Free Water MR Imaging of White Matter Microstructural Changes is a Sensitive Marker of Amyloid Positivity in Alzheimer's Disease
Background Extracellular free water (FW) resulting from white matter degeneration limits the sensitivity of diffusion tensor imaging (DTI) in predicting Alzheimer's disease (AD). Purpose To evaluate the sensitivity of FW‐DTI in detecting white matter microstructural changes in AD. To validate t...
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| Veröffentlicht in: | Journal of magnetic resonance imaging 2024-10, Vol.60 (4), p.1458-1469 |
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| container_title | Journal of magnetic resonance imaging |
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| creator | Sun, Xuan Zhao, Cui Chen, Si‐Yu Chang, Yan Han, Yu‐Liang Li, Ke Sun, Hong‐Mei Wang, Zhen‐Fu Liang, Ying Jia, Jian‐Jun |
| description | Background
Extracellular free water (FW) resulting from white matter degeneration limits the sensitivity of diffusion tensor imaging (DTI) in predicting Alzheimer's disease (AD).
Purpose
To evaluate the sensitivity of FW‐DTI in detecting white matter microstructural changes in AD. To validate the effectiveness of FW‐DTI indices to predict amyloid‐beta (Aβ) positivity in mild cognitive impairment (MCI) subtypes.
Study Type
Retrospective.
Population
Thirty‐eight Aβ‐negative cognitively healthy (CH) controls (68.74 ± 8.28 years old, 55% female), 15 Aβ‐negative MCI patients (MCI‐n) (68.87 ± 8.83 years old, 60% female), 29 Aβ‐positive MCI patients (MCI‐p) (73.03 ± 7.05 years old, 52% female), and 29 Aβ‐positive AD patients (72.93 ± 9.11 years old, 55% female).
Field Strength/Sequence
3.0T; DTI, T1‐weighted, T2‐weighted, T2 star‐weighted angiography, and Aβ PET (18F‐florbetaben or 11C‐PIB).
Assessment
FW‐corrected and standard diffusion indices were analyzed using trace‐based spatial statistics. Area under the curve (AUC) in distinguishing MCI subtypes were compared using support vector machine (SVM).
Statistical Tests
Chi‐squared test, one‐way analysis of covariance, general linear regression analyses, nonparametric permutation tests, partial Pearson's correlation, receiver operating characteristic curve analysis, and linear SVM. A P value |
| doi_str_mv | 10.1002/jmri.29189 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2902968488</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2902968488</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3579-f474546229c45660329d08468e7cc2b9b07a02b880b11191e533fd39af3293db3</originalsourceid><addsrcrecordid>eNp90U9P2zAYBnALDQFju-wDTJZ22DQp7LUdJ_ax6sYoomJimzhaTvKmdZc_YCdD5cRHx21hBw472dL7ex9Zfgh5x-CEAfAvq9a7E66Z0nvkiEnOEy5V9ireQYqEKcgPyesQVgCgdSoPyKFQcVEydUQeTj0ivbYDejq_orPWLly3oH1Nr5duQDq3w3bkSt-HwY_lMHrb0OnSdgsM1AVq6U_sghvc3432f6KO25N23fSuoj_67cgNa-o6Omnul-ha9B8D_eoC2oBvyH5tm4Bvn85j8vv026_pWXJx-X02nVwkpZC5Tuo0T2Waca7LVGYZCK4rUGmmMC9LXugCcgu8UAoKxphmKIWoK6FtHaWoCnFMPu1yb3x_O2IYTOtCiU1jO-zHYLgGrjOVKhXphxd01Y--i68zgnHFpAQQUX3eqc3PBI-1ufGutX5tGJhNL2bTi9n2EvH7p8ixaLH6R5-LiIDtwJ1rcP2fKHM-v5rtQh8ByZuW8w</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3128155003</pqid></control><display><type>article</type><title>Free Water MR Imaging of White Matter Microstructural Changes is a Sensitive Marker of Amyloid Positivity in Alzheimer's Disease</title><source>MEDLINE</source><source>Wiley Online Library All Journals</source><creator>Sun, Xuan ; Zhao, Cui ; Chen, Si‐Yu ; Chang, Yan ; Han, Yu‐Liang ; Li, Ke ; Sun, Hong‐Mei ; Wang, Zhen‐Fu ; Liang, Ying ; Jia, Jian‐Jun</creator><creatorcontrib>Sun, Xuan ; Zhao, Cui ; Chen, Si‐Yu ; Chang, Yan ; Han, Yu‐Liang ; Li, Ke ; Sun, Hong‐Mei ; Wang, Zhen‐Fu ; Liang, Ying ; Jia, Jian‐Jun</creatorcontrib><description>Background
Extracellular free water (FW) resulting from white matter degeneration limits the sensitivity of diffusion tensor imaging (DTI) in predicting Alzheimer's disease (AD).
Purpose
To evaluate the sensitivity of FW‐DTI in detecting white matter microstructural changes in AD. To validate the effectiveness of FW‐DTI indices to predict amyloid‐beta (Aβ) positivity in mild cognitive impairment (MCI) subtypes.
Study Type
Retrospective.
Population
Thirty‐eight Aβ‐negative cognitively healthy (CH) controls (68.74 ± 8.28 years old, 55% female), 15 Aβ‐negative MCI patients (MCI‐n) (68.87 ± 8.83 years old, 60% female), 29 Aβ‐positive MCI patients (MCI‐p) (73.03 ± 7.05 years old, 52% female), and 29 Aβ‐positive AD patients (72.93 ± 9.11 years old, 55% female).
Field Strength/Sequence
3.0T; DTI, T1‐weighted, T2‐weighted, T2 star‐weighted angiography, and Aβ PET (18F‐florbetaben or 11C‐PIB).
Assessment
FW‐corrected and standard diffusion indices were analyzed using trace‐based spatial statistics. Area under the curve (AUC) in distinguishing MCI subtypes were compared using support vector machine (SVM).
Statistical Tests
Chi‐squared test, one‐way analysis of covariance, general linear regression analyses, nonparametric permutation tests, partial Pearson's correlation, receiver operating characteristic curve analysis, and linear SVM. A P value <0.05 was considered statistically significant.
Results
Compared with CH/MCI‐n/MCI‐p, AD showed significant change in tissue compartment indices of FW‐DTI. No difference was found in the FW index among pair‐wise group comparisons (the minimum FWE‐corrected P = 0.114). There was a significant association between FW‐DTI indices and memory and visuospatial function. The SVM classifier with tissue radial diffusivity as an input feature had the best classification performance of MCI subtypes (AUC = 0.91), and the classifying accuracy of FW‐DTI was all over 89.89%.
Data Conclusion
FW‐DTI indices prove to be potential biomarkers of AD. The classification of MCI subtypes based on SVM and FW‐DTI indices has good accuracy and could help early diagnosis.
Evidence Level
4
Technical Efficacy
Stage 2</description><identifier>ISSN: 1053-1807</identifier><identifier>ISSN: 1522-2586</identifier><identifier>EISSN: 1522-2586</identifier><identifier>DOI: 10.1002/jmri.29189</identifier><identifier>PMID: 38100518</identifier><language>eng</language><publisher>Hoboken, USA: John Wiley & Sons, Inc</publisher><subject>Aged ; Aged, 80 and over ; Alzheimer Disease - diagnostic imaging ; Alzheimer's disease ; Amyloid beta-Peptides - metabolism ; Angiography ; Biomarkers ; Biomarkers - metabolism ; Brain - diagnostic imaging ; Classification ; Cognitive ability ; Cognitive Dysfunction - diagnostic imaging ; Degeneration ; diffusion tensor imaging ; Diffusion Tensor Imaging - methods ; Effectiveness ; Female ; Females ; Field strength ; free water ; Humans ; Magnetic resonance imaging ; Male ; Medical imaging ; Middle Aged ; Neurodegenerative diseases ; Permutations ; Population studies ; Positron-Emission Tomography ; Regression analysis ; Reproducibility of Results ; Retrospective Studies ; Sensitivity analysis ; Sensitivity and Specificity ; Spatial memory ; Statistical analysis ; Statistical tests ; Substantia alba ; support vector machine ; Support vector machines ; Tensors ; Water - chemistry ; white matter ; White Matter - diagnostic imaging ; β-Amyloid</subject><ispartof>Journal of magnetic resonance imaging, 2024-10, Vol.60 (4), p.1458-1469</ispartof><rights>2023 International Society for Magnetic Resonance in Medicine.</rights><rights>2024 International Society for Magnetic Resonance in Medicine</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3579-f474546229c45660329d08468e7cc2b9b07a02b880b11191e533fd39af3293db3</citedby><cites>FETCH-LOGICAL-c3579-f474546229c45660329d08468e7cc2b9b07a02b880b11191e533fd39af3293db3</cites><orcidid>0000-0002-0747-4132</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjmri.29189$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjmri.29189$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27903,27904,45553,45554</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38100518$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Sun, Xuan</creatorcontrib><creatorcontrib>Zhao, Cui</creatorcontrib><creatorcontrib>Chen, Si‐Yu</creatorcontrib><creatorcontrib>Chang, Yan</creatorcontrib><creatorcontrib>Han, Yu‐Liang</creatorcontrib><creatorcontrib>Li, Ke</creatorcontrib><creatorcontrib>Sun, Hong‐Mei</creatorcontrib><creatorcontrib>Wang, Zhen‐Fu</creatorcontrib><creatorcontrib>Liang, Ying</creatorcontrib><creatorcontrib>Jia, Jian‐Jun</creatorcontrib><title>Free Water MR Imaging of White Matter Microstructural Changes is a Sensitive Marker of Amyloid Positivity in Alzheimer's Disease</title><title>Journal of magnetic resonance imaging</title><addtitle>J Magn Reson Imaging</addtitle><description>Background
Extracellular free water (FW) resulting from white matter degeneration limits the sensitivity of diffusion tensor imaging (DTI) in predicting Alzheimer's disease (AD).
Purpose
To evaluate the sensitivity of FW‐DTI in detecting white matter microstructural changes in AD. To validate the effectiveness of FW‐DTI indices to predict amyloid‐beta (Aβ) positivity in mild cognitive impairment (MCI) subtypes.
Study Type
Retrospective.
Population
Thirty‐eight Aβ‐negative cognitively healthy (CH) controls (68.74 ± 8.28 years old, 55% female), 15 Aβ‐negative MCI patients (MCI‐n) (68.87 ± 8.83 years old, 60% female), 29 Aβ‐positive MCI patients (MCI‐p) (73.03 ± 7.05 years old, 52% female), and 29 Aβ‐positive AD patients (72.93 ± 9.11 years old, 55% female).
Field Strength/Sequence
3.0T; DTI, T1‐weighted, T2‐weighted, T2 star‐weighted angiography, and Aβ PET (18F‐florbetaben or 11C‐PIB).
Assessment
FW‐corrected and standard diffusion indices were analyzed using trace‐based spatial statistics. Area under the curve (AUC) in distinguishing MCI subtypes were compared using support vector machine (SVM).
Statistical Tests
Chi‐squared test, one‐way analysis of covariance, general linear regression analyses, nonparametric permutation tests, partial Pearson's correlation, receiver operating characteristic curve analysis, and linear SVM. A P value <0.05 was considered statistically significant.
Results
Compared with CH/MCI‐n/MCI‐p, AD showed significant change in tissue compartment indices of FW‐DTI. No difference was found in the FW index among pair‐wise group comparisons (the minimum FWE‐corrected P = 0.114). There was a significant association between FW‐DTI indices and memory and visuospatial function. The SVM classifier with tissue radial diffusivity as an input feature had the best classification performance of MCI subtypes (AUC = 0.91), and the classifying accuracy of FW‐DTI was all over 89.89%.
Data Conclusion
FW‐DTI indices prove to be potential biomarkers of AD. The classification of MCI subtypes based on SVM and FW‐DTI indices has good accuracy and could help early diagnosis.
Evidence Level
4
Technical Efficacy
Stage 2</description><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Alzheimer Disease - diagnostic imaging</subject><subject>Alzheimer's disease</subject><subject>Amyloid beta-Peptides - metabolism</subject><subject>Angiography</subject><subject>Biomarkers</subject><subject>Biomarkers - metabolism</subject><subject>Brain - diagnostic imaging</subject><subject>Classification</subject><subject>Cognitive ability</subject><subject>Cognitive Dysfunction - diagnostic imaging</subject><subject>Degeneration</subject><subject>diffusion tensor imaging</subject><subject>Diffusion Tensor Imaging - methods</subject><subject>Effectiveness</subject><subject>Female</subject><subject>Females</subject><subject>Field strength</subject><subject>free water</subject><subject>Humans</subject><subject>Magnetic resonance imaging</subject><subject>Male</subject><subject>Medical imaging</subject><subject>Middle Aged</subject><subject>Neurodegenerative diseases</subject><subject>Permutations</subject><subject>Population studies</subject><subject>Positron-Emission Tomography</subject><subject>Regression analysis</subject><subject>Reproducibility of Results</subject><subject>Retrospective Studies</subject><subject>Sensitivity analysis</subject><subject>Sensitivity and Specificity</subject><subject>Spatial memory</subject><subject>Statistical analysis</subject><subject>Statistical tests</subject><subject>Substantia alba</subject><subject>support vector machine</subject><subject>Support vector machines</subject><subject>Tensors</subject><subject>Water - chemistry</subject><subject>white matter</subject><subject>White Matter - diagnostic imaging</subject><subject>β-Amyloid</subject><issn>1053-1807</issn><issn>1522-2586</issn><issn>1522-2586</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp90U9P2zAYBnALDQFju-wDTJZ22DQp7LUdJ_ax6sYoomJimzhaTvKmdZc_YCdD5cRHx21hBw472dL7ex9Zfgh5x-CEAfAvq9a7E66Z0nvkiEnOEy5V9ireQYqEKcgPyesQVgCgdSoPyKFQcVEydUQeTj0ivbYDejq_orPWLly3oH1Nr5duQDq3w3bkSt-HwY_lMHrb0OnSdgsM1AVq6U_sghvc3432f6KO25N23fSuoj_67cgNa-o6Omnul-ha9B8D_eoC2oBvyH5tm4Bvn85j8vv026_pWXJx-X02nVwkpZC5Tuo0T2Waca7LVGYZCK4rUGmmMC9LXugCcgu8UAoKxphmKIWoK6FtHaWoCnFMPu1yb3x_O2IYTOtCiU1jO-zHYLgGrjOVKhXphxd01Y--i68zgnHFpAQQUX3eqc3PBI-1ufGutX5tGJhNL2bTi9n2EvH7p8ixaLH6R5-LiIDtwJ1rcP2fKHM-v5rtQh8ByZuW8w</recordid><startdate>202410</startdate><enddate>202410</enddate><creator>Sun, Xuan</creator><creator>Zhao, Cui</creator><creator>Chen, Si‐Yu</creator><creator>Chang, Yan</creator><creator>Han, Yu‐Liang</creator><creator>Li, Ke</creator><creator>Sun, Hong‐Mei</creator><creator>Wang, Zhen‐Fu</creator><creator>Liang, Ying</creator><creator>Jia, Jian‐Jun</creator><general>John Wiley & Sons, Inc</general><general>Wiley Subscription Services, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7TK</scope><scope>8FD</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-0747-4132</orcidid></search><sort><creationdate>202410</creationdate><title>Free Water MR Imaging of White Matter Microstructural Changes is a Sensitive Marker of Amyloid Positivity in Alzheimer's Disease</title><author>Sun, Xuan ; Zhao, Cui ; Chen, Si‐Yu ; Chang, Yan ; Han, Yu‐Liang ; Li, Ke ; Sun, Hong‐Mei ; Wang, Zhen‐Fu ; Liang, Ying ; Jia, Jian‐Jun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3579-f474546229c45660329d08468e7cc2b9b07a02b880b11191e533fd39af3293db3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Alzheimer Disease - diagnostic imaging</topic><topic>Alzheimer's disease</topic><topic>Amyloid beta-Peptides - metabolism</topic><topic>Angiography</topic><topic>Biomarkers</topic><topic>Biomarkers - metabolism</topic><topic>Brain - diagnostic imaging</topic><topic>Classification</topic><topic>Cognitive ability</topic><topic>Cognitive Dysfunction - diagnostic imaging</topic><topic>Degeneration</topic><topic>diffusion tensor imaging</topic><topic>Diffusion Tensor Imaging - methods</topic><topic>Effectiveness</topic><topic>Female</topic><topic>Females</topic><topic>Field strength</topic><topic>free water</topic><topic>Humans</topic><topic>Magnetic resonance imaging</topic><topic>Male</topic><topic>Medical imaging</topic><topic>Middle Aged</topic><topic>Neurodegenerative diseases</topic><topic>Permutations</topic><topic>Population studies</topic><topic>Positron-Emission Tomography</topic><topic>Regression analysis</topic><topic>Reproducibility of Results</topic><topic>Retrospective Studies</topic><topic>Sensitivity analysis</topic><topic>Sensitivity and Specificity</topic><topic>Spatial memory</topic><topic>Statistical analysis</topic><topic>Statistical tests</topic><topic>Substantia alba</topic><topic>support vector machine</topic><topic>Support vector machines</topic><topic>Tensors</topic><topic>Water - chemistry</topic><topic>white matter</topic><topic>White Matter - diagnostic imaging</topic><topic>β-Amyloid</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sun, Xuan</creatorcontrib><creatorcontrib>Zhao, Cui</creatorcontrib><creatorcontrib>Chen, Si‐Yu</creatorcontrib><creatorcontrib>Chang, Yan</creatorcontrib><creatorcontrib>Han, Yu‐Liang</creatorcontrib><creatorcontrib>Li, Ke</creatorcontrib><creatorcontrib>Sun, Hong‐Mei</creatorcontrib><creatorcontrib>Wang, Zhen‐Fu</creatorcontrib><creatorcontrib>Liang, Ying</creatorcontrib><creatorcontrib>Jia, Jian‐Jun</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of magnetic resonance imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sun, Xuan</au><au>Zhao, Cui</au><au>Chen, Si‐Yu</au><au>Chang, Yan</au><au>Han, Yu‐Liang</au><au>Li, Ke</au><au>Sun, Hong‐Mei</au><au>Wang, Zhen‐Fu</au><au>Liang, Ying</au><au>Jia, Jian‐Jun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Free Water MR Imaging of White Matter Microstructural Changes is a Sensitive Marker of Amyloid Positivity in Alzheimer's Disease</atitle><jtitle>Journal of magnetic resonance imaging</jtitle><addtitle>J Magn Reson Imaging</addtitle><date>2024-10</date><risdate>2024</risdate><volume>60</volume><issue>4</issue><spage>1458</spage><epage>1469</epage><pages>1458-1469</pages><issn>1053-1807</issn><issn>1522-2586</issn><eissn>1522-2586</eissn><abstract>Background
Extracellular free water (FW) resulting from white matter degeneration limits the sensitivity of diffusion tensor imaging (DTI) in predicting Alzheimer's disease (AD).
Purpose
To evaluate the sensitivity of FW‐DTI in detecting white matter microstructural changes in AD. To validate the effectiveness of FW‐DTI indices to predict amyloid‐beta (Aβ) positivity in mild cognitive impairment (MCI) subtypes.
Study Type
Retrospective.
Population
Thirty‐eight Aβ‐negative cognitively healthy (CH) controls (68.74 ± 8.28 years old, 55% female), 15 Aβ‐negative MCI patients (MCI‐n) (68.87 ± 8.83 years old, 60% female), 29 Aβ‐positive MCI patients (MCI‐p) (73.03 ± 7.05 years old, 52% female), and 29 Aβ‐positive AD patients (72.93 ± 9.11 years old, 55% female).
Field Strength/Sequence
3.0T; DTI, T1‐weighted, T2‐weighted, T2 star‐weighted angiography, and Aβ PET (18F‐florbetaben or 11C‐PIB).
Assessment
FW‐corrected and standard diffusion indices were analyzed using trace‐based spatial statistics. Area under the curve (AUC) in distinguishing MCI subtypes were compared using support vector machine (SVM).
Statistical Tests
Chi‐squared test, one‐way analysis of covariance, general linear regression analyses, nonparametric permutation tests, partial Pearson's correlation, receiver operating characteristic curve analysis, and linear SVM. A P value <0.05 was considered statistically significant.
Results
Compared with CH/MCI‐n/MCI‐p, AD showed significant change in tissue compartment indices of FW‐DTI. No difference was found in the FW index among pair‐wise group comparisons (the minimum FWE‐corrected P = 0.114). There was a significant association between FW‐DTI indices and memory and visuospatial function. The SVM classifier with tissue radial diffusivity as an input feature had the best classification performance of MCI subtypes (AUC = 0.91), and the classifying accuracy of FW‐DTI was all over 89.89%.
Data Conclusion
FW‐DTI indices prove to be potential biomarkers of AD. The classification of MCI subtypes based on SVM and FW‐DTI indices has good accuracy and could help early diagnosis.
Evidence Level
4
Technical Efficacy
Stage 2</abstract><cop>Hoboken, USA</cop><pub>John Wiley & Sons, Inc</pub><pmid>38100518</pmid><doi>10.1002/jmri.29189</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-0747-4132</orcidid></addata></record> |
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| identifier | ISSN: 1053-1807 |
| ispartof | Journal of magnetic resonance imaging, 2024-10, Vol.60 (4), p.1458-1469 |
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| language | eng |
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| subjects | Aged Aged, 80 and over Alzheimer Disease - diagnostic imaging Alzheimer's disease Amyloid beta-Peptides - metabolism Angiography Biomarkers Biomarkers - metabolism Brain - diagnostic imaging Classification Cognitive ability Cognitive Dysfunction - diagnostic imaging Degeneration diffusion tensor imaging Diffusion Tensor Imaging - methods Effectiveness Female Females Field strength free water Humans Magnetic resonance imaging Male Medical imaging Middle Aged Neurodegenerative diseases Permutations Population studies Positron-Emission Tomography Regression analysis Reproducibility of Results Retrospective Studies Sensitivity analysis Sensitivity and Specificity Spatial memory Statistical analysis Statistical tests Substantia alba support vector machine Support vector machines Tensors Water - chemistry white matter White Matter - diagnostic imaging β-Amyloid |
| title | Free Water MR Imaging of White Matter Microstructural Changes is a Sensitive Marker of Amyloid Positivity in Alzheimer's Disease |
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