Quantitative susceptibility mapping to evaluate the early stage of Alzheimer's disease
The objective of this study was to evaluate susceptibility changes caused by iron accumulation in cognitive normal (CN) elderly, those with amnestic mild cognitive impairment (aMCI), and those with early state AD, and to compare the findings with gray matter volume (GMV) changes caused by neuronal l...
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| Published in | NeuroImage clinical Vol. 16; pp. 429 - 438 |
|---|---|
| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Netherlands
Elsevier Inc
01.01.2017
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2213-1582 2213-1582 |
| DOI | 10.1016/j.nicl.2017.08.019 |
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| Abstract | The objective of this study was to evaluate susceptibility changes caused by iron accumulation in cognitive normal (CN) elderly, those with amnestic mild cognitive impairment (aMCI), and those with early state AD, and to compare the findings with gray matter volume (GMV) changes caused by neuronal loss. The participants included 19 elderly CN, 19 aMCI, and 19 AD subjects. The voxel-based quantitative susceptibility map (QSM) and GMV in the brain were calculated and the differences of those insides were compared among the three groups. The differences of the QSM data and GMVs among the three groups were investigated by voxel-based and region of interest (ROI)-based comparisons using a one-way analysis of covariance (ANCOVA) test with the gender and age as covariates. Finally, a receiver-operating-characteristic (ROC) curve analysis was performed. The voxel-based results showed that QSM demonstrated more areas with significant difference between the CN and AD groups compared to GMV. GMVs were decreased, but QSM values were increased in aMCI and AD groups compared with the CN group. QSM better differentiated aMCI from CN than GMV in the precuneus and allocortex regions. In the accumulation regions of iron and amyloid β, QSM can be used to differentiate between CN and aMCI groups, indicating a useful an auxiliary imaging for early diagnosis of AD.
•The susceptibility difference was more sensitive than GMV change in known regions of iron and the amyloid β accumulations.•QSM values were increased in patients compared with normal elderly and better differentiated aMCI from CN than GMV values.•The QSM technology can be used as an auxiliary imaging for early diagnosis of AD. |
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| AbstractList | AbstractThe objective of this study was to evaluate susceptibility changes caused by iron accumulation in cognitive normal (CN) elderly, those with amnestic mild cognitive impairment (aMCI), and those with early state AD, and to compare the findings with gray matter volume (GMV) changes caused by neuronal loss. The participants included 19 elderly CN, 19 aMCI, and 19 AD subjects. The voxel-based quantitative susceptibility map (QSM) and GMV in the brain were calculated and the differences of those insides were compared among the three groups. The differences of the QSM data and GMVs among the three groups were investigated by voxel-based and region of interest (ROI)-based comparisons using a one-way analysis of covariance (ANCOVA) test with the gender and age as covariates. Finally, a receiver-operating-characteristic (ROC) curve analysis was performed. The voxel-based results showed that QSM demonstrated more areas with significant difference between the CN and AD groups compared to GMV. GMVs were decreased, but QSM values were increased in aMCI and AD groups compared with the CN group. QSM better differentiated aMCI from CN than GMV in the precuneus and allocortex regions. In the accumulation regions of iron and amyloid β, QSM can be used to differentiate between CN and aMCI groups, indicating a useful an auxiliary imaging for early diagnosis of AD. The objective of this study was to evaluate susceptibility changes caused by iron accumulation in cognitive normal (CN) elderly, those with amnestic mild cognitive impairment (aMCI), and those with early state AD, and to compare the findings with gray matter volume (GMV) changes caused by neuronal loss. The participants included 19 elderly CN, 19 aMCI, and 19 AD subjects. The voxel-based quantitative susceptibility map (QSM) and GMV in the brain were calculated and the differences of those insides were compared among the three groups. The differences of the QSM data and GMVs among the three groups were investigated by voxel-based and region of interest (ROI)-based comparisons using a one-way analysis of covariance (ANCOVA) test with the gender and age as covariates. Finally, a receiver-operating-characteristic (ROC) curve analysis was performed. The voxel-based results showed that QSM demonstrated more areas with significant difference between the CN and AD groups compared to GMV. GMVs were decreased, but QSM values were increased in aMCI and AD groups compared with the CN group. QSM better differentiated aMCI from CN than GMV in the precuneus and allocortex regions. In the accumulation regions of iron and amyloid β, QSM can be used to differentiate between CN and aMCI groups, indicating a useful an auxiliary imaging for early diagnosis of AD. •The susceptibility difference was more sensitive than GMV change in known regions of iron and the amyloid β accumulations.•QSM values were increased in patients compared with normal elderly and better differentiated aMCI from CN than GMV values.•The QSM technology can be used as an auxiliary imaging for early diagnosis of AD. The objective of this study was to evaluate susceptibility changes caused by iron accumulation in cognitive normal (CN) elderly, those with amnestic mild cognitive impairment (aMCI), and those with early state AD, and to compare the findings with gray matter volume (GMV) changes caused by neuronal loss. The participants included 19 elderly CN, 19 aMCI, and 19 AD subjects. The voxel-based quantitative susceptibility map (QSM) and GMV in the brain were calculated and the differences of those insides were compared among the three groups. The differences of the QSM data and GMVs among the three groups were investigated by voxel-based and region of interest (ROI)-based comparisons using a one-way analysis of covariance (ANCOVA) test with the gender and age as covariates. Finally, a receiver-operating-characteristic (ROC) curve analysis was performed. The voxel-based results showed that QSM demonstrated more areas with significant difference between the CN and AD groups compared to GMV. GMVs were decreased, but QSM values were increased in aMCI and AD groups compared with the CN group. QSM better differentiated aMCI from CN than GMV in the precuneus and allocortex regions. In the accumulation regions of iron and amyloid β, QSM can be used to differentiate between CN and aMCI groups, indicating a useful an auxiliary imaging for early diagnosis of AD. The objective of this study was to evaluate susceptibility changes caused by iron accumulation in cognitive normal (CN) elderly, those with amnestic mild cognitive impairment (aMCI), and those with early state AD, and to compare the findings with gray matter volume (GMV) changes caused by neuronal loss. The participants included 19 elderly CN, 19 aMCI, and 19 AD subjects. The voxel-based quantitative susceptibility map (QSM) and GMV in the brain were calculated and the differences of those insides were compared among the three groups. The differences of the QSM data and GMVs among the three groups were investigated by voxel-based and region of interest (ROI)-based comparisons using a one-way analysis of covariance (ANCOVA) test with the gender and age as covariates. Finally, a receiver-operating-characteristic (ROC) curve analysis was performed. The voxel-based results showed that QSM demonstrated more areas with significant difference between the CN and AD groups compared to GMV. GMVs were decreased, but QSM values were increased in aMCI and AD groups compared with the CN group. QSM better differentiated aMCI from CN than GMV in the precuneus and allocortex regions. In the accumulation regions of iron and amyloid β, QSM can be used to differentiate between CN and aMCI groups, indicating a useful an auxiliary imaging for early diagnosis of AD. • The susceptibility difference was more sensitive than GMV change in known regions of iron and the amyloid β accumulations. • QSM values were increased in patients compared with normal elderly and better differentiated aMCI from CN than GMV values. • The QSM technology can be used as an auxiliary imaging for early diagnosis of AD. The objective of this study was to evaluate susceptibility changes caused by iron accumulation in cognitive normal (CN) elderly, those with amnestic mild cognitive impairment (aMCI), and those with early state AD, and to compare the findings with gray matter volume (GMV) changes caused by neuronal loss. The participants included 19 elderly CN, 19 aMCI, and 19 AD subjects. The voxel-based quantitative susceptibility map (QSM) and GMV in the brain were calculated and the differences of those insides were compared among the three groups. The differences of the QSM data and GMVs among the three groups were investigated by voxel-based and region of interest (ROI)-based comparisons using a one-way analysis of covariance (ANCOVA) test with the gender and age as covariates. Finally, a receiver-operating-characteristic (ROC) curve analysis was performed. The voxel-based results showed that QSM demonstrated more areas with significant difference between the CN and AD groups compared to GMV. GMVs were decreased, but QSM values were increased in aMCI and AD groups compared with the CN group. QSM better differentiated aMCI from CN than GMV in the precuneus and allocortex regions. In the accumulation regions of iron and amyloid β, QSM can be used to differentiate between CN and aMCI groups, indicating a useful an auxiliary imaging for early diagnosis of AD. Keywords: Alzheimer's disease (AD), Mild cognitive impairment (MCI), Quantitative susceptibility mapping (QSM), Gray matter volume The objective of this study was to evaluate susceptibility changes caused by iron accumulation in cognitive normal (CN) elderly, those with amnestic mild cognitive impairment (aMCI), and those with early state AD, and to compare the findings with gray matter volume (GMV) changes caused by neuronal loss. The participants included 19 elderly CN, 19 aMCI, and 19 AD subjects. The voxel-based quantitative susceptibility map (QSM) and GMV in the brain were calculated and the differences of those insides were compared among the three groups. The differences of the QSM data and GMVs among the three groups were investigated by voxel-based and region of interest (ROI)-based comparisons using a one-way analysis of covariance (ANCOVA) test with the gender and age as covariates. Finally, a receiver-operating-characteristic (ROC) curve analysis was performed. The voxel-based results showed that QSM demonstrated more areas with significant difference between the CN and AD groups compared to GMV. GMVs were decreased, but QSM values were increased in aMCI and AD groups compared with the CN group. QSM better differentiated aMCI from CN than GMV in the precuneus and allocortex regions. In the accumulation regions of iron and amyloid β, QSM can be used to differentiate between CN and aMCI groups, indicating a useful an auxiliary imaging for early diagnosis of AD.The objective of this study was to evaluate susceptibility changes caused by iron accumulation in cognitive normal (CN) elderly, those with amnestic mild cognitive impairment (aMCI), and those with early state AD, and to compare the findings with gray matter volume (GMV) changes caused by neuronal loss. The participants included 19 elderly CN, 19 aMCI, and 19 AD subjects. The voxel-based quantitative susceptibility map (QSM) and GMV in the brain were calculated and the differences of those insides were compared among the three groups. The differences of the QSM data and GMVs among the three groups were investigated by voxel-based and region of interest (ROI)-based comparisons using a one-way analysis of covariance (ANCOVA) test with the gender and age as covariates. Finally, a receiver-operating-characteristic (ROC) curve analysis was performed. The voxel-based results showed that QSM demonstrated more areas with significant difference between the CN and AD groups compared to GMV. GMVs were decreased, but QSM values were increased in aMCI and AD groups compared with the CN group. QSM better differentiated aMCI from CN than GMV in the precuneus and allocortex regions. In the accumulation regions of iron and amyloid β, QSM can be used to differentiate between CN and aMCI groups, indicating a useful an auxiliary imaging for early diagnosis of AD. |
| Author | Wang, Yi Jahng, Geon-Ho Ryu, Chang-Woo Park, Soonchan Lee, Soo Yeol Kim, Hyug-Gi Rhee, Hak Young Rhee, Sun Jung Lee, Kyung Mi |
| AuthorAffiliation | c Department of Neurology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, 892 Dongnam-ro, Gangdong-Gu, Seoul 05278, Republic of Korea b Department of Radiology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, 892 Dongnam-ro, Gangdong-Gu, Seoul 05278, Republic of Korea e Department of Biomedical Engineering and Radiology, Cornell University, 515 E 71st Street, Suite 102, New York, NY 10021, USA d Department of Radiology, Kyung Hee University Hospital, College of Medicine, Kyung Hee University, 23 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea a Department of Biomedical Engineering, Graduate School, Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Republic of Korea |
| AuthorAffiliation_xml | – name: c Department of Neurology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, 892 Dongnam-ro, Gangdong-Gu, Seoul 05278, Republic of Korea – name: a Department of Biomedical Engineering, Graduate School, Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Republic of Korea – name: d Department of Radiology, Kyung Hee University Hospital, College of Medicine, Kyung Hee University, 23 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea – name: e Department of Biomedical Engineering and Radiology, Cornell University, 515 E 71st Street, Suite 102, New York, NY 10021, USA – name: b Department of Radiology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, 892 Dongnam-ro, Gangdong-Gu, Seoul 05278, Republic of Korea |
| Author_xml | – sequence: 1 givenname: Hyug-Gi surname: Kim fullname: Kim, Hyug-Gi organization: Department of Biomedical Engineering, Graduate School, Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Republic of Korea – sequence: 2 givenname: Soonchan surname: Park fullname: Park, Soonchan organization: Department of Radiology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, 892 Dongnam-ro, Gangdong-Gu, Seoul 05278, Republic of Korea – sequence: 3 givenname: Hak Young surname: Rhee fullname: Rhee, Hak Young organization: Department of Neurology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, 892 Dongnam-ro, Gangdong-Gu, Seoul 05278, Republic of Korea – sequence: 4 givenname: Kyung Mi surname: Lee fullname: Lee, Kyung Mi organization: Department of Radiology, Kyung Hee University Hospital, College of Medicine, Kyung Hee University, 23 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea – sequence: 5 givenname: Chang-Woo surname: Ryu fullname: Ryu, Chang-Woo organization: Department of Radiology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, 892 Dongnam-ro, Gangdong-Gu, Seoul 05278, Republic of Korea – sequence: 6 givenname: Sun Jung surname: Rhee fullname: Rhee, Sun Jung organization: Department of Radiology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, 892 Dongnam-ro, Gangdong-Gu, Seoul 05278, Republic of Korea – sequence: 7 givenname: Soo Yeol surname: Lee fullname: Lee, Soo Yeol organization: Department of Biomedical Engineering, Graduate School, Kyung Hee University, 1732, Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 446-701, Republic of Korea – sequence: 8 givenname: Yi surname: Wang fullname: Wang, Yi organization: Department of Biomedical Engineering and Radiology, Cornell University, 515 E 71st Street, Suite 102, New York, NY 10021, USA – sequence: 9 givenname: Geon-Ho orcidid: 0000-0001-8881-1884 surname: Jahng fullname: Jahng, Geon-Ho email: ghjahng@khu.ac.kr, ghjahng@gmail.com organization: Department of Radiology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, 892 Dongnam-ro, Gangdong-Gu, Seoul 05278, Republic of Korea |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28879084$$D View this record in MEDLINE/PubMed |
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| PublicationDecade | 2010 |
| PublicationPlace | Netherlands |
| PublicationPlace_xml | – name: Netherlands |
| PublicationTitle | NeuroImage clinical |
| PublicationTitleAlternate | Neuroimage Clin |
| PublicationYear | 2017 |
| Publisher | Elsevier Inc Elsevier |
| Publisher_xml | – name: Elsevier Inc – name: Elsevier |
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| Snippet | The objective of this study was to evaluate susceptibility changes caused by iron accumulation in cognitive normal (CN) elderly, those with amnestic mild... AbstractThe objective of this study was to evaluate susceptibility changes caused by iron accumulation in cognitive normal (CN) elderly, those with amnestic... |
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| SubjectTerms | Aged Aging - metabolism Aging - pathology Alzheimer Disease - diagnostic imaging Alzheimer Disease - metabolism Alzheimer Disease - pathology Alzheimer's disease (AD) Amnesia - diagnostic imaging Amnesia - metabolism Amnesia - pathology Cognitive Dysfunction - diagnostic imaging Cognitive Dysfunction - metabolism Cognitive Dysfunction - pathology Female Gray Matter - diagnostic imaging Gray Matter - metabolism Gray Matter - pathology Gray matter volume Humans Iron - metabolism Magnetic Resonance Imaging - methods Male Middle Aged Mild cognitive impairment (MCI) Quantitative susceptibility mapping (QSM) Radiology Regular |
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| Title | Quantitative susceptibility mapping to evaluate the early stage of Alzheimer's disease |
| URI | https://www.clinicalkey.com/#!/content/1-s2.0-S2213158217302103 https://www.clinicalkey.es/playcontent/1-s2.0-S2213158217302103 https://www.ncbi.nlm.nih.gov/pubmed/28879084 https://www.proquest.com/docview/1936624950 https://pubmed.ncbi.nlm.nih.gov/PMC5577408 https://doaj.org/article/9ac4cc2e5ecb4b01a7d956174b189ee2 |
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