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Little is known about the association of cortical Aβ with depression and anxiety among cognitively normal (CN) elderly persons.
We conducted a cross-sectional study derived from the population-based Mayo Clinic Study of Aging in Olmsted County, Minnesota; involving CN persons aged ≥ 60 years that underwent PiB-PET scans and completed Beck Depression Inventory-II (BDI-II) and Beck Anxiety Inventory (BAI). Cognitive diagnosis was made by an expert consensus panel. Participants were classified as having abnormal (≥1.4; PiB+) or normal PiB-PET (<1.4; PiB−) using a global cortical to cerebellar ratio. Multi-variable logistic regression analyses were performed to calculate odds ratios (OR) and 95% confidence intervals (95% CI) after adjusting for age and sex.
Of 1,038 CN participants (53.1% males), 379 were PiB+. Each one point symptom increase in the BDI (OR = 1.03; 1.00–1.06) and BAI (OR = 1.04; 1.01–1.08) was associated with increased odds of PiB-PET+. The number of participants with BDI > 13 (clinical depression) was greater in the PiB-PET+ than PiB-PET- group but the difference was not significant (OR = 1.42; 0.83–2.43). Similarly, the number of participants with BAI > 10 (clinical anxiety) was greater in the PiB-PET+ than PiB-PET− group but the difference was not significant (OR = 1.77; 0.97–3.22).
As expected, depression and anxiety levels were low in this community-dwelling sample, which likely reduced our statistical power. However, we observed an informative albeit weak association between increased BDI and BAI scores and elevated cortical amyloid deposition. This observation needs to be tested in a longitudinal cohort study.
Iron plays an important role in normal neuronal metabolism. Excessive iron is, however, considered to be harmful because of its role in causing oxidative stress. It is well established in the literature that abnormal non-heme iron deposits (in different forms) occur in neurodegenerative disorders, including Alzheimer’s disease (AD), Huntington’s disease (HD), Parkinson’s disease (PD), multiple sclerosis, and neurodegenerative brain iron accumulation (NBIA). This suggests that oxidative stress resulting from imbalance in iron regulation may contribute to the pathological cascade in these diseases. The metabolism of brain iron and its potential role in causing various neurodegenerative disorders has been discussed in detail in Moos and Morgan  and Berg and Youdim. Iron imaging will play an important role in understanding the mechanisms and may be useful for early diagnosis of neurodegenerative disorders. This chapter has two objectives: to present the basics of iron signal detection using magnetic resonance imaging (MRI) and to examine the potential of MRI techniques for imaging abnormal iron deposits in various neurodegenerative disorders.
Iron signal in MRI
The MRI approach utilizes the nuclear magnetic resonance of atomic nuclei and, because of the abundance of protons in the human body (primarily in tissue water), MRI machines use signal from protons for imaging. The signal contrast in MR images mainly originates from differences in the proton density, longitudinal relaxation (T1) and transverse relaxation (T2) of protons in different tissues. Additionally in sequences such as gradient echo sequences where there is no 180° radiofrequency pulse to refocus the dephasing resulting from magnetic field inhomogeneity, the amplitude of the gradient echo carries a 1/T2* weighting where 1/T2* = 1/T2 + 1/T2′ where T2′ is the reversible contribution resulting from local magnetic field inhomogeneity. Since MRI detects changes in electromagnetic signals, changes in local magnetic field inhomogeneities caused by the presence of iron alter the signal contrast in the images. The presence of iron is mainly associated with reduction of T1, T2, and T2* relaxation of protons. There has been some recent work on using additional MRI contrasts such as diffusion tensor imaging (DTI) metrics and rotating frame relaxation constants, longitudinal (T1ρ) and transverse (T2ρ), to measure local iron content.
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