## 1 Introduction  
Alzheimer's disease (AD) places a huge burden on society and families [1]. The progression of AD is irreversible, and it is generally believed that early intervention is of critical importance in managing the disease [2]. Recent studies have indicated that subjective cognitive decline (SCD) may represent an early asymptomatic stage in the clinical progression of AD [3]. SCD refers to the subjective perception of a decline in cognitive abilities, despite no clear cognitive impairment detected in neuropsychological testing [4]. The risk of developing AD is higher in individuals with SCD [5; 6; 7]. Therefore, the diagnosis of SCD with objective biomarkers can be effective in preventing its conversion to mild cognitive impairment (MCI) and AD and has an important role in the successful use of interventional therapies [8]. The functional magnetic resonance imaging (fMRI), as a major tool, has been widely used in the cognitive research. In our previous studies, it has been shown that machine learning verification using diffusion tensor imaging (DTI) and fMRI resulted in identification accuracies of approximately 60% and 76.44% for SCD, respectively [9]. By integrating fMRI and DTI modalities, the ability to distinguish SCD from NC increased to an accuracy of 80.24% [10]. Furthermore, our recent dual-center study found that hypometabolism of the right middle temporal gyrus could differentiate between the NC and SCD groups with an AUC of 0.638-0.717 [11]. These findings indicate that fMRI have the potential to serve as a more sensitive and powerful biomarker for SCD, particularly in populations where abnormal interregional functional connectivity (FC) has been detected through fMRI analysis [12; 13; 14]. Besides, the researchers observed a reduction in FC in the bilateral hippocampus of the SCD group in contrast to the normal control (NC) group [15]. The traditional FC within the default mode network showed decreasing trend across the normal, SCD, and MCI group [16]. Altered FCs is also related to global cortical amyloid deposition [17] and neuropsychological scores [18]. However, there is high individual variety in the SCD population. Traditional FC calculations based on group comparisons do not show individual variety [19; 20; 21]. Hence, an individual FC index was required. Recently, studies have also attempted to accurately characterize FC strength, which contributes to the understanding of the neural underpinnings of cognitive abilities and behaviors [14; 22; 23; 24]. In this process, physiological and non-physiological noises easily interfere with blood oxygenation level dependent (BOLD) signals in fMRI, such as respiration, blood pressure changes, and head movements [25; 26; 27]. These non-neuronal noises can interfere with fMRI analysis [28; 29; 30]. To solve this problem, researchers have proposed a novel metric related to the degree of FC weighting, called proportional loss of functional connection strength (PLFCS), which is less susceptible to fMRI signal-to-noise interference than traditional FC metrics and avoids bias due to the introduction of proportional thresholds [31; 32]. The PLFCS incorporates the connection strength of normal controls for standardization and provides a good basis for eliminating individual differences. [PERSON] et al. used PLFCS to calculate the average strength of connections in AD. The findings show that average FC and tau burden are negatively correlated, with average FC declining along with tau burden [33]. However, presenting an individual proportion loss of functional connectivity strength (IPLFCS) remains challenging. The relationship between PLFCS and pathological molecular mechanisms as well as clinical manifestations at the SCD stage, such as amyloid deposition and clinical scale, is still unclear. In this study, we presented a novel IPLFCS analysis framework aimed at identifying SCD biomarkers. We further studied the relationship between IPLFCS biomarkers and amyloid deposition as well as neuropsychological manifestations.