We utilized the Freesurfer pipeline version 5.1.0 (http://surfer.nmr.mgh.harvard.edu/ ), which includes removal of non-brain tissue using a hybrid watershed/surface deformation procedure (Segonne et al. 2004 (link)), automated Talairach transformation, segmentation of the subcortical white matter and deep grey matter volumetric structures (Fischl et al. 2002 (link); Fischl et al. 2004a (link); Segonne et al. 2004 (link)) intensity normalization (Sled et al. 1998 (link)), tessellation of the grey matter white matter boundary, automated topology correction (Fischl et al. 2001 (link); Segonne et al. 2007 (link)), and surface deformation following intensity gradients to optimally place the grey/white and grey/cerebrospinal fluid borders at the location where the greatest shift in intensity defines the transition to the other tissue class (Dale et al. 1999 (link); Dale and Sereno 1993 (link); Fischl and Dale 2000 (link)). Once the cortical models are complete, registration to a spherical atlas takes place which utilizes individual cortical folding patterns to match cortical geometry across subjects (Fischl et al. 1999 (link)). This is followed by parcellation of the cerebral cortex into units based on gyral and sulcal structure (Desikan et al. 2006 (link); Fischl et al. 2004b (link)). The pipeline generated 68 cortical thickness, cortical volume, surface area, mean curvature, gaussian curvature, folding index and curvature index measures (34 from each hemisphere) and 46 regional subcortical volumes. Volumes of white matter hypointensities, optic chiasm, right and left vessel, and left and right choroid plexus were excluded from further analysis. Cortical thickness and volumetric measures from the right and left side were averaged (Fjell et al. 2009 (link); Walhovd et al. 2011 (link)). In total 259 variables obtained from the pipeline were used as input variables for the OPLS classification, 34 cortical regions (7 types of measures) and 21 regional volumes (Table 2 ). Figure 1 illustrates the location of both the cortical and subcortical regions. This segmentation approach has been used for multivariate classification of Alzheimer’s disease and healthy controls (Westman et al. 2011d (link)), neuropsychological-image analysis (Liu et al. 2010c (link), 2011 (link)), imaging-genetic analysis (Liu et al. 2010a (link), b (link)) and biomarker discovery (Thambisetty et al. 2010 (link)).
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Variable included in OPLS analysis
| Cortical measuresa | Subcortical measuresb |
|---|---|
| Banks of superior temporal sulcus | Third ventricle |
| Caudal anterior cingulate | Fourth ventricle |
| Caudal middle frontal gyrus | Inferior lateral ventricle |
| Cuneus cortex | Lateral ventricle |
| Entorhinal cortex | Cerebrospinal fluid (CSF) |
| Fusiform gyrus | Accumbens |
| Inferior parietal cortex | Amygdala |
| Inferior temporal gyrus | Brainstem |
| Isthmus of cingulate cortex | Caudate |
| Lateral occipital cortex | Cerebellum cortex |
| Lateral orbitofronral cortex | Cerebellum white matter |
| Lingual gyrus | Corpus callosum anterior |
| Medial orbitalfrontal cortex | Corpus callosum central |
| Middle temporal gyrus | Corpus callosum midanterior |
| Parahippocampal gyrus | Corpus callosum midposterior |
| Paracentral sulcus | Corpus callosum posterior |
| Frontal operculum | Hippocampus |
| Orbital operculum | Putamen |
| Triangular part of inferior frontal gyrus | Pallidum |
| Pericalcarine cortex | Thalamus proper |
| Postcentral gyrus | Ventral diencephalon (DC) |
| Posterior cingulate cortex | |
| Precentral gyrus | |
| Precuneus cortex | |
| Rostral anterior cingulate cortex | |
| Rostral middle frontal gyrus | |
| Superior frontal gyrus | |
| Superior parietal gyrus | |
| Superior temporal gyrus | |
| Supramarginal gyrus | |
| Frontal pole | |
| Temporal pole | |
| Transverse temporal cortex | |
| Insular |
259 variables in total included in OPLS analysis
aCortical measures = 34 regions (cortical volumes, cortical thickness, surface area, mean curvature, gaussian curvature, folding index and curvature index)
bSubcortical measures = 21 regions (volumes)
Representations of ROIs included as candidate input variables in the multivariate OPLS model.
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