DISTINCT FUNCTIONAL AND STRUCTURAL NETWORKS FOR SPATIAL AND VERBAL WORKING MEMORY

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Participants

Twenty-one healthy right-handed participants (11 males) with no known history of neurological disease participated in the study. Age of participants ranged from 19 to 37 years (mean age 26.05 years, SD 4.9). All participants gave informed consent with approval from the University of Auckland Human Participants Ethics Committee and had normal or corrected-to-normal vision.

Scanning procedure

Functional, diffusion-weighted and T1-weighted structural images were acquired on a 1.5 Tesla Siemens Avanto scanner (Erlangen, Germany) in a single session per participant. Total acquisition time was approximately 50 minutes. FMRI data was analysed using SPM8 (Wellcome Department of Imaging Neuroscience, London, UK; www.fil.ion.ucl.ac.uk), while DTI data was analysed using tools from the FMRIB Software Library (FSL, version 4.1.6, Oxford Centre for Functional MRI of the Brain [FMRIB], UK; [Behrens et al., 2003b; Smith et al., 2004; Woolrich et al., 2009] ) and the Freesurfer image analysis suite (Athinoula A. Martinos Center for Biomedical Imaging; http://surfer.nmr.mgh.harvard.edu/). Two male participants were excluded from analyses due to scanner malfunction, resulting in a total of 19 participants (9 males).

Experimental paradigm

Two language tasks were programmed using E-Prime software (http://www.pstnet.com) and carried out by each participant during scanning: verb generation and sentence comprehension. The verb generation task consisted of 10 blocks of 10 nouns and 5 blocks of words made up of Xs (number of Xs varied to correspond with the number of letters in each noun), which were presented centrally, every three seconds for a total time of 30 seconds per block. There were three conditions: silently generating a verb for every noun; silently repeating the presented nouns; and passively viewing the X words. There were five blocks of each condition which were presented sequentially in the above order until all 15 blocks were presented. Instructions for each block were presented prior to commencing each block. For the sentence comprehension task, participants were presented 10 blocks containing six sentences each, which were presented one word at a time in the centre of the screen. Each sentence consisted of 9 words (500 ms per word) with a 1000 ms fixation at the end of each sentence. The blocks alternated between a regular font and false font and participants were
instructed to passively read each sentence. The word stimuli for both tasks used were 55 point font-size. Data for two additional tasks were also collected but are not reported in the present paper.

fMRI data acquisition and preprocessing

For each participant, T2*-weighted echo-planar imaging (EPI) scans were acquired, which included two “dummy scans” to allow for signal saturation, parallel to the AC/PC line using the following parameters: repetition time (TR) = 2400 ms; echo time (TE) = 30 ms; field of view (FOV) = 192 mm2; 35 axial slices; matrix size = 64 x 64 mm; voxel size = 3 x 3 x 3 mm. A total of 270 T2*-weighted volumes were acquired during the verb generation task and a total of 180 T2*-weighted volumes were acquired for the sentence comprehension task. T1-weighted structural volumes were also acquired for each participant using a 3D magnetization-prepared rapidly acquired gradient echo (MP-RAGE) sequence with the following parameters: TR = 11 ms; TE = 4.94 ms; FOV = 256 mm2; 176 sagittal slices; matrix size = 256 x 256 mm; voxel size = 1 x 1 x 1 mm. The data was processed using SPM8 software following the standard preprocessing protocol (realignment, coregistration, normalisation and smoothing). Realignment involved realigning the first volume of each session with the rest of the volumes and also generating a mean of the functional volumes.
This mean of the functional scans was used for coregistration of the T1-weighted structural scan. Both structural and functional images were normalised to standard Montreal Neurological Institute (MNI) space and functional images were spatially smoothed used a Gaussian filter of 9 x 9 x 9 mm at full-width half maximum (FWHM). For both tasks, firstlevel analyses were performed for each participant using the general linear model (GLM). The conditions were modelled as a box-car function and convolved with a canonical haemodynamic response function. Movement regressors were also included in the model. A one-sample t-test was used on the contrast images for each task (verb generation vs. verb repeat; reading vs. false font) in a second-level random effects analysis to see the general patterns of activation during the two language tasks. To correct for multiple comparisons, a family-wise error (FWE) correction was applied at p < .05 for the verb generation and a Monte Carlo simulation for the sentence comprehension task (see PPI analyses for details).

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DTI data acquisition and analysis

Data were acquired on a 1.5 Tesla Siemens Avanto scanner (Erlangen, Germany) using a single shot spin-echo planar imaging sequence (45 slices; TR = 6600 ms; TE = 101; FOV = 230 mm2; matrix size = 128 x 128 mm; voxel size = 1.8 x 1.8 x 3 mm) in 30 non-collinear directions with a diffusion weighting of b = 1000 s/mm-2 with one scan at b = 0 s/mm-2. The sequence was applied three times and averaged in order to increase signal to noise ratio. Data were analysed using tools from FSL’s (Behrens et al., 2003b; Smith et al., 2004; Woolrich et al., 2009) Diffusion Toolbox (FDT, version 2.0). For each subject, the eddy current correction tool was used to correct for eddy current distortions and motion artefacts.
Diffusion-weighted images were registered to a standard MNI brain image using the FMRIB Linear Image Registration Tool (FLIRT). FA, mean diffusion (MD) and eigenvector maps were computed, which were then transformed to standard space using the FMRIB Non- Linear Image Registration Tool (FNIRT). The data were then run through the program BEDPOSTX to build probability distributions on diffusion parameters and model for crossing fibres at each voxel (Behrens et al., 2007), in preparation for tractography.

CHAPTER 1: INTRODUCTION
1.1 Combining fMRI and DTI
1.2 Diffusion Tensor Imaging and Tractography
1.3 Functional Connectivity .
1.4 Thesis Research Rationale
CHAPTER 2: STUDY 1 – REGIONAL DIFFERENCES IN CEREBRAL ASYMMETRIES OF HUMAN CORTICAL WHITE MATTER
2.1 Abstract .
2.2 Introduction .
2.3 Materials and Methods
2.4 Results
2.5 Discussion
2.6 Conclusion
CHAPTER 3 – STUDY 2: STRUCTURAL AND FUNCTIONAL CONNECTIVITY IN LANGUAGE PRODUCTION AND COMPREHENSION
3.1 Abstract
3.2 Introduction .
3.3 Materials and Methods .
3.4 Results .
3.5 Discussion
3.6 Conclusion
CHAPTER 4 – STUDY 3: DISTINCT FUNCTIONAL AND STRUCTURAL NETWORKS FOR SPATIAL AND VERBAL WORKING MEMORY
4.1 Abstract
4.2 Introduction .
4.3 Materials and Methods
4.4 Results .
4.5 Discussion .
4.6 Conclusion .
CHAPTER 5: GENERAL DISCUSSION
Appendices
Appendix A: Participant Information Sheet
Appendix B: Participant Consent Form
References