All MRI scans were processed using the FreeSurfer software package, freely available at http://surfer.nmr.mgh.harvard.edu . Multiple MPRAGE MRI acquisitions for each participant were motion corrected, averaged and normalized for intensity inhomogeneities to create a single image volume with relatively high contrast to noise (Dale et al., 1999 (link)). This averaged volume was used to locate the grey/white matter boundary (white matter surface) and this, in turn, was then used to locate the grey/CSF boundary (grey matter surface) (Fischl et al., 1999a (link); 2000 (link)). Cortical thickness measurements were then obtained by calculating the distance between the grey and the white matter surfaces at each point (per hemisphere) across the entire cortical mantle (Fischl et al., 2000 (link)). This cortical thickness measurement technique has been validated via histological (Rosas et al., 2002 (link)) as well as manual measurements (Salat et al., 2004 (link); Dickerson et al., 2009 (link)). The reliability of the cortical thickness measures as well as the other image analysis procedures presented here has been demonstrated across different manufacturer types, scanner upgrades, varying contrast-to-noise ratio, and the number of MPRAGE MRI acquisitions used (Han et al., 2006 (link); Fennema-Notestine et al., 2007 (link); Jovicich et al., 2009 (link)).
The neocortex of the brain on the MRI scans was then automatically subdivided into 32 gyral-based ROIs (in each hemisphere). To accomplish this, a registration procedure was used that aligns the cortical folding patterns (Fischl et al., 1999b ) and probabilistically assigns every point on the cortical surface to one of the 32 ROIs (Desikan et al., 2006 (link)). In addition, two non-neocortical regions of the brain, namely the amygdala and the hippocampus, were automatically delineated using an algorithm that examines variations in voxel intensities and spatial relationships to classify non-neocortical regions on MRI scans (Fischl et al., 2002 (link)).
The anatomic accuracy of the grey and white matter surfaces as well as each of the individual ROIs was carefully reviewed by a trained neuroanatomist (RSD), with particular attention to the medial temporal lobe where non-brain tissue, such as dura mater and temporal bone, often needs to be excluded. All of the MRI scans were processed on a Linux cluster machine with 230 nodes, each with a 2 GHz AMD Opteron CPU (Advanced Micro Devices, Sunnyvale, CA, USA) and 4 GB RAM. Processing time for each MRI scan was ∼25–40 h. The cluster machine allows for the processing of 230 MRI scans simultaneously.
In total, 34 neocortical and non-necortical ROIs were used in this study. For all of the analyses performed here, the mean thickness (only neocortical regions) and the volume (both neocortical and non-neocortical regions) of the right and the left hemispheres, for each ROI, were added together. In order to account for differences in head size, the total volume for each ROI was corrected using a previously validated estimate of the total intracranial volume (eTIV) (Buckner et al., 2004 (link)).Figure 1 shows all of the ROIs used in this study.
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The neocortex of the brain on the MRI scans was then automatically subdivided into 32 gyral-based ROIs (in each hemisphere). To accomplish this, a registration procedure was used that aligns the cortical folding patterns (Fischl et al., 1999b ) and probabilistically assigns every point on the cortical surface to one of the 32 ROIs (Desikan et al., 2006 (link)). In addition, two non-neocortical regions of the brain, namely the amygdala and the hippocampus, were automatically delineated using an algorithm that examines variations in voxel intensities and spatial relationships to classify non-neocortical regions on MRI scans (Fischl et al., 2002 (link)).
The anatomic accuracy of the grey and white matter surfaces as well as each of the individual ROIs was carefully reviewed by a trained neuroanatomist (RSD), with particular attention to the medial temporal lobe where non-brain tissue, such as dura mater and temporal bone, often needs to be excluded. All of the MRI scans were processed on a Linux cluster machine with 230 nodes, each with a 2 GHz AMD Opteron CPU (Advanced Micro Devices, Sunnyvale, CA, USA) and 4 GB RAM. Processing time for each MRI scan was ∼25–40 h. The cluster machine allows for the processing of 230 MRI scans simultaneously.
In total, 34 neocortical and non-necortical ROIs were used in this study. For all of the analyses performed here, the mean thickness (only neocortical regions) and the volume (both neocortical and non-neocortical regions) of the right and the left hemispheres, for each ROI, were added together. In order to account for differences in head size, the total volume for each ROI was corrected using a previously validated estimate of the total intracranial volume (eTIV) (Buckner et al., 2004 (link)).
Three-dimensional representations of all 34 ROIs examined in the current study (only one hemisphere is shown). All of the neocortical ROIs visible in (
