Relationship Between Sleep and Behavior in Autism Spectrum Disorder: Exploring the Impact of Sleep Variability.

Objective:The relationship between sleep (caregiver-reported and actigraphy-measured) and other caregiver-reported behaviors in children and adults with autism spectrum disorder (ASD) was examined, including the use of machine learning to identify sleep variables important in predicting anxiety in ASD. Methods:Caregivers of ASD (n = 144) and typically developing (TD) (n = 41) participants reported on sleep and other behaviors. ASD participants wore an actigraphy device at nighttime during an 8 or 10-week non-interventional study. Mean and variability of actigraphy measures for ASD participants in the week preceding midpoint and endpoint were calculated and compared with caregiver-reported and clinician-reported symptoms using a mixed effects model. An elastic-net model was developed to examine which sleep measures may drive prediction of anxiety. Results:Prevalence of caregiver-reported sleep difficulties in ASD was approximately 70% and correlated significantly (p < 0.05) with sleep efficiency measured by actigraphy. Mean and variability of actigraphy measures like sleep efficiency and number of awakenings were related significantly (p < 0.05) to ASD symptom severity, hyperactivity and anxiety. In the elastic net model, caregiver-reported sleep, and variability of sleep efficiency and awakenings were amongst the important predictors of anxiety. Conclusion:Caregivers report problems with sleep in the majority of children and adults with ASD. Reported problems and actigraphy measures of sleep, particularly variability, are related to parent reported behaviors. Measuring variability in sleep may prove useful in understanding the relationship between sleep problems and behavior in individuals with ASD. These findings may have implications for both intervention and monitoring outcomes in ASD

Language Model Applications to Spelling with Brain-Computer Interfaces

Within the Ambient Assisted Living (AAL) community, Brain-Computer Interfaces (BCIs) have raised great hopes as they provide alternative communication means for persons with disabilities bypassing the need for speech and other motor activities. Although significant advancements have been realized in the last decade, applications of language models (e.g., word prediction, completion) have only recently started to appear in BCI systems. The main goal of this article is to review the language model applications that supplement non-invasive BCI-based communication systems by discussing their potential and limitations, and to discern future trends. First, a brief overview of the most prominent BCI spelling systems is given, followed by an in-depth discussion of the language models appli

TB165: Chemical and Physical Properties of the Danforth, Elliotsville, Peacham, and Penquis Soil Map Units

The soils reported in this bulletin have developed in several different parent materials. The Danforth soil has developed from very deep, well drained, loose, high coarse fragment till derived from slate and fine-grained metasandstone. The Elliottsville soils have developed in moderately deep, well drained till derived from slates, metasandstones, phyllite and schists. The Penquis soils developed in moderately deep, well drained till of similar lithology as Elliottsville, but with a higher component of weathered and crushable rock fragments throughout the soil profile. Peacham soils are developed in very deep, very poorly drained, dense till derived from phyllite, schist, and granite.https://digitalcommons.library.umaine.edu/aes_techbulletin/1041/thumbnail.jp

Wearable devices for assessing function in Alzheimer’s disease: a European public involvement activity about the features and preferences of patients and caregivers

Background: Alzheimer's Disease (AD) impairs the ability to carry out daily activities, reduces independence and quality of life and increases caregiver burden. Our understanding of functional decline has traditionally relied on reports by family and caregivers, which are subjective and vulnerable to recall bias. The Internet of Things (IoT) and wearable sensor technologies promise to provide objective, affordable, and reliable means for monitoring and understanding function. However, human factors for its acceptance are relatively unexplored. Objective: The Public Involvement (PI) activity presented in this paper aims to capture the preferences, priorities and concerns of people with AD and their caregivers for using monitoring wearables. Their feedback will drive device selection for clinical research, starting with the study of the RADAR-AD project. Method: The PI activity involved the Patient Advisory Board (PAB) of the RADAR-AD project, comprised of people with dementia across Europe and their caregivers (11 and 10, respectively). A set of four devices that optimally represent various combinations of aspects and features from the variety of currently available wearables (e.g., weight, size, comfort, battery life, screen types, water-resistance, and metrics) was presented and experienced hands-on. Afterwards, sets of cards were used to rate and rank devices and features and freely discuss preferences. Results: Overall, the PAB was willing to accept and incorporate devices into their daily lives. For the presented devices, the aspects most important to them included comfort, convenience and affordability. For devices in general, the features they prioritized were appearance/style, battery life and water resistance, followed by price, having an emergency button and a screen with metrics. The metrics valuable to them included activity levels and heart rate, followed by respiration rate, sleep quality and distance. Some concerns were the potential complexity, forgetting to charge the device, the potential stigma and data privacy. Conclusions: The PI activity explored the preferences, priorities and concerns of the PAB, a group of people with dementia and caregivers across Europe, regarding devices for monitoring function and decline, after a hands-on experience and explanation. They highlighted some expected aspects, metrics and features (e.g., comfort and convenience), but also some less expected (e.g., screen with metrics)

Visual Preference for Biological Motion in Children and Adults with Autism Spectrum Disorder: An Eye-Tracking Study

Participants with autism spectrum disorder (ASD) (n = 121, mean [SD] age: 14.6 [8.0] years) and typically developing (TD) controls (n = 40, 16.4 [13.3] years) were presented with a series of videos representing biological motion on one side of a computer monitor screen and non-biological motion on the other, while their eye movements were recorded. As predicted, participants with ASD spent less overall time looking at presented stimuli than TD participants (P < 10-3) and showed less preference for biological motion (P < 10-5). Participants with ASD also had greater average latencies than TD participants of the first fixation on both biological (P < 0.01) and non-biological motion (P < 0.02). Findings suggest that individuals with ASD differ from TD individuals on multiple properties of eye movements and biological motion preference

Neural oscillations during cognitive processes in an <i>App</i> knock-in mouse model of Alzheimer's disease pathology

Multiple animal models have been created to gain insight into Alzheimer's disease (AD) pathology. Among the most commonly used models are transgenic mice overexpressing human amyloid precursor protein (APP) with mutations linked to familial AD, resulting in the formation of amyloid beta plaques, one of the pathological hallmarks observed in AD patients. However, recent evidence suggests that the overexpression of APP by itself can confound some of the reported observations. Therefore, we investigated in the present study the App(NL-G-F)model, an App knock-in (App-KI) mouse model that develops amyloidosis in the absence of APP-overexpression. Our findings at the behavioral, electrophysiological, and histopathological level confirmed an age-dependent increase in A beta 1-42 levels and plaque deposition in these mice in accordance with previous reports. This had apparently no consequences on cognitive performance in a visual discrimination (VD) task, which was largely unaffected in App(NL-G-F) mice at the ages tested. Additionally, we investigated neurophysiological functioning of several brain areas by phase-amplitude coupling (PAC) analysis, a measure associated with adequate cognitive functioning, during the VD task (starting at 4.5 months) and the exploration of home environment (at 5 and 8 months of age). While we did not detect age-dependent changes in PAC during home environment exploration for both the wild-type and the App(NL-G-F) mice, we did observe subtle changes in PAC in the wild-type mice that were not present in the App(NL-G-F) mice

Social attention to activities in children and adults with autism spectrum disorder: effects of context and age

BackgroundDiminished visual monitoring of faces and activities of others is an early feature of autism spectrum disorder (ASD). It is uncertain whether deficits in activity monitoring, identified using a homogeneous set of stimuli, persist throughout the lifespan in ASD, and thus, whether they could serve as a biological indicator ("biomarker") of ASD. We investigated differences in visual attention during activity monitoring in children and adult participants with autism compared to a control group of participants without autism.MethodsEye movements of participants with autism (n = 122; mean age [SD] = 14.5 [8.0] years) and typically developing (TD) controls (n = 40, age = 16.4 [13.3] years) were recorded while they viewed a series of videos depicting two female actors conversing while interacting with their hands over a shared task. Actors either continuously focused their gaze on each other's face (mutual gaze) or on the shared activity area (shared focus). Mean percentage looking time was computed for the activity area, actors' heads, and their bodies.ResultsCompared to TD participants, participants with ASD looked longer at the activity area (mean % looking time: 58.5% vs. 53.8%, p &lt; 0.005) but less at the heads (15.2% vs. 23.7%, p &lt; 0.0001). Additionally, within-group differences in looking time were observed between the mutual gaze and shared focus conditions in both participants without ASD (activity: Δ = - 6.4%, p &lt; 0.004; heads: Δ = + 3.5%, p &lt; 0.02) and participants with ASD (bodies: Δ = + 1.6%, p &lt; 0.002).LimitationsThe TD participants were not as well characterized as the participants with ASD. Inclusion criteria regarding the cognitive ability [intelligence quotient (IQ) &gt; 60] limited the ability to include individuals with substantial intellectual disability.ConclusionsDifferences in attention to faces could constitute a feature discriminative between individuals with and without ASD across the lifespan, whereas between-group differences in looking at activities may shift with development. These findings may have applications in the search for underlying biological indicators specific to ASD. Trial registration ClinicalTrials.gov identifier NCT02668991