Graph Neural Networks in Network Neuroscience

Bessadok, Alaa; Mahjoub, Mohamed Ali; Rekik, Islem

doi:10.1109/tpami.2022.3209686

Cited by 98 publications

(29 citation statements)

References 99 publications

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“…In terms of supervised learning, GNNs have been introduced into AD classification studies in a variety of ways using different types of data, especially MRI and PET brain data [6, 8–10, 35, 36]. More recent advancements of GNNs on AD have been proposed to provide more flexibility [11, 12].…”

Section: Discussionmentioning

confidence: 99%

Alzheimer's disease classification using cluster‐based labelling for graph neural network on heterogeneous data

McCombe

Bamrah

Sánchez-Bornot

et al. 2022

Healthcare Tech Letters

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Biomarkers for Alzheimer's disease (AD) diagnosis do not always correlate reliably with cognitive symptoms, making clinical diagnosis inconsistent. In this study, the performance of a graphical neural network (GNN) classifier based on data-driven diagnostic classes from unsupervised clustering on heterogeneous data is compared to the performance of a classifier using clinician diagnosis as an outcome. Unsupervised clustering on tau-positron emission tomography (PET) and cognitive and functional assessment data was performed. Five clusters embedded in a non-linear uniform manifold approximation and project (UMAP) space were identified. The individual clusters revealed specific feature characteristics with respect to clinical diagnosis of AD, gender, family history, age, and underlying neurological risk factors (NRFs). In particular, one cluster comprised mainly diagnosed AD cases. All cases within this cluster were re-labelled AD cases. The re-labelled cases are characterized by high cerebrospinal fluid amyloid beta (CSF Aβ) levels at a younger age, even though Aβ data was not used for clustering. A GNN model was trained using the relabelled data with a multiclass area-under-the-curve (AUC) of 95.2%, higher than the AUC of a GNN trained on clinician diagnosis (91.7%; p = 0.02). Overall, our work suggests that more objective cluster-based diagnostic labels combined with GNN classification may have value in clinical risk stratification and diagnosis of AD.

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Section: Discussionmentioning

confidence: 99%

Alzheimer's disease classification using cluster‐based labelling for graph neural network on heterogeneous data

McCombe

Bamrah

Sánchez-Bornot

et al. 2022

Healthcare Tech Letters

View full text Add to dashboard Cite

show abstract

“…To better exploit semantic relevance between neighbors, numerous studies have focused on relationship modeling via graph structures or attention mechanisms, where semantic context could then be extracted and aggregated into the corresponding center points. Graph neural networks (GNN) were firstly proposed by [27], and have been widely used in different fields, including semantic understanding [49], medical neuroimaging [50] and social networks [51], to describe the local and global contexts within unstructured data in recent years. For instance, super-point graph (SPG) [52] was constructed to realize semantic segmentation in a large scene.…”

Section: Graph-based Methodsmentioning

confidence: 99%

A Category-Contrastive Guided-Graph Convolutional Network Approach for the Semantic Segmentation of Point Clouds

Yang

Zhang

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The semantic segmentation of light detection and ranging (LiDAR) point clouds plays an important role in 3D scene intelligent perception and semantic modeling. The unstructured, sparse and uneven characteristics of point clouds pose great challenges to the representation of the local geometric shapes, which degrades semantic segmentation performance. To address the challenges of describing local geometric shapes due to unstructured and sparse 3D point clouds, this paper proposes a category-contrastive-guided graph convolutional network (CGGC-Net) for the semantic segmentation of LiDAR point clouds. First, a detailed geometric structure of the raw point clouds is encoded to represent the inherent geometric pattern within the local neighborhood. At the same time, the geometric structures information is transmitted across multiple layers, so that the geometric structure encoding information containing different receptive fields and richer neighborhood spatial structure can be aggregated. Following this, the graph convolution neural network uses the edge convolution layer to adaptively describe the semantic correlation between the query point and its neighboring points, and combines the attention mechanism to gather the surrounding feature information to the query point. As a result, the graph convolution neural network and attention mechanism are iteratively stacked for the aggregation and fusion of spatial context semantic information, to generate highly discriminative semantic feature representation. Finally, the super parameters of the model are learned through a multitask optimization strategy guided by category-aware contrastive loss and cross-entropy loss. Experiments are conducted on the public SemanticKITTI dataset and the Stanford Large-Scale 3D Indoor Spaces dataset to demonstrate the effectiveness and reliability of the proposed CGGC-Net from both quantitative and qualitative perspectives. The results indicate its capability of automatically classifying LiDAR point clouds, with a mean intersection-over-union (mIoU) of 58.4%. Moreover, multiple comparative experiments also demonstrate the superior performance of the proposed method, exceeding state-of-the-art methods.

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“…In the past decade, Graph Neural Networks (GNNs) have widely used in computer-aided diagnosis (Bessadok et al, 2022;Holzinger et al, 2021;Sun et al, 2020). GNNs such as Graph Convolutional Network (GCN, Kipf and Welling (2017)) and Graph Attention Network (GAT) (Veličković et al, 2017) leverage message passing mechanism to capture relationships between nodes and structure information in the graph.…”

Section: Graph Neural Networkmentioning

confidence: 99%

GATE: Graph CCA for Temporal Self-Supervised Learning for Label-Efficient fMRI Analysis

Liang

Wang

et al. 2023

IEEE Trans. Med. Imaging

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Prompt learning has demonstrated impressive efficacy in the fine-tuning of multimodal large models to a wide range of downstream tasks. Nonetheless, applying existing prompt learning methods for the diagnosis of neurological disorder still suffers from two issues: (i) existing methods typically treat all patches equally, despite the fact that only a small number of patches in neuroimaging are relevant to the disease, and (ii) they ignore the structural information inherent in the brain connection network which is crucial for understanding and diagnosing neurological disorders. To tackle these issues, we introduce a novel prompt learning model by learning graph prompts during the fine-tuning process of multimodal large models for diagnosing neurological disorders. Specifically, we first leverage GPT-4 to obtain relevant disease concepts and compute semantic similarity between these concepts and all patches. Secondly, we reduce the weight of irrelevant patches according to the semantic similarity between each patch and disease-related concepts. Moreover, we construct a graph among tokens based on these concepts and employ a graph convolutional network layer to extract the structural information of the graph, which is used to prompt the pre-trained multimodal large models for diagnosing neurological disorders. Extensive experiments demonstrate that our method achieves superior performance for neurological disorder diagnosis compared with state-of-the-art methods and validated by clinicians.

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Graph Neural Networks in Network Neuroscience

Cited by 98 publications

References 99 publications

Alzheimer's disease classification using cluster‐based labelling for graph neural network on heterogeneous data

Alzheimer's disease classification using cluster‐based labelling for graph neural network on heterogeneous data

A Category-Contrastive Guided-Graph Convolutional Network Approach for the Semantic Segmentation of Point Clouds

GATE: Graph CCA for Temporal Self-Supervised Learning for Label-Efficient fMRI Analysis

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