Upstate College of Medicine Scholarly Publications: Recent submissions
Now showing items 1-20 of 30
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Local Health Departments Tweeting About Ebola: Characteristics and MessagingContext: The first imported U.S. Ebola Hemorrhagic Fever case during the 2014 West Africa Ebola outbreak triggered an increase in online activity through various social media platforms, including Twitter. Objectives: The purpose of our study was to examine characteristics of local health departments (LHDs) tweeting about Ebola, in addition to how and when LHDs were communicating Ebola-related messages. Design: All tweets sent by 287 LHDs known to be using Twitter were collected from September 3 to November 2, 2014. Twitter data were merged with the 2013 National Association of County and City Health Officials (NACCHO) Profile study to assess LHD characteristics associated with sending Ebola-related tweets. To examine the content of Ebola tweets, we reviewed all such tweets and developed a codebook including four major message categories: information-giving, news update, event promotion, and preparedness. A timeline tracking the trends in Ebola tweets was created by aligning daily tweets with major Ebola news events posted on the Centers for Disease Control and Prevention (CDC) Ebola website. Results: Approximately 60% (n=174) of all LHDs using Twitter sent a total of 1 648 Ebola-related tweets during the study period. Sending more tweets in general (OR: 2.42; 95% CI: 1.00-5.84) and employing at least one Public Information Specialist (OR: 2.61; 95% CI: 1.14-5.95) significantly increased the odds that an LHD tweeted about Ebola. Of all the Ebola tweets collected, 78.6% were information-giving, 22.5% were on preparedness, 20.8% were news updates, and 10.3% were event promotion tweets. A temporal analysis of Ebola tweets indicated five distinct waves, each corresponding with major Ebola news events. Conclusions: Twitter has become a communication tool frequently used by many LHDs to respond to novel outbreaks, but messaging strategies vary widely across LHDs. We present several recommendations for LHDs using this novel communication channel during outbreaks and other emergent events.
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Role of Neighborhood Physical Disorder and Social Cohesion on Racial and Ethnic Disparities in Dementia RiskObjectives: To analyze how neighborhood physical disorder and social cohesion are associated with racial and ethnic disparities in dementia risk. Methods: Nine years of data (2011-2019) were retrieved from the National Health and Aging Trends Study, a nationally-representative U.S. older adult (age 65+) sample. Cox regression analyzed time to dementia diagnosis using composite scores for neighborhood physical disorder and social cohesion. Results: Higher baseline neighborhood physical disorder (Adjusted Hazard Ratio [aHR]=1.11, 95% Confidence Interval [CI]=1.01-1.23) and increased disorder at follow-up (aHR=1.10, 95% CI=1.01-1.19) significantly increased dementia risk. Hispanic older adults with higher physical disorder at baseline (aHR=0.62, 95% CI=0.49-0.79) and follow-up (aHR=0.81, 95% CI=0.67-0.98) had a significantly decreased dementia risk. There were no significant associations for social cohesion. Discussion: Physical but not social neighborhood characteristics are associated with dementia risk. Future research is needed to understand protective mechanisms for dementia among Hispanic older adults in neighborhoods with high physical disorder.
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Relationship between dementia, COVID‐19 risk, and adherence to COVID‐19 mitigation behaviors among older adults in the United StatesObjectives: To examine how dementia is associated with COVID-19 risk and adherence to COVID-19 mitigation behaviors, and whether mitigation behaviors mediate the relationship between dementia and COVID-19 risk. Methods/Design: We analyzed 2019 and 2020 data from the National Health and Aging Trends Study, a national prospective cohort study of United States older adults age 65+. Outcomes were COVID-19 diagnosis and adherence to COVID-19 mitigation behaviors (handwashing, mask-wearing, and social distancing). Results: Among the 3257 older adults in this study, 485 (14.9%) had dementia in 2019 and 98 (3.1%) were COVID-19 positive in 2020. Dementia significantly increased the odds of COVID-19 by 129% (odds ratio [OR] = 2.29, 95% confidence interval [CI] 1.32 to 3.97), and remained elevated after adjusting for sociodemographics and health (OR = 1.67, 95% CI 0.90 to 3.11). Dementia significantly decreased the odds of handwashing by 72% (OR = 0.28, 95% CI 0.17 to 0.44), which remained lower after adjusting for sociodemographics and health (OR = 0.53, 95% CI 0.23 to 1.21). Dementia was not significantly associated with mask-wearing and social distancing. The relationship between dementia and COVID-19 was primarily mediated by functional impairment, income, and residential setting. Conclusions: Dementia was associated with an increased COVID-19 risk and lower adherence to handwashing among U.S. older adults. Adherence to COVID-19 mitigation behaviors did not mediate COVID-19 risk by dementia status. For older adults with dementia, COVID-19 risk could be decreased by prioritizing health interventions.
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COVID-19 risk factors and predictors for handwashing, masking, and social distancing among a national prospective cohort of US older adultsObjectives: Older adults have a disproportionately higher COVID-19 risk, however, there is limited research investigating adherence to the major COVID-19 mitigation behaviors (handwashing, masking, social distancing) for older populations. We examined COVID-19 risk factors and predictors for adherence to COVID-19 mitigation behaviors among a national sample of U.S. older adults. Study Design: Data were retrieved for 3,257 respondents from the National Health and Aging Trends Study, a nationally representative prospective sample of U.S. Medicare beneficiaries age 65 or older. COVID-19 variables were collected in 2020, while all other data were collected in 2019. Methods: We utilized multiple logistic regression to analyze COVID-19 risk factors and predictors for handwashing, masking, and social distancing to minimize COVID-19 spread. Missing data were imputed, and all models applied survey sampling weights. Results: Factors significantly associated with increased odds of COVID-19 diagnosis among U.S. older adults were Hispanic ethnicity, low-income household, residential care or nursing home, and generalized anxiety disorder. We identified multiple factors significantly associated with adherence to handwashing, masking, and social distancing. Most notably, older males had a significantly lower odds of practicing all three COVID-19 mitigation behaviors, and Black older adults had a significantly higher odds of masking and handwashing. Conclusions: When prioritizing COVID-19 prevention efforts for older adults, risk factors that should be considered are race and ethnicity, income, residential setting, and anxiety. To effectively mitigate COVID-19 disease spread, public health professionals must also recognize sociodemographic and health factors may influence whether older adults adhere to handwashing, masking, and social distancing.
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Geospatial Distribution of Local Health Department Tweets and Online Searches About Ebola During the 2014 Ebola OutbreakObjective This study compared the geospatial distribution of Ebola tweets from local health departments (LHDs) to online searches about Ebola across the United States during the 2014 Ebola outbreak. Methods Between September and November 2014, we collected all tweets sent by 287 LHDs known to be using Twitter. Coordinates for each Ebola tweet were imported into ArcGIS 10.2.2 to display the distribution of tweets. Online searches with the search term “Ebola” were obtained from Google Trends. A Pearson correlation was conducted to access the relationship between online search activity and per capita number of LHD Ebola tweets by state. Results Ebola tweets from LHDs were concentrated in cities across the northeast states, including Philadelphia and New York City. In contrast, states with the highest online search queries for Ebola were primarily in the south, particularly Oklahoma and Texas. A weak, negative, non-significant correlation (r=-.03, p=.83, 95% CI -.30-.25) was observed between online search activity and per capita number of LHD Ebola tweets by state. Conclusions We recommend LHDs consider using social media to communicate possible disease outbreaks in a timely manner, and consider using online search data to tailor their messages to align with the public health interests of their constituents.
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Food Insecurity and COVID-19 Diagnosis: Findings from a National United States SampleThis study explores the association between experiencing food insecurity and COVID-19 diagnosis in the United States, and what sociodemographic characteristics moderate this relationship. We analyzed a national sample of adults in the United States (n=6,475). Multiple logistic regression results revealed respondents experiencing food insecurity had approximately 3.0 times significantly higher odds of a positive COVID-19 diagnosis (Odds Ratio [OR]=2.95, 95% Confidence Interval [CI]=1.38-6.32, p<.01), which remained significant after adjusting for sociodemographics and COVID-19 mitigation behaviors (OR=2.59, 95% CI=1.09-6.18, p<.05). Age group had a significant moderating effect (OR=42.55, 95% CI=3.13-579.15, p<.01). Results indicate experiencing food insecurity is associated with contracting COVID-19.
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Sleep Disturbances and Dementia Risk in Older Adults: Findings From 10 Years of National U.S. Prospective DataIntroduction: Prior research has identified a link between sleep disturbances and cognitive impairment, however, no study has examined this relationship using a national U.S. sample. This study examines how multiple longitudinal measures of sleep disturbances (sleep-initiation insomnia, sleep-maintenance insomnia, sleep medication usage) are associated with dementia risk. Methods: Ten annual waves (2011–2020) of prospective cohort data from a nationally representative U.S. sample of older adults age 65 and older were analyzed from the National Health and Aging Trends Study (NHATS). Sleep disturbances were converted into a longitudinal score and measured as sleep-initiation insomnia (trouble falling asleep in 30 minutes), sleep-maintenance insomnia (trouble falling asleep after waking up early), and sleep medication usage (taking medication to help sleep). Cox regression models analyzed time to dementia diagnosis for a sample of 6,284 respondents. Results: In the unadjusted model, sleep-initiation insomnia was significantly associated with a 51% increased dementia risk (hazard ratio [HR]=1.51, 95% confidence interval [CI]=1.19–1.90). Adjusted for sociodemographics, sleep medication usage was significantly associated with a 30% increased dementia risk (aHR=1.30, 95% CI=1.08–1.56). Adjusted for sociodemographics and health, sleep-maintenance insomnia was significantly associated with a 40% decreased dementia risk (aHR=0.60, 95% CI=0.46–0.77). Conclusions: These findings suggest sleep-initiation insomnia and sleep medication usage may elevate dementia risk. Based on the current evidence, sleep disturbances should be considered when assessing the risk profile for dementia. Future research is needed to examine other sleep disturbance measures and to explore mechanisms for decreased dementia risk among older adults with sleep-maintenance insomnia.
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Strategies for the Recruitment and Retention of Racial/Ethnic Minorities in Alzheimer Disease and Dementia Clinical ResearchBackground Racial/ethnic minorities have among the highest risks for Alzheimer disease and dementia, but remain underrepresented in clinical research studies. Objective To synthesize the current evidence on strategies to recruit and retain racial/ethnic minorities in Alzheimer disease and dementia clinical research. Method We conducted a systematic review by searching CINAHL, EMBASE, MEDLINE, PsycINFO, and Scopus. We included studies that met four criteria: (1) included a racial/ethnic minority group (African American, Latino, Asian, American Indian or Alaska Native, and Native Hawaiian or Other Pacific Islander); (2) implemented a recruitment or retention strategy for Alzheimer disease or dementia clinical research; (3) conducted within the U.S.; and (4) published in a peer-reviewed journal. Results Of the 19 included studies, 14 (73.7%) implemented recruitment strategies and 5 (26.3%) implemented both recruitment and retention strategies. Fifteen studies (78.9%) focused on African Americans, two (10.6%) on both African Americans and Latinos, and two (10.5%) on Asians. All articles were rated weak in study quality. Four major themes were identified for recruitment strategies: community outreach (94.7%), advertisement (57.9%), collaboration with health care providers (42.1%), and referral (21.1%). Three major themes were identified for retention strategies: follow-up communication (15.8%), maintain community relationship (15.8%), and convenience (10.5%). Conclusion Our findings highlight several promising recruitment and retention strategies investigators should prioritize when allocating limited resources, however, additional well-designed studies are needed. By recruiting and retaining more racial/ethnic minorities in Alzheimer disease and dementia research, investigators may better understand the heterogeneity of disease progression among marginalized groups. PROSPERO registration #CRD42018081979.
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ADHD and DAT1: Further evidence of paternal over-transmission of risk alleles and haplotypeWe [Hawi et al. (2005); Am J Hum Genet 77:958–965] reported paternal over-transmission of risk alleles in some ADHD-associated genes. This was particularly clear in the case of the DAT1 3′-UTR VNTR. In the current investigation, we analyzed three new samples comprising of 1,248 ADHD nuclear families to examine the allelic over-transmission of DAT1 in ADHD. The IMAGE sample, the largest of the three-replication samples, provides strong support for a parent of origin effect for allele 6 and the 10 repeat allele (intron 8 and 3′-UTR VNTR, respectively) of DAT1. In addition, a similar pattern of over-transmission of paternal risk haplotypes (constructed from the above alleles) was also observed. Some support is also derived from the two smaller samples although neither is independently significant. Although the mechanism driving the paternal overtransmission of the DAT risk alleles is not known, these finding provide further support for this phenomenon.
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Evidence for Similar Structural Brain Anomalies in Youth and Adult Attention-Deficit/Hyperactivity Disorder: A Machine Learning AnalysisAttention-deficit/hyperactivity disorder (ADHD) affects 5% of children world-wide. Of these, two-thirds continue to have impairing symptoms of ADHD into adulthood. Although a large literature implicates structural brain differences of the disorder, it is not clear if adults with ADHD have similar neuroanatomical differences as those seen in children with recent reports from the large ENIGMA-ADHD consortium finding structural differences for children but not for adults. This paper uses deep learning neural network classification models to determine if there are neuroanatomical changes in the brains of children with ADHD that are also observed for adult ADHD, and vice versa. We found that structural MRI data can significantly separate ADHD from control participants for both children and adults. Consistent with the prior reports from ENIGMA-ADHD, prediction performance and effect sizes were better for the child than the adult samples. The model trained on adult samples significantly predicted ADHD in the child sample, suggesting that our model learned anatomical features that are common to ADHD in childhood and adulthood. These results support the continuity of ADHD’s brain differences from childhood to adulthood. In addition, our work demonstrates a novel use of neural network classification models to test hypotheses about developmental continuity.
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Machine-Learning Prediction of Comorbid Substance Use Disorders in ADHD Youth Using Swedish Registry DataBackground: Children with attention-deficit/hyperactivity disorder (ADHD) have a high risk for substance use disorders (SUDs). Early identification of at-risk youth would help allocate scarce resources for prevention programs. Methods: Psychiatric and somatic diagnoses, family history of these disorders, measures of socioeconomic distress, and information about birth complications were obtained from the national registers in Sweden for 19,787 children with ADHD born between 1989 and 1993. We trained (a) a cross-sectional random forest (RF) model using data available by age 17 to predict SUD diagnosis between ages 18 and 19; and (b) a longitudinal recurrent neural network (RNN) model with the Long Short-Term Memory (LSTM) architecture to predict new diagnoses at each age. Results: The area under the receiver operating characteristic curve (AUC) was 0.73(95%CI 0.70–0.76) for the random forest model (RF). Removing prior diagnosis from the predictors, the RF model was still able to achieve significant AUCs when predicting all SUD diagnoses (0.69, 95%CI 0.66–0.72) or new diagnoses (0.67, 95%CI: 0.64, 0.71) during age 18–19. For the model predicting new diagnoses, model calibration was good with a low Brier score of 0.086. Longitudinal LSTM model was able to predict later SUD risks at as early as 2 years age, 10 years before the earliest diagnosis. The average AUC from longitudinal models predicting new diagnoses 1, 2, 5 and 10 years in the future was 0.63. Conclusions: Population registry data can be used to predict at-risk comorbid SUDs in individuals with ADHD. Such predictions can be made many years prior to age of the onset, and their SUD risks can be monitored using longitudinal models over years during child development. Nevertheless, more work is needed to create prediction models based on electronic health records or linked population registers that are sufficiently accurate for use in the clinic.
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Machine Learning And MRI-Based Diagnostic Models For ADHD: Are We There Yet?Machine learning (ML) has been applied to develop magnetic resonance imaging (MRI)-based diagnostic classifiers for attention-deficit/hyperactivity disorder (ADHD). This systematic review examines this literature to clarify its clinical significance and to assess the implications of the various analytic methods applied. We found that, although most of studies reported the classification accuracies, they varied in choice of MRI modalities, ML models, cross-validation and testing methods, and sample sizes. We found that the accuracies of cross-validation methods inflated the performance estimation compared with those of a held-out test, compromising the model generalizability. Test accuracies have increased with publication year but were not associated with training sample sizes. Improved test accuracy over time was likely due to the use of better ML methods along with strategies to deal with data imbalances. Ultimately, large multi-modal imaging datasets, and potentially the combination with other types of data, like cognitive data and/or genetics, will be essential to achieve the goal of developing clinically useful imaging classification tools for ADHD in the future.
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A seq2seq model to forecast the COVID-19 cases, deaths and reproductive R numbers in US countiesThe global pandemic of coronavirus disease 2019 (COVID-19) has killed almost two million people worldwide and over 400 thousand in the United States (US). As the pandemic evolves, informed policy-making and strategic resource allocation relies on accurate forecasts. To predict the spread of the virus within US counties, we curated an array of county-level demographic and COVID-19-relevant health risk factors. In combination with the county-level case and death numbers curated by John Hopkins university, we developed a forecasting model using deep learning (DL). We implemented an autoencoder-based Seq2Seq model with gated recurrent units (GRUs) in the deep recurrent layers. We trained the model to predict future incident cases, deaths and the reproductive number, R. For most counties, it makes accurate predictions of new incident cases, deaths and R values, up to 30 days in the future. Our framework can also be used to predict other targets that are useful indices for policymaking, for example hospitalization or the occupancy of intensive care units. Our DL framework is publicly available on GitHub and can be adapted for other indices of the COVID-19 spread. We hope that our forecasts and model can help local governments in the continued fight against COVID-19.
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Structural Brain Imaging Studies Offer Clues about the Effects of the Shared Genetic Etiology among Neuropsychiatric DisordersGenomewide association studies have found significant genetic correlations among many neuropsychiatric disorders. In contrast, we know much less about the degree to which structural brain alterations are similar among disorders and, if so, the degree to which such similarities have a genetic etiology. From the Enhancing Neuroimaging Genetics through Meta- Analysis (ENIGMA) consortium, we acquired standardized mean differences (SMDs) in regional brain volume and cortical thickness between cases and controls. We had data on 41 brain regions for: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), epilepsy, major depressive disorder (MDD), obsessive compulsive disorder (OCD), and schizophrenia (SCZ). These data had been derived from 24,360 patients and 37,425 controls. The SMDs were significantly correlated between SCZ and BD, OCD, MDD, and ASD. MDD was positively correlated with BD and OCD. BD was positively correlated with OCD and negatively correlated with ADHD. These pairwise correlations among disorders were correlated with the corresponding pairwise correlations among disorders derived from genomewide association studies (r = 0.494). Our results show substantial similarities in sMRI phenotypes among neuropsychiatric disorders and suggest that these similarities are accounted for, in part, by corresponding similarities in common genetic variant architectures.
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Shared polygenic risk for ADHD, executive dysfunction and other psychiatric disordersMany psychiatric disorders are associated with impaired executive functioning (EF). The associated EF component varies by psychiatric disorders, and this variation might be due to genetic liability. We explored the genetic association between five psychiatric disorders and EF in clinically-recruited attention deficit hyperactivity disorder (ADHD) children using polygenic risk score (PRS) methodology. Genome-wide association study (GWAS) summary data for ADHD, major depressive disorder (MDD), schizophrenia (SZ), bipolar disorder (BIP) and autism were used to calculate the PRSs. EF was evaluated by the Stroop test for inhibitory control, the trail-making test for cognitive flexibility, and the digital span test for working memory in a Chinese ADHD cohort (n = 1147). Exploratory factor analysis of the three measures identified one principal component for EF (EF-PC). Linear regression models were used to analyze the association between each PRS and the EF measures. The role of EF measures in mediating the effects of the PRSs on ADHD symptoms was also analyzed. The result showed the PRSs for MDD, ADHD and BIP were all significantly associated with the EF-PC. For each EF component, the association results were different for the PRSs of the five psychiatric disorders: the PRSs for ADHD and MDD were associated with inhibitory control (adjusted P = 0.0183 and 0.0313, respectively), the PRS for BIP was associated with working memory (adjusted P = 0.0416), and the PRS for SZ was associated with cognitive flexibility (adjusted P = 0.0335). All three EF measures were significantly correlated with ADHD symptoms. In mediation analyses, the ADHD and MDD PRSs, which were associated with inhibitory control, had significant indirect effects on ADHD symptoms through the mediation of inhibitory control. These findings indicate that the polygenic risks for several psychiatric disorders influence specific executive dysfunction in children with ADHD. The results helped to clarify the relationship between risk genes of each mental disorder and the intermediate cognitive domain, which may further help elucidate the risk genes and motivate efforts to develop EF measures as a diagnostic marker and future treatment target.
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Informativeness of Self-Reports of ADHD Symptoms in Monitoring Response to Stimulant Treatment in Clinically Referred Adults With ADHDTo investigate the informativeness of self-reports of ADHD symptoms in adults with ADHD in the clinical setting. Method: Subjects were clinically referred adults aged 19 years to 67 years of age of both sexes (N = 54). All subjects were on stable doses of stimulant and were considered responders to treatment. ADHD symptoms were assessed using the ADHD Investigator Symptom Rating Scale (AISRS) and the ADHD Self-Report Scale (ASRS). Spearman’s rank correlations were used to assess the correlations between clinician-assessed ADHD and patients’ self-reports. Results: Spearman’s rank correlation analysis found evidence of a strong, positive association between total scores on the AISRS and the ASRS (rs = .65, df = 52, p < .001). Conclusion: Results have important implications for the management and monitoring of treatment response in the clinical setting through patients’ self-report.(J. of Att. Dis. 2020; 24(3) 420-424)
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Agonal factors distort gene-expression patterns in human postmortem brainsAgonal factors, the conditions that occur just prior to death, can impact the molecular quality of postmortem brains, influencing gene expression results. Nevertheless, study designs using postmortem brain tissue rarely, if ever, account for these factors, and previous studies had not documented nor adjusted for agonal factors. Our study used gene expression data of 262 samples from ROSMAP with the following terminal states recorded for each donor: surgery, fever, infection, unconsciousness, difficulty breathing, and mechanical ventilation. Performed differential gene expression and weighted gene co-expression network analyses (WGCNA), fever and infection were the primary contributors to brain gene expression changes. Fever and infection also contributed to brain cell-type specific gene expression and cell proportion changes. Furthermore, the gene expression patterns implicated in fever and infection were unique to other agonal factors. We also found that previous studies of gene expression in postmortem brains were confounded by variables of hypoxia or oxygen level pathways. Therefore, correction for agonal factors through probabilistic estimation of expression residuals (PEER) or surrogate variable analysis (SVA) is recommended to control for unknown agonal factors. Our analyses revealed fever and infection contributing to gene expression changes in postmortem brains and emphasized the necessity of study designs that document and account for agonal factors.
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Risk variants and polygenic architecture of disruptive behavior disorders in the context of attention-deficit/hyperactivity disorderAttention-Deficit/Hyperactivity Disorder (ADHD) is a childhood psychiatric disorder often comorbid with disruptive behavior disorders (DBDs). Here, we report a GWAS meta-analysis of ADHD comorbid with DBDs (ADHD + DBDs) including 3802 cases and 31,305 controls. We identify three genome-wide significant loci on chromosomes 1, 7, and 11. A meta-analysis including a Chinese cohort supports that the locus on chromosome 11 is a strong risk locus for ADHD + DBDs across European and Chinese ancestries (rs7118422, P = 3.15×10−10, OR= 1.17). We find a higher SNP heritability for ADHD + DBDs (h2 SNP = 0.34) when compared to ADHD without DBDs (h2 SNP = 0.20), high genetic correlations between ADHD + DBDs and aggressive (rg = 0.81) and anti-social behaviors (rg = 0.82), and an increased burden (polygenic score) of variants associated with ADHD and aggression in ADHD + DBDs compared to ADHD without DBDs. Our results suggest an increased load of common risk variants in ADHD + DBDs compared to ADHD without DBDs, which in part can be explained by variants associated with aggressive behavior.
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Consortium neuroscience of attention deficit/hyperactivity disorder and autism spectrum disorder: The ENIGMA adventureNeuroimaging has been extensively used to study brain structure and function in individuals with attention deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) over the past decades. Two of the main shortcomings of the neuroimaging literature of these disorders are the small sample sizes employed and the heterogeneity of methods used. In 2013 and 2014, the ENIGMA-ADHD and ENIGMA-ASD working groups were respectively, founded with a common goal to address these limitations. Here, we provide a narrative review of the thus far completed and still ongoing projects of these working groups. Due to an implicitly hierarchical psychiatric diagnostic classification system, the fields of ADHD and ASD have developed largely in isolation, despite the considerable overlap in the occurrence of the disorders. The collaboration between the ENIGMA-ADHD and -ASD working groups seeks to bring the neuroimaging efforts of the two disorders closer together. The outcomes of case–control studies of subcortical and cortical structures showed that subcortical volumes are similarly affected in ASD and ADHD, albeit with small effect sizes. Cortical analyses identified unique differences in each disorder, but also considerable overlap between the two, specifically in cortical thickness. Ongoing work is examining alternative research questions, such as brain laterality, prediction of case–control status, and anatomical heterogeneity. In brief, great strides have been made toward fulfilling the aims of the ENIGMA collaborations, while new ideas and follow-up analyses continue that include more imaging modalities (diffusion MRI and resting-state functional MRI), collaborations with other large databases, and samples with dual diagnoses.
