Faculty Bibliography

Aberrant topological organization of whole-brain networks has been inconsistently reported in studies of patients with major depressive disorder (MDD), reflecting limited sample sizes. To address this issue, we utilized a big data sample of MDD patients from the REST-meta-MDD Project, including 821 MDD patients and 765 normal controls (NCs) from 16 sites. Using the Dosenbach 160 node atlas, we examined whole-brain functional networks and extracted topological features (e.g., global and local efficiency, nodal efficiency, and degree) using graph theory-based methods. Linear mixed-effect models were used for group comparisons to control for site variability; robustness of results was confirmed (e.g., multiple topological parameters, different node definitions, and several head motion control strategies were applied). We found decreased global and local efficiency in patients with MDD compared to NCs. At the nodal level, patients with MDD were characterized by decreased nodal degrees in the somatomotor network (SMN), dorsal attention network (DAN) and visual network (VN) and decreased nodal efficiency in the default mode network (DMN), SMN, DAN, and VN. These topological differences were mostly driven by recurrent MDD patients, rather than first-episode drug naive (FEDN) patients with MDD. In this highly powered multisite study, we observed disrupted topological architecture of functional brain networks in MDD, suggesting both locally and globally decreased efficiency in brain networks.

Objectives: Clinical trials in youth with autism spectrum disorder (ASD) have typically neglected the voices of the youth themselves. Besides raising ethical questions, this may also affect the quality of the data obtained. We examined differences in adverse event (AE) reporting between parents and parent-child dyads in an open trial of cannabidiol (CBD). We hypothesized that including youth with ASD in AE reporting would increase the number of total and related AEs independently of response.
Method(s): Twelve youth (ages 7-14 years) with ASD (verbally fluent, IQ >= 80) completed a 6-week, Phase 2 open trial of 98% CBD (Epidiolex [V], 100 mg/mL) at 3 or 6 mg/kg/day; target N = 30. An individualized target symptom domain was identified at baseline by clinician consensus from informant report, rating scales, and clinical observation. Responders were defined by Clinical Global Impression Scale-Improvement (CGI-I) <= 2 in their target symptom domain. AEs were assessed by phone with parents (weeks 1, 3, 5) and via the UKU (Udvalg for Kliniske Under-sogelser) Side Effects Rating Scale administered by clinicians to dyads (weeks 2, 4, 6). Clinician consensus determined the relatedness of AEs to treatment. Disease-related events (DREs) were considered adverse if the severity or frequency increased. In this interim analysis, we identified response to treatment and AEs, contrasted AE rates (parents vs dyads), and examined the relationship between treatment response and AE profile.
Result(s): All 12 initial participants completed the trial; 4 responded (33%). Clinical Global Impression Scale-Severity (CGI-S) improved significantly from pre- (M = 4.83; SD = 0.39) to posttreatment (M = 3.92; SD = 0.90) (t11 = 3.53; p < 0.004). The most frequent AEs were tiredness (n = 5) and increased emotionality (n = 3). Of 47 total AEs, all were mild and 39 were first reported by dyads. Of 14 related AEs, 9 were first reported by dyads. One DRE occurred: increased severity of restricted, repetitive behaviors. The number of AEs reported by dyads (M = 3.25; SD = 3.14) compared to parents alone (M = 0.67; SD = 0.89) was significantly higher (t11 = 2.18; p = 0.017). Responders and nonresponders did not differ significantly in the number of total or related AEs.
Conclusion(s): This interim analysis suggests that including the input of children with ASD in AE reporting captures a fuller profile of total and related AEs without compromising the study integrity or results. ASD, OLT, R
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OBJECTIVE:Some studies have suggested alterations of structural brain asymmetry in attention-deficit/hyperactivity disorder (ADHD), but findings have been contradictory and based on small samples. Here, we performed the largest ever analysis of brain left-right asymmetry in ADHD, using 39 datasets of the ENIGMA consortium. METHODS:We analyzed asymmetry of subcortical and cerebral cortical structures in up to 1,933 people with ADHD and 1,829 unaffected controls. Asymmetry Indexes (AIs) were calculated per participant for each bilaterally paired measure, and linear mixed effects modeling was applied separately in children, adolescents, adults, and the total sample, to test exhaustively for potential associations of ADHD with structural brain asymmetries. RESULTS:There was no evidence for altered caudate nucleus asymmetry in ADHD, in contrast to prior literature. In children, there was less rightward asymmetry of the total hemispheric surface area compared to controls (t = 2.1, p = .04). Lower rightward asymmetry of medial orbitofrontal cortex surface area in ADHD (t = 2.7, p = .01) was similar to a recent finding for autism spectrum disorder. There were also some differences in cortical thickness asymmetry across age groups. In adults with ADHD, globus pallidus asymmetry was altered compared to those without ADHD. However, all effects were small (Cohen's d from -0.18 to 0.18) and would not survive study-wide correction for multiple testing. CONCLUSION/CONCLUSIONS:Prior studies of altered structural brain asymmetry in ADHD were likely underpowered to detect the small effects reported here. Altered structural asymmetry is unlikely to provide a useful biomarker for ADHD, but may provide neurobiological insights into the trait.

Pediatric brain imaging holds significant promise for understanding neurodevelopment. However, the requirement to remain still inside a noisy, enclosed scanner remains a challenge. Verbal or visual descriptions of the process, and/or practice in MRI simulators are the norm in preparing children. Yet, the factors predictive of successfully obtaining neuroimaging data remain unclear. We examined data from 250 children (6-12 years, 197 males) with autism and/or attention-deficit/hyperactivity disorder. Children completed systematic MRI simulator training aimed to habituate to the scanner environment and minimize head motion. An MRI session comprised multiple structural, resting-state, task and diffusion scans. Of the 201 children passing simulator training and attempting scanning, nearly all (94%) successfully completed the first structural scan in the sequence, and 88% also completed the following functional scan. The number of successful scans decreased as the sequence progressed. Multivariate analyses revealed that age was the strongest predictor of successful scans in the session, with younger children having lower success rates. After age, sensorimotor atypicalities contributed most to prediction. Results provide insights on factors to consider in designing pediatric brain imaging protocols.

Serotonin and dopamine are associated with multiple psychiatric disorders. How they interact during development to affect subsequent behavior remains unknown. Knockout of the serotonin transporter or postnatal blockade with selective serotonin reuptake inhibitors (SSRIs) leads to novelty-induced exploration deficits in adulthood, potentially involving the dopamine system. Here, we show in the mouse that raphe nucleus serotonin neurons activate ventral tegmental area dopamine neurons via glutamate co-transmission and that this co-transmission is reduced in animals exposed postnatally to SSRIs. Blocking serotonin neuron glutamate co-transmission mimics this SSRI-induced hypolocomotion, while optogenetic activation of dopamine neurons reverses this hypolocomotor phenotype. Our data demonstrate that serotonin neurons modulate dopamine neuron activity via glutamate co-transmission and that this pathway is developmentally malleable, with high serotonin levels during early life reducing co-transmission, revealing the basis for the reduced novelty-induced exploration in adulthood due to postnatal SSRI exposure.

Rhythms of the brain are generated by neural oscillations across multiple frequencies. These oscillations can be decomposed into distinct frequency intervals associated with specific physiological processes. In practice, the number and ranges of decodable frequency intervals are determined by sampling parameters, often ignored by researchers. To improve the situation, we report on an open toolbox with a graphical user interface for decoding rhythms of the brain system (DREAM). We provide worked examples of DREAM to investigate frequency-specific performance of both neural (spontaneous brain activity) and neurobehavioral (in-scanner head motion) oscillations. DREAM decoded the head motion oscillations and uncovered that younger children moved their heads more than older children across all five frequency intervals whereas boys moved more than girls in the age of 7 to 9 years. It is interesting that the higher frequency bands contain more head movements, and showed stronger age-motion associations but weaker sex-motion interactions. Using data from the Human Connectome Project, DREAM mapped the amplitude of these neural oscillations into multiple frequency bands and evaluated their test-retest reliability. The resting-state brain ranks its spontaneous oscillation's amplitudes spatially from high in ventral-temporal areas to low in ventral-occipital areas when the frequency band increased from low to high, while those in part of parietal and ventral frontal regions are reversed. The higher frequency bands exhibited more reliable amplitude measurements, implying more inter-individual variability of the amplitudes for the higher frequency bands. In summary, DREAM adds a reliable and valid tool to mapping human brain function from a multiple-frequency window into brain waves.

Previous neuroimaging studies have revealed abnormal functional connectivity of brain networks in patients with major depressive disorder (MDD), but findings have been inconsistent. A recent big-data study found abnormal intrinsic functional connectivity within the default mode network in patients with recurrent MDD but not in first-episode drug-naïve patients with MDD. This study also provided evidence for reduced default mode network functional connectivity in medicated MDD patients, raising the question of whether previously observed abnormalities may be attributable to antidepressant effects. The present study (ClinicalTrials.gov identifier: NCT03294525) aimed to disentangle the effects of antidepressant treatment from the pathophysiology of MDD and test the medication normalization hypothesis. Forty-one first-episode drug-naïve MDD patients were administrated antidepressant medication (escitalopram or duloxetine) for 8 weeks, with resting-state functional connectivity compared between posttreatment and baseline. To assess the replicability of the big-data finding, we also conducted a cross-sectional comparison of resting-state functional connectivity between the MDD patients and 92 matched healthy controls. Both Network-Based Statistic analyses and large-scale network analyses revealed intrinsic functional connectivity decreases in extensive brain networks after treatment, indicating considerable antidepressant effects. Neither Network-Based Statistic analyses nor large-scale network analyses detected significant functional connectivity differences between treatment-naïve patients and healthy controls. In short, antidepressant effects are widespread across most brain networks and need to be accounted for when considering functional connectivity abnormalities in MDD.

Early life is a sensitive period in which enhanced neural plasticity allows the developing brain to adapt to its environment. This plasticity can also be a risk factor in which maladaptive development can lead to long-lasting behavioral deficits. Here, we test how early-life exposure to the selective-serotonin-reuptake-inhibitor, fluoxetine, affects motivation and dopaminergic signaling in adulthood. We show for the first time that mice exposed to fluoxetine in the early postnatal period exhibit a reduction in effort-related motivation. These mice also show blunted responses to amphetamine and reduced dopaminergic activation in a sucrose reward task.Interestingly, we find that the reduction in motivation can be rescued in the adult by administering bupropion, a dopamine-norepinephrine reuptake inhibitor used as an antidepressant and a smoke cessation aid, but not by fluoxetine. Taken together, our studies highlight the effects of early postnatal exposure of fluoxetine on motivation and demonstrate the involvement of the dopaminergic system in this process.Significance StatementThe developmental period is characterized by enhanced plasticity. During this period, environmental factors have the potential to lead to enduring behavioral changes. Here we show that exposure to the SSRI fluoxetine during a restricted period in early-life leads to a reduction in adult motivation. We further show that this reduction is associated with decreased dopaminergic responsivity. Finally, we show that motivational deficits induced by early-life fluoxetine exposure can be rescued by adult administration of bupropion but not by fluoxetine.

PURPOSE OF REVIEW/OBJECTIVE:Neuroimaging research on attention-deficit/hyperactivity disorder (ADHD) continues growing in extent and complexity, although it has yet to become clinically meaningful. We review recent MRI research on ADHD, to identify robust findings, current trends and challenges. RECENT FINDINGS/RESULTS:We identified 40 publications between January 2019 and September 2020 reporting or reviewing MRI research on ADHD. Four meta-analyses have presented conflicting results regarding across-study convergence of functional and resting-state functional (fMRI and R-fMRI) studies on ADHD. On the other hand, the Enhancing NeuroImaging Genetics Through Meta-Analysis international consortium has identified statistically robust albeit small differences in structural brain cortical and subcortical indices in children with ADHD versus typically developing controls. Other international consortia are harnessing open-science efforts and multimodal data (imaging, genetics, phenotypic) to shed light on the complex interplay of genetics, environment, and development in the pathophysiology of ADHD. We note growing research in 'prediction' science, which applies machine-learning analysis to identify biomarkers of disease based on big data. SUMMARY/CONCLUSIONS:Neuroimaging in ADHD is still far from informing clinical practice. Current large-scale, multimodal, and open-science initiatives represent promising paths toward untangling the neurobiology of ADHD.