1
|
Lin Q, Shahid S, Hone‐Blanchet A, Huang S, Wu J, Bisht A, Loring D, Goldstein F, Levey A, Crosson B, Lah J, Qiu D. Magnetic resonance evidence of increased iron content in subcortical brain regions in asymptomatic Alzheimer's disease. Hum Brain Mapp 2023; 44:3072-3083. [PMID: 36929676 PMCID: PMC10171513 DOI: 10.1002/hbm.26263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 02/06/2023] [Accepted: 02/22/2023] [Indexed: 03/18/2023] Open
Abstract
While iron over-accumulation has been reported in late stage Alzheimer's disease (AD), whether this occurs early in the asymptomatic stage of AD remains unknown. We aimed to assess brain iron levels in asymptomatic AD using quantitative MR relaxometry of effective transverse relaxation rate (R2*) and longitudinal relaxation rate (R1), and recruited 118 participants comprised of three groups including healthy young participants, and cognitively normal older individuals without or with positive AD biomarkers based on cerebrospinal fluid (CSF) proteomics analysis. Compared with the healthy young group, increased R2* was found in widespread cortical and subcortical regions in the older groups. Further, significantly higher levels of R2* were found in the cognitively normal older subjects with positive CSF AD biomarker (i.e., asymptomatic AD) compared with those with negative AD biomarker in subcortical regions including the left and right caudate, left and right putamen, and left and right globus pallidus (p < .05 for all regions), suggesting increased iron content in these regions. Subcortical R2* of some regions was found to significantly correlate with CSF AD biomarkers and neuropsychological assessments of visuospatial functions. In conclusion, R2* could be a valuable biomarker for studying early pathophysiological changes in AD.
Collapse
Affiliation(s)
- Qixiang Lin
- Department of Neurology, School of MedicineEmory UniversityAtlantaGeorgiaUSA
| | - Salman Shahid
- Department of Neurology, School of MedicineEmory UniversityAtlantaGeorgiaUSA
| | | | - Shuai Huang
- Department of Radiology and Imaging Sciences, School of MedicineEmory UniversityAtlantaGeorgiaUSA
| | - Junjie Wu
- Department of Radiology and Imaging Sciences, School of MedicineEmory UniversityAtlantaGeorgiaUSA
| | - Aditya Bisht
- Department of Neurology, School of MedicineEmory UniversityAtlantaGeorgiaUSA
| | - David Loring
- Department of Neurology, School of MedicineEmory UniversityAtlantaGeorgiaUSA
| | - Felicia Goldstein
- Department of Neurology, School of MedicineEmory UniversityAtlantaGeorgiaUSA
- Goizueta Alzheimer's Disease Research CenterEmory UniversityAtlantaGeorgiaUSA
| | - Allan Levey
- Department of Neurology, School of MedicineEmory UniversityAtlantaGeorgiaUSA
- Goizueta Alzheimer's Disease Research CenterEmory UniversityAtlantaGeorgiaUSA
| | - Bruce Crosson
- Department of Neurology, School of MedicineEmory UniversityAtlantaGeorgiaUSA
- Department of Radiology and Imaging Sciences, School of MedicineEmory UniversityAtlantaGeorgiaUSA
| | - James Lah
- Department of Neurology, School of MedicineEmory UniversityAtlantaGeorgiaUSA
- Goizueta Alzheimer's Disease Research CenterEmory UniversityAtlantaGeorgiaUSA
| | - Deqiang Qiu
- Department of Radiology and Imaging Sciences, School of MedicineEmory UniversityAtlantaGeorgiaUSA
- Goizueta Alzheimer's Disease Research CenterEmory UniversityAtlantaGeorgiaUSA
- Joint Department of Biomedical EngineeringEmory University and Georgia Institute of TechnologyAtlantaGeorgiaUSA
| |
Collapse
|
3
|
Dvorak AV, Swift-LaPointe T, Vavasour IM, Lee LE, Abel S, Russell-Schulz B, Graf C, Wurl A, Liu H, Laule C, Li DKB, Traboulsee A, Tam R, Boyd LA, MacKay AL, Kolind SH. An atlas for human brain myelin content throughout the adult life span. Sci Rep 2021; 11:269. [PMID: 33431990 PMCID: PMC7801525 DOI: 10.1038/s41598-020-79540-3] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 12/09/2020] [Indexed: 12/11/2022] Open
Abstract
Myelin water imaging is a quantitative neuroimaging technique that provides the myelin water fraction (MWF), a metric highly specific to myelin content, and the intra-/extra-cellular T2 (IET2), which is related to water and iron content. We coupled high-resolution data from 100 adults with gold-standard methodology to create an optimized anatomical brain template and accompanying MWF and IET2 atlases. We then used the MWF atlas to characterize how myelin content relates to demographic factors. In most brain regions, myelin content followed a quadratic pattern of increase during the third decade of life, plateau at a maximum around the fifth decade, then decrease during later decades. The ranking of mean myelin content between brain regions remained consistent across age groups. These openly available normative atlases can facilitate evaluation of myelin imaging results on an individual basis and elucidate the distribution of myelin content between brain regions and in the context of aging.
Collapse
Affiliation(s)
- Adam V Dvorak
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada. .,International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, BC, Canada.
| | | | - Irene M Vavasour
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.,Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Lisa Eunyoung Lee
- Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
| | - Shawna Abel
- Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
| | | | - Carina Graf
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.,International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, BC, Canada
| | - Anika Wurl
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
| | - Hanwen Liu
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.,International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, BC, Canada
| | - Cornelia Laule
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.,International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, BC, Canada.,Radiology, University of British Columbia, Vancouver, BC, Canada.,Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - David K B Li
- Radiology, University of British Columbia, Vancouver, BC, Canada.,Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
| | - Anthony Traboulsee
- Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
| | - Roger Tam
- Radiology, University of British Columbia, Vancouver, BC, Canada.,Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Lara A Boyd
- Department of Physical Therapy, University of British Columbia, Vancouver, BC, Canada
| | - Alex L MacKay
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.,Radiology, University of British Columbia, Vancouver, BC, Canada
| | - Shannon H Kolind
- Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada.,International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, BC, Canada.,Radiology, University of British Columbia, Vancouver, BC, Canada.,Medicine (Neurology), University of British Columbia, Vancouver, BC, Canada
| |
Collapse
|
4
|
Madan CR. Beyond volumetry: Considering age-related changes in brain shape complexity using fractal dimensionality. AGING BRAIN 2021; 1:100016. [PMID: 36911503 PMCID: PMC9997150 DOI: 10.1016/j.nbas.2021.100016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 05/07/2021] [Accepted: 05/09/2021] [Indexed: 10/21/2022] Open
Abstract
Gray matter volume for cortical, subcortical, and ventricles all vary with age. However, these volumetric changes do not happen on their own, there are also age-related changes in cortical folding and other measures of brain shape. Fractal dimensionality has emerged as a more sensitive measure of brain structure, capturing both volumetric and shape-related differences. For subcortical structures it is readily apparent that segmented structures do not differ in volume in isolation-adjacent regions must also vary in shape. Fractal dimensionality here also appears to be more sensitive to these age-related differences than volume. Given these differences in structure are quite prominent in structure, caution should be used when examining comparisons across age in brain function measures, as standard normalisation methods are not robust enough to adjust for these inter-individual differences in cortical structure.
Collapse
|