Layer-specific variation of iron content in cerebral cortex as a source of MRI contrast

Abstract

Recent advances in high-field MRI have dramatically improved the visualization of human brain anatomy in vivo. Most notably, in cortical gray matter, strong contrast variations have been observed that appear to reflect the local laminar architecture. This contrast has been attributed to subtle variations in the magnetic properties of brain tissue, possibly reflecting varying iron and myelin content. To establish the origin of this contrast, MRI data from postmortem brain samples were compared with electron microscopy and histological staining for iron and myelin. The results show that iron is distributed over laminae in a pattern that is suggestive of each region’s myeloarchitecture and forms the dominant source of the observed MRI contrast.

Much of the human cerebral cortex is organized in a network of functionally specialized regions. This understanding forms the basis of studies that attempt to expose the brain’s inner workings using modern functional imaging techniques such as PET and MRI (1, 2). Some of this functional specialization is reflected in the local cortical architecture, and may be observed in postmortem brain samples from the laminar and columnar variation in a number of tissue properties including cell shape (3), myelin content (4), metabolic state (5), neurochemical profile (6), and receptor density (7).

One of the most striking examples of this structure–function relationship is in the primary visual cortex (V1, or Brodmann area 17), which is involved in the early stages of visual information processing. Visual area V1 can be readily identified with postmortem tissue analysis based on the prominence of myelinated fibers in layer IVb, known as the line of Gennari. Many other brain areas show characteristic myelination patterns (8, 9), and systematic analyses of this type of structural information may provide important clues about the brain’s functional organization. Whole-brain postmortem studies of cellular distributions and myelin content in human brain sections at high resolution have previously been described (8, 10).

A number of attempts have been made to reveal laminar cortical structure in vivo with structural MRI, by exploiting contrast based on the altered density and NMR relaxation times of water protons in myelin-rich environments (11 –15). Although these studies have had some success, their impact has not been widespread as a result of the rather limited contrast and resolution available with conventional MRI. Nevertheless, much progress has been made over the last few years. Recent developments in high-field MRI (≥7 T) have increased spatial resolution to less than 300 μm (16 –18), and improved the ability to detect subtle variations in the magnetic susceptibility of tissue. These variations are reflected in a number of intrinsic MRI parameters, including the longitudinal relaxation rate R₁, the transverse relaxation rates R₂ and R₂*, and the NMR resonance frequency. R₂* indicates the reversible transverse relaxation rate and is, together with the resonance frequency, particularly sensitive to spatial magnetic susceptibility variations. Most notably, contrast based on resonance frequency shifts of tissue, derived from the phase of gradient-echo signals, has allowed the robust visualization of laminar structure in a number of neocortical regions, including primary visual and motor cortices (17) (Fig. 1).

Fig. 1.

Example of in vivo MRI contrast in the occipital lobe. (A) Localizer image shows region of interest for (B). (B) R₂* weighted magnitude and frequency images. The frequency image reflecting resonance frequency shifts. Intracortical contrast is particularly strong in the frequency image, revealing the centrally located Line of Gennari (arrows).

Despite this progress, the origin of cortical laminar contrast observed with high-field MRI remains unclear. What causes the magnetic properties to vary over cortical layers? Both iron and myelin have been suggested to alter the magnetic susceptibility (17, 19, 20), but experimental confirmation is lacking. In occipital cortex, the laminar pattern observed with MRI is consistent with the known myelin distribution (17), which would support a myelin-dominated contrast mechanism. However, the susceptibility differences seen in this region are opposite to what is expected for myelin-rich regions based on the contrast differences between white matter and cortex and therefore argue against the notion of a myelin-dominated contrast mechanism (17). Conversely, if iron was the main contributor to the observed contrast, this would suggest the intriguing possibility of a laminar variation of iron, which, like the myelin distribution, may be indicative of cortical functional specialization.

To investigate the possibility of a laminar variation of iron, we used a variety of imaging methods, including MRI, histochemical and immunofluorescence staining, and electron microscopy. We demonstrate that, in the human occipital cortex, (i) there is indeed a laminar variation of iron that resembles the frequency contrast seen in recent high-field MRI studies; (ii) this iron is a dominant contributor to R₂* and frequency contrast; and (iii) much of this iron appears to be stored within ferritin.

Results

Cortical Iron Distribution Is Consistent with MRI Contrast.

Although it has been recognized that iron is responsible for much of the MRI susceptibility-based contrast in iron-rich brain regions such as the subcortical nuclei (19 –22), very little is known about the element’s distribution in the cortex or its possible role in the laminar variation of MRI contrast. To explore the potential contribution of iron in MRI, correlative MRI with histochemical staining was performed on postmortem human brain tissue. Both iron and myelin staining were performed on a coronal slab, sectioned from the occipital lobe containing a substantial portion of V1 as well as parts of the secondary visual cortex (V2), and a section of the superior parietal cortex, respectively.

As can be seen from the results (Fig. 2 and Fig. S1), MRI contrast and iron staining show strong similarities. Areas of increased R₂* relaxation in the white matter show increased iron staining. In the gray matter, a laminar distribution of iron is observed with a pattern that resembles the MRI contrast. Moreover, the laminar iron staining pattern exhibits strong similarities with that of myelin. In the MRI of visual cortex tissue, much of the cortex surrounding the calcarine fissure shows a central stripe resembling the line of Gennari in striate cortex (i.e., V1). Furthermore, iron is also increased in subregions of the deeper cortical layers and in cortical gray matter around the V1/V2 border. This apparent correspondence between MRI and histology was also found in the superior parietal cortex. These results suggest the possibility that not only the distribution of myelin but also that of iron may reflect the functional subdivision of the neocortex.

Fig. 2.

Comparison of histochemical myelin and iron staining with MRI R₂* data in the visual cortex. The myelin stain shows the characteristic density increase in the line of Gennari in the pericalcarine cortex (solid arrow). The V1/V2 boundaries are indicated by asterisks. A dashed line represents the calcarine fissure. The distribution of intracortical iron mimics that of myelin, with elevated iron in the line of Gennari. The MRI data show a striking similarity with the iron stain, with increased R₂* in the line of Gennari (solid arrow), and in the deeper layers (open arrow), and subcortical white matter in area V2 (arrowhead).

Iron Colocalizes with Intracortical Fibers.

The similarity between the cortical iron and myelin distribution seen in the histochemical stains could reflect a microscopic colocalization at the cellular and molecular levels. Earlier work has found that, in white matter, much of the iron is found in the form of ferritin particles dispersed in the inner and outer loops of myelin (23 –25). This ferritin is thought to constitute a storage pool that primarily serves the iron needs of oligodendrocytes for the production and maintenance of myelin sheets surrounding nerve fibers (26, 27). In fact, higher magnification views in histochemical stains (Fig. S2) suggest that much of the iron is associated with axonal fibers. Comparison of iron and myelin stains obtained in this study suggests that this colocalization occurs not only in white matter fibers, but also in intracortical fibers. This is consistent with earlier findings (25) and furthermore suggests that myelin-associated iron is the dominant source of intracortical magnetic susceptibility–based contrast.

To confirm that the observed iron is indicative of ferritin, we performed double immunofluorescence staining with antibodies to myelin basic protein (MBP) and ferritin storage protein. The results (Fig. 3) clearly show a widespread colocalization of ferritin and myelinated fibers in both gray and white matter similar to what was observed for iron distribution patterns using histochemical stain.

Fig. 3.

Cellular colocalization of myelin and ferritin in the primary visual cortex. (A) Immunofluorescence costaining of MBP and ferritin protein shows strong colocalization in intracortical fibers on composite images (yellow in composite). The dashed lines represent gray/white matter boundaries. (Scale bar: 500 μm.) (B) Enlarged areas of superficial layers in the gray matter (sGM), the line of Gennari, deeper layers in the gray matter (dGM), and white matter (WM) all show widespread colocalization of MBP and ferritin. (Scale bar: 50 μm.) Image correlation between myelin and ferritin stains is indicated by r values.

Ferritin Iron Content Is Sufficient to Explain MRI Contrast.

Although the data presented here are consistent with the notion that ferritin is the primary source of the observed intracortical MRI contrast, it remains unclear whether the magnetic susceptibility of ferritin is sufficient to explain the MRI data. To clarify this, one would need to quantify both the concentration as well as the average iron loading of ferritin storage proteins. In previous work, the average gray matter content of ferritin-bound iron has been reported to be in the range of 30 to 50 μg/g tissue (wet weight) (28), but information about the laminar distribution is lacking.

To investigate laminar differences, we performed transmission EM (TEM) on brain samples obtained from regions within the line of Gennari and more superficial layers (layers II–III; Fig. 4). Data from 100 fields (700 × 700 × 100 nm) showed average iron particle concentrations of 351 ± 27 and 115 ± 51 (mean ± SD) for these two regions, respectively. Furthermore, analysis of electron energy loss spectra (EELS) suggested an average loading factor of 1,740 ± 580 Fe atoms per particle (n = 15; Figs. S3 and S4), which is within the range of literature values for samples of spleen and liver ferritin particles (29 –31). Based on these average values, one can calculate the laminar iron content to average 50 ± 4 and 16 ± 7 μg iron per gram tissue (wet weight) in the line of Gennari and superficial layers, respectively (Materials and Methods). Estimates based on theory and previous experimental work (Materials and Methods) indicate that this variation can lead to frequency and R₂* shifts in the line of Gennari of up to 5 and 13 Hz, respectively, which are sufficient to explain the in vivo results (17) and those obtained in tissue samples (as detailed later).

Fig. 4.

Distribution of individual ferritin particles measured by EM. (A) Sample bright-field TEM image shows scattered foci of reduced signal intensity, presumably originating from ferritin particles (arrow). (B) EELS spectrum of a single particle confirms iron as source of TEM contrast. (C) STEM-EELS spectroscopic image (3.5 nm/pixel) of iron suggests particle sizes of 1 to 2 pixels, consistent with the range of 3 to 8 nm reported for the ferritin core size (13). (Scale bars: 100 nm in A, 50 nm in C.)

Reduction of MRI Contrast After Chemical Extraction of Tissue Iron.

Having identified iron as a plausible source of intracortical MRI contrast from correlative analysis, can we establish a direct link? One way to address this question is to selectively manipulate tissue iron content and observe the effects on MRI contrast. Using a biochemical process to extract iron from postmortem tissue, Schenck and coworkers previously found that iron substantially affects R₂* relaxation in much of the brain (22). Applying their protocol to primary visual cortex, we observed an almost complete extinction of intracortical R₂* and frequency contrasts (Fig. 5 and Table 1): these went from 13.1/0.80 Hz before extraction to 2.7/0.02 Hz, respectively, after extraction. This finding solidifies the notion that intracortical contrast is predominantly caused by variations in tissue iron content. Note that some R₂* and frequency contrast remains after iron extraction, which is particularly the case for gray–white matter contrast. One explanation would be an ineffective iron extraction. However this was not evident from the comparison of iron stains of tissue without and with extraction, which showed a complete elimination of intracortical iron variations by extraction and a small remaining gray–white matter difference (Fig. S5). Therefore, it appears more likely that the remaining R₂* and frequency contrast is primarily caused by other factors such as variations in tissue myelin content, and it is possible that iron does not generally dominate the contrast outside the cerebral cortex.

Fig. 5.

Effect of iron extraction on MRI contrast in postmortem brain tissue. Iron extraction strongly reduces intracortical magnetic susceptibility–based contrast as evidenced from R₂*-weighted, R₂*, and frequency shift images. The frequency images show subtle signs of contrast reversal with extraction, suggesting a small, opposing frequency shift caused by the remaining myelin. The circular area of susceptibility shift at right bottom of postextraction images is caused by an entrapped air bubble.

Table 1.

Effect of iron extraction on MRI indicators of tissue magnetic susceptibility

	Gray matter (inside Gennari)		Gray matter (outside Gennari)		White matter		Gray matter difference (inside − outside Gennari)
Time point	Frequency	R2*	Frequency	R2*	Frequency	R2*	Frequency	R2*
Preextraction	0.82	39.6	0.02	26.5	−0.66	46.0	0.80	13.1
Postextraction	−0.28	17.7	−0.30	15.0	0.10	29.6	0.02	2.7
Difference	–	−21.9	–	−11.5	–	−16.4	−0.78	−10.4

Both R ₂* and relative frequency are strongly affected by iron extraction, with the strongest fractional changes observed in the line of Gennari. The contrast in both R ₂* and frequency between the line of Gennari and the surrounding gray matter is almost completely removed by iron extraction. All frequencies were measured relative to the water surrounding the tissue sample. As no global reference for water frequency was measured in pre- and postextraction experiments, direct comparison of frequencies for both measurements were not possible and therefore not included. All values in Hertz.

Are the reductions in R₂* with iron extraction in the various brain regions consistent with their putative iron concentrations? Based on previous work correlating iron histology and MRI in the basal ganglia (32), the observed reductions in R₂* values (ranging from 11.5 Hz in gray matter to 21.9 Hz in Gennari region) correspond to an iron content ranging from 30 to 63 μg/g (tissue wet weight) (32). This range is consistent with the previously established iron concentration in human occipital cortex of 45.5 ± 6.7 μg/g (28), further lending support for an iron-dominated contrast mechanism.

Discussion

The experiments described in this work establish a direct link between intracortical iron variations and susceptibility-based contrast observed in high-field MRI. This is important in several ways. First, it helps interpret the recent advances made in detecting the laminar architecture of human brain in vivo with high field MRI. Second, it suggests iron as an additional anatomical marker for the myeloarchitecture of the human cerebral cortex. Third, it suggests that high-field susceptibility-based MRI contrast may be used to reveal the local cortical architecture based on iron distribution, and may therefore provide improved contrast over conventional T₁-weighted techniques, which are primarily sensitized to myelin variations.

The importance of iron for MRI magnetic susceptibility–based contrast (including R₂*, R₂, and frequency shift) has been previously suggested for iron-rich gray matter areas such as the basal ganglia and for selected cortical regions (reviewed in ref. 20) and was confirmed with an iron extraction study (22). However, contributions from other sources have prevented the quantification of tissue iron content directly from the MRI measures. Most notably, the relationship between MRI frequency shift and iron content was found to be ambiguous within white matter (19). Some of the iron-induced shifts may be offset by an opposing (i.e., diamagnetic) susceptibility shift originating from lipids in myelin (17, 19), which are abundant in white matter (33). A diamagnetic susceptibility shift induced by myelin is consistent with the apparent frequency contrast reversal observed in the myelin-rich Gennari region after iron extraction (Fig. 5), and would explain the low contrast between white matter and cerebrospinal fluid observed in previous work (17). If confirmed, this would open the way to quantify iron content in both white matter and gray matter by combining R₂*, R₂, and frequency data, because these measures are differentially affected by iron and myelin content. For example, iron and myelin both increase R₂ and R₂*, whereas they have opposing effects on the resonance frequency (paramagnetic vs. diamagnetic shift, respectively). Furthermore, similar to contrast caused by deoxyhemoglobin (34, 35), R₂, R₂*, and frequency depend differently on the spatial distribution of iron and myelin. Accurate quantification of iron will require a better understanding of these interrelationships. It may also require prior removal of tissue geometry effects on frequency data by deconvolution methods (20, 36).

The combined results of fluorescence antibody staining, the iron extraction experiment, and quantitative scanning transmission electron microscopy (STEM)–EELS indicate that much of the intracortical frequency contrast seen ex vivo is caused by iron in ferritin. For in vivo MRI, an additional contribution could come from deoxyhemoglobin in blood, although theory and preliminary experimental data suggest that this contribution is negligible (17, 37).

The current finding of a laminar variation of iron is intriguing and merits further discussion. In both gray and white matter, both total iron (Fig. 2) and ferritin (Fig. 3) showed substantial colocalization with myelin. This is consistent with earlier observations and has been suggested to reflect the role of iron in the synthesis of cholesterol and lipids, which are key components of the myelin sheets surrounding nerve fibers (26, 38). Storage of iron in ferritin deposits close to myelination sites would ensure abundant availability for the myelination processes during development and/or myelin maintenance and repair afterward (38).

Interestingly, the MRI frequency data obtained here and in previous work (17) suggest that iron and myelin do not colocalize at constant proportions throughout the brain. That would lead to identical frequency shifts in subcortical white matter and the line of Gennari (relative to surrounding gray matter), inconsistent with the in vivo (Fig. 1) and ex vivo (Fig. 5 and Table 1) MRI observations. Rather, intracortical fibers appear to have a disproportionately high iron content. Imperfect ferritin–myelin colocalization is also seen in the iron-poor patches in subcortical white matter (Fig. S2), the origin of which is still poorly understood. Investigations into the precise anatomical relationship between ferritin and myelin distributions across the brain, and their alterations in demyelinating diseases such as multiple sclerosis may provide important clues about the biological role of ferritin. In fact, preliminary high-field MRI studies have already found substantial contrast variations in white matter regions (16, 17), allowing visualization of the major fiber bundles in normal brain, and lesions in diseases such as multiple sclerosis and brain tumors (39). The contribution of iron and myelin to contrast in these tissues is currently being investigated in this and other laboratories.

Materials and Methods

Human Volunteer and Brain Tissue Samples.

The MRI data presented in Fig. 1 were obtained from a normal human volunteer under an institutional review board–approved protocol after obtaining informed consent. Human postmortem brain samples were obtained from a 60-y-old male subject (occipital lobe) and a 27-year-old female subject (parietal lobe), each with no history of neurological disease. The brain tissue was fixed and preserved in 10% neutral buffered formalin for approximately 6 months. The postmortem interval before fixation was less than 24 h.

Iron and Myelin Staining.

For iron staining, paraffinized brain tissue slabs were cut into 20-μm sections with a Vibratome sectioning system (Vibratome). Deparaffinized sections (20 μm) were stained for iron using Perls stain (40) with intensification of the reaction product with 3,3′-diaminobenzidine (DAB) as described previously (41). To further improve the detection sensitivity, two modifications suggested by Smith et al. (42) were adopted, namely (i): increasing the concentrations of potassium ferrocyanide and hydrochloric acid and (ii) lengthening the incubation time and increasing the temperature. The procedure involved treating the sections with a freshly prepared 1:1 mixture of 5% potassium ferrocyanide and 5% HCl for 2 h at 37 °C. The sections were then washed with PBS solution three times for 10 min each. Subsequently, the sections were incubated in 0.5 mg/mL DAB for 10 min followed by immersion in a mixture of 0.5 mg/mL DAB and 0.01% H₂O₂ for 10 min in the dark. Sections were then washed with PBS solution three times for 10 min each, dehydrated in graded alcohol, air-dried, and mounted. Control sections were incubated with potassium ferrocyanide solution, but the hydrochloric acid was replaced with distilled water. No staining was observed in control sections.

For Luxol fast blue myelin staining, sections were incubated overnight at 56 °C in 0.1% Luxol fast blue solution (Luxol fast blue MBS 0.1 mg, 95% ethyl alcohol 100 mL, glacial acetic acid 0.5 mL). To differentiate the contrast between white and gray matter, slides were immersed in 0.05% lithium carbonate solution for tens of seconds. The slides were examined on an Eclipse E400 microscope (Nikon) with ×2 and ×10 objective lenses.

Tissue Iron Extraction.

To determine the contribution of iron to intracortical contrast, we used an iron extraction method described previously (22). Iron extraction was accomplished by reductive dissolution through the combined use of the reducing agent 2 mM sodium dithionite and the 1 mM iron chelating agent desferrioxamine. The iron extraction procedure lasted for 7 d and the chemical solutions were replaced daily. The high-resolution MRI scan (as detailed later) of formalin-fixed brain tissue samples of the primary visual cortex was performed before and after extraction of iron. The image plane was perpendicular to the main magnetic field.

Immunofluorescence Staining of Ferritin and Myelin.

The sections were incubated for 1 h at room temperature with 1/100 rabbit IgG anti-ferritin (Sigma) and 1 μg/mL chicken IgY anti-MBP (Aves Labs). The primary immunoreactions were visualized using 1 h incubation at room temperature in the presence of 1 μg/mL donkey anti-rabbit IgG–Alexa Fluor 488 (Invitrogen) and goat anti-chicken IgY–Alexa Fluor 647 (Invitrogen). Subsequently, the sections were scanned across multiple adjacent fields using an Axiovert 200M fluorescence microscope (Carl Zeiss) through a ×20 objective with Volocity Acquisition software (Improvision). Scanned images were then stitched together to form a larger field of view image using IDL 7.0 (ITT Visual Information Solutions) software. Subsequently, the Pearson product–moment correlation coefficient between MBP and ferritin stains was calculated for the tissue fields shown in Fig. 3B.

EM.

Thin trapezoidal brain slices containing superficial cortical layers and the line of Gennari were cut to lengths of 4 to 5 mm and fixed in a mixture of 2.5% paraformaldehyde and 2% glutaraldehyde in PBS solution for 2 h followed by an extensive wash in PBS solution. The samples were dehydrated in a series of ethanol solutions in water (30%, 50%, 75%, and 95%, for 10 min each) followed by 100% ethanol for 45 min with three changes. They were then infiltrated with Epon-Araldite (Ted Pella) for 2 d with 30% Epon-Araldite in ethanol for 2 h, 50% for 4 h, 75% for 16 h, and 100% for 1 d with two changes. Samples were polymerized at 60 °C for 2 d. Ultrathin sections of thickness 100 ± 10 nm were cut using an EM UC6 Ultramicrotome (Leica) and collected on copper grids covered with a Formvar/carbon support film.

TEM images of unstained sections were recorded with energy filtering and at a defocus of −4 to −6 μm using a Tecnai TF30 electron microscope (FEI) equipped with a GIF Tridiem imaging filter (Gatan), and operating at an accelerating voltage of 300 kV. Iron particles were counted in 100 randomly selected images obtained from the line of Gennari and 100 images from control regions in the more superficial layers (layers II–III). The full width of each square field was 700 nm. Approximately 40% of the areas from the line of Gennari contained more than five particles, and approximately 15% of the areas from the control region contained more than five particles.

To determine the iron loading of the particles, EELS were recorded across selected fields using a HB501 scanning transmission electron microscope (VG) operated at an accelerating voltage of 100 kV, and equipped with an ENFINA electron energy loss spectrometer (Gatan). From these data, Fe L_2,3 maps were extracted from three STEM-EELS spectrum-images that were acquired using Digital Micrograph software (Gatan).

The number of Fe atoms in each iron particle analyzed (n = 15) was calculated according to the following:

Here, is the number of carbon atoms in the analyzed region that includes the ferritin particle; is the integrated iron L_2,3 edge signal for energy window above the iron L_2,3 edge, and for collection semiangle β defined by the spectrometer entrance aperture; is the integrated carbon K edge signal for energy window above the carbon K edge and for collection semiangle β; is the partial iron L_2,3 inelastic scattering cross section; and is the partial carbon K inelastic scattering cross section.

The collection semiangle β was 20 mrad, was set to 10 eV to include the L₃ white-line resonance, and was set to 50 eV. A value for the scattering cross section was estimated by considering the fraction of the iron L_2,3 signal in the near-edge window relative to a 100 eV window, which was obtained using an experimentally determined iron L_2,3 spectrum (43).

Inelastic scattering cross sections inside the 100-eV window at the iron L_2,3 edge and inside a 50-eV window at the carbon K edge were determined from the Gatan EELS analysis software. The value of was estimated as 6.19 × 10⁻²⁵ m², and the value for was estimated as 8.47 × 10⁻²⁶ m². The numbers of carbon atoms contained in the analyzed regions of the ferritin particles were determined from the estimated specimen thickness (approximately 0.5 inelastic mean free paths), the density of carbon, and the area analyzed. There were approximately 10⁵ carbon atoms in each 50-nm² region containing a ferritin particle.

The tissue iron concentration was estimated by multiplying the concentration of particles (i.e., number of ferritin particles per unit volume) with the average loading of iron atoms per ferritin particle .

MRI Scanning.

All MRI scanning was performed on a Signa 7.0-T whole-body MRI scanner (GE Healthcare). The in vivo data were acquired as described previously (17). MRI scanning of postmortem brain tissue for comparison with iron histochemical staining was done using an eight-channel receive-only detector array designed for imaging of tissue slabs. A high resolution 3D multiecho gradient echo acquisition was performed with the following parameters: repetition time, 55 ms; echo time, 17.5/30.9/44.2 ms; flip angle, 10°; field of view, 156 × 156 × 30 mm³; matrix size, 1,024 × 1,024 × 200 (150 μm isotropic voxel size). The small tissue samples used for the iron extraction experiment were scanned using a dedicated, home-built four-channel receive-only detector array. High-resolution 2D multiecho gradient echo acquisition was performed with the following parameters: repetition time, 1.7 s; echo time, 16.2/33.3 ms; flip angle, 60°; slice thickness, 0.5 mm; 33 slices; field of view, 40 × 40 mm²; matrix size, 512 × 512; and repetition time, 2.0 s; echo time, 15.1/32.0 ms; flip angle, 60°; slice thickness, 0.5 mm; field of view, 65 × 65 mm²; matrix size, 512 × 512. Data processing, including image reconstruction, calculation of magnitude R₂*-weighted images and R₂* maps was performed with IDL 7.0 software. Images were reconstructed using a phase-sensitive noise-weighted channel combination (44), and the frequency image was analyzed after macroscopic background phase removal as described previously (17).

Estimation of Susceptibility Effects Originating from Ferritin Iron.

The dependence of R₂* relaxation rate on tissue iron content was estimated from previous work, which established a linear dependence with a slope of 0.05 Hz/T/ppm iron (i.e., μg/g wet weight) (32). A 34 μg/g iron concentration difference in the line of Gennari (relative to superficial layers), found with the TEM experiments, would correspond to an R₂* shift (at 7.0 T) of 13 Hz. An estimate for the associated resonance frequency shift was derived from previous calculations (45) of ferritin susceptibility shifts (from water) based on a number of assumptions, including an average magnetic moment of 3.78 Bohr magnetons per iron atom: , with c the iron concentration in μg/g. Associated frequency shifts are geometry and orientation-dependent (46), and are maximal for infinitely long structures parallel to the field: . For water protons at 7.0 T and c of 34 μg/g, this leads to a maximal 5 Hz frequency shift in the line of Gennari.