The definition of autism has not been static. The first studies to describe autism appeared in the 1930s and 1940s, says Francesca Happé, a professor of cognitive neuroscience at King's College London, who's been researching autism since 1988. "The original descriptions of autism are of children who have pretty high support needs, typically are very late to talk," she says. And the focus really was on children, of course, and largely on males." But the definition was broadened, Professor Happé says, when in the 1990s Asperger's syndrome was added to diagnostic manuals.

Health and Human Services Secretary Robert F. Kennedy Jr. conducts a news conference to discuss the Centers for Disease Control and Prevention's latest Autism and Developmental Disabilities Monitoring Network survey.Tom Williams/CQ Roll Call/AP While his anti-vaccine allies swooned and scientists cringed, HHS Secretary Robert F. Kennedy Jr. used his first-ever press conference this week, in response to new data showing an apparent increase in the number of autistic kids, to promote a variety of debunked, half-true, and deeply ableist ideas about autism. He painted the condition as a terrifying "disease" that "destroys," as he put it, children and their families. Kennedy made it clear he planned to use his powerful role as the person in charge of a massive federal agency devoted to protecting public health to promote the idea that autism is caused by "environmental factors," a still-speculative thesis that's clearly a short walk towards advancing his real aim: blaming vaccines. Kennedy has spent the last 20 years promoting anti-vaccine rhetoric, falsely and repeatedly claiming that vaccines are linked to autism. Yet as the press conference made clear, Kennedy knows startlingly little about autism.

Waymo, the autonomous driving tech company behind the human-less ride shares taking over America's major cities, is celebrating Autism Acceptance Month with everyone's favorite wholesome show: Netflix's Love On The Spectrum. In the latest episode of Driven -- Waymo's celebrity video series hosted by TV personality Andrew Freund -- the company shines a spotlight on beau-of-the-moment Connor Tomlinson, star of the show's recent hit season, as he hitches a ride in a Waymo. "It is mind blowing to be in a car that is fully autonomous," Tomlinson tells Freund. "This reminds me of that scene in Jurassic Park." For the second episode of the video series' second season, Waymo brought in national nonprofit the Autism Society.

In China, it is known as "the lonely disease". The Japanese term translates as "intentionally shut". Across the world, there is a perception of autistic people as aloof, socially awkward and isolated, seeming to not only lack the kind of automatic social instinct that enables successful interaction, but also the desire to achieve it. There is also a perception that autistic people tend to be men. For decades, researchers – myself included – have thought of autism as a predominantly male condition.

State-of-the-art approaches to causal discovery usually assume a fixed underlying causal model. However, it is often the case that causal models vary across domains or subjects, due to possibly omitted factors that affect the quantitative causal effects. As a typical example, causal connectivity in the brain network has been reported to vary across individuals, with significant differences across groups of people, such as autistics and typical controls. In this paper, we develop a unified framework for causal discovery and mechanism-based group identification. In particular, we propose a specific and shared causal model (SSCM), which takes into account the variabilities of causal relations across individuals/groups and leverages their commonalities to achieve statistically reliable estimation. The learned SSCM gives the specific causal knowledge for each individual as well as the general trend over the population. In addition, the estimated model directly provides the group information of each individual. Experimental results on synthetic and real-world data demonstrate the efficacy of the proposed method.

Integrating functional magnetic resonance imaging (fMRI) connectivity data with phenotypic textual descriptors ( e.g., disease label, demographic data) holds significant potential to advance our understanding of neurological conditions. However, existing cross-modal alignment methods often lack interpretability and risk introducing biases by encoding sensitive attributes together with diagnostic-related features. In this work, we propose NeuroLIP, a novel cross-modal contrastive learning framework. We introduce text token-conditioned attention ( TTCA) and cross-modal alignment via localized tokens ( CALT) to the brain region-level embeddings with each disease-related phenotypic token. It improves interpretability via token-level attention maps, revealing brain region-disease associations. To mitigate bias, we propose a loss for sensitive attribute disentanglement that maximizes the attention distance between disease tokens and sensitive attribute tokens, reducing unintended correlations in downstream predictions. Additionally, we incorporate a negative gradient technique that reverses the sign of CALT loss on sensitive attributes, further discouraging the alignment of these features. Experiments on neuroimaging datasets (ABIDE and ADHD-200) demonstrate NeuroLIP's superiority in terms of fairness metrics while maintaining the overall best standard metric performance. Qualitative visualization of attention maps highlights neuroanatomical patterns aligned with diagnostic characteristics, validated by the neuroscientific literature.

In this paper we propose a novel method that provides contrastive explanations justifying the classification of an input by a black box classifier such as a deep neural network. Given an input we find what should be minimally and sufficiently present (viz.

In many biological and medical contexts, we construct a large labeled corpus by aggregating many sources to use in target prediction tasks. Unfortunately, many of the sources may be irrelevant to our target task, so ignoring the structure of the dataset is detrimental. This work proposes a novel approach, the Multiple Domain Matching Network (MDMN), to exploit this structure. MDMN embeds all data into a shared feature space while learning which domains share strong statistical relationships. These relationships are often insightful in their own right, and they allow domains to share strength without interference from irrelevant data. This methodology builds on existing distribution-matching approaches by assuming that source domains are varied and outcomes multi-factorial. Therefore, each domain should only match a relevant subset. Theoretical analysis shows that the proposed approach can have a tighter generalization bound than existing multiple-domain adaptation approaches. Empirically, we show that the proposed methodology handles higher numbers of source domains (up to 21 empirically), and provides state-of-the-art performance on image, text, and multi-channel time series classification, including clinical outcome data in an open label trial evaluating a novel treatment for Autism Spectrum Disorder.

Autism Spectrum Disorder (ASD) is a complex neurological condition characterized by varied developmental impairments, especially in communication and social interaction. Accurate and early diagnosis of ASD is crucial for effective intervention, which is enhanced by richer representations of brain activity. The brain functional connectome, which refers to the statistical relationships between different brain regions measured through neuroimaging, provides crucial insights into brain function. Traditional static methods often fail to capture the dynamic nature of brain activity, in contrast, dynamic brain connectome analysis provides a more comprehensive view by capturing the temporal variations in the brain. We propose BrainTWT, a novel dynamic network embedding approach that captures temporal evolution of the brain connectivity over time and considers also the dynamics between different temporal network snapshots. BrainTWT employs temporal random walks to capture dynamics across different temporal network snapshots and leverages the Transformer's ability to model long term dependencies in sequential data to learn the discriminative embeddings from these temporal sequences using temporal structure prediction tasks. The experimental evaluation, utilizing the Autism Brain Imaging Data Exchange (ABIDE) dataset, demonstrates that BrainTWT outperforms baseline methods in ASD classification.