7 Years of Developing Seed Techniques for Alzheimer’s Disease Diagnosis Using Brain Image and Connectivity Data Largely Bypassed Prediction for Prognosis

Soussia, Mayssa; Rekik, Islem

doi:10.1007/978-3-030-32281-6_9

Cited by 5 publications

(3 citation statements)

References 58 publications

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“…While several review papers already exist, they are all different from our review. For instance, (37,38,51,40,44) and (1,2,3,4,5,6,14,17,35) do not discuss specific GNN-based methods for solving neuroscience problems, but instead act as a reference for a specific topic (i.e., GNN or neuroscience). Therefore, it is necessary to provide a high-quality review that analyzes the trends and highlights the future directions for the applications of GNNs to the field of connectomics, which can generalize to the broader field of "omics" (e.g., genomics) (90,91).…”

Section: Functional Brain Graphmentioning

confidence: 99%

“…Thus, this diversity in resolution will certainly increase the performance of early disease diagnosis (15) as it helps better capture the multi-level nested complexity of the brain as a connectome. Ultimately, knowing that neurological disorders affect the brain in different ways, one may boost the diagnosis by leveraging the complementary information present in multiple modalities such as functional and morphological connectivities (7,17,16). Therefore, the third axis refers to the domain in which the brain data was collected, which is commonly referred to in network neuroscience as the 'neuroimaging modality' (e.g., functional MRI or diffusion MRI) utilized to generate the brain connectome type (e.g., functional or structural).…”

Section: Introductionmentioning

confidence: 99%

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Graph Neural Networks in Network Neuroscience

Bessadok¹,

Mahjoub²,

Rekik³

2021

Preprint

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Noninvasive medical neuroimaging has yielded many discoveries about the brain connectivity. Several substantial techniques mapping morphological, structural and functional brain connectivities were developed to create a comprehensive road map of neuronal activities in the human brain -namely brain graph. Relying on its non-Euclidean data type, graph neural network (GNN) provides a clever way of learning the deep graph structure and it is rapidly becoming the state-of-the-art leading to enhanced performance in various network neuroscience tasks. Here we review current GNN-based methods, highlighting the ways that they have been used in several applications related to brain graphs such as missing brain graph synthesis and disease classification. We conclude by charting a path toward a better application of GNN models in network neuroscience field for neurological disorder diagnosis and population graph integration.

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Section: Functional Brain Graphmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Graph Neural Networks in Network Neuroscience

Bessadok¹,

Mahjoub²,

Rekik³

2021

Preprint

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“…Multi-resolution neuroimaging has spanned several neuroscientific works thanks to the rich and complementary information that it provides [19,7]. Existing works showed that the diversity in resolution allows early disease diagnosis [14,21]. While super-resolution images provide more details about brain anatomy and function they correspondingly increase the scan time.…”

Section: Introductionmentioning

confidence: 99%

Inter-Domain Alignment for Predicting High-Resolution Brain Networks Using Teacher-Student Learning

Demir¹,

Bessadok²,

Rekik³

2021

Preprint

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Accurate and automated super-resolution image synthesis is highly desired since it has the great potential to circumvent the need for acquiring high-cost medical scans and a time-consuming preprocessing pipeline of neuroimaging data. However, existing deep learning frameworks are solely designed to predict high-resolution (HR) image from a low-resolution (LR) one, which limits their generalization ability to brain graphs (i.e., connectomes). A small body of works has focused on superresolving brain graphs where the goal is to predict a HR graph from a single LR graph. Although promising, existing works mainly focus on superresolving graphs belonging to the same domain (e.g., functional), overlooking the domain fracture existing between multimodal brain data distributions (e.g., morphological and structural). To this aim, we propose a novel inter-domain adaptation framework namely, Learn to SuperResolve Brain Graphs with Knowledge Distillation Network (L2S-KDnet), which adopts a teacher-student paradigm to superresolve brain graphs. Our teacher network is a graph encoder-decoder that firstly learns the LR brain graph embeddings, and secondly learns how to align the resulting latent representations to the HR ground truth data distribution using an adversarial regularization. Ultimately, it decodes the HR graphs from the aligned embeddings. Next, our student network learns the knowledge of the aligned brain graphs as well as the topological structure of the predicted HR graphs transferred from the teacher. We further leverage the decoder of the teacher to optimize the student network. In such a way, we are not only bringing the learned embeddings from both networks closer to each other but also their predicted HR graphs. L2S-KDnet presents the first TS architecture tailored for brain graph super-resolution synthesis that is based on inter-domain alignment. Our experimental results demonstrate substantial performance gains over benchmark methods.

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