A Novel Biomarker of Compensatory Recruitment of Face Emotional Imagery Networks in Autism Spectrum Disorder

A neurophysiological signature of dynamic emotion recognition associated with social communication skills and cortical gamma-aminobutyric acid levels in children

Introduction

Emotion recognition is a core feature of social perception. In particular, perception of dynamic facial emotional expressions is a major feature of the third visual pathway. However, the classical N170 visual evoked signal does not provide a pure correlate of such processing. Indeed, independent component analysis has demonstrated that the N170 component is already active at the time of the P100, and is therefore distorted by early components. Here we implemented, a dynamic face emotional paradigm to isolate a more pure face expression selective N170. We searched for a neural correlate of perception of dynamic facial emotional expressions, by starting with a face baseline from which a facial expression evolved. This allowed for a specific facial expression contrast signal which we aimed to relate with social communication abilities and cortical gamma-aminobutyric acid (GABA) levels.

Methods

We recorded event-related potentials (ERPs) and Magnetic Resonance (MRS) measures in 35 typically developing (TD) children, (10-16 years) sex-matched, during emotion recognition of an avatar morphing/unmorphing from neutral to happy/sad expressions. This task allowed for the elimination of the contribution low-level visual components, in particular the P100, by morphing baseline isoluminant neutral faces into specific expressions, isolating dynamic emotion recognition. Therefore, it was possible to isolate a dynamic face sensitive N170 devoid of interactions with earlier components.

Results

We found delayed N170 and P300, with a hysteresis type of dependence on stimulus trajectory (morphing/unmorphing), with hemispheric lateralization. The delayed N170 is generated by an extrastriate source, which can be related to the third visual pathway specialized in biological motion processing. GABA levels in visual cortex were related with N170 amplitude and latency and predictive of worse social communication performance (SCQ scores). N170 latencies reflected delayed processing speed of emotional expressions and related to worse social communication scores.

Discussion

In sum, we found a specific N170 electrophysiological signature of dynamic face processing related to social communication abilities and cortical GABA levels. These findings have potential clinical significance supporting the hypothesis of a spectrum of social communication abilities and the identification of a specific face-expression sensitive N170 which can potentially be used in the development of diagnostic and intervention tools.

Machine learning approaches for electroencephalography and magnetoencephalography analyses in autism spectrum disorder: A systematic review

There are growing application of machine learning models to study the intricacies of non-linear and non-stationary characteristics of electroencephalography (EEG) and magnetoencephalography (MEG) data in neurobiologically complex and heterogeneous conditions such as autism spectrum disorder (ASD). Such tools have potential diagnostic applications, and given the highly heterogeneous presentation of ASD, might prove fruitful in early detection and therefore could facilitate very early intervention. We conducted a systematic review (PROSPERO ID#CRD42021257438) by searching PubMed, EMBASE, and PsychINFO for machine learning approaches for EEG and MEG analyses in ASD. Thirty-nine studies were identified, of which the majority (18) used support vector machines for classification; other successful methods included deep learning. Thirty-seven studies were found to employ EEG and two were found to employ MEG. This systematic review indicate that machine learning methods can be used to classify ASD, predict ASD diagnosis in high-risk infants as early as 3 months of age, predict ASD symptom severity, and classify states of cognition in ASD with high accuracy. Replication studies testing validity, reproducibility and generalizability in tandem with randomized controlled trials in ASD populations will likely benefit the field.

The relationship between alexithymia, interoception, and neural functional connectivity during facial expression processing in autism spectrum disorder

Neural processing differences of emotional facial expressions, while common in autism spectrum disorder (ASD), may be related to co-occurring alexithymia and interoceptive processing differences rather than autism per se. Here, we investigate relationships between alexithymia, interoceptive awareness of emotions, and functional connectivity during observation of facial expressions in youth (aged 8-17) with ASD (n = 28) compared to typically developing peers (TD; n = 37). Behaviorally, we found no significant differences between ASD and TD groups in interoceptive awareness of emotions, though alexithymia severity was significantly higher in the ASD group. In the ASD group, increased alexithymia was significantly correlated with lower interoceptive sensation felt during emotion. Using psycho-physiological interaction (PPI) analysis, the ASD group showed higher functional connectivity between the left ventral anterior insula and the left lateral prefrontal cortex than the TD group when viewing facial expressions. Further, alexithymia was associated with reduced left anterior insula-right precuneus connectivity and reduced right dorsal anterior insula-left ventral anterior insula connectivity when viewing facial expressions. In the ASD group, the degree of interoceptive sensation felt during emotion was positively correlated with left ventral anterior insula-right IFG connectivity when viewing facial expressions. However, across all participants, neither alexithymia nor interoceptive awareness of emotions predicted connectivity between emotion-related brain regions when viewing emotional facial expressions. To summarize, we found that in ASD compared to TD: 1) there is stronger connectivity between the insula and lateral prefrontal cortex; and 2) differences in interhemispheric and within left hemisphere connectivity between the insula and other emotion-related brain regions are related to individual differences in interoceptive processing and alexithymia. These results highlight complex relationships between alexithymia, interoception, and brain processing in ASD.

A Bayesian-optimized design for an interpretable convolutional neural network to decode and analyze the P300 response in autism

OBJECTIVE

P300 can be analyzed in autism spectrum disorder (ASD) to derive biomarkers and can be decoded in BCIs to reinforce ASD impaired skills. Convolutional neural networks (CNNs) have been proposed for P300 decoding, outperforming traditional algorithms but they i) do not investigate optimal designs in different training conditions; ii) lack in interpretability. To overcome these limitations, an interpretable CNN (ICNN), that we recently proposed for motor decoding, has been modified and adopted here, with its optimal design searched via Bayesian optimization.

APPROACH

The ICNN provides a straightforward interpretation of spectral and spatial features learned to decode P300. The Bayesian-optimized (BO) ICNN design was investigated separately for different training strategies (within-subject, within-session, and cross-subject) and BO models were used for the subsequent analyses. Specifically, transfer learning (TL) potentialities were investigated by assessing how pretrained cross-subject BO models performed on a new subject vs. random-initialized models. Furthermore, within-subject BO-derived models were combined with an Explanation Technique (ICNN+ET) to analyze P300 spectral and spatial features.

MAIN RESULTS

The ICNN resulted comparable or even outperformed existing CNNs, at the same time being lighter. Bayesian-optimized ICNN designs differed depending on the training strategy, needing more capacity as the training set variability increased. Furthermore, TL provided higher performance than networks trained from scratch. The ICNN+ET analysis suggested the frequency range [2, 5.8] Hz as the most relevant, and spatial features showed a right-hemispheric parietal asymmetry. The ICNN+ET-derived features, but not ERP-derived features, resulted significantly and highly correlated to ADOS clinical scores.

SIGNIFICANCE

This study substantiates the idea that a CNN can be designed both accurate and interpretable for P300 decoding, with an optimized design depending on the training condition. The novel ICNN-based analysis tool was able to better capture ASD neural signatures than traditional ERP analysis, possibly paving the way for identifying novel biomarkers.

Parametric fMRI analysis of videos of variable arousal levels reveals different dorsal vs ventral activation preferences between autism and controls

Atypical sensory processing is now considered a ubiquitous feature of autism spectrum disorder (ASD) and is responsible for the atypical sensory-based behaviours seen in these individuals. Specifically, emotional arousal is a critical ASD target since it comprises emotion regulation and sensory processing, two core aspects of autism. So, in this project, we used task-based fMRI and a well-catalogued dataset of videos with variable arousal levels to characterize the sensory processing of emotional arousal content in ASD and typically developed controls. Our analysis revealed a difference in the secondary attention network where ASD individuals showed a clear yet lateralized preference to the dorsal attention network, whereas the neurotypical individuals preferred the ventral attention network.

Classification of erroneous actions using EEG frequency features: implications for BCI performance. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021;2021:629-632. [PMID: 34891372 DOI: 10.1109/embc46164.2021.9630509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Several studies have demonstrated that error-related neuronal signatures can be successfully detected and used to improve the performance of brain-computer interfaces. However, this has been tested mainly in well-controlled environments and based on temporal features, such as the amplitude of event-related potentials. In this study, we propose a classification algorithm combining frequency features and a weighted SVM to detect the neuronal signatures of errors committed in a complex saccadic go/no-go task. We follow the hypothesis that frequency features yield better discrimination performance in complex tasks, generalize better, and require fewer pre-processing steps. When combining temporal and frequency features, we achieved a balanced classification accuracy of 75% - almost the same as using only frequency features. On the other hand, when using only temporal features, the balanced accuracy decreased to 66%. These findings show that subjects' performance can be automatically detected based on frequency features of error-related neuronal signatures. Additionally, our results revealed that features computed in the pre-response time contribute to the discrimination between correct and erroneous responses, which suggests the existence of error-related patterns even before response execution.

Review: Posed vs. Genuine Facial Emotion Recognition and Expression in Autism and Implications for Intervention

Different styles of social interaction are one of the core characteristics of autism spectrum disorder (ASD). Social differences among individuals with ASD often include difficulty in discerning the emotions of neurotypical people based on their facial expressions. This review first covers the rich body of literature studying differences in facial emotion recognition (FER) in those with ASD, including behavioral studies and neurological findings. In particular, we highlight subtle emotion recognition and various factors related to inconsistent findings in behavioral studies of FER in ASD. Then, we discuss the dual problem of FER – namely facial emotion expression (FEE) or the production of facial expressions of emotion. Despite being less studied, social interaction involves both the ability to recognize emotions and to produce appropriate facial expressions. How others perceive facial expressions of emotion in those with ASD has remained an under-researched area. Finally, we propose a method for teaching FER [FER teaching hierarchy (FERTH)] based on recent research investigating FER in ASD, considering the use of posed vs. genuine emotions and static vs. dynamic stimuli. We also propose two possible teaching approaches: (1) a standard method of teaching progressively from simple drawings and cartoon characters to more complex audio-visual video clips of genuine human expressions of emotion with context clues or (2) teaching in a field of images that includes posed and genuine emotions to improve generalizability before progressing to more complex audio-visual stimuli. Lastly, we advocate for autism interventionists to use FER stimuli developed primarily for research purposes to facilitate the incorporation of well-controlled stimuli to teach FER and bridge the gap between intervention and research in this area.

Effects of anodal multichannel transcranial direct current stimulation (tDCS) on social-cognitive performance in healthy subjects: A randomized sham-controlled crossover pilot study

Recent studies suggest that temporoparietal junction (TPJ) modulation can influence attention and social cognition performance. Nevertheless, no studies have used multichannel transcranial direct current stimulation (tDCS) over bilateral TPJ to estimate the effects on these neuropsychological functions. The project STIPED is using optimized multichannel stimulation as an innovative treatment approach for chronic pediatric neurodevelopmental disorders, namely in children/adolescents with Autism Spectrum Disorder (ASD). In this pilot study, we aim to explore whether anodal multichannel tDCS coupled with a Joint Attention Task (JAT) influences social-cognitive task performance relative to sham stimulation, both in an Emotion Recognition Task (ERT) and in a Mooney Faces Detection Task (MFDT), as well as to evaluate this technique's safety and tolerability. Twenty healthy adults were enrolled in a randomized, single-blinded, sham-controlled, crossover study. During two sessions, participants completed the ERT and the MFDT before and after 20min of sham or anodal tDCS over bilateral TPJ. No significant differences on performance accuracy and reaction time were found between stimulation conditions for all tasks, including the JAT. A significant main time effect for overall accuracy and reaction time was found for the MFDT. Itching was the most common side effect and stimulation conditions detection was at chance level. Results suggest that multichannel tDCS over bilateral TPJ does not affect performance of low-level emotional recognition tasks in healthy adults. Although preliminary safety and tolerability are demonstrated, further studies over longer periods will be pursued to investigate the clinical efficacy in children/adolescents with ASD, where social cognition impairments are preponderant.

Detection of Emotional Sensitivity Using fNIRS Based Dynamic Functional Connectivity

In this study, we proposed an analytical framework to identify dynamic task-based functional connectivity (FC) features as new biomarkers of emotional sensitivity in nursing students, by using a combination of unsupervised and supervised machine learning techniques. The dynamic FC was measured by functional Near-Infrared Spectroscopy (fNIRS), and computed using a sliding window correlation (SWC) analysis. A k -means clustering technique was applied to derive four recurring connectivity states. The states were characterized by both graph theory and semi-metric analysis. Occurrence probability and state transition were extracted as dynamic FC network features, and a Random Forest (RF) classifier was implemented to detect emotional sensitivity. The proposed method was trialled on 39 nursing students and 19 registered nurses during decision-making, where we assumed registered nurses have developed strategies to cope with emotional sensitivity. Emotional stimuli were selected from International Affective Digitized Sound System (IADS) database. Experiment results showed that registered nurses demonstrated single dominant connectivity state of task-relevance, while nursing students displayed in two states and had higher level of task-irrelevant state connectivity. The results also showed that students were more susceptive to emotional stimuli, and the derived dynamic FC features provided a stronger discriminating power than heart rate variability (accuracy of 81.65% vs 71.03%) as biomarkers of emotional sensitivity. This work forms the first study to demonstrate the stability of fNIRS based dynamic FC states as a biomarker. In conclusion, the results support that the state distribution of dynamic FC could help reveal the differentiating factors between the nursing students and registered nurses during decision making, and it is anticipated that the biomarkers might be used as indicators when developing professional training related to emotional sensitivity.