Box 1.

How to Measure the Fornix with Diffusion-Weighted Imaging

1. It is important to collect many diffusion directions (>30), because the fornix is highly curved and small relative to voxel size.

2. You must account for the cerebrospinal fluid (CSF) in the third ventricle surrounding the fornix to get accurate measurements. The diffusion properties of CSF differ greatly from that of white matter—namely, diffusion is completely free in CSF. CSF may inadvertently be included in the voxels identified as part of the fornix, and there is also the problem of pulsatile motion in CSF (Gunbey et al., 2014; Hodgetts et al., 2017; Sullivan et al., 2010). It is, therefore, paramount that researchers account for this in their analyses that involve the fornix. Kaufmann and colleagues (2017) proposed the following strategies for CSF remediation: During acquisition, fluid-attenuated inversion recovery (FLAIR) prepulses can be used (Cheng et al., 2011). Alternatively, during analysis, two steps can be taken. First, ventricular volume should be regressed out. Second, free-water elimination methods should be used (Pasternak et al., 2009). These strategies are essential when conducting a between-subject designs since ventricular alterations may correlate with age or certain disease states. For instance, it is known that neurologically normal older adults have relatively larger ventricles than younger adults (Apostolova et al., 2012). In addition, individuals with mild cognitive impairment and Alzheimer's Disease (Apostolova et al., 2012), multiple sclerosis (Dalton et al., 2006), anorexia nervosa (Kaufmann et al., 2017), and schizophrenia (Sayo et al., 2012) commonly have ventricular enlargement relative to matched controls.

3. Table 2 shows that most studies on the fornix have used an FMRIB Software Library package called tract-based spatial statistics (TBSS), which is appropriate for some, but not all analyses, and has known pitfalls (Bach et al., 2014). Although TBSS is lauded for its ability to correct for misregistration/misalignment of white matter maps (Oishi and Lyketsos, 2014), this benefit is not applicable to thin, circular tracts such as the fornix. Bach and colleagues (2014) critique this method and provide recommendations for how to use this technique. In the absence of using TBSS, investigators are left with tractography (note that the fornix is absent from many automated diffusion imaging toolboxes such as AFQ and TRACULA). Some laboratories have successfully employed probabilistic fiber tracking techniques by using seed, waypoint, and target masks to reconstruct the fornix (e.g., Bennett et al., 2015; Ngo et al., 2017). Propelled by the need for a reliable fornix template, one group chose to create one by using probabilistic tractography across all participants (n = 120), registering them to standard space, then merging them to create a template (Brown et al., 2017). Other groups have employed streamline tractography to reconstruct the fornix bundle in each individual's native space (Perea et al., 2018; Rabin et al., 2019).

4. Finally, before getting started, it is important to understand common diffusion-weighted imaging pitfalls. Jones and Cercignani (2010) provide an excellent discussion.