Investigators have sought to reconstruct WM pathways from diffusion images by computationally tracing the direction of maximal diffusion. Borrowing from a hydrodynamic analogy, the diffusion images provide the flow vector field, and the WM pathways represent flow streamlines. The reconstruction of WM pathways from diffusion imaging is a technique referred to as diffusion tractography.
Diffusion tractography has been applied widely in both research and clinical applications, but the technique has yet to be validated histologically in any systematic fashion. The tractography technique will need to be validated against established tracer methods before the tractography reconstructions can be interpreted with any reasonable confidence. lists the advantages and disadvantages of the diffusion MR method compared with conventional histological techniques. In general, there is a trade-off between the invasiveness and model-dependence of a method. For example, diffusion MR tractography is non-invasive but requires a model of the relationship between the diffusion signal and the underlying myeloarchitecture. Invasive tracers such as horseradish peroxidase and biocytin can follow the continuity of an axon without need for a mathematical model of the transport process.
Comparison of diffusion MRI and histological tract-tracing methods.
The accuracy of diffusion MR tractography is currently limited by a number of factors including the low spatial resolution of the MR image relative to the tract curvature (Lori et al. 2002
), the ambiguity between crossing and bending fibres (Basser et al. 2000
; Tuch 2004
) and distortions in the diffusion images (Jezzard & Clare 1999
). The latter problem hampers accurate registration between diffusion images and conventional T1-weighted structural images from which anatomical labels are conventionally obtained. More fundamentally, the tractography method is based on a presumed correspondence between the measured diffusion function and the underlying myeloarchitecture, although this relationship has never been tested.
The macaque monkey provides an ideal model for histological validation of the tractography method due to the phylogenetic proximity between humans and macaques, and the gyrencephalic structure of macaque cortex. Additionally, there is a large body of existing knowledge on the anatomic connectivity of the macaque brain. In particular, validation of diffusion tractography against the CoCoMac database (Stephan et al. 2001
; Kotter 2004
) is a promising avenue of research. The CoCoMac database contains a catalogue of reported tracing results in the macaque in a format which can be directly compared with the MR.
The macaque also provides an excellent validation model due to the ability to use comparable MR acquisition protocols in both humans and macaques. Parker and colleagues (2002
) have previously demonstrated the technical feasibility of acquiring high resolution diffusion tensor imaging (DTI) in the macaque. It has recently been shown that a high angular resolution diffusion imaging (HARDI) method called q
-ball imaging (QBI) can resolve multiple intravoxel axon orientations in human WM (Tuch et al. 2003
; Tuch 2004
). This capability promises to substantially boost the accuracy of the diffusion MR tract reconstructions. Other HARDI methods include persistent angular structure MRI (PASMRI; Jansons & Alexander 2003
), mixture model decomposition (Tuch et al. 2002
; Parker & Alexander 2003
), generalized DTI (Ozarslan & Mareci 2003
; Liu et al. 2004
), spherical harmonic transformation (Frank 2001
) and spherical harmonic deconvolution (Tournier et al. 2004
In contrast, QBI poses no assumptions on the underlying diffusion process and can resolve multiple intravoxel diffusion orientations. Rather than measuring the diffusion tensor, QBI measures the diffusion orientation distribution function (ODF). The diffusion ODF ψ
) describes the probability distribution for a water molecule to displace in a direction u
, where u
is a unit vector with no polarity, also called a director. In anatomic regions containing multiple axon orientations, the diffusion ODF exhibits multiple diffusion peaks, or multimodal diffusion (MMD; Tuch et al. 2003
; Tuch 2004
). The QBI method is thus capable of detecting the presence of multiple diffusion directions within an individual voxel.
The resolution of a QBI experiment is defined in diffusion displacement space and is determined by the experimental diffusion wavevector q
). The diffusion wavevector q
is proportional to the duration δ
and magnitude g
of the diffusion-sensitizing magnetic field gradient; specifically, q
is the gyromagnetic ratio (Callaghan 1993
). In QBI, the diffusion wavevector defines a projection function ρ
), which describes the radial fall-off of the projection beam in diffusion space. For a standard single wavevector QBI acquisition, the projection function is a Bessel beam, ρ
) (Tuch 2004
). The projection function has a width in the order of microns for a conventional QBI acquisition. The microscopic resolving power of QBI enables the technique to resolve multiple diffusion orientations within an individual imaging voxel. Previously, such intravoxel complexity has confounded diffusion imaging methods, such as DTI, which are only capable of resolving a single diffusion orientation within each imaging voxel (Wiegell et al. 2000
; Pierpaoli et al. 2001
It is important to note that the resolution of a QBI acquisition has dimensions length and not angle. Thus, the diffusion wavevector does not provide a direct indication of the angular resolution. The angular resolution also depends on the q-space sampling density on the sphere. The final resolution will be a function of the sampling wavevector, the sampling density and any smoothing operations applied in post-processing. Developing an imaging framework for QBI which will allow for the description of the angular resolution for a given sampling and reconstruction scheme is an area of active research.
QBI has demonstrated the capability of resolving complex intravoxel WM architecture in the human brain (Tuch et al. 2003
; Tuch 2004
) but it is not clear if QBI can also resolve intravoxel WM structure in the macaque brain. If the macaque is to serve as a model for histological validation of diffusion MR tractography, it will be necessary to show that QBI can also resolve complex WM architecture in the macaque. The objectives of the present study were therefore twofold: to demonstrate that QBI can resolve subvoxel architecture in macaque WM, and to develop a platform for future validation of diffusion tractography against histological tracer methods and the CoCoMac database.
In macaque WM, QBI resolved intravoxel architecture in a number of anatomic regions. QBI resolved intravoxel WM structure in, for example, the dorsolateral convexity, the pontine decussation, the pulvinar and temporal subcortical WM. The intravoxel fibre crossings were consistent with the known association, projection and callosal fibre architecture of the macaque brain. The paper concludes by discussing remaining challenges for the diffusion tractography project.