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Open Access

Peer-reviewed

Research Article

The Correlation of Hippocampal T2-Mapping with Neuropsychology Test in Patients with Alzheimer’s Disease

  • Zhuren Luo ,

    Contributed equally to this work with: Zhuren Luo, Xiongjie Zhuang

    Affiliation Department of Radiology, The First Affiliated Hospital, Xiamen University, Xiamen, P. R. China

    ⨯
  • Xiongjie Zhuang ,

    Contributed equally to this work with: Zhuren Luo, Xiongjie Zhuang

    xiongjiezhuang@gmail.com

    Affiliation Department of Radiology, The First Affiliated Hospital, Xiamen University, Xiamen, P. R. China

    ⨯
  • Dushyant Kumar,

    Affiliations Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany, Multiple Sclerosis Imaging Section, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany

    ⨯
  • Xiurong Wu,

    Affiliation Department of Radiology, The First Affiliated Hospital, Xiamen University, Xiamen, P. R. China

    ⨯
  • Cen Yue,

    Affiliation Department of Neurology, The First Affiliated Hospital, Xiamen University, Xiamen, P. R. China

    ⨯
  • Chengkun Han,

    Affiliation Department of Radiology, The First Affiliated Hospital, Xiamen University, Xiamen, P. R. China

    ⨯
  • Jiancheng Lv

    Affiliation Department of Radiology, The First Affiliated Hospital, Xiamen University, Xiamen, P. R. China

    ⨯

Abstract

Objectives

1) To deduce T2, the inverse of the transverse relaxation rate (R2), in the hippocampus of healthy adults; 2) to investigate the brain iron deposition in Alzheimer’s disease (AD) patients and age-matched healthy controls using T2-values.

Methods

T2-weighted data from the bilateral-hippocampi of ten AD patients and sixty healthy controls were collected at six echo time points using multi-slice multi-echo turbo spin echo (MSME-TSE) imaging on a 3.0 T MR-scanner, followed by the neuropsychological testing. The correlations between T2-values and Mini-Mental State Examination (MMSE) scores were investigated on group-wise basis (covariates in the group-wise analyses: gender, age, side and healthy/AD).

Results

There were no significant differences in hippocampal T2-values on intra-gender and inter-gender basis (P > 0.05). Hippocampal T2-values of both sides were similar (right: 85.2±2.4 milliseconds; left: 85.3±2.5 milliseconds). The bilateral hippocampal T2 values correlated moderately with age (right: r = -0.59; left: -0.58; P < 0.001). The AD-group had significantly lower T2-values in the hippocampus when compared to normal controls (P < 0.001) and such low T2-values had a strong positive correlation with the MMSE score (R2 = 0.97; P < 0.05).

Conclusion

Patients with AD showed significantly lower T2 values, which can be attributed to the increased iron depositions in the hippocampus. A positive correlation between T2-values and cognition scores suggests that quantitative T2 can be used in the early diagnosis of AD and in the monitoring of the treatment response.

Citation: Luo Z, Zhuang X, Kumar D, Wu X, Yue C, Han C, et al. (2013) The Correlation of Hippocampal T2-Mapping with Neuropsychology Test in Patients with Alzheimer’s Disease. PLoS ONE 8(9): e76203. https://doi.org/10.1371/journal.pone.0076203

Editor: Krish Sathian, Emory University, United States of America

Received: April 22, 2013; Accepted: August 21, 2013; Published: September 30, 2013

Copyright: © 2013 Luo et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work was financially supported by the Youth Foundation of Health Department of Fujian Province of China (No. 2012-2-75) (http://www.fjphb.gov.cn/show.aspx?ctlgid=448882&id=77384). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Alzheimer’s disease (AD) is the most common cause of dementia for the elderly. It is pathologically characterized by the presence of senile plaques (SPs) and neurofibrillary degeneration (NFD) in cortical regions of the brain [1-3]. The redox-active biometals have been suggested to play considerable roles in the generation of the oxidative stress and in the modulation of amyloid-ß (Aß). Additionally, iron is recognized as a major cause of oxidative stress in AD. There is a close connection between the iron deposition and the AD on both regional and cellular levels. Postmortem biochemical studies have reported elevated iron concentrations in the hippocampus, cortical lobes, and basal ganglia regions of AD brains compared to controls [4-8]. Furthermore, the increased iron accumulation is shown in both SPs and NFD regions that are major sites for the catalytic redox activity [9,10]. Increasing evidences indicate that oxidative stress is one of the earliest events in the genesis of AD, and iron may play a crucial role [11]. Iron concentrations are elevated in cortex and basal ganglia in AD patients [4-6,12,13] indicating a disruption of iron homeostasis in the brain. Higher iron concentrations in AD brains may increase the possibility of free iron-catalyzed lipid peroxidation, which may cause cell membrane damages and subsequent cell deaths. Based on these findings, it is possible that iron chelators and inhibitors of the iron-dependent oxidative stress and lipid peroxidation (e.g., antioxidants or free radical scavengers) may have a therapeutic value [14-16]. Therefore, a quantitative measurement is required to assess and monitor the concentrations of iron deposited in the brain, which might provide a biomarker for early detection and design of therapeutic interventions.

Iron, in the form of ferritin, can reduce T2 relaxation times or increase R2 (=1/T2) values, and so we applied quantitative MR imaging proton transverse relaxation rate (R2) which has the potential to measure brain iron content indirectly and manifest other features of AD pathology in vivo [17]. R2 (=1/T2), the transverse relaxation rate, describes the rate of dephasing of the hydrogen nuclei in specific structures [18] in the presence of external magnetic field. The R2 value of proton depends on volume and surface interaction effects of confining structures/ compartments [19] and hence, the proton in different environments (chemical or magnetic) would have different R2 values. Iron deposition causes local distortions of the effective magnetic field which enhances the relaxation rates of diffusing protons resulting in the increase of R2 (or decrease of T2) values. As a part of the middle temporal lobe composing the memory system, the hippocampus is one of the regions which is susceptible to damage from AD. Therefore, we chose hippocampus as the region of interest (ROI) for measurement of T2 (and R2) in this study.

In this study, we applied quantitative MR imaging to measure the mean hippocampal T2 relaxation times (and R2 values) in 60 healthy adults, and then assess differences in T2 values in the hippocampi between patients with AD and normal controls which can be attributed to different iron accumulation levels in both groups. The main objectives of this study were: 1) to provide baseline data for the early diagnosis and the longitudinal monitoring of AD with hippocampal T2 relaxation times; 2) to prospectively investigate the abnormal iron deposition in the hippocampus of patients with AD using hippocampal T2 relaxation times (and R2 values) as a surrogate; and 3) to explore the relationship between the reduction in average T2 values, likely due to the iron level, and the neuropsychological tests in these patients reporting memory loss.

Materials and Methods

Ethics statement

The study was approved by the Ethics Review Board of the First Affiliated Hospital of Xiamen University. The written informed consents were obtained from both groups: AD patients and healthy volunteers. In case the participants (AD patients) had impaired ability to consent, written consents were obtained from the next-of-kin or the care giver on their behalf.

Study population

Ten AD patients and 60 healthy adult volunteers of whom 10 controls were age-matched to the AD group were included in this prospective study (please refer to Table 1 for the demographic details and neuropsychological test scores of the participants). All the participants were right-handed. These patients underwent a series of neurological tests and a battery of neuropsychological assessments, which included the Mini-Mental State Examination (MMSE) and the Acitivity of Daily Living (ADL), to rule out other causes of cognitive impairment. We choose the MMSE score to indicate the cognitive level and not the ADL as the ADL test needs much more time than the MMSE and has less specificity with cognitive level. All cases meet the NINCDS/ADRDA [20] criteria for clinically probable AD. Sixty healthy adult volunteers were recruited, with age ranging from 18 to 70 years. They were further divided into three subgroups according to the latest principles of age group set by WHO: 1) youth group of 20 cases, mean age: 34 ± 6 years; 2) middle-aged group of 20 cases, mean age: 51 ± 3 years; and 3) elderly group of 20 cases, mean age: 65 ± 4 years.

Control groupAD group
No. of individuals1010
Age (y)65±466±3
Gender % (no.) of men30 (3)40 (4)
Education (y)9.12±2.629.06±2.31
MMSE28.50±2.8718.90±2.99*
ADL21.82±2.0441.18±12.09*

Table 1. Demographic details and neuropsychological test scores of the participants.

Note: Only 10 age-matched healthy control data were included. Data are expressed as mean ±SD, except for gender. MMSE: Mini Mental State Examination. ADL: Activity of Daily Living.
*Significant difference between control and AD group (P < 0.05, two-tailed t-test).
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Exclusion criteria included the following: patients with other brain diseases or with other causes of dementia supported by pathological brain scan and clinical findings, including significant cerebrovascular diseases (cortical infarctions, multiple lacunas lesions and chronic subdural hematoma); Parkinson’s disease; Huntington’s disease; Pick’s disease; Creutzfeldt-Jakob disease; Normal pressure hydrocephalus; Dementia with Lewy’s bodies; Corticobasal ganglionic degeneration; Progressive supranuclear palsy; Cancer (brain tumor or meningeal neoplasms); infection (AIDS, Neurosyphilis or Progressive multifocal leukoencephalopathy); Metabolic disorders (Hypothyroidism or Vitamin B12 deficiency) and patients with depression or dysthymia according to the DSM-IV criteria. Control subjects underwent a structured interview to exclude patients with cognitive dysfunction, substance abuse, depression, and other cerebral pathology.

Image acquisition

All the MR images were obtained using a 3.0-T MR system (Achieva 3.0 T TX; Philips Healthcare, Netherlands) equipped with an eight-channel head coil. The head was immobilized in the head coil with foam padding. Conventional axial T1- and T2-weighted images were acquired for screening of space-occupying lesions and cerebrovascular diseases. A multi-slice multi-echo turbo spin echo sequence (MSME-TSE; sequence name on Philips scanner: sT 2Cal_TSE) was used to get the T2 map and was taken in parallel to the coronal-oblique images of hippocampus with the following parameters: A pulse repetition time (TR) of 2000 msec and 6 echo times (TE) of 20, 40, 60, 80, 100, and 120 msec were used. Flip angle = 90°, number of slice = 5 slices, slice thickness = 3 mm, slice gap = 0 mm, NSA = 1, FOV = 160 mm × 160 mm, and matrix size = 380 × 310.

Image Analysis

The raw data acquired using the sequence sT 2Cal_TSE were transferred to a separate workstation (Philips Extended WorkSpace version 2.6.3.1), where the data were processed by a self-coded program to obtain the T2 map. After that, the T2 relaxation times [21] were calculated on the T2 map (Figure 1). R2 is defined as the reciprocal of the proton transverse relaxation time, T2 (i.e., R2 = 1/T2×1000). The units for T2 and R2 are millisecond (msec) and second-1 (sec-1), respectively. Regions of interest (ROIs) were first delineated on the intermediate echo time images (60 msec or 80 msec, Figure 1). ROIs were set to include the maximum contours of the hippocampus and exclude the hippocampal boundaries. Furthermore, the alveus and fimbria of hippocampus, and the cerebrospinal fluid (CSF) in the gyri uncinatus of the hippocampal head should be ruled out to reduce CSF partial volume effects. Manual tracing a ROI in a sample subject was illustrated on the representative long axial images of hippocampus in Figure 1. A trained neuroradiologist with more than 15 years of experience, who was blinded as to the subjects’ exact group, manually traced the ROIs. All the ROIs were remeasured two months later by the same reader on the same images. The final values were the means of the two measurements.

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Figure 1. T2-weighted images at various echo time-points and resultant T2-map.

Row 1 & Row 2 consist of six T2-weighted images, taken at echo times of 20, 40, 60, 80, 100, and 120 ms, covering the entire hippocampus of an AD patient and an elderly control. The single image in Row 3 is the corresponding T2 map. The bilateral hippocampal atrophy was visually found accompanied with a varied degree of decreased T2 values (or increased R2 values) in the AD patient. Illustration of the ROI selection on the representative spin-echo images (TE = 60, 80, 100 and 120 milliseconds) of a patient with AD and an elderly control. The hippocampal region for which T2 data were acquired is shown as representative regions of interest. Note: ROIs required include the hippocampal contours to be as large as possible but not involving its boundaries, and avoiding visible cystic areas and CSF in the hippocampal fissure.

https://doi.org/10.1371/journal.pone.0076203.g001

Statistical analysis

Group differences in age, education, and MMSE or ADL score were analyzed using one-way analysis of variance (ANOVA) with least significant differences post hoc analysis. Sex differences between groups were assessed by a χ2 test. The paired-sample t test was used to analyze the differences between the left and the right side of the hippocampus, and Student’s t test was employed to figure out if there were gender wise differences and ANOVA was adopted to identify the differences of T2 values among these subgroups in the healthy volunteers. Furthermore, the relationship between the T2-values and the age of subjects was analyzed using the Pearson’s Correlation test. Group differences in T2 (and R2) values were tested for significance by using one-tailed t test. Iron levels tend to increase with age, but typically reach a plateau in the elderly population [22]. Therefore, to eliminate the effect of age itself on iron levels between groups, an analysis of covariance (ANCOVA) (with age as the covariate) was also used to assess T2 (and R2) differences. To investigate the relationship between T2 (and R2) values in the hippocampus and MMSE scores for the participants with AD, a Pearson’s correlation coefficient, adjusted for age, was used to assess the direction, strength, and significance of the correlations. All statistical computations and analyses were carried out using the SPSS statistical package (SPSS for Windows, version 13.0; SPSS Inc., Chicago, IL), and the results were declared statistically significant when associated with a two-sided P < 0.05.

Results

Demographics, Clinical Data

There were no significant differences in age, sex and education levels (P > 0.05) between the age-matched elderly controls (N = 10) and the AD group (N = 10) (Table 1). The AD group had significantly lower MMSE score (P < 0.05, Table 1) in comparison to the control group.

Hippocampal T2 of normal adults and its relationship with age, gender and side

The hippocampal T2 values of both sides were (85.2±2.4) milliseconds (right hippocampus) and (85.3±2.5) milliseconds (left hippocampus), respectively, and there were no significant differences in hippocampal T2 values on the left and the right side of the same sex group of healthy volunteers (t = 0.62, P = 0.5383). There were no further significant gender-wise differences (P > 0.05). The bilateral hippocampal T2 values correlated moderately with age (r = -0.59 for right side and -0.58 for left side, respectively; P < 0.001) (Figure 2).

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Figure 2. Scatter plots of T2-values in bilateral hippocampus of normal controls at various ages.

Scatter plots illustrate T2 values on both sides of hippocampus correlated moderately with age in normal controls. The correlation coefficients are -0.59 and -0.58 for right side (A) and left side (B), respectively at P < 0.001.

https://doi.org/10.1371/journal.pone.0076203.g002

T2 (and R2) differences between both groups

After ANCOVA adjustment for age, the AD group had significantly lower T2 values (61.7 ± 2.7 ms; t = -12.262, P < 0.001) and significantly higher R2 values (t = 9.121, P < 0.001) in the hippocampus, compared to controls (T2 values: 83.2 ± 2.8 ms).

T2 (and R2) correlations with neuropsychological test scores

After controlling for the age-related bias, Pearson’s correlation test revealed that the T2 values in the hippocampus had a strong positive correlation with the MMSE score (the coefficient of determination, denoted as R2 = 0.97, P < 0.05), while the R2 values in the hippocampus had a strong negative correlation with the MMSE score (R2 = 0.95, P < 0.05) (Figure 3).

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Figure 3. Scatter plot of T2 (or R2)-values of hippocampus vs. MMSE score.

Scatter plot shows the T2 (or R2)-values of the hippocampus and the MMSE score in AD patients were positively (or negatively) correlated with a coefficient of determination of 0.97 (or 0.95) (P < 0.05 for both) controlling for the age related bias.

https://doi.org/10.1371/journal.pone.0076203.g003

Discussion

R2 versus R2’ or R2*

Many investigators [23-26] have proposed R2*, the free induction decay rate due to the presence of magnetic field gradient irregularities and minor differences in chemical environment, and even R2’, the difference between R2* and R2, to be a specific marker of tissue iron. At the same high field strength (3.0 T), Gelman et al [23] reported a strong correlation (r = 0.92) between R2 and postmortem iron concentrations. Those researchers [23-26], however, suggest R2’ or R2* may be a more specific measure of iron in the brain as cortical white and gray matter regions with similar iron concentrations have similar R2’ or R2* values, but significantly different R2 values. Other factors, such as the increased water content, may also contribute to changes in R2 values besides tissue iron concentrations. However, in the case of gray matter (e.g., hippocampus as ROIs in our study), where water concentrations are similar, iron appears to be the dominant factor in determining R2, and the iron-related specificity of R2 would, therefore, be enhanced. Hence, increase in R2 values of the gray matter are thought to reflect increase in the iron contents of this brain tissue type.

The inferred R2 values from the measured data also vary depending on the inter-echo spacing and the field strength of MRI scanner being used in the experiment [27], which would account for slightly different results reported in the literature.

Comparison of T2 (and R2) values between AD patients and age-matched controls

T2 (and R2) differences between the AD group and the healthy control group suggest iron concentration has increased in the hippocampal region and a disruption of iron homeostasis in the brain has happened in those with memory complaints, consistent with incipient AD pathogenesis and biochemical data. Excessive brain iron deposition would promote the aggregation of β-amyloid peptide and increase β-amyloid toxicity [11], and its neurotoxicity can, therefore, lead to nerve cell death. Previous histochemical stains reveal iron in amorphous amyloid plaques, in neurofibrillary tangles, and in cortical neurons in AD brains [28,29]. This may account for the severe encephalatrophy, especially in the hippocampus located in the temporal lobe of brain, observed from MR imaging in patients with AD. Haley et al [30] conducted a study of 10 patients with AD and 40 healthy participants, and found T2 values in the right hippocampus of AD patients to be significantly reduced compared to normal aged participants, which is consistent with the results of this study. Schenck et al [31] analyzed the differences in T2 relaxation times of hippocampus between patients with AD and normal controls using 3.0 T MRI, and argued that while iron accumulation in hippocampi of AD patients resulted in reduced T2 relaxation times, it may be partly offset by the prolongation of T2 relaxation times due to the increase of the free water content, and thereby, affecting the sensitivity of estimating iron level in the hippocampus by T2 values.

The preliminary study of House et al [32] and the work of Campeau et al [33] indicated that hippocampal R2 values in the AD group were essentially unchanged compared to the controls. However, our results contradicted their findings. A 7 T MR imaging study by Huesgen et al [34] has also reported a non-significant reduction in T2 relaxation times (R2 increase) of AD hippocampus. They explained that AD progression accelerates the neuronal degeneration and the hippocampal atrophy. Atrophy of brain tissue may increase the amount of CSF, thus reducing R2 and counteracting any iron increases. The final R2 values, therefore, remain unchanged. It is interesting to note that some researchers [35-37] have reported R2 reductions in the hippocampus of AD patients. Nevertheless, the reported absence of an association between hippocampal volumes and T2 relaxation times in AD [34,38] suggests that the atrophy, resulting in increased water contents, is not the dominant mechanism driving R2 reduction in this brain region. The hippocampus is rich in myelin relative to other gray matter regions. A loss of myelin in AD hippocampi has been inferred from a decrease in 2’3’-cyclic nucleotide-3’-phosphodiesterase (CNPase) activity [39], which suggests a possible mechanism for reducing hippocampal R2 values in AD, and is analogous to the pathologic processes reducing R2 in white matter. Hence, demyelination and atrophy cause the hippocampal R2 values to decline.

Wang et al [40] studied the animal model of AD in mice using histochemical staining for senile plaques and T2 mapping. They found that senile plaques were deposited as early as 4 months in transgenic mouse model of AD. Iron depositions in the hippocampus and the cortex were detected by Perl’s-DAB (stain for iron) as early as 6 months of age, and there was an overall increase in number and load of plaques and iron with age. They further found that T2 values decreased in the cortical and the hippocampal regions of adult mice group, and it tended to shorten with age. They [40] believed that shortening of T2 values in AD transgenic mouse may be associated with the interaction between Aβ peptide and iron. In the early days iron deposition was not as obvious as senile plaques. As a result, it reduced T2 values in AD transgenic mouse which may be related to a deposition of Aβ peptide. However, in the elder mice group, both Aβ and iron contributed, with iron taking a leading role, resulting in reduced T2 values. Their preclinical study established iron accumulation to be the dominant factor in the reduction of T2 value in AD.

Correlations of T2 (and R2) values with MMSE score

One of the most important findings in our measurement was a strongly positive correlation between the T2 relaxation times in the hippocampus and the MMSE score (R2=0.97, P < 0.05) or a strongly negative correlation between the R2 and the MMSE score (R2=0.95, P < 0.05) for the patients with AD. The results establish a connection between the iron deposition in the hippocampus and the severity of AD patients, and suggest that iron deposition relates to the pathology and the progression in AD. Such information may assist in the diagnosis of AD, as well as in yielding a clinical biomarker that would be valuable in the monitoring of AD. House et al [32] reported negative correlations in the gray matter and positive correlations in the white matter between R2 and cognition/memory scores, that is, in participants with memory problems, R2 tends to increase in gray matter and decrease in white matter. There are, however, some exceptions to these general trends. R2 in the internal capsule showed mainly weak negative correlations with MMSE score, perhaps reflecting the influence of interdigitating gray-matter bridges between the caudate and the putamen. R2 values in the left hippocampus were positively correlated weakly with some memory scores and the MMSE results. As discussed above, the high myelin content of the hippocampus relative to other gray matter regions could make this structure vulnerable to R2-reducing processes that are more pronounced in white matter, and which ultimately obscure correlations with memory scores. Laasko et al [35] found there was no correlation between T2 values and memory test scores in their AD patients, but found a significant negative correlation between the hippocampal T2 values and MMSE scores. This observation is equivalent to a positive correlation between R2-values and MMSE scores, which is contrary to our own findings. The work of Wang et al [37] mentioned that the right hippocampal T2 was correlated with cognitive performance in AD, whereas the amygdaloid T2 was not. They [37] argued that this might be partly explained by their study sample and the fact that most of the patients in their study had moderate dementia. They further argued these results could not conclude whether the measurement of amygdaloid T2 helps in monitoring the cognitive deterioration of AD. Their work motivated us to focus on the measurement in one specific region- hippocampus.

Limitations

There are two main limitations of our study. First of all, our study included limited number of AD patients. Further evaluation in larger samples is required, especially, more patients with mild cognitive impairment recruited in the following studies. Another limitation of this study is that only R2 values in the hippocampus were measured. Although AD is traditionally characterized as a gray matter disease, white matter changes were considered only a secondary phenomenon related to the neuronal degeneration. However, histopathological, biochemical, and MR imaging changes in AD white matter have been observed in several studies [41-44]. Some early research [42] indicated that white matter changes in AD were not purely a secondary phenomenon related to neuronal degeneration, while reports from the past few years [45,46] suggest that demyelination may have closer links to AD pathogenesis than previously thought. House et al [32,47] showed that R2-values and iron concentrations in various brain regions of AD patients were correlated, though hippocampal analysis was not included. Higher water content, associated with decreasing protein and lipid levels, can also contribute to R2 reduction in white matter of AD patient if iron concentration is fixed. This would explain that in normal cortical brain tissue, where gray and adjacent white matter have similar iron concentrations, the R2 of the gray matter is smaller than R2 of the white matter [48,49], and would account for variance in R2-values reported in various AD research studies.

Conclusions

Patients with AD showed significantly lower T2 values suggesting increased iron depositions in the hippocampus. In addition, R2 values from hippocampus in AD showed opposite correlation with cognition scores. Therefore, quantitative R2 measurements in the hippocampus might offer useful means for the early diagnosis and the monitoring of AD, and provide an indication of the treatment response.

Author Contributions

Conceived and designed the experiments: ZL XZ. Performed the experiments: ZL CY CH JL. Analyzed the data: XW ZL CH XZ. Contributed reagents/materials/analysis tools: XZ DK. Wrote the manuscript: ZL XZ DK.

References

  1. 1. Giannakopoulos P, Hof PR, Giannakopoulos AS, Herrmann FR, Michel JP et al. (1995) Regional distribution of neurofibrillary tangles and senile plaques in the cerebral cortex of very old patients. Arch Neurol 52: 1150-1159. doi:https://doi.org/10.1001/archneur.1995.00540360028012. PubMed: 7492288.
  2. 2. Pearson RC, Esiri MM, Hiorns RW, Wilcock GK, Powell TP (1985) Anatomical correlates of the distribution of the pathological changes in the neocortex in Alzheimer’s disease. Proc Natl Acad Sci U S A 82: 4531-4534. doi:https://doi.org/10.1073/pnas.82.13.4531. PubMed: 3859874.
  3. 3. Braak H, Braak E, Bohl J (1993) Staging of Alzheimer-related cortical destruction. Eur Neurol 33: 403-408. doi:https://doi.org/10.1159/000116984. PubMed: 8307060.
  4. 4. Loeffler DA, Connor JR, Juneau PL, Snyder BS, Kanaley L et al. (1995) Transferrin and iron in normal, Alzheimer’s disease, and Parkinson’s disease brain regions. J Neurochem 65: 710-716. PubMed: 7616227.
  5. 5. Connor JR, Snyder BS, Beard JL, Fine RE, Mufson EJ (1992) Regional distribution of iron and iron-regulatory proteins in the brain in aging and Alzheimer’s disease. J Neurosci Res 31: 327-335. doi:https://doi.org/10.1002/jnr.490310214. PubMed: 1573683.
  6. 6. Cornett CR, Markesbery WR, Ehmann WD (1998) Imbalances of trace elements related to oxidative damage in Alzheimer’s disease brain. Neurotoxicology 19: 339-346. PubMed: 9621340.
  7. 7. Dedman DJ, Treffry A, Candy JM, Taylor GA, Morris CM et al. (1992) Iron and aluminium in relation to brain ferritin in normal individuals and Alzheimer’s-disease and chronic renal-dialysis patients. Biochem J 287: 509-514. PubMed: 1445209.
  8. 8. Ehmann WD, Markesbery WR, Alauddin M, Hossain TI, Brubaker EH (1986) Brain trace elements in Alzheimer’s disease. Neurotoxicology 7: 195-206. PubMed: 3714121.
  9. 9. Sayre LM, Perry G, Harris PL, Liu Y, Schubert KA et al. (2000) In situ oxidative catalysis by neurofibrillary tangles and senile plaques in Alzheimer’s disease: a central role for bound transition metals. J Neurochem 74: 270-279. PubMed: 10617129.
  10. 10. Collingwood JF, Mikhaylova A, Davidson M, Batich C, Streit WJ et al. (2005) In situ characterization and mapping of iron compounds in Alzheimer’s disease tissue. J Alzheimers Dis 7: 267-272. PubMed: 16131727.
  11. 11. Honda K, Casadesus G, Petersen RB, Perry G, Smith MA (2004) Oxidative stress and redox-active iron in Alzheimer’s disease. Ann N Y Acad Sci 1012: 179-182. doi:https://doi.org/10.1196/annals.1306.015. PubMed: 15105265.
  12. 12. Deibel MA, Ehmann WD, Markesbery WR (1996) Copper, iron, and zinc imbalances in severely degenerated brain regions in Alzheimer’s disease: possible relation to oxidative stress. J Neurol Sci 143: 137-142. doi:https://doi.org/10.1016/S0022-510X(96)00203-1. PubMed: 8981312.
  13. 13. Thompson CM, Markesbery WR, Ehmann WD, Mao YX, Vance DE (1988) Regional brain trace-element studies in Alzheimer’s disease. Neurotoxicology 9: 1-7. PubMed: 3393299.
  14. 14. Doraiswamy PM, Finefrock AE (2004) Metals in our minds: therapeutic implications for neurodegenerative disorders. Lancet Neurol 3: 431-434. doi:https://doi.org/10.1016/S1474-4422(04)00809-9. PubMed: 15207800.
  15. 15. Zheng H, Weiner LM, Bar-Am O, Epsztejn S, Cabantchik ZI et al. (2005) Design, synthesis, and evaluation of a novel bifunctional iron-chelator as a potential agent for neuroprotection in Alzheimer’s, Parkinson’s, and other neurodegenerative diseases. Bioorg Med Chem 13: 773-783. doi:https://doi.org/10.1016/j.bmc.2004.10.037. PubMed: 15653345.
  16. 16. Liu G, Garrett MR, Men P, Zhu X, Perry G et al. (2005) Nanoparticle and other metal chelation therapeutics in Alzheimer’s disease. Biochim Biophys Acta 1741: 246-252. doi:https://doi.org/10.1016/j.bbadis.2005.06.006. PubMed: 16051470.
  17. 17. Hasan KM, Walimuni IS, Kramer LA, Narayana PA (2012) Human brain iron mapping using atlas-based T2 relaxometry. Magn Reson Med 67: 731-739. doi:https://doi.org/10.1002/mrm.23054. PubMed: 21702065.
  18. 18. Brittenham GM, Badman DG, National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) Workshop (2003) Noninvasive measurement of iron: report of an NIDDK workshop. Blood 101: 15-19. doi:https://doi.org/10.1182/blood-2002-06-1723. PubMed: 12393526.
  19. 19. Brownstein KR, Tarr CE (1977) Spin-lattice relaxation in a system governed by diffusion. J Magn Reson 26: 17-24.
  20. 20. McKhann G, Drachman D, Folstein M, Katzman R, Price D et al. (1984) Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 34: 939-944. doi:https://doi.org/10.1212/WNL.34.7.939. PubMed: 6610841.
  21. 21. Thomas LO, Boyko OB, Anthony DC, Burger PC (1993) MR detection of brain iron. AJNR Am J Neuroradiol 14: 1043-1048. PubMed: 8237678.
  22. 22. Hallgren B, Sourander P (1958) The effect of age on the non-haemin iron in the human brain. J Neurochem 3: 41-51. doi:https://doi.org/10.1111/j.1471-4159.1958.tb12607.x. PubMed: 13611557.
  23. 23. Gelman N, Gorell JM, Barker PB, Savage RM, Spickler EM et al. (1999) MR imaging of human brain at 3.0 T: preliminary report on transverse relaxation rates and relation to estimated iron content. Radiology 210: 759-767. PubMed: 10207479.
  24. 24. Novellino F, Cherubini A, Chiriaco C, Morelli M, Salsone M et al. (2013) Brain iron deposition in essential tremor: A quantitative 3-tesla magnetic resonance imaging study. Mov Disord 28: 196-200. doi:https://doi.org/10.1002/mds.25263. PubMed: 23238868.
  25. 25. Cherubini A, Péran P, Caltagirone C, Sabatini U, Spalletta G (2009) Aging of subcortical nuclei: microstructural, mineralization and atrophy modifications measured in vivo using MRI. NeuroImage 48: 29-36. doi:https://doi.org/10.1016/j.neuroimage.2009.06.035. PubMed: 19540925.
  26. 26. Péran P, Cherubini A, Assogna F, Piras F, Quattrocchi C et al. (2010) Magnetic resonance imaging markers of Parkinson’s disease nigrostriatal signature. Brain 133: 3423-3433. doi:https://doi.org/10.1093/brain/awq212. PubMed: 20736190.
  27. 27. Kolind SH, Mädler B, Fischer S, Li DK, MacKay AL (2009) Myelin water imaging: Implementation and development at 3.0T and comparison to 1.5T measurements. Magn Reson Med 62: 106-115. doi:https://doi.org/10.1002/mrm.21966. PubMed: 19353659.
  28. 28. LeVine SM (1997) Iron deposits in multiple sclerosis and Alzheimer’s disease brains. Brain Res 760: 298-303. doi:https://doi.org/10.1016/S0006-8993(97)00470-8. PubMed: 9237552.
  29. 29. Smith MA, Harris PL, Sayre LM, Perry G (1997) Iron accumulation in Alzheimer disease is a source of redox-generated free radicals. Proc Natl Acad Sci U S A 94: 9866-9868. doi:https://doi.org/10.1073/pnas.94.18.9866. PubMed: 9275217.
  30. 30. Haley AP, Knight-Scott J, Fuchs KL, Simnad VI, Manning CA (2004) Shortening of hippocampal spin-spin relaxation time in probable Alzheimer’s disease: a 1H magnetic resonance spectroscopy study. Neurosci Lett 362: 167-170. doi:https://doi.org/10.1016/j.neulet.2004.01.031. PubMed: 15158006.
  31. 31. Schenck JF, Zimmerman EA, Li Z, Adak S, Saha A et al. (2006) High-field magnetic resonance imaging of brain iron in Alzheimer disease. Top Magn Reson Imaging 17: 41-50. doi:https://doi.org/10.1097/01.rmr.0000245455.59912.40. PubMed: 17179896.
  32. 32. House MJ, St Pierre TG, Foster JK, Martins RN, Clarnette R (2006) Quantitative MR imaging R2 relaxometry in elderly participants reporting memory loss. AJNR Am J Neuroradiol 27: 430-439. PubMed: 16484425.
  33. 33. Campeau NG, Petersen RC, Felmlee JP, O’Brien PC, Jack CR Jr (1997) Hippocampal transverse relaxation times in patients with Alzheimer disease. Radiology 205: 197-201. PubMed: 9314985.
  34. 34. Huesgen CT, Burger PC, Crain BJ, Johnson GA (1993) In vitro MR microscopy of the hippocampus in Alzheimer’s disease. Neurology 43: 145-152. doi:https://doi.org/10.1212/WNL.43.1_Part_1.145. PubMed: 8423879.
  35. 35. Laakso MP, Partanen K, Soininen H, Lehtovirta M, Hallikainen M et al. (1996) MR T2 relaxometry in Alzheimer’s disease and age-associated memory impairment. Neurobiol Aging 17: 535-540. doi:https://doi.org/10.1016/S0197-4580(96)00036-X. PubMed: 8832627.
  36. 36. Kirsch SJ, Jacobs RW, Butcher LL, Beatty J (1992) Prolongation of magnetic resonance T2 time in hippocampus of human patients marks the presence and severity of Alzheimer’s disease. Neurosci Lett 134: 187-190. doi:https://doi.org/10.1016/0304-3940(92)90513-7. PubMed: 1589144.
  37. 37. Wang H, Yuan H, Shu L, Xie J, Zhang D (2004) Prolongation of T2 relaxation times of hippocampus and amygdala in Alzheimer’s disease. Neurosci Lett 363: 150-153. doi:https://doi.org/10.1016/j.neulet.2004.03.061. PubMed: 15172104.
  38. 38. Pitkänen A, Laakso M, Kälviäinen R, Partanen K, Vainio P et al. (1996) Severity of hippocampal atrophy correlates with the prolongation of MRI T2 relaxation time in temporal lobe epilepsy but not in Alzheimer’s disease. Neurology 46: 1724-1730. doi:https://doi.org/10.1212/WNL.46.6.1724. PubMed: 8649578.
  39. 39. Reinikainen KJ, Pitkänen A, Riekkinen PJ (1989) 2’, 3’-cyclic nucleotide-3’-phosphodiesterase activity as an index of myelin in the post-mortem brains of patients with Alzheimer’s disease. Neurosci Lett 106: 229-232. doi:https://doi.org/10.1016/0304-3940(89)90230-9. PubMed: 2555748.
  40. 40. Wang D, Zhang LH, Xu W, Du XX, Zhan YQ et al. (2010) Iron and senile plaques deposition in transgenic mouse model of Alzheimer’s disease and influence on MR T2 relaxation times. Chin J Neurol 43: 626-631.
  41. 41. Englund E, Brun A, Persson B (1987) Correlations between histopathologic white matter changes and proton MR relaxation times in dementia. Alzheimer Dis Assoc Disord 1: 156-170. doi:https://doi.org/10.1097/00002093-198701030-00008. PubMed: 3453747.
  42. 42. Englund E, Brun A, Alling C (1998) White matter changes in dementia of Alzheimer’s type: biochemical and neuropathological correlates. Brain 111: 1425-1439.
  43. 43. Sjöbeck M, Englund E (2003) Glial levels determine severity of white matter disease in Alzheimer’s disease: a neuropathological study of glial changes. Neuropathol Appl Neurobiol 29: 159-169. doi:https://doi.org/10.1046/j.1365-2990.2003.00456.x. PubMed: 12662323.
  44. 44. Bartzokis G, Cummings JL, Sultzer D, Henderson VW, Nuechterlein KH et al. (2003) White matter structural integrity in healthy aging adults and patients with Alzheimer disease. Arch Neurol 60: 393-398. doi:https://doi.org/10.1001/archneur.60.3.393. PubMed: 12633151.
  45. 45. Bartzokis G (2004) Age-related myelin breakdown: a developmental model of cognitive decline and Alzheimer’s disease. Neurobiol Aging 25: 5-18. doi:https://doi.org/10.1016/S0197-4580(04)80016-2. PubMed: 14675724.
  46. 46. Roher AE, Weiss N, Kokjohn TA, Kuo YM, Kalback W et al. (2002) Increased A beta peptides and reduced cholesterol and myelin proteins characterize white matter degeneration in Alzheimer’s disease. Biochemistry 41: 11080-11090. doi:https://doi.org/10.1021/bi026173d. PubMed: 12220172.
  47. 47. House MJ, St Pierre TG, Kowdley KV, Montine T, Connor J et al. (2007) Correlation of proton transverse relaxation rates (R2) with iron concentrations in postmortem brain tissue from Alzheimer’s disease patients. Magn Reson Med 57: 172-180. doi:https://doi.org/10.1002/mrm.21118. PubMed: 17191232.
  48. 48. Besson JA, Best PV, Skinner ER (1992) Post-mortem proton magnetic resonance spectrometric measures of brain regions in patients with a pathological diagnosis of Alzheimer’s disease and multi-infarct dementia. Br J Psychiatry 160: 187-190. doi:https://doi.org/10.1192/bjp.160.2.187. PubMed: 1540758.
  49. 49. Breger RK, Rimm AA, Fischer ME, Papke RA, Haughton VM (1989) T1 and T2 measurements on a 1.5-T commercial MR imager. Radiology 171: 273-276. PubMed: 2928538.