In vivo correlation between axon diameter and conduction velocity in the human brain

Horowitz, Assaf; Barazany, Daniel; Tavor, Ido; Bernstein, Moran; Yovel, Galit; Assaf, Yaniv

doi:10.1007/s00429-014-0871-0

Cited by 135 publications

(132 citation statements)

References 55 publications

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“…(39) coming from the term ~ A growing with the external radii (Table 2), can rationalize correlation between r app and neuronal signal propagation speed (amount of myelin) (Horowitz et al, 2014). The observed decrease of r app with increasing gradients (Huang et al, 2015) is consistent with moving up-wards from regime II towards the green domain in Fig.…”

Section: Resultsmentioning

confidence: 97%

Mesoscopic structure of neuronal tracts from time-dependent diffusion

2015

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Interpreting brain diffusion MRI measurements in terms of neuronal structure at a micrometer level is an exciting unresolved problem. Here we consider diffusion transverse to a bundle of fibers, and show theoretically, as well as using Monte Carlo simulations and measurements in a phantom made of parallel fibers mimicking axons, that the time dependent diffusion coefficient approaches its macroscopic limit slowly, in a (lnt)/t fashion. The logarithmic singularity arises due to short range disorder in the fiber packing. We identify short range disorder in axonal fibers based on histological data from the splenium, and argue that the time dependent contribution to the overall diffusion coefficient from the extra-axonal water dominates that of the intra-axonal water. This dominance may explain the bias in measuring axon diameters in clinical settings. The short range disorder is also reflected in the linear frequency dependence of the diffusion coefficient measured with oscillating gradients, in agreement with recent experiments. Our results relate the measured diffusion to the mesoscopic structure of neuronal tissue, uncovering the sensitivity of diffusion metrics to axonal arrangement within a fiber tract, and providing an alternative interpretation of axonal diameter mapping techniques.

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Section: Resultsmentioning

confidence: 97%

Mesoscopic structure of neuronal tracts from time-dependent diffusion

2015

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“…Previous analyses of PGSE data usually assume that the extracellular diffusion is hindered (ie, extracellular diffusion coefficient is a constant independent of diffusion time), and therefore

S_{e x} (PGSE) = S_{0, e x} \cdot \exp (- b \cdot D_{e x}) .

…”

Section: Methodsmentioning

confidence: 99%

Impact of transcytolemmal water exchange on estimates of tissue microstructural properties derived from diffusion MRI

Jiang

Xie

et al. 2016

Magn. Reson. Med.

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Purpose To investigate the influence of transcytolemmal water exchange on estimates of tissue microstructural parameters derived from diffusion MRI using conventional PGSE and IMPULSED methods. Methods Computer simulations were performed to incorporate a broad range of intracellular water life times τin (50 – ∞ ms), cell diameters d (5 – 15 μm), and intrinsic diffusion coefficient Din (0.6 – 2 μm2/ms) for different values of signal to noise ratio (10 to 50). For experiments, murine erythroleukemia (MEL) cancer cells were cultured and treated with saponin to selectively change cell membrane permeability. All fitted microstructural parameters from simulations and experiments in vitro were compared with ground-truth values. Results Simulations showed that, for both PGSE and IMPULSED methods, cell diameter d can be reliably fit with sufficient SNR (≥ 50), while intracellular volume fraction fin is intrinsically underestimated due to transcytolemmal water exchange. Din can be reliably fit only with sufficient SNR and using the IMPULSED method with short diffusion times. These results were confirmed with those obtained in the cell culture experiments in vitro. Conclusion For the sequences and models considered in this study, transcytolemmal water exchange has minor impacts on fittings of d and Din with physiologically relevant membrane permeabilities if SNR is sufficient (> 50), but fin is intrinsically underestimated.

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“…The clustering based on ADD was able to segment the corpus callosum into several regions that fitted known morphological zones of these samples (Figure ). Recently, ADD was demonstrated in human brain and was also shown to correlate with conduction velocity measures of interhemispheric transmission time . Hence, correlations between in vivo measures of axonal diameters and reaction time measures during cognitive paradigms will be of particular interest in the near future.…”

Section: Anatomymentioning

confidence: 97%

The role of diffusion MRI in neuroscience

2017

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Diffusion‐weighted imaging has pushed the boundaries of neuroscience by allowing us to examine the white matter microstructure of the living human brain. By doing so, it has provided answers to fundamental neuroscientific questions, launching a new field of research that had been largely inaccessible. We briefly summarize key questions that have historically been raised in neuroscience concerning the brain's white matter. We then expand on the benefits of diffusion‐weighted imaging and its contribution to the fields of brain anatomy, functional models and plasticity. In doing so, this review highlights the invaluable contribution of diffusion‐weighted imaging in neuroscience, presents its limitations and proposes new challenges for future generations who may wish to exploit this powerful technology to gain novel insights.

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In vivo correlation between axon diameter and conduction velocity in the human brain

Cited by 135 publications

References 55 publications

Mesoscopic structure of neuronal tracts from time-dependent diffusion

Mesoscopic structure of neuronal tracts from time-dependent diffusion

Impact of transcytolemmal water exchange on estimates of tissue microstructural properties derived from diffusion MRI

The role of diffusion MRI in neuroscience

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