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Copyright of Voces de la Educacion is the property of Voces de la Educacion and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

There are a number of key features which make olfaction difficult to study; subjective processes of odor detection, discrimination and identification, and individualistic odor hedonic perception and associated odor memories. In this systematic review we explore the role functional near-infrared spectroscopy (fNIRS) has played in understanding olfactory perception in humans. fNIRS is an optical neuroimaging technique able to measure changes in brain hemodynamics and oxygenation related to neural electrical activity. Adhering to PRISMA guidelines, results of this search found that generally the majority of studies involving healthy adult subjects observed increased activity in response to odors. Other population types were also observed, such as infants, individuals with autism, attention deficit hyperactivity disorder (ADHD), post-traumatic stress disorder (PTSD), mild cognitive impairment (MCI) and dysosmia. fNIRS coverage heavily favored the prefrontal cortex, temporal and parietal regions. This review finds that odor induced cortical activation is dependent on multiple factors, such as odorant type, gender and population type. This review also finds that there is room for improvement in areas such as participant diversity, use of wearable fNIRS systems, physiological monitoring and multi-distance channels. [ABSTRACT FROM AUTHOR]

This paper explores research in how students with Attention Deficit Hyperactive Disorder (ADHD) experience temporality. Using a phenomenological lens, the author looks for clues that indicate that students with ADHD sense time differently than those without ADHD and uses the writings on temporality by philosopher Martin Heidegger to underpin the importance of subjectivity in temporal perception. Her goal is to establish a philosophical framework for developing practical approaches to teaching art to secondary students that address the difference in temporal perception of students with ADHD in an effort to create a more inclusive learning environment. [ABSTRACT FROM AUTHOR]

Prior studies conducted within the realm of environmental psychology and public health have shown that air pollution exposure exerts significant effects on both physical and psychological well-being, especially for children. The roles of air pollution exposure are being increasingly recognized as essential factors influencing children's restoration. This systematic review provides an overview of existing knowledge of the impact of air pollution on children's mental health and restorative experience in both outdoor and indoor environments. A list of keywords for paper selection was derived from a systematic investigation of the literature on children's restorative environments. A total of 228 studies were initially identified, 18 of which met the eligibility criteria. This systematic review summarizes distinctive categories of air pollutants and discusses the assessments for both air pollution exposure and children's restorative outcomes. Additionally, 16 barriers in air pollution exposure to children's restorative experience were identified. The findings of this systematic review were concluded in an integrated framework, which have important implications for developing evidence-based and cross-disciplinary research on the air quality and children's restoration. [ABSTRACT FROM AUTHOR]

If having one child with Attention Deficit Hyperactivity Disorder (ADHD) contributes to family stress and dysfunction, then what happens when more than one family member has ADHD? This paper explores this question by drawing on findings from a multi-case study that explored the voices of stakeholders (child and parent/carer) affected by ADHD in Ireland. There were eight case studies (families) included. Each case had one parent/carer with ADHD and a child with ADHD. Eight children (aged 7–17 years; Mean = 12.6; S.D. = 3.4) and ten parents (2 males) participated. Four parentw/carers reported a diagnosis of ADHD themselves (2 females) and 6 mothers participated who had a spouse with ADHD. Triangulation was achieved using multiple interviews (parent/carers and child), a demographic survey, and creative methods with the children to contribute to a highly contextualised understanding of stakeholders' experiences. Research findings demonstrated that there may be positive and negative consequences when both parent and child have ADHD. On the one hand, it may contribute to greater dysfunction, when parents with ADHD struggle to stick to routines and remain calm and organised. On the other hand, children with ADHD may feel a sense of belonging and less different, parents believe they have greater understanding, and shared interactional preferences may have benefits. Findings will be discussed in terms of their implications for practice with families and future research. [ABSTRACT FROM AUTHOR]

Background: Attention deficit and hyperactivity disorder (ADHD) is one of the most common mental disorders in childhood. Recently, several studies showed the high suicide risk of patients with ADHD; however, most of these studies had a cross-sectional design. Aims: The aim of the current research is to complete a systematic review of published studies which investigate the suicide risk of ADHD patients with longitudinal design. Methods: The systematic search was made on OVID Medline, PsychInfo, PubMed, Scopus, and Web of Science. The search terms were (ADHD OR attention deficit hyperactivity disorder) AND (suicide OR suicidal OR suicidality) AND (follow-up OR longitudinal study OR prospective study). The inclusion criteria were as follows: written in English; the participants were under 18 years at baseline; longitudinal, prospective studies; ADHD population at baseline and at follow-up; and suicide behavior as a primary outcome. The exclusion criteria were as follows: the study did not contain empirical data and reviews/meta-analyses and studies which aimed to investigate the drug treatment efficacy of ADHD. Results: After the screening process, 18 papers were included in the systematic review. Ten articles were altogether published in the last 5 years. The range of follow-up periods varied between 2 and 17 years. Several different assessment tools were used to investigate the symptoms and/or the diagnosis of ADHD and the suicidal risk. Nine studies enrolled children aged under 12 at baseline, and three studies used birth cohort data, where there was no strict age-based inclusion criteria. A total of 17 studies found a positive association between ADHD diagnosis at baseline and the presence of suicidal behavior and/or attempts at the follow-up visits. Limitations: The main limitation of this review is the methodological heterogeneity of the selected studies. A further limitation is the relatively low number of studies that examined a population with balanced gender ratios. Additionally, only one study published data about the treatment of ADHD. Finally, though we carefully chose the keywords, we still may be missing some relevant papers on this topic. Conclusions: In spite of the methodological diversity of the included studies, the results of the current systematic review highlight the importance of screening suicidality in the long term in patients with ADHD. Therefore, further studies that compare the suicidal risk of treated and untreated groups of ADHD patients in the long term are needed. [ABSTRACT FROM AUTHOR]

The genetic architecture of neurodevelopmental disorders is largely polygenic, non-specific, and pleiotropic. This complex genetic architecture makes the search for specific etiological mechanisms that contribute to neurodevelopmental risk more challenging. Monogenic disorders provide an opportunity to focus in on how well-articulated signaling pathways contribute to risk for neurodevelopmental outcomes. This paper will focus on neurofibromatosis type 1 (NF1), a rare monogenic disorder that is associated with varied neurodevelopmental outcomes. Specifically, this paper will provide a brief overview of NF1 and its phenotypic associations with autism spectrum disorder, attention-deficit/hyperactivity disorder, and specific learning disorders, describe how variation within the NF1 gene increases risk for neurodevelopmental disorders via altered Ras signaling, and provide future directions for NF1 research to help elucidate the genetic architecture of neurodevelopmental disorders in the general population. [ABSTRACT FROM AUTHOR]

Esports is an electronic sport known as competitive video gaming, which has seen tremendous growth over the past few years. Unlike traditional sports such as hockey, baseball, and soccer, Esports is an interconnection of multiple platforms, computing, gaming, and media into a sport event. A systematic review was conducted to collate and review all the research studies concerning Esports from a health perspective and analyze various physical and psychological distress regarding Esports. Relevant published papers were identified through the electronic databases Google Scholar, PubMed, and Research Gate. As a result of the systematic research review, seven articles were identified that investigated three major domains (i) Esports and physical issues, (ii) Esports and psychological distress, and (iii) Esports and addiction. The result indicated that Esports excessive play and its competitive nature lead to physical and psychological problems. Physical issues include eye fatigue, blurry vision, low back pain, tension headache, wrist pain, hand pain, and poor posture while gaming. Psychological issues include depression, anxiety, apathy, uncooperative attitude, tense, sleep disturbances, mental distress, aggressive affect and behaviors, distress in social life, and emotional disturbances. The studies also found that long hours of online gaming was associated with the presence of depression, social phobia, obsession–compulsion, interpersonal sensitivity, hostility, phobic anxiety, paranoid ideation, psychoticism attention-deficit hyperactivity disorder, and gaming addiction. This review draws insight into the problematic side of Esports and suggested that future studies must focus on the interventions to deal with the negative impact of Esports. [ABSTRACT FROM AUTHOR]

Although earlier research has shown that individual differences on the spectrum of attention deficit hyperactivity disorder (ADHD) are highly heritable, emerging evidence suggests that symptoms are associated with complex interactions between genes and environmental influences. This study investigated whether a genetic predisposition [Note that the term 'genetic predisposition' was used in this manuscript to refer to an estimate based on twin modeling (an individual's score on the latent trait that resembles additive genetic influences) in the particular population being examined.] for the symptom dimensions hyperactivity and inattention determines the extent to which unique-environmental influences explain variability in these symptoms. To this purpose, we analysed a sample drawn from the Twins Early Development Study (TEDS) that consisted of item-level scores of 2168 16-year-old twin pairs who completed both the Strengths and Difficulties Questionnaire (SDQ; Goodman, in J Child Psychol Psychiatry 38:581–586, 1997) and the Strength and Weaknesses of ADHD Symptoms and Normal Behavior (SWAN; Swanson, in Paper presented at the meeting of the American Psychological Association, Los Angeles, 1981) questionnaire. To maximize the psychometric information to measure ADHD symptoms, psychometric analyses were performed to investigate whether the items from the two questionnaires could be combined to form two longer subscales. In the estimation of genotype-environment interaction, we corrected for error variance heterogeneity in the measurement of ADHD symptoms through the application of item response theory (IRT) measurement models. A positive interaction was found for both hyperactivity (e.g., β 1 = 2.20 with 95% highest posterior density interval equal to [1.79;2.65] and effect size equal to 3.00) and inattention (e.g., β 1 = 2.16 with 95% highest posterior density interval equal to [1.56;2.79] and effect size equal to 3.07). These results indicate that unique-environmental influences were more important in creating individual differences in both hyperactivity and inattention for twins with a genetic predisposition for these symptoms than for twins without such a predisposition. [ABSTRACT FROM AUTHOR]

Attention Deficit Hyperactivity Disorder (ADHD) is a frequent learning disorder affecting about 5%–8% of the student population globally. Currently, the traditional methods for ADHD diagnosis are not fully specified, due to difficulties in identifying the particular factors that cause this disorder. In this paper, we present a novel system for diagnosing ADHD, which does not need special equipment. Instead, it is based on the application of machine learning (ML), using data gathered from gameplay sessions of a serious game named "ADHD360", developed for this purpose. Participants were recruited with particular criteria in order to generate data for the study. The benefits of our approach include less subjectivity in the decision process, cost-efficiency and easier accessibility than the typical procedure. To this end, special data preprocessing steps and ML techniques were applied. Our models achieved up to 85.7% F1-score performance metric in predicting correctly a user's label (ADHD or not) from his/her gameplay session in ADHD360. Our method also proved to be efficient using only a small amount of data for the training procedure. The results of our systems are very promising, indicating notable ability of the tool to distinguish players that probably suffer from ADHD than those who do not. [ABSTRACT FROM AUTHOR]

Background: Substance use disorders (SUD) often co-occur with attention deficit hyperactivity disorder (ADHD). Although the short-term effects of some specific interventions have been investigated in randomized clinical trials, little is known about the long-term clinical course of treatment-seeking SUD patients with comorbid ADHD. Aims: This paper presents the protocol and baseline clinical characteristics of the International Naturalistic Cohort Study of ADHD and SUD (INCAS) designed and conducted by the International Collaboration on ADHD and Substance Abuse (ICASA) foundation. The overall aim of INCAS is to investigate the treatment modalities provided to treatment-seeking SUD patients with comorbid ADHD, and to describe the clinical course and identify predictors for treatment outcomes. This ongoing study employs a multicentre observational prospective cohort design. Treatment-seeking adult SUD patients with comorbid ADHD are recruited, at 12 study sites in nine different countries. During the follow-up period of nine months, data is collected through patient files, interviews, and self-rating scales, targeting a broad range of cognitive and clinical symptom domains, at baseline, four weeks, three months and nine months. Results: A clinically representative sample of 578 patients (137 females, 441 males) was enrolled during the recruitment period (June 2017-May 2021). At baseline, the sample had a mean age (SD) of 36.7 years (11.0); 47.5% were inpatients and 52.5% outpatients; The most prevalent SUDs were with alcohol 54.2%, stimulants 43.6%, cannabis 33.1%, and opioids 14.5%. Patients reported previous treatments for SUD in 71.1% and for ADHD in 56.9%. Other comorbid mental disorders were present in 61.4% of the sample: major depression 31.5%, post-traumatic stress disorder 12.1%, borderline personality disorder 10.2%. Conclusions: The first baseline results of this international cohort study speak to its feasibility. Data show that many SUD patients with comorbid ADHD had never received treatment for their ADHD prior to enrolment in the study. Future reports on this study will identify the course and potential predictors for successful pharmaceutical and psychological treatment outcomes. Trial registration: ISRCTN15998989 20/12/2019. [ABSTRACT FROM AUTHOR]

The automatic identification of Attention Deficit Hyperactivity Disorder (ADHD) is essential for developing ADHD diagnosis tools that assist healthcare professionals. Recently, there has been a lot of interest in ADHD detection from EEG signals because it seemed to be a rapid method for identifying and treating this disorder. This paper proposes a technique for detecting ADHD from EEG signals with the nonlinear features extracted using tunable Q-wavelet transform (TQWT). The 16 channels of EEG signal data are decomposed into the optimal amount of time-frequency sub-bands using the TQWT filter banks. The unique feature vectors are evaluated using Katz and Higuchi nonlinear fractal dimension methods at each decomposed levels. An Artificial Neural Network classifier with a 10-fold cross-validation method is found to be an effective classifier for discriminating ADHD and normal subjects. Different performance metrics reveal that the proposed technique could effectively classify the ADHD and normal subjects with the highest accuracy. The statistical analysis showed that the Katz and Higuchi nonlinear feature estimation methods provide potential features that can be classified with high accuracy, sensitivity, and specificity and is suitable for automatic detection of ADHD. The proposed system is capable of accurately distinguishing between ADHD and non-ADHD subjects with a maximum accuracy of 100%. [ABSTRACT FROM AUTHOR]

Attention deficit hyperactivity disorder (ADHD) is a very common disorder in children and adults. A connection with sleep disorders, and above all, disorders of the circadian rhythm are the subject of research and debate. The circadian system can be represented on different levels. There have been a variety of studies examining 24-h rhythms at the behavioral and endocrine level. At the molecular level, these rhythms are based on a series of feedback loops of core clock genes and proteins. In this paper, we compared the circadian rhythms at the behavioral, endocrine, and molecular levels between children with ADHD and age- and BMI-matched controls, complementing the previous data in adults. In a minimally invasive setting, sleep was assessed via a questionnaire, actigraphy was used to determine the motor activity and light exposure, saliva samples were taken to assess the 24-h profiles of cortisol and melatonin, and buccal mucosa swaps were taken to assess the expression of the clock genes BMAL1 and PER2. We found significant group differences in sleep onset and sleep duration, cortisol secretion profiles, and in the expression of both clock genes. Our data suggest that the analysis of circadian molecular rhythms may provide a new approach for diagnosing ADHD in children and adults. [ABSTRACT FROM AUTHOR]

Copyright of Clínica Contemporánea: Revista de Diagnóstico Psicológico, Psicoterapia y Salud is the property of Colegio Oficial de Psicologos de Madrid and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Frontal lobe function may not universally explain all forms of attention deficit hyperactivity disorder (ADHD) but the frontal lobe hypothesis described supports an internally consistent model for integrating the numerous behaviors associated with ADHD. The paper examines the developmental trajectories of frontal and prefrontal lobe development, framing ADHD as maturational dysregulation concluding that the cognitive, motor, and behavioral abilities of the presumptive majority of ADHD children may not primarily be disordered or dysfunctional but reflect maturational dysregulation that is inconsistent with the psychomotor and cognitive expectations for the child's chronological and mental age. ADHD children demonstrate decreased activation of the right and middle prefrontal cortex. Prefrontal and frontal lobe regions have an exuberant network of shared pathways with the diencephalic region, also having a regulatory function in arousal as well as with the ascending reticular formation which has a capacity for response suppression to task-irrelevant stimuli. Prefrontal lesions oftentimes are associated with the regulatory breakdown of goal-directed activity and impulsivity. In conclusion, a presumptive majority of childhood ADHD may result from maturational dysregulation of the frontal lobes with effects on the direct, indirect and/or, hyperdirect pathways. [ABSTRACT FROM AUTHOR]

Bipolar Disorder (BD) and Attention Deficit and Hyperactivity Disorder (ADHD) are mental disorders with high degree of lifetime comorbidity. Both BD and ADHD are disorders with onset in childhood and early adolescence. Both disorders are often undiagnosed, misdiagnosed, and sometimes overdiagnosed, leading to high rates of morbidity and disability. The psychiatric and behavioral symptoms associated with ADHD and BD have significant overlap. Albeit the existence of a large body of literature, it is far from being clear whether comorbidity can be explained by the confounding overlap of operationally defined criteria or whether it reflects a genuine comorbidity of two biologically distinct disorders. The aim of this paper is to recognize and/or differentiate the pattern of ADHD across the course of BD from a nosological point of view, focusing on specific clinical and neurobiological dimensions. We found that some critical issues may help to fulfill the purpose of our perspective. We suggest that the relationship between ADHD and BD, based on clinical, developmental, and epidemiological commonalities, can be better clarified using four different scenarios. [ABSTRACT FROM AUTHOR]

This narrative review describes, in detail, the relationships between Developmental Coordination Disorder (DCD) and most prevalent associated comorbidities in their complexity, heterogeneity and multifactoriality. The research has been conducted on the main scientific databases, excluding single case papers. Blurred borders between the different nosographic entities have been described and advances in this field have been highlighted. In this multifaceted framework a specific profiling for co-occurring DCD, ADHD and ASD signs and symptoms is proposed, confirming the need for a multidisciplinary approach to define new diagnostic paradigms in early childhood. [ABSTRACT FROM AUTHOR]

For attention deficit hyperactivity disorder (ADHD), a common neurological disease, accurate identification is the basis for treatment. In this paper, a novel end-to-end representation learning framework for ADHD classification of functional magnetic resonance imaging (fMRI) sequences is proposed. With such a framework, the complexity of the sequence representation learning neural network decreases, the overfitting problem of deep learning for small samples cases is solved effectively, and superior classification performance is achieved. Specifically, a data conversion module was designed to convert a two-dimensional sequence into a three-dimensional image, which expands the modeling area and greatly reduces the computational complexity. The transfer learning method was utilized to freeze or fine-tune the parameters of the pre-trained neural network to reduce the risk of overfitting in the cases with small samples. Hierarchical feature extraction can be performed automatically by combining the sequence representation learning modules with a weighted cross-entropy loss. Experiments were conducted both with individual imaging sites and combining them, and the results showed that the classification average accuracies with the proposed framework were 73.73% and 72.02%, respectively, which are much higher than those of the existing methods. [ABSTRACT FROM AUTHOR]

Copyright of Revista Latinoamericana de Psicopatologia Fundamental is the property of Associacao Universitaria de Pesquisa em Psicopatologia Fundamental and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Children with Attention-Deficit/Hyperactivity Disorder (ADHD) face a range of learning difficulties in the school environment, thus several strategies have been developed to enhance or optimise their performance in school. One possible way is to actively enable appropriate restlessness using dynamic seats. In this paper, an assessment of the efficacy of a dynamic seat while solving school task is presented and compared to classic chair and therapy ball. To test the effectiveness of active seat, a study that examined task solving performance while observing the intensity of movement, in-seat behaviour and psychophysiological responses (electrodermal activity, facial temperature) was designed. A total of 23 school-aged children participated in the study, 11 children with a combined type of ADHD and 12 children without disorders. Children with ADHD achieved the best results when sitting in the active seat, where the most intense movement and best in-seat behaviour was observed. At the same time, psychophysiological parameters indicate that when performing better at the task children with ADHD were not too challenged and were consequently less agitated. Results have suggested that for a better cognitive performance of children with ADHD, it is crucial to provide a comfortable and pleasant workspace that enables them the right amount of restlessness. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Psychiatry & Clinical Psychology / Psychiatria i Psychologia Kliniczna is the property of Medical Communications Sp. z o.o. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The notion of Attention Deficit Hyperactivity Disorder (ADHD) may have its origins in 1763, when Scottish physician Sir Arthur Crichton observed people who could be easily distracted to a degree approaching the nature of delirium. Since then, the notion of ADHD matured and aroused controversy concerning whether it is a real illness and the motives behind particular methods of its treatments. Despite the controversy, ADHD is well established as a research subject and a frequently diagnosed disorder. Thus, the aim of this paper is not to deal with controversies but rather to show the evolution of methods used for the detection of this disorder. Literature analysis revealed that those methods varied from classical psychological tests/questionnaires to analyses of: eye-motion, micro-motion, brain activity, and behavioral patterns in virtual reality. The main conclusion is that the above methods tend to be heavily computer-aided and that the future belongs to mobile solutions incorporated into complex e-health systems. [ABSTRACT FROM AUTHOR]

Functional near-infrared spectroscopy (fNIRS), a growing neuroimaging modality, has been utilized over the past few decades to understand the neuronal behavior in the brain. The technique has been used to assess the brain hemodynamics of impaired cohorts as well as able-bodied. Neuroimaging is a critical technique for patients with impaired cognitive or motor behaviors. The portable nature of the fNIRS system is suitable for frequent monitoring of the patients who exhibit impaired brain activity. This study comprehensively reviews brain-impaired patients: The studies involving patient populations and the diseases discussed in more than 10 works are included. Eleven diseases examined in this paper include autism spectrum disorder, attention-deficit hyperactivity disorder, epilepsy, depressive disorders, anxiety and panic disorder, schizophrenia, mild cognitive impairment, Alzheimer's disease, Parkinson's disease, stroke, and traumatic brain injury. For each disease, the tasks used for examination, fNIRS variables, and significant findings on the impairment are discussed. The channel configurations and the regions of interest are also outlined. Detecting the occurrence of symptoms at an earlier stage is vital for better rehabilitation and faster recovery. This paper illustrates the usability of fNIRS for early detection of impairment and the usefulness in monitoring the rehabilitation process. Finally, the limitations of the current fNIRS systems (i.e., nonexistence of a standard method and the lack of well-established features for classification) and future research directions are discussed. The authors hope that the findings in this paper would lead to advanced breakthrough discoveries in the fNIRS field in the future. [ABSTRACT FROM AUTHOR]

Childhood epilepsy represents a complex group of seizure disorders with neuropsychological deficits and diverse outcomes during developmental stages and later in life. Epilepsy can lead to neurodevelopmental disorders, such as attention-deficit and hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). This study based on review of literature, the association of etiologically diagnosed childhood epilepsy with the subsequent risks of neurodevelopmental disorders. Papers for this review were selected from established databases like PubMed, Proquest, and ScienceDirect among many. It has shown high comorbidity between epilepsy and intellectual deficits. However, it merely hints at common neurological mechanisms and does not adequately imply causation. Medications for treating epilepsy in children are also implicated in the development of neurodevelopmental disorders later in life. These claims are also explored in the review. There may be common mechanisms between epilepsy and neurodevelopmental disorders, which has to be further explored to develop causal factors. There is not much literature on the neurodevelopmental effects of antiepileptic drugs in the postnatal phase. [ABSTRACT FROM AUTHOR]

Studying functional brain connectivity plays an important role in understanding how human brain functions and neuropsychological diseases such as autism, attention-deficit hyperactivity disorder, and Alzheimer's disease (AD). Functional magnetic resonance imaging (fMRI) is one of the most popularly used tool to construct functional brain connectivity. However, the presence of noises and outliers in fMRI blood oxygen level dependent (BOLD) signals might lead to unreliable and unstable results in the construction of connectivity matrix. In this paper, we propose a pipeline that enables us to estimate robust and stable connectivity matrix, which increases the detectability of group differences. In particular, a low-rank plus sparse (L + S) matrix decomposition technique is adopted to decompose the original signals, where the low-rank matrix L recovers the essential common features from regions of interest, and the sparse matrix S catches the sparse individual variability and potential outliers. On the basis of decomposed signals, we construct connectivity matrix using the proposed novel concentration inequality-based sparse estimator. In order to facilitate the comparisons, we also consider correlation, partial correlation, and graphical Lasso-based methods. Hypothesis testing is then conducted to detect group differences. The proposed pipeline is applied to rs-fMRI data in Alzheimer's disease neuroimaging initiative to detect AD-related biomarkers, and we show that the proposed pipeline provides accurate yet more stable results than using the original BOLD signals. [ABSTRACT FROM AUTHOR]

Objectives To review the literature about the environmental impact on children's mental, behavior, and neurodevelopmental disorders. Sources of data A nonsystematic review of papers published on MEDLINE-PubMed was carried out using the terms environment and mental health or psychiatric disorders or neurodevelopmental disorders. Summary of findings Psychopathology emerges at different developmental times as the outcome of complex interactions between nature and nurture and may impact each person in different ways throughout childhood and determine adult outcomes. Mental health is intertwined with physical health and is strongly influenced by cultural, social and economic factors. The worldwide prevalence of psychiatric disorders in children and adolescents is 13.4%, and the most frequent are anxiety, disruptive behavior disorders, attention deficit hyperactivity disorder and depression. Neurodevelopment begins at the embryonic stage and continues through adulthood with genetic differences, environmental exposure, and developmental timing acting synergistically and contingently. Early life experiences have been linked to a dysregulation of the neuroendocrine-immune circuitry which results in alterations of the brain during sensitive periods. Also, the environment may trigger modifications on the epigenome of the differentiating cell, leading to changes in the structure and function of the organs. Over 200 million children under 5 years are not fulfilling their developmental potential due to the exposure to multiple risk factors, including poverty, malnutrition and unsafe home environments. Conclusions Continued support for the promotion of a protective environment that comprises effective parent-child interactions is key in minimizing the effects of neurodevelopmental disorders throughout the lifetime. [ABSTRACT FROM AUTHOR]

Background: ADHD is one of the most prevalent mental health disorders among children and adolescents. Household socio-economic status (SES) in early childhood is inversely related to ADHD later in childhood or adolescence. We conducted a systematic review to examine psychological, social and behavioural factors that mediate these relationships (PROSPERO Registration number: CRD42020182832). Methods and findings: We searched Medline, EMBASE, PsychINFo, and Web of Science from inception until May 2020. Both authors independently reviewed abstracts and identified papers for inclusion. We sought primary observational studies (cohort, cross-sectional and case control studies) of general population-based samples of children and adolescents aged 18 and under that investigated potential mediators of the relationships between SES and ADHD. Studies based upon non-general population-based samples, twins or biochemical/physiological changes were excluded. Direct and indirect effects derived from standard validated mediation analysis were extracted for potential mediators. We assessed risk of bias using a modified NIH tool and synthesised quantitative data without meta-analysis according to the (SWiM) protocol because of heterogeneity between included studies. Family adversity, paternal and maternal ADHD symptoms, Home Learning Environment, breastfeeding duration and a combined fine motor and language score at age 2 may lie on the SES-ADHD pathway. Evidence concerning the influence of maternal depression/anxiety and adverse parenting was inconsistent across studies. There was no evidence that mother's health-related behaviour, family characteristics, child's consumption of fizzy drinks or other developmental characteristics at birth/during infancy lie on the SES-ADHD pathway. Publication bias may have been introduced by our decision not to search grey literature, not to approach study authors and limit the search to the English language. Conclusions: Evidence for mediation of the SES-ADHD pathway in childhood/adolescence is under-researched. Maternal mental health, family adversity, parenting and health-related behaviours warrant further research based on longitudinal data and employing the most advanced mediation analysis methods. [ABSTRACT FROM AUTHOR]

Reading is a multifaceted skillset that has the potential to profoundly impact a child's academic performance and achievement. Mastery of reading skills is often an area of difficulty for children during their academic journey, particularly for children with Attention Deficit/Hyperactivity Disorder (ADHD), Specific Learning Disorder with Impairment in Reading (SLD-R), or children with a comorbid diagnosis of both ADHD and SLD-R. ADHD is characterized by executive functioning and impulse control deficits, as well as inattention and impulsivity. Among the academic struggles experienced by children with ADHD are challenges with word reading, decoding, or reading comprehension. Similarly, children with SLD-R frequently encounter difficulties in the development of appropriate reading skills. SLD-R incorporates dysfunctions in basic visual and auditory processes that result in difficulties with decoding and spelling words. There have been limited empirical studies investigating the efficacy of interventions to improve the reading ability of children with both ADHD and SLD-R. Research studies that have focused on reading interventions for children from this population have predominantly included the use of medication treatments with stimulants (e.g., methylphenidate) and non-stimulants (e.g., atomoxetine). This review paper will present and integrate findings from empirical studies on successful medication treatments for children with comorbid ADHD and SLDR. Furthermore, this paper will extend findings from empirically successful medication treatments to provide directions for future research. [ABSTRACT FROM AUTHOR]

Attention deficit hyperactivity disorder (ADHD) is associated with lower than average intelligence quotient (IQ) scores. However, research done on this disorder often excludes participants based on lower than average IQ's (i.e., between 70 and 85). The purpose of this paper is to alert researchers to the consequences of excluding participants based on IQ's within this range and to highlight the importance of providing a clear rationale when choosing to exclude participants based on IQ. Next, we offer recommendations for researching ADHD and their relative benefits and drawbacks of these approaches. Overall this paper emphasizes that including participants who have lower than average IQ in research on ADHD may promote a more realistic understanding of the condition and in turn improve our ability to treat it. [ABSTRACT FROM AUTHOR]

This paper summarizes the present knowledge on how positive allosteric modulators (PAMs) interact with the ligand-binding domain (LBD) of AMPA and kainate receptors, based on structure determinations. AMPA and kainate receptors belong to the family of ionotropic glutamate receptors that are responsible for mediating the majority of fast excitatory neurotransmission. These receptors have been related to brain disorders, e.g. Alzheimer's disease and attention deficit hyperactivity disorder. PAMs are small molecules that potentiate AMPA and kainate receptor currents by interfering with receptor desensitization. Therefore, PAMs are considered to be of interest for the development of pharmacological tools. Whereas PAMs for AMPAreceptors have been known for several years, only recently have PAMs for kainate receptors been reported. Today, >80 structures are available for AMPA receptors with PAMs. These PAMs bind at the interface between two LBD subunits in the vicinity of residue 775, which is important for functional differences between flip and flop isoforms of AMPA receptors. PAMs can be divided into five classes based on their binding mode. The most potent PAM reported to date belongs to class 3, which comprises dimerized PAMs. Three structures of the kainate receptor GluK1 were determined with PAMs belonging to class 2. One PAM enhances kainate receptor currents 5-to 59-fold but shows 100-fold lower potency compared to AMPA receptors. Selective PAMs for kainate receptors will be of great use as pharmacological tools for functional investigations in vivo and might potentially prove useful as drugs in controlling the activity of neuronal networks. [ABSTRACT FROM AUTHOR]

Asymmetric chorea unrelated to structural lesions is typically due to systemic etiologies, such as metabolic, autoimmune, or other inflammatory disorders. This is an editorial commenting on a paper by Batot C, Chea M, Zeidan S, et al. Clinical and radiological follow up of Pfizer-BioNTech COVID-19 vaccine-induced hemichorea-hemiballismus. Tremor and Other Hyperkinetic Movements; 2022; 12(1). DOI: https://doi.org/10.5334/tohm.688. A 90-year-old patient is reported who developed hemichorea shortly after his second vaccination against COVID-19. Hypometabolism was noted in the contralateral striatum. This case provides potential insights and raises questions about mechanisms of immunemediated hemichorea. [ABSTRACT FROM AUTHOR]

Research has documented how music training boosts executive functioning and that music therapy has been associated with a reduction in symptoms of Attention-Deficit Hyperactivity Disorder (ADHD). This paper reviews the evidence on whether music training has the potential to improve two executive functions--attentional control and inhibitory control--in children with ADHD. As the research on how music training affects children with ADHD is limited, studies on the benefits of music training for similar neurodiverse conditions and the effect of music training on attention and inhibition in neurotypical individuals were reviewed. This data may act as preliminary evidence for how music training may benefit children with ADHD, but further research must involve these children to confirm that these findings apply to them. [ABSTRACT FROM AUTHOR]

Resting-state functional MRI (rs-fMRI) has been increasingly applied in the research of brain cognitive science and psychiatric diseases. However, previous studies only focused on specific activation areas of the brain, and there are few studies on the inactivation areas. This may overlook much information that explains the brain's cognitive function. In this paper, we propose a relatively inert network (RIN) and try to explore its important role in understanding the cognitive mechanism of the brain and the study of mental diseases, using adult attention deficit hyperactivity disorder (ADHD) as an example. Here, we utilize methods based on group independent component analysis (GICA) and t -test to identify RIN and calculate its corresponding time series. Through experiments, alterations in the RIN and the corresponding activation network (AN) in adult ADHD patients are observed. And compared with those in the left brain, the activation changes in the right brain are greater. Further, when the RIN functional connectivity is introduced as a feature to classify adult ADHD patients from healthy controls (HCs), the classification accuracy rate is 12% higher than that of the original functional connectivity feature. This was also verified by testing on an independent public dataset. These findings confirm that the RIN of the brain contains much information that will probably be neglected. Moreover, this research provides an effective new means of exploring the information integration between brain regions and the diagnosis of mental illness. [ABSTRACT FROM AUTHOR]

Background: Attention-deficit/hyperactivity disorder (ADHD) is a neuro-developmental disease commonly seen in children and it is diagnosed via extensive interview procedures, behavioral studies, third-party observations, and comprehensive personal history. ADHD causes regional atrophy in brain regions and alters the pattern of functional brain connectivity networks. Automated/computerized methods based on magnetic resonance imaging (MRI) can replace subjective methods for the identification of ADHD.Objectives: The aim of this study was to analyze various machine-learning algorithms for ADHD by feeding in vital input features extracted from functional brain connectivity and different existing methods and to review factors crucial for the diagnosis of ADHD.Methods: This paper is a concise review of machine learning methods for the diagnosis of ADHD from MRI. Techniques for feature extraction, dimensionality reduction/feature selection, and classification, employed in the computerized techniques for the diagnosis of ADHD from MRI and the accuracy of classification offered by the individual methods, are focussed on the review.Conclusions: Machine learning algorithms with features of functional brain connectivity networks as input, with hierarchical sparse feature elimination, exhibits the highest accuracy. Augmentation of the behavioral features does not contribute much to increased accuracy. The level of accuracy offered by the frameworks meant for the computer-aided diagnosis of ADHD, available in the literature, does not justify their feasibility in clinical practice. Computerized methods that exploit highly specific biomarkers of ADHD like brain iron concentration in Globus Pallidus, Putamen, Caudate nucleus, and thalamus as features are not available. [ABSTRACT FROM AUTHOR]

This paper extends the understanding of the relation between ADHD and reading comprehension, through examining how this relation differs depending on the quantile an individual falls in for each. Samples from three twin projects around the United States were used (Florida Twin Project, Colorado component of International Longitudinal Twin Study of Early Reading Development, & Western Reserve Reading and Math Projects). Phenotypic analysis using quantile regression showed relations between ADHD related behaviors and reading comprehension to be stronger in the lower quantiles of reading comprehension in two of three samples. A new method was developed extending this analysis into the bivariate genetic space. Results of this quantile genetic analysis revealed that overlapping common environmental influences accounted for a larger proportion of variance in the lower quantiles of these variables in two of three samples. Finally, in all three samples the phenotypic relation was strongest when shared environmental influences accounted for a larger proportion of the overall variance. [ABSTRACT FROM AUTHOR]

The Obesogen field developed from two separate scientific research areas, endocrine disruptors and the Developmental Origins of Health and Disease (DOHaD). Endocrine Disrupting Chemicals (EDCs) are exogenous chemicals or mixtures of chemicals that interfere with the action of hormones. Exposure to EDCs during early development (DOHaD) has been shown to increase susceptibility to a variety of diseases including infertility, asthma, breast and prostate cancer, early puberty, susceptibility to infections, heart disease, autoimmune disease, and attention deficit hyperactivity disorder/learning disability. The effects of EDCs on obesity and fat cell development first gained attention around the turn of the twenty-first century. In 2002 Dr. Paula Baillie-Hamilton wrote the first review article focusing on environmental chemicals and obesity. She suggested that the obesity epidemic correlated with the increased production of chemicals after World War II. Baillie-Hamilton identified studies showing that exposures to a variety of chemicals led to weight gain. Shortly after that a commentary on an article showing that nonylphenol would increase fat cell differentiation in vitro noted the Baillie-Hamilton article and made the point that perhaps obesity was due in part to exposure to EDCs. In 2006 the field of DOHaD/EDCs and obesity made a giant leap forward when Dr. Bruce Blumberg published a paper showing that tributyltin could lead to weight gain in mice and coined the term obesogen for a chemical that caused weight gain and lead to obesity. In 2011, the NIEHS developed the first funding initiative focused on obesogens. In the following years there have been several workshops focused on obesogens. This paper describes these early days that lead to the obesogen hypotheses and the growth of the field for a decade, leading to its prominence today, and provides some insight into where the field is moving. [ABSTRACT FROM AUTHOR]

Attention deficit hyperactivity disorder (ADHD), categorized by the lack of attention and focus, is one of the most common cognitive disorders. Since electroencephalogram (EEG) signals carry wide-ranging insights about cognition skills, the potential of using EEG signals to detect ADHD has a significant potential. EEG can be recorded utilizing wireless EEG reading devices often used by brain–computer interface researchers. In parallel-to-affordable EEG devices, serious games have been recently employed in the rehabilitation of multiple cognitive deficits. In this paper, we put the two things together, and we investigate the integration of an EEG-controlled serious game that trains and strengthens patients’ attention ability while using machine learning to detect their attention level. Our pilot experiments with healthy individuals show an accuracy of up to 96% in classifying the EEG data to detect the correct game control type during gameplay, while our extended experiments with ADHD patients show an accuracy of up to 98% with a standard uncertainty of 0.16% in detecting ADHD patients. [ABSTRACT FROM AUTHOR]

Brain connectivity networks based on magnetic resonance imaging (MRI) or functional MRI (fMRI) data provide a straightforward way to quantify the structural or functional systems of the brain. Currently, there are several network descriptors developed for representing and analyzing brain connectivity networks. However, most of them are designed for unweighted networks, regardless of the valuable weight information of edges, or do not take advantage of the ordinal relationship of weighted edges (even though they are designed for weighted networks). In this paper, we propose a new network descriptor (i.e., ordinal pattern that contains a sequence of weighted edges) for brain connectivity network analysis. Compared with previous network properties, the proposed ordinal patterns cannot only take advantage of the weight information of edges but also explicitly model the ordinal relationship of weighted edges in brain connectivity networks. We further develop an ordinal pattern-based learning framework for brain disease diagnosis using resting-state fMRI data. Specifically, we first construct a set of brain functional connectivity networks, where each network is corresponding to a particular subject. We then develop an algorithm to identify ordinal patterns that frequently appear in brain connectivity networks of patients and normal controls. We further perform discriminative ordinal pattern selection and extract feature representations for subjects based on the selected ordinal patterns, followed by a learning model for automated brain disease diagnosis. Experimental results on both Alzheimer’s Disease Neuroimaging Initiative and attention deficit hyperactivity disorder-200 data sets demonstrate that our method outperforms the several state-of-the-art approaches in the tasks of disease classification and clinical score regression. [ABSTRACT FROM AUTHOR]

This paper studies how in utero exposure to maternal stress from family ruptures affects later mental health. We find that prenatal exposure to the death of a maternal relative increases take-up of ADHD medications during childhood and anti-anxiety and depression medications in adulthood. Further, family ruptures during pregnancy depress birth outcomes and raise the risk of perinatal complications necessitating hospitalization. Our results suggest large welfare gains from preventing fetal stress from family ruptures and possibly from economically induced stressors such as unemployment. They further suggest that greater stress exposure among the poor may partially explain the intergenerational persistence of poverty. (JEL I12, J12, J13) [ABSTRACT FROM AUTHOR]

A previous paper from our lab (Shalom, 2009) presented evidence that the medial part of the prefrontal cortex is involved in the integration of raw, unintegrated information into coherent, wholistic mental representations such as perceptual objects, episodic memories, emotional states, and motor actions. It has used this analysis to classify some challenges encountered by people with Autism Spectrum Disorders, linking different types of difficulties in integration with different subareas of the medial prefrontal cortex. The current paper performs a similar analysis for the corresponding subareas of the lateral prefrontal cortex. It presents evidence that the lateral part of the prefrontal cortex is involved in the selection/inhibition of perceptual, memory, emotion, and motor aspects of processing. It then uses this analysis to classify challenges encountered by people with ADHD, linking different types of difficulties in selection/inhibition to different subareas of the lateral prefrontal cortex. [ABSTRACT FROM AUTHOR]

Distance learning in response to the COVID-19 pandemic presented tremendous challenges for many families. Parents were expected to support children's learning, often while also working from home. Students with Attention Deficit Hyperactivity Disorder (ADHD) are at particularly high risk for setbacks due to difficulties with organization and increased risk of not participating in scheduled online learning. This paper explores how smartwatch technology, including timing notifications, can support children with ADHD during distance learning due to COVID-19. We implemented a 6-week pilot study of a Digital Health Intervention (DHI) with ten families. The DHI included a smartwatch and a smartphone. Google calendars were synchronized across devices to guide children through daily schedules. After the sixth week, we conducted parent interviews to understand the use of smartwatches and the impact on children's functioning, and we collected physiological data directly from the smartwatch. Our results demonstrated that children successfully adopted the use of the smartwatch, and parents believed the intervention was helpful, especially in supporting the development of organizational skills in their children. Overall, we illustrate how even simple DHIs, such as using smartwatches to promote daily organization and task completion, have the potential to support children and families, particularly during periods of distance learning. We include practical suggestions to help professionals teach children with ADHD to use smartwatches to improve organization and task completion, especially as it applies to supporting remote instruction. [ABSTRACT FROM AUTHOR]

Chronic diseases are gradually becoming the main threat to human health. By designing an efficient hospital management platform to quickly identify the corresponding chronic diseases, it can effectively reduce the labor cost, improve the accuracy of disease identification, and improve treatment efficiency. ADHD is a common behavioral disorder in school-age children, and it is also one of the most common chronic health problems in this period. The internationally recognized prevalence of ADHD is 3%–9%. ADHD often brings adverse effects on children's life and studying and at the same time increases difficulties for their families. Therefore, this paper designs an intelligent management platform for public hospitals based on a deep learning algorithm, evaluates the current situation and influencing factors of ADHD children through the child adaptive behavior scale and the family function assessment scale, and designs its intelligent platform by using a new technology of fNIRS. According to the nonlinearity and unsteadiness of the fNIRS signal, this paper proposes a motion noise removal method based on EMD algorithm methods: to automatically identify children with ADHD and improve the cognitive function of children with ADHD by intervention technology. The data are from the outpatients of the Department of Child Psychology of the First People's Hospital of Tianshui City in Gansu Province in 2018. The results showed that there were significant differences in the adaptive behavior scale (CABS) and fad scores between the two groups. In the seven dimensions of family function, there were significant differences between the two groups (P < 0.01). fNIRS management platform can effectively identify ADHD patients with high recognition accuracy. The intelligent management platform can significantly reduce the number of physical examination personnel, prolong the diagnosis and treatment time, reduce a lot of repetitive work, and improve the efficiency of diagnosis and treatment. At the same time, this technology also provides great help for better research and improvement of ADHD patients and provides a reference for the information intelligent construction of modern hospitals. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Psychiatry & Clinical Psychology / Psychiatria i Psychologia Kliniczna is the property of Medical Communications Sp. z o.o. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Inattention is one of the most significant clinical symptoms for evaluating attention deficit hyperactivity disorder (ADHD). Previous inattention estimations were performed using clinical scales. Recently, predictive models for inattention have been established for brain-behavior estimation using neuroimaging features. However, the performance of inattention estimation could be improved for conventional brain-behavior models with additional feature selection, machine learning algorithms, and validation procedures. This paper aimed to propose a unified framework for inattention estimation from resting state fMRI to improve the classical brain-behavior models. Phase synchrony was derived as raw features, which were selected with minimum-redundancy maximum-relevancy (mRMR) method. Six machine learning algorithms were applied as regression methods. 100 runs of 10-fold cross-validations were performed on the ADHD-200 datasets. The relevance vector machines (RVMs) based on the mRMR features for the brain-behavior models significantly improve the performance of inattention estimation. The mRMR-RVM models could achieve a total accuracy of 0.53. Furthermore, predictive patterns for inattention were discovered by the mRMR technique. We found that the bilateral subcortical-cerebellum networks exhibited the most predictive phase synchrony patterns for inattention. Together, an optimized strategy named mRMR-RVM for brain-behavior models was found for inattention estimation. The predictive patterns might help better understand the phase synchrony mechanisms for inattention. [ABSTRACT FROM AUTHOR]

Children diagnosed with attention deficit hyperactivity disorder (ADHD) experience a variety of difficulties related to three primary symptoms: hyperactivity, inattention and impulsivity. The most common type of ADHD has a combination of all three symptom areas. These core symptoms may negatively impact the academic and social performance of children throughout their school life. The AHA (ADHD-Augmented) project focused specifically on the impact of digital technologies' intervention on literacy skills of children that participated in the pilot study and were diagnosed with ADHD prior to the intervention. Existing research has shown that augmented reality (AR) can improve academic outcomes by stimulating pupils' attention. AHA project aimed at implementing an evidence-based intervention to improve ADHD children's reading and spelling abilities through the enhancement of an existing literacy programme with AR functionality. The present paper reports preliminary findings of the pilot study aimed at evaluating the effectiveness of the AHA system in promoting the acquisition of literacy skills in a sample of children diagnosed with ADHD compared to the literacy programme as usual. Background information on the main characteristics and difficulties related to the teaching and learning process associated with children diagnosed with ADHD are first introduced; the design and methodology of the AHA project intervention are also described. The preliminary findings have shown that AHA project succeeded in delivering an AR solution within an existing online literacy programme, which integrates a set of specific technologies and supports interactive educational content, services, assessment, and feedback. [ABSTRACT FROM AUTHOR]

In the past two decades, network-based analysis has garnered considerable attention for analyzing time series data across various fields. Time series data can be transformed into graphs or networks using different methods, with the visibility graph (VG) being a widely utilized approach. The VG holds extensive applications in comprehending, identifying, and predicting specific characteristics of time series data. Its practicality extends to domains such as medicine, economics, meteorology, tourism, and others. This research presents a scoping review of scholarly articles published in reputable English-language journals and conferences, focusing on VG-based analysis methods related to brain disorders. The aim is to provide a foundation for further and future research endeavors, beginning with an introduction to the VG and its various types. To achieve this, a systematic search and refinement of relevant articles were conducted in two prominent scientific databases: Google Scholar and Scopus. A total of 51 eligible articles were selected for a comprehensive analysis of the topic. These articles categorized based on publication year, type of VG used, rationale for utilization, machine learning algorithms employed, frequently occurring keywords, top authors and universities, evaluation metrics, applied network properties, and brain disorders examined, such as Epilepsy, Alzheimer's disease, Autism, Alcoholism, Sleep disorders, Fatigue, Depression, and other related conditions. Moreover, there are recommendations for future advancements in research, which involve utilizing cutting-edge techniques like graph machine learning and deep learning. Additionally, the exploration of understudied medical conditions such as attention deficit hyperactivity disorder and Parkinson's disease is also suggested. [ABSTRACT FROM AUTHOR]