https://ogma.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:43429 P ≤ .05). Only GPC showed positive correlation with all fatigue domains (r = .537, P ≤ .05). On the other hand, Glx-upper, NAA-2, GSH+Hca, and fucose-3 showed negative correlations with all fatigue domains (r = –.345 to –.580, P ≤ .05). While tyrosine showed positive correlation with MFIS (r = .499, P ≤ .05), cognitive fatigue was negatively correlated with total GSH (r = –.530, P ≤ .05). No correlations were found between lesion load or brain volumes with fatigue score. CONCLUSIONS: Our results suggest that fatigue in MS is strongly correlated with an imbalance in neurometabolites but not structural brain measurements.]]> Mon 19 Sep 2022 09:29:20 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:40208 Mon 01 Aug 2022 09:10:25 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:47224 Fri 16 Dec 2022 10:37:25 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:47876 0.12). in vivo results showed statistically significant diurnal variations (P ≤ 0.05, F > 3.88) for 22 resonances. Bonferroni post-hoc testing showed there was statistically significant increases in metabolite ratios between 0700 and 1700 and these include different moieties of N-acetylaspartate, creatine, choline, myo-inositol, lipids, fucose, glutathione, and homocarnosine. Data Conclusion: 2D L-COSY can detect diurnal physiological variability in neuro-metabolite levels. Thus, time of the day should be considered when planning MRS studies to avoid confounding results. Level of Evidence: 1. Technical Efficacy Stage: 1.]]> Fri 03 Feb 2023 15:17:52 AEDT ]]>