Predicting the course of ADHD symptoms through the integration of childhood genomic, neural, and cognitive features

Distinct developmental changes in regional gray matter volume and covariance in individuals with attention-deficit hyperactivity disorder: A longitudinal voxel-based morphometry study

BACKGROUND

Very few studies have investigated longitudinal clinical cohorts of attention-deficit/hyperactivity disorder (ADHD). Moreover, how baseline brain changes could affect the development of ADHD symptoms later in life remains elusive. Therefore, we aimed to fill this gap by exploring brain and clinical changes in youth with ADHD using a longitudinal design.

METHODS

This prospective study consisted of 74 children and adolescents with ADHD and 50 age-, sex-, intelligence-matched typically developing controls (TDC), evaluated at baseline (aged 7-19 years) and re-evaluated 5.3 years later (a mean follow-up latency). We applied voxel-based morphometry to characterize brain structures, followed by both mass-univariate and multivariate structural covariance statistics to identify brain regions with significant diagnosis-by-time interactions from late childhood/adolescence to early adulthood. We used the cross-lagged panel model to investigate the longitudinal association between structural brain metrics and core ADHD symptoms.

RESULTS

The mass-univariate statistic revealed significant diagnosis-by-time interactions in the right striatum and the sixth lobule of the cerebellum. This was expressed by increased striatal and decreased cerebellar volume in ADHD, while TDC showed inverse volume changes over time. The multivariate method showed significant diagnosis-by-time interactions in a structural covariance network consisting of the regions involved in the functional sensory-motor and default-mode networks. Higher baseline right striatal and cerebellar volumes were associated with elevated ADHD symptoms at follow-up.

CONCLUSIONS

Our findings suggest a temporal association between the divergent development of striatal and cerebellar regions and dynamical ADHD phenotypic expression through young adulthood. These results highlight a potential brain marker of future outcomes.

Machine learning in attention-deficit/hyperactivity disorder: new approaches toward understanding the neural mechanisms

Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent and heterogeneous neurodevelopmental disorder in children and has a high chance of persisting in adulthood. The development of individualized, efficient, and reliable treatment strategies is limited by the lack of understanding of the underlying neural mechanisms. Diverging and inconsistent findings from existing studies suggest that ADHD may be simultaneously associated with multivariate factors across cognitive, genetic, and biological domains. Machine learning algorithms are more capable of detecting complex interactions between multiple variables than conventional statistical methods. Here we present a narrative review of the existing machine learning studies that have contributed to understanding mechanisms underlying ADHD with a focus on behavioral and neurocognitive problems, neurobiological measures including genetic data, structural magnetic resonance imaging (MRI), task-based and resting-state functional MRI (fMRI), electroencephalogram, and functional near-infrared spectroscopy, and prevention and treatment strategies. Implications of machine learning models in ADHD research are discussed. Although increasing evidence suggests that machine learning has potential in studying ADHD, extra precautions are still required when designing machine learning strategies considering the limitations of interpretability and generalization.

Longitudinal trajectories of childhood and adolescent attention deficit hyperactivity disorder diagnoses in three cohorts

Background

Attention deficit/hyperactivity disorder (ADHD) is usually conceptualized as a childhood-onset neurodevelopmental disorder, in which symptoms either decrease steadily into adulthood or remain stable. A recent study challenged this view, reporting that for most with ADHD, diagnostic status fluctuates with age. We ask if such a 'fluctuating' ADHD symptom trajectory subgroup is present in other population-based and clinic-based cohorts, centered on childhood and adolescence.

Methods

Cohorts were the population-based Adolescent Brain Cognitive Development (ABCD: N = 9735), Neurobehavioral Clinical Research (NCR: N = 258), and the Nathan Kline Institute-Rockland (NKI-Rockland: N = 149). All participants had three or more assessments spanning different age windows. Participants were categorized into developmental diagnostic subgroups: fluctuant ADHD (defined by two or more switches between meeting and not meeting ADHD criteria), remitting ADHD, persisting ADHD, emerging ADHD and never affected. Data were collected between 2011 and 2022. Analyses were performed between May 2022 and April 2023.

Findings

A subgroup with fluctuant child and adolescent ADHD diagnoses was found in all cohorts (29.3% of participants with ADHD in ABCD, 26.6% in NCR and 17% in NKI-Rockland). While the proportion of those with fluctuant ADHD increased with the number of assessments, it never constituted the dominant subgroup.

Interpretation

We provide further evidence in three cohorts for the existence of a fluctuant ADHD diagnostic subgroup during childhood and adolescence, albeit in a minority of cases. Such fluctuant child and adolescent ADHD diagnoses may suggest a natural history more akin to relapsing-remitting mood disorders and/or a marked sensitivity to environmental shifts that occur across development.

Funding

Intramural programs of the NHGRI and NIMH.

The Oregon ADHD-1000: A new longitudinal data resource enriched for clinical cases and multiple levels of analysis

The fields of developmental psychopathology, developmental neuroscience, and behavioral genetics are increasingly moving toward a data sharing model to improve reproducibility, robustness, and generalizability of findings. This approach is particularly critical for understanding attention-deficit/hyperactivity disorder (ADHD), which has unique public health importance given its early onset, high prevalence, individual variability, and causal association with co-occurring and later developing problems. A further priority concerns multi-disciplinary/multi-method datasets that can span different units of analysis. Here, we describe a public dataset using a case-control design for ADHD that includes: multi-method, multi-measure, multi-informant, multi-trait data, and multi-clinician evaluation and phenotyping. It spans > 12 years of annual follow-up with a lag longitudinal design allowing age-based analyses spanning age 7-19 + years with a full age range from 7 to 21. Measures span genetic and epigenetic (DNA methylation) array data; EEG, functional and structural MRI neuroimaging; and psychophysiological, psychosocial, clinical and functional outcomes data. The resource also benefits from an autism spectrum disorder add-on cohort and a cross sectional case-control ADHD cohort from a different geographical region for replication and generalizability. Datasets allowing for integration from genes to nervous system to behavior represent the "next generation" of researchable cohorts for ADHD and developmental psychopathology.

Evidence from "big data" for the default-mode hypothesis of ADHD: a mega-analysis of multiple large samples

We sought to identify resting-state characteristics related to attention deficit/hyperactivity disorder, both as a categorical diagnosis and as a trait feature, using large-scale samples which were processed according to a standardized pipeline. In categorical analyses, we considered 1301 subjects with diagnosed ADHD, contrasted against 1301 unaffected controls (total N = 2602; 1710 males (65.72%); mean age = 10.86 years, sd = 2.05). Cases and controls were 1:1 nearest neighbor matched on in-scanner motion and key demographic variables and drawn from multiple large cohorts. Associations between ADHD-traits and resting-state connectivity were also assessed in a large multi-cohort sample (N = 10,113). ADHD diagnosis was associated with less anticorrelation between the default mode and salience/ventral attention (B = 0.009, t = 3.45, p-FDR = 0.004, d = 0.14, 95% CI = 0.004, 0.014), somatomotor (B = 0.008, t = 3.49, p-FDR = 0.004, d = 0.14, 95% CI = 0.004, 0.013), and dorsal attention networks (B = 0.01, t = 4.28, p-FDR < 0.001, d = 0.17, 95% CI = 0.006, 0.015). These results were robust to sensitivity analyses considering comorbid internalizing problems, externalizing problems and psychostimulant medication. Similar findings were observed when examining ADHD traits, with the largest effect size observed for connectivity between the default mode network and the dorsal attention network (B = 0.0006, t = 5.57, p-FDR < 0.001, partial-r = 0.06, 95% CI = 0.0004, 0.0008). We report significant ADHD-related differences in interactions between the default mode network and task-positive networks, in line with default mode interference models of ADHD. Effect sizes (Cohen's d and partial-r, estimated from the mega-analytic models) were small, indicating subtle group differences. The overlap between the affected brain networks in the clinical and general population samples supports the notion of brain phenotypes operating along an ADHD continuum.

Examining the impact of ADHD polygenic risk scores on ADHD and associated outcomes: A systematic review and meta-analysis

Early identification of attention-deficit/hyperactivity disorder (ADHD) is critical for mitigating the many negative functional outcomes associated with its diagnosis. Because of the strong genetic basis of ADHD, the use of polygenic risk scores (PRS) could potentially aid in the early identification of ADHD and associated outcomes. Therefore, a systematic search of the literature on the association between ADHD and PRS in pediatric populations was conducted. All articles were screened for a priori inclusion and exclusion criteria, and, after careful review, 33 studies were included in our systematic review and 16 studies with extractable data were included in our meta-analysis. The results of the review were categorized into three common themes: the associations between ADHD-PRS with 1) the diagnosis of ADHD and ADHD symptoms 2) comorbid psychopathology and 3) cognitive and educational outcomes. Higher ADHD-PRS were associated with increased odds of having a diagnosis (OR = 1.37; p<0.001) and more symptoms of ADHD (β = 0.06; p<0.001). While ADHD-PRS were associated with a persistent diagnostic trajectory over time in the systematic review, the meta-analysis did not confirm these findings (OR = 1.09; p = 0.62). Findings showed that ADHD-PRS were associated with increased odds for comorbid psychopathology such as anxiety/depression (OR = 1.16; p<0.001) and irritability/emotional dysregulation (OR = 1.14; p<0.001). Finally, while the systematic review showed that ADHD-PRS were associated with a variety of negative cognitive outcomes, the meta-analysis showed no significant association (β = 0.08; p = 0.07). Our review of the available literature suggests that ADHD-PRS, together with risk factors, may contribute to the early identification of children with suspected ADHD and associated disorders.

Neuroprotection in late life attention-deficit/hyperactivity disorder: A review of pharmacotherapy and phenotype across the lifespan

For decades, psychostimulants have been the gold standard pharmaceutical treatment for attention-deficit/hyperactivity disorder (ADHD). In the United States, an astounding 9% of all boys and 4% of girls will be prescribed stimulant drugs at some point during their childhood. Recent meta-analyses have revealed that individuals with ADHD have reduced brain volume loss later in life (>60 y.o.) compared to the normal aging brain, which suggests that either ADHD or its treatment may be neuroprotective. Crucially, these neuroprotective effects were significant in brain regions (e.g., hippocampus, amygdala) where severe volume loss is linked to cognitive impairment and Alzheimer's disease. Historically, the ADHD diagnosis and its pharmacotherapy came about nearly simultaneously, making it difficult to evaluate their effects in isolation. Certain evidence suggests that psychostimulants may normalize structural brain changes typically observed in the ADHD brain. If ADHD itself is neuroprotective, perhaps exercising the brain, then psychostimulants may not be recommended across the lifespan. Alternatively, if stimulant drugs are neuroprotective, then this class of medications may warrant further investigation for their therapeutic effects. Here, we take a bottom-up holistic approach to review the psychopharmacology of ADHD in the context of recent models of attention. We suggest that future studies are greatly needed to better appreciate the interactions amongst an ADHD diagnosis, stimulant treatment across the lifespan, and structure-function alterations in the aging brain.

Investigation of white matter functional networks underlying different behavioral profiles in attention-deficit/hyperactivity disorder

Background

Cortical functional network alterations have been widely accepted as the neural basis of attention-deficit/hyperactivity disorder (ADHD). Recently, white matter has also been recognized as a novel neuroimaging marker of psychopathology and has been used as a complement to cortical functional networks to investigate brain-behavior relationships. However, disorder-specific features of white matter functional networks (WMFNs) are less well understood than those of gray matter functional networks. In the current study, we constructed WMFNs using a new strategy to characterize behavior-related network features in ADHD.

Methods

We recruited 46 drug-naïve boys with ADHD and 46 typically developing (TD) boys, and used clustering analysis on resting-state functional magnetic resonance imaging data to generate WMFNs in each group. Intrinsic activity within each network was extracted, and the associations between network activity and behavior measures were assessed using correlation analysis.

Results

Nine WMFNs were identified for both ADHD and TD participants. However, boys with ADHD showed a splitting of the inferior corticospinal-cerebellar network and lacked a cognitive control network. In addition, boys with ADHD showed increased activity in the dorsal attention network and somatomotor network, which correlated positively with attention problems and hyperactivity symptom scores, respectively, while they presented decreased activity in the frontoparietal network and frontostriatal network in association with poorer performance in response inhibition, working memory, and verbal fluency.

Conclusions

We discovered a dual pattern of white matter network activity in drug-naïve ADHD boys, with hyperactive symptom-related networks and hypoactive cognitive networks. These findings characterize two distinct types of WMFN in ADHD psychopathology.

Brain Signatures During Reward Anticipation Predict Persistent Attention-Deficit/Hyperactivity Disorder Symptoms

OBJECTIVE

Children experiencing attention-deficit/hyperactivity disorder (ADHD) symptoms may retain symptoms into adulthood, but little is known about the underlying mechanism.

METHOD

To identify biomarkers of persistent ADHD symptom development, we carried out whole-brain analyses of neuroimaging data during the anticipation phase of the Monetary-Incentive-Delay (MID) task in 1,368 adolescents recruited by the IMAGEN Consortium at age 14 years, whose behavioral measurements were followed up longitudinally at age 16. In particular, we focused on comparing individuals with persistent high ADHD symptoms at both ages 14 and 16 years to unaffected control individuals, but also exploring which individuals demonstrating symptom remission (with high ADHD symptoms at age 14 but much reduced at age 16).

RESULTS

We identified reduced activations in the medial frontal cortex and the thalamus during reward anticipation as neuro-biomarkers for persistent ADHD symptoms across time. The genetic relevance of the above findings was further supported by the associations of the polygenic risk scores of ADHD with both the persistent and control status and the activations of both brain regions. Furthermore, in an exploratory analysis, the thalamic activation might also help to distinguish persons with persistent ADHD from those remitted in both an exploratory sample (odds ratio = 9.43, p < .001) and an independent generalization sample (odds ratio = 4.64, p = .003).

CONCLUSION

Using a well-established and widely applied functional magnetic resonance imaging task, we have identified neural biomarkers that could discriminate ADHD symptoms that persist throughout adolescence from controls and potentially those likely to remit during adolescent development as well.

Is genetic risk of ADHD mediated via dopaminergic mechanism? A study of functional connectivity in ADHD and pharmacologically challenged healthy volunteers with a genetic risk profile

Recent GWAS allow us to calculate polygenic risk scores for ADHD. At the imaging level, resting-state fMRI analyses have given us valuable insights into changes in connectivity patterns in ADHD patients. However, no study has yet attempted to combine these two different levels of investigation. For this endeavor, we used a dopaminergic challenge fMRI study (L-DOPA) in healthy participants who were genotyped for their ADHD, MDD, schizophrenia, and body height polygenic risk score (PRS) and compared results with a study comparing ADHD patients and healthy controls. Our objective was to evaluate how L-DOPA-induced changes of reward-system-related FC are dependent on the individual polygenic risk score. FMRI imaging was used to evaluate resting-state functional connectivity (FC) of targeted subcortical structures in 27 ADHD patients and matched controls. In a second study, we evaluated the effect of ADHD and non-ADHD PRS in a L-DOPA-based pharmaco-fMRI-challenge in 34 healthy volunteers. The functional connectivity between the putamen and parietal lobe was decreased in ADHD patients. In healthy volunteers, the FC between putamen and parietal lobe was lower in ADHD high genetic risk participants. This direction of connectivity was reversed during L-DOPA challenge. Further findings are described for other dopaminergic subcortical structures. The FC between the putamen and the attention network showed the most consistent change in patients as well as in high-risk participants. Our results suggest that FC of the dorsal attention network is altered in adult ADHD as well as in healthy controls with higher genetic risk.

Longitudinal changes of ADHD symptoms in association with white matter microstructure: A tract-specific fixel-based analysis

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HI symptom remission is associated with more follow-up lCST FD.

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Combined symptom remission is associated with more follow-up lCST FC.

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Altered white matter development may be moderated by preceding symptom trajectory.

Background

Variation in the longitudinal course of childhood attention deficit/hyperactivity disorder (ADHD) coincides with neurodevelopmental maturation of brain structure and function. Prior work has attempted to determine how alterations in white matter (WM) relate to changes in symptom severity, but much of that work has been done in smaller cross-sectional samples using voxel-based analyses. Using standard diffusion-weighted imaging (DWI) methods, we previously showed WM alterations were associated with ADHD symptom remission over time in a longitudinal sample of probands, siblings, and unaffected individuals. Here, we extend this work by further assessing the nature of these changes in WM microstructure by including an additional follow-up measurement (aged 18 – 34 years), and using the more physiologically informative fixel-based analysis (FBA).

Methods

Data were obtained from 139 participants over 3 clinical and 2 follow-up DWI waves, and analyzed using FBA in regions-of-interest based on prior findings. We replicated previously reported significant models and extended them by adding another time-point, testing whether changes in combined ADHD and hyperactivity-impulsivity (HI) continuous symptom scores are associated with fixel metrics at follow-up.

Results

Clinical improvement in HI symptoms over time was associated with more fiber density at follow-up in the left corticospinal tract (lCST) (t_max = 1.092, standardized effect[SE] = 0.044, p_FWE = 0.016). Improvement in combined ADHD symptoms over time was associated with more fiber cross-section at follow-up in the lCST (t_max = 3.775, SE = 0.051, p_FWE = 0.019).

Conclusions

Aberrant white matter development involves both lCST micro- and macrostructural alterations, and its path may be moderated by preceding symptom trajectory.