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Background Hypertrophic cardiomyopathy (HCM) is characterized by increased left ventricular wall thickness, cardiomyocyte hypertrophy, and fibrosis. Adverse cardiac risk characterization has been performed using late gadolinium enhancement (LGE), native T1, and extracellular volume (ECV). Relaxation time constants are affected by background field inhomogeneity. T1ρ utilizes a spin-lock pulse to decrease the effect of unwanted relaxation. The objective of this study was to study T1ρ as compared to T1, ECV, and LGE in HCM patients. Methods HCM patients were recruited as part of the Novel Markers of Prognosis in Hypertrophic Cardiomyopathy study, and healthy controls were matched for comparison. In addition to cardiac functional imaging, subjects underwent T1 and T1ρ cardiovascular magnetic resonance imaging at short-axis positions at 1.5T. Subjects received gadolinium and underwent LGE imaging 15–20 min after injection covering the entire heart. Corresponding basal and mid short axis LGE slices were selected for comparison with T1 and T1ρ. Full-width half-maximum thresholding was used to determine the percent enhancement area in each LGE-positive slice by LGE, T1, and T1ρ. Two clinicians independently reviewed LGE images for presence or absence of enhancement. If in agreement, the image was labeled positive (LGE + +) or negative (LGE −−); otherwise, the image was labeled equivocal (LGE + −). Results In 40 HCM patients and 10 controls, T1 percent enhancement area (Spearman’s rho = 0.61, p Conclusions Hyperenhancement areas as measured by T1ρ and LGE are moderately correlated. T1, T1ρ, and ECV were elevated in HCM patients compared to controls, irrespective of the presence of LGE. These findings warrant additional studies to investigate the prognostic utility of T1ρ imaging in the evaluation of HCM patients.

The 22q11.2 deletion syndrome (22q11DS) is a developmental genetic syndrome associated with a 30% risk for developing schizophrenia. Lateral ventricles and subcortical structures are abnormal in this syndrome as well as in schizophrenia. Here, we investigated whether these structures are related in young adults with 22q11DS with and without prodromal symptoms (PS) for schizophrenia and whether abnormalities in volumes are associated with global functioning. MR images were acquired on a 3T scanner from 51 individuals with 22q11DS and 30 healthy controls (mean age: 21±2 years). Correlations were performed to evaluate the relationship between ventricular and subcortical volumes, with Global Assessment of Functioning (GAF) and Premorbid Adjustment Scale (PAS) in each group. Lateral ventricular volumes correlated negatively with subcortical volumes in individuals with 22q11DS. In individuals with 22q11DS with PS only, GAF correlated positively with volumes of the lateral ventricles and negatively with subcortical volumes. PAS correlated negatively with lateral ventricle volumes, and positively with volumes of subcortical structures. The results suggest a common neurodevelopmental mechanism related to the growth of these brain structures. Further, the ratio between the volumes and clinical measures could potentially be used to characterize individuals with 22q11DS and those from the general population for the risk of the development of schizophrenia.

Resting-state functional MRI (rfMRI) correlates activity across brain regions to identify functional connectivity networks. The Human Connectome Project (HCP) for Early Psychosis has adopted the protocol of the HCP Lifespan Project, which collects 20 min of rfMRI data. However, because it is difficult for psychotic patients to remain in the scanner for long durations, we investigate here the reliability of collecting less than 20 min of rfMRI data. Varying durations of data were taken from the full datasets of 11 subjects. Correlation matrices derived from varying amounts of data were compared using the Bhattacharyya distance, and the reliability of functional network ranks was assessed using the Friedman test. We found that correlation matrix reliability improves steeply with longer windows of data up to 11–12 min, and ≥14 min of data produces correlation matrices within the variability of those produced by 18 min of data. The reliability of network connectivity rank increases with increasing durations of data, and qualitatively similar connectivity ranks for ≥10 min of data indicates that 10 min of data can still capture robust information about network connectivities.