Face memory and face recognition in children and adolescents with attention deficit hyperactivity disorder: A systematic review

Facial expression recognition ability and its neuropsychological mechanisms in children with attention deficit and hyperactive disorder

Attention deficit and hyperactive disorder (ADHD) is a chronic neurodevelopmental disorder characterized by inattention, hyperactivity-impulsivity, and working memory deficits. Social dysfunction is one of the major challenges faced by children with ADHD. It has been found that children with ADHD can't perform as well as typically developing children on facial expression recognition (FER) tasks. Generally, children with ADHD have some difficulties in FER, while some studies suggest that they have no significant differences in accuracy of specific emotion recognition compared with typically developing children. The neuropsychological mechanisms underlying these difficulties are as follows. First, neuroanatomically. Compared to typically developing children, children with ADHD show smaller gray matter volume and surface area in the amygdala and medial prefrontal cortex regions, as well as reduced density and volume of axons/cells in certain frontal white matter fiber tracts. Second, neurophysiologically. Children with ADHD exhibit increased slow-wave activity in their electroencephalogram, and event-related potential studies reveal abnormalities in emotional regulation and responses to angry faces when facing facial stimuli. Third, psychologically. Psychosocial stressors may influence FER abilities in children with ADHD, and sleep deprivation in ADHD children may significantly increase their recognition threshold for negative expressions such as sadness and anger. This article reviews research progress over the past three years on FER abilities of children with ADHD, analyzing the FER deficit in children with ADHD from three dimensions: neuroanatomy, neurophysiology and psychology, aiming to provide new perspectives for further research and clinical treatment of ADHD.

An assessment of dynamic facial emotion recognition and theory of mind in children with ADHD: An eye-tracking study

Deficits in social cognition in attention deficit hyperactivity disorder (ADHD) have been associated with difficulties in functioning. Since recognizing emotional facial expressions is essential for developing the perceptual components of the theory of mind (ToM), it is important to assess this relationship in children with ADHD. This study therefore compared the recognition of emotional stimuli and gaze patterns between children with ADHD and healthy children using eye-tracking with dynamic facial images. It also examined the relationship between facial emotion recognition accuracy, gaze patterns, ToM scores, and ADHD symptoms. Children with ADHD aged 8-13 (n = 47) and a control group (n = 38) completed a facial emotion recognition test, ToM tests, and the Conners' Parent Rating Scale. Participants' gaze patterns in response to dynamic facial emotion expressions were recorded using eye-tracking technology. Children with ADHD exhibited significantly lower accuracy in the recognition of the facial expressions of disgust and anger. The percentage fixation in the eye region was also significantly lower for happy, angry, sad, disgusted, and neutral emotions in the children with ADHD compared to the control group. No relationship was determined between the percentage of fixations on facial areas of interests and ADHD symptoms or ToM tests. This study provides evidence that children with ADHD experience deficits in visual attention to emotional cues. In addition, it suggests that facial emotion recognition deficits in children with ADHD represent a separate domain of social cognition that develops independently of ToM skills and core symptoms. Understanding and treating the social difficulties of individuals with ADHD may help improve their social functioning.

Differences in facial emotion recognition between bipolar disorder and other clinical populations: A systematic review and meta-analysis

Facial emotion (or expression) recognition (FER) is a domain of affective cognition impaired across various psychiatric conditions, including bipolar disorder (BD). We conducted a systematic review and meta-analysis searching for eligible articles published from inception to April 26, 2023, in PubMed/MEDLINE, Scopus, EMBASE, and PsycINFO to examine whether and to what extent FER would differ between people with BD and those with other mental disorders. Thirty-three studies comparing 1506 BD patients with 1973 clinical controls were included in the present systematic review, and twenty-six of them were analyzed in random-effects meta-analyses exploring the discrepancies in discriminating or identifying emotional stimuli at a general and specific level. Individuals with BD were more accurate in identifying each type of emotion during a FER task compared to individuals diagnosed with schizophrenia (SCZ) (SMD = 0.27; p-value = 0.006), with specific differences in the perception of anger (SMD = 0.46; p-value = 1.19e-06), fear (SMD = 0.38; p-value = 8.2e-04), and sadness (SMD = 0.33; p-value = 0.026). In contrast, BD patients were less accurate than individuals with major depressive disorder (MDD) in identifying each type of emotion (SMD = -0.24; p-value = 0.014), but these differences were more specific for sad emotional stimuli (SMD = -0.31; p-value = 0.009). No significant differences were observed when BD was compared with children and adolescents diagnosed with attention-deficit/hyperactivity disorder. FER emerges as a potential integrative instrument for guiding diagnosis by enabling discrimination between BD and SCZ or MDD. Enhancing the standardization of adopted tasks could further enhance the accuracy of this tool, leveraging FER potential as a therapeutic target.

Facial emotion recognition in children and youth with attention-deficit/hyperactivity disorder and irritability

The ability to recognize emotions evident in people's faces contributes to social functioning and might be affected by ADHD and irritability. Given their high co-occurrence, we examined the relative contribution of ADHD and irritability to facial emotion recognition (FER). We hypothesized that irritability but not ADHD traits would predict increased likelihood of misrecognizing emotions as negative, and that FER performance would explain the association of ADHD and irritability traits with social skills. FER was measured using the Reading the Mind in the Eyes Test (RMET) in children (6-14 years old) referred for ADHD assessment (n = 304) and healthy controls (n = 128). ADHD, irritability and social skills were measured using parent ratings. We used repeated measure logistics regression, comparing the effects across emotion valence of images (i.e., neutral/positive/negative). High irritability but not ADHD diagnosis predicted lower RMET accuracy. ADHD traits predicted lower RMET accuracy in younger but not older participants, whereas irritability predicted poorer accuracy at all ages. ADHD traits predicted lower RMET accuracy across all emotion valences, whereas irritability predicted increased probability of misrecognizing neutral and positive but not negative emotions. Irritability did not increase the probability for erroneously recognizing emotions as negative. ADHD and irritability traits fully explained the association between RMET and social skills. ADHD and irritability traits might impact the ability to identify emotions portrayed in faces. However, irritability traits appear to selectively impair recognition of neutral and positive but not negative emotions. ADHD and irritability are important when examining the link between FER and social difficulties.

Establishing the Relationship Between Attention Deficit Hyperactivity Disorder and Emotional Facial Expression Recognition Deficit: A Systematic Review

OBJECTIVE

In this review, we examined if there is a deficit in facial recognition of emotion (FER) in children, adolescents, and adults with attention deficit hyperactivity disorder (ADHD).

BACKGROUND

Emotional regulation is impaired in ADHD. Although a facial emotion recognition deficit has been described in this condition, the underlying causal mechanisms remain unclear.

METHODS

The search was performed in six databases in September 2022. Studies assessing children, adolescents, or adults with isolated or comorbid ADHD that evaluated participants using a FER task were included.

RESULTS

Twelve studies out of 385 were selected, with participants ranging in age from 6 to 37.1 years. A deficit in FER specific to ADHD, or secondary to comorbid autism spectrum disorder, anxiety, and oppositional symptoms, was found.

CONCLUSIONS

There is a FER deficit in patients with ADHD. Adults showed improved recognition accuracy, reflecting partial compensation. ADHD symptoms and comorbidities appear to influence FER deficits.

Relative Density-Based Intuitionistic Fuzzy SVM for Class Imbalance Learning

The support vector machine (SVM) has been combined with the intuitionistic fuzzy set to suppress the negative impact of noises and outliers in classification. However, it has some inherent defects, resulting in the inaccurate prior distribution estimation for datasets, especially the imbalanced datasets with non-normally distributed data, further reducing the performance of the classification model for imbalance learning. To solve these problems, we propose a novel relative density-based intuitionistic fuzzy support vector machine (RIFSVM) algorithm for imbalanced learning in the presence of noise and outliers. In our proposed algorithm, the relative density, which is estimated by adopting the k-nearest-neighbor distances, is used to calculate the intuitionistic fuzzy numbers. The fuzzy values of the majority class instances are designed by multiplying the score function of the intuitionistic fuzzy number by the imbalance ratio, and the fuzzy values of minority class instances are assigned the intuitionistic fuzzy membership degree. With the help of the strong capture ability of the relative density to prior information and the strong recognition ability of the intuitionistic fuzzy score function to noises and outliers, the proposed RIFSVM not only reduces the influence of class imbalance but also suppresses the impact of noises and outliers, and further improves the classification performance. Experiments on the synthetic and public imbalanced datasets show that our approach has better performance in terms of G-Means, F-Measures, and AUC than the other class imbalance classification algorithms.

Effect of short-term methylphenidate on social impairment in children with attention deficit/hyperactivity disorder: systematic review

Attention Deficit/Hyperactivity disorder (ADHD) is one of the most common disorders in school-age children. In addition to learning difficulties associated with the disorder's core symptoms of inattention and hyperactivity, children with ADHD display substantial social impairments. Methylphenidate (MPH) in formulations such as Ritalin or Concerta mitigates inattention and hyperactivity, but the effects of the therapy on social behavior in children with ADHD are not clear. This review aims to determine the effectiveness of short term (up to 6 months) MPH treatment on three domains of social skills in children aged 6-14 with ADHD: (i) Recognition of nonverbal emotional expressions, which are a marker of inherent (unlearned) social understanding, (ii) theory of mind (ToM) components that relate to learned cognition and social communication, and (iii) social competence in everyday environments. 15 relevant studies were identified based on inclusion/exclusion criteria. The results show mixed effects: the overall social performance as evaluated by parents, teachers or peers, and some components of ToM, were found to improve following a weeks-long course of MPH treatment. However, the effects of the medication are less clear when evaluating momentary/nonverbal social responses such as reactions to emotional facial expressions. While the findings of this review indicate that an MPH medication regime of order weeks to months could improve, to a degree, social impairment in children with ADHD, more studies are required to identify the medications' mechanism and confirm such a conclusion.

Clinical assessment and voxel-based morphometry study of untreated Adult Attention deficit hyperkinetic disorders patients.. [DOI: 10.1101/2022.05.28.22271305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Purpose

Adult ADHD is one of the most undiagnosed diseases mainly because of the misperception that ADHD is a childhood disease. In this study, we assess the characteristic features of adult ADHD using clinical assessment and structural Magnetic resonance imaging (sMRI)

Methods

We obtained structural MRI data from the UCLA Consortium for Neuropsychiatric Phenomics for 21 untreated adult ADHD patients and 21 age and gender propensity-matched control patients. For clinical assessment, we compared the scores of Barrat impulsivity score, Dickman impulsivity inventory II, and Eysenck’s Impulsivity Inventory. We then compared grey matter volume (GMV) between ADHD and control patients using a two-sample t-test. We also assessed the correlation between different clinical assessments and GMV.

Results

Based on our results, adult ADHD showed significantly higher impulsivity scores, however, no significant difference in functional impulsivity scores or empathy summary scores. For sMRI, there was a significant decrease of GMV of the left cuneus in female ADHD patients. For clinical assessment scales, only the motor impulsiveness subdomain showed a significant positive correlation with the GMV of the left precuneus.

Conclusions

In this study, we assessed the characteristic sMRI features and clinical assessment scores for untreated adult ADHD. Our results show that a study with a bigger sample size can identify diagnostic features for adult ADHD.

Deep Learning-Based Imbalanced Classification With Fuzzy Support Vector Machine

Imbalanced classification is widespread in the fields of medical diagnosis, biomedicine, smart city and Internet of Things. The imbalance of data distribution makes traditional classification methods more biased towards majority classes and ignores the importance of minority class. It makes the traditional classification methods ineffective in imbalanced classification. In this paper, a novel imbalance classification method based on deep learning and fuzzy support vector machine is proposed and named as DFSVM. DFSVM first uses a deep neural network to obtain an embedding representation of the data. This deep neural network is trained by using triplet loss to enhance similarities within classes and differences between classes. To alleviate the effects of imbalanced data distribution, oversampling is performed in the embedding space of the data. In this paper, we use an oversampling method based on feature and center distance, which can obtain more diverse new samples and prevent overfitting. To enhance the impact of minority class, we use a fuzzy support vector machine (FSVM) based on cost-sensitive learning as the final classifier. FSVM assigns a higher misclassification cost to minority class samples to improve the classification quality. Experiments were performed on multiple biological datasets and real-world datasets. The experimental results show that DFSVM has achieved promising classification performance.

Facial emotion recognition impairment predicts social and emotional problems in children with (subthreshold) ADHD

Children with attention-deficit/hyperactivity disorder (ADHD) symptoms often experience social and emotional problems. Impaired facial emotion recognition has been suggested as a possible underlying mechanism, although impairments may depend on the type and intensity of emotions. We investigated facial emotion recognition in children with (subthreshold) ADHD and controls using a novel task with children's faces of emotional expressions varying in type and intensity. We further investigated associations between emotion recognition accuracy and social and emotional problems in the ADHD group. 83 children displaying ADHD symptoms and 30 controls (6-12 years) completed the Morphed Facial Emotion Recognition Task (MFERT). The MFERT assesses emotion recognition accuracy on four emotions using five expression intensity levels. Teachers and parents rated social and emotional problems on the Strengths and Difficulties Questionnaire. Repeated measures analysis of variance revealed that the ADHD group showed poorer emotion recognition accuracy compared to controls across emotions (small effect). The significant group by expression intensity interaction (small effect) showed that the increase in accuracy with increasing expression intensity was smaller in the ADHD group compared to controls. Multiple regression analyses within the ADHD group showed that emotion recognition accuracy was inversely related to social and emotional problems, but not prosocial behavior. Not only children with an ADHD diagnosis, but also children with subthreshold ADHD experience impairments in facial emotion recognition. This impairment is predictive for social and emotional problems, which may suggest that emotion recognition may contribute to the development of social and emotional problems in these children.

Data-driven analysis of gaze patterns in face perception: Methodological and clinical contributions

Gaze patterns during face perception have been shown to relate to psychiatric symptoms. Standard analysis of gaze behavior includes calculating fixations within arbitrarily predetermined areas of interest. In contrast to this approach, we present an objective, data-driven method for the analysis of gaze patterns and their relation to diagnostic test scores. This method was applied to data acquired in an adult sample (N = 111) of psychiatry outpatients while they freely looked at images of human faces. Dimensional symptom scores of autism, attention deficit, and depression were collected. A linear regression model based on Principal Component Analysis coefficients computed for each participant was used to model symptom scores. We found that specific components of gaze patterns predicted autistic traits as well as depression symptoms. Gaze patterns shifted away from the eyes with increasing autism traits, a well-known effect. Additionally, the model revealed a lateralization component, with a reduction of the left visual field bias increasing with both autistic traits and depression symptoms independently. Taken together, our model provides a data-driven alternative for gaze data analysis, which can be applied to dimensionally-, rather than categorically-defined clinical subgroups within a variety of contexts. Methodological and clinical contribution of this approach are discussed.

Increased pupil dilation to happy faces in children with hyperactive/impulsive symptoms of ADHD

Attention-deficit/hyperactivity disorder (ADHD) is associated with disrupted emotional processes including impaired regulation of approach behavior and positive affect, irritability, and anger. Enhanced reactivity to emotional cues may be an underlying process. Pupil dilation is an indirect index of arousal, modulated by the autonomic nervous system and activity in the locus coeruleus-noradrenergic system. In the current study, pupil dilation was recorded while 8- to 12- year old children (n = 71, 26 with a diagnosis of ADHD and 45 typically developing), viewed images of emotional faces. Parent-rated hyperactive/impulsive symptoms were uniquely linked to higher pupil dilation to happy, but not fearful, angry, or neutral faces. This was not explained by comorbid externalizing symptoms. Together, these results suggest that hyperactive/impulsive symptoms are associated with hyperresponsiveness to approach-related emotional cues across a wide range of symptom severity.

The Effect of Comorbid Attention-Deficit/Hyperactivity Disorder Symptoms on Face Memory in Children with Autism Spectrum Disorder: Insights from Transdiagnostic Profiles

Face memory impairments are common but heterogeneous in autism spectrum disorder (ASD), which may be influenced by co-occurrence with attention-deficit/hyperactivity disorder (ADHD). Here, we aimed to investigate the phenotype change of face memory in children with ASD comorbid ADHD symptoms, and discuss the potential role of executive function (EF). Ninety-eight children were analyzed in the present study, including ASD- (ASD-only, n = 24), ADHD (n = 23), ASD+ (with ADHD symptoms, n = 23) and neurotypical controls (NTC, n = 28). All participants completed two tests: face encoding and retrieving task and Wisconsin Card Sorting Test (WCST) for measuring face memory and EF, respectively. Results revealed that: compared with the NTC group, children with ASD- exhibited lower accuracy in both face encoding and retrieving, and participants with ASD+ showed lower accuracy only in the retrieving, whereas no differences were found among participants with ADHD. Moreover, in the ASD+ group, face encoding performance was correlated with response perseverative errors (RPE) and failure to maintain sets (FMS) of WCST; significantly, there were no group differences between ASD+ and NTC in these two indices. The transdiagnostic profiles indicated that comorbid ADHD symptoms could modulate the face encoding deficiency of ASD, which may be partially compensated by EF. Shared and distinct intervention strategies to improve social cognition are recommended for children undergoing treatment for each condition.

Configural processing of emotional bodies and faces in patients with attention deficit hyperactivity disorder

Introduction: Attention Deficit Disorder (ADHD) is associated with interpersonal problems and difficulties in inferring other peoples' emotions. Previous research has focused on face processing, mostly in children. Our study investigated configural processing of emotional bodies and faces in adults with ADHD in comparison with healthy controls, analyzing P100, N170 and P250 event-related potentials (ERPs) and relating them to (socio)cognitive functioning. Method: Nineteen patients with ADHD and 25 healthy controls were presented upright and inverted bodies and faces which had to be categorized as neutral, happy or angry while ERPs were recorded. Additionally, sociocognitive and executive functioning was assessed. Results: In ADHD patients relative to controls, recognition of emotions depicted by bodies but not by faces was impaired and P100 amplitudes were enhanced for angry bodies. Furthermore, patients showed enhanced P250 amplitudes in response to both bodies and faces, specifically for happy and neutral emotions. Larger N170 amplitudes to bodies and faces correlated with lower alexithymia scores only in controls, while enhanced P250 amplitudes to both categories were associated with poorer inhibition only in patients. Conclusion: Adults with ADHD show potentially compensatory enhanced semantic processing of emotional bodies and faces, as reflected by increased P250 amplitudes, associated with poorer executive functioning and subtle alterations of emotional and configural processing, as reflected by ERPs.

Recognition of emotional facial expressions in adolescents with attention deficit/hyperactivity disorder

INTRODUCTION

Attention Deficit/Hyperactivity Disorder (ADHD) is associated with impaired social competencies, due in part to an inability to determine emotional states through facial expressions. Social interactions are a critical component of adolescence, which raises the question of how do adolescents with ADHD cope with this impairment. Yet, previous reviews do not distinguish between children and adolescents. This review focuses on the ability of adolescents (defined by the World Health Organization as 10-19 years old) with ADHD to recognize emotional facial expressions, when compared to their typically-developing peers.

METHODS

Comprehensive database search and analysis yielded 9 relevant studies published between 2008 and 2018.

RESULTS

The studies reviewed here examined recognition of emotional facial expressions in adolescents with ADHD. Behavioral measures (reaction time, reaction time variance and recognition accuracy) show no statistically significant differences between adolescents with ADHD and their typically-developing peers. However, neural responses as recorded using functional Magnetic Resonance Imaging (fMRI) or Event Related Potentials (ERP) find differences in brain activity and the temporal evolution of the reaction between the two groups.

CONCLUSIONS

Studies of children and of adults with ADHD find deficiencies in the recognition of emotional facial expressions. However, this review shows that adolescents with ADHD perform comparably to their peers on accuracy and rate, although their neural processing is different. This suggests that the methodologies employed by the ADHD and typically-developing adolescents to asses facial expressions are different. Further study is needed to determine what these may be.

The nature and extent of emotion recognition and empathy impairments in children showing disruptive behaviour referred into a crime prevention programme

Childhood disruptive behaviour has been linked to later antisocial and criminal behaviour. Emotion recognition and empathy impairments, thought to be caused by inattention to the eye region, are hypothesised to contribute to antisocial and criminal behaviour. This is the first study to simultaneously examine emotion recognition and empathy impairments, their relationship, and the mechanism behind these impairments, in children with disruptive behaviour. We hypothesised that children with disruptive behaviour would exhibit negative emotion recognition and cognitive and affective empathy impairments, but that these impairments would not be due to reduced attention to the eye region. We expected these emotion impairments to be driven by disruptive behaviour. We also expected a relationship between emotion recognition and cognitive empathy only. Ninety-two children with disruptive behaviour, who were participating in a police crime prevention programme and rated by their schoolteacher using the Strengths and Difficulties Questionnaire (DB; mean age 8.8 years, 80% male), took part. There was a comparison group of 58 typically developing children (TD; mean age 9.7 years, 78% male). All children completed emotion recognition and empathy tasks, both with concurrent eye tracking to assess social attention. Not only were DB children significantly impaired in negative emotion and neutral emotion recognition, and in cognitive and affective empathy compared to the TD children, but severity of disruptive behaviour also predicted intensity of emotion impairments. There were no differences in social attention to the eye region. Negative emotion recognition and empathy impairments are already present in an identifiable group of children displaying disruptive behaviour. These findings provide evidence to encourage the use of targeted interventions.

Affinity and class probability-based fuzzy support vector machine for imbalanced data sets

The learning problem from imbalanced data sets poses a major challenge in data mining community. Although conventional support vector machine can generally show relatively robust performance in dealing with the classification problems of imbalanced data sets, it treats all training samples with the same contribution for learning, which results in the final decision boundary biasing toward the majority class especially in the presence of outliers or noises. In this paper, we propose a new affinity and class probability-based fuzzy support vector machine technique (ACFSVM). The affinity of a majority class sample is calculated according to support vector description domain (SVDD) model trained only by the given majority class training samples in kernel space similar to that used for FSVM learning. The obtained affinity can be used for identifying possible outliers and some border samples existing in the majority class training samples. In order to eliminate the effect of noises, we employ the kernel k-nearest neighbor method to determine the class probability of the majority class samples in the same kernel space as before. The samples with lower class probabilities are more likely to be noises and their contribution for learning seems to be reduced by their low memberships constructed by combining the affinities and the class probabilities. Thus, ACFSVM can pay more attention to the majority class samples with higher affinities and class probabilities while reducing their effects of the ones with lower affinities and class probabilities, eventually skewing the final classification boundary toward the majority class. In addition, the minority class samples are assigned relative high memberships to guarantee their importance for the model learning. The extensive experimental results on the different imbalanced datasets from UCI repository demonstrate that the proposed approach can achieve better generalization performance in terms of G-Mean, F-Measure, and AUC as compared to the other existing imbalanced dataset classification techniques.

Intellectual functioning and executive functions in children and adolescents with attention deficit hyperactivity disorder (ADHD) and specific learning disorder (SLD)

Several studies have shown neuropsychological deficits across multiple domains in attention deficit hyperactivity disorder (ADHD) and specific learning disorder (SLD), but differences and similarities between these disorders have been little considered. We were interested in analyzing the intellectual and executive profiles in a sample of children and adolescents, divided according to the diagnosis into the ADHD group and the SLD group, and in identifying the differences and similarities between these disorders. The sample included two clinical groups: the first included 36 children and adolescents with a diagnosis of ADHD (5-15 years; mean = 9.42; SD = 2.22) while the second included 36 children and adolescents with a diagnosis of SLD (7-15 years; mean = 9.43; SD = 2.25). The WISC-IV was used to measure intellectual ability and the NEPSY-II was employed to measure executive functions. The results showed that the SLD group had significantly higher scores than the ADHD group on the NEPSY-II in the inhibition, cognitive flexibility, short-term verbal memory and verbal working memory domains. The ANCOVA showed differences regarding the FSIQ of WISC-IV, in that the SLD group obtaining higher scores than ADHD group. Findings showed that ADHD children are more impaired than SLD children, in particular in cognitive inhibition, cognitive flexibility, verbal memory, working memory and intellectual functioning. The recognition of the strengths and weaknesses of children and adolescents with ADHD and SLD allows to outline an educational and clinical intervention focused on their specific executive and intellectual functioning.