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How do I get magnetic resonance spectroscopy (MRS) of the brain to see all the metabolites? Also brain molecular age

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Furthermore, the evaluation of metabolite patterns across brain regions uncovered high levels of free radical scavengers in the frontal cortex. This includes, notably, uric acid and carnosine, which are known to be antioxidants and neuroprotective agents (Fang, et al., 2013; Bae, et al., 2013;). This regional specificity may be linked to surveillance responses, which serve to reduce oxidative stress and protect against oxidative damage. Uric acid was found at lower levels in patients with cognitive impairment (e.g. Alzheimer’s disease, Parkinson’s disease, vascular-linked dementia) when compared to aged individuals who enjoy normal mental, motor and behavioral functions (Gong, et al., 2012; Kim, et al., 2006;). Comparable to uric acid, carnosine is a potent antioxidant that scavenges reactive oxygen species and unsaturated aldehydes leading to reduced oxidative, nitrosative, and glycemic stress (Bellia, et al., 2011). Like uric acid, carnosine levels are reduced in Alzheimer’s disease suggesting that carnosine deficiency affects cognitive function (Fonteh, et al., 2007). Frontal cortical areas have a complex role in cognitive function and the high content of uric acid and carnosine as homeostatic metabolites may be associated with the need for internal control by the brain in preventing oxidative damage.



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  • InquilineKea changed the title to How do I get magnetic resonance spectroscopy (MRS) of the brain to see all the metabolites? Also brain molecular age

It is also evident that some brains appear exceptional, either slowed or advanced for aging compared with the average. These exceptional cases show the highest absolute values of Δ age in our analysis described above.

An external file that holds a picture, illustration, etc. Object name is LSA-2019-00303_Fig4.jpg
Characterization of age-sensitive transcripts and molecular ages.

(A, B) The 537 increasing and 834 decreasing age-sensitive transcripts are visualized in the heat map (A). Top Ingenuity functional categories are shown for increasing or decreasing transcripts (A). (B, C) Venn diagrams show the intersection of age down-regulated transcripts and neuronal-specific transcripts (B) and age up-regulated transcripts and glial-specific transcripts (C).

We grouped the aging-sensitive transcripts functionally by Ingenuity software. Interestingly, for transcripts that are lower in older people, neurological disease genes were a top disease category (Fig 4A). This included genes that relate to PD (P = 2 × 10−3), Tauopathy (7 × 10−3), Huntington’s (P = 2 × 10−11), ALS (4 × 10−3), basal ganglia disorders (1 × 10−10), and other neurodegenerative disorders as well as to psychiatric disorders, including anxiety (P = 1 × 10−4), depression (2 × 10−4), bipolar disorder (1 × 10−3), and schizophrenia (2 × 10−7). More specific pathway categories for down-regulated genes include “glutamate signaling,” “dopamine feedback in cAMP signaling” (not shown, P = 1 × 10−5), and “rho GTPases” (Fig 4A). The rho-GTPases are particularly interesting because they are known regulators of synaptic spine formation and actin cytoskeletal dynamics (Morrison & Baxter, 2012; Lefort, 2015). Thus, these changes are consistent with earlier findings showing deficits in synaptic function and neuronal signaling

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