Dataset Information

Sparse multiple factor analysis to integrate genetic data, neuroimaging features, and attention-deficit/hyperactivity disorder domains.

ABSTRACT:

Objectives

We proposed the application of a multivariate cross-sectional framework based on a combination of a variable selection method and a multiple factor analysis (MFA) in order to identify complex meaningful biological signals related to attention-deficit/hyperactivity disorder (ADHD) symptoms and hyperactivity/inattention domains.

Methods

The study included 135 children from the general population with genomic and neuroimaging data. ADHD symptoms were assessed using a questionnaire based on ADHD-DSM-IV criteria. In all analyses, the raw sum scores of the hyperactivity and inattention domains and total ADHD were used. The analytical framework comprised two steps. First, zero-inflated negative binomial linear model via penalized maximum likelihood (LASSO-ZINB) was performed. Second, the most predictive features obtained with LASSO-ZINB were used as input for the MFA.

Results

We observed significant relationships between ADHD symptoms and hyperactivity and inattention domains with white matter, gray matter regions, and cerebellum, as well as with loci within chromosome 1.

Conclusions

Multivariate methods can be used to advance the neurobiological characterization of complex diseases, improving the statistical power with respect to univariate methods, allowing the identification of meaningful biological signals in Imaging Genetic studies.

SUBMITTER: Vilor-Tejedor N

PROVIDER: S-EPMC6877273 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Publications

Sparse multiple factor analysis to integrate genetic data, neuroimaging features, and attention-deficit/hyperactivity disorder domains.

Vilor-Tejedor Natàlia N Alemany Silvia S Cáceres Alejandro A Bustamante Mariona M Mortamais Marion M Pujol Jesús J Sunyer Jordi J González Juan R JR

International journal of methods in psychiatric research 20180814 3

<h4>Objectives</h4>We proposed the application of a multivariate cross-sectional framework based on a combination of a variable selection method and a multiple factor analysis (MFA) in order to identify complex meaningful biological signals related to attention-deficit/hyperactivity disorder (ADHD) symptoms and hyperactivity/inattention domains.<h4>Methods</h4>The study included 135 children from the general population with genomic and neuroimaging data. ADHD symptoms were assessed using a quest ...[more]

PMID: 30105890

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Project description:BackgroundA challenge facing clinical neuroscientists is how best to synthesize diverse and sometimes inconsistent evidence for neuropsychological deficits and brain system dysfunction found in psychiatric disorders into models that guide etiological and treatment research. Multiple-pathway models suggest that psychiatric symptoms might arise from pathophysiology in different neural systems. This study tested dual-pathway model predictions for attention-deficit/hyperactivity disorder (ADHD) that reward and executive function cognitive deficits should be related to abnormalities in corresponding functionally specialized neural systems.MethodsBehavioral inhibition and preference for immediate rewards were assessed in N = 251 adolescent boys and girls ages 12 to 18 diagnosed with DSM-IV combined-subtype ADHD or non-ADHD control subjects. Following taxometric analyses of test performance, the resulting subgroups were compared on a functional magnetic resonance imaging monetary incentive delay task probing reward anticipation and go/no-go task of motor response inhibition.ResultsThree ADHD subgroups were identified consistent with different proposed pathways-ADHD with executive function/motor inhibition deficits, ADHD with both executive and reward deficits, and ADHD with relatively normal test performance. Each cognitive domain mapped to different ADHD brain dysfunction features as expected. However, no brain abnormalities were found common to all ADHD subgroups despite the fact they had nearly identical ADHD-related clinical characteristics.ConclusionsThe results suggest that combined-subtype ADHD is a collection of discrete disorders for which a comparable behavioral end point arises through different neurobiological pathways. The findings raise caution about applying common cause, single-deficit conceptual models to individual ADHD patients and should prompt researchers to consider biologically defined, multifactorial etiological models for other psychiatric diagnoses.

Project description:ObjectivesLeveraging a large population-level morphologic, microstructural, and functional neuroimaging dataset, we aimed to elucidate the underlying neurobiology of attention-deficit hyperactivity disorder (ADHD) in children. In addition, we evaluated the applicability of machine learning classifiers to predict ADHD diagnosis based on imaging and clinical information.MethodsFrom the Adolescents Behavior Cognitive Development (ABCD) database, we included 1,798 children with ADHD diagnosis and 6,007 without ADHD. In multivariate logistic regression adjusted for age and sex, we examined the association of ADHD with different neuroimaging metrics. The neuroimaging metrics included fractional anisotropy (FA), neurite density (ND), mean-(MD), radial-(RD), and axial diffusivity (AD) of white matter (WM) tracts, cortical region thickness and surface areas from T1-MPRAGE series, and functional network connectivity correlations from resting-state fMRI.ResultsChildren with ADHD showed markers of pervasive reduced microstructural integrity in white matter (WM) with diminished neural density and fiber-tracks volumes - most notable in the frontal and parietal lobes. In addition, ADHD diagnosis was associated with reduced cortical volume and surface area, especially in the temporal and frontal regions. In functional MRI studies, ADHD children had reduced connectivity among default-mode network and the central and dorsal attention networks, which are implicated in concentration and attention function. The best performing combination of feature selection and machine learning classifier could achieve a receiver operating characteristics area under curve of 0.613 (95% confidence interval = 0.580-0.645) to predict ADHD diagnosis in independent validation, using a combination of multimodal imaging metrics and clinical variables.ConclusionOur study highlights the neurobiological implication of frontal lobe cortex and associate WM tracts in pathogenesis of childhood ADHD. We also demonstrated possible potentials and limitations of machine learning models to assist with ADHD diagnosis in a general population cohort based on multimodal neuroimaging metrics.

Dataset Information

Sparse multiple factor analysis to integrate genetic data, neuroimaging features, and attention-deficit/hyperactivity disorder domains.

Objectives

Methods

Results

Conclusions

Publications

Sparse multiple factor analysis to integrate genetic data, neuroimaging features, and attention-deficit/hyperactivity disorder domains.

Similar Datasets

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