Faculty Bibliography

Most diffusion magnetic resonance imaging studies of disease rely on statistical comparisons between large groups of patients and healthy participants to infer altered tissue states in the brain; however, clinical heterogeneity can greatly challenge their discriminative power. There is currently an unmet need to move away from the current approach of group-wise comparisons to methods with the sensitivity to detect altered tissue states at the individual level. This would ultimately enable the early detection and interpretation of microstructural abnormalities in individual patients, an important step towards personalized medicine in translational imaging. To this end, Detect was developed to advance diffusion magnetic resonance imaging tractometry towards single-patient analysis. By operating on the manifold of white-matter pathways and learning normative microstructural features, our framework captures idiosyncrasies in patterns along white-matter pathways. Our approach paves the way from traditional group-based comparisons to true personalized radiology, taking microstructural imaging from the bench to the bedside.

The noninvasive quantification of axonal morphology is an exciting avenue for gaining understanding of the function and structure of the central nervous system. Accurate non-invasive mapping of micron-sized axon radii using commonly applied neuroimaging techniques, that is, diffusion-weighted MRI, has been bolstered by recent hardware developments, specifically MR gradient design. Here the whole brain characterization of the effective MR axon radius is presented and the inter- and intra-scanner test-retest repeatability and reproducibility are evaluated to promote the further development of the effective MR axon radius as a neuroimaging biomarker. A coefficient-of-variability of approximately 10% in the voxelwise estimation of the effective MR radius is observed in the test-retest analysis, but it is shown that the performance can be improved fourfold using a customized along-tract analysis.

We provide a rich multi-contrast microstructural MRI dataset acquired on an ultra-strong gradient 3T Connectom MRI scanner comprising 5 repeated sets of MRI microstructural contrasts in 6 healthy human participants. The availability of data sets that support comprehensive simultaneous assessment of test-retest reliability of multiple microstructural contrasts (i.e., those derived from advanced diffusion, multi-component relaxometry and quantitative magnetisation transfer MRI) in the same population is extremely limited. This unique dataset is offered to the imaging community as a test-bed resource for conducting specialised analyses that may assist and inform their current and future research. The Microstructural Image Compilation with Repeated Acquisitions (MICRA) dataset includes raw data and computed microstructure maps derived from multi-shell and multi-direction encoded diffusion, multi-component relaxometry and quantitative magnetisation transfer acquisition protocols. Our data demonstrate high reproducibility of several microstructural MRI measures across scan sessions as shown by intra-class correlation coefficients and coefficients of variation. To illustrate a potential use of the MICRA dataset, we computed sample sizes required to provide sufficient statistical power a priori across different white matter pathways and microstructure measures for different statistical comparisons. We also demonstrate whole brain white matter voxel-wise repeatability in several microstructural maps. The MICRA dataset will be of benefit to researchers wishing to conduct similar reliability tests, power estimations or to evaluate the robustness of their own analysis pipelines.

BACKGROUND:Diffusion MRI is routinely used to evaluate brain injury in neonatal encephalopathy. Although abnormal mean diffusivity (MD) is often attributed to cytotoxic edema, the specific contribution from neuronal pathology is unclear. PURPOSE:To determine whether MD from high-resolution diffusion tensor imaging (DTI) can detect variable degrees of neuronal degeneration and pathology in piglets with brain injury induced by excitotoxicity or global hypoxia-ischemia (HI) with or without overt infarction. STUDY TYPE:Prospective. ANIMAL MODEL:Excitotoxic brain injury was induced in six neonatal piglets by intrastriatal stereotaxic injection of the glutamate receptor agonist quinolinic acid (QA). Three piglets underwent global HI or a sham procedure. Piglets recovered for 20-96 hours before undergoing MRI (n = 9). FIELD STRENGTH/SEQUENCE:-weighted imaging. ASSESSMENT:injury were assessed in the putamen and caudate. The cell bodies of normal neurons, degenerating neurons (excitotoxic necrosis, ischemic necrosis, or necrosis-apoptosis cell death continuum), and injured neurons with equivocal degeneration were counted by histopathology. STATISTICAL TESTS:injury and neuron counts were evaluated by descriptive analysis. RESULTS:-weighted MRI. DATA CONCLUSION:MD is more accurate than FA for detecting neuronal degeneration and loss during acute recovery from neonatal excitotoxic and HI brain injury. MD does not reliably detect nonfulminant, nascent, and potentially reversible neuronal injury. EVIDENCE LEVEL:1 TECHNICAL EFFICACY: Stage 2 J. Magn. Reson. Imaging 2020;52:1216-1226.

Recent advances in diffusion magnetic resonance imaging (dMRI) analysis techniques have improved our understanding of fibre-specific variations in white matter microstructure. Increasingly, studies are adopting multi-shell dMRI acquisitions to improve the robustness of dMRI-based inferences. However, the impact of b-value choice on the estimation of dMRI measures such as apparent fibre density (AFD) derived from spherical deconvolution is not known. Here, we investigate the impact of b-value sampling scheme on estimates of AFD. First, we performed simulations to assess the correspondence between AFD and simulated intra-axonal signal fraction across multiple b-value sampling schemes. We then studied the impact of sampling scheme on the relationship between AFD and age in a developmental population (n = 78) aged 8-18 (mean = 12.4, SD = 2.9 years) using hierarchical clustering and whole brain fixel-based analyses. Multi-shell dMRI data were collected at 3.0T using ultra-strong gradients (300 mT/m), using 6 diffusion-weighted shells ranging from b = 0 to 6,000 s/mm² . Simulations revealed that the correspondence between estimated AFD and simulated intra-axonal signal fraction was improved with high b-value shells due to increased suppression of the extra-axonal signal. These results were supported by in vivo data, as sensitivity to developmental age-relationships was improved with increasing b-value (b = 6,000 s/mm² , median R² = .34; b = 4,000 s/mm² , median R² = .29; b = 2,400 s/mm² , median R² = .21; b = 1,200 s/mm² , median R² = .17) in a tract-specific fashion. Overall, estimates of AFD and age-related microstructural development were better characterised at high diffusion-weightings due to improved correspondence with intra-axonal properties.

Various diffusion MRI (dMRI) measures have been proposed for characterising tissue microstructure over the last 15 years. Despite the growing number of experiments using different dMRI measures in assessments of white matter, there has been limited work on: 1) examining their covariance along specific pathways; and on 2) combining these different measures to study tissue microstructure. Indeed, it quickly becomes intractable for existing analysis pipelines to process multiple measurements at each voxel and at each vertex forming a streamline, highlighting the need for new ways to visualise or analyse such high-dimensional data. In a sample of 36 typically developing children aged 8-18 years, we profiled various commonly used dMRI measures across 22 brain pathways. Using a data-reduction approach, we identified two biologically-interpretable components that capture 80% of the variance in these dMRI measures. The first derived component captures properties related to hindrance and restriction in tissue microstructure, while the second component reflects characteristics related to tissue complexity and orientational dispersion. We then demonstrate that the components generated by this approach preserve the biological relevance of the original measurements by showing age-related effects across developmentally sensitive pathways. In summary, our findings demonstrate that dMRI analyses can benefit from dimensionality reduction techniques, to help disentangling the neurobiological underpinnings of white matter organisation.

BACKGROUND:Postmortem and imaging studies provide converging evidence that the frontal lobe myelination trajectory is dysregulated in schizophrenia (SZ) and suggest that early in treatment, antipsychotic medications increase intracortical myelin (ICM). We used magnetic resonance imaging to examine whether the ICM trajectory in SZ is dysregulated and altered by antipsychotic treatment. METHODS:We examined 93 subjects with SZ (64 men and 29 women) taking second-generation oral antipsychotics with medication exposures of 0-333 months in conjunction with 80 healthy control subjects (52 men and 28 women). Frontal lobe ICM volume was estimated using a novel dual contrast magnetic resonance imaging method that combines two images that track different tissue components. RESULTS:When plotted against oral antipsychotic exposure duration, ICM of subjects with SZ was higher as a function of medication exposure during the first year of treatment but declined thereafter. In the age range examined, ICM of subjects with SZ was lower with increased age, while ICM of healthy control subjects was not. CONCLUSIONS:In adults with SZ, the relationship between length of exposure to oral second-generation antipsychotics and ICM was positive during the first year of treatment but was negative after this initial period, consistent with suboptimal later adherence after initial adherence. This ICM trajectory resembles clinically observed antipsychotic response trajectory with high rates of remission in the first year followed by progressively lower response rates. The results support postmortem evidence that SZ pathophysiology involves ICM deficits and suggest that correcting these deficits may be an important mechanism of action for antipsychotics.

Iron accumulates gradually in the ageing brain. In Parkinson's disease, iron deposition within the substantia nigra is further increased, contributing to a heightened pro-oxidant environment in dopaminergic neurons. We hypothesise that individuals in high-income countries, where cereals and infant formulae have historically been fortified with iron, experience increased early-life iron exposure that predisposes them to age-related iron accumulation in the brain. Combined with genetic factors that limit iron regulatory capacity and/or dopamine metabolism, this may increase the risk of Parkinson's diseases. We propose to (a) validate a retrospective biomarker of iron exposure in children; (b) translate this biomarker to adults; (c) integrate it with in vivo brain iron in Parkinson's disease; and (d) longitudinally examine the relationships between early-life iron exposure and metabolism, brain iron deposition and Parkinson's disease risk. This approach will provide empirical evidence to support therapeutically addressing brain iron deposition in Parkinson's diseases and produce a potential biomarker of Parkinson's disease risk in preclinical individuals.

Iron deposition in the brain is a feature of normal aging, though in several neurodegenerative disorders, including Alzheimer's disease, the rate of iron accumulation is more advanced than in age-matched controls. Using laser ablation-inductively coupled plasma-mass spectrometry imaging we present here a pilot study that quantitatively assessed the iron content of white and gray matter in paraffin-embedded sections from the frontal cortex of Alzheimer's and control subjects. Using the phosphorus image as a confirmed proxy for the white/gray matter boundary, we found that increased intrusion of iron into gray matter occurs in the Alzheimer's brain compared to controls, which may be indicative of either a loss of iron homeostasis in this vulnerable brain region, or provide evidence of increased inflammatory processes as a response to chronic neurodegeneration. We also observed a trend of increasing iron within the white matter of the frontal cortex, potentially indicative of disrupted iron metabolism preceding loss of myelin integrity. Considering the known potential toxicity of excessive iron in the brain, our results provide supporting evidence for the continuous development of novel magnetic resonance imaging approaches for assessing white and gray matter iron accumulation in Alzheimer's disease.