Autism Research Institute

To evaluate repetitive motor behaviors (RMBs) in non-autistic toddlers using direct observational methods. This cohort study assessed RMBs in a community sample of 679 toddlers at 24 and 36 months of age. Initial analyses examined rates of RMBs in autistic (n = 65) vs nonautistic (n = 614) participants. Subsequent analyses focused on participants without autism, first examining clinical correlates of RMBs in the full non-autistic group and then comparing rates of RMBs in 2 non-autistic subgroups: 104 participants with other developmental concerns (ODC) and 510 participants with no developmental concerns. A total of 36% of non-autistic children demonstrated RMB at 24 and/or 36 months of age. RMBs were significantly more likely in the ODC (55%) than the no developmental concerns (33%) group. Non-autistic participants with RMBs had significantly lower communication scores at 24 months and, at both ages, significantly higher scores on 2 measures of autism-related symptomatology than those without RMBs; however, group means fell within the average range and effect sizes were small. There were no RMB differences based on sex. RMBs are not uncommon in 24- to 36-month-old children who do not meet diagnostic criteria for autism. Among non-autistic children, RMBs are most likely to occur in those with other developmental challenges but are also present in some typically developing children. The presence of RMBs in toddlers should be evaluated within the context of Diagnostic and Statistical Manual of Mental Disorders, fifth edition ASD criteria, and RMBs alone without social communication challenges core to ASD should not be viewed as automatically indicative of autism.

BackgroundAlzheimer's Disease and related dementias (ADRD) are complex disorders with increased risk in populations experiencing structural inequities, including those who live in disadvantaged neighborhoods as indexed by the Area Deprivation Index (ADI). Increasingly, evidence suggests multi‐etiology dementia neuropathology in these populations. Cerebrovascular Disease (CVD) is common in multi‐etiology dementia neuropathology. Currently, little work has investigated ADI's relationship with CVD. Through the Neighborhoods Study, a national study leveraging 21 Alzheimer's Disease Research Center (ADRC) brain banks to evaluate the adverse social exposomes relationship to neuropathology, we sought to understand weather high deprivation predicts CVD in this national sample of brain bank donors.Method21 ADRC brain banks and their brain donor participants were eligible (N = 7187) as part of the Neighborhoods Study. Addresses at death were linked to national ADI percentiles corresponding to year of death, organized into categorical statistical tertiles (1‐20 (N = 2773), 21‐50 (N = 2620), 51‐100 (N = 1794). Outcomes included presence of a selection of CVD pathologic changes and lesions (infarcts, hemorrhages, microinfarcts, and white matter rarefaction) as noted within NACC neuropathology data, as per method put forward by Godrich et al., 2022. We used a logistic regression adjusting for sex, age at death, and APOE carrier status.ResultWe found that ∼25% of donors lived in high deprivation contexts, 52% had at least one incidence of CVD, and the cohort identified as <1% American Indian or Native Alaskan, <1 % Asian or Pacific Islander, ∼1% Multiracial, ∼3% Black, and ∼95% White. We found that increased ADI predicted significantly increased odds of CVD neuropathology in our cohort (OR = 1.16, p <.001). Further, we found that the low deprivation had significantly fewer donors with CVD (48%) compared to the middle (54.6% CVD, OR = 1.30, p < .001) and highest tertiles (54.7% CVD, OR=1.34, p <.001); however, there was no significant difference between the middle and highest tertiles (p > .05).ConclusionWe found that more disadvantaged neighborhoods predict CVD neuropathology in a nationwide brain donor cohort in the Neighborhoods Study. Further work will investigate the mechanisms that might contribute to the prevalence of overlapping multi‐etiology neuropathology in persons residing in highly disadvantaged neighborhoods.

Multiple Systems Atrophy (MSA) is a rare, sporadic, age-related synucleinopathy characterized by Parkinson-like motor symptoms and ataxia. There is no therapy for MSA other than symptomatic treatment. MSA is characterized pathologically by glial cytoplasmic inclusions (GCI) of α-synuclein (αSyn) occurring in oligodendrocytes leading to loss of myelination in the brain. We recently utilized a peptide-mediated delivery method to systemically transport an anti-sense oligonucleotide (ASO) targeted to αSyn in a mouse model of MSA. We hypothesized that systemic delivery of αSyn ASO by peptide mediated delivery to a mouse model of MSA would reduce the αSyn accumulation in oligodendrocytes and reduce the overt pathology associated with MSA. Following monthly treatments of the αSyn ASO, we found increased myelination in the corpus callosum and the cerebellum. We also observed increased numbers of oligodendrocytes and reduced gliosis; however, we did not detect changes in overall αSyn in the areas of the brain we examined. Upon further analysis, we determined the peptide-mediated delivery of αSyn ASO was not taken up by oligodendrocytes. Thus, we have successfully alleviated some of the pathology associated with MSA in a mouse model; however, without direct delivery to oligodendrocytes, other approaches may need to supplement this therapy.

Variability in cognitive decline in preclinical Alzheimer's disease (AD) presents a significant challenge in evaluating treatment effects in clinical trials. This study developed models to forecast cognitive progression and assessed their potential to improve the precision of treatment effect estimates in future trials. Data were from the Phase III Anti-Amyloid Treatment trial of Solanezumab in amyloid-positive asymptomatic preclinical AD. Due to no significant cognitive decline differences between groups, the Solanezumab arm (n=549) was used for training, and the placebo arm (n=559) for validation. Cognitive decline was assessed as 24-week changes in Preclinical Alzheimer's Cognitive Composite (PACC) score over 216 weeks. A predictive model for PACC decline was developed using demographics, APOE ε4 status, and baseline clinical assessments (PACC composite score, its components, and CDR-SB), employing Stochastic Gradient Boosting. The added value of amyloid PET Centiloid (CL), plasma pTau217 (electrochemiluminescence assay), MRI morphometrics, and tau PET measures (375-participant substudy) were evaluated. Model performance was optimized via cross-validation and validated in the placebo group. The impact of baseline PACC decline predictions as an Alzheimer's Prognostic Covariate (APC) on treatment effect assessments was evaluated in trial simulations. A model with demographics, CL, and clinical assessments explained 16% of PACC decline at week 216 (R²=0.16), improving over CL or clinical assessments alone (p<0.05). Adding MRI increased R² to 0.23 (p<0.05); replacing CL with plasma pTau217 or tau PET raised it to 0.25 and 0.42, respectively. Without tau PET in the model, key baseline predictors were pTau217, CL, PACC, MMSE, inferior temporal area, and entorhinal volume. When tau PET was included, it became the strongest predictor, with fusiform, inferior temporal, and supramarginal regions being the most predictive. Simulations showed baseline prediction of PACC decline as APC can reduce treatment effect variance by 20.3%, increase power from 80% to 88%, or reduce sample size by 21.5%. Incorporating tau PET further reduces variance by 37%, increases power to 94%, and decreases sample size by 35%. Baseline tau PET was the strongest predictor of PACC decline. Using baseline-predicted PACC decline as APC can enhance treatment effect estimates and trial efficiency in preclinical AD.

INTRODUCTIONOver 220,000 people live with Down syndrome (DS) in the United States, with an average life expectancy of 63.5 years of age. The high risk for Alzheimer's disease (AD) in DS means studies must be accessible and inclusive. We share our experience of building a collaborative group of self‐advocates and care partners to provide meaningful feedback on research.METHODSOur research partnership group is supported by the Alzheimer's Clinical Trials Consortium—Down Syndrome (ACTC‐DS) and Alzheimer's Biomarker Consortium Down Syndrome (ABC‐DS). We synthesize feedback into themes and recommendations for researchers.RESULTSFeedback themes are broad, extending from the strong motivation to engage in research to the need to balance study burden and risk. Recommendations for researchers include providing support and connection to resources, sharing individual and study‐level results, flexibility, and respectful communication.DISCUSSIONOur collaborative approach will lead to more relevant and accessible research studies.HighlightsInclusive and accessible research is needed for Down syndrome and Alzheimer's disease.We built a collaborative group of self‐advocates and care partners to provide feedback.Feedback includes a strong motivation to learn about brain health and engage in research.Respectful communication includes representative images and tailored materials.Recommendations include the choice to learn individual results, flexibility, and support.

Electromagnetic radiation emission assessment of the powertrain is of great significance for controlling the overall electromagnetic compatibility (EMC) performance of the electric vehicle. However, conventional electromagnetic radiation testing methods not only rely on expensive semi-anechoic chamber environments but also hard to identify the main path in complex systems. This paper proposes a transfer function method incorporating coupling effects among multiple paths. This method enables rapid and accurate prediction of radiated emissions within the 0.15 MHz to 30 MHz frequency range and identifies the primary radiation paths, thereby facilitating product radiation reduction.

Abstract Educational attainment is a key social determinant of health; however, there is a paucity of research worldwide on the educational attainment of autistic people. Leveraging data from a large, anonymous online survey, the study matched 14,519 autistic adults and 37,458 non-autistic adults with high autistic traits (henceforth high AQ) 1:5 on age, gender, and UK region to non-autistic adults. Autistic and high AQ people had lower levels of educational attainment than others, on average; however, autistic people achieved postgraduate qualifications at similar levels to matched peers. This suggests that most autistic and high AQ people experience significant barriers to educational attainment, but some autistic people achieve advanced educational qualifications. In addition, it provides evidence that educational systems may fail to effectively support a wider range of learners than those with formally recognized special educational needs. As educational attainment predicts long-term health and financial stability, timely autism diagnosis and adaptations to national and international educational policies are urgently needed.

As the number of older autistic adults and adults with intellectual disabilities grows, expanding capacity to meet their needs is crucial. On November 23-25, 2023, a Think Tank on aging in autism and/or intellectual disabilities was convened, with national and international delegates within this field. The Think Tank consisted of presentations focusing on key issues as well as a series of panel presentations addressing first-person lived experience, family caregiving, service provision in the community, and physical and mental health-based care. Discussion reflected lived experiences and care needs in this population, with an ultimate aim of advancing healthcare and community support. Delegates, who represent perspectives as self-advocates, family caregivers, service providers, clinicians and researchers, ranked guidelines and areas of focus identified in the literature in terms of the most important priorities for the near-term development of capacity building resources.

Nonlinear longitudinal proportional effect models have been proposed to improve power and provide direct estimates of the proportional treatment effect in randomized clinical trials. These models assume a fixed proportional treatment effect over time, which can lead to bias and Type I error inflation when the assumption is violated. Even when the proportional effect assumption holds, these models are biased, and their inference is sensitive to the labeling of treatment groups. Typically, this bias favors the active group, inflates Type I error, and can result in one-sided testing. Conversely, the bias can make it more difficult to detect treatment harm, creating a safety concern.