Is ocd a neurodevelopmental disorder

Is ocd a neurodevelopmental disorder DEFAULT

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This hypothesis being proposed by a Georgetown University Medical Center neuroscientist is based on decades of research. It is published online and will be in the April issue of Neuroscience and Biobehavioral Reviews.

The proposed compensation allows individuals with autism to learn scripts for navigating social situations; helps people with obsessive-compulsive disorder or Tourette syndrome to control tics and compulsions; and provides strategies to overcome reading and language difficulties in those diagnosed with dyslexia, autism, or Specific Language Impairment, a developmental disorder of language.

"There are multiple learning and memory systems in the brain, but declarative memory is the superstar," says Michael Ullman, PhD, professor of neuroscience at Georgetown and director of the Brain and Language Laboratory. He explains that declarative memory can learn explicitly (consciously) as well as implicitly (non-consciously).

"It is extremely flexible, in that it can learn just about anything. Therefore it can learn all kinds of compensatory strategies, and can even take over for impaired systems," says Ullman.

"Nevertheless, in most circumstances, declarative memory won't do as good a job as these systems normally do, which is an important reason why individuals with the disorders generally still have noticeable problems despite the compensation," he adds.

Knowing that individuals with these disorders can rely on declarative memory leads to insights on how to improve diagnosis and treatment of these conditions. It could improve treatment in two ways, Ullman says. First, designing treatments that rely on declarative memory, or that improve learning in this system, could enhance compensation. Conversely, treatments that are designed to avoid compensation by declarative memory may strengthen the dysfunctional systems.

Ullman says compensation by declarative memory may also help explain an observation that has long puzzled scientists -- the fact that boys are diagnosed with these disorders more frequently than girls. "Studies suggest that girls and women are better than boys and men, on average, in their use of declarative memory. Therefore females are likely to compensate more successfully than males, even to the point of compensating themselves out of diagnosis more often than males," Ullman says.

Declarative memory may also compensate for dysfunctions in other disorders, he adds, including attention deficit hyperactivity disorder (ADHD) and even adult-onset disorders such as aphasia or Parkinson's disease.

The hypothesis may thus have powerful clinical and other implications for a wide variety of disorders. Ullman says.

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Materials provided by Georgetown University Medical Center. Note: Content may be edited for style and length.


Journal Reference:

  1. Michael T. Ullman, Mariel Y. Pullman. A compensatory role for declarative memory in neurodevelopmental disorders. Neuroscience & Biobehavioral Reviews, 2015; 51: 205 DOI: 10.1016/j.neubiorev.2015.01.008

Cite This Page:

Georgetown University Medical Center. "A brain system that appears to compensate for autism, OCD, and dyslexia." ScienceDaily. ScienceDaily, 12 February 2015. <www.sciencedaily.com/releases/2015/02/150212154635.htm>.

Georgetown University Medical Center. (2015, February 12). A brain system that appears to compensate for autism, OCD, and dyslexia. ScienceDaily. Retrieved October 23, 2021 from www.sciencedaily.com/releases/2015/02/150212154635.htm

Georgetown University Medical Center. "A brain system that appears to compensate for autism, OCD, and dyslexia." ScienceDaily. www.sciencedaily.com/releases/2015/02/150212154635.htm (accessed October 23, 2021).


Sours: https://www.sciencedaily.com/releases/2015/02/150212154635.htm

Examining overlap and homogeneity in ASD, ADHD, and OCD: a data-driven, diagnosis-agnostic approach

Abstract

The validity of diagnostic labels of autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and obsessive compulsive disorder (OCD) is an open question given the mounting evidence that these categories may not correspond to conditions with distinct etiologies, biologies, or phenotypes. The objective of this study was to determine the agreement between existing diagnostic labels and groups discovered based on a data-driven, diagnosis-agnostic approach integrating cortical neuroanatomy and core-domain phenotype features. A machine learning pipeline, called bagged-multiview clustering, was designed to discover homogeneous subgroups by integrating cortical thickness data and measures of core-domain phenotypic features of ASD, ADHD, and OCD. This study was conducted using data from the Province of Ontario Neurodevelopmental Disorders (POND) Network, a multi-center study in Ontario, Canada. Participants (n = 226) included children between the ages of 6 and 18 with a diagnosis of ASD (n = 112, median [IQR] age = 11.7[4.8], 21% female), ADHD (n = 58, median [IQR] age = 10.2[3.3], 14% female), or OCD (n = 34, median [IQR] age = 12.1[4.2], 38% female), as well as typically developing controls (n = 22, median [IQR] age = 11.0[3.8], 55% female). The diagnosis-agnostic groups were significantly different than each other in phenotypic characteristics (SCQ: χ2(9) = 111.21, p < 0.0001; SWAN: χ2(9) = 142.44, p < 0.0001) as well as cortical thickness in 75 regions of the brain. The analyses revealed disagreement between existing diagnostic labels and the diagnosis-agnostic homogeneous groups (normalized mutual information < 0.20). Our results did not support the validity of existing diagnostic labels of ASD, ADHD, and OCD as distinct entities with respect to phenotype and cortical morphology.

Introduction

Autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and obsessive compulsive disorder (OCD) are complex neurodevelopmental disorders. There is emerging evidence that these diagnostic categories may not correspond to conditions with distinct etiology1,2,3,4,5,6,7,8, biology9, or phenotype10, and that they may not represent distinct underlying mechanisms of dysfunction or predict treatment response11. In this context, several studies have revealed shared characteristics among ASD, ADHD, and OCD across various levels of analysis (e.g., etiology1,2,3,4,5,6,7,8, biology9,12,13, and phenotype10,14,15,16,17,18,19), as well as significant comorbidity among these disorders3,20,21. These studies commonly rely on case-control designs, which use diagnostic labels to define group-level statistics for comparisons. Although these approaches can identify group differences in means when distributions are close to normal, they cannot characterize group overlap in the presence of large within-group variability that may arise from existence of subgroups within each group. This is an important consideration when analyzing complex disorders, such as ASD, ADHD, and OCD, which present with strikingly large within-disorder heterogeneity in etiology21,22,23,24,25,26,27,28,29, neurobiology30,31,32,33,34,35,36, and phenotypic presentation37.

The between-group overlap and the large within-group heterogeneity motivate a shift away from traditional case-control designs to trans-diagnostic analyses based on diagnosis-agnostic and continuous measures. This approach may provide insight into the structure of individual variability in biology and phenotype, including discovery of homogeneous subgroups and/or continua characterized by different biologies. To this end, we propose a data-driven, diagnosis-agnostic approach to derive sub-groups that share biological and phenotypic characteristics. Previous attempts have been made to discover homogeneous subgroups within each disorder37,38,39,40,41,42 or on a single level of analysis16,43, however, to our knowledge cross-disorder, multi-level stratification has not been examined previously.

We examined homogeneity in neuroanatomy, measured by cortical thickness, and core-domain phenotypic characteristics of each disorder. Neuroanatomical similarities can provide an intermediate phenotype that links multiple genetic variants31 given that genetic findings are rare and unknown for the majority of individuals with ASD, OCD, and ADHD. Cortical thickness is a heritable measure of cortical columnar structure, suggested to reflect cellular maturational changes in the cortex (i.e., dendritic arborization and pruning, myelination), as well as cognitive and behavioral differences44,45,46.

Materials and methods

Participants

Participants were recruited through the Province of Ontario (Canada) Neurodevelopmental Disorders Network (POND), a multi-center research network studying neurodevelopmental disorders. Participants who had capacity to consent provided informed consent. For others, consent was obtained from guardians and assent was obtained from the participants. Ethics approval was obtained from the research ethics boards at Holland Bloorview Kids Rehabilitation Hospital and the Hospital for Sick Children.

The included participants were 6–18 years old, had sufficient English comprehension to complete the testing protocols, and did not have contraindications for MRI. For the clinical groups, a primary diagnosis of ASD, ADHD, or OCD was required. Diagnoses for the clinical groups were confirmed using in-depth assessments (ASD: Autism Diagnostic Observation Schedule–2 (ADOS)47 and Autism Diagnostic Interview–Revised (ADI-R)48; ADHD: Parent Interview for Child Symptoms (PICS)49; OCD: K-SADS and the Children’s Yale–Brown Obsessive Compulsive Scale (CY-BOCS)50. The controls did not have a neurodevelopmental, psychiatric and/or neurological diagnosis and were born after 35 weeks gestation.

Behavioral measures

Our analyses focused on primary domains affected in ASD, ADHD, and OCD, quantified using continuous measures of autism features (Social Communication Questionnaire (SCQ)51), inattention (inattentive subscale of the Strengths and Weaknesses of ADHD-symptoms and Normal Behavior (SWAN) rating scale52), and obsessive-compulsive traits (Toronto Obsessive Compulsive rating scale (TOCS)53). Participants also completed the Child-Behaviour Checklist (CBCL)59. Full-scale IQ was estimated using the age-appropriate Wechsler or Stanford-Binet scales.

Imaging data

Structural MRI data was collected on the 3-Tesla Siemens Trio TIM at the Hospital for Sick Children, in Toronto, Ontario for 184 participants. The remaining participants were scanned after a hardware updated to the Siemens Prisma scanner. Cortical thickness measures were extracted from T1-weighted images using the CIVET pipeline (version 2.1.0)54. The pipeline applies a non-uniformity correction on the images54 followed by stereotaxic registration to the Montreal Neurologic Institute (MNI ICBM152) template (non-linear 6th generation target)55,56. Next, brains were masked, extracted, and classified into gray matter, white matter, and cerebrospinal fluid. Tissue classification images were used to generate gray and white matter surfaces57,58,59,60,61. A surface-diffusion kernel was applied62, and regions were registered to the automated anatomical labeling atlas63,64,65. Cortical thickness measurements were taken from the distance between the two smoothed surfaces66. Quality assurance was carried out at the time of the scan for motion artifact, and was analyzed through the CIVET quality control (QC) analysis pipeline. Scans that were flagged on the QC analysis were manually reviewed for quality and excluded if needed.

Cortical thickness measurements from 76 regions of the brain were regressed against age, sex, and scanner type in a sequential manner and the z-scored residuals were used in subsequent analyses.

Analysis

Inspired by the concepts of multi-view clustering67 and bagging68, a machine learning pipeline was designed to analyze the multi-dimensional brain-behavior data. This pipeline (Fig. 1), called bagged-multiview clustering, integrates three features: (1) clustering to discover groups of participants who present with “similar” characteristics in both neuroanatomy and phenotype, (2) bagging to improve cluster stability, and (3) feature weight calculation to determine the cortical regions that contributed most to determining the clusters. The clustering analyses were performed using the Scikit-learn toolbox in Python. Statistical analyses were carried out using R 3.3.3 and Matlab 2017a.

Overview of the analytical pipeline.

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Bagged clustering

The bagged-multiview clustering pipeline consisted of bagging and spectral clustering69. Resampling methods such as bagging68 generate and aggregate decisions based on multiple random subsets of data to improve the accuracy, stability, and generalizability of machine learning algorithms69. For this study, a full run of the bagged-multiview clustering pipeline consisted of 50,000 subsamples, each using a random subset of 63.2% of participants, two (of three) dimensions of phenotypic data, cortical thickness measurements from seven (of 76) cortical regions, and the number of clusters randomly chosen between 2 and 15. The size of the random subsets was determined following seminal works in bagging70,71. The range for the number of clusters was determined based on visual inspection of affinity matrices. Each iteration generated a participant connectivity matrix, with entries of one if two participants were grouped in the same cluster and zero otherwise. To confirm that the clustering result was indicative of true connections between participants, the analyses were run on two sets created by (1) randomly sampling a uniform distribution across the range of the data, and (2) randomly permuting the cortical thickness and phenotypic data. The distribution of connectivity values due to random chance was computed. Two participants were deemed “similar” if they were grouped together more times than the 99th percentile of the connection values for the random data.

For each iteration of clustering, the following steps were performed. First, using the Gaussian similarity function, affinity matrices were computed for the SCQ, SWAN, and TOCS scores as well as cortical thickness measurements for each of the 76 cortical regions (total of 3 + 76 matrices). The parameter for the Gaussian kernel was set as the 75th percentile of pairwise distances in each measure. Second, a subset of cortex features were chosen for fusion with the phenotype data. The selection maximized within-to-between cluster similarity using sequential-feature-forward selection72. Within-cluster similarity was defined as the median of the medians similarities for participants in the same cluster. Between-cluster similarity was the median of the 99th percentile similarity between participants in one clusters and those in all other clusters (99th percentile chosen to deal with the sparsity of the matrix). The overall ratio was computed as the average of the within-to-between ratios, where the ratio for each cluster was weighted by the number of participants in that cluster. Participant similarities were obtained from an affinity matrix resulting from the fusion of cortical thickness and phenotypic matrices. The matrices corresponding to the same data type (cortical measurement or phenotype) were fused using element-wise arithmetic averaging. This type of fusion allows for clusters to match along any of the features combined. This procedure results in two affinity matrices: one for cortical thickness and one for phenotypic measurements. These two matrices were fused using a geometric mean. This requires the final clusters to match across cortical thickness and phenotypic dimensions. To further reduce variability and improve generalizability, the entire pipeline was run 10 times (×50,000 iteration each time) and the median of the participant similarity matrices was used to generate the results reported in the following sections. The 10 iterations were performed further to reduce variability in clustering in a computationally efficient manner.

Feature weight calculation

Feature weights were computed based on an adapted version of the permutation accuracy importance70,71. In particular, each feature’s prediction accuracy was calculated as the difference between the final labels and the labels generated in iterations where that feature was selected. The values of the feature were then permuted and the accuracy was again calculated. The feature weight was defined as the difference in accuracy before and after the feature is permuted.

Agreement between groups

To evaluate the agreement between diagnostic labels and the data-driven cluster assignments, four measures were used:

  • Normalized mutual information73: Roughly, this measure quantifies that amount of information shared between two clustering assignments. This measure takes on values between 0 (independent clusterings) and 1 (identical clusterings).

  • Adjusted Rand score74: This measure is based on counting item pairs who fall in the same or different clusters based on two clusterings. The adjusted Rand score ranges between 0 and 1, with 1 indicating perfect agreement between to clusterings.

  • Homogeneity75: This measure quantifies the extent to which each data-driven cluster contains only participants from a single diagnostic group (0 minimum homogeneity, 1 when each cluster contains only members of a single class).

  • Completeness75: This measures quantifies how well participants in the same diagnostic group are assigned to the same cluster (0 minimum completeness, 1 perfectly complete assignment).

SCQ, SWAN, and TOCS scores, as well as cortical thickness values were compared across clusters using Kruskal–Wallis tests.

Results

Participants

Participant demographic information is shown in Table 1. The diagnostic groups differed significantly in age (χ2(3) = 10.1, p = 0.02), full-scale IQ (χ2(3) = 31.4, p < 0.0001), and measures of core-domain symptomatology namely, SCQ (χ2(3) = 134.8, p < 0.0001), SWAN (χ2(3) = 69.5, p < 0.0001), and TOCS (χ2(3) = 80.2, p < 0.0001). The age difference did not survive correction for multiple comparisons. Post-hoc analyses showed that the proportion of male to female participants was higher in the ASD and ADHD groups compared to the OCD and TD groups. Participants in the ASD group had lower median IQ scores compared to the OCD and TD groups, and the ADHD group had lower median IQ compared to the OCD group. Thirty-eight of the 226 participants were missing IQ data.

Full size table

The ASD, ADHD, and OCD groups had significantly elevated scores compared to all groups on their respective core-domain measures (SCQ, SWAN, TOCS; p < 0.0001). Interestingly, the ADHD group had significantly higher SCQ scores compared to the TD controls, and the ASD group had significantly elevated SWAN and TOCS scores compared to the TD groups.

In the ASD group, 46 and 40% of the participants met clinical cut-offs on the SWAN and TOCS, respectively. In the ADHD group, 11 and 17% of participants met clinical cut-offs on the SCQ and TOCS. Of the participants in the OCD group, 8 and 24% met the cut-off on the SCQ and SWAN, respectively. None of the TD participants met the cut-offs for SCQ or SWAN, but 2 of the 22 exceeded the cut-off on the TOCS (eTable 1 in the Supplement). The distribution of each of the core-measures scores also evidenced overlap among the diagnostic groups with respect to all three measures (eFig. 1 in the Supplement).

Cluster-diagnosis agreement

The agreement was <0.2 for the normalized mutual information and adjusted rand scores for cluster numbers ranging from 2 to 14 (perfect agreement corresponds to a value of one). Homogeneity and completeness scores were less than 0.3, indicating that data-driven clusters do not represent a single diagnostic category (eFig. 2 in the Supplement).

Different levels in this figure correspond to a different number of clusters used to partition the similarity matrix generated by the bagged-multiview clustering pipeline.

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Clusters

Based on the within-to-between similarity ratio, a 10-cluster solution was chosen for the remaining analyses (eFig. 3 in the Supplement). Figure 2 graphically depicts how clusters emerge as the number of clusters increases. The figure was generated by changing the number of clusters in a spectral clustering algorithm applied to the similarity matrix generated by the bagged-multiview clustering pipeline.

The Kruskal–Wallis test did not show significant cluster differences in age or sex proportions (eFig. 4 in the Supplement). However, the clusters were significantly different in IQ (χ2(9) = 23.6, p = 0.005). Post-hoc testing showed that cluster 1 had significantly higher mean ranks than clusters 5 and 7 (p = 0.03).

Diagnostic labels

Figure 3 shows the percentage of participants from the four diagnostic categories falling into each of the ten clusters. Most clusters contained participants from multiple diagnostic groups. There was also a group of clusters with participants from the neurodevelopmental groups only (referred to as “neurodevelopmental clusters” from here on). There was also a small cluster of participants with ASD only (cluster 10), containing 12% of the participants with an ASD diagnosis.

Percentage of participants from the four diagnostic categories in each cluster.

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The Kruskal–Wallis test revealed a significant difference in SCQ and SWAN scores among clusters (SCQ: χ2(9) = 111.21, p < 0.0001; SWAN: χ2(9) = 142.44, p < 0.0001), but the cluster difference in TOCS scores was not significant (eFig. 5 in the Supplement). The clusters were also significantly different in CBCL Social Problems scores (χ2(9) = 56.3, p < 0.0001) and Attention Problems (χ2(9) = 94.8, p < 0.0001), but not OCD Problems. Kruskal–Wallis tests showed a significant effect of cluster on cortical thickness in all regions (Bonferroni corrected p < 0.002), except for the left lingual gyrus.

Figure 4 depicts participant-level SCQ and SWAN scores for the diagnostic groups, as well as the data-driven clusters. This figure highlights the differences between diagnostic classifications and the data-driven solution. The data-driven clusters broadly divide the SCQ-SWAN space into low and high SWAN scores based on a cut-off score of 6; within the low and high SWAN regions, a continuum of SCQ scores can be observed. The neurodevelopmental clusters fall within the high SWAN region, with the exception of the “pure” ASD group.

Poorly defined cluster (cluster 5) excluded from plots. Values perturbed by random Gaussian noise to enhance visualization.

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The participant similarity matrix (eFig. 6 in the Supplement) revealed significant overlap among clusters 1 through 3, and 6 through 9, suggesting a structure more consistent with a continuum rather than distinct clusters within these groups. The matrix also indicates that clusters 5 is poorly defined (low similarity among the participants in the cluster).

Cortical regions

Figure 5 visualizes the contribution of each cortical region to the clustering solution. The top ten highly weighted features are listed in eTable 2 in the Supplement for reference.

Red hues represent higher weights (more contribution to determining the clustering solution).

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The weight distribution among the regions was relatively uniformly decreasing (eFig. 7 in the Supplement), suggesting that no single region is driving the clustering results.

Cluster validity

The distribution of connection values used to derive clustering solutions for participant data showed no overlap with the randomly generated data (eFig. 8 in the Supplement).

Discussion

In this study, we used a data-driven, diagnosis-agnostic approach to examine overlap across three neurodevelopmental disorders (ASD, ADHD, and OCD). Overall, our results suggest that homogeneity in the variables examined in our analyses does not align well with existing diagnostic categories. Instead, we observed that differences in the domains primarily affected in these disorders may exist along a continuum that includes typical development.

Clusters

We started with a grouping of participants categorized into four diagnostic groups, which differed significantly on scores on SCQ, SWAN, and TOCS. Our analyses resulted in a new grouping of these participants into more homogeneous subgroups, which differed significantly in SCQ and SWAN scores as well as cortical thickness, but did not align well with the original diagnostic labels.

The majority of the data-driven clusters contained participants from multiple diagnostic categories, highlighting shared phenotypes and neurobiologies among the diagnostic groups. Social difficulties and inattention are commonly reported as shared features of ASD, ADHD, and OCD10,14,20,76,77. Several studies have also reported shared characteristics in brain structure, function, and connectivity9,12,78,79,80,81,82 in these disorders. Our results support the emerging recognition that the existing behaviorally-defined diagnostic labels may not capture etiologically, biologically, and phenomenologically homogeneous groups29,79,83,84,85,86,87,88,89.

Visually, our results are consistent with the notion that that the ASD-like features, and to some extent inattention traits, exist across a continuum that includes typical development. This model is supported by the substantial etiological overlap between these disorders and typical variation in social communication ability90 and inattention91. This is also consistent with the notion that multiple susceptibility genetic factors may interact with environmental conditions to lead to a continuous dimension of ASD-like and inattention traits, with neurodevelopmental disorders at the extremes of this continuum92,93. This motivates models of neurodevelopmental disorders which focus on continuous variations in traits instead of categorical diagnoses defined based on qualitative cut-offs. Future studies should consider examining other phenotypic characteristics and biological parameters (e.g., metabolic, immune, endocrine markers) to comprehensively describe this continuum.

The data-driven clusters differed significantly in SCQ and SWAN scores, but not TOCS. This pattern was also replicated using the CBCL measures of social, attention, and OCD problems. Moreover, the majority of participants with an OCD diagnosis clustered together with the typical controls. This has been observed in two other studies which examined social perception abilities10 and white matter structure9 using the same cohort. In addition, a study of a community sample found that those with a sibling with ASD showed more ADHD, but not OCD traits compared to those without a sibling with ASD20. Replication on larger samples is needed to further explore shared characteristics and differences across these disorders.

Finally, it is important to note that discovery of the exact clusters/subgroups that can be translated into clinical practice requires replication and integration of findings across a large number of studies and measures. This paper is a first step to accomplish this. Our results motivate a paradigm shift to challenge how ASD, ADHD, and OCD are currently defined, diagnosed, and treated. In particular, this paper adds to the evidence that these diagnoses may not exist as uniquely-defined diagnostic constructs, and highlights the need to discover other groupings that may be more closely aligned with biology and/or response to treatment.

Our results also have implications for the research community. Most existing studies commonly rely on case-control designs, which use diagnostic labels to define group-level statistics for comparisons. These approaches are often not able to characterize group overlap in the presence of large within-group variability that is revealed in our study. In this context, our results highlight the need to move beyond traditional statistical approaches to more advanced computational approaches to examine variability and overlap in/across these disorders. To our knowledge, this is the first examination of cross-disorder, multi-level stratification across ASD, ADHD, and OCD.

Cortical features

Our results add to the emerging evidence that the existing diagnostic categories may not be associated with unique patterns of difference in brain structure, paralleling a recent study showing significant heterogeneity in brain volume across 26 mouse models of ASD31.

Broadly, the regions contributing most to the data-driven groupings were involved in social function, emotion processing, language, attention, and inhibitory control. Many of these regions have been previously implicated in studies of cortical morphology in ASD (e.g., middle temporal gyrus94,95,96,97,98,99, supramarginal gyrus78,96,97,98,99, angular gyrus100, middle frontal gyrus94,96,99,100, cingulate94,97,99, inferior frontal gyrus96,98,99, postcentral gyrus96,98,99,100, inferior temporal gyrus94,98,99), ADHD (e.g., cingulate101,102,103,104, dorsolateral prefrontal cortex102, inferior frontal cortex102, anterior cingulate cortex13, temporoparietal regions13), and OCD (e.g., inferior frontal gyrus105, anterior cingulate cortex13,105, supramarginal gyrus105, dorsolateral prefrontal cortex13, middle frontal gyrus12).

Our results also overlap with those of the very few studies that have examined similarities and differences in brain structure across pairs of ASD, ADHD, and OCD. For example, disorder-specific differences in the left middle temporal gyrus95, right supramarginal gyrus78, and the prefrontal cortex106 have been reported for ASD and ADHD. Looking at ADHD and OCD, differences have been reported in the cingulate cortex and dorsolateral prefrontal cortex13. Decreased volume in the anterior cingulate cortex has been suggested as a shared finding in ASD and OCD 12.

Limitations

Our analyses were conducted on a single measure of cortical structure and three phenotypic measures as well as a specific age group. These levels of analyses may not fully capture homogeneity across the disorders. Future work should consider running similar types of analyses using multiple measures that can comprehensively characterize the variability across neurodevelopmental disorders. These include brain structure and function and core and comorbid behavioral domains across the life-span, as well as genetic, epigenetic, metabolic, immune, and endocrine markers.

The sample size used for the analyses reported in this paper was limited, with unequal distribution of participants across the diagnostic groups. Replication with larger sample sizes is needed.

To our knowledge, this is the first study of diagnosis-agnostic homogeneity across ASD, ADHD, and OCD using data-driven discovery. Homogeneity in the variables examined in our analyses did not align well with existing diagnostic categories in the sample studied. These results add to the emerging body of literature questioning the validity of existing diagnostic constructs with respect to having distinct biological and phenotype presentation. The results of this study also highlight the need for a shift from case-control models to more complex analyses that can cope with the large between-disorder overlap and within-disorder variability.

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Sours: https://www.nature.com/articles/s41398-019-0631-2
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Obsessive-compulsive disorder and attention-deficit/hyperactivity disorder: distinct associations with DNA methylation and genetic variation

  • Sarah J. Goodman1,
  • Christie L. Burton2,
  • Darci T. Butcher3,
  • Michelle T. Siu4,
  • Mathieu Lemire2,
  • Eric Chater-Diehl1,
  • Andrei L. Turinsky1,5,
  • Michael Brudno1,5,6,
  • Noam Soreni7,
  • David Rosenberg8,
  • Kate D. Fitzgerald9,
  • Gregory L. Hanna9,
  • Evdokia Anagnostou10,11,
  • Paul D. Arnold12,13,
  • Jennifer Crosbie2,
  • Russell Schachar2,14 &
  • Rosanna Weksberg1,11,15,16,17

Journal of Neurodevelopmental Disordersvolume 12, Article number: 23 (2020) Cite this article

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Abstract

Background

A growing body of research has demonstrated associations between specific neurodevelopmental disorders and variation in DNA methylation (DNAm), implicating this molecular mark as a possible contributor to the molecular etiology of these disorders and/or as a novel disease biomarker. Furthermore, genetic risk variants of neurodevelopmental disorders have been found to be enriched at loci associated with DNAm patterns, referred to as methylation quantitative trait loci (mQTLs).

Methods

We conducted two epigenome-wide association studies in individuals with attention-deficit/hyperactivity disorder (ADHD) or obsessive-compulsive disorder (OCD) (aged 4–18 years) using DNA extracted from saliva. DNAm data generated on the Illumina Human Methylation 450 K array were used to examine the interaction between genetic variation and DNAm patterns associated with these disorders.

Results

Using linear regression followed by principal component analysis, individuals with the most endorsed symptoms of ADHD or OCD were found to have significantly more distinct DNAm patterns from controls, as compared to all cases. This suggested that the phenotypic heterogeneity of these disorders is reflected in altered DNAm at specific sites. Further investigations of the DNAm sites associated with each disorder revealed that despite little overlap of these DNAm sites across the two disorders, both disorders were significantly enriched for mQTLs within our sample.

Conclusions

Our DNAm data provide insights into the regulatory changes associated with genetic variation, highlighting their potential utility both in directing GWAS and in elucidating the pathophysiology of neurodevelopmental disorders.

Background

Attention-deficit/hyperactivity disorder (ADHD) and obsessive-compulsive disorder (OCD) are common, heterogeneous disorders that can co-occur or occur with other neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD) and Tourette syndrome (TS) [1,2,3]. Elucidating the etiologies and pathophysiologies of these disorders has proven challenging as they have historically been classified based on varying clinical profiles, rather than underlying biology [4].

ADHD is characterized by inattention, hyperactivity, and impulsivity mostly in childhood but it can persist into adolescence and adulthood [5,6,7]. ADHD affects approximately 5% of children and adolescents, and 2.5% of adults [8]. Core features of OCD consist of recurrent and unwanted thoughts, urges, and repetitive behaviors or mental acts performed to reduce anxiety or a sense of dread [9]. These behaviors and thoughts can impair social and occupational functioning in individuals with OCD [9]. The estimated prevalence of OCD in childhood and adult populations is similar, approximately 1–3% [10, 11].

Both ADHD and OCD have been the focus of considerable genetic research, including a small number of genome-wide association studies (GWAS), given their relative heritability estimates of 70–80% and 40–65%, respectively [12,13,14,15,16]. Both disorders have been found to be polygenic in nature, with many common single nucleotide polymorphisms (SNPs) each conferring small risks [17,18,19,20,21,22]. However, there has been a notable lack of reproducible GWAS findings, which may be attributed to lack of statistical power but also heterogeneity in the disorders [23,24,25]. Accounting for this heterogeneity by examining symptom severity rather than diagnostic categories may help increase statistical power since individuals with more severe symptoms plausibly have a larger genetic load. The hypothesis that the manifestation of each disorder represents extremes of a quantitative trait may explain the heterogeneity of these disorders and the rarity of replicable risk variants despite strong heritability [26, 27].

In addition to genetics, epigenetic factors might mediate the expression of ADHD and OCD. Epigenetics refers to heritable changes to the chromatin state that are not due to changes in DNA sequence, such as those accompanying cellular reprogramming [28, 29]. DNA methylation (DNAm), the most commonly studied human epigenetic mark, can reflect both genetic and environmental influences in a quantitative and often stable manner [30, 31]. To that end, DNAm states in 20–80% of CpGs in the genome are thought to associated with genetic variation to some extent [32,33,34,35], and inter-individual variation of DNAm in a single CpG is best predicted by an interaction between genetics and environment [30]. Research in ADHD has identified numerous environmental risk factors including birth weight, early-life maltreatment, lead exposure, and maternal smoking during pregnancy [18, 36,37,38]. In contrast, there is currently a lack of convincing evidence for reproducible associations between OCD and environmental factors [14, 39].

In ADHD and OCD, a small number of DNAm studies, including candidate analyses and epigenome-wide association studies (EWAS), have been published. Most notably, a recent EWAS of ADHD performed on DNAm measured in whole blood, found a large degree of heterogeneity across three ADHD cohorts, with no differentially methylated sites replicating in the meta-analysis [40]. Additional ADHD EWAS have been performed in cord blood and saliva, the latter identifying differentially methylated sites in VIPR2, a gene encoding a protein that plays a role in circadian rhythm [41, 42]. Research into DNAm patterns associated with OCD is more limited. One epigenetic OCD analysis reported DNAm associations proximal to genes involved in actin binding, cell adhesion and transcriptional regulation [43]. Targeted analyses have also implicated BDNF and OXTR DNAm in OCD [44, 45].

While research into the epigenetic patterns underlying ADHD and OCD is still relatively nascent, EWAS of schizophrenia have provided strong evidence that epigenetic research can focus and strengthen genetic research [46,47,48]. An integrated analysis of genetics and DNAm in schizophrenia found that (1) differentially methylated sites associated with a diagnosis of schizophrenia replicated across independent cohorts, (2) differentially methylated sites corresponded to known schizophrenia GWAS loci, and (3) GWAS loci were enriched for methylation quantitative risk loci (mQTLs) [46, 49].

Here, we undertook a novel approach of incorporating genetics, phenotype, and epigenetics to identify DNAm correlates of ADHD and OCD. We hypothesized that disorder heterogeneity would be reflected in DNAm patterns and categorized individuals by their clinical profile to aid in identifying differentially methylated sites. We ran linear models of DNAm in ADHD or OCD cases vs. controls and compared the results to the same analyses run on a subset of ADHD or OCD cases selected based on severity or number of symptoms. We then assessed whether restricting heterogeneity of the phenotype led to a stronger epigenetic signal. We also tested the disorder-associated CpGs for their relatedness to nearby genetic variation, i.e., mQTLs, and finally, assessed how these mQTLs were positioned in independent GWAS findings. We found that DNAm is a better discriminator of more symptomatic cases of ADHD and OCD than the heterogeneous, full cohorts of cases, as compared to controls. As well, CpG sites differentially methylated between cases and controls, in both ADHD and OCD analyses, were enriched for mQTL associations.

Methods and materials

Participants

Information on participants can be found in Table 1.

Full size table

Participants for this study were collected from three unique cohorts: (1) Patients with OCD and matched controls were recruited from the Department of Psychiatry at the University of Michigan and surrounding community. The lifetime and current severity of OCD was assessed in patients with a modified version of the Children’s Yale-Brown Obsessive Compulsive Disorder Scale (CY-BOCS), with patients and their parents providing item scores retrospectively for the most severe episode of OCD and item scores for current severity. (2) Patients with ADHD or OCD were recruited through the Province of Ontario Neurodevelopmental Disorders Network (POND) from The Hospital for Sick Children (SickKids Hospital; Toronto), Holland Bloorview Kids Rehabilitation Hospital (Toronto), McMaster Children’s Hospital (Hamilton), or Lawson Health Research Institute (London). Participants were recruited if they had a primary clinical diagnosis of ADHD or OCD, sufficient English comprehension to complete required testing, and no contraindications for MRI. Diagnoses were established using the Parent Interview for Child Symptoms for ADHD and the CY-BOCS for OCD. (3) Age-, sex-, and tissue-matched control samples were measured in individuals recruited at the Ontario Science Centre in Toronto as part of the Spit for Science study [details published elsewhere (Crosbie et al.)] [50]. In total, 17,262 children and adolescents between 6 and 17 years were recruited. Participants were excluded if they had reported receiving a diagnosis of any mental illness from a physician or mental health professional in an electronic questionnaire (community diagnosis). Parents of children younger than 13 years filled out the questionnaires on their child’s behalf (referred to as “parental respondents”). Individuals age 15 and older completed the questionnaires for themselves, while those between the ages of 13 and 15 responded either for themselves or had parents fill out the questionnaire. Our previous work established that the incidence rates of self-reported diagnoses of NDDs in this community sample were comparable to population prevalence as are typically reported (see OMIM 20985; OMIM 143465; OMIM 164230) [50, 51]. Approval from research ethics boards was obtained at all participating institutions. For all patients, parental consent was obtained for children between 6 and 12 years of age. Individuals who were 13 years and older provided their own consent in addition to parental consent.

Sample selection of ADHD and OCD and symptom characterization

Samples selected from the three cohorts for the analysis presented here met a number of criteria. Firstly, we imposed a limit of one case, ADHD or OCD, per family. Cases could have symptoms of other disorders (e.g., ADHD case with some OCD symptoms) but not comorbid diagnoses at the time of data collection (e.g., child with ADHD and ASD). Individuals were required to be European Caucasian ancestry due to the strong association between DNAm and ethnicity or heritage [52, 53]. Detailed medication history was collected, and anyone with a history of seizure medication (e.g., valproic acid) was excluded due to known effects on one-carbon metabolism, the biochemical pathway in which methyl donors are produced. Following case selection, a similar number of age- and ancestry-matched controls were chosen.

We selected ADHD and OCD cases based on cutoffs on the SWAN and CY-BOCS, respectively [54,55,56,57]. For the ADHD sample, a threshold of ≥ 6 symptoms based on the SWAN was used which reflects the DSM-5 criteria [9].

For the OCD sample, a threshold of CY-BOCS total score ≥ 18 was used. We selected a slightly more conservative cutoff than that suggested for the CY-BOCS “moderate” symptom severity range. Twenty of 59 OCD samples did not have CY-BOCS scores and as such were excluded from analysis of the “more symptomatic” OCD subset. However, as there were no a priori requirements of disorder severity in the full OCD sample analysis, these individuals were included there.

DNAm data generation and preprocessing

Saliva was collected using Oragene OG-500 (DNA Genotek, Ottawa, ON) collection kits and stored at room temperature as per manufacturer’s instructions. DNA was extracted from saliva for all cases and controls using standard techniques. Extracted DNA was sodium bisulfite converted using the Qiagen EZ DNA Methylation kit (Qiagen, Valencia, CA), according to the manufacturer’s protocol. All DNA samples were processed according to the manufacturer’s protocol for DNAm analysis using the Illumina Human Methylation 450 K (450 K array) at The Centre for Applied Genomics (SickKids). The distribution of the samples on the arrays was randomized for all cases and controls and for age and sex.

Raw data (IDAT files) underwent pre-processing quality control and normalization prior to analysis, using the R package minfi [58]. Low quality probes were removed, as measured by the detection p value, as well as probes located on sex chromosomes, cross-reactive probes, SNP probes, and probes targeting CpG sites within 5 bp of an SNP with a minor allele frequency > 1%. Background signal subtraction and control normalization were then performed using the methods designed for the Illumina Genome Studio software. The final output consisted of 426,551 methylation values for each sample (Beta [β] values) ranging from 0 to 1, corresponding to the percent methylated probes measured at each CpG.

Prior to statistical analysis, buccal epithelial cell (BEC) and blood cell proportions were estimated from the methylation data using methods similar to those described in Houseman et al. for blood samples and Smith et al. for saliva samples [59, 60]. We used isolated cell types (GEO GSE46573, GEO GSE35069) as reference methylomes to identify CpGs differentially methylated by cell type and then predicted the cellular composition of each saliva sample [61, 62].

Genotyping data generation and preprocessing

The samples were genotyped as part of different genotyping projects, on a variety of genotyping arrays: Illumina HumanCoreExome, PsychArray, Omni2.5, and Affymetrix6.0. Samples for each array type were processed separately, using the same pipeline described below. Data for each sample was extracted from imputed data, combined and analyzed. Samples were excluded for the following technical reasons: if (1) their call rate was below 97% (2), if they were found to be outliers with respect to heterozygosity, where outliers are defined as a value at a distance greater than 6 times the interquartile range from the closest quartile, and (3) if the sex predicted from the genotypes differed from the reported sex. SNPs were excluded if (1) their call rate was below 97%, (2) deviated from the rules of Hardy-Weinberg equilibrium at an FDR < 1%, based on a set of homogeneous samples in terms of ancestry, and (3) were found to be duplicates of other SNPs, based on position and alleles, in which case the one with the highest call rate was retained. These statistics were computed using plink v1.90 [63].

Imputation was performed separately for each project, using Beagle v4.1 and companion program conform-gt with default values. A/T and C/G genotyped SNPs were removed prior to imputation. Data from phase 3, version 5 of the 1000 Genomes project, downloaded from http://bochet.gcc.biostat.washington.edu/beagle/1000_Genomes_phase3_v5a/b37.vcf/, was used as reference.

Principal components (PCs) were calculated from a set of autosomal, bi-allelic ancestry informative markers (AIM), calculated from samples from phase 3 of the 1000 Genomes project. We first pruned SNPs for linkage disequilibrium (r2 < 0.2 in 1500 kbp windows). Then, for each continental population, the top 1% SNPs with largest frequency differences between that population and all others were retained. We ignored SNPs in the MHC regions: chr8 7,000,000–13000000 [hg19] (8p23 inversion) and chr6 25,000,000-34,000,000.

Samples’ AIMs were extracted from the imputed data sets, as long as their imputation quality was AR2 > 0.8. Hard genotype calls were used. To identify outliers with respect to ancestry, i.e., non-Caucasian samples, data from samples were combined with data from the 1000 Genomes project (Supplementary Figure 1). PCs were calculated using plink v1.90, and outliers (as defined above) were identified from each of the top 3 principal components. Once ancestry outliers were removed, PCs were recomputed without 1000 Genomes samples and used as covariates in downstream statistical analyses.

GWAS datasets

Additional datasets used for investigating the relationship between genotype and ADHD or OCD diagnosis at SNPS of interest were attained through the Psychiatric Genetics Consortium (https://www.med.unc.edu/pgc/results-and-downloads). Summary statistics from Demontis et al. and IOCDF-GC and OCGAS were downloaded to assess genotype-phenotype correlations in independent samples of European ancestry [19, 22]. The ADHD GWAS was performed on 19,099 individuals with ADHD (and 34,194 matched controls from the European Caucasian subset), and the OCD GWAS was performed on 2688 individuals with OCD and 7037 matched controls [19, 22].

Statistical analyses

Analysis pipeline is summarized in Fig. 1.

Pipeline of statistical analysis. Black boxes and arrows indicate that the analyses were performed on our ADHD and OCD cases versus controls. Sample sizes for each comparison can be found in Table 2. Dashed boxes and arrows indicate analyses that were performed on independent samples and previously published (Demontis et al. 2017; IOCDF-GC and OCGAS 2017); summary statistics downloaded from the Psychiatric Genetic Consortium were used

Full size image

Genome-wide DNAm analyses were performed for each two-group comparison using the Limma package, which runs a linear regression on each CpG. Sex, age, and estimated buccal proportion were included as covariates, as well as batch, where appropriate; as all ADHD samples were run in a single batch, only controls from the same batch were included in these comparisons. While both buccal cell and granulocyte proportions were estimated, these measures were strongly inversely proportional and as such, the granulocyte measure was not included as a covariate. CpGs reported as significantly associated with ADHD or OCD were required to have a nominal p value < 0.05 and an absolute Δβ > 5%. Δβ is calculated as the difference in mean DNAm (β) between groups. While Benjamini-Hochberg correction for multiple testing was applied, it was not reported as no sites met a threshold of q value < 0.05.

Principal component analysis (PCA) was performed on mean-centered data using Qlucore Omics Explorer [QOE, www.qlucore.com] for visualization of case-control clustering. Silhouette scores were calculated using beta values and Manhattan distances for clustering.

mQTL identification was performed by first identifying all SNPs within 5 kb of any CpG significantly associated with ADHD or OCD in the more symptomatic samples subsets. SNPs with < 5% minor allele frequency (MAF) in our sample (either ADHD and appropriate controls or OCD and appropriate controls, depending on the SNP) were removed. Alleles at each SNP were coded as “0”, “1”, and “2”, and a Spearman correlation was run at each SNP-CpG pair. MQTLs were identified as SNP-CpG pairs with a Benjamini-Hochberg corrected correlation p value < 0.05 [52].

To test if the number of OCD- or ADHD-associated CpGs associated with mQTLs were significantly enriched compared to background CpGs (i.e., CpGs assayed on the EPIC array), we employed repeated random sampling. For each disorder, we randomly sampled a set of CpGs equal to the number of disorder-associated CpGs, 1000 times. For each iteration, the same methods used above for mQTL identification were applied: first, mapping all variable SNPs within 5 kb of each CpG, running correlations, and finally, correcting p values for false discovery rate. The output of each iteration was the sum of CpGs associated with at least one SNP (mQTL); combined, these 1000 sums were used to generate a random null distribution.

Finally, a logistic regression using disorder status as the outcome was run on each SNP that was significantly associated with an NDD-associated CpG (3283 SNPs correlated with ADHD-associated CpGs and 1150 SNPs correlated with OCD-associated CpGs), using the R package snpStats [64]. Principal components 1 and 2 calculated from the full genotyping array data were included as covariates to account for population substructure. Disorder-associated SNPs met a Benjamini-Hochberg corrected p value < 0.05.

Summary statistics from independent GWAS were downloaded for two sets of SNPs identified using the prior ADHD and OCD mQTL analyses. First, all SNPs identified as mQTLs and second, all SNPs tested in mQTL analyses but not significantly associated with DNA methylation, were assessed for their association to either ADHD or OCD, as reported in each GWAS [19, 22]. Q-Q plots and genomic inflation factors (λ) were generated from the GWAS p values of these subsets to assess if SNPs proximal to CpGs associated with a disorder were more likely to be associated with the disorders themselves, as indicated by positive skewing of observed p values and larger λ values, respectively.

Results

DNAm better distinguishes more symptomatic cases of ADHD and OCD from controls, as compared to more heterogeneous, full case sets

DNAm profiles of all ADHD and OCD samples (n = 22, n = 59, respectively) were compared with age-, and tissue-matched controls (n = 35, n = 54, respectively) at 426,551 sites using linear regression and covarying for sex, buccal cell proportion, and age. Buccal composition was estimated using DNAm and was included as a covariate despite no significant differences between cases and controls (data not shown), as cellular heterogeneity is strongly associated with DNAm. Batch was also included as a covariate for analysis of OCD samples and the corresponding controls as these were run in three batches, with equal numbers of cases and controls in each batch. No sites were identified as significantly associated with ADHD or OCD after Benjamini-Hochberg correction for multiple testing (all q > 0.05). As such, we set the criteria for significance to a nominal p < 0.05 and |Δβ| > 5% to identify sites likely to be true associations while remaining cognizant of the increased risk of false positives. At this threshold, 188 CpG sites were associated with ADHD, and 82 CpGs were associated with OCD (Supplementary Table 1). Seven sites were associated with both ADHD and OCD and mapped to the following genes: DNAJC15 (2 CpGs), C13orf39, DLGAP2, and PRDM9; two CpGs were intergenic.

As both ADHD and OCD are heterogeneous disorders, we repeated our analyses on subsets of cases that included only individuals who were more symptomatic, as determined by ≥ 6 SWAN symptoms for ADHD or CY-BOCS total score ≥ 18 for OCD (n = 15; n = 28, respectively). Of note, a higher CY-BOCS corresponds to more severe OCD symptoms, while a higher SWAN score is indicative of more ADHD symptoms, i.e., more behaviors reflecting inattention, hyperactivity, or impulsiveness. Although there were still no significant differences in buccal proportion found between cases and controls (both p values> 0.05), differences in the distribution of buccal proportion in the subset of ADHD and controls were apparent (Supplementary Figure 2). To better balance buccal proportion in ADHD cases and controls, controls were stratified by cell proportion, and eight samples were removed (remaining control samples n = 27). As well, controls were significantly older than the ADHD subset; this was the only comparison for which age differed significantly between cases and controls, and age was used as a covariate in all statistical models (Supplementary Figure 3).

Linear models, identical to those run on the full cohorts, were then applied to the more optimally matched groups, and disorder-associated CpGs were identified using the same criteria, i.e., nominal p < 0.05 and |Δβ| > 5%. In both ADHD and OCD, a greater number of sites were associated with the more symptomatic subsets than the full cohorts, which is likely due to the decreased heterogeneity of these subsets (299 ADHD-associated CpGs; 137 OCD-associated CpGs; Supplementary Table 2). Additionally, many significant CpGs mapped to the same gene, suggestive of differentially methylated regions (DMRs); these included POUF6 (6 CpGs), PRDM8 (4 CpGs), SNRPN (4 CpGs), and RASGEF1C (3 CpGs) associated with the ADHD subset, and NINJ2 (5 CpGs), PRKG1 (4 CpGs) and CES1 (2 CpGs) associated with the OCD subset (example DMRs shown in Fig. 2). The overlap in CpGs associated with both the full cohort and more symptomatic subset was greater than expected by chance in both ADHD and OCD (103 CpGs and 35 CpGs, respectively), as determined by random resampling 1000 times (all p < 0.0001; Table 2).

Differential methylation found in subset ADHD and OCD cohorts. CpG sites denoted by asterisks were differentially methylated in (a) CES1 in the subset of OCD (determined by CY-BOCS ≥ 18, n = 28) and (b) RASGEF1C in the subset of ADHD (SWAN symptoms ≥ 6, n = 15), as compared to controls. CpG sites denoted by two asterisks remained significant in full ADHD cohort. Lines represent mean methylation at each CpG in (1) controls, (2) the subset of more symptomatic cases, and (3) remaining cases. Green bars represent CpG islands

Full size image

Full size table

To assess how well the cases clustered separately from controls, i.e., how unique their methylation profiles were at the disorder-associated sites, we ran PCA on all four sets of disorder-associated sites (ADHD full cohort, ADHD more symptomatic subset, OCD full cohort, OCD more symptomatic subset) on the samples from which they were derived (Fig. 3a). In both ADHD and OCD, the more symptomatic subsets of cases clustered farther from controls, as compared to the full cohort. As well, PC1, which separated cases from controls in all comparisons, accounted for a larger proportion of the total variation in the PCA performed on the subsets, as compared to the full cohort (PC1 ADHD subset = 15%, PC1 ADHD full = 12%; PC1 OCD subset = 10%, PC1 OCD full = 9%).

PCA plots of NDD-associated CpGs and relative PC1 scores in controls, “less symptomatic”, and “more symptomatic” individuals with ADHD and OCD. a Samples sizes and number of CpGs input into PCA shown in bottom, right-hand corner of each facet. b PC1 scores of PCA run on 299 CpGs differentially methylated between controls and the more symptomatic ADHD subset, with “less symptomatic” samples included in PCA (n controls = 27, n ADHD less symptomatic = 7, n ADHD more symptomatic = 15). c PC1 scores of PCA run on 137 CpGs differentially methylated between controls and the more symptomatic OCD subset with “less symptomatic” samples included in PCA (n controls = 54, n OCD less symptomatic = 11, n OCD more symptomatic = 28, n = 20 removed due to missing CY-BOCS scores). Comparisons were performed using ANOVA and marked by asterisks if significant (Tukey p values < 0.05)

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To quantify the differences observed in the PCA plots, silhouette widths, a measure of the average distance between clusters, were compared using the beta values and Manhattan distance (Supplementary Figure 4). In both ADHD and OCD, average silhouette widths increased in the subsets containing only more symptomatic cases compared to controls. As well, among the samples included in both the full analysis and the subset silhouette widths were significantly greater (Wilcoxin signed-rank p values< 0.05). We then re-introduced the less symptomatic samples (ADHD n = 7, OCD n = 11) into the PCAs of 299 ADHD subset-associated CpGs and 137 OCD subset-associated CpGs and found that PC1 scores of these less symptomatic samples fell between more symptomatic cases and controls (Fig. 3b, c). Overall, these visualizations and quantitative tests all suggest that more symptomatic cases of ADHD and OCD demonstrate greater DNAm differences from controls.

Finally, to assess whether the difference in sample selection or CpG set was responsible for the greater separation between cases and controls in the more symptomatic subset, we performed PCA on the complete sample set using CpGs identified from the more symptomatic cohort. As well, we performed PCA on the more symptomatic sample set using CpGs identified from the complete cohort, in both ADHD and OCD samples. The more symptomatic samples remained more distantly clustered from controls, as compared to the complete cohort of cases (Supplementary Figure 5). Irrespective of CpG set, the methylation patterns of the more symptomatic individuals were more distinct from the control samples.

Disorder-associated CpGs were enriched for mQTLs

Next, we assessed disorder-associated CpGs for underlying mQTLs, given the common relationship between genetic DNAm variation, especially in NDD related loci. We filtered for variable SNPs within a 5-kb window of the two sets of disorder-associated CpGs identified using the more symptomatic subsets, as they had better separation from controls. We then ran Pearson correlations between genotypes, coded numerically, and DNAm to identify mQTLs. Of the 299 CpGs associated with ADHD, 263 were tested with SNPs within 5 kb, and 88% of those (232) were significantly associated with at least one SNP at an FDR-corrected p value < 0.05. A total of 6433 SNP-CpG pairs were tested, as one CpG could be tested against multiple SNPs within 5 kb, and 3283 were identified as mQTLs.

Of the 137 CpGs associated with OCD, 106 were tested with SNPs within 5 kb, and 81% of those (86) were significantly correlated with at least one SNP at an FDR-corrected p < 0.05. A total of 2882 SNP-CpG pairs were tested, and 1350 were identified as mQTLs. Select mQTL associations identified in ADHD and OCD can be seen in Fig. 4. For both ADHD- and OCD-associated CpGs sets, the number of CpGs associated with at least one mQTL was significantly enriched (p values< 0.001), as compared to 1000 iterations of randomly sampled CpGs (See Methods for greater detail; Supplementary Figure 6).

Boxplots of sample mQTLs identified in (a) ADHD cases and controls (n = 42) and (b) OCD cases and controls (n = 82). Cases and controls were combined for mQTL analysis, as depicted by boxplots

Full size image

Finally, we tested each SNP that was significantly associated with an NDD-associated CpG (3283 SNPs correlated with ADHD-associated CpGs and 1150 SNPs correlated with OCD-associated CpGs as identified in the mQTL analysis) against disorder status. No SNPs were significantly associated with OCD after FDR correction; however, 13 SNPs within a 3.5-kb distance and in perfect linkage disequilibrium were associated with ADHD (p values<0.05; Supplementary Table 3). These SNPs were intronic to the gene MAD1L1, a component of the mitotic spindle-assembly checkpoint. This finding suggests that DNAm may mediate the interaction between ADHD and genomic/genetic variation as this locus.

mQTL SNPs had skewed p values in independent GWAS of ADHD but not OCD

We assessed the summary statistics of two independent GWAS analyses for ADHD and OCD, of European decent, to examine whether mQTL SNPs, i.e., SNPs associated with disorder-associated CpGs, were independently related to disorder status in larger sample sizes.

From the results of Demontis et al. European cohort, we pulled all SNPs that were tested for mQTLs in the ADHD sample; 5064 of 5294 were available, which included 2760 of 2896 mQTL SNPs [19]. We generated Q-Q plots of these mQTL SNPs and the remaining 2304 SNPs that were tested for mQTLs, but not significant (Fig. 5; Table 3). The genomic inflation factor reported for the full GWAS, testing 8,094,094 SNPs, was 1.22. By comparison, the mQTL SNPs (i.e., those associated with ADHD-associated CpGs) had a λ=1.47 while in the remaining non-mQTL SNPs λ=1.23. This suggested that the discovery of epigenotype-genotype-phenotype relationships was dependent on associations between proximal SNPs and CpGs.

Q-Q plots of independently generated GWAS p values in (a) ADHD and (b) OCD. Plots show p value distribution of mQTL SNPs with disorder-associated CpGs (left), non-mQTL SNPs proximal to disorder-associated CpGs (middle), and SNPS from the full GWAS (right)

Full size image

Full size table

From the p values published in IOCDF-GC and OCGAS (2018) European cohorts, we assessed all SNPs that were tested for mQTLs in the OCD sample in this study in the same manner as described for ADHD [22]. Of 2202 SNPs tested for mQTLs, 1430 were available in the OCD GWAS, which included 737 of 1105 mQTL SNPs and the 693 remaining SNPs that were proximal, but not correlated with OCD-associated CpGs. Unlike the ADHD GWAS analysis, the genomic inflation factor of the OCD mQTL SNPs and non-mQTL SNPs was inflated, λ = 1.3 and 1.32, respectively, relative to the full GWAS, λ=1.03. However, Q-Q plots showed that “inflation” was limited to p values near the mid-point, while more significant p values were larger than expected, falling below the line of equality (y = x).

Discussion

Genetic and phenotypic heterogeneity of NDDs, including ADHD and OCD, have likely contributed to difficulties in uncovering the molecular etiologies of these disorders. Here, we found that differentially methylated CpGs were more readily identified by epigenome-wide analysis of both ADHD and OCD when groups were reduced to more symptomatic cases. Moreover, the majority of these CpGs were linked to mQTLs, associating with genetic variation at proximal SNPs.

Research into the epigenetic aberrations associated with ASD has provided insight into how epigenetic patterns in blood-derived DNA can be reflective of heterogenous neurodevelopmental phenotypes and how samples may be classified a priori using underlying genetic variation to better define subgroups of ASD [65]. We took a similar approach using phenotype rather than genotype to assess whether a subset of individuals with more endorsed symptoms of ADHD and OCD were more distinct from controls than larger, more heterogeneous cohorts of ADHD and OCD. To account for the possibility of higher comorbidity rates with more severe presentations, participants with multiple diagnoses were excluded. In both disorders, the reduced sets of more symptomatic cases exhibited differential methylation from controls at a greater number of CpGs than the larger cohorts, and they clustered more distinctly from controls. As well, multiple DMRs in both disorders either gained significant CpGs or had larger effect sizes in the subsets of cases with greater clinical severity (Fig. 2). This finding speaks to the potential utility of homogenous group of cases to improve the signal of epigenetic differences from controls.

In both the full OCD cohort and subset selected for greater OCD severity, there was no clear-cut distinction in clustering of cases and controls as visualized on a PCA plot (Fig. 3). Of note, our severity cutoff corresponded to “moderate” OCD on the CY-BOCS and children diagnosed with moderate OCD experience daily interference in their school and social performances; their obsessive thoughts are described as frequent and disturbing, and they can have difficulty controlling or resisting urges to perform compulsions. Nonetheless, in individuals with OCD, DNAm patterning showed greater overlap with that of neurotypical children than the ADHD group (versus controls). Interestingly, in brain imaging studies of NDDs, brain structural connectivity of individuals with ADHD differed more strongly from controls than the structural connectivity of individuals with OCD. Specifically, wide-spread fractional anisotropy, which measures brain tissue characteristics including fibre density and myelination, demonstrates significant reductions in both ASD and ADHD groups as compared to both controls and OCD groups; the OCD group was the most similar to controls, with differences in fractional anisotropy limited to the splenium [66]. Taken together with our findings, these results suggest that ADHD may be a more distinctive condition at the genetic, epigenetic and neurological levels than OCD as compared to neurotypical children.

We found 7 CpGs mapping to 5 genes (C13orf39, C17orf54, DNAJC15, LLGL2, POLS) that were associated with both ADHD and OCD in the more symptomatic samples (Supplementary Table 2). Additionally, both disorders were associated with altered DNAm at CpGs mapping to MAD1L1, MGC87042, PTPRN2, and SGK2; however, the specific associated CpGs were unique to each disorder. Notably, MAD1L1 has previously been associated with ADHD, as reported in an EWAS of DNAm data measured in saliva samples on the Illumina 450K HumanMethylation array [42]; Wilmot et al. found four CpGs mapping to MAD1L1 associated with ADHD at a nominal p value< 0.05 and Δβ > 2% [42]. In our sample, two CpGs in this gene were associated with ADHD (cg12376829, p value< 1.7 × 10–4, Δβ = − 6.9%; cg17545141, p value< 0.044, Δβ = 5.7%), and one was associated with OCD (cg03075889, p value< 0.037, Δβ = 16.1%). In sum, many of our findings suggest that there may be common epigenetic dysregulation across multiple NDDs as has been demonstrated previously for genomic variation.

The MAD1L1 gene has previously been reported as containing risk variants associated with both bipolar disorder and schizophrenia, in multiple studies, and more recently, with ADHD and anxiety [67,68,69,70,71]. In our analysis, MAD1L1 contained the only SNPs significantly associated disorder status; 13 SNPs in perfect linkage disequilibrium were associated with ADHD. This gene specifically merits further research with respect to both the genetic and epigenetic variation in associations with NDDs.

Assessing OCD- and ADHD-associated CpGs for associations with genetic variation, we discovered that 81% and 88% of significant sites were linked to mQTLs, respectively. These proportions are relatively high given that depending on tissue and developmental timing, between 20–80% of CpGs are predicted to be mQTL-associated [32,33,34,35]. This finding was consistent with previous research into epigenetic correlates of ADHD and schizophrenia, but this is the first demonstration of this finding for OCD [40, 48, 72]. In the context of schizophrenia, mQTLs have been proposed as representing SNPs with a functional annotation [40, 48]. The genetic variation across individuals harboring different SNPs can be associated with a regulatory change that is mediated by a specific DNAm change. Based on this postulation, we tested whether our mQTL SNPs, i.e., SNPs associated with disorder-associated CpGs, were more likely to be associated with disorder status in large, independent GWAS analyses run on ADHD and OCD groups. Using the summary statistics of our mQTL SNPs as compared to the whole genome, we saw a stronger trend towards lower p values in ADHD, but not OCD. One interpretation of this finding is that there is a stronger epigenotype-genotype-phenotype correlation in ADHD than OCD and therefore, incorporating DNAm into ADHD genetic research may be particularly fruitful as it has been in schizophrenia research. Although the OCD GWAS we used was the largest study to date, it is still likely underpowered, with no reported SNPs meeting genome-wide significance. As such, we cannot definitively say that DNAm would not be informative in future OCD GWAS analyses.

Our findings were limited by common issues that affect epigenetic research in NDDs. DNAm is strongly associated with tissue/cell type and here, we have analyzed saliva DNAm in cases and controls rather than brain, which is arguably the tissue of interest. As correlations between saliva and brain tissue are limited, we hesitate to interpret potential effects of these DNAm patterns on brain pathophysiology and how they relate to ADHD or OCD etiology. Nonetheless, DNAm studies in accessible tissues, such as saliva and peripheral blood, have contributed to the understanding of the pathophysiology of complex diseases, gene-environment interactions, and effects of prenatal exposures, all of which are pertinent to the study of neurodevelopmental disorders [48, 60, 73, 74]. As well, such accessible, quantitative measures may prove useful as molecular markers of each disorder, potentially prior to clinical presentation and predictive of later behavioral outcomes.

Furthermore, our study sample was small and likely underpowered, especially given that common genetic variants are believed to have small contributions to disorder risk, and it is plausible that similar effects are seen in epigenetics. However, based on our findings, we argue that the reduced power of selecting a subset of cases may be offset by the increased effect size, as seen in our DMRs. Finally, as both ADHD and OCD are believed to represent extremes of quantitative traits, it is likely that our control samples reflect the normative degree of heterogeneity seen in SWAN and CY-BOCS measures [56]. As such, our findings were likely affected by the ranges of non-syndromal ADHD and OCD traits in the control group. Future studies would ideally measure ADHD or OCD in both cases and controls to have a better understanding of the range of phenotypic variability and overlap in each group prior to assessing DNAm.

Conclusions

The enrichment of mQTLs in NDD-associated CpGs sites, presented here and in previous research studies, highlights the utility of DNAm as both an asset to genetic NDD research and a potential biomarker in itself. The DNAm patterns in ADHD and OCD provide evidence of potential epigenetic biomarkers mirroring the phenotypic heterogeneity of these NDDs. Across all NDD research, it is plausible that reducing NDD cohorts to more homogenous subgroups may be a useful method in uncovering stronger molecular correlates as we have shown here.

Availability of data and materials

The microarray data will be made publicly available in the GEO repository upon publication.

Abbreviations

Attention-deficit/hyperactivity disorder

Buccal epithelial cell

Differentially methylated region

DNA methylation

Epigenome-wide association study

Genome-wide association study

Methylation quantitative risk loci

Neurodevelopmental disorders

Obsessive-compulsive disorder

Principal component analysis

Single nucleotide polymorphism

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Sours: https://jneurodevdisorders.biomedcentral.com/articles/10.1186/s11689-020-09324-3
Understanding Obsessive Compulsive Disorder (OCD)

What is the research about?
Autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and obsessive-compulsive disorder (OCD) are neurodevelopmental disorders that impact how the brain functions. Emergent evidence suggests that ASD, ADHD, and OCD may not represent three distinguishable disorders based on the variability among the three disorders and their overlapping causes, biology, and treatment. The purpose of this study was to determine if groups created by categorizing POND participants diagnosed with ASD, ADHD or OCD based on their behavioural and biological characteristics corresponded to their diagnostic groups.

What did the researchers do?

This study used a data-driven approach that questioned the diagnostic accuracy of ASD, ADHD, and OCD. The researchers studied the behavioural and biological characteristics of participants from the POND Network with a primary diagnosis of ASD, ADHD, or OCD. The researchers analyzed the behavioural characteristics of participants by looking at social abilities, attention, and obsessive-compulsive traits. The researchers analyzed the biology of participants by studying brain images. The researchers removed the diagnostic labels and grouped the participants based on their behavioural and biological characteristics.

What did the researchers find?

The results suggested that the groups determined by grouping participants based on behaviour and biology data do not align well with the ASD, ADHD, or OCD diagnostic categories. The groups created by the researchers based on data contained participants from multiple diagnostic categories. Therefore, a child diagnosed with ASD may have more biological and behavioural characteristics in common with a child with OCD or ADHD than another child with ASD.

Take home message.

This paper challenges the way ASD, ADHD, and OCD are currently defined, diagnosed, and treated. The results from this paper add to the growing evidence that neurodevelopmental disorders may not be able to be classified by a single, unique diagnostic label. The researchers outline a need to create groups of neurodevelopmental disorders that better reflect the behavioural and biological characteristics to ensure more accurate interventions and care.

Notes

The full research article can be accessed at this link: https://www.nature.com/articles/s41398-019-0631-2

For PDF of the lay summary click here. Kushki et al. (2019) FINAL

Reference (APA):

Kushki, A., Anagnostou, E., Hammill, C. et al. Examining overlap and homogeneity in ASD, ADHD, and OCD: a data-driven, diagnosis-agnostic approach. Transl Psychiatry9, 318 (2019) doi:10.1038/s41398-019-0631-2

Sours: https://pond-network.ca/research/research-summaries/examining-overlap-and-homogeneity-in-asd-adhd-and-ocd/

Neurodevelopmental is ocd disorder a

Study shows shared brain biology is linked to symptoms that occur across different conditions

A team of scientists has found some similarities in brain impairments in children with autism spectrum disorder, attention deficit hyperactivity disorder (ADHD) or obsessive compulsive disorder (OCD).

The study, published online in July 2016 in the American Journal of Psychiatry, involved brain imaging of white matter in 200 children with autism, ADHD, OCD or no diagnosis. White matter is made up of bundles of nerve fibers that connect cell bodies across the brain, and enables communication between different brain regions.

“We found impairments in white matter in the main tract connecting the right and left hemispheres of the brain in children with either autism, ADHD or OCD, when compared to healthy children in the control group,” says Dr. Stephanie Ameis, first author on the study and Clinician Scientist at the Centre for Addiction and Mental Health (CAMH) Campbell Family Mental Health Research Institute. This particular white matter tract, the corpus callosum, is the largest in the brain and among the first to develop. 

The research team, from CAMH, the Hospital for Sick Children and Holland Bloorview Kids Rehabilitation Hospital, also found children with autism and ADHD showed more severe impairments affecting more of the brain’s white matter than those with OCD. This finding may reflect the fact that both autism and ADHD typically have an onset at a much younger age than OCD, and at a time when a number of different white matter tracts are going through rapid development, says Dr. Ameis.

Autism, ADHD and OCD have common symptoms and are linked by some of the same genes. Yet historically they have been studied as separate disorders. Together, these three neurodevelopmental disorders affect roughly 15 per cent of children and youth.

The study is part of a major Ontario initiative, the Province of Ontario Neurodevelopmental Disorders Network (POND), that is examining various childhood brain-related disorders collectively, to better understand their similarities and differences, and develop more effective and targeted therapies.  

Brain-behaviour link
Many of the behaviours that contribute to impairment in autism, ADHD and OCD, such as attention problems or social difficulties, occur across these conditions, and differ in severity from person to person. The researchers found that the brain’s white matter structure was associated with a spectrum of behavioural symptoms present across these diagnoses. Children with greater brain impairment also had higher impairments in functioning in daily life, regardless of their diagnosis, says Dr. Ameis, who is also appointed at the Hospital for Sick Children. 

This finding has implications for our understanding of the nature of brain-related disorders, notes senior author Dr. Evdokia Anagnostou of Holland Bloorview Kids Rehabilitation Hospital and head of the POND Network. By providing biological evidence that brain structure relates to a spectrum of behavioural symptoms that cut across different developmental conditions, it highlights the shared biology among such conditions. And it points to the potential that treatments targeting a spectrum of behaviours may be relevant for all three conditions. 

This study, the first to be published using the POND Network’s magnetic imaging resonance (MRI) data, was supported by the Ontario Brain Institute. 

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Sours: https://www.camh.ca/en/camh-news-and-stories/common-brain-changes-found-in-children-with-autism-adhd-and-ocd
Neurobiology of Obsessive Compulsive Disorder and Co Occurring Mental Health Issues

Inhibitory control is a component of executive function that governs the suppression of interference from irrelevant stimuli. Individuals diagnosed with many neuropsychiatric disorders, including many neurodevelopmental disorders such as ADHD (attention deficit hyperactivity disorder) and OCD (obsessive compulsive disorder), share neural dysfunction of inhibitory control.1

Although individuals diagnosed with OCD or ADHD share impairments in inhibitory control, they present with disorder-specific functional and structural brain abnormalities in frontostriatal region, according to new findings of a meta-analysis performed by investigators affiliated with King’s College London and Karolinska Institute in Stockholm, Sweden. The findings were published in JAMA Psychiatry.2

While the role of frontostriatal circuits is not completely elucidated, aberrant connectivity in frontostriatal circuits is known to impact cognitive processing, including specific components of executive function such as inhibitory control. Abnormalities of the frontal cortex and striatum have also been reported in many other neuropsychiatric disorders, including schizophrenia and depression.


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In the current meta-analysis, researchers assessed structural abnormalities by analyzing the voxel-based morphometry (VBM) data of whole-brain gray matter (GMV) volume, and they assessed brain activation patterns by analyzing functional magnetic resonance imaging (fMRI) data. They compared structural and functional imaging data of patients diagnosed with ADHD (VBM, n=931 and fMRI, n=489) or OCD (VBM, n=928 and fMRI, n=287) with data collected on age-matched, typically-developing individuals that served as controls in the same ADHD (VBM, n=822 and fMRI, n=591) and OCD (VBM, n=822 and fMRI, n=284) studies.

Data indicate that patients with ADHD show decreased GMV (left z = 1.904, P < .001; right z = 1.738, P < .001) and function (left z = 1.447, P < .001; right z = 1.229, P < .001) in bilateral basal ganglia/insula, when compared to patients with OCD (and controls). Patients with OCD, on contrary, show increased GMV and function in bilateral ganglia/insula, relative to control participants.

Patients with OCD show reduced GMV (z = 1.622, P < .001) and function (z = 2.133, P < .001) in rostral and dorsal anterior cingulate/medial prefrontal cortex. Patients with ADHD, on contrary, show reduced function predominantly in the right ventrolateral prefrontal cortex (z = 1.229, P < .001).

Deficits in cognitive (and motor) inhibitory control are observed in various neuropsychiatric and neurodevelopmental disorders. For example, the aberrant function of the corticostriatal circuitry (eg, basal ganglia and prefrontal cortex) is involved in reduced inhibitory control over involuntary impulses in disorders such as Tourette’s Syndrome. Also, increased activation of the insula, for example, is hypothesized to indicate an increased effort to inhibit responses in individuals diagnosed with autism. And, reduced activation of the anterior cingulate, for example, has been reported in patients with schizophrenia, another disorder that involves executive dysfunction, including poor inhibitory control.

Taken together, although patients with ADHD or OCD share behavioral impairments in inhibitory control, they present with distinctive striatal and frontal functional and structural biomarkers. “Disorder-specific neurofunctional biomarkers … provide useful targets for treatment with drugs that target these regions, or for nonpharmacological therapies such as fMRI-based neurofeedback, brain stimulation, or cognitive training of functions mediated by these regions,” the investigators concluded in their publication.

References

1. Brem S, Grünblatt E, Drechsler R, et al. The neurobiological link between OCD and ADHD. Atten Defic Hyperact Disord. 2014;6(3):175-202.

2. Norman LJ, Carlisi C, Lukito S, et al. Structural and functional brain abnormalities in attention-defiict/hyperactivity disorder and obsessive-compulsive disorder: a comparative meta-analysis. JAMA Psychiatry. 2016. doi: 10.1001/jamapsychiatry.2016.0700. [Epub ahead of print]

Topics:

Attention-Deficit/Hyperactivity DisorderNeurocognitiveNeurodevelopmental DisorderNeurologyNeuropsychiatricNeuropsychiatric SymptomsObsessive CompulsiveObsessive-Compulsive DisorderObsessive-Compulsive DisordersSours: https://www.psychiatryadvisor.com/home/topics/neurodevelopmental-disorder/adhd-and-ocd-individuals-differ-in-brain-structure-and-function/

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Sours: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3645849/


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