Unlocking Alzheimer's mysteries: A revolutionary leap in brain network analysis

Dementia stands as one of the most significant global health challenges of the 21st century, with over 50 million individuals worldwide currently affected, a number projected to triple by 2050, reaching 152 million, due to global population aging. Alzheimer’s disease (AD) is the predominant cause of dementia, accounting for 60–80% of all cases. Research on AD identifies two primary pathological hallmarks: the progressive accumulation of extracellular amyloid beta (Aβ) plaques and the presence of intracellular neurofibrillary tangles (NFTs). The accumulation of these pathological proteins in specific brain regions, followed by their dissemination throughout the broader brain network, leads to disruptions in both individual brain regions and their interconnections. Consequently, brain networks play a pivotal role in the development and progression of AD.

In a study (https://doi.org/10.1093/psyrad/kkad033) published in Psychoradiology on January 11, 2024, researchers from the University of Texas at Arlington and the University of Georgia have systematically summarized studies on brain networks within the context of AD, critically analyzed the strengths and weaknesses of existing methodologies, and offered novel perspectives and insights, intending to serve as inspiration for future research.

This study offers a comprehensive overview of the dynamic landscape of Alzheimer's disease (AD) research within the realm of brain network analysis. It underscores the pivotal role of brain networks in elucidating the mechanisms underpinning AD and their profound impact on disease progression. The review sheds light on the rich spectrum of graph-based methods employed in AD investigations, classifying them into traditional graph theory-based approaches and cutting-edge deep graph neural network-based techniques. These methodologies have significantly enriched our understanding of AD by unveiling intricate patterns within brain networks. Consequently, they have opened doors to pioneering diagnostic tools, predictive models, and the identification of potential biomarkers. Moreover, this review highlights numerous substantial challenges lying ahead. These challenges encompass issues such as the interpretability of complex models and the effective integration of multimodal data, especially within the context of limited medical datasets. Addressing these hurdles remains paramount for the continued advancement of AD research and its translation into clinical practice.

Lead researcher, Dr. Lu Zhang, states, “Today, we have easier access to diverse modalities of data and possess more powerful computational models. I firmly believe that based on these advancements, we will ultimately overcome Alzheimer's disease in the near future.”

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References

DOI

10.1093/psyrad/kkad033

Original Source URL

https://doi.org/10.1093/psyrad/kkad033

Funding information

This work was supported by National Institutes of Health (R01AG075582, RF1NS128534).

About Psychoradiology

Psychoradiology is an open-access journal co-published by Oxford University Press and West China Hospital. It has been indexed by Scopus, DOAJ, EBSCO and the APC is waived during its early stage. We welcome interdisciplinary submissions in the fields of radiology, psychology, psychiatry, neurology and neuroscience, as well as medical imaging, interventional medicine, artificial intelligence, and computer science, etc. A fast-track production mode will be adopted to ensure the manuscript is published as soon as possible.