Distributed effects of methylphenidate on the network structure of the resting brain: a connectomic pattern classification analysis

Correlation of altered intrinsic functional connectivity with impaired self-regulation in children and adolescents with ADHD

BACKGROUND

Attention-deficit hyperactivity disorder (ADHD) has a high prevalence of co-occurring impaired self-regulation (dysregulation), exacerbating adverse outcomes. Neural correlates underlying impaired self-regulation in ADHD remain inconclusive. We aimed to investigate the impact of dysregulation on intrinsic functional connectivity (iFC) in children with ADHD and the correlation of iFC with dysregulation among children with ADHD relative to typically developing controls (TDC).

METHODS

Resting-state functional MRI data of 71 children with ADHD (11.38 ± 2.44 years) and 117 age-matched TDC were used in the final analysis. We restricted our analyses to resting-state networks (RSNs) of interest derived from independent component analysis. Impaired self-regulation was estimated based on the Child Behavioral Checklist-Dysregulation Profile.

RESULTS

Children with ADHD showed stronger iFC than TDC in the left frontoparietal network, somatomotor network (SMN), visual network (VIS), default-mode network (DMN), and dorsal attention network (DAN) (FWE-corrected alpha < 0.05). After adding dysregulation levels as an extra regressor, the ADHD group only showed stronger iFC in the VIS and SMN. ADHD children with high dysregulation had higher precuneus iFC within DMN than ADHD children with low dysregulation. Angular gyrus iFC within DMN was positively correlated with dysregulation in the ADHD group but negatively correlated with dysregulation in the TDC group. Functional network connectivity showed ADHD had a greater DMN-DAN connection than TDC, regardless of the dysregulation level.

CONCLUSIONS

Our findings suggest that DMN connectivity may contribute to impaired self-regulation in ADHD. Impaired self-regulation should be considered categorical and dimensional moderators for the neural correlates of altered iFC in ADHD.

Graph analysis uncovers an opposing impact of methylphenidate on connectivity patterns within default mode network sub-divisions

BACKGROUND

The Default Mode Network (DMN) is a central neural network, with recent evidence indicating that it is composed of functionally distinct sub-networks. Methylphenidate (MPH) administration has been shown before to modulate impulsive behavior, though it is not yet clear whether these effects relate to MPH-induced changes in DMN connectivity. To address this gap, we assessed the impact of MPH administration on functional connectivity patterns within and between distinct DMN sub-networks and tested putative relations to variability in sub-scales of impulsivity.

METHODS

Fifty-five right-handed healthy adults underwent two resting-state functional MRI (rs-fMRI) scans, following acute administration of either MPH (20 mg) or placebo, via a randomized double-blind placebo-controlled design. Graph modularity analysis was implemented to fractionate the DMN into distinct sub-networks based on the impact of MPH (vs. placebo) on DMN connectivity patterns with other neural networks.

RESULTS

MPH administration led to an overall decreased DMN connectivity, particularly with the auditory, cinguloopercular, and somatomotor networks, and increased connectivity with the parietomedial network. Graph analysis revealed that the DMN could be fractionated into two distinct sub-networks, with one exhibiting MPH-induced increased connectivity and the other decreased connectivity. Decreased connectivity of the DMN sub-network with the cinguloopercular network following MPH administration was associated with elevated impulsivity and non-planning impulsiveness.

CONCLUSION

Current findings highlight the intricate effects of MPH administration on DMN rs-fMRI connectivity, uncovering its opposing impact on distinct DMN sub-divisions. MPH-induced dynamics in DMN connectivity patterns with other neural networks may account for some of the effects of MPH administration on impulsive behavior.

Brain connectivity changes to fast versus slow dopamine increases

The rewarding effects of stimulant drugs such as methylphenidate (MP) depend crucially on how fast they raise dopamine in the brain. Yet how the rate of drug-induced dopamine increases impacts brain network communication remains unresolved. We manipulated route of MP administration to generate fast versus slow dopamine increases. We hypothesized that fast versus slow dopamine increases would result in a differential pattern of global brain connectivity (GBC) in association with regional levels of dopamine D1 receptors, which are critical for drug reward. Twenty healthy adults received MP intravenously (0.5 mg/kg; fast dopamine increases) and orally (60 mg; slow dopamine increases) during simultaneous [11C]raclopride PET-fMRI scans (double-blind, placebo-controlled). We tested how GBC was temporally associated with slow and fast dopamine increases on a minute-to-minute basis. Connectivity patterns were strikingly different for slow versus fast dopamine increases, and whole-brain spatial patterns were negatively correlated with one another (rho = -0.54, pspin < 0.001). GBC showed "fast>slow" associations in dorsal prefrontal cortex, insula, posterior thalamus and brainstem, caudate and precuneus; and "slow>fast" associations in ventral striatum, orbitofrontal cortex, and frontopolar cortex (pFDR < 0.05). "Fast>slow" GBC patterns showed significant spatial correspondence with D1 receptor availability (estimated via normative maps of [11C]SCH23390 binding; rho = 0.22, pspin < 0.05). Further, hippocampal GBC to fast dopamine increases was significantly negatively correlated with self-reported 'high' ratings to intravenous MP across individuals (r(19) = -0.68, pbonferroni = 0.015). Different routes of MP administration produce divergent patterns of brain connectivity. Fast dopamine increases are uniquely associated with connectivity patterns that have relevance for the subjective experience of drug reward.

Higher-order functional brain networks and anterior cingulate glutamate + glutamine (Glx) in antipsychotic-naïve first episode psychosis patients

Human connectome studies have provided abundant data consistent with the hypothesis that functional dysconnectivity is predominant in psychosis spectrum disorders. Converging lines of evidence also suggest an interaction between dorsal anterior cingulate cortex (dACC) cortical glutamate with higher-order functional brain networks (FC) such as the default mode (DMN), dorsal attention (DAN), and executive control networks (ECN) in healthy controls (HC) and this mechanism may be impaired in psychosis. Data from 70 antipsychotic-medication naïve first-episode psychosis (FEP) and 52 HC were analyzed. 3T Proton magnetic resonance spectroscopy (1H-MRS) data were acquired from a voxel in the dACC and assessed correlations (positive FC) and anticorrelations (negative FC) of the DMN, DAN, and ECN. We then performed regressions to assess associations between glutamate + glutamine (Glx) with positive and negative FC of these same networks and compared them between groups. We found alterations in positive and negative FC in all networks (HC > FEP). A relationship between dACC Glx and positive and negative FC was found in both groups, but when comparing these relationships between groups, we found contrasting associations between these variables in FEP patients compared to HC. We demonstrated that both positive and negative FC in three higher-order resting state networks are already altered in antipsychotic-naïve FEP, underscoring the importance of also considering anticorrelations for optimal characterization of large-scale functional brain networks as these represent biological processes as well. Our data also adds to the growing body of evidence supporting the role of dACC cortical Glx as a mechanism underlying alterations in functional brain network connectivity. Overall, the implications for these findings are imperative as this particular mechanism may differ in untreated or chronic psychotic patients; therefore, understanding this mechanism prior to treatment could better inform clinicians.Clinical trial registration: Trajectories of Treatment Response as Window into the Heterogeneity of Psychosis: A Longitudinal Multimodal Imaging Study, NCT03442101 . Glutamate, Brain Connectivity and Duration of Untreated Psychosis (DUP), NCT02034253 .

An autonomic mode of brain activity

The relevance of interactions between autonomic and central nervous systems remains unclear for human brain function and health, particularly when both systems are challenged under sleep deprivation (SD). We measured brain activity (with fMRI), pulse and respiratory signals, and baseline brain amyloid beta burden (with PET) in healthy participants. We found that SD relative to rested wakefulness (RW) resulted in a significant increase in synchronized low frequency (LF, < 0.1 Hz) activity in an autonomically-related network (AN), including dorsal attention, visual, and sensorimotor regions, which we previously found to have consistent temporal coupling with LF pulse signal changes (regulated by sympathetic tone). SD resulted in a significant phase coherence between the LF component of the pulse signal and a medial network with peak effects in the midbrain reticular formation, and between LF component of the respiratory variations (regulated by respiratory motor output) and a cerebellar network. The LF power of AN during SD was significantly and independently correlated with pulse-medial network and respiratory-cerebellar network phase coherences (total adjusted R2 = 0.78). Higher LF power of AN during SD (but not RW) was associated with lower amyloid beta burden (Cohen's d = 0.8). In sum, SD triggered an autonomic mode of synchronized brain activity that was associated with distinct autonomic-central interactions. Findings highlight the direct relevance of global cortical synchronization to brain clearance mechanisms.

Effect of methylphenidate on functional controllability: a preliminary study in medication-naïve children with ADHD

Methylphenidate (MPH) is the recommended first-line treatment for attention-deficit/hyperactivity disorder (ADHD). While MPH's mechanism of action as a dopamine and noradrenaline transporter blocker is well known, how this translates to ADHD-related symptom mitigation is still unclear. As functional connectivity is reliably altered in ADHD, with recent literature indicating dysfunctional connectivity dynamics as well, one possible mechanism is through altering brain network dynamics. In a double-blind, placebo-controlled MPH crossover trial, 19 medication-naïve children with ADHD underwent two functional MRI scanning sessions (one on MPH and one on placebo) that included a resting state scan and two inhibitory control tasks; 27 typically developing (TD) children completed the same protocol without medication. Network control theory, which quantifies how brain activity reacts to system inputs based on underlying connectivity, was used to assess differences in average and modal functional controllability during rest and both tasks between TD children and children with ADHD (on and off MPH) and between children with ADHD on and off MPH. Children with ADHD on placebo exhibited higher average controllability and lower modal controllability of attention, reward, and somatomotor networks than TD children. Children with ADHD on MPH were statistically indistinguishable from TD children on almost all controllability metrics. These findings suggest that MPH may stabilize functional network dynamics in children with ADHD, both reducing reactivity of brain organization and making it easier to achieve brain states necessary for cognitively demanding tasks.

Cognitive enhancement: Effects of methylphenidate, modafinil, and caffeine on latent memory and resting state functional connectivity in healthy adults

Stimulants like methylphenidate, modafinil, and caffeine have repeatedly shown to enhance cognitive processes such as attention and memory. However, brain-functional mechanisms underlying such cognitive enhancing effects of stimulants are still poorly characterized. Here, we utilized behavioral and resting-state fMRI data from a double-blind randomized placebocontrolled study of methylphenidate, modafinil, and caffeine in 48 healthy male adults. The results show that performance in different memory tasks is enhanced, and functional connectivity (FC) specifically between the frontoparietal network (FPN) and default mode network (DMN) is modulated by the stimulants in comparison to placebo. Decreased negative connectivity between right prefrontal and medial parietal but also between medial temporal lobe and visual brain regions predicted stimulant-induced latent memory enhancement. We discuss dopamine's role in attention and memory as well as its ability to modulate FC between large-scale neural networks (e.g., FPN and DMN) as a potential cognitive enhancement mechanism.

Cortical D1 and D2 dopamine receptor availability modulate methylphenidate-induced changes in brain activity and functional connectivity

Dopamine signaling plays a critical role in shaping brain functional network organization and behavior. Prominent theories suggest the relative expression of D1- to D2-like dopamine receptors shapes excitatory versus inhibitory signaling, with broad consequences for cognition. Yet it remains unknown how the balance between cortical D1R versus D2R signaling coordinates the activity and connectivity of functional networks in the human brain. To address this, we collected three PET scans and two fMRI scans in 36 healthy adults (13 female/23 male; average age 43 ± 12 years), including a baseline D1R PET scan and two sets of D2R PET scans and fMRI scans following administration of either 60 mg oral methylphenidate or placebo (two separate days, blinded, order counterbalanced). The drug challenge allowed us to assess how pharmacologically boosting dopamine levels alters network organization and behavior in association with D1R-D2R ratios across the brain. We found that the relative D1R-D2R ratio was significantly greater in high-level association cortices than in sensorimotor cortices. After stimulation with methylphenidate compared to placebo, brain activity (as indexed by the fractional amplitude of low frequency fluctuations) increased in association cortices and decreased in sensorimotor cortices. Further, within-network resting state functional connectivity strength decreased more in sensorimotor than association cortices following methylphenidate. Finally, in association but not sensorimotor cortices, the relative D1R-D2R ratio (but not the relative availability of D1R or D2R alone) was positively correlated with spatial working memory performance, and negatively correlated with age. Together, these data provide a framework for how dopamine-boosting drugs like methylphenidate alter brain function, whereby regions with relatively higher inhibitory D2R (i.e., sensorimotor cortices) tend to have greater decreases in brain activity and connectivity compared to regions with relatively higher excitatory D1R (i.e., association cortices). They also support the importance of a balanced interaction between D1R and D2R in association cortices for cognitive function and its degradation with aging.

Joint PET and MRI analyses of cortical D1 and D2 dopamine receptors in healthy adults provide a framework for understanding how dopamine-boosting drugs alter brain function.

Increased Functional Segregation Related to the Salience Network in Unaffected Siblings of Youths With Attention-Deficit/Hyperactivity Disorder

OBJECTIVE

Although there are frequent reports of shared neurofunctional and neurostructural alterations among probands with attention-deficit/hyperactivity disorder (ADHD) and their unaffected siblings, there is little knowledge regarding whether abnormalities in the resting-state functional connectivity of ADHD probands is also expressed in unaffected siblings, or whether this unaffected (but at-risk) cohort manifests distinct patterns.

METHOD

We used a multivariate connectome-wide association study examining intrinsic functional connectivity with resting-state functional magnetic resonance imaging (MRI) in a sample (aged 8-17 years) of medication-naive ADHD probands (n = 56), their unaffected siblings (n = 55), and typically developing (TD) youths (n = 106).

RESULTS

ADHD probands showed, relative to TD youths, increased connectivity between the default-mode network (DMN) and task-positive networks. Relative to ADHD and TD groups, respectively, unaffected siblings showed increased connectivity within the salience network and reduced connectivity between the DMN and salience network. No shared alterations in functional connectivity among ADHD probands and their unaffected siblings were identified. These findings were largely confirmed by complementary pairwise connectomic comparisons. However, the main connectivity differences between ADHD and unaffected siblings were not replicated in a tightly age- and sex-matched subsample (20 proband-sibling pairs and 60 TD youths).

CONCLUSION

Our findings suggest that increased functional segregation related to the attention networks, especially the salience (ventral attention) system, may be a potential feature of at-risk siblings who remain unaffected by ADHD expression. Further replications are needed in other larger and sex-matched samples.

CLINICAL TRIAL REGISTRATION INFORMATION

Structural and Functional Connectivity of Frontostriatal and Frontoparietal Networks as Endophenotypes of ADHD; https://clinicaltrials.gov/; NCT01682915.

Unravelling the effects of methylphenidate on the dopaminergic and noradrenergic functional circuits

Functional magnetic resonance imaging (fMRI) can be combined with drugs to investigate the system-level functional responses in the brain to such challenges. However, most psychoactive agents act on multiple neurotransmitters, limiting the ability of fMRI to identify functional effects related to actions on discrete pharmacological targets. We recently introduced a multimodal approach, REACT (Receptor-Enriched Analysis of functional Connectivity by Targets), which offers the opportunity to disentangle effects of drugs on different neurotransmitters and clarify the biological mechanisms driving clinical efficacy and side effects of a compound. Here, we focus on methylphenidate (MPH), which binds to the dopamine transporter (DAT) and the norepinephrine transporter (NET), to unravel its effects on dopaminergic and noradrenergic functional circuits in the healthy brain at rest. We then explored the relationship between these target-enriched resting state functional connectivity (FC) maps and inter-individual variability in behavioural responses to a reinforcement-learning task encompassing a novelty manipulation to disentangle the molecular systems underlying specific cognitive/behavioural effects. Our main analysis showed a significant MPH-induced FC increase in sensorimotor areas in the functional circuit associated with DAT. In our exploratory analysis, we found that MPH-induced regional variations in the DAT and NET-enriched FC maps were significantly correlated with some of the inter-individual differences on key behavioural responses associated with the reinforcement-learning task. Our findings show that main MPH-related FC changes at rest can be understood through the distribution of DAT in the brain. Furthermore, they suggest that when compounds have mixed pharmacological profiles, REACT may be able to capture regional functional effects that are underpinned by the same cognitive mechanism but are related to engagement of distinct molecular targets.

Machine learning-based prediction of clinical pain using multimodal neuroimaging and autonomic metrics

Although self-report pain ratings are the gold standard in clinical pain assessment, they are inherently subjective in nature and significantly influenced by multidimensional contextual variables. Although objective biomarkers for pain could substantially aid pain diagnosis and development of novel therapies, reliable markers for clinical pain have been elusive. In this study, individualized physical maneuvers were used to exacerbate clinical pain in patients with chronic low back pain (N = 53), thereby experimentally producing lower and higher pain states. Multivariate machine-learning models were then built from brain imaging (resting-state blood-oxygenation-level-dependent and arterial spin labeling functional imaging) and autonomic activity (heart rate variability) features to predict within-patient clinical pain intensity states (ie, lower vs higher pain) and were then applied to predict between-patient clinical pain ratings with independent training and testing data sets. Within-patient classification between lower and higher clinical pain intensity states showed best performance (accuracy = 92.45%, area under the curve = 0.97) when all 3 multimodal parameters were combined. Between-patient prediction of clinical pain intensity using independent training and testing data sets also demonstrated significant prediction across pain ratings using the combined model (Pearson's r = 0.63). Classification of increased pain was weighted by elevated cerebral blood flow in the thalamus, and prefrontal and posterior cingulate cortices, and increased primary somatosensory connectivity to frontoinsular cortex. Our machine-learning approach introduces a model with putative biomarkers for clinical pain and multiple clinical applications alongside self-report, from pain assessment in noncommunicative patients to identification of objective pain endophenotypes that can be used in future longitudinal research aimed at discovery of new approaches to combat chronic pain.

Investigating the effects of subconcussion on functional connectivity using mass-univariate and multivariate approaches

There are concerns about the effects of subconcussive head impacts in sport, but the effects of subconcussion on brain connectivity are not well understood. We hypothesized that college football players experience changes in brain functional connectivity not found in athletes competing in lower impact sports or healthy controls. These changes may be spatially heterogeneous across participants, requiring analysis methods that go beyond mass-univariate approaches commonly used in functional MRI (fMRI). To test this hypothesis, we analyzed resting-state fMRI data from college football (n = 15), soccer (n = 12), and lacrosse players (n = 16), and controls (n = 29) collected at preseason and postseason time points. Regional homogeneity (ReHo) and degree centrality (DC) were calculated as measures of local and long-range functional connectivity, respectively. Standard voxel-wise analysis and paired support vector machine (SVM) classification studied subconcussion's effects on local and global functional connectivity. Voxel-wise analyses yielded minimal findings, but SVM classification had high accuracy for college football's ReHo (87%, p = 0.009) and no other group. The findings suggest subconcussion results in spatially heterogeneous changes in local functional connectivity that may only be detectible with multivariate analyses. To determine if voxel-wise and SVM analyses had similar spatial patterns, region-average t-statistic and SVM weight values were compared using a measure of ranking distance. T-statistic and SVM weight rankings exhibited significantly low ranking distance values for all groups and metrics, demonstrating that the analyses converged on a similar underlying effect. Overall, this research suggests that subconcussion in football may produce local functional connectivity changes similar to concussion.

Network Structure among Brain Systems in Adult ADHD is Uniquely Modified by Stimulant Administration

Current research in connectomics highlights that self-organized functional networks or "communities" of cortical areas can be detected in the adult brain. This perspective may provide clues to mechanisms of treatment response in psychiatric conditions. Here we examine functional brain community topology based on resting-state fMRI in adult Attention-Deficit/Hyperactivity Disorder (ADHD; n = 22) and controls (n = 31). We sought to evaluate ADHD patterns in adulthood and their modification by short term stimulants administration. Participants with ADHD were scanned one or two weeks apart, once with medication and once without; comparison participants were scanned at one time-point. Functional connectivity was estimated from these scans and community detection applied to determine cortical network topology. Measures of change in connectivity profile were calculated via a graph measure, termed the Node Dissociation Index (NDI). Compared to controls, several cortical networks had atypical connectivity in adults with ADHD when withholding stimulants, as measured by NDI. In most networks stimulants significantly reduced, but did not eliminate, differences in the distribution of connections between key brain systems relative to the control sample. These findings provide an enriched model of connectivity in ADHD and demonstrate how stimulants may exert functional effects by altering connectivity profiles in the brain.

Gamma-hydroxybutyrate increases brain resting-state functional connectivity of the salience network and dorsal nexus in humans

According to the triple network hypothesis the brain is equipped with three core neurocognitive networks: the default mode (DMN), the salience (SN), and the central executive (CEN) network. Moreover, the so called dorsal nexus, has met growing interest as it is a hub region connecting these three networks. Assessment of resting-state functional connectivity (rsFC) of these networks enables the elucidation of drug-induced brain alterations. Gamma-hydroxybutyrate (GHB) is a GHB/GABA-B receptor agonist that induces a paradoxical state of mixed stimulation and sedation at moderate doses, which makes it a valuable tool to investigate neural signatures of subjective drug effects. Employing a placebo-controlled, double-blind, randomized, cross-over design, we assessed the effects of GHB (35 mg/kg p. o.) in 19 healthy male subjects on DMN-, SN-, CEN-, and dorsal nexus-rsFC measured by functional magnet resonance imaging and applying independent component as well as seed-based analyses, while subjective drug effects were investigated using visual analog scales (VAS). Subjectively, GHB increased VAS ratings of a general drug effect, stimulation, and sedation. Intrinsic DMN-, and CEN-rsFC remained largely unchanged under GHB, but the drug increased SN-DMN-rsFC and SN-dorsal nexus-rsFC, while dorsal nexus-rsFC was reciprocally increased to both the SN (right anterior insula) and to the CEN (right middle frontal gyrus). Increased sedation significantly predicted the observed SN-dorsal nexus-rsFC. In conclusion, GHB generates a unique stimulant/sedative subjective state that is paralleled by a complex pattern of increased functional connectivity encompassing all three core neurocognitive networks of the brain, while increased SN-dorsal nexus-rsFC was demonstrated to be a potential signature of the sedative component of the drug effect.

Roadmap for optimizing the clinical utility of emotional stress paradigms in human neuroimaging research

The emotional stress response is relevant to a number of psychiatric disorders, including posttraumatic stress disorder (PTSD) in particular. Research using neuroimaging methods such as functional magnetic resonance imaging (fMRI) to probe stress-related neural processing have provided some insights into psychiatric disorders. Treatment providers and individual patients would benefit from clinically useful fMRI paradigms that provide information about patients' current brain state and responses to stress in order to inform the treatment selection process. However, neuroimaging has not yet made a meaningful impact on real-world clinical practice. This lack of clinical utility may be related to a number of basic psychometric properties that are often overlooked during fMRI task development. The goals of the current review are to discuss important methodological considerations for current human fMRI stress-related paradigms and to provide a roadmap for developing methodologically sound and clinically useful paradigms. This would include establishing various aspects of reliability, including internal consistency, test-retest and multi-site, as well as validity, including face, content, construct, and criterion. In addition, the establishment of standardized normative data from a large sample of participants would support our understanding of how any one individual compares to the general population. Addressing these methodological gaps will likely have a powerful effect on improving the replicability of findings and optimize our chances for improving real-world clinical outcomes.

Predictors for drug effects with brain disease: Shed new light from EEG parameters to brain connectomics

Though researchers spent a lot of effort to develop treatments for neuropsychiatric disorders, the poor translation of drug efficacy data from animals to human hampered the success of these therapeutic approaches in human. Pharmaceutical industry is challenged by low clinical success rates for new drug registration. To maximize the success in drug development, biomarkers are required to act as surrogate end points and predictors of drug effects. The pathology of brain disease could be in part due to synaptic dysfunction. Electroencephalogram (EEG), generating from the result of the postsynaptic potential discharge between cells, could be a potential measure to bridge the gaps between animal and human data. Here we discuss recent progress on using relevant EEG characteristics and brain connectomics as biomarkers to monitor drug effects and measure cognitive changes on animal models and human in real-time. It is expected that the novel approach, i.e. EEG connectomics, will offer a deeper understanding on the drug efficacy at a microcirculatory level, which will be useful to support the development of new treatments for neuropsychiatric disorders.

Effects of dexamphetamine-induced dopamine release on resting-state network connectivity in recreational amphetamine users and healthy controls

Dexamphetamine (dAMPH) is not only used for the treatment of attention deficit hyperactivity disorder (ADHD), but also as a recreational drug. Acutely, dAMPH induces release of predominantly dopamine (DA) in the striatum, and in the cortex both DA and noradrenaline. Recent animal studies have shown that chronic dAMPH administration can induce changes in the DA system following long-term exposure, as evidenced by reductions in DA transporters, D2/3 receptors and endogenous DA levels. However, only a limited number of studies have investigated the effects of dAMPH in the human brain. We used a combination of resting-state functional magnetic resonance imaging (rs-fMRI) and [(123)I]IBZM single-photon emission computed tomography (SPECT) (to assess baseline D2/3 receptor binding and DA release) in 15 recreational AMPH users and 20 matched healthy controls to investigate the short-, and long-term effects of AMPH before and after an acute intravenous challenge with dAMPH. We found that acute dAMPH administration reduced functional connectivity in the cortico-striatal-thalamic network. dAMPH-induced DA release, but not DA D2/3 receptor binding, was positively associated with connectivity changes in this network. In addition, acute dAMPH reduced connectivity in default mode networks and salience-executive-networks networks in both groups. In contrast to our hypothesis, no significant group differences were found in any of the rs-fMRI networks investigated, possibly due to lack of sensitivity or compensatory mechanisms. Our findings thus support the use of ICA-based resting-state functional connectivity as a tool to investigate acute, but not chronic, alterations induced by dAMPH on dopaminergic processing in the striatum.

Methylphenidate Modulates Functional Network Connectivity to Enhance Attention

Recent work has demonstrated that human whole-brain functional connectivity patterns measured with fMRI contain information about cognitive abilities, including sustained attention. To derive behavioral predictions from connectivity patterns, our group developed a connectome-based predictive modeling (CPM) approach (Finn et al., 2015; Rosenberg et al., 2016). Previously using CPM, we defined a high-attention network, comprising connections positively correlated with performance on a sustained attention task, and a low-attention network, comprising connections negatively correlated with performance. Validating the networks as generalizable biomarkers of attention, models based on network strength at rest predicted attention-deficit/hyperactivity disorder (ADHD) symptoms in an independent group of individuals (Rosenberg et al., 2016). To investigate whether these networks play a causal role in attention, here we examined their strength in healthy adults given methylphenidate (Ritalin), a common ADHD treatment, compared with unmedicated controls. As predicted, individuals given methylphenidate showed patterns of connectivity associated with better sustained attention: higher high-attention and lower low-attention network strength than controls. There was significant overlap between the high-attention network and a network with greater strength in the methylphenidate group, and between the low-attention network and a network with greater strength in the control group. Network strength also predicted behavior on a stop-signal task, such that participants with higher go response rates showed higher high-attention and lower low-attention network strength. These results suggest that methylphenidate acts by modulating functional brain networks related to sustained attention, and that changing whole-brain connectivity patterns may help improve attention.

SIGNIFICANCE STATEMENT

Recent work identified a promising neuromarker of sustained attention based on whole-brain functional connectivity networks. To investigate the causal role of these networks in attention, we examined their response to a dose of methylphenidate, a common and effective treatment for attention-deficit/hyperactivity disorder, in healthy adults. As predicted, individuals on methylphenidate showed connectivity signatures of better sustained attention: higher high-attention and lower low-attention network strength than controls. These results suggest that methylphenidate acts by modulating strength in functional brain networks related to attention, and that changing whole-brain connectivity patterns may improve attention.

Increased default-mode variability is related to reduced task-performance and is evident in adults with ADHD

Insufficient suppression and connectivity of the default mode network (DMN) is a potential mediator of cognitive dysfunctions across various disorders, including attention deficit/hyperactivity disorder (ADHD). However, it remains unclear if alterations in sustained DMN suppression, variability and connectivity during prolonged cognitive engagement are implicated in adult ADHD pathophysiology, and to which degree methylphenidate (MPH) remediates any DMN abnormalities. This randomized, double-blinded, placebo-controlled, cross-over clinical trial of MPH (clinicaltrials.gov/ct2/show/NCT01831622) explored large-scale brain network dynamics in 20 adults with ADHD on and off MPH, compared to 27 healthy controls, while performing a reward based decision-making task. DMN task-related activation, variability, and connectivity were estimated and compared between groups and conditions using independent component analysis, dual regression, and Bayesian linear mixed models. The results show that the DMN exhibited more variable activation patterns in unmedicated patients compared to healthy controls. Group differences in functional connectivity both between and within functional networks were evident. Further, functional connectivity between and within attention and DMN networks was sensitive both to task performance and case-control status. MPH altered within-network connectivity of the DMN and visual networks, but not between-network connectivity or temporal variability. This study thus provides novel fMRI evidence of reduced sustained DMN suppression in adults with ADHD during value-based decision-making, a pattern that was not alleviated by MPH. We infer from multiple analytical approaches further support to the default mode interference hypothesis, in that higher DMN activation variability is evident in adult ADHD and associated with lower task performance.

Biomarkers, designs, and interpretations of resting-state fMRI in translational pharmacological research: A review of state-of-the-Art, challenges, and opportunities for studying brain chemistry

A decade of research and development in resting-state functional MRI (RSfMRI) has opened new translational and clinical research frontiers. This review aims to bridge between technical and clinical researchers who seek reliable neuroimaging biomarkers for studying drug interactions with the brain. About 85 pharma-RSfMRI studies using BOLD signal (75% of all) or arterial spin labeling (ASL) were surveyed to investigate the acute effects of psychoactive drugs. Experimental designs and objectives include drug fingerprinting dose-response evaluation, biomarker validation and calibration, and translational studies. Common biomarkers in these studies include functional connectivity, graph metrics, cerebral blood flow and the amplitude and spectrum of BOLD fluctuations. Overall, RSfMRI-derived biomarkers seem to be sensitive to spatiotemporal dynamics of drug interactions with the brain. However, drugs cause both central and peripheral effects, thus exacerbate difficulties related to biological confounds, structured noise from motion and physiological confounds, as well as modeling and inference testing. Currently, these issues are not well explored, and heterogeneities in experimental design, data acquisition and preprocessing make comparative or meta-analysis of existing reports impossible. A unifying collaborative framework for data-sharing and data-mining is thus necessary for investigating the commonalities and differences in biomarker sensitivity and specificity, and establishing guidelines. Multimodal datasets including sham-placebo or active control sessions and repeated measurements of various psychometric, physiological, metabolic and neuroimaging phenotypes are essential for pharmacokinetic/pharmacodynamic modeling and interpretation of the findings. We provide a list of basic minimum and advanced options that can be considered in design and analyses of future pharma-RSfMRI studies. Hum Brain Mapp 38:2276-2325, 2017. © 2017 Wiley Periodicals, Inc.

Anything goes? Regulation of the neural processes underlying response inhibition in TBI patients

Despite evidence for beneficial use of methylphenidate in response inhibition, no studies so far have investigated the effects of this drug in the neurobiology of inhibitory control in traumatic brain injury (TBI), even though impulsive behaviours are frequently reported in this patient group. We investigated the neural basis of response inhibition in a group of TBI patients using functional magnetic resonance imaging and a stop-signal paradigm. In a randomised double-blinded crossover study, the patients received either a single 30mg dose of methylphenidate or placebo and performed the stop-signal task. Activation in the right inferior frontal gyrus (RIFG), an area associated with response inhibition, was significantly lower in patients compared to healthy controls. Poor response inhibition in this group was associated with greater connectivity between the RIFG and a set of regions considered to be part of the default mode network (DMN), a finding that suggests the interplay between DMN and frontal executive networks maybe compromised. A single dose of methylphenidate rendered activity and connectivity profiles of the patients RIFG near normal. The results of this study indicate that the neural circuitry involved in response inhibition in TBI patients may be partially restored with methylphenidate. Given the known mechanisms of action of methylphenidate, the effect we observed may be due to increased dopamine and noradrenaline levels.

Classics in Chemical Neuroscience: Methylphenidate

As the first drug to see widespread use for the treatment of attention deficit hyperactivity disorder (ADHD), methylphenidate was the forerunner and catalyst to the modern era of rapidly increasing diagnosis, treatment, and medication development for this condition. During its often controversial history, it has variously elucidated the importance of dopamine signaling in memory and attention, provoked concerns about pharmaceutical cognitive enhancement, driven innovation in controlled-release technologies and enantiospecific therapeutics, and stimulated debate about the impact of pharmaceutical sales techniques on the practice of medicine. In this Review, we will illustrate the history and importance of methylphenidate to ADHD treatment and neuroscience in general, as well as provide key information about its synthesis, structure-activity relationship, pharmacological activity, metabolism, manufacturing, FDA-approved indications, and adverse effects.

Neural Correlates of Symptom Improvement Following Stimulant Treatment in Adults with Attention-Deficit/Hyperactivity Disorder

OBJECTIVE

The purposes of this study were to examine the impact of 3 weeks of amphetamine administration on intrinsic connectome-wide connectivity patterns in adults with attention-deficit/hyperactivity disorder (ADHD) and explore the association between stimulant-induced symptom improvement and functional connectivity alteration.

METHODS

Participants included 19 adults (age 20-55 years) diagnosed with ADHD using the Diagnostic and Statistical Manual of Mental Disorders, 4th ed., Text Revision (DSM-IV-TR) criteria per the Adult Clinician Diagnostic Scale taking part in amphetamine trials. For each patient, two 6-minute resting-state functional magnetic resonance imaging (R-fMRI) scans were acquired at baseline and after treatment. A fully data-driven multivariate analytic approach (i.e., multivariate distance matrix regression [MDMR]) was applied to R-fMRI data to characterize the distributed pharmacological effects in the entire functional connectome. Clinical efficacy was assessed using ADHD rating scale with adult prompts and the Adult Self-Report Scale v1.1 Symptom Checklist. We linked stimulant-induced functional connectivity changes to symptom amelioration using Spearman's correlation.

RESULTS

Three weeks of administration of a stimulant significantly reduced ADHD symptoms. MDMR-based analyses on R-fMRI data highlighted the left dorsolateral prefrontal cortex (DLPFC, a key cognitive control region) and the medial prefrontal cortex (MPFC, the anterior core of default network) whose distributed patterns of functional connectivity across the entire brain were altered by psychostimulants. Follow-up intrinsic functional connectivity revealed that stimulants specifically decreased the positive functional connectivity between DLPFC-insula, DLPFC-anterior cingulate cortex, and MPFC-insula. Importantly, these functional connectivity changes are associated with symptom improvement.

CONCLUSION

These results suggested that ADHD is associated with increased functional integration or decreased functional segregation between core regions of cognitive control, default, and salience networks. The apparent normalization of intrinsic functional interaction in these circuits (i.e., increased functional segregation) may underlie the clinical benefits produced by 3 weeks of amphetamine treatment.

Characterising resting-state functional connectivity in a large sample of adults with ADHD

Attention-deficit/hyperactivity disorder (ADHD) is a common childhood psychiatric disorder that often persists into adulthood. While several studies have identified altered functional connectivity in brain networks during rest in children with ADHD, few studies have been performed on adults with ADHD. Existing studies have generally investigated small samples. We therefore investigated aberrant functional connectivity in a large sample of adult patients with childhood-onset ADHD, using a data-driven, whole-brain approach. Adults with a clinical ADHD diagnosis (N=99) and healthy, adult comparison subjects (N=113) underwent a 9-minute resting-state fMRI session in a 1.5T MRI scanner. After elaborate preprocessing including a thorough head-motion correction procedure, group independent component analysis (ICA) was applied from which we identified six networks of interest: cerebellum, executive control, left and right frontoparietal and two default-mode networks. Participant-level network maps were obtained using dual-regression and tested for differences between patients with ADHD and controls using permutation testing. Patients showed significantly stronger connectivity in the anterior cingulate gyrus of the executive control network. Trends were also observed for stronger connectivity in the cerebellum network in ADHD patients compared to controls. However, there was considerable overlap in connectivity values between patients and controls, leading to relatively low effect sizes despite the large sample size. These effect sizes were slightly larger when testing for correlations between hyperactivity/impulsivity symptoms and connectivity strength in the executive control and cerebellum networks. This study provides important insights for studies on the neurobiology of adult ADHD; it shows that resting-state functional connectivity differences between adult patients and controls exist, but have smaller effect sizes than existing literature suggested.

Growth Charting of Brain Connectivity Networks and the Identification of Attention Impairment in Youth

IMPORTANCE

Intrinsic connectivity networks (ICNs), important units of brain functional organization, demonstrate substantial maturation during youth. In addition, interrelationships between ICNs have been reliably implicated in attention performance. It is unknown whether alterations in ICN maturational profiles can reliably detect impaired attention functioning in youth.

OBJECTIVE

To use a network growth charting approach to investigate the association between alterations in ICN maturation and attention performance.

DESIGN, SETTING, AND PARTICIPANTS

Data were obtained from the publicly available Philadelphia Neurodevelopmental Cohort, a prospective, population-based sample of 9498 youths who underwent genomic testing, neurocognitive assessment, and neuroimaging. Data collection was conducted at an academic and children's hospital health care network between November 1, 2009, and November 30, 2011, and data analysis was conducted between February 1, 2015, and January 15, 2016.

MAIN OUTCOMES AND MEASURES

Statistical associations between deviations from normative network growth were assessed as well as 2 main outcome measures: accuracy during the Penn Continuous Performance Test and diagnosis with attention-deficit/hyperactivity disorder.

RESULTS

Of the 9498 individuals identified, 1000 youths aged 8 to 22 years underwent brain imaging. A sample of 519 youths who met quality control criteria entered analysis, of whom 25 (4.8%) met criteria for attention-deficit/hyperactivity disorder. The mean (SD) age of the youth was 15.7 (3.1) years, and 223 (43.0%) were male. Participants' patterns of deviations from normative maturational trajectories were indicative of sustained attention functioning (R2 = 24%; F6,512 = 26.89; P < 2.2 × 10-16). Moreover, these patterns were found to be a reliable biomarker of severe attention impairment (peak receiver operating characteristic curve measured by area under the curve, 79.3%). In particular, a down-shifted pattern of ICN maturation (shallow maturation), rather than a right-shifted pattern (lagged maturation), was implicated in reduced attention performance (Akaike information criterion relative likelihood, 3.22 × 1026). Finally, parallel associations between ICN dysmaturation and diagnosis of attention-deficit/hyperactivity disorder were identified.

CONCLUSIONS AND RELEVANCE

Growth charting methods are widely used to assess the development of physical or other biometric characteristics, such as weight and head circumference. To date, this is the first demonstration that this method can be extended to development of functional brain networks to identify clinically relevant conditions, such as dysfunction of sustained attention.

Abnormalities in large scale functional networks in unmedicated patients with schizophrenia and effects of risperidone

Objective

To describe abnormalities in large scale functional networks in unmedicated patients with schizophrenia and to examine effects of risperidone on networks.

Material and methods

34 unmedicated patients with schizophrenia and 34 matched healthy controls were enrolled in this longitudinal study. We collected resting state functional MRI data with a 3T scanner at baseline and six weeks after they were started on risperidone. In addition, a group of 19 healthy controls were scanned twice six weeks apart. Four large scale networks, the dorsal attention network, executive control network, salience network, and default mode network were identified with seed based functional connectivity analyses. Group differences in connectivity, as well as changes in connectivity over time, were assessed on the group's participant level functional connectivity maps.

Results

In unmedicated patients with schizophrenia we found resting state connectivity to be increased in the dorsal attention network, executive control network, and salience network relative to control participants, but not the default mode network. Dysconnectivity was attenuated after six weeks of treatment only in the dorsal attention network. Baseline connectivity in this network was also related to clinical response at six weeks of treatment with risperidone.

Conclusions

Our results demonstrate abnormalities in large scale functional networks in patients with schizophrenia that are modulated by risperidone only to a certain extent, underscoring the dire need for development of novel antipsychotic medications that have the ability to alleviate symptoms through attenuation of dysconnectivity.

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We found widespread functional dysconnectivity in unmedicated patients with schizophrenia.

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Large scale functional networks appear differentially affected in the disorder.

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Attenuation of dysconnectivity with risperidone is seen only to a limited extent.

Estimating multivariate similarity between neuroimaging datasets with sparse canonical correlation analysis: an application to perfusion imaging

An increasing number of neuroimaging studies are based on either combining more than one data modality (inter-modal) or combining more than one measurement from the same modality (intra-modal). To date, most intra-modal studies using multivariate statistics have focused on differences between datasets, for instance relying on classifiers to differentiate between effects in the data. However, to fully characterize these effects, multivariate methods able to measure similarities between datasets are needed. One classical technique for estimating the relationship between two datasets is canonical correlation analysis (CCA). However, in the context of high-dimensional data the application of CCA is extremely challenging. A recent extension of CCA, sparse CCA (SCCA), overcomes this limitation, by regularizing the model parameters while yielding a sparse solution. In this work, we modify SCCA with the aim of facilitating its application to high-dimensional neuroimaging data and finding meaningful multivariate image-to-image correspondences in intra-modal studies. In particular, we show how the optimal subset of variables can be estimated independently and we look at the information encoded in more than one set of SCCA transformations. We illustrate our framework using Arterial Spin Labeling data to investigate multivariate similarities between the effects of two antipsychotic drugs on cerebral blood flow.

Modality-spanning deficits in attention-deficit/hyperactivity disorder in functional networks, gray matter, and white matter

Previous neuroimaging investigations in attention-deficit/hyperactivity disorder (ADHD) have separately identified distributed structural and functional deficits, but interconnections between these deficits have not been explored. To unite these modalities in a common model, we used joint independent component analysis, a multivariate, multimodal method that identifies cohesive components that span modalities. Based on recent network models of ADHD, we hypothesized that altered relationships between large-scale networks, in particular, default mode network (DMN) and task-positive networks (TPNs), would co-occur with structural abnormalities in cognitive regulation regions. For 756 human participants in the ADHD-200 sample, we produced gray and white matter volume maps with voxel-based morphometry, as well as whole-brain functional connectomes. Joint independent component analysis was performed, and the resulting transmodal components were tested for differential expression in ADHD versus healthy controls. Four components showed greater expression in ADHD. Consistent with our a priori hypothesis, we observed reduced DMN-TPN segregation co-occurring with structural abnormalities in dorsolateral prefrontal cortex and anterior cingulate cortex, two important cognitive control regions. We also observed altered intranetwork connectivity in DMN, dorsal attention network, and visual network, with co-occurring distributed structural deficits. There was strong evidence of spatial correspondence across modalities: For all four components, the impact of the respective component on gray matter at a region strongly predicted the impact on functional connectivity at that region. Overall, our results demonstrate that ADHD involves multiple, cohesive modality spanning deficits, each one of which exhibits strong spatial overlap in the pattern of structural and functional alterations.

Connectomics in psychiatric research: advances and applications

Psychiatric disorders disturb higher cognitive functions and severely compromise human health. However, the pathophysiological mechanisms underlying psychiatric disorders are very complex, and understanding these mechanisms remains a great challenge. Currently, many psychiatric disorders are hypothesized to reflect "faulty wiring" or aberrant connectivity in the brains. Imaging connectomics is arising as a promising methodological framework for describing the structural and functional connectivity patterns of the human brain. Recently, alterations of brain networks in the connectome have been reported in various psychiatric disorders, and these alterations may provide biomarkers for disease diagnosis and prognosis for the evaluation of treatment efficacy. Here, we summarize the current achievements in both the structural and functional connectomes in several major psychiatric disorders (eg, schizophrenia, attention-deficit/hyperactivity disorder, and autism) based on multi-modal neuroimaging data. We highlight the current progress in the identification of these alterations and the hypotheses concerning the aberrant brain networks in individuals with psychiatric disorders and discuss the research questions that might contribute to a further mechanistic understanding of these disorders from a connectomic perspective.

Lag in maturation of the brain's intrinsic functional architecture in attention-deficit/hyperactivity disorder

Attention-deficit/hyperactivity disorder (ADHD) is among the most common psychiatric disorders of childhood, and there is great interest in understanding its neurobiological basis. A prominent neurodevelopmental hypothesis proposes that ADHD involves a lag in brain maturation. Previous work has found support for this hypothesis, but examinations have been limited to structural features of the brain (e.g., gray matter volume or cortical thickness). More recently, a growing body of work demonstrates that the brain is functionally organized into a number of large-scale networks, and the connections within and between these networks exhibit characteristic patterns of maturation. In this study, we investigated whether individuals with ADHD (age 7.2-21.8 y) exhibit a lag in maturation of the brain's developing functional architecture. Using connectomic methods applied to a large, multisite dataset of resting state scans, we quantified the effect of maturation and the effect of ADHD at more than 400,000 connections throughout the cortex. We found significant and specific maturational lag in connections within default mode network (DMN) and in DMN interconnections with two task positive networks (TPNs): frontoparietal network and ventral attention network. In particular, lag was observed within the midline core of the DMN, as well as in DMN connections with right lateralized prefrontal regions (in frontoparietal network) and anterior insula (in ventral attention network). Current models of the pathophysiology of attention dysfunction in ADHD emphasize altered DMN-TPN interactions. Our finding of maturational lag specifically in connections within and between these networks suggests a developmental etiology for the deficits proposed in these models.

Disease prediction based on functional connectomes using a scalable and spatially-informed support vector machine

Substantial evidence indicates that major psychiatric disorders are associated with distributed neural dysconnectivity, leading to a strong interest in using neuroimaging methods to accurately predict disorder status. In this work, we are specifically interested in a multivariate approach that uses features derived from whole-brain resting state functional connectomes. However, functional connectomes reside in a high dimensional space, which complicates model interpretation and introduces numerous statistical and computational challenges. Traditional feature selection techniques are used to reduce data dimensionality, but are blind to the spatial structure of the connectomes. We propose a regularization framework where the 6-D structure of the functional connectome (defined by pairs of points in 3-D space) is explicitly taken into account via the fused Lasso or the GraphNet regularizer. Our method only restricts the loss function to be convex and margin-based, allowing non-differentiable loss functions such as the hinge-loss to be used. Using the fused Lasso or GraphNet regularizer with the hinge-loss leads to a structured sparse support vector machine (SVM) with embedded feature selection. We introduce a novel efficient optimization algorithm based on the augmented Lagrangian and the classical alternating direction method, which can solve both fused Lasso and GraphNet regularized SVM with very little modification. We also demonstrate that the inner subproblems of the algorithm can be solved efficiently in analytic form by coupling the variable splitting strategy with a data augmentation scheme. Experiments on simulated data and resting state scans from a large schizophrenia dataset show that our proposed approach can identify predictive regions that are spatially contiguous in the 6-D "connectome space," offering an additional layer of interpretability that could provide new insights about various disease processes.

Volitional regulation of emotions produces distributed alterations in connectivity between visual, attention control, and default networks

The ability to volitionally regulate emotions is critical to health and well-being. While patterns of neural activation during emotion regulation have been well characterized, patterns of connectivity between regions remain less explored. It is increasingly recognized that the human brain is organized into large-scale intrinsic connectivity networks (ICNs) whose interrelationships are altered in characteristic ways during psychological tasks. In this fMRI study of 54 healthy individuals, we investigated alterations in connectivity within and between ICNs produced by the emotion regulation strategy of reappraisal. In order to gain a comprehensive picture of connectivity changes, we utilized connectomic psychophysiological interactions (PPI), a whole-brain generalization of standard single-seed PPI methods. In particular, we quantified PPI connectivity pair-wise across 837 ROIs placed throughout the cortex. We found that compared to maintaining one's emotional responses, engaging in reappraisal produced robust and distributed alterations in functional connections involving visual, dorsal attention, frontoparietal, and default networks. Visual network in particular increased connectivity with multiple ICNs including dorsal attention and default networks. We interpret these findings in terms of the role of these networks in mediating critical constituent processes in emotion regulation, including visual processing, stimulus salience, attention control, and interpretation and contextualization of stimuli. Our results add a new network perspective to our understanding of the neural underpinnings of emotion regulation, and highlight that connectomic methods can play a valuable role in comprehensively investigating modulation of connectivity across task conditions.

Disrupted network architecture of the resting brain in attention-deficit/hyperactivity disorder

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent psychiatric disorders of childhood. Neuroimaging investigations of ADHD have traditionally sought to detect localized abnormalities in discrete brain regions. Recent years, however, have seen the emergence of complementary lines of investigation into distributed connectivity disturbances in ADHD. Current models emphasize abnormal relationships between default network-involved in internally directed mentation and lapses of attention-and task positive networks, especially ventral attention network. However, studies that comprehensively investigate interrelationships between large-scale networks in ADHD remain relatively rare.

METHODS

Resting state functional magnetic resonance imaging scans were obtained from 757 participants at seven sites in the ADHD-200 multisite sample. Functional connectomes were generated for each subject, and interrelationships between seven large-scale brain networks were examined with network contingency analysis.

RESULTS

ADHD brains exhibited altered resting state connectivity between default network and ventral attention network [P < 0.0001, false discovery rate (FDR)-corrected], including prominent increased connectivity (more specifically, diminished anticorrelation) between posterior cingulate cortex in default network and right anterior insula and supplementary motor area in ventral attention network. There was distributed hypoconnectivity within default network (P = 0.009, FDR-corrected), and this network also exhibited significant alterations in its interconnections with several other large-scale networks. Additionally, there was pronounced right lateralization of aberrant default network connections.

CONCLUSIONS

Consistent with existing theoretical models, these results provide evidence that default network-ventral attention network interconnections are a key locus of dysfunction in ADHD. Moreover, these findings contribute to growing evidence that distributed dysconnectivity within and between large-scale networks is present in ADHD.