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Background Increasing evidence suggests a complex role of family influences, such as the exposure to parent psychopathology through parenting behavior, in parent-to-child psychopathology transmission. Parenting behaviour could represent a relevant target of psychoeducative intervention. Given these premises, we aimed to evaluate homotypic and heterotypic relationships between parent and child psychopathology, mediated by parenting behaviours, taking into account the constructs of parent and offspring internalizing and externalizing psychopathology. Methods Internalizing and externalizing symptoms in 272 clinically-referred subjects (mean age = 14.5 ± 2.3; F = 23.5%) and their parents (mothers n = 272, fathers n = 242) were assessed through the Child Behavior Checklist and the Adult Self Report; four areas of parenting behaviours were investigated through the Family Life Questionnaire. Multiple mediation models were built, considering mother and father psychopathology scales as independent variables, parenting measures and family functioning as mediators (Affirmation, Rules, Discipline and Special Allowances), child psychopathology scales as dependent variables and demographic variables as covariates. Results Regression models showed a significant effect of maternal internalizing symptomatology on child externalizing behavioral problems; high levels of maternal pathology predicted high levels of children’s psychopathology. A total mediating effect of parenting measures was found: high levels of internalizing symptoms in mothers predicted low levels of affirmation, which in turn predicted high levels of externalizing psychopathology in children. Conclusions Our study results confirmed the existence of interdependent links between mothers’ psychiatric symptomatology, parenting behaviour and offspring outcomes, specifically in an Italian context. On a clinical and rehabilitation basis, this work offers suggestions about parenting practices, specifically maternal, involved in the maintenance of child psychopathology.

Individual responses to methylphenidate (MPH) can significantly differ in children with attention-deficit/hyperactivity disorder (ADHD) in terms of the extent of clinical amelioration, optimal dosage needed, possible side effects, and short- and long-term duration of the benefits. In the present repeated-measures observational study, we undertook a proof-of-concept study to determine whether clustering analysis could be useful to characterize different clusters of responses to MPH in children with ADHD. We recruited 33 children with ADHD who underwent a comprehensive clinical, cognitive, and neurophysiological assessment before and after one month of MPH treatment. Symptomatology changes were assessed by parents and clinicians. The neuropsychological measures used comprised pen-and-paper and computerized tasks. Functional near-infrared spectroscopy was used to measure cortical hemodynamic activation during an attentional task. We developed an unsupervised machine learning algorithm to characterize the possible clusters of responses to MPH in our multimodal data. A symptomatology improvement was observed for both clinical and neuropsychological measures. Our model identified distinct clusters of amelioration that were related to symptom severity and visual-attentional performances. The present findings provide preliminary evidence that clustering analysis can potentially be useful in identifying different responses to MPH in children with ADHD, highlighting the importance of a personalized medicine approach within the clinical framework.

Recently, there has been an increase in telemedicine applied to child neuropsychiatry, such as the use of online platforms to collect remotely case histories and demographic and behavioral information. In the present proof-of-concept study, we aimed to understand to what extent information parents and teachers provide through online questionnaires overlaps with clinicians’ diagnostic conclusions on attention-deficit/hyperactivity disorder (ADHD). Moreover, we intended to explore a possible role that autism spectrum disorders (ASD) symptoms played in this process. We examined parent- and teacher-rated questionnaires collected remotely and an on-site evaluation of intelligence quotients from 342 subjects (18% females), aged 3–16 years, and referred for suspected ADHD. An easily interpretable machine learning model—decision tree (DT)—was built to simulate the clinical process of classifying ADHD/non-ADHD based on collected data. Then, we tested the DT model’s predictive accuracy through a cross-validation approach. The DT classifier’s performance was compared with those that other machine learning models achieved, such as random forest and support vector machines. Differences in ASD symptoms in the DT-identified classes were tested to address their role in performing a diagnostic error using the DT model. The DT identified the decision rules clinicians adopt to classify an ADHD diagnosis with an 82% accuracy rate. Regarding the cross-validation experiment, our DT model reached a predictive accuracy of 74% that was similar to those of other classification algorithms. The caregiver-reported ADHD core symptom severity proved the most discriminative information for clinicians during the diagnostic decision process. However, ASD symptoms were a confounding factor when ADHD severity had to be established. Telehealth procedures proved effective in obtaining an automated output regarding a diagnostic risk, reducing the time delay between symptom detection and diagnosis. However, this should not be considered an alternative to on-site procedures but rather as automated support for clinical practice, enabling clinicians to allocate further resources to the most complex cases.

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children and adolescents, with environmental and biological causal influences. Pharmacological medication is the first choice in ADHD treatment; recently, many studies have concentrated on dietary supplementation approaches to address nutritional deficiencies, to which part of non-responses to medications have been imputed. This review aims to evaluate the efficacy of non-pharmacological supplementations in children or adolescents with ADHD. We reviewed 42 randomized controlled trials comprised of the following supplementation categories: polyunsaturated fatty acids (PUFAs), peptides and amino acids derivatives, single micronutrients, micronutrients mix, plant extracts and herbal supplementations, and probiotics. The reviewed studies applied heterogeneous methodologies, thus making it arduous to depict a systematic overview. No clear effect on single cognitive, affective, or behavioral domain was found for any supplementation category. Studies on PUFAs and micronutrients found symptomatology improvements. Peptides and amino acids derivatives, plant extracts, herbal supplementation, and probiotics represent innovative research fields and preliminary results may be promising. In conclusion, such findings, if confirmed through future research, should represent evidence for the efficacy of dietary supplementation as a support to standard pharmacological and psychological therapies in children and adolescents with ADHD.

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Recently there has been an increase in telemedicine applied to child neuropsychiatry, such as the use of online platforms to remotely collect anamnestic and behavioral information. In the present proof-of-concept study, we aimed to understand to what extent information provided by parents and teachers through online questionnaires overlaps with clinicians’ diagnostic conclusions on Attention-Deficit/Hyperactivity Disorder (ADHD). Moreover, we intended to explore a possible role played by autism spectrum disorders (ASD) symptoms in this process. We examined parent- and teacher-rated questionnaires collected remotely and an on-site evaluation of intelligence quotients from 342 subjects (18% females), aged 3 to 16 years, referred for suspected ADHD. A machine learning model (decision tree, DT) was built to simulate the clinical process of classifying ADHD/non-ADHD. Differences in ASD symptoms in the DT-identified classes were tested to address their role in performing a diagnostic error using the DT model. The DT identified the decision rules adopted by clinicians to classify ADHD diagnosis and proved accurate in 82% of our subjects. The caregiver-reported ADHD core symptom severity proved the most discriminative information for clinicians during the diagnostic decision process. However, ASD symptoms were a confounding factor when ADHD severity had to be established. Telehealth procedures proved effective in obtaining an automated output regarding a diagnostic risk, reducing the time delay between symptom detection and diagnosis. However, this should not be considered as an alternative to on-site procedures but as automated support for clinical practice, enabling clinicians to allocate more resources to the most complex cases.

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