Technical Report #501. Structural MRI Laboratory Manual

Transcription

1 Technical Report #501 Structural MRI Laboratory Manual MR Image Acquisition and Image Processing Tools And Neuroanatomical Regions of Interest (ROI) Robert W. McCarley, M.D. and Martha E. Shenton, Ph.D. Copyright 2008: McCarley and Shenton

2 Structural MRI Laboratory Manual MR Image Acquisition and Image Processing Tools And Neuroanatomical Regions of Interest (ROI) Table of Contents Overview Page 2-3 Section I. Image Acquisition and Image Processing Tools. Pages 3-8 Section 2. Regions of Interest. Pages 8-27 I. Neocortical Gray Matter ROI Page 8 II. Temporal Lobe ROI Pages 8-13 III. Frontal and Prefrontal Cortex ROI Pages IV. Parietal Lobe ROI Pages 21 V. Basal Ganglia and Thalamus ROI Pages VI. Cerebellar and Brainstem ROI Pages VII. Orbito-Frontal Sulcal-Gyral Pattern and Cortex ROI Pages VIII. Occipital Lobe ROI Page 27 IX. Power Analyses Pages 27 Section 3. References. Pages Figures: Pages 34-55

3 OVERVIEW This Technical Report provides information on details of image acquisition and image processing tools in addition to the region of interest (ROI) definitions for brain regions often used in schizophrenia research. It is a snapshot of current image acquisition and processing procedures and our intended alterations in the near future as we move to use of 3 T scanners at all sites. The ROI definitions are based on several different studies that span over many years. Of note, with improved spatial resolution in the images, and with improved segmentation procedures for classifying different tissues, the reliability and accuracy of our measurements have increased over time. Training on ROIs for small brain ROI, are, however, nonetheless still labor intensive and time consuming, and are based on the use of specific MR data sets that are used for reliability purposes and included in our ROI library. These training ROIs are from our most state-of-the-art images, using the most state-of-the-art segmentation procedure, and as such, are periodically updated based on changes in either spatial resolution and/or segmentation algorithms. Of note, this Appendix is considered multipurpose in our two laboratories, and is used by several investigators for their own individual grant applications. It was originally written by Drs. Shenton and McCarley, but multiple individuals have contributed to the ROI definitions, most of which are now published, and the appendix has been revised multiple times with the help of many investigators. Voxel-based morphometry (VBM) and manually drawn ROI. It is probably worth commenting on the current state of VBM and its utility in our subject population vs. manually drawn ROI, which are the current standard (see review in Shenton et al., 2001). We recently did a systematic comparison of VBM results vs. manually drawn ROI in our first episode schizophrenic and manic subjects and their controls (Kubicki et al., 2002). A simple summary is that VBM and the manual ROI were congruent in some comparisons but not in others. We are seeking to understand the basis of the discrepancies, but, in the meantime, we would see methodological problems arising with the exclusive use of VBM in populations of subjects with schizophrenia and bipolar disorder. VBM will likely prove very useful in suggesting possible abnormalities in regions not covered by manual ROI, and, indeed a VBM finding of an insula abnormality in schizophrenia was subsequently confirmed by manual ROI analysis (Kasai et al., 2003). This Technical Report is considered multipurpose in our laboratories and is used by several investigators. The web-publishing was done to facilitate access both within the lab and also for workers outside our laboratory who wish to have a convenient reference to our definitions. It was originally begun by Drs. McCarley and Shenton, but now multiple individuals have contributed to the ROI definitions, most of which are now published in peer-reviewed journals. We emphasize to the reader that this Technical Report has been revised now several times, and will likely continue to be revised in the future as our methodology advances. The work that involves image processing and measuring regions of interest will partly take place at the Brockton VA and partly in the Psychiatry Neuroimaging Laboratory, Brigham and Women s Hospital, directed by Dr. Shenton (see which is closely linked to the Surgical Planning Laboratory (SPL) also at Brigham and Women s Hospital (BWH). We also note scans for the first episode sample will be acquired at McLean Hospital while those for Brockton VA patients will be acquired at BWH, an acquisition arrangement that has been has been in use more than 8 years and has proved fruitful in terms of publications documenting scientific advances. What is new in our acquisition protocol from the last technical report is the use of a GE 3T scanner at BWH and use of a 3T Siemens scanner at McLean that will begin in January 2009, once we have determined the equivalence of MRI data acquired at McLean to that acquired at the 3 T scanner at BWH. We went through the same determination of equivalence of data for the 1.5 T scanners when we began to use the McLean site and the BWH used a 1.5 T scanner. In Section I, we describe the MR acquisition protocol as well as recent additions to the image processing tools used to measure specific ROI. In Section 2, we describe specific ROI and McCarley, Robert - 2

4 neuroanatomical landmarks. The ROI defined in this Technical Report include: I. Neocortical Gray Matter ROI, followed by II. Temporal Lobe ROI, III. Frontal and Prefrontal Lobe ROI, IV. Parietal Lobe ROI, V. Basal Ganglia and Thalamus ROI, VI. Cerebellar and Brainstem ROI, VII. Orbitofrontal Sulco-Gyral Pattern and Cortex ROI, and, VIII. Occipital Lobe ROI. Some of these ROI definitions have been published, including: many of the Temporal Lobe ROI (Shenton et al., 1992; Kwon et al., 1999; Hirayasu et al., 2000), Prefrontal Lobe ROI for whole gray and white matter (Wible et al., 1995; 1997), Parietal Lobe ROI (Niznikiewicz et al., 2000; Nierenberg et al., 2005), Basal Ganglia ROI (Hokama et al., 1995), and, Cerebellar and Brain Stem ROI (Levitt et al., 1999). A description of the Thalamus ROI is also published (Portas et al., 1998). We also review criteria used for defining orbital frontal sulcal gyral patterns. In Section 3 we provide references for the technical descriptions in Sections 1 and 2. SECTION I: MR Acquisition Protocol and Image Processing Tools. 3T MRI Acquisition Protocol at BWH. Structural MRI (smri). 3T. For the Structural MRI volume measurements, images will be acquired using a 3T whole body MRI Echospeed system (General Electric Medical Systems, Milwaukee) at BWH in Boston, MA. We will use an 8 Channel coil in order to perform parallel imaging using ASSET (Array Spatial Sensitivity Encoding techniques, GE) with a SENSE-factor (speed-up) of 2. The structural MRI acquisition protocol will include two MRI pulse sequences. The first results in contiguous spoiled gradient-recalled acquisition (fastspgr) with the following parameters; TR=7.4ms, TE=3ms, TI=600, 10 degree flip angle, 25.6cm2 field of view, matrix=256x256. The voxel dimensions are 1x1x1 mm. The second- XETA (extended Echo Train Acquisition) produces a series of contiguous T2-weighted images (TR=2500ms, TE=80ms, 25.6 cm2 field of view, 1 mm slice thickness). Voxel dimensions are 1x1x1 mm. This latter sequence is used as the additional channel of information for brain segmentation. Total scan time for the structural protocol is 11 minutes. Artifact Reduction. For both the XETA and fastspgr acquisitions, flow compensation and presaturation of a slab inferior to the head will be used to reduce flow related artifacts and to obtain low intra-arterial signal intensity. These parameters have been optimized for our application so that, in combination with our specialized image filtering, they afford the best trade-off between high spatial resolution and high SNR. Together, these two acquisition sequences thus provide the technical benefit of high spatial resolution, covering the whole brain, combined with the clinical benefit of a short time in the magnet (about 20 minutes including set up time). MRI information will be transferred onto a network of 18 SUN workstations and two supercomputers for processing. MRI Acquisition Protocol at McLean. 1.5 T. The current protocol, to be continued with subjects in the McLean longitudinal study who began on the 1.5T, follows exactly the protocol that was previously in use for the 1.5 T scanner at BWH. 3D Fourier Transform Spoiled Gradient- Recalled Acquisition in Steady State (3DFT SPGR) Images. This pulse sequence affords excellent gray and white matter contrast for evaluation of brain structures. Imaging parameters will be: TR=35-msec, TE=5-msec, one repetition, 45 degree nutation angle, 24-cm field of view, 1.0 NEX, matrix=256 X 256 (192 phase encoding steps) X 124. Voxel dimensions will be: X X 1.5-mm. Data will be stored and analyzed as mm thickness coronal slices. Dual Echo, Spin Echo T2 Weighted Sequence. The imaging parameters are: Repetition Time (TR)=3000ms, Echo Times (TE)=30 and 80ms, 24-cm field of view, four interleaved acquisitions with 3 mm slice thickness. This will result in a series of contiguous double echo (proton density and T2-weighted) images (52 levels/104 slices). Voxel (volume of pixel) dimensions will be 1X1X1X3- mm. Scan duration is 25 min, total duration, including setup, is 34 min. McLean 3T scanner (usage to begin January 2009.) For the Structural MRI volume measurements, images will be acquired using a 3T whole body MRI Siemens Trio 3T scanner (Siemes Medical Solutions USA, Inc., Malvern, PA). Protocol parameters will follow as much as McCarley, Robert - 3

5 possible those used at BWH. We will use an 8 Channel coil in order to perform parallel imaging using GRAPPA (Generalized Autocalibrating Partially Parallel Acquisition) with acceleration factor of 2. The structural MRI acquisition protocol will include two MRI pulse sequences. The first results in contiguous gradient-echo acquisition (MP-RAGE- Magnetization Prepared Rapid Gradient Echo) with the following parameters: TR=7.4ms, TE=2.7ms, T1=600, 10 degree flip angel, 25.6cm2 field of view, matrix=256x256. The voxel dimensions are 1X1X1-mm. The second- SPACE (Sampling Perfection with Application optimized Contrasts using different flip angle Evolution) produces a series of contiguous T2-weighted images (TR=2500ms, TE=80ms, 25.6cm2 field of view, 1-mm slice thickness, voxel dimensions: 1X1X1-mm. Scanner Variability Over Time and Scanner Compatibility. Of note, we have scanned and rescanned 5 individuals over a short period of time (< 1 week) and we have found that geometrically complicated and large ROI like the STG have no more inter-scan variation with the same rater than the intra-rater reliability done on the same scan rescored 6 months later (sufficiently long for the rater to forget the original scan scoring). Signal-to-Noise-Ratio (SNR), Resolution, Contrast, and Field Inhomogeneities. At both scanner sites, BWH and McLean, programs from GE, including Top Level Tests (TLT) are performed every day to check the SNR, resolution, contrast, and field Inhomogeneities. The field Inhomogeneities are monitored using cylindrical water filled phantoms. Additionally, image geometric linearity is monitored daily with a 100 mm square cross phantom. Similar Siemens programs will be used for the Siemens 3T scanner at McLean. Image Processing Tools and Procedures McLean Data Transfer. Each MR image data set will then be transferred to CD and maintained and archived in duplicate copy at the McLean Laboratory of Dr. Salisbury (our collaborator in first episode studies). This information in DICOM format will be transferred to the BWH PNL for our processing, as we have successfully done for many years for the McLean GE 1.5 T data. McLean Data Processing for Siemens 3T data. Because of the interoperability of the Slicer ( our analysis tool described below, these images will be directly readable by it, although we will save them in NRRD (Nearly Raw Raster Data), our preferred file format prior to processing. Using the slicer we have successfully processed Siemens MRI data from Mass. General Hospital. Thus the image processing steps described below will also be applicable to the Siemens 3T data from McLean. Image Processing Tools. The 3D Slicer ( is a freely available, open-source software for visualization, registration, segmentation, and quantification of medical neuroimaging data, and was developed in a collaboration between the MIT Artificial Intelligence Lab and the Surgical Planning Lab at BWH, with input from investigators McCarley and Shenton on desirable features for MRI analysis of images from schizophrenia and schizophrenia spectrum subjects and their controls. The newest version of slicer was released in 2007, version 2.7. (A completely new version of slicer, Slicer3, will be released soon. 3D Slicer is natively designed to be available on multiple platforms, including Windows, Linux and Mac OS X. Slicer's capabilities include: 1) interactive visualization of images, 2) manual editing, 3) fusion and co-registering of data, 4) automatic segmentation, 5) analysis of diffuse tensor imaging data, and 6) visualization of tracking information for imageguided surgical procedures. Slicer has been used for processing and analysis of the MRI data from schizophrenia and spectrum subjects and their controls for more than a decade. Segmentation Tools: Expectation-Maximization (EM) Algorithm. For segmentation, the iterative expectation-maximization (EM) algorithm combines the statistical classification of tissue classes with the automatic identification of intensity inhomogeneities in the images. Accurate tissue McCarley, Robert - 4

6 segmentation in MR images is a difficult problem because of the spatial inhomogeneities in pixel intensity. For example, a pixel representing white matter in the upper left of an image is often much brighter than a pixel representing white matter in the lower right side of the same slicing. The EM segmenter alternates two computational stages. In one stage, the spatial intensity inhomogeneities are estimated, and then in a second stage, this estimate is used to improve the accuracy of the tissue classification. An initial semi-automated segmentation (the algorithm used for segmentation in our previous papers) is used as a starting point or as input to the EM segmenter, and then the algorithm improves the segmentation in several iterations of the two steps (see Wells et al., 1994 for a detailed discussion). This algorithm allows for the use of the same statistical model or semi-automated segmentation (the input or starting point) to be used for all of the scans of a given acquisition type, hence eliminating error due to differences between users with regard to tissue classification. More specifically, earlier label maps for tissue classification were created for each individual subject. This can lead to errors based on individual differences among the individuals creating the label maps. With the new algorithm, label maps are created once for all cases in a study, and these maps are used as the input or starting point. Further, the addition of the estimation of inhomogeneities allows for a more consistent segmentation of scans across magnet upgrades, and/or across different imaging sites. The segmentations computed using this algorithm were more consistent in estimating tissue classes than the semi-automated segmentation procedures when the segmentation was compared among 5 raters (Wells et al., 1994). In previous papers (e.g., Wible et al., 1995), the measurement of the gray/white matter volume in a cortical region required slice by slice editing of the boundary on each slice. The segmentations obtained using the EM segmenter are more accurate for the whole brain, and require less editing. This segmenter has made it possible to measure cortical regions faster, allowing us to accurately measure more regions in a greater number of subjects. A new segmentation method is now in use, which uses the EM Segmenter, described above, in conjunction with a statistical framework that combines the registration of an atlas with the segmentation of magnetic resonance images. We use the same Expectation Maximization-based algorithm to find a solution within the model, which simultaneously estimates image inhomogeneities, anatomical labelmap, and a mapping from the atlas to the image space. An example of the approach is given in a recent publication for a brain structure-dependent affine mapping approach (see Pohl et al., 2005a). The algorithm produces high quality segmentations for brain tissues as well as their substructures. We demonstrate the approach on a set of 22 magnetic resonance images. In addition, we show that the approach performs better than similar methods, which separate the registration and segmentation problems. The segmentation method developed by Pohl et al. (2004) has recently been applied to MR images from subjects with schizophrenia and schizophrenia spectrum disorders and their controls in order to partition the images into the major three tissue classes: gray matter, white matter, and CSF (Koo et al., 2006; Nakamura et al., 2007). The method, as noted above, is based on use of an expectation-maximization (EM) algorithm, which simultaneously estimates the inhomogeneities in the images and segments the images into the three major tissue classes. The algorithm analyzes both SPGR and T2-weighted MR images (Pohl et al., 2004), and uses spatial priors (Guimond et al., 2001) to increase the accuracy of the approach. Spatial priors capture the probability of a certain tissue class being present at a certain location in the 3D volume. When compared to other state-of-the-art algorithms (Bouix et al. 2004), the method produces highly accurate segmentations of the three major tissue classes as it combines prior information, image inhomogeneity correction, and dual channel analysis. The final step measures the volume of the different tissue classes using the medical imaging software 3D-Slicer (Pieper et al., 2004). The voxel volumes of gray and white matter and CSF are summed yielding the total intracranial contents (ICC). Koo et al. (2006), using McCarley, Robert - 5

7 this method, were able to discern smaller neocortical gray matter and larger sulcal CSF volumes in neuroleptic-naïve females diagnosed with schizotypal personality disorder. This kind of algorithm development is important as small differences between groups, such as those investigated in schizophrenia and schizophrenia related disorders require that tools be sufficiently sensitive to detect very subtle volume differences, which while small, may nonetheless be quite important in the etiology of these disorders. Different segmentation algorithms have been compared by a computer scientist, Dr. Sylvain Bouix, who works with Dr. Shenton in the Psychiatry Neuroimaging Laboratory, who found our segmentation program performed well in comparison with others in the field (Bouix et al., 2007). The reader is also referred to publications cited in the references at the end of this Appendix (see Bouix et al., 2004; Liu et al., 2004; Martin-Fernandez et al., 2005; Pohl et al., 2002, 2005a, 2005b). FSL software. We used the FSL software for bias field correction for the 3T images. FSL is the product of the Analysis Group at the Oxford Centre for Functional MRI of the Brain (FMRIB), and FSL is shorthand for FMRIB s Software Library (overview in Smith et al., 2004) and includes tools for structural MRI analysis ( The particular FSL implementation for bias field correction for the 3T images was developed by Zhang et al. (2001). It is based on a Markov Field Model using a modification of the expectation-maximization algorithm of Wells et al (1996) for bias field correction. A number of new image processing tools have been developed in our image processing laboratory, and they are currently implemented in our studies. 3D volume editor The presence of supercomputers at the Brigham and Women s laboratory site aids and speeds image processing. For example, one tool that is useful is called the 3D volume editor, incorporated into slicer This program displays coronal, sagittal, and axial planes of any image series automatically. The slice number is orthogonal for each of the 3 planes, so that each can be viewed at any anterior/posterior or dorsal/ventral level. A segmentation file can also be viewed as an overlay onto the MR image, and some editing subroutines are available. Changing to a new slice, editing a volume of segmentation, or segmentation of a volume using thresholding are all computations that can be done instantaneously in this editor. In addition, any changes to the segmentation are immediately updated in a window in which the segmentation is rendered in 3D. The 3D rendering can also be viewed in any orientation. This editor contains routines for line drawing of ROI, connectivity, dilation and erosion, island removal and magnification. Realignment and Reslicing programs were developed to compensate for head tilt and rotation during MR acquisition, and for realigning the brain along any chosen axes, currently implemented in Slicer. Head tilt or rotation can interfere with identifying landmarks and midline structures while constructing ROI. A plane that minimizes the square distance error is fitted to a set of user chosen axes to align the brain, and then the image is resampled into isotropic voxels. The voxel size is set to the smallest dimension of the original voxels, in this case 1 mm. Cubic interpolation is used to determine the intensity values of the resampled scans for MR Images. The intensity of a voxel in the resulting scan is set to a linear combination of the intensity values of the voxel s eight nearest neighbors with the weights linearly decreasing when the distance between the voxel centers. Segmented images and ROI can also be realigned and resampled. For the segmented slices, the interpolation scheme had to be modified, as the original tri-linear interpolation algorithm produces a scan with label values that did not exist in the original scan. It assumes continuous range of values in the images, and therefore is not applicable in this case. It was modified to what is called tri-linear voting, where the weights are computed identically to the original method, but are used as votes for the corresponding label values, rather than as weights for computing the McCarley, Robert - 6

8 linear combination of the labels. The resulting label is the label that receives the highest vote. Using this method, resampling after automatic tissue segmentation preservers the segmentation obtained in the original, unaligned image, with no variation in intracranial contents and minimal change in gray (1%), white (none) and CSF (5%) classification, the same order of difference observed when manual classification is performed by different raters. Summary of 3T Processing Protocol Prior to ROI Analysis. 1. First, the T2 image will be realigned to the T1 using a rigid body registration algorithm (Slicer). Note that both the T1 & T2 images have voxels of 1x1x1mm 2. FSL will be used to extract the intracranial contents (ICC) mask which will be further edited by human raters. The ICC mask has skull, skin and non-cns elements stripped from the MRI image. 3. Both realigned T1 and T2 images will then be corrected for bias field inhomogeneities using FSL. 4. Finally, the two images will be used as input to Slicer EMAtlasBrainClassifier designed by Kilian Pohl (see above), with slightly modified parameters better suited to the 3T. COMPARISON OF SEGMENTATION OF IMAGES FROM GE 1.5 T AND GE 3.0T SCANNER AT BWH. Separation of brain tissue into distinct tissue classes of gray matter, white matter and CSF in segmentation depends on the signal intensity differences of these tissue classes. This contrast is lessened by partial volumes, where different classes of tissue are mixed in one voxel. The larger the voxel, the greater the partial volume effect. The 3T scanner has smaller voxels (1x1x1mm) than the 1.5 T scanner (1.5x.9375x.9375mm); the 3T scanner thus lessens the partial volume effect and increases the accuracy of segmentation. However, for the 3T acquisitions, segmentation is complicated by larger inhomogeneities in the magnetic field, referred to as a bias field, with the bias field being greater for the 3T than the 1.5 T scanner, necessitating a bias field correction, as described above. For our comparison we scanned 5 subjects with schizophrenia and 5 age-matched subjects both on the 1.5 T scanner used previously in our studies and on the 3 T scanner. Segmentation and bias field correction were as described above. To illustrate features of the 1.5T vs. 3T we chose scans of the same subject with schizophrenia. As shown in Figure 1A, 3T shows superior resolution with less partial volume effect, especially evident in the better segmentation seen in more detail of white matter in basal ganglia and in cortex. A coronal slice view of Heschl s gyrus and the superior temporal gyrus in Figure 1B also reveals greater detail in the raw images and greater accuracy of segmentation in the 3T scanner. To compare 1.5T vs. 3T quantitatively across all subjects we evaluated neocortical gray matter and white matter and supratentorial CSF (subcortical nuclei and cerebellum/brain stem were very poorly done in 1.5 T and were not compared). While the sample is much too small for statistical evaluation and for final conclusions, the preliminary data are interesting. Overall in our 10 subject comparison the superior quality of the 3T scanner with bias correction compared with the 1.5 T scans (3T-1.5T values) let to a mean increase in gray matter of.7%, a decrease in CSF of 1.1% and an increase in white matter of 6.3%. Interestingly, the healthy controls and schizophrenic patients showed reverse trends in gray matter and CSF for the 3T-1.5T differences, with 3T gray matter in controls increasing by 2.9% and decreasing in schizophrenics by 1.5%, while 3T CSF showed controls decreasing by 4% and schizophrenics increasing by 8.8%. White matter increased in both groups, 3.8% in controls and 8.8% in schizophrenics. These data suggest that the differences between controls and schizophrenics we observed using 1.5T images in neocortical McCarley, Robert - 7

9 comparisons (Nakamura et al., 2007) would have shown a more pronounced reduction in gray matter and an increase in CSF in schizophrenics compared with controls, and thus the 3T data confirms, with even stronger emphasis, our findings using 1.5T. SECTION 2: REGIONS OF INTEREST (ROI). Insert Figure 1 About Here I. NEOCORTICAL GRAY MATTER. The procedure for isolating neocortex is described elsewhere (Koo et al., 2006). Briefly, neocortical ROI delineation included all six-layered neocortex and excluded the major portion of nonneocortical cortex, including limbic cortical areas (with the exception of the pyriform cortex) and most of paralimbic cortex, with the exception of portions of cingulate, insula, and temporal pole (for anatomic description: Mesulam, 1985). We describe this ROI as neocortical gray matter (NCGM) because the included regions of non-six-layer cortex comprise less than 5% of the GM volume in the ROI. Consistent with exclusion of medial temporal gray matter structures, the LV ROI did not include the very small temporal horn portion. NCGM was manually parcellated into three lobar ROI, frontal, temporal, and parieto-occipital (Figure 2). This parcellation mainly used sulcal boundaries because these are more faithful to brain anatomy than a purely geometric parcellation. The frontal lobe was separated from the parietooccipital lobe by the central sulcus on the convexity, a boundary that is constant, easily identifiable, and traceable with little interindividual variation (Ono et al., 1990). The central sulcus was initially traced on the axial plane, and subsequently these trace lines were used on the coronal plane to separate frontal and parietal lobes. For frontal GM on the medial wall, the posterior terminus was the most posterior coronal slice containing corpus callosum. The frontal lobe was clearly separated from the temporal lobe by the Sylvian fissure and circular insular sulcus. The posterior temporal lobe terminus was geometrically defined as the most posterior coronal slice where the fornix could be clearly seen along the lateral ventricles, as in our previous studies (Hirayasu, et al., 2000). Occasionally, especially in the right hemisphere, the Sylvian fissure steeply ascended posteriorly through the parieto-occipital region. In this case, the superior boundary separating the temporal lobe from the parieto-occipital lobe was defined as the most superior axial slice where Heschl s gyrus could be seen. The parieto-occipital lobe was automatically defined by its contiguous boundaries with the frontal and temporal lobes. Insert Figure 2 About Here II. TEMPORAL LOBE ROI. Introduction. Table 1, below, defines the landmarks used for the regions of interest in our laboratory, and thus we include some ROIs that go beyond the ROIs proposed in the current application. We therefore list some regions not proposed because we think it important to have developed rules for ROI for brain regions that might prove interesting in terms of unhypothesized findings on fmri scans done on subjects in this REAP, such as the thalamus and cerebellum. Daniels et al. (1987) was the primary anatomical MRI atlas used. Interrater reliability was high, and is discussed at the end of this section. Figure 3 provides a lateral view of the brain which includes many of the gyri that are measured, including superior temporal gyrus, middle temporal gyrus, inferior temporal gyrus, etc. (see Figure 3 at end of this Appendix). Insert Figure 3 About Here Table 1 Region Most Anterior Slice Most Posterior slice McCarley, Robert - 8

10 Anterior amygdala-hippocampus (amygdala) White-matter tract (temporal stem) linking temporal lobe with the rest of brain Last slice before the appearance of the mammillary bodies Posterior amygdalahippocampus (hippocampus) Superior temporal gyrus Anterior region Posterior region First appearance of the mammillary bodies Landmarks for anterior and posterior hippocampus Landmarks for amygdala - anterior hippocampus Landmarks for posterior hippocampus Last appearance of fibers of the crux of the fornix Landmarks for anterior and posterior hippocampus Landmarks for amygdala - anterior hippocampus Landmarks for posterior hippocampus Middle temporal gyrus, Inferior temporal gyrus, Fusiform gyrus, Parahippocampal gyrus Landmarks for superior temporal gyrus Landmarks for superior temporal gyrus Temporal Lobe Anterior pole Second slice showing temporal lobe Slice immediately anterior to start of superior temporal gyrus & amygdala (1) Amygdala-Hippocampus. This region was subdivided into anterior and posterior segments, with the subiculum included with each hippocampal subdivision. A) Anterior Amygdala- Hippocampal Complex (Amygdala weighted). The contiguous gray matter of the anterior hippocampus and the amygdala could not be reliably differentiated and so were grouped together. The most anterior slice of this ROI was objectively defined as the slice showing the temporal stem. As illustrated in Figure 151 (p. 366) of Crosby et al. (1962), this objectively definable fiber connection is nearly coextensive anteriorly with the less objectively definable (on MRI) onset of the anterior amygdala. Figure 4 depicts a coronal 1.5mm slice that shows an outline of the amygdala as well as an outline of the parahippocampal gyrus and the whole temporal lobe. Regions of interest (ROIs) were drawn on multiple slices that included the region of interest (see Figure 4). Insert Figure 4 about Here B) Posterior Amygdala-Hippocampal Complex (Hippocampal weighted). The coronal slice that showed the onset of the mammillary bodies was used as the anterior landmark. The most posterior coronal section to be included was defined by the last appearance of the fibers of the fornix traveling dorsally from hippocampus along the medio-ventral border of the lateral ventricles; this slice also contained the splenium of the corpus callosum. A visual inspection of the MR scans of all cases indicated that these definitions included all of the amygdala/hippocampal complex within a precision equal to the slice thickness (i.e., mm). To determine further the validity of the anterior and posterior landmarks, i.e., whether or not they included the entire extent of amygdala-hippocampal complex, the length of this complex for each subject was computed from the number of 1.5-mm slices used. Mean values (+S.D.) were: mm (normal controls) and mm (schizophrenics) for right hippocampus, and McCarley, Robert - 9

11 mm (normal controls) and mm (schizophrenics) for left hippocampus. These values did not differ from post-mortem data collected from 60 adult brains (Duvernoy, 1988), which showed an average length of mm for the hippocampus. In addition, schizophrenics and normal controls in the New England Journal of Medicine study (Shenton et al., 1992) did not differ on entire hippocampal length; they also did not differ in the lengths of either the anterior or posterior segments. Posterior segment lengths were mm (normal controls) and mm (schizophrenics) on the right, and mm (normal controls), and mm (schizophrenics) on the left. Anterior segment lengths were mm (normal controls), and mm (schizophrenics) on the right, and mm (normal controls) and mm (schizophrenics) on the left. (2) Parahippocampal Gyrus (PHG) Gray Matter ROI. Non-subicular portions of this gyrus were defined laterally by the collateral sulcus and a demarcation line drawn across the narrow portion of the gyral isthmus at the deepest portion of the collateral sulcus. The anterior and posterior extent used the same landmarks as for the anterior and posterior amygdalahippocampus. (3) Superior Temporal Gyrus (STG) Gray Matter ROI. In addition to measuring medial temporal lobe regions, the neocortical STG gray matter was also examined. This region has been reported to show abnormalities in schizophrenia and is also important because of its critical role in auditory and language processing (e.g., Geschwind and Levitsky, 1968; Galaburda et al., 1984; Steinmetz et al., 1989; Shenton et al., 1997; Shenton 2001). The medial extent of STG was defined by the limiting fissure of the insula; from here a line was drawn through the gray matter. For the quantitative measurements, the anterior and posterior borders of the STG were the same as for the amygdala-hippocampal complex. (4) Middle Temporal Gyrus. For the middle temporal gyrus (MTG), we used criteria similar to Kim et al. (1999) and Crespo-Facorro et al. (2000). Before tracing, we drew two guidelines on the sagittal slice in each hemisphere to assure the borders (see Figure 5). We identified STG and anterior occipital sulcus on the sagittal slice where STG could be seen most clearly. These gyri were the superior border of the MTG, and the inferior temporal gyrus (ITG) was used as the inferior border. Manual drawings of MTG were performed on the coronal plane referring the guidelines (slice A in Figure 5). On the slice of a transition area, MTG and ITG were divided referring the guidelines (slice B in Figure 5). The most anterior slice was the first one in which the white matter tract linking the temporal lobe with the rest of the brain (temporal stem) could be seen. The most posterior slice was determined by the anterior tip of parietoccipital sulcus in midsagittal plane. MTG was thus defined as the gyrus just inferior to the STG (the parallel and superior temporal sulci will serve as the boundaries). The ventral boundary of the gyrus, the middle temporal sulcus, was the most inferior sulcus on the lateral surface of the temporal lobe. Further work evaluating the Middle Temporal Gyrus in schizophrenia has been completed in our laboratory by Onitsuka et al. (2004) and by Kuroki et al. (2006). Reliabilities were > 0.95 ICC. Insert Figure 5 About Here (5) Inferior Temporal Gyrus. The middle temporal sulcus constitutes the dorsal border of this gyrus on the lateral surface. The medial border constitutes the fusiform gyrus and lateral occipitotemporal sulcus, and the inferior temporal sulcus (ITS) constitutes the superior border for the inferior temporal gyrus (ITG). The occipitotemporal sulcus was used to determine the medial border. This sulcus is interrupted frequently (the proportion of a single continuous sulcus is 48% for the right side and 24% for the left side). In such interrupted cases, the border was decided as the prominent sulcus on the coronal and axial slices. The most anterior and posterior slice were the same as MTG. Manual drawings of ITG were also performed on the coronal plane. Figure 5 McCarley, Robert - 10

12 shows STG, MTG, ITG, fusiform gyrus (FG) in a coronal slice as well as 3-dimensional reconstructions. Further work evaluating the Inferior Temporal Gyrus in schizophrenia has been completed in our laboratory by Onitsuka et al. (2004) and by Kuroki et al. (2006). Reliabilities were > 0.95 (interclass correlation coefficient). (6) The Fusiform Gyrus. This gyrus is defined as the first gyrus lateral to the parahippocampal gyrus. Bordered medially by the collateral sulcus and laterally by the lateral occipitotemporal sulcus. The fusiform gyrus (FG) is a spindle-shaped structure that is coextensive with the length of the temporal lobe, at a lateral distance lateral to the parahippocampal gyrus. Anatomically, the collateral sulcus forms the medial border of FG along its entire length. The occipitotemporal sulcus forms the lateral border of FG along its entire length. In some anatomical definitions, the anterior and posterior transverse collateral sulci are used to define the anterior and posterior FG boundaries. However, the anterior and posterior borders are often hard to identify reliably on MR images, and, consequently, different landmarks have to be used for the segmentation of this structure. In the current study, we used criteria similar to the work of Kim et al. (2000) who provided detailed guidelines for FG measurement in the parcellation of the temporal lobe. Drawing for FG was performed on the coronal plane. We found it essential to refer to axial and sagittal orientations for cases in which the borders were ambiguous on coronal slices. The anterior landmark was reliably defined by one slice posterior to the appearance of the mamillary body. The posterior landmark was determined by the anterior tip of the parietoccipital sulcus in midsagittal plane. These landmarks were chosen because they were the most reliable for delineating FG, although small amounts of the anterior and posterior parts of FG were excluded. This approach prevented erroneous inclusion of parts of another structure in FG measurement. The collateral sulcus and occipitotemporal sulcus were used to determine medial and lateral FG borders respectively. In some cases, these sulci were interrupted or duplicated particularly in the posterior part near the preoccipital incisura. In these sections, the more laterally located sulcus was used as the border (see Figure 6 for a 3D view of the ventral surface of the brain and for a slice showing the ROI on a 1.5mm coronal slice; see also Lee et al., 2002). This gyrus has also been investigated in schizotypal personality disorder by our research group, where subjects with schizotypal personality disorder did not show reduced gray matter volume of the fusiform gyrus compared with normal controls (see Dickey et al., 2003). Insert Figure 6 About Here (7) Temporal Pole. For the delineation of the temporal pole, we used the same criteria as Kim et al. (2000) and Crespo-Facorro et al. (2000), which were similar to those of Gur et al. (2000). The posterior border of the temporal pole was defined as the coronal plane where there was no frontotemporal junction. The lateral, medial, superior, and inferior boundaries were defined simply by the natural limits of the temporal lobe, and the anterior boundary was the rostral end of the temporal lobe tissue adjacent to the sphenoid bone (see Figure 7). Insert Figure 7 About Here (8) Whole Temporal Lobe ROI. The ROI included both white and gray matter volume. Two definitions were used. The first portion included that part of temporal lobe that was coextensive with the anterior-posterior extent of the hippocampal and STG ROI. The second portion added the most anterior temporal pole region, a region we will refer to simply as the temporal pole and had the following definition: the most anterior portion started with the second slice on which temporal lobe appeared (the first slice included meninges) and the posterior portion was the slice anterior to the beginning of the anterior amygdala-hippocampal complex. Visual inspection of 3D reconstruction showed that for the right temporal lobe, the posterior McCarley, Robert - 11

13 landmark (the same as for STG and PHG) included almost all of the temporal lobe (in most cases, within mm) as defined by the extent of the Sylvian Fissure. However, in accord with LeMay (1990), the left temporal lobe, as defined by the extent of the Sylvian Fissure, extended beyond (was more posterior to) the slice defined by the end of the posterior hippocampus ROI. To allow objective comparisons of volumes based on the same slice definition, this most posterior extent of the left temporal lobe ROI was not included in the computation of volumes, and this restriction should be kept in mind. It should be emphasized, nevertheless, that the volume definition used did include most of the left planum temporale (PT) and almost all of Heschl s gyrus. Use of this objective and reliable scheme also resulted in anterior and posterior landmarks that applied equally to left and right temporal lobes and to normal controls and schizophrenics. For the temporal lobe boundary, a line was drawn along the cortical gray matter surface of the temporal lobe, following the Sylvian Fissure up to the Sylvian point, and then a line was drawn diagonally from the Sylvian point to the upper most portion of the amygdala-hippocampal complex and then medially to the lateral ventricle if this was present, or along the amygdala-hippocampal complex, back to the temporal lobe. All areas of CSF within the temporal lobe were excluded and only the gray and white matter values were included in the volumetric analyses. Left and right were computed separately. (9) Localization of Structures Within STG- Heschl's Gyrus and PT. For the measurement of Heschl's gyrus we will use Pfeifer's definition of Heschl's gyrus, Heschl's sulcus, and the anterior border of the PT (translated by Steinmetz et al., 1989). The anterior border of the PT is defined by Heschl's sulcus, and, if needed, a line is drawn that extends its trajectory to the lateral surface of the STG. In cases where there are two transverse gyri (frequent on the right), we will follow "Pfeifer's norm". That is, if the two gyri come from a common stem, then they will be both classified as Heschl's gyrus. The posterior border of the PT will be the same as for the STG. In accordance with the criteria of Barta et al., 1995, Heschl s gyrus extends from the posterior margin of the insula near the opercular branch of the postcentral gyrus, transverses the superior aspect of the temporal lobe and terminates in the lateral border of the superior temporal gyrus. (For more details see Kwon et al., 1999; see also Figure 8). Insert Figure 8 About Here Axial images (reformatted from coronal scans) were first used to mark the outline of Heschl s gyrus in order to accurately indicate the location of Heschl s gyrus on coronal images. The images were then converted back to the coronal plane and the markers were used as a guide to outline the gray matter. As a last step, ROI were checked on sagittal images to confirm the accuracy of the boundaries. This subdivision of posterior STG is under development, and is not proposed in the current application. Heschl s gyrus and planum temporale have also been evaluated in schizotypal personality disorder (Dickey et al., 2002) as well as in first episode patients diagnosed with schizophrenia (Kasai et al., 2003a). In the Kasai et al. study, progression of volume reduction was reported at follow up in both Heschl s gyrus and planum temporale in patients diagnosed with schizophrenia, but not in bipolar first episode patients or controls. (10) Temporal Horn. This includes all CSF within the segment of the lateral ventricle running approximately in the plane of the temporal lobe, with the most posterior slice being the 2 nd slice anterior to the most posterior slice of the hippocampus (this definition excludes the portion of the lateral ventricle running dorso-ventrally). Interrater Reliability. For our interrater reliability studies we use the intraclass correlation McCarley, Robert - 12

14 coefficient, and independent raters trace regions of interest, blind to diagnostic group. For our original study (Shenton et al., 1992), Dr. Martha Shenton measured the temporal lobe regions of interest, blind to diagnosis. A second rater, also blind to diagnosis, measured the temporal regions for three normal controls and three schizophrenic patients selected randomly from each group. Additionally, four raters, blind to diagnosis, rated superior frontal gyrus for one case selected randomly and segmented into four regions (12 slices each). The average intraclass correlation was r i =0.86. Since this time we have conducted further reliability studies, for both intra- and interrater reliability, and results have all been r i >0.90. For interrater reliability for the temporal pole, three raters, blinded to group membership, independently drew ROIs. Ten cases were selected at random and the raters edited every other slice. The intraclass correlation coefficient was 0.99/0.98 for left/right temporal pole gray matter, and 0.99/0.99 for left/right temporal pole white matter, respectively. For Heschl s gyrus and planum temporale, interrater reliability was based on three independent raters who drew ROIs for 10 cases selected randomly. Interrater reliability was r i =0.92 for left Heschl s gyrus, r i =0.90 for right Heschl s gyrus, and r i =0.93 for left planum temporale and r i =0.91 for right planum temporale (see Kwon et al., 1999). In a later study by Hirayasu et al. (2000), interrater reliability was also computed by three independent raters for ten cases selected randomly. For Heschl s gyrus r i =0.88 for left Heschl s gyrus, r i =0.88 for right Heschl s gyrus, and r i =0.99 for left planum temporale and r i =0.95 for right planum temporale. Interrater reliability for the middle and inferior temporal gyrus, based on three independent raters and 10 cases, was: r i =0.98 for left MTG, r i =0.98 for right MTG, r i =0.964 for left ITG, and r i =0.97 for right ITG. Interrater reliability for the fusiform gyrus was computed by 3 independent raters for ten cases that were selected randomly. Interrater reliability for the three raters was: for left FG, for right FG (see Lee et al., 2002). III. PREFRONTAL CORTEX ROI. Overview. The prefrontal ROI are described below, beginning with total gray and white matter of the prefrontal cortex, and ending with ROI definitions for the individual gyri of the prefrontal lobe. Reliability is high, and is discussed in conjunction with each subdivision of this section. (1) Total Prefrontal Gray Matter and White Matter. The boundaries for the prefrontal region have been described in detail elsewhere (Wible et al., 1995; see Figure 9). Briefly, the gray matter measurements extended from the most anterior slice containing gray matter to three slices anterior to the temporal stem. The posterior landmark was determined by first locating the most anterior slice that contained the temporal stem (the white matter tract connecting the temporal and frontal lobes), then moving anteriorly three slices. This landmark was chosen because it was reliable and it controlled for any difference between schizophrenics and controls in lateral asymmetries (reliability for all anterior-posterior landmarks is discussed below). Insert Figure 9 About Here The prefrontal cortex gray matter measure stops a few slices anterior to the most inferior aspect of the precentral sulcus, and the posterior bound differed slightly on the left and right, reflecting the different anteroposterior onset of left and right temporal stems. Thus, the gray matter volume compared in this study excluded Brodmann s area 4 (motor cortex) and at least parts of area 6 (supplementary and premotor cortices). Reliability. To ensure a high degree of reliability, different anteroposterior landmarks were used for the prefrontal white matter volume than for the prefrontal gray matter volume. Anteriorly, the white matter was measured beginning with the first slice that contained white matter and McCarley, Robert - 13

15 extended posteriorly to the slice immediately anterior to the slice that contained the lateral ventricles. These landmarks were chosen for the white matter because it is invaded posteriorly by the gray matter structures of basal ganglia and the claustrum, both of which were not accurately segmented by the present semi-automated segmentation procedures and would have required extensive manual editing. Data on 29 cases were segmented by two different raters, each working on half of the cases. Interrater reliability was assessed by having each rater segment a random case that was initially processed by the other rater. A third rater also segmented the two cases, resulting in three estimates of volume (from three raters) for the two cases (one control and one schizophrenic patient). The intraclass correlation for three raters (Drs. Wible and Hokama, and Research Assistant I-han Chou) was r i =0.98. The inter-rater reliability of the first two raters was of interest, since they performed the segmentation. The average percent error for the first two raters was computed by subtracting the volumes obtained on the same case by the two raters for each category of tissue measured (i.e., white and gray matter on the left and right) and dividing the difference by the first rater s volume score; the average percentage difference between raters was 1.75% for the schizophrenic case and 3.63% for the control case. More recently, interrater reliability was computed by three independent raters based on ten cases where Intraclass correlation coefficients were: for left prefrontal gray, for right prefrontal gray, for left prefrontal white, and for right prefrontal white (Hirayasu et al., 2001). Intrarater reliability was obtained for the two raters who segmented each of the brains. After the segmentation was completed for all cases, inter-rater reliability was determined by having each of the two raters reapply the image processing stages to a randomly selected case that this rater had previously segmented. This procedure produced two estimates of all four prefrontal ROI (left and right gray and white matter) for two cases. The average percentage difference between the first and second segmentations of the case was 3.25% for rater CGW, and the correlation between segmentations was r=0.98. For rater HH, the percentage difference was 4.25%, and the correlation between segmentations was r=0.99. Intra-rater and inter-rater reliability for the identification of the landmarks used to delineate gray and white matter boundaries were also assessed. Three cases for each of the two raters were chosen randomly from those initially processed by that rater, resulting in a total of six cases that were used for landmark reliability. The landmarks for gray and white matter on the left and right were judged blindly for all six cases by the two raters, resulting in landmark values for each case from the original segmentation, a second judgment from the original rater, and a third judgment from the rater who had not originally processed the case. The intraclass correlation for landmark reliability over six cases each rated three times was r i =0.99. For further information the reader is referred to Wible et al. (1995). (2) Parcellation of the Prefrontal Cortex into ROI. The prefrontal cortex was divided into insular, orbital, inferior, middle, superior, cingulate, and polar portions. The landmarks and methods used to parcellate the prefrontal cortex will be described for each of these areas. The delineating sulci used to define the ROI will be discussed in terms of three anterior-posterior levels of the prefrontal cortex (designated anterior, intermediate, and posterior); for some regions the boundaries changed at these transition points. The dorsolateral and ventromedial boundaries will be described only for the orbital region, and the dorsolateral boundary will be described for each remaining region. The other regions will be described in turn from the most ventral to the most dorsal. Each region's dorsal boundary was the same as the ventral boundary of the region immediately superior to it. A. Insular Region. The insular region was determined by visualizing sagittal views of the McCarley, Robert - 14

16 brain. It was bounded dorsally and ventrally by the circular insular sulcus. In the most posterior extent, the insula was divided from the orbital cortex by designating cortex on the ventral surface orbital cortex, and on the lateral surface, insular cortex. In the most anterior extent, it consisted of a small gyrus between the orbital and inferior gyrus regions). Kasai et al. (2003b) illustrate the definition. B. Orbital Region. Most recently, we have focused on delineating the orbital frontal region from other portions of the prefrontal cortex. The ventral surface of the frontal lobe, traditionally referred to as the orbitofrontal cortex (OFC), extends from the frontal pole rostrally to the anterior perforated substance caudally. The frontal operculum and the ventromedial margin of the cerebral hemisphere form its lateral and medial boundaries, respectively. Greater variability exists among the sulcal and gyral patterns of the human OFC. In order to specify OFC more specifically and more anatomically, we classified OFC into three subregions primarily based on sulcal information (Chiavaras and Petrides 2000), and including: Gyrus Rectus (GR); Middle Orbital Gyri (MiOG); and Lateral Orbital Gyrus (LOG). Of note, Medial, Anterior, and Posterior Orbital Gyri (MOG, AOG, and POG, respectively) were combined into MiOG, to ensure reliability of ROI definition in view of the extreme variability of H-shaped sulci dividing MiOG. The detailed boundary definition is next described. Figure 10 provides an overview of the ROI definitions and Figure 11 shows a 3D reconstruction. Boundary Definitions (Based on sulci). (1) GR (Gyrus Rectus). Anterior: the most anterior slice where olfactory sulcus can be seen clearly. Posterior: GR disappear itself before olfactory trigone and subcallosal gyrus appear. Lateral: olfactory sulcus. Medical: supraorbital sulcus. (2) MidOG (Middle Orbital Gyrus) Anterior: One slice posterior to the slice at one fourth anterior point between the most anterior slice of brain parenchyma and the most anterior slice where corpus callosum are separately seen above and below the septum. Posterior: MidOG (POG and MOG) disappear. Lateral: -Anterior part: lateral portion of H-shaped sulci -Transitional part: some obscure slices (up to 4~5 coronal 1.5mm slices) between anterior and -posterior parts -Posterior part (where POG disappears): circular insular sulcus -Medial: olfactory sulcus (3) LOG (Lateral Orbital Gyrus) Anterior: the most anterior slice where both lateral orbital sulcus and the lateral ramus of H-shaped sulci can be seen clearly. Posterior: the most posterior slice where lateral ramus of H-shaped sulci can be seen clearly. Lateral: lateral orbital sulcus Medial: lateral portion of H-shaped sulci These parcellations can be seen in Figure 10, and are adapted from Chiavaras and Petrides (2000). Insert Figure 10 and Figure 11 About Here McCarley, Robert - 15

17 C. Inferior Frontal Gyrus. At all levels, the dorsal boundary of this gyrus was the inferior frontal sulcus, which was identified primarily from 3D reconstructions. At an intermediate level, the inferior frontal gyrus includes the pars orbitalis, which was visible on the slice as a small gyrus situated immediately inferior to the circular insular gyrus. The pars orbitalis was difficult to distinguish from the insular cortex on coronal slices, and therefore was identified primarily from 3-D reconstruction. Reformatting the scans in the sagittal plane can also aid in identification of the pars orbitalis, where the gyrus is a C shaped structure bordered by the insular cortex. Note also that the true border of the inferior frontal gyrus often lies on the lateral, not ventral surface. However, we found the lateral orbital sulcus to be difficult to consistently identify, and so at levels anterior to the pars orbitalis, we chose to use the most ventral and lateral orbital sulcus as the boundary. The middle and superior frontal gyri extend more anteriorly than the inferior frontal gyrus, so at the most anterior level, the gyrus usually occupied a relatively small part of the brain. In the 3-D reconstruction, the inferior frontal gyrus was identified by the appearance of the pars opercularis, triangularis, and orbitalis. If the border of the inferior frontal gyrus in the most anterior end of the measured region was unclear, then the superior boundary of the gyrus was determined by extending the last clear boundary horizontally to the anterior most end of the measured region. In a more recent method, Kawashima, et al. (in preparation) also segmented the inferior frontal gyrus, along with the medial frontal and superior frontal gyri, as part of a prefrontal cortex parcellation (see Figure 12). The posterior boundary of the inferior frontal gyrus (IFG) was the most anterior slice that contained the genu of corpus callosum as same as that of SFG and MFG. The inferior frontal sulcus represented the superior boundary of IFG. The guidelines to be followed when the inferior frontal sulcus existed as an interrupted sulcus were described under tracing guidelines of the MFG. The anterior limit of the IFG was defined by the most anterior slice that contained the inferior frontal sulcus. The inferior boundary of the IFG consisted of the lateral orbital sulcus anteriorly and the superior circular sulcus of insula posteriorly. When both the lateral orbital sulcus and the superior circular sulcus were visualize on each coronal slice, the superior circular sulcus was chosen as a boundary. The guidelines to be followed when the lateral orbital sulcus could not be visualized in consecutive coronal slices were described under tracing guidelines of the IFG. D. Middle Frontal Gyrus. The middle frontal gyrus was the most difficult to identify, and was primarily segmented using 3D reconstructions. In the coronal plane, the gyrus was often split into an inferior and superior portion by the middle frontal sulcus, and so consisted of at least two separate gyri with a deep sulcus between them. The middle frontal gyrus was usually defined after first identifying the superior and inferior frontal gyri. The middle frontal gyrus was also part of Kawashima's segmentation (see Figure 12). The superior frontal sulcus formed the superior boundary of the MFG. The guidelines to be followed when the superior frontal sulcus existed as an interrupted sulcus are described under tracing guidelines of the SFG. Inferiorly, the inferior frontal sulcus formed the boundary of the MFG. When the inferior frontal sulcus was interrupted into two or three segments, the most-superior one was chosen as a boundary on each coronal slice. More anteriorly, the lateral orbital sulcus constituted the inferior boundary. When the lateral orbital sulcus could not be visualized in consecutive coronal slices, the tracing from the coronal section that last contained the sulcus was copied onto the neighboring slices. If the inferior frontal sulcus joined the lateral orbital sulcus, and these sulci intersected at a coronal plane anterior to FP, the inferior frontal sulcus also formed the inferior boundary for MFG in the anterior part. We did not use the frontomarginal sulcus as a landmark because it was highly variable and made reliable definition problematic. The posterior boundary of MFG was formed by the most-anterior slice that contained the genu of corpus McCarley, Robert - 16

18 callosum as same as that of SFG. The anterior limit of the MFG was determined by the FP. E. Superior Frontal Gyrus. This gyrus was also identified primarily from the 3D reconstructions. At posterior levels, the gyrus often consisted of a large single gyrus in the superior aspect that was bounded medially by the cingulate gyrus. At anterior levels, anterior to the corpus callosum, the superior frontal gyrus was arbitrarily defined as tissue occupying most of the medial aspect of the brain. The genu of the cingulate gyrus was included in the measurement at this level; the suborbital sulcus was the inferior boundary. Near the frontal pole, a transverse component often invaded the space usually occupied by the middle frontal gyrus; these transverse gyri were included in the superior region. The superior frontal gyrus was defined to consist of the first large gyrus on the superior aspect of the brain, although occasionally it bifurcated into two or more branches. Other gyri were included if they joined the most superior gyrus at points between the precentral sulcus and the frontal pole, and if the two gyri appeared to be parallel to each other. In the coronal plane, especially at anterior levels, the middle frontal sulcus was relatively deep and gave the appearance of grouping gyri above and below it into two groups. However, it is important to note that the gyrus above the middle frontal sulcus most often consists of the superior portion of the middle frontal gyrus, not the superior frontal gyrus. Kawashima et al. also segmented the superior frontal gyrus (see Figure 12). The posterior border of the superior frontal gyrus (SFG) was determined by the most posterior anterior slice that contained the genu of corpus callosum. On the lateral aspect of the cerebral hemisphere, the inferior boundary was the superior frontal sulcus. When the sulcus was interrupted into two or three segments, the most-inferior one was chosen as a boundary on each coronal slice. Inferomedially, the cingulate sulcus formed the boundary of the SFG. In case of a double-parallel type of cingulate sulcus, the most-inferior one was selected as a boundary on each coronal slice. The paracingulate sulcus, if present, was considered as part of the SFG. More anteriorly, on the medial aspect, the superior rostral sulcus constituted the inferior boundary. If the superior rostral sulcus was unconnected to the cingulate sulcus, the inferior boundary of the SFG was completed by extending the posterior aspect of the superior rostral sulcus to intersect the cingulate sulcus on coronal slice. Anteriorly, the SFG was limited by the posterior extent of the FP. Insert Figure 12 About Here F. Cingulate Gyrus. The cingulate gyrus, defined as the one or two gyri superior to the corpus callosum, was outlined manually on a workstation (see Figure 13). The cingulate gyrus was bounded superiorly by the cingulate sulcus, and inferiorly by the callosal sulcus on each of the coronal slices. The anatomical landmark for dividing the cingulate gyrus into anterior and posterior cingulate regions was a vertical line (Bush et al., 1999) passing through the anterior commissure point in the mid-sagittal slice. Within the anterior cingulate gyrus, further parcellations were made forming subgenual (Drevets et al., 1997), affective (antero-rostral; Bush, Luu, and Posner, 2000; Crespo-Facorro et al., 1999), and cognitive (antero-caudal; Bush, Luu, and Posner, 2000) subregion ROIs. G. Subgenual Cingulate Gyrus. We defined this region according to Drevets et al., (1997), who reported it as significantly reduced in patients with affective disorder. Accordingly, we defined the subgenual subregion as the cingulate area under the corpus callosum, bounded anteriorly by the line passing through the anterior margin of the genu of corpus callosum, and posteriorly one slice anterior to the internal capsule that divides the striatum. The affective subregion (Bush, Luu, and Posner, 2000) was bounded anteriorly by the cingulate sulcus and posteriorly above the corpus callosum by a line (Crespo-Facorro et al., 1999) passing through the most anterior point of McCarley, Robert - 17

19 the inner surface of the genu of the corpus callosum, and anterior to the subgenual division below the corpus callosum. The cognitive subregion (Bush, Luu, and Posner, 2000) was defined as the remaining ACC between the affective subregion and posterior cingulate gyrus. The posterior cingulate subregion extended to the line passing through the most posterior end of corpus callosum (Noga et al., 1995). We did not include the most posterior part of the posterior cingulate division, often termed the retrosplenial cortex (Vogt, Absher, and Bush, 2000; Maddock, 1999), since there are no clear MRI boundaries to define it. Some, but not all, brains contained a paracingulate sulcus, parallel to the cingulate sulcus. The paracingulate sulcus was judged as present if it measured at least 20-mm in length in a sagittal view, and if the paracingulate gyrus was clearly independent from the cingulate and superior frontal gyri on coronal slices. When the paracingulate sulcus was present, the paracingulate gyrus, which comprises approximately Brodmann area 32, was excluded from the cingulate gyrus measurement. In order to examine the effect of differential presence of the paracingulate gyrus, the numbers of cases with paracingulate sulci present were compared among groups. Since the paracingulate sulcus can be present on one hemisphere but not on the other, its presence or absence was examined in both hemispheres. (Insert Figure 13 About Here) H. Frontal Pole. The frontal pole measurement was arbitrarily defined as the anterior-most 10 slices of brain. This grouping was done because the fusing of gyri makes reliable differentiation in this region problematic. In Kawashima's method, the frontal pole (FP) (see Figure 12, above) measurement was arbitrarily defined as the anterior-most 15 slices of brain (equivalent to mm), because the fusing of gyri makes reliable differentiation problematic (e.g., when the lateral surfaces of the frontal lobe reach the frontal pole, the longitudinally oriented frontal gyri are interrupted by transversal folds: the transverse frontopolar gyri). The FP extends onto the lateral, orbital and medial surfaces of the cortex. Notes on parcellation. The rules for classifying difficult or unusual sulcal/gyral patterns. Long transverse gyri. Transverse gyri (with the exception of the superior frontopolar gyrus) were classified using 3D surface according to the region they occupied. For example, if a transverse gyrus from the middle frontal region invaded the inferior frontal region, that part of the gyrus would be classified as inferior frontal gyrus. Discontinuous or unclear sulcal boundaries. If a sulcal boundary was not present on a slice, the tissue was segmented so that the boundary between regions was a straight line between two regions where there were clear boundaries. Reliability. In our previous study (Wible et al., 1995), one rater (CGW) did the parcellation of prefrontal gray matter for all of the cases. The intra-rater reliability was assessed by having this rater blindly parcellate the gray matter for the two randomly chosen cases, one schizophrenic, and one control case. The second segmentations for each of the two cases were done months after the initial segmentation was completed. The percent error was calculated by taking the absolute value of the difference score between the initial and second segmentation results for rater CGW. The volumes for each individual prefrontal region, left and right hemisphere separately, were used in the calculation. The average percent error was 9.5% and 3.8% for the schizophrenic and control cases respectively. The intraclass correlation for the intrarater reliability for the two cases for two segmentations was r i =.97. The intraclass correlations for the intrarater reliability calculated separately for the two cases for two segmentations were r i =.95 and r i =.99 for the schizophrenic and control cases, respectively. Inter-rater reliability was assessed by having 2 raters parcellate 2 McCarley, Robert - 18

20 cases (one control, one schizophrenic). These raters were instructed with the rules for parcellation in one short session. The percent error was calculated by taking the absolute value of the difference scores between the volumes for each raters segmentation and the average value of the three segmentations. The average percent error was 6.7% for both the schizophrenic and control cases. The intraclass correlations for the three raters were r i =.97 and r i =.96 for the schizophrenic and control cases, respectively. Reliability was also done for Kawashima's method. All editing for definition of ROI was done blind to diagnosis. Interrater reliability for each region of interest was evaluated in three randomly selected cases assessed by three independent raters (T.K., M.N., and D.C.) blind to diagnosis. Intraclass correlation coefficients were 0.99 for the left FP, 0.99 for the right FP, 0.98 for the left SFG, 0.97 for the right SFG, 0.96 for the left MFG, 0.97 for the right MFG, 0.94 for the left IFG, and 0.95 for the right IFG. IV. PARIETAL LOBE ROI. Initial Steps. Several steps were followed for the delineating the regions of interest (ROI) within the parietal lobe. The first editing step made use of a re-slice editing algorithm that constructed sagittal and axial images of each brain from the original coronal images. Specific markings were made on several sagittal slices to define certain boundaries for the ROI (described below). These markings then appeared on the coronal slices, where the ROI outlining was performed by manual tracing (with a computer pointing device). The final editing step made use of a surface rendering algorithm (Cline, 1991), which made possible a three-dimensional view of the relevant structures. The three-dimensional images of the ROI could then be viewed individually or within the context of the entire cortex, and images could be rotated around x, y, and z axes, to achieve the best possible visualization of each ROI. After examining the three-dimensional images, the coronal slices were then reassessed and any necessary corrections were made on the original editing. Once the editing was complete, volumetric measurements of the ROI were automatically derived (as with the whole brain data) by summing the voxels for each ROI across all relevant slices (see Figure 14). Insert Figure 14 About Here The boundaries for the ROI in this study were determined with the help of an anatomical atlas (Duvernoy, 1991). All ROI definitions were identical for each hemisphere. For all boundaries that involved cutting planes (see below), we corrected for head rotation (tilt) around all three axes (this was done prior to our head alignment program but would still have been necessary depending upon individual landmarks). Head rotation about the fronto-occipital axis was measured by a line drawn perpendicular to the interhemispheric fissure on a coronal slice at the level of the parietal lobe. Head rotation about the vertical (z) axis was measured by a line drawn perpendicular to the interhemispheric fissure on an axial slice at the level of the parietal lobe. Head rotation about the bitemporal axis was measured by a line drawn from the most anterior point of the corpus callosum to the most posterior point on a midsagittal slice. This reference line was more reliably determined than an anterior to posterior commissure line, which was verified to be virtually parallel with the callosal line (mean difference angle < 3 degrees) for the 30 cases reported here. After correction for rotation about the fronto-occipital and vertical axes, all brain rotation about the bitemporal axis was corrected to match the brain with the least rotation (brain with the callosal line most nearly horizontal). Medial Surface. The parietal lobe is bounded by the frontal lobe, occipital lobe, and cingulate gyrus. The fronto-parietal border was defined by the central sulcus and the marginal ramus of the cingulate sulcus and, since the sulci do not intersect, by a vertical line extending from the most posterior portion of the central sulcus to the cingulate sulcus. This line was extended McCarley, Robert - 19

21 laterally in the coronal plane (perpendicular to the sagittal plane). The parieto-occipital fissure was a clear anatomical boundary separating the parietal and occipital lobes. The parietal lobe and cingulate gyrus were bounded anteriorly by the subparietal sulcus. In the absence of a clear anatomical division, we defined the posterior and ventral parieto-cingulate border by a vertical line extending from the subparietal sulcus to the occipito-parietal fissure. This line was extended laterally in the coronal plane (perpendicular to the sagittal plane). Lateral Surface. Anteriorly, the central sulcus is seen as the parieto-frontal lobe boundary. The Sylvian fissure bounded the parietal and temporal lobes anteriorly. More posteriorly, the ventral bound of the parietal lobe was defined by the three cutting planes, which were all perpendicular to the sagittal plane. Plane A began at the dorsal level of the Sylvian fissure on the most posterior coronal slice of the postcentral gyrus, and continued posteriorly using the same vertical (z) position, for 15 mm (10 coronal slices). Plane B of the ventral parietal boundary was defined by two parallel lines. The first line was drawn on a midsagittal slice from the most anterior point of the corpus callosum extending posteriorly at a 9 degree angle to the callosal reference line. The second line defining this plane was drawn on a more lateral sagittal slice, using the same coordinates as the first boundary line; these lines defined the cutting plane B. The 9 degree angle between the reference line and the boundary line was selected so as to include the maximum amount of parietal lobe gray matter without including any (or at least only minimal amounts of) temporal or occipital lobe tissue. The posterior boundary of the parietal lobe was the cutting plane C, defined by two parallel lines. The first line was drawn through the parieto-occipital fissure on a midsagittal slice. The second line was drawn on a more lateral sagittal slice using the same coordinates as the first line. These lines defined the cutting plane C. Left and right parietal hemispheres were separated by the interhemispheric fissure. Parcellation of the Parietal lobe. The ROI within the parietal lobe included the postcentral gyrus (PCG), the superior parietal gyrus (SPG), and the inferior parietal lobule (IPL), comprised of the angular gyrus and the supramarginal gyrus. The PCG was separated from the SPG and the IPL by the postcentral sulcus. The IPL was separated from the SPG by the intra-parietal sulcus. The IPL was further subdivided into the angular gyrus (AG) and the supramarginal gyrus (SMG). In the absence of a clear, consistent anatomical boundary between the AG and the SMG, the bound between these two IPL structures was defined by the coronal slice midway between the most posterior and most anterior coronal slices of the IPL. Reliability. The investigators were "blind" to subject diagnosis throughout the entire image processing stage, and remained blind for reliability testing. Both inter- and intra-rater reliability were measured for each of the parietal regions using intraclass r. For inter-rater reliability, three judges measured each of the parietal regions on 10 slices coronal (2 sets of 5 contiguous slices) on three randomly selected brains, thus producing 6 measures for each parietal region (i.e. a left and right measure for each of three brains). Using these six measures for each parietal region, intraclass r estimates of reliability were determined to be 0.96 for the IPL, 0.96 for the SPG, and 0.97 for the PCG. Intra-rater reliability was computed using all of the slices from one randomly selected brain measured by the primary investigator (RD) at two separate times (approximately one year apart). For intra-rater reliability, intraclass r values for the parietal regions were 0.97 for the IPL, 0.98 for the SPG and 0.94 for the PCG. Thus, all reliability measures were very satisfactory. This work has been published in Niznikiewicz et al. (2000). Newer criteria have also been developed by Nierenberg et al. (2005). V. BASAL GANGLIA AND THALAMUS ROI. (1) Basal Ganglia McCarley, Robert - 20

22 Overview of ROI Definition. These ROI are described in detail, together with illustrations of their boundaries on MRI sections in: Hokama H, Shenton ME, Nestor PG, Kikinis R, Levitt JJ, Metcalf D, Wible CG, O'Donnell BF, Jolesz FA, McCarley RW. (1995) Caudate, putamen, and globus pallidus volume in schizophrenia: A quantitative MRI study. Psychiatry Research: Neuroimaging. 61: We here provide a brief summary for the reader of this application. Crosby et al. (1962), Carpenter (1978), and Duvernoy (1991) were used as primary anatomical references. Throughout the development of the ROI we were quite conscious of partial volume (PV) constraints on reliability: when voxels include more than one tissue component, such as both gray and white matter, reliability is greatly reduced. Our rule was that if reliable tracing of the boundaries of a portion of a ROI could not be performed, this portion was excluded from analysis (such as most of the tail of the caudate--see ROI descriptions below). The basic definitions of landmarks used for the basal ganglia ROI (caudate, putamen, and globus pallidus) are described below, where the entire extent of ROI on coronal slices for one case. A. Caudate Nucleus. This ROI included the head & body of the caudate and the tail portion as it curved ventrally abutting the lateral portion of the atrium of the ventricle. Tracing of the tail portion stopped when the tail portion turned to course anteriorly, since, even with our small voxels, PV effects rendered more extensive tracing unreliable. The caudate ROI also included most of the nucleus accumbens; the accumbens is ontogenetically and phylogenetically related to the caudate-putamen and cannot be reliably differentiated on MRI images (see Figure 15). Insert Figure 15 About Here B. Putamen. This included its ventral extension, termed the peduncle of the lentiform nucleus. C. Globus Pallidus (GP). The medial and lateral GP are separated by a very thin white matter layer (medial medullary lamina) which, because of its thinness and consequent PV effects, is lumped together with medial and lateral GP to form the GP ROI. Reliability. This was assessed in several ways. Anterior-Posterior boundaries. Each of 3 raters was within + one 1.5 mm slice for all 3 ROI. Interrater reliability of manual definition of ROI within the AP bounds was assessed on 10 coronal slices from 2 randomly selected cases; left and right sides of all three basal ganglia ROI were delineated by three separate raters (HH, MES, CGW). This interrater procedure follows that previously used by us and by the literature on basal ganglia measurements (e.g., Elkashef et al., 1994), and focuses on the main source of variation, that of manual tracing of ROI by individual raters. [Parenthetically, a multiple slice assessment is preferable to a single slice assessment because it captures the range of difficulty inherent in tracing structures whose boundaries and ease of definition differ from slice to slice.] Mean intraclass correlation coefficients, computed using the fixed rater model of Shrout and Fleiss (1979), were high for caudate (0.955), putamen (0.918), and globus pallidus (0.967). Intrarater reliability. HH measured all cases. Reliability was assessed by duplicate measurements, 6 months apart, by HH on the entire data set of two randomly selected cases. There was an excellent agreement for caudate (4.4 and 4.3% volume differences on the two cases for the two measurements), putamen (2.0% and 3.2% differences), and globus pallidus (0.5% difference for both cases). Segmentation reliability. The excellent reliability measurements for the segmentation of McCarley, Robert - 21

23 total gray and white matter, and of CSF on the double echo spin echo images have been described elsewhere (Kikinis et al., 1992; Shenton et al., 1992). (2) Thalamus. Overview of ROI Definition. These ROI are described in detail, together with illustrations of their boundaries on MRI sections in Portas et al. (1998). We here provide a brief summary. Thalamic Boundary Definition. The automated segmentation procedures produced the separation of gray and white matter based on differences in signal intensity values (Kikinis et al., 1990; Cline et al., 1990). Manual segmentation of the thalamus occurred in consecutive slices (out of an average of 120 slices over the entire brain). In order to overcome the problem of partial volume (PV) effects, we decided to include 50% of the PV area. The definitions of the landmarks used for the thalamus are described as follows. Since the most anterior boundary was difficult to resolve objectively and reliably, we chose to use a clear anatomical landmark as the anterior bound, the mammillary bodies of the hypothalamus. The ventralis anterior nucleus is just dorsal to the hypothalamus, bounded laterally by the internal capsule, dorsally by the lateral ventricle, and medially by the third ventricle. The posterior boundary was defined on the slice showing the thalamus merging under the crus fornix. The thalamus was medially defined using the third ventricle. The inferior border was defined as the point of merger with the brainstem; the superior border was defined by the main body of the lateral ventricle. Duvernoy, 1991; De Armond 1989; Roberts, 1971; Haines, 1991, were used as primary anatomical references. Intrarater and Interrater Reliability. Intra- and interrater reliability was conducted by three raters (CP, IF, RD). Since CP measured all cases, intrarater reliability on the thalamic segmentation was assessed six months apart on three randomly selected cases. The volume difference between the first and the second measurement was negligible in all three cases (< 1%). Interrater reliability, estimated by intraclass correlation coefficients, for three randomly selected cases across three raters was:.93 for total thalamic volume,.93 for right thalamic volume and.91 for left thalamic volume. VI. Cerebellar and Brainstem ROI. (1) Cerebellar (ROI) Definitions: Cerebellum, Vermis and Brainstem ROI. The cerebellum was masked from the rest of the brain prior to segmentation. The gray and white matter of the combined cerebellum/brainstem ROI (defined below), derived from the segmented SPGR images of the whole brain, initially were converted into a single pixel value. Once the cerebellum was separated and masked from the rest of the brain (as defined below), it was then re-segmented into gray and white matter using the automatic segmenter of Wells et al. (1994). The masked cerebellum gray-scale image was then segmented into gray and white matter, and the segmentation was manually edited in 3 planes, (both the MR image and the segmented image can be formatted into sagittal, axial and coronal planes), and finally checked using a 3-D reconstruction program.. Duvernoy (1995), Angevine et al. (1961), and DeArmond et al. (1976) were the primary anatomical atlases used to assist in the neuroanatomical ROI definitions. A. The Combined Cerebellum/Brainstem ROI: The rostral and dorsal boundaries of this combined structure were composed of the rostral plane of the brainstem, as defined below, and the tentorium cerebelli. The ventral and inferior bounds were defined by the subarachnoid cerebrospinal (CSF) cisterns and the caudal plane of the brainstem, as defined below; the lateral bounds were formed by subarachnoid CSF cisterns and the traverse sinuses (see Figure 16). Insert Figure 16 About Here B. The Brainstem of This Combined Cerebellum/Brainstem Structure. In the sagittal McCarley, Robert - 22

24 MR image, this ROI was positioned with the long axis coinciding with the perpendicular axis of the screen. The rostral plane bounding the brainstem was defined by one ventral and two dorsal points in order to correct for any head tilt and angulation of the brain stem with respect to the MR axis: a) the height of the ventral point was defined on the midsagittal slice (at the level of the cerebral aqueduct) by the voxel immediately rostral to the point of deepest penetration of CSF into the mammillary body arch; the ventral extent of this point was defined as the most ventral coronal slice where both mammillary bodies and crus cerebri were present, and b) the dorsal points were defined, on a coronal slice, as the voxels immediately rostral to the right and left superior colliculi at the point of their maximal dorsal extent. The caudal boundary of the brainstem was defined as the points representing the most superior aspect of the odontoid process (visualized on coronal and sagittal slices); the caudal plane passes through these points and was perpendicular to the long axis of the brainstem. The ventral, dorsal and lateral bounds of the brainstem were formed by the surrounding subarachnoid CSF cisterns, blood vessels and cranial nerves. Manual editing, determined in coronal slices, was used to separate the brainstem crus cerebri from these adjacent structures; and its lateral extent was defined, bilaterally, as the most lateral sagittal slice in which they were still present. C. The Cerebellum ROI. The cerebellum, using manual tracing, was separated from the combined cerebellum/brainstem structure by cutting perpendicularly to the direction of the fiber tracts of the cerebellar peduncles using an axial view. The separated cerebellar segmented image was then merged with an MR gray scale image of the whole brain, using a merger program, resulting in a separate gray scale MR cerebellar image which will then be re-segmented into grey and white matter using an automated segmentation algorithm as described above (Wells et al., 1994). D. The Brainstem ROI. The above separation of the cerebellum automatically yields a separated brainstem. Because of the complexity of gray and white matter spatial organization in the brainstem, no effort at this time was made to segment the brainstem into gray and white matter tissue types; rather, a total brainstem volume was acquired. E. The Vermis ROI. The cerebellar vermis were then separated from the cerebellar hemispheres using manual tracing in all 3 planes with sagittal slices offering the clearest view. The vermis was seen in about 10 or 11 1mm thick reformatted sagittal slices. The posterior bounds of the more lateral sagittal slices will require separation from the hemispheres (as both the natural twisting of the cerebellar vermis, Latin for worm, and head tilt result in overlapping of vermian and hemispheric structures on sagittal view); this separation was facilitated by the characteristic radial sulci pattern of the vermis in sagittal slices, which differs from the hemispheric sulci pattern, and by the use of reformatting of the image into 3 planes. The anterior inferior bound of the vermis was formed by the hemispheric tonsils which require manual separation; the tonsils characteristic horizontal sulci pattern contrasting with the radial sulci pattern of the vermis, together with reformatting of the image into 3 planes, permit a precise separation. The lateral extent of the vermis was difficult to define objectively. We chose to define it by using a combination of criteria; on sagittal view its maximum extent was defined by: 1) that sagittal slice prior to the slice where the prepyramidal fissure no longer was visualized (the prepyramidal fissure was restricted to the vermis (Angevine et al., 1961); 2) that most lateral sagittal slice where the corpus medullare retains a characteristic vermian shape (that is, the primary and secondary branches emanating from a relatively sparse corpus medullare core were still clearly discernable; the characteristic shape and size of the corpus medullare in the vermis vs in the hemispheres was described by Press et al., 1989 and Courchesne et al., 1989). Additionally, on coronal and axial views, the previously traced prepyramidal fissure can be well visualized, as can the lateral extent of the vermis, at certain levels, offering a further check on our definition. We give such great emphasis to defining the lateral extent of the vermis objectively because (this has not been done in previous studies of the McCarley, Robert - 23

25 vermis) and the total vermian volume so depends on the way this boundary was defined. F. Gray and White Matter Cerebellar Hemisphere ROI. With the delineation of the vermis, the left and right gray and white matter hemisphere ROI were automatically defined. G. Gray and White Matter Vermis ROI. The vermis was manually parcellated into 3 gray matter regions and a single total vermis white matter region. The gray matter regions were parcellated using the sagittal plane: 1) vermian lobules I-V, (the lingula, central and culmen); 2) vermian lobules V-VII (the declive, folium and tuber vermis); and 3) vermian lobules VIII-X ( the pyramid, uvula and nodulus). The boundary between vermian lobules I-V and vermian lobules V- VII was defined by tracing, in all sagittal vermian slices, the primary fissure from its point of connection to the surface of cerebellar cortex to its point of connection to the corpus medullare which surrounds the roof of the 4th ventricle; the boundary between vermis lobules Vl-Vll and lobules Vlll-X were similarly defined in all sagittal vermian slices by tracing along the prepyramidal fissure to the corpus medullare surrounding the roof of the 4th ventricle (See Fig 4; Courchesne et al. (1994), in their influential studies in autism, traced the same fissures to define 3 vermian regions but did so, only, in one unsegmented 5mm vermian sagittal slice, yielding a combined gray and white matter measure for their vermian lobule regions). All manual tracings were done under magnification. The total vermis white matter region was generated automatically by the segmentation program; all white matter pixels lying within the vermis were manually labeled as vermis white matter. Interrater Reliability. Interrater reliability was computed for all ROI, and, in our pilot data, was r i >.90 (intraclass correlation). These ROI are under development, and acceptable reliability for future work will continue to be r i >.90. If it is less than.90, further training will take place until all raters show a better than.90 interrater reliability. Intrarater reliability is in the process of being computed. This work has been published in Levitt et al. (1999). VII. Orbito-Frontal Cortex (OFC): A. Sulcal Gyral Pattern Identification: Sulcal Pattern Identification. We based our sulcal pattern identification on previous work by Chiavaras and Petrides (2000). These investigators classified the OFC sulco-gyral pattern into 3 types (type I, type II, type III) in each hemisphere. This visual classification was based on the continuity of the Medial and Lateral Orbital Sulci (MOS, LOS, respectively) (See Figure 17 and Figure 18). In type I, rostral and caudal portions of the LOS were connected, while the MOS were clearly interrupted between rostral and caudal portions of MOS. In type II, rostral and caudal portions of both the MOS and LOS were connected, and continuous MOS and LOS were jointed by the horizontally oriented Transverse Orbital Sulcus (TOS). In type III, rostral and caudal portions of both MOS and LOS were interrupted. To evaluate the sulcal pattern precisely and consistently, neighboring sulci including the Olfactory Sulcus (Olf), Intermediate Orbital Sulcus (IOS), Posterior Orbital Sulcus (POS), and Sulcus Fragmentosus (Fr) were also identified as a landmark. Of note, Chiavaras and Petrides reported that IOS was identified in all of 100 observed hemispheres where 19% showed double IOS (medial and lateral). POS was observed in 77%, and Fr was observed in only 10 % of the 100 hemispheres. Three-dimensional information was also used in this study to provide reliable classification of the OFC sulco-gyral pattern, using a software package for medical image analysis [3D slicer: on a workstation. The sulco-gyral pattern identification was done using mainly axial slices. At first, the most inferior level (axial plane) where Olf can be seen clearly was identified, and then moving up to the superior level where Olf could be seen discontinuously. At this level, caudal portions of MOS and LOS can be seen connected by TOS. It is important to identify this sulcal complex, because McCarley, Robert - 24

26 these three sulcal portions are always connected. At this point, POS could be identified, if present. For the next step, continuity between rostral and caudal MOS was examined at the intersection with TOS, observing several axial slices (0.9375mm). If the rostral and caudal MOS were separated, the sulco-gyral pattern could be type I or III, and if not, type II. If MOS could be seen discontinuously distant from the TOS intersection level, its anterior fragment was identified as IOS or Fr, and the rostral and caudal MOS were considered as continued (type II). For the last step, the continuity between rostral and caudal LOS was examined observing several axial slices. If they were connected, the sulco-gyral pattern could be type I, if not, type III. The rostral LOS can be seen most laterally in the axial plane, however, short sulcus, which is oriented vertical rather than parallel to Olf, was not regarded as rostral LOS. At this step, one or two IOS could be identified between MOS and LOS. It should be emphasized that proper and consistent realignment of brain images are very important for reliable identification of the sulco-gyral pattern. The sulco-gyral pattern classification in each hemisphere of the 100 subjects was done by one rater (Dr. Motoaki Nakamura) blinded to subject group. (See Figure 17 and Figure 18.) Inter-Rater Reliability. For assessing interrater reliability, two raters (Motoaki Nakamura, Toshiro Kawashima), blinded to diagnoses, independently evaluated the sulcal pattern for 25 random cases. The intraclass correlation coefficients were for left hemisphere and for right hemisphere. Figure 17 depicts the landmarks used for defining the sulcal-gyral pattern. Insert Figure 17 and 18 About Here B. Orbito-Frontal Cortex Volume: We also used the above landmarks to help evaluate volume in the orbito-frontal cortex (Figure 19). Table 2 shows the landmarks used to define the Gyrus Rectus (GR), Middle Orbital Gyrus (MiOG), and Lateral Orbital Gyrus (LOG). Insert Figure 19 About Here Table 2 Inter-Rater Reliability: All manual delineations were performed by Motoaki Nakamura, McCarley, Robert - 25

27 who was blind to subject group. For assessing interrater reliability, three raters (Motoaki Nakamura, Adam Cohen and Toshiro Kawashima), also blind to subject diagnosis, independently delineated left and right GR, MiOG and LOG for seven randomly selected cases. The intraclass correlation coefficients were 0.95 (left GR), 0.96 (right GR), 0.99 (left MiOG), 0.96 (right MiOG), 0.96 (left LOG) and 0.99 (right LOG). VIII. OCCIPITAL LOBE. Figure 20 shows the medial and lateral view of the threedimensional reconstruction of the occipital lobe. The following steps were used to define the occipital lobe. First, the parietooccipital sulcus (POS) was identified on the midsagittal plane for each hemisphere. Second, the anterior tip of the POS was identified, as well as the posterior tip of the POS that corresponds to the parietooccipital fissure (see Figure 20). The occipital lobe was defined as beginning at one slice posterior to the plane that contains the anterior tip of the POS, identified on the midsagittal plane, and ending in the last slice in the coronal plane, including the posterior tip of occipital lobe. For the medial surface, the boundary between the parietal and occipital lobe was the POS. For the lateral surface, the rater (Toshiaki Onitsuka) drew a guideline connecting the parietooccipital fissure and the superior temporal sulcus, or anterior occipital sulcus, on the first, beginning slice of occipital lobe. This guideline was defined as the boundary between the parietal and occipital lobe for the lateral surface. This guideline was seen as a point on each coronal image (see Figure 21c). The parietal and occipital lobe were divided operationally by extending the guideline across the tissue bridge of white matter, horizontally and medially up to the intraparietal sulcus (see Figure 21 c&d). The primary visual area (PVA) was defined as the area between one gyrus above the calcarine fissure and one gyrus below the sulcus on each coronal image. The rater drew two guidelines at 3-5 slices laterally from the medial surface to determine the gyri above and below calcarine fissure (see Figure 21b). The lines were drawn extending the sulcal course across the tissue bridge of white matter. These guidelines were seen as points on each coronal image and the rater delineated the primary visual area referring to the lines (see Figure 21c). Manual drawings of the ROIs were then performed on the realigned and resampled coronal slices (see Figure 21d). Insert Figure 20 & 21 About Here Inter-Rater Reliability. Interrater reliability was computed for the ROIs by 3 independent raters (Toshi Onitsuka, Noriomi Kuroki, and Susan S. Demeo), who were blinded to diagnostic group membership. Six cases were selected randomly for interrater reliability. Three raters measured the occipital lobe on every third slice. An intraclass correlation coefficient was used to compute interrater reliability. For the three raters, the intraclass correlations were: 0.93 for the left PVA, 0.90 for the right PVA, 0.98 for the left VAA, 0.98 for the right VAA. IX. Power Analyses. Statistical power is related to the probabilities of Type I and Type II errors; to estimate power the sample size, the effect size, and the significance levels are all important (Cohen, 1977). We have pilot data from which we have been able to estimate effect sizes using projected sample sizes of n=15 to n=25, and significance levels of p=0.05. Based on our MRI data, the projected power for detecting differences between groups for left posterior STG is >98% (mean difference 0.8 ml, sd=0.86 ml), and for left anterior hippocampus-amygdala the power is >99% (mean difference 0.5 ml, sd=0.6ml), and this was with an n of 15 subjects. Shape measures of hippocampus were also based on small n s (n=15 in schizophrenic group) and differences were detected. We therefore think that the subject ns are sufficient to detect differences between groups if differences are present. Additionally, we note that the expected magnitude of our clinical correlations (e.g., SAPS and TDI) has ranged from 0.41 to.81 with our MR measures, with a power of 97% for n=15. We thus believe that the sample size proposed here will be sufficient to detect associations/differences between variables. We note, however, that with McCarley, Robert - 26

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34 Smith, S.M., Jenkinson, M., Woolrich, M.W., Beckmann, C.F., Behrens, T.E.J., Johansen-Berg, H., Bannister, P.R., De Luca, M., Drobnjak, I., Flitney, D.E., Niazy, R., Saunders, J., Vickers, J., Zhang, Y., De Stefano, N., Brady, J.M., Matthews, P.M. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 23(S1): Steinmetz, H., Rademacher, J., Huang, Y., et al. (1989). Cerebral Asymmetry: MR planimetry of the human planum temporale. Journal of Computer Assisted Tomography 13(6): Wells III, W.M., Grimson, W.E.L., Kikinis, R., Jolesz, F.A. (1994). Statistical intensity correction and segmentation of MRI data. In SPIE, volume 2359 Visualization in Biomedical Computing (pp ). Wible, C.G., Shenton, M.E., Hokama, H.,Kikinis, F.A., Metcalf, D., McCarley, R.W. (1995). Prefrontal cortex and schizophrenia: A quantitative magnetic resonance imaging study. Archives of General Psychiatry 52: Wible, C.G., Shenton, M.E., Fischer, I.A., Allard, J.E., Kikinis, R., Jolesz, F.A., Iosifescu, D.V., McCarley, R.W. (1997). Parcellation of the human prefrontal cortex using MRI. Psychiatry Research. 76(1): Zhang, Y., Brady, M., Smith, S. (2001). Segmentation of brain MR images through a hidden Markov random field model and the expectation maximization algorithm. IEEE Trans. on Medical Imaging 20(1): McCarley, Robert - 33

35 Figure 1 Figure 1. Part A. Comparison of raw images (top row) and segmented images (bottom row), 1.5 T scanner, 3T scanner uncorrected, and 3T scanner with Bias Field Correction, same subject with schizophrenia. SPGR pulse sequence acquisition, axial slices at approximately the same level. In the top row, note that 1.5 T has more partial volume effect than 3T, evident in neocortical region gray-white borders and in basal ganglia. In 3T uncorrected image (top,middle) posterior white matter has higher signal ( whiter ) than anterior white matter with a corresponding posterior> anterior white matter segmentation bias. The Bias Field correction nullifies this signal intensity bias and the corresponding segmentation inhomogeneity. Note that for the Bias Field Correction non-brain components have been stripped. Figure 1. Part B. Coronal section through superior temporal gyrus (STG, box) from 1.5 T and 3T scan of same subject with schizophrenia, showing raw and segmented images. Note less blurring in 3T image = less partial volume effect and greater detail of white-gray matter junction, especially in Heschl s gyrus, the medial bump on STG. McCarley, Robert - 34

36 Figure 2 Figure 2. Expectation-maximization atlas tissue segmentation and regions of interest (ROI). Top, left: Examples of spoiled-gradient-recalled images and tissue segmentation. Tissue has been segmented and parcellated into ROI of neocortical gray matter (NCGM, green on right, blue on left hemisphere), cerebral white matter (CWM, yellow on right, beige on left hemisphere), sulcal cerebrospinal fluid (SCSF, red on right, brown on left side), and lateral ventricles (LV, dark blue on right and purple on left side). Note the exclusion of subcortical nuclei, the medial temporal region, and all infratentorial tissue. Top, right: Three-dimensional reconstructions of brain tissue ROI. Bottom: Upper portion shows lobar parcellation of NCGM into frontal lobe (green on right, blue on left hemisphere), temporal lobe (purple on right, pink on left hemisphere), and parieto-occipital lobe (red on right, brown on left hemisphere). Lower part is a three-dimensional reconstruction. L, left; R, right; A, anterior; P, posterior. McCarley, Robert - 35

37 Figure 3 Figure 3. A lateral view of the brain shows the major sulci and gyri in the brain. The ROIs that we have delineated include many of the gyri depicted here (i.e., superior temporal gyrus, middle temporal gyrus, inferior temporal gyrus, superior frontal gyrus, supramarginal gyrus, angular gyrus, etc.). (From Carpenter and Sutin, 1983, Human Neuroanatomy: Williams & Wilkins). McCarley, Robert - 36

38 Figure 4 Figure 4. Coronal 1.5mm slice showing medial temporal and neocortical structures. The region bordering the Sylvian fissure on the right (subject left) is the superior temporal gyrus. The almondshaped region in the medial portion of the temporal lobe is the amygdala and the region demarcated beneath is the parahippocampal gyrus. The whole temporal lobe is outlined on the left (subject right). [From Shenton et al., 1992, courtesy of The New England Journal of Medicine.] McCarley, Robert - 37

39 PANEL A. Figure 5 PANEL B. Panel A. Sagittal and coronal MR images showing delineation of MTG and ITG. A red line signifies the STS. A yellow line signifies the ITS. The vertical dotted lines A and B correspond to the slices of A and B, respectively. On the slice A, the gray matter of MTG is shown in orange and the gray matter of ITG is shown in purple (subject left). Below is a magnified view that corresponds to the red square. On the slice of a transition area (slice B), MTG and ITG were delineated referring the guidelines (red and yellow dots). Panel B. Delineation of temporal subregions in a coronal image. The gray matter of MTG is shown in orange (subject left) and light blue (subject right). The gray matter of ITG is shown in purple (subject left) and yellow (subject right). The gray matters of left FG, right FG, left STG, right STG are shown in light green, red, blue, green, respectively. (b) Frontal view tilted to the foreground of left temporal lobe subregions of a three-dimensional reconstruction. (c) Ventral view of bilateral temporal lobe subregions. (d) Left lateral view of temporal lobe subregions. McCarley, Robert - 38

40 PANEL A Figure 6 PANEL B Figure 6 Panel A. Three dimensional reconstruction of the ventral surface of the brain. The fusiform gyrus region of interest is red on subject left and yellow on subject right. Panel B. Coronal SPGR resampled image with isotropic voxels (.9375 mm) showing the outline of the fusiform gray matter ROI on subject left (yellow) and subject right (blue). McCarley, Robert - 39

41 Figure 7 Figure 7. 2-D and 3-D presentation of the paralimbic regions of interest. This figure is based on MRI data of a control subject. The gray matter of the anterior insular cortex is colored blue on subject left and orange on subject right. The gray matter of the posterior insular cortex is colored light blue on subject left and yellow on subject right. The gray matter of the temporal pole is colored purple on subject left and green on subject right. The white matter of the bilateral temporal pole is colored ivory. Panel A, B, and C: Delineation of the paralimbic regions of interest on coronal slices. Panel A represents the rostal part of the anterior insular cortex adjacent to the orbital cortex, and the temporal pole. Panel B represents the caudal portion of the anterior insular cortex. Panel C represents the middle portion of the posterior insular cortex. Panel D: Sagittal view of the insular cortex and temporal pole in the left hemisphere. The coronal lines A, B, and C correspond to the planes of Panel A, B, and C, respectively. Panel E and F: 3-D reconstruction of insular cortex (E) and temporal pole (F) gray matter superimposed on the axial plane. McCarley, Robert - 40

42 Figure 8 Figure 8. Panel A. Delineation of Heschl s gyrus and planum temporale in a coronal slice, based on MRI data of a control subject. The gray matter of Heschl s gyrus is labeled dark blue on subject left and green on subject right. The gray matter of planum temporale is light blue on subject left and yellow on subject right. Panel B. 3-D reconstruction of Heschl s gyrus and planum temporale gray matter superimposed on the axial plane. Each region is labeled using the same color as that in Panel A. McCarley, Robert - 41

43 Figure 9 Figure 9. Gray matter of the prefrontal cortex was measured starting anteriorly from the first slice that contained brain tissue. The posterior land mark was determined by first locating the most anterior slice that contained the temporal stem (the white matter tract connecting the temporal and frontal lobes), then moving anteriorly three slices. Anteriorly, the white matter was measured beginning with the first slice that contained white matter and extended posteriorly to the slice immediately anterior to the slice that contained the lateral ventricles. Prefrontal segmented images on a coronal slice are shown in Fig. 1A, and Fig. 1B, C, and D illustrate the anterior-posterior boundaries of the gray and white matter regions of interest (ROI). Panel A. Coronal slice (1.5 mm) through the prefrontal region of a normal control subject. Prefrontal gray matter is outlined in red. Panel B. Three-dimensional reconstruction of prefrontal white matter (yellow) with semitransparent gray matter (gray matter) on a midsagittal MR slice viewed from the left side. This illustrates the anterior-posterior extent of gray and white matter ROI. Panel C. Top-down view of a three-dimensional reconstruction of prefrontal gray matter (beige) on an axial MR slice. Panel D. Top-down view of a three-dimensional reconstruction of prefrontal white matter (yellow) on an axial MR slice. McCarley, Robert - 42

44 Figure 10 Figure 10. (Adapted and Modified from Chiavaras and Petrides, 2000) OFC Subregions and Neighboring Sulci. Abbreviations: Olf, olfactory sulcus; MOS, medial orbital sulcus (-r: rostral, -c: caudal); TOS, transverse orbital sulcus; LOS, lateral ramus of H-shaped sulci (-r: rostral, -c: caudal); IOS, intermediate orbital sulcus (-m: medial, -l: lateral); POS, posterior orbital sulcus; Fr, sulcus fragmentosus. McCarley, Robert - 43

45 Figure 11 Figure 11. The 3-D Reconstructed ROI of OFC Subregions. Abbreviations: GR, Gyrus Rectus; MiOG, Middle Orbital Gyri; LOG, Lateral Orbital Gyrus. McCarley, Robert - 44

46 Figure 12 Figure 12. Regions of interest (ROI). Top: The frontal pole (FP), the superior (SFG), middle (MFG), and inferior frontal gyrus (IFG) are shown on a coronal slice (left) and a sagittal slice (right). Bottom: Three-dimensional reconstructions of ROIs and other grey matter in coronal view (left) and sagittal view (right). McCarley, Robert - 45

47 Figure 13 Figure 13. Cingulate gyrus subregions Regions of Interest. Three-dimensional reconstruction of the cingulate gyrus gray matter according to subregions (subgenual, affective, cognitive, and posterior divisions), seen in sagittal and coronal views. On sagittal view of left cingulate gyrus, subgenual division is color-coded by yellow, affective division by pink, cognitive division by blue, and posterior division by green. McCarley, Robert - 46

48 Figure 14 Figure 14. A 3-D surface rendering of the cortex (gray), with the gyri of the parietal lobe color coded as follows: postcentral gyrus (blue), superior parietal gyrus (green), supramarginal gyrus (red), and angular gyrus (yellow). (See text for detailed description of boundaries). McCarley, Robert - 47

49 Figure 15 Figure 15. Three dimensional renderings of left and right head of the caudate nucleus (shaded blue) and left and right posterior caudate nucleus (shaded red) superimposed on MRI coronal and axial slices in a normal control (top) and SPD subject (bottom). McCarley, Robert - 48

50 Figure 16 Regions of Interest in Gray and White Matter of the Cerebellum and Vermis of One Subject in an MRI Comparison of Patients With Schizophrenia and Healthy Comparison Subjects Figure 16. For the coronal image (left side), a combination of criteria were used to define objectively the indistinct lateral extent of the vermis: the last sagittal slice bilaterally where the prepyramidal fissure was visualized (the prepyramidal fissure is restricted to the vermis) and where the corpus medullare retained a characteristic vermian shape. Additionally, using a surface rendering program, we created a three-dimensional reconstruction of the cerebellar hemispheric and vermian white matter, alone, facilitating their separation. For the sagittal image (right side), the vermis was parcellated into three gray matter regions (lobules I V, VI VII, and VIII X) by tracing the primary and prepyramidal fissures in the sagittal plane in turn, defining total vermis white matter. McCarley, Robert - 49

51 Figure 17 H-shaped sulcus and its variation in human brain. A. Schema of orbitofrontal sulci and major gyri. H-shaped sulcus is traced by red dotted line, dividing orbitofrontal cortex into 4 gyri of medial, anterior, posterior, and lateral orbital gyri. B. Example of three sulcal pattern. Three main orbitofrontal sulco-gyral types are defined based on the continuity of the medial and lateral orbital sulci. Type I expresses most frequently and type III expresses least frequently in healthy population. C. Schema of major three types of sulcal patterns of H-shaped sulcus. Abbreviations: Olf, olfactory sulcus; MOS, medial orbital sulcus (-r: rostral, -c: caudal); TOS, transverse orbital sulcus; LOS, lateral orbital sulcus (-r: rostral, -c: caudal); IOS, intermediate orbital sulcus (-m: medial, -l: lateral); POS, posterior orbital sulcus; Fr, sulcus fragmentosus; R, right hemisphere, L, left hemisphere. Panel A, B, C were adapted and modified from the previous paper (Chiavaras and Petrides, 2000). McCarley, Robert - 50

52 Figure 18 MRI images of major three types of H-shaped sulcus. Examples of the major three sulco-gyral patterns from six different subjects. On the axial plane of SPGR (spoiled gradient-recalled images), sulci of type I, II, III are delineated with green, blue and pink color, respectively. Upper and lower column demonstrate left and right hemisphere. At this level, olfactory sulcus cannot be observed in most cases. Abbreviations: L, left hemisphere; R, right hemisphere. McCarley, Robert - 51

53 Figure 19 (Panel A) A MR Images of Three Orbitofrontal Subregions. Panel A: 3D reconstruction of the three orbitofrontal subregions of Gyrus Rectus (GR; left: blue, right: green), Middle Orbital Gyri (MiOG; left: brown, right: red), and Lateral Orbital Gyrus (LOG; left: purple, right: light green), superimposed on axial plane of SPGR image. Panel B: Orbitofrontal ROIs in axial and coronal planes of SPGR images. See method section for their boundary definition. McCarley, Robert - 52

54 B Figure 19 (Panel B) MR Images of Three Orbitofrontal Subregions. Panel A: 3D reconstruction of the three orbitofrontal subregions of Gyrus Rectus (GR; left: blue, right: green), Middle Orbital Gyri (MiOG; left: brown, right: red), and Lateral Orbital Gyrus (LOG; left: purple, right: light green), superimposed on axial plane of SPGR image. Panel B: Orbitofrontal ROIs in axial and coronal planes of SPGR images. See method section for their boundary definition. McCarley, Robert - 53

55 Figure 20 Figure 20. Delineation of PVA and VAA in occipital lobe. PVA is shown in purple, and VAA is shown in blue. For the medial surface (left), the border between parietal lobe and occipital lobe is the parietooccipital sulcus. PVA is defined as the area including one gyrus above and one gyrus below the calcarine fissure. For the lateral surface (right), the border between the two lobes is delineated by a guideline connecting the parietooccipital fissure and the superior temporal sulcus on the most anterior slice of occipital lobe (The guideline is shown in red). McCarley, Robert - 54

56 Figure 21 Figure 21. Sagittal and coronal MR images showing delineation of PVA and VAA. (a) The rater identifies the parietooccipital sulcus and the calcarine fissure on the midsaggital plane. (b) The yellow lines are the guidelines extending the sulcal courses used to delineate PVA and VAA. (c) On a coronal slice PVA and VAA are delineated by referring to the guidelines (yellow dots). In part B, here shown as yellow dots. On the lateral surface, the parietal lobe and occipital lobe are operationally separated by extending the guideline (the red dot) horizontally and medially across the tissue bridge of white matter horizontally and medially up to the intraparietal sulcus. (d) A coronal view of PVA and VAA delineation. White matter and gray matter were shown in light yellow and light blue respectively. The gray matter of PVA is shown in orange (subject left) and purple (subject right). The gray matter of VAA is shown in red (subject left) and blue (subject right). McCarley, Robert - 55