Diffusion Magnetic Resonance Imaging Techniques in Neural Connectivity Analysis

Diffusion magnetic resonance imaging (dMRI) encompasses a suite of non-invasive methods for characterising the microstructural properties of neural tissue and mapping the pathways that underlie brain connectivity. Early approaches such as diffusion tensor imaging (DTI) provided measures of anisotropic water diffusion, notably fractional anisotropy, to infer the orientation of principal fibre bundles. However, DTI is limited in regions where multiple fibre populations intersect within a single voxel. High angular resolution diffusion imaging (HARDI) and advanced models overcome this by sampling many gradient directions or multiple b-values, enabling reconstruction of fibre orientation distributions (FODs) via techniques such as spherical deconvolution. These methods estimate the angular profile of diffusion within each voxel and support both deterministic and probabilistic tractography algorithms to trace long-range connections. Recent innovations include multi-shell acquisitions that disentangle contributions from white matter, grey matter and cerebrospinal fluid, and continuous modelling frameworks that exploit spatial correlations to improve robustness. Machine-learning approaches have begun to refine FOD estimation in data-limited settings. Together, these developments have enhanced the fidelity of in vivo connectome mapping, informing studies of development, ageing, neurological disease and neurosurgical planning, and yielding reproducible metrics of structural connectivity at the individual and group level.

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Technical terms

Diffusion Tensor Imaging (DTI): A model that characterises water diffusion in terms of a tensor, yielding measures such as fractional anisotropy and principal diffusion directions.

High Angular Resolution Diffusion Imaging (HARDI): A sampling scheme employing many diffusion-encoding directions to capture complex fibre configurations beyond the tensor model.

Fractional Anisotropy (FA): A scalar index derived from the diffusion tensor that quantifies the degree of directional preference of water diffusion.

Fiber Orientation Distribution (FOD): An angular function representing the densities of underlying fibre populations within a voxel.

Spherical Deconvolution: A mathematical technique to recover the FOD by deconvolving a tissue response function from the measured diffusion signal.

Multi-shell Diffusion MRI: An acquisition strategy collecting data at multiple b-values to distinguish tissue types and model microstructural heterogeneity.

Tractography: A computational method for reconstructing white matter pathways by following local fibre orientation estimates through the brain volume.

References

1. Implicit neural representation of multi-shell constrained spherical deconvolution for continuous modeling of diffusion MRI. Imaging Neuroscience (2025).
2. Modelling white matter with spherical deconvolution: How and why?. NMR in Biomedicine (2018).
3. Spherical Deconvolution of Multichannel Diffusion MRI Data with Non-Gaussian Noise Models and Spatial Regularization. PLOS ONE (2015).
4. Generalized Richardson-Lucy (GRL) for analyzing multi-shell diffusion MRI data. NeuroImage (2020).