Diffusion-weighted image analysis along the perivascular space (DWI-ALPS) for evaluating interstitial fluid status: age dependence in normal subjects.

Toshiaki Taoka, Rintaro Ito, Rei Nakamichi, Toshiki Nakane, Mayuko Sakai, Kazushige Ichikawa, Hisashi Kawai, Shinji Naganawa
Author Information
  1. Toshiaki Taoka: Department of Innovative Biomedical Visualization (iBMV), Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan. ttaoka@med.nagoya-u.ac.jp. ORCID
  2. Rintaro Ito: Department of Innovative Biomedical Visualization (iBMV), Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan.
  3. Rei Nakamichi: Department of Radiology, Nagoya University, Nagoya, Aichi, Japan.
  4. Toshiki Nakane: Department of Radiology, Nagoya University, Nagoya, Aichi, Japan.
  5. Mayuko Sakai: Canon Medical Systems Corporation, Otawara, Japan.
  6. Kazushige Ichikawa: Department of Radiological Technology, Nagoya University Hospital, Nagoya, Aichi, Japan.
  7. Hisashi Kawai: Department of Radiology, Aichi Medical University, Nagakute, Japan.
  8. Shinji Naganawa: Department of Radiology, Nagoya University, Nagoya, Aichi, Japan.

Abstract

PURPOSE: The purpose of this study was to evaluate the interstitial fluid status in a wide range of age groups using diffusion-weighted image analysis along the perivascular space (DWI-ALPS) method, which is a simplified variation of diffusion tensor image analysis along the perivascular space (DTI-ALPS).
MATERIALS AND METHODS: This retrospective study included data from 128 patients who underwent clinical magnetic resonance imaging (MRI) studies, including DWI, and were found to have no abnormal findings in the brain on MRI. Three motion-probing gradients of the DWI were applied in an orthogonal direction to the imaging plane. Apparent diffusion coefficient images in the x-, y-, and z-axes were retrospectively generated, and composite color images were created to locate the projection and association fiber area on the slice including the body of the lateral ventricle. ALPS indices were calculated, and correlations with age were evaluated using linear and second-degree regression analysis. Linear regression analysis was also performed for a subgroup of patients older than 40 years. In addition, an analysis of variance (ANOVA) test among the generations was performed.
RESULTS: The linear regression analysis between age and the ALPS index showed a correlation coefficient of -0.20 for all age group and -0.51 for the subgroup older than 40 years. The second-degree regression analysis showed a correlation coefficient of 0.39. ANOVA showed that the 40's generation showed a statistically significant higher value of ALPS index compared to all other generations except for the 30's generation. While, the 70's generation showed a statistically significant lower value of the ALPS index compared to all other generations.
CONCLUSIONS: The analysis of the DWI-APLS method showed a correlation between age and the ALPS index in second-degree distribution which peaked in the 40's generation. This finding in normal subjects may be fundamental in the analysis of disease cases. We tried to evaluate the glymphatic system status in a wide range of age groups using diffusion-weighted image analysis along the perivascular space (DWI-ALPS) method, and the results showed a correlation between age and the ALPS index in second-degree distribution which peaked in the 40's generation.

Keywords

References

  1. Parkinsonism Relat Disord. 2021 Jan;82:56-60 [PMID: 33248394]
  2. Ann Neurol. 2020 Mar;87(3):357-369 [PMID: 31916277]
  3. Neuroradiology. 2012 Apr;54(4):335-43 [PMID: 21611726]
  4. Nature. 2018 Aug;560(7717):185-191 [PMID: 30046111]
  5. Front Aging Neurosci. 2021 Nov 08;13:756249 [PMID: 34819849]
  6. Brain. 2022 Aug 27;145(8):2785-2795 [PMID: 34919648]
  7. Neuron. 2015 Jan 21;85(2):296-302 [PMID: 25611508]
  8. Oxid Med Cell Longev. 2021 Feb 16;2021:4034509 [PMID: 33680283]
  9. BMJ Open. 2021 Nov 26;11(11):e054885 [PMID: 34836909]
  10. Magn Reson Med Sci. 2020 Dec 1;19(4):375-381 [PMID: 32023561]
  11. Neuroimage. 2021 Jan 1;224:117441 [PMID: 33039618]
  12. Eur Radiol. 2003 Jan;13(1):6-11 [PMID: 12541104]
  13. J Neurol. 2022 Apr;269(4):2133-2139 [PMID: 34510256]
  14. Brain Behav. 2022 Mar;12(3):e2504 [PMID: 35107879]
  15. Neuropathology. 1999 Jan;19(1):93-111 [PMID: 19519653]
  16. Front Neurol. 2022 Jan 04;12:789918 [PMID: 35082748]
  17. Front Neurosci. 2021 Apr 22;15:674898 [PMID: 33967688]
  18. Jpn J Radiol. 2022 Feb;40(2):147-158 [PMID: 34390452]
  19. Brain. 2020 Aug 1;143(8):2576-2593 [PMID: 32705145]
  20. Sci Transl Med. 2012 Aug 15;4(147):147ra111 [PMID: 22896675]
  21. Neuron. 2010 Nov 4;68(3):409-27 [PMID: 21040844]
  22. Front Oncol. 2021 Oct 13;11:725354 [PMID: 34722268]
  23. Front Pharmacol. 2012 Mar 29;3:46 [PMID: 22479246]
  24. Neurology. 2022 Feb 22;98(8):e829-e838 [PMID: 34906982]
  25. Front Neurol. 2022 Feb 28;13:843883 [PMID: 35295837]
  26. Neuroimage. 2021 Sep;238:118257 [PMID: 34118396]
  27. Curr Gerontol Geriatr Res. 2019 Jun 20;2019:5675014 [PMID: 31320896]
  28. Eur J Clin Pharmacol. 2003 Aug;59(4):297-302 [PMID: 12845506]
  29. Front Hum Neurosci. 2020 Aug 13;14:300 [PMID: 32922272]
  30. J Neurosci. 2017 Mar 15;37(11):2870-2877 [PMID: 28188218]
  31. J Neuroimaging. 2021 May;31(3):569-578 [PMID: 33556226]
  32. Jpn J Radiol. 2017 Apr;35(4):172-178 [PMID: 28197821]
  33. Front Aging Neurosci. 2020 Dec 21;12:559603 [PMID: 33408625]
  34. Front Aging Neurosci. 2021 Nov 15;13:773951 [PMID: 34867300]
  35. Radiographics. 2017 Jan-Feb;37(1):281-297 [PMID: 28076020]
  36. JCI Insight. 2018 Jul 12;3(13): [PMID: 29997300]
  37. Mol Imaging Biol. 2012 Dec;14(6):771-6 [PMID: 22476967]
  38. Magn Reson Med. 2000 Aug;44(2):259-68 [PMID: 10918325]
  39. Sci Adv. 2021 May 21;7(21): [PMID: 34020948]
  40. Parkinsonism Relat Disord. 2021 Aug;89:98-104 [PMID: 34271425]
  41. Tissue Barriers. 2017 Oct 2;5(4):e1373897 [PMID: 28956691]
  42. Acta Neurol Scand. 2022 Apr;145(4):464-470 [PMID: 34918348]
  43. Magn Reson Med Sci. 2019 Apr 10;18(2):163-169 [PMID: 30393275]
  44. AJNR Am J Neuroradiol. 2022 Jan;43(1):48-55 [PMID: 34794943]
  45. Front Oncol. 2021 Sep 23;11:744318 [PMID: 34631582]
  46. Front Neurol. 2022 Jan 25;12:809438 [PMID: 35145471]
  47. Ann Neurol. 2014 Dec;76(6):845-61 [PMID: 25204284]

Grants

  1. 21K07563/KAKENHI

MeSH Term

Adult
Diffusion Magnetic Resonance Imaging
Extracellular Fluid
Glymphatic System
Humans
Image Processing, Computer-Assisted
Retrospective Studies

Word Cloud

Created with Highcharts 10.0.0analysisageshowedALPSimagemethodindexgenerationalongperivascularspacesecond-degreeregressioncorrelationusingDWI-ALPSMRIcoefficientgenerations40'sstudyevaluateinterstitialfluidstatuswiderangegroupsdiffusion-weighteddiffusionpatientsimagingincludingDWIimageslinearperformedsubgroupolder40 yearsANOVA-0statisticallysignificantvaluecompareddistributionpeakednormalsubjectssystemPURPOSE:purposesimplifiedvariationtensorDTI-ALPSMATERIALSANDMETHODS:retrospectiveincludeddata128underwentclinicalmagneticresonancestudiesfoundabnormalfindingsbrainThreemotion-probinggradientsappliedorthogonaldirectionplaneApparentx-y-z-axesretrospectivelygeneratedcompositecolorcreatedlocateprojectionassociationfiberareaslicebodylateralventricleindicescalculatedcorrelationsevaluatedLinearalsoadditionvariancetestamongRESULTS:20group51039higherexcept30's70'slowerCONCLUSIONS:DWI-APLSfindingmayfundamentaldiseasecasestriedglymphaticresultsDiffusion-weightedevaluatingstatus:dependenceDTI–ALPSDWI–ALPSDiffusionGlymphatic

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