Things to Do in L.A. Tap to enable a layout that focuses on the article. Health Secretary Robert F. Kennedy Jr. speaks about autism in the White House on Monday as President Trump and Centers for Medicare & Medicaid Services Administrator Dr. Mehmet Oz look on. This is read by an automated voice. Please report any issues or inconsistencies here . On Monday, President Trump led a White House press event where he and many of his administration's health leaders told the public that taking Tylenol during pregnancy increases the risk of autism in children.

General movements (GMs) are spontaneous, coordinated body movements in infants that offer valuable insights into the developing nervous system. Assessed through the Prechtl GM Assessment (GMA), GMs are reliable predictors for neurodevelopmental disorders. However, GMA requires specifically trained clinicians, who are limited in number. To scale up newborn screening, there is a need for an algorithm that can automatically classify GMs from infant video recordings. This data poses challenges, including variability in recording length, device type, and setting, with each video coarsely annotated for overall movement quality. In this work, we introduce a tool for extracting features from these recordings and explore various machine learning techniques for automated GM classification.

Divergent brain connectivity is thought to underlie the behavioral and cognitive symptoms observed in many neurodevelopmental disorders. Quantifying divergence from neurotypical connectivity patterns offers a promising pathway to inform diagnosis and therapeutic interventions. While advanced neuroimaging techniques, such as diffusion MRI (dMRI), have facilitated the mapping of brain's structural connectome, the challenge lies in accurately modeling developmental trajectories within these complex networked structures to create robust neurodivergence markers. In this work, we present the Brain Representation via Individualized Deep Generative Embedding (BRIDGE) framework, which integrates normative modeling with a bio-inspired deep generative model to create a reference trajectory of connectivity transformation as part of neurotypical development. This will enable the assessment of neurodivergence by comparing individuals to the established neurotypical trajectory. BRIDGE provides a global neurodivergence score based on the difference between connectivity-based brain age and chronological age, along with region-wise neurodivergence maps that highlight localized connectivity differences. Application of BRIDGE to a large cohort of children with autism spectrum disorder demonstrates that the global neurodivergence score correlates with clinical assessments in autism, and the regional map offers insights into the heterogeneity at the individual level in neurodevelopmental disorders. Together, the neurodivergence score and map form powerful tools for quantifying developmental divergence in connectivity patterns, advancing the development of imaging markers for personalized diagnosis and intervention in various clinical contexts.

Intellectual and Developmental Disabilities (IDDs), such as Down syndrome, Fragile X syndrome, Rett syndrome, and autism spectrum disorder, usually manifest at birth or early childhood. IDDs are characterized by significant impairment in intellectual and adaptive functioning, and both genetic and environmental factors underpin IDD biology. Molecular and genetic stratification of IDDs remain challenging mainly due to overlapping factors and comorbidity. Advances in high throughput sequencing, imaging, and tools to record behavioral data at scale have greatly enhanced our understanding of the molecular, cellular, structural, and environmental basis of some IDDs. Fueled by the “big data” revolution, artificial intelligence (AI) and machine learning (ML) technologies have brought a whole new paradigm shift in computational biology. Evidently, the ML-driven approach to clinical diagnoses has the potential to augment classical methods that use symptoms and external observations, hoping to push the personalized treatment plan forward. Therefore, integrative analyses and applications of ML technology have a direct bearing on discoveries in IDDs. The application of ML to IDDs can potentially improve screening and early diagnosis, advance our understanding of the complexity of comorbidity, and accelerate the identification of biomarkers for clinical research and drug development. For more than five decades, the IDDRC network has supported a nexus of investigators at centers across the USA, all striving to understand the interplay between various factors underlying IDDs. In this review, we introduced fast-increasing multi-modal data types, highlighted example studies that employed ML technologies to illuminate factors and biological mechanisms underlying IDDs, as well as recent advances in ML technologies and their applications to IDDs and other neurological diseases. We discussed various molecular, clinical, and environmental data collection modes, including genetic, imaging, phenotypical, and behavioral data types, along with multiple repositories that store and share such data. Furthermore, we outlined some fundamental concepts of machine learning algorithms and presented our opinion on specific gaps that will need to be filled to accomplish, for example, reliable implementation of ML-based diagnosis technology in IDD clinics. We anticipate that this review will guide researchers to formulate AI and ML-based approaches to investigate IDDs and related conditions.

With the outlook of improving communication and social abilities of people with ASD, we propose to extend the paradigm of robot-based imitation games to ASD teenagers. In this paper, we present an interaction scenario adapted to ASD teenagers, propose a computational architecture using the latest machine learning algorithm Openpose for human pose detection, and present the results of our basic testing of the scenario with human caregivers. These results are preliminary due to the number of session (1) and participants (4). They include a technical assessment of the performance of Openpose, as well as a preliminary user study to confirm our game scenario could elicit the expected response from subjects.

When Grayson Kollins was two and a half years old--just shortly after the birth of his younger sister--his parents noticed that he had all but stopped uttering the sentences and phrases that up until then he had been using to communicate. In addition, his daycare provider mentioned that Grayson had begun repeating phrases over and over, and lacked interest in playing with other children. Grayson's father Scott Kollins, Ph.D., a clinical psychologist and professor of psychiatry and behavioral sciences in the School of Medicine at Duke, was well aware of the symptoms of autism spectrum disorder, or ASD, a neurodevelopmental disorder that affects the ability to socially interact and communicate with others. Although it usually manifests early in life, it is a lifelong condition and can have profound effects on learning, employment, and personal relationships. Prompted by these early symptoms, Grayson's parents subsequently had him assessed, and he received a clinical diagnosis of ASD.

When Grayson Kollins was two and a half years old--just shortly after the birth of his younger sister--his parents noticed that he had all but stopped uttering the sentences and phrases that up until then he had been using to communicate. In addition, his daycare provider mentioned that Grayson had begun repeating phrases over and over, and lacked interest in playing with other children. Grayson's father Scott Kollins, Ph.D., a clinical psychologist and professor of psychiatry and behavioral sciences in the School of Medicine at Duke, was well aware of the symptoms of autism spectrum disorder, or ASD, a neurodevelopmental disorder that affects the ability to socially interact and communicate with others. Although it usually manifests early in life, it is a lifelong condition and can have profound effects on learning, employment, and personal relationships. Prompted by these early symptoms, Grayson's parents subsequently had him assessed, and he received a clinical diagnosis of ASD.

Williams Syndrome, a rare neurodevelopmental disorder that affects about 1 in 10,000 babies born in the United States, produces a range of symptoms including cognitive impairments, cardiovascular problems, and extreme friendliness, or hypersociability. In a study of mice, MIT neuroscientists have garnered new insight into the molecular mechanisms that underlie this hypersociability. They found that loss of one of the genes linked to Williams Syndrome leads to a thinning of the fatty layer that insulates neurons and helps them conduct electrical signals in the brain. The researchers also showed that they could reverse the symptoms by boosting production of this coating, known as myelin. This is significant, because while Williams Syndrome is rare, many other neurodevelopmental disorders and neurological conditions have been linked to myelination deficits, says Guoping Feng, the James W. and Patricia Poitras Professor of Neuroscience and a member of MIT's McGovern Institute for Brain Research.

One of the biggest problems in media today is so-called "fake news," which is so highly pernicious in part because it superficially resembles the real thing. AI tools promise to help identify it, but in order for it to do so, researchers have found that the best way is for that AI to learn to create fake news itself -- a double-edged sword, though perhaps not as dangerous as it sounds. Grover is a new system created by the University of Washington and Allen Institute for AI (AI2) computer scientists that is extremely adept at writing convincing fake news on myriad topics and as many styles -- and as a direct consequence is also no slouch at spotting it. The paper describing the model is available here. The idea of a fake news generator isn't new -- in fact, OpenAI made a splash recently by announcing that its own text-generating AI was too dangerous to release publicly. But Grover's creators believe we'll only get better at fighting generated fake news by putting the tools to create it out there to be studied.

For many years, parents have raised concerns that their child might be addicted to video games. Now, a new ruling by the World Health Organization (WHO) gives credence to those beliefs, as the agency agreed this weekend to recognize gaming addiction as a mental health disorder. To be diagnosed with this disorder, people must be playing video games so much that it'takes precedence over other life interests.' A new decision from the WHO has recognized'gaming addiction' as a mental health disorder. The change has been reflected in the WHO's International Classification of Diseases (ICD), a list used by health providers as a guideline for diagnosing patients. Gaming disorder falls under the WHO's list of'mental, behavioral or neurodevelopmental disorders' and closely mirrors the language used by the WHO to describe'gambling disorder.'