Inattentiveness in attention-deficit/hyperactivity disorder

Distinct structural brain network properties in children with familial versus non-familial attention-deficit/hyperactivity disorder (ADHD)

Attention-deficit/hyperactivity disorder (ADHD) is among the most prevalent, inheritable, and heterogeneous childhood-onset neurodevelopmental disorders. Children with a hereditary background of ADHD have heightened risk of having ADHD and persistent impairment symptoms into adulthood. These facts suggest distinct familial-specific neuropathological substrates in ADHD that may exist in anatomical components subserving attention and cognitive control processing pathways during development. The objective of this study is to investigate the topological properties of the gray matter (GM) structural brain networks in children with familial ADHD (ADHD-F), non-familial ADHD (ADHD-NF), as well as matched controls. A total of 452 participants were involved, including 132, 165 and 155 in groups of ADHD-F, ADHD-NF and typically developed children, respectively. The GM structural brain network was constructed for each group using graph theoretical techniques with cortical and subcortical structures as nodes and correlations between volume of each pair of the nodes within each group as edges, while controlled for confounding factors using regression analysis. Relative to controls, children in both ADHD-F and ADHD-NF groups showed significantly higher nodal global and nodal local efficiencies in the left caudal middle frontal gyrus. Compared to controls and ADHD-NF, children with ADHD-F showed distinct structural network topological patterns associated with right precuneus (significantly higher nodal global efficiency and significantly higher nodal strength), left paracentral gyrus (significantly higher nodal strength and trend toward significantly higher nodal local efficiency) and left putamen (significantly higher nodal global efficiency and trend toward significantly higher nodal local efficiency). Our results for the first time in the field provide evidence of familial-specific structural brain network alterations in ADHD, that may contribute to distinct clinical/behavioral symptomology and developmental trajectories in children with ADHD-F.

Machine learning-enabled detection of attention-deficit/hyperactivity disorder with multimodal physiological data: a case-control study

BACKGROUND

Attention-Deficit/Hyperactivity Disorder (ADHD) is a multifaceted neurodevelopmental psychiatric condition that typically emerges during childhood but often persists into adulthood, significantly impacting individuals' functioning, relationships, productivity, and overall quality of life. However, the current diagnostic process exhibits limitations that can significantly affect its overall effectiveness. Notably, its face-to-face and time-consuming nature, coupled with the reliance on subjective recall of historical information and clinician subjectivity, stand out as key challenges. To address these limitations, objective measures such as neuropsychological evaluations, imaging techniques and physiological monitoring of the Autonomic Nervous System functioning, have been explored.

METHODS

The main aim of this study was to investigate whether physiological data (i.e., Electrodermal Activity, Heart Rate Variability, and Skin Temperature) can serve as meaningful indicators of ADHD, evaluating its utility in distinguishing adult ADHD patients. This observational, case-control study included a total of 76 adult participants (32 ADHD patients and 44 healthy controls) who underwent a series of Stroop tests, while their physiological data was passively collected using a multi-sensor wearable device. Univariate feature analysis was employed to identify the tests that triggered significant signal responses, while the Informative k-Nearest Neighbors (KNN) algorithm was used to filter out less informative data points. Finally, a machine-learning decision pipeline incorporating various classification algorithms, including Logistic Regression, KNN, Random Forests, and Support Vector Machines (SVM), was utilized for ADHD patient detection.

RESULTS

Results indicate that the SVM-based model yielded the optimal performance, achieving 81.6% accuracy, maintaining a balance between the experimental and control groups, with sensitivity and specificity of 81.4% and 81.9%, respectively. Additionally, integration of data from all physiological signals yielded the best results, suggesting that each modality captures unique aspects of ADHD.

CONCLUSIONS

This study underscores the potential of physiological signals as valuable diagnostic indicators of adult ADHD. For the first time, to the best of our knowledge, our findings demonstrate that multimodal physiological data collected via wearable devices can complement traditional diagnostic approaches. Further research is warranted to explore the clinical applications and long-term implications of utilizing physiological markers in ADHD diagnosis and management.

Differential Impact of Serotonin Signaling Methylphenidate on Young versus Adult: Insights from Behavioral and Dorsal Raphe Nucleus Neuronal Recordings from Freely Behaving Rats

Methylphenidate (MPD) remains a cornerstone pharmacological intervention for managing ADHD, yet its increasing usage among ordinary youth and adults outside clinical contexts necessitates a thorough investigation into its developmental effects. This study seeks to simultaneously investigate the behavioral and neuronal changes within the dorsal raphe (DR) nucleus, a center of serotonergic neurons in the mammalian brain, before and after the administration of varying doses of acute and chronic MPD in freely behaving young and adult rats implanted with DR recording electrodes. Wireless neuronal and behavioral recording systems were used over 10 consecutive experimental days. Eight groups were examined: saline, 0.6, 2.5, and 10.0 mg/kg MPD for both young and adult rats. Six daily MPD injections were administered on experimental days 1 to 6, followed by a three-day washout period and MPD re-administration on experimental day 10 (ED10). The analysis of neuronal activity recorded from 504 DR neurons (DRNs) in young rats and 356 DRNs in adult rats reveals significant age-dependent differences in acute and chronic MPD responses. This study emphasizes the importance of aligning electrophysiological evaluations with behavioral outcomes following extended MPD exposure, elucidating the critical role of DRNs and serotonin signaling in modulating MPD responses and delineating age-specific variations in young versus adult rat models.

"Unattended, distracting or irrelevant": Theoretical implications of terminological choices in auditory selective attention research

For seventy years, auditory selective attention research has focused on studying the cognitive mechanisms of prioritizing the processing a 'main' task-relevant stimulus, in the presence of 'other' stimuli. However, a closer look at this body of literature reveals deep empirical inconsistencies and theoretical confusion regarding the extent to which this 'other' stimulus is processed. We argue that many key debates regarding attention arise, at least in part, from inappropriate terminological choices for experimental variables that may not accurately map onto the cognitive constructs they are meant to describe. Here we critically review the more common or disruptive terminological ambiguities, differentiate between methodology-based and theory-derived terms, and unpack the theoretical assumptions underlying different terminological choices. Particularly, we offer an in-depth analysis of the terms 'unattended' and 'distractor' and demonstrate how their use can lead to conflicting theoretical inferences. We also offer a framework for thinking about terminology in a more productive and precise way, in hope of fostering more productive debates and promoting more nuanced and accurate cognitive models of selective attention.

Machine learning in biosignals processing for mental health: A narrative review

Machine Learning (ML) offers unique and powerful tools for mental health practitioners to improve evidence-based psychological interventions and diagnoses. Indeed, by detecting and analyzing different biosignals, it is possible to differentiate between typical and atypical functioning and to achieve a high level of personalization across all phases of mental health care. This narrative review is aimed at presenting a comprehensive overview of how ML algorithms can be used to infer the psychological states from biosignals. After that, key examples of how they can be used in mental health clinical activity and research are illustrated. A description of the biosignals typically used to infer cognitive and emotional correlates (e.g., EEG and ECG), will be provided, alongside their application in Diagnostic Precision Medicine, Affective Computing, and brain-computer Interfaces. The contents will then focus on challenges and research questions related to ML applied to mental health and biosignals analysis, pointing out the advantages and possible drawbacks connected to the widespread application of AI in the medical/mental health fields. The integration of mental health research and ML data science will facilitate the transition to personalized and effective medicine, and, to do so, it is important that researchers from psychological/ medical disciplines/health care professionals and data scientists all share a common background and vision of the current research.

Distinct Thalamic and Frontal Neuroanatomical Substrates in Children with Familial vs. Non-Familial Attention-Deficit/Hyperactivity Disorder (ADHD)

Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent, inheritable, and heterogeneous neurodevelopmental disorder. Children with a family history of ADHD are at elevated risk of having ADHD and persisting its symptoms into adulthood. The objective of this study was to investigate the influence of having or not having positive family risk factor in the neuroanatomy of the brain in children with ADHD. Cortical thickness-, surface area-, and volume-based measures were extracted and compared in a total of 606 participants, including 132, 165, and 309 in groups of familial ADHD (ADHD-F), non-familial ADHD (ADHD-NF), and typically developed children, respectively. Compared to controls, ADHD probands showed significantly reduced gray matter surface area in the left cuneus. Among the ADHD subgroups, ADHD-F showed significantly increased gray matter volume in the right thalamus and significantly thinner cortical thickness in the right pars orbitalis. Among ADHD-F, an increased volume of the right thalamus was significantly correlated with a reduced DSM-oriented t-score for ADHD problems. The findings of this study may suggest that a positive family history of ADHD is associated with the structural abnormalities in the thalamus and inferior frontal gyrus; these anatomical abnormalities may significantly contribute to the emergence of ADHD symptoms.

A Robust Movement Quantification Algorithm of Hyperactivity Detection for ADHD Children Based on 3D Depth Images

Attention deficit hyperactivity disorder (ADHD) is one of the most common childhood mental disorders. Hyperactivity is a typical symptom of ADHD in children. Clinicians diagnose this symptom by evaluating the children's activities based on subjective rating scales and clinical experience. In this work, an objective system is proposed to quantify the movements of children with ADHD automatically. This system presents a new movement detection and quantification method based on depth images. A novel salient object extraction method is proposed to segment body regions. In movement detection, we explore a new local search algorithm to detect any potential motions of children based on three newly designed evaluation metrics. In the movement quantification, two parameters are investigated to quantify the participation degree and the displacements of each body part in the movements. This system is tested by a depth dataset of children with ADHD. The movement detection results of this dataset mainly range from 91.0% to 95.0%. The movement quantification results of children are consistent with the clinical observations. The public MSR Action 3D dataset is tested to validate the performance of this system.

Abnormal spontaneous neural activity in hippocampal-cortical system of patients with obsessive-compulsive disorder and its potential for diagnosis and prediction of early treatment response

Early brain functional changes induced by pharmacotherapy in patients with obsessive-compulsive disorder (OCD) in relation to drugs per se or because of the impact of such drugs on the improvement of OCD remain unclear. Moreover, no neuroimaging biomarkers are available for diagnosis of OCD and prediction of early treatment response. We performed a longitudinal study involving 34 patients with OCD and 36 healthy controls (HCs). Patients with OCD received 5-week treatment with paroxetine (40 mg/d). Resting-state functional magnetic resonance imaging (fMRI), regional homogeneity (ReHo), support vector machine (SVM), and support vector regression (SVR) were applied to acquire and analyze the imaging data. Compared with HCs, patients with OCD had higher ReHo values in the right superior temporal gyrus and bilateral hippocampus/parahippocampus/fusiform gyrus/cerebellum at baseline. ReHo values in the left hippocampus and parahippocampus decreased significantly after treatment. The reduction rate (RR) of ReHo values was positively correlated with the RRs of the scores of Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) and obsession. Abnormal ReHo values at baseline could serve as potential neuroimaging biomarkers for OCD diagnosis and prediction of early therapeutic response. This study highlighted the important role of the hippocampal-cortical system in the neuropsychological mechanism underlying OCD, pharmacological mechanism underlying OCD treatment, and the possibility of building models for diagnosis and prediction of early treatment response based on spontaneous activity in the hippocampal-cortical system.

Neurological state changes indicative of ADHD in children learned via EEG-based LSTM networks

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that pervasively interferes with the lives of individuals starting in childhood.

OBJECTIVE

To address the subjectivity of current diagnostic approaches, many studies have been dedicated to efforts to identify the differences between ADHD and neurotypical (NT) individuals using EEG and continuous performance tests (CPT).

APPROACH

In this study, we proposed EEG-based long short-term memory (LSTM) networks that utilize deep learning techniques with learning the cognitive state transition to discriminate between ADHD and NT children via EEG signal processing. A total of thirty neurotypical children and thirty ADHD children participated in CPT tests while being monitored with EEG. Several architectures of deep and machine learning were applied to three EEG data segments including resting state, cognitive execution, and a period containing a fusion of those.

MAIN RESULTS

The experimental results indicated that EEG-based LSTM networks produced the best performance with an average accuracy of 90.50 ± 0.81 % in comparison with the deep neural networks, the convolutional neural networks, and the support vector machines with learning the cognitive state transition of EEG data. Novel observations of individual neural markers showed that the beta power activity of the O1 and O2 sites contributed the most to the classifications, subjects exhibited decreased beta power in the ADHD group, and had larger decreases during cognitive execution.

SIGNIFICANCE

These findings showed that the proposed EEG-based LSTM networks are capable of extracting the varied temporal characteristics of high-resolution electrophysiological signals to differentiate between ADHD and NT children, and brought a new insight to facilitate the diagnosis of ADHD. The registration numbers of the institutional review boards are 16MMHIS021 and EC1070401-F.

Application of Artificial Intelligence in the MRI Classification Task of Human Brain Neurological and Psychiatric Diseases: A Scoping Review

Artificial intelligence (AI) for medical imaging is a technology with great potential. An in-depth understanding of the principles and applications of magnetic resonance imaging (MRI), machine learning (ML), and deep learning (DL) is fundamental for developing AI-based algorithms that can meet the requirements of clinical diagnosis and have excellent quality and efficiency. Moreover, a more comprehensive understanding of applications and opportunities would help to implement AI-based methods in an ethical and sustainable manner. This review first summarizes recent research advances in ML and DL techniques for classifying human brain magnetic resonance images. Then, the application of ML and DL methods to six typical neurological and psychiatric diseases is summarized, including Alzheimer's disease (AD), Parkinson's disease (PD), major depressive disorder (MDD), schizophrenia (SCZ), attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD). Finally, the limitations of the existing research are discussed, and possible future research directions are proposed.

PLS-DA Model for the Evaluation of Attention Deficit and Hyperactivity Disorder in Children and Adolescents through Blood Serum FTIR Spectra

Attention deficit and hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders of childhood. It affects ~10% of the world's population of children, and about 30-50% of those diagnosed in childhood continue to show ADHD symptoms later, with 2-5% of adults having the condition. Current diagnosis of ADHD is based on the clinical evaluation of the patient, and on interviews performed by clinicians with parents and teachers of the children, which, together with the fact that it shares common symptoms and frequent comorbidities with other neurodevelopmental disorders, makes the accurate and timely diagnosis of the disorder a difficult task. Despite the large effort to identify reliable biomarkers that can be used in a clinical environment to support clinical diagnosis, this goal has never been achieved hitherto. In the present study, infrared spectroscopy was used together with multivariate statistical methods (hierarchical clustering and partial least-squares discriminant analysis) to develop a model based on the spectra of blood serum samples that is able to distinguish ADHD patients from healthy individuals. The developed model used an approach where the whole infrared spectrum (in the 3700-900 cm^-1 range) was taken as a holistic imprint of the biochemical blood serum environment (spectroscopic biomarker), overcoming the need for the search of any particular chemical substance associated with the disorder (molecular biomarker). The developed model is based on a sensitive and reliable technique, which is cheap and fast, thus appearing promising to use as a complementary diagnostic tool in the clinical environment.

Developmental exposure to 17α-hydroxyprogesterone caproate impairs adult delayed reinforcement and reversal learning in male and female rats

Women with a history of unexplained miscarriage are frequently prescribed the synthetic progestin, 17α-hydroxyprogesterone caproate (17-OHPC) during the middle trimester of pregnancy. However, little is known about the long-term behavioural effects of 17-OHPC. Work in rodents suggests that the developing brain is sensitive to progestins. Neonatal 17-OHPC impairs adult performance in set-shifting and delay discounting. The present study tested the effects of 17-OHPC (0.5 mg kg^-1 ) or vehicle administration from postnatal days 1-14 on cognitive function in adulthood in rats. Cognitive function was assessed in males and females (n = 8-10 per group) by operant responding for sugar pellets, measuring delayed reinforcement or reversal learning. For delayed reinforcement, the rat must wait 15 seconds for pellets after responding on a lever. Delay is signalled by a light or is unsignalled. For reversal learning, the rat must respond on the lever under a stimulus light, and then learn to respond on the unlit lever. For delayed reinforcement, rats earned more pellets under signalled vs unsignalled conditions. Likewise, males made more responses and earned more pellets compared to females. Under signalled conditions, 17-OHPC-treated rats earned fewer pellets than controls. For reversal learning, the results were similar. Females required more trials than males to respond correctly for the new rule, and 17-OHPC-treated rats required more trials than controls. This suggests that 17-OHPC exposure during development may impair cognitive function. Considering that questions have been raised as to the efficacy of 17-OHPC to prevent miscarriage, it may be necessary to rethink the use of progestin therapy during pregnancy.

Attention-deficit/hyperactivity disorder in children and adolescents: An event-related potential study of working memory

Working memory (WM) impairments have been frequently observed as an important feature of attention-deficit/hyperactivity disorder (ADHD). Event-related potential (ERP) differences between ADHD and healthy controls (HC) would be expected during WM task performance. Especially, the so-called slow wave (SW), which is related to the retention process, might present amplitude differences in ADHD. In this ERP study participated twenty-nine ADHD children and adolescents and thirty-four HC. WM performance was assessed using the Working Memory Test Battery for Children (WMTB-C), and ERPs were analyzed with a Delayed Match-To-Sample (DMTS) task. ADHD sample showed worse behavioral performance in both WMTB-C and DMTS task, and higher SW amplitude during the retention phase of the DMTS task. Additionally, the principal component analysis indicated that the scores on the component explaining the centro-parietal SW were significantly different between ADHD subjects and HC. The observed impaired neurophysiological activity during the encoding and retention periods in ADHD, which would be the origin of the behavioral deficits in WM task performance, might be reflecting a delayed maturation of the neural processes underlying the centro-parietal SW.

Intracellular mechanisms and behavioral changes in mouse model of attention deficit hyperactivity disorder: Importance of age-specific NMDA receptor blockade

Exposure of NMDA receptor antagonists during developmental stages leads to behavioral consequences like attention deficit hyperactivity disorder (ADHD). However, the underlying molecular mechanisms have remained poorly understood. Herein, we studied the phosphorylated Akt (pAkt) and caspase-3, the key regulators of neuronal cell survival/death, as the probable downstream targets of MK-801 often used to engender ADHD-like condition. Swiss albino mice at postnatal days (PND) 7, 14 or 21 were injected with a single dose of MK-801 and evaluated for hyperactivity (open field test) and memory deficit at adolescence (PND 30) and adult stages (PND 60). PND 7 or 14 treatment groups (but not PND 21) consistently showed hyperactivity at the adolescence stage. A significant increase in working and reference memory errors in radial arm maze was noted at the adolescence age. PND 7 group continued to display the symptoms even in adulthood. All the treatment groups showed a significant decrease in the percent alterations (Y-maze) and discrimination index (novel object recognition test) at adolescence age. A significant increase in caspase-3 expression was noted in the prefrontal cortex (PFC) and hippocampus, whereas increased pAkt was noticed only in the hippocampus, following a single injection of MK-801 at PND 7. Concurrently, PND 7 treatment group showed significantly decreased neuronal nuclei (NeuN) expression (a marker for mature neurons) in the dentate gyrus, cornu ammonis-3 and PFC, but not in cornu ammonis-1, at adolescence age. We suggest that the observed symptoms of ADHD at adolescence and adulthood stages may be linked to alteration in pAkt and caspase-3 followed MK-801 treatment at PND 7.

A Probabilistic Classification Procedure Based on Response Time Analysis Towards a Quick Pre-Diagnosis of Student’s Attention Deficit

A classification methodology based on an experimental study is proposed towards a fast pre-diagnosis of attention deficit. Our sample consisted of school-aged children between 8 and 12 years from Valencia, Spain. The study was based on the response time (RT) to visual stimuli in computerized tasks. The process of answering consecutive questions usually follows an ex-Gaussian distribution of the RTs. Specifically, we seek to propose a simple automatic classification scheme of children based on the most recent evidence of the relationship between RTs and ADHD. Specifically, the prevalence percentage and reported evidence for RTs in relation to ADHD or to attention deficit symptoms were taken as reference in our study. We explain step by step how to go from the computer-based experiments and through the data analysis. Our desired aim is to provide a methodology to determine quickly those children who behave differently from the mean child in terms of response times and thus are potential candidates to be diagnosed for ADHD or any another cognitive disorder related to attention deficit. This is highly desirable as there is an urgent need for objective instruments to diagnose attention deficit symptomatology. Most of the methodologies available nowadays lead to an overdiagnosis of ADHD and are not based on direct measurement but on interviews of people related to the child such as parents or teachers. Although the ultimate diagnosis must be made by a psychologist, the selection provided by a methodology like ours could allow them to focus on assessing a smaller number of candidates which would help save time and other resources.

Executive functions in children with dyslexia

INTRODUCTION

This study aimed to verify whether children with dyslexia have difficulties in executive functions (shifting, working memory, inhibition).

METHODS

A sample of 47 children (ages 8-13 years) participated in the study: 24 who were dyslexic and 23 controls with typical development. A battery of neuropsychological tests was used.

RESULTS

Results revealed executive function difficulties among the dyslexic children when compared with controls, encompassing selective attention modulation processes, shifting, and inhibitory control. These difficulties appeared to be affected by phonological working memory deficits, typically associated with dyslexia.

CONCLUSION

Our findings support the consensus among scholars regarding the central involvement of phonological skill dysfunctions in dyslexia.

Latent class analysis of attention and white matter correlation in children with attention-deficit/hyperactivity disorder

This study aimed to explore attentional patterns among children with inattentive attention-deficit/hyperactivity disorder (ADHD-I) and children with typical development (TD), using a latent class analysis (LCA). Patterns of brain connectivity were also explored. The sample comprised 29 ADHD-I and 29 TD matched children. An LCA was conducted to reclassify subjects according to their attentional performance, considering cognitive measures of attention and behavioral symptoms, regardless of group of origin. The new clusters were then compared in respect to brain white matter measurements (extracted from diffusion tensor imaging). Participants were rearranged in 2 new latent classes, according to their performance in an attention task and the results of behavioral scales, resulting in groups with more homogeneous attentional profiles. A comparison of the 2 new classes using the white matter measurements revealed increased fractional anisotropy in the left inferior fronto-occipital fasciculus and left inferior longitudinal fasciculus for the class composed by participants with a higher risk of attentional problems. The findings indicated that it was possible to observe variability regarding neuropsychological profile, accompanied by underpinning neurobiological differences, even among individuals with the same disorder subtype - inattentive ADHD. This specific data-driven clustering analysis may help to enhance understanding of the pathophysiology of the disorder's phenotypes.

Novel and rapid LC-MS/MS method for quantitative analysis of methylphenidate in dried blood spots

AIM

Development and validation of a novel, sensitive, specific and rapid dried blood spots (DBS)-LC-MS/MS method for methylphenidate (MPH), an attention-deficit hyperactivity disorder drug. Methodology & results: Protein precipitation with acetonitrile was used to extract MPH from the DBS cards. Chromatographic separation was achieved on a Zorbax C18 column using an isocratic mobile phase composed of acetonitrile and 5 mM ammonium formate buffer (20:80, v/v) at a flow rate of 0.5 ml/min. MPH was quantified over a linear range of 200-25,000 pg/ml.

CONCLUSION

The clinical DBS-LC-MS/MS method was successfully validated as per the US FDA's Bioanalytical Method Validation Guidance to support an ongoing pediatric pharmacokinetic study.

Comparative Analysis of the WISC between Two ADHD Subgroups

OBJECTIVE

The prevalence of attention deficit/hyperactivity disorder (ADHD) in school-age children is 7.2%, and ADHD is divided into clinical subtypes.

METHODS

The current study explored whether specific cognitive profiles as assessed using the Wechsler Intelligence Scale for Children (WISC)-IV could be obtained for each clinical ADHD subtype (ADHD-Inattentive type and ADHD-Combined type) and investigated the correlation between WISC scores and parental age at their children's birth or birthweight. The enrolled sample comprised 12 ADHD-I and 15 ADHD-C subjects.

RESULTS

An impaired Processing Speed Index was found in ADHD-I. The age of the father at the child's birth and birthweight positively correlated with the full scale intelligence quotient (FSIQ) score in the WISC assessment.

CONCLUSION

Inattentiveness within the behaviors of the children with ADHD-I is partly due to the impaired processing speed, therefore effective support for ADHD will be conducted if educator decreases their speaking speed. Since biological basis of ADHD is still largely unknown, future studies using both psychological and biological methods will reveal the etiology of ADHD. These scientific assessments will provide information for more effective approaches in the care of children with ADHD.

A current view on contactin-4, -5, and -6: Implications in neurodevelopmental disorders

Contactins (Cntns) are a six-member subgroup of the immunoglobulin cell adhesion molecule superfamily (IgCAMs) with pronounced brain expression and function. Recent genetic studies of neuropsychiatric disorders have pinpointed contactin-4 (CNTN4), contactin-5 (CNTN5) and contactin-6 (CNTN6) as candidate genes in neurodevelopmental disorders, particularly in autism spectrum disorders (ASDs), but also in intellectual disability, schizophrenia (SCZ), attention-deficit hyperactivity disorder (ADHD), bipolar disorder (BD), alcohol use disorder (AUD) and anorexia nervosa (AN). This suggests that they have important functions during neurodevelopment. This suggestion is supported by data showing that neurite outgrowth, cell survival and neural circuit formation can be affected by disruption of these genes. Here, we review the current genetic data about their involvement in neuropsychiatric disorders and explore studies on how null mutations affect mouse behavior. Finally, we highlight to role of protein-protein interactions in the potential mechanism of action of Cntn4, -5 and -6 and emphasize that complexes with other membrane proteins may play a role in neuronal developmental functions.

Developmental inter-relations between early maternal depression, contextual risks, and interpersonal stress, and their effect on later child cognitive functioning

BACKGROUND

Maternal depression and contextual risks (e.g. poverty) are known to impact children's cognitive and social functioning. However, few published studies have examined how stress in the social environment (i.e. interpersonal stress) might developmentally inter-relate with maternal depression and contextual risks to negatively affect a child in these domains. This was the purpose of the current study.

METHOD

Mother-child pairs (n = 6979) from the Avon Longitudinal Study of Parents were the study participants. Mothers reported on depression, contextual risks, and interpersonal stress between pregnancy and 33 months child age. At age 8, the children underwent cognitive assessments and the mothers reported on the children's social cognitive skills.

RESULTS

Maternal depression, contextual risks, and interpersonal stress showed strong continuity and developmental inter-relatedness. Maternal depression and contextual risks directly predicted a range of child outcomes, including executive functions and social cognitive skills. Interpersonal stress worked indirectly via maternal depression and contextual risks to negatively affect child outcomes.

CONCLUSION

Maternal depression and contextual risks each increased interpersonal stress in the household, which, in turn, contributed to reduced child cognitive and social functioning.