Functional MRI, BOLD, and language development

Functional MRI (fMRI) has become a standard tool to study regional brain activation in response to differing sensorimotor or cognitive tasks. The contrast for fMRI is referred to as blood oxygen level dependent (BOLD), and is based on the

hemodynamic response, such that blood releases oxygen to active neurons at a greater rate than inactive neurons (for a review, see Logothetis and Pfeuffer, 2004).

Oxyhemoglobin and deoxyhemoglobin have different magnetic susceptibilities, with oxyhemoglobin being diamagnetic, and deoxyhemoglobin being paramagnetic. MRI can detect the resulting magnetic signal variation between oxygenated and deoxygenated blood which gives rise to the BOLD signal. The BOLD signal itself results from a

number of physiological variations that accompany neural activity, including CBF, cerebral blood volume and cerebral oxygenation metabolic rate. Because of this, task-specific BOLD signal changes cannot be directly quantified in physiological units, but are typically expressed as a relative percent of signal change.

There are numerous studies that have investigated reading and language ability in children using BOLD fMRI. For expressive and receptive language tasks, BOLD studies have shown that left lateralization of language ability increases from ages 5-25 years (Szaflarski et al., 2006; Brauer and Friederici 2007; Holland et al., 2007). Knoll et al.

(2012) investigated 4-7 year olds and found that children with stronger grammar skills show a more similar activation to adults (left inferior frontal and superior temporal gyri), where as children with average or weak grammer skills show a more widespread

activation. In one of the few imaging studies to investigate preschoolers, Sroka et al.

(2015) examined functional activation in a group of 30 children 3-5 years during a passive listening to story-task, and found children with higher vocabulary scores showed increased gray matter left-lateralization and greater activation in bilateral thalamus, hippocampus, and left angular gyrus

5.1.3 Hypothesis

Findings from BOLD fMRI studies suggest that young children with better language skills have strengthened, more adult-like (i.e. left lateralized) functional

networks responsible for language. ASL allows for quantification of CBF, independent of cognitive tasks, although no ASL studies to date have assessed language ability in

preschool-aged children. The goal of this study was to use ASL to investigate the

relationship between language ability and CBF in a group of typically developing

children aged 3-5 years. We hypothesized that preschool children with stronger language skills would have a higher level of brain function, and a positive relationship would exist between language ability and CBF, such that stronger language skills would relate to higher measures for CBF in left hemisphere inferior-frontal, temporal-parietal, and occipital-temporal language regions.

5.2 Methods

5.2.1 Participants

41 children (3.7 ± 0.4 years 18 females, 23 males) were included in the analysis, based on the quality of their T1W structural and perfusion-weighted images.

5.2.2 Language assessments

Each participant’s language ability was assessed using the Phonological Processing and Speeded Naming subtests from the NEPSY-II (Korkman et al., 2007).

5.2.3 MRI scanning

Perfusion-weighted images were acquired using a 3D fast spin echo sequence, with a spiral trajectory and pseudocontinous labeling, TR of 4.6 s, TE of 10.7 ms, 4mm slice thickness, and post label delay of 1.5 s. The imaging plane was placed near the base of the cerebellum, with the label plane ~2cm below. For CBF quantification, the

following parameters were used: labeling efficiency 0.8, partition coefficient 0.9, T1 of

blood 1.6s. T1-weighted anatomical images were acquired with a spoiled gradient echo sequence, TR of 8.2 ms, TE of 3.8 ms, flip angle of 12^o, and 0.9mm isotropic voxels.

5.2.4 Image analysis

A whole-brain voxel-wise analysis (Figure 22) was performed using software in the FSL suite. For each subject, skull stripping was performed with BET, and gray matter was segmented with FAST. A brain template and gray matter mask specific to the study were created by co-registering and averaging all subjects’ skull stripped structural

images, as well as their segmented gray matter images. Each subjects perfusion weighted data was registered to the study template using FLIRT (Jenkinson et al., 2002) and the same forward registration matrix was applied to the CBF maps. (CBF maps have poor alignment when they are directly registered to a structural image). CBF maps were smoothed using a 10mm full-width-half-maximum Gaussian kernel and masked with the gray matter template. Voxel wise cross-subject statistics was performed using

permutation based non-parametric testing (RANDOMISE), with 5000 permutations, family wise error rate correction for multiple comparisons, and threshold free cluster enhancement (Winkler et al., 2014). For this study, CBF values were independently compared with age-standardized Phonological Processing and Speeded Naming scaled scores, while controlling for child’s age, sex, and maternal years of post-secondary education. After analysis, significant clusters were thresholded at p<0.05 and labeled using the Harvard-Oxford cortical structural atlas (Desikan et al, 2006).

Figure 22. Workflow used for whole-brain voxel based analysis of CBF data.

To confirm findings, a separate post hoc analysis was used to isolate mean CBF values for specific cortical regions that were shown to be significant from the whole brain analysis. Structural images were first processed through the Freesurfer pipeline (Fischl et al., 2012), which included skull stripping, segmentation of gray and white matter, and cortical parcelation with the Desikan-Killiany atlas (Desikan et al., 2006). Perfusion-weighted images were registered to skull stripped structural images, and the inverse normalization was used to register the subjects gray matter mask, as well as their cortical parcelations into native CBF space. The gray matter mask was binarized to the CBF image, and mean values for CBF were extracted for cortical regions of interest. CBF

values were then entered into a general linear model (SPSS) and compared with language scores, while controlling for age, gender, and maternal years of postsecondary education.

5.3 Results

5.3.1 Language assessments

For the Phonological Processing test, children achieved a mean aged-standardized score of 10.5 ± 2.9 with a range between 4-16. For Speeded Naming, the mean scaled score was 10.8 ± 2.8, and a range of 4-16.