Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 189

The DOI matches its Crossref record.

Spatially localized 1H NMR spectra of metabolites in the human brain.

Open access
Hanstock, C C, Rothman, D L, Prichard, J W, Jue, T, Shulman, R G·1988·Proceedings of the National Academy of Sciences

Abstract

Using a surface coil, we have obtained 1H NMR spectra from metabolites in the human brain. Localization was achieved by combining depth pulses with image-selected in vivo spectroscopy magnetic field gradient methods. 1H spectra in which total creatine (3.03 ppm) has a signal/noise ratio of 95:1 were obtained in 4 min from 14 ml of brain. A resonance at 2.02 ppm consisting predominantly of N-acetylaspartate was measured relative to the creatine peak in gray and white matter, and the ratio was lower in the white matter. The spin-spin relaxation times of N-acetylaspartate and creatine were measured in white and gray matter and while creatine relaxation times were the same in both, the N-acetylaspartate relaxation time was longer in white matter. Lactate was detected in the normoxic brain and the average of three measurements was approximately equal to 0.5 mM from comparison with the creatine plus phosphocreatine peak, which was assumed to be 10.5 mM.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1073/pnas.85.6.1821

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 189

Metadata

Authors
Hanstock, C. C., Rothman, D. L., Prichard, J. W., Jue, T., & Shulman, R. G.
Venue
Proceedings of the National Academy of Sciences
Published
1988-03
Volume
85
Issue
6
Pages
1821-1825
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W1970959659
Cited by
189
References
0
FWCI
17.91
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Spatially localized 1H NMR spectra of metabolites in the human brain. https://app.cochat.ai/papers/10.1073/pnas.85.6.1821
The paper
Hanstock, C. C., Rothman, D. L., Prichard, J. W., Jue, T., & Shulman, R. G. (1988). Spatially localized 1H NMR spectra of metabolites in the human brain. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.85.6.1821

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.