Revolutionary Leap in Brain Network Analysis Leads to Breakthrough in Alzheimer's Research

The pivotal role of brain networks in Alzheimer’s disease research is emphasized in a recent study, giving insights into methodologies and upcoming challenges. The study accentuates the importance of advances in the integration of data and model interpretability to improve research and clinical practices, viewing the overcoming of Alzheimer’s disease as possible with current technological advancements.

Worldwide, dementia is a significant health problem in the 21st century, affecting over 50 million people globally. As the world's population ages, this number is expected to skyrocket to 152 million by 2050. Alzheimer’s disease (AD) is the most common form of dementia, accounting for 60–80% of all cases of dementia.

The primary pathological characteristics identified in AD research are the gradual buildup of extracellular amyloid beta (Aβ) plaques and the occurrence of intracellular neurofibrillary tangles (NFTs).

The buildup of these pathological proteins in certain brain regions, followed by their spread throughout the wider brain network, results in disruptions in both individual brain regions and their interconnections. As a result, brain networks play a key role in the onset and progression of AD.

Researchers from the University of Texas at Arlington and the University of Georgia have systematically summarized studies on brain networks related to AD in a study recently published in Psychoradiology. They critically assessed the strengths and weaknesses of existing methodologies and offered fresh perspectives and insights, hoping to inspire future research.

The study provides a comprehensive survey of the dynamic landscape of Alzheimer’s disease (AD) research in the field of brain network analysis. It underscores the significance of brain networks in unraveling the mechanisms behind AD and their significant impact on disease progression.

Structural connectivity (SC), usually estimated using fiber bundles derived from diffusion MRI, refers to anatomical links. Functional connectivity (FC) and effective connectivity (EC) are typically inferred through the correlation of nodal activities based on BOLD-fMRI or EEG/MEG. Credit: Psychoradiology

The review highlights the wide-ranging graph-based methods used in AD investigations, dividing them into traditional graph theory-based approaches and advanced deep graph neural network-based techniques. These methodologies have significantly expanded our understanding of AD by uncovering complex patterns within brain networks. As a result, they have laid the foundation for innovative diagnostic tools, predictive models, and the discovery of potential biomarkers.

In addition, this review emphasizes numerous significant challenges that lie ahead. These challenges include understanding complex models and effectively integrating multimodal data, particularly in the context of limited medical datasets. Overcoming these obstacles is crucial for the ongoing progress of AD research and its implementation in clinical practice.

Lead researcher Dr. Lu Zhang states, "Today, we have easier access to diverse modalities of data and possess more sophisticated computational models. I am confident that with these advancements, we will eventually overcome Alzheimer’s disease in the near future.”

"Exploring Alzheimer’s disease: a comprehensive brain connectome-based survey” by Lu Zhang, Junqi Qu, Haotian Ma, Tong Chen, Tianming Liu and Dajiang Zhu was published on 11 January 2024 in Psychoradiology.