Graph neural networks (GNN) have emerged as a popular tool for modelling
functional magnetic resonance imaging (fMRI) datasets. Many recent studies have
reported significant improvements in disorder classification performance via
more sophisticated GNN designs and highlighted salient features that could be
potential biomarkers of the disorder. In this review, we provide an overview of
how GNN and model explainability techniques have been applied on fMRI datasets
for disorder prediction tasks, with a particular emphasis on the robustness of
biomarkers produced for neurodegenerative diseases and neuropsychiatric
disorders. We found that while most studies have performant models, salient
features highlighted in these studies vary greatly across studies on the same
disorder and little has been done to evaluate their robustness. To address
these issues, we suggest establishing new standards that are based on objective
evaluation metrics to determine the robustness of these potential biomarkers.
We further highlight gaps in the existing literature and put together a
prediction-attribution-evaluation framework that could set the foundations for
future research on improving the robustness of potential biomarkers discovered
via GNNs.

通过模型解释技术，本文综述了图神经网络在功能磁共振成像数据集上应用于疾病预测任务中的应用，特别关注所生成的神经变性疾病和神经精神障碍的潜在生物标记的稳健性，并建议建立新的标准来评估这些潜在生物标记的稳健性。

使用图神经网络从功能磁共振成像中发现神经系统疾病的稳健生物标志物：综述

Discovering robust biomarkers of neurological disorders from functional  MRI using graph neural networks: A Review

The characterisation of the brain as a "connectome", in which the connections
are represented by correlational values across timeseries and as summary
measures derived from graph theory analyses, has been very popular in the last
years. However, although this representation has advanced our understanding of
the brain function, it may represent an oversimplified model. This is because
the typical fMRI datasets are constituted by complex and highly heterogeneous
timeseries that vary across space (i.e., location of brain regions). We compare
various modelling techniques from deep learning and geometric deep learning to
pave the way for future research in effectively leveraging the rich spatial and
temporal domains of typical fMRI datasets, as well as of other similar
datasets. As a proof-of-concept, we compare our approaches in the homogeneous
and publicly available Human Connectome Project (HCP) dataset on a supervised
binary classification task. We hope that our methodological advances relative
to previous "connectomic" measures can ultimately be clinically and
computationally relevant by leading to a more nuanced understanding of the
brain dynamics in health and disease. Such understanding of the brain can
fundamentally reduce the constant specialised clinical expertise in order to
accurately understand brain variability.

通过比较深度学习和几何深度学习的各种建模技术来为未来的研究铺平道路，以有效利用典型的 fMRI 数据集以及其他类似数据集的丰富的时空域，从而为人们对健康和疾病中大脑动态的更加细致的理解、以及减少对专业临床专家的需求提供方法。

构建基于时空预测的脑数据表示模型

Towards a predictive spatio-temporal representation of brain data

Language-brain encoding experiments evaluate the ability of language models
to predict brain responses elicited by language stimuli. The evaluation
scenarios for this task have not yet been standardized which makes it difficult
to compare and interpret results. We perform a series of evaluation experiments
with a consistent encoding setup and compute the results for multiple fMRI
datasets. In addition, we test the sensitivity of the evaluation measures to
randomized data and analyze the effect of voxel selection methods. Our
experimental framework is publicly available to make modelling decisions more
transparent and support reproducibility for future comparisons.

本研究旨在通过一系列基于一致编码的评估实验，探究语言模型预测大脑对语言刺激的反应能力。同时，为了支持未来比较的可重复性，我们公开了实验框架，并分析了评估测量的敏感性以及偏重效应的影响。