Comprehensive and stratified meta-analysis of diffusion tensor image analysis along the perivascular space in Alzheimer's disease: Linking glymphatic dysfunction with cognitive decline and amyloid pathology

Abstract

Background

The glymphatic system clears brain waste, including amyloid-β (Aβ), and it is shown that its dysfunction may contribute to Alzheimer's disease (AD) pathology. This dysfunction can be evaluated using the diffusion tensor image analysis along the perivascular space (DTI-ALPS) index.

Objective

This study summarizes the AD literature on the glymphatic system evaluated through neuroimaging methods.

Methods

We searched PubMed, Scopus, Embase, and Web of Science databases to find relevant neuroimaging studies.

Results

24 studies were included in this systematic review and meta-analysis. We observed a significant reduction in DTI-ALPS index among patients with AD, compared to healthy controls (standardized mean difference (SMD) of −1.044 (95% CI: −1.304, −0.784) in DTI studies with 1000 s/mm2 b-values and an SMD of −1.063 (95% CI: −1.278, −0.847) in studies with b-value of 2000 s/mm2). Moreover, our study reflected a significant correlation between the DTI-ALPS index and cognitive function assessed by Mini-Mental State Examination (95% CI: 0.37 to 0.51, z-score: 0.44), Montreal Cognitive Assessment (95% CI: 0.45 to 0.61, z-score: 0.54), and Clinical Dementia Rating (95% CI: −0.63 to −0.28, z-score: −0.47).

Conclusions

In conclusion, our systematic review and meta-analysis revealed a significant dysfunction of the glymphatic system in patients with AD, compared to healthy participants. These findings suggest the DTI-ALPS index as a linked index to cognitive performance among patients with AD and as a potential parameter in assessing the progression of AD.

Keywords

ALPS index Alzheimer's disease cognitive decline diffusion tensor image glymphatic system neuroimaging

Introduction

Alzheimer's disease (AD) is a degenerative disorder marked by atypical demeanor, decreased consciousness, reduction in functional autonomy, and cognitive impairment.¹ AD imposes a serious disease burden, with a prevalence of 682.48 per 100,000 and being responsible for 1.62 million dementia cases worldwide.² Moreover, AD is the predominant cause of dementia, with accountability for almost 75% of the cases.³

The major pathological hallmarks of AD are the external accumulation of amyloid-β (Aβ) peptides in the interstitial fluid (ISF) and the intra-neuronal development of neurofibrillary tangles, which are the results of hyperphosphorylated tau aggregation.⁴ Nowadays, several neuroimaging methods, such as magnetic resonance imaging (MRI), Positron emission tomography (PET), and functional near-infrared spectroscopy, are mostly used to observe AD pathology and evaluate disease progression.^5–7 Also, mounting manifestations demonstrate the role of a diminished brain clearance system in the neuropathophysiology of AD.^8–10

Recent studies have shown that the glymphatic system is an essential division of brain clearance, being responsible for the disposal of soluble proteins like Aβ, metabolic waste, and unneeded circulating fluids from the nervous system.^11,12 The glymphatic system is organized around the perivascular space (PVS), which includes periarterial pathways for fluid influx and perivenous pathways for fluid efflux.¹³ The circulating cerebrospinal fluid (CSF) enters the periaqueductal area via the subarachnoid cavity.¹⁴ Astrocytic end feet expressing aquaporin-4 (AQP4) facilitate the exchange of CSF with ISF within the brain parenchyma.¹⁵ Then, the traversed fluid ultimately clears the brain from the metabolic waste and outflows from the brain through the perivenous space.¹⁶ Altogether, the glymphatic system and its cooperating components accelerate the disposal of waste such as Aβ, and further investigation of this mechanism can illuminate the pathological aspects of AD.¹⁷ Thus, measuring the function of the glymphatic system can provide deeper insight into AD pathology.

Studies that investigated the functionality of PVS and the glymphatic system were severely restricted due to the fallbacks of the previous intrathecal method for tracking the tracers.¹⁸ Nonetheless, the limitations of intrathecal injection for assessing the glymphatic system can be overcome using modern non-invasive methods such as diffusion tensor imaging (DTI).¹⁹ Using this DTI-based method, the diffusion index of water along the PVS can be measured as a parameter coined as the diffusion tensor image analysis along the perivascular space (DTI-ALPS) index.¹⁹ The three-dimensional DTI-ALPS evaluates the passage of water molecules at the plane of the lateral ventricle body in its medullary veins, association fibers, and projection fibers denoting X-, Y-, and Z-axes, respectively.¹⁹ Water molecules passing the PVS through the X-axis (medullary veins) produce a greater diffusion signal compared to similar molecules passing the Y- or Z-axes (association and fibers projection).¹⁹ Then, the variance between signals can be assigned to the fluid current in the PVS and indicate the functional status of the glymphatic system.¹⁹ Some studies have revealed the diminished functionality of the glymphatic system and reduced DTI-ALPS in patients with AD, suggesting this index as a potential parameter in observing AD pathology and its progression.^19,20

Previously, Khalafi et al. 2025, in a systematic review study, reported reduced DTI-ALPS index in patients with AD, compared to healthy individuals.²¹ However, since the publication of that study, several large-scale and methodologically diverse investigations have emerged, including cohorts published between 2024 and 2025. Additionally, their study did not stratify studies based on diffusion b-values, which can potentially increase heterogeneity, nor assessed the association between glymphatic dysfunction and amyloid burden assessed by PET.²¹

Therefore, due to the potential role of the glymphatic system in AD, the present systematic review and meta-analysis aims to provide an updated and methodologically refined synthesis of the literature by (i) incorporating the most recent neuroimaging studies, (ii) stratifying analyses based on diffusion b-values (1000 versus 2000 s/mm²), (iii) performing the first pooled meta-analysis of correlations between the DTI-ALPS index and amyloid PET standardized uptake value ratios (SUVR), and (iv) integrating evidence from complementary glymphatic imaging approaches. This approach allows a more comprehensive evaluation of glymphatic dysfunction and its clinical relevance in AD.

Methods

The current systematic review and meta-analysis aimed to assess the glymphatic system in AD by imaging modalities. All steps, including search strategy, screening, data extraction, statistical analysis, and drafting were conducted according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA).²²

Eligibility criteria

The inclusion criteria for this study were as follows: (1) original research articles focused on AD, (2) assessment of the glymphatic system through imaging modalities, (3) studies evaluated the function of the glymphatic system, and (4) articles published in English. Exclusion criteria included review papers, case-reports and case-series studies, letters, oral presentations, and articles with insufficient data. Additionally, studies examining the glymphatic system in diseases other than AD or using non-imaging assessment methods were excluded.

Information sources and search strategy

The key terms related to “Alzheimer's Disease”, “Glymphatic System”, and “Neuroimaging” combined with appropriate Boolean operators (OR, AND), were comprehensively searched across electronic databases, including PubMed, Scopus, EMBASE, and Web of Science in May 2025. Also, a manual search of references from relevant systematic reviews was conducted to ensure no relevant studies were overlooked. All search strategies in each database are provided in the Supplemental Material.

Selection process

The articles were imported into EndNote version 20 for management. Two independent reviewers (R.Z. and A.H.K.) initially screened the titles and abstracts to determine their relevance based on the inclusion criteria. The full texts of selected articles were then thoroughly reviewed by the same reviewers. Any disagreements were resolved through discussion or by involving a third reviewer (F.N.) to ensure accuracy. Only studies that fully met the inclusion criteria were included in the final analysis.

Data collection process and data items

Data extraction for each selected study was performed independently by three reviewers (R.Z., A.H.K., T.T.), with any disagreements resolved by a senior reviewer (F.N.). A standardized extraction form, developed by F.N. and R.Z., was utilized to systematically collect the following information: general details (title, author and publication year, country, study design, and sample size), patient demographics (total number of participants, number of male and female participants, mean age, and education), imaging data (imaging modality, DTI b-values of 1000/2000 s/mm2, and DTI-ALPS index), along with cognitive and clinical indices including Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Alzheimer's Disease Assessment Scale (ADAS), and Clinical Dementia Rating (CDR).

Study risk of bias assessment

Three independent reviewers (R.Z., A.H.K., T.T.) evaluated the risk of bias for each study using the Newcastle-Ottawa Quality Assessment Scale (NOS) for case-control, cross-sectional studies with case-control design, and cohort studies.²³ The NOS for case-control studies examined biases across three categories: selection, comparability, and exposure. Similarly, the NOS for cohort studies assessed biases within three categories: selection, comparability, and outcome. Each question could be awarded a maximum of one star, except for the comparability domain, which could receive up to two stars.

Statistical analysis

All statistical analyses were conducted using Python. For the meta-analysis, we calculated pooled effect estimates with corresponding 95% confidence intervals (CIs). In addition, to address methodological variability across diffusion tensor imaging protocols, we stratified studies based on the diffusion b-value used for DTI-ALPS index calculation (1000 versus 2000 s/mm²). The diffusion b-value can potentially affect the DTI-ALPS index as it influences sensitivity to water diffusivity and microstructural properties of perivascular spaces. Thus, analyses were separated by b-value to reduce heterogeneity and to improve the interpretability and comparability of pooled effect sizes. Separate pooled estimates were calculated for each b-value subgroup using fixed-effects or random-effects models based on the degree of between-study heterogeneity. Heterogeneity among studies was assessed using the I² statistic and Cochran's Q test. An I² value greater than 50% indicated substantial heterogeneity, and a random-effects model was applied accordingly; otherwise, a fixed-effects model was used.

Results

Study selection

Our initial search was conducted on four major databases, resulting in an overall inclusion of 2356 studies (485 records by PubMed, 824 records by Scopus, 530 records by Embase, and 517 records by Web of Science). After removing duplicate studies (n = 626), the remaining 1730 studies were screened based on the title and abstract, which resulted in the exclusion of 1607 studies. The remaining records were considered for the full-text assessment, resulting in the exclusion of 98 studies due to being a conference paper (n = 19),^24–42 not being a human study (n = 2),⁴³ not being about AD (n = 33),^44–76 not being about glymphatic system (n = 36),^77–112 not being a neuroimaging study (n = 2),^113,114 not being an original study (n = 7),^115–121 Finally, 24 articles were included in our systematic review and meta-analysis. More details on the inclusion and exclusion process were provided in the PRISMA flow diagram in Figure 1.

Figure 1.

PRISMA flowchart.

Study characteristics

Table 1 provides information about the extracted characteristics of the included studies. Among the included studies, 18 were cross-sectional studies with a case-control design,^19,122–138 four of them were longitudinal cohorts,^139–142 and the remaining two studies were retrospective cohorts.^143,144 Three of the included studies consisted of several cohorts, which are reported separately.^124,126,140 The number of female participants was greater than that of males in all included studies except for four with more males.^{122,126,134,140} Also, the mean age of participants was more than 60 years old across all studies incorporated in this review. Furthermore, all studies consisted of both healthy participants and those with cognitive impairment, except for one study with only patients with AD.¹⁴² One of the most common questionnaires used to assess cognitive performance is the MMSE, which was used in all studies to evaluate cognition in both healthy controls and patients with AD, except for five studies which did not use MMSE,^{19,131,138,141,142} and one study which measured MMSE score in only AD groups.¹⁴⁴ Other applied cognitive tests included the CDR, the MoCA, and the ADAS assessment tools. Moreover, all eligible studies utilized the DTI imaging method along with other imaging modalities such as PET, T1w MRI, and FLAIR, except for two studies that used DTI solely^125,142 and one with only PET/MRI imaging.¹³⁰ In addition, all of the included DTI studies utilized a DTI b-value of 1000 s/mm2 except for two studies with only a b-value of 2000 s/mm2,^136,139 and two studies with b-values of both 1000 and 2000 s/mm2,^126,137 along with two other studies giving no information on DTI b-value.^138,141 Additionally, every study that met the inclusion criteria measured the DTI-ALPS index and compared it between healthy participants and patients with cognitive impairment, except for four studies.^{19,124,138,142} Some included studies were removed from our meta-analysis to reduce the heterogeneity.^{124,129,142,144} Also, some studies employed other imaging methods to assess the function of the glymphatic system, including the CSF water fraction (CSFF) and resting-state functional magnetic resonance imaging (rsfMRI).^130,138

Table 1.

Characteristics of the included studies.

Author, year	Country	Study type	Imaging method	DTI b-value (s/mm²)	ROIs of DTI-ALPS (Number, diameter)	Sample size (AD/CN)	Number of females (AD/CN)	Mean age (years)	MMSE score	DTI-ALPS index
Kamagata et al., 2022¹²²	Japan	Cross-sectional (with case-control design)	DTI, T1w MRI, FLAIR	1000	Association fibers & Projection fibers at the level of lateral ventricle body (5 mm)	111 (36/31)	49 (14/17)	1. AD: 74.28 ± 8.76 2. CN: 73.86 ± 4.91	3. AD: 23.39 ± 1.96 4. CN: 28.97 ± 1.32	5. AD: 1.24 ± 0.21 6. CN: 1.34 ± 0.11
Kim et al., 2024¹²³	South Korea	Cross-sectional (with case-control design)	DTI, SWI, T1w MRI, T2w, FLAIR	1000	Projection neural fibers & association neural fibers & subcortical neural fibers (N = 9, 5 mm)	80 (65/15)	55 (48/7)	7. AD: 78.3 ± 8.3 8. CN: 70.7 ± 6.2	9. AD: 18.1 ± 5.8 10. CN: 25.5 ± 3.6	11. AD: 1.48 ± 0.04 12. CN: 1.78 ± 0.08
Li et al., 2024 (SILCODE cohort)¹²⁴	China	Cross-sectional (with case-control design)	DTI, PET	1000	SLF & SCR at the level of lateral ventricle (N = 4, 5 mm)	654 (92/215)	377 (53/130)	13. AD: 68.87 ± 9.67 14. CN: 67.03 ± 6.18	15. AD: 17.83 ± 5.58 16. CN: 24.42 ± 3.75	-
Li et al., 2024 (ADNI cohort)¹²⁴	China	Cross-sectional (with case-control design)	DTI, PET	1000	SLF & SCR at the level of lateral ventricle (N = 4, 5 mm)	650 (104/197)	329 (36/119)	17. AD: 74.86 ± 8.11 18. CN: 72.20 ± 8.86	19. AD: 22.90 ± 2.68 20. CN: 28.98 ± 1.25	-
Liang et al., 2023¹²⁵	China	Cross-sectional (with case-control design)	DTI	1000	SLF & Projection fibers in the left hemisphere (N = 4	105 (38/28)	66 (28/12)	21. AD: 72.05 ± 6.95 22. CN: 67.75 ± 6.09	23. AD: 17.71 ± 5.34 24. CN: 28.61 ± 0.83	25. AD: 1.15 ± 0.07 26. CN: 1.44 ± 0.07
Schirge et al., 2025 (ADNI cohort)¹²⁶	Germany	Cross-sectional (with case-control design)	DTI, FLAIR	1000	Association fibers & Projection fibers at the level of lateral ventricle body and centrum semiovale (N = 4, 5 mm)	92 (43/49)	37 (24/13)	27. AD: 73.7 ± 7.6 28. CN: 69.8 ± 6.8	29. AD: 25.2 ± 4.3 30. CN: 29.2 ± 0.9	31. AD: 1.08 ± 0.24 32. CN: 1.19 ± 0.13
Schirge et al., 2025 (DELCODE cohort)¹²⁶	Germany	Cross-sectional (with case-control design)	DTI, FLAIR	1000	Association fibers & Projection fibers at the level of lateral ventricle body and centrum semiovale (N = 4, 5 mm)	121 (54/67)	60 (27/33)	33. AD: 73.4 ± 6.5 34. CN: 69.4 ± 6.1	35. AD: 26 ± 3.1 36. CN: 29.4 ± 0.9	37. AD: 1.26 ± 0.18 38. CN: 1.34 ± 0.20
Schirge et al., 2025 (ActiGliA cohort)¹²⁶	Germany	Cross-sectional (with case-control design)	DTI, T1w MRI	2000	Association fibers & Projection fibers at the level of lateral ventricle body and centrum semiovale (N = 4, 5 mm)	34 (16/18)	15 (6/9)	39. AD: 70.75 ± 7.47 40. CN: 70.94 ± 7.08	41. AD: 24.13 ± 2.99 42. CN: 29.56 ± 0.71	43. AD: 1.22 ± 0.10 44. CN: 1.36 ± .014
Matsushita et al., 2024¹⁴³	Japan	Retrospective cohort	DTI, T1w MRI, PET	1000	Association fibers & Projection fibers at the level of lateral ventricle body (N = 4, 5 mm)	58 (29/29)	30 (15/15)	45. AD: 78.3 ± 6.8 46. CN: 78.3 ± 6.8	47. AD: 24.1 ± 4.4 48. CN: 29.2 ± 1.0	49. AD: 1.3 ± 0.1 50. CN: 1.4 ± 0.1
Nakaya et al., 2025¹²⁷	Japan	Cross-sectional (with case-control design)	DTI, T1w MRI	1000	Association fibers & Projection fibers at the level of lateral ventricle body (N = 2, 5 mm)	106 (26/31)	57 (10/19)	51. AD: 73.2 ± 9.23 52. CN: 72.63 ± 5.69	53. AD: 23.5 ± 1.86 54. CN: 28.83 ± 1.58	55. AD: 1.20 ± 0.15 56. CN: 1.52 ± 0.22
Okazawa et al., 2024¹³⁹	Japan	Prospective cohort	DTI, T1 W MRI, PET	1000, 2000	Upper paraventricular region at the level of the lateral ventricles (N = 4)	83 (56/27)	44 (28/16)	57. AD: 69.9 ± 10.6 58. CN: 69.3 ± 9.6	59. AD: 23.5 ± 3.8 60. CN: 27.8 ± 3.9	61. 61. b-value=1000: 62. 62. AD: 1.29 ± 0.12 63. 63. CN: 1.44 ± 0.09 64. 64. b-value=2000 65. 65. AD: 1.28 ± 0.13 66. 66. CN: 1.38 ± 0.12
Ota et al., 2022¹²⁸	Japan	Cross-sectional (with case-control design)	DTI, T1w MRI, PET	1000	Association fibers & Projection fibers at the level of lateral ventricle body (N = 4, 5 mm)	57 (21/36)	35 (14/21)	67. AD: 71.3 ± 9.1 68. CN: 64.6 ± 7.6	69. AD: 20.8 ± 3.0 70. CN: 29.2 ± 1.1	71. AD: 1.19 ± 0.14 72. CN: 1.39 ± 0.16
Chang et al., 2023¹²⁹	Taiwan	Cross-sectional (with case-control design)	DTI, T1w MRI	1000	SLF & SCR at the level of lateral ventricle (5 mm)	267 (130/137)	155 (78/77)	73. AD: 60.15 ± 3.67 74. CN: 58.52 ± 5.71	75. AD: 19.55 ± 7.77 76. CN: 28.01 ± 1.80	77. AD: 1.03 ± 0.05 78. CN: 1.00 ± 0.03
Guo et al., 2025 (ADNI cohort)¹⁴⁰	China	Prospective cohort	DTI, T1w MRI	1000	Atlas-based ROIs (ICBM-DTI-81)	180 (35/35)	77 (12/18)	79. AD: 75 80. CN: 73	81. AD: 24 82. CN: 29	83. AD: 1.13 ± 0.15 84. CN: 1.35 ± 0.11
Guo et al., 2025 (CTPCS-HEAD cohort)¹⁴⁰	China	Prospective cohort	DTI, T1w MRI	1000	Atlas-based ROIs (ICBM-DTI-81)	127 (19/25)	77 (11/13)	85. AD: 74 86. CN: 68	87. AD: 19 88. CN: 29	89. AD: 1.15 ± 0.07 90. CN: 1.30 ± 0.07
Han et al., 2020¹³⁰	USA	Cross-sectional (with case-control design)	fMRI, PET	-	-	118 (7/31)	60 (5/16)	91. AD: 79.53 ± 4.55 92. CN: 74.46 ± 5.78	93. AD: 22.00 ± 2.38 94. CN: 28.77 ± 1.26	-
Hong et al., 2025¹³¹	China	Cross-sectional (with case-control design)	DTI, T1w MRI, FLAIR, PET	1000	Bilateral periventricular projection & association ROIs (N = 5, 6 mm)	133 (19/88)	83 (10/54)	95. AD: 75.7 ± 9.6 96. CN: 75.2 ± 8.4	-	97. AD: 1.154 ± 0.184 98. CN: 1.355 ± 0.155
Hsu et al., 2022¹³²	Taiwan	Cross-sectional (with case-control design)	DTI, T1w MRI, PET	1000	Atlas-based ROIs (ICBM-DTI-81)	50 (37/13)	36 (27/9)	99. AD: 63.2 ± 4.7 100. CN: 61.0 ± 7.1	101. AD: 17.7 ± 6.7 102. CN: 28.0 ± 1.2	103. AD: 1.4 ± 0.2 104. CN: 1.5 ± 0.1
Huang et al., 2024¹⁴¹	China	Prospective cohort	DTI, T1w MRI, FLAIR, PET	-	SCR, PCR & SLF (N = 4, 4 mm)	419 (47/235)	229 (25/150)	105. AD: 68.0 ± 5.36 106. CN: 68.7 ± 3.85	-	107. AD: 1.17 ± 0.14 108. CN: 1.28 ± 0.19
Jiao et al., 2025¹³³	China	Cross-sectional (with case-control design)	DTI, T1w MRI, FLAIR, PET	1000	SCR & SLF (N = 4, 5 mm)	53 (18/19)	35 (14/19)	109. AD: 67.3 ± 8.44 110. CN: 59.4 ± 7.21	111. AD: 17.2 ± 6.61 112. CN: 28.4 ± 1.59	113. AD: 1.107 ± 0.123 114. CN: 1.286 ± 0.15
Jungwon et al., 2024¹⁴²	South Korea	Prospective cohort	DTI	1000	Association fibers & Projection fibers at the level of lateral ventricle body (N = 4, 3.6 mm)	16 (16/0)	9 (9/0)	115. AD: 73.50 ± 6.57 116. CN: -	-	-
Shang et al., 2024¹³⁴	China	Cross-sectional (with case-control design)	DTI, T1w MRI, T2w, FLAIR	1000, 2000	SCR & SLF (N = 4, 3 mm)	104 (79/25)	42 (30/12)	117. AD: 69.27 ± 6.23 118. CN: 67.36 ± 7.13	119. AD: 18.62 ± 3.04 120. CN: 29.24 ± 0.97	121. AD: 1.315 ± 0.076 122. CN: 1.586 ± 0.048
Taoka et al., 2017¹⁹	Japan	Cross-sectional (with case-control design)	DTI, SWI	1000, 2000	Projection, association, & subcortical fiber areas at the level of the lateral ventricle body (N = 6, 5 mm)	31 (16/0)	17 (NA/NA)	-	-	-
You et al., 2025¹⁴⁴	South Korea	Retrospective cohort	DTI, T1w MRI, T2w MRI, FLAIR	1000	Projection, association, & subcortical fiber areas at the level of the lateral ventricle body (N = 6, 5 mm)	359 (100/0)	220 (60/0)	123. AD: 77.73 ± 7.33 124. CN: -	125. AD: 21.00 ± 5.19 126. CN: -	127. AD: 0.98 ± 0.12 128. CN: -
Zhang et al., 2024¹³⁵	China	Cross-sectional (with case-control design)	DTI, T1w MRI, T2w, FLAIR	1000	Association fibers & Projection fibers at the level of lateral ventricle body (N = 4, 6 mm)	56 (15/26)	29 (6/15)	129. AD: 66.9 ± 9.5 130. CN: 63.4 ± 7.4	131. AD: 9.5 ± 7.6 132. CN: 26.2 ± 1.9	133. AD: 1.42 ± 0.16 134. CN: 1.57 ± 0.16
Zhong et al., 2023a¹³⁶	China	Cross-sectional (with case-control design)	DTI, MRI	2000	Association fibers & Projection fibers at the level of lateral ventricle body (N = 4, 5 mm)	300 (69/111)	163 (38/57)	135. AD: 74.0 ± 8.5 136. CN: 68.6 ± 7.5	137. AD: 14.9 ± 5.8 138. CN: 27.0 ± 2.2	139. AD: 2.10 ± 0.17 140. CN: 2.30 ± 0.20
Zhong et al., 2023b¹³⁷	China	Cross-sectional (with case-control design)	DTI, MRI	1000, 2000	Association fibers & Projection fibers at the level of lateral ventricle body (N = 4, 5 mm)	159 (36/65)	89 (19/35)	141. AD: 74.8 ± 8.0 142. CN: 67.9 ± 6.5	143. AD: 14.9 ± 5.5 144. CN: 27.0 ± 2.1	145. AD: 2.03 ± 0.16 146. CN: 2.25 ± 0.17
Zhou et al., 2023¹³⁸	USA	Cross-sectional (with case-control design)	DTI, T1w MRI, T2w, FLAIR, PET	-	-	21 (7/14)	15 (5/10)	147. AD: 70.29 ± 11.18 148. CN: 64.79 ± 13.62	-	-

DTI: Diffusion tensor imaging; SWI: Susceptibility-weighted imaging; MRI: Magnetic resonance imaging; T1w MRI: T1-weighted MRI; T2w MRI: T2-weighted MRI; FLAIR: Fluid-attenuated inversion recovery; PET: Positron emission tomography; MMSE: Mini-Mental State Examination; AD: Alzheimer's disease; CN: Healthy controls; ROI: Region of interest; SLF: Superior longitudinal fasciculus; SCR: Superior corona radiata; PCR: Posterior corona radiata.

Risk of bias assessment in studies

We assess the quality of selected studies by the NOS for cross-sectional studies with case-control designs and cohort studies. Detailed scores of cross-sectional studies with case-control designs are represented in Supplemental Table 1. Approximately two-thirds of studies demonstrated adequate case definitions and representativeness in the selection bias category. Among 18 studies, two achieved fully satisfactory marks in the selection bias subscales.^127,129 Regarding the comparability domain, more than half of the studies scored for age as potential confounders. Notably, Ota et al. achieved the maximum scores for this domain.¹²⁸ In terms of exposure ascertainment, all studies demonstrated reliable methods, with several reporting non-response rates to support validity. The overall quality scores ranged from 1 to 8 points, with higher scores indicating more rigorous quality.

Moreover, detailed scores of cohort studies are shown in Supplemental Table 1. Most studies demonstrated adequate representativeness of the exposed cohort, appropriate selection of the non-exposed cohort, and ascertainment of exposure, with full marks scoring for Huang et al. in the selection bias category.¹⁴¹ In the comparability category only one study was controlled for age confounding.¹³⁹ Moreover, all studies satisfactorily assessed outcomes, although none reported on the adequacy of follow-up time.

The DTI-ALPS index is significantly reduced in patients with AD

Overall, 18 DTI studies were included in our meta-analysis comparing the ALPS index among patients with AD and healthy controls. To reduce heterogeneity among the included studies, we divided them based on the amount of the applied DTI b-value. Among these, 14 studies used a b-value of 1000 s/mm², the results of which reported a standardized mean difference (SMD) of −1.044 (95% CI: −1.304, −0.784) for the function of the glymphatic system. These studies reflected a substantial heterogeneity (χ² = 46.90, df = 13, I² = 72.30%, p < 0.001) (Figure 2A). To assess for the substantial heterogeneity, we conducted a sensitivity analysis, which revealed that removal of any single study did not materially change the pooled effect size or its statistical significance. Considering studies with a b-value of 2000 s/mm², four studies were included in our subgroup meta-analysis reporting an SMD of −1.063 (95% CI: −1.278, −0.847). In addition, no significant heterogeneity was observed among studies using a b-value of 2000 s/mm² (χ² = 2.61, df = 3, I² = 0.00%, p = 0.456) (Figure 2B). The results of the meta-analysis including all studies with higher heterogeneity are provided in Supplemental Figure 1.

Figure 2.

Standardized mean difference (SMD) of the DTI-ALPS index between patients with AD and healthy controls. A) DTI studies using a b-value of 1000 s/mm², B) DTI studies using a b-value of 2000 s/mm². DTI-ALPS index: Diffusion Tensor Image Analysis along the Perivascular Space, AD: Alzheimer's disease, Schirge et al. 2025 (A): ADNI cohort, Schirge et al. 2025 (B): DELCODE cohort, Schirge et al. 2025 (C): ActiGliA cohort, Guo et al. 2025 (A): ADNI cohort, Guo et al. 2025 (B): CTPCS-HEAD cohort, Zhong (A): Zhong et al., 2023a, Zhong (B): Zhong et al., 2023b.

The DTI-ALPS index is significantly correlated with cognitive performance

We conducted several meta-analyses to evaluate the correlation between ALPS index and cognitive function in various cognitive tests including MMSE, MoCA, CDR, ADAS-11, and ADAS-13. Among studies using MMSE, 10 studies utilized a b-value of 1000 s/mm², revealing a pooled r of 0.44 (95% CI: 0.37, 0.51). These studies showed a moderate heterogeneity (χ² = 13.24, df = 9, I² = 32%, p = 0.15) (Figure 3A). While MMSE studies using a b-value of 2000 s/mm² reflected an overall r of 0.24 (95% CI: 0.16, 0.32) with no significant heterogeneity reported among them (χ² = 2.04, df = 2, I² = 2%, p = 0.36) (Figure 3B).

Figure 3.

Correlation of DTI-ALPS index with MMSE scores. A) DTI studies using a b-value of 1000 s/mm², B) DTI studies using a b-value of 2000 s/mm². DTI-ALPS index: Diffusion Tensor Image Analysis along the Perivascular Space, MMSE: Mini-Mental State Examination, Zhong (A): Zhong et al., 2023a, Zhong (B): Zhong et al., 2023b.

In addition, among MoCA studies, four of them used a b-value of 1000 s/mm², and three were based on a b-value of 2000 s/mm². MoCA studies with a b-value of 1000 s/mm² reported a significant correlation between the MoCA score and ALPS index (95% CI: 0.45, 0.61, z-score: 0.54). No heterogeneity was reported among these studies (χ² = 0.74, df = 3, I² = 0%, p = 0.86) (Figure 4A). On the other hand, MoCA studies with a b-value of 2000 also reflected a significant correlation (95% CI: 0.16, 0.32), reflecting no significant heterogeneity (χ² = 2.04, df = 2, I² = 2%, p = 0.36) (Figure 4B).

Figure 4.

Correlation of DTI-ALPS index with MoCA scores. A) DTI studies using a b-value of 1000 s/mm², B) DTI studies using a b-value of 2000 s/mm². DTI-ALPS index: Diffusion Tensor Image Analysis along the Perivascular Space, MoCA: Montreal Cognitive Assessment, Zhong (A): Zhong et al., 2023a, Zhong (B): Zhong et al., 2023b.

Moreover, four studies evaluated the correlation between the DTI-ALPS index and CDR score. These studies reflected a significant negative correlation between the performance of participants in CDR and the DTI-APLS index (95% CI: −0.63; −0.28, z-score: −0.47). The heterogeneity among these studies was substantial (χ² = 16.99, df = 3, I² = 82%, p < 0.001) (Figure 5). Due to substantial heterogeneity, we conducted a sensitivity analysis. The results indicated that the observed association was robust and not driven by any single study.

Figure 5.

Correlation of DTI-ALPS index with CDR scores. DTI-ALPS index: Diffusion Tensor Image Analysis along the Perivascular Space, CDR: Clinical Dementia Rating.

Finally, we pooled the results of two studies to assess the correlation of the DTI-ALPS index with ADAS-11 and ADAS-13 scores among participants. Regarding the performance of participants in ADAS-11, our meta-analysis reported a significant correlation with the DTI-ALPS index (95% CI: −0.66; −0.30, z-score: −0.50) with a substantial heterogeneity observed among these two studies (χ² = 2.64, df = 1, I² = 62%, p = 0.01) (Figure 6A). On the other hand, our meta-analysis of these studies to observe the correlation between the DTI-ALPS index and ADAS-13 reflected a significant correlation with a z-score of −0.48 (95% CI: −0.70; −0.18). Also, substantial heterogeneity was reported for these studies (χ² = 5.55, df = 1, I² = 82%, p = 0.02) (Figure 6B). Given the small number of available studies, heterogeneity estimates should be interpreted with caution.

Figure 6.

Correlation of DTI-ALPS index with ADAS scores. A) Correlation of DTI-ALPS index with ADAS-11, B) Correlation of DTI-ALPS index with ADAS-13. DTI-ALPS index: Diffusion Tensor Image Analysis along the Perivascular Space, ADAS: Alzheimer's Disease Assessment Scale.

The ALPS index is significantly correlated to the standardized uptake value ratio (SUVR)

A total of four studies were included in our subgroup meta-analysis to observe the correlation of SUVR, which assess Aβ burden, with the DTI-ALPS index. Among these studies, only Okazawa et al. 2024 reported SUVR using Centiloid scales.¹³⁹ Two of these studies used florbetapir to measure SUVR. One of them assessed amyloid burden within 18 predefined cortical regions of interest (ROIs), including bilateral frontal, parietal, temporal, occipital lobes, anterior and posterior cingulate cortex, precuneus, parahippocampus, and sensory-motor cortex.¹³² The other study measured amyloid burden in the whole cerebral cortex.¹⁴³ Both studies referenced tau burden to the cerebellum cortex. Overall, these studies reflected a significant positive correlation with the DTI-ALPS index (95% CI: 0.12; 0.52, z-score: 0.34) and no heterogeneity (χ² = 0.03, df = 1, I² = 0%, p = 0.85) (Figure 7A). Two remaining studies used Pittsburg compound B (PiB) PET imaging to assess SUVR, both assessing amyloid burden in the whole brain and referencing it to the whole cerebellum cortex.^128,139 These studies, however, reported a negative correlation between SUVR and the DTI-ALPS index (95% CI: −0.71; −0.50, z-score: −0.62). Additionally, there was no significant heterogeneity among these studies (χ² = 0.65, df = 1, I² = 0%, p = 0.42) (Figure 7B). The funnel plots of risk of bias assessment for all conducted meta-analyses are provided in Supplemental Figures 2–4.

Figure 7.

Correlation of DTI-ALPS index with SUVR. A) Studies using florbetapir to assess SUVR, B) Studies using ¹¹C-PiB to assess SUVR. DTI-ALPS index: Diffusion Tensor Image Analysis along the Perivascular Space, SUVR: Standardized Uptake Value Ratio, PiB: Pittsburgh Compound B.

Discussion

Previous studies suggested different imaging methods, such as MRI, FDG-PET, and DTI, to assess brain alterations in connection to cognitive decline among patients with AD. However, recent studies suggesting dysfunction of the glymphatic system in patients with AD as a primary pathology in AD disorder introduced the DTI-ALPS index to evaluate the progression of AD and facilitate its diagnosis. The findings of our results consistently reflected a significant difference in DTI-ALPS between patients with AD and healthy participants. Besides that, our meta-analysis also reported a significant correlation between this DTI index and the performance of participants in cognitive evaluations, including MMSE, MoCA, CDR, ADAS-11, and ADAS-13 and other imaging findings such as Aβ burden assessed by SUVR values.

It is shown that the abnormal accumulation of Aβ plaques, along with tau deposits causing neurodegenerative changes, is the most pivotal underlying pathology for AD.¹⁴⁵ Recent studies suggested that impairments of the lymphatic and glymphatic systems can play a crucial role in the neuroinflammation process across the AD spectrum.^146,147 The glymphatic system is a particular kind of PVS network in the brain tissue that, along with the lymphatic system, increases the clearance of waste products, including Aβ and tau, in the brain.¹⁴⁸ This system helps the interchange of CSF and ISF by the influx of CSF into the brain parenchyma and removing ISF from the perivascular space.¹³ It is reported that the glymphatic system is involved in the pathogenesis of several neurologic disorders, such as stroke, Parkinson's disease, and traumatic brain injury.^149–151 One of the most vital parts of the glymphatic system is AQP4, which is mostly found in the foot processes of astrocytes.¹⁵² Some studies suggested diffusion and convection as two main kinds of transportation for molecules in the brain tissue, which are significantly dependent on the function of AQP4.¹³ In addition, It has been demonstrated that insufficient function of AQP4 can lead to impairment of the glymphatic system, reducing interstitial clearance in the brain tissue by more than 50%.¹³ Recent studies suggested that AQP4 dislocation in PVS may accelerate the aggregation of toxic depositions in the brain tissue of patients with neurodegenerative disorders.¹⁵³ Chandra et al. demonstrated that AQP4 has an essential role in the Aβ accumulation as well as cognitive decline in patients with AD, suggesting a significant association between AQP4 and the function of the glymphatic system.¹⁵⁴ The results of our study also indicated a significant impairment in the glymphatic system among patients with AD. Thus, assessing the function of the glymphatic system may provide more information on the pathophysiology of AD.

Using the DTI-ALPS index to assess the function of the glymphatic system has its own advantages and disadvantages. First, it is widely accessible, requires no contrast agents or radioactive tracers, and can be derived from conventional diffusion MRI data.^19,155 Moreover, it draws regionally specific information on perivascular diffusivity that may be sensitive to microstructural changes associated with aging and neurodegeneration.^155,156 However, it is noteworthy that the DTI-ALPS index cannot measure the function of the entire glymphatic system.¹⁵⁶ Instead, it is an indirect diffusion-based proxy derived from water diffusivity along perivascular spaces in periventricular white matter, reflecting a specific anatomical component of perivascular fluid transport.^156,157 Additionally, its specificity to periventricular white matter limits generalization of measured function to whole brain regions, and the index may be influenced by factors unrelated to glymphatic function, including white-matter integrity, vascular changes, and diffusion acquisition parameters.¹⁹ Consequently, DTI-ALPS should be interpreted cautiously and in conjunction with other imaging markers when inferring glymphatic-related dysfunction.

Recently, reduced DTI-ALPS index, an indirect indicator of impaired function of the glymphatic system, has been extensively observed in patients with AD.¹⁵⁸ The same studies reported a significant correlation between the DTI-ALPS index and the performance of participants in functional tests such as the MMSE and the Unified Parkinson's Disease Rating Scale (UPDRS) score among patients with neurodegenerative diseases.^20,159,160 Also, the included studies in our review reported reduced levels of the DTI-ALPS index in patients with AD continuum compared to healthy controls.^{124,129,142,144} In addition, these studies reflected a significant correlation between the DTI-ALPS index and cognitive function of patients with early AD, as assessed by the MMSE and SCR tools.^{124,129,142,144} The results of our meta-analysis demonstrated that the DTI-ALPS index is significantly reduced in patients with AD, indicating impaired activity of the glymphatic system in these patients. Also, our study revealed a significant correlation between the DTI-ALPS index and cognitive function in patients with AD across various cognitive tests, including the MMSE, MoCA, CDR, ADAS-11, and ADAS-13.

We reported substantial heterogeneity among studies evaluating the correlation between glymphatic function and ADAS-11, ADAS-13, and CDR cognitive tests, compared to those using MMSE and MoCA. This difference can originate from the distinctive nature and clinical application of these cognitive measures. CDR and ADAS are multidimensional instruments that integrate functional status, clinician judgment, and multiple cognitive domains.^161,162 These features enhance the sensitivity of CDR and ADAS tests to disease staging and clinical severity; however, this can in turn make them vulnerable to inter-rater variability and differences in assessment protocols across studies.^162–166 Notably, it is shown that, compared to MMSE and MoCA, CDR and ADAS tend to exhibit nonlinear relationships with disease progression, particularly in AD.^166–169 Thus, different factors, including disease severity distribution, diagnostic criteria, and sample composition across studies, can influence associations involving CDR or ADAS. However, after conducting a sensitivity analysis, we found a stable direction and significancy of reported associations among studies using CDR and ADAS tests.

In addition, amyloid PET is another imaging method to assess Aβ burden, mostly measured by SUVR values.^170,171 This index can be evaluated by using different tracers, such as 18F-AV-45 (florbetapir), PiB, and fluorodeoxyglucose (FDG), reflecting the function of perivascular spaces.^172–174 It is shown that PET tracers based on ¹⁸F have a longer half-life compared to those based on ¹¹C such as PiB increasing their applicability.¹⁷⁵ To calculate SUVR, the mean activity concentration of different brain areas such as temporal, parietal, frontal, and precuneus, along with the posterior and anterior cingulate is measured and compared with a reference region which is often the cerebellum.^176,177 Recent PET studies have reported a significant correlation between SUVR values and the performance of patients with AD in MMSE and ADAS assessments, as well as plasma levels of p-tau 181 in these patients.^178,179 Moreover, Park et al. reported that patients with cognitive decline exhibit higher amounts of SUVR compared to healthy participants.⁷³ The results of this study also reported a negative correlation between SUVR in the paracentral cortex and DTI-ALPS index in older adults.⁷³ The results of our meta-analysis also suggested a significant correlation between SUVR and the DTI-ALPS index. These findings suggest SUVR, along with DTI-ALPS, as two potential values in assessing the function of the glymphatic system and perivascular spaces to evaluate the progression of AD.

However, an intriguing finding of the present meta-analysis was that the direction of the association between the DTI-ALPS index and amyloid burden differed according to PET tracer type. Studies using florbetapir reported a positive association, while those using PiB reflected a negative association. This discrepancy can be explained by differences in reference regions, signal dynamic range, and scaling methods, as well as disease severity and tracer binding properties.^{173,180–183} For instance, it is shown that PiB mostly binds fibrillar amyloid plaques and is highly sensitive to early amyloid deposition,¹⁷³ whereas florbetapir exhibits more tendency to both fibrillar and diffuse amyloid forms and is more commonly applied in clinically symptomatic populations.¹⁸⁰ Thus, measures of glymphatic function may relate differently to early versus established amyloid pathology, potentially resulting in opposite correlation directions across tracers.

Although the present meta-analysis mainly focused on studies employing the DTI-ALPS approach, some related investigations using alternative neuroimaging techniques have also reported glymphatic dysfunction in AD. Zhou et al., in their study, attempt to evaluate the function of the glymphatic system using a non-invasive imaging method called the CSFF. The results of this study also suggested impaired function of the glymphatic system in patients with AD along with a significant correlation between CSFF and Aβ depositions.¹³⁸ Another study used the combination of the global blood oxygen level-dependent signal and CSF signal, which are measured by rsfMRI, reporting the dysfunction in the glymphatic system across patients with cognitive decline.¹³⁰

A similar systematic review on the alterations of the glymphatic system in AD was conducted by Khalafi et al.²¹ Their study also suggested a significant reduction in the DTI-ALPS index among patients with AD, as well as a moderate correlation with cognitive scores. However, several methodological aspects differentiate our study from theirs. First, our analysis comprised a larger number of studies (24 versus 19) and included the most recent datasets published in 2024 to 2025, providing a more current and comprehensive synthesis of the available evidence. In addition, we categorized our meta-analyses by diffusion b-value (1000 versus 2000 s/mm²), reducing heterogeneity and resulting in more accurate effect size estimates, a methodological refinement not implemented in the aforementioned study. Also, beyond cognitive outcomes, we quantitatively examined correlations between the DTI-ALPS index and amyloid-PET SUVR values, presenting the first pooled evidence linking glymphatic dysfunction to amyloid accumulation. Finally, we expanded the discussion to encompass alternative neuroimaging indicators of glymphatic function, such as CSFF and resting-state fMRI coupling metrics, thus offering a wider interpretive framework beyond diffusion-based measures.

Our study includes some limitations which are worth mentioning. First of all, the included studies utilized different numbers and sizes of ROIs to measure the DTI-ALPS index, which can be a potential cause of heterogeneity among their results. Also, the included studies evaluating SUVR applied different kinds of PET tracers, which prevented the meta-analysis of all SUVR studies. Furthermore, some of the included studies shared similar datasets for participant inclusion, without clearly specifying inclusion identifiers, which may introduce dependency among effect estimates. Finally, the included studies consisted of participants in different stages of dementia, which can be another explanation for the observed heterogeneity among studies reporting the DTI-ALPS index.

In conclusion, our meta-analysis attempted to assess the difference in DTI-ALPS index between patients with cognitive decline and healthy controls as an indirect marker of glymphatic system function between these participants. Moreover, we evaluated the potential correlation of the DTI-ALPS index with cognitive assessments, such as the MMSE and MoCA, and Aβ burden in patients with AD. The results of our study reported a significant difference in the DTI-ALPS index between patients with cognitive impairment and healthy controls, suggesting a distinctive dysfunction of the glymphatic system in these patients. In addition, our study reflected a significant correlation between DTI-ALPS and cognitive function in patients with AD. Further studies are recommended to assess the association between the DTI-ALPS index and plasma and CSF biomarkers in patients with AD to shed more light on the reliability of these biomarkers in assessing AD progression and suggesting effective treatments.

Supplemental Material

sj-docx-1-alr-10.1177_25424823261424508 - Supplemental material for Comprehensive and stratified meta-analysis of diffusion tensor image analysis along the perivascular space in Alzheimer's disease: Linking glymphatic dysfunction with cognitive decline and amyloid pathology

Supplemental material, sj-docx-1-alr-10.1177_25424823261424508 for Comprehensive and stratified meta-analysis of diffusion tensor image analysis along the perivascular space in Alzheimer's disease: Linking glymphatic dysfunction with cognitive decline and amyloid pathology by Rasa Zafari, Tina Taherkhani, Amirhossein Kamroo and Fardin Nabizadeh in Journal of Alzheimer's Disease Reports

Footnotes

Acknowledgements

The authors have no acknowledgments to report.

ORCID iDs

Rasa Zafari

Tina Taherkhani

Amirhossein Kamroo

Ethical considerations

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Author contribution(s)

Rasa Zafari: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Writing – original draft; Writing – review & editing.

Tina Taherkhani: Conceptualization; Data curation; Investigation; Methodology; Writing – original draft.

Amirhossein Kamroo: Conceptualization; Data curation; Investigation; Methodology; Writing – original draft.

Fardin Nabizadeh: Conceptualization; Project administration; Supervision; Writing – review & editing.

Funding

The authors received no financial support for the research, authorship, and publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

The dataset presented in the study is available on request from the corresponding author during submission or after publication.

Supplemental material

Supplemental material for this article is available online.

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