Back

Age dependency of neurometabolite T1 relaxation times

Murali-Manohar, S.; Zoellner, H. J.; Hupfeld, K. E.; Song, Y.; Carter, E. E.; Yedavalli, V.; Hui, S. C. N.; Simicic, D.; Gudmundson, A. T.; Simegn, G. L.; Davies-Jenkins, C. W.; Oeltzschner, G.; Porges, E. C.; Edden, R. A. E.

2024-10-02 neuroscience
10.1101/2024.09.30.615917 bioRxiv
Show abstract

PurposeTo measure T1 relaxation times of metabolites at 3T in a healthy aging population and investigate age dependence. MethodsA cohort of 101 healthy adults were recruited with approximately 10 male and 10 female participants in each decade band: 18-29, 30-39, 40-49, 50-59, and 60+ years old. Inversion-recovery PRESS data (TE/TR: 30/2000 ms) were acquired at 8 inversion times (TIs) (300, 400, 511, 637, 780, 947, 1148 and 1400 ms) from voxels in white-matter-rich centrum semiovale (CSO) and gray-matter-rich posterior cingulate cortex (PCC). Modeling of TI-series spectra was performed in Osprey 2.5.0. Quantified metabolite amplitudes for total N-acetylaspartate (tNAA2.0), total creatine at 3.0 ppm (tCr3.0) and 3.9 ppm (tCr3.9), total choline (tCho), myo-inositol (mI), and the sum of glutamine and glutamate (Glx) were modeled to calculate T1 relaxation times of metabolites. ResultsT1 relaxation times of tNAA2.0 in CSO and tNAA2.0, tCr3.0, mI and Glx in PCC decreased with age. These correlations remained significant when controlling for cortical atrophy. T1 relaxation times were significantly different between PCC and CSO for all metabolites except tCr3.0. We also propose linear models for predicting metabolite T1s at 3T to be used in future aging studies. ConclusionMetabolite T1 relaxation times change significantly with age, an effect that will be important to consider for accurate quantitative MRS, particularly in studies of aging.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.