widespread brain cortex in ASD brains. For whole brain regional C-scores of ASD patients, orbitofrontal middle cortex L, inferior triangular frontal gyrus L, middle occipital gyrus L, postcentral gyrus L, supramarginal L, supramarginal R, cerebellum 8 L, and cerebellum 10 Rare endowed with significantly different C-scores.At brain subsystems level, C-scores in left hemisphere, right hemisphere, top hemisphere, bottom hemisphere, frontal lobe, parietal lobe, occipital lobe, cerebellum sub systems are abnormal in ASD patients.

CONCLUSIONS

The ASD brains have whole-brain abnormity on widespread regions. Through the strict evidence-based study, it was found that the changeability index (C-score) is a meaningful biological marker to explore cortical activity in ASD.

Brain circuits signaling the absence of emotion in body language

Adaptive social behavior and mental well-being depend on not only recognizing emotional expressions but also, inferring the absence of emotion. While the neurobiology underwriting the perception of emotions is well studied, the mechanisms for detecting a lack of emotional content in social signals remain largely unknown. Here, using cutting-edge analyses of effective brain connectivity, we uncover the brain networks differentiating neutral and emotional body language. The data indicate greater activation of the right amygdala and midline cerebellar vermis to nonemotional as opposed to emotional body language. Most important, the effective connectivity between the amygdala and insula predicts people's ability to recognize the absence of emotion. These conclusions extend substantially current concepts of emotion perception by suggesting engagement of limbic effective connectivity in recognizing the lack of emotion in body language reading. Furthermore, the outcome may advance the understanding of overly emotional interpretation of social signals in depression or schizophrenia by providing the missing link between body language reading and limbic pathways. The study thus opens an avenue for multidisciplinary research on social cognition and the underlying cerebrocerebellar networks, ranging from animal models to patients with neuropsychiatric conditions.

Social endophenotypes in autism spectrum disorder: A scoping review

Endophenotypes are measurable markers of genetic vulnerability to current or future disorder. Autism spectrum disorder (ASD) is well-suited to be examined within an endophenotype framework given past and current emphases on the broader autism phenotype and early detection. We conducted a scoping review to identify potential socially-related endophenotypes of ASD. We focused on paradigms related to sociality (e.g., theory of mind (TOM), social attention), which comprise most of this literature. We integrated findings from traditional behavioral paradigms with brain-based measures (e.g., electroencephalography, functional magnetic resonance imaging). Broadly, infant research regarding social attention and responsivity (Research Domain Criteria (RDoC) domain of affiliation) and attention to faces and voices (social communication) finds consistent abnormality in vulnerable infant siblings. Several additional paradigms that have shown differences in vulnerable infants and young children include animacy perception tasks (perception and understanding of others), measures of recognition and response to familiar faces (attachment), and joint attention and false-belief tasks (understanding mental states). Research areas such as alexithymia (the perception and understanding of self), empathic responding, and vocal prosody may hold interest; however, challenges in measurement across populations and age ranges is a limiting factor. Future work should address sex differences and age dependencies, specificity to ASD, and heterogeneous genetic pathways to disorder within samples individuals with ASD and relatives.

Modulation of humor ratings of bad jokes by other people's laughter

Laughter is a positive vocal emotional expression: most laughter is found in social interactions [1]. We are overwhelmingly more likely to laugh when we are with other people [1], and laughter can play a very important communicative role [2]. We do of course also laugh at humor - but can laughter influence how funny we actually perceive the humorous material to be? In this study, we show that the presence of laughter enhances how funny people find jokes and that this effect is increased for spontaneous laughter. This effect was present for both neurotypical and autistic participants, indicating similarities in their implicit processing of laughter.

Visual Perception in Autism Spectrum Disorder: A Review of Neuroimaging Studies

Although autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social impairments, patients with ASD frequently manifest atypical sensory behaviors. Recently, atypical sensory perception in ASD has received much attention, yet little is known about its cause or neurobiology. Herein, we review the findings from neuroimaging studies related to visual perception in ASD. Specifically, we examined the neural underpinnings of visual detection, motion perception, and face processing in ASD. Results from neuroimaging studies indicate that atypical visual perception in ASD may be influenced by attention or higher order cognitive mechanisms, and atypical face perception may be affected by disrupted social brain network. However, there is considerable evidence for atypical early visual processing in ASD. It is likely that visual perceptual abnormalities are independent of deficits of social functions or cognition. Importantly, atypical visual perception in ASD may enhance difficulties in dealing with complex and subtle social stimuli, or improve outstanding abilities in certain fields in individuals with Savant syndrome. Thus, future research is required to elucidate the characteristics and neurobiology of autistic visual perception to effectively apply these findings in the interventions of ASD.

Atypical Brain Structures as a Function of Gray Matter Volume (GMV) and Gray Matter Density (GMD) in Young Adults Relating to Autism Spectrum Traits

Individuals with autistic traits are those who present in the normal population with characteristics of social, communication, personality, and cognitive impairments but do not meet the clinical threshold for autism spectrum disorder (ASD). Most studies have focused on the abnormalities in ASD patients rather than on individuals with autistic traits. In this study, we focused on the behaviors of a large sample (N = 401) of Chinese individuals with different levels of autistic traits, measured using the Autism Spectrum Quotient, and applied voxel-based morphometry (VBM) to determine their association to differences in brain structure. The results mainly showed that the correlation between gray matter volume (GMV) and gray matter density of the brain and the Autism Spectrum Quotient was significant in these regions: the right middle frontal gyrus, which are involved in social processing and social reasoning; the left parahippocampal gyrus, which is involved in socioemotional behaviors and unconscious relational memory encoding; and the right superior parietal lobule, which are involved in cognitive control and the ability to show attention to detail. These findings reveal that people with autistic traits in the normal population have atypical development in GMV and gray matter density, which may affect their social functioning and communication ability.

ESTIMATING REPRODUCIBLE FUNCTIONAL NETWORKS ASSOCIATED WITH TASK DYNAMICS USING UNSUPERVISED LSTMS

We propose a method for estimating more reproducible functional networks that are more strongly associated with dynamic task activity by using recurrent neural networks with long short term memory (LSTMs). The LSTM model is trained in an unsupervised manner to learn to generate the functional magnetic resonance imaging (fMRI) time-series data in regions of interest. The learned functional networks can then be used for further analysis, e.g., correlation analysis to determine functional networks that are strongly associated with an fMRI task paradigm. We test our approach and compare to other methods for decomposing functional networks from fMRI activity on 2 related but separate datasets that employ a biological motion perception task. We demonstrate that the functional networks learned by the LSTM model are more strongly associated with the task activity and dynamics compared to other approaches. Furthermore, the patterns of network association are more closely replicated across subjects within the same dataset as well as across datasets. More reproducible functional networks are essential for better characterizing the neural correlates of a target task.