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Abstract #4035

Brain Iron Deposition in Healthy Aging and association with Alzheimer’s Disease Pathology Revealed by Quantitative Susceptibility Mapping
Qixiang Lin1, Junjie Wu2, Shuai Huang2, Aditya Bisht1, Allan Levey1,3, James Lah1,3, and Deqiang Qiu2,3,41Department of Neurology, School of Medicine, Emory University, Atlanta, GA, United States, 2Department of Radiology and Imaging Sciences, School of Medicine, Emory University, Atlanta, GA, United States, 3Goizueta Alzheimer’s Disease Research Center, Emory University, Atlanta, GA, United States, 4Joint Department of BioMedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, United States

Synopsis

This study aims to evaluate brain iron depositions in healthy aging and asymptomatic Alzheimer’s Disease using Quantitative Susceptibility Mapping (QSM) MR technique. 3D-GRE scans were performed in 146 healthy old and 58 healthy young participants. Whole-brain voxel-wise analysis showed increased susceptibility value in bilateral basal ganglia and widespread cortical regions. Significant correlations were found between CSF biomarkers of AD and susceptibility value in the caudate and left orbital frontal area. Together these results suggest microstructural alterations associated with healthy aging and AD-related CSF biomarkers. QSM measurements might provide sensitive neuroimaging biomarkers for iron deposition during normal aging and neurodegeneration diseases.

Introduction

The accumulation of iron in the brain is a hallmark of aging and neurodegenerative diseases such as Alzheimer’s Disease (AD)1. Iron is essential for supporting many normal functions and plays vital roles in many biological processes in human brain including neurotransmitter synthesis, DNA replication, the formation of myelin etc2. Recent studies have shown increased iron deposition in the symptomatic stages of AD patients3-5. However, the evaluation of the brain iron level in pre-symptomatic AD remains largely under-studied and whether the iron deposition is related to the cerebrospinal fluid (CSF) biomarkers of AD is also unknown. Advances in quantitative susceptibility mapping (QSM) MRI techniques have allowed us to noninvasively measure the tissue iron concentration in deep grey matter and cortex6,7. Here, we utilized a multi-echo GRE protocol to quantify tissue apparent magnetic susceptibility (AMS) as a measure of iron depositions and their relationship with CSF biomarkers of AD in a large group of healthy old participants.

Materials and Methods

Participants: A total cohort of 204 participants including 58 healthy young (HY) participants (mean ± SD age: 26.7 ± 5.0; 35 males) and 146 cognitively normal healthy old participants (HO) (age: 65.2 ± 6.4; 41 males) were included from the Emory Brain Image Project, a component of the Emory Healthy Brain Study. MRI acquisitions: MRI data were acquired on a Siemens Magnetom Prisma 3T scanner with a 32-channel phased-array head coil. High-resolution magnitude and phase images were acquired with a multi-echo 3D Spoiled Gradient Echo (ME-GRE) protocol with the following parameters: TR = 37 ms, flip angle: 15° with 5 echoes, TE = 6.61, 12.85, 19.09, 25.33, 31.57 ms, voxel size = 0.72×0.72×1.44 mm3. 3D T1-weighted (T1w) image were acquired using a MPRAGE sequence with the following parameters: TR = 2300 ms, TE = 2.96 ms, TI = 900 ms, flip angle = 9°, 208 sagittal slices with slice thickness = 1 mm, in-plane matrix size = 256 × 240, isotropic voxel size.CSF biomarker Collection: Lumbar punctures were performed in the HO participants to obtain CSF samples, from which amyloid-β 1-42(Aβ), total tau (T-tau), and phosphorylated tau (pTau) were measured. T-tau/Aβ ratio was calculated as an indicator of Aβ and tau burden.Data analysis: First, apparent magnetic susceptibility images were reconstructed using an in-house QSM toolbox7 including phase unwarping, background field removal and magnetic field dipole inversion. We chose a frontal white matter region as the reference for AMS values. Second, the magnitude image of ME-GRE was corregistered to T1w structural image. Then both T1w and QSM images were spatially normalized to the MNI space using the nonlinear registration of Advanced Normalization Tools followed by smoothing with 5-mm FWHM kernel. Statistical analysis: Voxel-wise general linear model (GLM) analysis was applied to detect brain cortical and subcortical regions showing significantly different AMS values between HY and HO groups using randomise tools of FSL with 5000 permutation tests and Threshold-Free Cluster Enhancement (TFCE) to correct for the multiple comparisons. A corrected voxel-level p-value <0.005 was considered statistically significant. Another GLM model was performed to study the association between tissue magnetic susceptibility values and CSF biomarkers in the HO group with age as covariates. Permutation test with 5000 permutations and TFCE was performed to correct for the multiple comparisons with a corrected p <0.05.

Results

Figure 2A shows averaged magnetic susceptibility maps for the HY and HO groups. Compared with HY group, significantly increased magnetic susceptibility values were found in the HO group in subcortical regions including the left caudate, the left putamen, the left pallidum, the left accumbens, the right caudate, the right putamen, the right pallidum, the right amygdala, the right hippocampus, and widespread cortical regions as well as brain stem areas (Fig. 2B). Decreased magnetic susceptibility value was found primarily in left and right anterior insula area, left thalamus, right thalamus and left and right posterior cingula gyrus (Fig. 2B). The magnetic susceptibility values of the right caudate and the left orbital frontal cortex were significantly positively correlated with Total tau/Aβ ratio in the HO group. The magnetic susceptibility values of the left caudate, right caudate and left orbital frontal cortex were significantly positively correlated with pTau in HO group (Fig. 3). Figure 4 shows the scatter plots for mean susceptibility values of each significant cluster and corresponding CSF biomarkers in the HO group adjusted for the effects of age.

Discussion & Conclusion

In this study, we used 3D ME-GRE images and QSM to quantify brain magnetic susceptibility in a large group of healthy young and aged subjects. We found widespread changes in the tissue magnetic susceptibility of the human aging brain and identified brain regions showing association of magnetic susceptibility with CSF biomarkers of AD, suggesting that the iron deposition is related to the healthy aging and AD pathology. Significant correlations between brain magnetic susceptibility values and CSF biomarkers suggest that iron elevation in subcortical and cortical regions might be related to amyloid-β and phosphorylated tau depositions which are considered the underlying pathology for AD8,9. QSM offers a promising opportunity for investigating tissue iron concentration and could be potential imaging markers for both studying normal aging and early detection of AD.

Acknowledgements

The study was supported by Goizueta Foundation and National Institutes of Health (R21AG064405, R01AG070937, P30AG066511)

References

1. Ward RJ, Zucca FA, Duyn JH, Crichton RR, Zecca L. The role of iron in brain ageing and neurodegenerative disorders. Lancet Neurol 2014; 13(10): 1045-60.

2. Zecca L, Youdim MB, Riederer P, Connor JR, Crichton RR. Iron, brain ageing and neurodegenerative disorders. Nat Rev Neurosci 2004; 5(11): 863-73.

3. van Bergen JM, Li X, Hua J, et al. Colocalization of cerebral iron with Amyloid beta in Mild Cognitive Impairment. Sci Rep 2016; 6: 35514.

4. Kim HG, Park S, Rhee HY, et al. Quantitative susceptibility mapping to evaluate the early stage of Alzheimer's disease. Neuroimage Clin 2017; 16: 429-38.

5. Moon Y, Han SH, Moon WJ. Patterns of Brain Iron Accumulation in Vascular Dementia and Alzheimer's Dementia Using Quantitative Susceptibility Mapping Imaging. J Alzheimers Dis 2016; 51(3): 737-45.

6. Langkammer C, Schweser F, Krebs N, et al. Quantitative susceptibility mapping (QSM) as a means to measure brain iron? A post mortem validation study. Neuroimage 2012; 62(3): 1593-9.

7. Qiu D, Chan GC-F, Chu J, et al. MR Quantitative Susceptibility Imaging for the Evaluation of Iron Loading in the Brains of Patients with β-Thalassemia Major. American Journal of Neuroradiology 2014; 35(6): 1085-90.

8. Shaw LM, Vanderstichele H, Knapik-Czajka M, et al. Qualification of the analytical and clinical performance of CSF biomarker analyses in ADNI. Acta Neuropathol 2011; 121(5): 597-609.

9. Sperling RA, Aisen PS, Beckett LA, et al. Toward defining the preclinical stages of Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement 2011; 7(3): 280-92.

Qixiang Lin1, Junjie Wu2, Shuai Huang2, Aditya Bisht1, Allan Levey1,3, James Lah1,3, and Deqiang Qiu2,3,4
1Department of Neurology, School of Medicine, Emory University, Atlanta, GA, United States, 2Department of Radiology and Imaging Sciences, School of Medicine, Emory University, Atlanta, GA, United States, 3Goizueta Alzheimer’s Disease Research Center, Emory University, Atlanta, GA, United States, 4Joint Department of BioMedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, United States

Synopsis

This study aims to evaluate brain iron depositions in healthy aging and asymptomatic Alzheimer’s Disease using Quantitative Susceptibility Mapping (QSM) MR technique. 3D-GRE scans were performed in 146 healthy old and 58 healthy young participants. Whole-brain voxel-wise analysis showed increased susceptibility value in bilateral basal ganglia and widespread cortical regions. Significant correlations were found between CSF biomarkers of AD and susceptibility value in the caudate and left orbital frontal area. Together these results suggest microstructural alterations associated with healthy aging and AD-related CSF biomarkers. QSM measurements might provide sensitive neuroimaging biomarkers for iron deposition during normal aging and neurodegeneration diseases.

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