Abstract：
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.
CONCLUSIONS: 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.
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.
CONCLUSIONS: 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.
摘要：
目的：准确识别主观认知功能减退（SCD）的个体对于神经退行性疾病的早期干预和预防至关重要。分形维数(FD)已经成为一种稳健和可复制的度量，超越传统的几何度量，在表征大脑结构的复杂分形几何特性中。然而，FD在确定SCD个体方面的有效性尚不清楚.可以建议使用3D区域FD方法来表征和量化精确灰质的空间复杂性，提供认知老化的见解，并帮助自动识别患有SCD的个体。
方法：本研究引入了一种新颖的基于整数比率的3D盒计数分形分析（IRBCFA），以量化结构磁共振成像（MRI）数据中的区域分形维数（FD）。该创新方法通过适应任意的盒子尺寸，克服了传统的盒子计数技术的局限性，从而提高小FD估计的精度，然而在神经上意义重大，大脑区域。
结果：将IRBCFA应用于两个公开可用的数据集，OASIS-3和ADNI,由520和180个科目组成，分别。该方法确定了主要在边缘系统内的区分性感兴趣区域(ROI)，额顶区,枕上-颞区,和基底神经节-丘脑区。这些ROI与认知功能表现出显著的相关性，包括执行功能，记忆,社会认知，和感官知觉，提示它们作为SCD神经影像学标志物的潜力。在这些ROI上训练的识别模型表现出卓越的性能，在发现数据集上实现超过93%的准确率，在独立测试数据集上超过87%。此外，数据集之间的交换实验揭示了判别ROI的大量重叠，突出了我们方法在不同人群中的稳健性。
结论：我们的研究结果表明，IRBCFA可以作为量化灰质空间复杂性的有价值的工具，提供认知老化的见解，并帮助自动识别患有SCD的个体。该方法证明的通用性和鲁棒性使其成为神经退行性疾病研究的有前途的工具，并为临床应用提供了潜力。
方法：本研究引入了一种新颖的基于整数比率的3D盒计数分形分析（IRBCFA），以量化结构磁共振成像（MRI）数据中的区域分形维数（FD）。该创新方法通过适应任意的盒子尺寸，克服了传统的盒子计数技术的局限性，从而提高小FD估计的精度，然而在神经上意义重大，大脑区域。
结果：将IRBCFA应用于两个公开可用的数据集，OASIS-3和ADNI,由520和180个科目组成，分别。该方法确定了主要在边缘系统内的区分性感兴趣区域(ROI)，额顶区,枕上-颞区,和基底神经节-丘脑区。这些ROI与认知功能表现出显著的相关性，包括执行功能，记忆,社会认知，和感官知觉，提示它们作为SCD神经影像学标志物的潜力。在这些ROI上训练的识别模型表现出卓越的性能，在发现数据集上实现超过93%的准确率，在独立测试数据集上超过87%。此外，数据集之间的交换实验揭示了判别ROI的大量重叠，突出了我们方法在不同人群中的稳健性。
结论：我们的研究结果表明，IRBCFA可以作为量化灰质空间复杂性的有价值的工具，提供认知老化的见解，并帮助自动识别患有SCD的个体。该方法证明的通用性和鲁棒性使其成为神经退行性疾病研究的有前途的工具，并为临床应用提供了潜力。
