Unified framework for brain connectivity-based biomarkers in neurodegenerative disorders

Sung-Woo Kim; Yeong-Hun Song; Hee Jin Kim; Young Noh; Sang Won Seo; Duk L Na; Joon-Kyung Seong

doi:10.3389/fnins.2022.975299

Unified framework for brain connectivity-based biomarkers in neurodegenerative disorders

Front Neurosci. 2022 Sep 20:16:975299. doi: 10.3389/fnins.2022.975299. eCollection 2022.

Authors

Sung-Woo Kim¹, Yeong-Hun Song², Hee Jin Kim^{3

4

5}, Young Noh^{6

7}, Sang Won Seo^{3

4

8

9}, Duk L Na^{3

10}, Joon-Kyung Seong^{2

11

12}

Affiliations

¹ Department of Bio-Convergence Engineering, Korea University, Seoul, South Korea.
² Department of Artificial Intelligence, Korea University, Seoul, South Korea.
³ Department of Neurology, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea.
⁴ Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, South Korea.
⁵ Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, South Korea.
⁶ Department of Neurology, Gil Medical Center, Gachon University of College of Medicine, Incheon, South Korea.
⁷ Neuroscience Research Institute, Gachon University, Incheon, South Korea.
⁸ Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Seoul, South Korea.
⁹ Alzheimer's Disease Convergence Research Center, Samsung Medical Center, Seoul, South Korea.
¹⁰ Neuroscience Center, Samsung Medical Center, Seoul, South Korea.
¹¹ School of Biomedical Engineering, Korea University, Seoul, South Korea.
¹² Interdisciplinary Program in Precision Public Health, Korea University, Seoul, South Korea.

Abstract

Background: Brain connectivity is useful for deciphering complex brain dynamics controlling interregional communication. Identifying specific brain phenomena based on brain connectivity and quantifying their levels can help explain or diagnose neurodegenerative disorders.

Objective: This study aimed to establish a unified framework to identify brain connectivity-based biomarkers associated with disease progression and summarize them into a single numerical value, with consideration for connectivity-specific structural attributes.

Methods: This study established a framework that unifies the processes of identifying a brain connectivity-based biomarker and mapping its abnormality level into a single numerical value, called a biomarker abnormality summarized from the identified connectivity (BASIC) score. A connectivity-based biomarker was extracted in the form of a connected component associated with disease progression. BASIC scores were constructed to maximize Kendall's rank correlation with the disease, considering the spatial autocorrelation between adjacent edges. Using functional connectivity networks, we validated the BASIC scores in various scenarios.

Results: Our proposed framework was successfully applied to construct connectivity-based biomarker scores associated with disease progression, characterized by two, three, and five stages of Alzheimer's disease, and reflected the continuity of brain alterations as the diseases advanced. The BASIC scores were not only sensitive to disease progression, but also specific to the trajectory of a particular disease. Moreover, this framework can be utilized when disease stages are measured on continuous scales, resulting in a notable prediction performance when applied to the prediction of the disease.

Conclusion: Our unified framework provides a method to identify brain connectivity-based biomarkers and continuity-reflecting BASIC scores that are sensitive and specific to disease progression.

Keywords: Alzheimer's disease; Kendall's rank correlation; Laplacian regularization; biomarker scores; brain connectivity; connected component; connectivity-based biomarker.