Evidence for distinct neuro-metabolic phenotypes in humans
Standard
Evidence for distinct neuro-metabolic phenotypes in humans. / Wu, Bofan; Bagshaw, Andrew P; Hickey, Clayton; Kühn, Simone; Wilson, Martin.
in: NEUROIMAGE, Jahrgang 249, 118902, 01.04.2022.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
Harvard
APA
Vancouver
Bibtex
}
RIS
TY - JOUR
T1 - Evidence for distinct neuro-metabolic phenotypes in humans
AU - Wu, Bofan
AU - Bagshaw, Andrew P
AU - Hickey, Clayton
AU - Kühn, Simone
AU - Wilson, Martin
N1 - Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.
PY - 2022/4/1
Y1 - 2022/4/1
N2 - Advances in magnetic resonance imaging have shown how individual differences in the structure and function of the human brain relate to health and cognition. The relationship between individual differences and the levels of neuro-metabolites, however, remains largely unexplored - despite the potential for the discovery of novel behavioural and disease phenotypes. In this study, we measured 14 metabolite levels, normalised as ratios to total-creatine, with 1H magnetic resonance spectroscopy (MRS) acquired from the bilateral anterior cingulate cortices of six healthy participants, repeatedly over a period of four months. ANOVA tests revealed statistically significant differences of 3 metabolites and 3 commonly used combinations (total-choline, glutamate + glutamine and total-N-acetylaspartate) between the participants, with scyllo-inositol (F=85, p=6e-26) and total-choline (F=39, p=1e-17) having the greatest discriminatory power. This was not attributable to structural differences. When predicting individuals from the repeated MRS measurements, a leave-one-out classification accuracy of 88% was achieved using a support vector machine based on scyllo-inositol and total-choline levels. Accuracy increased to 98% with the addition of total-N-acetylaspartate and myo-inositol - demonstrating the efficacy of combining MRS with machine learning and metabolomic methodology. These results provide evidence for the existence of neuro-metabolic phenotypes, which may be non-invasively measured using widely available 3 Tesla MRS. Establishing these phenotypes in a larger cohort and investigating their connection to brain health and function presents an important area for future study.
AB - Advances in magnetic resonance imaging have shown how individual differences in the structure and function of the human brain relate to health and cognition. The relationship between individual differences and the levels of neuro-metabolites, however, remains largely unexplored - despite the potential for the discovery of novel behavioural and disease phenotypes. In this study, we measured 14 metabolite levels, normalised as ratios to total-creatine, with 1H magnetic resonance spectroscopy (MRS) acquired from the bilateral anterior cingulate cortices of six healthy participants, repeatedly over a period of four months. ANOVA tests revealed statistically significant differences of 3 metabolites and 3 commonly used combinations (total-choline, glutamate + glutamine and total-N-acetylaspartate) between the participants, with scyllo-inositol (F=85, p=6e-26) and total-choline (F=39, p=1e-17) having the greatest discriminatory power. This was not attributable to structural differences. When predicting individuals from the repeated MRS measurements, a leave-one-out classification accuracy of 88% was achieved using a support vector machine based on scyllo-inositol and total-choline levels. Accuracy increased to 98% with the addition of total-N-acetylaspartate and myo-inositol - demonstrating the efficacy of combining MRS with machine learning and metabolomic methodology. These results provide evidence for the existence of neuro-metabolic phenotypes, which may be non-invasively measured using widely available 3 Tesla MRS. Establishing these phenotypes in a larger cohort and investigating their connection to brain health and function presents an important area for future study.
KW - Adult
KW - Biological Variation, Population
KW - Female
KW - Gyrus Cinguli/diagnostic imaging
KW - Humans
KW - Magnetic Resonance Imaging
KW - Magnetic Resonance Spectroscopy
KW - Male
KW - Phenotype
KW - Support Vector Machine
U2 - 10.1016/j.neuroimage.2022.118902
DO - 10.1016/j.neuroimage.2022.118902
M3 - SCORING: Journal article
C2 - 35033676
VL - 249
JO - NEUROIMAGE
JF - NEUROIMAGE
SN - 1053-8119
M1 - 118902
ER -