Abstract
In recent years, there has been ever-increasing interest in combining functional magnetic resonance imaging (fMRI) and diffusion magnetic resonance imaging (dMRI) for better understanding the link between cortical activity and connectivity, respectively. However, it is challenging to detect and validate fMRI activity in key sub-cortical areas such as the thalamus, given that they are prone to susceptibility artifacts due to the partial volume effects (PVE) of surrounding tissues (GM/WM interface). This is especially true on relatively low-field clinical MR systems (e.g., 1.5 T). We propose to overcome this limitation by using a spatial denoising technique used in structural MRI and more recently in diffusion MRI called non-local means (NLM) denoising, which uses a patch-based approach to suppress the noise locally. To test this, we measured fMRI in 20 healthy subjects performing three block-based tasks : eyes-open closed (EOC) and left/right finger tapping (FTL, FTR). Overall, we found that NLM yielded more thalamic activity compared to traditional denoising methods. In order to validate our pipeline, we also investigated known structural connectivity going through the thalamus using HARDI tractography: the optic radiations, related to the EOC task, and the cortico-spinal tract (CST) for FTL and FTR. To do so, we reconstructed the tracts using functionally based thalamic and cortical ROIs to initiates seeds of tractography in a two-level coarse-to-fine fashion. We applied this method at the single subject level, which allowed us to see the structural connections underlying fMRI thalamic activity. In summary, we propose a new fMRI processing pipeline which uses a recent spatial denoising technique (NLM) to successfully detect sub-cortical activity which was validated using an advanced dMRI seeding strategy in single subjects at 1.5 T.
Highlights
In order to validate our pipeline, we investigated known structural connectivity going through the thalamus using high angular resolution diffusion imaging (HARDI) tractography: the optic radiations, related to the eyes-open closed (EOC) task, and the cortico-spinal tract (CST) for FTL and FTR
We showed that performing a non-local means (NLM) denoising technique allows the recovery of small sub-cortical activations, which were evaluated by a new HARDI tractography seeding strategy
Using our group analysis of both the functional magnetic resonance imaging (fMRI) activation maps and streamline occurrence score (Figure 8), we qualitatively showed in an inter subject and inter modal analysis how the streamline occurrence score of the CST and optic radiations streamlines intersect with the fMRI region of interest (ROI) (Figure 8)
Summary
Combining functional and diffusion magnetic resonance imaging (fMRI and dMRI, respectively) provides a unique non-invasive approach for investigating the structural architecture linking areas which are functionally active during cognitive processing. fMRI can be used to localize activation areas within cortex that represent changes in cortical blood flow, volume, and oxygen metabolism (Blood-Oxygenation-Level-Dependent or BOLD signal) associated with “active” brain tissue (Kwong et al, 1992; Turner, 1992; Bandettini et al, 1993; Menon and Kim, 1999; Buxton, 2002).Even during relatively simple tasks, several activation areas can be seen, possibly reflecting networks of cerebral connectivity. Combining functional and diffusion magnetic resonance imaging (fMRI and dMRI, respectively) provides a unique non-invasive approach for investigating the structural architecture linking areas which are functionally active during cognitive processing. Diffusion MRI (dMRI), a non-invasive technique based on the observed anisotropic diffusion of water molecules along white-matter (WM) fibers, can be used to approximate and reconstruct WM tracts between such activation areas (Descoteaux et al, 2009; Descoteaux and Poupon, 2014). The probable direction of the diffusion in each voxel can be represented either by diffusion tensor imaging (DTI) (Basser et al, 1994; Basser and Pierpaoli, 1996; Pierpaoli et al, 1996) or more recently by high angular resolution diffusion imaging (HARDI) (Descoteaux et al, 2009; Tournier et al, 2012; Descoteaux and Poupon, 2014). There is a growing interest in combining dMRI and fMRI for studying large-scale networks in vivo (Zhu et al, 2013)
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