Association of White Matter Structure With Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder

Aoki, Yuta; Yoncheva, Yuliya N.; Chen, Bosi; Nath, Tanmay; Sharp, Dillon W.; Lazar, Mariana; Velasco, Pablo; Milham, Michael P.; Martino, Adriana Di

doi:10.1001/jamapsychiatry.2017.2573

Cited by 131 publications

(141 citation statements)

References 70 publications

(142 reference statements)

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“…Others have also reported on the absence of clear biological distinctions on structural or functional neuroimaging measures when comparing different NDD diagnostic groups [13][14][15][16][17][18] Data-driven clustering approaches offer a methodological alternative to conventional comparisons between clinically defined groups. This alternative approach may better disentangle heterogeneity within and across current diagnostic categories to identify participant subgroups that may be more similar to each other in brain or behavior [10].…”

Section: Introductionmentioning

confidence: 99%

Integration of Brain and Behavior Measures for Identification of Data-Driven Groups Cutting Across Children with ASD, ADHD, or OCD

Jacobs

Voineskos

Hawco

et al. 2020

Preprint

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Autism spectrum disorder (ASD), obsessive-compulsive disorder (OCD) and attentiondeficit/hyperactivity disorder (ADHD) are clinically and biologically heterogeneous neurodevelopmental disorders (NDDs). The objective of the present study was to integrate brain imaging and behavioral measures to identify new brain-behavior subgroups cutting across these disorders. A subset of the data from the Province of Ontario Neurodevelopmental Disorder (POND) Network including participants with different NDDs (aged 6-16 years) that underwent cross-sectional T1-weighted and diffusion-weighted magnetic resonance imaging (MRI) scanning on the same 3T scanner, and behavioral/cognitive assessments was used. Similarity Network Fusion was applied to integrate cortical thickness, subcortical volume, white matter fractional anisotropy (FA), and behavioral measures in 176 children with ASD, ADHD or OCD with complete data that passed quality control. Normalized mutual information (NMI) was used to determine top contributing model features. Bootstrapping, out-of-model outcome measures and supervised machine learning were each used to examine stability and evaluate the new groups. Cortical thickness in socio-emotional and attention/executive networks and inattention symptoms comprised the top ten features driving participant similarity and differences between four transdiagnostic groups. Subcortical volumes (pallidum, nucleus accumbens, thalamus) were also different among groups, although white matter FA showed limited differences. Features driving participant similarity remained stable across resampling, and the new groups showed significantly different scores on everyday adaptive functioning. Our findings open the possibility of studying new data-driven groups that represent children with NDDs more similar to each other than others within their own diagnostic group. Such new groups can be evaluated longitudinally for prognostic utility and could be stratified for clinical trials targeted toward each group's unique brain and behavioral profiles.

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Section: Introductionmentioning

confidence: 99%

Integration of Brain and Behavior Measures for Identification of Data-Driven Groups Cutting Across Children with ASD, ADHD, or OCD

Jacobs

Voineskos

Hawco

et al. 2020

Preprint

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“…Approaches differ in how heterogeneity is decomposed, from utilizing theoretical a priori known stratifiers 21,22 or dimensions, to completely data-driven approaches 12,[23][24][25] . Models for understanding heterogeneity also differ considerably, with some conceptualizing distinctions as categorical/qualitative, continuous/dimensional, and/or where distinctions or similarities may cut across diagnostic boundaries [25][26][27] . Work can also differ with regards to aims that are specific to understanding heterogeneity within one level of analysis 28, 29 , while others attempt to extend models to explain heterogeneity across levels [30][31][32][33][34][35] .The purpose of this paper is not to provide an in-depth review of the literature on these areas.…”

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confidence: 99%

Big data approaches to decomposing heterogeneity across the autism spectrum

Lombardo

Lai

Baron‐Cohen

2018

Preprint

120

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Autism is a diagnostic label based on behavior. While the diagnostic criteria attempts to maximize clinical consensus, it also masks a wide degree of heterogeneity between and within individuals at multiple levels of analysis. Understanding this multi-level heterogeneity is of high clinical and translational importance. Here we present organizing principles to frame the work examining multi-level heterogeneity in autism. Theoretical concepts such as 'spectrum' or 'autisms' reflect non-mutually exclusive explanations regarding continuous/dimensional or categorical/qualitative variation between and within individuals. However, common practices of small sample size studies and case-control models are suboptimal for tackling heterogeneity. Big data is an important ingredient for furthering our understanding heterogeneity in autism. In addition to being 'feature-rich', big data should be both 'broad' (i.e. large sample size) and 'deep' (i.e. multiple levels of data collected on the same individuals). These characteristics help ensure the results from a population are more generalizable and facilitate evaluation of the utility of different models of heterogeneity. A model's utility can be shown by its ability to explain clinically or mechanistically important phenomena, but also by explaining how variability manifests across different levels of analysis. The directionality for explaining variability across levels can be bottom-up or top-down, and should include the importance of development for characterizing change within individuals. While progress can be made with 'supervised' models built upon a priori or theoretically predicted distinctions or dimensions of importance, it will become increasingly important to complement such work with unsupervised data-driven discoveries that leverage unknown and multivariate distinctions within big data. Without a better understanding of how to model heterogeneity between autistic people, progress towards the goal of precision medicine may be limited.. CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/278788 doi: bioRxiv preprint first posted online Mar. 9, 2018; 3 Autism occurs in approximately 1-2% of the population 1 and autistic individuals'wellbeing is a major public health issue. In economic terms, the lifetime individual cost of autism is estimated at $2.4 (£1.5) million in the USA and UK and annual population costs are around $268 billion in the USA 2,3 . While interest in and science on autism has been growing rapidly, progress towards translating scientific knowledge into high-impact clinical practice has been less rapid. We are still far from delivering more effective intervention and support, more precise and earlier diagnosis, better understanding and prediction of prognosis and development, and personalization of support and intervention. All of these points are within the scope of stratified psychiatry 4 and precision me...

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“…Neuroimaging studies contrasting either ASD or ADHD against typically developing people (TD) have shown that both disorders are characterized by abnormal connectivity in function as well as structure (reviewed in (Anagnostou and Taylor 2011, Aoki et al 2013a, Castellanos and Aoki 2016, Aoki et al 2018)). Thus, some prior studies using diffusion tensor imaging (DTI) have enrolled the three groups (ASD, ADHD, and TD) and reported on similarities and distinctions in white matter between the disorders compared with TD (Ameis et al 2016, Aoki et al 2017, Chiang et al 2017). Results of categorical analyses contrasting the three diagnostic groups vary, possibly because of heterogeneity of the developmental disorders (Pelphrey et al 2011, Ecker and Murphy 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Results of categorical analyses contrasting the three diagnostic groups vary, possibly because of heterogeneity of the developmental disorders (Pelphrey et al 2011, Ecker and Murphy 2014). However, they consistently emphasize the corpus callosum (Ameis et al 2016, Aoki et al 2017, Chiang et al 2017). Besides categorical analysis, dimensional analyses were also conducted in these studies in which all the participants were allocated to one group and the relationship between DTI parameters and ASD symptoms was examined.…”

Section: Introductionmentioning

confidence: 99%

White matter alterations in autism spectrum disorder and attention-deficit/hyperactivity disorder in relation to sensory profile

Ohta

Aoki

Itahashi

et al. 2019

Preprint

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Word Count words2 Abstract Background. Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) have high rates of co-occurrence and share atypical behavioral characteristics, including sensory problems. The present diffusion tensor imaging (DTI) study was conducted to examine whether and how white matter abnormalities are observed in adult populations with developmental disabilities (DD) and to determine how brain-sensory relationships are either shared between or distinct to ASD and ADHD.Methods. We collected DTI data from adult developmental disorder (DD) populations (a primary diagnosis of ASD: n=105, ADHD: n=55) as well as age and sex matched typically developed (TD) participants (n=58). Voxel-wise fractional anisotropy (FA), mean diffusivity, axial diffusivity, and radial diffusivity (RD) were analyzed using tract-based spatial statistics. The severities of sensory problems were assessed using the Adolescent/Adult Sensory Profile (AASP). Results. Categorical analyses identified voxel clusters showing significant effects of DD on FA andRD in the posterior portion of the corpus callosum and its extension in the right hemisphere. Furthermore, regression analyses using the AASP scores revealed that slopes in relationships of FA or RD with the degree of sensory problems were parallel between the two DDs in large parts of the affected corpus callosum regions, although a small but significant cluster did exist showing interaction between the diagnosis of DD and an AASP subscale score on RD. 3 Conclusions. These results indicate that white matter abnormalities and their relationships to sensory problems are largely shared between ASD and ADHD, with localized abnormalities showing significant between-diagnosis differences within DD. (247 words)

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Association of White Matter Structure With Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder

Cited by 131 publications

References 70 publications

Integration of Brain and Behavior Measures for Identification of Data-Driven Groups Cutting Across Children with ASD, ADHD, or OCD

Integration of Brain and Behavior Measures for Identification of Data-Driven Groups Cutting Across Children with ASD, ADHD, or OCD

Big data approaches to decomposing heterogeneity across the autism spectrum

White matter alterations in autism spectrum disorder and attention-deficit/hyperactivity disorder in relation to sensory profile

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