Glymphatic system dysfunction is linked to cognitive decline in dementia continuum. Diffusion magnetic resonance imaging markers like the Diffusion Tensor Image Analysis Along the Perivascular Space (DTI-ALPS) index and whole-brain free water (FW) fraction allow non-invasive evaluation of glymphatic activity.
Objective
This study assessed whether DTI-ALPS and FW fraction are associated with cognitive performance and examined the influence of factors such as education on these relationships across dementia stages.
Methods
Cognitively normal, mild cognitive impairment, and dementia participants were recruited from both the community and the memory clinic at Chang Gung Memorial Hospital, Keelung. Brain MRI and neuropsychological assessments were performed.
Results
A total of 127 individuals (Clinical Dementia Rating 0, 0.5, 1–3; N = 75, 43, 6) completed the study. Both DTI-ALPS index and FW fraction differed among groups (p < 0.001) and correlated with cognitive performance (DTI-ALPS: β = 0.35; FW: β = −0.50; both p < 0.001). Mediation analysis showed that DTI-ALPS was positively associated with Montreal Cognitive Assessment (total effect b = 21.94, p < 0.001), largely mediated by FW fraction (indirect β = 0.22, 95% CI [0.14–0.33]). After adjusting for age, sex, and education, the total effect remained significant (b = 10.93, p = 0.018), while the direct effect was not (b = 8.15, p = 0.080), and the indirect effect via FW persisted (β = 0.05, 95% CI [0.01–0.13]). Education moderated these associations (DTI-ALPS x education: b = −2.07, p = 0.033; FW x education: b = 5.59, p = 0.034). In addition, sleep-medication use interacted with FW fraction in relation to cognitive performance (FW x sleep-medication: b = −58.87, p = 0.044).
Conclusions
DTI-ALPS index and FW fraction provided complementary insights into glymphatic dysfunction linked to cognitive decline. Higher DTI-ALPS values were associated with better cognition, mediated by lower whole-brain FW, but with attenuated effects in individuals with higher education.
JackCRJrAndrewsJSBeachTG, et al.Revised criteria for diagnosis and staging of Alzheimer's disease: Alzheimer's Association Workgroup. Alzheimers Dement2024; 20: 5143–5169.
2.
AisenPSCummingsJJackCRJr, et al.On the path to 2025: understanding the Alzheimer's disease continuum. Alzheimers Res Ther2017; 9: 60.
3.
JackCRJrBennettDABlennowK, et al.NIA-AA Research framework: toward a biological definition of Alzheimer's disease. Alzheimers Dement2018; 14: 535–562.
4.
IliffJJWangMLiaoY, et al.A paravascular pathway facilitates CSF flow through the brain parenchyma and the clearance of interstitial solutes, including amyloid beta. Sci Transl Med2012; 4: 147ra111.
5.
SandroneSMoreno-ZambranoDKipnisJ, et al.A (delayed) history of the brain lymphatic system. Nat Med2019; 25: 538–540.
6.
IliffJJLeeHYuM, et al.Brain-wide pathway for waste clearance captured by contrast-enhanced MRI. J Clin Invest2013; 123: 1299–1309.
7.
TaokaTMasutaniYKawaiH, et al.Evaluation of glymphatic system activity with the diffusion MR technique: diffusion tensor image analysis along the perivascular space (DTI-ALPS) in Alzheimer's disease cases. Jpn J Radiol2017; 35: 172–178.
8.
SiowTYTohCHHsuJL, et al.Association of sleep, neuropsychological performance, and gray matter volume with glymphatic function in community-dwelling older adults. Neurology2022; 98: e829–e838.
9.
WeiYCHsuCHHuangWY, et al.Vascular risk factors and astrocytic marker for the glymphatic system activity. Radiol Med2023; 128: 1148–1161.
10.
HsuJLWeiYCTohCH, et al.Magnetic resonance images implicate that glymphatic alterations mediate cognitive dysfunction in Alzheimer disease. Ann Neurol2023; 93: 164–174.
11.
SatheAYangYSchillingKG, et al.Free-water: a promising structural biomarker for cognitive decline in aging and mild cognitive impairment. Imaging Neurosci (Camb)2024; 2: 1–16.
12.
OforiEDeKoskySTFeboM, et al.Free-water imaging of the hippocampus is a sensitive marker of Alzheimer's disease. Neuroimage Clin2019; 24: 101985.
13.
HsuCHChongSTKungYC, et al.Integrated diffusion image operator (iDIO): a pipeline for automated configuration and processing of diffusion MRI data. Hum Brain Mapp2023; 44: 2669–2683.
14.
JenkinsonMSmithS. A global optimisation method for robust affine registration of brain images. Med Image Anal2001; 5: 143–156.
15.
JenkinsonMBannisterPBradyM, et al.Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage2002; 17: 825–841.
16.
BasserPJMattielloJLeBihanD. Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B1994; 103: 247–254.
17.
YokotaHVijayasarathiACekicM, et al.Diagnostic performance of glymphatic system evaluation using diffusion tensor imaging in idiopathic normal pressure hydrocephalus and mimickers. Curr Gerontol Geriatr Res2019; 2019: 5675014.
18.
MoriSOishiKJiangH, et al.Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. Neuroimage2008; 40: 570–582.
19.
PasternakOMaier-HeinKBaumgartnerC, et al.The estimation of free-water corrected diffusion tensors. In: WestinC-FVilanovaABurgethB (eds) Visualization and processing of tensors and higher order descriptors for multi-valued data. Berlin, Heidelberg: Springer, 2014, pp.249–270.
20.
GaryfallidisEBrettMAmirbekianB, et al.Dipy, a library for the analysis of diffusion MRI data. Front Neuroinform2014; 8: 8.
21.
PasternakOSochenNGurY, et al.Free water elimination and mapping from diffusion MRI. Magn Reson Med2009; 62: 717–730.
22.
TsaiC-FLeeW-JWangS-J, et al.Psychometrics of the Montreal Cognitive Assessment (MoCA) and its subscales: validation of the Taiwanese version of the MoCA and an item response theory analysis. Int Psychogeriatr2011; 24: 651–658.
23.
FolsteinMFFolsteinSEMcHughPR. "Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res1975; 12: 189–198.
24.
WeiYCChenCKLinC, et al.Normative data of Mini-mental state examination, Montreal cognitive assessment, and Alzheimer's disease assessment scale-cognitive subscale of community-dwelling older adults in Taiwan. Dement Geriatr Cogn Disord2022; 51: 365–376.
25.
MohsRCKnopmanDPetersenRC, et al.Development of cognitive instruments for use in clinical trials of antidementia drugs: additions to the Alzheimer's Disease Assessment Scale that broaden its scope: The Alzheimer's Disease Cooperative Study. Alzheimer Dis Assoc Disord1997; 11: S13–S21.
26.
WechslerD. WAIS-III : administration and scoring manual : Wechsler Adult Intelligence Scale. San Antonio, TX: Psychological Corporation, 1997.
27.
MonschAUBondiMWButtersN, et al.Comparisons of verbal fluency tasks in the detection of dementia of the Alzheimer type. Arch Neurol1992; 49: 1253–1258.
28.
Tomaszewski FariasSMungasDHarveyDJ, et al.The measurement of everyday cognition: development and validation of a short form of the Everyday Cognition scales. Alzheimers Dement2011; 7: 593–601.
29.
GalvinJERoeCMPowlishtaKK, et al.The AD8: a brief informant interview to detect dementia. Neurology2005; 65: 559–564.
30.
WeiYCHuangLYChenCK, et al.Subjective cognitive decline in the community is affected at multiple aspects of mental health and life quality: a cross-sectional study of the community medicine of Keelung Chang Gung Memorial Hospital. Dement Geriatr Cogn Dis Extra2019; 9: 152–162.
31.
BjellandIDahlAAHaugTT, et al.The validity of the Hospital Anxiety and Depression Scale. An updated literature review. J Psychosom Res2002; 52: 69–77.
32.
WangGLHsuSHFengAC, et al.The HADS and the DT for screening psychosocial distress of cancer patients in Taiwan. Psychooncology2011; 20: 639–646.
33.
MontorioIIzalM. The Geriatric Depression Scale: a review of its development and utility. Int Psychogeriatr1996; 8: 103–112.
34.
HaysRDDiMatteoMR. A short-form measure of loneliness. J Pers Assess1987; 51: 69–81.
35.
WuC-hYaoG. Psychometric analysis of the short-form UCLA Loneliness Scale (ULS-8) in Taiwanese undergraduate students. Pers Individ Dif2008; 44: 1762–1771.
36.
MarinRSBiedrzyckiRCFirinciogullariS. Reliability and validity of the Apathy Evaluation Scale. Psychiatry Res1991; 38: 143–162.
37.
LeeSHWenMCChaoCC, et al.Apathy in late-life depression among Taiwanese patients. Int Psychogeriatr2008; 20: 328–337.
38.
TsaiPSWangSYWangMY, et al.Psychometric evaluation of the Chinese version of the Pittsburgh Sleep Quality Index (CPSQI) in primary insomnia and control subjects. Qual Life Res2005; 14: 1943–1952.
39.
JohnsMW. Reliability and factor analysis of the Epworth Sleepiness Scale. Sleep1992; 15: 376–381.
40.
LeePHMacfarlaneDJLamTH, et al.Validity of the International Physical Activity Questionnaire Short Form (IPAQ-SF): a systematic review. Int J Behav Nutr Phys Act2011; 8: 115.
41.
MalmstromTKMorleyJE. SARC-F: a simple questionnaire to rapidly diagnose sarcopenia. J Am Med Dir Assoc2013; 14: 531–532.
42.
GlynnNWSantanastoAJSimonsickEM, et al.The Pittsburgh Fatigability scale for older adults: development and validation. J Am Geriatr Soc2015; 63: 130–135.
43.
LinCGlynnNWGmelinT, et al.Validation of the traditional Chinese version of the Pittsburgh Fatigability Scale for Older Adults. Clin Gerontol2022; 45: 606–618.
44.
MacKinnonDPValenteMJGonzalezO. The correspondence between causal and traditional mediation analysis: the link is the mediator by treatment interaction. Prev Sci2020; 21: 147–157.
45.
HayesAF. Introduction to mediation, moderation, and conditional process analysis : a regression-based approach. Second edition ed. New York: Guilford Press, 2018.
46.
SternYBarnesCAGradyC, et al.Brain reserve, cognitive reserve, compensation, and maintenance: operationalization, validity, and mechanisms of cognitive resilience. Neurobiol Aging2019; 83: 124–129.
47.
KamagataKSaitoYAndicaC, et al.Noninvasive magnetic resonance imaging measures of glymphatic system activity. J Magn Reson Imaging2024; 59: 1476–1493.
48.
JungwonJLeeJChoiC-H, et al.DTI-ALPS index as a predictor of cognitive decline over 1 year. Neuroradiology2024; 67: 163–170.
49.
HongHTozerDJMarkusHS. Relationship of perivascular space markers with incident dementia in cerebral small vessel disease. Stroke2024; 55: 1032–1040.
50.
LiangTChangFHuangZ, et al.Evaluation of glymphatic system activity by diffusion tensor image analysis along the perivascular space (DTI-ALPS) in dementia patients. Br J Radiol2023; 96: 20220315.
51.
TaokaTItoRNakamichiR, et al.Diffusion Tensor Image Analysis Along the Perivascular Space (DTI-ALPS): revisiting the meaning and significance of the method. Magn Reson Med Sci2024; 23: 268–290.
52.
MaillardPFletcherESinghB, et al.Cerebral white matter free water: a sensitive biomarker of cognition and function. Neurology2019; 92: e2221–e2231.
53.
NedergaardMGoldmanSA. Glymphatic failure as a final common pathway to dementia. Science2020; 370: 50–56.
54.
LiuXMaillardPBarisanoG, et al.MRI Free water mediates the association between diffusion tensor image analysis along the perivascular space and executive function in four independent middle to aged cohorts. Alzheimers Dement2025; 21: e14453.
55.
KressBTIliffJJXiaM, et al.Impairment of paravascular clearance pathways in the aging brain. Ann Neurol2014; 76: 845–861.
56.
SatpathiSReidRIPrzybelskiSA, et al.Evaluation and interpretation of DTI-ALPS, a proposed surrogate marker for glymphatic clearance, in a large population-based sample. Alzheimers Res Ther2025; 17: 191.
57.
Arce RenteríaMVonkJMJFelixG, et al.Illiteracy, dementia risk, and cognitive trajectories among older adults with low education. Neurology2019; 93: e2247–e2256.
58.
WangNXuHDhingraR, et al.The impact of later-life learning on trajectories of cognitive function among U.S. older adults. Innov Aging2025; 9: igaf023.
59.
Arenaza-UrquijoEMVemuriP. Improving the resistance and resilience framework for aging and dementia studies. Alzheimers Res Ther2020; 12: 41.
60.
AlberiniCMCruzEDescalziG, et al.Astrocyte glycogen and lactate: new insights into learning and memory mechanisms. Glia2018; 66: 1244–1262.
61.
GibbsMEHutchinsonDHertzL. Astrocytic involvement in learning and memory consolidation. Neurosci Biobehav Rev2008; 32: 927–944.
62.
KolAAdamskyAGroysmanM, et al.Astrocytes contribute to remote memory formation by modulating hippocampal-cortical communication during learning. Nat Neurosci2020; 23: 1229–1239.
63.
RasmussenMKMestreHNedergaardM. The glymphatic pathway in neurological disorders. Lancet Neurol2018; 17: 1016–1024.
64.
ZhouLYangWLiuY, et al.Moderating effect of education on glymphatic function and cognitive performance in mild cognitive impairment. Front Aging Neurosci2024; 16: 1399943.
65.
LeeSYooREChoiSH, et al.Contrast-enhanced MRI T1 mapping for quantitative evaluation of putative dynamic glymphatic activity in the human brain in sleep-wake states. Radiology2021; 300: 661–668.
66.
Davoodi-BojdEDingGZhangL, et al.Modeling glymphatic system of the brain using MRI. Neuroimage2019; 188: 616–627.
67.
SepehrbandFCabeenRPChoupanJ, et al.Perivascular space fluid contributes to diffusion tensor imaging changes in white matter. Neuroimage2019; 197: 243–254.
68.
DickieBRParkerGJMParkesLM. Measuring water exchange across the blood-brain barrier using MRI. Prog Nucl Magn Reson Spectrosc2020; 116: 19–39.
69.
Le BihanD. What can we see with IVIM MRI?Neuroimage2019; 187: 56–67.
70.
YamadaSHiratsukaSOtaniT, et al.Usefulness of intravoxel incoherent motion MRI for visualizing slow cerebrospinal fluid motion. Fluids Barriers CNS2023; 20: 16.
