Assessment of the glymphatic function in children with attention-deficit/hyperactivity disorder.

OBJECTIVES: Whether the alternation of the glymphatic system exists in neurodevelopmental disease still remains unclear. In this study, we investigated structural and functional changes in the glymphatic system in the treatment-naïve attention-deficit/hyperactivity disorder (ADHD) children by quantitatively measuring the Virchow-Robin spaces (VRS) volume and diffusion tensor image-analysis along the perivascular space (DTI-ALPS). METHODS: Forty-seven pediatric ADHD patients and 52 age- and gender-matched typically developing (TD) children were recruited in this prospective study. The VRS volume was calculated using a semi-automated approach in axial T2-weighted images. Diffusivities along the x-, y-, and z-axes in the projection, association, and subcortical neural fiber areas were measured. The ALPS index, a ratio that accentuated water diffusion along the perivascular space, was calculated. The Mann-Whitney U test was used to compare the quantitative parameters; Pearson's correlation was used to analyze the correlation with clinical symptoms. RESULTS: The cerebral VRS volume (mean, 15.514 mL vs. 11.702 mL) and the VRS volume ratio in the ADHD group were larger than those in the TD group (all p < 0.001). The diffusivity along the x-axis in association fiber area and ALPS index were significantly smaller in the ADHD group vs. TD group (mean, 1.40 vs.1.59, p < 0.05 after false discovery rate adjustment). Besides, the ALPS index was related to inattention symptoms of ADHD (r = - 0.323, p < 0.05). CONCLUSIONS: Our study suggests that the glymphatic system alternation may participate in the pathogenesis of ADHD, which may be a new research direction for exploring the mechanisms of psycho-behavioral developmental disorders. Moreover, the VRS volume and ALPS index could be used as the metrics for diagnosing ADHD. CLINICAL RELEVANCE STATEMENT: Considering the potential relevance of the glymphatic system for exploring the mechanisms of attention deficit/hyperactivity, the Virchow-Robin spaces volume and the analysis along the perivascular space index could be used as additional metrics for diagnosing the disorder. KEY POINTS: • Increased Virchow-Robin space volume and decreased analysis along the perivascular space index were found in the treatment-naïve attention-deficit/hyperactivity disorder children. • The results of this study indicate that the glymphatic system alternation may have a valuable role in the pathogenesis of attention-deficit/hyperactivity disorder. • The analysis along the perivascular space index is correlated with inattention symptoms of attention-deficit/hyperactivity disorder children.

Nonuniformity of Whole-Cerebral Neural Resource Allocation, a Neuromarker of the Broad-Task Attention.

The neural basis of attention is thought to involve the allocation of limited neural resources. However, the quantitative validation of this hypothesis remains challenging. Here, we provide quantitative evidence that the nonuniform allocation of neural resources across the whole cerebral gray matter reflects the broad-task process of sustained attention. We propose a neural measure for the nonuniformity of whole-cerebral allocation using functional magnetic resonance imaging. We found that this measure was significantly correlated with conventional indicators of attention level, such as task difficulty and pupil dilation. We further found that the broad-task neural correlates of the measure belong to frontoparietal and dorsal attention networks. Finally, we found that patients with attention-deficit/hyperactivity disorder showed abnormal decreases in the level of the proposed measure, reflecting the executive dysfunction. This study proposes a neuromarker suggesting that the nonuniform allocation of neural resources may be the broad-task neural basis of sustained attention.

Children with attention-deficit/hyperactivity disorder spend more time in hyperconnected network states and less time in segregated network states as revealed by dynamic connectivity analysis.

Previous studies in children with attention-deficit/hyperactivity disorder (ADHD) have observed functional brain network disruption on a whole-brain level, as well as on a sub-network level, particularly as related to the default mode network, attention-related networks, and cognitive control-related networks. Given behavioral findings that children with ADHD have more difficulty sustaining attention and more extreme moment-to-moment fluctuations in behavior than typically developing (TD) children, recently developed methods to assess changes in connectivity over shorter time periods (i.e., "dynamic functional connectivity"), may provide unique insight into dysfunctional network organization in ADHD. Thus, we performed a dynamic functional connectivity (FC) analysis on resting state fMRI data from 38 children with ADHD and 79 TD children. We used Hidden semi-Markov models (HSMMs) to estimate six network states, as well as the most probable sequence of states for each participant. We quantified the dwell time, sojourn time, and transition probabilities across states. We found that children with ADHD spent less total time in, and switched more quickly out of, anticorrelated states involving the default mode network and task-relevant networks as compared to TD children. Moreover, children with ADHD spent more time in a hyperconnected state as compared to TD children. These results provide novel evidence that underlying dynamics may drive the differences in static FC patterns that have been observed in ADHD and imply that disrupted FC dynamics may be a mechanism underlying the behavioral symptoms and cognitive deficits commonly observed in children with ADHD.

Efficient robust doubly adaptive regularized regression with applications.

We consider the problem of estimation and variable selection for general linear regression models. Regularized regression procedures have been widely used for variable selection, but most existing methods perform poorly in the presence of outliers. We construct a new penalized procedure that simultaneously attains full efficiency and maximum robustness. Furthermore, the proposed procedure satisfies the oracle properties. The new procedure is designed to achieve sparse and robust solutions by imposing adaptive weights on both the decision loss and the penalty function. The proposed method of estimation and variable selection attains full efficiency when the model is correct and, at the same time, achieves maximum robustness when outliers are present. We examine the robustness properties using the finite-sample breakdown point and an influence function. We show that the proposed estimator attains the maximum breakdown point. Furthermore, there is no loss in efficiency when there are no outliers or the error distribution is normal. For practical implementation of the proposed method, we present a computational algorithm. We examine the finite-sample and robustness properties using Monte Carlo studies. Two datasets are also analyzed.

Assessment of Susceptibility Risk Factors for ADHD in Imaging Genetic Studies.

OBJECTIVE: ADHD consists of a count of symptoms that often presents heterogeneity due to overdispersion and excess of zeros. Statistical inference is usually based on a dichotomous outcome that is underpowered. The main goal of this study was to determine a suited probability distribution to analyze ADHD symptoms in Imaging Genetic studies. METHOD: We used two independent population samples of children to evaluate the consistency of the standard probability distributions based on count data for describing ADHD symptoms. RESULTS: We showed that the zero-inflated negative binomial (ZINB) distribution provided the best power for modeling ADHD symptoms. ZINB reveals a genetic variant, rs273342 (Microtubule-Associated Protein [MAPRE2]), associated with ADHD ( p value = 2.73E-05). This variant was also associated with perivascular volumes (Virchow-Robin spaces; p values < 1E-03). No associations were found when using dichotomous definition. CONCLUSION: We suggest that an appropriate modeling of ADHD symptoms increases statistical power to establish significant risk factors.

Real-time motion analytics during brain MRI improve data quality and reduce costs.

Head motion systematically distorts clinical and research MRI data. Motion artifacts have biased findings from many structural and functional brain MRI studies. An effective way to remove motion artifacts is to exclude MRI data frames affected by head motion. However, such post-hoc frame censoring can lead to data loss rates of 50% or more in our pediatric patient cohorts. Hence, many scanner operators collect additional 'buffer data', an expensive practice that, by itself, does not guarantee sufficient high-quality MRI data for a given participant. Therefore, we developed an easy-to-setup, easy-to-use Framewise Integrated Real-time MRI Monitoring (FIRMM) software suite that provides scanner operators with head motion analytics in real-time, allowing them to scan each subject until the desired amount of low-movement data has been collected. Our analyses show that using FIRMM to identify the ideal scan time for each person can reduce total brain MRI scan times and associated costs by 50% or more.

Network-level assessment of reward-related activation in patients with ADHD and healthy individuals.

INTRODUCTION: Reward processing is a key aspect of cognitive control processes, putatively instantiated by mesolimbic and mesocortical brain circuits. Deficient signaling within these circuits has been associated with psychopathology. We applied a network discovery approach to assess specific functional networks associated with reward processing in participants with attention-deficit/hyperactivity disorder (ADHD). METHODS: To describe task-related processes in terms of integrated functional networks, we applied independent component analysis (ICA) to task response maps of 60 healthy participants who performed a monetary incentive delay (MID) task. The resulting components were interpreted on the basis of their similarity with group-level task responses as well as their similarity with brain networks derived from resting state fMRI analyses. ADHD-related effects on network characteristics including functional connectivity and communication between networks were examined in an independent sample comprising 150 participants with ADHD and 48 healthy controls. RESULTS: We identified 23 components to be associated with 4 large-scale functional networks: the default-mode, visual, executive control, and salience networks. The salience network showed a specific association with reward processing as well as the highest degree of within-network integration. ADHD was associated with decreased functional connectivity between the salience and executive control networks as well as with peripheral brain regions. CONCLUSIONS: Reward processing as measured with the MID task involves one reward-specific and three general functional networks. Participants with ADHD exhibited alterations in connectivity of both the salience and executive control networks and associated brain regions during task performance. Hum Brain Mapp 38:2359-2369, 2017. © 2017 Wiley Periodicals, Inc.

Learning effective connectivity from fMRI using autoregressive hidden Markov model with missing data.

BACKGROUND: Effective connectivity (EC) analysis of neuronal groups using fMRI delivers insights about functional-integration. However, fMRI signal has low-temporal resolution due to down-sampling and indirectly measures underlying neuronal activity. NEW METHOD: The aim is to address above issues for more reliable EC estimates. This paper proposes use of autoregressive hidden Markov model with missing data (AR-HMM-md) in dynamically multi-linked (DML) framework for learning EC using multiple fMRI time series. In our recent work (Dang et al., 2016), we have shown how AR-HMM-md for modelling single fMRI time series outperforms the existing methods. AR-HMM-md models unobserved neuronal activity and lost data over time as variables and estimates their values by joint optimization given fMRI observation sequence. RESULTS: The effectiveness in learning EC is shown using simulated experiments. Also the effects of sampling and noise are studied on EC. Moreover, classification-experiments are performed for Attention-Deficit/Hyperactivity Disorder subjects and age-matched controls for performance evaluation of real data. Using Bayesian model selection, we see that the proposed model converged to higher log-likelihood and demonstrated that group-classification can be performed with higher cross-validation accuracy of above 94% using distinctive network EC which characterizes patients vs. CONTROLS: The full data EC obtained from DML-AR-HMM-md is more consistent with previous literature than the classical multivariate Granger causality method. COMPARISON: The proposed architecture leads to reliable estimates of EC than the existing latent models. CONCLUSIONS: This framework overcomes the disadvantage of low-temporal resolution and improves cross-validation accuracy significantly due to presence of missing data variables and autoregressive process.