Publications by Year: 2019

OBJECTIVE: Detecting brain abnormalities in clinical high-risk populations before the onset of psychosis is important for tracking pathological pathways and for identifying possible intervention strategies that may impede or prevent the onset of psychotic disorders. Co-occurring cellular and extracellular white matter alterations have previously been implicated after a first psychotic episode. The authors investigated whether or not cellular and extracellular alterations are already present in a predominantly medication-naive cohort of clinical high-risk individuals experiencing attenuated psychotic symptoms. METHODS: Fifty individuals at clinical high risk, of whom 40 were never medicated, were compared with 50 healthy control subjects, group-matched for age, gender, and parental socioeconomic status. 3-T multishell diffusion MRI data were obtained to estimate free-water imaging white matter measures, including fractional anisotropy of cellular tissue (FA) and the volume fraction of extracellular free water (FW). RESULTS: Significantly lower FA was observed in the clinical high-risk group compared with the healthy control group, but no statistically significant FW alterations were observed between groups. Lower FA in the clinical high-risk group was significantly associated with a decline in Global Assessment of Functioning Scale (GAF) score compared with highest GAF score in the previous 12 months. CONCLUSIONS: Cellular but not extracellular alterations characterized the clinical high-risk group, especially in those who experienced a decline in functioning. These cellular changes suggest an early deficit that possibly reflects a predisposition to develop attenuated psychotic symptoms. In contrast, extracellular alterations were not observed in this clinical high-risk sample, suggesting that previously reported extracellular abnormalities may reflect an acute response to psychosis, which plays a more prominent role closer to or at onset of psychosis.

Compartmental diffusion MRI models that account for intravoxel incoherent motion (IVIM) of blood perfusion allow for estimation of the fractional volume of the microvascular compartment. Conventional IVIM models are known to be biased by not accounting for partial volume effects caused by free water and cerebrospinal fluid (CSF), or for tissue-dependent relaxation effects. In this work, a three-compartment model (tissue, free water and blood) that includes relaxation terms is introduced. To estimate the model parameters, in vivo human data were collected with multiple echo times (TE), inversion times (TI) and b-values, which allowed a direct relaxation estimate alongside estimation of perfusion, diffusion and fractional volume parameters. Compared to conventional two-compartment models (with and without relaxation compensation), the three-compartment model showed less effects of CSF contamination. The proposed model yielded significantly different volume fractions of blood and tissue compared to the non-relaxation-compensated model, as well as to the conventional two-compartment model, suggesting that previously reported parameter ranges, using models that do not account for relaxation, should be reconsidered.

PURPOSE: To demonstrate the feasibility of multidimensional diffusion MRI to probe and quantify microscopic fractional anisotropy (µFA) in human kidneys in vivo. METHODS: Linear tensor encoded (LTE) and spherical tensor encoded (STE) renal diffusion MRI scans were performed in 10 healthy volunteers. Respiratory triggering and image registration were used to minimize motion artefacts during the acquisition. Kidney cortex-medulla were semi-automatically segmented based on fractional anisotropy (FA) values. A model-free analysis of LTE and STE signal dependence on b-value in the renal cortex and medulla was performed. Subsequently, µFA was estimated using a single-shell approach. Finally, a comparison of conventional FA and µFA is shown.

Computational methods are crucial for the analysis of diffusion magnetic resonance imaging (MRI) of the brain. Computational diffusion MRI can provide rich information at many size scales, including local microstructure measures such as diffusion anisotropies or apparent axon diameters, whole-brain connectivity information that describes the brain’s wiring diagram and population-based studies in health and disease. Many of the diffusion MRI analyses performed today were not possible five, ten or twenty years ago, due to the requirements for large amounts of computer memory or processor time. In addition, mathematical frameworks had to be developed or adapted from other fields to create new ways to analyze diffusion MRI data. The purpose of this review is to highlight recent computational and statistical advances in diffusion MRI and to put these advances into context by comparison with the more traditional computational methods that are in popular clinical and scientific use. We aim to provide a high-level overview of interest to diffusion MRI researchers, with a more in-depth treatment to illustrate selected computational advances.