干旱区生态环境知识资源中心(Arid): Accurate identification of individuals with subjective cognitive decline using 3D regional fractal dimensions on structural magnetic resonance imaging

Arid

DOI	10.1016/j.cmpb.2024.108281
	Accurate identification of individuals with subjective cognitive decline using 3D regional fractal dimensions on structural magnetic resonance imaging
	Ni, Huangjing; Xue, Jing; Qin, Jiaolong; Zhang, Yu
通讯作者	Qin, JL
来源期刊	COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
ISSN	0169-2607
EISSN	1872-7565
出版年	2024
卷号	254
英文摘要	Background and Objective: Accurate identification of individuals with subjective cognitive decline (SCD) is crucial for early intervention and prevention of neurodegenerative diseases. Fractal dimensionality (FD) has emerged as a robust and replicable measure, surpassing traditional geometric metrics, in characterizing the intricate fractal geometrical properties of brain structure. Nevertheless, the effectiveness of FD in identifying individuals with SCD remains largely unclear. A 3D regional FD method can be suggested to characterize and quantify the spatial complexity of the precise gray matter, providing insights into cognitive aging and aiding in the automated identification of individuals with SCD. Methods: This study introduces a novel integer ratio based 3D box -counting fractal analysis (IRBCFA) to quantify regional fractal dimensions (FDs) in structural magnetic resonance imaging (MRI) data. The innovative method overcomes limitations of conventional box -counting techniques by accommodating arbitrary box sizes, thereby enhancing the precision of FD estimation in small, yet neurologically significant, brain regions. Results: The application of IRBCFA to two publicly available datasets, OASIS -3 and ADNI, consisting of 520 and 180 subjects, respectively. The method identified discriminative regions of interest (ROIs) predominantly within the limbic system, fronto-parietal region, occipito-temporal region, and basal ganglia -thalamus region. These ROIs exhibited significant correlations with cognitive functions, including executive functioning, memory, social cognition, and sensory perception, suggesting their potential as neuroimaging markers for SCD. The identification model trained on these ROIs demonstrated exceptional performance achieving over 93 % accuracy on the discovery dataset and exceeding 87 % on the independent testing dataset. Furthermore, an exchange experiment between datasets revealed a substantial overlap in discriminative ROIs, highlighting the robustness of our method across diverse populations. Conclusion: Our findings indicate that IRBCFA can serve as a valuable tool for quantifying the spatial complexity of gray matter, providing insights into cognitive aging and aiding in the automated identification of individuals with SCD. The demonstrated generalizability and robustness of this method position it as a promising tool for neurodegenerative disease research and offer potential for clinical applications.
英文关键词	Fractal dimension Integer ratio based 3D box-counting fractal analysis Structural magnetic resonance imaging Subjective cognitive decline individual identification
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001261192800001
WOS关键词	WHITE-MATTER ; COMPLEXITY ; PATTERNS ; CORTEX ; CONNECTIVITY ; PROGRAM
WOS类目	Computer Science, Interdisciplinary Applications ; Computer Science, Theory & Methods ; Engineering, Biomedical ; Medical Informatics
WOS研究方向	Computer Science ; Engineering ; Medical Informatics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403247
推荐引用方式 GB/T 7714	Ni, Huangjing,Xue, Jing,Qin, Jiaolong,et al. Accurate identification of individuals with subjective cognitive decline using 3D regional fractal dimensions on structural magnetic resonance imaging[J],2024,254.
APA	Ni, Huangjing,Xue, Jing,Qin, Jiaolong,&Zhang, Yu.(2024).Accurate identification of individuals with subjective cognitive decline using 3D regional fractal dimensions on structural magnetic resonance imaging.COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE,254.
MLA	Ni, Huangjing,et al."Accurate identification of individuals with subjective cognitive decline using 3D regional fractal dimensions on structural magnetic resonance imaging".COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 254(2024).