Aim 3 Neuroimaging Data Analysis

Longitudinal Structural Neuroimaging Data Analysis

Preprocessing:

Structural MR data from baseline and post-intervention scans were preprocessed using tools in the FMRIB Software Library (Image Analysis Group, FMRIB, Oxford, UK; http://www.fmrib.ox.ac.uk/fsl/; (S. M. Smith et al., 2004)). All high-resolution MPRAGE images were pre-processed using the following steps: (1) non-brain matter was removed using the brain extraction technique in FSL (S. M. Smith & Nichols, 2009). (2) All brain-extracted images were visually inspected for any residual non-brain matter, and any residual matter was then manually removed from the image. (3) Next, these brain-extracted images were segmented into gray matter, white matter, and cerebrospinal fluid using FSL’s automated segmentation technique

(Zhang, Brady, & Smith, 2001). Values were thresholded at >.2 to eliminate voxels that are of questionable tissue type. (4) Next, all images (across time points) were averaged to create a

study-specific template that was adjusted for sample and time specific variation. The partial volume estimate maps of gray matter were then registered to the study specific template (Jenkinson & Smith, 2001). (5) Each voxel of each registered gray matter image was modulated by applying the Jacobian determinant from the transformation matrix (Good et al., 2001b). (6) These modulated images were concatenated into a 4D image, which was smoothed using a 3 mm Gaussian kernel. Statistical analyses were conducted on these segmented, registered, modulated, and smoothed gray matter images.

Longitudinal Whole-Brain Volumetric Analyses

Intervention-related volumetric changes across the whole-brain were examined using voxel-based morphometry (VBM), a classic semi-automated approach for identifying voxelwise partial volume estimates. VBM analysis computes the probability that each voxel in a structural MR image is cerebrospinal fluid, gray matter, or white matter and yields statistical maps for each voxel type (see (Ashburner & Friston, 2000) for a detailed description of VBM methods). Voxels are then classified into the structural category with the highest probability and can be statistically analyzed between subjects. Separate statistical maps are created for gray matter voxels and white matter voxels, which can then be used for volumetric analysis. For the current study, I limited my investigation to gray matter statistical maps, as the advent of Diffusion Tensor Imaging has resulted in infrequent use of VBM to assess white matter volume. VBM has shown to be a reliable method for analyzing gray matter data from healthy older adults (S. J. Colcombe et al., 2006; Good et al., 2001a; Good et al., 2001b); and provides estimates that are similar to manual tracing in this population (Kennedy & Raz, 2009).

Longitudinal VBM analyses have been done in prior depression treatment trials to examine differences treatment effects on brain structure (R. Smith, Chen, Baxter, Fort, & Lane,

2013). Longitudinal regression models were conducted using FSL to examine the main effects of group and time, and a group x time interaction in predicting change in regional partial volume estimates, while adjusting for age and scanner type (i.e., 3T vs. 7T). Given that this dataset was underpowered to detect interactions, independent t-tests were additionally used to examine the presence of post-intervention group differences in voxelwise gray matter volume that were not present at baseline. This analysis was used to identify possible trends in exercise-related regional volumetric changes that can be tested in larger clinical trials.

Vertex-based morphological Brain Changes

Additionally, vertex-based estimates of regional gray matter thicknesss were generated using Freesurfer, version 6.0. Additionally, vertex-based estimates of regional gray matter thicknesss were generated using Freesurfer, version 6.0. The methods used to in the surface- based processing pipeline in Freesurfer are described in detail in Fischl et al., 1999. Briefly, 1) the image is registered with the MNI305 atlas, 2) the B1 bias field is estimated by measuring variation in white matter intensity, and images are bias-corrected on a voxelwise basis, 3) non- brain better is removed, 4) voxels are classified as white matter or non-white matter, 5) the white surface (between white and gray matter) and pial surface (between gray matter and CSF) are generated for each hemisphere and further refined, 6) the white and pial surfaces are overlaid on the original T1-weighted image, and 7) the distance between the white and pial surface is used to estimate gray matter thickness at each location in the cortex.

In this study, the exploration of exercise-related changes in cortical thickness was limited to prefrontal, anterior cingulate, and medial temporal cortical regions, given that these have previously been associated with depression and appear to be sensitive to and exercise-related structural brain changes. Regional cortical thickness was calculated using the following steps: 1)

registration of the MPRAGE to the MNI atlas, 2) removal of non-brain matter, 3) segmentation of white matter segmentation, 4) generation of the ‘white surface’ in each hemisphere (i.e., surface between white matter and gray matter), 5) generation of the pial surface (i.e., surface between gray matter and CSF) , and 6) measurement of the distance between the white and pial surfaces at each location on the cortex .

Volumetric changes in Hippocampal Subfields

Numerous automated methods have been developed to segment the hippocampus in high- resolution structural MR images (Dill, Franco, & Pinho, 2015). An automated segmentation tool in Freesurfer, version 6.0 was used to segment hippocampal subfields using the T1 image only. This automated segmentation method uses a statistical model of image formation around the hippocampus to segment hippocampal subfields in each hemisphere, using Bayesian inference, and has been validated against manual tracing of hippocampal subfields (Leemput et al., 2009). Change in volume (in mm3) of these hippocampal subfields was examined using paired-samples t-tests.

Functional Connectivity Analysis

Preprocessing

Functional MR data from baseline and post-intervention scans were pre-processed using tools in the FMRIB Software Library (Image Analysis Group, FMRIB, Oxford, UK; http://www.fmrib.ox.ac.uk/fsl/; (S. M. Smith et al., 2004)). Preprocessing of the functional data included four key steps: 1) removing non-brain matter from the images using the brain extraction technique (BET) tool in FSL (S. M. Smith & Nichols, 2009), 2) rigid body motion correction, 3) temporal filtering with a low pass and high pass filter, and 5) spatial smoothing using a 6 mm

3D Gaussian kernel. Functional MR data from baseline and post-intervention scans were pre- processed using tools in the FMRIB Software Library (Image Analysis Group, FMRIB, Oxford, UK; http://www.fmrib.ox.ac.uk/fsl/; (S. M. Smith et al., 2004)). Preprocessing of the functional data included four key steps: 1) removing non-brain matter from the images using the brain extraction technique (BET) tool in FSL (S. M. Smith & Nichols, 2009), 2) rigid body motion correction, 3) temporal filtering with a low pass and high pass filter, and 5) spatial smoothing using a 6 mm 3D Gaussian kernel. First, non-brain matter was removed from both the functional image and structural anatomical image. All brain-extracted images were visually inspected for any residual non-brain matter, and any residual matter was manually removed from the image by creating a brain mask and multiplying the manually revised brain mask by the original image. The data was then corrected for motion in 6 directions using the MCFLIRT tool in FSL. If excessive motion (peaks >1.7 mm) was found, the total motion for each participant in each direction was included as a covariate in the regression analyses to correct for motion. Next, the low pass filter was applied at 0.1 Hz (25) and the high-pass filter was separately applied at 0.01 Hz in the process of completing the first-level analysis for each participant. Finally, the data was spatially smoothed by adjusting voxel values based on nearby voxel values using a 3D 6 mm Gaussian kernel.

Registration

After completing preprocessing steps, the preprocessed functional images were registered to each participant’s respective structural anatomical MPRAGE image for baseline and post- intervention scans. FSL’s Linear Registration Technique (FLIRT) was used to transform each participant’s functional image into standard space. Registration for each participant was visually

Seed region extraction: Given the important role of the hippocampus in depression and its

sensitivity to exercise-based interventions, a seed-based resting state analysis was conducted, using the left and right hippocampus as the primary seeds. In this type of analysis, the mean time series signal (across volumes) from regions-of-interest (i.e., seeds) is extracted from each participant and is correlated with every other voxel in the brain. All seed regions were determined separately for pre- and post-intervention MR images.

The left and right hippocampal seeds were segmented from the MPRAGE image using FreeSurfer image analysis suite (version 6.0; http://surfer.nmr.mgh.harvard.edu). The tool performs subcortical structural segmentation using the method presented in Fischl et al. (2002). First, each image is linearly aligned with an average template. A frequency histogram of possible structures (defined by the spatial template prior) is used to compute the probability of a given anatomical label (e.g., hippocampus) occurring at a given location. The prior of a given spatial arrangement of different labels (i.e., subcortical structures) is also incorporated into the segmentation. Using this process, the hippocampal seeds were determined from the MPRAGE image for each participant at each time point. The hippocampal seeds were then registered to the 4D functional image, and the mean time series (during the 6.5 minute passive viewing paradigm) was extracted for the left and right hippocampus.

Analysis

First, Pearson and Chi-square statistics were used to summarize demographic, physical, cognitive, and brain characteristics as appropriate. Mean differences between groups for all outcome measures were examined using independent samples t-tests. All analyses were two- tailed. Next, within-subject analyses were conducted on the preprocessed resting state data. Separate general linear models were conducted for both hippocampal seeds (left and right) on

each individual participant at each time point, controlling for the six rigid body directional motion parameters. Including the volume-by-volume motion parameters allowed for additional control against motion-related noise by accounting for the variability of movement throughout the time series for each participant. This within-subject analysis yielded two statistical parametric maps representing brain regions that were either positively or negatively functionally correlated with the seed regions (left and right hippocampus) for each participant at each time point (baseline, post-intervention). Fisher’s r-to-z transformations were conducted on these maps and resulting z-score statistics were analyzed in the group-level analysis. The first goal in examining functional connectivity patterns in this dataset was to examine whether adding aerobic exercise to antidepressant medication treatment for depression yields greater changes in hippocampal connectivity patterns relative to treatment with antidepressant medication alone in adults with Major Depression. As such, the first step was to determine brain regions where connectivity changed across time as a function of group. Given the very small sample size of this study and the high likelihood of being insufficiently powered to detect Time x Group interaction effects, a more simplistic analytic approach (i.e., independent samples t-test examining group differences at each time point) was used to explore group differences in intervention-related change in hippocampal functional connectivity patterns. First, group differences in hippocampal functional connectivity patterns were examined post-intervention. Next, group differences in hippocampal functional connectivity patterns were tested at baseline. Group differences in hippocampal connectivity patterns post-intervention that were not present at baseline were hypothesized to be due to intervention-related effects. To examine the direction of change in functional correlations observed post-intervention (i.e., increase in positive correlation vs. attenuation of negative correlation) and to conduct additional analyses with behavioral variables,

the beta-values and z-scores in clusters that were functionally correlated with the hippocampal seeds post-intervention were extracted at both time points for each participant. These regions were then compared against participant fitness to examine whether change in fitness related to change in connectivity. Pearson correlations were run between percent change values (i.e., post- intervention – baseline, normalized by baseline value) for functional connectivity and percent change values for estimated VO2 max and functional connectivity post-intervention was examined. We hypothesized that group differences in functional connectivity post-intervention would likely be attributable to intervention-related changes in fitness levels. If similar regions were correlated with the hippocampal seeds in both sets of analyses (i.e., group differences and change in fitness), it may suggest that change in fitness mediates group differences in functional connectivity observed post-intervention (although a formal mediation analysis was not conducted). To examine general medication-treatment related changes in hippocampal connectivity patterns, the association between percent change in depression severity (i.e., MADRS score) and functional connectivity post-intervention was examined. Finally, the association between depression severity at baseline and functional connectivity at baseline was examined in order to help interpret post-intervention group differences in functional connectivity post-intervention.