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Multimodal MR Images-Based Diagnosis of Early Adolescent Attention-Deficit/Hyperactivity Disorder Using Multiple Kernel Learning.

ABSTRACT: 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.

SUBMITTER: Zhou X

PROVIDER: S-EPMC8477011 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ImportanceAppropriate diagnosis of attention-deficit/hyperactivity disorder (ADHD) can improve some short-term outcomes in children and adolescents, but little is known about the association of a diagnosis with their quality of life (QOL).ObjectiveTo compare QOL in adolescents with and without an ADHD diagnosis.Design, setting, and participantsThis cohort study followed an emulated target trial design using prospective, observational data from the Longitudinal Study of Australian Children, a representative, population-based prospective cohort study with biennial data collection from 2006 to 2018 with 8 years of follow-up (ages 6-7 to 14-15 years). Propensity score matching was used to ensure children with and without ADHD diagnosis were well matched on a wide range of variables, including hyperactive/inattentive (H/I) behaviors. Eligible children were born in 1999 to 2000 or 2003 to 2004 and did not have a previous ADHD diagnosis. All incident ADHD cases were matched with controls. Data were analyzed from July 2021 to January 2022.ExposuresIncident parent-reported ADHD diagnosis at age 6 to 7, 8 to 9, 10 to 11, 12 to 13, or 14 to 15.Main outcomes and measuresQuality of life at age 14 to 15 was measured with Child Health Utility 9D (CHU9D) and 8 other prespecified, self-reported measures mapped to the World Health Organization's QOL domains. Pooled regression models were fitted for each outcome, with 95% CIs and P values calculated using bootstrapping to account for matching and repeat observations.ResultsOf 8643 eligible children, a total of 393 adolescents had an ADHD diagnosis (284 [72.2%] boys; mean [SD] age, 10.03 [0.30] years; mean [SD] H/I Strengths and Difficulties Questionnaire score, 5.05 [2.29]) and were age-, sex-, and H/I score-matched with 393 adolescents without ADHD diagnosis at time zero. Compared with adolescents without diagnosis, those with an ADHD diagnosis reported similar QOL on CHU9D (mean difference, -0.03; 95% CI, -0.07 to 0.01; P = .10), general health (mean difference, 0.11; 95% CI, -0.04 to 0.27; P = .15), happiness (mean difference, -0.18; 95% CI, -0.37 to 0.00; P = .05), and peer trust (mean difference, 0.65; 95% CI, 0.00 to 1.30; P = .05). Diagnosed adolescents had worse psychological sense of school membership (mean difference, -2.58; 95% CI, -1.13 to -4.06; P < .001), academic self-concept (mean difference, -0.14; 95% CI, -0.02 to -0.26; P = .02), and self-efficacy (mean difference, -0.20; 95% CI, -0.05 to -0.33; P = .007); displayed more negative social behaviors (mean difference, 1.56; 95% CI, 0.55 to 2.66; P = .002); and were more likely to harm themselves (odds ratio, 2.53; 95% CI, 1.49 to 4.37; P < .001) than adolescents without diagnosis.Conclusions and relevanceIn this cohort study, ADHD diagnosis was not associated with any self-reported improvements in adolescents' QOL compared with adolescents with similar levels of H/I behaviors but no ADHD diagnosis. ADHD diagnosis was associated with worse scores in some outcomes, including significantly increased risk of self-harm. A large, randomized clinical trial with long-term follow-up is needed.

Project description:BACKGROUND: Psychostimulants are first line of therapy for paediatric and adolescent AD/HD. The evidence suggests that up to 30% of those prescribed stimulant medications do not show clinically significant outcomes. In addition, many children and adolescents experience side-effects from these medications. As a result, parents are seeking alternate interventions for their children. Complementary and alternative medicine therapies for behavioural disorders such as AD/HD are increasing with as many as 68% of parents having sought help from alternative practitioners, including chiropractors. OBJECTIVE: The review seeks to answer the question of whether chiropractic care can reduce symptoms of inattention, impulsivity and hyperactivity for paediatric and adolescent AD/HD. METHODS: Electronic databases (Cochrane CENTRAL register of Controlled Trials, Cochrane Database of Systematic reviews, MEDLINE, PsycINFO, CINAHL, Scopus, ISI Web of Science, Index to Chiropractic Literature) were searched from inception until July 2009 for English language studies for chiropractic care and AD/HD. Inclusion and exclusion criteria were applied to select studies. All randomised controlled trials were evaluated using the Jadad score and a checklist developed from the CONSORT (Consolidated Standards of Reporting Trials) guidelines. RESULTS: The search yielded 58 citations of which 22 were intervention studies. Of these, only three studies were identified for paediatric and adolescent AD/HD cohorts. The methodological quality was poor and none of the studies qualified using inclusion criteria. CONCLUSIONS: To date there is insufficient evidence to evaluate the efficacy of chiropractic care for paediatric and adolescent AD/HD. The claim that chiropractic care improves paediatric and adolescent AD/HD, is only supported by low levels of scientific evidence. In the interest of paediatric and adolescent health, if chiropractic care for AD/HD is to continue, more rigorous scientific research needs to be undertaken to examine the efficacy and effectiveness of chiropractic treatment. Adequately-sized RCTs using clinically relevant outcomes and standardised measures to examine the effectiveness of chiropractic care verses no-treatment/placebo control or standard care (pharmacological and psychosocial care) are needed to determine whether chiropractic care is an effective alternative intervention for paediatric and adolescent AD/HD.

Project description:Objective: The Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5), classifies attention-deficit/hyperactivity disorder (ADHD) as a neurodevelopmental disorder, with symptoms becoming apparent as early as the preschool years. Early recognition can lead to interventions such as parent/teacher-administered behavior therapy, the recommended first-line treatment for preschool patients. There are few data, however, to inform the use of second-line, pharmacotherapy options in this population. In this review, we identified recent literature on the diagnosis and treatment of ADHD in preschool children. Methods: A PubMed and clinicaltrials.gov search was conducted for trials assessing efficacy or safety of ADHD medications in children aged <6 years. Diagnostic methods and criteria focusing on recognition of ADHD in preschool children were also surveyed. Results: The DSM-5 describes different manifestations of ADHD in preschool versus school-aged children, but does not list separate criteria by age group. Importantly, behaviors indicative of ADHD in older children may be developmentally appropriate in preschool children. Several behavioral rating scales have been validated in children younger than 6 years of age for assessing ADHD. The Preschool ADHD Treatment Study (PATS) has provided the most extensive efficacy and safety data on methylphenidate (MPH) for ADHD in preschoolers to date, with significant improvement in ADHD symptoms observed with MPH compared with placebo, although adverse event-related discontinuation was higher in PATS compared with studies of MPH for ADHD in school-aged children. Since PATS was conducted, few studies designed to assess ADHD medication effectiveness in preschool children have been published. One article reported significant improvement in ADHD symptoms with MPH (immediate release) versus placebo, two studies showed no difference between MPH and risperidone or MPH plus risperidone in relief of ADHD symptoms, and one study demonstrated the efficacy of atomoxetine versus placebo for ADHD symptoms in preschoolers. Conclusions: Further research is needed on pharmacotherapy for preschool children with ADHD.

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Multimodal MR Images-Based Diagnosis of Early Adolescent Attention-Deficit/Hyperactivity Disorder Using Multiple Kernel Learning.

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