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Noninvasive quantification of axon radii using diffusion MRI

  1. Jelle Veraart  Is a corresponding author
  2. Daniel Nunes
  3. Umesh Rudrapatna
  4. Els Fieremans
  5. Derek K Jones
  6. Dmitry S Novikov
  7. Noam Shemesh
  1. Champalimaud Centre for the Unknown, Portugal
  2. New York University School of Medicine, United States
  3. University of Antwerp, Belgium
  4. Cardiff University, United Kingdom
  5. Australian Catholic University, Australia
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Cite this article as: eLife 2020;9:e49855 doi: 10.7554/eLife.49855

Abstract

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.

Introduction

Axons facilitate connectivity between distant neurons. Along with myelination, the axon radius determines the conduction velocity, thereby shaping the timing of neuronal computations and communication (Waxman, 1980). Using a model of action potential neurophysiology (Rushton, 1951), it has been shown that the axon radius explains the largest proportion of variance in conduction velocity (Drakesmith et al., 2019). Histological studies demonstrated that axon sizes vary widely within the human brain, ranging from 0.1μm to more than 3μm (Aboitiz et al., 1992; Innocenti et al., 2015; Liewald et al., 2014), and across species (Olivares et al., 2001; Schüz and Preiβl, 1996; Liewald et al., 2014). Moreover, axon radii have been shown to be altered in various disease processes. For example, direct axon counting in post-mortem tissue has suggested that smaller axons may be preferentially susceptible to axonal injury in multiple sclerosis (Evangelou et al., 2001) due to inflammation (Campbell et al., 2014). Electron microscopy has revealed a higher percentage of small-radius axons and a lower percentage of large-radius axons in several anatomically and functionally distinct segments of the corpus callosum in autistic subjects compared to healthy controls (Wegiel et al., 2018). From the animal literature, morphometric analysis of adult rat brains showed reduced axonal radii without axonal loss after chronic alcohol feeding (Kjellström and Conradi, 1993). Such studies indicate that non-invasive metrics capable of reporting on features of the axon radius distribution could provide important neuroimaging biomarkers for basic research and clinical applications.

A particularly relevant neuroimaging modality attuned to the microarchitecture of living brain tissue is diffusion-weighted MRI (dMRI). dMRI is sensitive to the thermal motion of water molecules and their interference with microscopic boundaries, such as imparted by cells and subcellular structures in the brain (Tanner, 1979; Le Bihan, 2003; Le Bihan et al., 1986; Callaghan et al., 1988; Basser et al., 1994; Jones, 2010; Beaulieu, 2002; Novikov et al., 2019). Applications of dMRI specialize in revealing macroscopic brain connections (Jbabdi et al., 2015) and in the interpretation of contrast differences in diffusion-weighted images (Moseley et al., 1990; Baron et al., 2015). However, reproducible and specific biomarkers for studying disease onset and progression non-invasively and quantitatively in the entire brain, in particular vis-a-vis axonal properties, would confer clear advantages. Several studies have used various methods to report on axon radius parameters; still, despite many attempts, axon radius mapping using dMRI remains highly contested (Assaf et al., 2013; Horowitz et al., 2015; Alexander et al., 2010; Innocenti et al., 2015; Xu et al., 2014; Burcaw et al., 2015; Ong et al., 2008; Ong and Wehrli, 2010). Discrepancies between histology and dMRI-derived axon radii uncovered various confounding factors, for example orientation dispersion (Drobnjak et al., 2016; Nilsson et al., 2012), time-dependent extra-axonal diffusion overshadowing the intra-axonal signal at low diffusion weighting (Burcaw et al., 2015; Fieremans et al., 2016; Lee et al., 2018), weak signal attenuation for typically very narrow axons, especially in the realistic experimental regime of long diffusion gradient duration (van Gelderen et al., 1994; Neuman, 1974), and/or putative shrinkage during tissue preparation (Barazany et al., 2009; Innocenti et al., 2015; Aboitiz et al., 1992).

Recent advances in biophysical modeling and hardware prompted a revival of MR axon radius mapping (McNab et al., 2013; Huang et al., 2015; Jones et al., 2018). First, several of the most crucial confounding factors have been removed using powder-averaging concepts (Callaghan et al., 1979; Jespersen et al., 2013; Kaden et al., 2016). Averaging diffusion-weighted signals that are isotropically distributed on a sphere with constant diffusion-weighting strength b has been shown to factor out the orientation dispersion (Jespersen et al., 2013Kaden et al., 2016Mollink et al., 2017), thereby eliminating one of the most important confounding factors in axon radius mapping (Nilsson et al., 2012). Second, gradient systems capable of producing relatively strong gradient pulses have been introduced in human scanners (Jones et al., 2018). Third, it has been shown that dMRI can be made specific to a particular water population restricted into long, yet micrometer-thin cylindrical objects by imparting high diffusion-weighting regimes (McKinnon et al., 2017; Veraart et al., 2019). Often, an axon is too narrow to yield a measurable diffusion-weighted MR signal decay, hence the popular use of ‘sticks’ (Behrens et al., 2003; Kroenke et al., 2004) when referring to axons (and possibly glial cell processes) within the context of biophysical modeling of white matter using dMRI.

The intuition behind promoting specificity to intra-axonal water comes from Callaghan’s model (Callaghan et al., 1979) of diffusion inside infinitely narrow one-dimensional randomly-oriented cylinders, as applied to intra-neurite diffusion by Kroenke et al. (2004). The spatial Fourier transform e-Da(𝐪𝐧^)2t of the diffusion propagator (with respect to the diffusion wave vector 𝐪) for a single stick as measured with MRI (Callaghan, 1991), averaged over the orientations 𝐧^ of the sticks, yields the asymptotic scale-invariant power law S¯=dcosθeDaq2tcos2θ1/bα as a function of the diffusion weighting parameter b=q2t (Le Bihan et al., 1986), with the scaling exponent α=1/2. Evidently, this power law scaling should be only approximate, for q1/r, where r is the cylinder radius. Its observation (McKinnon et al., 2017; Veraart et al., 2019) in the range 6ms/μm2b10ms/μm2 is a manifestation of the insensitivity of dMRI to the transverse axonal dimensions on clinical scanners. However, for sufficiently strong diffusion weighting, the power law scaling eventually breaks down, and the dMRI measurement becomes sensitive to the axonal diameter.

Technically, this work addresses the detection and the interpretation of the deviation from the radius-insensitive α= 1/2 power law signal behavior at the largest possible b (by varying q at fixed diffusion time t), in rat and human white matter. Indeed, either sensitivity of MR to a finite axonal radius, or a notable exchange rate between intra- and extra-axonal water at the clinical dMRI time scales t∼100 ms, would alter the very particular power law scaling (Kroenke et al., 2004; Jensen et al., 2016; McKinnon et al., 2017; Veraart et al., 2019).

Following theoretical considerations, we demonstrate the breaking of the power law scaling at very high b-values in ex vivo rodent brains, from which metrics associated with the axon radius distribution can be mapped quantitatively. Confocal microscopy of the rat corpus callosum (CC) validated that (i) the signal arises mainly from the intra-axonal space, and (ii) the MR-derived axon radius metrics are in good quantitative agreement with those derived from histology. We then observe the same signal signatures in living human brain on the Connectom 3T scanner, that is, a high performance research scanner with a maximal gradient amplitude of 300 mT/m – a fourfold increase compared to state-of-the art clinical scanners (Glasser et al., 2016). Our findings both validate the mechanism with which axon radii are weighted in dMRI (Burcaw et al., 2015), and demonstrate the accuracy of which properties of the radius distributions can be estimated. After validating and evaluating our methodology in rat and human brain, we further discuss the impact of axon radius measurements in health and disease.

Theory

Power law scaling

In most biophysical models for diffusion in white matter, axons (and possibly glial cell processes) are represented by zero-radius impermeable ‘sticks’, characterized by locally one-dimensional diffusion, that is radial intra-axonal diffusivity Da 0 (Kroenke et al., 2004; Behrens et al., 2003; Jespersen et al., 2007; Jespersen et al., 2010Fieremans et al., 2011; Sotiropoulos et al., 2012; Zhang et al., 2012; Novikov et al., 2014; Novikov et al., 2018; Novikov et al., 2019; Jensen et al., 2016; Reisert et al., 2017; McKinnon et al., 2017; Veraart et al., 2019). The stick model then yields an asymptotic intra-axonal orientationally averaged signal decay,

(1) S¯(b)βb-α+γ,bDa1,

with an intercept γ (discussed below), the power law exponent α=1/2, and the coefficient β=π/4f/(Da)1/2 where f is the T2-weighted axonal water fraction (Veraart et al., 2018; Lampinen et al., 2019) and Da the parallel intra-axonal diffusivity. This particular signal decay only holds in the absence of extra-axonal signal, which is assumed to decay exponentially fast and, as such, to be fully suppressed at sufficiently high b-values (McKinnon et al., 2017; Veraart et al., 2019). Therefore, we restrict our in vivo analysis to b>6ms/μm2 (Veraart et al., 2019). Our lower bound on the b-value is significantly higher than previous predictions from Monte Carlo simulations (Raffelt et al., 2012), thereby minimizing the likelihood of residual extra-axonal signal contributions. For the ex vivo analysis, we increase this lower bound to b=20ms/μm2 to compensate for the reduced diffusivities in fixed tissue (Shepherd et al., 2009).

Breaking of the power law

The following computations always assume that the signal is normalized to S|b=01. Sensitivity of MR to either finite axon radius or notable exchange rate between intra- and extra-cellular water would break the b-1/2-scaling at large b as follows:

  • Finite axon radius: A finite Da> 0 results in a truncated power law:

(2) S¯(b)βe-bDa+𝒪(b2)b-1/2+fim,
(3) S¯βξe-Da/ξ2+fim,ξ=1/b
  • around any finite point ξ0 predicts the ξ0 intercept γ<fim, Figure 1. The always negative difference ϵ=γ-fim<0 depends on β, Da, and ξ0; its maximal magnitude |ϵmax|=β2Da/e=fπ2eDa/Da is achieved at the curve’s inflection point ξ*2=2Da. Hence, the lower bound fim-|ϵmax| for the ξ0 intercept γ may be negative. A negative γ is biophysically implausible if the stick model holds, Da0; however, γ<0 becomes a natural consequence of a finite Da>0 (and hence, of a finite axonal diameter), Figure 1, in the case when the extrapolated negative intercept overcomes the positive immobile fraction fim. Recently, fim was shown to be negligible in healthy human white matter (Dhital et al., 2018; Tax et al., 2019; Veraart et al., 2019). Therefore, a negative intercept is a novel hallmark of MR sensitivity to the inner axon diameter, even if the signal scaling might appear linear as a function of b-1/2 for b-ranges accessible on human MR scanners. Importantly, the finite axon radius model, Equation 3, is poorly conditioned as a result of which the simultaneous estimation of fim and Da is practically impossible, especially for human MR experiments, see Figure 1. An accurate and precise measurement of Da depends on the prior knowledge of fim and requires a dedicated measurement (Dhital et al., 2018; Tax et al., 2019).

  • Exchange: The spherical integration of the two-compartment ‘Kärger’ model (Kärger, 1985) with a finite exchange rate >0 yields approximately the following signal decay:

    (4) S¯(b)β(b-1/2+cb-3/2)+fim,cTE/De>0,
  • with De the radial diffusivity in the interstitial space, and TE, the echo time, during which exchange can happen. Importantly, Equation 4 is convex as a function of ξ=b-1/2.

The relative fit quality of the models (i.e., Equations 1, 2, and 4) to the dMRI signal decays can be evaluated qualitatively (convex versus concave shape) or statistically by means of the corrected Akaike information criterion (AICc) (Burnham and Anderson, 2002).

Breakdown of power law scaling: Top left: A nonzero Da would result in a truncated power-law signal decay.

Although the resulting signal nonlinearity might be too subtle to be discerned within the achievable b-range, even for (pre-)clinical systems with strong diffusion-weighting gradients, the concavity of the curves plotted as function of ξ=1/b for ξ>ξ*=2Da means that even the smallest Da will result in an extrapolated ξ0 intercept γ<fim when the power law, Equation 1, is used to approximately describe the signal in the delineated b-ranges. The intercept is maximally negative at the inflection point ξ* (colored dots), beyond which each curve becomes convex, and the negative intercept γ of the linear extrapolation starts to decrease. In all plots here, diffusivities and b-values are expressed in µm2/ms and ms/µm2, respectively. Top right: One representative curve (Da=0.020) is shown to highlight the differences between the physically plausible dot compartment fim>0, and the intercept γ. The dot compartment is a positive signal fraction of a biophysical compartment, whereas the intercept is a parameter of the power-law approximation, Equation 1. Their difference ϵ depends on various parameters, including the axonal signal fraction, diffusivities, the axon radius, and the scan protocol. The predicted signal decay for the exchange model (dash-dotted; Equation 4) is convex in the entire b-range, where the signal decay for the finite axon radius model (dotted; Equation 2) is concave until the inflection point. Bottom: The optimization landscape of Equation 3 shows a shallow valley, relative to the noise floor, for a simulation that mimics the human component of the study. (Bottom left) The valley is shown in a 2D projection of the landscape (shown as a function of radius instead of Da, see Equation 10). (Bottom right) The fit objective function along the valley is shown (red line) in comparison to the noise floor (dashed line) with an unrealistically high SNR of 250 for the non-DW signal. The red dot indicates the ground truth value.

From diffusivity to effective MR radius

The radial signal attenuation Sc(r) inside the cylinder of radius r in the Gaussian phase approximation (van Gelderen et al., 1994):

(5) lnSc(r)=2g2r4D0m=1tcαm6(αm21)[2αm2δtc2+2e2αm2δ/tc+2e2αm2Δ/tce2αm2(Δδ)/tce2αm2(Δ+δ)/tc]+𝒪(g4)bDa(r)+𝒪(b2),

with b=g2δ2(Δ-δ/3) and tc=r2/D0 defines the connection between the intra-axonal radial diffusivity Da and the radius r. Here, D0 is the diffusivity of the axoplasm, g the gradient of the Larmor frequency, αm is the mth root of dJ1(α)/dα=0, where J1(α) is the Bessel function of the first kind, and δ and Δ are the gradient duration and separation, respectively (Stejskal, 1965).

In the long-pulse limit, that is when δtc, the dependence on Δ drops out (Neuman, 1974), and Equation 5 approaches the Neuman’s limit

(6) lnSc(r)=-κr4,κ=748g2δD0,δtc.

This limit practically applies to the majority of axons. Importantly, the attenuation is proportional to the fourth power of the radius r and, as such, it is very weak for narrow axons. Hence the low sensitivity of dMRI to the inner axon diameter.

For an unknown distribution h(r) of axons with radii r, the total intra-axonal signal attenuation becomes a volume-average over the histogram bins ri (Packer and Rees, 1972):

(7) S[h(r)]ih(ri)ri2Sc(ri)ih(ri)ri2=r2Sc(r)r2,

such that the signal contribution of an axon scales quadratically with its radius r. The Taylor expansion of the net signal attenuation S demonstrates the sensitivity of the dMRI signal to the distribution’s higher order moments:

(8) S[h(r)]=r2(1κr4+𝒪(r8))/r21κr6/r2eκreff4Sc(reff),

 where the effective axon radius:

(9) reff(r6/r2)1/4

captures the contribution from the whole distribution h(r) in a single metric (Burcaw et al., 2015). The ability to represent the whole distribution by the ratio of its 6th and 2nd moments relies on almost all axons falling into the Neuman’s limit, Equation 6. Representing Sc(reff)e-bDa, we can calculate

(10) rMR=(487δ(Δ-δ/3)D0Da)1/4,

the MRI estimate of reff after estimating Da from the orientation-averaged signal using Equation 2.

Note that the effective radius, Equation 9, is heavily weighted by the tail of h(r). Physically, this happens due to the combination of the weak NMR signal attenuation by small radii, lnSr4, in the diffusion-narrowing (Neuman’s) regime (Neuman, 1974), and of the subsequent volume-weighting that emphasizes the thickest axons by an extra factor of r2 (Packer and Rees, 1972; Alexander et al., 2010). The error associated with these modeling assumptions is discussed in the Results section.

Results

Simulations

Accuracy

First, we evaluate the accuracy of axon radius mapping as a function of r for axon radius distributions extracted from histology; Figure 2 (left and middle panels). We used a simulation framework based on the matrix formalism for diffusion signal attenuation within fully restricted cylinders (Callaghan, 1997), as implemented in the MISST toolbox (Drobnjak et al., 2010), while mimicking the entire experimental setup, for both the human and preclinical experiments.

Simulations on accuracy and precision of MR-based axon radius mapping.

First, the left and middle panel show the difference between the estimated, r^, and theoretical, r, effective MR radius associated with various realistic axon caliber distributions (solid dots with different color for different distributions) for the clinical and preclinical setups, respectively. Axon caliber distributions were adopted from Aboitiz et al. (1992) and Innocenti et al. (2015) for the clinical simulations (see Figure 7), whereas various axon distributions (see Figure 4) derived from our own histology were used for the preclinical simulation. The average radii, r¯, of the axon caliber distribution are shown for comparison (open dots). Additionally, the accuracy of the framework for a system with single cylinder with radius r is shown (black line). Second (right figure), the feasibility to measure Da with statistical significance in case of scan settings and SNR for the Connectom (300 mT/m; blue), Aeon (1500 mT/m; green) protocol, respectively. For comparison, we also assessed the feasibility for the Prisma protocol as described in Veraart et al. (2019) (80mT/m; red). The shaded areas illustrate the 95% confidence intervals derived from Cramér-Rao lower bound analysis of model, Equation 2 with fim=0. The corresponding minimal cylinder radius r that allows for the observation of significant Da(r), r=0.76μm, 1.41 µm and 3.24 µm for Aeon, Connectom, and Prisma, respectively, is indicated by the vertical lines. In all plots, diffusivities and radii are expressed in μm2/ms and μm, respectively.

In the case of diffusion restricted in a single cylinder with radius r, the error in the estimated radius r^ increases with r. Indeed, the missing higher-order 𝒪(g4) corrections to Equations (5)-(6) set an upper bound on the achievable accuracy for large axons, as estimated recently (Lee et al., 2018).

The combined error in the estimation of reff associated with the approximations made in Equation 6 and Equation 8 is only 5% for the human set-up when considering the axon radius distribution provided by Aboitiz et al. (1992); the distributions of Caminiti et al. (2009) result in a subpercent error. Additionally, we show the errors in the estimation of reff for the axon caliber distributions that were observed in our different histological sections while considering the scan parameters from our fixed tissue experiments. The shorter diffusion timings increase the approximation errors, leading to an underestimation up to 9%.

Feasibility and precision

Figure 2 (right panel) shows a theoretical lower bound on the 95% confidence interval in the voxelwise estimation of Da from Equation 2, as predicted using a Cramér-Rao lower bound analysis (Kay, 1993). Using the dependence DaDa(reff), Equation 8, that approximately identifies reff with the single cylinder radius in van Gelderen’s model, can be used to translate the lower bound on Da to that on reff.

Notably, it follows from Figure 2, that an estimate of Da exceeds zero with a statistical threshold of p>0.05, if the corresponding reff>1.41μm and reff>0.76μm, when mimicking the diffusion acquisition and SNR on the Siemens Connectom (Gmax=300mT/m) and Bruker Aeon (Gmax=1500mT/m) MR scanners, respectively. In comparison, for a typical acquisition on a modern clinical scanner with Gmax=80mT/m, this lower bound is 3.2 μm (Veraart et al., 2019).

Preclinical data

Dot compartment

Because fim has been reported to be significant in fixed tissue by Stanisz et al. (1997), we first have to estimate the signal fraction of the immobile water compartment fim in the three fixed brain samples from a dedicated MRI acquisition (see Materials and methods). The estimate f^im is in the range of 8 to 17% with a median value of 13%. The range is defined here by the 5th and 95th percentile of the distribution of the estimated dot fractions in all CC voxels, across the three samples.

Breaking the power law

Figure 3 shows the signal decay, averaged across all CC voxels, based on diffusion measurements in the three rat brain samples with b up to 100ms/μm2. We notice that an extrapolation to infinite b, that is 1/b0, yields a small but significant negative offset γ, of the order of a few per cent of the non-attenuated S|b=0 signal, in all three samples after subtracting the f^im from the diffusion measurements.

Breaking of the power law.

The ROI- and spherically averaged signal decay is shows for the different fixed samples of the rat CC (a) and human subjects (b) and as a function of 1/b and on a double logarithmic scale. The data deviate from the power law scaling with exponent 1/2 that is predicted by the stick model (i.e. nonlinear signal decay in log-log plot), thereby demonstrating sensitivity of the signal to the radial intra-axonal signal. The fits of Equation 1 are shown in dashed lines. In all plots, b is expressed in ms/μm2.

We re-evaluate the validity of a perfect stick assumption in the high b-regimes using a AIC analysis. To study fit robustness with respect to the number of degrees of freedom by considering the full, nested, and extended models to Equation 1. Specifically, we evaluated the following models:

  1.  fim+βb-α

  2.   fim+βb1/2;

  3.  fim+βe-bDab-1/2

  4.   fim+β(b-1/2+cb-3/2)

  5.   βb-α

  6.  βb-1/2

  7.  βe-bDab-1/2

  8.  β(b-1/2+cb-3/2)

Our analysis shows that a truncated power law (vii), which explicitly accounts for Da>0 (and hence does not require a negative intercept parameter), yet sets fim=0, fits the experimental data significantly better than pure power law forms (models (i), (ii), (v), and (vi)) (the difference in AICc<2), with or without an offset γ, if the immobile (dot) compartment is corrected for, that is when using S¯(b)=S¯(b)-f^im. Without dot-correcting the ex vivo data, the power law form (ii) with an intercept outperforms the other models. In that case, the intercept γ is negative, while fim is defined to be positive. Hence, the intercept encodes both the still water fraction and a negative offset to the intercept associated with the sensitivity to the axon diameter, such that the overall γ<0.

Axon radius estimation and histological validation

Axon radii were estimated from the diffusion MRI data for the different CC ROIs (Figure 4) along with the axon radius distributions extracted histologically, Figure 4. The errors between the associated tail-weighted effective radii and MR-derived rMR vary between 5 and 21% in the different ROIs. Notably, a consistent residual overestimation was observed, whereas the previous simulations (Figure 2) predicted an underestimation.

Histological validation, part I.

The axon radius distributions for different ROIs of rat CC#1 are shown (blue bars).The associated tail-weighted effective radii are shown in the black lines, whereas the corresponding MR estimates are shown by the red lines. In all plots, r is expressed in μm.

To further examine the correlations between the MR-derived parameters and underlying microstructure, we analyzed 16 patches with the same size as an imaging voxel, that is 100 × 100 μm2 within the genu of the CC of the second sample, CC#2. The confocal light microscopy images of two of those patches are shown for the various stainings, Figure 5. Two notable biological components other than axons were highlighted, namely, astrocytic processes and (neuronal or glial) cell bodies, which were found to have volume fractions of about 5%. Although the radius of the astrocytic processes cannot be measured due to their random orientations w.r.t. the image plane, it is clear that some of the processes have a large diameter, for example up to 7 μm in the first patch. The average cell body radius is 2.6 μm, with an effective radius of 4.3 μm. It is worth highlighting that the T2-weighted signal fraction of both cell types remains unknown since the corresponding relaxation times are unknown. This unknown difference between compartmental volume (histology) and signal (MRI) fractions remains the Achilles’s heel of comparisons between MRI measurements and histological evaluations of tissue microstructure.

Histological validation, part II.

(left) For a second fixed brain sample, CC#2, the confocal microscopy images, stained for neurofilaments (red), astrocytes (green), and cell bodies (blue), are shown for two representative 100×100μm2-patches that are positioned within the Genu (microscopy image of CC shown for ROI positioning). The abundance of astrocytes and cell bodies, both representing 5% of the volume, is clear in both patches. The astrocytic processes can have a large diameter, up to 7 µm in the first patch. A detailed analysis of the radius distribution of the astrocytic processes is not possible due to their random orientation w.r.t. the image plane. (middle) Axon radius distributions for all 16 patches of the Genu (each patch has different color in the bar plot). (right) Boxplots represent the distribution of the average and effective radius of the axon radii distribution that were extracted from each of the 16 patches within the genu. The effective radius reff is larger than the average r¯, respective medians are 1.06 and 0.61 µm. The boxplots for the MR-derived axon radius measurements for 16 MR voxels within the genu for the three fixed CC samples are also shown. In all plots, radii are expressed in μm.

Within each of the 16 patches, we extracted the axon radii distribution and derived the average r¯ and effective radius reff. The box plots of those metrics are shown in Figure 5. The median r¯ and median effective radius reff across all patches are 0.61 and 1.06 μm , respectively. In comparison, the median rMR, derived from dMRI in 16 voxels within the genu of the CC, is 1.16, 1.10, and 1.19 μm for the three rat samples, respectively. The median MR-derived effective axon radius is between 81 and 97% larger than the median r¯, whereas the error to the median reff, as derived from histology, is only 3.4 to 12.8%.

Parameter maps

ROI measurements provided robust estimation, but a remaining question is whether dMRI could be used to map the effective MR radius in a voxelwise manner. Figure 6 shows the maps of the MR-derived effective axon radii for all three rat CC’s. The maps appear smooth with very few outlier voxels, suggesting that the estimation is robust even when voxelwise data is used. Furthermore, the qualitative trends are in good agreement with previously reported observations of larger axons in the body of the CC in comparison to the genu and splenium (Barazany et al., 2009). Inter-subject variability is not very large and can be attributed to slightly different slice positions. When computing the effective radius of the CC-averaged signal r¯MR, the intersubject variability nearly nullifies. Indeed, we estimate r¯MR=1.22,1.25 and 1.25 µm in the three samples, respectively.

Effective radii in the CC.

Maps of the effective radii derived from diffusion MR data, for the 3 samples of the rat CC.

Towards human applicability

Breaking the power law

To assess the applicability of this approach in more realistic settings available for human imaging, experiments were performed in human subjects on the Connectom scanner, which is capable of producing 300 mT/m gradient amplitudes. The dMRI signal decay curves, averaged across all WM voxels, with b-values up to 25 ms/µm2 for the four human subjects are shown in Figure 3. Importantly, we find that — in excellent correspondence with the previous preclinical data — the linear extrapolation of the signal decay as a function of 1/b to 1/b0 produces a significant negative offset γ in all subjects.

Note that the dot compartment was not measured directly, because previous dedicated studies revealed a negligible dot compartment, that is fim=0, in the healthy white matter (Dhital et al., 2018; Veraart et al., 2019; Tax et al., 2019); see Discussion.

The AICc analysis of various models demonstrated that also for the human white matter, the truncated power law (vii) with Da>0 and negligible dot compartment fim=0 fits the data significantly better than pure power law forms, with or without intercept. However, this statistical analysis cannot be interpreted as a data-driven justification for fim=0 because of the degeneracy of Equation 3, as highlighted in Figure 1.

Comparison with histology

Since direct histological evaluation in volunteers is unfeasible, we turn to validate the MR-derived metrics in humans with previously reported literature of human corpus callosum microstructure (Aboitiz et al., 1992; Innocenti et al., 2015). In Figure 7, the MR-derived metrics were directly compared with axon radius distributions of multiple histological studies (Aboitiz et al., 1992; Innocenti et al., 2015). Various reports and histological studies show a good correspondence for the bulk of the distributions, represented by the average radius r¯, that is the average radius r¯ only ranges between 0.54 and 0.69 µm. In histological samples, the corresponding effective radius reff dominated by large axons, shows strong variability. Indeed, compared to r¯, reff varies more than three-fold, from 0.91 to 2.9μm.

Comparing the effective radius from histology and in vivo dMRI.

(top - histology) Axon radius distributions of multiple histological studies and human CC samples show a good correspondence for the bulk of the distribution, represented by the average radius r¯ (dashed-dotted lines). Due to mesoscopic fluctuations of the large axons in histological samples, the corresponding effective radius reff dominated by large axons, shows strong variability (dotted lines). (bottom - MRI) The four Connectom subjects show good correspondence in terms of reff. The distribution describing reff for all voxels in the midbody of the CC for all four subjects falls almost entirely within the range spanned by values predicted by histology, with no need to account for potential shrinkage (Horowitz et al., 2015) during tissue preparation. In all plots, radii are expressed in μm.

The four human subjects show good correspondence in terms of rMR. In Figure 7, we show the individual and combined distributions describing rMR for all voxels in the midbody of the CC for all four subjects. It is apparent that the combined distribution falls almost entirely within the range spanned by reff-values as predicted from histology – even without introducing a putative axonal shrinkage factor (maximally 35% [Aboitiz et al., 1992], and typically within 10% [Tang et al., 1997]).

Parameter distribution and maps

In Figure 8, the distribution and map of Da for WM voxels in all human subjects, estimated using the ODF-independent model, Equation 2 with fim=0, are shown. Considering the statistical bound from Figure 2, it is to be expected that the estimated Da is biophysically meaningful for the vast majority of WM voxels for the Connectom scanner (Figure 8 shows a representative map of Da and associated rMR for a single subject of the Connectom cohort), whereas similar measurements on a modern clinical scanner result in a biophysically implausible negative Da in approximately 35% of all WM voxels. Note that the data from a clinical scanner (Siemens Prisma with 80 mT/m gradients) are adopted from our recent work (Veraart et al., 2019). This suggests that estimating Da and the associated effective axonal radius rMR is only possible on MR systems with ultra-strong gradients (Jones et al., 2018; Huang et al., 2015). The spatial variability as well as the observed asymmetry between the hemispheres in, for example, the occipital lobes was noted for all subjects. However, our cohort is too small and not sufficiently characterized to study the whole brain characterization or the role of lateralization in large axons of human brain (Eichert et al., 2019; Liewald et al., 2014; Lebel and Beaulieu, 2009).

Distribution and maps of Da and rMR.

(left) The distribution of Da estimated via Equation 3 for all WM voxels (all scanner-specific subjects pooled) shown for both scan set-ups. In agreement with Figure 2, Prisma (80mT/m) data shows a much lower precision for the estimator of Da. Despite the small yet positive mean value and the associated negative offset γ in Figure 3, a large number of WM voxels yield biophysically implausible Da<0 values. Precision drastically improves on the Connectom scanner (300 mT/m). (right) Maps of Da, and of the effective MR radius heavily weighted by the tail of axonal distribution (Figure 7), for a single subject. Here, rMR is derived from Da via Equation 10. In all plots, diffusivities and radii are expressed in μm2/ms and μm, respectively.

Gray matter

It is worth examining the power law scaling also in areas outside the white mater. Therefore, Figure 9 shows the diffusion-weighted signal decay, averaged over all cortical gray matter (GM) voxels as a function of b in the human subjects. The signal scaling in the WM is shown for qualitative comparison. The non-linear scaling of the isotropically-averaged signal as a function of 1/b of all human subjects indicates strong deviations from the ‘stick’ model in the cortical GM, (McKinnon et al., 2017; Palombo et al., 2019). Accounting for a finite neurite radius, Equation 2, does not describe the data well either. Instead, the convex signal decay as a function of 1/b at high b-values is in good agreement with the anisotropic exchange model that we derived from the expansion of the anisotropic Kärger model in the powers of inverse b, Equation 4. Both the finite radius and exchange model predictions are shown in the absence of an immobile water fraction. The exchange model fits the data better than all other evaluated models in all subjects according to an AIC analysis (data not shown). The residence time within the neurites 1/ varies from approximately 10 to 15 ms or 20 to 30 ms if we assume De=1μm2/ms or De=0.5μm2/ms, respectively. Dedicated experiments are required for a more precise measurement of the exchange rate.

Signal decay in the GM.

The spherically-averaged signal decay in the WM and GM is shown for all human subjects as a function of 1/b. The consistent non-linear scaling of the signal as a function of 1/b demonstrates deviations from the ‘stick’ model in the cortical GM. In contrast to the WM, the convex signal decay in the GM is better described by an anisotropic exchange model of two compartments (Equation 4), than the finite radius model (Equation 3). In all plots, b is expressed in ms/μm2 .

Discussion

What do we measure with dMRI?

Noninvasively estimating metrics associated with axon radius distributions is a formidable task, yet it could have a strong impact for numerous areas of research including neuroscience, biomedicine and even for clinical research and applications. Histological studies have extensively reported axon diameters 2r to be in the range 0.5 − 2 µm for human WM (Aboitiz et al., 1992; Caminiti et al., 2009; Liewald et al., 2014; Tang et al., 1997), with only 1% of all axons having a diameter larger than 3 µm (Caminiti et al., 2009). A vigorous debate has emerged in the MRI and neuroanatomy communities as in vivo, MRI-derived axon diameters are reported to fall within the range 3.5 −15 µm (Alexander et al., 2010; Horowitz et al., 2015; Huang et al., 2015). On the MRI side, the discrepancy has been attributed to the long diffusion pulses that strongly reduce the signal attenuation of protons restricted in a narrow cylinder (van Gelderen et al., 1994; Burcaw et al., 2015). Therefore, the time-dependence of extra-axonal diffusion De(t) (Burcaw et al., 2015; Fieremans et al., 2016; Lee et al., 2018), and the undulation or along-axon caliber variation (Nilsson et al., 2012; Brabec, 2019; Özarslan et al., 2018; Lee et al., 2019), potentially overshadow the relatively small Da.

On the other hand, shrinkage during tissue fixation has been suggested as a potential shortcoming of histology (Barazany et al., 2009; Horowitz et al., 2015), implying that in vivo axons are thicker than their histologically reported values.

This study aimed at investigating what the dMRI signal can measure in terms of axon radius, as well as provide insight into the aforementioned debate. Our wide range of diffusion weightings in both human and preclinical dMRI enables a suppression of the extra-axonal contribution (that otherwise biases the radii Burcaw et al., 2015; Fieremans et al., 2016), thereby allowing us to shed light on this controversy. We claim that the effective MR radii measured in this study (rMR) quantitatively agree with those derived from histology — to the extent that histology correctly captures the tail of the axonal radii distribution h(r). That is, rMR obtained from dMRI appears to be a self-averaging quantity in each imaging voxel, as large MRI voxels ensure that the moments of h(r) sampled from a voxel represent well the ‘true’ underlying h(r) in that WM region, so that the spatial variations in rMR stem mainly from genuine biological variations of the tails of axon distributions across the brain.

Mesoscopic fluctuations

Histology-derived reff are prone to mesoscopic fluctuations due to small sampling sizes, Figure 7. Despite a good correspondence of the bulk of axon radii distributions obtained from different histological studies and samples (Aboitiz et al., 1992; Caminiti et al., 2009; Liewald et al., 2014; Tang et al., 1997), the tail of the distribution is typically coarsely sampled, with only a few spikes representing the occasional observation of large axons within the relatively small histological sections, Figure 7. It is precisely for the detectable large r, that the relative fluctuations for the bin counts Ni are observed for bin values of Ni1 (Table 2 of Aboitiz et al. (1992) and Figure 5), according to the Poissonian statistics governing each Ni. Not surprisingly, reff derived from discrete histological histograms exhibits strong fluctuations, as depicted by dotted vertical lines in Figure 7a and the error bars in Figure 5.

Humans

Although the average radius r¯, as reported in human literature (Aboitiz et al., 1992; Innocenti et al., 2015), only ranges between 0.54 and 0.69μm, the corresponding reff varies from 0.91 to 2.9μm. In comparison, the average dMRI-derived rMR estimated from the four Connectom data sets within the same region-of-interest, the midbody of the CC, only varied from 2.48 to 2.82μm (Figure 7b).

Rodents

The average radius, as measured in this study, varies between 0.54 and 0.68 μm across the 16 patches of the genu of the CC, while the associated reff varies from 0.81 to 1.30 μm. The MR-derived effective axon radii rMR vary similarly, that is 0.94 to 1.4 μm across several voxels within the genu of the CC for all three scanned samples.

For dMRI, the variability in the estimation of rMR is determined by thermal MRI noise, and genuine anatomical – inter-voxel and inter-subject – differences. For human MRI, the mesoscopic fluctuations are much weaker, due to the large MRI voxels in comparison to the histological samples. Indeed, the variance in the estimation of rMR is expected to decay inversely with the number of axons within a field of view. However, for rodent MRI, in which the MRI voxels have the same surface area as the histological patches, the precision in the estimation of the effective radius is similar for both modalities.

Overall, dMRI provides a precise measurement of the largest axons, which are captured within an MRI voxel. In contrast, histology, so far, mainly probes the bulk that consists of smaller axons with high precision. Therefore, both modalities are complementary, especially in human MRI for which the voxels are significantly larger than a typical histological sample.

Measuring the bulk of the axon distribution using MRI

As the signal attenuation inside axons, Equations (5)-(6), scales as lnSg2reff4, getting to two-times smaller reff would require another four-fold increase in gradients. However, even with stronger gradient systems, the main bottleneck might be the missing prior knowledge about the shape of the expected axon radius distribution h(r). Even when assuming a particular functional form of h(r), one is limited to estimating a single parameter to describe the axon radius distribution, whereas realistic distributions such as the generalized extreme value distribution (Sepehrband et al., 2016) are parameterized by at least two variables. Hence, the reconstruction of h(r) from only reff is technically ill-posed and, as such, prone to mis– or over–interpretation due to biases towards user-defined distribution shapes and parameters, even more so if confounding factors such as dispersion or fixed diffusivities are ignored (Assaf et al., 2008; Barazany et al., 2009; Horowitz et al., 2015; McNab et al., 2013).

With unknown h(r), only a single metric representing the entire distribution, that is reff, for which the strength of tail-weighting is determined by the gradient pulse width, can be estimated reliably. In the best case, that is the narrow-pulse limit δtc, see 'From Da to effective MR radius’, reff will depend on the fourth rather than the sixth order moment of h(r), that is reffr4/r2 (Burcaw et al., 2015), thereby reducing, but not eliminating the difference between reff and r¯. Other methods, such as oscillating gradient diffusion weighting or double diffusion encoding, may provide other sources of contrast encoding different aspects of the size distribution (Jiang et al., 2016), although the low-frequency limit of the oscillating-gradient attenuation has been shown to be equivalent to the Neuman’s limit, not providing any extra information (Novikov et al., 2019). It can be hypothesized that the combination of methods could perhaps recover more accurate information on the underlying h(r).

Dot fraction

The presence of isotropic immobile water fim has been conjectured by Stanisz et al. (1997) as water possibly restricted inside the soma of various cell types, such as neurons or oligodendrocytes. Several previous studies, for example Veraart et al. (2019), Tax et al. (2019), and Dhital et al. (2019), demonstrated with various diffusion encoding strategies that in vivo dMRI is practically insensitive, that is < 0.2%, to such signal contributions in the healthy white matter of the living human brain, excluding the cerebellum (Tax et al., 2019). The lack of sensitivity of dMRI to immobile water might be explained by a small volume fraction, a short T2 relaxation time, and/or a fast water exchange rate on the scale of our diffusion time Δ=30ms for treating them as coming from separate compartments. In contrast, the dot compartment has been observed in fixed brain samples in various studies (Stanisz et al., 1997; Alexander et al., 2010). The origin of this signal contribution is not well understood yet, but the still water compartment needs to be considered when validating or studying biophysical models in fixed tissue.

In this work, for the human experiments, we build upon the previously reported observations to fix fim=0 in the healthy white matter to avoid fitting degeneracies that are associated with the poor conditioning of model (iii). However, any underestimation of the dot compartment, for example due to fixing fim=0, leads directly to an underestimation of the effective MR radius, see Figure 1. Therefore, in future studies, especially those that focus on the developing, aging, or pathological brain, we encourage the independent measurement of the dot compartment to complement the axon radius acquisitions. The fast measurement of the dot compartment is promoted by the availability of spherical diffusion-encoding, as demonstrated by Dhital et al. (2019), and Tax et al. (2019).

In our ex vivo experiments, the measurement of the dot compartment is based on the diffusion-weighing in the direction parallel to the average fiber direction in the CC at the maximal b-value of 100 ms/µm2. The measured signal fraction of such a still water compartment in our fixed brain samples was in the range of 8 to 17%, in line with Stanisz et al. (1997). Applying a radial or planar diffusion-weighting filter prior to this measurement would suppress any contribution of anisotropic signal compartments, such as crossing or dispersed axons, to the isotropically restricted dot compartment. Although we aimed to minimize this confounding factor by using a very high b-value (Dhital et al., 2019), the dot compartment fraction, and as such the effective MR radius, might be slightly overestimated because of various complex fiber configurations. Additional confounding factors are listed in the following section.

Confounding factors

The apparent discrepancy between histology and dMRI, when confounding factors such as extra-axonal water (Burcaw et al., 2015; Fieremans et al., 2016; Lee et al., 2018) and orientational dispersion (Drobnjak et al., 2016Nilsson et al., 2012) are addressed, is mainly a result of the difference between r¯ and reff – that is between the bulk and the tail of axonal distribution. This already provides an important insight into the discussion on why the radii reported in the literature vary so much between the methods. When comparing apples-to-apples, despite the excellent agreement observed in this study between rMR and its histological counterpart reff, in our own histological validation we still observed a small, yet consistent overestimation of between 5 and 20% in axon radius rMR using dMRI. Aside from the previously discussed dot compartment, various other factors might contribute to this discrepancy.

First, an underlying assumption of all studies targeting the measurement of the axon radius is specificity: that the signal observed at these high b-values could be attributed exclusively to the intra-axonal space. However, this assumption is not established nor in our opinion is it justified given that water resides in all cellular compartments of the central nervous system. We cannot exclude that water trapped in other ‘stick’-like features such as the radiating processes of astrocytes contribute observable signals; it has been previously reported, but also observed in our histological sample, that such glial processes can have large diameters, up to 7μm in our sample. In the future, this contribution could be investigated using the increased cellular specificity of (diffusion-weighted) spectroscopy (Palombo et al., 2016; Shemesh et al., 2017; Ligneul et al., 2019).

Second, Stanisz et al. (1997) and, more recently, Palombo et al. (2019) demonstrated that at shorter diffusion times, the signal contribution from cell bodies might be characterized by a specific b-value dependent signature (Neuman, 1974 and Murday and Cotts, 1968) that might enable the extraction of MR effective cell body sizes in both the white and gray matter (Palombo et al., 2019). In this study, the potential b-value dependent signal contribution of cell bodies was unaccounted for, and, given our and other (e.g. Sampaio-Baptista et al., 2019) observations of a finite cell body volume fraction, the axon radius measurements could be biased. However, deviations to the power law scaling due to the presence of cell bodies are more likely to be expected in the GM because of larger volume density of large somas in comparison to the WM (Palombo et al., 2019). In our histological sample, we observed a significant volume fraction of cell bodies in the genu of the CC, that is 5%, but due to unknown compartmental relaxation times, the associated, yet more important, signal fraction is unknown (Lampinen et al., 2019). Regardless, a biophysical model parameterized by various volume fractions, axon radii, soma sizes, and compartmental diffusivities may be poorly conditioned and degenerate.

Third, along-axon undulations (Nilsson et al., 2012) and curvature (Özarslan et al., 2018) might result in an increased apparent radial diffusivity and, as such, contribute to an overestimation of the axon radius using dMRI, especially for increasingly long diffusion times (Lee et al., 2019; Brabec, 2019).

Finally, the estimation of the MR effective axon radius depends on the unknown intrinsic diffusivity D0 of the axoplasm. In ex vivo samples of a well-aligned WM bundle, one could estimate D0 directly by exploring the time dependence of the apparent diffusivity at very short diffusion times, (Mitra et al., 1993). In this study, we were not able to achieve a reproducible and precise estimate of D0 and opted to use the longitudinal diffusivity Da as a proxy for D0, with DaD0. Therefore, we might actually underestimate the positive bias in the estimation of r^eff(D0)1/4 (Equation 10). However, the propagation of the error in the estimation of D0 to r^eff is strongly reduced by the fourth root relation between both metrics.

Inter-species variability

In our study, the effective MR radius in humans was significantly higher than in rats when comparing similar regions of interest, for example the midbody of the CC. This difference is in agreement with several studies that compared the callosal fiber composition as a function of the brain size of various mammals and concluded that large brains have more large axons and an increased maximal radius, whereas the bulk of axons is not altered (Olivares et al., 2001; Schüz and Preiβl, 1996; Liewald et al., 2014). Since the effective MR radius is predominantly sensitive to the larger axons, observed differences between humans and rats will be amplified when comparing effective MR radii. Overall, this observation favors future application of MR axon radius mapping in species with relatively large brain sizes.

Gray matter

Although this work mainly focuses on the WM, we do report significantly different signal scaling for the cortical GM. We suggest that the proton exchange between dendrites and interstitial water might explain this scaling behavior, in particular due to the convex scaling with b-1/2. However, the abundance of cell bodies in the gray matter might confound this analysis (Palombo et al., 2019). Moreover, the study of the cortical GM is challenged by its low SNR and susceptibility to partial voluming. Nonetheless, we conclude that the stick assumption does not hold in the cortical GM and that biophysical models building upon that assumption must be interpreted with caution if applied to tissue regions outside of WM.

Conclusion

In summary, we provide a realistic perspective on MR axon radius mapping by showing MR-derived effective radii that have good quantitative agreement with histology. First, we compared the MR-derived axon radii directly to confocal microscopy of the same rat brain samples. Second, the distribution of dMRI-derived rMR of the living human brain falls almost entirely within the range spanned by histology-derived reff that has been reported in the literature — even without introducing a putative axonal shrinkage factor. This estimation is inherently bound to a single scalar reff that encodes moments of the axon distribution, which is – by virtue of the signal encoding – dominated by the largest axons. Therefore, the average axon radius r¯ and reff can be practically considered as two complementary metrics probing the underlying axon caliber distribution: histology, so far, mainly probes its bulk, that is r¯, while dMRI probes rMR=r^eff, that is its tail. Due to the intrinsic bias of MR-derived axon radii to larger axons, clinical applications should focus on pathologies that specifically target those larger axons, until other methods are developed that probe the smaller axon diameter.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional
information
Antibodyanti-Neurofilament 160/200 (Mouse monoclonal)Sigma AldrichCat# N2912 (clone RMdo20)2.5 µg/mL
Antibodyanti-GFAP (rabbit polyclonal)Thermo Fisher ScientificCat# PA1-100191:1000
software. algorithmImageJimagej.nih.gov/ij/RRID: SCR0030701.52q
software. algorithmFSLfsl.fmrib.ox.ac.uk/fsl/RRID: SCR002823v6
software. algorithmMRtrixwww.mrtrix.orgRRID: SCR006971v3.0
software. algorithmFreeSurfersurfer.nmr.mgh.harvard.eduRRID: SCR001847v6.0.0
OtherDAPISigma AldrichCat# D9542500nM

MRI of fixed rat brain tissue

Ethics

Animals used in this study were handled in agreement with the European FELASA guide-lines and all procedures were approved by the Champalimaud Animal Welfare Body and by the national authorities, Direção Geral de Alimentação e Veterinária, Lisbon, Portugal, under the approved protocol number 0421/000/000/2016. All animal care procedures were conducted in agreement with the European Directive 2010/63, at the vivarium of the Champalimaud Foundation, a research facility part of CONGENTO, project number Lisboa-01–0145-FEDER-022170.

Sample preparation

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Three Long Evans rats (Female, 12-weeks-old) were transcardially perfused using 4% paraformaldehyde. The extracted brains were kept for 24 hr in 4% paraformaldehyde and washed using PBS over two days (changed daily). Given our focus on the CC of the rat brains, we will refer to the samples as CC#1, CC#2, and CC#3.

MRI scanning

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  1. Multi-shell dMRI data: The three samples were scanned on an 16.4T MR scanner (Bruker BioSpin) at room temperature with Δ/δ= 20/7.1ms interfaced with an AVANCE IIIHD console and a micro2.5 imaging probe with maximal gradient amplitude Gmax= 1500mT/m. Diffusion-weighting was applied using a RARE sequence in the midsagittal plane along 60 gradient directions for a densely sampled spectrum of 18 different b-values up to 100 ms/µm2. Furthermore, TR/TE=2400/30.4ms and the spatial resolution was 100 × 100 × 850µm3.

  2. ‘dot fraction’ fim: we acquired 60 repeated measurements of diffusion-weighing applied in the direction parallel to the average fiber direction in the corpus callosum (CC) at the maximal b-value of 100 ms/µm2. The average fiber orientation was defined as the first eigenvector of the dyadic tensor (Jones, 2003) that was computed from the voxelwise first eigenvectors of the diffusion tensors that were estimated by fitting the DTI model to the lowest b-values, that is b<5ms/μm2, of the multi-shell data in each voxel within the manually segmented CC (Basser et al., 1994).

The average SNR for S|b=0 was 195 and some examples of the acquired images at various low and high b-values are shown in Figure 10, where the quality of the raw data can be evaluated. Notably, since images are spherically averaged over many directions, the signal is characterized by high SNR even at high b-values.

Raw data.

The spherically-averaged diffusion-weighted images, prior to any other image corrections, are shown for various low and high b-values for one rat brain sample (a) and one human subject (b).

Data analysis

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From the multi-shell data, the spherically-averaged signals S¯(b) are estimated per b-value as the zeroth order spherical harmonic using a Rician maximum likelihood estimator of the even order spherical harmonic coefficients up to the 6th order (Sijbers et al., 1998).

The spatially localized dot fraction fim is computed as the signal estimated from the repeated (N=60) measurements in the direction parallel to the principal fibre direction using a Rician maximum likelihood estimator with pre-computed noise level (Veraart et al., 2016), normalized by the respective non-diffusion weighted signal. We compute the dot-free signal in each voxel as follows S¯(b)=S¯(b)-fim. In the remainder of the work, analyses were done on both ‘dot contaminated’ S¯(b) and ‘dot free’ S¯(b) signals.

The intra-axonal radial diffusivity D^a is estimated voxelwise by fitting Equation 2 with fim=0 to S¯(b20) using a nonlinear least squares estimator (code is available for download on GitHub [Veraart and Novikov, 2019; https://github.com/NYU-DiffusionMRI/AxonRadiusMapping; copy archived at https://github.com/elifesciences-publications/AxonRadiusMapping]).

Thereafter, the estimated effective axon radius rMR is derived from D^a using Equation 6. The alternative approach, that is the simultaneous estimation of D^a and fim from S¯(b20) is very poorly conditioned. Hence, disentangling both parameters from only the linearly-encoded multi-shell data is impossible, even at unrealistically high SNR.

Histology of fixed rat brain tissue

Full details of the immunohistochemistry for sample preparation, confocal microscopy, and image analysis are provided in Nunes et al. (2017). Study-specific elements are described below.

Sample preparation

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After MRI scanning, free-floating horizontal sections 50 μm-thick were collected from the medial lateral center of two rat brains, CC#1 and CC#2, corresponding to the MR imaged volume. For CC#2, we used antibodies against neurofilaments 160/200 (axonal marker; Sigma-Aldrich, cat.# N2912) and GFAP (astrocytes marker; ThermoFisher Scientific, cat.# PA1-10019), as well as a staining for cell bodies using DAPI (Sigma-Adrich, cat.# D9542). For CC#1, the staining was limited to the neurofilaments to focus on the axon radius count.

Confocal microscopy

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A Zeiss LSM 710 laser scanning confocal microscope was used for immunohistochemistry image acquisition. A tile scan using a 10× objective (EC Plan Neofluar, numerical aperture = 0.3, Zeiss, Germany) was used to cover the entire CC. Various ROIs were imaged using a 63× immersion objective (Plan Apochromat, numerical aperture = 1.4, Zeiss, Germany) in confocal mode, with pixel resolution of 65 × 65 × 150 nm3 and field-of-view of 135 × 135 µm2 (Figure 11). The placement of the ROIs is shown in Figures 4 and 5 for CC#1. and CC#2, respectively.

For two brain samples, MR scanning (a, color encoded FA map) was followed by low (b) and high (c) resolution confocal microscopy with staining for neurofilaments to identify the axons.

The low-resolution image was used to position various ROIs, whereas the axon caliber distributions were extracted from the high-resolution image of the corresponding ROIs. The long axes of fitted ellipsoids served as proxies for the respective axon diameters (d).

Confocal microscopy data analysis

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Images were processed using the ImageJ software. Noise suppression of the confocal single frames was done using a subsequent application of a 2D anisotropic diffusion filter and bandpass filtering in the frequency domain, (Nunes et al., 2017). Thereafter, axons were identified as particles with a minimum area size of 0.2 µm2 and a circularity larger than 0.4 in the confocal images that were stained for neurofilaments. The number of extracted axons varied from about 500 to 2000, depending on the placement of the ROI. The long axes of fitted ellipsoids served as proxies for the respective axon radii. For each ROI, we obtain a distribution of axon radii h(r) from which we compute the associate effective radius reff using Equation 9.

In vivo MRI of human brain

Ethics

Data were acquired after obtaining written informed consent and consent to publish. The project was approved by the Cardiff University School of Psychology Ethics Committee (approval number EC.06.05.02.891).

Subjects

Four healthy volunteers (3 males and 1 female between 22 and 45 years) were recruited for this study. We will refer to the human subjects as 22M, 25F, 32M, and 45M to encode both the age and gender. The data were collected under the approval of the Cardiff University School of Psychology Ethics Committee.

MRI scanning

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All four subjects were scanned on a Siemens Connectom 3T MR scanner using a 32-channel receiver coil. The 300 mT/m gradient system was used to achieve b-values up to 25ms/μm2. The diffusion gradients were characterized by Δ/δ=30/13ms and maximal gradient amplitude of 289 mT/m. Diffusion weighting was applied along 60 isotropically distributed gradient directions (Jones et al., 1999) for b= 1, 3, 5, 7, 9, 11, 12.1, 13.5, 15, 16.9, 19.1, 21.7, and 25ms/μm2, with TR/T: 3500/62ms, matrix: 74 × 74, and 42 slices with a spatial resolution of 3 × 3 × 3mm3. The average SNR for S|b=0 was 52. See Figure 11 for the image quality and contrast at various b-values.

The dot compartment was not measured directly (see Dhital et al., 2018 and Tax et al., 2019).

Data analysis

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Image processing was done according to the DESIGNER pipeline (Ades-Aron et al., 2018) using the FSL (Smith et al., 2004) and MRtrix (Tournier et al., 2019) software packages. In particular, MPPCA noise estimation and denoising (Veraart et al., 2016) were used for estimating noise maps σ(x) by exploiting the inherent redundancy in dMRI data. The positive signal bias, inherent to low-SNR magnitude MR data, was removed by using the method of moments (Koay and Basser, 2006), where the denoised signal was used as a proxy for the Rician expectation value. Denoised and Rice-floor-corrected images were subsequently corrected for Gibbs ringing (Kellner et al., 2016), geometric eddy current distortions and subject motion (Andersson and Sotiropoulos, 2016). The pipeline is available on https://github.com/NYU-DiffusionMRI/DESIGNER (Ades-Aron and Veraart, 2018). We used tract-density imaging (Calamante et al., 2010) based on whole-brain probabilistic fiber-tracking (Tournier et al., 2019) of the b=5ms/μm2-shell for identifying all WM voxels. To avoid voxels affected by partial voluming with the gray matter (GM), an additional, more conservative, segmentation was obtained by omitting all voxels with a fractional anisotropy smaller than 0.6. In addition, the cortical GM was segmented using FreeSurfer (Dale et al., 1999).

References

  1. 1
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6
  7. 7
  8. 8
  9. 9
  10. 10
  11. 11
  12. 12
  13. 13
  14. 14
  15. 15
    Information and Likelihood Theory: A Basis for Model Selection and Inference
    1. KP Burnham
    2. DR Anderson
    (2002)
    In: K. P Burnham, D. R Anderson, editors. Model Selection and Multimodel Inference. Springer. pp. 49–97.
  16. 16
  17. 17
  18. 18
  19. 19
    Principles of Nuclear Magnetic Resonance Microscopy
    1. PT Callaghan
    (1991)
    Oxford: Clarendon Press.
  20. 20
  21. 21
  22. 22
  23. 23
  24. 24
  25. 25
  26. 26
  27. 27
  28. 28
  29. 29
  30. 30
  31. 31
  32. 32
  33. 33
  34. 34
  35. 35
  36. 36
  37. 37
  38. 38
  39. 39
  40. 40
  41. 41
  42. 42
  43. 43
  44. 44
  45. 45
    Diffusion MRI: Theory, Methods and Applications
    1. DK Jones
    (2010)
    Oxford University Press.
  46. 46
  47. 47
  48. 48
  49. 49
    Fundamentals of Statistical Signal Processing
    1. S Kay
    (1993)
    Prentice Hall PTR.
  50. 50
  51. 51
  52. 52
  53. 53
  54. 54
  55. 55
  56. 56
  57. 57
  58. 58
  59. 59
  60. 60
  61. 61
  62. 62
  63. 63
  64. 64
  65. 65
  66. 66
  67. 67
  68. 68
  69. 69
  70. 70
  71. 71
  72. 72
  73. 73
  74. 74
  75. 75
  76. 76
  77. 77
  78. 78
  79. 79
  80. 80
  81. 81
  82. 82
  83. 83
  84. 84
    Oligodendrocytes
    1. C Sampaio-Baptista
    2. K Diosi
    3. H Johansen-Berg
    (2019)
    In: D. A Lyons, L Kegel, editors. Magnetic Resonance Techniques for Imaging White Matter. Springer. pp. 397–407.
  85. 85
  86. 86
  87. 87
  88. 88
  89. 89
  90. 90
  91. 91
  92. 92
  93. 93
  94. 94
  95. 95
  96. 96
  97. 97
  98. 98
  99. 99
  100. 100
  101. 101
  102. 102
    Axon radius mapping
    1. J Veraart
    2. DS Novikov
    (2019)
    Axon radius mapping, https://github.com/NYU-DiffusionMRI/AxonRadiusMapping.
  103. 103
  104. 104
  105. 105
  106. 106

Decision letter

  1. Floris P de Lange
    Senior Editor; Radboud University, Netherlands
  2. Birte Forstmann
    Reviewing Editor; University of Amsterdam, Netherlands
  3. Birte Forstmann
    Reviewer; University of Amsterdam, Netherlands
  4. Saad Jbabdi
    Reviewer; University of Oxford, United Kingdom
  5. Robert Mulkern
    Reviewer

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

This study focuses on a longstanding and important question in the field of diffusion MRI, namely the measurement of axon diameters. Importantly, until now, the accurate estimation of axon diameter mapping with non-invasive techniques such as diffusion MRI has been elusive due to a lack of sensitivity in the signal. The authors provide compelling evidence using sophisticated modeling that axon diameters can be estimated for largest axons when eliminating confounding factors such as extra-axonal water and axonal orientation dispersion. Data of fixed rat brains and optical microscopy of the same specimen are presented showing good quantitative agreement for MR-derived axon diameters. Finally, in vivo data from Connectom 3T scanning is presented which shows the feasibility of mapping axon diameters in healthy subjects. The work is therefore of interest to a broad scientific audience ranging from physicists to cognitive neuroscientists.

Decision letter after peer review:

Thank you for submitting your article "Noninvasive quantification of axon diameter using diffusion MRI' for consideration by eLife. Your article has been reviewed by three peer reviewers, including Birte Forstmann as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen Floris de Lange as the Senior Editor.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

Overall, this manuscript is well written, interesting, timely and will help resolve the debate in the field. We therefore suggest revising the manuscript to address the points raised by the reviewers which are outline below.

Reviewer 1:

This paper introduces an interesting and surprisingly simple method for estimating axon diameter (potentially in vivo). Their approach relies on three key ingredients:

– ensure that the diffusion sensitisation (b value) is high enough to eliminate extra-axonal water

– use powder averaging across diffusion gradient orientations to eliminate confounds due to orientation of axons

– estimate intra-axonal perpendicular diffusivity from a simple power-law formula predicted by the theory

The intra-axonal perpendicular diffusivity is then used to estimate an "effective axonal radius" which describes the tail of the axon radius distribution under certain assumptions.

In general, this is a potentially important contribution. I have the following comments which I am sure the authors will be able to address:

1) Presentation

I found the presentation of the theory unnecessarily dense and difficult to follow. A simple diagram might help. Something like Figure 1 but with a single line showing the prediction from a model with fim as well as Dperp>0 (so with non-zero intercept of the nonlinear part and a negative intercept of the linear part). It would also be helpful to indicate on the same diagram that the slope on the right-hand side depends on Dpara, and that the intercept is fim, but the 'dashed line' intercept is a function of fim and Dperp (and Dpara?). The x-axis could be double-labelled with both 1/b and b. And also it would help to have vertical sections in the graph indicating the b value regimes (e.g. clinically-feasible, vs. Connectom vs. small-bore scanners, vs. low-b regime).

2) Modelling

Having played with the models (i)-(viii) an little, I see lots of degeneracy between fim and Dperp over a wide range of parameter values which is not surprising: unless some curvature is visible in the data (above the noise level), it is difficult to disentangle the contributions of fim and Dperp to the negative intercept. In the data that is shown (e.g. Figure 5), the points fall in a straight line and so there is no curvature to help disentangle fim and Dperp. The authors assumed that fim=0 for the in vivo data, but is this really justified (could there not still be very slow diffusing water that is unmodelled) and what about ex vivo?

In general, some sort of analysis of when the degeneracy breaks down (as a function of the max b value attainable and the other params like Dpara) would be helpful here. For example looking at the full posterior distribution and not just point estimates of the parameters (I don't find AIC values very helpful compared to looking at the full posterior distribution).

3) Axon diameter estimation

It would be helpful if the authors could unpack how they get to reff as a function of Dperp. I can see that the diffusion in a cylinder formula gives rise to a r4 dependence of the log(S) in the regime that the acquisition are made in. But then to go from there to reff (which is the ratio of 6th to 2nd moment) is a stretch for me. Is it simply by doing a Taylor expansion of S=exp(-a*r4) around zero inside the integral in Equation 7? If that is the case then perhaps an appendix would not hurt. Also, it is not clear how accurate it is to use the Taylor expansion.

Also on axon diameters, the authors make it quite clear that they don't like methods that make explicit distributional assumptions on the axon diameter (e.g. AxCaliber) – but I think it would be interesting to compare them with the author's approach. Looking at the histology data that is presented, one wonders how accurate a gamma distribution would be. With a distributional assumption there would be no need for the Taylor expansion above, and everything can be done keeping the exponentials and directly inverting the equations to get the parameters of the gamma distribution. How does this compare to the reff proposed by the authors?

4) Are the results biologically sound?

Generally, I found that there was not enough in terms of showing results that indicate the technique actually works well, e.g. in Figure 8: is there a way to show a similar map from histology?. Or in general show that inter-areas variation in radius from histo correlate with inter-area variation from MR in the ex vivo data. The only comparison between the two modalities is done in Figure 7 in a single region.

Similarly for the in vivo data: Figure 10: is there any evidence that the intervoxel variation is meaningful? There are zones of reduced radius in the lateral frontal lobes near the cortex – are those meaningful?

5) Exchange

The authors present an interesting extra source of information, in that an exchange model makes a different prediction at high b values and they found evidence for exchange in GM. Can you convince the reader that this is not just partial volume effects (e.g. multiple pools of water with different diffusion coefficients and no exchange?) would that for example induce a curve with the opposite convexity? As GM is likely to have more partial volume issues I think this is a valid question that needs addressing.

Also on exchange: My understanding of the Karger model is that it assumes that exchange happens in situ (a molecule would change its behaviour from e.g. slow to fast diffusion with some probability instantaneously). But in reality exchange happens at the membrane. Does that invalidate the equations? Can the equations be derived here in an appendix?

6) Presentation of the data

Single voxel data is never shown and so it is difficult to tell how noisy the signal vs. 1/sqrt(b) curves actually are.

7) Data sharing

The authors are to be commended on sharing their data. However the way they have done it is not optimal in that they only provide raw data with no particular documentation or curation. The shared data set would strongly benefit if you would add the following:

– include preprocessed data not just raw data (including the Dperp and reff maps) – or at least provide code to generate the maps and do the preprocessing

– match data format between human and rodent

– include documentation

– avoid lsm format as it is proprietary – maybe use tiff instead?

– include processed histo data?

Reviewer 2:

This study focuses on a longstanding and important question in the field of diffusion MRI, namely the measurement of axon diameters. Importantly, until now, the accurate estimation of axon diameter mapping with non-invasive techniques such as diffusion MRI has been elusive due to a lack of sensitivity in the signal. The authors provide compelling evidence using sophisticated modeling that axon diameters can be estimated for largest axons when eliminating confounding factors such as extra-axonal water and axonal orientation dispersion. Data of fixed rat brains and optical microscopy of the same specimen are presented showing good quantitative agreement for MR-derived axon diameters. Finally, in vivo data from Connectom 3T scanning is presented which shows the feasibility of mapping axon diameters in healthy subjects.

Major comments:

Generalizability of the data:

My main concern is that the MRI-based axon diameter modeling was only evaluated in the corpus callosum. It would be important to see whether the modeling also holds in other fiber tracts, e.g., fronto-occipital fasciculus.

This is something that the authors should ideally address, but in case this is not feasible, at least comment on.

Reviewer 3:

This is an impressive work combining well-thought out theory with experimental data only recently available, particularly for the human studies, using the Connectom system to provide gradient strengths some 4 times larger than available on commercial scanners. The mix of pre-clinical data with rat CC for which histological distributions of axon diameters was measured, with human data (using somewhat less gradient strength than available on the animal system) and only literature histology is justified and actually adds strength to the comparison of experimental with theoretical considerations. The lack of any attempt to measure the "dot" component in humans is less justifiable in my view though that might have significantly added to the scan time and further comments on this might be appreciated. The authors recognize the limitations of their assessment in having to rely upon a rather "weighted" version of the distribution which gives an output index well into the tail of the distribution, the larger axons, but at least the measures are getting closer to the actual size of the median axon values than those reported in the past with more standard gradient strengths and perhaps dubious modeling. It also would be helpful to perhaps add to Figure 1 or another figure the curves that would be anticipated theoretically from the exchange model of Equation 4 at such high b values, emphasizing the difference between concave and convex theoretical curves that the authors, I assume, deem to eliminate the latter model given the experimental data. Finally as a major point, in the Data Analysis section the authors explain reff or rMR from the data but this description is difficult to follow. For example, in Equation 2, how are the O(b2) taken into account if they are. Then, assume we now have Da(perpendicular) how does one use that with Equations 5 and 9 to get reff. This must be clarified. People should be able to replicate this calculation from what is in this text.

https://doi.org/10.7554/eLife.49855.sa1

Author response

Reviewer 1:

[…]

In general, this is a potentially important contribution. I have the following comments which I am sure the authors will be able to address:

1) Presentation

I found the presentation of the theory unnecessarily dense and difficult to follow. A simple diagram might help. Something like Figure 1 but with a single line showing the prediction from a model with fim as well as Dperp>0 (so with non-zero intercept of the nonlinear part and a negative intercept of the linear part). It would also be helpful to indicate on the same diagram that the slope on the right-hand side depends on Dpara, and that the intercept is fim, but the 'dashed line' intercept is a function of fim and Dperp (and Dpara?). The x-axis could be double-labelled with both 1/sqrt(b) and b. And also it would help to have vertical sections in the graph indicating the b value regimes (e.g. clinically-feasible, vs. Connectom vs. small-bore scanners, vs. low-b regime).

The theoretical sections, mainly “Breaking the power law” has been revised to improve readability. Following the suggestion of the reviewer, we amended Figure 1 to visualize the difference between fimand the intercept. While fimis a signal fraction of full restricted isotropic diffusion, the intercept is a convoluted metric that combines fimwith a b value dependent offset that relates to the sensitivity of MR to the radius.

2) Modelling

Having played with the models (i)-(viii) an little, I see lots of degeneracy between fim and Dperp over a wide range of parameter values which is not surprising: unless some curvature is visible in the data (above the noise level), it is difficult to disentangle the contributions of fim and Dperp to the negative intercept. In the data that is shown (e.g. Figure 5), the points fall in a straight line and so there is no curvature to help disentangle fim and Dperp. The authors assumed that fim=0 for the in vivo data, but is this really justified (could there not still be very slow diffusing water that is unmodelled) and what about ex vivo?

In general, some sort of analysis of when the degeneracy breaks down (as a function of the max b value attainable and the other params like Dpara) would be helpful here. For example looking at the full posterior distribution and not just point estimates of the parameters (I don't find AIC values very helpful compared to looking at the full posterior distribution).

The degeneracy is an intrinsic limitation of multi-compartmental modeling of diffusion MRI data. Various strategies to resolve this problem have been attempted during the past decade, ranging from imposing constraints or priors, to complement the data with orthogonal measurements. The former strategy has been contested because of the potential biases that might arise from inaccurate priors, whereas the exploring novel acquisition strategies to resolve the degeneracies is currently widely studied. A promising avenue is complementing the classical Stjeskal-Tanner diffusion-weighting – as used in this study – with planar and spherical diffusion-weighted strategies. Whereas linear encoding is best-suited to probe elongated cellular structures, such as axons, the spherical encoding is most sensitive to spherical objects such as cell bodies. Tax et al., 2019, and Dhital et al., 2018, used this spherical encoding to quantify the signal fraction fimin healthy white matter. In previous work (Veraart et al., 2019) we made a similar observation, i.e. fim<0.2%, using linear diffusion encoding at high b when only considering the parallel diffusion directions. Here, we adopt the conclusive result of fimnot being significant for the human data in the major white matter structures.

Tissue fixation and possibly temperature-induced alterations of the microstructure has resulted in a non-zero fimin ex vivo studies. To avoid the need for the simultaneous estimation of fimand Dperp, we included a dedicated experiment to quantify fimprior to the axon diameter mapping. We recommend a similar strategy when studying clinical cohorts in future studies.

The optimization landscape of Equation 2 (shown in updated Figure 1) reveals that disentangling fimand Dperpis impossible, even at unrealistically high SNR. Indeed, the contrast in error function along a valley that runs through the landscape is minimal in comparison to the noise floor, see Author response image 1. In the revised manuscript, we highlight the intrinsic degeneracy of axon diameter mapping with unknown dot compartment explicitly. We dedicate a more extensive section to the dot compartment in which we discuss the degeneracy, overinterpretation of AIC analysis, and need for measurement of the dot compartment in atypical cohorts.

Author response image 1
The optimization landscape of model (III) shows a shallow valley, relative to the noise floor, for a simulation that mimics the human component of the study.

(left) The valley is shown in a 2D projection of the landscape (plot shown as a function of radius instead of Daperp). (right) The fit objective function along the valley is shown (red line) in comparison to the noise floor with an unrealistically high SNR of 250 for the non-DW signal.

3) Axon diameter estimation

It would be helpful if the authors could unpack how they get to reff as a function of Dperp. I can see that the diffusion in a cylinder formula gives rise to a r4 dependence of the log(S) in the regime that the acquisition are made in. But then to go from there to reff (which is the ratio of 6th to 2nd moment) is a stretch for me. Is it simply by doing a Taylor expansion of S=exp(-a*r4) around zero inside the integral in Equation 7? If that is the case then perhaps an appendix would not hurt. Also, it is not clear how accurate it is to use the Taylor expansion.

reff is derived from the Taylor expansion of <r2 exp(-a*r4)>/<r2>. We revised section “From Dperp to effective MR radius” to guide the reader through the mathematics behind the effective MR radius more rigorously. The accuracy of the Taylor expansion, and the model in general, for realistic settings has been addressed in a simulation section, Figure 4.

Also on axon diameters, the authors make it quite clear that they don't like methods that make explicit distributional assumptions on the axon diameter (e.g. AxCaliber) – but I think it would be interesting to compare them with the author's approach. Looking at the histology data that is presented, one wonders how accurate a Gamma distribution would be. With a distributional assumption there would be no need for the Taylor expansion above, and everything can be done keeping the exponentials and directly inverting the equations to get the parameters of the Gamma distribution. How does this compare to the reff proposed by the authors?

Sepehrband et al., 2016, published a comprehensive study on the accuracy of various parametric distribution to describe the axon distribution in the mouse corpus callosum. The generalized extreme valuedistribution consistently fitted the observed distributions better than other distribution functions, including the Gamma distribution. Most importantly, well-fitting distributions are parametrized by two or more parameters, so trying reconstructing the parametric distribution from the MR effective radius is ill-posed if these parameters are to be estimated from the same data. We simply don’t have enough information to estimate all parameters of such a well-fitting distribution. Instead of making additional assumption that will further bias the axon diameter estimates, we encourage future research directions in which the current approach is complemented with oscillating or short gradient pulse experiments to decode additional, lower-order, moments of the distribution. This might provide an avenue to reconstruct the parametric distribution based on MRI, but current hardware limits prevent us from conducting this experiment.

4) Are the results biologically sound?

Generally, I found that there was not enough in terms of showing results that indicate the technique actually works well, e.g. in Figure 8: is there a way to show a similar map from histology?. Or in general show that inter-areas variation in radius from histo correlate with inter-area variation from MR in the ex vivo data. The only comparison between the two modalities is done in Figure 7 in a single region.

Similarly for the in vivo data: Figure 10: is there any evidence that the intervoxel variation is meaningful? There are zones of reduced radius in the lateral frontal lobes near the cortex – are those meaningful?

The direct comparison between MRI and histology is done in 20 different patches, covering 4 different locations within the corpus callosum and two samples. For all those samples, we reported the accuracy, demonstrating good quantitative agreement, despite a small remaining overestimation. The potential confounding factors that might have contributed to this observation, yet we were unable to eliminate, are discussed in depth in the Discussion. Aside from this direct comparison, we do rely on a qualitative and indirect comparison for other brain structures, including the human corpus callosum. The trend of axon sizes within the corpus callosum, as shown in Figure 8, has been reported in various species, including the rat (Barazany et al., 2009 and Sargon et al., 2003), rhesus monkey (Lamantia et al., 1990), and human (e.g. Aboitiz et al., 1992). Although we don’t emphasize it in the manuscript, one might even argue that the slightly increased radii in the rostrum of the corpus callosum are in qualitative agreement with Sargon et al., 2003.

A thorough comparison of our MR results with the histological literature is challenged by the dependency on the axon diameter distribution to calculate the corresponding effective radius, as discussed in issue #4 above. The corpus callosum is best characterized in that respect and, as such, our validation component is limited to that commissural fiber. Please consider Author response image 2 in which we show the same trend in our human data.

Author response image 2
Effective MR radius for various segments of the human CC, including rostrum (light blue) and genu to splenium (from left to right), for each of the 4 subjects.

Each subject is represented by a subject-specific marker. The segmentation of the CC is shown on the right hand side.

The lack of such data in various regions across the human brain, especially in regions such as the frontal lobes, prevents us from making strong claims on meaning. Instead, we would claim that, now, we can start relying on axon diameter mapping using MRI to explore inter- and along-tract variability in the entire living brain.

5) Exchange

The authors present an interesting extra source of information, in that an exchange model makes a different prediction at high b values and they found evidence for exchange in GM. Can you convince the reader that this is not just partial volume effects (e.g. multiple pools of water with different diffusion coefficients and no exchange?) would that for example induce a curve with the opposite convexity? As GM is likely to have more partial volume issues I think this is a valid question that needs addressing.

We don’t want to make the claim that exchange is the only possible explanation for the different signal decay, nor can we convince the reader that it is the most likely explanation. Palombo et el., 2019, recently demonstrated that the presence of a (non-)exchanging compartments such as somas might confound our interpretation. However, the functional form of the exchange with a “stick” compartment is novel, and we believe that bringing it up as a viable possibility has value. Especially because the convex scaling is not expected from a partial-volume contribution of a non-exchanging non-stick compartment, such as a spherical cell. By reporting the signal decay in the gray matter and posing the hypothesis of exchange, we would like to thereby trigger further research to explore different relevant biophysical processes in the gray matter, in order to develop the most adequate models of diffusion in GM. Hence, as stated in the Discussion, we acknowledge that exchange is just one out of a few different avenues to be explored and investigated.

Also on exchange: My understanding of the Karger model is that it assumes that exchange happens in situ (a molecule would change its behaviour from e.g. slow to fast diffusion with some probability instantaneously). But in reality exchange happens at the membrane. Does that invalidate the equations? Can the equations be derived here in an appendix?

As clarified by Fieremans et al., 2010, the Karger model (KM) is applicable for diffusion times t long enough so that the medium, coarse-grainedover the corresponding diffusion length L(t), can be viewed as one where exchange happens everywhere, as the reviewer has indeed noted.

For the intra-neurite water, this means that the exchange should be slow enough, so that it is “barrier-limited” rather than “diffusion-limited” (per Karger’s original terminology). Our estimates of the residence times exceeding 10ms ensure that this is the case, as most neurite diameters are under 1 micron and the corresponding diffusion time is therefore an order of magnitude slower. These estimates are in line with other measurements, e.g., by Yang et al., 2018, whose exchange times exceeded 100 ms. For the extra-neurite water, technically, the KM applicability requires L(t) ≫ lc,or, equivalently, ttc,where lcis the correlation length of the structure. Practically, the same requirements ensure that the tortuosity limit has been reached in each compartment separately, i.e., diffusion in each compartment has become Gaussian (neglecting the exchange); this Gaussian diffusion is what is assumed by the Karger model’s equations (our only difference is that we made this Gaussian diffusion anisotropic, as compared to isotropic diffusion originally considered by Karger). For our diffusion time, the diffusion length in the plane transverse to the neurites, Lt=2∙2Det≈2∙2∙130≈11μmexceeds the mean distance between them; it also exceeds the distance between beads along neurites (3-7 microns). The exact tissue parameters remain controversial while the EM segmentation and tissue quantification is underway. However, the beauty of employing very strong diffusion weighting is that the extra-neurite compartment is suppressed, therefore the above issues become irrelevant.

All in all, we believe KM may be asymptotically applicable here, in the view of suppressing the extra-neurite signal at strong b, as well as because the estimated exchange time a posteriorijustifies the barrier-limited exchange for the intra-neurite water. However, as KM may still be too simplistic, while giving an approximate range, we are not making strong claims about the numerical value of the exchange time in gray matter, because it is an entirely different active area of investigation, and the relevance of different biophysical effects there is still under intense debate. Our relatively short estimated exchange time (on the scale of clinically used diffusion times) may indeed explain the difference in the functional form between high- b signal in GM from a “stick” seen in WM, and adds value to the current debate about the need to include exchange in GM modeling.

6) Presentation of the data

Single voxel data is never shown and so it is difficult to tell how noisy the signal vs 1/sqrt(b) curves actually are.

Please consider Author response image 3 in which we show the spherically-averaged signal decay as a function of 1/sqrt(b) for all individual voxels of the WM of one human subject (faded colored lines) and for the average across all WM voxels (blue line with markers, cf. Figure 5B). In addition, to highlight the precision, we show the signal decay in 10 arbitrarily chosen individual WM voxels. We would like to highlight that Figures 8 and 10 in the manuscript show voxel-by-voxel results that demonstrate the precision qualitatively.

Author response image 3
Signal decay in a single voxel.

(left) The spherically-averaged signal decay is shown as a function of 1/b for all individual voxels of the WM of one human subject (faded colored lines) and for the average across all WM voxels (blue line with markers, Figure 5B). (right) The signal decay for individual voxels is shown for 10 arbitrarily-chosen WM voxel. The dashed box is positioned the same for all graphs.

7) Data sharing

The authors are to be commended on sharing their data. However the way they have done it is not optimal in that they only provide raw data with no particular documentation or curation. The shared data set would strongly benefit if you would add the following:

– include preprocessed data not just raw data (including the Dperp and reff maps) – or at least provide code to generate the maps and do the preprocessing

– match data format between human and rodent

– include documentation

– avoid lsm format as it is proprietary – maybe use tiff instead?

– include processed histo data?

Since there is no consensus within the neuroimaging community on how to process dMRI data, especially with a strong diffusion-weighting, we opt to share the raw data, for both MRI and microscopy, and not limit the users to the considerations and choices that we made. We would like to highlight that all image processing was done using publicly available software tools, i.e. FSL, Freesurfer, MRtrix, and ImageJ – as listed in the Key resources table. The code for the axon diameter fitting is also made available on our GitHub.

We now match the data format, NiFTi, for humans and rodents; provide the microscopic images in LSM and TIFF format, and include a more comprehensive documentation of the data in the Dryad Digital Repository.

Reviewer 2:

[…]

Major comments:

Generalizability of the data:

My main concern is that the MRI-based axon diameter modeling was only evaluated in the corpus callosum. It would be important to see whether the modeling also holds in other fiber tracts, e.g., fronto-occipital fasciculus.

This is something that the authors should ideally address, but in case this is not feasible, at least comment on.

Our work focuses on theory, validation, and human feasibility. To demonstrate the human feasibility, we opted to limit ourselves to the corpus callosum because it’s the tract which has been characterized most thoroughly across multiple histological studies. Nonetheless, in Author response image 4, we show that the inter-tract variability of the effective MR axon diameter is higher than the inter-subject variability to provide on optimistic outlook on whole brain characterization in future studies. A comment is made in the revised manuscript.

Author response image 4
Average effective MR radius within various tracts (color encoded as shown on the right; both hemispheres were considered simultaneously) for the 4 human subjects (encoded by marker).

The line segments show the mean across the subjects. The inter-tract variability exceeds the inter-subject variability. We hypothesize that the hereby introduced technique can be used in future studies to characterize the typical, developing, or pathological brain in a wide range of species, including humans, rodents, or non-human primates.

Reviewer 3:

[…]

The lack of any attempt to measure the "dot" component in humans is less justifiable in my view though that might have significantly added to the scan time and further comments on this might be appreciated.

The justification of a negligible dot compartment in the healthy white matter was found in various recent publications, Dhital et al., 2018, Veraart et al., 2019, and Tax et al., 2019, which concluded independently from each other, using different techniques, that such a compartment is not significant in the major white matter pathways in the healthy adult brain. Note that the study of Tax et al. was performed on exactly the same scanner. That being said, when moving towards clinical or preclinical applications, we encourage the independent measurement of the dot compartment to complement to axon diameter acquisitions. The fast measurement of the dot compartment is promoted by the availability of spherical diffusion-encoding. We do comment more extensively on this discussion point in the revised manuscript.

It also would be helpful to perhaps add to Figure 1 or another figure the curves that would be anticipated theoretically from the exchange model of Equation 4 at such high b values, emphasizing the difference between concave and convex theoretical curves that the authors, I assume, deem to eliminate the latter model given the experimental data.

The curve is now shown in Figure 1 to showcase the significantly different signal decay.

Finally as a major point, in the Data Analysis section the authors explain reff or rMR from the data but this description is difficult to follow. For example, in Equation 2, how are the O(b2) taken into account if they are. Then, assume we now have Da(perpendicular) how does one use that with Equations 5 and 9 to get reff. This must be clarified. People should be able to replicate this calculation from what is in this text.

This comment is in line with comments 1 and 3 from reviewer #1. We include the derivation of reff using Equations 5 to 8 explicitly. The error associated with modelling approximations, e.g. omitting O(b2) or the Taylor expansion in Equation 9, is estimated using a realistic simulation framework, as shown in Figure 4.

https://doi.org/10.7554/eLife.49855.sa2

Article and author information

Author details

  1. Jelle Veraart

    1. Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
    2. Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, United States
    3. imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium
    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing
    For correspondence
    jelle.veraart@nyulangone.org
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0781-0420
  2. Daniel Nunes

    Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Visualization, Methodology, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8882-3228
  3. Umesh Rudrapatna

    CUBRIC, School of Psychology, Cardiff University, Cardiff, United Kingdom
    Contribution
    Data curation, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
  4. Els Fieremans

    Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, United States
    Contribution
    Conceptualization, Funding acquisition, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1384-8591
  5. Derek K Jones

    1. CUBRIC, School of Psychology, Cardiff University, Cardiff, United Kingdom
    2. Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Australia
    Contribution
    Resources, Data curation, Funding acquisition, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
  6. Dmitry S Novikov

    Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, United States
    Contribution
    Conceptualization, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing
    Contributed equally with
    Noam Shemesh
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4213-3050
  7. Noam Shemesh

    Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
    Contribution
    Conceptualization, Resources, Data curation, Supervision, Visualization, Methodology, Writing - original draft, Writing - review and editing
    Contributed equally with
    Dmitry S Novikov
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6681-5876

Funding

Fonds Wetenschappelijk Onderzoek (12S1615N)

  • Jelle Veraart

National Institute of Neurological Disorders and Stroke (R01NS088040)

  • Els Fieremans
  • Dmitry S Novikov

National Institute of Biomedical Imaging and Bioengineering (P41 EB017183)

  • Els Fieremans
  • Dmitry S Novikov

H2020 European Research Council (679058)

  • Noam Shemesh
  • Daniel Nunes

Engineering and Physical Sciences Research Council (EP/M029778/1)

  • Derek K Jones

Wellcome (096646/Z/11/Z)

  • Derek K Jones

Wellcome (104943/Z/14/Z)

  • Derek K Jones

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

All authors would like to thank Prof. Mark D Does (Vanderbilt University) for the remmiRARE sequence used in this study, supported by grant number NIH EB019980, and Dr. Erika Raven for discussions and comments on the manuscript. JV is a Postdoctoral Fellow of the Research Foundation - Flanders (FWO; grant number 12S1615N). DN and NS are supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Starting Grant, agreement No. 679058). EF and DSN were supported by the NIH/NINDS award R01NS088040 and research was performed as part of the Center of Advanced Imaging Innovation and Research (CAI2R, www.cai2r.net), an NIBIB Biomedical Technology Resource Center (NIH P41 EB017183). The Connectom data were acquired at the UK National Facility for in vivo MR Imaging of Human Tissue Microstructure funded by the EPSRC (grant EP/M029778/1), and The Wolfson Foundation. DKJ is supported by a Wellcome Trust Investigator Award (096646/Z/11/Z) and a Wellcome Trust Strategic Award (104943/Z/14/Z).

Ethics

Human subjects: Human studies were carried out under a protocol (EC.06.05.02.891) approved by Cardiff University School of Psychology Ethics Committee. Written informed consent and consent to publish was obtained from all participants.

Animal experimentation: Animals used in this study were handled in agreement with the European FELASA guide-lines and all procedures were approved by the Champalimaud Animal Welfare Body and by the national authorities, Direção Geral de Alimentação e Veterinária, Lisbon, Portugal, under the approved protocol number 0421/000/000/2016. All animal care procedures were conducted in agreement with the European Directive 2010/63, at the vivarium of the Champalimaud Foundation, a research facility part of CONGENTO, project number Lisboa-01-0145-FEDER-022170.

Senior Editor

  1. Floris P de Lange, Radboud University, Netherlands

Reviewing Editor

  1. Birte Forstmann, University of Amsterdam, Netherlands

Reviewers

  1. Birte Forstmann, University of Amsterdam, Netherlands
  2. Saad Jbabdi, University of Oxford, United Kingdom
  3. Robert Mulkern

Publication history

  1. Received: July 2, 2019
  2. Accepted: January 7, 2020
  3. Version of Record published: February 12, 2020 (version 1)
  4. Version of Record updated: February 13, 2020 (version 2)

Copyright

© 2020, Veraart et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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