Supplementary Information:
MRI acquisition
A subgroup of patients (n=13) underwent brain MRI on a 1.5T GE Signa (Milwaukee, WI, USA) at visits 2 and 4 (before/after iPod) using an 8-channel phase-array head coil.
Diffusion tensor images were acquired of the whole brain in the axial plane using a single shot, spin-echo echo-planar imaging sequence with repetition time(TR)=11000ms, echo time(TE)=90ms, 50 directions, 96 x 96 image matrix, 40 axial slices, 2.5mm slice thickness, b-value=1000s/mm2, 5 non-diffusion weighted T2 volumes (b-value = 0 s/mm2), number of excitations (NEX)=1.
A high resolution structural brain images were acquired using a 3D spoiled gradient recalled (SPGR) sequence (TE=5.2ms, TR=12.4ms, TI=450ms, Slice thickness =1.2mm, field of view (FOV)=240mm, 256 x 256 matrix).
Resting-state functional MRI data was acquired using a gradient echo – echo planar imaging (GE-EPI) sequence with TR=2000ms, TE=40ms, FOV= 240mm, 64x64, 200 volumes, slice thickness =3.75mm and no slice gap.
Image Analysis:
DTI analysis:
All image analysis was done using tools provided in FMRIB’s Software Library (FSL) v5.0 (ref 1-3). Diffusion images were corrected for eddy current related distortions and simple head motion using affine registration to a reference volume. Voxelwise statistical analysis of the fractional anisotropy (FA), mean diffusivity (MD), longitudinal diffusivity (LD) and radial diffusivity (RD) data was carried out using TBSS (Tract-Based Spatial Statistics1), part of FSL2. First, FA images were created by fitting a tensor model to the raw diffusion data using FDT, and then brain-extracted using BET. All subjects' FA data were then aligned into a common space using the nonlinear registration tool FNIRT3, which uses a b-spline representation of the registration warp field4. Next, the mean FA image was created and thinned to create a mean FA skeleton which represents the centres of all tracts common to the group. Each subject's aligned FA data was then projected onto this skeleton. A script tbss_non_FA was used to create the same skeletonized version for MD, LD and RD data and the resulting images were fed into voxelwise cross-subject statistics using a paired t-test design and non-parametric permutation testing tool randomise. 5000 permutations were used for each possible statistical contrast, and the results were corrected for multiple comparisons across space using a threshold- free cluster enhancement (TFCE) approach with family wised error (FWE) corrected p-value of 0.05 (ref)
Resting-state analysis
Resting state images were pre-processed using the Multivariate Exploratory Linear Optimized Decomposition into Independent Components (MELODIC) FSL (FMRIB's Software Library, www.fmrib.ox.ac.uk/fsl) toolbox. The images were motion corrected and smoothed with a Gaussian kernel of full-width at half-maximum of 8mm. high-pass temporal filtering with a cut-off of 60s was applied to the data and registered to standard-space MNI template.
Group MELODIC with temporal concatenation was used to extract 30 independent components using Probabilistic Independent Component Analysis (PICA). Thirty components were found to provide the best separation between components of interest and artifacts while not overly separating out the Resting state networks (RSNs). This subjective selection was based on RSNs identified in previous literature. Statistical analysis using a paired t-test design was performed to explore if any components change after ipod therapy, using a dual regression technique5, 6 followed by a voxel wise analyses using a non-parametric permutation testing (5000 permutations) with randomise function of FSL. Threshold-Free Cluster Enhancement (TFCE)7 was used with a significance threshold of p0.05, FWE-corrected for multiple comparisons. The results were probabilistic statistical maps evaluating the increase or decrease in resting state connectivity after ipod therapy. The statistical maps were then up-sampled to MNI 2mm standard brain template to localize the areas of RSNs pre. vs. post differences.
References
1. Smith SM, Jenkinson M, Johansen-Berg H, et al. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 2006;31:1487-505.
2. Smith SM, Jenkinson M, Woolrich MW, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 2004;23 Suppl 1:S208-19.
3. Andersson JLR, Jenkinson M, Smith S. Non-linear Registration aka Spatial normalisation. FMRIB Technical report TR07JA1 from www.fmrib.ox.ac.uk/analysis/techrep 2007.
4. Rueckert D, Sonoda LI, Hayes C, et al. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging 1999;18:712-21.
5. Beckmann CF, Mackay CE, Filippini N, et al. Group comparison of resting-state FMRI data using multi-subject ICA and dual regression. OHBM 2009.
6. Filippini N, MacIntosh BJ, Hough MG, et al. Distinct patterns of brain activity in young carriers of the APOE-epsilon4 allele. Proc Natl Acad Sci U S A 2009;106:7209-14.
7. Smith SM, Nichols TE. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage 2009;44:83-98.