Impact of b‐value on estimates of apparent fibre density

Abstract

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

In vivo microstructural heterogeneity of white matter lesions in healthy elderly and Alzheimer's disease participants using tissue compositional analysis of diffusion MRI data

“…Finally, using SS3T-CSD, the resulting WM FOD is then also not weighted by the signal from lower b-value(s), and consequently the resulting apparent fibre density is less biased by extra-axonal signal and more specifically sensitised to intra-axonal volume. The latter is consistent with recent findings by Genc et al, (2020) , where use of higher b-values was found to result in more accurate estimates of WM fibre density using simulated data, and corroborated by their in vivo studies.…”

“…This issue can be overcome by modelling GM and CSF signal fractions, for example by using multi-shell multi-tissue CSD ( Jeurissen et al, 2014 ). However, this requires multi-shell data, typically implying the additional use of low b-values, which introduces some amount of extra-axonal signal ( Genc et al, 2020 ). Alternatively, a recently introduced variant of the CSD method, called single-shell 3-tissue CSD (SS3T-CSD) ( Dhollander and Connelly, 2016a , Dhollander et al, 2016 ), is able to additionally estimate the GM and CSF compartments using single-shell diffusion data alone.…”

Disruption of brainstem monoaminergic fibre tracts in multiple sclerosis as a putative mechanism for cognitive fatigue: a fixel-based analysis

“…After the pre-processing, for each subject a whole-brain mask was defined and a FOD was computed from the highest b-value only (b = 2000 s/mm 2 ) using the Dhollander method ( Dhollander et al, 2016 ). We chose to select only this b-value to avoid any potential extra-axonal signal contribution ( Genc et al, 2020 ), as previously done in other studies ( Dimond et al, 2020 ). The Dhollander method is designed to rely on multi-tissue CSD even in absence of multi-shell data ( Dhollander et al, 2019 ).…”

The photosensitive phase acts as a sensitive window for seasonal multisensory neuroplasticity in male and female starlings

“…Fixel-based analysis can distinguish between different fiber bundles within a voxel and estimate FD for each fiber bundle separately (FD1 and FD2). For a complete description and interpretation of these measures we refer to the following reviews of DTI analysis (Beaulieu, 2002;Beaulieu, 2014;Song et al, 2002;Zatorre et al, 2012) and fixel based analysis (Genc et al, 2020;Raffelt et al, 2017) It is important to note that fixel-based analysis was mainly created to study the human or mammalian brain. The mammalian cortex has a laminar organization with large white matter tracts interconnecting both hemispheres through the corpus callosum, whereas the avian telencephalon has a nuclear organization with distinct white matter tracts organized in lamina (Jarvis et al, 2005).…”