# Dynamic Multi-Task Graph Isomorphism Network for Classification of Alzheimer's Disease
## 1 Introduction  
Alzheimer's disease (AD) is described as a fatal degenerative dementia [1]. It affects patients' memory, thinking, and communication abilities, accounting for 60% to 70% of progressive cognitive impairment cases in the elderly [2]. Currently, approximately 50 million individuals worldwide are affected by Alzheimer's disease and related dementias. Owing to the rise in life expectancy, it is anticipated that by 2050, the global patient population will reach 139 million [3, 4], which poses significant impacts on society, the patients themselves, and their families. Alzheimer's disease progresses slowly, and there is still no effective treatment to prevent, halt, or reverse the disease [5]. In this context, Artificial Intelligence is emerging as a promising tool for not only enhancing the efficiency and effectiveness of healthcare services, but also for providing optimal strategies to clinicians [6]. Specifically, in early diagnosis, functional magnetic resonance imaging (fMRI) and deep learning methods play a critical role [7]. Functional MRI is a neuroimaging technique that utilizes measurements of cerebral blood flow to visualize brain activity [8]. This technique has been widely used for the automated diagnosis of brain disorders, thereby providing a rich source of information about the brain's functional connectivity. Recent studies suggest that AD's functional connectivity undergoes extensive changes, providing important clues for understanding the disease mechanism and searching for effective treatments.However, diagnosticaccuracy remains a challenge, and there is a need for non-invasive biomarkers that can detect prod understanding of Alzheimer's disease, contributing to its improved classification and diagnosis. In neuroimaging research, age and gender have been proven to be related to the occurrence and progression of Alzheimer's disease. Age is the greatest risk factor for Alzheimer's disease, with the incidence rate increasing significantly with age [13]. Gender differences may also affect brain structure and functional changes, thereby influencing the incidence and progression of Alzheimer's disease [14]. Therefore, by incorporating age prediction and gender classification as auxiliary tasks, we can better classify and diagnose Alzheimer's disease. Although multi-task learning [12] has also been successfully applied in Alzheimer's disease classification research, previous studies have not adequately addressed the task conflict issue during training, particularly in terms of dynamic weight balancing [15]. Each task may optimize model parameters in different directions, leading to interference between tasks and adversely affecting model performance. An efficient classification method for Alzheimer's disease was developed with the core motivation and objective of playing a crucial role in early diagnosis. Limitations of the use of traditional machine learning and graph neural networks in processing graph data were identified, and potential weight imbalance issues in multi-task learning during Alzheimer's disease classification were acknowledged. The interpretability of the model was emphasized due to its paramount importance in the field of neuroscience, as it aids in understanding the underlying mechanisms of the disease and provides clues for finding effective treatments. A new Dynamic Multi-Task Graph Isomorphism model for Alzheimer's disease classification was developed. The main component of the model is a Graph Isomorphism Network (GIN), which classifies fMRI images of Alzheimer's disease as its primary task. By adopting an improved GIN to analyze the brain functional network and learn node features on adjacency matrices, the network's representational capability was significantly enhanced. Simultaneously, age prediction and gender classification were incorporated as auxiliary tasks to guide the classification of fMRI images of Alzheimer's disease.