Purpose Two‐dimensional creatine CEST (2D‐CrCEST), with a slice thickness of 10‐20 mm and temporal resolution (τRes) of about 30 seconds, has previously been shown to capture the creatine‐recovery kinetics in healthy controls and in patients with abnormal creatine‐kinase kinetics following the mild plantar flexion exercise. Since the distribution of disease burden may vary across the muscle length for many musculoskeletal disorders, there is a need to increase coverage in the slice‐encoding direction. Here, we demonstrate the feasibility of 3D‐CrCEST with τRes of about 30 seconds, and propose an improved voxel‐wise B1+‐calibration approach for CrCEST. Methods The current 7T study with enrollment of 5 volunteers involved collecting the baseline CrCEST imaging for the first 2 minutes, followed by 2 minutes of plantar flexion exercise and then 8 minutes of postexercise CrCEST imaging, to detect the temporal evolution of creatine concentration following exercise. Results Very good repeatability of 3D‐CrCEST findings for activated muscle groups on an intraday and interday basis was established, with coefficient of variance of creatine recovery constants (τCr) being 7%‐15.7%, 7.5%, and 5.8% for lateral gastrocnemius, medial gastrocnemius, and peroneus longus, respectively. We also established a good intraday and interday scan repeatability for 3D‐CrCEST and also showed good correspondence between τCr measurements using 2D‐CrCEST and 3D‐CrCEST acquisitions. Conclusion In this study, we demonstrated for the first time the feasibility and the repeatability of the 3D‐CrCEST method in calf muscle with improved B1+ correction to measure creatine‐recovery kinetics within a large 3D volume of calf muscle.

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Aims Skeletal muscle (SkM) abnormalities may impact exercise capacity in patients with heart failure with preserved ejection fraction (HFpEF). We sought to quantify differences in SkM oxidative phosphorylation capacity (OxPhos), fibre composition, and the SkM proteome between HFpEF, hypertensive (HTN), and healthy participants. Methods and results Fifty-nine subjects (20 healthy, 19 HTN, and 20 HFpEF) performed a maximal-effort cardiopulmonary exercise test to define peak oxygen consumption (VO 2, peak ), ventilatory threshold (VT), and VO 2 efficiency (ratio of total work performed to O 2 consumed). SkM OxPhos was assessed using Creatine Chemical-Exchange Saturation Transfer (CrCEST, n = 51), which quantifies unphosphorylated Cr, before and after plantar flexion exercise. The half-time of Cr recovery (t 1/2, Cr ) was taken as a metric of in vivo SkM OxPhos. In a subset of subjects (healthy = 13, HTN = 9, and HFpEF = 12), percutaneous biopsy of the vastus lateralis was performed for myofibre typing, mitochondrial morphology, and proteomic and phosphoproteomic analysis. HFpEF subjects demonstrated lower VO 2,peak , VT, and VO 2 efficiency than either control group (all P < 0.05). The t 1/2, Cr was significantly longer in HFpEF (P = 0.005), indicative of impaired SkM OxPhos, and correlated with cycle ergometry exercise parameters. HFpEF SkM contained fewer Type I myofibres (P = 0.003). Proteomic analyses demonstrated (a) reduced levels of proteins related to OxPhos that correlated with exercise capacity and (b) reduced ERK signalling in HFpEF. Conclusions Heart failure with preserved ejection fraction patients demonstrate impaired functional capacity and SkM OxPhos. Reductions in the proportions of Type I myofibres, proteins required for OxPhos, and altered phosphorylation signalling in the SkM may contribute to exercise intolerance in HFpEF.

Background and Purpose:Recent studies indicate disrupted functional mechanisms of salience network (SN) regions-right anterior insula, left anterior insula, and anterior cingulate cortex-in mild cognitive impairment (MCI). However, the underlying anatomical and molecular mechanisms in these regions are not clearly understood yet. It is also unknown whether integration of multimodal-anatomical and molecular-markers could predict cognitive impairment better in MCI.Methods: Herein we quantified anatomical volumetric markers via structural MRI and molecular amyloid markers via PET with Pittsburgh compound B in SN regions of MCI (n = 33) and healthy controls (n = 27). From these markers, we built support vector machine learning models aiming to estimate cognitive dysfunction in MCI. Results:We found that anatomical markers are significantly reduced and molecular markers are significantly elevated in SN nodes of MCI compared to healthy controls (p < .05).These altered markers in MCI patients were associated with their worse cognitive performance (p < .05). Our machine learning-based modeling further suggested that the integration of multimodal markers predicts cognitive impairment in MCI superiorly compared to using single modality-specific markers.Conclusions: These findings shed light on the underlying anatomical volumetric and molecular amyloid alterations in SN regions and show the significance of multimodal markers integration approach in better predicting cognitive impairment in MCI.

Recent studies indicate disrupted functional mechanisms of salience network regions, especially right anterior insula (RAI), left AI (LAI), and anterior cingulate cortex (ACC), in mild cognitive impairment (MCI). However, the underlying neuro-anatomical and neuro-molecular mechanisms in these regions are not clearly understood yet. It is also unknown whether integration of multi-modal neuro-anatomical and neuro-molecular markers could predict cognitive impairment better in MCI. Herein we quantified neuro-anatomical volumetric markers via structural magnetic resonance imaging (sMRI) and neuro-molecular amyloid markers via positron emission tomography with Pittsburgh compound B (PET PiB) in SN regions of MCI (n = 33) and healthy controls (n = 27). We found that neuro-anatomical markers are significantly reduced while neuro-molecular markers are significantly elevated in SN nodes of MCI compared to healthy controls (p < 0.05). These altered markers in MCI patients were associated with their worse cognitive performance (p < 0.05). Our machine learning-based modeling further suggested that the integration of multi-modal markers predicts cognitive impairment in MCI superiorly compared to using single modality-specific markers. Overall, these findings shed light on the underlying neuro-anatomical volumetric and neuro-molecular amyloid alterations in SN regions and show the significance of multi-modal markers integration approach in better predicting cognitive impairment in MCI.