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Related Concept Videos

Aging01:26

Aging

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Aging is a complex biological phenomenon influenced by various processes that affect cellular and systemic functions. Several prominent theories attempt to explain its mechanisms, highlighting cellular limitations, oxidative damage, and hormonal changes as central factors in aging.
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Related Experiment Video

Updated: Feb 16, 2026

Isolation of Intermediate Filament Proteins from Multiple Mouse Tissues to Study Aging-associated Post-translational Modifications
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Altered macromolecular pattern and content in the aging human brain.

Małgorzata Marjańska1, Dinesh K Deelchand1, James S Hodges2

  • 1Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota, Minneapolis, MN, USA.

NMR in Biomedicine
|December 22, 2017
PubMed
Summary
This summary is machine-generated.

Brain macromolecular patterns change with age. Older adults have higher macromolecular content, necessitating age-specific spectra for accurate metabolite analysis in magnetic resonance spectroscopy studies.

Keywords:
7 Tmacromoleculesmagnetic resonance spectroscopyultrahigh field

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Area of Science:

  • Neuroimaging
  • Biophysics
  • Gerontology

Background:

  • Proteins contribute to brain magnetic resonance spectroscopy (MRS) signals, distinct from metabolites.
  • These macromolecular resonances possess unique relaxation properties (T1, T2).
  • Understanding age-related changes in brain macromolecules is crucial for interpreting MRS data.

Purpose of the Study:

  • To investigate age-dependent alterations in macromolecular patterns and content in the human brain.
  • To focus on adults over 66 years of age using ultrahigh-field in vivo MRS.
  • To determine if age-specific macromolecular spectra are required for accurate metabolite quantification.

Main Methods:

  • Utilized 7 Tesla (7T) ultrahigh-field magnetic resonance spectroscopy (MRS) in vivo.
  • Acquired metabolite spectra using stimulated echo acquisition mode (STEAM) in occipital cortex and posterior cingulate cortex.
  • Acquired macromolecular spectra using inversion recovery STEAM sequence in the occipital cortex.
  • Quantified macromolecular content using LCModel and spectral integration.

Main Results:

  • Apparent age-associated differences in macromolecular patterns were observed in both metabolite and inversion recovery spectra.
  • The most significant age differences were noted for macromolecular resonances at 1.7 and 2 ppm.
  • Older adults exhibited higher macromolecular content in both studied brain regions.

Conclusions:

  • Significant age-related differences exist in brain macromolecular patterns and content.
  • Age-specific macromolecular spectra are essential for accurate metabolite quantification in cross-sectional age studies.
  • These findings offer insights into the aging process and its impact on brain composition.