Uncertainty of Vowel Predictions as a Digital Biomarker for Ataxic Dysarthria.

Dysarthria is a common manifestation across cerebellar ataxias leading to impairments in communication, reduced social connections, and decreased quality of life. While dysarthria symptoms may be present in other neurological conditions, ataxic dysarthria is a perceptually distinct motor speech disorder, with the most prominent characteristics being articulation and prosody abnormalities along with distorted vowels. We hypothesized that uncertainty of vowel predictions by an automatic speech recognition system can capture speech changes present in cerebellar ataxia. Speech of participants with ataxia (N=61) and healthy controls (N=25) was recorded during the "picture description" task. Additionally, participants' dysarthric speech and ataxia severity were assessed on a Brief Ataxia Rating Scale (BARS). Eight participants with ataxia had speech and BARS data at two timepoints. A neural network trained for phoneme prediction was applied to speech recordings. Average entropy of vowel tokens predictions (AVE) was computed for each participant's recording, together with mean pitch and intensity standard deviations (MPSD and MISD) in the vowel segments. AVE and MISD demonstrated associations with BARS speech score (Spearman's rho=0.45 and 0.51), and AVE demonstrated associations with BARS total (rho=0.39). In the longitudinal cohort, Wilcoxon pairwise signed rank test demonstrated an increase in BARS total and AVE, while BARS speech and acoustic measures did not significantly increase. Relationship of AVE to both BARS speech and BARS total, as well as the ability to capture disease progression even in absence of measured speech decline, indicates the potential of AVE as a digital biomarker for cerebellar ataxia.

Complexity analysis of head movements in autistic toddlers.

BACKGROUND: Early differences in sensorimotor functioning have been documented in young autistic children and infants who are later diagnosed with autism. Previous research has demonstrated that autistic toddlers exhibit more frequent head movement when viewing dynamic audiovisual stimuli, compared to neurotypical toddlers. To further explore this behavioral characteristic, in this study, computer vision (CV) analysis was used to measure several aspects of head movement dynamics of autistic and neurotypical toddlers while they watched a set of brief movies with social and nonsocial content presented on a tablet. METHODS: Data were collected from 457 toddlers, 17-36 months old, during their well-child visit to four pediatric primary care clinics. Forty-one toddlers were subsequently diagnosed with autism. An application (app) displayed several brief movies on a tablet, and the toddlers watched these movies while sitting on their caregiver's lap. The front-facing camera in the tablet recorded the toddlers' behavioral responses. CV was used to measure the participants' head movement rate, movement acceleration, and complexity using multiscale entropy. RESULTS: Autistic toddlers exhibited significantly higher rate, acceleration, and complexity in their head movements while watching the movies compared to neurotypical toddlers, regardless of the type of movie content (social vs. nonsocial). The combined features of head movement acceleration and complexity reliably distinguished the autistic and neurotypical toddlers. CONCLUSIONS: Autistic toddlers exhibit differences in their head movement dynamics when viewing audiovisual stimuli. Higher complexity of their head movements suggests that their movements were less predictable and less stable compared to neurotypical toddlers. CV offers a scalable means of detecting subtle differences in head movement dynamics, which may be helpful in identifying early behaviors associated with autism and providing insight into the nature of sensorimotor differences associated with autism.

Enhancing early autism prediction based on electronic records using clinical narratives.

Recent work has shown that predictive models can be applied to structured electronic health record (EHR) data to stratify autism likelihood from an early age (<1 year). Integrating clinical narratives (or notes) with structured data has been shown to improve prediction performance in other clinical applications, but the added predictive value of this information in early autism prediction has not yet been explored. In this study, we aimed to enhance the performance of early autism prediction by using both structured EHR data and clinical narratives. We built models based on structured data and clinical narratives separately, and then an ensemble model that integrated both sources of data. We assessed the predictive value of these models from Duke University Health System over a 14-year span to evaluate ensemble models predicting later autism diagnosis (by age 4 years) from data collected from ages 30 to 360 days. Our sample included 11,750 children above by age 3 years (385 meeting autism diagnostic criteria). The ensemble model for autism prediction showed superior performance and at age 30 days achieved 46.8% sensitivity (95% confidence interval, CI: 22.0%, 52.9%), 28.0% positive predictive value (PPV) at high (90%) specificity (CI: 2.0%, 33.1%), and AUC4 (with at least 4-year follow-up for controls) reaching 0.769 (CI: 0.715, 0.811). Prediction by 360 days achieved 44.5% sensitivity (CI: 23.6%, 62.9%), and 13.7% PPV at high (90%) specificity (CI: 9.6%, 18.9%), and AUC4 reaching 0.797 (CI: 0.746, 0.840). Results show that incorporating clinical narratives in early autism prediction achieved promising accuracy by age 30 days, outperforming models based on structured data only. Furthermore, findings suggest that additional features learned from clinician narratives might be hypothesis generating for understanding early development in autism.

Parent strategies for expanding food variety: Reflections of 19,239 adults with symptoms of Avoidant/Restrictive Food Intake Disorder.

OBJECTIVE: To characterize helpful parent feeding strategies using reflections on childhood eating experiences of adults with symptoms of Avoidant/Restrictive Food Intake Disorder (ARFID). METHOD: We explored a unique text-based dataset gathered from a population of N = 19,239 self-identified adult "picky eaters." The sample included adults with symptoms of ARFID as evidenced by marked interference in psychosocial functioning, weight loss/sustained low weight, and/or nutritional deficiency (likely ARFID), and non-ARFID participants. We leveraged state-of-the-art natural language processing (NLP) methods to classify feeding strategies that were perceived as helpful or not helpful. The best classifiers that distinguished helpful approaches were further analyzed using qualitative coding according to a grounded theory approach. RESULTS: NLP reliably and accurately classified the perceived helpfulness of caregivers' feeding strategies (82%) and provided information about features of helpful parent strategies using recollections of adults with varying degrees of food avoidance. Strategies perceived as forceful were regarded as not helpful. Positive and encouraging strategies were perceived as helpful in improving attitudes toward food and minimizing social discomfort around eating. Although food variety improved, adults still struggled with a degree of avoidance/restriction. DISCUSSION: Adults perceived that positive parent feeding strategies were helpful even though they continued to experience some degree of food avoidance. Creating a positive emotional context surrounding food and eating with others may help to eliminate psychosocial impairment and increase food approach in those with severe food avoidance. Nevertheless, additional tools to optimize parent strategies and improve individuals' capacity to incorporate avoided foods and cope with challenging eating situations are needed.

Deep-learning based fully automatic segmentation of the globus pallidus interna and externa using ultra-high 7 Tesla MRI.

Deep brain stimulation (DBS) surgery has been shown to dramatically improve the quality of life for patients with various motor dysfunctions, such as those afflicted with Parkinson's disease (PD), dystonia, and essential tremor (ET), by relieving motor symptoms associated with such pathologies. The success of DBS procedures is directly related to the proper placement of the electrodes, which requires the ability to accurately detect and identify relevant target structures within the subcortical basal ganglia region. In particular, accurate and reliable segmentation of the globus pallidus (GP) interna is of great interest for DBS surgery for PD and dystonia. In this study, we present a deep-learning based neural network, which we term GP-net, for the automatic segmentation of both the external and internal segments of the globus pallidus. High resolution 7 Tesla images from 101 subjects were used in this study; GP-net is trained on a cohort of 58 subjects, containing patients with movement disorders as well as healthy control subjects. GP-net performs 3D inference in a patient-specific manner, alleviating the need for atlas-based segmentation. GP-net was extensively validated, both quantitatively and qualitatively over 43 test subjects including patients with movement disorders and healthy control and is shown to consistently produce improved segmentation results compared with state-of-the-art atlas-based segmentations. We also demonstrate a postoperative lead location assessment with respect to a segmented globus pallidus obtained by GP-net.

A scalable computational approach to assessing response to name in toddlers with autism.

BACKGROUND: This study is part of a larger research program focused on developing objective, scalable tools for digital behavioral phenotyping. We evaluated whether a digital app delivered on a smartphone or tablet using computer vision analysis (CVA) can elicit and accurately measure one of the most common early autism symptoms, namely failure to respond to a name call. METHODS: During a pediatric primary care well-child visit, 910 toddlers, 17-37 months old, were administered an app on an iPhone or iPad consisting of brief movies during which the child's name was called three times by an examiner standing behind them. Thirty-seven toddlers were subsequently diagnosed with autism spectrum disorder (ASD). Name calls and children's behavior were recorded by the camera embedded in the device, and children's head turns were coded by both CVA and a human. RESULTS: CVA coding of response to name was found to be comparable to human coding. Based on CVA, children with ASD responded to their name significantly less frequently than children without ASD. CVA also revealed that children with ASD who did orient to their name exhibited a longer latency before turning their head. Combining information about both the frequency and the delay in response to name improved the ability to distinguish toddlers with and without ASD. CONCLUSIONS: A digital app delivered on an iPhone or iPad in real-world settings using computer vision analysis to quantify behavior can reliably detect a key early autism symptom-failure to respond to name. Moreover, the higher resolution offered by CVA identified a delay in head turn in toddlers with ASD who did respond to their name. Digital phenotyping is a promising methodology for early assessment of ASD symptoms.

A Shared Vision for Machine Learning in Neuroscience.

With ever-increasing advancements in technology, neuroscientists are able to collect data in greater volumes and with finer resolution. The bottleneck in understanding how the brain works is consequently shifting away from the amount and type of data we can collect and toward what we actually do with the data. There has been a growing interest in leveraging this vast volume of data across levels of analysis, measurement techniques, and experimental paradigms to gain more insight into brain function. Such efforts are visible at an international scale, with the emergence of big data neuroscience initiatives, such as the BRAIN initiative (Bargmann et al., 2014), the Human Brain Project, the Human Connectome Project, and the National Institute of Mental Health's Research Domain Criteria initiative. With these large-scale projects, much thought has been given to data-sharing across groups (Poldrack and Gorgolewski, 2014; Sejnowski et al., 2014); however, even with such data-sharing initiatives, funding mechanisms, and infrastructure, there still exists the challenge of how to cohesively integrate all the data. At multiple stages and levels of neuroscience investigation, machine learning holds great promise as an addition to the arsenal of analysis tools for discovering how the brain works.

Automatic localization of the subthalamic nucleus on patient-specific clinical MRI by incorporating 7 T MRI and machine learning: Application in deep brain stimulation.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has shown clinical potential for relieving the motor symptoms of advanced Parkinson's disease. While accurate localization of the STN is critical for consistent across-patients effective DBS, clear visualization of the STN under standard clinical MR protocols is still challenging. Therefore, intraoperative microelectrode recordings (MER) are incorporated to accurately localize the STN. However, MER require significant neurosurgical expertise and lengthen the surgery time. Recent advances in 7 T MR technology facilitate the ability to clearly visualize the STN. The vast majority of centers, however, still do not have 7 T MRI systems, and fewer have the ability to collect and analyze the data. This work introduces an automatic STN localization framework based on standard clinical MRIs without additional cost in the current DBS planning protocol. Our approach benefits from a large database of 7 T MRI and its clinical MRI pairs. We first model in the 7 T database, using efficient machine learning algorithms, the spatial and geometric dependency between the STN and its adjacent structures (predictors). Given a standard clinical MRI, our method automatically computes the predictors and uses the learned information to predict the patient-specific STN. We validate our proposed method on clinical T2 W MRI of 80 subjects, comparing with experts-segmented STNs from the corresponding 7 T MRI pairs. The experimental results show that our framework provides more accurate and robust patient-specific STN localization than using state-of-the-art atlases. We also demonstrate the clinical feasibility of the proposed technique assessing the post-operative electrode active contact locations.

Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning.

We present a sparse Bayesian unmixing algorithm BusineX: Bayesian Unmixing for Sparse Inference-based Estimation of Fiber Crossings (X), for estimation of white matter fiber parameters from compressed (under-sampled) diffusion MRI (dMRI) data. BusineX combines compressive sensing with linear unmixing and introduces sparsity to the previously proposed multiresolution data fusion algorithm RubiX, resulting in a method for improved reconstruction, especially from data with lower number of diffusion gradients. We formulate the estimation of fiber parameters as a sparse signal recovery problem and propose a linear unmixing framework with sparse Bayesian learning for the recovery of sparse signals, the fiber orientations and volume fractions. The data is modeled using a parametric spherical deconvolution approach and represented using a dictionary created with the exponential decay components along different possible diffusion directions. Volume fractions of fibers along these directions define the dictionary weights. The proposed sparse inference, which is based on the dictionary representation, considers the sparsity of fiber populations and exploits the spatial redundancy in data representation, thereby facilitating inference from under-sampled q-space. The algorithm improves parameter estimation from dMRI through data-dependent local learning of hyperparameters, at each voxel and for each possible fiber orientation, that moderate the strength of priors governing the parameter variances. Experimental results on synthetic and in-vivo data show improved accuracy with a lower uncertainty in fiber parameter estimates. BusineX resolves a higher number of second and third fiber crossings. For under-sampled data, the algorithm is also shown to produce more reliable estimates.

Probabilistic fluorescence-based synapse detection.

Deeper exploration of the brain's vast synaptic networks will require new tools for high-throughput structural and molecular profiling of the diverse populations of synapses that compose those networks. Fluorescence microscopy (FM) and electron microscopy (EM) offer complementary advantages and disadvantages for single-synapse analysis. FM combines exquisite molecular discrimination capacities with high speed and low cost, but rigorous discrimination between synaptic and non-synaptic fluorescence signals is challenging. In contrast, EM remains the gold standard for reliable identification of a synapse, but offers only limited molecular discrimination and is slow and costly. To develop and test single-synapse image analysis methods, we have used datasets from conjugate array tomography (cAT), which provides voxel-conjugate FM and EM (annotated) images of the same individual synapses. We report a novel unsupervised probabilistic method for detection of synapses from multiplex FM (muxFM) image data, and evaluate this method both by comparison to EM gold standard annotated data and by examining its capacity to reproduce known important features of cortical synapse distributions. The proposed probabilistic model-based synapse detector accepts molecular-morphological synapse models as user queries, and delivers a volumetric map of the probability that each voxel represents part of a synapse. Taking human annotation of cAT EM data as ground truth, we show that our algorithm detects synapses from muxFM data alone as successfully as human annotators seeing only the muxFM data, and accurately reproduces known architectural features of cortical synapse distributions. This approach opens the door to data-driven discovery of new synapse types and their density. We suggest that our probabilistic synapse detector will also be useful for analysis of standard confocal and super-resolution FM images, where EM cross-validation is not practical.

Use of a Digital Modified Checklist for Autism in Toddlers - Revised with Follow-up to Improve Quality of Screening for Autism.

OBJECTIVES: To assess changes in quality of care for children at risk for autism spectrum disorders (ASD) due to process improvement and implementation of a digital screening form. STUDY DESIGN: The process of screening for ASD was studied in an academic primary care pediatrics clinic before and after implementation of a digital version of the Modified Checklist for Autism in Toddlers - Revised with Follow-up with automated risk assessment. Quality metrics included accuracy of documentation of screening results and appropriate action for positive screens (secondary screening or referral). Participating physicians completed pre- and postintervention surveys to measure changes in attitudes toward feasibility and value of screening for ASD. Evidence of change was evaluated with statistical process control charts and χ2 tests. RESULTS: Accurate documentation in the electronic health record of screening results increased from 54% to 92% (38% increase, 95% CI 14%-64%) and appropriate action for children screening positive increased from 25% to 85% (60% increase, 95% CI 35%-85%). A total of 90% of participating physicians agreed that the transition to a digital screening form improved their clinical assessment of autism risk. CONCLUSIONS: Implementation of a tablet-based digital version of the Modified Checklist for Autism in Toddlers - Revised with Follow-up led to improved quality of care for children at risk for ASD and increased acceptability of screening for ASD. Continued efforts towards improving the process of screening for ASD could facilitate rapid, early diagnosis of ASD and advance the accuracy of studies of the impact of screening.

A tetraploid intermediate precedes aneuploid formation in yeasts exposed to fluconazole.

Candida albicans, the most prevalent human fungal pathogen, is generally diploid. However, 50% of isolates that are resistant to fluconazole (FLC), the most widely used antifungal, are aneuploid and some aneuploidies can confer FLC resistance. To ask if FLC exposure causes or only selects for aneuploidy, we analyzed diploid strains during exposure to FLC using flow cytometry and epifluorescence microscopy. FLC exposure caused a consistent deviation from normal cell cycle regulation: nuclear and spindle cycles initiated prior to bud emergence, leading to "trimeras," three connected cells composed of a mother, daughter, and granddaughter bud. Initially binucleate, trimeras underwent coordinated nuclear division yielding four daughter nuclei, two of which underwent mitotic collapse to form a tetraploid cell with extra spindle components. In subsequent cell cycles, the abnormal number of spindles resulted in unequal DNA segregation and viable aneuploid progeny. The process of aneuploid formation in C. albicans is highly reminiscent of early stages in human tumorigenesis in that aneuploidy arises through a tetraploid intermediate and subsequent unequal DNA segregation driven by multiple spindles coupled with a subsequent selective advantage conferred by at least some aneuploidies during growth under stress. Finally, trimera formation was detected in response to other azole antifungals, in related Candida species, and in an in vivo model for Candida infection, suggesting that aneuploids arise due to azole treatment of several pathogenic yeasts and that this can occur during the infection process.

Structure and accessibility of HA trimers on intact 2009 H1N1 pandemic influenza virus to stem region-specific neutralizing antibodies.

Rapid antigenic variation of HA, the major virion surface protein of influenza A virus, remains the principal challenge to the development of broader and more effective vaccines. Some regions of HA, such as the stem region proximal to the viral membrane, are nevertheless highly conserved across strains and among most subtypes. A fundamental question in vaccine design is the extent to which HA stem regions on the surface of the virus are accessible to broadly neutralizing antibodies. Here we report 3D structures derived from cryoelectron tomography of HA on intact 2009 H1N1 pandemic virions in the presence and absence of the antibody C179, which neutralizes viruses expressing a broad range of HA subtypes, including H1, H2, H5, H6, and H9. By fitting previously derived crystallographic structures of trimeric HA into the density maps, we deduced the locations of the molecular surfaces of HA involved in interaction with C179. Using computational methods to distinguish individual unliganded HA trimers from those that have bound C179 antibody, we demonstrate that ∼75% of HA trimers on the surface of the virus have C179 bound to the stem domain. Thus, despite their close packing on the viral membrane, the majority of HA trimers on intact virions are available to bind anti-stem antibodies that target conserved HA epitopes, establishing the feasibility of universal influenza vaccines that elicit such antibodies.

Automatic clustering and population analysis of white matter tracts using maximum density paths.

We introduce a framework for population analysis of white matter tracts based on diffusion-weighted images of the brain. The framework enables extraction of fibers from high angular resolution diffusion images (HARDI); clustering of the fibers based partly on prior knowledge from an atlas; representation of the fiber bundles compactly using a path following points of highest density (maximum density path; MDP); and registration of these paths together using geodesic curve matching to find local correspondences across a population. We demonstrate our method on 4-Tesla HARDI scans from 565 young adults to compute localized statistics across 50 white matter tracts based on fractional anisotropy (FA). Experimental results show increased sensitivity in the determination of genetic influences on principal fiber tracts compared to the tract-based spatial statistics (TBSS) method. Our results show that the MDP representation reveals important parts of the white matter structure and considerably reduces the dimensionality over comparable fiber matching approaches.

Structural mechanism of trimeric HIV-1 envelope glycoprotein activation.

HIV-1 infection begins with the binding of trimeric viral envelope glycoproteins (Env) to CD4 and a co-receptor on target T-cells. Understanding how these ligands influence the structure of Env is of fundamental interest for HIV vaccine development. Using cryo-electron microscopy, we describe the contrasting structural outcomes of trimeric Env binding to soluble CD4, to the broadly neutralizing, CD4-binding site antibodies VRC01, VRC03 and b12, or to the monoclonal antibody 17b, a co-receptor mimic. Binding of trimeric HIV-1 BaL Env to either soluble CD4 or 17b alone, is sufficient to trigger formation of the open quaternary conformation of Env. In contrast, VRC01 locks Env in the closed state, while b12 binding requires a partial opening in the quaternary structure of trimeric Env. Our results show that, despite general similarities in regions of the HIV-1 gp120 polypeptide that contact CD4, VRC01, VRC03 and b12, there are important differences in quaternary structures of the complexes these ligands form on native trimeric Env, and potentially explain differences in the neutralizing breadth and potency of antibodies with similar specificities. From cryo-electron microscopic analysis at ∼9 Å resolution of a cleaved, soluble version of trimeric Env, we show that a structural signature of the open Env conformation is a three-helix motif composed of α-helical segments derived from highly conserved, non-glycosylated N-terminal regions of the gp41 trimer. The three N-terminal gp41 helices in this novel, activated Env conformation are held apart by their interactions with the rest of Env, and are less compactly packed than in the post-fusion, six-helix bundle state. These findings suggest a new structural template for designing immunogens that can elicit antibodies targeting HIV at a vulnerable, pre-entry stage.

Estimation of the CSA-ODF using Bayesian compressed sensing of multi-shell HARDI.

PURPOSE: Diffusion MRI provides important information about the brain white matter structures and has opened new avenues for neuroscience and translational research. However, acquisition time needed for advanced applications can still be a challenge in clinical settings. There is consequently a need to accelerate diffusion MRI acquisitions. METHODS: A multi-task Bayesian compressive sensing (MT-BCS) framework is proposed to directly estimate the constant solid angle orientation distribution function (CSA-ODF) from under-sampled (i.e., accelerated image acquisition) multi-shell high angular resolution diffusion imaging (HARDI) datasets, and accurately recover HARDI data at higher resolution in q-space. The proposed MT-BCS approach exploits the spatial redundancy of the data by modeling the statistical relationships within groups (clusters) of diffusion signal. This framework also provides uncertainty estimates of the computed CSA-ODF and diffusion signal, directly computed from the compressive measurements. Experiments validating the proposed framework are performed using realistic multi-shell synthetic images and in vivo multi-shell high angular resolution HARDI datasets. RESULTS: Results indicate a practical reduction in the number of required diffusion volumes (q-space samples) by at least a factor of four to estimate the CSA-ODF from multi-shell data. CONCLUSION: This work presents, for the first time, a multi-task Bayesian compressive sensing approach to simultaneously estimate the full posterior of the CSA-ODF and diffusion-weighted volumes from multi-shell HARDI acquisitions. It demonstrates improvement of the quality of acquired datasets by means of CS de-noising, and accurate estimation of the CSA-ODF, as well as enables a reduction in the acquisition time by a factor of two to four, especially when "staggered" q-space sampling schemes are used. The proposed MT-BCS framework can naturally be combined with parallel MR imaging to further accelerate HARDI acquisitions.

Molecular architecture of native HIV-1 gp120 trimers.

The envelope glycoproteins (Env) of human and simian immunodeficiency viruses (HIV and SIV, respectively) mediate virus binding to the cell surface receptor CD4 on target cells to initiate infection. Env is a heterodimer of a transmembrane glycoprotein (gp41) and a surface glycoprotein (gp120), and forms trimers on the surface of the viral membrane. Using cryo-electron tomography combined with three-dimensional image classification and averaging, we report the three-dimensional structures of trimeric Env displayed on native HIV-1 in the unliganded state, in complex with the broadly neutralizing antibody b12 and in a ternary complex with CD4 and the 17b antibody. By fitting the known crystal structures of the monomeric gp120 core in the b12- and CD4/17b-bound conformations into the density maps derived by electron tomography, we derive molecular models for the native HIV-1 gp120 trimer in unliganded and CD4-bound states. We demonstrate that CD4 binding results in a major reorganization of the Env trimer, causing an outward rotation and displacement of each gp120 monomer. This appears to be coupled with a rearrangement of the gp41 region along the central axis of the trimer, leading to closer contact between the viral and target cell membranes. Our findings elucidate the structure and conformational changes of trimeric HIV-1 gp120 relevant to antibody neutralization and attachment to target cells.

Scalp surface estimation and head registration using sparse sampling and 3D statistical models.

Registering the head and estimating the scalp surface are important for various biomedical procedures, including those using neuronavigation to localize brain stimulation or recording. However, neuronavigation systems rely on manually-identified fiducial head targets and often require a patient-specific MRI for accurate registration, limiting adoption. We propose a practical technique capable of inferring the scalp shape and use it to accurately register the subject's head. Our method does not require anatomical landmark annotation or an individual MRI scan, yet achieves accurate registration of the subject's head and estimation of its surface. The scalp shape is estimated from surface samples easily acquired using existing pointer tools, and registration exploits statistical head model priors. Our method allows for the acquisition of non-trivial shapes from a limited number of data points while leveraging their object class priors, surpassing the accuracy of common reconstruction and registration methods using the same tools. The proposed approach is evaluated in a virtual study with head MRI data from 1152 subjects, achieving an average reconstruction root-mean-square error of 2.95 mm, which outperforms a common neuronavigation technique by 2.70 mm. We also characterize the error under different conditions and provide guidelines for efficient sampling. Furthermore, we demonstrate and validate the proposed method on data from 50 subjects collected with conventional neuronavigation tools and setup, obtaining an average root-mean-square error of 2.89 mm; adding landmark-based registration improves this error to 2.63 mm. The simulation and experimental results support the proposed method's effectiveness with or without landmark annotation, highlighting its broad applicability.

Autism Caregiver Coaching in Africa (ACACIA): Protocol for a type 1-hybrid effectiveness-implementation trial.

BACKGROUND: While early autism intervention can significantly improve outcomes, gaps in implementation exist globally. These gaps are clearest in Africa, where forty percent of the world's children will live by 2050. Task-sharing early intervention to non-specialists is a key implementation strategy, given the lack of specialists in Africa. Naturalistic Developmental Behavioral Interventions (NDBI) are a class of early autism intervention that can be delivered by caregivers. As a foundational step to address the early autism intervention gap, we adapted a non-specialist delivered caregiver coaching NDBI for the South African context, and pre-piloted this cascaded task-sharing approach in an existing system of care. OBJECTIVES: First, we will test the effectiveness of the caregiver coaching NDBI compared to usual care. Second, we will describe coaching implementation factors within the Western Cape Department of Education in South Africa. METHODS: This is a type 1 effectiveness-implementation hybrid design; assessor-blinded, group randomized controlled trial. Participants include 150 autistic children (18-72 months) and their caregivers who live in Cape Town, South Africa, and those involved in intervention implementation. Early Childhood Development practitioners, employed by the Department of Education, will deliver 12, one hour, coaching sessions to the intervention group. The control group will receive usual care. Distal co-primary outcomes include the Communication Domain Standard Score (Vineland Adaptive Behavior Scales, Third Edition) and the Language and Communication Developmental Quotient (Griffiths Scales of Child Development, Third Edition). Proximal secondary outcome include caregiver strategies measured by the sum of five items from the Joint Engagement Rating Inventory. We will describe key implementation determinants. RESULTS: Participant enrolment started in April 2023. Estimated primary completion date is March 2027. CONCLUSION: The ACACIA trial will determine whether a cascaded task-sharing intervention delivered in an educational setting leads to meaningful improvements in communication abilities of autistic children, and identify implementation barriers and facilitators. TRIAL REGISTRATION: NCT05551728 in Clinical Trial Registry (https://clinicaltrials.gov).