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[Preprint]. 2025 May 15:arXiv:2501.04706v2. [Version 2]

Associations between iron and mean kurtosis in iron-rich grey matter nuclei in aging

Jason Langley 1,+, Kitzia Solis 2, Vala Masjedizadeh 3, Murphy Shao 1, Ilana Bennett 2, Xiaoping P Hu 1,3
PMCID: PMC12133083  PMID: 40463705

Abstract

Mean kurtosis in iron-rich grey matter has values similar to that seen in white matter. We suspect these elevated values may be related to iron. Multi-shell diffusion and multi-echo gradient echo acquisitions were used to derive mean kurtosis and R2*, respectively. Mean kurtosis and R2* were measured in subcortical grey matter nuclei and white matter tracts in 93 older adults and 62 younger adults. Grey matter regions exhibited higher mean kurtosis and R2* in the older adult group whereas white matter regions had reduced mean kurtosis in the older adult group. Grey matter mean kurtosis was significantly correlated with R2* iron-rich grey matter nuclei in both groups. Our findings indicate that higher mean kurtosis in iron-rich grey matter structures may be due to either increased tissue complexity or to decreases in signal-to-noise ratios from iron deposition.

Keywords: kurtosis, iron, grey matter, aging

1. Introduction

Magnetic resonance imaging (MRI) techniques that are sensitive to the diffusion of water have been used to study effects of normal aging1–3 or disease4–6 on brain tissue microstructure. Diffusion tensor imaging (DTI) is a commonly used approach that models tissue as a single compartment with the assumption that diffusion is Gaussian. However, the cells, organelles, and cell membranes that comprise tissue can introduce barriers to diffusion and cause it to be a non-Gaussian process. Diffusion kurtosis imaging (DKI) is able to characterize non-Gaussian diffusion by relaxing this assumption7 and measures the degree to which diffusion differs from a Gaussian process, known as kurtosis. Higher kurtosis values are thought to be indicative of more complex tissue microstructure8,9.

Application of DKI to aging has consistently revealed lower kurtosis values in white matter tracts of older adults relative to younger adults10–15, consistent with evidence that aging is accompanied by degradation of white matter16,17, which would manifest as lower MK8,18. In contrast, results have been mixed in subcortical grey matter nuclei, with lower kurtosis in older than younger adults in the caudate and thalamus, but higher kurtosis in older than younger adults in the putamen11,12. Higher kurtosis has also been seen in the globus pallidus, putamen, and substantia nigra in participants with neurodegenerative disorders (Parkinson’s disease) relative to age-matched controls19,20. Whereas the mechanism for this increase is unknown19, its occurrence in grey matter nuclei that accumulate iron throughout the lifespan21–25 suggests that iron content may affect kurtosis. Iron is known to alter DTI metrics (mean diffusivity, fractional anisotropy) within iron-rich grey matter nuclei in aging and disease26–28 and has been attributed to an interaction between diffusion encoding gradients and the magnetic fields generated by iron deposits29,30. However, the effect of iron on diffusion kurtosis remains unknown.

The purpose of the present study was to test the hypothesis that elevated iron is a mechanism driving elevated kurtosis values in subcortical grey matter. In younger and older adults who underwent DKI, we characterized age group differences in iron and kurtosis in subcortical grey matter structures as well as in white matter tracts and then examined relationships between measures of iron and kurtosis in subcortical grey matter structures. To further test this relationship, we measured kurtosis in an agarose phantom with four levels of iron concentrations.

2. Methods

2.1. Discovery Cohort

2.2.1. Participants.

A cohort consisting of 110 older adults (65 female/45 male; mean age=69.3 years ± 6.3 years; age range: 60 years–87 years) and 63 younger adults (42 female/21 male; mean age=20.2 years ± 2.0 years; age range: 18 years–28 years) participated in this study. Each participant gave written informed consent prior to enrolling in the study as approved by the local institutional review board.

2.1.2. Structural acquisition and processing.

Images from a T1-weighted MP-RAGE sequence (echo time (TE)/repetition time (TR)/inversion time=3.02/2600/800 ms, flip angle=8°, voxel size=0.8×0.8×0.8 mm3) were used for registration from subject space to common space. T1-weighted images were analyzed with FMRIB Software Library (FSL). A transformation was derived between individual subject space to MNI152 T1-weighted space using FMRIB’s Linear Image Registration Tool (FLIRT) and FMRIB’s Nonlinear Image Registration Tool (FNIRT) in the FSL software package31,32.

2.1.3. Diffusion acquisition and processing.

Diffusion-weighted data were acquired using a spin-echo EPI with bipolar diffusion encoding gradients with the following parameters: TE/TR = 102/3500 ms, FOV = 212×182 mm, voxel size = 1.7×1.7×1.7 mm, 64 axial slices, six b=0 images, and multiband acceleration factor = 4. Diffusion-weighting was applied in 64 directions with two b-values (b=1500 s/mm2 and b=3000 s/mm2). Six b=0 images with reversed phase-encoding were acquired to correct for susceptibility distortions.

Diffusion data were denoised in mrtrix33, then corrected for motion, eddy current and susceptibility distortions using eddy in FSL34. Next, skull stripping of the T1-weighted image and susceptibility corrected b=0 image was performed using the brain extraction tool in the FSL software package35. Mean kurtosis (MK) was estimated in DIPY36.

2.1.4. Iron acquisition and processing.

Multi-echo data were collected with a 6-echo 3D gradient recalled echo sequence: TE1/∆TE/TR =4/6/40 ms, FOV=192×224 mm2, matrix size=192×224×96, and slice thickness=1.7 mm. R2* was calculated from the magnitude data assuming a monoexponential decay.

2.1.5. Regions of interest.

Globus pallidus, putamen, caudate nucleus, thalamus, and hippocampus regions of interest (ROIs) were defined using the Harvard-Oxford subcortical atlas and the dentate nucleus was defined using a previously published atlas37. MK and mean R2* were measured in each grey matter ROI. White matter ROIs for the superior longitudinal fasciculus, forceps major, and forceps minor were defined using the Johns-Hopkins white matter atlas and mean MK was measured in each ROI. For each ROI, voxels with MK values that were negative or greater than 4 were excluded.

2.2. ADNI Replication Cohort

2.2.1. Database and participants.

Data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu) were used to replicate the observed relationship between iron and kurtosis. Up-to-date information can be found at www.adni-info.org. The ADNI study was approved by the local Institutional Review Boards of all participating sites. Study subjects and, if applicable, their legal representatives, gave written informed consent at the time of enrollment for imaging data, genetic sample collection and clinical questionnaires.

The ADNI3 database was queried for individuals with T1-weighted, multi-shell diffusion MRI, and multi-echo gradient echo (2D-GRE) MRI images at the same scanning visit. From this cohort, we selected all individuals with a diagnostic status of control at the time of the visit, which included 72 control participants (48 female/24 male; mean age=73.4 years ± 7.5 years; age range: 56 years–90 years). Details regarding image acquisition parameters can be found at www.adni-info.org/methods. Imaging data were downloaded between December 2019 and January 2024.

2.2.2. Image processing.

Susceptibility images were processed as described in the previous sections. Diffusion data were first denoised 33 then corrected for motion and eddy currents using EDDY. Next, field maps were constructed and used to correct magnetic field inhomogeneities in the diffusion images using FUGUE as previously described38. Mean iron and kurtosis were extracted from each grey matter ROI as described above.

2.3. Phantom Experiment

An agar phantom (2.5% agarose) was constructed with 4 vials of ferric citrate embedded in the phantom (2.5% agarose; ferric citrate concentrations: 0.06 mMol, 0.09 mMol, 0.12 mMol, 0.15 mMol). The phantom was scanned with a diffusion-weighted acquisition (TE/TR=70/4000 ms; voxel size=1.4 mm isotropic, 30 diffusion directions with b=1000 s/mm2 and b=2000 s/mm2, 3 b=0 images were acquired, 15 averages). A second diffusion acquisition with identical parameters but with reverse phase encoding was acquired to correct for susceptibility distortions. Data were processed as in the human scans. Data were denoised in mrtrix33, then corrected for eddy current and susceptibility distortions using eddy in FSL. Mean kurtosis (MK) was estimated in DIPY36. ROIs in were placed in each vial and a control ROI was placed in a region with no iron at the center of the phantom.

2.4. Statistical Analysis

All statistical analyses were performed using IBM SPSS Statistics software version 24 (IBM Corporation, Somers, NY, USA) and results are reported as mean ± standard deviation. A P value less than 0.05 was considered significant for all statistical tests. Age group MK and R2* comparisons between the young and older cohort were made using a 2 Group (younger, older) × 9 (subcortical grey matter and white matter) Region analysis of variance (ANOVA) for MK comparison and separate 2 Group (younger, older) × 6 Region (subcortical grey matter) ANOVA for R2*. Significant interactions were followed with post hoc between-group two-tailed t-tests for each region.

Within older adults, relationships between age and both MK and iron (R2*) measures were assessed with separate Pearson correlations. These relationships were not assessed in younger adults due to their restricted age range.

To assess the relationship between iron and MK in subcortical grey matter, Pearson correlations between iron measures (R2*) and MK in grey matter ROIs were performed separately in each age group.

In the phantom experiment, the effect of iron concentration on MK was tested with a 5 Concentration (0, 0.06, 0.09, 0.12, 0.15) one-way ANOVA. If the interaction was significant, post hoc comparisons between MK values in each vial were performed using respective two-tailed t-tests.

3. Results

3.1. Discovery Cohort

3.1.1. Age group differences in mean kurtosis.

Maps of MK in each age group are shown in Figure 1. Significant main effects of Group (P<10−3; F=90.411) and region (P<10−3; F=790.539) were seen in the 2 Group (younger, older) × 9 (subcortical grey matter and white matter) Region ANOVA. A significant interaction was observed between Group and Region (P<10−3; F=31.656). Post hoc t-tests revealed that the older adult group had higher MK than the younger group in the putamen (P<10−3), globus pallidus (P=0.002), caudate nucleus (P<10−3), dentate nucleus (P<10−3), and hippocampus (P<10−3), but not in the thalamus (P=0.393). In white matter ROIs, the older adult group had lower MK than the younger group in the superior longitudinal fasciculus (P<10−3), forceps major (P=0.011), and forceps minor (P<10−3). These comparisons are shown in Figures 2 and 3 and mean values for each group are summarized in Table 1.

Figure 1.

Figure 1.

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Group average images for mean kurtosis (left column), R2* (middle column), and susceptibility (right column) in younger (top row) and older (bottom row) participants. These images were created by transforming each participant’s mean kurtosis, R2* map, or susceptibility map to Montreal Neurological Institute (MNI) common space and averaging within each group.

Figure 2.

Figure 2.

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Group comparisons of mean kurtosis (MK; shown in A), R2* (shown in B), and susceptibility (denoted Δχ; shown in C) for all grey matter ROIs considered in this analysis.

Figure 3.

Figure 3.

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Group comparisons of mean kurtosis in the superior longitudinal fasciculus, forceps major, and forceps minor are shown in the top row (A-C). Within older adults, associations between age and MK in these white matter tracts are shown in the bottom row (D-F). Significant correlations were seen between MK and age in each white matter tract (Ps<10−3).

Table 1.

Age group differences in mean kurtosis

Younger Older t
Putamen 0.75 ± 0.05 0.95 ± 0.11 173.86
Globus Pallidus 1.22 ± 0.12 1.31 ± 0.22 10.37
Caudate Nucleus 0.64 ± 0.04 0.70 ± 0.06 44.33
Dentate Nucleus 0.99 ± 0.06 1.06 ± 0.10 28.04
Thalamus 0.88 ± 0.05 0.89 ± 0.06 0.794
Hippocampus 0.66 ± 0.03 0.70 ± 0.04 33.94
Sup. Long. Fasc. 0.99 ± 0.03 0.96 ± 0.05 22.83
Forceps Major 0.77 ± 0.03 0.75 ± 0.04 6.54
Forceps Minor 0.89 ± 0.04 0.85 ± 0.06 20.47
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Notes. Mean kurtosis values in subcortical grey matter regions of interest and white matter tracts. Data is presented as mean ± standard deviation. Post hoc t-tests were used for group comparisons of MK in each ROI from which the t-statistics are shown.

3.1.2. Effects of age on mean kurtosis in older adults.

In subcortical grey matter structures of the older adult group, older age was correlated with lower MK in the globus pallidus (r=−0.206; P=0.033), caudate nucleus (r=−0.224; P=0.020), thalamus (r=−0.264; P=0.006), and hippocampus (r=−0.269; P=0.005). No significant correlations were observed in the putamen (r=0.082; P=0.401) or dentate nucleus (r=−0.145; P=0.135). In white matter tracts of the older adult group, older age was significantly associated with lower MK in the superior longitudinal fasciculus (r=− 0.442; P<10−3), forceps major (r=−0.339; P<10−3), and forceps minor (r=−0.370; P<10−3).

3.1.3. Age group differences in R2*.

Maps of mean R2* and susceptibility in each age group are shown in Figure 1. Significant main effects of Group (P<10−3; F=293.350) and region (P<10−3; F=620.713) were seen in the 2 Group (younger, older) × 6 (subcortical grey matter) Region ANOVA of R2*. A significant interaction between Group and Region was observed (P<10−3; F=36.437). Post hoc t-tests revealed that the older adult group had higher mean R2* than the younger group in the putamen (P<10−3), globus pallidus (P<10−3), caudate nucleus (P<10−3), dentate nucleus (P<10−3), and hippocampus (P<10−3), but not in the thalamus (P=0.568).

Significant main effects of Group (P<10−3; F=80.156) and region (P<10−3; F=11123.864) were seen in the 2 Group (younger, older) × 6 (subcortical grey matter) Region ANOVA of susceptibility. A significant interaction between Group and Region was observed (P<10−3; F=48.973). Post hoc t-tests revealed that the older adult group had higher mean susceptibility than the younger group in the putamen (P<10−3), globus pallidus (P=0.030), caudate nucleus (P<10−3), and dentate nucleus (P<10−3). Lower susceptibility was observed in the thalamus (P<10−3) and hippocampus (P<10−3) of older adults relative to younger adults. These comparisons are shown in Figure 2 and are summarized in Table 2.

Table 2.

Age group differences in grey matter iron metrics

Younger Older t
Putamen 21.0 ± 1.4 30.7 ± 5.1 206.18
Globus Pallidus R2* 33.9 ± 3.3 39.1 ± 5.7 41.47
Caudate Nucleus R2* 19.2 ± 1.5 22.4 ± 3.6 43.62
Dentate Nucleus R2* 24.4 ± 2.7 28.0 ± 4.2 34.34
Thalamus R2* 20.2 ± 1.2 20.1 ± 1.8 0.33
Hippocampus R2* 15.9 ± 1.5 17.8 ± 1.9 44.01
Putamen Δχ 36 ± 9 80 ± 24 175.83
Globus Pallidus Δχ 138 ± 8 147 ± 28 4.82
Caudate Nucleus Δχ 37 ± 9 48 ± 15 25.67
Dentate Nucleus Δχ 48 ± 21 71 ± 24 37.28
Thalamus Δχ 18 ± 6 4 ± 11 78.46
Hippocampus Δχ 9 ± 10 2 ± 11 15.97
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Notes. R2* and susceptibility, denoted Δχ, values in subcortical grey matter regions of interest. Data is presented as mean ± standard deviation and units for R2* and Δχ are s−1 and ppb, respectively. Post hoc t-tests were used for group comparisons of R2* in each ROI from which the t-statistics are shown.

3.1.4. Effects of age on R2* within older adults.

Older age was correlated with lower R2* in the caudate nucleus (r=−0.292; P=0.001) and hippocampus (r=−0.194; P=0.044). No significant correlations between age and R2* were observed in the putamen (r=0.064; P=0.517), globus pallidus (r=0.086; P=0.383), dentate nucleus (r=−0.118; P=0.230), and thalamus (r=−0.183; P=0.061). No significant correlations were seen between age and susceptibility in any grey matter ROI (Ps>0.061).

3.1.5. Relationships between MK and diffusivity.

In the older adult cohort, significant negative correlations were seen between MK and MD in the superior longitudinal fasciculus (r=−0.834; P<10−3), forceps major (r=−0.713; P<10−3), and forceps minor (r=−0.712; P<10−3) with higher MK related to lower MD. MK and MD were negatively correlated in the putamen (r=−0.329; P<10−3), globus pallidus (r=−0.781; P<10−3), and caudate nucleus (r=−0.490; P<10−3), dentate nucleus (r=−0.580; P<10−3), thalamus (r=−0.314; P<10−3), and hippocampus (r=−0.412; P<10−3). Similar correlations were observed in each ROI in the younger adult cohort (Ps<10−3).

3.1.6. Relationships between MK and iron.

In the older adult group, MK and R2* showed significant positive associations in all grey matter ROIs, including the putamen (r=0.702; P<10−3), globus pallidus (r=0.475; P<10−3), caudate nucleus (r=0.602; P<10−3), dentate nucleus (r=0.667; P<10−3), thalamus (r=0.202; P=0.038), and hippocampus (r=0.327; P=0.001). The MK-R2* correlations in the older adult group remained significant after controlling for age (Ps<0.027). In the younger group, significant positive correlations were seen between MK and R2* in the putamen (r=0.366; P=0.004), globus pallidus (r=0.702; P<10−3), caudate nucleus (r=0.289; P=0.037), dentate nucleus (r=0.654; P<10−3), thalamus (r=0.265; P=0.043), and hippocampus (r=0.283; P=0.030), These associations are plotted in Figure 4.

Figure 4.

Figure 4.

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Correlations between MK and R2* in the putamen (A), globus pallidus (B), caudate nucleus (C), dentate nucleus (D), thalamus (E), and hippocampus (F). The association between MK and R2* in all subcortical ROIs is shown in G.

In the older adult group, MK and susceptibility were significantly associated in the putamen (r=0.606; P<10−3), globus pallidus (r=0.473; P<10−3), caudate nucleus (r=0.293; P=0.003), dentate nucleus (r=0.546; P<10−3), thalamus (r=0.276; P=0.004). Controlling for age did not alter the significance of these correlations (Ps>0.014). No association was seen between susceptibility and MK in the hippocampus (r=−0.327; P=0.001). In the younger adult group, significant positive associations were seen between MK and susceptibility in the putamen (r=0.366; P-0.004), globus pallidus (r=0.827; P<10−3), caudate nucleus (r=0.292; P=0.025), and dentate nucleus (r=0.567; P<10−3) but no significant relationships between MK and susceptibility were seen in the thalamus (r=0.208; P=0.115) or hippocampus (r=0.071; P=0.594). These associations are plotted in Figure 5.

Figure 5.

Figure 5.

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Correlations between MK and susceptibility (denoted Δχ) in the putamen (A), globus pallidus (B), caudate nucleus (C), dentate nucleus (D), thalamus (E), and hippocampus (F). The association between MK and susceptibility in all subcortical ROIs is shown in G.

3.1.7. Relationships between MK and SNR.

In the older adult group, no significant associations were seen between SNR and MK in the putamen (r=−0.176; P=0.075), globus pallidus (r=−0.057; P=0.567), caudate nucleus (r=−0.132; P=0.183), dentate nucleus (r=−0.181; P=0.067), thalamus (r=−0.121; P=0.222), or hippocampus (r=−0.109; P=0.275). Similarly, no significant associations were observed between SNR and MK in any ROI in the younger adult group (Ps>0.124). These associations are plotted in Figure 6.

Figure 6.

Figure 6.

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Correlations between MK and SNR from the b=3000 s/mm2 shell in the putamen (A), globus pallidus (B), caudate nucleus (C), dentate nucleus (D), thalamus (E), and hippocampus (F). The association between MK and SNR in all subcortical ROIs is shown in G.

3.2. Replication Cohort

3.2.1. Effects of age on MK and iron.

Significant correlations were seen between age and MK in the globus pallidus (r=− 0.352; P=0.003), caudate nucleus (r=−0.380; P=0.001), dentate nucleus (r=−0.234; P=0.049), thalamus (r=−0.472; P<10−3), and hippocampus (r=−0.318; P=0.008) with higher age associated with lower MK. No association was observed between MK and age in the putamen (r=0.135; P=0.266). Older age was correlated with lower R2* (r=−0.275; P=0.021) and susceptibility (r=−0.248; P=0.035) in the caudate nucleus. Iron measures were not correlated with age in other grey matter ROIs (Ps>0.088).

3.2.2. Relationships between MK and iron.

MK and R2* were significantly positively associated in iron-rich grey matter ROIs, including the putamen (r=0.504; P=0.007), globus pallidus (r=0.244; P=0.043), caudate nucleus (r=0.685; P<10−3), and dentate nucleus (r=0.331; P=0.005). The MK-R2* correlations remained significant after controlling for age (Ps<0.042). No association between MK and R2* was seen in the thalamus (r=0.231; P=0.056) or hippocampus (r=−0.102; P=0.406). Significant correlations were seen between MK and susceptibility in the putamen (r=0.565; P<10−3), globus pallidus (r=0.309; P=0.010), caudate nucleus (r=0.480; P<10−3), dentate nucleus (r=0.627; P<10−3). No association was seen between MK and susceptibility in the thalamus (r=0.120; P=0.330) and hippocampus (r=0.200; P=0.105).

3.3. Phantom experiment

The b=0 image and corresponding MK map for the agarose phantom with 4 vials containing different concentrations of ferric citrate and agar without ferric citrate are shown in Figure 7. The ANOVA revealed a significant main effect of iron concentration (F=247.96, P<10−3) and post hoc pairwise t-tests revealed significant differences in MK between all vials (Ps<10−3). Mean MK in agar without ferric citrate was 0.59 ± 0.01. MK was found to increase as ferric citrate concentration increased with mean MK values of 0.62 ± 0.017, 0.67 ± 0.018, 0.73 ± 0.032, and 0.77 ± 0.038 in vials with 0.06 mMol, 0.09 mMol, 0.12 mMol, and 0.15 mMol, respectively (Figure 7).

Figure 7.

Figure 7.

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The b=0 image is shown in A, mean kurtosis image is shown in B, and the relationship between ferric citrate concentration and mean kurtosis is shown in C.

4. Discussion

This study examined how age affects MK in subcortical grey matter structures and in white matter tracts as well as assessed the relationship between MK and iron content in subcortical grey matter. In a discovery cohort, we found that age differentially affects MK in white matter and subcortical grey matter structures with lower white matter MK and higher gray matter MK in older relative to younger participants. Further, MK was significantly correlated with age in white matter tracts with lower MK associated with higher age. In subcortical iron-rich grey matter of older adults, both a discovery and replication cohort revealed that higher MK was significantly correlated to higher iron measures (R2* and susceptibility). Finally, the phantom experiment found higher MK as iron concentration is increased.

Postmortem studies in humans have found age-related reductions in white matter volume and fibers in older adults 16,17. These reductions should manifest as decreased MK 8,18. In agreement with this, we observed older adults had, on average, lower MK in white matter tracts as compared to the younger adult group and a negative correlation between white matter tract MK and age in older adults. Prior imaging studies using DKI to examine age-related differences within white matter tracts found negative correlations between MK and age 10–14 and our correlations between MK and age in the older adult group replicate these findings.

We observed age-related increases in MK of all subcortical grey matter nuclei expect the thalamus. Few studies have applied the kurtosis model to examine how aging affects subcortical grey matter. An earlier imaging study examining age-related changes MK in subcortical grey matter nuclei found differences depend on the nuclei under consideration, with age positively correlated with MK in the putamen and but negative correlated with MK in the caudate nucleus, globus pallidus, and thalamus 11. Our results are in partial agreement with this study since we observed negative correlations between age and MK in the caudate nucleus, hippocampus, and thalamus (trending with P=0.06) of the discovery cohort. The ADNI cohort showed negative correlations between age and MK in the globus pallidus, caudate nucleus, dentate nucleus, hippocampus, and thalamus. No relationship between age and MK was observed in the putamen of either cohort.

Histological and imaging studies have found that iron accrues in subcortical grey matter nuclei throughout life 21,23–25. Consistent with these results, iron-sensitive MRI metrics were found to be elevated in older adults for all grey matter nuclei except for the thalamus ROI. Iron deposition has been found to alter DTI metrics from a single-tensor model (mean diffusivity, fractional anisotropy) within iron-rich grey matter nuclei in aging and disease 26–28. Interestingly, MK was found to be correlated with iron measures in all subcortical grey matter nuclei in the older adult discovery cohort as well as in all iron-rich grey matter nuclei in the ADNI replication cohort. In the younger adult discovery cohort, MK was correlated with iron in all grey matter structures except the thalamus.

MK in white matter was found to have values similar to values seen iron-rich subcortical grey matter nuclei. The similarity of these values may be due to the fact that these nuclei are permeated by small white matter bundles 39–41 and this observation agrees with an earlier study 39. Alternatively, the elevated MK values in iron-rich subcortical grey matter may be due to the influence of iron. In both younger and older cohorts, structures with higher tissue R2* or susceptibility values had, on average, higher MK. Consistent with this result and results showing correlations between MK and iron measures, the phantom experiment found that MK increased as iron concentration increased.

In subcortical grey matter, iron is primarily stored in ferritin and accumulation of ferritin may impede diffusion and lead to higher kurtosis values. Alternatively, iron content in subcortical grey matter nuclei will increase tissue R2 or R2* and reduce SNR and this reduction in SNR may increase MK 42,43. However, MK was not correlated with SNR in the younger or older adult groups. Finally, the association between MK and iron measures in grey matter nuclei may be due to the interaction between diffusion encoding gradients with the magnetic fields generated by iron deposits 29,30.

The results presented here suggest iron content alters MK. The dependence of MK on iron may be problematic in interpreting MK differences in neurological conditions where iron is deposited. For example, in Parkinson’s disease, iron deposition occurs alongside neuronal loss in the basal ganglia 44–47 and studies have found MK is increased in the basal ganglia of populations with Parkinson’s disease 19,20. The mechanism for these increases is unknown 19 since higher kurtosis values are thought to indicate more complex tissue microstructure 8,18. Given the relationship between iron and MK presented here, the increases in MK may be an artifact of iron deposition. However, additional studies are needed to confirm that the relationship holds in pathologic populations.

This study is not without caveats. Older adults tend to have elevated iron levels as compared to younger adults 21,23–25 and this iron deposition will increase tissue R2, thereby reducing the SNR. One limitation in the calculation of MK is SNR and low SNR may positively bias MK 42. Here, we employed a denoising strategy to increase SNR 33 and mitigate the positive bias on MK. While no significant associations were observed between MK and SNR in any grey matter nuclei, we cannot rule out that noise biased MK values in the subcortical grey matter ROIs.

We examined age-related differences in MK in two older adult cohorts using gradient strengths identical to those used in the HCP Lifespan (b=1500 s/mm2 and b=3000 s/mm2)48 and ADNI studies (b=1000 s/mm2 and b=2000 s/mm2)49 and these results may aid in the interpretation of MK differences due to age or pathology. We observed age-related increases in MK and iron measures (R2* and susceptibility) in iron-rich grey matter structures, but age-related reductions in MK in white matter. Significant correlations were seen between MK and iron measures (R2* and susceptibility) in iron-rich grey matter structures of a discovery cohort consisting of younger adults and older adults as well as in a replication cohort of older adult controls from the ADNI database. Finally, MK was found to be related to iron concentration in a phantom. These converging findings indicate that higher MK may be related to iron content in iron-rich grey matter structures.

Acknowledgements

This work was supported by R21-AG080282 and R01-AG072607 from the National Institutes of Health/ National Institute on Aging. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

References

  • 1.Behler A., Kassubek J. & Muller H.P. Age-Related Alterations in DTI Metrics in the Human Brain-Consequences for Age Correction. Front Aging Neurosci 13, 682109 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Bender A.R., Volkle M.C. & Raz N. Differential aging of cerebral white matter in middle-aged and older adults: A seven-year follow-up. Neuroimage 125, 74–83 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Bennett I.J., Madden D.J., Vaidya C.J., Howard D.V. & Howard J.H. Jr. Age-related differences in multiple measures of white matter integrity: A diffusion tensor imaging study of healthy aging. Hum Brain Mapp 31, 378–390 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Schwarz S.T., et al. Diffusion tensor imaging of nigral degeneration in Parkinson’s disease: A region-of-interest and voxel-based study at 3 T and systematic review with meta-analysis. Neuroimage Clin 3, 481–488 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Zhang Y., et al. Diffusion tensor imaging of the nigrostriatal fibers in Parkinson’s disease. Mov Disord 30, 1229–1236 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Langley J., et al. Diffusion tensor imaging of the substantia nigra in Parkinson’s disease revisited. Hum Brain Mapp 37, 2547–2556 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Jensen J.H., Helpern J.A., Ramani A., Lu H. & Kaczynski K. Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med 53, 1432–1440 (2005). [DOI] [PubMed] [Google Scholar]
  • 8.Szczepankiewicz F., et al. The link between diffusion MRI and tumor heterogeneity: Mapping cell eccentricity and density by diffusional variance decomposition (DIVIDE). Neuroimage 142, 522–532 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Cheung M.M., et al. Does diffusion kurtosis imaging lead to better neural tissue characterization? A rodent brain maturation study. Neuroimage 45, 386–392 (2009). [DOI] [PubMed] [Google Scholar]
  • 10.Das S.K., Wang J.L., Bing L., Bhetuwal A. & Yang H.F. Regional Values of Diffusional Kurtosis Estimates in the Healthy Brain during Normal Aging. Clin Neuroradiol 27, 283–298 (2017). [DOI] [PubMed] [Google Scholar]
  • 11.Gong N.J., Wong C.S., Chan C.C., Leung L.M. & Chu Y.C. Aging in deep gray matter and white matter revealed by diffusional kurtosis imaging. Neurobiol Aging 35, 2203–2216 (2014). [DOI] [PubMed] [Google Scholar]
  • 12.Latt J., et al. Regional values of diffusional kurtosis estimates in the healthy brain. J Magn Reson Imaging 37, 610–618 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Coutu J.P., Chen J.J., Rosas H.D. & Salat D.H. Non-Gaussian water diffusion in aging white matter. Neurobiol Aging 35, 1412–1421 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Benitez A., Jensen J.H., Falangola M.F., Nietert P.J. & Helpern J.A. Modeling white matter tract integrity in aging with diffusional kurtosis imaging. Neurobiol Aging 70, 265–275 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Falangola M.F., et al. Age-related non-Gaussian diffusion patterns in the prefrontal brain. J Magn Reson Imaging 28, 1345–1350 (2008). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Tang Y., Nyengaard J.R., Pakkenberg B. & Gundersen H.J. Age-induced white matter changes in the human brain: a stereological investigation. Neurobiol Aging 18, 609–615 (1997). [DOI] [PubMed] [Google Scholar]
  • 17.Marner L., Nyengaard J.R., Tang Y. & Pakkenberg B. Marked loss of myelinated nerve fibers in the human brain with age. J Comp Neurol 462, 144–152 (2003). [DOI] [PubMed] [Google Scholar]
  • 18.Hui E.S., Cheung M.M., Qi L. & Wu E.X. Towards better MR characterization of neural tissues using directional diffusion kurtosis analysis. Neuroimage 42, 122–134 (2008). [DOI] [PubMed] [Google Scholar]
  • 19.Wang J.J., et al. Parkinson disease: diagnostic utility of diffusion kurtosis imaging. Radiology 261, 210–217 (2011). [DOI] [PubMed] [Google Scholar]
  • 20.Welton T., et al. Microstructure of Brain Nuclei in Early Parkinson’s Disease: Longitudinal Diffusion Kurtosis Imaging. J Parkinsons Dis 13, 233–242 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Hallgren B. & Sourander P. The effect of age on the non-haemin iron in the human brain. J Neurochem 3, 41–51 (1958). [DOI] [PubMed] [Google Scholar]
  • 22.Aquino D., et al. Age-related iron deposition in the basal ganglia: quantitative analysis in healthy subjects. Radiology 252, 165–172 (2009). [DOI] [PubMed] [Google Scholar]
  • 23.Bilgic B., Pfefferbaum A., Rohlfing T., Sullivan E.V. & Adalsteinsson E. MRI estimates of brain iron concentration in normal aging using quantitative susceptibility mapping. Neuroimage 59, 2625–2635 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Haacke E.M., et al. Correlation of putative iron content as represented by changes in R2* and phase with age in deep gray matter of healthy adults. J Magn Reson Imaging 32, 561–576 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Pfefferbaum A., Adalsteinsson E., Rohlfing T. & Sullivan E.V. MRI estimates of brain iron concentration in normal aging: comparison of field-dependent (FDRI) and phase (SWI) methods. Neuroimage 47, 493–500 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Pfefferbaum A., Adalsteinsson E., Rohlfing T. & Sullivan E.V. Diffusion tensor imaging of deep gray matter brain structures: effects of age and iron concentration. Neurobiol Aging 31, 482–493 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Langley J., Huddleston D.E. & Hu X. Nigral diffusivity, but not free water, correlates with iron content in Parkinson’s disease. Brain Commun 3, fcab251 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Langley J., Hussain S., Flores J.J., Bennett I.J. & Hu X. Characterization of age-related microstructural changes in locus coeruleus and substantia nigra pars compacta. Neurobiol Aging 87, 89–97 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Zhong J., Kennan R.P. & Gore J.C. Effects of susceptibility variations on NMR measurements of diffusion. J Magn Reson 95, 267–280 (1991). [Google Scholar]
  • 30.Novikov D.S., Reisert M. & Kiselev V.G. Effects of mesoscopic susceptibility and transverse relaxation on diffusion NMR. J Magn Reson 293, 134–144 (2018). [DOI] [PubMed] [Google Scholar]
  • 31.Smith S.M., et al. Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 23 Suppl 1, S208–219 (2004). [DOI] [PubMed] [Google Scholar]
  • 32.Woolrich M.W., et al. Bayesian analysis of neuroimaging data in FSL. NeuroImage 45, S173–186 (2009). [DOI] [PubMed] [Google Scholar]
  • 33.Veraart J., et al. Denoising of diffusion MRI using random matrix theory. Neuroimage 142, 394–406 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Andersson J.L., Skare S. & Ashburner J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. NeuroImage 20, 870–888 (2003). [DOI] [PubMed] [Google Scholar]
  • 35.Smith S.M. Fast robust automated brain extraction. Hum Brain Mapp 17, 143–155 (2002). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Henriques R.N., et al. Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project. Front Hum Neurosci 15, 675433 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.He N., et al. Improved Neuroimaging Atlas of the Dentate Nucleus. Cerebellum 16, 951–956 (2017). [DOI] [PubMed] [Google Scholar]
  • 38.Langley J., Bennett I.J., Hu X.P. & Alzheimer’s Disease Neuroimaging, I. Examining iron-related off-target binding effects of (18)F-AV1451 PET in the cortex of Abeta+ individuals. Eur J Neurosci 60, 3614–3628 (2024). [DOI] [PubMed] [Google Scholar]
  • 39.Maiter A., et al. Investigating the relationship between diffusion kurtosis tensor imaging (DKTI) and histology within the normal human brain. Sci Rep 11, 8857 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Percheron G., Yelnik J. & Francois C. A Golgi analysis of the primate globus pallidus. III. Spatial organization of the striatopallidal complex. J Comp Neurol 227, 214–227 (1984). [DOI] [PubMed] [Google Scholar]
  • 41.Yelnik J., Francois C., Percheron G. & Heyner S. Golgi study of the primate substantia nigra. I. Quantitative morphology and typology of nigral neurons. J Comp Neurol 265, 455–472 (1987). [DOI] [PubMed] [Google Scholar]
  • 42.Glenn G.R., Tabesh A. & Jensen J.H. A simple noise correction scheme for diffusional kurtosis imaging. Magn Reson Imaging 33, 124–133 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Jensen J.H. & Helpern J.A. MRI quantification of non-Gaussian water diffusion by kurtosis analysis. NMR Biomed 23, 698–710 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Dexter D.T., et al. Increased nigral iron content in postmortem parkinsonian brain. Lancet 2, 1219–1220 (1987). [DOI] [PubMed] [Google Scholar]
  • 45.Langley J., et al. Reproducible detection of nigral iron deposition in 2 Parkinson’s disease cohorts. Mov Disord 34, 416–419 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Du G., et al. Combined R2* and diffusion tensor imaging changes in the substantia nigra in Parkinson’s disease. Mov Disord 26, 1627–1632 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.He N., et al. Region-specific disturbed iron distribution in early idiopathic Parkinson’s disease measured by quantitative susceptibility mapping. Hum Brain Mapp 36, 4407–4420 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Harms M.P., et al. Extending the Human Connectome Project across ages: Imaging protocols for the Lifespan Development and Aging projects. Neuroimage 183, 972–984 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Weiner M.W., et al. The Alzheimer’s Disease Neuroimaging Initiative 3: Continued innovation for clinical trial improvement. Alzheimers Dement 13, 561–571 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]

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