Share：
Share this content in WeChat
X
[Chinese][PDF]3744
• Review •
Advances of multiparametric MRI and machine learning in cognitive impairment related to cerebral small vessel disease
HUANG Jing  LUO Tianyou 

Cite this article as: HUANG J, LUO T Y. Advances of multiparametric MRI and machine learning in cognitive impairment related to cerebral small vessel disease[J]. Chin J Magn Reson Imaging, 2024, 15(2): 172-177. DOI:10.12015/issn.1674-8034.2024.02.027.


[Abstract] With the aging of the population, the prevalence of age-related cerebral small vessel disease (CSVD) is on the rise. CSVD frequently results in cognitive impairment and dementia, making it a pressing public health concern. Nevertheless, the pathogenesis of cognitive impairment related to CSVD has not yet been fully elucidated, and there is a lack of effective methods for early diagnosis and treatment. With the rapid development of neuroimaging technology and artificial intelligence, multiparametric MRI and machine learning are playing an increasingly important role in the auxiliary diagnosis and pathogenesis exploration of cognitive impairment related to CSVD. This article provides a review of the relevant research progress in recent years, aiming to provide comprehensive and objective imaging evidence for elucidating the neural mechanisms and early diagnosis of cognitive impairment related to CSVD.
[Keywords] cerebral small vessel disease；cognitive impairment；magnetic resonance imaging；multiparametric magnetic resonance imaging；machine learning

HUANG Jing   LUO Tianyou*  

Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China

Corresponding author: LUO T Y, E-mail: ltychy@sina.com

Conflicts of interest   None.

Received  2023-11-07
Accepted  2024-02-02
DOI: 10.12015/issn.1674-8034.2024.02.027
Cite this article as: HUANG J, LUO T Y. Advances of multiparametric MRI and machine learning in cognitive impairment related to cerebral small vessel disease[J]. Chin J Magn Reson Imaging, 2024, 15(2): 172-177. DOI:10.12015/issn.1674-8034.2024.02.027.

[1]
ZHANG W, CHENG Z, FU F, et al. Prevalence and clinical characteristics of white matter hyperintensities in Migraine: a meta-analysis[J/OL]. Neuroimage Clin, 2023, 37: 103312 [2023-11-07]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9827384. DOI: 10.1016/j.nicl.2023.103312.
[2]
WANG Y L, CHEN W Q, YE J Y, et al. Cerebral small vessel disease: looking back on 2022[J]. Chin J Stroke, 2023, 18(1): 17-41. DOI: 10.3969/j.issn.1673-5765.2023.01.002.
[3]
DUERING M, BIESSELS G J, BRODTMANN A, et al. Neuroimaging standards for research into small vessel disease-advances since 2013[J]. Lancet Neurol, 2023, 22(7): 602-618. DOI: 10.1016/s1474-4422(23)00131-x.
[4]
HU W L, YANG L, LI X T, et al. Consensus of chinese experts on the diagnosis and treatment of cerebrovascular diseases 2021[J]. Chin J Stroke, 2021, 16(7): 716-726. DOI: 10.3969/j.issn.1673-5765.2021.07.013.
[5]
CANNISTRARO R J, BADI M, EIDELMAN B H, et al. CNS small vessel disease: a clinical review[J]. Neurology, 2019, 92(24): 1146-1156. DOI: 10.1212/wnl.0000000000007654.
[6]
NGANDU T, LEHTISALO J, SOLOMON A, et al. A 2 year multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive decline in at-risk elderly people (FINGER): a randomised controlled trial[J]. Lancet, 2015, 385(9984): 2255-2263. DOI: 10.1016/S0140-6736(15)60461-5.
[7]
PENG D. Clinical practice guideline for cognitive impairment of cerebral small vessel disease[J]. Aging Med (Milton)2019, 2(2): 64-73. DOI: 10.1002/agm2.12073.
[8]
EDWARDS J D, JACOVA C, SEPEHRY A A, et al. A quantitative systematic review of domain-specific cognitive impairment in lacunar stroke[J]. Neurology, 2013, 80(3): 315-322. DOI: 10.1212/WNL.0b013e31827deb85.
[9]
HAMILTON O K L, BACKHOUSE E V, JANSSEN E, et al. Cognitive impairment in sporadic cerebral small vessel disease: A systematic review and meta-analysis[J]. Alzheimers Dement, 2021, 17(4): 665-685. DOI: 10.1002/alz.12221.
[10]
LIU Q, ZHANG X. Multimodality neuroimaging in vascular mild cognitive impairment: A narrative review of current evidence[J/OL]. Front Aging Neurosci, 2023, 15: 1073039 [2023-11-07]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10050753. DOI: 10.3389/fnagi.2023.1073039.
[11]
LAMPE L, KHARABIAN-MASOULEH S, KYNAST J, et al. Lesion location matters: The relationships between white matter hyperintensities on cognition in the healthy elderly[J]. J Cereb Blood Flow Metab, 2019, 39(1): 36-43. DOI: 10.1177/0271678x17740501.
[12]
ALBER J, ALLADI S, BAE H J, et al. White matter hyperintensities in vascular contributions to cognitive impairment and dementia (VCID): Knowledge gaps and opportunities[J]. Alzheimers Dement (N. Y), 2019, 5: 107-117. DOI: 10.1016/j.trci.2019.02.001.
[13]
MELAZZINI L, MACKAY C E, BORDIN V, et al. White matter hyperintensities classified according to intensity and spatial location reveal specific associations with cognitive performance[J/OL]. Neuroimage Clin, 2021, 30: 102616 [2023-11-07]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7995650. DOI: 10.1016/j.nicl.2021.102616.
[14]
MAILLARD P, CARMICHAEL O, FLETCHER E, et al. Coevolution of white matter hyperintensities and cognition in the elderly[J]. Neurology, 2012, 79(5): 442-448. DOI: 10.1212/WNL.0b013e3182617136.
[15]
KIM S J, LEE D K, JANG Y K, et al. The effects of longitudinal white matter hyperintensity change on cognitive decline and cortical thinning over three years[J/OL]. J Clin Med, 2020, 9(8): 2663 [2023-11-07]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7465642. DOI: 10.3390/jcm9082663.
[16]
SHAABAN C E, JORGENSEN D R, GIANAROS P J, et al. Cerebrovascular disease: neuroimaging of cerebral small vessel disease[J]. Prog Mol Biol Transl Sci, 2019, 165: 225-255. DOI: 10.1016/bs.pmbts.2019.07.008.
[17]
ZHAO C, HUANG W J, FENG F, et al. Abnormal characterization of dynamic functional connectivity in Alzheimer's disease[J]. Neural Regen Res, 2022, 17(9): 2014-2021. DOI: 10.4103/1673-5374.332161.
[18]
FEIN G, DI SCLAFANI V, TANABE J, et al. Hippocampal and cortical atrophy predict dementia in subcortical ischemic vascular disease[J]. Neurology, 2000, 55(11): 1626-1635. DOI: 10.1212/wnl.55.11.1626.
[19]
WONG F C C, YATAWARA C, LOW A, et al. Cerebral small vessel disease influences hippocampal subfield atrophy in mild cognitive impairment[J]. Transl Stroke Res, 2021, 12(2): 284-292. DOI: 10.1007/s12975-020-00847-4.
[20]
LI M, LI Y, LIU Y, et al. Altered hippocampal subfields volumes is associated with memory function in type 2 diabetes mellitus[J/OL]. Front Neurol, 2021, 12: 756500 [2023-11-07]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8657943. DOI: 10.3389/fneur.2021.756500.
[21]
LI X, LI D, LI Q, et al. Hippocampal subfield volumetry in patients with subcortical vascular mild cognitive impairment[J/OL]. Sci Rep, 2016, 6: 20873 [2023-11-07]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4753487. DOI: 10.1038/srep20873.
[22]
TULADHAR A M, VAN NORDEN A G, DE LAAT K F, et al. White matter integrity in small vessel disease is related to cognition[J]. Neuroimage Clin, 2015, 7: 518-524. DOI: 10.1016/j.nicl.2015.02.003.
[23]
MASCALCHI M, SALVADORI E, TOSCHI N, et al. DTI-derived indexes of brain WM correlate with cognitive performance in vascular MCI and small-vessel disease. a TBSS study[J]. Brain Imaging Behav, 2019, 13(3): 594-602. DOI: 10.1007/s11682-018-9873-5.
[24]
CHEN H F, HUANG L L, LI H Y, et al. Microstructural disruption of the right inferior fronto-occipital and inferior longitudinal fasciculus contributes to WMH-related cognitive impairment[J]. CNS. Neurosci Ther, 2020, 26(5): 576-588. DOI: 10.1111/cns.13283.
[25]
BRANDHOFE A, STRATMANN C, SCHÜRE J R, et al. T(2) relaxation time of the normal-appearing white matter is related to the cognitive status in cerebral small vessel disease[J]. J Cereb Blood Flow Metab, 2021, 41(7): 1767-1777. DOI: 10.1177/0271678x20972511.
[26]
PETERSEN M, FREY B M, SCHLEMM E, et al. Network localisation of white matter damage in cerebral small vessel disease[J/OL]. Sci Rep, 2020, 10(1): 9210 [2023-11-07]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7280237. DOI: 10.1038/s41598-020-66013-w.
[27]
FREY B M, PETERSEN M, SCHLEMM E, et al. White matter integrity and structural brain network topology in cerebral small vessel disease: the Hamburg city health study[J]. Hum Brain Mapp, 2021, 42(5): 1406-1415. DOI: 10.1002/hbm.25301.
[28]
DU J, ZHU H, ZHOU J, et al. Structural brain network disruption at preclinical stage of cognitive impairment due to cerebral small vessel disease[J]. Neuroscience, 2020, 449: 99-115. DOI: 10.1016/j.neuroscience.2020.08.037.
[29]
LIANG T, CHANG F, HUANG Z, et al. Evaluation of glymphatic system activity by diffusion tensor image analysis along the perivascular space (DTI-ALPS) in dementia patients[J/OL]. Br J Radiol, 2023, 96(1146): 20220315 [2023-11-07]. https://academic.oup.com/bjr/article/96/1146/20220315/7450741?login=false. DOI: 10.1259/bjr.20220315.
[30]
TIAN Y, CAI X, ZHOU Y, et al. Impaired glymphatic system as evidenced by low diffusivity along perivascular spaces is associated with cerebral small vessel disease: a population-based study[J]. Stroke Vasc Neurol, 2023, 8(5): 413-423. DOI: 10.1136/svn-2022-002191.
[31]
KE Z, MO Y, LI J, et al. Glymphatic dysfunction mediates the influence of white matter hyperintensities on episodic memory in cerebral small vessel disease[J/OL]. Brain Sci, 2022, 12(12): 1611 [2023-11-07]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9775074. DOI: 10.3390/brainsci12121611.
[32]
SONG H, RUAN Z, GAO L, et al. Structural network efficiency mediates the association between glymphatic function and cognition in mild VCI: a DTI-ALPS study[J/OL]. Front Aging Neurosci, 2022, 14: 974114 [2023-11-07]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9708722. DOI: 10.3389/fnagi.2022.974114.
[33]
ZHUANG Y, SHI Y, ZHANG J, et al. Neurologic factors in patients with vascular mild cognitive impairment based on fMRI[J]. World Neurosurg, 2021, 149: 461-469. DOI: 10.1016/j.wneu.2020.11.120.
[34]
LI H, JIA X, LI Y, et al. Aberrant amplitude of low-frequency fluctuation and degree centrality within the default mode network in patients with vascular mild cognitive impairment[J/OL]. Brain. Sci, 2021, 11(11): 1534 [2023-11-07]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8615791. DOI: 10.3390/brainsci11111534.
[35]
FENG M, WEN H, XIN H, et al. Altered spontaneous brain activity related to neurologic dysfunction in patients with cerebral small vessel disease[J/OL]. Front Aging Neurosci, 2021, 13: 731585 [2023-11-07]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8718906. DOI: 10.3389/fnagi.2021.731585.
[36]
SHI Q, CHEN H, JIA Q, et al. Altered granger causal connectivity of resting-state neural networks in patients with leukoaraiosis-associated cognitive impairment-a cross-sectional study[J/OL]. Front Neurol, 2020, 11: 457 [2023-11-07]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7325959. DOI: 10.3389/fneur.2020.00457.
[37]
SCHULZ M, MALHERBE C, CHENG B, et al. Functional connectivity changes in cerebral small vessel disease - a systematic review of the resting-state MRI literature[J/OL]. BMC Med, 2021, 19(1): 103 [2023-11-07]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8097883. DOI: 10.1186/s12916-021-01962-1.
[38]
WANG Y, LIU X, HU Y, et al. Impaired functional network properties contribute to white matter hyperintensity related cognitive decline in patients with cerebral small vessel disease[J/OL]. BMC Med Imaging, 2022, 22(1): 40 [2023-11-07]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8908649. DOI: 10.1186/s12880-022-00769-7.
[39]
XIN H, WEN H, FENG M, et al. Disrupted topological organization of resting-state functional brain networks in cerebral small vessel disease[J]. Hum Brain Mapp, 2022, 43(8): 2607-2620. DOI: 10.1002/hbm.25808.
[40]
GESIERICH B, TULADHAR A M, TELGTE A TER, et al. Alterations and test-retest reliability of functional connectivity network measures in cerebral small vessel disease[J]. Hum Brain Mapp, 2020, 41(10): 2629-2641. DOI: 10.1002/hbm.24967.
[41]
SCHUFF N, MATSUMOTO S, KMIECIK J, et al. Cerebral blood flow in ischemic vascular dementia and Alzheimer's disease, measured by arterial spin-labeling magnetic resonance imaging[J]. Alzheimers Dement, 2009, 5(6): 454-462. DOI: 10.1016/j.jalz.2009.04.1233.
[42]
TU M C, CHUNG H W, HSU Y H, et al. Stage-dependent cerebral blood flow and leukoaraiosis couplings in subcortical ischemic vascular disease and alzheimer's disease[J]. J Alzheimers Dis, 2022, 86(2): 729-739. DOI: 10.3233/jad-215405.
[43]
SUN Y, CAO W, DING W, et al. Cerebral blood flow alterations as assessed by 3D ASL in cognitive impairment in patients with subcortical vascular cognitive impairment: a marker for disease severity[J/OL]. Front Aging Neurosci, 2016, 8: 211 [2023-11-07]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5005930. DOI: 10.3389/fnagi.2016.00211.
[44]
LI Y, LI M, ZHANG X, et al. Higher blood-brain barrier permeability is associated with higher white matter hyperintensities burden[J]. J Neurol, 2017, 264(7): 1474-1481. DOI: 10.1007/s00415-017-8550-8.
[45]
KERKHOFS D, WONG S M, ZHANG E, et al. Baseline blood-brain barrier beakage and bongitudinal microstructural tissue damage in the periphery of white matter hyperintensities[J/OL]. Neurology, 2021, 96(17): e2192-e2200 [2023-11-07]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8166427. DOI: 10.1212/wnl.0000000000011783.
[46]
DOBRYNINA L A, SHAMTIEVA K V, KREMNEVA E I, et al. Daily blood pressure profile and blood-brain barrier permeability in patients with cerebral small vessel disease[J/OL]. Sci Rep, 2022, 12(1): 7723 [2023-11-07]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9095696. DOI: 10.1038/s41598-022-11172-1.
[47]
WANG X, SHI Y, CHEN Y, et al. Blood-brain barrier breakdown is a sensitive biomarker of cognitive and language impairment in patients with white matter hyperintensities[J]. Neurol Ther, 2023, 12(5): 1745-1758. DOI: 10.1007/s40120-023-00527-z.
[48]
KERKHOFS D, WONG S M, ZHANG E, et al. Blood-brain barrier leakage at baseline and cognitive decline in cerebral small vessel disease: a 2-year follow-up study[J]. Geroscience, 2021, 43(4): 1643-1652. DOI: 10.1007/s11357-021-00399-x.
[49]
WALSH J, TOZER D J, SARI H, et al. Microglial activation and blood-brain barrier permeability in cerebral small vessel disease[J]. Brain, 2021, 144(5): 1361-1371. DOI: 10.1093/brain/awab003.
[50]
WONG S M, JANSEN J F A, ZHANG C E, et al. Blood-brain barrier impairment and hypoperfusion are linked in cerebral small vessel disease[J/OL]. Neurology, 2019, 92(15): e1669-e1677 [2023-11-07]. https://www.neurology.org/doi/10.1212/WNL.0000000000007263?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub%20%200pubmed. DOI: 10.1212/wnl.0000000000007263.
[51]
ZHOU X, HU X, ZHANG C, et al. Aberrant functional connectivity and structural atrophy in subcortical vascular cognitive impairment: relationship with cognitive impairments[J/OL]. Front Aging Neurosci, 2016, 8: 14 [2023-11-07]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4736471. DOI: 10.3389/fnagi.2016.00014.
[52]
CHEN X, HUANG L, YE Q, et al. Disrupted functional and structural connectivity within default mode network contribute to WMH-related cognitive impairment[J/OL]. Neuroimage Clin, 2019, 24: 102088 [2023-11-07]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6861557. DOI: 10.1016/j.nicl.2019.102088.
[53]
DE LUCA A, KUIJF H, EXALTO L, et al. Multimodal tract-based MRI metrics outperform whole brain markers in determining cognitive impact of small vessel disease-related brain injury[J]. Brain Struct Funct, 2022, 227(7): 2553-2567. DOI: 10.1007/s00429-022-02546-2.
[54]
SUNDARESAN V, ZAMBONI G, ROTHWELL P M, et al. Triplanar ensemble U-Net model for white matter hyperintensities segmentation on MR images[J]. Med Image Anal, 2021, 73: 102184 [2023-11-07]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8505759. DOI: 10.1016/j.media.2021.102184.
[55]
ORTIZ-RAMÓN R, VALDÉS HERNÁNDEZ M D C, GONZÁLEZ-CASTRO V, et al. Identification of the presence of ischaemic stroke lesions by means of texture analysis on brain magnetic resonance images[J]. Comput Med Imaging Graph, 2019, 74: 12-24. DOI: 10.1016/j.compmedimag.2019.02.006.
[56]
LIANG L, ZHOU P, LU W, et al. An anatomical knowledge-based MRI deep learning pipeline for white matter hyperintensity quantification associated with cognitive impairment[J/OL]. Comput Med Imaging Graph, 2021, 89: 101873 [2023-11-07]. https://www.sciencedirect.com/science/article/abs/pii/S0895611121000215?via%3Dihub. DOI: 10.1016/j.compmedimag.2021.101873.
[57]
SUNDARESAN V, ZAMBONI G, DINSDALE N K. Comparison of domain adaptation techniques for white matter hyperintensity segmentation in brain MR images[J/OL]. Med Image Anal, 2021, 74: 102215 [2023-11-07]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8573594. DOI: 10.1016/j.media.2021.102215.
[58]
CIULLI S, CITI L, SALVADORI E, et al. Prediction of impaired performance in trail making test in MCI patients with small vessel disease using DTI data[J]. IEEE J Biomed Health Inform, 2016, 20(4): 1026-1033. DOI: 10.1109/jbhi.2016.2537808.

PREV The application and progress of dynamic functional connectivity in idiopathic generalized epilepsy
NEXT Research progress on freezing of gait in Parkinson,s disease based on multimodal MRI
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn