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Individuals with intellectual and developmental disabilities (IDD) continue to experience disparities in health and well-being despite improved provisions of person-centered care. Patient-centered outcomes research (PCOR) translates evidence into practice for meaningful outcomes. This piece describes findings from an environmental scan and stakeholder outreach to identify and prioritize opportunities to enhance IDD PCOR data infrastructure. These opportunities include developing a standardized research definition; advancing data standards for service systems; improving capture of IDD at point of care; developing standardized outcome measures; and encouraging Medicaid data use for IDD research. Within this piece, we discuss the implications of addressing data gaps for enhanced research. While the identified activities provide a path towards advancing IDD PCOR data infrastructure, collaborative efforts between government, researchers, and others are paramount., (©AAIDD.)

Objective: Diseases such as age-related macular degeneration and retinitis pigmentosa cause the degradation of the photoreceptor layer. One approach to restore vision is to electrically stimulate the surviving retinal ganglion cells with a microelectrode array such as epiretinal implants. Epiretinal implants are known to generate visible anisotropic shapes elongated along the axon fascicles of neighboring retinal ganglion cells. Recent work has demonstrated that to obtain isotropic pixel-like shapes, it is possible to map axon fascicles and avoid stimulating them by inactivating electrodes or lowering stimulation current levels. Avoiding axon fascicule stimulation aims to remove brushstroke-like shapes in favor of a more reduced set of pixel-like shapes., Approach: In this study, we propose the use of isotropic and anisotropic shapes to render intelligible images on the retina of a virtual patient in a reinforcement learning environment named rlretina. The environment formalizes the task as using brushstrokes in a stroke-based rendering task., Main Results: We train a deep reinforcement learning agent that learns to assemble isotropic and anisotropic shapes to form an image. We investigate which error-based or perception-based metrics are adequate to reward the agent. The agent is trained in a model-based data generation fashion using the psychophysically validated axon map model to render images as perceived by different virtual patients. We show that the agent can generate more intelligible images compared to the naive method in different virtual patients., Significance: This work shares a new way to address epiretinal stimulation that constitutes a first step towards improving visual acuity in artificially-restored vision using anisotropic phosphenes., (Creative Commons Attribution license.)

Despite decades of research, the features of an input audio stimulus that are encoded in an electroencephalogram (EEG) are still not clearly identified. We wish to investigate whether a frequency-band coupling model that estimates the cortical neural activity from EEGs can capture the important features of an input audio stimulus. To do so, EEG recordings were acquired from 8 subjects during a listening task where the vowels a, i and u were randomly presented. The neural activity was estimated from the EEG using a frequency-band coupling model that combined the EEG's phase in the delta band (2 Hz-4 Hz) and its amplitude in the gamma band (30 Hz-100 Hz). To investigate if the estimated neural activity could capture relevant features of an input audio stimulus, we fitted a generalized linear model (GLM) to the estimated neural activity and applied a statistical relative deviance metric to evaluate how important is the input audio stimulus in the estimated neural activity. We demonstrate that the input audio stimulus is the main component explaining the estimated neural activity and that other aspects such as the contribution of the surrounding network dynamics do not contribute significantly to the estimated neural activity. These results confirm that the features of the EEG used in the coupling model, namely the phase of the delta band and the power of the gamma band, do encode relevant aspects of an input audio signal. This non-invasive approach could be used, for example, to study how the presence of spectro-temporal features in the estimated neural activity is modified depending on different listening conditions or types of input sounds., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Sensory systems, such as the visual or auditory system, are highly non linear. It is therefore not easy to predict the effect of additive inputs on the spiking activity of related brain structures. Here, we propose two metrics to study the effect of additive covariates on the spiking activity of neurons. These metrics are directly obtained from a generalized linear model. We apply these metrics to the study of the effect of additive input audio noise on the spiking activity of neurons in the auditory system. To do so, we combine clean vocalisations with natural stationary or non-stationary noises and record activity in the auditory system while presenting the noisy vocalisations. We found that non-stationary noise has a greater effect on the neural activity than stationary noise. We observe that the results, obtained using the proposed metrics, is more consistent with current knowledge in auditory neuroscience than the results obtained when using a common metric from the literature, the extraction index.

Background: People with developmental disabilities (DD) are often not included as participants in research owing to a variety of ethical and practical challenges. One major challenge is that traditional measurement instruments may not be accessible to people with DD. Participatory research approaches promise to increase the participation of marginalized communities in research, but few partnerships have successfully used such approaches to conduct quantitative studies people with DD., Objective: To use a community-based participatory research (CBPR) approach to create an accessible, computer-assisted survey about violence and health in people with DD, and to psychometrically test adapted health instruments., Methods: Our academic-community partnership, composed of academic researchers, people with DD, and supporters, collaboratively selected and modified data collection instruments, conducted cognitive interviews and pilot tests, and then administered the full survey to 350 people with DD., Results: Although team members sometimes had opposing accommodation needs and adaptation recommendations, academic and community partners were able to work together successfully to adapt instruments to be accessible to participants with a wide range of DD. Results suggest the adapted health instruments had strong content validity and all but one had good to excellent internal consistency reliability (alpha, 0.81-0.94). The majority of participants (75%) responded that all or most of the questions were easy to understand., Conclusions: Researchers should consider using participatory approaches to adapting instruments so people with DD can be validly included in research.

We study the impact of different encoding models and spectro-temporal representations on the accuracy of Bayesian decoding of neural activity recorded from the central auditory system. Two encoding models, a generalized linear model (GLM) and a generalized bilinear model (GBM), are compared along with three different spectro-temporal representations of the input stimuli: a spectrogram and two bio-inspired representations, i.e. a gammatone filter bank (GFB) and a spikegram. Signal to noise ratios between the reconstructed and original representations are used to evaluate the decoding, or reconstruction accuracy. We experimentally show that the reconstruction accuracy is best with the spikegram representation and worst with the spectrogram representation and, furthermore, that using a GBM instead of a GLM significantly increases the reconstruction accuracy. In fact, our results show that the spikegram reconstruction accuracy with a GBM fitting yields an SNR that is 3.3 dB better than when using the standard decoding approach of reconstructing a spectrogram with GLM fitting.

We propose a point process model of spiking activity from auditory neurons. The model takes account of the neuron's intrinsic dynamics as well as the spectrotemporal properties of an input stimulus. A discrete Volterra expansion is used to derive the form of the conditional intensity function. The Volterra expansion models the neuron's baseline spike rate, its intrinsic dynamics-spiking history-and the stimulus effect which in this case is the analog of the spectrotemporal receptive field (STRF). We performed the model fitting efficiently in a generalized linear model framework using ridge regression to address properly this ill-posed maximum likelihood estimation problem. The model provides an excellent fit to spiking activity from 55 auditory nerve neurons. The STRF-like representation estimated jointly with the neuron's intrinsic dynamics may offer more accurate characterizations of neural activity in the auditory system than current ones based solely on the STRF.