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An independent components and functional connectivity analysis of resting state fMRI data points to neural network dysregulation in adult ADHD.

ABSTRACT: Spontaneous fluctuations can be measured in the brain that reflect dissociable functional networks oscillating at synchronized frequencies, such as the default mode network (DMN). In contrast to its diametrically opposed task-positive counterpart, the DMN predominantly signals during a state of rest, and inappropriate regulation of this network has been associated with inattention, a core characteristic of attention-deficit/hyperactivity disorder (ADHD). To examine whether abnormalities can be identified in the DMN component of patients with ADHD, we applied an independent components analysis to resting state functional magnetic resonance imaging data acquired from 22 male medication-naïve adults with ADHD and 23 neurotypical individuals. We observed a stronger coherence of the left dorsolateral prefrontal cortex (dlPFC) with the DMN component in patients with ADHD which correlated with measures of selective attention. The increased left dlPFC-DMN coherence also surfaced in a whole-brain replication analysis involving an independent sample of 9 medication-naïve adult patients and 9 controls. In addition, a post hoc seed-to-voxel functional connectivity analysis using the dlPFC as a seed region to further examine this region's suggested connectivity differences uncovered a higher temporal coherence with various other neural networks and confirmed a reduced anticorrelation with the DMN. These results point to a more diffuse connectivity between functional networks in patients with ADHD. Moreover, our findings suggest that state-inappropriate neural activity in ADHD is not confined to DMN intrusion during attention-demanding contexts, but also surfaces as an insufficient suppression of dlPFC signaling in relation to DMN activity during rest. Together with previous findings, these results point to a general dysfunction in the orthogonality of functional networks.

SUBMITTER: Hoekzema E

PROVIDER: S-EPMC6869838 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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An independent components and functional connectivity analysis of resting state fMRI data points to neural network dysregulation in adult ADHD.

Hoekzema Elseline E Carmona Susana S Ramos-Quiroga J Antoni JA Richarte Fernández Vanesa V Bosch Rosa R Soliva Juan Carlos JC Rovira Mariana M Bulbena Antonio A Tobeña Adolf A Casas Miguel M Vilarroya Oscar O

Human brain mapping 20130218 4

Spontaneous fluctuations can be measured in the brain that reflect dissociable functional networks oscillating at synchronized frequencies, such as the default mode network (DMN). In contrast to its diametrically opposed task-positive counterpart, the DMN predominantly signals during a state of rest, and inappropriate regulation of this network has been associated with inattention, a core characteristic of attention-deficit/hyperactivity disorder (ADHD). To examine whether abnormalities can be i ...[more]

PMID: 23417778

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Project description:The human brain at rest exhibits intrinsic dynamics transitioning among the multiple metastable states of the inter-regional functional connectivity. Accordingly, the demand for exploring the state-specific functional connectivity increases for a deeper understanding of mental diseases. Functional connectivity, however, lacks information about the directed causal influences among the brain regions, called effective connectivity. This study presents the dynamic causal modeling (DCM) framework to explore the state-dependent effective connectivity using spectral DCM for the resting-state functional MRI (rsfMRI). We established the sequence of brain states using the hidden Markov model with the multivariate autoregressive coefficients of rsfMRI, summarizing the functional connectivity. We decomposed the state-dependent effective connectivity using a parametric empirical Bayes scheme that models the effective connectivity of consecutive windows with the time course of the discrete states as regressors. We showed the plausibility of the state-dependent effective connectivity analysis in a simulation setting. To test the clinical applicability, we applied the proposed method to characterize the state- and subtype-dependent effective connectivity of the default mode network in children with combined-type attention deficit hyperactivity disorder (ADHD-C) compared with age-matched, typically developed children (TDC). All 88 children were subtyped according to the occupation times (i.e., dwell times) of the three dominant functional connectivity states, independently of clinical diagnosis. The state-dependent effective connectivity differences between ADHD-C and TDC according to the subtypes and those between the subtypes of ADHD-C were expressed mainly in self-inhibition, magnifying the importance of excitation inhibition balance in the subtyping. These findings provide a clear motivation for decomposing the state-dependent dynamic effective connectivity and state-dependent analysis of the directed coupling in exploring mental diseases.

Project description:BACKGROUND:Adult attention-deficit/hyperactivity disorder (ADHD) is a serious and frequent psychiatric disorder of multifactorial pathogenesis. Several lines of evidence support the idea that ADHD is, in its core, a disorder of dysfunctional brain connectivity within and between several neurofunctional networks. The primary aim of this study was to investigate associations between the functional connectivity within resting state brain networks and the individual severity of core ADHD symptoms (inattention, hyperactivity, and impulsivity). METHODS:Resting state functional magnetic resonance imaging (rs-fMRI) data of 38 methylphenidate-naïve adults with childhood-onset ADHD (20 women, mean age 40.5 years) were analyzed using independent component analysis (FSL's MELODIC) and FSL's dual regression technique. For motion correction, standard volume-realignment followed by independent component analysis-based automatic removal of motion artifacts (FSL's ICA-AROMA) were employed. To identify well-established brain networks, the independent components found in the ADHD group were correlated with brain networks previously found in healthy participants (Smith et al. PNAS 2009;106:13040-5). To investigate associations between functional connectivity and individual symptom severity, sex, and age, linear regressions were performed. RESULTS:Decomposition of resting state brain activity of adults with ADHD resulted in similar resting state networks as previously described for healthy adults. No significant differences in functional connectivity were seen between women and men. Advanced age was associated with decreased functional connectivity in parts of the bilateral cingulate and paracingulate cortex within the executive control network. More severe hyperactivity was associated with increased functional connectivity in the left putamen, right caudate nucleus, right central operculum and a portion of the right postcentral gyrus within the auditory/sensorimotor network. CONCLUSIONS:The present study supports and extends our knowledge on the involvement of the striatum in the pathophysiology of ADHD, in particular, in the pathogenesis of hyperactivity. Our results emphasize the usefulness of dimensional analyses in the study of ADHD, a highly heterogeneous disorder. TRIAL REGISTRATION:ISRCTN12722296 ( https://doi.org/10.1186/ISRCTN12722296 ).

Project description:Different machine learning algorithms have recently been used for assisting automated classification of independent component analysis (ICA) results from resting-state fMRI data. The success of this approach relies on identification of artifact components and meaningful functional networks. A limiting factor of ICA is the uncertainty of the number of independent components (NIC). We aim to develop a framework based on support vector machines (SVM) and optimized feature-selection for automated classification of independent components (ICs) and use the framework to investigate the effects of input NIC on the ICA results. Seven different resting-state fMRI datasets were studied. 18 features were devised by mimicking the empirical criteria for manual evaluation. The five most significant (p < 0.01) features were identified by general linear modeling and used to generate a classification model for the framework. This feature-optimized classification of ICs with SVM (FOCIS) framework was used to classify both group and single subject ICA results. The classification results obtained using FOCIS and previously published FSL-FIX were compared against manually evaluated results. On average the false negative rate in identifying artifact contaminated ICs for FOCIS and FSL-FIX were 98.27 and 92.34%, respectively. The number of artifact and functional network components increased almost linearly with the input NIC. Through tracking, we demonstrate that incrementing NIC affects most ICs when NIC < 33, whereas only a few limited ICs are affected by direct splitting when NIC is incremented beyond NIC > 40. For a given IC, its changes with increasing NIC are individually specific irrespective whether the component is a potential resting-state functional network or an artifact component. Using FOCIS, we investigated experimentally the ICA dimensionality of resting-state fMRI datasets and found that the input NIC can critically affect the ICA results of resting-state fMRI data.

Dataset Information

An independent components and functional connectivity analysis of resting state fMRI data points to neural network dysregulation in adult ADHD.

Publications

An independent components and functional connectivity analysis of resting state fMRI data points to neural network dysregulation in adult ADHD.

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