Network connectivity abnormality profile supports a categorical-dimensional hybrid model of ADHD

Developmental Trajectories and Differences in Functional Brain Network Properties of Preterm and At-Term Neonates

Premature infants, born before 37 weeks of gestation can have alterations in neurodevelopment and cognition, even when no anatomical lesions are evident. Resting-state functional neuroimaging of naturally sleeping babies has shown altered connectivity patterns, but there is limited evidence on the developmental trajectories of functional organization in preterm neonates. By using a large dataset from the developing Human Connectome Project, we explored the differences in graph theory properties between at-term (n = 332) and preterm (n = 115) neonates at term-equivalent age, considering the age subgroups proposed by the World Health Organization for premature birth. Leveraging the longitudinal follow-up for some preterm participants, we characterized the developmental trajectories for preterm and at-term neonates, for this purpose linear, quadratic, and log-linear mixed models were constructed with gestational age at scan as an independent fixed-effect variable and random effects were added for the intercept and subject ID. Significance was defined at p < 0.05, and the model with the lowest Akaike Information Criterion (AIC) was selected as the best model. We found significant differences between groups in connectivity strength, clustering coefficient, characteristic path length and global efficiency. Specifically, at term-equivalent ages, higher connectivity, clustering coefficient and efficiency are identified for neonates born at later postmenstrual ages. Similarly, the characteristic path length showed the inverse pattern. These results were consistent for a variety of connectivity thresholds at both the global (whole brain) and local level (brain regions). The brain regions with the greatest differences between groups include primary sensory and motor regions and the precuneus which may relate to the risk factors for sensorimotor and behavioral deficits associated with premature birth. Our results also show non-linear developmental trajectories for premature neonates, but decreased integration and segregation even at term-equivalent age. Overall, our results confirm altered functional connectivity, integration and segregation properties of the premature brain despite showing rapid maturation after birth.

Compressed cerebro-cerebellar functional gradients in children and adolescents with attention-deficit/hyperactivity disorder

Both cortical and cerebellar developmental differences have been implicated in attention-deficit/hyperactivity disorder (ADHD). Recently accumulating neuroimaging studies have highlighted hierarchies as a fundamental principle of brain organization, suggesting the importance of assessing hierarchy abnormalities in ADHD. A novel gradient-based resting-state functional connectivity analysis was applied to investigate the cerebro-cerebellar disturbed hierarchy in children and adolescents with ADHD. We found that the interaction of functional gradient between diagnosis and age was concentrated in default mode network (DMN) and visual network (VN). At the same time, we also found that the opposite gradient changes of DMN and VN caused the compression of the cortical main gradient in ADHD patients, implicating the co-occurrence of both low- (visual processing) and high-order (self-related thought) cognitive dysfunction manifesting in abnormal cerebro-cerebellar organizational hierarchy in ADHD. Our study provides a neurobiological framework to better understand the co-occurrence and interaction of both low-level and high-level functional abnormalities in the cortex and cerebellum in ADHD.

Corticostriatal causality analysis in children and adolescents with attention-deficit/hyperactivity disorder

AIM

The effective connectivity between the striatum and cerebral cortex has not been fully investigated in attention-deficit/hyperactivity disorder (ADHD). Our objective was to explore the interaction effects between diagnosis and age on disrupted corticostriatal effective connectivity and to represent the modulation function of altered connectivity pathways in children and adolescents with ADHD.

METHODS

We performed Granger causality analysis on 300 participants from a publicly available Attention-Deficit/Hyperactivity Disorder-200 dataset. By computing the correlation coefficients between causal connections between striatal subregions and other cortical regions, we estimated the striatal inflow and outflow connection to represent intermodulation mechanisms in corticostriatal pathways.

RESULTS

Interactions between diagnosis and age were detected in the superior occipital gyrus within the visual network, medial prefrontal cortex, posterior cingulate gyrus, and inferior parietal lobule within the default mode network, which is positively correlated with hyperactivity/impulsivity severity in ADHD. Main effect of diagnosis exhibited a general higher cortico-striatal causal connectivity involving default mode network, frontoparietal network and somatomotor network in ADHD compared with comparisons. Results from high-order effective connectivity exhibited a disrupted information pathway involving the default mode-striatum-somatomotor-striatum-frontoparietal networks in ADHD.

CONCLUSION

The interactions detected in the visual-striatum-default mode networks pathway appears to be related to the potential distraction caused by long-term abnormal information input from the retina in ADHD. Higher causal connectivity and weakened intermodulation may indicate the pathophysiological process that distractions lead to the impairment of motion planning function and the inhibition/control of this unplanned motion signals in ADHD.

Altered Intrinsic Brain Spontaneous Activities in Children With Autism Spectrum Disorder Comorbid ADHD

OBJECTIVE

The study involved 17 children with Autism Spectrum Disorder (ASD), 21 with ADHD, 30 with both (ASD + ADHD), and 28 typically developing children (TD).

METHODS

The amplitude of low-frequency fluctuations (ALFF) was measured as a regional brain function index. Intrinsic functional connectivity (iFC) was also analyzed using the region of interest (ROI) identified in ALFF analysis. Statistical analysis was done via one-way ANCOVA, Gaussian random field (GRF) theory, and post-hoc pair-wise comparisons.

RESULTS

The ASD + ADHD group showed increased ALFF in the left middle frontal gyrus (MFG.L) compared to the TD group. In terms of global brain function, the ASD group displayed underconnectivity in specific regions compared to the ASD + ADHD and TD groups.

CONCLUSION

The findings contribute to understanding the neural mechanisms underlying ASD + ADHD.

Atypical cortical networks in children at high-genetic risk of psychiatric and neurodevelopmental disorders

Although many genetic risk factors for psychiatric and neurodevelopmental disorders have been identified, the neurobiological route from genetic risk to neuropsychiatric outcome remains unclear. 22q11.2 deletion syndrome (22q11.2DS) is a copy number variant (CNV) syndrome associated with high rates of neurodevelopmental and psychiatric disorders including autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD) and schizophrenia. Alterations in neural integration and cortical connectivity have been linked to the spectrum of neuropsychiatric disorders seen in 22q11.2DS and may be a mechanism by which the CNV acts to increase risk. In this study, magnetoencephalography (MEG) was used to investigate electrophysiological markers of local and global network function in 34 children with 22q11.2DS and 25 controls aged 10-17 years old. Resting-state oscillatory activity and functional connectivity across six frequency bands were compared between groups. Regression analyses were used to explore the relationships between these measures, neurodevelopmental symptoms and IQ. Children with 22q11.2DS had altered network activity and connectivity in high and low frequency bands, reflecting modified local and long-range cortical circuitry. Alpha and theta band connectivity were negatively associated with ASD symptoms while frontal high frequency (gamma band) activity was positively associated with ASD symptoms. Alpha band activity was positively associated with cognitive ability. These findings suggest that haploinsufficiency at the 22q11.2 locus impacts short and long-range cortical circuits, which could be a mechanism underlying neurodevelopmental and psychiatric vulnerability in this high-risk group.

Bayesian dynamical system analysis of the effects of methylphenidate in children with attention-deficit/hyperactivity disorder: a randomized trial

Methylphenidate is a widely used and effective treatment for attention-deficit/hyperactivity disorder (ADHD), yet the underlying neural mechanisms and their relationship to changes in behavior are not fully understood. Specifically, it remains unclear how methylphenidate affects brain and behavioral dynamics, and the interplay between these dynamics, in individuals with ADHD. To address this gap, we used a novel Bayesian dynamical system model to investigate the effects of methylphenidate on latent brain states in 27 children with ADHD and 49 typically developing children using a double-blind, placebo-controlled crossover design. Methylphenidate remediated greater behavioral variability on a continuous performance task in children with ADHD. Children with ADHD exhibited aberrant latent brain state dynamics compared to typically developing children, with a single latent state showing particularly abnormal dynamics, which was remediated by methylphenidate. Additionally, children with ADHD showed brain state-dependent hyper-connectivity in the default mode network, which was also remediated by methylphenidate. Finally, we found that methylphenidate-induced changes in latent brain state dynamics, as well as brain state-related functional connectivity between salience and default mode networks, were correlated with improvements in behavioral variability. Taken together, our findings reveal a novel latent brain state dynamical process and circuit mechanism underlying the therapeutic effects of methylphenidate in childhood ADHD. We suggest that Bayesian dynamical system models may be particularly useful for capturing complex nonlinear changes in neural activity and behavioral variability associated with ADHD. Our approach may be of value to clinicians and researchers investigating the neural mechanisms underlying pharmacological treatment of psychiatric disorders.

The role of single trial variability in event related potentials in children with attention deficit hyperactivity disorder

OBJECTIVE

Children with attention deficit hyperactivity disorder (ADHD) show attenuated mean P3 component amplitudes compared to typically developing (TD) children. This finding may be the result of individual differences in P3 amplitudes, P3 latencies, and/or greater single trial variability (STV) in amplitude or latency, suggesting neural "noise."

METHODS

Event related potentials (ERPs) from 75 children with ADHD and 29 TD children were recorded with electroencephalography (EEG). Caregivers provided ratings on child ADHD symptoms. Single-trial ERP amplitudes and latencies were extracted from the P3 component time window during a visual oddball task. Additionally, we computed individual-centered and trial-centered P3 amplitudes to account for inter-individual and inter-trial variability in the timing of the P3 peak.

RESULTS

In line with prior research, greater ADHD symptom severity was associated with reduced mean P3 amplitude. This correlation was no longer significant after correcting for inter-trial differences in P3 latency. In contrast, greater ADHD symptom severity was associated with reduced STV in P3 amplitude.

CONCLUSIONS

Our results suggest that attenuated average P3 amplitude in ADHD samples is due to a consistent reduction in strength of the neurophysiological signal at the single trial level, as well as increased inter-trial variability in the timing of P3 peak amplitudes. The traditional method of extracting P3 amplitudes based on a single time window for all trials may not adequately capture variability in P3 latencies associated with ADHD.

SIGNIFICANCE

Inter- and intra-individual differences in brain signatures should be considered in models of neurobiological differences in neurodevelopmental samples.

Multiple measurement analysis of resting-state fMRI for ADHD classification in adolescent brain from the ABCD study

Attention deficit hyperactivity disorder (ADHD) is one of the most common psychiatric disorders in school-aged children. Its accurate diagnosis looks after patients' interests well with effective treatment, which is important to them and their family. Resting-state functional magnetic resonance imaging (rsfMRI) has been widely used to characterize the abnormal brain function by computing the voxel-wise measures and Pearson's correlation (PC)-based functional connectivity (FC) for ADHD diagnosis. However, exploring the powerful measures of rsfMRI to improve ADHD diagnosis remains a particular challenge. To this end, this paper proposes an automated ADHD classification framework by fusion of multiple measures of rsfMRI in adolescent brain. First, we extract the voxel-wise measures and ROI-wise time series from the brain regions of rsfMRI after preprocessing. Then, to extract the multiple functional connectivities, we compute the PC-derived FCs including the topographical information-based high-order FC (tHOFC) and dynamics-based high-order FC (dHOFC), the sparse representation (SR)-derived FCs including the group SR (GSR), the strength and similarity guided GSR (SSGSR), and sparse low-rank (SLR). Finally, these measures are combined with multiple kernel learning (MKL) model for ADHD classification. The proposed method is applied to the Adolescent Brain and Cognitive Development (ABCD) dataset. The results show that the FCs of dHOFC and SLR perform better than the others. Fusing multiple measures achieves the best classification performance (AUC = 0.740, accuracy = 0.6916), superior to those from the single measure and the previous studies. We have identified the most discriminative FCs and brain regions for ADHD diagnosis, which are consistent with those of published literature.

Functional connectivity based brain signatures of behavioral regulation in children with ADHD, DCD, and ADHD-DCD

Behavioral regulation problems have been associated with daily-life and mental health challenges in children with neurodevelopmental conditions such as attention-deficit/hyperactivity disorder (ADHD) and developmental coordination disorder (DCD). Here, we investigated transdiagnostic brain signatures associated with behavioral regulation. Resting-state fMRI data were collected from 115 children (31 typically developing (TD), 35 ADHD, 21 DCD, 28 ADHD-DCD) aged 7-17 years. Behavioral regulation was measured using the Behavior Rating Inventory of Executive Function and was found to differ between children with ADHD (i.e., children with ADHD and ADHD-DCD) and without ADHD (i.e., TD children and children with DCD). Functional connectivity (FC) maps were computed for 10 regions of interest and FC maps were tested for correlations with behavioral regulation scores. Across the entire sample, greater behavioral regulation problems were associated with stronger negative FC within prefrontal pathways and visual reward pathways, as well as with weaker positive FC in frontostriatal reward pathways. These findings significantly increase our knowledge on FC in children with and without ADHD and highlight the potential of FC as brain-based signatures of behavioral regulation across children with differing neurodevelopmental conditions.

Altered neural flexibility in children with attention-deficit/hyperactivity disorder

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders of childhood, and is often characterized by altered executive functioning. Executive function has been found to be supported by flexibility in dynamic brain reconfiguration. Thus, we applied multilayer community detection to resting-state fMRI data in 180 children with ADHD and 180 typically developing children (TDC) to identify alterations in dynamic brain reconfiguration in children with ADHD. We specifically evaluated MR derived neural flexibility, which is thought to underlie cognitive flexibility, or the ability to selectively switch between mental processes. Significantly decreased neural flexibility was observed in the ADHD group at both the whole brain (raw p = 0.0005) and sub-network levels (p < 0.05, FDR corrected), particularly for the default mode network, attention-related networks, executive function-related networks, and primary networks. Furthermore, the subjects with ADHD who received medication exhibited significantly increased neural flexibility (p = 0.025, FDR corrected) when compared to subjects with ADHD who were medication naïve, and their neural flexibility was not statistically different from the TDC group (p = 0.74, FDR corrected). Finally, regional neural flexibility was capable of differentiating ADHD from TDC (Accuracy: 77% for tenfold cross-validation, 74.46% for independent test) and of predicting ADHD severity using clinical measures of symptom severity (R2: 0.2794 for tenfold cross-validation, 0.156 for independent test). In conclusion, the present study found that neural flexibility is altered in children with ADHD and demonstrated the potential clinical utility of neural flexibility to identify children with ADHD, as well as to monitor treatment responses and disease severity.

White Matter Microstructure in Attention-Deficit/Hyperactivity Disorder: A Systematic Tractography Study in 654 Individuals

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by age-inappropriate levels of inattention and/or hyperactivity-impulsivity. ADHD has been related to differences in white matter (WM) microstructure. However, much remains unclear regarding the nature of these WM differences and which clinical aspects of ADHD they reflect. We systematically investigated whether fractional anisotropy (FA) is associated with current and/or lifetime categorical diagnosis, impairment in daily life, and continuous ADHD symptom measures.

METHODS

Diffusion-weighted imaging data were obtained from 654 participants (322 unaffected, 258 affected, 74 subthreshold; 7-29 years of age). We applied automated global probabilistic tractography on 18 major WM pathways. Linear mixed-effects regression models were used to examine associations of clinical measures with overall brain and tract-specific FA.

RESULTS

There were significant interactions of tract with all ADHD variables on FA. There were no significant associations of FA with current or lifetime diagnosis, nor with impairment. Lower FA in the right cingulum angular bundle was associated with higher hyperactivity-impulsivity symptom severity (p_{familywise error} = .045). There were no significant effects for other tracts.

CONCLUSIONS

This is the first time global probabilistic tractography has been applied to an ADHD dataset of this size. We found no evidence for altered FA in association with ADHD diagnosis. Our findings indicate that associations of FA with ADHD are not uniformly distributed across WM tracts. Continuous symptom measures of ADHD may be more sensitive to FA than diagnostic categories. The right cingulum angular bundle in particular may play a role in symptoms of hyperactivity and impulsivity.

Intrinsic Functional Connectivity in the Default Mode Network Differentiates the Combined and Inattentive Attention Deficit Hyperactivity Disorder Types

Neuroimaging studies have revealed neurobiological differences in ADHD, particularly studies examining connectivity disruption and anatomical network organization. However, the underlying pathophysiology of ADHD types remains elusive as it is unclear whether dysfunctional network connections characterize the underlying clinical symptoms distinguishing ADHD types. Here, we investigated intrinsic functional network connectivity to identify neural signatures that differentiate the combined (ADHD-C) and inattentive (ADHD-I) presentation types. Applying network-based statistical (NBS) and graph theoretical analysis to task-derived intrinsic connectivity data from completed fMRI scans, we evaluated default mode network (DMN) and whole-brain functional network topology in a cohort of 34 ADHD participants (aged 8-17 years) defined using DSM-IV criteria as predominantly inattentive (ADHD-I) type (n = 15) or combined (ADHD-C) type (n = 19), and 39 age and gender-matched typically developing controls. ADHD-C were characterized from ADHD-I by reduced network connectivity differences within the DMN. Additionally, reduced connectivity within the DMN was negatively associated with ADHD-RS hyperactivity-impulsivity subscale score. Compared with controls, ADHD-C but not ADHD-I differed by reduced connectivity within the DMN; inter-network connectivity between the DMN and somatomotor networks; the DMN and limbic networks; and between the somatomotor and cingulo-frontoparietal, with ventral attention and dorsal attention networks. However, graph-theoretical measures did not significantly differ between groups. These findings provide insight into the intrinsic networks underlying phenotypic differences between ADHD types. Furthermore, these intrinsic functional connectomic signatures support neurobiological differences underlying clinical variations in ADHD presentations, specifically reduced within and between functional connectivity of the DMN in the ADHD-C type.

Frontal corticostriatal functional connectivity reveals task positive and negative network dysregulation in relation to ADHD, sex, and inhibitory control

Frontal corticostriatal circuits (FCSC) are involved in self-regulation of cognition, emotion, and motor function. While these circuits are implicated in attention-deficit/hyperactivity disorder (ADHD), the literature establishing FCSC associations with ADHD is inconsistent. This may be due to study variability in considerations of how fMRI motion regression was handled between groups, or study specific differences in age, sex, or the striatal subregions under investigation. Given the importance of these domains in ADHD it is crucial to consider the complex interactions of age, sex, striatal subregions and FCSC in ADHD presentation and diagnosis. In this large-scale study of 362 8-12 year-old children with ADHD (n = 165) and typically developing (TD; n = 197) children, we investigate associations between FCSC with ADHD diagnosis and symptoms, sex, and go/no-go (GNG) task performance. Results include: (1) increased striatal connectivity with age across striatal subregions with most of the frontal cortex, (2) increased frontal-limbic striatum connectivity among boys with ADHD only, mostly in default mode network (DMN) regions not associated with age, and (3) increased frontal-motor striatum connectivity to regions of the DMN were associated with greater parent-rated inattention problems, particularly among the ADHD group. Although diagnostic group differences were no longer significant when strictly controlling for head motion, with motion possibly reflecting the phenotypic variance of ADHD itself, the spatial distribution of all symptom, age, sex, and other ADHD group effects were nearly identical to the initial results. These results demonstrate differential associations of FCSC between striatal subregions with the DMN and FPN in relation to age, ADHD, sex, and inhibitory control.

Annual Research Review: The transdiagnostic revolution in neurodevelopmental disorders

Practitioners frequently use diagnostic criteria to identify children with neurodevelopmental disorders and to guide intervention decisions. These criteria also provide the organising framework for much of the research focussing on these disorders. Study design, recruitment, analysis and theory are largely built on the assumption that diagnostic criteria reflect an underlying reality. However, there is growing concern that this assumption may not be a valid and that an alternative transdiagnostic approach may better serve our understanding of this large heterogeneous population of young people. This review draws on important developments over the past decade that have set the stage for much-needed breakthroughs in understanding neurodevelopmental disorders. We evaluate contemporary approaches to study design and recruitment, review the use of data-driven methods to characterise cognition, behaviour and neurobiology, and consider what alternative transdiagnostic models could mean for children and families. This review concludes that an overreliance on ill-fitting diagnostic criteria is impeding progress towards identifying the barriers that children encounter, understanding underpinning mechanisms and finding the best route to supporting them.

Latent brain state dynamics distinguish behavioral variability, impaired decision-making, and inattention

Children with Attention Deficit Hyperactivity Disorder (ADHD) have prominent deficits in sustained attention that manifest as elevated intra-individual response variability and poor decision-making. Influential neurocognitive models have linked attentional fluctuations to aberrant brain dynamics, but these models have not been tested with computationally rigorous procedures. Here we use a Research Domain Criteria approach, drift-diffusion modeling of behavior, and a novel Bayesian Switching Dynamic System unsupervised learning algorithm, with ultrafast temporal resolution (490 ms) whole-brain task-fMRI data, to investigate latent brain state dynamics of salience, frontoparietal, and default mode networks and their relation to response variability, latent decision-making processes, and inattention. Our analyses revealed that occurrence of a task-optimal latent brain state predicted decreased intra-individual response variability and increased evidence accumulation related to decision-making. In contrast, occurrence and dwell time of a non-optimal latent brain state predicted inattention symptoms and furthermore, in a categorical analysis, distinguished children with ADHD from controls. Importantly, functional connectivity between salience and frontoparietal networks predicted rate of evidence accumulation to a decision threshold, whereas functional connectivity between salience and default mode networks predicted inattention. Taken together, our computational modeling reveals dissociable latent brain state features underlying response variability, impaired decision-making, and inattentional symptoms common to ADHD. Our findings provide novel insights into the neurobiology of attention deficits in children.

What have we really learned from functional connectivity in clinical populations?

Functional connectivity (FC), or the statistical interdependence of blood-oxygen dependent level (BOLD) signals between brain regions using fMRI, has emerged as a widely used tool for probing functional abnormalities in clinical populations due to the promise of the approach across conceptual, technical, and practical levels. With an already vast and steadily accumulating neuroimaging literature on neurodevelopmental, psychiatric, and neurological diseases and disorders in which FC is a primary measure, we aim here to provide a high-level synthesis of major concepts that have arisen from FC findings in a manner that cuts across different clinical conditions and sheds light on overarching principles. We highlight that FC has allowed us to discover the ubiquity of intrinsic functional networks across virtually all brains and clarify typical patterns of neurodevelopment over the lifespan. This understanding of typical FC maturation with age has provided important benchmarks against which to evaluate divergent maturation in early life and degeneration in late life. This in turn has led to the important insight that many clinical conditions are associated with complex, distributed, network-level changes in the brain, as opposed to solely focal abnormalities. We further emphasize the important role that FC studies have played in supporting a dimensional approach to studying transdiagnostic clinical symptoms and in enhancing the multimodal characterization and prediction of the trajectory of symptom progression across conditions. We highlight the unprecedented opportunity offered by FC to probe functional abnormalities in clinical conditions where brain function could not be easily studied otherwise, such as in disorders of consciousness. Lastly, we suggest high priority areas for future research and acknowledge critical barriers associated with the use of FC methods, particularly those related to artifact removal, data denoising and feasibility in clinical contexts.

Inhibition-related modulation of salience and frontoparietal networks predicts cognitive control ability and inattention symptoms in children with ADHD

Attention-deficit hyperactivity disorder (ADHD) is associated with pervasive impairments in attention and cognitive control. Although brain circuits underlying these impairments have been extensively investigated with resting-state fMRI, little is known about task-evoked functional brain circuits and their relation to cognitive control deficits and inattention symptoms in children with ADHD. Children with ADHD and age, gender and head motion matched typically developing (TD) children completed a Go/NoGo fMRI task. We used multivariate and dimensional analyses to investigate impairments in two core cognitive control systems: (i) cingulo-opercular "salience" network (SN) anchored in the right anterior insula, dorsal anterior cingulate cortex (rdACC), and ventrolateral prefrontal cortex (rVLPFC) and (ii) dorsal frontoparietal "central executive" (FPN) network anchored in right dorsolateral prefrontal cortex (rDLPFC) and posterior parietal cortex (rPPC). We found that multivariate patterns of task-evoked effective connectivity between brain regions in SN and FPN distinguished the ADHD and TD groups, with rDLPFC-rPPC connectivity emerging as the most distinguishing link. Task-evoked rdACC-rVLPFC connectivity was positively correlated with NoGo accuracy, and negatively correlated with severity of inattention symptoms. Brain-behavior relationships were robust against potential age, gender, and head motion confounds. Our findings highlight aberrancies in task-evoked modulation of SN and FPN connectivity in children with ADHD. Crucially, cingulo-frontal connectivity was a common locus of deficits in cognitive control and clinical measures of inattention symptoms. Our study provides insights into a parsimonious systems neuroscience model of cognitive control deficits in ADHD, and suggests specific circuit biomarkers for predicting treatment outcomes in childhood ADHD.

A Review of the Default Mode Network in Autism Spectrum Disorders and Attention Deficit Hyperactivity Disorder

Functional magnetic resonance imaging (fMRI) has been widely used to examine the relationships between brain function and phenotypic features in neurodevelopmental disorders. Techniques such as resting-state functional connectivity (FC) have enabled the identification of the primary networks of the brain. One fMRI network, in particular, the default mode network (DMN), has been implicated in social-cognitive deficits in autism spectrum disorders (ASD) and attentional deficits in attention deficit hyperactivity disorder (ADHD). Given the significant clinical and genetic overlap between ASD and ADHD, surprisingly, no reviews have compared the clinical, developmental, and genetic correlates of DMN in ASD and ADHD and here we address this knowledge gap. We find that, compared with matched controls, ASD studies show a mixed pattern of both stronger and weaker FC in the DMN and ADHD studies mostly show stronger FC. Factors such as age, intelligence quotient, medication status, and heredity affect DMN FC in both ASD and ADHD. We also note that most DMN studies make ASD versus ADHD group comparisons and fail to consider ASD+ADHD comorbidity. We conclude, by identifying areas for improvement and by discussing the importance of using transdiagnostic approaches such as the Research Domain Criteria (RDoC) to fully account for the phenotypic and genotypic heterogeneity and overlap of ASD and ADHD.

Shared alterations in resting-state brain connectivity in adults with attention-deficit/hyperactivity disorder and their unaffected first-degree relatives

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is a developmental condition that often persists into adulthood with extensive negative consequences on quality of life. Despite emerging evidence indicating the genetic basis of ADHD, investigations into the familial expression of latent neurocognitive traits remain limited.

METHODS

In a group of adult ADHD probands (n = 20), their unaffected first-degree relatives (n = 20) and typically developing control participants (n = 20), we assessed endophenotypic alterations in the default mode network (DMN) connectivity during resting-state functional magnetic resonance imaging in relation to cognitive performance and clinical symptoms. In an external validation step, we also examined the dimensional nature of this neurocognitive trait in a sample of unrelated healthy young adults (n = 100) from the Human Connectome Project (HCP).

RESULTS

The results illustrated reduced anti-correlations between the posterior cingulate cortex/precuneus and right middle frontal gyrus that was shared between adult ADHD probands and their first-degree relatives, but not with healthy controls. The observed connectivity alterations were linked to higher ADHD symptoms that was mediated by performance in a sustained attention task. Moreover, this brain-based neurocognitive trait dimensionally explained ADHD symptom variability in the HCP sample.

CONCLUSIONS

Alterations in the default mode connectivity may represent a dimensional endophenotype of ADHD, hence a significant aspect of the neuropathophysiology of this disorder. As such, brain network organisation can potentially be employed as an important neurocognitive trait to enhance statistical power of genetic studies in ADHD and as a surrogate efficacy endpoint in the development of novel pharmaceuticals.

Supervised machine learning for diagnostic classification from large-scale neuroimaging datasets

There are growing concerns about the generalizability of machine learning classifiers in neuroimaging. In order to evaluate this aspect across relatively large heterogeneous populations, we investigated four disorders: Autism spectrum disorder (N = 988), Attention deficit hyperactivity disorder (N = 930), Post-traumatic stress disorder (N = 87) and Alzheimer's disease (N = 132). We applied 18 different machine learning classifiers (based on diverse principles) wherein the training/validation and the hold-out test data belonged to samples with the same diagnosis but differing in either the age range or the acquisition site. Our results indicate that overfitting can be a huge problem in heterogeneous datasets, especially with fewer samples, leading to inflated measures of accuracy that fail to generalize well to the general clinical population. Further, different classifiers tended to perform well on different datasets. In order to address this, we propose a consensus-classifier by combining the predictive power of all 18 classifiers. The consensus-classifier was less sensitive to unmatched training/validation and holdout test data. Finally, we combined feature importance scores obtained from all classifiers to infer the discriminative ability of connectivity features. The functional connectivity patterns thus identified were robust to the classification algorithm used, age and acquisition site differences, and had diagnostic predictive ability in addition to univariate statistically significant group differences between the groups. A MATLAB toolbox called Machine Learning in NeuroImaging (MALINI), which implements all the 18 different classifiers along with the consensus classifier is available from Lanka et al. (2019) The toolbox can also be found at the following URL: https://github.com/pradlanka/malini .

Association of Prenatal Acetaminophen Exposure Measured in Meconium With Risk of Attention-Deficit/Hyperactivity Disorder Mediated by Frontoparietal Network Brain Connectivity

Importance

Despite evidence of an association between prenatal acetaminophen exposure and attention-deficit/hyperactivity disorder (ADHD) in offspring, the drug is not contraindicated during pregnancy, possibly because prior studies have relied on maternal self-report, failed to quantify acetaminophen dose, and lacked mechanistic insight.

Objective

To examine the association between prenatal acetaminophen exposure measured in meconium (hereinafter referred to as meconium acetaminophen) and ADHD in children aged 6 to 7 years, along with the potential for mediation by functional brain connectivity.

Design, Setting, and Participants

This prospective birth cohort study from the Centre Hospitalier Université de Sherbrooke in Sherbrooke, Québec, Canada, included 394 eligible children, of whom 345 had meconium samples collected at delivery and information on ADHD diagnosis. Mothers were enrolled from September 25, 2007, to September 10, 2009, at their first prenatal care visit or delivery and were followed up when children were aged 6 to 7 years. When children were aged 9 to 11 years, resting-state brain connectivity was assessed with magnetic resonance imaging. Data for the present study were collected from September 25, 2007, to January 18, 2020, and analyzed from January 7, 2019, to January 22, 2020.

Exposures

Acetaminophen levels measured in meconium.

Main Outcomes and Measures

Physician diagnosis of ADHD was determined at follow-up when children were aged 6 to 7 years or from medical records. Resting-state brain connectivity was assessed with magnetic resonance imaging; attention problems and hyperactivity were assessed with the Behavioral Assessment System for Children Parent Report Scale. Associations between meconium acetaminophen levels and outcomes were estimated with linear and logistic regressions weighted on the inverse probability of treatment to account for potential confounders. Causal mediation analysis was used to test for mediation of the association between prenatal acetaminophen exposure and hyperactivity by resting-state brain connectivity.

Results

Among the 345 children included in the analysis (177 boys [51.3%]; mean [SD] age, 6.58 [0.54] years), acetaminophen was detected in 199 meconium samples (57.7%), and ADHD was diagnosed in 33 children (9.6%). Compared with no acetaminophen, detection of acetaminophen in meconium was associated with increased odds of ADHD (odds ratio [OR], 2.43; 95% CI, 1.41-4.21). A dose-response association was detected; each doubling of exposure increased the odds of ADHD by 10% (OR, 1.10; 95% CI, 1.02-1.19). Children with acetaminophen detected in meconium showed increased negative connectivity between frontoparietal and default mode network nodes to clusters in the sensorimotor cortices, which mediated an indirect effect on increased child hyperactivity (14%; 95% CI, 1%-26%).

Conclusions and Relevance

Together with the multitude of other cohort studies showing adverse neurodevelopment associated with prenatal acetaminophen exposure, this work suggests caution should be used in administering acetaminophen during pregnancy. Research into alternative pain management strategies for pregnant women could be beneficial.

The Subgrouping Structure of Newborns with Heterogenous Brain-Behavior Relationships

The presence of heterogeneity/subgroups in infants and older populations against single-domain brain or behavioral measures has been previously characterized. However, few attempts have been made to explore heterogeneity at the brain-behavior relationship level. Such a hypothesis posits that different subgroups of infants may possess qualitatively different brain-behavior relationships that could ultimately contribute to divergent developmental outcomes even with relatively similar brain phenotypes. In this study, we aimed to explore such relationship-level heterogeneity and delineate the subgrouping structure of newborns with differential brain-behavior associations based on a typically developing sample of 81 infants with 3-week resting-state functional magnetic resonance imaging scans and 4-year intelligence quotient (IQ) measures. Our results not only confirmed the existence of relationship-level heterogeneity in newborns but also revealed divergent developmental outcomes associated with two subgroups showing similar brain functional connectivity but contrasting brain-behavior relationships. Importantly, further analyses unveiled an intriguing pattern that the subgroup with higher 4-year IQ outcomes possessed brain-behavior relationships that were congruent to their functional connectivity pattern in neonates while the subgroup with lower 4-year IQ not, providing potential explanations for the observed IQ differences. The characterization of heterogeneity at the brain-behavior relationship level may not only improve our understanding of the patterned intersubject variability during infancy but could also pave the way for future development of heterogeneity-inspired, personalized, subgroup-specific models for better prediction.

Aberrant functional connectivity in resting state networks of ADHD patients revealed by independent component analysis

Background

ADHD is one of the most common psychiatric disorders in children and adolescents. Altered functional connectivity has been associated with ADHD symptoms. This study aimed to investigate abnormal changes in the functional connectivity of resting-state brain networks (RSNs) among adolescent patients with different subtypes of ADHD.

Methods

The data were obtained from the ADHD-200 Global Competition, including fMRI data from 88 ADHD patients (56 patients of ADHD-Combined, ADHD-C and 32 patients of ADHD-Inattentive, ADHD-I) and 67 typically developing controls (TD-C). Group ICA was utilized to research aberrant brain functional connectivity within the different subtypes of ADHD.

Results

In comparison with the TD-C group, the ADHD-C group showed clusters of decreased functional connectivity in the left inferior occipital gyrus (p = 0.0041) and right superior occipital gyrus (p = 0.0011) of the dorsal attention network (DAN), supplementary motor area (p = 0.0036) of the executive control network (ECN), left supramarginal gyrus (p = 0.0081) of the salience network (SN), middle temporal gyrus (p = 0.0041), and superior medial frontal gyrus (p = 0.0055) of the default mode network (DMN), while the ADHD-I group showed decreased functional connectivity in the right superior parietal gyrus (p = 0.0017) of the DAN and left middle temporal gyrus (p = 0.0105) of the DMN. In comparison with the ADHD-I group, the ADHD-C group showed decreased functional connectivity in the superior temporal gyrus (p = 0.0062) of the AN, inferior temporal gyrus (p = 0.0016) of the DAN, and the dorsolateral superior frontal gyrus (p = 0.0082) of the DMN. All the clusters surviving at p < 0.05 (AlphaSim correction).

Conclusion

The results suggested that decreased functional connectivity within the DMN and DAN was responsible, at least in part, for the symptom of inattention in ADHD-I patients. Similarly, we believed that the impaired functional connectivity within networks may contribute to the manifestations of ADHD-C patients, including inattention, hyperactivity/impulsivity, and unconscious movements.

Associations Between Resting-State Functional Connectivity and a Hierarchical Dimensional Structure of Psychopathology in Middle Childhood

BACKGROUND

Previous research from the Adolescent Brain Cognitive Development (ABCD) Study delineated and validated a hierarchical 5-factor structure with a general psychopathology (p) factor at the apex and 5 specific factors (internalizing, somatoform, detachment, neurodevelopmental, externalizing) using parent-reported child symptoms. The present study is the first to examine associations between dimensions from a hierarchical structure and resting-state functional connectivity (RSFC) networks.

METHODS

Using 9- to 11-year-old children from the ABCD Study baseline sample, we examined the variance explained by each hierarchical structure level (p-factor, 2-factor, 3-factor, 4-factor, and 5-factor models) in associations with RSFC. Analyses were first conducted in a discovery dataset (n = 3790), and significant associations were examined in a replication dataset (n = 3791).

RESULTS

There were robust associations between the p-factor and lower connectivity within the default mode network, although stronger effects emerged for the neurodevelopmental factor. Neurodevelopmental impairments were also related to variation in RSFC networks associated with attention to internal states and external stimuli. Analyses revealed robust associations between the neurodevelopmental dimension and several RSFC metrics, including within the default mode network, between the default mode network with cingulo-opercular and "Other" (unassigned) networks, and between the dorsal attention network with the Other network.

CONCLUSIONS

The hierarchical structure of psychopathology showed replicable links to RSFC associations in middle childhood. The specific neurodevelopmental dimension showed robust associations with multiple RSFC metrics. These results show the utility of examining associations between intrinsic brain architecture and specific dimensions of psychopathology, revealing associations especially with neurodevelopmental impairments.

Topological Data Analysis Reveals Robust Alterations in the Whole-Brain and Frontal Lobe Functional Connectomes in Attention-Deficit/Hyperactivity Disorder

Attention-deficit/hyperactivity disorder (ADHD) is a developmental disorder characterized by difficulty to control the own behavior. Neuroimaging studies have related ADHD with the interplay of fronto-parietal attention systems with the default mode network (DMN; Castellanos and Aoki, 2016). However, some results have been inconsistent, potentially due to methodological differences in the analytical strategies when defining the brain functional network, i.e., the functional connectivity threshold and/or the brain parcellation scheme. Here, we make use of topological data analysis (TDA) to explore the brain connectome as a function of the filtration value (i.e., the connectivity threshold), instead of using a static connectivity threshold. Specifically, we characterized the transition from all nodes being isolated to being connected into a single component as a function of the filtration value. We explored the utility of such a method to identify differences between 81 children with ADHD (45 male, age: 7.26–17.61 years old) and 96 typically developing children (TDC; 59 male, age: 7.17–17.96 years old), using a public dataset of resting state (rs)fMRI in human subjects. Results were highly congruent when using four different brain segmentations (atlases), and exhibited significant differences for the brain topology of children with ADHD, both at the whole-brain network and the functional subnetwork levels, particularly involving the frontal lobe and the DMN. Therefore, this is a solid approach that complements connectomics-related methods and may contribute to identify the neurophysio-pathology of ADHD.

Disrupted functional brain connectivity networks in children with attention-deficit/hyperactivity disorder: evidence from resting-state functional near-infrared spectroscopy

Significance: Attention-deficit/hyperactivity disorder (ADHD) is the most common psychological disease in childhood. Currently, widely used neuroimaging techniques require complete body confinement and motionlessness and thus are extremely hard for brain scanning of ADHD children. Aim: We present resting-state functional near-infrared spectroscopy (fNIRS) as an imaging technique to record spontaneous brain activity in children with ADHD. Approach: The brain functional connectivity was calculated, and the graph theoretical analysis was further applied to investigate alterations in the global and regional properties of the brain network in the patients. In addition, the relationship between brain network features and core symptoms was examined. Results: ADHD patients exhibited significant decreases in both functional connectivity and global network efficiency. Meanwhile, the nodal efficiency in children with ADHD was also found to be altered, e.g., increase in the visual and dorsal attention networks and decrease in somatomotor and default mode networks, compared to the healthy controls. More importantly, the disrupted functional connectivity and nodal efficiency significantly correlated with dimensional ADHD scores. Conclusions: We clearly demonstrate the feasibility and potential of fNIRS-based connectome technique in ADHD or other neurological diseases in the future.

Differentiating Boys with ADHD from Those with Typical Development Based on Whole-Brain Functional Connections Using a Machine Learning Approach

PURPOSE

In recent years, machine learning techniques have received increasing attention as a promising approach to differentiating patients from healthy subjects. Therefore, some resting-state functional magnetic resonance neuroimaging (R-fMRI) studies have used interregional functional connections as discriminative features. The aim of this study was to investigate ADHD-related spatially distributed discriminative features derived from whole-brain resting-state functional connectivity patterns using machine learning.

PATIENTS AND METHODS

We measured the interregional functional connections of the R-fMRI data from 40 ADHD patients and 28 matched typically developing controls. Machine learning was used to discriminate ADHD patients from controls. Classification performance was assessed by permutation tests.

RESULTS

The results from the model with the highest classification accuracy showed that 85.3% of participants were correctly identified using leave-one-out cross-validation (LOOV) with support vector machine (SVM). The majority of the most discriminative functional connections were located within or between the cerebellum, default mode network (DMN) and frontoparietal regions. Approximately half of the most discriminative connections were associated with the cerebellum. The cerebellum, right superior orbitofrontal cortex, left olfactory cortex, left gyrus rectus, right superior temporal pole, right calcarine gyrus and bilateral inferior occipital cortex showed the highest discriminative power in classification. Regarding the brain-behaviour relationships, some functional connections between the cerebellum and DMN regions were significantly correlated with behavioural symptoms in ADHD (P < 0.05).

CONCLUSION

This study indicated that whole-brain resting-state functional connections might provide potential neuroimaging-based information for clinically assisting the diagnosis of ADHD.

New Insights on the Effects of Methylphenidate in Attention Deficit Hyperactivity Disorder

This narrative review describes an overview of the multiple effects of methylphenidate (MPH) in attention-deficit/hyperactivity disorder (ADHD) and its potential neurobiological targets. It addressed the following aspects: 1) MPH effects on attention and executive functions in ADHD; 2) the relation between MPH efficacy and dopamine transporter gene (DAT) polymorphism; and 3) the role of MPH as an epigenetic modulator in ADHD. Literature analysis showed that MPH, the most commonly used psychostimulant in the therapy of ADHD, acts on multiple components of the disorder. Marked improvements in attentional and executive dysfunction have been observed in children with ADHD during treatment with MPH, as well as reductions in neurological soft signs. MPH efficacy may be influenced by polymorphisms in the DAT, and better responses to treatment were associated with the 10/10 genotype. Innovative lines of research have suggested that ADHD etiopathogenesis and its neuropsychological phenotypes also depend on the expression levels of human endogenous retrovirus (HERV). In particular, several studies have revealed that ADHD is associated with HERV-H over-expression and that MPH administration results in decreased expression levels of this retroviral family and a reduction in the main symptoms of the disorder. In conclusion, there is a confirmed role for MPH as an elective drug in the therapy of ADHD alone or in association with behavioral therapy. Its effectiveness can vary based on DAT polymorphisms and can act as a modulator of HERV-H gene expression, pointing to targets for a precision medicine approach.

Building functional connectivity neuromarkers of behavioral self-regulation across children with and without Autism Spectrum Disorder

Behavioral self-regulation develops rapidly during childhood and struggles in this area can have lifelong negative outcomes. Challenges with self-regulation are common to several neurodevelopmental conditions, including Autism Spectrum Disorder (ASD). Little is known about the neural expression of behavioral regulation in children with and without neurodevelopmental conditions.

We examined whole-brain brain functional correlations (FC) and behavioral regulation through connectome predictive modelling (CPM). CPM is a data-driven protocol for developing predictive models of brain–behavior relationships and assessing their potential as ‘neuromarkers’ using cross-validation. The data stems from the ABIDE II and comprises 276 children with and without ASD (8–13 years).

We identified networks whose FC predicted individual differences in behavioral regulation. These network models predicted novel individuals’ inhibition and shifting from FC data in both a leave-one-out, and split halves, cross-validation. We observed commonalities and differences, with inhibition relying on more posterior networks, shifting relying on more anterior networks, and both involving regions of the DMN.

Our findings substantially add to our knowledge on the neural expressions of inhibition and shifting across children with and without a neurodevelopmental condition. Given the numerous behavioral issues that can be quantified dimensionally, refinement of whole-brain neuromarker techniques may prove useful in the future.

Parsing Heterogeneity in Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder with Individual Connectome Mapping

Traditional diagnostic systems for neurodevelopmental disorders define diagnostic categories that are heterogeneous in behavior and underlying neurobiological alterations. The goal of this study was to parse heterogeneity in a core executive function (EF), cognitive flexibility, in children with a range of abilities (N = 132; children with autism spectrum disorder, attention-deficit/hyperactivity disorder [ADHD], and typically developing children) using directed functional connectivity profiles derived from resting-state functional magnetic resonance imaging data. Brain regions activated in response to a cognitive flexibility task in adults were used to guide region-of-interest selection to estimate individual connectivity profiles in this study. We expected to find subgroups of children who differed in their network connectivity metrics and symptom measures. Unexpectedly, we did not find a stable or valid subgrouping solution, which suggests that categorical models of the neural substrates of cognitive flexibility in children may be invalid. Exploratory analyses revealed dimensional associations between network connectivity metrics and ADHD symptomatology and EF ability across the entire sample. Results shed light on the validity of conceptualizing the neural substrates of cognitive flexibility categorically in children. Ultimately, this work may provide a foundation for the development of a revised nosology focused on neurobiological substrates as an alternative to traditional symptom-based classification systems.

Linked anatomical and functional brain alterations in children with attention-deficit/hyperactivity disorder

Objectives

Neuroimaging studies have independently demonstrated brain anatomical and functional impairments in participants with ADHD. The aim of the current study was to explore the relationship between structural and functional brain alterations in ADHD through an integrated analysis of multimodal neuroimaging data.

Methods

We performed a multimodal analysis to integrate resting-state functional magnetic resonance imaging (MRI), structural MRI, and diffusion-weighted imaging data in a large, single-site sample of children with and without diagnosis for ADHD. The inferred subject contributions were fed into regression models to investigate the relationships between diagnosis, symptom severity, gender, and age.

Results

Compared with controls, children with ADHD diagnosis showed altered white matter microstructure in widespread white matter fiber tracts as well as greater gray matter volume (GMV) in bilateral frontal regions, smaller GMV in posterior regions, and altered functional connectivity (FC) in default mode and fronto-parietal networks. Age-related growth of GMV of bilateral occipital lobe, FC in frontal regions as well as age-related decline of GMV in medial regions seen in controls appeared reversed in children with ADHD. In the whole group, higher symptom severity was related to smaller GMV in widespread regions in bilateral frontal, parietal, and temporal lobes, as well as greater GMV in intracalcarine and temporal cortices.

Conclusions

Through a multimodal analysis approach we show that structural and functional alterations in brain regions known to be altered in subjects with ADHD from unimodal studies are linked across modalities. The brain alterations were related to clinical features of ADHD, including disorder status, age, and symptom severity.

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Multimodal imaging analysis provides new insights in interconnected imaging findings.

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Co-occurrence of structural and functional brain alterations were observed in ADHD.

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The identified multimodal alterations are related to clinical features of ADHD.

A data mining and item response mixture modeling method to retrospectively measure Diagnostic and Statistical Manual of Mental Disorders-5 attention deficit hyperactivity disorder in the 1970 British Cohort Study

OBJECTIVE

To facilitate future outcome studies, we aimed to develop a robust and replicable method for estimating a categorical and dimensional measure of Diagnostic and Statistical Manual of Mental Disorders-5 (DSM-5) attention deficit hyperactivity disorder (ADHD) in the 1970 British Cohort Study (BCS70).

METHOD

Following a data mining framework, we mapped DSM-5 ADHD symptoms to age 10 BCS70 data (N = 11,426) and derived a 16-item scale (α = 0.85). Mapping was validated by an expert panel. A categorical subgroup was derived (n = 594, 5.2%), and a zero-inflated item response theory (IRT) mixture model fitted to estimate a dimensional measure.

RESULTS

Subgroup composition was comparable with other ADHD samples. Relative risk ratios (ADHD/not ADHD) included boys = 1.38, unemployed fathers = 2.07, below average reading = 2.58, and depressed parent = 3.73. Our estimated measures correlated with two derived reference scales: Strengths and Difficulties Questionnaire hyperactivity (r = 0.74) and a Rutter/Conners-based scale (r = 0.81), supporting construct validity. IRT model items (symptoms) had moderate to high discrimination (0.90-2.81) and provided maximum information at average to moderate theta levels of ADHD (0.5-1.75).

CONCLUSION

We extended previous work to identify ADHD in BCS70, derived scales from existing data, modeled ADHD items with IRT, and adjusted for a zero-inflated distribution. Psychometric properties were promising, and this work will enable future studies of causal mechanisms in ADHD.

Hyperactivity/restlessness is associated with increased functional connectivity in adults with ADHD: a dimensional analysis of resting state fMRI

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 ).

An Integrated Analysis of Neural Network Correlates of Categorical and Dimensional Models of Attention-Deficit/Hyperactivity Disorder

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is a heterogeneous neurodevelopmental disorder, putatively induced by dissociable dysfunctional biobehavioral pathways. Here, we present a proof-of-concept study to parse ADHD-related heterogeneity in its underlying neurobiology by investigating functional connectivity across multiple brain networks to 1) disentangle categorical diagnosis-related effects from dimensional behavior-related effects and 2) functionally map these neural correlates to neurocognitive measures.

METHODS

We identified functional connectivity abnormalities related to ADHD across 14 networks within a large resting-state functional magnetic resonance imaging dataset (n = 409; age = 17.5 ± 3.3 years). We tested these abnormalities for their association with the categorical ADHD diagnosis and with dimensional inattention and hyperactivity/impulsivity scores using a novel modeling framework, creating orthogonalized models. Next, we evaluated the relationship of these findings with neurocognitive measures (working memory, response inhibition, reaction time variability, reward sensitivity).

RESULTS

Within the default mode network, we mainly observed categorical ADHD-related functional connectivity abnormalities, unrelated to neurocognitive measures. Clusters within the visual networks primarily related to dimensional scores of inattention and reaction time variability, while findings within the sensorimotor networks were mainly linked to hyperactivity/impulsivity and both reward sensitivity and working memory. Findings within the cerebellum network and salience network related to both categorical and dimensional ADHD measures and were linked to response inhibition and reaction time variability.

CONCLUSIONS

This proof-of-concept study identified ADHD-related neural correlates across multiple functional networks, showing distinct categorical and dimensional mechanisms and their links to neurocognitive functioning.

Growing a social brain

It has long been assumed that social animals, such as humans, are born with a brain system that has evolved to support social affiliation. However, the evidence does not necessarily support this assumption. Alternatively, social animals can be defined as those who cannot survive alone and rely on members from their group to regulate their ongoing physiology (or allostasis). The rather simple evolutionary constraint of social dependency for survival can be sufficient to make the social environment vitally salient, and to provide the ultimate driving force for socially crafted brain development and learning. In this Perspective, we propose a framework for sociality and specify a set of hypotheses on the mechanisms of social development and underlying neural systems. The theoretical shift proposed here implies that profound human characteristics, including but not limited to sociality, are acquired at an early age, while social interactions provide key wiring instructions that determine brain development.

Novel in silico multivariate mapping of intrinsic and anticorrelated connectivity to neurocognitive functional maps supports the maturational hypothesis of ADHD

From childhood to adolescence, strengthened coupling in frontal, striatal and parieto-temporal regions associated with cognitive control, and increased anticorrelation between task-positive and task-negative circuits, subserve the reshaping of behavior. ADHD is a common condition peaking in adolescence and regressing in adulthood, with a wide variety of cognitive control deficits. Alternate hypotheses of ADHD emphasize lagging circuitry refinement versus categorical differences in network function. However, quantifying the individual circuit contributions to behavioral findings, and relative roles of maturational versus categorical effects, is challenging in vivo or in meta-analyses using task-based paradigms within the same pipeline, given the multiplicity of neurobehavioral functions implicated. To address this, we analyzed 46 positively-correlated and anticorrelated circuits in a multivariate model in resting-state data from 504 age- and gender-matched youth, and created a novel in silico method to map individual quantified effects to reverse inference maps of 8 neurocognitive functions consistently implicated in ADHD, as well as dopamine and hyperactivity. We identified only age- and gender-related effects in intrinsic connectivity, and found that maturational refinement of circuits in youth with ADHD occupied 3-10x more brain locations than in typical development, with the footprint, effect size and contribution of individual circuits varying substantially. Our analysis supports the maturational hypothesis of ADHD, suggesting lagging connectivity reorganization within specific subnetworks of fronto-parietal control, ventral attention, cingulo-opercular, temporo-limbic and cerebellar sub-networks contribute across neurocognitive findings present in this complex condition. We present the first analysis of anti-correlated connectivity in ADHD and suggest new directions for exploring residual and non-responsive symptoms.

Resting-State Brain Signal Variability in Prefrontal Cortex Is Associated With ADHD Symptom Severity in Children

Atypical brain function in attention-deficit/hyperactivity disorder (ADHD) has been identified using both task-activation and functional connectivity fMRI approaches. Recent work highlights the potential for another measure derived from functional neuroimaging data, brain signal variability, to reveal insights into clinical conditions. Higher brain signal variability has previously been linked with optimal behavioral performance. At present, little is known regarding the relationship between resting-state brain signal variability and ADHD symptom severity. The current study examined the relationship between a measure of moment-to-moment brain signal variability called mean-square successive difference (MSSD) and ADHD symptomatology in a group of children (7–12 years old) with (n = 40) and without (n = 30) a formal diagnosis of ADHD. A categorical analysis comparing subjects with and without a clinical diagnosis of ADHD showed no differences in MSSD between groups. A dimensional analysis revealed a positive relationship between MSSD and overall ADHD symptom severity and inattention across children with and without an ADHD diagnosis. Specifically, this positive relationship was found in medial prefrontal areas comprising the default mode network. These results demonstrate a link between intrinsic brain signal variability and ADHD symptom severity that cuts across diagnostic categories, and point to a locus of dysfunction consistent with previous neuroimaging literature.

Aberrant Time-Varying Cross-Network Interactions in Children With Attention-Deficit/Hyperactivity Disorder and the Relation to Attention Deficits

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is thought to stem from aberrancies in large-scale cognitive control networks. However, the exact nature of aberrant brain circuit dynamics involving these control networks is poorly understood. Using a saliency-based triple-network model of cognitive control, we tested the hypothesis that dynamic cross-network interactions among the salience, central executive, and default mode networks are dysregulated in children with ADHD, and we investigated how these dysregulations contribute to inattention.

METHODS

Using functional magnetic resonance imaging data from 140 children with ADHD and typically developing children from two cohorts (primary cohort = 80 children, replication cohort = 60 children) in a case-control design, we examined both time-averaged and dynamic time-varying cross-network interactions in each cohort separately.

RESULTS

Time-averaged measures of salience network-centered cross-network interactions were significantly lower in children with ADHD compared with typically developing children and were correlated with severity of inattention symptoms. Children with ADHD displayed more variable dynamic cross-network interaction patterns, including less persistent brain states, significantly shorter mean lifetimes of brain states, and intermittently weaker cross-network interactions. Importantly, dynamic time-varying measures of cross-network interactions were more strongly correlated with inattention symptoms than with time-averaged measures of functional connectivity. Crucially, we replicated these findings in the two independent cohorts of children with ADHD and typically developing children.

CONCLUSIONS

Aberrancies in time-varying engagement of the salience network with the central executive network and default mode network are a robust and clinically relevant neurobiological signature of childhood ADHD symptoms. The triple-network neurocognitive model provides a novel, replicable, and parsimonious dynamical systems neuroscience framework for characterizing childhood ADHD and inattention.

Resting state brain networks in the prairie vole

Resting state functional magnetic resonance imaging (rsfMRI) has shown the hierarchical organization of the human brain into large-scale complex networks, referred as resting state networks. This technique has turned into a promising translational research tool after the finding of similar resting state networks in non-human primates, rodents and other animal models of great value for neuroscience. Here, we demonstrate and characterize the presence of resting states networks in Microtus ochrogaster, the prairie vole, an extraordinary animal model to study complex human-like social behavior, with potential implications for the research of normal social development, addiction and neuropsychiatric disorders. Independent component analysis of rsfMRI data from isoflurane-anestethized prairie voles resulted in cortical and subcortical networks, including primary motor and sensory networks, but also included putative salience and default mode networks. We further discuss how future research could help to close the gap between the properties of the large scale functional organization and the underlying neurobiology of several aspects of social cognition. These results contribute to the evidence of preserved resting state brain networks across species and provide the foundations to explore the use of rsfMRI in the prairie vole for basic and translational research.

Maternal Systemic Interleukin-6 During Pregnancy Is Associated With Newborn Amygdala Phenotypes and Subsequent Behavior at 2 Years of Age

BACKGROUND

Maternal inflammation during pregnancy increases the risk for offspring psychiatric disorders and other adverse long-term health outcomes. The influence of inflammation on the developing fetal brain is hypothesized as one potential mechanism but has not been examined in humans.

METHODS

Participants were adult women (N = 86) who were recruited during early pregnancy and whose offspring were born after 34 weeks' gestation. A biological indicator of maternal inflammation (interleukin-6) that has been shown to influence fetal brain development in animal models was quantified serially in early, mid-, and late pregnancy. Structural and functional brain magnetic resonance imaging scans were acquired in neonates shortly after birth. Infants' amygdalae were individually segmented for measures of volume and as seeds for resting state functional connectivity. At 24 months of age, children completed a snack delay task to assess impulse control.

RESULTS

Higher average maternal interleukin-6 concentration during pregnancy was prospectively associated with larger right amygdala volume and stronger bilateral amygdala connectivity to brain regions involved in sensory processing and integration (fusiform, somatosensory cortex, and thalamus), salience detection (anterior insula), and learning and memory (caudate and parahippocampal gyrus). Larger newborn right amygdala volume and stronger left amygdala connectivity were in turn associated with lower impulse control at 24 months of age, and mediated the association between higher maternal interleukin-6 concentrations and lower impulse control.

CONCLUSIONS

These findings provide new evidence in humans linking maternal inflammation during pregnancy with newborn brain and emerging behavioral phenotypes relevant for psychiatric disorders. A better understanding of intrauterine conditions that influence offspring disease susceptibility is warranted to inform targeted early intervention and prevention efforts.

Inconsistency in Abnormal Brain Activity across Cohorts of ADHD-200 in Children with Attention Deficit Hyperactivity Disorder

Many papers have shown results from the multi-site dataset of resting-state fMRI (rs-fMRI) in attention deficit hyperactivity disorder (ADHD), a data-sharing project named ADHD-200. However, few studies have illustrated that to what extent the pooled findings were consistent across cohorts. The present study analyzed three voxel-wise whole-brain metrics, i.e., amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and degree centrality (DC) based on the pooled dataset as well as individual cohort of ADHD-200. In addition to the conventional frequency band of 0.01-0.08 Hz, sub-frequency bands of 0-0.01, 0.01-0.027, 0.027-0.073, 0.073-0.198, and 0.198-0.25 Hz, were assessed. While the pooled dataset showed abnormal activity in some brain regions, e.g., the bilateral sensorimotor cortices, bilateral cerebellum, and the bilateral lingual gyrus, these results were highly inconsistent across cohorts, even across the three cohorts from the same research center. The standardized effect size was rather small. These findings suggested a high heterogeneity of spontaneous brain activity in ADHD. Future studies based on multi-site large-sample dataset should be performed on pooled data and single cohort data, respectively and the effect size must be shown.

White Matter Microstructural Alterations in Children with ADHD: Categorical and Dimensional Perspectives

Studies of brain alterations in children with attention-deficit/hyperactivity disorder (ADHD) have shown heterogeneous results. The aims of the current study were to investigate white matter microstructure in children using both categorical and dimensional definitions of ADHD and to determine the functional consequences of observed alterations. In a large single-site sample of children (aged 8-15 years) with ADHD (n=83) and healthy controls (n=122), we used tract-based spatial statistics on diffusion tensor imaging data to investigate whole-skeleton differences of fractional anisotropy (FA), mean, axial, and radial diffusivity (MD, AD, RD), and mode of anisotropy related to ADHD status (categorical) and symptom severity (dimensional). For categorical differences observed, we analyzed their association with cognitive functioning in working memory and inhibition. Compared with healthy controls, children with ADHD showed decreased FA and increased RD in widespread, overlapping brain regions, mainly in corpus callosum (CC) and major tracts in the left hemisphere. Decreased FA was associated with inhibition performance in the participants with ADHD. Using dimensional definitions, greater hyperactivity/impulsivity symptom severity was associated with higher FA also in widespread regions, mainly in CC and major tracts in the right hemisphere. Our study showed white matter alterations to be related to ADHD status and symptom severity in patients. The coexistence of decreased FA and increased RD in the absence of alterations in MD or AD might indicate altered myelination as a pathophysiological factor in ADHD.

Neurological soft signs are associated with attentional dysfunction in children with attention deficit hyperactivity disorder

INTRODUCTION

Inattention is one of the core symptoms of Attention Deficit Hyperactivity Disorder (ADHD). Most of patients with ADHD show motor impairment, consisting in the persistence of neurological soft signs (NSS). Our aim was to evaluate attentional and motor functioning in an ADHD sample and healthy children (HC) and possible link between attentional dysfunction and motor impairment in ADHD.

METHOD

Twenty-seven drug-naive patients with ADHD and 23 HC were tested with a test battery, measuring different aspects of attention. Motor evaluation has provided three primary variables: overflow movements (OM), dysrhythmia and total speed of timed activities.

RESULTS

Compared to HC, patients were impaired in a considerable number of attentional processes and showed a greater number of NSS. Significant correlations between disturbances of attention and motor abnormalities were observed in ADHD group.

CONCLUSION

Our findings suggest that attentional processes could be involved in the pathophysiology of the NSS and add scientific evidence to the predictive value of NSS as indicators of the severity of functional impairment in ADHD. Given the marked improvement or complete resolution of NSS following treatment with methylphenidate, we suggest that evaluation of NSS is useful to monitor the effectiveness of pharmacological treatment with MPH in ADHD.

Neural Connectivity Evidence for a Categorical-Dimensional Hybrid Model of Autism Spectrum Disorder

BACKGROUND

Autism spectrum disorder (ASD) encompasses a complex manifestation of symptoms that include deficits in social interaction and repetitive or stereotyped interests and behaviors. In keeping with the increasing recognition of the dimensional characteristics of ASD symptoms and the categorical nature of a diagnosis, we sought to delineate the neural mechanisms of ASD symptoms based on the functional connectivity of four known neural networks (i.e., default mode network, dorsal attention network, salience network, and executive control network).

METHODS

We leveraged an open data resource (Autism Brain Imaging Data Exchange) providing resting-state functional magnetic resonance imaging data sets from 90 boys with ASD and 95 typically developing boys. This data set also included the Social Responsiveness Scale as a dimensional measure of ASD traits. Seed-based functional connectivity was paired with linear regression to identify functional connectivity abnormalities associated with categorical effects of ASD diagnosis, dimensional effects of ASD-like behaviors, and their interaction.

RESULTS

Our results revealed the existence of dimensional mechanisms of ASD uniquely affecting each network based on the presence of connectivity-behavioral relationships; these were independent of diagnostic category. However, we also found evidence of categorical differences (i.e., diagnostic group differences) in connectivity strength for each network as well as categorical differences in connectivity-behavioral relationships (i.e., diagnosis-by-behavior interactions), supporting the coexistence of categorical mechanisms of ASD.

CONCLUSIONS

Our findings support a hybrid model for ASD characterization that includes a combination of categorical and dimensional brain mechanisms and provide a novel understanding of the neural underpinnings of ASD.

Functional Connectivity of the Infant Human Brain: Plastic and Modifiable

Infancy is a critical and immensely important period in human brain development. Subtle changes during this stage may be greatly amplified with the unfolding of different developmental processes, exerting far-reaching consequences. Studies of the structure and behavioral manifestations of the infant brain are fruitful. However, the specific functional brain mechanisms that enable the execution of different behaviors remained elusive until the advent of functional connectivity fMRI (fcMRI), which provides an unprecedented opportunity to probe the infant functional brain development in vivo. Since its inception, a burgeoning field of infant brain functional connectivity study has emerged and thrived during the past decade. In this review, we describe (1) findings of normal development of functional connectivity networks and their relationships to behaviors and (2) disruptions of the normative functional connectivity development due to identifiable genetic and/or environmental risk factors during the first 2 years of human life. Technical considerations of infant fcMRI are also provided. It is our hope to consolidate previous findings so that the field can move forward with a clearer picture toward the ultimate goal of fcMRI-based objective methods for early diagnosis/identification of risks and evaluation of early interventions to optimize developing functional connectivity networks in this critical developmental window.

Intrinsic Functional Connectivity in Attention-Deficit/Hyperactivity Disorder: A Science in Development

Functional magnetic resonance imaging (fMRI) without an explicit task, i.e., resting state fMRI, of individuals with Attention-Deficit/Hyperactivity Disorder (ADHD) is growing rapidly. Early studies were unaware of the vulnerability of this method to even minor degrees of head motion, a major concern in the field. Recent efforts are implementing various strategies to address this source of artifact along with a growing set of analytical tools. Availability of the ADHD-200 Consortium dataset, a large-scale multi-site repository, is facilitating increasingly sophisticated approaches. In parallel, investigators are beginning to explicitly test the replicability of published findings. In this narrative review, we sketch out broad, overarching hypotheses being entertained while noting methodological uncertainties. Current hypotheses implicate the interplay of default, cognitive control (frontoparietal) and attention (dorsal, ventral, salience) networks in ADHD; functional connectivities of reward-related and amygdala-related circuits are also supported as substrates for dimensional aspects of ADHD. Before these can be further specified and definitively tested, we assert the field must take on the challenge of mapping the "topography" of the analytical space, i.e., determining the sensitivities of results to variations in acquisition, analysis, demographic and phenotypic parameters. Doing so with openly available datasets will provide the needed foundation for delineating typical and atypical developmental trajectories of brain structure and function in neurodevelopmental disorders including ADHD when applied to large-scale multi-site prospective longitudinal studies such as the forthcoming Adolescent Brain Cognitive Development study.