Altered temporal features of intrinsic connectivity networks in boys with combined type of attention deficit hyperactivity disorder

Wang, Xun‐Heng; Li, Lihua

doi:10.1016/j.ejrad.2015.02.018

Cited by 15 publications

(13 citation statements)

References 30 publications

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“…Following the argument above regarding mitigation of the non-dominant hand in controls, it is plausible that stronger within-hemisphere connections of the left thalamus and stronger between-hemisphere connections of the right cerebellum may be an attempt to mitigate sensorimotor deficits of the dominant right hand in children with DCD and ADHD. Our findings also support a previous resting-state fMRI study of ADHD that demonstrated disruptions within frontal-striatal-cerebellar circuits ( Cao et al, 2006 ), as well other studies that observed alterations in cognitive and attention brain networks ( Lin et al, 2015 , Carmona et al, 2015 , Wang and Li, 2015 , dos Santos Siqueira et al, 2014 , Mattfeld et al, 2014 ); some of these studies suggest there is an interaction between these altered networks and changes within networks associated with motor and sensory functions ( Carmona et al, 2015 , dos Santos Siqueira et al, 2014 ). Our findings provide direct evidence of how the motor network is altered in DCD and ADHD, and serve to foster future studies of these interactions.…”

Section: Discussionsupporting

confidence: 91%

Atypical within- and between-hemisphere motor network functional connections in children with developmental coordination disorder and attention-deficit/hyperactivity disorder

McLeod

Langevin

Dewey

et al. 2016

NeuroImage: Clinical

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Developmental coordination disorder (DCD) and attention-deficit hyperactivity disorder (ADHD) are highly comorbid neurodevelopmental disorders; however, the neural mechanisms of this comorbidity are poorly understood. Previous research has demonstrated that children with DCD and ADHD have altered brain region communication, particularly within the motor network. The structure and function of the motor network in a typically developing brain exhibits hemispheric dominance. It is plausible that functional deficits observed in children with DCD and ADHD are associated with neurodevelopmental alterations in within- and between-hemisphere motor network functional connection strength that disrupt this hemispheric dominance. We used resting-state functional magnetic resonance imaging to examine functional connections of the left and right primary and sensory motor (SM1) cortices in children with DCD, ADHD and DCD + ADHD, relative to typically developing children. Our findings revealed that children with DCD, ADHD and DCD + ADHD exhibit atypical within- and between-hemisphere functional connection strength between SM1 and regions of the basal ganglia, as well as the cerebellum. Our findings further support the assertion that development of atypical motor network connections represents common and distinct neural mechanisms underlying DCD and ADHD. In children with DCD and DCD + ADHD (but not ADHD), a significant correlation was observed between clinical assessment of motor function and the strength of functional connections between right SM1 and anterior cingulate cortex, supplementary motor area, and regions involved in visuospatial processing. This latter finding suggests that behavioral phenotypes associated with atypical motor network development differ between individuals with DCD and those with ADHD.

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Section: Discussionsupporting

confidence: 91%

Atypical within- and between-hemisphere motor network functional connections in children with developmental coordination disorder and attention-deficit/hyperactivity disorder

McLeod

Langevin

Dewey

et al. 2016

NeuroImage: Clinical

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“…This direction indeed provides a more holistic picture of the underlying neurobiology. Growing evidence from both functional and structural connectivity studies highlight brain connectivity differences in ADHD and between subtypes, which extends support for specific key networks that may underlie the combined and inattentive types (Carmona et al, 2015, Park et al, 2016, Iannaccone et al, 2015, Wang and Li, 2015). Correspondingly, diffusion tensor imaging (DTI) studies provide further support for distinct structural and white matter connectivity disturbances between the ADHD-C and ADHD-I subtype (Hong et al, 2014, Lei et al, 2014a, Svatkova et al, 2016, Ercan et al, 2016).…”

Section: Introductionmentioning

confidence: 63%

Regional brain network organization distinguishes the combined and inattentive subtypes of Attention Deficit Hyperactivity Disorder

Saad

Griffiths

Kohn

et al. 2017

NeuroImage: Clinical

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Attention Deficit Hyperactivity Disorder (ADHD) is characterized clinically by hyperactive/impulsive and/or inattentive symptoms which determine diagnostic subtypes as Predominantly Hyperactive-Impulsive (ADHD-HI), Predominantly Inattentive (ADHD-I), and Combined (ADHD-C). Neuroanatomically though we do not yet know if these clinical subtypes reflect distinct aberrations in underlying brain organization.We imaged 34 ADHD participants defined using DSM-IV criteria as ADHD-I (n = 16) or as ADHD-C (n = 18) and 28 matched typically developing controls, aged 8–17 years, using high-resolution T1 MRI. To quantify neuroanatomical organization we used graph theoretical analysis to assess properties of structural covariance between ADHD subtypes and controls (global network measures: path length, clustering coefficient, and regional network measures: nodal degree). As a context for interpreting network organization differences, we also quantified gray matter volume using voxel-based morphometry.Each ADHD subtype was distinguished by a different organizational profile of the degree to which specific regions were anatomically connected with other regions (i.e., in “nodal degree”). For ADHD-I (compared to both ADHD-C and controls) the nodal degree was higher in the hippocampus. ADHD-I also had a higher nodal degree in the supramarginal gyrus, calcarine sulcus, and superior occipital cortex compared to ADHD-C and in the amygdala compared to controls. By contrast, the nodal degree was higher in the cerebellum for ADHD-C compared to ADHD-I and in the anterior cingulate, middle frontal gyrus and putamen compared to controls. ADHD-C also had reduced nodal degree in the rolandic operculum and middle temporal pole compared to controls. These regional profiles were observed in the context of no differences in gray matter volume or global network organization.Our results suggest that the clinical distinction between the Inattentive and Combined subtypes of ADHD may also be reflected in distinct aberrations in underlying brain organization.

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“…There are two types of ICN-based temporal patterns: univariate features within ICN and bivariate features between ICNs. Altered intra-ICN amplitude of low frequency fluctuations and inter-ICN functional connectivity were found in children with ADHD 28 . Moreover, the intra-ICN entropy and inter-ICN synchrony might predict the clinical symptoms for ADHD 29 .…”

Section: Introductionmentioning

confidence: 94%

Identifying individuals with attention deficit hyperactivity disorder based on temporal variability of dynamic functional connectivity

Wang

Yun

2018

Sci Rep

Self Cite

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Attention deficit hyperactivity disorder (ADHD) is a common disorder that emerges in school-age children. The diagnostic model based on neuroimaging features could be beneficial for ADHD in twofold: identifying individuals with ADHD and discovering the discriminative patterns for patients. The dynamic functional connectivity of ADHD remains unclear. Towards this end, 100 children with ADHD and 140 normal controls were obtained from the ADHD-200 Consortium. The raw features were derived from the temporal variability between intrinsic connectivity networks (ICNs) as well as the demographic and covariate variables. The diagnostic model was based on the support vector machines (SVMs). The performance of diagnostic model was analyzed using leave-one-out cross-validation (LOOCV) and 10-folds cross-validations (CVs). The diagnostic model based on inter-ICN variability outperformed that based on inter-ICN functional connectivity and inter-ICN phase synchrony. The LOOCV achieved total accuracy of 78.75%, the sensitivity of 76%, and the specificity of 80.71%. The 10-folds CVs achieved average prediction accuracy of 75.54% ± 1.34%, average sensitivity of 70.5% ± 2.34%, and average specificity of 77.44% ± 1.47%. In addition, the discriminative patterns for ADHD were discovered using SVMs. The discriminative patterns confirmed with previous findings. In summary, individuals with ADHD could be identified through inter-ICN variability, which could be potential biomarkers for diagnostic model of ADHD.

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Altered temporal features of intrinsic connectivity networks in boys with combined type of attention deficit hyperactivity disorder

Cited by 15 publications

References 30 publications

Atypical within- and between-hemisphere motor network functional connections in children with developmental coordination disorder and attention-deficit/hyperactivity disorder

Atypical within- and between-hemisphere motor network functional connections in children with developmental coordination disorder and attention-deficit/hyperactivity disorder

Regional brain network organization distinguishes the combined and inattentive subtypes of Attention Deficit Hyperactivity Disorder

Identifying individuals with attention deficit hyperactivity disorder based on temporal variability of dynamic functional connectivity

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