Causal mapping of the functional organisation of the human brain requires
evidence of \textit{necessity} available at adequate scale only from
pathological lesions of natural origin. This demands inferential models with
sufficient flexibility to capture both the observable distribution of
pathological damage and the unobserved distribution of the neural substrate.
Current model frameworks -- both mass-univariate and multivariate -- either
ignore distributed lesion-deficit relations or do not model them explicitly,
relying on featurization incidental to a predictive task. Here we initiate the
application of deep generative neural network architectures to the task of
lesion-deficit inference, formulating it as the estimation of an expressive
hierarchical model of the joint lesion and deficit distributions conditioned on
a latent neural substrate. We implement such deep lesion deficit inference with
variational convolutional volumetric auto-encoders. We introduce a
comprehensive framework for lesion-deficit model comparison, incorporating
diverse candidate substrates, forms of substrate interactions, sample sizes,
noise corruption, and population heterogeneity. Drawing on 5500 volume images
of ischaemic stroke, we show that our model outperforms established methods by
a substantial margin across all simulation scenarios, including comparatively
small-scale and noisy data regimes. Our analysis justifies the widespread
adoption of this approach, for which we provide an open source implementation:
this https URL

本研究采用深度生成神经网络架构进行损伤缺陷推断的任务，并与现有方法进行比较。结果表明，该模型在所有情况下均显著优于已有方法，包括相对较小和嘈杂的数据情况。

深度变分损伤缺陷映射

Deep Variational Lesion-Deficit Mapping

Pathological brain appearances may be so heterogeneous as to be intelligible
only as anomalies, defined by their deviation from normality rather than any
specific pathological characteristic. Amongst the hardest tasks in medical
imaging, detecting such anomalies requires models of the normal brain that
combine compactness with the expressivity of the complex, long-range
interactions that characterise its structural organisation. These are
requirements transformers have arguably greater potential to satisfy than other
current candidate architectures, but their application has been inhibited by
their demands on data and computational resource. Here we combine the latent
representation of vector quantised variational autoencoders with an ensemble of
autoregressive transformers to enable unsupervised anomaly detection and
segmentation defined by deviation from healthy brain imaging data, achievable
at low computational cost, within relative modest data regimes. We compare our
method to current state-of-the-art approaches across a series of experiments
involving synthetic and real pathological lesions. On real lesions, we train
our models on 15,000 radiologically normal participants from UK Biobank, and
evaluate performance on four different brain MR datasets with small vessel
disease, demyelinating lesions, and tumours. We demonstrate superior anomaly
detection performance both image-wise and pixel-wise, achievable without
post-processing. These results draw attention to the potential of transformers
in this most challenging of imaging tasks.

通过将向量量化变分自编码器的潜在表示与自回归变换器集合相结合，我们在相对较小的数据范围内，以低计算成本实现了无监督的异常检测和分割，并在合成和真实病变实验中将其与现有方法进行了比较，证明了变换器在这一最具挑战性的成像任务中的潜力。