Searched for: person:novikd01
in-biosketch:yes
The present and the future of microstructure MRI: From a paradigm shift to "normal science"
Novikov, Dmitry S
The aspiration of imaging tissue microstructure with MRI is to uncover micrometer-scale tissue features within millimeter-scale imaging voxels, in vivo. This kind of super-resolution has fueled a paradigm shift within the biomedical imaging community. However, what feels like an on-going revolution in MRI, has been conceptually experienced in physics decades ago; from this point of view, our current developments can be seen as Thomas Kuhn's "normal science" stage of progress. While the concept of model-based quantification below the nominal imaging resolution is not new, its possibilities in neuroscience and neuroradiology are only beginning to be widely appreciated. This disconnect calls for communicating the progress of tissue microstructure MR imaging to its potential users. Here, a number of recent research developments are outlined in terms of the overarching concept of coarse-graining the tissue structure over an increasing diffusion length. A variety of diffusion models and phenomena are summarized on the phase diagram of diffusion MRI, with the unresolved problems and future directions corresponding to its unexplored domains.
PMID: 33096152
ISSN: 1872-678x
CID: 4642622
The impact of realistic axonal shape on axon diameter estimation using diffusion MRI
Lee, Hong-Hsi; Jespersen, Sune N; Fieremans, Els; Novikov, Dmitry S
To study axonal microstructure with diffusion MRI, axons are typically modeled as straight impermeable cylinders, whereby the transverse diffusion MRI signal can be made sensitive to the cylinder's inner diameter. However, the shape of a real axon varies along the axon direction, which couples the longitudinal and transverse diffusion of the overall axon direction. Here we develop a theory of the intra-axonal diffusion MRI signal based on coarse-graining of the axonal shape by 3-dimensional diffusion. We demonstrate how the estimate of the inner diameter is confounded by the diameter variations (beading), and by the local variations in direction (undulations) along the axon. We analytically relate diffusion MRI metrics, such as time-dependent radial diffusivity D⊥(t) and kurtosis K⊥(t), to the axonal shape, and validate our theory using Monte Carlo simulations in synthetic undulating axons with randomly positioned beads, and in realistic axons reconstructed from electron microscopy images of mouse brain white matter. We show that (i) In the narrow pulse limit, the inner diameter from D⊥(t) is overestimated by about twofold due to a combination of axon caliber variations and undulations (each contributing a comparable effect size); (ii) The narrow-pulse kurtosis K⊥|t→∞ deviates from that in an ideal cylinder due to caliber variations; we also numerically calculate the fourth-order cumulant for an ideal cylinder in the wide pulse limit, which is relevant for inner diameter overestimation; (iii) In the wide pulse limit, the axon diameter overestimation is mainly due to undulations at low diffusion weightings b; and (iv) The effect of undulations can be considerably reduced by directional averaging of high-b signals, with the apparent inner diameter given by a combination of the axon caliber (dominated by the thickest axons), caliber variations, and the residual contribution of undulations.
PMID: 32798676
ISSN: 1095-9572
CID: 4566312
A time-dependent diffusion MRI signature of axon caliber variations and beading
Lee, Hong-Hsi; Papaioannou, Antonios; Kim, Sung-Lyoung; Novikov, Dmitry S; Fieremans, Els
MRI provides a unique non-invasive window into the brain, yet is limited to millimeter resolution, orders of magnitude coarser than cell dimensions. Here, we show that diffusion MRI is sensitive to the micrometer-scale variations in axon caliber or pathological beading, by identifying a signature power-law diffusion time-dependence of the along-fiber diffusion coefficient. We observe this signature in human brain white matter and identify its origins by Monte Carlo simulations in realistic substrates from 3-dimensional electron microscopy of mouse corpus callosum. Simulations reveal that the time-dependence originates from axon caliber variation, rather than from mitochondria or axonal undulations. We report a decreased amplitude of time-dependence in multiple sclerosis lesions, illustrating the potential sensitivity of our method to axonal beading in a plethora of neurodegenerative disorders. This specificity to microstructure offers an exciting possibility of bridging across scales to image cellular-level pathology with a clinically feasible MRI technique.
PMCID:7341838
PMID: 32636463
ISSN: 2399-3642
CID: 4517972
In vivo observation and biophysical interpretation of time-dependent diffusion in human cortical gray matter
Lee, Hong-Hsi; Papaioannou, Antonios; Novikov, Dmitry S; Fieremans, Els
The dependence of the diffusion MRI signal on the diffusion time t is a hallmark of tissue microstructure at the scale of the diffusion length. Here we measure the time-dependence of the mean diffusivity D(t) and mean kurtosis K(t) in cortical gray matter and in 25 gray matter sub-regions, in 10 healthy subjects. Significant diffusivity and kurtosis time-dependence is observed for t=21.2-100 ms, and is characterized by a power-law tail ∼t-ϑ with dynamical exponent ϑ. To interpret our measurements, we systematize the relevant scenarios and mechanisms for diffusion time-dependence in the brain. Using effective medium theory formalisms, we derive an exact relation between the power-law tails in D(t) and K(t). The estimated power-law dynamical exponent ϑ≃1/2 in both D(t) and K(t) is consistent with one-dimensional diffusion in the presence of randomly positioned restrictions along neurites. We analyze the short-range disordered statistics of synapses on axon collaterals in the cortex, and perform one-dimensional Monte Carlo simulations of diffusion restricted by permeable barriers with a similar randomness in their placement, to confirm the ϑ=1/2 exponent. In contrast, the Kärger model of exchange is less consistent with the data since it does not capture the diffusivity time-dependence, and the estimated exchange time from K(t) falls below our measured t-range. Although we cannot exclude exchange as a contributing factor, we argue that structural disorder along neurites is mainly responsible for the observed time-dependence of diffusivity and kurtosis. Our observation and theoretical interpretation of the t-1/2 tail in D(t) and K(t) alltogether establish the sensitivity of a macroscopic MRI signal to micrometer-scale structural heterogeneities along neurites in human gray matter in vivo.
PMID: 32585341
ISSN: 1095-9572
CID: 4493492
Diffusion MRI biomarkers of white matter microstructure vary nonmonotonically with increasing cerebral amyloid deposition
Dong, Jian W; Jelescu, Ileana O; Ades-Aron, Benjamin; Novikov, Dmitry S; Friedman, Kent; Babb, James S; Osorio, Ricardo S; Galvin, James E; Shepherd, Timothy M; Fieremans, Els
Beta amyloid (Aβ) accumulation is the earliest pathological marker of Alzheimer's disease (AD), but early AD pathology also affects white matter (WM) integrity. We performed a cross-sectional study including 44 subjects (23 healthy controls and 21 mild cognitive impairment or early AD patients) who underwent simultaneous PET-MR using 18F-Florbetapir, and were categorized into 3 groups based on Aβ burden: Aβ- [mean mSUVr ≤1.00], Aβi [1.00 < mSUVr <1.17], Aβ+ [mSUVr ≥1.17]. Intergroup comparisons of diffusion MRI metrics revealed significant differences across multiple WM tracts. Aβi group displayed more restricted diffusion (higher fractional anisotropy, radial kurtosis, axonal water fraction, and lower radial diffusivity) than both Aβ- and Aβ+ groups. This nonmonotonic trend was confirmed by significant continuous correlations between mSUVr and diffusion metrics going in opposite direction for 2 cohorts: pooled Aβ-/Aβi and pooled Aβi/Aβ+. The transient period of increased diffusion restriction may be due to inflammation that accompanies rising Aβ burden. In the later stages of Aβ accumulation, neurodegeneration is the predominant factor affecting diffusion.
PMID: 32111392
ISSN: 1558-1497
CID: 4324492
Multi-parametric quantitative in vivo spinal cord MRI with unified signal readout and image denoising
Grussu, Francesco; Battiston, Marco; Veraart, Jelle; Schneider, Torben; Cohen-Adad, Julien; Shepherd, Timothy M; Alexander, Daniel C; Fieremans, Els; Novikov, Dmitry S; Gandini Wheeler-Kingshott, Claudia A M
Multi-parametric quantitative MRI (qMRI) of the spinal cord is a promising non-invasive tool to probe early microstructural damage in neurological disorders. It is usually performed in vivo by combining acquisitions with multiple signal readouts, which exhibit different thermal noise levels, geometrical distortions and susceptibility to physiological noise. This ultimately hinders joint multi-contrast modelling and makes the geometric correspondence of parametric maps challenging. We propose an approach to overcome these limitations, by implementing state-of-the-art microstructural MRI of the spinal cord with a unified signal readout in vivo (i.e. with matched spatial encoding parameters across a range of imaging contrasts). We base our acquisition on single-shot echo planar imaging with reduced field-of-view, and obtain data from two different vendors (vendor 1: Philips Achieva; vendor 2: Siemens Prisma). Importantly, the unified acquisition allows us to compare signal and noise across contrasts, thus enabling overall quality enhancement via multi-contrast image denoising methods. As a proof-of-concept, here we provide a demonstration with one such method, known as Marchenko-Pastur (MP) Principal Component Analysis (PCA) denoising. MP-PCA is a singular value (SV) decomposition truncation approach that relies on redundant acquisitions, i.e. such that the number of measurements is large compared to the number of components that are maintained in the truncated SV decomposition. Here we used in vivo and synthetic data to test whether a unified readout enables more efficient MP-PCA denoising of less redundant acquisitions, since these can be denoised jointly with more redundant ones. We demonstrate that a unified readout provides robust multi-parametric maps, including diffusion and kurtosis tensors from diffusion MRI, myelin metrics from two-pool magnetisation transfer, and T1 and T2 from relaxometry. Moreover, we show that MP-PCA improves the quality of our multi-contrast acquisitions, since it reduces the coefficient of variation (i.e. variability) by up to 17% for mean kurtosis, 8% for bound pool fraction (myelin-sensitive), and 13% for T1, while enabling more efficient denoising of modalities limited in redundancy (e.g. relaxometry). In conclusion, multi-parametric spinal cord qMRI with unified readout is feasible and provides robust microstructural metrics with matched resolution and distortions, whose quality benefits from multi-contrast denoising methods such as MP-PCA.
PMID: 32360689
ISSN: 1095-9572
CID: 4429722
Noninvasive quantification of axon radii using diffusion MRI
Veraart, Jelle; Nunes, Daniel; Rudrapatna, Umesh; Fieremans, Els; Jones, Derek K; Novikov, Dmitry S; Shemesh, Noam
Axon caliber plays a crucial role in determining conduction velocity and, consequently, in the timing and synchronization of neural activation. Noninvasive measurement of axon radii could have significant impact on the understanding of healthy and diseased neural processes. Until now, accurate axon radius mapping has eluded in vivo neuroimaging, mainly due to a lack of sensitivity of the MRI signal to micron-sized axons. Here, we show how - when confounding factors such as extra-axonal water and axonal orientation dispersion are eliminated - heavily diffusion-weighted MRI signals become sensitive to axon radii. However, diffusion MRI is only capable of estimating a single metric, the effective radius, representing the entire axon radius distribution within a voxel that emphasizes the larger axons. Our findings, both in rodents and humans, enable noninvasive mapping of critical information on axon radii, as well as resolve the long-standing debate on whether axon radii can be quantified.
PMCID:7015669
PMID: 32048987
ISSN: 2050-084x
CID: 4304432
Multi -parametric quantitative in vivo spinal cord MRI with unified signal readout and image denoising
Grussu, Francesco; Battiston, Marco; Veraart, Jelle; Schneider, Torben; Cohen-Adad, Julien; Shepherd, Timothy M.; Alexander, Daniel C.; Fieremans, Els; Novikov, Dmitry S.; Wheeler-Kingshott, Claudia A. M. Gandini
ISI:000542370300008
ISSN: 1053-8119
CID: 4525782
Chapter 11: Model-based Analysis of Advanced Diffusion Data
Veraart, J; Lemberskiy, G; Baete, S; Novikov, D S; Fieremans, E
The diagnosis of various disorders is hindered by the lack of an imaging technique that reveals the architecture of living tissue at the fine resolution of the associated pathological processes. Indeed, even the most powerful imaging techniques such as MRI can only resolve or visualize biological tissue down to the scale of a cubic millimetre. However, MRI may be able to reveal what happens on a much finer scale, as it is sensitive to the random thermal motion of water molecules and, more importantly, their interactions with surrounding cells constituting the microstructure of the tissue. The gap between being sensitive and specific is bridged by the development of a tissue model that decomposes the MRI signal into components that probe relevant features of the underlying microstructure, typically affected by pathology. Hence, biophysical modelling is potentially a diagnostic tool that allows scientists to identify problems that arise in the unexplored depths of our organs, driving forward treatment and understanding of disease progression. In this chapter, we will introduce the main concepts of multiparametric modelling, lay out a general framework of multi-compartmental models, and discuss limitations and challenges.
Copyright
EMBASE:633348060
ISSN: 2044-253x
CID: 4666312
A resting state fMRI analysis pipeline for pooling inference across diverse cohorts: an ENIGMA rs-fMRI protocol
Adhikari, Bhim M; Jahanshad, Neda; Shukla, Dinesh; Turner, Jessica; Grotegerd, Dominik; Dannlowski, Udo; Kugel, Harald; Engelen, Jennifer; Dietsche, Bruno; Krug, Axel; Kircher, Tilo; Fieremans, Els; Veraart, Jelle; Novikov, Dmitry S; Boedhoe, Premika S W; van der Werf, Ysbrand D; van den Heuvel, Odile A; Ipser, Jonathan; Uhlmann, Anne; Stein, Dan J; Dickie, Erin; Voineskos, Aristotle N; Malhotra, Anil K; Pizzagalli, Fabrizio; Calhoun, Vince D; Waller, Lea; Veer, Ilja M; Walter, Hernik; Buchanan, Robert W; Glahn, David C; Hong, L Elliot; Thompson, Paul M; Kochunov, Peter
Large-scale consortium efforts such as Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) and other collaborative efforts show that combining statistical data from multiple independent studies can boost statistical power and achieve more accurate estimates of effect sizes, contributing to more reliable and reproducible research. A meta- analysis would pool effects from studies conducted in a similar manner, yet to date, no such harmonized protocol exists for resting state fMRI (rsfMRI) data. Here, we propose an initial pipeline for multi-site rsfMRI analysis to allow research groups around the world to analyze scans in a harmonized way, and to perform coordinated statistical tests. The challenge lies in the fact that resting state fMRI measurements collected by researchers over the last decade vary widely, with variable quality and differing spatial or temporal signal-to-noise ratio (tSNR). An effective harmonization must provide optimal measures for all quality data. Here we used rsfMRI data from twenty-two independent studies with approximately fifty corresponding T1-weighted and rsfMRI datasets each, to (A) review and aggregate the state of existing rsfMRI data, (B) demonstrate utility of principal component analysis (PCA)-based denoising and (C) develop a deformable ENIGMA EPI template based on the representative anatomy that incorporates spatial distortion patterns from various protocols and populations.
PMID: 30191514
ISSN: 1931-7565
CID: 3271572