Building comprehensive brain connectomes has proved of fundamental importance
in resting-state fMRI (rs-fMRI) analysis. Based on the foundation of brain
network, spatial-temporal-based graph convolutional networks have dramatically
improved the performance of deep learning methods in rs-fMRI time series
classification. However, existing works either pre-define the brain network as
the correlation matrix derived from the raw time series or jointly learn the
connectome and model parameters without any topology constraint. These methods
could suffer from degraded classification performance caused by the deviation
from the intrinsic brain connectivity and lack biological interpretability of
demonstrating the causal structure (i.e., effective connectivity) among brain
regions. Moreover, most existing methods for effective connectivity learning
are unaware of the downstream classification task and cannot sufficiently
exploit useful rs-fMRI label information. To address these issues in an
end-to-end manner, we model the brain network as a directed acyclic graph (DAG)
to discover direct causal connections between brain regions and propose
Spatial-Temporal DAG Convolutional Network (ST-DAGCN) to jointly infer
effective connectivity and classify rs-fMRI time series by learning brain
representations based on nonlinear structural equation model. The optimization
problem is formulated into a continuous program and solved with score-based
learning method via gradient descent. We evaluate ST-DAGCN on two public
rs-fMRI databases. Experiments show that ST-DAGCN outperforms existing models
by evident margins in rs-fMRI classification and simultaneously learns
meaningful edges of effective connectivity that help understand brain activity
patterns and pathological mechanisms in brain disease.

通过建立大脑网络连接图，提出了一种空间 - 时间有向无环图卷积网络（ST-DAGCN），结合基于非线性结构方程模型的大脑表征学习方法，以推断功能连接性和分类脑部功能磁共振成像时间序列，并在两个公共数据库上进行验证，结果表明 ST-DAGCN 在脑部功能磁共振成像分类中明显优于现有模型，同时学习到有意义的功能连接边以帮助理解脑活动模式和疾病机制。

空间 - 时间 DAG 卷积网络用于端到端关节有效连接学习和静息态 fMRI 分类

Spatial-Temporal DAG Convolutional Networks for End-to-End Joint  Effective Connectivity Learning and Resting-State fMRI Classification

Alzheimer's disease (AD) is an irreversible brain disease that can
dramatically reduce quality of life, most commonly manifesting in older adults
and eventually leading to the need for full-time care. Early detection is
fundamental to slowing its progression; however, diagnosis can be expensive,
time-consuming, and invasive. In this work we develop a neural model based on a
CNN-LSTM architecture that learns to detect AD and related dementias using
targeted and implicitly-learned features from conversational transcripts. Our
approach establishes the new state of the art on the DementiaBank dataset,
achieving an F1 score of 0.929 when classifying participants into AD and
control groups.

本研究采用基于 CNN-LSTM 体系结构的神经模型，使用定向和隐式学习的方法从对话转录中学习，从而检测阿尔茨海默病及相关痴呆疾病的存在，实现了对 DementiaBank 数据集的新的最先进性能，将参与者分类为 AD 和对照组的 F1 分数达到 0.929。