Atypical Amygdala-Neocortex Interaction During Dynamic Facial Expression Processing in Autism Spectrum Disorder

Meta-analysis of structural and functional abnormalities in behavioral addictions

BACKGROUND

The incidence of behavioral addictions (BAs) associated with scientific and technological advances has been increasing steadily. Unfortunately, a large number of studies on the structural and functional abnormalities have shown poor reproducibility, and it remains unclear whether different addictive behaviors share common underlying abnormalities. Therefore, our objective was to conduct a quantitative meta-analysis of different behavioral addictions to provide evidence-based evidence of common structural and functional changes.

METHODS

We conducted systematic searches in PubMed, Web of Science and Scopus from January 2010 to December 2023, supplementing reference lists of high-quality relevant meta-analyses and reviews, to identify eligible voxel-based morphometry (VBM) and functional magnetic resonance imaging (fMRI) studies. Using anisotropic seed-based D-Mapping (AES-SDM) meta-analysis methods, we compared brain abnormalities between BAs and healthy controls (HCs).

RESULTS

There were 11 GMV studies (287 BAs and 292 HCs) and 26 fMRI studies (577 BAs and 545 HCs) that met inclusion criteria. Compared with HCs, BAs demonstrated significant reductions in gray matter volume (GMV) in (1) right anterior cingulate gyri extending into the adjacent superior frontal gyrus, as well as in the left inferior frontal gyrus and right striatum. (2) the bilateral precuneus, right supramarginal gyrus, and right fusiform gyrus were hyperfunction; (3) the left medial cingulate gyrus extended to the superior frontal gyrus, the left inferior frontal gyrus, and right middle temporal gyrus had hypofunction.

CONCLUSIONS

Our study identified structural and functional impairments in brain regions involved in executive control, cognitive function, visual memory, and reward-driven behavior in BAs. Notably, fronto-cingulate regions may serve as common biomarkers of BAs.

Abnormal amygdala functional connectivity and deep learning classification in multifrequency bands in autism spectrum disorder: A multisite functional magnetic resonance imaging study

Previous studies have explored resting-state functional connectivity (rs-FC) of the amygdala in patients with autism spectrum disorder (ASD). However, it remains unclear whether there are frequency-specific FC alterations of the amygdala in ASD and whether FC in specific frequency bands can be used to distinguish patients with ASD from typical controls (TCs). Data from 306 patients with ASD and 314 age-matched and sex-matched TCs were collected from 28 sites in the Autism Brain Imaging Data Exchange database. The bilateral amygdala, defined as the seed regions, was used to perform seed-based FC analyses in the conventional, slow-5, and slow-4 frequency bands at each site. Image-based meta-analyses were used to obtain consistent brain regions across 28 sites in the three frequency bands. By combining generative adversarial networks and deep neural networks, a deep learning approach was applied to distinguish patients with ASD from TCs. The meta-analysis results showed frequency band specificity of FC in ASD, which was reflected in the slow-5 frequency band instead of the conventional and slow-4 frequency bands. The deep learning results showed that, compared with the conventional and slow-4 frequency bands, the slow-5 frequency band exhibited a higher accuracy of 74.73%, precision of 74.58%, recall of 75.05%, and area under the curve of 0.811 to distinguish patients with ASD from TCs. These findings may help us to understand the pathological mechanisms of ASD and provide preliminary guidance for the clinical diagnosis of ASD.

Meta-analytic connectivity modelling of functional magnetic resonance imaging studies in autism spectrum disorders

Social and non-social deficits in autism spectrum disorders (ASD) persist into adulthood and may share common regions of aberrant neural activations. The current meta-analysis investigated activation differences between ASD and neurotypical controls irrespective of task type. Activation likelihood estimation meta-analyses were performed to examine consistent hypo-activated and/or hyper-activated regions for all tasks combined, and for social and non-social tasks separately; meta-analytic connectivity modelling and behavioral/paradigm analyses were performed to examine co-activated regions and associated behaviors. One hundred studies (mean age range = 18-41 years) were included. For all tasks combined, the ASD group showed significant (p < .05) hypo-activation in one cluster around the left amygdala (peak - 26, -2, -20, volume = 1336 mm³, maximum ALE = 0.0327), and this cluster co-activated with two other clusters around the right cerebellum (peak 42, -56, -22, volume = 2560mm³, maximum ALE = 0.049) Lobule VI/Crus I and the left fusiform gyrus (BA47) (peak - 42, -46, -18, volume = 1616 mm³, maximum ALE = 0.046) and left cerebellum (peak - 42, -58, -20, volume = 1616mm³, maximum ALE = 0.033) Lobule VI/Crus I. While the left amygdala was associated with negative emotion (fear) (z = 3.047), the left fusiform gyrus/cerebellum Lobule VI/Crus I cluster was associated with language semantics (z = 3.724) and action observation (z = 3.077). These findings highlight the left amygdala as a region consistently hypo-activated in ASD and suggest the potential involvement of fusiform gyrus and cerebellum in social cognition in ASD. Future research should further elucidate if and how amygdala-fusiform/cerebellar connectivity relates to social and non-social cognition in adults with ASD.

Effects of temporal properties of facial expressions on the perceived intensity of emotion

A series of multiple facial expressions can be temporally perceived as an averaged facial expression in a process known as ensemble perception. This study examined the effect of temporal parameters on the perceived intensity of facial expression in each emotion, and how the effect varies with autistic traits in typically developing people. In the experiment, we presented facial expressions that switched from emotional to neutral expressions, and vice versa, for 3 s. Participants rated the overall perceived intensity of the facial emotions as a whole rather than rating individual items within the set. For the two tasks, a ratio of duration of emotional faces to duration of neutral faces (emotional ratio) and the timing for transitions were manipulated individually. The results showed that the intensity of facial emotion was perceived more strongly when the presentation ratio increased and when the emotional expression was presented last. The effects were different among the emotions (e.g. relatively weak in the anger expression). Moreover, the perceived intensity of angry expressions decreased with autistic traits. These results suggest that the properties and individual differences in the facial ensemble of each emotion affect emotional perceptions.

The sight of one’s own body: Could qEEG help predict the treatment response in anorexia nervosa?

Aims of the study

The study aims to identify the differences in brain activity between participants with anorexia nervosa and healthy control using visual stimulus conditions combined with the quantitative dense-array EEG recording analysis method called Brain Activation Sequences (BAS).

Materials and methods

23 participants with anorexia nervosa and 21 healthy controls were presented with visual stimuli, including the subject’s facial expressions and body images. The 128-channel EEG data were processed using BAS and displayed as activity in up to 66 brain regions. Subsequent cluster analysis was used to identify groups of participants exhibiting area-specific activation patterns.

Results

Cluster analysis identified three distinct groups: one including all healthy controls (HC) and two consisting of all participants with anorexia (AN-I with 19 participants and AN-II with four participants). The AN-I and AN-II groups differed in their response to treatment. Comparisons of HC vs. AN confirmed the dominance of the right cerebral hemisphere in participants with anorexia nervosa in two of the three reported conditions. The facial expressions condition, specifically the facial reaction expressing disgust, indicates the existence of a social attentional bias toward faces, whereas emotions remained undetected in participants. High limbic activity, medial frontal gyrus involvement, low fusiform cortex activity, and milder visual cortex activity in healthy controls compared to participants indicate that the facial expression stimulus is perceived by healthy subjects primarily as an emotion, not as the face itself. In the body image condition, participants showed higher activity in the fusiform gyrus and right insula, indicating activation of the brain’s “fear network.”

Conclusion

The study describes a specific pattern of brain activation in response to facial expression of disgust and body images that likely contributes to social-cognitive and behavioral impairments in anorexia. In addition, the substantial difference in the pattern of brain activation within the participants with AN and its association with treatment resistance deserves special attention because of its potential to develop a clinically useful prediction tool and identify potential targets for, for example, neuromodulatory treatments and/or individualized psychotherapy.

Dynamic Causal Modeling on the Identification of Interacting Networks in the Brain: A Systematic Review

Dynamic causal modeling (DCM) has long been used to characterize effective connectivity within networks of distributed neuronal responses. Previous reviews have highlighted the understanding of the conceptual basis behind DCM and its variants from different aspects. However, no detailed summary or classification research on the task-related effective connectivity of various brain regions has been made formally available so far, and there is also a lack of application analysis of DCM for hemodynamic and electrophysiological measurements. This review aims to analyze the effective connectivity of different brain regions using DCM for different measurement data. We found that, in general, most studies focused on the networks between different cortical regions, and the research on the networks between other deep subcortical nuclei or between them and the cerebral cortex are receiving increasing attention, but far from the same scale. Our analysis also reveals a clear bias towards some task types. Based on these results, we identify and discuss several promising research directions that may help the community to attain a clear understanding of the brain network interactions under different tasks.

Dynamic Causal Modeling Self-Connectivity Findings in the Functional Magnetic Resonance Imaging Neuropsychiatric Literature

Dynamic causal modeling (DCM) is a method for analyzing functional magnetic resonance imaging (fMRI) and other functional neuroimaging data that provides information about directionality of connectivity between brain regions. A review of the neuropsychiatric fMRI DCM literature suggests that there may be a historical trend to under-report self-connectivity (within brain regions) compared to between brain region connectivity findings. These findings are an integral part of the neurologic model represented by DCM and serve an important neurobiological function in regulating excitatory and inhibitory activity between regions. We reviewed the literature on the topic as well as the past 13 years of available neuropsychiatric DCM literature to find an increasing (but still, perhaps, and inadequate) trend in reporting these results. The focus of this review is fMRI as the majority of published DCM studies utilized fMRI and the interpretation of the self-connectivity findings may vary across imaging methodologies. About 25% of articles published between 2007 and 2019 made any mention of self-connectivity findings. We recommend increased attention toward the inclusion and interpretation of self-connectivity findings in DCM analyses in the neuropsychiatric literature, particularly in forthcoming effective connectivity studies of substance use disorders.

Atypical development of emotional face processing networks in autism spectrum disorder from childhood through to adulthood

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MEG connectivity to emotional faces in ASD and typical controls 6–39 years of age was investigated.

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Distinct age-related changes in connectivity were observed in the groups to happy and angry faces.

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Age-related between-group differences in functional connectivity were found in gamma band.

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Emotion-specific age-related between-group differences were seen in beta.

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Findings highlight specific neurodevelopmental trajectories to emotional faces in ASD vs. TD.

Impairments in social functioning are hallmarks of autism spectrum disorder (ASD) and atypical functional connectivity may underlie these difficulties. Emotion processing networks typically undergo protracted maturational changes, however, those with ASD show either hyper- or hypo-connectivity with little consensus on the functional connectivity underpinning emotion processing. Magnetoencephalography was used to investigate age-related changes in whole-brain functional connectivity of eight regions of interest during happy and angry face processing in 190 children, adolescents and adults (6–39 years) with and without ASD. Findings revealed age-related changes from child- through to mid-adulthood in functional connectivity in controls and in ASD in theta, as well as age-related between-group differences across emotions, with connectivity decreasing in ASD, but increasing for controls, in gamma. Greater connectivity to angry faces was observed across groups in gamma. Emotion-specific age-related between-group differences in beta were also found, that showed opposite trends with age for happy and angry in ASD. Our results establish altered, frequency-specific developmental trajectories of functional connectivity in ASD, across distributed networks and a broad age range, which may finally help explain the heterogeneity in the literature.

Alterations in Rapid Social Evaluations in Individuals with High Autism Traits

Typically developing adults with low and high Autism Spectrum Quotient (AQ) scores made rapid social evaluations of neutral faces when these were primed by briefly presented emotional faces. High AQ participants rated neutral faces as more threatening than low AQ participants, regardless of the prime condition. Both groups rated target neutral faces as more threatening with fearful compared with neutral primes, while neither group demonstrated an effect of happy primes on the ratings of neutral target faces. These results demonstrate subtle anomalies in rapid visual processing of emotional faces across the broader autism spectrum. They suggest that higher autism traits may be associated with a generalized threat bias in rapid social evaluations.

Neurocognitive Mechanisms Underlying Social Atypicalities in Autism: Weak Amygdala's Emotional Modulation Hypothesis

Autism spectrum disorder (ASD) is a neurodevelopmental condition associated with atypicalities in social interaction. Although psychological and neuroimaging studies have revealed divergent impairments in psychological processes (e.g., emotion and perception) and neural activity (e.g., amygdala, superior temporal sulcus, and inferior frontal gyrus) related to the processing of social stimuli, it remains difficult to integrate these findings. In an effort to resolve this issue, we review our psychological and functional magnetic resonance imaging (fMRI) findings and present a hypothetical neurocognitive model. Our psychological study showed that emotional modulation of reflexive joint attention is impaired in individuals with ASD. Our fMRI study showed that modulation from the amygdala to the neocortex during observation of dynamic facial expressions is reduced in the ASD group. Based on these findings and other evidence, we hypothesize that weak modulation from the amygdala to the neocortex-through which emotion rapidly modulates various types of perceptual, cognitive, and motor processing functions-underlies the social atypicalities in individuals with ASD.