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The aim of this systematic review was to collect and align the research on social cognition impairments in adults with Attention-deficit/hyperactivity disorder (ADHD). In particular, we selected and analyzed papers on emotion recognition and processing, Theory of Mind (TOM), empathy, and other facets of social cognition as decision making. We identified 16 papers published between 2012 and 2022 which meet inclusion criteria. Papers search, selection, and extraction followed the PRISMA guidelines. In order to summarize data from papers, we used a narrative synthesis approach. Results show different evidence of impairment in social cognition domains in adults with ADHD. Our systematic review suggests the importance of promoting more research on this topic because it is essential to keep in mind that social cognition plays a central role in socialization and social relationships. [ABSTRACT FROM AUTHOR]

The article "Insights in ADHD: 2023" published in Frontiers in Psychiatry discusses seven papers that cover various aspects of Attention-Deficit/Hyperactivity Disorder (ADHD). The papers explore topics such as prevalence, clinical management, dose optimization, neurocognitive mechanisms, and the association between ADHD and external factors. The studies provide insights into the higher burden of illness among adults with ADHD, the importance of a comprehensive differential diagnosis for accurate treatment, the potential indicators of subclinical ADHD, the influence of ADHD on neuro-ophthalmologic function, and the role of emotion dysregulation and depressive symptoms in ADHD. The article emphasizes the need for further scientific investigations to fill the knowledge gaps surrounding ADHD. [Extracted from the article]

Background: An increasing number of studies have focused on the role of gut microbiota in the treatment of ADHD, but its related molecular mechanisms are not yet clear, and there is still room for development of studies targeting this area. This study analyzes publications from 2012 to 2021 in a comprehensive and multi-faceted visualization, with the aim of grasping the existing research profile and guiding scholars to make more in-depth studies. Methods: The 1,677 articles and 298 review articles on gut microbiota in ADHD were retrieved from the Web of Science Core Collection. CiteSpace, VOSviewer, Microsoft Excel 2019, Scimago Graphica, Bibliometrix and Pajek metrics software were used for visualization and analysis of the included literature. Results: On August 3, 2022, a total of 1975 English-language articles on gut microbiota in ADHD were retrieved from Web of Science Core Collection (WoSCC) from January 2012 to December 2021, with a steady upward trend in the number of articles published in this field over the decade. The top three countries in terms of the number of articles published are the United States, China, and Spain. Meanwhile, CONSEJO SUPERIOR DE INVESTIGACIONES CIENTIFICAS CSIC, UNIV OF CALIFORNIA SYSTEM, and UDICE FRENCH RESEARCH UNIV have made significant contributions in this field. In the analysis of the published journals, PLoS One was not only the first in terms of number of articles published but also the most cited. Wang J was the most prolific author and CAPORASO JG ranked first in terms of co-cited authors. In addition, "Diet rapidly and reproducibly alters the human gut microbiome," published by David LA et al., has the highest citation frequency in this field. The most frequently occurring keyword was "gut microbiota." Conclusion: The results of this paper clarify the current status of research on gut microbiota in ADHD. Based on the research on the mechanism of gut microbiota in other diseases, there is reason to believe that the exploration of gut microbiota in ADHD must be increasingly mature. And the study speculates that future research may focus on "nutrition supplements," "lipid metabolism," and "gut brain axis." It is imperative to promote a closer international cooperation among scholars in this field. [ABSTRACT FROM AUTHOR]

Introduction: A problem that applied researchers and practitioners often face is the fact that different institutions within research consortia use different scales to evaluate the same construct which makes comparison of the results and pooling challenging. In order to meaningfully pool and compare the scores, the scales should be harmonized. The aim of this paper is to use different test equating methods to harmonize the ADHD scores from Child Behavior Checklist (CBCL) and Strengths and Difficulties Questionnaire (SDQ) and to see which method leads to the result. Methods: Sample consists of 1551 parent reports of children aged 10-11.5 years from Raine study on both CBCL and SDQ (common persons design). We used linear equating, kernel equating, Item Response Theory (IRT), and the following machine learning methods: regression (linear and ordinal), random forest (regression and classification) and Support Vector Machine (regression and classification). Efficacy of the methods is operationalized in terms of the root-mean-square error (RMSE) of differences between predicted and observed scores in cross-validation. Results and discussion: Results showed that with single group design, it is the best to use the methods that use item level information and that treat the outcome as interval measurement level (regression approach). [ABSTRACT FROM AUTHOR]

Psychiatric disorders are categorized on the basis of presence and absence of diagnostic criteria using classification systems such as the international classification of diseases (ICD) and the diagnostic and statistical manual for mental disorders (DSM). The research domain criteria (RDoC) initiative provides an alternative dimensional framework for conceptualizing mental disorders. In the present paper, we studied neural and behavioral effects of central stimulant (CS) medication in adults with attention deficit hyperactivity disorder (ADHD) and healthy controls using categorical and dimensional stratifications. AX-Continuous Performance Task (AX-CPT) was utilized for the later purpose, and participants were classified as "reactive" or "proactive" based on their baseline proactive behavioral index (PBI). Out of the 65 individuals who participated (33 healthy controls and 32 patients with ADHD), 53 were included in the final analysis that consisted of 31 healthy controls and 22 ADHD patients. For the dimensional stratification, a median split of PBI scores divided participants into "reactive" and "proactive" groups irrespective of whether they had ADHD or not. Participants performed AX-CPT in conjunction with functional magnetic resonance imaging (fMRI) before and after CS medication. We found no significant within or between group CS effect when participants were categorically assigned as healthy controls and ADHD patients. For the dimensional stratification, however, CS selectively increased activation in frontoparietal cognitive areas and induced a shift towards proactive control mode in the reactive group, without significantly affecting the proactive group. In conclusion, the neural and behavioral effects of CS were more clear-cut when participants were stratified into dimensional groups rather than diagnostic categories. [ABSTRACT FROM AUTHOR]

Computer aided diagnosis methods play an important role in Attention Deficit Hyperactivity Disorder (ADHD) identification. Dynamic functional connectivity (dFC) analysis has been widely used for ADHD diagnosis based on resting-state functional magnetic resonance imaging (rs-fMRI), which can help capture abnormalities of brain activity. However, most existing dFC-based methods only focus on dependencies between two adjacent timestamps, ignoring global dynamic evolution patterns. Furthermore, the majority of these methods fail to adaptively learn dFCs. In this paper, we propose an adaptive spatial-temporal neural network (ASTNet) comprising three modules for ADHD identification based on rs-fMRI time series. Specifically, we first partition rs-fMRI time series into multiple segments using non-overlapping sliding windows. Then, adaptive functional connectivity generation (AFCG) is used to model spatial relationships among regions-of-interest (ROIs) with adaptive dFCs as input. Finally, we employ a temporal dependency mining (TDM) module which combines local and global branches to capture global temporal dependencies from the spatially-dependent pattern sequences. Experimental results on the ADHD-200 dataset demonstrate the superiority of the proposed ASTNet over competing approaches in automated ADHD classification. [ABSTRACT FROM AUTHOR]

The aimof this systematic review was to collect and align the research on social cognition impairments in adults with Attention-deficit/hyperactivity disorder (ADHD). In particular, we selected and analyzed papers on emotion recognition and processing, Theory of Mind (TOM), empathy, and other facets of social cognition as decision making. We identified 16 papers published between 2012 and 2022 which meet inclusion criteria. Papers search, selection, and extraction followed the PRISMA guidelines. In order to summarize data from papers, we used a narrative synthesis approach. Results show different evidence of impairment in social cognition domains in adults with ADHD. Our systematic review suggests the importance of promoting more research on this topic because it is essential to keep in mind that social cognition plays a central role in socialization and social relationships. [ABSTRACT FROM AUTHOR]

Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder affecting a large percentage of the adult population. A series of ongoing efforts has led to the development of a hybrid AI algorithm (a combination of a machine learningmodel and a knowledge-basedmodel) for assisting adult ADHDdiagnosis, and its clinical trial currently operating in the largest National Health Service (NHS) for adults with ADHD in the UK. Most recently, more data was made available that has lead to a total collection of 501 anonymized records as of 2022 July. This prompted the ongoing research to carefully examine the model by retraining and optimizing the machine learning algorithm in order to update the model with better generalization capability. Based on the large data collection so far, this paper also pilots a study to examine the effectiveness of variables other than the Diagnostic Interview for ADHD in adults (DIVA) assessment, which adds considerable cost in the screenining process as it relies on specially trained senior clinicians. Results reported in this paper demonstrate that the newly trained machine learning model reaches an accuracy of 75.03% when all features are used; the hybrid model obtains an accuracy of 93.61%. Exceeding what clinical experts expected in the absence of DIVA, achieving an accuracy of 65.27% using a rule-based machine learning model alone encourages the development of a cost effective model in the future. [ABSTRACT FROM AUTHOR]

Introduction: Virtual Reality (VR) is revolutionizing healthcare research and practice by offering innovative methodologies across various clinical conditions. Advances in VR technology enable the creation of controllable, multisensory 3D environments, making it an appealing tool for capturing and quantifying behavior in realistic scenarios. This paper details the application of VR as a tool for neurocognitive evaluation, specifically in attention process assessment, an area of relevance for informing the diagnosis of childhood health conditions such as Attention Deficit Hyperactivity Disorder (ADHD). Methods: The data presented focuses on attention performance results from a large sample (n = 837) of neurotypicalmale and female children (ages 6-13) tested on a visual continuous performance task, administered within an immersive VR classroom environment. This data was collected to create a normative baseline database for use to inform comparisons with the performances of children with ADHD to support diagnostic decision-making in this area. Results: Results indicate systematic improvements on most metrics across the age span, and sex differences are noted on key variables thought to reflect differential measures of hyperactivity and inattention in children with ADHD. Results support VR technology as a safe and viable option for testing attention processes in children, under stimulus conditions that closely mimic ecologically relevant challenges found in everyday life. Discussion: In response to these stimulus conditions, VR can support advanced methods for capturing and quantifying users' behavioral responses. VR offers a more systematic and objective approach for clinical assessment and intervention and provides conceptual support for its use in a wide variety of healthcare contexts. [ABSTRACT FROM AUTHOR]

S-palmitoylation is a reversible posttranslational modification, and the palmitoylation reaction in human-derived cells is mediated by the zDHHC family, which is composed of S-acyltransferase enzymes that possess the DHHC (Asp-His-His-Cys) structural domain. zDHHC proteins form an autoacylation intermediate, which then attaches the fatty acid to cysteine a residue in the target protein. zDHHC proteins sublocalize in different neuronal structures and exert dif-ferential effects on neurons. In humans, many zDHHC proteins are closely related to human neu-rological disor-ders. This review focuses on a variety of neurological disorders, such as AD (Alz-heimer's disease), HD (Huntington's disease), SCZ (schizophrenia), XLID (X-linked intellectual disability), attention deficit hyperactivity disorder and glioma. In this paper, we will discuss and summarize the research progress regarding the role of zDHHC proteins in these neu-rological disorders. [ABSTRACT FROM AUTHOR]

Introduction: Attention-deficit/hyperactivity disorder (ADHD) symptoms in adults interfere with parental functioning. Dysfunctional parental cognitions may play a role in this impairment. Despite the importance of parental cognitions on parents and children's outcomes, up to now, no systematic review or meta-analysis of these findings is available. To fill this gap, this meta-analysis aimed to evaluate the relationship between adult ADHD symptoms and parental cognitions. Methods: We conducted searches in Web of Science, PubMed, and ProQuest from January 2000 to June 2023. Studies were included if they provided data on the relationship between parental ADHD symptoms and parental cognitions by means of a row correlational coefficient, or means and standard deviation were reported for each study group. A randomeffects model was used. Publication bias was assessed by funnel plot and Rosenthal's fail-safe N. Moderator analyses were conducted by means of subgroup analysis and meta-regression analyses. Results: Fifteen published papers were included (N = 2851), and 51 effect sizes were analysed. The weighted mean effect size was small but significant (Fisher's Z = 0.186, k = 15, 95% CI [0.120 - 0.252], z = 5.539, p < 0.001), indicating that ADHD symptoms in adults are associated with more negative and less positive parental cognitions. The Fail-Safe N analysis suggested a robust effect. Tweedie's trim and fill results suggested that five studies were missing; after five missing studies had been imputed, the mean overall effect size dropped to 0.116 (0.080 - 0.152). There was significant heterogeneity among effect sizes. The methodology of the study was found to be a significant moderator. Meta-regression analyses revealed that the lower age of the parent and the child were related to more negative parental cognitions. Discussion: Though the analysis might be inflated by publication bias, our results suggest a significant association between ADHD symptom level and dysfunctional parental cognitions. Biased negative perceptions of the parental role, the child and co-parenting may play a central mediator role between parental ADHD and parent and child outcomes. Given the familiar nature of ADHD, targeting dysfunctional parental cognitions in parent training programs is warranted. [ABSTRACT FROM AUTHOR]

Introduction: Mental restlessness reported by adult with Attention Deficit Hyperactivity Disorder (ADHD) has been mainly explained by excessive mind wandering. However, the description of a mind constantly on the go is also akin to racing thoughts, predominantly described in bipolar disorder. This paper aimed at disentangling mind wandering from racing thoughts in adult with ADHD. Associations between those mental phenomena and the ADHD symptomatology were also investigated. Methods: To this aim, 84 adults with ADHD completed self-reported questionnaires, including the Mind Wandering-Deliberate and Mind Wandering-Spontaneous questionnaires, the Racing and Crowded Thoughts Questionnaire and the Daydreaming Frequency Scale. Factorial analysis and multiple linear regressions were performed. Results: The factor analysis yielded a two-factor solution. The first factor encompassed the three facets of racing thoughts and was predicted by emotional lability. The second comprised deliberated-MW, spontaneous-MW and daydreaming, but was neither related to the ADHD symptoms, nor functional impairment. Discussion: These findings suggest that MW and racing thoughts are two distinguishable mental phenomena. Racing thoughts appear to be a relevant hypothesis to explain the mental restlessness in adult ADHD. [ABSTRACT FROM AUTHOR]

Introduction: Ventriculomegaly (VM) is a fetal brain malformation which may present independently (isolated form) or in association with different cerebral malformations, genetic syndromes or other pathologies (non-isolated form). Methods: This paper aims to study the effect of ventriculomegaly on the internal tridimensional architecture of fetal brains by way of Klingler's dissection. Ventriculomegaly was diagnosed using fetal ultrasonography during pregnancy and subsequently confirmed by necropsy. Taking into consideration the diameter of the lateral ventricle (measured at the level of the atrium), the brains were divided into two groups:moderate ventriculomegaly (with atrial diameter between 13 and 15 mm) and severe ventriculomegaly (with atrial diameter above 15 mm). Results and discussion: The results of each dissection were described and illustrated, then compared with age-matched reference brains. In the pathological brains, fascicles in direct contact with the enlarged ventricles were found to be thinner and displaced inferiorly, the opening of the uncinate fasciculus was wider, the fornix was no longer in contact with the corpus callosum and the convexity of the corpus callosumwas inverted. We have studied the prevalence of neurodevelopmental delay in children born with ventriculomegaly in the literature and discovered that a normal developmental outcome was found in over 90% of the mild VM cases, approximately 75% of the moderate and 60% in severe VM, with the correlated neurological impairments ranging from attention deficits to psychiatric disorders. [ABSTRACT FROM AUTHOR]

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common brain diseases among children. The current criteria of ADHD diagnosis mainly depend on behavior analysis, which is subjective and inconsistent, especially for children. The development of neuroimaging technologies, such as magnetic resonance imaging (MRI), drives the discovery of brain abnormalities in structure and function by analyzing multimodal neuroimages for computer-aided diagnosis of brain diseases. This paper proposes a multimodal machine learning framework that combines the Boruta based feature selection and Multiple Kernel Learning (MKL) to integrate the multimodal features of structural and functional MRIs and Diffusion Tensor Images (DTI) for the diagnosis of early adolescent ADHD. The rich and complementary information of the macrostructural features, microstructural properties, and functional connectivities are integrated at the kernel level, followed by a support vector machine classifier for discriminating ADHD from healthy children. Our experiments were conducted on the comorbidity-free ADHD subjects and covariable-matched healthy children aged 9–10 chosen from the Adolescent Brain and Cognitive Development (ABCD) study. This paper is the first work to combine structural and functional MRIs with DTI for early adolescents of the ABCD study. The results indicate that the kernel-level fusion of multimodal features achieves 0.698 of AUC (area under the receiver operating characteristic curves) and 64.3% of classification accuracy for ADHD diagnosis, showing a significant improvement over the early feature fusion and unimodal features. The abnormal functional connectivity predictors, involving default mode network, attention network, auditory network, and sensorimotor mouth network, thalamus, and cerebellum, as well as the anatomical regions in basal ganglia, are found to encode the most discriminative information, which collaborates with macrostructure and diffusion alterations to boost the performances of disorder diagnosis. [ABSTRACT FROM AUTHOR]

Currently, the diagnosis of attention deficit hyperactivity disorder (ADHD) is solely based on behavioral tests prescribed by the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5). However, biomarkers can be more objective and accurate for diagnosis and evaluating treatment efficacy. Thus, this review aimed to identify potential biomarkers for ADHD. Search terms "ADHD," and "biomarker" combined with one of "protein," "blood/serum," "gene," and "neuro" were used to identify human and animal studies in PubMed, Ovid Medline, and Web of Science. Only papers in English were included. Potential biomarkers were categorized into radiographic, molecular, physiologic, or histologic markers. The radiographic analysis can identify specific activity changes in several brain regions in individuals with ADHD. Several molecular biomarkers in peripheral blood cells and some physiologic biomarkers were found in a small number of participants. There were no published histologic biomarkers for ADHD. Overall, most associations between ADHD and potential biomarkers were properly controlled. In conclusion, a series of biomarkers in the literature are promising as objective parameters to more accurately diagnose ADHD, especially in those with comorbidities that prevent the use of DSM-5. However, more research is needed to confirm the reliability of the biomarkers in larger cohort studies. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most common neurodegenerative disorder with the majority of patients classified as sporadic AD (sAD), in which etiopathogenesis remains unresolved. Though sAD is argued to be a polygenic disorder, apolipoprotein E (APOE) +4, was found three decades ago to pose the strongest genetic risk for sAD. Currently, the only clinically approved diseasemodifying drugs for AD are aducanumab (Aduhelm) and lecanemab (Leqembi). All other AD treatment options are purely symptomatic with modest benefits. Similarly, attention-deficit hyperactivity disorder (ADHD), is one of the most common neurodevelopmental mental disorders in children and adolescents, acknowledged to persist in adulthood in over 60% of the patients. Moreover, for ADHD whose etiopathogenesis is not completely understood, a large proportion of patients respond well to treatment (first-line psychostimulants, e.g., methylphenidate/MPH), however, no disease-modifying therapy exists. Interestingly, cognitive impairments, executive, and memory deficits seem to be common in ADHD, but also in early stages of mild cognitive impairment (MCI), and dementia, including sAD. Therefore, one of many hypotheses is that ADHD and sAD might have similar origins or that they intercalate with one another, as shown recently that ADHD may be considered a risk factor for sAD. Intriguingly, several overlaps have been shown between the two disorders, e.g., inflammatory activation, oxidative stress, glucose and insulin pathways, wingless-INT/mammalian target of rapamycin (Wnt/mTOR) signaling, and altered lipid metabolism. Indeed, Wnt/mTOR activities were found to be modified by MPH in several ADHD studies. Wnt/mTOR was also found to play a role in sAD and in animal models of the disorder. Moreover, MPH treatment in the MCI phase was shown to be successful for apathy including some improvement in cognition, according to a recent meta-analysis. In several AD animal models, ADHD-like behavioral phenotypes have been observed indicating a possible interconnection between ADHD and AD. In this concept paper, we will discuss the various evidence in human and animal models supporting the hypothesis in which ADHD might increase the risk for sAD, with common involvement of the Wnt/mTOR-pathway leading to lifespan alteration at the neuronal levels. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

Objectives: This paper reviews the literature on intentional discontinuation of psychostimulants in ADHD to summarize what is known about clinical course of controlled discontinuation and guide practitioners who are considering stopping these medications for youth with ADHD. Methods: A systematic search was executed in Cochrane CENTRAL, EMBASE, Psychinfo, and MEDLINE databases to identify all articles that addressed the topic of deprescribing of psychotropic medications in children and adolescents. Keywords and search strings were developed using "PICO" framework, involving Population of interest (<18 y.o.), Intervention ("discontinuation," "deprescribing," and synonyms), Comparator (continuation of specific medications), and Outcomes. Ten reviewers conducted the initial screen via a single reviewer system. Articles that met a set of three inclusionary criteria were selected for full text review and identification as specific to discontinuation of stimulants in ADHD. Results: The literature review identified 35 articles specifically addressing intentional deprescribing, discontinuation, tapering, or withdrawal of stimulants for children and adolescents with ADHD. In addition to providing broad support for the efficacy of stimulants to treat ADHD and reduce negative outcomes, there is a distinct population of children and adolescents with ADHD who do not relapse or deteriorate when taken off medications for ADHD. The majority of articles addressed either the re-emergence of ADHD symptoms or side effects, both desired and adverse, following discontinuation of stimulants. While confirming the ability of stimulants to treat ADHD in youth, our results support periodic consideration of trials of stopping medications to determine continued need. Conclusions: This systematic review summarizes the literature on deprescribing stimulants for ADHD in children and adolescents. Further research is needed to determine the optimal duration of treatment, identify patients that may benefit from medication discontinuation, and inform evidence-based guidelines for discontinuation when appropriate. More research is needed to understand and define the subgroup of youth who may succeed with stimulant discontinuation. [ABSTRACT FROM AUTHOR]

Attention deficit hyperactivity disorder (ADHD) is a heterogeneous neurodevelopmental disorder that affects 5% of the pediatric and adult population worldwide. The diagnosis remains essentially clinical, based on history and exam, with no available biomarkers. In this paper, we describe a convolutional neural network (CNN) with a four-layer architecture combining filtering and pooling, which we train using stacked multi-channel EEG time-frequency decompositions (spectrograms) of electroencephalography data (EEG), particularly of event-related potentials (ERP) from ADHD patients (n = 20) and healthy controls (n = 20) collected during the Flanker Task, with 2800 samples for each group. We treat the data as in audio or image classification approaches, where deep networks have proven successful by exploiting invariances and compositional features in the data. The model reaches a classification accuracy of 88% ± 1.12%, outperforming the Recurrent Neural Network and the Shallow Neural Network used for comparison, and with the key advantage, compared with other machine learning approaches, of avoiding the need for manual selection of EEG spectral or channel features. The event-related spectrograms also provide greater accuracy compared to resting state EEG spectrograms. Finally, through the use of feature visualization techniques such as DeepDream , we show that the main features exciting the CNN nodes are a decreased power in the alpha band and an increased power in the delta-theta band around 100 ms for ADHD patients compared to healthy controls, suggestive of attentional and inhibition deficits, which have been previously suggested as pathophyisiological signatures of ADHD. While confirmation with larger clinical samples is necessary, these results suggest that deep networks may provide a useful tool for the analysis of EEG dynamics even from relatively small datasets, highlighting the potential of this methodology to develop biomarkers of practical clinical utility. [ABSTRACT FROM AUTHOR]

This document is a corrigendum for an article titled "Neurocognitive and cerebellar function in ADHD, autism and spinocerebellar ataxia." The corrigendum addresses errors in Table 1 and a sentence in the article. The errors have been corrected, and the authors state that they do not change the scientific conclusions of the article. The document also includes a note from the publisher stating that the claims expressed in the article are solely those of the authors and do not necessarily represent the views of their affiliated organizations or the publisher. [Extracted from the article]

Background: Thyroid hormones (THs) are essential for brain development. Numerous studies have identified significant links between thyroid dysfunction and cognitive function. However, research on the significance and necessity of thyroid function tests in diagnosis of neurological disorders is limited and subject to controversy. Methods: Our study employed a combination of meta-analysis and case-control design. For the meta-analysis, we conducted a systematic search of online databases for studies that compared thyroid function tests in children with neurological disorders to controls. In our case-control study, we recruited a total of 11836 children, comprising 7035 cases and 4801 healthy controls. Wilcoxon Rank Sum Test was used to determine characteristics of thyroid function between the cases and healthy controls. In order to exclude the false discovery rate (FDR), the Benjamini-Hochberg (BH) procedure is applied. Results: A total of 12 relevant literature sources were included in the meta-analysis. Compared with controls, free thyroxine (FT4) levels were significantly decreased in neurological disorders in meta-analysis (MD = -0.29, 95% CI: -0.50 to -0.09), whereas thyroid-stimulating hormone (TSH) levels showed no significant difference (MD = -0.07, 95% CI: -0.36 to 0.21). In our case-control study, levels of free thyroxine (FT4), total triiodothyronine (TT3), total thyroxine (TT4), and anti-thyroglobulin antibodies (TG-Ab) were notably reduced among individuals with neurological disorders, compared with healthy controls (P <0.001, P< 0.001, P =0.036, P =0.006). However, thyroid-stimulating hormone (TSH) levels did not show any statistically significant differences among the cases and controls. Conclusions: Our research demonstrates that, in comparison to controls, children with neurological disorders exhibited a significant decrease in FT4 levels, while TSH levels remained unchanged. This finding provides a reference for potential serum marker of neurological disorders in children. Replication in future studies with the assessment of THs is needed to determine whether thyroid function should be included as a routine screening in these children. [ABSTRACT FROM AUTHOR]

Background and objective: A growing body of research evidence suggests that many patients with attention deficit hyperactivity disorder (ADHD) have difficulties with emotion regulation. Non-invasive brain stimulation (NIBS), which mainly includes transcranial electrical stimulation (tES) and repetitive transcranial magnetic stimulation (rTMS), has been considered a potential new direction in the treatment of emotion dysregulation in ADHD patients. The key components of tES are transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS). However, there is no systematic evaluation exploring the effects of non-invasive brain stimulation on emotion regulation in ADHD patients. Therefore, this systematic review aimed to summarize the effects of NIBS on emotion regulation in ADHD patients. Methods: This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched PubMed, Embase, Web of Science, and the Cochrane Library electronic databases up to 1 July 2024. We also hand-searched the reference lists of retrieved articles and reviews. Assessing risk of bias using the Cochrane Assessment Tool. Results: Through database search, we obtained a total of 1134 studies, of which 5 met the inclusion criteria. Statistically significant improvements in emotion regulation in children with ADHD were observed in 1 study after treatment with tDCS. In the remaining 4 studies (2 with tDCS and 2 with rTMS), there were no statistically significant changes in emotion regulation in ADHD patients after treatment with either tDCS or rTMS. Conclusions: The data from our preliminary study do not allow us to draw definitive conclusions that non-invasive brain stimulation improves emotion regulation in ADHD patients. This is because there is a paucity of literature on the effects of tES or rTMS on emotion regulation in ADHD patients and a limited number of randomized controlled trials. More high-quality multicenter randomized controlled trials exploring the efficacy of non-invasive brain stimulation on emotion regulation in ADHD patients are needed in the future to provide strong evidence for definitive conclusions before it can be considered as a potential treatment option. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/ , identifier CRD42024569041. [ABSTRACT FROM AUTHOR]

Multiple lines of evidence indicate that altered dopamine signaling may be involved in neuropsychiatric disorders and common behavioral traits. Here we critically review evidence collected during the past 40-plus years supporting the role of dopamine dysfunction in attention deficit hyperactivity disorder (ADHD). We recapitulate the basic components of dopaminergic signaling in the central nervous system, focusing on core enzymes, transporters and receptors involved in monoaminergic functions, particularly in striatal and cortical regions. We summarize key human brain imaging and genetic studies reporting associations between dopaminergic neurotransmission and behavioral traits, with an emphasis on ADHD. We also consider ADHD in the context of animal models and single gene, metabolic, and neurological disorders with established dysfunction of the dopaminergic system. Examining the evidence in this way leads us to conclude that there is evidence for the involvement of dopamine but limited evidence for a hypo-dopaminergic state per se as a key component of ADHD. We propose a path forward to increase our understanding of dopamine signaling in human behavioral traits and disorders that should particularly focus on its role in clinical subgroups, during brain development and how it interacts with other neurotransmitter systems. [ABSTRACT FROM AUTHOR]

Background: To date, there are no official diagnostic criteria for the frequently reported phenomenon of exercise addiction. Therefore, the aim of the present study was to investigate how mental disorders, specifically depression and attention-deficit hyperactivity disorder (ADHD), are related to exercise addiction (EA). Methods: A total of 173 participants aged between 18 and 70 years, who reported exercising more than 10 h a week and continued to exercise despite injury or illness, answered questionnaires including the Exercise Dependence Scale, the Beck Depression Inventory, and the Homburger ADHD scale for adults. Multiple linear regression analyses were performed adjusting for relevant confounders (age, gender) and stepwise regression was used to identify which of the two mental disorders is the more influential predictor of EA. Results: Pearson correlation analysis showed that depressive symptoms [r (171) = 0.422, p < 0.00] and ADHD symptoms [r (171) = 0.308, p < 0.001] were positively correlated with EA symptoms. The relation between depressive symptoms and EA remained after adjusting for confounders in the regression model (B = 20.531; t(170) = 5.950; 95% CI [13.719, 27.343]; p < 0.001). Similarly, the positive link between ADHD symptoms and EA persisted after controlling for confounders (B = 15.507; t(170) = 3.771; 95% CI [7.389, 23.625]; p < 0.001). Additionally, a stepwise regression model identified that depressive symptoms are a stronger predictor for EA than ADHD symptoms. Conclusion: Depressive symptoms seem to be a stronger predictor for EA compared to ADHD symptoms in frequent exercisers. Although individuals with ADHD May exercise extensively, they might be less at risk for EA than individuals with depression. These results contribute to the complex characterization of the psychiatric profile of individuals with exercise addiction, and underline the need for further research elucidating the interplay between mental disorders and EA. [ABSTRACT FROM AUTHOR]

Introduction: Children with Attention Deficit/Hyperactivity Disorder (ADHD) participated in a randomized clinical trial comparing animal-assisted intervention (AAI) to psychosocial treatment as usual (TAU). This brief report describes effects of AAI on acute HPA axis reactivity and regulation. Saliva was collected before, during, and after psychosocial intervention sessions with and without therapy dogs and later assayed for cortisol (ug/dL). Methodology: Thirty-nine participants (n = 39) with ADHD, aged 7-9 years (79% male) provided saliva at 3 points during 90-minute sessions; (i) upon arrival, (ii) +20 minutes, and (iii) 15 minutes prior to departure, on 3 occasions across an 8-week intervention (weeks 1, 4, and 8). Cortisol slopes calculated within each session were compared across the intervention weeks to determine within subject and between group effect sizes. Spearman's correlations between baseline individual neurodevelopmental symptoms and in-session acute cortisol responses were also evaluated. Results: No significant between group differences were observed in cortisol responsiveness at week-1. By week-4, in-session changes in cortisol were evident, with significantly greater decreases in the AAI group (Cohen's d = -.40). This pattern was also observed at week-8, with an even stronger effect-size (d = -0.60). Concurrent symptoms of autism were associated with the in-session acute cortisol response. Specifically, higher parent-reported symptom scores were associated with steeper decreases in cortisol across the session at week 1 (r = -0.42, p <.01) and week-8 (r = -0.34 p =.05). At week-8 this association was stronger in the AAI group (r = -0.53) versus TAU (r = -0.25), with Cohen's q = 0.413). Discussion: AAI may influence acute HPA reactivity and regulation for children with ADHD. Concurrent symptoms of ADHD and autism may be related to individual differences in the nature of the effect. Implications of these findings for AAI as an alternative, or complementary intervention for ADHD are discussed. Clinical trial registration: ClinicalTrials.gov, identifier NCT05102344. [ABSTRACT FROM AUTHOR]

Aim: Developmental disabilities (DD) often persist into adulthood, necessitating early and continuous treatment. Although pharmacotherapy is a viable treatment option, managing medication can be challenging. Prior research has not explored medication use among patients with DD in Japan. Thus, this study aimed to identify the medication challenges faced by these patients. Methods: A questionnaire survey was administered to 200 outpatients to collect data on the number of prescribed medications, the timing of administration, the frequency of missed doses, and the use of medication notebooks. This was a prospective observational study without intervention and a random sampling. Results: The survey revealed that 57.0% of the participants were non-adherent to their medication regimen. Specifically, medication non-adherence rates were 44.7% among individuals with autism spectrum disorder (ASD), 86.9% for those with attention deficit hyperactivity disorder (ADHD), and 30.4% for patients with comorbid ASD and ADHD. Despite 48.3% of the participants taking measures to prevent forgetting their medication, 65.3% of them still failed to take their medication as prescribed. The possession rate of medication record books was 96.0%. Conclusion: The study indicates that the frequency of medication non-adherence among patients with DD in Japan mirrors that in other countries. Patients who reported taking preventative measures still experienced high rates of non-adherence, suggesting limited effectiveness of these strategies. It is essential to develop more effective measures to improve adherence, enhance disease awareness, and increase understanding of medication instructions. The high possession rate of medication record books suggests they could play a significant role in managing DD, and their use is expected to increase in the future. [ABSTRACT FROM AUTHOR]

Aim: Developmental disabilities (DD) often persist into adulthood, necessitating early and continuous treatment. Although pharmacotherapy is a viable treatment option, managing medication can be challenging. Prior research has not explored medication use among patients with DD in Japan. Thus, this study aimed to identify the medication challenges faced by these patients. Methods: A questionnaire survey was administered to 200 outpatients to collect data on the number of prescribed medications, the timing of administration, the frequency of missed doses, and the use of medication notebooks. This was a prospective observational study without intervention and a random sampling. Results: The survey revealed that 57.0% of the participants were non-adherent to their medication regimen. Specifically, medication non-adherence rates were 44.7% among individuals with autism spectrum disorder (ASD), 86.9% for those with attention deficit hyperactivity disorder (ADHD), and 30.4% for patients with comorbid ASD and ADHD. Despite 48.3% of the participants taking measures to prevent forgetting their medication, 65.3% of them still failed to take their medication as prescribed. The possession rate of medication record books was 96.0%. Conclusion: The study indicates that the frequency of medication non-adherence among patients with DD in Japan mirrors that in other countries. Patients who reported taking preventative measures still experienced high rates of non-adherence, suggesting limited effectiveness of these strategies. It is essential to develop more effective measures to improve adherence, enhance disease awareness, and increase understanding of medication instructions. The high possession rate of medication record books suggests they could play a significant role in managing DD, and their use is expected to increase in the future. [ABSTRACT FROM AUTHOR]

The neuropsychological deficits in attention-deficit/hyperactivity disorder (ADHD) may present clinical features similar to mild and/or major neurocognitive disorder and may act as a confounding factor, making it difficult to detect cognitive decline. In this paper, we present the results of longitudinal neuropsychological evaluations in two elderly women with ADHD. Three neuropsychological assessments were performed in two women with ADHD (60 and 77 years old) between 2010 and 2013 at intervals varying from 12 to 15 months. We used structural magnetic resonance imaging to rule out significant abnormalities that could account for cognitive impairment. The results showed two different cognitive profiles with fluctuations in performance over these 2 years, sometimes with improvement and sometimes with decline of some functions such as attention, memory, inhibitory control, and reaction time. To minimize confounding aspects of these fluctuations in clinical practice, we used a longer follow-up with the application of a reliable change index and a minimum of three spaced assessments to provide a more consistent baseline cognitive profile. Our findings did not indicate a consistent cognitive decline, suggesting a less pessimistic perspective about cognitive impairments that could be a prodrome of ADHD-related dementia. [ABSTRACT FROM AUTHOR]

Attention deficit hyperactivity disorder (ADHD) is a disorder most frequently diagnosed in children and adolescents. Although ADHD can be effectively treated with psychostimulants, a significant proportion of patients discontinue treatment because of adverse events or insufficient improvement of symptoms. In addition, cognitive abilities that are frequently impaired in ADHD are not directly targeted by medication. Therefore, additional treatment options, especially to improve cognitive abilities, are needed. Because of its relatively easy application, well-established safety, and low cost, transcranial direct current stimulation (tDCS) is a promising additional treatment option. Further research is needed to establish efficacy and to integrate this treatment into the clinical routine. In particular, limited evidence regarding the use of tDCS in children, lack of clear translational guidelines, and general challenges in conducting research with vulnerable populations pose a number of practical and ethical challenges to tDCS intervention studies. In this paper, we identify and discuss ethical issues related to research on tDCS and its potential therapeutic use for ADHD in children and adolescents. Relevant ethical issues in the tDCS research for pediatric ADHD center on safety, risk/benefit ratio, information and consent, labeling problems, and nonmedical use. Following an analysis of these issues, we developed a list of recommendations that can guide clinicians and researchers in conducting ethically sound research on tDCS in pediatric ADHD. [ABSTRACT FROM AUTHOR]

Brain functional imaging data, especially functional magnetic resonance imaging (fMRI) data, have been employed to reflect functional integration of the brain. Alteration in brain functional connectivity (FC) is expected to provide potential biomarkers for classifying or predicting brain disorders. In this paper, we present a comprehensive review in order to provide guidance about the available brain FC measures and typical classification strategies. We survey the state-of-the-art FC analysis methods including widely used static functional connectivity (SFC) and more recently proposed dynamic functional connectivity (DFC). Temporal correlations among regions of interest (ROIs), data-driven spatial network and functional network connectivity (FNC) are often computed to reflect SFC from different angles. SFC can be extended to DFC using a sliding-window framework, and intrinsic connectivity states along the time-varying connectivity patterns are typically extracted using clustering or decomposition approaches. We also briefly summarize window-less DFC approaches. Subsequently, we highlight various strategies for feature selection including the filter, wrapper and embedded methods. In terms of model building, we include traditional classifiers as well as more recently applied deep learning methods. Moreover, we review representative applications with remarkable classification accuracy for psychosis and mood disorders, neurodevelopmental disorder, and neurological disorders using fMRI data. Schizophrenia, bipolar disorder, autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), Alzheimer's disease and mild cognitive impairment (MCI) are discussed. Finally, challenges in the field are pointed out with respect to the inaccurate diagnosis labeling, the abundant number of possible features and the difficulty in validation. Some suggestions for future work are also provided. [ABSTRACT FROM AUTHOR]

Everyday human behavior relies upon extraordinary feats of coordination within the brain. In this perspective paper, we argue that the rich temporal structure of music provides an informative context in which to investigate how the brain coordinates its complex activities in time, and how that coordination can be disrupted. We bring insights from the neuroscience of musical rhythm to considerations of timing deficits in Attention Deficit/Hyperactivity Disorder (ADHD), highlighting the significant overlap between neural systems involved in processing musical rhythm and those implicated in ADHD. We suggest that timing deficits warrant closer investigation since they could lead to the identification of potentially informative phenotypes, tied to neurobiological and genetic factors. Our novel interdisciplinary approach builds upon recent trends in both fields of research: in the neuroscience of rhythm, an increasingly nuanced understanding of the specific contributions of neural systems to rhythm processing, and in ADHD, an increasing focus on differentiating phenotypes and identifying distinct etiological pathways associated with the disorder. Finally, we consider the impact of musical experience on rhythm processing and the potential value of musical rhythm in therapeutic interventions. [ABSTRACT FROM AUTHOR]

Introduction: Nociplastic pain (NP), a third category of chronic pain, offers a framework for elucidating its pathophysiology and treatment strategies. One of the characteristics of NP is comorbidity of symptoms other than pain, such as psychological and cognitive problems; therefore, these can be clues to understanding NP. Recently, we reported several cases with comorbid symptoms of attention-deficit/hyperactivity disorder (ADHD). Notably, ADHD medications, including methylphenidate (MP) and atomoxetine, improved chronic pain as well as the symptoms of ADHD. However, in clinical settings, identifying comorbid ADHD in patients with chronic pain is challenging, and underlying mechanisms have not been elucidated. To explore the common characteristics of brain function in patients with ADHD-comorbid NP, we identified brain regions where cerebral blood flow (CBF) distributions changed between pre- and post-treatment using single-photon emission computed tomography (SPECT). Additionally, we examined brain regions where CBF values correlated with clinical scores. Methods: We retrospectively studied 65 patients (mean age 53 ± 14 years; 30 males and 35 females) with ADHD-comorbid NP who underwent CBF-SPECT before and after ADHD medication initiation. Clinical scores included the clinical global impression severity (CGI-S), pain numerical rating scale, hospital anxiety and depression scale, pain catastrophizing scale, and Conners’ adult ADHD rating scale-self report scores. Voxel-based statistical methods were used to compare pre- and post-treatment CBF-SPECT images to identify significant differences and investigate brain regions correlated with clinical scores. Results: The CBF was higher in the precuneus, insular gyrus, and thalamus before treatment than after treatment (paired t-test, cluster-definition p < 0.001, cluster-extent threshold p < 0.05, with family-wise error [FWE] correction). The hyperperfusion in the precuneus was positively correlated with the CGI-S score and significantly reduced after treatment with MP (paired t-test, cluster-definition p < 0.005, cluster-extent threshold p < 0.05, with FWE correction). Conclusion: The finding of precuneal hyperperfusion may provide insight into the mechanisms of NP and help identify patients who would benefit most from ADHD medications. [ABSTRACT FROM AUTHOR]

Background: To date, there are no official diagnostic criteria for the frequently reported phenomenon of exercise addiction. Therefore, the aim of the present study was to investigate how mental disorders, specifically depression and attention-deficit hyperactivity disorder (ADHD), are related to exercise addiction (EA). Methods: A total of 173 participants aged between 18 and 70 years, who reported exercising more than 10 h a week and continued to exercise despite injury or illness, answered questionnaires including the Exercise Dependence Scale, the Beck Depression Inventory, and the Homburger ADHD scale for adults. Multiple linear regression analyses were performed adjusting for relevant confounders (age, gender) and stepwise regression was used to identify which of the two mental disorders is the more influential predictor of EA. Results: Pearson correlation analysis showed that depressive symptoms [r (171) = 0.422, p < 0.00] and ADHD symptoms [r (171) = 0.308, p < 0.001] were positively correlated with EA symptoms. The relation between depressive symptoms and EA remained after adjusting for confounders in the regression model (B = 20.531; t(170) = 5.950; 95% CI [13.719, 27.343]; p < 0.001). Similarly, the positive link between ADHD symptoms and EA persisted after controlling for confounders (B = 15.507; t(170) = 3.771; 95% CI [7.389, 23.625]; p < 0.001). Additionally, a stepwise regression model identified that depressive symptoms are a stronger predictor for EA than ADHD symptoms. Conclusion: Depressive symptoms seem to be a stronger predictor for EA compared to ADHD symptoms in frequent exercisers. Although individuals with ADHD May exercise extensively, they might be less at risk for EA than individuals with depression. These results contribute to the complex characterization of the psychiatric profile of individuals with exercise addiction, and underline the need for further research elucidating the interplay between mental disorders and EA. [ABSTRACT FROM AUTHOR]

Background: To date, there are no official diagnostic criteria for the frequently reported phenomenon of exercise addiction. Therefore, the aim of the present study was to investigate how mental disorders, specifically depression and attention-deficit hyperactivity disorder (ADHD), are related to exercise addiction (EA). Methods: A total of 173 participants aged between 18 and 70 years, who reported exercising more than 10 h a week and continued to exercise despite injury or illness, answered questionnaires including the Exercise Dependence Scale, the Beck Depression Inventory, and the Homburger ADHD scale for adults. Multiple linear regression analyses were performed adjusting for relevant confounders (age, gender) and stepwise regression was used to identify which of the two mental disorders is the more influential predictor of EA. Results: Pearson correlation analysis showed that depressive symptoms [r (171) = 0.422, p < 0.00] and ADHD symptoms [r (171) = 0.308, p < 0.001] were positively correlated with EA symptoms. The relation between depressive symptoms and EA remained after adjusting for confounders in the regression model (B = 20.531; t(170) = 5.950; 95% CI [13.719, 27.343]; p < 0.001). Similarly, the positive link between ADHD symptoms and EA persisted after controlling for confounders (B = 15.507; t(170) = 3.771; 95% CI [7.389, 23.625]; p < 0.001). Additionally, a stepwise regression model identified that depressive symptoms are a stronger predictor for EA than ADHD symptoms. Conclusion: Depressive symptoms seem to be a stronger predictor for EA compared to ADHD symptoms in frequent exercisers. Although individuals with ADHD May exercise extensively, they might be less at risk for EA than individuals with depression. These results contribute to the complex characterization of the psychiatric profile of individuals with exercise addiction, and underline the need for further research elucidating the interplay between mental disorders and EA. [ABSTRACT FROM AUTHOR]

This article explores the use of convolutional neural networks (CNNs) in the detection and diagnosis of Attention Deficit Hyperactivity Disorder (ADHD). CNNs, which are adept at image recognition, can analyze brain imaging data to automatically identify relevant brain structures and abnormalities associated with ADHD. The integration of deep learning technology into ADHD diagnosis represents a significant advancement, allowing for more objective and data-driven approaches. The article also highlights specific studies that demonstrate the effectiveness of CNNs in diagnosing ADHD using EEG signals and MRI scans. The authors suggest that future research should focus on integrating different types of data and developing personalized treatment plans while considering ethical considerations and explainable AI models. Overall, CNNs are seen as a promising tool in improving ADHD care and patient outcomes. [Extracted from the article]

Introduction: In this prospective cohort study over 40 years we investigated the effect of childhood attention-deficit/hyperactivity disorder (ADHD) and subthreshold ADHD on cognitive performance in adulthood. Methods: The cohort comprised individuals with mild perinatal risks. Childhood ADHD group (cADHD, n = 39) was compared to a group with subthreshold childhood attention or hyperactivity symptoms (cAP; n = 79), a group with similar perinatal risks but no ADHD symptoms (n = 255), and to controls without ADHD symptoms or perinatal risks (n = 69). The groups were assessed with multiple neuropsychological measures in domains of verbal reasoning, perceptual skills, memory, working memory, attention, executive functions, and speed. Grouplevel differences and frequencies of deficient functioning were analyzed. Results: Overall, the groups' performance differed in all cognitive domains at age 40. Verbal reasoning, perceptual skills, memory, and speed had the largest effect sizes (0.51-0.62). The cADHD group's performance was lower than the other groups' on 13 out of 21 measures. The cAP group performed poorer than controls on five measures. In the cADHD group, 23% had three or more deficient cognitive domains, compared to 4-6% in the other groups. Discussion: Childhood ADHD is associated with impaired cognitive functioning in adulthood on several cognitive domains whereas childhood subthreshold ADHD is linked to fewer cognitive deficits. Task complexity was linked to poorer performance within the ADHD group. Our results add to the scarce longitudinal evidence of cognitive outcomes related to childhood ADHD and subthreshold symptoms. [ABSTRACT FROM AUTHOR]

The cerebellum plays a major role in balance, motor control and sensorimotor integration, but also in cognition, language, and emotional regulation. Several neuropsychiatric disorders such as attention deficit-hyperactivity disorder (ADHD), autism spectrum disorder (ASD), as well as neurological diseases such as spinocerebellar ataxia type 3 (SCA3) are associated with differences in cerebellar function. Morphological abnormalities in different cerebellar subregions produce distinct behavioral symptoms related to the functional disruption of specific cerebro-cerebellar circuits. The specific contribution of the cerebellum to typical development may therefore involve the optimization of the structure and function of cerebro-cerebellar circuits underlying skill acquisition in multiple domains. Here, we review cerebellar structural and functional differences between healthy and patients with ADHD, ASD, and SCA3, and explore how disruption of cerebellar networks affects the neurocognitive functions in these conditions. We discuss how cerebellar computations contribute to performance on cognitive and motor tasks and how cerebellar signals are interfaced with signals from other brain regions during normal and dysfunctional behavior. We conclude that the cerebellum plays a role in many cognitive functions. Still, more clinical studies with the support of neuroimaging are needed to clarify the cerebellum's role in normal and dysfunctional behavior and cognitive functioning. [ABSTRACT FROM AUTHOR]

Background: Attention Deficit Hyperactivity Disorder (ADHD) frequently persists into adulthood. There are practice guidelines that outline the requirements for the assessment and treatment of adults. Nevertheless, guidelines specifying what constitutes a good quality diagnostic assessment and report and the competencies required to be a specialist assessor are lacking. This can lead to variation in the quality and reliability of adult ADHD assessments. Poor quality assessments may not be accepted as valid indicators of the presence of ADHD by other clinicians or services, resulting in wasteful re-assessments and delays in providing treatment. To address this issue the UK Adult ADHD Network (UKAAN) proposes a quality framework for adult ADHD assessments - the Adult ADHD Assessment Quality Assurance Standard (AQAS). Methods: The co-authors agreed on five questions or themes that then guided the development of a set of consensus statements. An initial draft was reviewed and amended in an iterative process to reach a final consensus. Results: What constitutes a high-quality diagnostic assessment and report was agreed by consensus of the co-authors. The resulting guideline emphasises the need to evaluate impairment, describes core competencies required by the assessor and highlights the importance of linking the diagnosis to an appropriate post-diagnostic discussion. Assessments should be completed in the context of a full psychiatric and neurodevelopmental review, and need good interview skills, using a semi-structured interview with open questioning and probing to elicit real life examples of symptoms and impairments. It is recommended that 2 hours or more is required for an adequate assessment including both the diagnostic assessment and initial post-assessment discussions. Conclusion: The AQAS has been developed as a practical resource to support reliable and valid diagnostic assessments of adult ADHD. It is intended to complement formal training. A secondary objective is to empower patients by providing them with evidence-based information on what to expect from an assessment and assessment report. [ABSTRACT FROM AUTHOR]