Advances in Connectome-Wide Association Studies (CWAS) Along the Neurodegeneration Trajectory

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Background

Brain structural and functional alterations have been consistently proposed to be involved in the neurobiological underpinnings of aging and neurodegenerative disorders, such as Alzheimer's disease and Parkinson’s disease. Despite the considerable amount of neuroimaging research conducted in this area over the last decade, pathological perturbations of the brain are rarely confined to a single region. Instead, they often spread via axonal pathways to influence other regions. Patterns of such disease propagation are constrained by the extraordinarily complex, yet highly organized, topology of the underlying neural architecture; the so‑called connectome. It is becoming increasingly accepted that connectome reorganization plays a key role in determining cognitive or motor disability.
Correspondingly, hypothesis-free connectome-wide association studies (CWAS), may potentially allow for the identification of novel neural correlates of neurodegeneration at the whole-brain scale. Further elucidating how brain-network topology can shape neural responses to damage is needed to better understand disease status and progression.

The aim of this Research Topic is to explore novel CWAS-based analytical methods to precisely map the brain’s structural or functional reorganization along the aging or neurodegeneration trajectory. Using diffusion or functional MRI techniques, we focus on pinpointing and quantifying the brain disconnection signature at different levels and its relationship with cognitive or motor disability.

In addition, one of the most exciting recent hypotheses in neuroscience is that neurodegenerative diseases may be caused by neuron-to-neuron propagation of prion-like misfolded proteins. Given the evidences that brain atrophy due to neurodegeneration are observed spatially correlated with specific brain subnetworks, neuroscientists are debating over whether it explains the above hypothesis, or it only reflects selective vulnerability or neural deafferentation. It would be challenging to further validate if neuropathology propagates along the brain connectome by using dynamic CWAS approaches.

Moreover, deep neural networks allow for end-to-end disease identification with promising accuracy, by embedding connectome features into a low-dimensional space and simultaneously preserving high-order nonlinear information. Thus, we welcome novel frameworks or applications leveraging cutting-edge CWAS methods to uncover insights into the mechanisms or clinical translation of aging and neurodegeneration.

We welcome research articles and review articles on the following topics among others:
• Brain network studies of multivariate statistical methods to avoid false-positive connections in aging or neurodegenerative disorders.
• New approaches using multidisciplinary design (e.g., graph theory, control theory, agent-based model) to clarify and validate brain imaging findings associated with neuropathology in elderly people.
• Advanced brain connectivity analytical methods, such as whole-brain semi-empirical modeling and perturbation approaches, high-order interactions and meta-connectivity, or other advanced methods (decoding, multi-feature frameworks).
• Novel frameworks and guidelines to establish end-to-end CWAS-based model for individual diagnosis or prognosis, preferring graph embedding and graph neural network (GNN) algorithm research.
• The reproducibility and reliability of the novel CWAS methods to identify connectome hallmarks of aging or neurodegenerative disorders with multi-center data.

Keywords: CWAS, network reorganization, brain connectivity gradient, network controllability, deep neural network

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