Impaired causal awareness and associated cortical-basal ganglia structural changes in youth psychiatric disorders

Facial Emotions Are Accurately Encoded in the Neural Signal of Those With Autism Spectrum Disorder: A Deep Learning Approach

BACKGROUND

Individuals with autism spectrum disorder (ASD) exhibit frequent behavioral deficits in facial emotion recognition (FER). It remains unknown whether these deficits arise because facial emotion information is not encoded in their neural signal or because it is encodes but fails to translate to FER behavior (deployment). This distinction has functional implications, including constraining when differences in social information processing occur in ASD, and guiding interventions (i.e., developing prosthetic FER vs. reinforcing existing skills).

METHODS

We utilized a discriminative and contemporary machine learning approach-deep convolutional neural networks-to classify facial emotions viewed by individuals with and without ASD (N = 88) from concurrently recorded electroencephalography signals.

RESULTS

The convolutional neural network classified facial emotions with high accuracy for both ASD and non-ASD groups, even though individuals with ASD performed more poorly on the concurrent FER task. In fact, convolutional neural network accuracy was greater in the ASD group and was not related to behavioral performance. This pattern of results replicated across three independent participant samples. Moreover, feature importance analyses suggested that a late temporal window of neural activity (1000-1500 ms) may be uniquely important in facial emotion classification for individuals with ASD.

CONCLUSIONS

Our results reveal for the first time that facial emotion information is encoded in the neural signal of individuals with (and without) ASD. Thus, observed difficulties in behavioral FER associated with ASD likely arise from difficulties in decoding or deployment of facial emotion information within the neural signal. Interventions should focus on capitalizing on this intact encoding rather than promoting compensation or FER prostheses.

Basal ganglia correlates of wellbeing in early adolescence

It has been suggested that biological markers that define mental health are different to those that define mental illness. The basal ganglia changes dramatically over adolescence and has been linked to wellbeing and mental health disorders in young people. However, there remains a paucity of research on wellbeing and brain structure in early adolescence. This cross-sectional study examined relationships between grey matter volume (GMV) of basal ganglia regions (caudate, putamen, pallidum and nucleus accumbens) and self-reported wellbeing (COMPAS-W), in a sample of Australian adolescents aged 12 years (N = 49, M = 12.6, 46.9% female). Significant negative associations were found between left hemisphere caudate GMV and scores on 'total wellbeing', 'composure' and 'positivity'. The results of this study indicate that smaller caudate GMV at age 12 is linked to increased subjective wellbeing. While seemingly counter-intuitive, our finding is consistent with previous research of decreased GMV in the pons and increased COMPAS-W scores in adults. Our results suggest that protective neurobiological factors may be identifiable early in adolescence and be linked to specific types of wellbeing (such as positive affect and optimism). This has implications for interventions targeted at building resilience against mental health disorders in young people.

Quantifying deep grey matter atrophy using automated segmentation approaches: A systematic review of structural MRI studies

The deep grey matter (DGM) nuclei of the brain play a crucial role in learning, behaviour, cognition, movement and memory. Although automated segmentation strategies can provide insight into the impact of multiple neurological conditions affecting these structures, such as Multiple Sclerosis (MS), Huntington's disease (HD), Alzheimer's disease (AD), Parkinson's disease (PD) and Cerebral Palsy (CP), there are a number of technical challenges limiting an accurate automated segmentation of the DGM. Namely, the insufficient contrast of T1 sequences to completely identify the boundaries of these structures, as well as the presence of iso-intense white matter lesions or extensive tissue loss caused by brain injury. Therefore in this systematic review, 269 eligible studies were analysed and compared to determine the optimal approaches for addressing these technical challenges. The automated approaches used among the reviewed studies fall into three broad categories, atlas-based approaches focusing on the accurate alignment of atlas priors, algorithmic approaches which utilise intensity information to a greater extent, and learning-based approaches that require an annotated training set. Studies that utilise freely available software packages such as FIRST, FreeSurfer and LesionTOADS were also eligible, and their performance compared. Overall, deep learning approaches achieved the best overall performance, however these strategies are currently hampered by the lack of large-scale annotated data. Improving model generalisability to new datasets could be achieved in future studies with data augmentation and transfer learning. Multi-atlas approaches provided the second-best performance overall, and may be utilised to construct a "silver standard" annotated training set for deep learning. To address the technical challenges, providing robustness to injury can be improved by using multiple channels, highly elastic diffeomorphic transformations such as LDDMM, and by following atlas-based approaches with an intensity driven refinement of the segmentation, which has been done with the Expectation Maximisation (EM) and level sets methods. Accounting for potential lesions should be achieved with a separate lesion segmentation approach, as in LesionTOADS. Finally, to address the issue of limited contrast, R2*, T2* and QSM sequences could be used to better highlight the DGM due to its higher iron content. Future studies could look to additionally acquire these sequences by retaining the phase information from standard structural scans, or alternatively acquiring these sequences for only a training set, allowing models to learn the "improved" segmentation from T1-sequences alone.

Clinical, neurocognitive and demographic factors associated with functional impairment in the Australian Brain and Mind Youth Cohort Study (2008-2016)

OBJECTIVES

We sought to determine the unique and shared contributions of clinical, neurocognitive and demographic factors to functional impairment in a large, transdiagnostic, clinical cohort of adolescents and young adults.

DESIGN

Cross-sectional baseline data from a prospective, cohort study.

SETTING

Help-seeking youth referred from outpatient services were recruited to the Brain and Mind Youth Cohort (2008-2016) in Sydney, Australia.

PARTICIPANTS

In total, 1003 outpatients were recruited, aged between 12 and 36 years (mean= 20.4 years, 54% female), with baseline diagnoses of affective, psychotic, developmental or behavioural disorders.

INTERVENTIONS

Treatment as usual.

PRIMARY OUTCOME MEASURES

Social and occupational functioning was used to index level of functional impairment. Structural equation modelling was used to examine associations between neurocognition, core clinical symptoms and alcohol and substance use, and clinician-rated and researcher-rated functional impairment. Moderator analyses were conducted to determine the potential influence of demographic and clinical factors (eg, medication exposure).

RESULTS

Independent of diagnosis, we found that neurocognitive impairments, and depressive, anxiety and negative symptoms, were significantly associated with functioning. The association of neurocognition with social and occupational functioning remained significant even when constraining for age (15-25-year-olds only) or diagnosis (affective disorders only) in the final model.

CONCLUSIONS

This study demonstrated that, in a clinically representative sample of youth, the key determinants of functioning may not be disorder specific. Further, evidence of neurocognitive dysfunction suggests that interventions that target cognition and functioning should not necessarily be reserved just for older adults with established illness.