Specific White Matter Tracts and Diffusion Properties Predict Conversion From Mild Cognitive Impairment to Alzheimer’s Disease
Identifying biomarkers that can assess the risk of developing Alzheimer’s Disease (AD) remains a significant challenge. In this study, we investigated the integrity levels of brain white matter in 34 patients with mild cognitive impairment (MCI) who later converted to AD and 53 stable MCI patients....
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2021-07-01
|
| Series: | Frontiers in Aging Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnagi.2021.711579/full |
| _version_ | 1818641761132609536 |
|---|---|
| author | David B. Stone Sephira G. Ryman Alexandra P. Hartman Christopher J. Wertz Andrei A. Vakhtin Alzheimer’s Disease Neuroimaging Initiative |
| author_facet | David B. Stone Sephira G. Ryman Alexandra P. Hartman Christopher J. Wertz Andrei A. Vakhtin Alzheimer’s Disease Neuroimaging Initiative |
| author_sort | David B. Stone |
| collection | DOAJ |
| description | Identifying biomarkers that can assess the risk of developing Alzheimer’s Disease (AD) remains a significant challenge. In this study, we investigated the integrity levels of brain white matter in 34 patients with mild cognitive impairment (MCI) who later converted to AD and 53 stable MCI patients. We used diffusion tensor imaging (DTI) and automated fiber quantification to obtain the diffusion properties of 20 major white matter tracts. To identify which tracts and diffusion measures are most relevant to AD conversion, we used support vector machines (SVMs) to classify the AD conversion and non-conversion MCI patients based on the diffusion properties of each tract individually. We found that diffusivity measures from seven white matter tracts were predictive of AD conversion with axial diffusivity being the most predictive diffusion measure. Additional analyses revealed that white matter changes in the central and parahippocampal terminal regions of the right cingulate hippocampal bundle, central regions of the right inferior frontal occipital fasciculus, and posterior and anterior regions of the left inferior longitudinal fasciculus were the best predictors of conversion from MCI to AD. An SVM based on these white matter tract regions achieved an accuracy of 0.75. These findings provide additional potential biomarkers of AD risk in MCI patients. |
| first_indexed | 2024-12-16T23:32:18Z |
| format | Article |
| id | doaj.art-85d33f5df07e4f0bb632782681f9dccd |
| institution | Directory Open Access Journal |
| issn | 1663-4365 |
| language | English |
| last_indexed | 2024-12-16T23:32:18Z |
| publishDate | 2021-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Aging Neuroscience |
| spelling | doaj.art-85d33f5df07e4f0bb632782681f9dccd2022-12-21T22:11:49ZengFrontiers Media S.A.Frontiers in Aging Neuroscience1663-43652021-07-011310.3389/fnagi.2021.711579711579Specific White Matter Tracts and Diffusion Properties Predict Conversion From Mild Cognitive Impairment to Alzheimer’s DiseaseDavid B. StoneSephira G. RymanAlexandra P. HartmanChristopher J. WertzAndrei A. VakhtinAlzheimer’s Disease Neuroimaging InitiativeIdentifying biomarkers that can assess the risk of developing Alzheimer’s Disease (AD) remains a significant challenge. In this study, we investigated the integrity levels of brain white matter in 34 patients with mild cognitive impairment (MCI) who later converted to AD and 53 stable MCI patients. We used diffusion tensor imaging (DTI) and automated fiber quantification to obtain the diffusion properties of 20 major white matter tracts. To identify which tracts and diffusion measures are most relevant to AD conversion, we used support vector machines (SVMs) to classify the AD conversion and non-conversion MCI patients based on the diffusion properties of each tract individually. We found that diffusivity measures from seven white matter tracts were predictive of AD conversion with axial diffusivity being the most predictive diffusion measure. Additional analyses revealed that white matter changes in the central and parahippocampal terminal regions of the right cingulate hippocampal bundle, central regions of the right inferior frontal occipital fasciculus, and posterior and anterior regions of the left inferior longitudinal fasciculus were the best predictors of conversion from MCI to AD. An SVM based on these white matter tract regions achieved an accuracy of 0.75. These findings provide additional potential biomarkers of AD risk in MCI patients.https://www.frontiersin.org/articles/10.3389/fnagi.2021.711579/fullAlzheimer’s diseasediffusion tensor imagingsupport vector machinemild cognitive impairmentautomated fiber quantificationtractography |
| spellingShingle | David B. Stone Sephira G. Ryman Alexandra P. Hartman Christopher J. Wertz Andrei A. Vakhtin Alzheimer’s Disease Neuroimaging Initiative Specific White Matter Tracts and Diffusion Properties Predict Conversion From Mild Cognitive Impairment to Alzheimer’s Disease Frontiers in Aging Neuroscience Alzheimer’s disease diffusion tensor imaging support vector machine mild cognitive impairment automated fiber quantification tractography |
| title | Specific White Matter Tracts and Diffusion Properties Predict Conversion From Mild Cognitive Impairment to Alzheimer’s Disease |
| title_full | Specific White Matter Tracts and Diffusion Properties Predict Conversion From Mild Cognitive Impairment to Alzheimer’s Disease |
| title_fullStr | Specific White Matter Tracts and Diffusion Properties Predict Conversion From Mild Cognitive Impairment to Alzheimer’s Disease |
| title_full_unstemmed | Specific White Matter Tracts and Diffusion Properties Predict Conversion From Mild Cognitive Impairment to Alzheimer’s Disease |
| title_short | Specific White Matter Tracts and Diffusion Properties Predict Conversion From Mild Cognitive Impairment to Alzheimer’s Disease |
| title_sort | specific white matter tracts and diffusion properties predict conversion from mild cognitive impairment to alzheimer s disease |
| topic | Alzheimer’s disease diffusion tensor imaging support vector machine mild cognitive impairment automated fiber quantification tractography |
| url | https://www.frontiersin.org/articles/10.3389/fnagi.2021.711579/full |
| work_keys_str_mv | AT davidbstone specificwhitemattertractsanddiffusionpropertiespredictconversionfrommildcognitiveimpairmenttoalzheimersdisease AT sephiragryman specificwhitemattertractsanddiffusionpropertiespredictconversionfrommildcognitiveimpairmenttoalzheimersdisease AT alexandraphartman specificwhitemattertractsanddiffusionpropertiespredictconversionfrommildcognitiveimpairmenttoalzheimersdisease AT christopherjwertz specificwhitemattertractsanddiffusionpropertiespredictconversionfrommildcognitiveimpairmenttoalzheimersdisease AT andreiavakhtin specificwhitemattertractsanddiffusionpropertiespredictconversionfrommildcognitiveimpairmenttoalzheimersdisease AT alzheimersdiseaseneuroimaginginitiative specificwhitemattertractsanddiffusionpropertiespredictconversionfrommildcognitiveimpairmenttoalzheimersdisease |