scholarly journals Surface-based shared and distinct resting functional connectivity in attention-deficit hyperactivity disorder and autism spectrum disorder

2018 ◽  
Vol 214 (06) ◽  
pp. 339-344 ◽  
Author(s):  
Minyoung Jung ◽  
Yiheng Tu ◽  
Joel Park ◽  
Kristen Jorgenson ◽  
Courtney Lang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Attention Deficit Hyperactivity Disorder ◽  
Functional Connectivity ◽  
Attention Deficit ◽  
Autism Spectrum ◽  
Diagnostic Methods ◽  
Spectrum Disorder ◽  
Typical Development ◽  
Hyperactivity Disorder

BackgroundBoth attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are neurodevelopmental disorders with a high prevalence. They are often comorbid and both exhibit abnormalities in sustained attention, yet common and distinct neural patterns of ASD and ADHD remain unidentified.AimsTo investigate shared and distinct functional connectivity patterns in a relatively large sample of boys (7- to 15-year-olds) with ADHD, ASD and typical development matched by age, gender and IQ.MethodWe applied machine learning techniques to investigate patterns of surface-based brain resting-state connectivity in 86 boys with ASD, 83 boys with ADHD and 125 boys with typical development.ResultsWe observed increased functional connectivity within the limbic and somatomotor networks in boys with ASD compared with boys with typical development. We also observed increased functional connectivity within the limbic, visual, default mode, somatomotor, dorsal attention, frontoparietal and ventral attention networks in boys with ADHD compared with boys with ASD. In addition, using a machine learning approach, we were able to discriminate typical development from ASD, typical development from ADHD and ASD from ADHD with accuracy rates of 76.3%, 84.1%, and 79.3%, respectively.ConclusionsOur results may shed new light on the underlying mechanisms of ASD and ADHD and facilitate the development of new diagnostic methods for these disorders.Declaration of interestJ.K. holds equity in a startup company, MNT.

scholarly journals Neural correlates of shared sensory symptoms in autism and attention-deficit/hyperactivity disorder

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Takashi Itahashi ◽  
Junya Fujino ◽  
Taku Sato ◽  
Haruhisa Ohta ◽  
Motoaki Nakamura ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Attention Deficit Hyperactivity Disorder ◽  
Functional Connectivity ◽  
Attention Deficit ◽  
Neural Correlates ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Hyperactivity Disorder ◽  
Sensory Symptom ◽  
Brain Functional Connectivity

Abstract Symptoms of autism spectrum disorder and attention-deficit/hyperactivity disorder often co-occur. Among these, sensory impairment, which is a core diagnostic feature of autism spectrum disorder, is often observed in children with attention-deficit/hyperactivity disorder. However, the underlying mechanisms of symptoms that are shared across disorders remain unknown. To examine the neural correlates of sensory symptoms that are associated with autism spectrum disorder and attention-deficit/hyperactivity disorder, we analysed resting-state functional MRI data obtained from 113 people with either autism spectrum disorder or attention-deficit/hyperactivity disorder (n = 78 autism spectrum disorder, mean age = 29.5; n = 35 attention-deficit/hyperactivity disorder, mean age = 31.2) and 96 neurotypical controls (mean age = 30.6, range: 20–55 years) using a cross-sectional study design. First, we used a multi-dimensional approach to examine intrinsic brain functional connectivity related to sensory symptoms in four domains (i.e. low registration, sensation seeking, sensory sensitivity and sensation avoidance), after controlling for age, handedness and head motion. Then, we used a partial least squares correlation to examine the link between sensory symptoms related to intrinsic brain functional connectivity and neurodevelopmental symptoms measured using the Autism Spectrum Quotient and Conners’ Adult Attention-Deficit/Hyperactivity Disorder Rating Scale, regardless of diagnosis. To test whether observed associations were specific to sensory symptoms related to intrinsic brain functional connectivity, we conducted a control analysis using a bootstrap framework. The results indicated that transdiagnostic yet distinct intrinsic brain functional connectivity neural bases varied according to the domain of the examined sensory symptom. Partial least squares correlation analysis revealed two latent components (latent component 1: q < 0.001 and latent component 2: q < 0.001). For latent component 1, a set of intrinsic brain functional connectivity was predominantly associated with neurodevelopmental symptom-related composite score (r = 0.64, P < 0.001), which was significantly correlated with Conners’ Adult Attention-Deficit/Hyperactivity Disorder Rating Scale total T scores (r = −0.99, q < 0.001). For latent component 2, another set of intrinsic brain functional connectivity was positively associated with neurodevelopmental symptom-related composite score (r = 0.58, P < 0.001), which was eventually positively associated with Autism Spectrum Quotient total scores (r = 0.92, q < 0.001). The bootstrap analysis showed that the relationship between intrinsic brain functional connectivity and neurodevelopmental symptoms was relative to sensory symptom-related intrinsic brain functional connectivity (latent component 1: P = 0.003 and latent component 2: P < 0.001). The current results suggest that sensory symptoms in individuals with autism spectrum disorder and those with attention-deficit/hyperactivity disorder have shared neural correlates. The neural correlates of the sensory symptoms were associated with the severity of both autism spectrum disorder and attention-deficit/hyperactivity disorder symptoms, regardless of diagnosis.

scholarly journals Machine Learning Methods for Diagnosing Autism Spectrum Disorder and Attention- Deficit/Hyperactivity Disorder Using Functional and Structural MRI: A Survey

2021 ◽  
Vol 14 ◽  
Author(s):  
Taban Eslami ◽  
Fahad Almuqhim ◽  
Joseph S. Raiker ◽  
Fahad Saeed
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Imaging Techniques ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Hyperactivity Disorder ◽  
Machine Learning Model

Here we summarize recent progress in machine learning model for diagnosis of Autism Spectrum Disorder (ASD) and Attention-deficit/Hyperactivity Disorder (ADHD). We outline and describe the machine-learning, especially deep-learning, techniques that are suitable for addressing research questions in this domain, pitfalls of the available methods, as well as future directions for the field. We envision a future where the diagnosis of ASD, ADHD, and other mental disorders is accomplished, and quantified using imaging techniques, such as MRI, and machine-learning models.

scholarly journals Commentary on… the overlapping and distinct resting functional connectivity between autism spectrum disorder and attention-deficit hyperactivity disorder

2019 ◽  
Vol 214 (06) ◽  
pp. 345-346
Author(s):  
Chirag Mehra ◽  
Michael Absoud
Keyword(s):  
Autism Spectrum Disorder ◽  
Attention Deficit Hyperactivity Disorder ◽  
Functional Connectivity ◽  
Attention Deficit ◽  
Neurodevelopmental Disorders ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Neural Connectivity ◽  
Resonance Imaging ◽  
Hyperactivity Disorder

SummaryAltered neural connectivity in neurodevelopmental disorders is likely subtle, meaning that neuroimaging literature studying development has produced heterogeneous findings. A recent study, published in this issue, illustrates the translational potential of functional connectivity magnetic resonance imaging findings as a biomarker for attention-deficit hyperactivity disorder and autism spectrum disorder. Importantly, it highlights the overlap between disorders, emphasising the need for transdiagnostic and dimensional approaches in neurodevelopment.Declaration of interestNone.

Attention Deficit Hyperactivity Disorder in Autism Spectrum Disorder

2020 ◽  
pp. 158-176
Author(s):  
Karen Bearss ◽  
Aaron J. Kaat
Keyword(s):  
Autism Spectrum Disorder ◽  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Autism Spectrum ◽  
Developmental Trajectory ◽  
Spectrum Disorder ◽  
Adhd Symptoms ◽  
Functional Impairments ◽  
Hyperactivity Disorder ◽  
Delayed Recognition

This chapter will review the available evidence on individuals with co-occurring diagnoses of autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD). This chapter contends that children diagnosed with both disorders (ASD+ADHD) are a subset of the ASD population that is at risk for delayed recognition of their ASD diagnosis, poor treatment response, and poorer functional outcomes compared to those with ASD without ADHD. Specifically, the chapter highlights the best estimates of the prevalence of the comorbidity, the developmental trajectory of people with co-occurring ASD and ADHD, how ADHD symptoms change across development, overlapping genetic and neurobiological risk factors, psychometrics of ADHD diagnostic instruments in an ASD population, neuropsychological and functional impairments associated with co-occurring ASD and ADHD, and the current state of evidence-based treatment for both ASD and ADHD symptoms. Finally, the chapter discusses fruitful avenues of research for improving understanding of this high-risk comorbidity so that mechanism-to-treatment pathways for ADHD in children with ASD can be better developed.

scholarly journals Medications for attention-deficit/hyperactivity disorder in individuals with or without coexisting autism spectrum disorder: analysis of data from the Swedish prescribed drug register

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Viktoria Johansson ◽  
Sven Sandin ◽  
Zheng Chang ◽  
Mark J. Taylor ◽  
Paul Lichtenstein ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Clinical Studies ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Prescribing Patterns ◽  
Adhd Medication ◽  
Hyperactivity Disorder ◽  
Drug Register

Abstract Background Clinical studies found that medication for attention-deficit/hyperactivity disorder (ADHD) is effective in coexisting autism spectrum disorder (ASD), but current research is based on small clinical studies mainly performed on children or adolescents. We here use register data to examine if individuals with ADHD and coexisting ASD present differences in the prescribing patterns of ADHD medication when compared to individuals with pure ADHD. Methods Data with information on filled prescriptions and diagnoses was retrieved from the Swedish Prescribed Drug Register and the National Patient Register. We identified 34,374 individuals with pure ADHD and 5012 individuals with ADHD and coexisting ASD, aged between 3 and 80 years. The first treatment episode with ADHD medications (≥ 2 filled prescriptions within 90 days) and daily doses of methylphenidate during a 3-year period was measured. Odds ratios (ORs) were calculated for the likelihood of being prescribed ADHD medication in individuals with and without ASD and Wilcoxon rank-sum test was used to compare group differences in dose per day. Results Individuals with ADHD and coexisting ASD were less likely to start continuous treatment with ADHD medication (ADHD 80.5%; ADHD with ASD 76.2%; OR, 0.80; 95% confidence interval, 0.75-0.86), were less likely to be prescribed methylphenidate, and were more commonly prescribed second line treatments such as dexamphetamine, amphetamine, or modafinil. No group difference was observed for atomoxetine. In adults with ADHD and coexisting ASD, methylphenidate was prescribed in lower daily doses over three years as compared to individuals with pure ADHD. Conclusions The findings indicate that there are differences in the medical treatment of individuals with or without ASD. If these differences are due to different medication responses in ASD or due to other factors such as clinicians’ perceptions of medication effects in patients with ASD, needs to be further studied.

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