Brain disorders in the early and late life of humans potentially share
pathological alterations in brain functions. However, the key evidence from
neuroimaging data for pathological commonness remains unrevealed. To explore
this hypothesis, we build a deep learning model, using multi-site functional
magnetic resonance imaging data (N=4,410, 6 sites), for classifying 5 different
brain disorders from healthy controls, with a set of common features. Our model
achieves 62.6(1.9)% overall classification accuracy on data from the 6
investigated sites and detects a set of commonly affected functional
subnetworks at different spatial scales, including default mode, executive
control, visual, and limbic networks. In the deep-layer feature representation
for individual data, we observe young and aging patients with disorders are
continuously distributed, which is in line with the clinical concept of the
"spectrum of disorders". The revealed spectrum underlying early- and late-life
brain disorders promotes the understanding of disorder comorbidities in the
lifespan.

利用多个不同来源的功能性磁共振成像数据（4,410 名受试者，6 个位置）建立深度学习模型，能够识别 5 种脑部疾病，包括默认模式、执行控制、视觉和边缘网络等共同受损的功能子网络，并发现了早期和晚期大脑疾病共同谱系。

深度学习揭示多种年轻和老年脑部疾病的共同特征谱：基于脑功能网络的研究

Deep learning reveals the common spectrum underlying multiple brain disorders in youth and elders from brain functional networks

Can models with particular structure avoid being biased towards spurious
correlation in out-of-distribution (OOD) generalization? Peters et al. (2016)
provides a positive answer for linear cases. In this paper, we use a functional
modular probing method to analyze deep model structures under OOD setting. We
demonstrate that even in biased models (which focus on spurious correlation)
there still exist unbiased functional subnetworks. Furthermore, we articulate
and demonstrate the functional lottery ticket hypothesis: full network contains
a subnetwork that can achieve better OOD performance. We then propose Modular
Risk Minimization to solve the subnetwork selection problem. Our algorithm
learns the subnetwork structure from a given dataset, and can be combined with
any other OOD regularization methods. Experiments on various OOD generalization
tasks corroborate the effectiveness of our method.

本文研究了深度模型在无法识别的数据集上的偏见及其缓解策略，提出了一种名为 Modular Risk Minimization 的算法，通过学习子网络结构来提高模型对未知数据的泛化性能。

子网络结构是否是超出分布泛化的关键？

Can Subnetwork Structure be the Key to Out-of-Distribution  Generalization?

We propose a novel perspective to understand deep neural networks in an
interpretable disentanglement form. For each semantic class, we extract a
class-specific functional subnetwork from the original full model, with
compressed structure while maintaining comparable prediction performance. The
structure representations of extracted subnetworks display a resemblance to
their corresponding class semantic similarities. We also apply extracted
subnetworks in visual explanation and adversarial example detection tasks by
merely replacing the original full model with class-specific subnetworks.
Experiments demonstrate that this intuitive operation can effectively improve
explanation saliency accuracy for gradient-based explanation methods, and
increase the detection rate for confidence score-based adversarial example
detection methods.

本文提出了一种理解深度神经网络的新视角，通过提取类特定的功能子网络来实现可解释的解缠结形式，并将其应用于视觉解释和对抗性示例检测任务中。实验证明，这种直观的操作可以有效地提高基于梯度的解释方法的解释显著性准确性，并增加基于置信度分数的对抗性示例检测方法的检测率。