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Abstract Action video-games (AVGs) could improve reading efficiency, enhancing not only visual attention but also phonological processing. Here we tested the AVG effects upon three consolidated language-based predictors of reading development in a sample of 79 pre-readers at-risk and 41 non-at-risk for developmental dyslexia. At-risk children were impaired in either phonemic awareness (i.e., phoneme discrimination task), phonological working memory (i.e., pseudoword repetition task) or rapid automatized naming (i.e., RAN of colours task). At-risk children were assigned to different groups by using an unequal allocation randomization: (1) AVG (n = 43), (2) Serious Non-Action Video Game (n = 11), (3) treatment-as-usual (i.e., speech therapy, n = 11), and (4) waiting list (n = 14). Pre- and post-training comparisons show that only phonemic awareness has a significantly higher improvement in the AVG group compared to the waiting list, the non-AVG, and the treatment-as-usual groups, as well as the combined active groups (n = 22). This cross-modal plastic change: (i) leads to a recovery in phonemic awareness when compared to the not-at-risk pre-readers; (ii) is present in more than 80% of AVG at-risk pre-readers, and; (iii) is maintained at a 6-months follow-up. The present findings indicate that this specific multisensory attentional training positively affects how phonemic awareness develops in pre-readers at risk for developmental dyslexia, paving the way for innovative prevention programs.

Background Interactive telemedicine applications have been progressively introduced in the assessment of cognitive and literacy skills. However, there is still a lack of research focusing on the validity of this methodology for the neuropsychological assessment of children with Specific Learning Disorder (SLD). Methods Seventy-nine children including 40 typically developing children (18 males, age 11.5 ± 1.06) and 39 children with SLD (24 males, age 12.3 ± 1.28) were recruited. Each participant underwent the same neuropsychological battery assessing reading accuracy, speed, and comprehension, writing, numerical processing, computation, and semantic numerical sense, twice (once during an in-person session (I) and once during a remote (R) home-based videoconference session). Four groups were subsequently defined based on the administration order. Repeated-measure-ANOVAs with assessment type (R vs. I testing) as within-subject factor and diagnosis (SLD vs. TR) and administration order (R-I vs. I-R) as between-subject factors, and between-group t-tests comparing the two assessment types within each time of administration, were run. Results No differences emerged between I and R assessments of reading accuracy and speed, numerical processing, and computation; on the contrary, potential biases against R assessment emerged when evaluating skills in writing, reading comprehension, and semantic numerical sense. However, regardless of the assessment type, the scores obtained with I and R assessments within the same administration time point overlapped. Discussion These results partially support the validity and reliability of the assessment of children's learning skills via a remote home-based videoconferencing system. Implementing telemedicine as an assessment tool may increase timely access to primary health care and to support research activity.

BackgroundDiagnosis of child and adolescent psychopathologies involves a multifaceted approach, integrating clinical observations, behavioral assessments, medical history, cognitive testing, and familial context information. Digital technologies, especially internet-based platforms for administering caregiver-rated questionnaires, are increasingly used in this field, particularly during the screening phase. The ascent of digital platforms for data collection has propelled advanced psychopathology classification methods such as supervised machine learning (ML) into the forefront of both research and clinical environments. This shift, recently called psycho-informatics, has been facilitated by gradually incorporating computational devices into clinical workflows. However, an actual integration between telemedicine and the ML approach has yet to be fulfilled. ObjectiveUnder these premises, exploring the potential of ML applications for analyzing digitally collected data may have significant implications for supporting the clinical practice of diagnosing early psychopathology. The purpose of this study was, therefore, to exploit ML models for the classification of attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) using internet-based parent-reported socio-anamnestic data, aiming at obtaining accurate predictive models for new help-seeking families. MethodsIn this retrospective, single-center observational study, socio-anamnestic data were collected from 1688 children and adolescents referred for suspected neurodevelopmental conditions. The data included sociodemographic, clinical, environmental, and developmental factors, collected remotely through the first Italian internet-based screening tool for neurodevelopmental disorders, the Medea Information and Clinical Assessment On-Line (MedicalBIT). Random forest (RF), decision tree, and logistic regression models were developed and evaluated using classification accuracy, sensitivity, specificity, and importance of independent variables. ResultsThe RF model demonstrated robust accuracy, achieving 84% (95% CI 82-85; P

IntroductionTachidino is a web-based platform for remote treatment of reading and spelling disorders. The purpose of the present study was to investigate the possible impact of different clinical conditions on the efficacy of treatment. The focus was on possible ADHD comorbidity-related effects on the outcomes of the Tachidino treatment, and the impact of previous treatments, such as speech and language therapy or the repetition of the same Tachidino program.Methods136 children with developmental dyslexia received four-weeks treatment via the Tachidino platform. Improvements in reading and writing scores were compared between different subgroups.ResultsNo gross differences emerged in treatment effectiveness between groups of children. Children receiving treatment improved significantly more than untreated children.DiscussionTreatment with Tachidino brought significant benefits for all children, irrespective of comorbidity, clinical history or treatment repetition. Comparison with an untreated control group (waiting list) made it possible to exclude that improvement was due to test–retest learning effects.

Developmental dyslexia is a very common learning disorder causing an impairment in reading ability. Although the core deficit underlying dyslexia is still under debate, significant agreement is reached in the literature that dyslexia is related to a specific deficit in the phonological representation of speech sounds. Many studies also reported an association between reading skills and music. These findings suggest that interventions aimed at enhancing basic auditory skills of children with DD may impact reading abilities. However, music education alone failed to produce improvements in reading skills comparable to those resulting from traditional intervention methods for DD. Therefore, a computer-assisted intervention method, called Rhythmic Reading Training (RRT), which combines sublexical reading exercises with rhythm processing, was implemented. The purpose of the present study was to compare the effectiveness of RRT and that of an intervention resulting from the combination of two yet validated treatments for dyslexia, namely, Bakker’s Visual Hemisphere-Specific Stimulation (VHSS) and the Action Video Game Training (AVG). Both interventions, administered for 13 h over 9 days, significantly improved reading speed and accuracy of a group of Italian students with dyslexia aged 8–14. However, each intervention program produced improvements that were more evident in specific reading parameters: RRT was more effective for improvement of pseudoword reading speed, whereas VHSS + AVG was more effective in increasing general reading accuracy. Such different effects were found to be associated with different cognitive mechanisms, namely, phonological awareness for RRT and rapid automatized naming for VHSS + AVG, thus explaining the specific contribution of each training approach. Clinical Trial registration: ClinicalTrials.gov NCT02791841.

The current gold standard for diagnosis of attention deficit/hyperactivity disorder (ADHD) includes subjective measures, such as clinical interview, observation, and rating scales. The significant heterogeneity of ADHD symptoms represents a challenge for this assessment and could prevent an accurate diagnosis. The aim of this work was to investigate the ability of a multi-domain profile of measures, including blood fatty acid (FA) profiles, neuropsychological measures, and functional measures from near-infrared spectroscopy (fNIRS), to correctly recognize school-aged children with ADHD. To answer this question, we elaborated a supervised machine-learning method to accurately discriminate 22 children with ADHD from 22 children with typical development by means of the proposed profile of measures. To assess the performance of our classifier, we adopted a nested 10-fold cross validation, where the original dataset was split into 10 subsets of equal size, which were used repeatedly for training and testing. Each subset was used once for performance validation. Our method reached a maximum diagnostic accuracy of 81% through the combining of the predictive models trained on neuropsychological, FA profiles, and deoxygenated-hemoglobin features. With respect to the analysis of a single-domain dataset per time, the most discriminant neuropsychological features were measures of vigilance, focused and sustained attention, and cognitive flexibility; the most discriminating blood FAs were linoleic acid and the total amount of polyunsaturated fatty acids. Finally, with respect to the fNIRS data, we found a significant advantage of the deoxygenated-hemoglobin over the oxygenated-hemoglobin data in terms of predictive accuracy. These preliminary findings show the feasibility and applicability of our machine-learning method in correctly identifying children with ADHD based on multi-domain data. The present machine-learning classification approach might be helpful for supporting the clinical practice of diagnosing ADHD, even fostering a computer-aided diagnosis perspective.