Faculty Bibliography

Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high SNR images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a three-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing, and comparisons with microscopy. In each section, we attempt to provide guidelines and recommendations but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing and point toward open-source software and databases specific to small animal and ex vivo imaging.

Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected considerations and recommendations from the diffusion community on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We, then, give recommendations for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including preprocessing, model-fitting, and tractography. Finally, we provide an online resource that lists publicly available preclinical dMRI datasets and software packages to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. Although we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.

Joint modeling of diffusion and relaxation has seen growing interest due to its potential to provide complementary information about tissue microstructure. For brain white matter (WM), we designed an optimal diffusion-relaxometry MRI protocol that samples multiple b-values, B-tensor shapes, and echo times (TE). This variable-TE protocol (27 min) has as subsets a fixed-TE protocol (15 min) and a two-shell dMRI protocol (7 min), both characterizing diffusion only. We assessed the sensitivity, specificity, and reproducibility of these protocols with synthetic experiments and in six healthy volunteers. Compared with the fixed-TE protocol, the variable-TE protocol enables estimation of the free water fraction while also capturing compartmental

The Adolescent Brain and Cognitive Development (ABCD) project is the largest study of adolescent brain development. ABCD longitudinally tracks 11,868 participants aged 9-10 years from 21 sites using standardized protocols for multi-site MRI data collection and analysis. While the multi-site and multi-scanner study design enhances the robustness and generalizability of analysis results, it may also introduce nonbiological variances including scanner-related variations, subject motion, and deviations from protocols. ABCD imaging data were collected biennially within a period of ongoing maturation in cortical thickness and integrity of cerebral white matter. These changes can bias the classical test-retest methodologies, such as intraclass correlation coefficients (ICC). We developed a site-wise adaptive ICC (AICC) to evaluate the reliability of imaging-derived phenotypes while accounting for ongoing brain development. AICC iteratively estimates the population-level age-related brain development trajectory using a weighted mixed model and updates age-corrected site-wise reliability until convergence. We evaluated the test-retest reliability of regional fractional anisotropy (FA) measures from diffusion tensor imaging and cortical thickness (CT) from structural MRI data for each site. The mean AICC for 20 FA tracts across sites was 0.61 ± 0.19, lower than the mean AICC for CT in 34 regions across sites, 0.76 ± 0.12. Remarkably, sites using Siemens scanners consistently showed significantly higher AICC values compared with those using GE/Philips scanners for both FA (AICC = 0.71 ± 0.12 vs. 0.46 ± 0.17, p < 0.001) and CT (AICC = 0.80 ± 0.10 vs. 0.69 ± 0.11, p < 0.001). These findings demonstrate site-and-scanner related variations in data quality and underscore the necessity for meticulous data curation in subsequent association analyses.

22q11.2 deletion syndrome, or 22q11.2DS, is a genetic syndrome associated with high rates of schizophrenia and autism spectrum disorders, in addition to widespread structural and functional abnormalities throughout the brain. Experimental animal models have identified neuronal connectivity deficits, e.g., decreased axonal length and complexity of axonal branching, as a primary mechanism underlying atypical brain development in 22q11.2DS. However, it is still unclear whether deficits in axonal morphology can also be observed in people with 22q11.2DS. Here, we provide an unparalleled in vivo characterization of white matter microstructure in participants with 22q11.2DS (12-15 years) and those undergoing typical development (8-18 years) using a customized magnetic resonance imaging scanner which is sensitive to axonal morphology. A rich array of diffusion MRI metrics are extracted to present microstructural profiles of typical and atypical white matter development, and provide new evidence of connectivity differences in individuals with 22q11.2DS. A recent, large-scale consortium study of 22q11.2DS identified higher diffusion anisotropy and reduced overall diffusion mobility of water as hallmark microstructural alterations of white matter in individuals across a wide age range (6-52 years). We observed similar findings across the white matter tracts included in this study, in addition to identifying deficits in axonal morphology. This, in combination with reduced tract volume measurements, supports the hypothesis that abnormal microstructural connectivity in 22q11.2DS may be mediated by densely packed axons with disproportionately small diameters. Our findings provide insight into the in vivo white matter phenotype of 22q11.2DS, and promote the continued investigation of shared features in neurodevelopmental and psychiatric disorders.

Background Exhaustion of cerebrovascular reactivity (CVR) portends increased stroke risk. Acetazolamide-augmented blood oxygenation level-dependent (BOLD) MRI has been used to estimate CVR, but low signal-to-noise conditions relegate its use to terminal CVR (CVR_end) measurements that neglect dynamic features of CVR. Purpose To demonstrate comprehensive characterization of acetazolamide-augmented BOLD MRI response in chronic steno-occlusive disease using a computational framework to precondition signal time courses for dynamic whole-brain CVR analysis. Materials and Methods This study focused on retrospective analysis of consecutive patients with unilateral chronic steno-occlusive disease who underwent acetazolamide-augmented BOLD imaging for recurrent minor stroke or transient ischemic attack at an academic medical center between May 2017 and October 2020. A custom principal component analysis-based denoising pipeline was used to correct spatially varying non-signal-bearing contributions obtained by a local principal component analysis of the MRI time series. Standard voxelwise CVR_end maps representing terminal responses were produced and compared with maximal CVR (CVR_max) as isolated from binned (per-repetition time) denoised BOLD time course. A linear mixed-effects model was used to compare CVR_max and CVR_end in healthy and diseased hemispheres. Results A total of 23 patients (median age, 51 years; IQR, 42-61, 13 men) who underwent 32 BOLD examinations were included. Processed MRI data showed twofold improvement in signal-to-noise ratio, allowing improved isolation of dynamic characteristics in signal time course for sliding window CVR_max analysis to the level of each BOLD repetition time (approximately 2 seconds). Mean CVR_max was significantly higher than mean CVR_end in diseased (5.2% vs 3.8%, P < .01) and healthy (5.5% vs 4.0%, P < .01) hemispheres. Several distinct time-signal signatures were observed, including nonresponsive; delayed/blunted; brisk; and occasionally nonmonotonic time courses with paradoxical features in normal and abnormal tissues (ie, steal and reverse-steal patterns). Conclusion A principal component analysis-based computational framework for analysis of acetazolamide-augmented BOLD imaging can be used to measure unsustained CVR_max through twofold improvements in signal-to-noise ratio. © RSNA, 2023 Supplemental material is available for this article.

BACKGROUND:Renal diffusion-weighted imaging (DWI) involves microstructure and microcirculation, quantified with diffusion tensor imaging (DTI), intravoxel incoherent motion (IVIM), and hybrid models. A better understanding of their contrast may increase specificity. PURPOSE/OBJECTIVE:To measure modulation of DWI with cardiac phase and flow-compensated (FC) diffusion gradient waveforms. STUDY TYPE/METHODS:Prospective. POPULATION/METHODS:Six healthy volunteers (ages: 22-48 years, five females), water phantom. FIELD STRENGTH/SEQUENCE/UNASSIGNED:3-T, prototype DWI sequence with 2D echo-planar imaging, and bipolar (BP) or FC gradients. 2D Half-Fourier Single-shot Turbo-spin-Echo (HASTE). Multiple-phase 2D spoiled gradient-echo phase contrast (PC) MRI. ASSESSMENT/RESULTS:), for each tissue (cortex/medulla, segmented on b0/FA respectively), phase, and waveform (BP, FC). Monte Carlo water diffusion simulations aided data interpretation. STATISTICAL TESTS/METHODS:Mixed model regression probed differences between tissue types and pulse sequences. Univariate general linear model analysis probed variations among cardiac phases. Spearman correlations were measured between diffusion metrics and renal artery velocities. Statistical significance level was set at P < 0.05. RESULTS:, MD for FC. FA correlated significantly with velocity. Monte Carlo simulations indicated medullary measurements were consistent with a 34 μm tubule diameter. DATA CONCLUSION/CONCLUSIONS:Cardiac gating and flow compensation modulate of measurements of renal diffusion. EVIDENCE LEVEL/METHODS:2 TECHNICAL EFFICACY STAGE: 2.

Background: 22q11.2 Deletion Syndrome, or 22q11.2DS, is a genetic syndrome associated with high rates of schizophrenia, autism, and attention deficit hyperactivity disorder, in addition to widespread structural and functional abnormalities throughout the brain. Experimental animal models have identified neuronal connectivity deficits, e.g., decreased axonal length and complexity of axonal branching, as a primary mechanism underlying atypical brain development in 22q11.2DS. However, it is still unclear whether deficits in axonal morphology can also be observed in people with 22q11.2DS.
Method(s): To explore axonal morphology in depth, it is necessary to move beyond current state-of-the-art MRI techniques to achieve enhanced cellular specificity in developmental populations. Here, we present an in-depth characterization of white matter microstructure in both typically developing (n=92) and 22q11.2DS (n=6) participants using ultra-strong gradients and a multi-shell diffusion MRI acquisition, including b-values up to 6000 s/mm2. The scanner and unique high-b shell protocol enable for the first time sensitivity to axon morphology. We then conducted a novel multi-parametric analysis to probe microstructural properties underlying disrupted axonal morphology, to better describe previous observations of white matter hypo-connectivity in 22q11.2DS.
Result(s): We observed increased diffusion anisotropy and reduced water mobility across all white matter tracts, in addition to identifying deficits in axonal morphology. This, in combination with reduced tract volume measurements, supports the hypothesis that microstructural connectivity in 22q11.2DS is mediated by densely packed axons with disproportionately small diameters.
Conclusion(s): Our findings provide insight into the in vivo mechanistic features of 22q11.2DS, and promote further investigation of shared features in neurodevelopmental and psychiatric disorders. Supported By: ER: UK Marshall-Sherfield Fellowship; JV: NIH P41 EB-017183; R01 NS088040); RK: SUAG/047 G101400; JD: Wellcome Trust 102003/Z/13/Z; MvdB: MRC MR/T033045/1, NIMH U01 MH119738-01, Wellcome Trust ISSF; DKJ: Wellcome Trust 096646/Z/11/Z and Wellcome Trust 104943/Z/14/Z. Keywords: 22q11 Deletion Syndrome, Diffusion MRI, Developmental Neuroimaging, Structural MRI
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The free water elimination (FWE) model and its kurtosis variant (DKI-FWE) can separate tissue and free water signal contributions, thus providing tissue-specific diffusional information. However, a downside of these models is that the associated parameter estimation problem is ill-conditioned, necessitating the use of advanced estimation techniques that can potentially bias the parameter estimates. In this work, we propose the T₂-DKI-FWE model that exploits the T₂ relaxation properties of both compartments, thereby better conditioning the parameter estimation problem and providing, at the same time, an additional potential biomarker (the T₂ of tissue). In our approach, the T₂ of tissue is estimated as an unknown parameter, whereas the T₂ of free water is assumed known a priori and fixed to a literature value (1573 ms). First, the error propagation of an erroneous assumption on the T₂ of free water is studied. Next, the improved conditioning of T₂-DKI-FWE compared to DKI-FWE is illustrated using the Cramér-Rao lower bound matrix. Finally, the performance of the T₂-DKI-FWE model is compared to that of the DKI-FWE and T₂-DKI models on both simulated and real datasets. The error due to a biased approximation of the T₂ of free water was found to be relatively small in various diffusion metrics and for a broad range of erroneous assumptions on its underlying ground truth value. Compared to DKI-FWE, using the T₂-DKI-FWE model is beneficial for the identifiability of the model parameters. Our results suggest that the T₂-DKI-FWE model can achieve precise and accurate diffusion parameter estimates, through effective reduction of free water partial volume effects and by using a standard nonlinear least squares approach. In conclusion, incorporating T₂ relaxation properties into the DKI-FWE model improves the conditioning of the model fitting, while only requiring an acquisition scheme with at least two different echo times.