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A Review of the Default Mode Network in Autism Spectrum Disorders and Attention Deficit Hyperactivity Disorder.

ABSTRACT: Functional magnetic resonance imaging (fMRI) has been widely used to examine the relationships between brain function and phenotypic features in neurodevelopmental disorders. Techniques such as resting-state functional connectivity (FC) have enabled the identification of the primary networks of the brain. One fMRI network, in particular, the default mode network (DMN), has been implicated in social-cognitive deficits in autism spectrum disorders (ASD) and attentional deficits in attention deficit hyperactivity disorder (ADHD). Given the significant clinical and genetic overlap between ASD and ADHD, surprisingly, no reviews have compared the clinical, developmental, and genetic correlates of DMN in ASD and ADHD and here we address this knowledge gap. We find that, compared with matched controls, ASD studies show a mixed pattern of both stronger and weaker FC in the DMN and ADHD studies mostly show stronger FC. Factors such as age, intelligence quotient, medication status, and heredity affect DMN FC in both ASD and ADHD. We also note that most DMN studies make ASD versus ADHD group comparisons and fail to consider ASD+ADHD comorbidity. We conclude, by identifying areas for improvement and by discussing the importance of using transdiagnostic approaches such as the Research Domain Criteria (RDoC) to fully account for the phenotypic and genotypic heterogeneity and overlap of ASD and ADHD.

SUBMITTER: Harikumar A

PROVIDER: S-EPMC8112713 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorders (ASD) are two of the most represented neurodevelopmental conditions in childhood. The diagnostic shift introduced by the DSM-5, allowing a combined diagnosis of ADHD and ASD, poses different clinical challenges related to diagnostic overshadowing, accuracy of clinical judgment and potential delay in an ASD diagnosis in children presenting with ADHD. Here we tried to disentangle the clinical phenotype and specificity of the two co-occurring conditions in relation to autism traits and empathy, by comparing children with ASD with and without comorbid ADHD with children presenting ADHD only and children with typical development. The child versions of the Autism Quotient (C-AQ) and Empathy Quotient (C-EQ) were administered to a total sample of 198 male children between 6 and 14 years old with age appropriate language skills and normal intelligence. Univariate analysis demonstrated no significant differences in the C-AQ total and subscale scores as well as the C-EQ between children with ASD and children with ASD + ADHD, while children with ADHD alone presented an intermediate phenotype between ASD and TD. Furthermore, a receiver operating characteristic (ROC) analysis was applied to discriminate among the different phenotypes. We found that the C-AQ and C-EQ were accurate at distinguishing with satisfactory reliability between: (a) ASD vs. non- ASD (N-ASD) groups comprising both ADHD and TD children (Area Under the Curve AUC 88% for C-AQ and 81% for C-EQ); (b) ASD and TD (AUC 92% for C-AQ and 95% for C-EQ); (c) ASD and ADHD (AUC 80% for C-AQ and 68% for C-EQ). Our data confirm the reliability of the C-AQ and C-EQ as behavioral markers to differentiate ASD (regardless of comorbid ADHD) from an ADHD condition and TD. Interestingly, in our sample an ADHD condition does not increase the severity of the clinical phenotype in terms of autism traits distribution and empathy, suggesting that the psychological measures detected by the two quantitative instruments are independent of ADHD traits. This evidence will contribute to the translational efforts in developing better tailored treatments and preventive strategies.

Project description:BackgroundMaternal diabetes has been associated with a risk of neurodevelopmental disorders (NDDs) in offspring, though the common co-occurrence of autism spectrum disorders (ASD), attention-deficit/hyperactivity disorder (ADHD) and intellectual disability (ID) is rarely considered, nor is the potential for confounding by shared familial factors (e.g. genetics).MethodsThis population-based cohort study used data from Psychiatry Sweden, a linkage of Swedish national registers, to follow 2 369 680 individuals born from 1987 to 2010. We used population-averaged logit models to examine the association between exposure to maternal type 1 diabetes mellitus (T1DM), pre-gestational type 2 diabetes mellitus (T2DM) or gestational diabetes mellitus (GDM), and odds of NDDs in offspring. Subgroup analysis was then performed to investigate the timings of GDM diagnosis during pregnancy and its effect on the odds of NDDs in offspring. We compared these results to models considering paternal lifetime T1DM and T2DM as exposures.ResultsOverall, 45 678 individuals (1.93%) were diagnosed with ASD, 20 823 (0.88%) with ID and 102 018 (4.31%) with ADHD. All types of maternal diabetes were associated with odds of NDDs, with T2DM most strongly associated with any diagnosis of ASD (odds ratioadjusted 1.37, 95% confidence interval 1.03-1.84), ID (2.09, 1.53-2.87) and ADHD (1.43, 1.16-1.77). Considering common co-morbid groups, the associations were strongest between maternal diabetes and diagnostic combinations that included ID. Paternal T1DM and T2DM diagnoses were also associated with offspring NDDs, but these associations were weaker than those with maternal diabetes. Diagnosis of GDM between 27 and 30 weeks of gestation was generally associated with the greatest risk of NDDs in offspring, with the strongest associations for outcomes that included ID.ConclusionThe association of maternal diabetes with NDDs in offspring varies depending on the co-morbid presentation of the NDDs, with the greatest odds associated with outcomes that included ID. Results of paternal-comparison studies suggest that the above associations are likely to be partly confounded by shared familial factors, such as genetic liability.

Project description:Neuroimaging studies have revealed neurobiological differences in ADHD, particularly studies examining connectivity disruption and anatomical network organization. However, the underlying pathophysiology of ADHD types remains elusive as it is unclear whether dysfunctional network connections characterize the underlying clinical symptoms distinguishing ADHD types. Here, we investigated intrinsic functional network connectivity to identify neural signatures that differentiate the combined (ADHD-C) and inattentive (ADHD-I) presentation types. Applying network-based statistical (NBS) and graph theoretical analysis to task-derived intrinsic connectivity data from completed fMRI scans, we evaluated default mode network (DMN) and whole-brain functional network topology in a cohort of 34 ADHD participants (aged 8–17 years) defined using DSM-IV criteria as predominantly inattentive (ADHD-I) type (n = 15) or combined (ADHD-C) type (n = 19), and 39 age and gender-matched typically developing controls. ADHD-C were characterized from ADHD-I by reduced network connectivity differences within the DMN. Additionally, reduced connectivity within the DMN was negatively associated with ADHD-RS hyperactivity-impulsivity subscale score. Compared with controls, ADHD-C but not ADHD-I differed by reduced connectivity within the DMN; inter-network connectivity between the DMN and somatomotor networks; the DMN and limbic networks; and between the somatomotor and cingulo-frontoparietal, with ventral attention and dorsal attention networks. However, graph-theoretical measures did not significantly differ between groups. These findings provide insight into the intrinsic networks underlying phenotypic differences between ADHD types. Furthermore, these intrinsic functional connectomic signatures support neurobiological differences underlying clinical variations in ADHD presentations, specifically reduced within and between functional connectivity of the DMN in the ADHD-C type.

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A Review of the Default Mode Network in Autism Spectrum Disorders and Attention Deficit Hyperactivity Disorder.

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