Exploring Brain and Ventricular Boundary Shift Integral Associations with Beta Amyloid and Neurofilament Light in Alzheimer’s Disease

Exploring Brain and Ventricular Boundary Shift Integral Associations with Beta Amyloid and Neurofilament Light in Alzheimer’s Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring Brain and Ventricular Boundary Shift Integral Associations with Beta Amyloid and Neurofilament Light in Alzheimer’s Disease Negin Ashoori, Hamideh Nasiri, Sahba Shahbazi, Faezeh Rezaei, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6589854/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Alzheimer’s disease (AD) pathology develops over decades, creating a prolonged preclinical phase. Sensitive biomarkers are needed to detect neurodegeneration early. We aimed to determine whether baseline cerebrospinal fluid Aβ1–42 (CSF Aβ1–42) and plasma neurofilament light chain (NfL) levels are associated with brain atrophy rates in early AD. Methods We analyzed data from 76 participants in the ADNI cohort (30 cognitively normal [CN], 32 with mild cognitive impairment [MCI], 14 with AD). Baseline CSF Aβ1–42 and plasma NfL levels were measured. Whole-brain, ventricular, and hippocampal volume changes over 12 months were quantified with the Boundary Shift Integral (BSI) on serial T1-weighted MRIs. Linear regression models tested associations between each biomarker and 12-month brain atrophy, adjusting for age, sex, and education (significance threshold p < 0.05). Results Lower baseline CSF Aβ1–42 was significantly associated with greater whole-brain, ventricular, and hippocampal volume loss over 12 months in the MCI and AD groups (p < 0.05). No significant association was found between CSF Aβ1–42 and atrophy in CN. Baseline plasma NfL showed no significant relationship with 12-month atrophy in any group. Conclusion These findings help clarify the differential relationship between key pathological markers (Aβ accumulation vs. axonal injury reflected by NfL) and longitudinal structural brain changes by directly comparing these associations using multiple BSI metrics across the AD spectrum. The results highlight that BSI is sensitive to Aβ-related neurodegeneration but may capture different aspects of pathology than plasma NfL, significantly impacting our understanding of biomarker dynamics and supporting BSI's potential for tracking early AD changes. This warrants further longitudinal validation to establish its clinical utility. Boundary Shift Integral Neurofilament Light (NfL) Alzheimer’s Disease Beta-Amyloid Mild Cognitive Impairment 1. INTRODUCTION Alzheimer’s disease (AD), characterized by progressive memory loss and cognitive impairment, is the most common neurodegenerative disorder and the leading cause of dementia, affecting millions worldwide. AD’s pathological processes start decades before clinical symptoms appear. This has increased focus on early detection and intervention during preclinical stages when neuroprotection might still be effective. Consequently, the study of biomarkers in preclinical AD has become a topic of significant interest ( 1 ) ( 2 ). Neurodegeneration is a hallmark of AD ( 3 ). It is hypothesized to result from an imbalance in beta-amyloid (Aβ) synthesis and clearance, leading to extracellular Aβ aggregation. This aggregation initiates a cascade of pathological events that culminate in neuronal death, brain atrophy, and cognitive impairment ( 4 ). According to the latest diagnostic guidelines from the National Institute on Aging and Alzheimer’s Association (NIA-AA), neurodegeneration, as an imaging biomarker, is recommended to be integrated with hallmark fluid biomarkers of AD, such as Aβ and pathological tau ( 5 ). Imaging techniques, such as magnetic resonance imaging (MRI), have been utilized to assess brain atrophy in AD ( 6 ). However, in the early stages of the disease, the degree of tissue loss may not cause structural volume changes large enough to deviate significantly from normal ranges. As a result, direct measures of longitudinal change may serve as more sensitive diagnostic markers. The Boundary Shift Integral (BSI), a semi-automated imaging technique, provides a precise and reliable metric for assessing regional and global cerebral volume changes ( 7 ). BSI quantifies volume changes by measuring boundary displacements in specific brain structures using serial three-dimensional (3D) MRI scans ( 7 ). Consequently, BSI has emerged as a promising early marker for AD. However, its potential diagnostic value and associations with fluid biomarkers remain poorly understood ( 8 ). Decreased concentrations of Aβ1–42 in cerebrospinal fluid (CSF) have been observed in patients with mild cognitive impairment (MCI) and AD. Additionally, elevated plasma levels of neurofilament light chain (NfL), an axonal cytoskeleton protein and nonspecific marker of neurodegeneration, are associated with Alzheimer’s dementia( 9 ) ( 10 ). While relationships between brain atrophy and individual biomarkers like CSF Aβ1 − 42​ or plasma NfL have been studied, direct comparisons assessing how atrophy rates across multiple brain regions (measured by BSI) correlate differently with both biomarkers across the full CN-MCI-AD spectrum are limited. Clarifying these differential associations is essential for understanding the links between amyloidosis, neurodegeneration, and structural change. Therefore, using ADNI data, this study comprehensively compares whole brain, ventricular, and hippocampal BSI metrics with baseline CSF Aβ1 − 42​ and plasma NfL levels across CN, MCI, and AD participants. We hypothesized that BSI values would correlate significantly, but potentially differentially, with baseline Aβ1 − 42​ and NfL levels in the MCI and AD groups, but not in CN controls. These relationships were examined using general linear regression models adjusted for relevant covariates, aiming to refine pathophysiological understanding and potentially optimize biomarker use in AD. 2. METHODS AND MATERIALS 2.1. Participants Data for this study were obtained from the ADNI database (http://adni.loni.usc.edu). Established in 2003 as a public-private partnership led by Dr. Michael W. Weiner, ADNI brings together researchers from academic institutions and private companies to develop accurate biomarkers for early AD diagnosis and monitoring. The database includes a wealth of brain images, genetic data, clinical and neuropsychological assessments, and biochemical markers from participants across over 50 sites in the United States and Canada (www.adni-info.org). Participants aged 55–90 years from the ADNI-2 cohort were included, provided they were in good general health, fluent in English or Spanish, and willing to undergo neuroimaging, lumbar punctures, longitudinal follow-ups, DNA extraction, and blood and urine examinations. Detailed inclusion and exclusion criteria have been previously described. Key exclusion criteria included a Hachinski Ischemic Score >4, a Geriatric Depression Scale score ≥6, use of non-approved medications, recent changes in permitted medications, and fewer than six years of education or equivalent work experience. Participants were classified into CN, MCI, or AD groups based on ADNI clinical criteria (11). Demographic data, apolipoprotein E4 (ApoE ɛ4) status, cognitive scores, CSF Aβ1-42 levels, and plasma NfL levels were collected for 30 CN participants, 32 MCI subjects, and 14 AD patients from the baseline ADNI-2 cohort. Additionally, ventricular and brain BSI measures were extracted from T1-weighted MRI scans (http://adni.loni.usc.edu). 2.2. Image acquisition and processing BSI metrics, preprocessed atrophy measurements from the ADNI database, were used in this study. BSI quantifies atrophy by measuring boundary displacements in brain and ventricular regions between baseline and follow-up T1-weighted MRI scans. Regions of interest, including the whole brain, whole ventricle (left and right lateral ventricles, third and fourth ventricles), and bilateral hippocampus, were delineated automatically using multiatlas propagation and segmentation (MAPS), with expert corrections applied as needed (7) (12). The 12-month scans were aligned to baseline images using a 9-degree-of-freedom transformation, and BSI values were computed. T1 MRI scans were acquired using standardised protocols validated across multiple platforms. Participating sites underwent rigorous scanner validation and quality assurance procedures, including using a fluid-filled phantom during acquisitions. Details of these procedures are available elsewhere (13) (www.loni.ucla.edu/ADNI). Quality control (QC) protocols were implemented during BSI calculations, including cross-sectional QC to assess image quality, artifacts, and anatomical coverage and longitudinal QC to address issues such as variations in acquisition protocols, positional discrepancies, and registration errors. Scans that failed QC were excluded (www.adni-info.org). The study analyzed four BSI metrics: ventricular BSI (VBSI), Right hippocampus BSI (RHBSI), Left hippocampus BSI (LHBSI), and whole-brain k-means clustering differential bias-corrected BSI (KMNDBCBBSI, referred to as KN-BSI). 2.3. Fluid biomarkers measurements Baseline plasma NfL levels were obtained from the ADNI database, where they were analyzed atat the Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Sweden. Plasma NfL was measured using the Simoa technique, which employs monoclonal antibodies and purified bovine NfL for calibration. All samples were analyzed in duplicate, with a detection sensitivity of <1.0 pg/mL (9). Baseline CSF Aβ1-42 levels were also sourced from the ADNI database. CSF was collected via lumbar puncture at baseline using 20- or 24-gauge spinal needles. Samples were transferred to polypropylene tubes, frozen on dry ice within one hour, and shipped to the ADNI Biomarker Core at the University of Pennsylvania Medical Center. After preparation, Aβ1-42 was measured using the multiplex xMAP Luminex platform with Innogenetics immunoassay reagents. Detailed assay protocols have been previously published (14). 2.4. Cognitive assessment Cognition was assessed using the Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-cog), the Clinical Dementia Rating (CDR) scale, and the Mini-Mental State Examination (MMSE). Data for these assessments were obtained from the ADNI neuropsychological battery. The ADAS-cog evaluates cognitive decline severity and includes the ADAS-cog-11 (11 tasks; score range: 0–70), ADAS-cog-13 (adds 2 tasks; score range: 0–85), and ADAS-Q4, which focuses on delayed word recall (15). Higher scores indicate greater impairment.(16). The MMSE, a widely used tool for assessing cognitive function, comprises 11 items with a maximum score of 30, where higher scores denote better cognitive performance (17). MMSE scores ranged from 24 to 30 for CN and MCI groups and 20 to 26 for AD patients. The CDR evaluates dementia severity on a scale of 0–3. Scores were 0 for CN participants, 0.5 with a memory box score ≥0.5 for MCI, and 0.5–1 for AD (11). 2.5. Statistical analyses Data analyses were performed using Python. The Shapiro-Wilk test was used to assess normality. Continuous variables were presented as mean (SD), and categorical variables as frequencies. ANOVA was applied to normally distributed data, while the Kruskal-Wallis test was used for non-normal data. Partial correlation analysis examined relationships between biomarker levels and BSI measures. General linear regression models assessed the predictive power of biomarkers for BSI metrics across cognitive groups (CN/MCI/AD), adjusting for age, gender, and education. P-values were corrected using the Benjamini-Hochberg method, with FDR-adjusted p-values <0.05 considered significant. 3. RESULTS The study included 30 CN participants, 32 subjects with MCI, and 14 AD patients. There were no significant differences in age across the groups (CN: 73.44 ± 6.92 years; MCI: 72.26 ± 8.24 years; AD: 73.09 ± 7.37 years; P = 0.835). Similarly, gender distribution was comparable between the groups ( P = 0.908). However, educational level differed significantly across groups ( P = 0.028). The prevalence of ApoE4 positivity was notably higher in the AD group ( P = 0.004). Aβ levels were significantly lower in the AD group ( P = 0.001), while NfL levels were significantly elevated in AD patients ( P = 0.014). Regarding clinical assessments, both CDR-SB and ADAS-Cog scores showed significant worsening from CN to AD. Additionally, the AD group's MMSE scores were significantly lower (Table 1 ). Table 1 Demographic characteristics of the study population. CN (n = 30) MCI (n = 32) AD (n = 14) P value Age 73.44 ± 6.92 72.26 ± 8.24 73.09 ± 7.37 0.835 Gender (F / M) 13 / 17 14 / 18 7 / 7 0.908 Education 17.63 ± 2.70 15.94 ± 2.90 16.07 ± 2.62 0.028 ApoE4 (+ / -) 7 / 23 18 / 14 10 / 4 0.004 Amyloid β 1296.74 ± 378.04 993.82 ± 448.17 771.51 ± 432.03 0.001 NfL 36.54 ± 20.23 37.91 ± 17.59 49.74 ± 17.63 0.014 CDR-SB 0.07 ± 0.17 1.52 ± 0.91 5.25 ± 1.34 < 0.001 ADAS-Cog 11 4.30 ± 2.23 10.16 ± 5.10 18.36 ± 7.28 < 0.001 ADAS-Cog 13 6.80 ± 3.09 16.09 ± 8.11 27.43 ± 9.59 < 0.001 ADAS-Cog Q4 2.03 ± 1.50 5.16 ± 3.27 7.93 ± 2.40 < 0.001 MMSE 29.70 ± 0.60 27.38 ± 2.17 22.21 ± 2.55 < 0.001 CN, cognitively normal; MCI, mild cognitive impairment; AD, Alzheimer's disease; ApoE4, apolipoprotein E4; NfL, neurofilament light; CDR-SB, Clinical Dementia Rating-Sum of Boxes; ADAS-Cog, Alzheimer’s Disease Assessment Scale- Cognitive Subscale; MMSE, Mini-Mental State Examination; F, female; M, male. Significant differences were observed in all BSI metrics across the groups. As presented in Table 2 , whole brain KN-BSI values progressively increased from CN to AD ( P < 0.001). Similarly, ventricular BSI was significantly higher in the AD group ( P = 0.002). Both right and left hippocampal BSI also showed progressive increases from CN to AD ( P < 0.001 for both). Table 2 Comparison of different BSI metrics between diagnostic groups. CN (n = 30) MCI (n = 32) AD (n = 14) P value Whole brain KN-BSI 11.44 ± 6.11 18.02 ± 10.55 28.25 ± 14.14 < 0.001 Ventricular BSI 2.92 ± 1.79 4.82 ± 3.49 6.38 ± 3.86 0.002 Right side hippocampal BSI 0.05 ± 0.06 0.11 ± 0.11 0.21 ± 0.12 < 0.001 Left side hippocampal BSI 0.05 ± 0.05 0.10 ± 0.10 0.17 ± 0.12 < 0.001 CN, cognitively normal; MCI, mild cognitive impairment; AD, Alzheimer's disease; BSI, boundary shift integral. Table 3 presents the correlation analysis between BSI metrics and biomarkers, including Aβ and NfL, adjusted for age, gender, and education. Significant negative correlations were found between Aβ and whole brain KN-BSI in the MCI ( r = -0.46, P = 0.015) and AD ( r = -0.85, P = 0.002) groups. Similarly, ventricular BSI showed significant negative correlations with Aβ in both the MCI ( r = -0.48, P = 0.014) and AD ( r = -0.67, P = 0.015) groups. Regional hippocampal atrophy, as measured by right and left hippocampal BSI, demonstrated significant negative correlations with Aβ in both the MCI and AD groups. Specifically, right hippocampal BSI correlated negatively in the MCI ( r = -0.48, P = 0.014) and AD ( r = -0.75, P = 0.011) groups, while left hippocampal BSI followed a similar pattern in MCI ( r = -0.44, P = 0.017) and AD ( r = -0.71, P = 0.014). In contrast, the correlations between NfL and BSI metrics were less pronounced and did not reach statistical significance. Table 3 Correlation analysis of BSI metrics and biomarkers adjusted for age, gender, and education. Amyloid β NfL CN (n = 30) MCI (n = 32) AD (n = 14) CN (n = 30) MCI (n = 32) AD (n = 14) r P value r P value r P value r P value r P value r P value Whole brain KN-BSI − 0.23 0.286 − 0.46 0.015 − 0.85 0.002 0.29 0.452 0.03 0.861 − 0.43 0.452 Ventricular BSI - 0.22 0.286 - 0.48 0.014 - 0.67 0.015 0.15 0.604 - 0.19 0.529 - 0.32 0.529 Right side hippocampal BSI 0.04 0.833 - 0.48 0.014 - 0.75 0.011 0.41 0.279 0.24 0.452 - 0.22 0.604 Left side hippocampal BSI 0.08 0.722 - 0.44 0.017 - 0.71 0.014 - 0.07 0.830 - 0.06 0.830 - 0.39 0.452 NfL, neurofilament light; CN, cognitively normal; MCI, mild cognitive impairment; AD, Alzheimer's disease; BSI, boundary Tables 4 and 5 show the results of the linear regression analysis. For whole brain KN-BSI, a significant inverse relationship with Aβ was observed in both the MCI (standardized β = -2.68, P = 0.016) and AD (standardized β = -4.78, P = 0.004) groups. Similarly, ventricular BSI exhibited significant negative associations with Aβ in the MCI (standardized β = -2.82, P = 0.016) and AD (standardized β = -2.70, P = 0.024) groups. A significant negative association was also found for right hippocampal BSI in both the MCI (standardized β = -2.83, P = 0.016, adjusted R² = 0.15) and AD (standardized β = -3.43, P = 0.015) groups. Consistent findings were observed for left hippocampal BSI, with significant negative associations in the MCI (standardized β = -2.56, P = 0.016) and AD (standardized β = -2.99, P = 0.020) groups. No significant associations were observed in the CN group. Moreover, the regression analysis across all cognitive groups did not demonstrate significant associations between BSI metrics and NfL. Table 4 Linear regression analysis of BSI metrics and amyloid β adjusted for age, gender, and education. CN (n = 30) MCI (n = 32) AD (n = 14) Adjusted R2 Standardized β P value Adjusted R2 Standardized β P value Adjusted R2 Standardized β P value Whole brain KN-BSI -0.04 -1.20 0.531 0.21 -2.68 0.016 0.66 -4.78 0.004 Ventricular BSI 0.00 -1.14 0.531 0.15 -2.82 0.016 0.36 -2.70 0.024 Right side hippocampal BSI 0.19 0.20 0.843 0.15 -2.83 0.016 0.45 -3.43 0.015 Left side hippocampal BSI -0.11 0.42 0.843 0.16 -2.56 0.016 0.28 -2.99 0.020 CN, cognitively normal; MCI, mild cognitive impairment; AD, Alzheimer's disease; BSI, boundary shift integral. Table 5 Linear regression analysis of BSI metrics and NfL adjusted for age, gender, and education. CN (n = 30) MCI (n = 32) AD (n = 14) Adjusted R2 Standardized β P value Adjusted R2 Standardized β P value Adjusted R2 Standardized β P value Whole brain KN-BSI -0.03 1.38 0.360 < 0.01 -0.22 0.982 0.01 -1.45 0.459 Ventricular BSI -0.04 0.63 0.536 -0.06 -0.95 0.698 -0.05 -1.00 0.459 Right side hippocampal BSI 0.37 2.61 0.060 -0.05 1.14 0.698 -0.22 -0.67 0.518 Left side hippocampal BSI -0.07 -1.11 0.370 -0.04 0.02 0.982 -0.22 -1.27 0.459 CN, cognitively normal; MCI, mild cognitive impairment; AD, Alzheimer's disease; BSI, boundary shift integral. 4. DISCUSSION Our study investigated the potential associations between CSF Aβ1–42 and NfL levels with brain atrophy rates, as measured by BSI metrics, across three participant groups: CN, MCI, and AD. We hypothesized that Aβ and NfL levels could independently predict BSI measures, serving as potential diagnostic biomarkers for early-stage AD. Our findings revealed a strong association between all four BSI measures and CSF Aβ levels in MCI and AD participants. This indicates that higher BSI measures are independently associated with lower levels of CSF Aβ, suggesting the potential diagnostic value of BSI in the early stages of AD. In contrast, we did not observe any associations between plasma NfL levels and BSI measures, indicating a more complex relationship between this biomarker and brain volume loss. Finally, as expected, no significant association was found between fluid biomarkers and BSI among CN individuals. The most remarkable finding of our study was that BSI measures of the whole brain, ventricles, and right and left hippocampus were independently predicted by CSF Aβ levels in MCI subjects. Similar results were noted in AD patients, in contrast to the CN group, where this association was absent. One explanation for this finding is related to the direct role of Aβ in the complex pathology of AD. Reductions in CSF Aβ have been shown to occur throughout the course of AD and are believed to reflect the sequestration of CSF Aβ into plaques. According to a widely accepted model, the genesis of Aβ plaques in the brain, even before the manifestation of symptoms, triggers a neurodegenerative cascade, which ultimately leads to brain atrophy ( 4 ). Therefore, since Aβ plaques have been correlated with brain atrophy ( 18 ), a larger load of these plaques, as reflected by CSF Aβ levels, is expected to result in a higher rate of neuronal loss, consequently leading to an increased brain atrophy rate. It is essential to emphasize the importance of a sensitive and accurate measurement technique such as BSI ( 7 ), with the ability to detect subtle structural changes in the brain before significant cognitive decline occurs. The BSI methodology has been enhanced through various studies aimed at improving its accuracy and robustness against imaging artifacts. Techniques such as tissue-specific intensity normalization have been developed to refine BSI measurements, ensuring that the results are not skewed by variations in imaging conditions ( 12 ). Therefore, given that the BSI method is a precise metric for assessing brain atrophy rates ( 7 ) and is sensitive to the effects of pathological processes such as Aβ deposition ( 19 ) ( 20 ), a strong linear relationship between BSI and CSF Aβ values in MCI and AD patients was anticipated. Another important consideration is that while patients with MCI do not necessarily progress to AD, and a proportion of them revert to normal cognition ( 21 ), this group still strongly represents the earliest changes in AD pathology. In fact, many early pathological changes of AD occur in MCI patients even when no dementia is present ( 22 ). Therefore, our findings in MCI subjects, demonstrating a strong linear relationship between four different BSI measures and an established marker of AD, Aβ, similar to the pattern observed in AD patients, further supports the notion that these relationships reflect brain changes that precede the onset of AD. This underscores the potential of BSI values as an early diagnostic biomarker for AD ( 23 ). Our observations in MCI subjects, is in keeping with other cross-sectional studies on brain atrophy rate in MCI cohorts. However, only a limited number of these studies utilized the BSI method ( 3 ) ( 21 ). Similar to our findings, rates of hippocampal atrophy were shown to be associated with lower CSF Aβ in MCI subjects.This is particularly relevant as the hippocampus is a critical region affected in AD, and its atrophy is closely associated with cognitive decline. The atrophy rates examined in these studies were calculated using different methods than BSI ( 24 ) ( 18 ) ( 8 ) ( 25 ). The current study extends those findings by demonstrating that, in addition to the left and right hippocampus, which are among the earliest sites of tissue loss in AD, whole brain atrophy and ventricular expansion rates obtained with BSI are also associated with lower CSF Aβ levels in MCI subjects. These observations were consistent with prior studies. Barnes et al. reported the same correlation between CSF Aβ and whole brain atrophy rate in the MCI group using the BSI method ( 3 ). Additionally, the observed relationships between CSF Aβ and ventricular expansion in the present study confirm the results of other previous studies, which used different measurement methods( 26 ) ( 8 ) ( 25 ). In contrast to our results, one study reported no significant correlation between whole brain atrophy rates and CSF Aβ levels among MCI subjects. No regional measures were included in this study ( 27 ). One explanation for this finding may relate to using a different and possibly less refined method for measuring atrophy rates. Another consideration is that an MCI cohort generally represents a bimodal sample of individuals with prodromal AD and high rates of atrophy, as well as those with no AD pathology and low atrophy rates ( 3 ). Therefore, the results of such analyses depend on the homogeneity level of the examined MCI cohort. This is supported by findings from a longitudinal study conducted in 2021, which utilized the BSI method. According to this study, higher rates of brain and ventricular atrophy were associated with lower baseline CSF Aβ, exclusively in MCI subjects who did not return to a normal cognitive state after one year of follow-up. However, this link was not observed in the reverted MCI group ( 21 ). This further emphasizes the diversity of MCI cohorts enrolled in different studies and their potential impact on study outcomes. Despite Our observations in AD patients, many controversial findings have been reported in prior studies. In one study, increased ventricular expansion was found to be related to decreased CSF Aβ levels in AD patients, which aligns with our results( 26 ). In contrast, another study detected a positive correlation between atrophy rates and CSF Aβ levels in the AD group, which differs from our findings of a negative correlation ( 25 ). It is noteworthy that this correlation was observed across different brain regions compared to our study. Additionally, two other studies found no association between CSF Aβ levels and atrophy rates in the hippocampus( 24 ) and whole brain ( 3 ) among patients with AD. One possible explanation for these controversies could be the disease-stage-specific impact of CSF biomarkers on brain atrophy. AD patients with lower CSF Aβ concentrations may exhibit more advanced AD pathology, potentially leading to a plateau in their rates of volume loss, which results in a slower progression of brain atrophy. Therefore, the differences in Aβ relationships to structural brain changes in this group of patients could be attributed to variations in the sensitivity of processing techniques used for detecting atrophy. Other factors, such as disease duration, may account for the variance in brain volume loss at this stage of the disease. In contrast to our findings regarding Aβ, we did not observe any associations between plasma NfL levels and BSI measures in the MCI and AD groups. While previous studies have consistently demonstrated the prognostic value of NfL in predicting cognitive decline and disease severity in AD patients ( 28 ) ( 29 ) ( 10 ), the complexity of AD pathology might contribute to the lack of detectable linear relationships between BSI measures and NfL in this cohort. One possible explanation is that NfL reflects broad neuronal injury, but BSI metrics, which capture more specific structural changes, may not fully overlap with the neuronal damage that NfL levels indicate. NfL is not specific to Alzheimer’s disease; it is a marker of general neurodegeneration, correlating with tau pathology and neurofibrillary tangles ( 30 ) ( 31 ), which are hallmarks of AD. However, BSI measures may be more sensitive to the effects of other pathological processes ( 12 ) ( 19 ), such as amyloid-beta deposition or regional brain atrophy, which are not always tightly correlated with NfL levels. Additionally, the longitudinal nature of neurodegenerative processes and the timing of sample collection may further complicate the association between NfL and structural brain changes. NfL levels have been shown to rise significantly before clinical symptoms appear, particularly in familial AD, suggesting that NfL could reflect earlier stages of neuronal injury ( 32 ) ( 33 ). If BSI metrics capture structural changes at later stages, when neuronal loss is more advanced, the temporal discordance between NfL elevation and detectable BSI alterations might account for the lack of significant associations in cross-sectional analyses. Another important consideration is the potential confounding influence of other biomarkers. Previous research has emphasized that NfL’s prognostic value is largely independent of amyloid pathology ( 28 ) ( 29 ). This independence suggests that amyloid and tau biomarkers, which may also drive structural changes in the brain, could influence BSI metrics more directly than NfL. Finally, while plasma NfL is a valuable biomarker for monitoring disease progression and neuronal injury ( 30 ), the relatively subtle structural changes detectable by BSI metrics may not always align with fluctuations in NfL levels, particularly when examined cross-sectionally. Longitudinal studies, which capture dynamic changes over time, have been more successful in demonstrating the relationship between rising NfL levels and worsening cognitive performance ( 29 ) ( 34 ). Therefore, the lack of a significant association in this study may underscore the importance of temporal context in interpreting NfL levels in relation to brain structural integrity. Taken together, relatively few studies have combined CSF biomarker levels and brain atrophy rates using a cross-sectional design, and fewer have utilized BSI for this purpose. A unique feature of this study was the examination of four different BSI metrics in three groups of participants, which, to our knowledge, has not been conducted to date. However, some limitations need to be considered for the present study. The major limitation of this research was a relatively small sample size which restricts the generalizability of the findings. Moreover, while our study aimed to control for major confounding variables such as age, sex, and education, we acknowledge that genetic factors like ApoE genotype and comorbidities could also influence the findings. However, we could not include ApoE status in our analysis due to dataset limitations. Future studies with access to comprehensive genetic profiles could further clarify these relationships. Additionally, the cross-sectional nature of this study limits our ability to establish causality or determine the temporal relationships between fluid biomarker levels and BSI measures. Another point is that while an MCI cohort is enriched with subjects in the mildest stages of AD, it is a heterogeneous classification, with a considerable portion of individuals never progressing to AD and not exhibiting AD pathology ( 3 ). Therefore, since we do not know how many of the MCI subjects in this study will ultimately develop AD, we cannot determine the diagnostic value of BSI measures in AD. Thus, longitudinal studies would provide more robust evidence regarding the link between brain atrophy rates and fluid biomarkers in AD. Furthermore, the study concentrated exclusively on a particular set of biomarkers (Aβ1–42 and NfL), overlooking other potentially relevant markers of neurodegeneration or neuroinflammation that could confound the results. Finally, while this study enhances our understanding of the link between brain atrophy and fluid biomarkers of AD, especially during the earliest stage of the disease, its limitations underscore the need for larger, longitudinal investigations incorporating a broader range of biomarkers and additional brain regions. 5. Conclusion This study focused on AD biomarkers such as Aβ and NfL to answer the question of whether rates of brain atrophy acquired by the BSI method could independently be predicted by these fluid markers. We found that lower CSF Aβ1–42 levels in MCI and AD patients are associated with increased global, hippocampal, and ventricular BSI measures, whereas in CN individuals, no association was found. Surprisingly, we did not find significant correlations between plasma NfL levels and BSI measures in our subjects, suggesting a more intricate relationship between NfL and brain atrophy in the context of AD pathology. These findings highlight the complex relationship between fluid biomarkers and BSI measures in patients within the Alzheimer’s dementia spectrum, suggesting that rates of brain atrophy obtained through a highly refined technique such as BSI can potentially serve as an early diagnostic marker of AD, especially in pre-dementia stages. We believe BSI can assist in identifying individuals at risk of progressing from MCI to AD and enable early intervention strategies. Future longitudinal studies with larger samples are needed to confirm these associations and explore the temporal dynamics between biomarkers and brain atrophy. Declarations Ethics approval and consent to participate This study is a secondary analysis of anonymized data from the ADNI database. The original ADNI study was conducted in accordance with the Declaration of Helsinki and approved by the institutional review boards (IRBs) of all participating centers. All participants or their authorized representatives provided written informed consent. A full list of participating institutions whose IRBs approved the ADNI study procedures can be found here: https://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf. Ethics approval was obtained from the institutional review boards of each institution involved: Oregon Health and Science University; University of Southern California; University of California—San Diego; University of Michigan; Mayo Clinic, Rochester; Baylor College of Medicine; Columbia University Medical Center; Washington University, St. Louis; University of Alabama at Birmingham; Mount Sinai School of Medicine; Rush University Medical Center; Wien Center; Johns Hopkins University; New York University; Duke University Medical Center; University of Pennsylvania; University of Kentucky; University of Pittsburgh; University of Rochester Medical Center; University of California, Irvine; University of Texas Southwestern Medical School; Emory University; University of Kansas, Medical Center; University of California, Los Angeles; Mayo Clinic, Jacksonville; Indiana University; Yale University School of Medicine; McGill University, Montreal-Jewish General Hospital; Sunnybrook Health Sciences, Ontario; U.B.C. Clinic for AD & Related Disorders; and Cognitive Neurology—St. Joseph’s, Ontario; Cleveland Clinic Lou Ruvo Center for Brain Health; Northwestern University; Premiere Research Inst (Palm Beach Neurology); Georgetown University Medical Center; Brigham and Women’s Hospital; Stanford University; Banner Sun Health Research Institute; Boston University; Howard University; Case Western Reserve University; University of California, Davis—Sacramento; Neurological Care of CNY; Parkwood Hospital; University of Wisconsin; University of California, Irvine—BIC; Banner Alzheimer’s Institute; Dent Neurologic Institute; Ohio State University; Albany Medical College; Hartford Hospital, Olin Neuropsychiatry Research Center; Dartmouth-Hitchcock Medical Center; Wake Forest University Health Sciences; Rhode Island Hospital; Butler Hospital; UC San Francisco; Medical University South Carolina; St. Joseph’s Health Care Nathan Kline Institute; University of Iowa College of Medicine; and Cornell University and University of South Florida: USF Health Byrd Alzheimer’s Institute. Consent for publication The authors confirm that they have approved the final version of this manuscript for submission and publication. All authors consent to the publication of this article in the BMC Neurology Journal. Furthermore, all authors have read and approved the final manuscript and agree to be responsible for the accuracy and integrity of the work. This study utilized fully anonymized secondary data from the ADNI repository, and no identifying personal or clinical details are included. Data availability The data used in this research were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and are publicly available at https://adni.loni.usc.edu/ upon registration and acceptance of data use terms. Conflicts of interest The authors have no conflicts of interest to declare. Funding Not applicable. Author contributions All authors listed have made a fundamental, direct, and intellectual contribution to the work and have approved it for publication. Acknowledgments Data used in preparation of this paper were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (https://adni.loni.usc.edu/). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this article. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ ADNI_Acknowledgement_List.pdf. Declaration of generative AI and AI-assisted technologies in the writing process While preparing this manuscript, the authors used Sider in a limited manner solely to enhance language clarity, grammar, and readability. The authors did not rely on generative AI to produce original content. After using this service, the authors carefully reviewed, revised, and edited the manuscript and take full responsibility for the content presented in this publication. 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Plasma Neurofilament Light Chain Levels Are Associated With Cortical Hypometabolism in Alzheimer Disease Signature Regions. J Neuropathol Exp Neurol [Internet]. 2019;78(8):709–16. Available from: https://academic.oup.com/jnen/article/78/8/709/5532311 Mattsson N, Andreasson U, Zetterberg H, Blennow K. Association of Plasma Neurofilament Light With Neurodegeneration in Patients With Alzheimer Disease. JAMA Neurol [Internet]. 2017;74(5):557. Available from: http://archneur.jamanetwork.com/article.aspx?doi=10.1001/jamaneurol.2016.6117 Petersen RC, Aisen PS, Beckett LA, Donohue MC, Gamst AC, Harvey DJ et al. Alzheimer’s Disease Neuroimaging Initiative (ADNI). Neurology [Internet]. 2010;74(3):201–9. Available from: https://www.neurology.org/doi/ 10.1212/WNL.0b013e3181cb3e25 Leung KK, Clarkson MJ, Bartlett JW, Clegg S, Jack CR, Weiner MW et al. Robust atrophy rate measurement in Alzheimer’s disease using multi-site serial MRI: Tissue-specific intensity normalization and parameter selection. Neuroimage [Internet]. 2010;50(2):516–23. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1053811909013482 Jack CR, Bernstein MA, Fox NC, Thompson P, Alexander G, Harvey D, et al. The Alzheimer’s Disease Neuroimaging Initiative (ADNI): MRI methods. J Magn Reson Imaging. 2008;27(4):685–91. Shaw LM, Vanderstichele H, Knapik-Czajka M, Figurski M, Coart E, Blennow K et al. Qualification of the analytical and clinical performance of CSF biomarker analyses in ADNI. Acta Neuropathol [Internet]. 2011;121(5):597–609. Available from: http://link.springer.com/ 10.1007/s00401-011-0808-0 Yagi T, Kanekiyo M, Ito J, Ihara R, Suzuki K, Iwata A et al. Identification of prognostic factors to predict cognitive decline of patients with early Alzheimer’s disease in the Japanese Alzheimer’s Disease Neuroimaging Initiative study. Alzheimer’s Dement Transl Res Clin Interv [Internet]. 2019;5(1):364–73. Available from: https://alz-journals.onlinelibrary.wiley.com/doi/ 10.1016/j.trci.2019.06.004 Rosen W. A new rating scale for Alzheimer’s disease. Am J Psychiatry [Internet]. 1984;141(11):1356–64. Available from: http://psychiatryonline.org/doi/abs/ 10.1176/ajp.141.11.1356 Folstein MF, Folstein SE, McHugh PR. Mini-mental state. J Psychiatr Res [Internet]. 1975;12(3):189–98. Available from: https://linkinghub.elsevier.com/retrieve/pii/0022395675900266 de Leon MJ, DeSanti S, Zinkowski R, Mehta PD, Pratico D, Segal S et al. Longitudinal CSF and MRI biomarkers improve the diagnosis of mild cognitive impairment. Neurobiol Aging [Internet]. 2006;27(3):394–401. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0197458005001971 Prados F, Cardoso MJ, Leung KK, Cash DM, Modat M, Fox NC, et al. Measuring brain atrophy with a generalized formulation of the boundary shift integral. Neurobiol Aging. 2015;36:S1. Leung KK, Clarkson MJ, Bartlett JW, Clegg S, Jack CR, Weiner MW et al. Robust atrophy rate measurement in Alzheimer’s disease using multi-site serial MRI: Tissue-specific intensity normalization and parameter selection. NeuroImage. 2010;50(2). Moradi K, Faghani S, Abdolalizadeh A, Khomeijani-Farahani M, Ashraf-Ganjouei A. Biological Features of Reversion from Mild Cognitive Impairment to Normal Cognition: A Study of Cerebrospinal Fluid Markers and Brain Volume. J Alzheimer’s Dis Rep. 2021;5(1). Guzman VA, Carmichael OT, Schwarz C, Tosto G, Zimmerman ME, Brickman AM. White matter hyperintensities and amyloid are independently associated with entorhinal cortex volume among individuals with mild cognitive impairment. Alzheimer’s Dement. 2013;9(5 SUPPL.). Guo J, Wang Z, Liu R, Huang Y, Zhang N, Zhang R, Memantine. Donepezil, or Combination Therapy—What is the best therapy for Alzheimer’s Disease? A Network Meta-Analysis. Brain Behav [Internet]. 2020;10(11). Available from: https://onlinelibrary.wiley.com/doi/ 10.1002/brb3.1831 Schuff N, Woerner N, Boreta L, Kornfield T, Shaw LM, Trojanowski JQ et al. MRI of hippocampal volume loss in early Alzheimer’s disease in relation to ApoE genotype and biomarkers. Brain [Internet]. 2008;132(4):1067–77. Available from: https://academic.oup.com/brain/article-lookup/doi/ 10.1093/brain/awp007 Tosun D, Schuff N, Truran-Sacrey D, Shaw LM, Trojanowski JQ, Aisen P et al. Relations between brain tissue loss, CSF biomarkers, and the ApoE genetic profile: a longitudinal MRI study. Neurobiol Aging [Internet]. 2010;31(8):1340–54. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0197458010002058 Chou YY, Leporé N, Avedissian C, Madsen SK, Parikshak N, Hua X et al. Mapping correlations between ventricular expansion and CSF amyloid and tau biomarkers in 240 subjects with Alzheimer’s disease, mild cognitive impairment and elderly controls. Neuroimage [Internet]. 2009;46(2):394–410. Available from: https://linkinghub.elsevier.com/retrieve/pii/S105381190900158X Sluimer JD, Bouwman FH, Vrenken H, Blankenstein MA, Barkhof F, van der Flier WM et al. Whole-brain atrophy rate and CSF biomarker levels in MCI and AD: A longitudinal study. Neurobiol Aging [Internet]. 2010;31(5):758–64. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0197458008002315 Lehmann S, Schraen-Maschke S, Vidal JS, Blanc F, Paquet C, Allinquant B et al. Blood Neurofilament Levels Predict Cognitive Decline across the Alzheimer’s Disease Continuum. Int J Mol Sci [Internet]. 2023;24(24):17361. Available from: https://www.mdpi.com/1422-0067/24/24/17361 Mielke MM, Syrjanen JA, Blennow K, Zetterberg H, Vemuri P, Skoog I et al. Plasma and CSF neurofilament light. Neurology [Internet]. 2019;93(3). Available from: https://www.neurology.org/doi/ 10.1212/WNL.0000000000007767 Sugarman MA, Zetterberg H, Blennow K, Tripodis Y, McKee AC, Stein TD et al. A longitudinal examination of plasma neurofilament light and total tau for the clinical detection and monitoring of Alzheimer’s disease. Neurobiol Aging [Internet]. 2020;94:60–70. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0197458020301731 Sánchez-Valle R, Heslegrave A, Foiani MS, Bosch B, Antonell A, Balasa M et al. Serum neurofilament light levels correlate with severity measures and neurodegeneration markers in autosomal dominant Alzheimer’s disease. Alzheimers Res Ther [Internet]. 2018;10(1):113. Available from: https://alzres.biomedcentral.com/articles/ 10.1186/s13195-018-0439-y Weston PSJ, Poole T, O’Connor A, Heslegrave A, Ryan NS, Liang Y et al. Longitudinal measurement of serum neurofilament light in presymptomatic familial Alzheimer’s disease. Alzheimers Res Ther [Internet]. 2019;11(1):19. Available from: https://alzres.biomedcentral.com/articles/ 10.1186/s13195-019-0472-5 Weston PSJ, Poole T, Ryan NS, Nair A, Liang Y, Macpherson K et al. Serum neurofilament light in familial Alzheimer disease. Neurology [Internet]. 2017;89(21):2167–75. Available from: https://www.neurology.org/doi/ 10.1212/WNL.0000000000004667 Ou YN, Hu H, Wang ZT, Xu W, Tan L, Yu JT. Plasma neurofilament light as a longitudinal biomarker of neurodegeneration in Alzheimer’s disease. Brain Sci Adv [Internet]. 2019;5(2):94–105. Available from: https://www.sciopen.com/article/ 10.26599/BSA.2019.9050011 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6589854","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":475871834,"identity":"44eba41b-cf60-42c6-8cac-f121a3f68d9f","order_by":0,"name":"Negin Ashoori","email":"","orcid":"","institution":"Islamic Azad University Ardabil","correspondingAuthor":false,"prefix":"","firstName":"Negin","middleName":"","lastName":"Ashoori","suffix":""},{"id":475871835,"identity":"2fa4921b-e7b2-4eb7-9bd9-775961673481","order_by":1,"name":"Hamideh Nasiri","email":"","orcid":"","institution":"Zanjan University of Medical Science","correspondingAuthor":false,"prefix":"","firstName":"Hamideh","middleName":"","lastName":"Nasiri","suffix":""},{"id":475871836,"identity":"3dcc3591-ee7f-451c-a6f0-e6d083e52cda","order_by":2,"name":"Sahba Shahbazi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYBAC+QYeNiBVl8AgwQOkDWyg4ga4tRgcAGs5DNOSxsBDUAsDWMsBqBaGw1AteIAB+9ljD34wHMjjn9178OOPgvOJ+xmYH35gKLiH2y89eemGPQx1xRJ3ziVLSBjcTuxhYDOWYDAoxm3NgRwzoJOYExtu5BhIGIC1MJgBbU/AreX8GzPJP0At82/kGP9IMDgH1ML+Db+WGzlm0iBbNgAZEgcMDgC18OC3xeDGG3NjGYPDiRtv5KVZNhgkG/cc5imWSMCjRb4/x+zhm4q6xHk3cg/f/PHHTra9vX3jhw9/8DgMYhcyhxmICWkYBaNgFIyCUYAfAABCh1IiqGbU7wAAAABJRU5ErkJggg==","orcid":"","institution":"University of Tehran","correspondingAuthor":true,"prefix":"","firstName":"Sahba","middleName":"","lastName":"Shahbazi","suffix":""},{"id":475871837,"identity":"47d7ee09-f0ec-4631-96b4-121d33be7944","order_by":3,"name":"Faezeh 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Najafabad","correspondingAuthor":false,"prefix":"","firstName":"Mahtab","middleName":"","lastName":"Khosravi","suffix":""},{"id":475871841,"identity":"a4396fa9-c499-49b0-8f02-8d3165eca733","order_by":7,"name":"Niloofar Malakouti","email":"","orcid":"","institution":"Isfahan University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Niloofar","middleName":"","lastName":"Malakouti","suffix":""},{"id":475871842,"identity":"06027f5b-eec3-4d32-b9a9-f2c7b20090ac","order_by":8,"name":"Zahra Babaei Aghdam","email":"","orcid":"","institution":"Tabriz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zahra","middleName":"Babaei","lastName":"Aghdam","suffix":""},{"id":475871843,"identity":"56cbc343-57de-4c81-abfa-304e1a597c7b","order_by":9,"name":"Masoud Noroozi","email":"","orcid":"","institution":"University of Isfahan","correspondingAuthor":false,"prefix":"","firstName":"Masoud","middleName":"","lastName":"Noroozi","suffix":""},{"id":475871844,"identity":"1f7696b0-20ab-47c4-abae-6e8a921ed81e","order_by":10,"name":"Mahsa Mayeli","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mahsa","middleName":"","lastName":"Mayeli","suffix":""}],"badges":[],"createdAt":"2025-05-04 18:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6589854/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6589854/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88210376,"identity":"43988bbf-40fa-41da-91e8-8e6026e78f19","added_by":"auto","created_at":"2025-08-04 05:01:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":852470,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6589854/v1/4fbd3e33-b523-4e21-9b92-5e3d6b636d31.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring Brain and Ventricular Boundary Shift Integral Associations with Beta Amyloid and Neurofilament Light in Alzheimer’s Disease","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eAlzheimer\u0026rsquo;s disease (AD), characterized by progressive memory loss and cognitive impairment, is the most common neurodegenerative disorder and the leading cause of dementia, affecting millions worldwide. AD\u0026rsquo;s pathological processes start decades before clinical symptoms appear. This has increased focus on early detection and intervention during preclinical stages when neuroprotection might still be effective. Consequently, the study of biomarkers in preclinical AD has become a topic of significant interest (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNeurodegeneration is a hallmark of AD (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). It is hypothesized to result from an imbalance in beta-amyloid (Aβ) synthesis and clearance, leading to extracellular Aβ aggregation. This aggregation initiates a cascade of pathological events that culminate in neuronal death, brain atrophy, and cognitive impairment (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). According to the latest diagnostic guidelines from the National Institute on Aging and Alzheimer\u0026rsquo;s Association (NIA-AA), neurodegeneration, as an imaging biomarker, is recommended to be integrated with hallmark fluid biomarkers of AD, such as Aβ and pathological tau (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eImaging techniques, such as magnetic resonance imaging (MRI), have been utilized to assess brain atrophy in AD (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). However, in the early stages of the disease, the degree of tissue loss may not cause structural volume changes large enough to deviate significantly from normal ranges. As a result, direct measures of longitudinal change may serve as more sensitive diagnostic markers. The Boundary Shift Integral (BSI), a semi-automated imaging technique, provides a precise and reliable metric for assessing regional and global cerebral volume changes (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). BSI quantifies volume changes by measuring boundary displacements in specific brain structures using serial three-dimensional (3D) MRI scans (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Consequently, BSI has emerged as a promising early marker for AD. However, its potential diagnostic value and associations with fluid biomarkers remain poorly understood (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDecreased concentrations of Aβ1\u0026ndash;42 in cerebrospinal fluid (CSF) have been observed in patients with mild cognitive impairment (MCI) and AD. Additionally, elevated plasma levels of neurofilament light chain (NfL), an axonal cytoskeleton protein and nonspecific marker of neurodegeneration, are associated with Alzheimer\u0026rsquo;s dementia(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile relationships between brain atrophy and individual biomarkers like CSF Aβ1\u0026thinsp;\u0026minus;\u0026thinsp;42​ or plasma NfL have been studied, direct comparisons assessing how atrophy rates across multiple brain regions (measured by BSI) correlate differently with \u003cem\u003eboth\u003c/em\u003e biomarkers across the full CN-MCI-AD spectrum are limited. Clarifying these differential associations is essential for understanding the links between amyloidosis, neurodegeneration, and structural change. Therefore, using ADNI data, this study comprehensively compares whole brain, ventricular, and hippocampal BSI metrics with baseline CSF Aβ1\u0026thinsp;\u0026minus;\u0026thinsp;42​ and plasma NfL levels across CN, MCI, and AD participants. We hypothesized that BSI values would correlate significantly, but potentially differentially, with baseline Aβ1\u0026thinsp;\u0026minus;\u0026thinsp;42​ and NfL levels in the MCI and AD groups, but not in CN controls. These relationships were examined using general linear regression models adjusted for relevant covariates, aiming to refine pathophysiological understanding and potentially optimize biomarker use in AD.\u003c/p\u003e"},{"header":"2. METHODS AND MATERIALS","content":"\u003cp\u003e\u003cstrong\u003e2.1. Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData for this study were obtained from the ADNI database (http://adni.loni.usc.edu). Established in 2003 as a public-private partnership led by Dr. Michael W. Weiner, ADNI brings together researchers from academic institutions and private companies to develop accurate biomarkers for early AD diagnosis and monitoring. The database includes a wealth of brain images, genetic data, clinical and neuropsychological assessments, and biochemical markers from participants across over 50 sites in the United States and Canada (www.adni-info.org).\u003c/p\u003e\n\u003cp\u003eParticipants aged 55\u0026ndash;90 years from the ADNI-2 cohort were included, provided they were in good general health, fluent in English or Spanish, and willing to undergo neuroimaging, lumbar punctures, longitudinal follow-ups, DNA extraction, and blood and urine examinations. Detailed inclusion and exclusion criteria have been previously described. Key exclusion criteria included a Hachinski Ischemic Score \u0026gt;4, a Geriatric Depression Scale score \u0026ge;6, use of non-approved medications, recent changes in permitted medications, and fewer than six years of education or equivalent work experience. Participants were classified into CN, MCI, or AD groups based on ADNI clinical criteria (11).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDemographic data, apolipoprotein E4 (ApoE ɛ4) status, cognitive scores, CSF A\u0026beta;1-42 levels, and plasma NfL levels were collected for 30 CN participants, 32 MCI subjects, and 14 AD patients from the baseline ADNI-2 cohort. Additionally, ventricular and brain BSI measures were extracted from T1-weighted MRI scans (http://adni.loni.usc.edu).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. Image acquisition and processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBSI metrics, preprocessed atrophy measurements from the ADNI database, were used in this study. BSI quantifies atrophy by measuring boundary displacements in brain and ventricular regions between baseline and follow-up T1-weighted MRI scans. Regions of interest, including the whole brain, whole ventricle (left and right lateral ventricles, third and fourth ventricles), and bilateral hippocampus, were delineated automatically using multiatlas propagation and segmentation (MAPS), with expert corrections applied as needed (7) (12). The 12-month scans were aligned to baseline images using a 9-degree-of-freedom transformation, and BSI values were computed.\u003c/p\u003e\n\u003cp\u003eT1 MRI scans were acquired using standardised protocols validated across multiple platforms. Participating sites underwent rigorous scanner validation and quality assurance procedures, including using a fluid-filled phantom during acquisitions. Details of these procedures are available elsewhere (13) (www.loni.ucla.edu/ADNI).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQuality control (QC) protocols were implemented during BSI calculations, including cross-sectional QC to assess image quality, artifacts, and anatomical coverage and longitudinal QC to address issues such as variations in acquisition protocols, positional discrepancies, and registration errors. Scans that failed QC were excluded (www.adni-info.org). The study analyzed four BSI metrics: ventricular BSI (VBSI), Right hippocampus BSI (RHBSI), Left hippocampus BSI (LHBSI), and whole-brain k-means clustering differential bias-corrected BSI (KMNDBCBBSI, referred to as KN-BSI).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3. Fluid biomarkers measurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline plasma NfL levels were obtained from the ADNI database, where they were analyzed atat the Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Sweden. Plasma NfL was measured using the Simoa technique, which employs monoclonal antibodies and purified bovine NfL for calibration. All samples were analyzed in duplicate, with a detection sensitivity of \u0026lt;1.0 pg/mL\u0026nbsp;(9).\u003c/p\u003e\n\u003cp\u003eBaseline CSF A\u0026beta;1-42 levels were also sourced from the ADNI database. CSF was collected via lumbar puncture at baseline using 20- or 24-gauge spinal needles. Samples were transferred to polypropylene tubes, frozen on dry ice within one hour, and shipped to the ADNI Biomarker Core at the University of Pennsylvania Medical Center. After preparation, A\u0026beta;1-42 was measured using the multiplex xMAP Luminex platform with Innogenetics immunoassay reagents. Detailed assay protocols have been previously published (14).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4. Cognitive assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCognition was assessed using the Alzheimer\u0026rsquo;s Disease Assessment Scale-Cognitive subscale (ADAS-cog), the Clinical Dementia Rating (CDR) scale, and the Mini-Mental State Examination (MMSE). Data for these assessments were obtained from the ADNI neuropsychological battery.\u003c/p\u003e\n\u003cp\u003eThe ADAS-cog evaluates cognitive decline severity and includes the ADAS-cog-11 (11 tasks; score range: 0\u0026ndash;70), ADAS-cog-13 (adds 2 tasks; score range: 0\u0026ndash;85), and ADAS-Q4, which focuses on delayed word recall (15). Higher scores indicate greater impairment.(16).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe MMSE, a widely used tool for assessing cognitive function, comprises 11 items with a maximum score of 30, where higher scores denote better cognitive performance (17). MMSE scores ranged from 24 to 30 for CN and MCI groups and 20 to 26 for AD patients. The CDR evaluates dementia severity on a scale of 0\u0026ndash;3. Scores were 0 for CN participants, 0.5 with a memory box score \u0026ge;0.5 for MCI, and 0.5\u0026ndash;1 for AD (11).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5. Statistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData analyses were performed using Python. The Shapiro-Wilk test was used to assess normality. Continuous variables were presented as mean (SD), and categorical variables as frequencies. ANOVA was applied to normally distributed data, while the Kruskal-Wallis test was used for non-normal data. Partial correlation analysis examined relationships between biomarker levels and BSI measures. General linear regression models assessed the predictive power of biomarkers for BSI metrics across cognitive groups (CN/MCI/AD), adjusting for age, gender, and education. P-values were corrected using the Benjamini-Hochberg method, with FDR-adjusted p-values \u0026lt;0.05 considered significant.\u003c/p\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003eThe study included 30 CN participants, 32 subjects with MCI, and 14 AD patients. There were no significant differences in age across the groups (CN: 73.44\u0026thinsp;\u0026plusmn;\u0026thinsp;6.92 years; MCI: 72.26\u0026thinsp;\u0026plusmn;\u0026thinsp;8.24 years; AD: 73.09\u0026thinsp;\u0026plusmn;\u0026thinsp;7.37 years; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.835). Similarly, gender distribution was comparable between the groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.908). However, educational level differed significantly across groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.028). The prevalence of ApoE4 positivity was notably higher in the AD group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004). Aβ levels were significantly lower in the AD group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), while NfL levels were significantly elevated in AD patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014). Regarding clinical assessments, both CDR-SB and ADAS-Cog scores showed significant worsening from CN to AD. Additionally, the AD group's MMSE scores were significantly lower (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic characteristics of the study population.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCN (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMCI (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAD (n\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.44\u0026thinsp;\u0026plusmn;\u0026thinsp;6.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.26\u0026thinsp;\u0026plusmn;\u0026thinsp;8.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.09\u0026thinsp;\u0026plusmn;\u0026thinsp;7.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (F / M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 / 17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 / 18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 / 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.63\u0026thinsp;\u0026plusmn;\u0026thinsp;2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.94\u0026thinsp;\u0026plusmn;\u0026thinsp;2.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.07\u0026thinsp;\u0026plusmn;\u0026thinsp;2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApoE4 (+ / -)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 / 23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 / 14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 / 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmyloid β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1296.74\u0026thinsp;\u0026plusmn;\u0026thinsp;378.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e993.82\u0026thinsp;\u0026plusmn;\u0026thinsp;448.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e771.51\u0026thinsp;\u0026plusmn;\u0026thinsp;432.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNfL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.54\u0026thinsp;\u0026plusmn;\u0026thinsp;20.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.91\u0026thinsp;\u0026plusmn;\u0026thinsp;17.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.74\u0026thinsp;\u0026plusmn;\u0026thinsp;17.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDR-SB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADAS-Cog 11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.30\u0026thinsp;\u0026plusmn;\u0026thinsp;2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.16\u0026thinsp;\u0026plusmn;\u0026thinsp;5.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.36\u0026thinsp;\u0026plusmn;\u0026thinsp;7.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADAS-Cog 13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.80\u0026thinsp;\u0026plusmn;\u0026thinsp;3.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.09\u0026thinsp;\u0026plusmn;\u0026thinsp;8.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.43\u0026thinsp;\u0026plusmn;\u0026thinsp;9.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADAS-Cog Q4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.16\u0026thinsp;\u0026plusmn;\u0026thinsp;3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.93\u0026thinsp;\u0026plusmn;\u0026thinsp;2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.38\u0026thinsp;\u0026plusmn;\u0026thinsp;2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.21\u0026thinsp;\u0026plusmn;\u0026thinsp;2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCN, cognitively normal; MCI, mild cognitive impairment; AD, Alzheimer's disease; ApoE4, apolipoprotein E4; NfL, neurofilament light; CDR-SB, Clinical Dementia Rating-Sum of Boxes; ADAS-Cog, Alzheimer\u0026rsquo;s Disease Assessment Scale- Cognitive Subscale; MMSE, Mini-Mental State Examination; F, female; M, male.\u003c/p\u003e \u003cp\u003eSignificant differences were observed in all BSI metrics across the groups. As presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, whole brain KN-BSI values progressively increased from CN to AD (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, ventricular BSI was significantly higher in the AD group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002). Both right and left hippocampal BSI also showed progressive increases from CN to AD (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of different BSI metrics between diagnostic groups.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCN (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMCI (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAD (n\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhole brain KN-BSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e11.44\u0026thinsp;\u0026plusmn;\u0026thinsp;6.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e18.02\u0026thinsp;\u0026plusmn;\u0026thinsp;10.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e28.25\u0026thinsp;\u0026plusmn;\u0026thinsp;14.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVentricular BSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.82\u0026thinsp;\u0026plusmn;\u0026thinsp;3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e6.38\u0026thinsp;\u0026plusmn;\u0026thinsp;3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight side hippocampal BSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft side hippocampal BSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCN, cognitively normal; MCI, mild cognitive impairment; AD, Alzheimer's disease; BSI, boundary shift integral.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the correlation analysis between BSI metrics and biomarkers, including Aβ and NfL, adjusted for age, gender, and education. Significant negative correlations were found between Aβ and whole brain KN-BSI in the MCI (\u003cem\u003er\u003c/em\u003e = -0.46, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015) and AD (\u003cem\u003er\u003c/em\u003e = -0.85, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) groups. Similarly, ventricular BSI showed significant negative correlations with Aβ in both the MCI (\u003cem\u003er\u003c/em\u003e = -0.48, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014) and AD (\u003cem\u003er\u003c/em\u003e = -0.67, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015) groups. Regional hippocampal atrophy, as measured by right and left hippocampal BSI, demonstrated significant negative correlations with Aβ in both the MCI and AD groups. Specifically, right hippocampal BSI correlated negatively in the MCI (\u003cem\u003er\u003c/em\u003e = -0.48, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014) and AD (\u003cem\u003er\u003c/em\u003e = -0.75, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011) groups, while left hippocampal BSI followed a similar pattern in MCI (\u003cem\u003er\u003c/em\u003e = -0.44, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017) and AD (\u003cem\u003er\u003c/em\u003e = -0.71, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014). In contrast, the correlations between NfL and BSI metrics were less pronounced and did not reach statistical significance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation analysis of BSI metrics and biomarkers adjusted for age, gender, and education.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAmyloid β\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNfL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCN (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMCI (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eAD (n\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eCN (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eMCI (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eAD (n\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhole brain KN-BSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVentricular BSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight side\u003c/p\u003e \u003cp\u003ehippocampal BSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft side\u003c/p\u003e \u003cp\u003ehippocampal BSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNfL, neurofilament light; CN, cognitively normal; MCI, mild cognitive impairment; AD, Alzheimer's disease; BSI, boundary\u003c/p\u003e \u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e show the results of the linear regression analysis. For whole brain KN-BSI, a significant inverse relationship with Aβ was observed in both the MCI (standardized β = -2.68, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016) and AD (standardized β = -4.78, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) groups. Similarly, ventricular BSI exhibited significant negative associations with Aβ in the MCI (standardized β = -2.82, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016) and AD (standardized β = -2.70, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.024) groups. A significant negative association was also found for right hippocampal BSI in both the MCI (standardized β = -2.83, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016, adjusted R\u0026sup2; = 0.15) and AD (standardized β = -3.43, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015) groups. Consistent findings were observed for left hippocampal BSI, with significant negative associations in the MCI (standardized β = -2.56, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016) and AD (standardized β = -2.99, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020) groups. No significant associations were observed in the CN group. Moreover, the regression analysis across all cognitive groups did not demonstrate significant associations between BSI metrics and NfL.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLinear regression analysis of BSI metrics and amyloid β adjusted for age, gender, and education.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCN (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMCI (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAD (n\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandardized β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStandardized β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStandardized β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhole brain KN-BSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-4.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVentricular\u003c/p\u003e \u003cp\u003eBSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight side hippocampal\u003c/p\u003e \u003cp\u003eBSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft side hippocampal BSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-2.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCN, cognitively normal; MCI, mild cognitive impairment; AD, Alzheimer's disease; BSI, boundary shift integral.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLinear regression analysis of BSI metrics and NfL adjusted for age, gender, and education.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCN (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMCI (n\u0026thinsp;=\u0026thinsp;32)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAD (n\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandardized β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStandardized β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003cp\u003eR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eStandardized β\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP\u003c/p\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhole brain KN-BSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVentricular\u003c/p\u003e \u003cp\u003eBSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight side hippocampal\u003c/p\u003e \u003cp\u003eBSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft side hippocampal BSI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCN, cognitively normal; MCI, mild cognitive impairment; AD, Alzheimer's disease; BSI, boundary shift integral.\u003c/p\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eOur study investigated the potential associations between CSF Aβ1\u0026ndash;42 and NfL levels with brain atrophy rates, as measured by BSI metrics, across three participant groups: CN, MCI, and AD. We hypothesized that Aβ and NfL levels could independently predict BSI measures, serving as potential diagnostic biomarkers for early-stage AD.\u003c/p\u003e \u003cp\u003eOur findings revealed a strong association between all four BSI measures and CSF Aβ levels in MCI and AD participants. This indicates that higher BSI measures are independently associated with lower levels of CSF Aβ, suggesting the potential diagnostic value of BSI in the early stages of AD. In contrast, we did not observe any associations between plasma NfL levels and BSI measures, indicating a more complex relationship between this biomarker and brain volume loss. Finally, as expected, no significant association was found between fluid biomarkers and BSI among CN individuals.\u003c/p\u003e \u003cp\u003eThe most remarkable finding of our study was that BSI measures of the whole brain, ventricles, and right and left hippocampus were independently predicted by CSF Aβ levels in MCI subjects. Similar results were noted in AD patients, in contrast to the CN group, where this association was absent. One explanation for this finding is related to the direct role of Aβ in the complex pathology of AD. Reductions in CSF Aβ have been shown to occur throughout the course of AD and are believed to reflect the sequestration of CSF Aβ into plaques. According to a widely accepted model, the genesis of Aβ plaques in the brain, even before the manifestation of symptoms, triggers a neurodegenerative cascade, which ultimately leads to brain atrophy (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Therefore, since Aβ plaques have been correlated with brain atrophy (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), a larger load of these plaques, as reflected by CSF Aβ levels, is expected to result in a higher rate of neuronal loss, consequently leading to an increased brain atrophy rate. It is essential to emphasize the importance of a sensitive and accurate measurement technique such as BSI (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), with the ability to detect subtle structural changes in the brain before significant cognitive decline occurs. The BSI methodology has been enhanced through various studies aimed at improving its accuracy and robustness against imaging artifacts. Techniques such as tissue-specific intensity normalization have been developed to refine BSI measurements, ensuring that the results are not skewed by variations in imaging conditions (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Therefore, given that the BSI method is a precise metric for assessing brain atrophy rates (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) and is sensitive to the effects of pathological processes such as Aβ deposition (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), a strong linear relationship between BSI and CSF Aβ values in MCI and AD patients was anticipated.\u003c/p\u003e \u003cp\u003eAnother important consideration is that while patients with MCI do not necessarily progress to AD, and a proportion of them revert to normal cognition (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), this group still strongly represents the earliest changes in AD pathology. In fact, many early pathological changes of AD occur in MCI patients even when no dementia is present (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Therefore, our findings in MCI subjects, demonstrating a strong linear relationship between four different BSI measures and an established marker of AD, Aβ, similar to the pattern observed in AD patients, further supports the notion that these relationships reflect brain changes that precede the onset of AD. This underscores the potential of BSI values as an early diagnostic biomarker for AD (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Our observations in MCI subjects, is in keeping with other cross-sectional studies on brain atrophy rate in MCI cohorts. However, only a limited number of these studies utilized the BSI method (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Similar to our findings, rates of hippocampal atrophy were shown to be associated with lower CSF Aβ in MCI subjects.This is particularly relevant as the hippocampus is a critical region affected in AD, and its atrophy is closely associated with cognitive decline.\u003c/p\u003e \u003cp\u003eThe atrophy rates examined in these studies were calculated using different methods than BSI (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). The current study extends those findings by demonstrating that, in addition to the left and right hippocampus, which are among the earliest sites of tissue loss in AD, whole brain atrophy and ventricular expansion rates obtained with BSI are also associated with lower CSF Aβ levels in MCI subjects. These observations were consistent with prior studies. Barnes et al. reported the same correlation between CSF Aβ and whole brain atrophy rate in the MCI group using the BSI method (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Additionally, the observed relationships between CSF Aβ and ventricular expansion in the present study confirm the results of other previous studies, which used different measurement methods(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). In contrast to our results, one study reported no significant correlation between whole brain atrophy rates and CSF Aβ levels among MCI subjects. No regional measures were included in this study (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). One explanation for this finding may relate to using a different and possibly less refined method for measuring atrophy rates. Another consideration is that an MCI cohort generally represents a bimodal sample of individuals with prodromal AD and high rates of atrophy, as well as those with no AD pathology and low atrophy rates (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Therefore, the results of such analyses depend on the homogeneity level of the examined MCI cohort. This is supported by findings from a longitudinal study conducted in 2021, which utilized the BSI method. According to this study, higher rates of brain and ventricular atrophy were associated with lower baseline CSF Aβ, exclusively in MCI subjects who did not return to a normal cognitive state after one year of follow-up. However, this link was not observed in the reverted MCI group (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). This further emphasizes the diversity of MCI cohorts enrolled in different studies and their potential impact on study outcomes.\u003c/p\u003e \u003cp\u003eDespite Our observations in AD patients, many controversial findings have been reported in prior studies. In one study, increased ventricular expansion was found to be related to decreased CSF Aβ levels in AD patients, which aligns with our results(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). In contrast, another study detected a positive correlation between atrophy rates and CSF Aβ levels in the AD group, which differs from our findings of a negative correlation (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). It is noteworthy that this correlation was observed across different brain regions compared to our study. Additionally, two other studies found no association between CSF Aβ levels and atrophy rates in the hippocampus(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) and whole brain (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) among patients with AD. One possible explanation for these controversies could be the disease-stage-specific impact of CSF biomarkers on brain atrophy. AD patients with lower CSF Aβ concentrations may exhibit more advanced AD pathology, potentially leading to a plateau in their rates of volume loss, which results in a slower progression of brain atrophy. Therefore, the differences in Aβ relationships to structural brain changes in this group of patients could be attributed to variations in the sensitivity of processing techniques used for detecting atrophy. Other factors, such as disease duration, may account for the variance in brain volume loss at this stage of the disease.\u003c/p\u003e \u003cp\u003eIn contrast to our findings regarding Aβ, we did not observe any associations between plasma NfL levels and BSI measures in the MCI and AD groups. While previous studies have consistently demonstrated the prognostic value of NfL in predicting cognitive decline and disease severity in AD patients (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), the complexity of AD pathology might contribute to the lack of detectable linear relationships between BSI measures and NfL in this cohort. One possible explanation is that NfL reflects broad neuronal injury, but BSI metrics, which capture more specific structural changes, may not fully overlap with the neuronal damage that NfL levels indicate. NfL is not specific to Alzheimer\u0026rsquo;s disease; it is a marker of general neurodegeneration, correlating with tau pathology and neurofibrillary tangles (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), which are hallmarks of AD. However, BSI measures may be more sensitive to the effects of other pathological processes (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), such as amyloid-beta deposition or regional brain atrophy, which are not always tightly correlated with NfL levels. Additionally, the longitudinal nature of neurodegenerative processes and the timing of sample collection may further complicate the association between NfL and structural brain changes. NfL levels have been shown to rise significantly before clinical symptoms appear, particularly in familial AD, suggesting that NfL could reflect earlier stages of neuronal injury (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). If BSI metrics capture structural changes at later stages, when neuronal loss is more advanced, the temporal discordance between NfL elevation and detectable BSI alterations might account for the lack of significant associations in cross-sectional analyses. Another important consideration is the potential confounding influence of other biomarkers. Previous research has emphasized that NfL\u0026rsquo;s prognostic value is largely independent of amyloid pathology (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). This independence suggests that amyloid and tau biomarkers, which may also drive structural changes in the brain, could influence BSI metrics more directly than NfL. Finally, while plasma NfL is a valuable biomarker for monitoring disease progression and neuronal injury (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), the relatively subtle structural changes detectable by BSI metrics may not always align with fluctuations in NfL levels, particularly when examined cross-sectionally. Longitudinal studies, which capture dynamic changes over time, have been more successful in demonstrating the relationship between rising NfL levels and worsening cognitive performance (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Therefore, the lack of a significant association in this study may underscore the importance of temporal context in interpreting NfL levels in relation to brain structural integrity.\u003c/p\u003e \u003cp\u003eTaken together, relatively few studies have combined CSF biomarker levels and brain atrophy rates using a cross-sectional design, and fewer have utilized BSI for this purpose. A unique feature of this study was the examination of four different BSI metrics in three groups of participants, which, to our knowledge, has not been conducted to date. However, some limitations need to be considered for the present study. The major limitation of this research was a relatively small sample size which restricts the generalizability of the findings. Moreover, while our study aimed to control for major confounding variables such as age, sex, and education, we acknowledge that genetic factors like ApoE genotype and comorbidities could also influence the findings. However, we could not include ApoE status in our analysis due to dataset limitations. Future studies with access to comprehensive genetic profiles could further clarify these relationships. Additionally, the cross-sectional nature of this study limits our ability to establish causality or determine the temporal relationships between fluid biomarker levels and BSI measures. Another point is that while an MCI cohort is enriched with subjects in the mildest stages of AD, it is a heterogeneous classification, with a considerable portion of individuals never progressing to AD and not exhibiting AD pathology (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Therefore, since we do not know how many of the MCI subjects in this study will ultimately develop AD, we cannot determine the diagnostic value of BSI measures in AD. Thus, longitudinal studies would provide more robust evidence regarding the link between brain atrophy rates and fluid biomarkers in AD. Furthermore, the study concentrated exclusively on a particular set of biomarkers (Aβ1\u0026ndash;42 and NfL), overlooking other potentially relevant markers of neurodegeneration or neuroinflammation that could confound the results. Finally, while this study enhances our understanding of the link between brain atrophy and fluid biomarkers of AD, especially during the earliest stage of the disease, its limitations underscore the need for larger, longitudinal investigations incorporating a broader range of biomarkers and additional brain regions.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study focused on AD biomarkers such as Aβ and NfL to answer the question of whether rates of brain atrophy acquired by the BSI method could independently be predicted by these fluid markers. We found that lower CSF Aβ1\u0026ndash;42 levels in MCI and AD patients are associated with increased global, hippocampal, and ventricular BSI measures, whereas in CN individuals, no association was found. Surprisingly, we did not find significant correlations between plasma NfL levels and BSI measures in our subjects, suggesting a more intricate relationship between NfL and brain atrophy in the context of AD pathology. These findings highlight the complex relationship between fluid biomarkers and BSI measures in patients within the Alzheimer\u0026rsquo;s dementia spectrum, suggesting that rates of brain atrophy obtained through a highly refined technique such as BSI can potentially serve as an early diagnostic marker of AD, especially in pre-dementia stages. We believe BSI can assist in identifying individuals at risk of progressing from MCI to AD and enable early intervention strategies. Future longitudinal studies with larger samples are needed to confirm these associations and explore the temporal dynamics between biomarkers and brain atrophy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is a secondary analysis of anonymized data from the ADNI database. The original ADNI study was conducted in accordance with the Declaration of Helsinki and approved by the institutional review boards (IRBs) of all participating centers. All participants or their authorized representatives provided written informed consent. A full list of participating institutions whose IRBs approved the ADNI study procedures can be found here: https://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.\u003c/p\u003e\n\u003cp\u003eEthics approval was obtained from the institutional review boards of each institution involved: Oregon Health and Science University; University of Southern California; University of California\u0026mdash;San Diego; University of Michigan; Mayo Clinic, Rochester; Baylor College of Medicine; Columbia University Medical Center; Washington University, St. Louis; University of Alabama at Birmingham; Mount Sinai School of Medicine; Rush University Medical Center; Wien Center; Johns Hopkins University; New York University; Duke University Medical Center; University of Pennsylvania; University of Kentucky; University of Pittsburgh; University of Rochester Medical Center; University of California, Irvine; University of Texas Southwestern Medical School; Emory University; University of Kansas, Medical Center; University of California, Los Angeles; Mayo Clinic, Jacksonville; Indiana University; Yale University School of Medicine; McGill University, Montreal-Jewish General Hospital; Sunnybrook Health Sciences, Ontario; U.B.C. Clinic for AD \u0026amp; Related Disorders; and Cognitive Neurology\u0026mdash;St. Joseph\u0026rsquo;s, Ontario; Cleveland Clinic Lou Ruvo Center for Brain Health; Northwestern University; Premiere Research Inst (Palm Beach Neurology); Georgetown University Medical Center; Brigham and Women\u0026rsquo;s Hospital; Stanford University; Banner Sun Health Research Institute; Boston University; Howard University; Case Western Reserve University; University of California, Davis\u0026mdash;Sacramento; Neurological Care of CNY; Parkwood Hospital; University of Wisconsin; University of California, Irvine\u0026mdash;BIC; Banner Alzheimer\u0026rsquo;s Institute; Dent Neurologic Institute; Ohio State University; Albany Medical College; Hartford Hospital, Olin Neuropsychiatry Research Center; Dartmouth-Hitchcock Medical Center; Wake Forest University Health Sciences; Rhode Island Hospital; Butler Hospital; UC San Francisco; Medical University South Carolina; St. Joseph\u0026rsquo;s Health Care Nathan Kline Institute; University of Iowa College of Medicine; and Cornell University and University of South Florida: USF Health Byrd Alzheimer\u0026rsquo;s Institute.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that they have approved the final version of this manuscript for submission and publication. All authors consent to the publication of this article in the BMC Neurology Journal. Furthermore, all authors have read and approved the final manuscript and agree to be responsible for the accuracy and integrity of the work. This study utilized fully anonymized secondary data from the ADNI repository, and no identifying personal or clinical details are included.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this research were obtained from the Alzheimer\u0026rsquo;s Disease Neuroimaging Initiative (ADNI) and are publicly available at https://adni.loni.usc.edu/ upon registration and acceptance of data use terms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors listed have made a fundamental, direct, and intellectual contribution to the work and have approved it for publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData used in preparation of this paper were obtained from the Alzheimer\u0026rsquo;s Disease Neuroimaging Initiative (ADNI) database (https://adni.loni.usc.edu/). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this article. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ ADNI_Acknowledgement_List.pdf.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the writing process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile preparing this manuscript, the authors used Sider in a limited manner solely to enhance language clarity, grammar, and readability. The authors did not rely on generative AI to produce original content. After using this service, the authors carefully reviewed, revised, and edited the manuscript and take full responsibility for the content presented in this publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eXiong Y lan, Meng T, Luo J, Zhang H. The Potential of Neurofilament Light as a Biomarker in Alzheimer\u0026rsquo;s Disease. Eur Neurol [Internet]. 2021;84(1):6\u0026ndash;15. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://karger.com/doi/10.1159/000513008\u003c/span\u003e\u003cspan address=\"https://karger.com/doi/10.1159/000513008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan CC, Yu JT, Tan L. Biomarkers for Preclinical Alzheimer\u0026rsquo;s Disease. J Alzheimer\u0026rsquo;s Dis [Internet]. 2014;42(4):1051\u0026ndash;69. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.sciopen.com/article/\u003c/span\u003e\u003cspan address=\"https://www.sciopen.com/article/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.26599/BSA.2019.9050011\u003c/span\u003e\u003cspan address=\"10.26599/BSA.2019.9050011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Boundary Shift Integral, Neurofilament Light (NfL), Alzheimer’s Disease, Beta-Amyloid, Mild Cognitive Impairment","lastPublishedDoi":"10.21203/rs.3.rs-6589854/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6589854/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAlzheimer\u0026rsquo;s disease (AD) pathology develops over decades, creating a prolonged preclinical phase. Sensitive biomarkers are needed to detect neurodegeneration early. We aimed to determine whether baseline cerebrospinal fluid Aβ1\u0026ndash;42 (CSF Aβ1\u0026ndash;42) and plasma neurofilament light chain (NfL) levels are associated with brain atrophy rates in early AD.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed data from 76 participants in the ADNI cohort (30 cognitively normal [CN], 32 with mild cognitive impairment [MCI], 14 with AD). Baseline CSF Aβ1\u0026ndash;42 and plasma NfL levels were measured. Whole-brain, ventricular, and hippocampal volume changes over 12 months were quantified with the Boundary Shift Integral (BSI) on serial T1-weighted MRIs. Linear regression models tested associations between each biomarker and 12-month brain atrophy, adjusting for age, sex, and education (significance threshold p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eLower baseline CSF Aβ1\u0026ndash;42 was significantly associated with greater whole-brain, ventricular, and hippocampal volume loss over 12 months in the MCI and AD groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). No significant association was found between CSF Aβ1\u0026ndash;42 and atrophy in CN. Baseline plasma NfL showed no significant relationship with 12-month atrophy in any group.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThese findings help clarify the differential relationship between key pathological markers (Aβ accumulation vs. axonal injury reflected by NfL) and longitudinal structural brain changes by directly comparing these associations using multiple BSI metrics across the AD spectrum. The results highlight that BSI is sensitive to Aβ-related neurodegeneration but may capture different aspects of pathology than plasma NfL, significantly impacting our understanding of biomarker dynamics and supporting BSI's potential for tracking early AD changes. This warrants further longitudinal validation to establish its clinical utility.\u003c/p\u003e","manuscriptTitle":"Exploring Brain and Ventricular Boundary Shift Integral Associations with Beta Amyloid and Neurofilament Light in Alzheimer’s Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-26 15:00:24","doi":"10.21203/rs.3.rs-6589854/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cf6e9e1d-0cbe-40a9-a8c8-823f39174c0c","owner":[],"postedDate":"June 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-04T04:53:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-26 15:00:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6589854","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6589854","identity":"rs-6589854","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}