Subcortical Brain Volume, Regional Cortical Thickness, and Cortical Surface Area Across Disorders: Findings From the ENIGMA ADHD, ASD, and OCD Working Groups

Methylphenidate for attention-deficit/hyperactivity disorder in adults: a narrative review

RATIONALE

Psychostimulants, including methylphenidate (MPH), are the mainstay of pharmacotherapy for attention-deficit/hyperactivity disorder (ADHD) in adults. Even though MPH is the most commonly used medication for ADHD these days, there are relatively few resources available that provide comprehensive insight into the pharmacological and clinical features of the compound.

OBJECTIVE

The aim of this paper is to provide an up-to-date outline of the pharmacology and clinical utility of MPH for ADHD in adult patients.

METHODS

While conducting the narrative review, we applied structured search strategies covering the two major online databases (MEDLINE and Cochrane Central Register of Controlled Trials). In addition, we performed handsearching of reference lists of relevant papers.

RESULTS

Methylphenidate exhibits multimodal mechanism of action, working primarily as a dopamine and noradrenaline reuptake inhibitor. It also protects the dopaminergic system against the ongoing 'wearing off' (by securing a substantial reserve pool of the neurotransmitter, stored in the presynaptic vesicles). In placebo-controlled trials, MPH was shown to be moderately effective both against the core ADHD symptoms (standardized mean difference [SMD], 0.49; 95% confidence interval [CI], 0.35-0.64), and the accompanying emotion regulation deficits (SMD, 0.34; 95% CI, 0.23-0.45). The most common adverse events related to long-term treatment with MPH are decreased appetite (~ 20%), dry mouth (15%), heart palpitations (13%), gastrointestinal infections (~ 10%), and agitation/feeling restless (~ 10%).

CONCLUSIONS

There is substantial body of evidence to suggest that MPH is an effective and safe treatment option for adults with ADHD.

Ensemble classification of autism spectrum disorder using structural magnetic resonance imaging features

Background

Autism spectrum disorder (ASD) is characterized by a spectrum of social and communication impairments and rigid and stereotyped behaviors that have a neurodevelopmental origin. Although many imaging studies have reported structural and functional alterations in multiple brain regions, clinically useful diagnostic imaging biomarkers for ASD remain unavailable.

Methods

In this study, we applied machine learning (ML) models to regional volumetric and cortical thickness data from the largest structural magnetic resonance imaging (sMRI) dataset available from the Enhancing Neuro Imaging Genetics Through Meta-Analysis (ENIGMA) consortium (1833 subjects with ASD and 1838 without ASD; age range: 1.5-64; average age: 15.6; male/female ratio: 4.2:1).

Results

The highest classification accuracy on a hold-out test set was achieved using a stacked Extra Tree Classifier. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.62 (95% confidence interval [CI]: 0.57, 0.68) and the area under the precision-recall curve was 0.58. Learning curve analysis showed the good fit of the model and suggests that more training examples will not likely benefit model performance.

Conclusions

Our results suggest that sMRI volumetric and cortical thickness data alone may not provide clinically sufficient useful diagnostic biomarkers for ASD. Developing clinically useful imaging classifiers for ASD will benefit from combining other data modalities or feature types, such as functional MRI data and raw images that can leverage other machine learning (ML) techniques such as convolutional neural networks.

The World Federation of ADHD International Consensus Statement: 208 Evidence-based conclusions about the disorder

BACKGROUND

Misconceptions about ADHD stigmatize affected people, reduce credibility of providers, and prevent/delay treatment. To challenge misconceptions, we curated findings with strong evidence base.

METHODS

We reviewed studies with more than 2000 participants or meta-analyses from five or more studies or 2000 or more participants. We excluded meta-analyses that did not assess publication bias, except for meta-analyses of prevalence. For network meta-analyses we required comparison adjusted funnel plots. We excluded treatment studies with waiting-list or treatment as usual controls. From this literature, we extracted evidence-based assertions about the disorder.

RESULTS

We generated 208 empirically supported statements about ADHD. The status of the included statements as empirically supported is approved by 80 authors from 27 countries and 6 continents. The contents of the manuscript are endorsed by 366 people who have read this document and agree with its contents.

CONCLUSIONS

Many findings in ADHD are supported by meta-analysis. These allow for firm statements about the nature, course, outcome causes, and treatments for disorders that are useful for reducing misconceptions and stigma.

The Monash Autism-ADHD genetics and neurodevelopment (MAGNET) project design and methodologies: a dimensional approach to understanding neurobiological and genetic aetiology

Background

ASD and ADHD are prevalent neurodevelopmental disorders that frequently co-occur and have strong evidence for a degree of shared genetic aetiology. Behavioural and neurocognitive heterogeneity in ASD and ADHD has hampered attempts to map the underlying genetics and neurobiology, predict intervention response, and improve diagnostic accuracy. Moving away from categorical conceptualisations of psychopathology to a dimensional approach is anticipated to facilitate discovery of data-driven clusters and enhance our understanding of the neurobiological and genetic aetiology of these conditions. The Monash Autism-ADHD genetics and neurodevelopment (MAGNET) project is one of the first large-scale, family-based studies to take a truly transdiagnostic approach to ASD and ADHD. Using a comprehensive phenotyping protocol capturing dimensional traits central to ASD and ADHD, the MAGNET project aims to identify data-driven clusters across ADHD-ASD spectra using deep phenotyping of symptoms and behaviours; investigate the degree of familiality for different dimensional ASD-ADHD phenotypes and clusters; and map the neurocognitive, brain imaging, and genetic correlates of these data-driven symptom-based clusters.

Methods

The MAGNET project will recruit 1,200 families with children who are either typically developing, or who display elevated ASD, ADHD, or ASD-ADHD traits, in addition to affected and unaffected biological siblings of probands, and parents. All children will be comprehensively phenotyped for behavioural symptoms, comorbidities, neurocognitive and neuroimaging traits and genetics.

Conclusion

The MAGNET project will be the first large-scale family study to take a transdiagnostic approach to ASD-ADHD, utilising deep phenotyping across behavioural, neurocognitive, brain imaging and genetic measures.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13229-021-00457-3.

Whole Brain White Matter Tract Deviation and Idiosyncrasy From Normative Development in Autism and ADHD and Unaffected Siblings Link With Dimensions of Psychopathology and Cognition

OBJECTIVE

The heterogeneity of autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) preclude definitive identification of neurobiomarkers and biological risks. High clinical overlap suggests multifaceted circuit-level alterations across diagnoses, which remains elusive. This study investigated whether individuals with ADHD or ASD and their unaffected siblings constitute a spectrum of neurodevelopmental conditions in terms of white matter etiology.

METHODS

Sex-specific white matter tract normative development was modeled from diffusion MRI of 626 typically developing control subjects (ages 5-40 years; 376 of them male). Individualized metrics estimating white matter tract deviation from the age norm were derived for 279 probands with ADHD, 175 probands with ASD, and their unaffected siblings (ADHD, N=121; ASD, N=72).

RESULTS

ASD and ADHD shared diffuse white matter tract deviations in the commissure and association tracts (rho=0.54; p<0.001), while prefrontal corpus callosum deviated more remarkably in ASD (effect size=-0.36; p<0.001). Highly correlated deviance patterns between probands and unaffected siblings were found in both ASD (rho=0.69; p<0.001) and ADHD (rho=0.51; p<0.001), but only unaffected sisters of ASD probands showed a potential endophenotype in long-range association fibers and projection fibers connecting prefrontal regions. ADHD and ASD shared significant white matter tract idiosyncrasy (rho=0.55; p<0.001), particularly in tracts connecting prefrontal regions, not identified in either sibling group. Canonical correlation analysis identified multiple dimensions of psychopathology/cognition across categorical entities; autistic, visual memory, intelligence/planning/inhibition, nonverbal-intelligence/attention, working memory/attention, and set-shifting/response-variability were associated with distinct sets of white matter tract deviations.

CONCLUSIONS

When conceptualizing neurodevelopmental disorders as white matter tract deviations from normative patterns, ASD and ADHD are more alike than different. The modest white matter tract alterations in siblings suggest potential endophenotypes in these at-risk populations. This study further delineates brain-driven dimensions of psychopathology/cognition, which may help clarify within-diagnosis heterogeneity and high between-diagnosis co-occurrence.

Shared and Distinct Topologically Structural Connectivity Patterns in Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder

Background

Previous neuroimaging studies have described shared and distinct neurobiological mechanisms between autism spectrum disorders (ASDs) and attention-deficit/hyperactivity disorder (ADHD). However, little is known about the similarities and differences in topologically structural connectivity patterns between the two disorders.

Methods

Diffusion tensor imaging (DTI) and deterministic tractography were used to construct the brain white matter (WM) structural networks of children and adolescents (age range, 6–16 years); 31 had ASD, 34 had ADHD, and 30 were age- and sex-matched typically developing (TD) individuals. Then, graph theoretical analysis was performed to investigate the alterations in the global and node-based properties of the WM structural networks in these groups. Next, measures of ASD traits [Social Responsiveness Scale (SRS)] and ADHD traits (Swanson, Nolan, and Pelham, version IV scale, SNAP-IV) were correlated with the alterations to determine the functional significance of such changes.

Results

First, there were no significant differences in the global network properties among the three groups; moreover, compared with that of the TD group, nodal degree (Ki) of the right amygdala (AMYG.R) and right parahippocampal gyrus (PHG.R) were found in both the ASD and ADHD groups. Also, the ASD and ADHD groups shared four additional hubs, including the left middle temporal gyrus (MTG.L), left superior temporal gyrus (STG.L), left postcentral gyrus (PoCG.L), and right middle frontal gyrus (MFG.R) compared with the TD group. Moreover, the ASD and ADHD groups exhibited no significant differences regarding regional connectivity characteristics. Second, the ADHD group showed significantly increased nodal betweenness centrality (Bi) of the left hippocampus (HIP.L) compared with the ASD group; also, compared with the ADHD group, the ASD group lacked the left anterior cingulate gyrus (ACG.L) as a hub. Last, decreased nodal efficiency (Enodal) of the AMYG.R, Ki of the AMYG.R, and Ki of the PHG.R were associated with higher SRS scores in the ASD group. Decreased Ki of the PHG.R was associated with higher SRS scores in the full sample, whereas decreased Bi of the PHG.R was associated with lower oppositional defiance subscale scores of the SNAP-IV in the ADHD group, and decreased Bi of the HIP.L was associated with lower inattention subscale scores of the SNAP-IV in the full sample.

Conclusion

From the perspective of the topological properties of brain WM structural networks, ADHD and ASD have both shared and distinct features. More interestingly, some shared and distinct topological properties of WM structures are related to the core symptoms of these disorders.

Cross-Diagnosis Structural Correlates of Autistic-Like Social Communication Differences

Social communication differences are seen in autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and obsessive–compulsive disorder (OCD), but the brain mechanisms contributing to these differences remain largely unknown. To address this gap, we used a data-driven and diagnosis-agnostic approach to discover brain correlates of social communication differences in ASD, ADHD, and OCD, and subgroups of individuals who share similar patterns of brain-behavior associations. A machine learning pipeline (regression clustering) was used to discover the pattern of association between structural brain measures (volume, surface area, and cortical thickness) and social communication abilities. Participants (n = 416) included children with a diagnosis of ASD (n = 192, age = 12.0[5.6], 19% female), ADHD (n = 109, age = 11.1[4.1], 18% female), or OCD (n = 50, age = 12.3[4.2], 42% female), and typically developing controls (n = 65, age = 11.6[7.1], 48% female). The analyses revealed (1) associations with social communication abilities in distributed cortical and subcortical networks implicated in social behaviors, language, attention, memory, and executive functions, and (2) three data-driven, diagnosis-agnostic subgroups based on the patterns of association in the above networks. Our results suggest that different brain networks may contribute to social communication differences in subgroups that are not diagnosis-specific.

Artificial intelligence applications in psychoradiology

One important challenge in psychiatric research is to translate findings from brain imaging research studies that identified brain alterations in patient groups into an accurate diagnosis at an early stage of illness, prediction of prognosis before treatment, and guidance for selection of effective treatments that target patient-relevant pathophysiological features. This is the primary aim of the field of Psychoradiology. Using databases collected from large samples at multiple centers, sophisticated artificial intelligence (AI) algorithms may be used to develop clinically useful image analysis pipelines that can help physicians diagnose, predict, and make treatment decisions. In this review, we selectively summarize psychoradiological research using magnetic resonance imaging of the brain to explore the neural mechanism of psychiatric disorders, and outline progress and the path forward for the combination of psychoradiology and AI for complementing clinical examinations in patients with psychiatric disorders, as well as limitations in the application of AI that should be considered in future translational research.

Explainable Deep Learning for Personalized Age Prediction With Brain Morphology

Predicting brain age has become one of the most attractive challenges in computational neuroscience due to the role of the predicted age as an effective biomarker for different brain diseases and conditions. A great variety of machine learning (ML) approaches and deep learning (DL) techniques have been proposed to predict age from brain magnetic resonance imaging scans. If on one hand, DL models could improve performance and reduce model bias compared to other less complex ML methods, on the other hand, they are typically black boxes as do not provide an in-depth understanding of the underlying mechanisms. Explainable Artificial Intelligence (XAI) methods have been recently introduced to provide interpretable decisions of ML and DL algorithms both at local and global level. In this work, we present an explainable DL framework to predict the age of a healthy cohort of subjects from ABIDE I database by using the morphological features extracted from their MRI scans. We embed the two local XAI methods SHAP and LIME to explain the outcomes of the DL models, determine the contribution of each brain morphological descriptor to the final predicted age of each subject and investigate the reliability of the two methods. Our findings indicate that the SHAP method can provide more reliable explanations for the morphological aging mechanisms and be exploited to identify personalized age-related imaging biomarker.

Hippocampal functional connectivity development during the first two years indexes 4-year working memory performance

The hippocampus is a key limbic region involved in higher-order cognitive processes including learning and memory. Although both typical and atypical functional connectivity patterns of the hippocampus have been well-studied in adults, the developmental trajectory of hippocampal connectivity during infancy and how it relates to later working memory performance remains to be elucidated. Here we used resting state fMRI (rsfMRI) during natural sleep to examine the longitudinal development of hippocampal functional connectivity using a large cohort (N = 202) of infants at 3 weeks (neonate), 1 year, and 2 years of age. Next, we used multivariate modeling to investigate the relationship between both cross-sectional and longitudinal growth in hippocampal connectivity and 4-year working memory outcome. Results showed robust local functional connectivity of the hippocampus in neonates with nearby limbic and subcortical regions, with dramatic maturation and increasing connectivity with key default mode network (DMN) regions resulting in adult-like topology of the hippocampal functional connectivity by the end of the first year. This pattern was stabilized and further consolidated by 2 years of age. Importantly, cross-sectional and longitudinal measures of hippocampal connectivity in the first year predicted subsequent behavioral measures of working memory at 4 years of age. Taken together, our findings provide insight into the development of hippocampal functional circuits underlying working memory during this early critical period.

Magnitude and variability of structural brain abnormalities in neuropsychiatric disease: protocol for a network meta-analysis of MRI studies

INTRODUCTION

Structural MRI is the most frequently used method to investigate brain volume alterations in neuropsychiatric disease. Previous meta-analyses have typically focused on a single diagnosis, thereby precluding transdiagnostic comparisons.

METHODS AND ANALYSIS

We will include all structural MRI studies of adults that report brain volumes for participants from at least two of the following diagnostic groups: healthy controls, schizophrenia, schizoaffective disorder, delusional disorder, psychotic depression, clinical high risk for psychosis, schizotypal personality disorder, psychosis unspecified, bipolar disorder, autism spectrum disorder, major depressive disorder, attention deficit hyperactivity disorder, obsessive compulsive disorder, post-traumatic stress disorder, emotionally unstable personality disorder, 22q11 deletion syndrome, generalised anxiety disorder, social anxiety disorder, panic disorder, mixed anxiety and depression. Network meta-analysis will be used to synthesise eligible studies. The primary analysis will examine standardised mean difference in average volume, a secondary analysis will examine differences in variability of volumes.

DISCUSSION

This network meta-analysis will provide a transdiagnostic integration of structural neuroimaging studies, providing researchers with a valuable summary of a large literature.

PROSPERO REGISTRATION NUMBER

CRD42020221143.

Associations of subclinical autistic-like traits with brain structural variation using diffusion tensor imaging and voxel-based morphometry

Background

Previous case–control studies of autistic spectrum disorder (ASD) have identified altered brain structure such as altered frontal and temporal cortex volumes, or decreased fractional anisotropy (FA) within the inferior fronto-occipital fasciculus in patients. It remains unclear whether subclinical autistic-like traits might also be related to variation in these brain structures.

Methods

In this study, we analyzed magnetic resonance imaging (MRI) data of 250 psychiatrically healthy subjects phenotyped for subclinical autistic-like traits using the Autism Spectrum Quotient (AQ). For data analysis, we used voxel-based morphometry of T1-MRIs (Computational Anatomy Toolbox) and tract-based spatial statistics for diffusion tensor imaging data.

Results

AQ attention switching subscale correlated negatively with FA values in the bilateral uncinate fasciculus as well as the bilateral inferior fronto-occipital fasciculus. Higher AQ attention switching subscale scores were associated with increased mean diffusivity and radial diffusivity values in the uncinate fasciculus, while axial diffusivity values within this tract show a negative correlation. AQ attention to detail subscale correlated positively with gray matter volume in the right pre- and postcentral gyrus.

Conclusions

We demonstrate that individuals with higher levels of autism-spectrum-like features show decreased white matter integrity in tracts associated with higher-level visual processing and increased cortical volume in areas linked to movement sequencing and working memory. Our results resemble regional brain structure alterations found in individuals with ASD. This offers opportunities to further understand the etiology and pathogenesis of the disorder and shows a subclinical continuum perspective.

Neuroimaging in attention-deficit/hyperactivity disorder

PURPOSE OF REVIEW

Neuroimaging research on attention-deficit/hyperactivity disorder (ADHD) continues growing in extent and complexity, although it has yet to become clinically meaningful. We review recent MRI research on ADHD, to identify robust findings, current trends and challenges.

RECENT FINDINGS

We identified 40 publications between January 2019 and September 2020 reporting or reviewing MRI research on ADHD. Four meta-analyses have presented conflicting results regarding across-study convergence of functional and resting-state functional (fMRI and R-fMRI) studies on ADHD. On the other hand, the Enhancing NeuroImaging Genetics Through Meta-Analysis international consortium has identified statistically robust albeit small differences in structural brain cortical and subcortical indices in children with ADHD versus typically developing controls. Other international consortia are harnessing open-science efforts and multimodal data (imaging, genetics, phenotypic) to shed light on the complex interplay of genetics, environment, and development in the pathophysiology of ADHD. We note growing research in 'prediction' science, which applies machine-learning analysis to identify biomarkers of disease based on big data.

SUMMARY

Neuroimaging in ADHD is still far from informing clinical practice. Current large-scale, multimodal, and open-science initiatives represent promising paths toward untangling the neurobiology of ADHD.

Postural Control in Childhood: Investigating the Neurodevelopmental Gradient Hypothesis

Neurodevelopmental disorders (NDDs) have been suggested to lie on a gradient continuum, all resulting from common brain disturbances, but with different degrees of impairment severity. This case-control study aimed to assess postural stability against such hypothesis in 104 children/adolescents aged 5–17, of whom 81 had NDDs and 23 were healthy controls. Compared to healthy controls, Autism Spectrum Disorder (ASD) resulted in the most severely impaired neurodevelopmental condition, followed by Attention Deficit Hyperactive Disorder (ADHD) and Tourette Syndrome (TS). In particular, while ASD children/adolescents performed worse than healthy controls in a number of sensory conditions across all parameters, ADHD children/adolescents performed worse than healthy controls only in the sway area for the most complex sensory conditions, when their vision and somatosensory functions were both compromised, and performance in Tourette Syndrome (TS) was roughly indistinguishable from that of healthy controls. Finally, differences were also observed between clinical groups, with ASD children/adolescents, and to a much lesser extent ADHD children/adolescents, performing worse than TS children/adolescents, especially when sensory systems were not operationally accurate. Evidence from this study indicates that poor postural control may be a useful biomarker for risk assessment during neurodevelopment, in line with predictions from the gradient hypothesis.

Early onset obsessive-compulsive disorder: the biological and clinical phenotype

Moving from a behavioral-based to a biological-based classification of mental disorders is a crucial step toward a precision-medicine approach in psychiatry. In the last decade, a big effort has been made in order to stratify genetic, immunological, neurobiological, cognitive, and clinical profiles of patients. Making the case of obsessive-compulsive disorder (OCD), a lot have been made in this direction. Indeed, while the Diagnostic and Statistical Manual of Mental Disorders (DSM) diagnosis of OCD aimed to delineate a homogeneous group of patients, it is now clear that OCD is instead an heterogeneous disorders both in terms of neural networks, immunological, genetic, and clinical profiles. In this view, a convergent amount of literature, in the last years, indicated that OCD patients with an early age at onset seem to have a specific clinical and biological profile, suggesting it as a neurodevelopmental disorder. Also, these patients tend to have a worse outcome respect to adult-onset patients and there is growing evidence that early-interventions could potentially improve their prognosis. Therefore, the aim of the present paper is to review the current available genetic, immunological, neurobiological, cognitive, and clinical data in favor of a more biologically precise subtype of OCD: the early-onset subtype. We also briefly resume current available recommendations for the clinical management of this specific population.

Alternatives to Pharmacological and Psychotherapeutic Treatments in Psychiatric Disorders

Nowadays, most of the patients affected by psychiatric disorders are successfully treated with psychotherapy and pharmacotherapy. Nevertheless, according to the disease, a variable percentage of patients results resistant to such modalities, and alternative methods can then be considered. The purpose of this review is to summarize the techniques and results of invasive modalities for several treatment-resistant psychiatric diseases. A literature search was performed to provide an up-to-date review of advantages, disadvantages, efficacy, and complications of Deep-Brain Stimulation, Magnetic Resonance-guided Focused-Ultrasound, radiofrequency, and radiotherapy lesioning for depression, obsessive-compulsive disorder, schizophrenia, addiction, anorexia nervosa, and Tourette’s syndrome. The literature search did not strictly follow the criteria for a systematic review: due to the large differences in methodologies and patients’ cohort, we tried to identify the highest quality of available evidence for each technique. We present the data as a comprehensive, narrative review about the role, indication, safety, and results of the contemporary instrumental techniques that opened new therapeutic fields for selected patients unresponsive to psychotherapy and pharmacotherapy.

Autism Spectrum Disorder and Childhood Apraxia of Speech: Early Language-Related Hallmarks across Structural MRI Study

Autism Spectrum Disorder (ASD) and Childhood Apraxia of Speech (CAS) are developmental disorders with distinct diagnostic criteria and different epidemiology. However, a common genetic background as well as overlapping clinical features between ASD and CAS have been recently reported. To date, brain structural language-related abnormalities have been detected in both the conditions, but no study directly compared young children with ASD, CAS and typical development (TD). In the current work, we aim: (i) to test the hypothesis that ASD and CAS display neurostructural differences in comparison with TD through morphometric Magnetic Resonance Imaging (MRI)-based measures (ASD vs. TD and CAS vs. TD); (ii) to investigate early possible disease-specific brain structural patterns in the two clinical groups (ASD vs. CAS); (iii) to evaluate predictive power of machine-learning (ML) techniques in differentiating the three samples (ASD, CAS, TD). We retrospectively analyzed the T1-weighted brain MRI scans of 68 children (age range: 34-74 months) grouped into three cohorts: (1) 26 children with ASD (mean age ± standard deviation: 56 ± 11 months); (2) 24 children with CAS (57 ± 10 months); (3) 18 children with TD (55 ± 13 months). Furthermore, a ML analysis based on a linear-kernel Support Vector Machine (SVM) was performed. All but one brain structures displayed significant higher volumes in both ASD and CAS children than TD peers. Specifically, ASD alterations involved fronto-temporal regions together with basal ganglia and cerebellum, while CAS alterations are more focused and shifted to frontal regions, suggesting a possible speech-related anomalies distribution. Caudate, superior temporal and hippocampus volumes directly distinguished the two conditions in terms of greater values in ASD compared to CAS. The ML analysis identified significant differences in brain features between ASD and TD children, whereas only some trends in the ML classification capability were detected in CAS as compared to TD peers. Similarly, the MRI structural underpinnings of two clinical groups were not significantly different when evaluated with linear-kernel SVM. Our results may represent the first step towards understanding shared and specific neural substrate in ASD and CAS conditions, which subsequently may contribute to early differential diagnosis and tailoring specific early intervention.

Brain Age Prediction With Morphological Features Using Deep Neural Networks: Results From Predictive Analytic Competition 2019

Morphological changes in the brain over the lifespan have been successfully described by using structural magnetic resonance imaging (MRI) in conjunction with machine learning (ML) algorithms. International challenges and scientific initiatives to share open access imaging datasets also contributed significantly to the advance in brain structure characterization and brain age prediction methods. In this work, we present the results of the predictive model based on deep neural networks (DNN) proposed during the Predictive Analytic Competition 2019 for brain age prediction of 2638 healthy individuals. We used FreeSurfer software to extract some morphological descriptors from the raw MRI scans of the subjects collected from 17 sites. We compared the proposed DNN architecture with other ML algorithms commonly used in the literature (RF, SVR, Lasso). Our results highlight that the DNN models achieved the best performance with MAE = 4.6 on the hold-out test, outperforming the other ML strategies. We also propose a complete ML framework to perform a robust statistical evaluation of feature importance for the clinical interpretability of the results.

Serum Levels of Vitamin A and Vitamin D and Their Association With Symptoms in Children With Attention Deficit Hyperactivity Disorder

Objective: To measure levels of vitamin A (VA) and vitamin D (VD) and the symptomatic association of their co-deficiencies on attention deficit hyperactivity disorder (ADHD) in Chinese children (6-9 years). Methods: Eighty-two children (69 boys and 13 girls; mean age = 7.1 ± 0.9 years at the time of the diagnosis) with ADHD were recruited as ADHD group. A total of 106 healthy children were recruited as the healthy control (HC) group. Serum levels of retinol and 25-hydroxyvitamin D (25(OH)D) of all children were evaluated using high-performance liquid chromatography (HPLC) and HPLC-tandem mass spectrometry. The Swanson, Nolan, and Pelham IV Rating Scale (SNAP-IV) was employed to assess the clinical symptoms of ADHD. Results: Children suffering from ADHD had significantly reduced serum levels of retinol and 25(OH)D compared with those of HCs, and the prevalence of VA deficiency and VD deficiency were higher in children suffering from ADHD. Serum concentrations of 25(OH)D and retinol were linked closely with the presence or absence of ADHD after adjustment for age, body mass index, season of blood sampling, and sun exposure. Serum concentrations of 25(OH)D and retinol showed a negative correlation with the total scores of SNAP-IV. Children with ADHD as well as VA and VD co-deficiency had increased SNAP-IV total scores and ADHD inattention subscale scores. Conclusion: VA deficiency and VD deficiency in children with ADHD were increased in comparison with that in HCs. VA and VD co-deficiency associated with ADHD symptom severity. Attention should be paid to regular testing of VA levels and VD levels. However, the mechanism of VA and VD in ADHD needs to be further studied. Interventional studies on VA and VD supplementation are recommended to further verify the relationship between VA and VD co-deficiency and ADHD.