RESUMEN DE LA DESCRIPCIÓN DEL PROYECTO:

6.4.1 fMRI background

Functional magnetic resonance imaging (fMRI) has made it possible to infer on brain activity in vivo throughout the brain, either while performing a task or with the brain at rest (Biswal et al. 1995). fMRI exploits a mechanism described in the previous section: Magnetic susceptibility. The change from oxyhemoglobin to deoxyhemoglobin alters the measured MRI signal and is therefore described as the blood oxygen level-dependent (BOLD) signal. The BOLD response is governed by 3 consecutive physiological processes: neuronal response, signaling and vascular response.

Using simultaneous fMRI and electrophysiology it was found that the measured BOLD signal reflects the input and the intracortical synaptic processing rather than the spiking output of a given brain area (Logothetis et al. 2001). When such activation is present, there will be an increase in demand for oxygen and nutrients. The local neuronal environment will signal to the blood vessels (arterioles) to increase cerebral blood flow (CBF) in order to satisfy this demand. At onset of activation the resulting consumption of oxygen causes the weakly diamagnetic oxyhemoglobin to release O2, transforming it to paramagnetic

deoxyhemoglobin, leading to a drop in measured MRI signal. This signaling occurs both through metabolic signaling from the increase local energy consumption and through neurotransmitter signaling. The respective contributions of the two factors differs across the brain. In response to the signaling the local and upstream vasculature responds by adapting the tone of the vascular wall in order to increase the local cerebral blood flow and volume (Figure 9).

The described cascade has been validated in healthy subjects (Logothetis 2008; Logothetis et al. 2001), however, all three steps leading to BOLD signal can be affected by disease pathology. This can be advantageous when investigating baseline changes such as in resting state analysis, but can strongly affect results in task-fMRI where controls are compared to diseases populations.

Figure 9: Adapted from (Iannetti and Wise 2007). Schematic overview of the generation of the blood

oxygen level-dependent (BOLD) signal.

6.4.2 fMRI resting state analysis

At the advent of fMRI analysis, the focus was on task-related fMRI in which the brain’s activity during a task is compared to the baseline activity to identify task-associated brain regions. It was observed that the BOLD signal has slow spontaneous fluctuations at rest (< 0.1 Hz), but this was initially regarded as noise resulting from vascular properties instead of brain activity. Later it was observed that this activity at rest is synchronous across brain regions, indicating functional connectivity between regions (Biswal et al. 1995) (Figure 10). The significance of resting state BOLD signal correlations was given little importance until visual representations of one of the key resting state networks, the default mode network (DMN), were published and were found relevant in a neuroscience context (Greicius et al. 2003). Task-related fMRI was not used in this work and therefore the next sections only describe resting state fMRI (rsfMRI) processing.

Figure 10: Adapted from (Smith et al. 2013). Basic concept of rsfMRI: calculate correlation of temporal

activity across voxels and as such brain regions (nodes, indicated as A, B, C).

6.4.3 fMRI resting state processing

Before the rsfMRI scan subjects are asked to “lie still, do not think of anything in particular and to not fall asleep” while the BOLD signal is measured for 8 minutes (values between 5-15 minutes are common in rsfMRI studies). Most common MRI sequence to measure this signal is gradient echo planar imaging (EPI) in which one 2D slice is acquired to cover the whole brain within 2-3s with a spatial resolution of around 3-5mm in each dimension. Consecutively a structural scan with higher resolution is acquired in order to map activations to brain regions.

Various software packages are available to process the raw 4D rsfMRI data using standardized approaches. In this work the CONN-toolbox (Whitfield-Gabrieli and Nieto-Castanon 2012) is used in which the following pre-processing steps are performed:

1. Analyze and realign changes in orientation of the rsfMRI data over time due to head motion. 2. Warp the high resolution structural scan to Montreal Neurological Institute (MNI) standardized-

space and use the deformation matrixes to also warp the rsfMRI data to this space.

3. Use the structural image in MNI space to generate tissue maps in order to separate non-brain regions, gray matter and white matter.

4. Smoothing of the rsfMRI data is applied to improve signal-to-noise ratio and statistical power. 5. Filtering of rsfMRI data to remove the slowest temporal drifts.

6. Regression of motion parameters (estimated in step 1) and signal changes from the CSF.

7. Independent component analysis (ICA) identifying noise components which are subsequently removed.

After pre-processing temporal synchronicity between spatially separated voxels needs to be calculated (Figure 10). This can either be done in a seed-based approach, in which a seed region is selected and time courses in all other voxels are compared to the seed region, or an ICA analysis to identify similar components throughout the brain (Cole, Smith, and Beckmann 2010). While seed-based approach allows for less variation due to possible underlying changes since the seed is fixed, ICA requires manual identification of significant components and is less established. Both techniques are used throughout currently published works, with a tentative preference for ICA in more recent works.

When the networks are identified, comparisons can be made on a group or linear-correlation basis. Either participants are grouped based on some classifier and the same network is compared between the two groups, or within the selected network voxels are weighted by a linear value for each subject and evaluated for significance within the whole sample. The statistics involved for these large 4D datasets is called Statistical Parametric Mapping (SPM) (Friston 1994), in which a General Linear Model (GLM) is used to describe the statistical contrast being tested as a correlation between measurements and a design matrix with a noise term. Correction for multiple testing is handled by applying Gaussian random fields theory (Friston 1994) after T-testing of each voxel for significance.