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Fulltext From neurons to brain networks, pharmacodynamics of stimulant medication for ADHD

Parlatini V, Bellato A, Murphy D, Cortese S

Neurosci Biobehav Rev, 2024 Sep;164:105841.
PMID: 39098738 DOI: 10.1016/j.neubiorev.2024.105841

Stimulants represent the first line pharmacological treatment for attention-deficit/hyperactivity disorder (ADHD) and are among the most prescribed psychopharmacological treatments. Their mechanism of action at synaptic level has been extensively studied. However, it is less clear how their mechanism of action determines clinically observed benefits. To help bridge this gap, we provide a comprehensive review of stimulant effects, with an emphasis on nuclear medicine and magnetic resonance imaging (MRI) findings. There is evidence that stimulant-induced modulation of dopamine and norepinephrine neurotransmission optimizes engagement of task-related brain networks, increases perceived saliency, and reduces interference from the default mode network. An acute administration of stimulants may reduce brain alterations observed in untreated individuals in fronto-striato-parieto-cerebellar networks during tasks or at rest. Potential effects of prolonged treatment remain controversial. Overall, neuroimaging has fostered understanding on stimulant mechanism of action. However, studies are often limited by small samples, short or no follow-up, and methodological heterogeneity. Future studies should address age-related and longer-term effects, potential differences among stimulants, and predictors of treatment response.
Association Between Single-Dose and Longer Term Clinical Response to Stimulants in Attention-Deficit/Hyperactivity Disorder: A Systematic Review of Randomized Controlled Trials

Parlatini V, Bellato A, Roy S, Murphy D, Cortese S

J Child Adolesc Psychopharmacol, 2024 Oct;34(8):337-345.
PMID: 39027968 DOI: 10.1089/cap.2024.0038

Objectives: Stimulants, such as methylphenidate (MPH) and amphetamines, represent the first-line pharmacological option for attention-deficit/hyperactivity disorder (ADHD). Randomized controlled trials (RCTs) have demonstrated beneficial effects at a group level but could not identify characteristics consistently associated with varying individual response. Thus, more individualized approaches are needed. Experimental studies have suggested that the neurobiological response to a single dose is indicative of longer term response. It is unclear whether this also applies to clinical measures. Methods: We carried out a systematic review of RCTs testing the association between the clinical response to a single dose of stimulants and longer term improvement. Potentially suitable single-dose RCTs were identified from the MED-ADHD data set, the European ADHD Guidelines Group RCT Data set (https://med-adhd.org/), as updated on February 1, 2024. Quality assessment was carried out using the Cochrane Risk of Bias (RoB) 2.0 tool. Results: A total of 63 single-dose RCTs (94% testing MPH, 85% in children) were identified. Among these, only a secondary analysis of an RCT tested the association between acute and longer term clinical response. This showed that the clinical improvement after a single dose of MPH was significantly associated with symptom improvement after a 4-week MPH treatment in 46 children (89% males) with ADHD. The risk of bias was rated as moderate. A further RCT used near-infrared spectroscopy, thus did not meet the inclusion criteria, and reported an association between brain changes under a single-dose and longer term clinical response in 22 children (82% males) with ADHD. The remaining RCTs only reported single-dose effects on neuropsychological, neuroimaging, or neurophysiological measures. Conclusion: This systematic review highlighted an important gap in the current knowledge. Investigating how acute and long-term response may be related can foster our understanding of stimulant mechanism of action and help develop stratification approaches for more tailored treatment strategies. Future studies need to investigate potential age- and sex-related differences.
Fulltext Commentary: Using QbTest for monitoring pharmacological treatment response in ADHD - are we there yet?

Bellato A, Parlatini V, Groom MJ, Hall CL, Hollis C, Simonoff E, et al.

J Child Psychol Psychiatry, 2025 Feb;66(2):266-270.
PMID: 39513414 DOI: 10.1111/jcpp.14071

Individuals with attention-deficit/hyperactivity disorder (ADHD) exhibit varied responses to pharmacological treatments (e.g. stimulants and non-stimulants). Accurately and promptly detecting treatment-related improvements, response failure, or deterioration poses significant challenges, as current monitoring primarily relies on subjective ratings. In this commentary, we critically evaluate the evidence supporting the use of QbTest for objectively monitoring ADHD treatment response in clinical practice. We also offer recommendations for future research, advocating for rigorous clinical trials and longitudinal studies to further explore the potential utilisation of QbTest and other tools for monitoring treatment responses in individuals with ADHD.
A state-of-the-art overview of candidate diagnostic biomarkers for Attention-deficit/hyperactivity disorder (ADHD)

Parlatini V, Bellato A, Gabellone A, Margari L, Marzulli L, Matera E, et al.

Expert Rev Mol Diagn, 2024 Apr;24(4):259-271.
PMID: 38506617 DOI: 10.1080/14737159.2024.2333277

INTRODUCTION: Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental conditions and is highly heterogeneous in terms of symptom profile, associated cognitive deficits, comorbidities, and outcomes. Heterogeneity may also affect the ability to recognize and diagnose this condition. The diagnosis of ADHD is primarily clinical but there are increasing research efforts aiming at identifying biomarkers that can aid the diagnosis.
AREAS COVERED: We first discuss the definition of biomarkers and the necessary research steps from discovery to implementation. We then provide a broad overview of research studies on candidate diagnostic biomarkers in ADHD encompassing genetic/epigenetic, biochemical, neuroimaging, neurophysiological and neuropsychological techniques. Finally, we critically appraise current limitations in the field and suggest possible ways forward.
EXPERT OPINION: Despite the large number of studies and variety of techniques used, no promising biomarkers have been identified so far. Clinical and biological heterogeneity as well as methodological limitations, including small sample size, lack of standardization, confounding factors, and poor replicability, have hampered progress in the field. Going forward, increased international collaborative efforts are warranted to support larger and more robustly designed studies, develop multimodal datasets to combine biomarkers and improve diagnostic accuracy, and ensure reproducibility and meaningful clinical translation.
Fulltext Latest clinical frontiers related to autism diagnostic strategies

Cortese S, Bellato A, Gabellone A, Marzulli L, Matera E, Parlatini V, et al.

Cell Rep Med, 2025 Feb 18;6(2):101916.
PMID: 39879991 DOI: 10.1016/j.xcrm.2024.101916

The diagnosis of autism is currently based on the developmental history, direct observation of behavior, and reported symptoms, supplemented by rating scales/interviews/structured observational evaluations-which is influenced by the clinician's knowledge and experience-with no established diagnostic biomarkers. A growing body of research has been conducted over the past decades to improve diagnostic accuracy. Here, we provide an overview of the current diagnostic assessment process as well as of recent and ongoing developments to support diagnosis in terms of genetic evaluation, telemedicine, digital technologies, use of machine learning/artificial intelligence, and research on candidate diagnostic biomarkers. Genetic testing can meaningfully contribute to the assessment process, but caution is required when interpreting negative results, and more work is needed to strengthen the transferability of genetic information into clinical practice. Digital diagnostic and machine-learning-based analyses are emerging as promising approaches, but larger and more robust studies are needed. To date, there are no available diagnostic biomarkers. Moving forward, international collaborations may help develop multimodal datasets to identify biomarkers, ensure reproducibility, and support clinical translation.
The management of sleep disturbances in children with attention-deficit/hyperactivity disorder (ADHD): an update of the literature

Cortese S, Fusetto Veronesi G, Gabellone A, Margari A, Marzulli L, Matera E, et al.

Expert Rev Neurother, 2024 May 13.
PMID: 38738544 DOI: 10.1080/14737175.2024.2353692

INTRODUCTION: Sleep disorders represent an important comorbidity in individuals with ADHD. While the links between ADHD and sleep disturbances have been extensively investigated, research on the management of sleep disorders in individuals with ADHD is relatively limited, albeit expanding.
AREAS COVERED: The authors searched PubMed, Medline, PsycInfo, Embase+Embase Classic, Web of Sciences databases, and clinicaltrials.gov up to 4 January 2024, for randomized controlled trials (RCTs) of any intervention for sleep disorders associated with ADHD. They retained 16 RCTs (eight on pharmacological and eight on non-pharmacological interventions), supporting behavioral intervention and melatonin, and nine ongoing RCTs registered on clinicaltrials.gov.
EXPERT OPINION: The pool of RCTs testing interventions for sleep disorders in individuals with ADHD is expanding. However, to inform clinical guidelines, there is a need for additional research in several areas, including 1) RCTs based on a precise phenotyping of sleep disorders; 2) pragmatic RCTs recruiting neurodevelopmental populations representative of those seen in clinical services; 3) trials testing alternative interventions (e.g. suvorexant or light therapy) or ways to deliver them (e.g. online); 4) sequential and longer-term RCTs; 5) studies testing the impact of sleep interventions on outcomes other than sleep; 6) and implementation of advanced evidence synthesis and precision medicine approaches.
Fulltext Individualized prediction models in ADHD: a systematic review and meta-regression

Salazar de Pablo G, Iniesta R, Bellato A, Caye A, Dobrosavljevic M, Parlatini V, et al.

Mol Psychiatry, 2024 Dec;29(12):3865-3873.
PMID: 38783054 DOI: 10.1038/s41380-024-02606-5

There have been increasing efforts to develop prediction models supporting personalised detection, prediction, or treatment of ADHD. We overviewed the current status of prediction science in ADHD by: (1) systematically reviewing and appraising available prediction models; (2) quantitatively assessing factors impacting the performance of published models. We did a PRISMA/CHARMS/TRIPOD-compliant systematic review (PROSPERO: CRD42023387502), searching, until 20/12/2023, studies reporting internally and/or externally validated diagnostic/prognostic/treatment-response prediction models in ADHD. Using meta-regressions, we explored the impact of factors affecting the area under the curve (AUC) of the models. We assessed the study risk of bias with the Prediction Model Risk of Bias Assessment Tool (PROBAST). From 7764 identified records, 100 prediction models were included (88% diagnostic, 5% prognostic, and 7% treatment-response). Of these, 96% and 7% were internally and externally validated, respectively. None was implemented in clinical practice. Only 8% of the models were deemed at low risk of bias; 67% were considered at high risk of bias. Clinical, neuroimaging, and cognitive predictors were used in 35%, 31%, and 27% of the studies, respectively. The performance of ADHD prediction models was increased in those models including, compared to those models not including, clinical predictors (β = 6.54, p = 0.007). Type of validation, age range, type of model, number of predictors, study quality, and other type of predictors did not alter the AUC. Several prediction models have been developed to support the diagnosis of ADHD. However, efforts to predict outcomes or treatment response have been limited, and none of the available models is ready for implementation into clinical practice. The use of clinical predictors, which may be combined with other type of predictors, seems to improve the performance of the models. A new generation of research should address these gaps by conducting high quality, replicable, and externally validated models, followed by implementation research.