DTI-ALPS Is Associated with Temporolimbic Amyloid but Not Plasma p-Tau181 Across the Alzheimer’s Disease Continuum

DTI-ALPS Is Associated with Temporolimbic Amyloid but Not Plasma p-Tau181 Across the Alzheimer’s Disease Continuum | 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 DTI-ALPS Is Associated with Temporolimbic Amyloid but Not Plasma p-Tau181 Across the Alzheimer’s Disease Continuum Rasa Zafari, Amirhossein Kamroo, Tina Taherkhani, Mahsa Heidari-Foroozan, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9382890/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background: Alzheimer's disease (AD) is the most common cause of dementia, characterized by progressive aggregation of misfolded proteins. Accumulation of amyloid-beta (Aβ) and hyperphosphorylated tau (p-tau) is the hallmark pathology of AD. Diffusion tensor imaging along the perivascular spaces (DTI-ALPS) has been proposed as an MRI marker related to perivascular diffusivity patterns and has been used in studies of neurodegenerative disease. Aims: This study investigates the association between the association between DTI-ALPS indices, regional amyloid-PET burden, and plasma p-tau181 across the AD spectrum. Methods: Data from 410 individuals was enrolled from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. DTI-ALPS was used as an imaging proxy related to perivascular diffusivity patterns. Results: Mean DTI-ALPS declined progressively from healthy controls to AD. Moreover, females reflected higher DTI-ALPS indices compared with males. No significant associations were observed between perivascular diffusivity patterns and plasma concentration of p-tau 181 in patients with cognitive decline. In contrast, the global cortical amyloid-PET SUVR was associated with the mean [β = -0.163] and right DTI-ALPS indices in the MCI group [β = -0.202]. Moreover, we observed stronger associations between DTI-ALPS index and amyloid-PET SUVR in the temporal pole cortex [β = -0.199], entorhinal cortex [β = -0.224], and parahippocampal gyrus in patients with MCI [β = -0.281]. Conclusions: Reduced DTI-ALPS indices were associated with increased temporolimbic amyloid deposition, particularly in individuals with MCI. These findings suggest DTI-ALPS-derived perivascular diffusivity as an imaging marker associated with amyloid burden in prodromal AD. Alzheimer's Disease Mild Cognitive Impairment Perivascular clearance Amyloid Glymphatic System DTI-ALPS index Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Alzheimer's disease (AD) is the most prevalent cause of dementia worldwide and it is a prominent challenge for healthcare providers, due to its considerable socioeconomic burden [ 1 , 2 ]. The central driver for the development of AD is the aggregation of misfolded amyloid-beta (Aβ) plaques and hyperphosphorylated tau (p-tau) proteins [ 3 – 5 ]. Beyond their central role in AD pathophysiology, Aβ and p-tau are measurable hallmarks of disease pathology [ 6 , 7 ]. Regarding the p-tau proteins, different p-tau isoforms of p-tau 231, p-tau 217, and p-tau 181 have been extensively investigated in the context of AD [ 8 – 10 ]. Specifically, p-tau 181 is a well-established indicator of neuronal damage in AD, which has revealed promising evaluation accuracy both in cerebrospinal fluid (CSF) sampling and positron emission tomography (PET) [ 11 – 14 ]. It is extensively reported that plasma p-tau181 represents downstream tau-related neurodegeneration and may reflect a systemic correlate of protein accumulation within the central nervous system (CNS) [ 15 , 16 ]. While plasma p-tau181 reflects tau pathology and neurodegeneration, it remains unclear whether circulating tau levels are directly influenced by alterations in perivascular clearance mechanisms. Beyond excessive protein aggregation, emerging evidence implies dysfunction of the neurovascular unit and brain barrier systems in the pathogenesis of AD [ 17 ]. It is shown that age-related alterations in the blood-brain barrier (BBB), perivascular spaces, and astrocytic aquaporin-4 (AQP4) polarization can result in impaired clearance of Aβ and p-tau in the brain tissue [ 18 ]. Such clearance failure is proposed to contribute to neurodegenerative processes [ 18 ]. As mentioned, recent studies have suggested that impairments in the clearance system of the brain can contribute to the excessive accumulation of p-tau deposits [ 19 ]. The glymphatic system is a pivotal clearance system in the brain, involved in the drainage of harmful waste, and plays a vital role in preserving the normal function of the brain [ 20 , 21 ]. The function of the glymphatic system heavily relies on brain perivascular spaces (PVSs), consisting of periarterial spaces in arteries and perivenous spaces in veins [ 22 ]. Periarterial spaces are the entrance pathway for the influx of CSF into the brain parenchyma [ 23 ]. After the entry, AQP-4 channels of the astrocytes allow further fluid and material exchange between the CSF of the periarterial spaces and the interstitial fluid (ISF) [ 24 ]. Followed by the exchange, the efflux of the waste-containing CSF occurs through the perivenous spaces [ 25 ]. Interruptions in the function of this perivascular exchange system, particularly in aging, can exacerbate regional accumulation of misfolded proteins, particularly within vulnerable temporolimbic circuits [ 26 ]. Different approaches could be implemented for the clinical assessment of the glymphatic system, including infrared (IR) imaging [ 27 ], single-photon emission computed tomography (SPECT) [ 28 ], and contrast-enhanced or non-contrast magnetic resonance imaging (MRI) [ 29 , 30 ]. Among the non-contrast MRI-based techniques, diffusion tensor imaging-based approaches (DTI) have been used to derive indices related to perivascular water diffusivity, which may reflect aspects of perivascular fluid dynamics [ 31 ]. DTI measures the diffusion of water molecules in the anatomical structures. The type of DTI used for the assessment of the fluid dynamics in the glymphatic system is called DTI along the perivascular spaces (DTI-ALPS) [ 32 ]. The DTI-ALPS measures water diffusion in three axes of subcortical fibers, association fibers, and projection fibers at the level of the lateral ventricle body [ 33 ]. Subcortical fibers as the X-axis, association fibers as the Y-axis, and projection fibers serve as the Z-axis [ 34 ]. Ultimately, the ratio of X-axis water diffusion to the average of Y/Z-axes water diffusion indicates the DTI-ALPS quantified index that has been proposed as an indirect imaging marker related to water diffusivity along perivascular spaces, and has been interpreted as a potential proxy of perivascular fluid dynamics [ 35 ]. Therefore, in this study, we aimed to examine whether DTI-ALPS indices are associated with regional amyloid deposition in temporolimbic structures and whether such alterations relate to circulating p-tau181 across the Alzheimer’s disease continuum. We hypothesized that the altered diffusivity in perivascular system would be associated with an exacerbated accumulation of Aβ in the brain and potentially elevated plasma levels of p-tau 181 among patients with cognitive impairment. This study provides a more in-depth insight into the neuronal pathophysiology and abnormal functionality of the glymphatic system in patients with cognitive impairments. Methods Subject Cohort and Data Acquisition Data for 410 individuals, including cognitively normal controls and participants with varying degrees of cognitive impairment were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Inclusion of the participants relied on the availability of complete diffusion-weighted imaging (DWI) sequences, T1-weighted anatomical scans, plasma biomarker data and amyloid-PET measurements. Datasets that showed significant motion artifact or structural anomalies, as determined by standardized ADNI quality control protocols, were excluded. Demographic covariates including age, sex, years of education, clinical scores comprised of MMSE and ADAS-11, APOE ε4 status, and biomarker levels were extracted. Magnetic Resonance Imaging Acquisition All imaging data were acquired according to the standardized ADNI acquisition protocol across multiple sites. 3 Tesla MRI systems were used. The diffusion-weighted imaging protocol utilized multiple non-colinear gradient directions with a b-value of 1000 s/mm², supplemented by multiple b0 reference images. High-resolution 3D T1-weighted anatomical images were obtained at 1.0 mm isotropic resolution. Amyloid-PET data were processed and quantified as standard uptake value ratio (SUVR) in accordance with ADNI pipelines. Diffusion Data Preprocessing and Tensor Modeling Diffusion-weighted images were first visually inspected to ensure the absence of gross artifacts and motion-related distortions. Preprocessing was then performed using the FMRIB Software Library (FSL, version 6.0) and MRtrix3 [ 36 , 37 ]. The diffusion data were denoised using the Marchenko-Pastur principal component analysis–based algorithm (dwidenoise) [ 38 ] and corrected for Gibbs ringing artifacts (mrdegibbs) [ 39 ]. Eddy-current and motion-induced distortions were corrected using FSL’s eddy, with outlier replacement and slice-to-volume motion correction enabled when appropriate. Following preprocessing, diffusion tensors were fitted voxel-wise using FSL’s dtifit to generate diffusion tensor–derived maps, including the three principal diffusivities (Dxx, Dyy, Dzz) corresponding to diffusivity along the x-, y-, and z-axes of the diffusion tensor, respectively. These maps were subsequently used for the calculation of the DTI-ALPS index [ 32 ]. Fractional anisotropy (FA) maps were also generated and used for spatial normalization to the JHU-ICBM 1 mm FA template using linear registration (flirt) [ 40 ]. All processing steps were carried out in the native diffusion space prior to registration to minimize interpolation effects. Quantification of Perivascular Fluid Dynamics The function of the glymphatic system was calculated using the DTI-ALPS method. At the level of the centrum semiovale, the perivascular spaces accompanying the medullary veins run predominantly along the left–right (x) axis, while the projection fibers (within the superior corona radiata, SCR) are oriented in the superior-inferior (z) direction and the association fibers (within the superior longitudinal fasciculus, SLF) run in the anterior-posterior (y) direction. Because of this nearly orthogonal geometry, diffusion along the x-axis in this region primarily reflects water movement within perivascular channels, whereas diffusion along the y- and z-axes represents diffusion perpendicular to these spaces [ 41 ]. Four regions of interest (ROIs) were placed bilaterally in the left and right SCR and SLF using predefined templates in the JHU-ICBM FA space. The individual diffusion tensor components, Dxx, Dyy, and Dzz, representing diffusivity along the x, y, and z axes, respectively, were extracted from these ROIs. The DTI-ALPS index was computed separately for each hemisphere using the formula: $$\:DTI-ALPS\:index=\frac{{D}_{x\left(Projection\:ROI\right)}}{{D}_{y\left(Projection\:ROI\right)}}+\frac{{D}_{x\left(Association\:ROI\right)}}{{D}_{z\left(Association\:ROI\right)}}$$ Dxx, Dyy, and Dzz correspond to the directional diffusivities within the projection (SCR) and association (SLF) fibers. A higher ALPS index indicates greater diffusivity along the perivascular-space axis, which has been interpreted as potentially reflecting more preserved perivascular fluid dynamics; however, it does not directly quantify glymphatic flow. All diffusion metrics were calculated in the native diffusion space to minimize interpolation errors and then spatially normalized for group-level analysis. The per-subject ALPS index was exported to a tabular file for subsequent statistical analysis. Statistical Modeling Framework All statistical analyses were performed using SPSS software (Version 20). The normality of variable distributions was evaluated using the Kolmogorov-Smirnov and Shapiro-Wilk tests. To evaluate the association between the DTI-ALPS index and core Alzheimer's disease biomarkers, multivariable linear regression models were used. The primary models assessed the relationship between the mean DTI-ALPS index (dependent variable) and plasma p-tau 181 levels, as well as global amyloid-PET SUVR, adjusted for the covariates of age, sex, and years of education. Secondary analyses examined hemispheric-specific indices and their relationship with amyloid-PET SUVR in the temporal pole cortex, inferior temporal cortex, middle temporal cortex, entorhinal cortex, and parahippocampal gyrus, due to their involvement in early-stage Alzheimer’s disease and their known vulnerability to amyloid accumulation. A p value < 0.05 was considered statistically significant. To account for multiple regional comparisons, false discovery rate (FDR) correction was applied using the Benjamini-Hochberg procedure across amyloid-PET regional analyses, with q < 0.05 considered statistically significant. In exploratory analyses, we also compared effect sizes across diagnostic groups to evaluate whether DTI-ALPS-amyloid associations were stage-specific (HC vs MCI vs AD), given the diagnostic focus of biomarker-based stratification. Results Demographic and clinical features of participants The baseline cohort data of 410 individuals, consisting of 217 males and 193 females, were used in this study (Fig. 1). The overall mean age was 71.91 ± 6.98, and the mean years of education was 16.21 ± 2.68 among all participants. In addition, the mean scores for the Mini-Mental State Examination (MMSE) and Alzheimer's Disease Assessment Scale (ADAS-11) were 27.96 ± 2.32 and 8.63 ± 6.07, respectively. Moreover, 42.19% of individuals reflected at least one APOE ɛ4. We found a significant difference in the cognitive performance of participants in both the MMSE [F (2,407) = 224.356, P value < 0.001, η² = 0.524] and the ADAS-11 questionnaires [F (2,407) = 196.801, P value < 0.001, η² = 0.492]. Nevertheless, the plasma level of p-tau 181 was significantly different among individuals with cognitive impairment and healthy controls [F (2,407) = 15.586, P value < 0.001, η² = 0.071]. Considering imaging biomarkers, our analysis demonstrated considerable differences between groups in global cortical amyloid-PET SUVR [F (2,407) = 60.000, P value < 0.001, η² = 0.228], as well as amyloid-PET SUVR in temporal pole cortex [F (2,407) = 15.797, P value < 0.001, η² = 0.072], middle temporal cortex [F (2,407) = 37.188, P value < 0.001, η² = 0.155], inferior temporal cortex [F (2,407) = 41.020, P value < 0.001, η² = 0.168], and parahippocampal gyrus [F (2,407) = 25.319, P value < 0.001, η² = 0.111]. Additionally, no significant differences were observed in the mean DTI-ALPS index among patients with cognitive impairment and healthy individuals [F (2,407) = 0.441, P value = 0.644, η² = 0.002] (Fig. 2). Table 1 summarizes demographic characteristics of participants. Table 1. Demographic characteristics. HC (n = 174) MCI (n = 193) AD (n = 43) P value Age (years) 72.26 ± 5.94 71.09 ± 7.37 74.18 ± 8.50 0.022 Sex (F/M) 79/95 111/82 27/16 0.026 Education (years) 16.57 ± 2.59 16.06 ± 2.64 15.42 ± 2.97 0.022 MMSE 28.98 ± 1.26 28.05 ± 1.69 23.42 ± 1.86 <0.001 ADAS11 5.47 ± 2.90 8.92 ± 4.49 20.09 ± 7.44 <0.001 APOE Ɛ4 <0.001 Without Ɛ4 123 100 14 One Ɛ4 48 77 23 Two Ɛ4 3 19 6 Left DTI-ALPS 1.22 ± 0.19 1.22 ± 0.21 1.21 ± 0.26 0.945 Right DTI-ALPS 1.24 ± 0.18 1.21 ± 0.20 1.20 ± 0.23 0.268 Mean DTI-ALPS 1.23 ± 0.17 1.22 ± 0.20 1.20 ± 0.23 0.644 Plasma p-tau181 15.16 ± 9.98 17.77 ± 9.71 24.47 ± 10.33 <0.001 Global Cortical Amyloid SUVR 1.15 ± 0.16 1.27 ± 0.22 1.51 ± 0.21 <0.001 Temporal Pole Amyloid SUVR 0.92 ± 0.13 0.96 ± 0.16 1.07 ± 0.21 <0.001 Inferior Temporal Amyloid SUVR 1.15 ± 0.14 1.21 ± 0.17 1.39 ± 0.18 <0.001 Middle Temporal Amyloid SUVR 1.06 ± 0.15 1.13 ± 0.18 1.30 ± 0.18 <0.001 Entorhinal Cortex Amyloid SUVR 0.94 ± 0.09 0.95 ± 0.12 0.97 ± 0.13 0.378 Parahippocampal Gyrus Amyloid SUVR 0.99 ± 0.11 1.03 ± 0.13 1.14 ± 0.14 <0.001 Values are showed as mean ± SD or raw numbers of patients. Results of ANOVA analysis between groups noted as p value. APOE Ɛ4: Apolipoprotein E ɛ4 genotype, MMSE: Mini Mental State Examination, AD: Alzheimer’s disease, ADAS 11: Alzheimer’s Disease Assessment Scale-Cognitive Subscale 11 Items, HC: Healthy Controls, DTI-ALPS: Diffusion Tensor Imaging Along Perivascular Space, MCI: Mild Cognitive Impairment, SUVR: Standardized Uptake Value, p-tau181: Tau Protein Phosphorylated at Threonine 181. Association of DTI-ALPS indices with cognitive performance and demographic characteristics We conducted a linear regression model to investigate whether there is a significant association between DTI-ALPS indices and demographic features, as well as cognitive function among the participants. Unlike patients with AD [P value = 0.136], our analysis revealed a significant relationship between the DTI-ALPS index and the age of the healthy participants [standardized β = -0.314, P value < 0.001, 95% CI= -0.457 to -0.172, Adjusted R 2 = 0.099] and the MCI group [standardized β = -0.209, P value < 0.05, 95% CI= -0.349 to -0.070, Adjusted R 2 = 0.044]. However, no significant associations were shown between the DTI-ALPS index and the years of education among patients with AD [P value = 0.586] and healthy individuals [P value = 0.482]. Additionally, the DTI-ALPS index was not associated with the cognitive performance of participants with AD in MMSE [P value = 0.143] or ADAS-11 [P value = 0.166]. Sex difference in DTI-ALPS indices We found a significant association between the sex of healthy individuals and the mean DTI-ALPS index [standardized β = 0.264, P value < 0.001, 95% CI= 0.119 to 0.409, Adjusted R 2 = 0.064]. Then, we conducted an independent t-test to compare the DTI-ALPS index among participants. Unlike patients with cognitive impairment, healthy participants reflected a significant difference in the DTI-ALPS index between the two sexes, with a higher glymphatic activity in females [t (173) = -3.594, P value < 0.001, η² = 0.069]. Similar results on the significant difference of the glymphatic function were also observed among all participants [t ( 40 8) = -2.239, P value < 0.05, η² = 0.012] (Fig. 3). Association of DTI-ALPS indices with plasma p-tau 181 To assess the association between DTI-ALPS indices and plasma levels of p-tau 181, we used a multivariable linear regression model adjusted for age, sex, APOE ɛ4, and years of education. Our analysis reported a negative but insignificant association between the mean DTI-ALPS index and plasma concentrations of p-tau 181 among patients with AD [standardized β = -0.204, P value = 0.255, 95% CI= -0.561 to 0.153, Adjusted R 2 = 0.004]. Also, there were no significant associations between DTI-ALPS index and p-tau181 in patients with MCI or healthy participants (Fig. 4). Association of DTI-ALPS indices with Amyloid-PET We observed the association between DTI-ALPS index and global cortical amyloid-PET SUVR, as well as amyloid SUVR in different brain regions, including the temporal cortex, the entorhinal cortex, and the parahippocampal gyrus. The amyloid-PET SUVR of the global cortex was negatively associated with the mean [standardized β = -0.109, P value = 0.047, 95% CI= -0.214 to -0.004, Adjusted R 2 = 0.073] and right DTI-ALPS indices among all participants [standardized β = -0.127, P value = 0.047, 95% CI= -0.233 to -0.022, Adjusted R 2 = 0.069]. However, after separating groups, only patients with MCI reflected significant associations, with global cortical SUVR linked to both the mean DTI-ALPS index [standardized β = -0.163, P = 0.047, 95% CI= -0.325 to -0.002, Adjusted R 2 = 0.058] and the right DTI-ALPS index [standardized β = -0.202, P = 0.014, 95% CI= -0.364 to -0.041, Adjusted R 2 = 0.057] (Fig. 5A). In the temporal pole cortex, we found no significant associations between DTI-ALPS index and amyloid uptake when considering all participants. In contrast, among patients with MCI, the temporal pole SUVR of amyloid was significantly associated with the mean DTI-ALPS index [standardized β = -0.199, P value < 0.05, 95% CI= -0.343 to -0.054, Adjusted R 2 = 0.074] (Fig. 5B), as well as the DTI-ALPS index in the right and left hemispheres [standardized β = - 0.196, P value < 0.05, 95% CI= -0.342 to -0.050, Adjusted R 2 = 0.062 in the right hemisphere VS standardized β = - 0.175, P value < 0.05, 95% CI= -0.320 to -0.029, Adjusted R 2 = 0.063 in the left hemisphere]. For the middle and inferior temporal cortices, no associations emerged in any group, except for a nominal association between the right DTI-ALPS index and amyloid-PET SUVR in the middle temporal cortex in patients with MCI [standardized β = -0.169, P value < 0.05, 95% CI= -0.332 to -0.007, Adjusted R 2 = 0.048], which did not survive FDR correction (Fig. 5C, D). In the entorhinal cortex, lower glymphatic function was associated with more accumulated amyloid plaques when combining all participants [standardized β = -0.134, P value < 0.05, 95% CI= -0.229 to -0.039, Adjusted R 2 = 0.081]. In addition, unlike patients with AD, significant association was found between the mean DTI-ALPS index and amyloid-PET SUVR in the entorhinal cortex among patients with MCI [standardized β = -0.224, P value < 0.05, 95% CI= -0.363 to -0.084, Adjusted R 2 = 0.087] (Figure 5E). Also, a similar associational trend was shown between amyloid uptake in the entorhinal cortex and the glymphatic function in patients with MCI in the right [standardized β = -0.240, P value < 0.001, 95% CI= -0.380 to -0.100, Adjusted R 2 = 0.083], and left hemispheres [standardized β = -0.179, P value < 0.05, 95% CI= -0.320 to -0.038, Adjusted R 2 = 0.066]. In addition, among all participants, the mean DTI-ALPS index was negatively associated with amyloid-PET SUVR in the parahippocampal gyrus [standardized β = -0.151, P value < 0.05, 95% CI= -0.250 to -0.051, Adjusted R 2 = 0.084]. This trend was also true for glymphatic function in both the right and left hemispheres [standardized β = -0.154, P value < 0.05, 95% CI= -0.253 to -0.054, Adjusted R 2 = 0.022 in the right hemisphere Vs. standardized β = -0.129, P value < 0.05, 95% CI= -0.228 to -0.029, Adjusted R 2 = 0.070 in the left hemisphere]. Unlike the two other groups, impaired function of the glymphatic system was significantly associated with increased amyloid SUVR in the parahippocampal gyrus of individuals with MCI [standardized β = -0.281, P value < 0.001, 95% CI= -0.428 to -0.134, Adjusted R 2 = 0.106] (Figure 5F). This pattern was mirrored in both the right and left DTI-ALPS indices [standardized β = -0.290, P value < 0.001, 95% CI= -0.438 to -0.143, Adjusted R 2 = 0.099 in the right hemisphere Vs. standardized β = -0.236, P value < 0.05, 95% CI= -0.384 to -0.087, Adjusted R 2 = 0.083 in the left hemisphere] (Table 2). However, after FDR correction across all regional and hemispheric analyses in the MCI group, associations within the parahippocampal gyrus, entorhinal cortex, and temporal pole remained significant for mean, right, and left DTI-ALPS indices, while only the right DTI-ALPS association with global cortical amyloid survived correction. Table 2. Analyses Results for linear regression of DTI-ALPS indices and plasma p-Tau181 and amyloid-PET SUVR. Plasma p-Tau181 Global Cortical Amyloid SUVR Temporal Pole Amyloid SUVR Inferior Temporal Amyloid SUVR Middle Temporal Amyloid SUVR Entorhinal Cortex Amyloid SUVR Parahippocampal Gyrus Amyloid SUVR Beta P-value CI 95% Beta P-value CI 95% Beta P-value CI 95% Beta P-value CI 95% Beta P-value CI 95% Beta P-value CI 95% Beta P-value CI 95% HC Mean DTI-ALPS -0.073 0.314 -0.217 to 0.070 -0.013 0.856 -0.160 to 0.133 0.091 0.223 -0.056 to 0.237 -0.026 0.729 -0.173 to 0.121 0.096 0.206 -0.053 to 0.246 -0.043 0.554 -0.188 to 0.101 -0.023 0.757 -0.173 to 0.126 Right DTI-ALPS -0.073 0.311 -0.216 to 0.069 0.016 0.833 -0.130 to 0.161 0.134 0.070 -0.011 to 0.278 -0.020 0.784 -0.167 to 0.126 0.095 0.208 -0.054 to 0.244 -0.029 0.686 -0.173 to 0.114 -0.025 0.742 -0.173 to 0.123 Left DTI-ALPS -0.064 0.391 -0.211 to 0.083 -0.039 0.605 -0.189to 0.111 0.038 0.621 -0.113 to 0.188 -0.028 0.716 -0.179 to 0.123 0.085 0.276 -0.069 to 0.239 -0.051 0.497 -0.199 to 0.097 -0.019 0.806 -0.172 to 0.134 MCI Mean DTI-ALPS -0.083 0.277 -0.233 to 0.067 -0.163 0.047 -0.325 to -0.002 -0.199 0.007* -0.343 to -0.054 -0.141 0.088 -0.303 to 0.021 -0.075 0.364 -0.239 to 0.088 -0.224 0.002* -0.363 to -0.084 -0.281 <0.001* -0.428 to -0.134 Right DTI-ALPS -0.111 0.149 -0.261 to 0.040 -0.202 0.014* -0.364 to -0.041 -0.196 0.009* -0.342 to -0.050 -0.169 0.041 -0.332 to -0.007 -0.097 0.247 -0.261 to 0.067 -0.240 <0.001* -0.380 to -0.100 -0.290 <0.001* -0.438 to -0.143 Left DTI-ALPS -0.046 0.549 -0.197 to 0.105 -0.105 0.205 -0.268 to 0.058 -0.175 0.019* -0.320 to -0.029 -0.095 0.250 -0.259 to 0.068 -0.045 0.586 -0.209 to 0.119 -0.179 0.013* -0.320 to -0.038 -0.236 0.002* -0.384 to -0.087 AD Mean DTI-ALPS -0.204 0.255 -0.561 to 0.153 -0.105 0.508 -0.425 to 0.214 -0.101 0.527 -0.423 to 0.220 -0.098 0.538 -0.420 to 0.223 -0.015 0.925 -0.346 to 0.315 -0.024 0.887 -0.359 to 0.311 -0.021 0.900 -0.356 to 0.315 Right DTI-ALPS -0.156 0.391 -0.520 to 0.208 -0.103 0.523 -0.427 to 0.221 -0.046 0.778 -0.373 to 0.281 -0.062 0.704 -0.388 to 0.264 -0.035 0.832 -0.369 to 0.299 0.046 0.784 -0.293 to 0.385 0.047 0.781 -0.292 to 0.386 Left DTI-ALPS -0.227 0.201 -0.581 to 0.126 -0.098 0.536 -0.416 to 0.220 -0.141 0.376 -0.459 to 0.177 -0.122 0.443 -0.440 to 0.196 0.003 0.985 -0.325 to 0.332 -0.082 0.619 -0.414 to 0.250 -0.078 0.636 -0.411 to 0.254 *Survived FDR correction (q < 0.05). Each cell shows the standardized Beta coefficient/p-value, extracted from the linear regression analysis between right, left, and total DTI-ALPS indices and plasma p-Tau18 and amyloid SUVR. All results were adjusted for APOE genotype, age, sex, and years of education. ALPS: Along Perivascular Space, SUVR: Standardized Uptake Value, p-tau181: Tau Protein Phosphorylated at Threonine 181, AD: Alzheimer’s disease, HC: Healthy Controls, MCI: Mild Cognitive Impairment Discussion To the best of our knowledge, the present study is the first investigation that observes the association between the DTI-ALPS index and plasma levels of p-tau 181, as well as the amyloid-PET findings among patients across the AD spectrum. We found that the DTI-ALPS index is negatively associated with the age of healthy participants. Additionally, a significant association was reported between the DTI-ALPS index and amyloid-PET SUVR in the entorhinal cortex and parahippocampal gyrus among all participants. It is also shown that unlike other temporal regions the amyloid-PET SUVR of the inferior temporal cortex was associated with the right DTI-ALPS index among all participants. In addition, patients in the MCI group reflected a significant association between the glymphatic function in the right hemisphere, as assessed indirectly by the DTI-ALPS index, and amyloid-PET SUVR of the global cortex. In addition, the amyloid SUVR in the temporal pole, entorhinal cortex, and parahippocampal gyrus of patients with MCI was significantly associated with DTI-ALPS index. However, no associations were found between the perivascular diffusivity patterns and amyloid-PET findings in patients with AD and there were no significant associations between the DTI-ALPS index and the plasma level of p-tau181 in either group. The absence of associations between DTI-ALPS and plasma p-tau181 suggests that glymphatic impairment may relate more closely to regional cortical amyloid deposition than to circulating tau biomarkers. The DTI-ALPS index was first proposed by Taoka et al. (2017) as an indirect imaging biomarker to assess the glymphatic system function [32]. The DTI-ALPS index is calculated by comparing water diffusivity along perivascular spaces with diffusivity in projection and association fibers at the level of the lateral ventricles [42]. Specifically, it is introduced as the ratio of diffusivity along the x-axis (parallel to perivascular spaces) to the average diffusivity along the y- and z-axes (perpendicular directions), reflecting the efficiency of fluid transport in perivascular pathways. The DTI-ALPS index, as an indicator of the perivascular diffusivity patterns, has been extensively utilized by previous studies to enhance understanding of neurodegenerative disorders. Recent studies have reported reduced DTI-ALPS indices in patients with AD and MCI, which have been interpreted as reflecting deficits in perivascular diffusivity patterns [43-47]. A recent meta-analysis on DTI-ALPS index in Parkinson’s disease (PD) and AD has reported not only significant changes in the DTI-ALPS index in either PD and AD in comparison to healthy controls, but also has reported an acceptable accuracy for the DTI-ALPS index in distinguishing patients with AD from those suffering from PD [48]. In contrast, our findings did not reflect a significant difference in the DTI-ALPS index between participants with AD spectrum and the control group. This result may be attributed to factors such as the small sample size, especially in the AD group, or heterogeneous disease stages. We reported a negative association between the DTI-ALPS index and the age of participants. This finding aligns with the results of previous studies, reporting a considerable correlation between age and the function of the glymphatic system [49-51]. These findings suggest an impaired function for the perivascular diffusivity patterns in older individuals. Additionally, we demonstrated a significant difference in the DTI-ALPS index between the two sexes. The evidence also extensively reports higher amounts of the DTI-ALPS index in females than in males, both in healthy participants and those with various neurodegenerative disorders [48, 52-54]. This difference between the two sexes can be due to multiple reasons. It is shown that female sexual hormones, like estrogen and progesterone, increase the expression of AQP-4 channels and thus enhance the clearance throughout the glymphatic system [55]. Moreover, females have higher resting cerebral blood flow and their better arterial pulsatility can lead to increased DTI-ALPS index compared to males [56, 57]. P-tau 181, p-tau 217, and p-tau 231 are among the most commonly assessed biomarkers in studies investigating AD [58]. While it is shown that p-tau 217 and p-tau 231 increase in the early stages of AD continuum, compared to the plasma and CSF levels of p-tau 181, recent studies have suggested a similar diagnostic reliability for these biomarkers among patients with AD spectrum [59]. Moreover, p-tau 181 is shown to be a more accurate biomarker in the observation of the stage and severity of cognitive decline among patients with AD continuum [16]. Based on previous studies, p-tau 181 CSF levels and related PET findings have become strong biomarkers for the early diagnosis of AD; however, the invasive and high-cost evaluation process restricts their availability, particularly in developing countries [60]. Thus, finding an available imaging technique associated with established AD biomarkers can promise a more cost-effective and non-invasive method in monitoring disease progression among patients with cognitive decline. However, our study revealed no considerable associations between the DTI-ALPS index and the plasma concentration of p-tau 181 among patients with AD spectrum. This finding is in line with previous studies, reporting no correlations between the DTI-ALPS index and plasma or CSF levels of p-tau 181 across different neurodegenerative diseases, such as PD and AD [61, 62]. The stronger associations observed in MCI may reflect that impairments of perivascular clearance exhibit more measurable influence during the prodromal stage of AD. In other words, widespread neurodegeneration, vascular remodeling, and structural tissue loss during advanced stages of the AD continuum may mask the association between fluid transport dysfunction and protein accumulation reported in early stages. This pattern aligns with models proposing that barrier and clearance dysfunction precede overt neuronal loss [63, 64]. Although, a recent mouse model of AD has demonstrated that impairment of AQP4 leads to increased p-tau 181 accumulation in the CSF [65]. Some studies have also revealed negative correlations between t-tau and p-tau181 CSF levels and DTI-ALPS index among patients with AD and MCI [66]. Such inconsistent findings highlight the need for more longitudinal studies with larger sample sizes to provide more sufficient evidence on the role of the glymphatic system in the clearance of tau tangles and to assess the validity of DTI-ALPS as a non-invasive imaging method in the assessment of disease progression among patients with AD continuum. We chose the temporal pole cortex, inferior temporal cortex, middle temporal cortex, entorhinal cortex, and parahippocampal gyrus as our ROIs in amyloid-PET SUVR measurement. The temporal lobe is among the key areas of the brain tissue involved in AD and its damage can lead to considerable cognitive impairment among patients with AD spectrum [67]. Among the temporal lobe subregions, the temporal pole cortex, inferior temporal cortex, and middle temporal cortex are constantly reported to exhibit significant synaptic loss and cortical thinning, contributing to apathy in patients with cognitive impairment [68-70]. In addition, the parahippocampal gyrus and entorhinal cortex are among the brain regions affected in early stages of AD, causing deficits in recent memory and spatial navigation [71]. The predominance of associations within temporolimbic structures may reflect the vulnerability of these regions to impaired perivascular clearance. Therefore, impaired perivascular exchange in aging may preferentially facilitate protein accumulation in these anatomically vulnerable circuits. Moreover, these findings suggest that assessing the relationship between amyloid-PET burden in these ROIs and the DTI-ALPS index may offer deeper insights into how perivascular diffusivity imapirment contributes to waste accumulation within temporolimbic circuits across the AD spectrum. The results of the present study demonstrated a significant association between amyloid SUVR in temporal pole cortex, inferior temporal cortex, entorhinal cortex, and parahippocampal gyrus, and the DTI-ALPS index, particularly among patients with MCI. To observe amyloid PET in patients with AD spectrum, several compounds, such as Pittsburgh compound B (11C-PiB), 18 F-flutemetamol (FMM), and 18 F-florbetapir (FBP), are used as tracers [72]. Our study utilized FBP as the PET tracer, which has been previously shown to have a longer half-life and affinity to Aβ deposits, compared to other PET tracers [73]. Recent studies have indicated a significant association between amyloid-PET and postmortem amyloid burden among patients with AD continuum, suggesting amyloid-PET as a potential imaging method in the assessment of AD pathology [74]. Amyloid PET SUVR is typically calculated by dividing the tracer uptake in targeted cortical regions by that in a reference region, such as the cerebellar cortex or whole cerebellum, to normalize for nonspecific binding and intersubject variability [75]. This quantitative measure is reported to be a reliable indicator of Aβ deposition and is widely used to assess disease burden and progression in cognitive impairment [74]. Moreover, Aβ SUVR is derived by normalizing Aβ concentrations to a reference measure, offering a standardized index of amyloid burden that complements PET-based quantification [76]. Reduced DTI-ALPS index, reflecting altered perivascular diffusivity patterns, is associated with more aggregated amyloid deposits, as indicated by a higher amyloid SUVR, suggesting that impaired perivascular clearance can contribute to greater accumulation of amyloid deposits in both the CSF and the brain tissue [62]. In other words, amyloid burden and neurodegeneration may represent pathological processes associated with alterations in DTI-ALPS indices and cognitive decline. These findings suggest that integrating DTI-ALPS indices with amyloid PET and neurodegenerative biomarkers may help characterize disease-related alterations in early AD, which needs validation in longitudinal studies (Fig. 6). Despite providing novel insights into the relationship between glymphatic system function, tau pathology, and amyloid burden in the AD spectrum, this study has several limitations. First, the cross-sectional design precludes causal inferences, limiting the ability to determine whether impaired glymphatic clearance precedes or follows amyloid and tau accumulation. Second, while the DTI-ALPS index provides a noninvasive proxy of the glymphatic function, it captures only perivascular water diffusivity at a single anatomical level and may not fully represent global glymphatic clearance. Additionally, plasma p-tau181 and amyloid SUVR were used as biomarkers, but longitudinal measures and simultaneous multimodal imaging could strengthen the understanding of dynamic interactions between glymphatic function and proteinopathies. Conclusions In summary, this study demonstrates that reduced DTI-ALPS indices, reflecting impaired perivascular fluid transport within the glymphatic system, are associated with increased temporolimbic amyloid deposition among individuals with cognitive impairment. These findings support the concept that impaired perivascular fluid transport may contribute to region-specific vulnerability to amyloid accumulation during the early phases of AD, consistent with emerging models of neurovascular unit and barrier dysfunction in aging. Future studies should employ longitudinal designs with repeated DTI-ALPS and PET assessments to elucidate temporal relationships and further explore the mechanistic links between perivascular transport alterations and neurodegenerative processes. Abbreviations 1C-PiB: Carbon-11 Pittsburgh Compound B Aβ: Amyloid-beta AD: Alzheimer’s disease ADAS: Alzheimer’s Disease Assessment Scale ADNI: Alzheimer’s Disease Neuroimaging Initiative AQP-4: Aquaporin-4 CSF: Cerebrospinal fluid DTI: Diffusion tensor imaging DTI-ALPS index: Diffusion Tensor Image Analysis along the Perivascular Space index DWI: Diffusion-weighted imaging FBP: Florbetapir FMM: Flutemetamol FSL: FMRIB Software Library ISF: Interstitial fluid MCI: Mild cognitive impairment MMSE: Mini-Mental State Examination MRI: Magnetic resonance imaging PD: Parkinson’s disease PET: Positron emission tomography p-tau: Phosphorylated tau PVS: Perivascular space ROI: Region of interest SCR: Superior corona radiata SLF: Superior longitudinal fasciculus SPECT: Single-photon emission computed tomography SUVR: Standardized uptake value ratio Declarations Acknowledgements. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hofmann-La Roche Ltd. and its afliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfzer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Ethical considerations. This study used de-identified data obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. ADNI study procedures were approved by the institutional review boards of participating institutions and conducted in accordance with the Declaration of Helsinki and its later amendments. Consent to participate. Written informed consent was obtained from all participants or their legally authorized representatives within the ADNI study. The present secondary analysis of de-identified data did not require additional institutional review or consent. Consent for publication. This manuscript has been approved for publication by all authors. Declaration of conflicting interest. The authors have no conflicts of interest to disclose Funding. 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Impairment of paravascular clearance pathways in the aging brain. Ann Neurol. 2014;76(6):845-61. doi: 10.1002/ana.24271. Sweeney MD, Sagare AP, Zlokovic BV. Blood-brain barrier breakdown in Alzheimer disease and other neurodegenerative disorders. Nat Rev Neurol. 2018;14(3):133-50. doi: 10.1038/nrneurol.2017.188. Harrison IF, Ismail O, Machhada A, Colgan N, Ohene Y, Nahavandi P, et al. Impaired glymphatic function and clearance of tau in an Alzheimer's disease model. Brain. 2020;143(8):2576-93. doi: 10.1093/brain/awaa179. Yu P, Shen L, Tang L. Multimodal DTI-ALPS and hippocampal microstructural signatures unveil stage-specific pathways in Alzheimer's disease progression. Front Aging Neurosci. 2025;17:1609793. doi: 10.3389/fnagi.2025.1609793. Migliaccio R, Cacciamani F. Chapter 25 - The temporal lobe in typical and atypical Alzheimer disease. In: Miceli G, Bartolomeo P, Navarro V, editors. Handbook of Clinical Neurology. Elsevier; 2022. p. 449-66. Scheff SW, Price DA, Schmitt FA, Scheff MA, Mufson EJ. Synaptic loss in the inferior temporal gyrus in mild cognitive impairment and Alzheimer's disease. J Alzheimers Dis. 2011;24(3):547-57. doi: 10.3233/jad-2011-101782. de Flores R, Das SR, Xie L, Wisse LEM, Lyu X, Shah P, et al. Medial Temporal Lobe Networks in Alzheimer's Disease: Structural and Molecular Vulnerabilities. J Neurosci. 2022;42(10):2131-41. doi: 10.1523/jneurosci.0949-21.2021. Arnold SE, Hyman BT, Van Hoesen GW. Neuropathologic changes of the temporal pole in Alzheimer's disease and Pick's disease. Arch Neurol. 1994;51(2):145-50. doi: 10.1001/archneur.1994.00540140051014. Karimani F, Asgari Taei A, Abolghasemi-Dehaghani MR, Safari MS, Dargahi L. Impairment of entorhinal cortex network activity in Alzheimer's disease. Front Aging Neurosci. 2024;16:1402573. doi: 10.3389/fnagi.2024.1402573. Sanaat A, Hu Y, Boccalini C, Salimi Y, Mansouri Z, Teixeira EPA, et al. Tracer-Separator: A Deep Learning Model for Brain PET Dual-Tracer ( 18 F-FDG and Amyloid) Separation. Clin Nucl Med. 2025;50(1):1-10. doi: 10.1097/rlu.0000000000005511. Wong DF, Rosenberg PB, Zhou Y, Kumar A, Raymont V, Ravert HT, et al. In vivo imaging of amyloid deposition in Alzheimer disease using the radioligand 18F-AV-45 (florbetapir [corrected] F 18). J Nucl Med. 2010;51(6):913-20. doi: 10.2967/jnumed.109.069088. Clark CM, Pontecorvo MJ, Beach TG, Bedell BJ, Coleman RE, Doraiswamy PM, et al. Cerebral PET with florbetapir compared with neuropathology at autopsy for detection of neuritic amyloid-β plaques: a prospective cohort study. Lancet Neurol. 2012;11(8):669-78. doi: 10.1016/s1474-4422(12)70142-4. Klunk WE, Engler H, Nordberg A, Wang Y, Blomqvist G, Holt DP, et al. Imaging brain amyloid in Alzheimer's disease with Pittsburgh Compound-B. Ann Neurol. 2004;55(3):306-19. doi: 10.1002/ana.20009. Hansson O, Seibyl J, Stomrud E, Zetterberg H, Trojanowski JQ, Bittner T, et al. CSF biomarkers of Alzheimer's disease concord with amyloid-β PET and predict clinical progression: A study of fully automated immunoassays in BioFINDER and ADNI cohorts. Alzheimers Dement. 2018;14(11):1470-81. doi: 10.1016/j.jalz.2018.01.010. Additional Declarations No competing interests reported. Supplementary Files GraphicalAbstract.tiff Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 08 May, 2026 Reviewers agreed at journal 27 Apr, 2026 Reviewers invited by journal 27 Apr, 2026 Editor assigned by journal 27 Apr, 2026 Submission checks completed at journal 10 Apr, 2026 First submitted to journal 10 Apr, 2026 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. 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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-9382890","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":634136232,"identity":"eac3b1ce-6e6a-4ebd-bcb7-4852ba842153","order_by":0,"name":"Rasa Zafari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYPACOQYGZuaDDz4AmWzsxGkxBmphSzacAdLCTLQWBh4zaR4Qm5AW8/bjDz/+qDGQN29nMJC2+bVNno+ZgfHDxxzcWmTO5BhLSBwzMJxzmCHBOLfvtmEbMwOz5MxtuLVIMOQwSBiw/WGcwcxwIDm35zYjUAsbMy8+LfzPH/9I+GdgP4OZseGwZc9te8JaJBLMJA62GSTOYGZmbGb4cTuRCC1vzCwb+wySZzCzMTP2NtxObgNqxe8X/vTHN398M7CdwX/++48ff27bzm9vPvjhIx4tqICxDUw2EKseBP6QongUjIJRMApGCgAAiZpKuYSPNdwAAAAASUVORK5CYII=","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Rasa","middleName":"","lastName":"Zafari","suffix":""},{"id":634136235,"identity":"adab621c-133c-46a7-a0bd-76d37a9ef921","order_by":1,"name":"Amirhossein Kamroo","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Amirhossein","middleName":"","lastName":"Kamroo","suffix":""},{"id":634136237,"identity":"b69311a8-1802-43cc-b5e2-4e240899e82c","order_by":2,"name":"Tina Taherkhani","email":"","orcid":"","institution":"Tehran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Tina","middleName":"","lastName":"Taherkhani","suffix":""},{"id":634136239,"identity":"77e9f89e-935d-4653-80ba-ef31853861d0","order_by":3,"name":"Mahsa Heidari-Foroozan","email":"","orcid":"","institution":"Shahid Beheshti University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mahsa","middleName":"","lastName":"Heidari-Foroozan","suffix":""},{"id":634136240,"identity":"bbf3853f-825e-4d0e-aa0a-6763a228779b","order_by":4,"name":"Fardin Nabizdeh","email":"","orcid":"","institution":"Iran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Fardin","middleName":"","lastName":"Nabizdeh","suffix":""},{"id":634136241,"identity":"bac70e84-1f59-47ab-a241-979cb1eb7a90","order_by":5,"name":"Mohammad Hadi Aarabi","email":"","orcid":"","institution":"Université de Bordeaux, CNRS, CEA","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Hadi","lastName":"Aarabi","suffix":""}],"badges":[],"createdAt":"2026-04-10 20:23:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9382890/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9382890/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108806206,"identity":"39076013-3cae-4ca5-91b8-7a4e7403571b","added_by":"auto","created_at":"2026-05-08 15:27:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2617854,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of ADNI data applications. HC: Healthy controls, MCI: Mild cognitive impairment, AD: Alzheimer’s disease.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9382890/v1/6be6bc2817e325246beb6327.png"},{"id":108805745,"identity":"90c5067f-0b84-4e73-8544-98b3caedf682","added_by":"auto","created_at":"2026-05-08 15:26:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1051206,"visible":true,"origin":"","legend":"\u003cp\u003eBaseline DTI-ALPS indices among participants. DTI-ALPS index: Diffusion Tensor Image Analysis along the Perivascular Space index, HC: Healthy controls, MCI: Mild cognitive impairment, AD: Alzheimer’s disease.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9382890/v1/d7559c89ec84667f74af3e5d.png"},{"id":108630185,"identity":"794a76fa-317d-4eb5-bc7e-59f45bde852a","added_by":"auto","created_at":"2026-05-06 16:35:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1096644,"visible":true,"origin":"","legend":"\u003cp\u003eSex differences in the function of the glymphatic system. Females demonstrated higher DTI-ALPS indices compared with males. DTI-ALPS index: Diffusion Tensor Image Analysis along the Perivascular Space index.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9382890/v1/793e0fb3c9747bd3b1984f67.png"},{"id":108630188,"identity":"8b1b8121-86a5-4bd1-b2c2-93ebf29fca48","added_by":"auto","created_at":"2026-05-06 16:35:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":554766,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between baseline DTI-ALPS index and plasma p-tau 181. No significant associations were found between the baseline DTI-ALPS index and plasma p-tau 181 in any groups. DTI-ALPS index: Diffusion Tensor Image Analysis along the Perivascular Space index, HC: Healthy controls, MCI: Mild cognitive impairment, AD: Alzheimer’s disease.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9382890/v1/331e677f9b35acf84d06244d.png"},{"id":108805167,"identity":"ef4d81e6-ed20-4250-bf46-ddce33189159","added_by":"auto","created_at":"2026-05-08 15:25:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3694034,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between DTI-ALPS index and regional amyloid-PET SUVR. Scatterplots show the relationship between baseline DTI-ALPS index and amyloid-PET SUVR in the global cortex (A), temporal pole (B), middle temporal cortex (C), inferior temporal cortex (D), entorhinal cortex (E), and parahippocampal gyrus (F), stratified by HC, MCI, and AD groups. Significant negative associations were observed primarily in the MCI group. SUVR: Standardized uptake value ratio, DTI-ALPS index: Diffusion Tensor Image Analysis along the Perivascular Space index, HC: Healthy controls, MCI: Mild cognitive impairment, AD: Alzheimer’s disease.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9382890/v1/2df013bb2c8b1497e6ce2076.png"},{"id":108976859,"identity":"9b1c7184-37fe-4fdd-a16b-866ba2d49270","added_by":"auto","created_at":"2026-05-11 11:29:12","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2885742,"visible":true,"origin":"","legend":"\u003cp\u003eProposed relationship between DTI-ALPS indices and amyloid accumulation in Alzheimer’s disease. Impaired perivascular fluid transport may contribute to decreased perivascular removal of Aβ and increased amyloid deposition, reflected by higher PET SUVR. Accumulation of Aβ within the ISF is proposed to interact with neurodegenerative processes and cognitive decline (Created with BioRender.com). Aβ: Amyloid-beta, PET: Positron emission tomography, SUVR: Standardized uptake value ratio, ISF: Interstitial fluid.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-9382890/v1/98ce5a95cecd06e8a5266539.png"},{"id":108980891,"identity":"f433a22c-7a68-4bf9-a1c8-06b7ec534a9c","added_by":"auto","created_at":"2026-05-11 12:12:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11365943,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9382890/v1/377b038b-33fc-4c94-a8c7-058763c60acd.pdf"},{"id":108630182,"identity":"fe1963ef-c2f1-4fc9-ad72-131a7a49a6f3","added_by":"auto","created_at":"2026-05-06 16:35:03","extension":"tiff","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1326936,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.tiff","url":"https://assets-eu.researchsquare.com/files/rs-9382890/v1/a9dbc692f6a568539958b705.tiff"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDTI-ALPS Is Associated with Temporolimbic Amyloid but Not Plasma p-Tau181 Across the Alzheimer’s Disease Continuum\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAlzheimer's disease (AD) is the most prevalent cause of dementia worldwide and it is a prominent challenge for healthcare providers, due to its considerable socioeconomic burden [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The central driver for the development of AD is the aggregation of misfolded amyloid-beta (Aβ) plaques and hyperphosphorylated tau (p-tau) proteins [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Beyond their central role in AD pathophysiology, Aβ and p-tau are measurable hallmarks of disease pathology [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Regarding the p-tau proteins, different p-tau isoforms of p-tau 231, p-tau 217, and p-tau 181 have been extensively investigated in the context of AD [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Specifically, p-tau 181 is a well-established indicator of neuronal damage in AD, which has revealed promising evaluation accuracy both in cerebrospinal fluid (CSF) sampling and positron emission tomography (PET) [\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. It is extensively reported that plasma p-tau181 represents downstream tau-related neurodegeneration and may reflect a systemic correlate of protein accumulation within the central nervous system (CNS) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. While plasma p-tau181 reflects tau pathology and neurodegeneration, it remains unclear whether circulating tau levels are directly influenced by alterations in perivascular clearance mechanisms.\u003c/p\u003e \u003cp\u003eBeyond excessive protein aggregation, emerging evidence implies dysfunction of the neurovascular unit and brain barrier systems in the pathogenesis of AD [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. It is shown that age-related alterations in the blood-brain barrier (BBB), perivascular spaces, and astrocytic aquaporin-4 (AQP4) polarization can result in impaired clearance of Aβ and p-tau in the brain tissue [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Such clearance failure is proposed to contribute to neurodegenerative processes [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs mentioned, recent studies have suggested that impairments in the clearance system of the brain can contribute to the excessive accumulation of p-tau deposits [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The glymphatic system is a pivotal clearance system in the brain, involved in the drainage of harmful waste, and plays a vital role in preserving the normal function of the brain [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The function of the glymphatic system heavily relies on brain perivascular spaces (PVSs), consisting of periarterial spaces in arteries and perivenous spaces in veins [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Periarterial spaces are the entrance pathway for the influx of CSF into the brain parenchyma [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. After the entry, AQP-4 channels of the astrocytes allow further fluid and material exchange between the CSF of the periarterial spaces and the interstitial fluid (ISF) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Followed by the exchange, the efflux of the waste-containing CSF occurs through the perivenous spaces [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Interruptions in the function of this perivascular exchange system, particularly in aging, can exacerbate regional accumulation of misfolded proteins, particularly within vulnerable temporolimbic circuits [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Different approaches could be implemented for the clinical assessment of the glymphatic system, including infrared (IR) imaging [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], single-photon emission computed tomography (SPECT) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and contrast-enhanced or non-contrast magnetic resonance imaging (MRI) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Among the non-contrast MRI-based techniques, diffusion tensor imaging-based approaches (DTI) have been used to derive indices related to perivascular water diffusivity, which may reflect aspects of perivascular fluid dynamics [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDTI measures the diffusion of water molecules in the anatomical structures. The type of DTI used for the assessment of the fluid dynamics in the glymphatic system is called DTI along the perivascular spaces (DTI-ALPS) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The DTI-ALPS measures water diffusion in three axes of subcortical fibers, association fibers, and projection fibers at the level of the lateral ventricle body [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Subcortical fibers as the X-axis, association fibers as the Y-axis, and projection fibers serve as the Z-axis [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Ultimately, the ratio of X-axis water diffusion to the average of Y/Z-axes water diffusion indicates the DTI-ALPS quantified index that has been proposed as an indirect imaging marker related to water diffusivity along perivascular spaces, and has been interpreted as a potential proxy of perivascular fluid dynamics [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, in this study, we aimed to examine whether DTI-ALPS indices are associated with regional amyloid deposition in temporolimbic structures and whether such alterations relate to circulating p-tau181 across the Alzheimer\u0026rsquo;s disease continuum. We hypothesized that the altered diffusivity in perivascular system would be associated with an exacerbated accumulation of Aβ in the brain and potentially elevated plasma levels of p-tau 181 among patients with cognitive impairment. This study provides a more in-depth insight into the neuronal pathophysiology and abnormal functionality of the glymphatic system in patients with cognitive impairments.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSubject Cohort and Data Acquisition\u003c/h2\u003e \u003cp\u003eData for 410 individuals, including cognitively normal controls and participants with varying degrees of cognitive impairment were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Inclusion of the participants relied on the availability of complete diffusion-weighted imaging (DWI) sequences, T1-weighted anatomical scans, plasma biomarker data and amyloid-PET measurements. Datasets that showed significant motion artifact or structural anomalies, as determined by standardized ADNI quality control protocols, were excluded. Demographic covariates including age, sex, years of education, clinical scores comprised of MMSE and ADAS-11, APOE ε4 status, and biomarker levels were extracted.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMagnetic Resonance Imaging Acquisition\u003c/h3\u003e\n\u003cp\u003eAll imaging data were acquired according to the standardized ADNI acquisition protocol across multiple sites. 3 Tesla MRI systems were used. The diffusion-weighted imaging protocol utilized multiple non-colinear gradient directions with a b-value of 1000 s/mm\u0026sup2;, supplemented by multiple b0 reference images. High-resolution 3D T1-weighted anatomical images were obtained at 1.0 mm isotropic resolution. Amyloid-PET data were processed and quantified as standard uptake value ratio (SUVR) in accordance with ADNI pipelines.\u003c/p\u003e\n\u003ch3\u003eDiffusion Data Preprocessing and Tensor Modeling\u003c/h3\u003e\n\u003cp\u003eDiffusion-weighted images were first visually inspected to ensure the absence of gross artifacts and motion-related distortions. Preprocessing was then performed using the FMRIB Software Library (FSL, version 6.0) and MRtrix3 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The diffusion data were denoised using the Marchenko-Pastur principal component analysis\u0026ndash;based algorithm (dwidenoise) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] and corrected for Gibbs ringing artifacts (mrdegibbs) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Eddy-current and motion-induced distortions were corrected using FSL\u0026rsquo;s eddy, with outlier replacement and slice-to-volume motion correction enabled when appropriate.\u003c/p\u003e \u003cp\u003eFollowing preprocessing, diffusion tensors were fitted voxel-wise using FSL\u0026rsquo;s dtifit to generate diffusion tensor\u0026ndash;derived maps, including the three principal diffusivities (Dxx, Dyy, Dzz) corresponding to diffusivity along the x-, y-, and z-axes of the diffusion tensor, respectively. These maps were subsequently used for the calculation of the DTI-ALPS index [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Fractional anisotropy (FA) maps were also generated and used for spatial normalization to the JHU-ICBM 1 mm FA template using linear registration (flirt) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. All processing steps were carried out in the native diffusion space prior to registration to minimize interpolation effects.\u003c/p\u003e\n\u003ch3\u003eQuantification of Perivascular Fluid Dynamics\u003c/h3\u003e\n\u003cp\u003eThe function of the glymphatic system was calculated using the DTI-ALPS method. At the level of the centrum semiovale, the perivascular spaces accompanying the medullary veins run predominantly along the left\u0026ndash;right (x) axis, while the projection fibers (within the superior corona radiata, SCR) are oriented in the superior-inferior (z) direction and the association fibers (within the superior longitudinal fasciculus, SLF) run in the anterior-posterior (y) direction. Because of this nearly orthogonal geometry, diffusion along the x-axis in this region primarily reflects water movement within perivascular channels, whereas diffusion along the y- and z-axes represents diffusion perpendicular to these spaces [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFour regions of interest (ROIs) were placed bilaterally in the left and right SCR and SLF using predefined templates in the JHU-ICBM FA space. The individual diffusion tensor components, Dxx, Dyy, and Dzz, representing diffusivity along the x, y, and z axes, respectively, were extracted from these ROIs. The DTI-ALPS index was computed separately for each hemisphere using the formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:DTI-ALPS\\:index=\\frac{{D}_{x\\left(Projection\\:ROI\\right)}}{{D}_{y\\left(Projection\\:ROI\\right)}}+\\frac{{D}_{x\\left(Association\\:ROI\\right)}}{{D}_{z\\left(Association\\:ROI\\right)}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eDxx, Dyy, and Dzz correspond to the directional diffusivities within the projection (SCR) and association (SLF) fibers. A higher ALPS index indicates greater diffusivity along the perivascular-space axis, which has been interpreted as potentially reflecting more preserved perivascular fluid dynamics; however, it does not directly quantify glymphatic flow.\u003c/p\u003e \u003cp\u003eAll diffusion metrics were calculated in the native diffusion space to minimize interpolation errors and then spatially normalized for group-level analysis. The per-subject ALPS index was exported to a tabular file for subsequent statistical analysis.\u003c/p\u003e\n\u003ch3\u003eStatistical Modeling Framework\u003c/h3\u003e\n\u003cp\u003eAll statistical analyses were performed using SPSS software (Version 20). The normality of variable distributions was evaluated using the Kolmogorov-Smirnov and Shapiro-Wilk tests. To evaluate the association between the DTI-ALPS index and core Alzheimer's disease biomarkers, multivariable linear regression models were used. The primary models assessed the relationship between the mean DTI-ALPS index (dependent variable) and plasma p-tau 181 levels, as well as global amyloid-PET SUVR, adjusted for the covariates of age, sex, and years of education. Secondary analyses examined hemispheric-specific indices and their relationship with amyloid-PET SUVR in the temporal pole cortex, inferior temporal cortex, middle temporal cortex, entorhinal cortex, and parahippocampal gyrus, due to their involvement in early-stage Alzheimer\u0026rsquo;s disease and their known vulnerability to amyloid accumulation. A p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. To account for multiple regional comparisons, false discovery rate (FDR) correction was applied using the Benjamini-Hochberg procedure across amyloid-PET regional analyses, with q\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant. In exploratory analyses, we also compared effect sizes across diagnostic groups to evaluate whether DTI-ALPS-amyloid associations were stage-specific (HC vs MCI vs AD), given the diagnostic focus of biomarker-based stratification.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDemographic and clinical features of participants\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe baseline cohort data of 410 individuals, consisting of 217 males and 193 females, were used in this study (Fig. 1). The overall mean age was 71.91 \u0026plusmn; 6.98, and the mean years of education was 16.21 \u0026plusmn; 2.68 among all participants. In addition, the mean scores for the Mini-Mental State Examination (MMSE) and Alzheimer\u0026apos;s Disease Assessment Scale (ADAS-11) were 27.96 \u0026plusmn; 2.32 and 8.63 \u0026plusmn; 6.07, respectively. Moreover, 42.19% of individuals reflected at least one APOE ɛ4. We found a significant difference in the cognitive performance of participants in both the MMSE [F (2,407) = 224.356, P value \u0026lt; 0.001, \u0026eta;\u0026sup2; = 0.524] and the ADAS-11 questionnaires [F (2,407) = 196.801, P value \u0026lt; 0.001, \u0026eta;\u0026sup2; = 0.492].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNevertheless, the plasma level of p-tau 181 was significantly different among individuals with cognitive impairment and healthy controls [F (2,407) = 15.586, P value \u0026lt; 0.001, \u0026eta;\u0026sup2; = 0.071]. Considering imaging biomarkers, our analysis demonstrated considerable differences between groups in global cortical amyloid-PET SUVR [F (2,407) = 60.000, P value \u0026lt; 0.001, \u0026eta;\u0026sup2; = 0.228], as well as amyloid-PET SUVR in temporal pole cortex [F (2,407) = 15.797, P value \u0026lt; 0.001, \u0026eta;\u0026sup2; = 0.072], middle temporal cortex [F (2,407) = 37.188, P value \u0026lt; 0.001, \u0026eta;\u0026sup2; = 0.155], inferior temporal cortex [F (2,407) = 41.020, P value \u0026lt; 0.001, \u0026eta;\u0026sup2; = 0.168], and parahippocampal gyrus [F (2,407) = 25.319, P value \u0026lt; 0.001, \u0026eta;\u0026sup2; = 0.111]. Additionally, no significant differences were observed in the mean DTI-ALPS index among patients with cognitive impairment and healthy individuals [F (2,407) = 0.441, P value = 0.644, \u0026eta;\u0026sup2; = 0.002] (Fig. 2). Table 1 summarizes demographic characteristics of participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Demographic characteristics.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"711\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3483%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHC\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 174)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMCI\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 193)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3989%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 43)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3483%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e72.26 \u0026plusmn; 5.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e71.09 \u0026plusmn; 7.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3989%;\"\u003e\n \u003cp\u003e74.18 \u0026plusmn; 8.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3483%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex (F/M)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e79/95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e111/82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3989%;\"\u003e\n \u003cp\u003e27/16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.026\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3483%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e16.57 \u0026plusmn; 2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e16.06 \u0026plusmn; 2.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3989%;\"\u003e\n \u003cp\u003e15.42 \u0026plusmn; 2.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3483%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMMSE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e28.98 \u0026plusmn; 1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e28.05 \u0026plusmn; 1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3989%;\"\u003e\n \u003cp\u003e23.42 \u0026plusmn; 1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3483%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eADAS11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e5.47 \u0026plusmn; 2.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e8.92 \u0026plusmn; 4.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3989%;\"\u003e\n \u003cp\u003e20.09 \u0026plusmn; 7.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3483%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAPOE Ɛ4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3989%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3483%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWithout Ɛ4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3989%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3483%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOne Ɛ4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3989%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3483%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTwo Ɛ4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3989%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3483%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLeft DTI-ALPS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e1.22 \u0026plusmn; 0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e1.22 \u0026plusmn; 0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3989%;\"\u003e\n \u003cp\u003e1.21 \u0026plusmn; 0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3483%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRight DTI-ALPS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e1.24 \u0026plusmn; 0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e1.21 \u0026plusmn; 0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3989%;\"\u003e\n \u003cp\u003e1.20 \u0026plusmn; 0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e\u003cspan dir=\"RTL\"\u003e0.268\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3483%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean DTI-ALPS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e1.23 \u0026plusmn; 0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e1.22 \u0026plusmn; 0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3989%;\"\u003e\n \u003cp\u003e1.20 \u0026plusmn; 0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e0.644\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3483%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlasma p-tau181\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e15.16 \u0026plusmn; 9.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e17.77 \u0026plusmn; 9.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3989%;\"\u003e\n \u003cp\u003e24.47 \u0026plusmn; 10.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3483%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlobal Cortical Amyloid SUVR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e1.15 \u0026plusmn; 0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e1.27 \u0026plusmn; 0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3989%;\"\u003e\n \u003cp\u003e1.51 \u0026plusmn; 0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3483%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTemporal Pole Amyloid SUVR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e0.92 \u0026plusmn; 0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e0.96 \u0026plusmn; 0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3989%;\"\u003e\n \u003cp\u003e1.07 \u0026plusmn; 0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3483%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInferior Temporal Amyloid SUVR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e1.15 \u0026plusmn; 0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e1.21 \u0026plusmn; 0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3989%;\"\u003e\n \u003cp\u003e1.39 \u0026plusmn; 0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3483%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiddle Temporal Amyloid SUVR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e1.06 \u0026plusmn; 0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e1.13 \u0026plusmn; 0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3989%;\"\u003e\n \u003cp\u003e1.30 \u0026plusmn; 0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3483%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEntorhinal Cortex Amyloid SUVR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e0.94 \u0026plusmn; 0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e0.95 \u0026plusmn; 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3989%;\"\u003e\n \u003cp\u003e0.97 \u0026plusmn; 0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.378\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.3483%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParahippocampal Gyrus Amyloid SUVR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e0.99 \u0026plusmn; 0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e1.03 \u0026plusmn; 0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3989%;\"\u003e\n \u003cp\u003e1.14 \u0026plusmn; 0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.0843%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues are showed as mean \u0026plusmn; SD or raw numbers of patients.\u003c/p\u003e\n\u003cp\u003eResults of ANOVA analysis between groups noted as p value.\u003c/p\u003e\n\u003cp\u003eAPOE Ɛ4: Apolipoprotein E ɛ4 genotype, MMSE: Mini Mental State Examination, AD: Alzheimer\u0026rsquo;s disease, ADAS 11: Alzheimer\u0026rsquo;s Disease Assessment Scale-Cognitive Subscale 11 Items, HC: Healthy Controls, DTI-ALPS: Diffusion Tensor Imaging Along Perivascular Space, MCI: Mild Cognitive Impairment, SUVR: Standardized Uptake Value, p-tau181: Tau Protein Phosphorylated at Threonine 181.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAssociation of DTI-ALPS indices with cognitive performance and demographic characteristics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a linear regression model to investigate whether there is a significant association between DTI-ALPS indices and demographic features, as well as cognitive function among the participants. Unlike patients with AD [P value = 0.136], our analysis revealed a significant relationship between the DTI-ALPS index and the age of the healthy participants [standardized \u0026beta; = -0.314, P value \u0026lt; 0.001,\u0026nbsp;95% CI= -0.457 to -0.172, Adjusted R\u003csup\u003e2\u003c/sup\u003e= 0.099] and the MCI group [standardized \u0026beta; = -0.209, P value \u0026lt; 0.05,\u0026nbsp;95% CI= -0.349 to -0.070, Adjusted R\u003csup\u003e2\u003c/sup\u003e= 0.044]. However, no significant associations were shown between the DTI-ALPS index and the years of education among patients with AD [P value = 0.586] and healthy individuals [P value = 0.482]. Additionally, the DTI-ALPS index was not associated with the cognitive performance of participants with AD in MMSE [P value = 0.143] or ADAS-11 [P value = 0.166].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSex difference in DTI-ALPS indices\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe found a significant association between the sex of healthy individuals and the mean DTI-ALPS index [standardized \u0026beta; = 0.264, P value \u0026lt; 0.001,\u0026nbsp;95% CI= 0.119 to 0.409, Adjusted R\u003csup\u003e2\u003c/sup\u003e= 0.064]. Then, we conducted an independent t-test to compare the DTI-ALPS index among participants. Unlike patients with cognitive impairment, healthy participants reflected a significant difference in the DTI-ALPS index between the two sexes, with a higher glymphatic activity in females [t (173) = -3.594, P value \u0026lt; 0.001,\u0026nbsp;\u0026eta;\u0026sup2; = 0.069]. Similar results on the significant difference of the glymphatic function were also observed among all participants [t (\u003cspan dir=\"RTL\"\u003e40\u003c/span\u003e8) = -2.239, P value \u0026lt; 0.05,\u0026nbsp;\u0026eta;\u0026sup2; = 0.012] (Fig. 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAssociation of DTI-ALPS indices with plasma p-tau 181\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the association between DTI-ALPS indices and plasma levels of p-tau 181, we used a multivariable linear regression model adjusted for age, sex, APOE ɛ4, and years of education. Our analysis reported a negative but insignificant association between the mean DTI-ALPS index and plasma concentrations of p-tau 181 among patients with AD [standardized\u0026nbsp;\u0026beta; = -0.204, P value = 0.255, 95% CI= -0.561 to 0.153, Adjusted R\u003csup\u003e2\u003c/sup\u003e= 0.004]. Also, there were no significant associations between DTI-ALPS index and p-tau181 in patients with MCI or healthy participants (Fig. 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAssociation of DTI-ALPS indices with Amyloid-PET\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe observed the association between DTI-ALPS index and global cortical amyloid-PET SUVR, as well as amyloid SUVR in different brain regions, including the temporal cortex, the entorhinal cortex, and the parahippocampal gyrus. The amyloid-PET SUVR of the global cortex was negatively associated with the mean [standardized \u0026beta; = -0.109, P value = 0.047, 95% CI= -0.214 to -0.004, Adjusted R\u003csup\u003e2\u003c/sup\u003e= 0.073] and right DTI-ALPS indices among all participants [standardized \u0026beta; = -0.127, P value = 0.047, 95% CI= -0.233 to -0.022, Adjusted R\u003csup\u003e2\u003c/sup\u003e= 0.069]. However, after separating groups, only patients with MCI reflected significant associations, with global cortical SUVR linked to both the mean DTI-ALPS index [standardized \u0026beta; = -0.163, P = 0.047, 95% CI= -0.325 to -0.002, Adjusted R\u003csup\u003e2\u003c/sup\u003e= 0.058] and the right DTI-ALPS index [standardized \u0026beta; = -0.202, P = 0.014, 95% CI= -0.364 to -0.041, Adjusted R\u003csup\u003e2\u003c/sup\u003e= 0.057] (Fig. 5A).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the temporal pole cortex, we found no significant associations between DTI-ALPS index and amyloid uptake when considering all participants. In contrast, among patients with MCI, the temporal pole SUVR of amyloid was significantly associated with the mean DTI-ALPS index [standardized \u0026beta; = -0.199, P value \u0026lt; 0.05, 95% CI= -0.343 to -0.054, Adjusted R\u003csup\u003e2\u003c/sup\u003e= 0.074] (Fig. 5B), as well as the DTI-ALPS index in the right and left hemispheres [standardized \u0026beta; = - 0.196, P value \u0026lt; 0.05, 95% CI= -0.342 to -0.050, Adjusted R\u003csup\u003e2\u003c/sup\u003e= 0.062 in the right hemisphere VS standardized \u0026beta; = - 0.175, P value \u0026lt; 0.05, 95% CI= -0.320 to -0.029, Adjusted R\u003csup\u003e2\u003c/sup\u003e= 0.063 in the left hemisphere]. For the middle and inferior temporal cortices, no associations emerged in any group, except for a nominal association between the right DTI-ALPS index and amyloid-PET SUVR in the middle temporal cortex in patients with MCI [standardized \u0026beta; = -0.169, P value \u0026lt; 0.05, 95% CI= -0.332 to -0.007, Adjusted R\u003csup\u003e2\u003c/sup\u003e= 0.048], which did not survive FDR correction (Fig. 5C, D).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the entorhinal cortex, lower glymphatic function was associated with more accumulated amyloid plaques when combining all participants [standardized \u0026beta; = -0.134, P value \u0026lt; 0.05, 95% CI= -0.229 to -0.039, Adjusted R\u003csup\u003e2\u003c/sup\u003e= 0.081]. In addition, unlike patients with AD, significant association was found between the mean DTI-ALPS index and amyloid-PET SUVR in the entorhinal cortex among patients with MCI [standardized \u0026beta; = -0.224, P value \u0026lt; 0.05, 95% CI= -0.363 to -0.084, Adjusted R\u003csup\u003e2\u003c/sup\u003e= 0.087] (Figure 5E). Also, a similar associational trend was shown between amyloid uptake in the entorhinal cortex and the glymphatic function in patients with MCI in the right [standardized \u0026beta; = -0.240, P value \u0026lt; 0.001, 95% CI= -0.380 to -0.100, Adjusted R\u003csup\u003e2\u003c/sup\u003e= 0.083], and left hemispheres [standardized \u0026beta; = -0.179, P value \u0026lt; 0.05, 95% CI= -0.320 to -0.038, Adjusted R\u003csup\u003e2\u003c/sup\u003e= 0.066].\u003c/p\u003e\n\u003cp\u003eIn addition, among all participants, the mean DTI-ALPS index was negatively associated with amyloid-PET SUVR in the parahippocampal gyrus [standardized \u0026beta; = -0.151, P value \u0026lt; 0.05, 95% CI= -0.250 to -0.051, Adjusted R\u003csup\u003e2\u003c/sup\u003e= 0.084]. This trend was also true for glymphatic function in both the right and left hemispheres [standardized \u0026beta; = -0.154, P value \u0026lt; 0.05, 95% CI= -0.253 to -0.054, Adjusted R\u003csup\u003e2\u003c/sup\u003e= 0.022 in the right hemisphere Vs. standardized \u0026beta; = -0.129, P value \u0026lt; 0.05, 95% CI= -0.228 to -0.029, Adjusted R\u003csup\u003e2\u003c/sup\u003e= 0.070 in the left hemisphere]. Unlike the two other groups, impaired function of the glymphatic system was significantly associated with increased amyloid SUVR in the parahippocampal gyrus of individuals with MCI [standardized \u0026beta; = -0.281, P value \u0026lt; 0.001, 95% CI= -0.428 to -0.134, Adjusted R\u003csup\u003e2\u003c/sup\u003e= 0.106] (Figure 5F). This pattern was mirrored in both the right and left DTI-ALPS indices [standardized \u0026beta; = -0.290, P value \u0026lt; 0.001, 95% CI= -0.438 to -0.143, Adjusted R\u003csup\u003e2\u003c/sup\u003e= 0.099 in the right hemisphere Vs. standardized \u0026beta; = -0.236, P value \u0026lt; 0.05, 95% CI= -0.384 to -0.087, Adjusted R\u003csup\u003e2\u003c/sup\u003e= 0.083 in the left hemisphere] (Table 2). However, after FDR correction across all regional and hemispheric analyses in the MCI group, associations within the parahippocampal gyrus, entorhinal cortex, and temporal pole remained significant for mean, right, and left DTI-ALPS indices, while only the right DTI-ALPS association with global cortical amyloid survived correction.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Analyses Results for linear regression of DTI-ALPS indices and plasma p-Tau181 and amyloid-PET SUVR.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"810\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlasma p-Tau181\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlobal Cortical Amyloid SUVR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTemporal Pole Amyloid SUVR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInferior Temporal Amyloid SUVR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiddle Temporal Amyloid SUVR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEntorhinal Cortex Amyloid SUVR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParahippocampal Gyrus Amyloid SUVR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI 95%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI 95%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI 95%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI 95%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI 95%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI 95%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBeta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI 95%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean DTI-ALPS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.217 to 0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.160 to 0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.056 to 0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.173 to 0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e-0.053 to 0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e-0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e0.554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e-0.188 to 0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e0.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.173 to 0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRight DTI-ALPS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.216 to 0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.130 to 0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.011 to 0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.167 to 0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e-0.054 to 0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e-0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e0.686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e-0.173 to 0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.173 to 0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLeft DTI-ALPS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.211 to 0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.189to 0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.113 to 0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.179 to 0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e-0.069 to 0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e-0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e0.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e-0.199 to 0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.172 to 0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMCI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean DTI-ALPS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.233 to 0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.163\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.047\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.325 to -0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.199\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.343 to -0.054\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.303 to 0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e-0.239 to 0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.224\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.363 to -0.084\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.281\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.428 to -0.134\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRight DTI-ALPS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.261 to 0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.202\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.014*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.364 to -0.041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.196\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.342 to -0.050\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.169\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.332 to -0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e-0.261 to 0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.240\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.380 to -0.100\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.290\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.438 to -0.143\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLeft DTI-ALPS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.197 to 0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.268 to 0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.175\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.019*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.320 to -0.029\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.259 to 0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e-0.209 to 0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.179\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.320 to -0.038\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.236\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.384 to -0.087\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" rowspan=\"3\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean DTI-ALPS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.561 to 0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.425 to 0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.423 to 0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.420 to 0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e-0.346 to 0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e-0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e0.887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e-0.359 to 0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e0.900\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.356 to 0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRight DTI-ALPS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.520 to 0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.427 to 0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.373 to 0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.388 to 0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e-0.369 to 0.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e-0.293 to 0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e0.781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.292 to 0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLeft DTI-ALPS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.581 to 0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.536\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.416 to 0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.459 to 0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.440 to 0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e-0.325 to 0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e-0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003e-0.414 to 0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35px;\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e-0.411 to 0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Survived FDR correction (q \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eEach cell shows the standardized Beta coefficient/p-value, extracted from the linear regression analysis between right, left, and total DTI-ALPS indices and plasma p-Tau18 and amyloid SUVR. All results were adjusted for APOE genotype, age, sex, and years of education. ALPS: Along Perivascular Space, SUVR: Standardized Uptake Value, p-tau181: Tau Protein Phosphorylated at Threonine 181, AD: Alzheimer\u0026rsquo;s disease, HC: Healthy Controls, MCI: Mild Cognitive Impairment\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, the present study is the first investigation that observes the association between the DTI-ALPS index and plasma levels of p-tau 181, as well as the amyloid-PET findings among patients across the AD spectrum. We found that the DTI-ALPS index is negatively associated with the age of healthy participants. Additionally, a significant association was reported between the DTI-ALPS index and amyloid-PET SUVR in the entorhinal cortex and parahippocampal gyrus among all participants. It is also shown that unlike other temporal regions the amyloid-PET SUVR of the inferior temporal cortex was associated with the right DTI-ALPS index among all participants.\u003c/p\u003e\n\u003cp\u003eIn addition, patients in the MCI group reflected a significant association between the glymphatic function in the right hemisphere, as assessed indirectly by the DTI-ALPS index, and amyloid-PET SUVR of the global cortex. In addition, the amyloid SUVR in the temporal pole, entorhinal cortex, and parahippocampal gyrus of patients with MCI was significantly associated with DTI-ALPS index. However, no associations were found between the perivascular diffusivity patterns and amyloid-PET findings in patients with AD and there were no significant associations between the DTI-ALPS index and the plasma level of p-tau181 in either group. The absence of associations between DTI-ALPS and plasma p-tau181 suggests that glymphatic impairment may relate more closely to regional cortical amyloid deposition than to circulating tau biomarkers.\u003c/p\u003e\n\u003cp\u003eThe DTI-ALPS index was first proposed by Taoka et al. (2017) as an indirect imaging biomarker to assess the glymphatic system function [32]. The DTI-ALPS index is calculated by comparing water diffusivity along perivascular spaces with diffusivity in projection and association fibers at the level of the lateral ventricles [42]. Specifically, it is introduced as the ratio of diffusivity along the x-axis (parallel to perivascular spaces) to the average diffusivity along the y- and z-axes (perpendicular directions), reflecting the efficiency of fluid transport in perivascular pathways. The DTI-ALPS index, as an indicator of the perivascular diffusivity patterns, has been extensively utilized by previous studies to enhance understanding of neurodegenerative disorders. Recent studies have reported reduced DTI-ALPS indices in patients with AD and MCI, which have been interpreted as reflecting deficits in perivascular diffusivity patterns [43-47]. A recent meta-analysis on DTI-ALPS index in Parkinson\u0026rsquo;s disease (PD) and AD \u0026nbsp;has reported not only significant changes in the DTI-ALPS index in either \u0026nbsp;PD and AD in comparison to healthy controls, but also has reported an acceptable accuracy for the DTI-ALPS index in distinguishing patients with AD from those suffering from PD [48]. In contrast, our findings did not reflect a significant difference in the DTI-ALPS index between participants with AD spectrum and the control group. This result may be attributed to factors such as the small sample size, especially in the AD group, or heterogeneous disease stages.\u003c/p\u003e\n\u003cp\u003eWe reported a negative association between the DTI-ALPS index and the age of participants. This finding aligns with the results of previous studies, reporting a considerable correlation between age and the function of the glymphatic system [49-51]. These findings suggest an impaired function for the perivascular diffusivity patterns in older individuals. Additionally, we demonstrated a significant difference in the DTI-ALPS index between the two sexes. The evidence also extensively reports higher amounts of the DTI-ALPS index in females than in males, both in healthy participants and those with various neurodegenerative disorders [48, 52-54]. This difference between the two sexes can be due to multiple reasons. It is shown that female sexual hormones, like estrogen and progesterone, increase the expression of AQP-4 channels and thus enhance the clearance throughout the glymphatic system [55]. Moreover, females have higher resting cerebral blood flow and their better arterial pulsatility can lead to increased DTI-ALPS index compared to males [56, 57].\u003c/p\u003e\n\u003cp\u003eP-tau 181, p-tau 217, and p-tau 231 are among the most commonly assessed biomarkers in studies investigating AD [58]. While it is shown that p-tau 217 and p-tau 231 increase in the early stages of AD continuum, compared to the plasma and CSF levels of p-tau 181, recent studies have suggested a similar diagnostic reliability for these biomarkers among patients with AD spectrum [59]. Moreover, p-tau 181 is shown to be a more accurate biomarker in the observation of the stage and severity of cognitive decline among patients with AD continuum [16]. Based on previous studies, p-tau 181 CSF levels and related PET findings have become strong biomarkers for the early diagnosis of AD; however, the invasive and high-cost evaluation process restricts their availability, particularly in developing countries [60]. Thus, finding an available imaging technique associated with established AD biomarkers can promise a more cost-effective and non-invasive method in monitoring disease progression among patients with cognitive decline.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, our study revealed no considerable associations between the DTI-ALPS index and the plasma concentration of p-tau 181 among patients with AD spectrum. This finding is in line with previous studies, reporting no correlations between the DTI-ALPS index and plasma or CSF levels of p-tau 181 across different neurodegenerative diseases, such as PD and AD [61, 62]. The stronger associations observed in MCI may reflect that impairments of perivascular clearance exhibit more measurable influence during the prodromal stage of AD. In other words, widespread neurodegeneration, vascular remodeling, and structural tissue loss during advanced stages of the AD continuum may mask the association between fluid transport dysfunction and protein accumulation reported in early stages. This pattern aligns with models proposing that barrier and clearance dysfunction precede overt neuronal loss [63, 64]. Although, a recent mouse model of AD has demonstrated that impairment of AQP4 leads to increased p-tau 181 accumulation in the CSF [65]. Some studies have also revealed negative correlations between t-tau and p-tau181 CSF levels and DTI-ALPS index among patients with AD and MCI [66]. Such inconsistent findings highlight the need for more longitudinal studies with larger sample sizes to provide more sufficient evidence on the role of the glymphatic system in the clearance of tau tangles and to assess the validity of DTI-ALPS as a non-invasive imaging method in the assessment of disease progression among patients with AD continuum.\u003c/p\u003e\n\u003cp\u003eWe chose the temporal pole cortex, inferior temporal cortex, middle temporal cortex, entorhinal cortex, and parahippocampal gyrus as our ROIs in amyloid-PET SUVR measurement. The temporal lobe is among the key areas of the brain tissue involved in AD and its damage can lead to considerable cognitive impairment among patients with AD spectrum [67]. Among the temporal lobe subregions, the temporal pole cortex, inferior temporal cortex, and middle temporal cortex are constantly reported to exhibit significant synaptic loss and cortical thinning, contributing to apathy in patients with cognitive impairment [68-70]. In addition, the parahippocampal gyrus and entorhinal cortex are among the brain regions affected in early stages of AD, causing deficits in recent memory and spatial navigation [71]. The predominance of associations within temporolimbic structures may reflect the vulnerability of these regions to impaired perivascular clearance. Therefore, impaired perivascular exchange in aging may preferentially facilitate protein accumulation in these anatomically vulnerable circuits. Moreover, these findings suggest that assessing the relationship between amyloid-PET burden in these ROIs and the DTI-ALPS index may offer deeper insights into how perivascular diffusivity imapirment contributes to waste accumulation within temporolimbic circuits across the AD spectrum.\u003c/p\u003e\n\u003cp\u003eThe results of the present study demonstrated a significant association between amyloid SUVR in temporal pole cortex, inferior temporal cortex, entorhinal cortex, and parahippocampal gyrus, and the DTI-ALPS index, particularly among patients with MCI. To observe amyloid PET in patients with AD spectrum, several compounds, such as Pittsburgh compound B (11C-PiB), 18 F-flutemetamol (FMM), and 18 F-florbetapir (FBP), are used as tracers [72]. Our study utilized FBP as the PET tracer, which has been previously shown to have a longer half-life and affinity to A\u0026beta; deposits, compared to other PET tracers [73]. Recent studies have indicated a significant association between amyloid-PET and postmortem amyloid burden among patients with AD continuum, suggesting amyloid-PET as a potential imaging method in the assessment of AD pathology [74]. Amyloid PET SUVR is typically calculated by dividing the tracer uptake in targeted cortical regions by that in a reference region, such as the cerebellar cortex or whole cerebellum, to normalize for nonspecific binding and intersubject variability [75]. This quantitative measure is reported to be a reliable indicator of A\u0026beta; deposition and is widely used to assess disease burden and progression in cognitive impairment [74]. Moreover, A\u0026beta; SUVR is derived by normalizing A\u0026beta; concentrations to a reference measure, offering a standardized index of amyloid burden that complements PET-based quantification [76]. Reduced DTI-ALPS index, \u0026nbsp;reflecting altered perivascular diffusivity patterns, is associated with more aggregated amyloid deposits, as indicated by a higher amyloid SUVR, suggesting that impaired perivascular clearance can contribute to greater accumulation of amyloid deposits in both the CSF and the brain tissue [62]. In other words, amyloid burden and neurodegeneration may represent pathological processes associated with alterations in DTI-ALPS indices and cognitive decline. These findings suggest that integrating DTI-ALPS indices with amyloid PET and neurodegenerative biomarkers may help characterize disease-related alterations in early AD, which needs validation in longitudinal studies (Fig. 6).\u003c/p\u003e\n\u003cp\u003eDespite providing novel insights into the relationship between glymphatic system function, tau pathology, and amyloid burden in the AD spectrum, this study has several limitations. First, the cross-sectional design precludes causal inferences, limiting the ability to determine whether impaired glymphatic clearance precedes or follows amyloid and tau accumulation. Second, while the DTI-ALPS index provides a noninvasive proxy of the glymphatic function, it captures only perivascular water diffusivity at a single anatomical level and may not fully represent global glymphatic clearance. Additionally, plasma p-tau181 and amyloid SUVR were used as biomarkers, but longitudinal measures and simultaneous multimodal imaging could strengthen the understanding of dynamic interactions between glymphatic function and proteinopathies.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, this study demonstrates that reduced DTI-ALPS indices, reflecting impaired perivascular fluid transport within the glymphatic system, are associated with increased temporolimbic amyloid deposition among individuals with cognitive impairment. These findings support the concept that impaired perivascular fluid transport may contribute to region-specific vulnerability to amyloid accumulation during the early phases of AD, consistent with emerging models of neurovascular unit and barrier dysfunction in aging. Future studies should employ longitudinal designs with repeated DTI-ALPS and PET assessments to elucidate temporal relationships and further explore the mechanistic links between perivascular transport alterations and neurodegenerative processes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e1C-PiB: Carbon-11 Pittsburgh Compound B\u003c/p\u003e\n\u003cp\u003eA\u0026beta;: Amyloid-beta\u003c/p\u003e\n\u003cp\u003eAD: Alzheimer\u0026rsquo;s disease\u003c/p\u003e\n\u003cp\u003eADAS: Alzheimer\u0026rsquo;s Disease Assessment Scale\u003c/p\u003e\n\u003cp\u003eADNI: Alzheimer\u0026rsquo;s Disease Neuroimaging Initiative\u003c/p\u003e\n\u003cp\u003eAQP-4: Aquaporin-4\u003c/p\u003e\n\u003cp\u003eCSF: Cerebrospinal fluid\u003c/p\u003e\n\u003cp\u003eDTI: Diffusion tensor imaging\u003c/p\u003e\n\u003cp\u003eDTI-ALPS index: Diffusion Tensor Image Analysis along the Perivascular Space index\u003c/p\u003e\n\u003cp\u003eDWI: Diffusion-weighted imaging\u003c/p\u003e\n\u003cp\u003eFBP: Florbetapir\u003c/p\u003e\n\u003cp\u003eFMM: Flutemetamol\u003c/p\u003e\n\u003cp\u003eFSL: FMRIB Software Library\u003c/p\u003e\n\u003cp\u003eISF: Interstitial fluid\u003c/p\u003e\n\u003cp\u003eMCI: Mild cognitive impairment\u003c/p\u003e\n\u003cp\u003eMMSE: Mini-Mental State Examination\u003c/p\u003e\n\u003cp\u003eMRI: Magnetic resonance imaging\u003c/p\u003e\n\u003cp\u003ePD: Parkinson\u0026rsquo;s disease\u003c/p\u003e\n\u003cp\u003ePET: Positron emission tomography\u003c/p\u003e\n\u003cp\u003ep-tau: Phosphorylated tau\u003c/p\u003e\n\u003cp\u003ePVS: Perivascular space\u003c/p\u003e\n\u003cp\u003eROI: Region of interest\u003c/p\u003e\n\u003cp\u003eSCR: Superior corona radiata\u003c/p\u003e\n\u003cp\u003eSLF: Superior longitudinal fasciculus\u003c/p\u003e\n\u003cp\u003eSPECT: Single-photon emission computed tomography\u003c/p\u003e\n\u003cp\u003eSUVR: Standardized uptake value ratio\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements.\u003c/strong\u003e Data collection and sharing for this project was funded by the Alzheimer\u0026rsquo;s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer\u0026rsquo;s Association; Alzheimer\u0026rsquo;s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hofmann-La Roche Ltd. and its afliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research \u0026amp; Development, LLC.; Johnson \u0026amp; Johnson Pharmaceutical Research \u0026amp; Development LLC.; Lumosity; Lundbeck; Merck \u0026amp; Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfzer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer\u0026rsquo;s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical considerations.\u003c/strong\u003e This study used de-identified data obtained from the Alzheimer\u0026rsquo;s Disease Neuroimaging Initiative (ADNI) database. ADNI study procedures were approved by the institutional review boards of participating institutions and conducted in accordance with the Declaration of Helsinki and its later amendments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate.\u0026nbsp;\u003c/strong\u003eWritten informed consent was obtained from all participants or their legally authorized representatives within the ADNI study. The present secondary analysis of de-identified data did not require additional institutional review or consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication.\u0026nbsp;\u003c/strong\u003eThis manuscript has been approved for publication by all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of conflicting interest.\u003c/strong\u003e The authors have no conflicts of interest to disclose\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding.\u003c/strong\u003e This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions.\u0026nbsp;\u003c/strong\u003eRZ and FN Designed the study; RZ collected data and interrupted data, and analyzed the data; RZ, TT, MHF, and AK wrote the draft version of the manuscript; RZ created the graphical abstract, Figures, and Tables; MHA and FN supervised the paper. The manuscript was revised and approved by all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability.\u0026nbsp;\u003c/strong\u003eData used in this study are available from the Alzheimer\u0026rsquo;s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu) upon application and approval.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLane CA, Hardy J, Schott JM. Alzheimer\u0026apos;s disease. Eur J Neurol. 2018;25(1):59-70. doi: 10.1111/ene.13439.\u003c/li\u003e\n\u003cli\u003eTay LX, Ong SC, Tay LJ, Ng T, Parumasivam T. Economic Burden of Alzheimer\u0026apos;s Disease: A Systematic Review. Value Health Reg Issues. 2024;40:1-12. doi: 10.1016/j.vhri.2023.09.008.\u003c/li\u003e\n\u003cli\u003eRawat P, Sehar U, Bisht J, Selman A, Culberson J, Reddy PH. Phosphorylated Tau in Alzheimer\u0026apos;s Disease and Other Tauopathies. 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Alzheimers Dement. 2018;14(11):1470-81. doi: 10.1016/j.jalz.2018.01.010.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"brain-imaging-and-behavior","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bior","sideBox":"Learn more about [Brain Imaging and Behavior](https://www.springer.com/journal/11682)","snPcode":"11682","submissionUrl":"https://submission.nature.com/new-submission/11682/3","title":"Brain Imaging and Behavior","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Alzheimer's Disease, Mild Cognitive Impairment, Perivascular clearance, Amyloid, Glymphatic System, DTI-ALPS index","lastPublishedDoi":"10.21203/rs.3.rs-9382890/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9382890/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eAlzheimer's disease (AD) is the most common cause of dementia, characterized by progressive aggregation of misfolded proteins. Accumulation of amyloid-beta (Aβ) and hyperphosphorylated tau (p-tau) is the hallmark pathology of AD. Diffusion tensor imaging along the perivascular spaces (DTI-ALPS) has been proposed as an MRI marker related to perivascular diffusivity patterns and has been used in studies of neurodegenerative disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAims: \u003c/strong\u003eThis study investigates the association between the association between DTI-ALPS indices, regional amyloid-PET burden, and plasma p-tau181 across the AD spectrum.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eData from 410 individuals was enrolled from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. DTI-ALPS was used as an imaging proxy related to perivascular diffusivity patterns.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eMean DTI-ALPS declined progressively from healthy controls to AD. Moreover, females reflected higher DTI-ALPS indices compared with males. No significant associations were observed between perivascular diffusivity patterns and plasma concentration of p-tau 181 in patients with cognitive decline. In contrast, the global cortical amyloid-PET SUVR was associated with the mean [β = -0.163] and right DTI-ALPS indices in the MCI group [β = -0.202]. Moreover, we observed stronger associations between DTI-ALPS index and amyloid-PET SUVR in the temporal pole cortex [β = -0.199], entorhinal cortex [β = -0.224], and parahippocampal gyrus in patients with MCI [β = -0.281].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Reduced DTI-ALPS indices were associated with increased temporolimbic amyloid deposition, particularly in individuals with MCI. These findings suggest DTI-ALPS-derived perivascular diffusivity as an imaging marker associated with amyloid burden in prodromal AD.\u003c/p\u003e","manuscriptTitle":"DTI-ALPS Is Associated with Temporolimbic Amyloid but Not Plasma p-Tau181 Across the Alzheimer’s Disease Continuum","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-06 16:34:58","doi":"10.21203/rs.3.rs-9382890/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"292134925240204915630020808471359609744","date":"2026-05-08T19:00:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"126640542141372596848010207885427766052","date":"2026-04-27T14:34:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-27T14:00:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-27T13:55:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-11T01:55:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Brain Imaging and Behavior","date":"2026-04-10T20:16:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"brain-imaging-and-behavior","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bior","sideBox":"Learn more about [Brain Imaging and Behavior](https://www.springer.com/journal/11682)","snPcode":"11682","submissionUrl":"https://submission.nature.com/new-submission/11682/3","title":"Brain Imaging and Behavior","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c790f977-c60e-426c-9c4a-4056e3a95876","owner":[],"postedDate":"May 6th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"292134925240204915630020808471359609744","date":"2026-05-08T19:00:37+00:00","index":24,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T16:34:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-06 16:34:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9382890","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9382890","identity":"rs-9382890","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}