The Hidden Power of Normalization: Exponential Capacity Control in Deep Neural Networks

arXiv:2511.00958v1 Announce Type: cross Abstract: Normalization methods are fundamental components of modern deep neural networks (DNNs). Empirically, they are known to stabilize optimization dynamics and

FeNN-DMA: A RISC-V SoC for SNN acceleration

arXiv:2511.00732v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) are a promising, energy-efficient alternative to standard Artificial Neural Networks (ANNs) and are particularly well-suited to

Parameter Interpolation Adversarial Training for Robust Image Classification

arXiv:2511.00836v1 Announce Type: cross Abstract: Though deep neural networks exhibit superior performance on various tasks, they are still plagued by adversarial examples. Adversarial training has

Region-Aware Reconstruction Strategy for Pre-training fMRI Foundation Model

arXiv:2511.00443v1 Announce Type: cross Abstract: The emergence of foundation models in neuroimaging is driven by the increasing availability of large-scale and heterogeneous brain imaging datasets.

FlashEVA: Accelerating LLM inference via Efficient Attention

arXiv:2511.00576v1 Announce Type: cross Abstract: Transformer models have revolutionized natural language processing, achieving state-of-the-art performance and demonstrating remarkable scalability. However, their memory demands, particularly due

Antidepressant prescribing trends for adults with and without autism in the United Kingdom from 1997 to 2023, a population-based cohort study using Clinical Practice Research Datalink Aurum

October 25, 2025

Background: Antidepressant prescribing and autism diagnoses have both increased in recent years, however antidepressant prescribing patterns for autistic adults are not well understood. Aims: (1) To describe antidepressant trends in autistic and non-autistic adults in the UK from 1997 to 2023. (2) To compare trends between autistic adults and age- and sex-matched non-autistic adults. (3) To investigate how trends vary by intellectual disability (ID) status, age-group and sex. Methods: Using population-representative primary care records from the Clinical Practice Research Datalink Aurum, we defined three autistic groups – autistic adults with ID, autistic adults without ID and all autistic adults – and four non-autistic groups – all non-autistic adults and an age- and sex-matched comparator group for each autistic group. In each calendar year we calculated: annual, lifetime and new antidepressant prescribing; indications for serotonin-selective reuptake inhibitor initiation; average doses prescribed for citalopram, fluoxetine, and sertraline; and the proportion of courses that lasted over 1, 2 and 3 years. We performed primary analyses in 16-64 year olds and repeated them with stratification by sex and age-group. Results: In all, 34,173,295 non-autistic adults and 172,242 autistic adults were included (47,011 with ID and 125,231 without ID). 30% of autistic adults and 14.7% of non-autistic adults were prescribed an antidepressant in 2023. Annual prescribing, average doses and course durations increased for all groups during the study period, and were higher for autistic adults than non-autistic adults. Annual, new and lifetime prescribing were highest in autistic adults without ID, whereas course durations were highest in autistic adults with ID. Recording of depression and anxiety as indications was lower for those with ID. Trends were largely similar across sex and age strata, with higher prescribing among females. Conclusions: Antidepressant prescribing for autistic adults has increased since 1997 and patterns of use vary by intellectual disability status.

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