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A method of basal forebrain anatomical standardization for functional image analysis
Psychiatry Research: Neuroimaging Section 84 Ž1998. 113᎐125
A method of basal forebrain anatomical standardization for functional image analysis Monte S. Buchsbaum a,U , James H. Fallonb , Tse-Chung Wei a , Steven Guich b , Jacqueline Spiegel-Cohena , Matthew Hamiltonb , Cheuk Tang a a
Department of Psychiatry, Mount Sinai School of Medicine, Box 1505, One Gusta¨ e L. Le¨ y Place, New York, NY 10029-6574, USA b Department of Anatomy and Neurobiology, Uni¨ ersity of California, Ir¨ ine, College of Medicine, 364 Med. Surg. II, Ir¨ ine, CA 92697-1275, USA Received 6 June 1998; received in revised form 28 September 1998; accepted 12 October 1998
Abstract Functional as well as structural assessment of the basal forebrain has mostly focused on the dorsal caudate and putamen in axial slices where they are easily outlined or their centers located with stereotaxic methods. The more ventral extent of the basal forebrain, where the irregular form and indistinct boundaries of the nucleus accumbens and substantia innominata are difficult to trace and where the brain’s ventral surface may contribute partial volume artifacts to measurement, has been less studied. We present a method based on coronal sections, landmarks placed on clearly visible anchor points, and the computational technique of thin-plate spline warping which allows the alignment of groups of individuals to common coordinates for pixel-by-pixel statistical mapping. The reliability of the landmarks across independent raters yields a median absolute difference of 1.3᎐1.6 mm. The validity of the method is confirmed by variance maps which reveal significant decreases in variance over spindle and bounding box alignment. 䊚 1998 Elsevier Science Ireland Ltd. All rights reserved. Keywords: Positron emission tomography; Magnetic resonance imaging; Striatum; Pallidum; Caudate; Putamen; Nucleus accumbens; Substantia innominata
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Corresponding author. Tel.: q1 212 2415294; fax: q1 212 4230819; e-mail: [email protected]
0925-4927r98r$ - see front matter 䊚 1998 Elsevier Science Ireland Ltd. All rights reserved. P I I: S 0 9 2 5 - 4 9 2 7 Ž 9 8 . 0 0 0 5 2 - 3
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1. Introduction 1.1. Anatomical standardization for functional image analysis
al., 1991; Friston, 1995.. Recent comparisons of these two variants have revealed good agreement ŽDesmond and Lim, 1997.. 1.2. Assessment of the striatum
During the first decade of functional imaging studies, anatomical regions were assessed primarily by the generation of geometric regions or circumferential strips along the brain edge ŽBuchsbaum et al., 1982a; Harris et al., 1991., stereotaxic placement of regions of interest Že.g. Buchsbaum et al., 1982b, 1984a., or drawing of regions on the functional image Že.g. Chugani et al., 1987.. These methods were limited by the need to select individual brain areas in advance and their inability to reveal unanticipated functional units of the cortex. The evaluation of every pixel in an image with a statistical map, as suggested by Bartels and Subach Ž1976., offered an alternative approach, which was used for electroencephalographic topography Že.g. Duffy et al., 1981. and computed tomographic brain images ŽJernigan et al., 1979.. Variants of significance probability mapping have been applied for group map contrasts and topographic display of statistical parameters such as test᎐retest reliability Že.g. Buchsbaum et al., 1982a, 1984a,b; Friston et al., 1991; Friston, 1995. over the last two decades. In order for the statistical test, most commonly a t-test comparing groups or a paired t-test comparing two conditions studied in a group, to be interpretable, all of the pixels containing the same anatomical structure must be aligned across subjects. Otherwise, each pixel location would contain information from a mixture of different anatomical structures and no real functional map could be created. It should be noted that all standard stereotaxic approaches to locating electrode positions or regions of interest require similar adjustment to describe coordinates of action or assessment Že.g. Buchsbaum et al., 1984b.. Approaches to standardization of brain slice images have included the use of proportional brain bounding boxes based on brain length and width ŽAndreasen, 1995. and linear adjustments of the brain in different regions based on the atlas of Talairach and Tournoux Ž1988. developed further by Friston and his associates ŽFriston et
The size, shape, and location of the striatum provide localization problems different from those associated with major cortical surface areas. When linear proportional adjustment based on brain edges is used to locate the center of the dorsal caudate, errors in the 2᎐3-mm range are typical as estimated by applying the method to actual magnetic resonance images ŽMRI. ŽBuchsbaum et al., 1992.. This degree of error is small compared with the 8᎐10-mm resolution of older positron emission tomography ŽPET. scanners but sizable compared with the 4.5-mm resolution of current scanners and the 6᎐10-mm width of the caudate, for example. While axial slices provide clear boundaries of the dorsal caudate and putamen, the more ventral outlines at or below the level of the internal capsule are often ambiguous. Because of these difficulties, we Že.g. Shihabuddin et al., 1998. and other investigators have tended to leave more ventral areas of the basal forebrain not fully evaluated. Various approaches to the evaluation of the striatum have been used. Holcomb et al. Ž1996. used an MRI-based technique that combined circular regions of interest ŽROIs. across dorsoventral levels. Gur et al. Ž1995., applying a geometric flexible template method, similarly obtained averages across all slices in which the striatum was present. Bartlett et al. Ž1996. used a square 5 = 5-mm box placed in the center of the MRI templates coregistered to the PET image of the striatum. Bremner et al. Ž1997. traced the dorsal caudate on MRI wtheir Fig. 1 approx. corresponding to Talairach and Tournoux Ž1988. level 18᎐20 mm Ždorsal to the anterior commissure to posterior commissure line.x and transferred the caudate outline to the coregistered PET. Thus, both stereotaxic atlas and MRI coregistration methods have been used, but data analysis has been largely confined to dorsal striatum, with emphasis on the dorsal-most caudate. In an important theoretical article, Early et al.
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Fig. 1. Non-linear change in edges of the striatum. Left: Average MRI from 25 individuals based on thin-plate spline warping of edge, midline, and striatal landmarks. Middle: Matching atlas illustration ŽTalairach and Tournoux, 1988. with landmarks applied. Initial square grid superimposed shows original rectilinear coordinates. Right: Atlas standardized to average coordinates of normal subjects with deformation grid from thin-plate spline method indicating non-linear change. Such non-linearity would not be corrected for in the proportional standardization in a purely atlas-based method as opposed to an MRI-based non-linear method of anatomical standardization for statistical probability mapping. Atlas was digitized at resolution matching the MRI field of view Že.g. 256 = 256 pixels. and, for this reason, small scale numbers at margin are blurred; this has no effect on the transformation.
Ž1989. highlighted the ventral striatum and ventral pallidum in their model of thought disorder in schizophrenia. In analyzing their own data, however, these investigators carried out only a limited assessment of the basal forebrain Žno lower than the dorsoventral level of the anterior commissure ŽTalairach and Tournoux, zs 0 mm, a level approximately halfway from the dorsalmost aspect of the caudate nucleus at zs q24 mm to the ventral-most tip of the nucleus accumbens at zs y20 mm.. Obstacles to a more thorough examination of the basal forebrain in imaging studies include partial volume effects and the variations in the exact angle of brain orientation, which may especially affect assessment of inferior areas on MRI. To attempt to overcome these problems, a method based both on aligning coronal sections and individual landmarks appears desirable. With the advent of high-resolution PET and thin-slice MRI instruments, the possibility of creating accurate and detailed statistical contrast images based on an individual’s own brain as an atlas has broadened our scientific tools for exploration. Accurate anatomical localization can
be provided by coregistering MRIs to the functional scan. Because brain activity does not necessarily coincide with brain structure, the plastic transformation method ŽFriston et al., 1991., which relies upon the functional image intensity profile, may create local positional biases based on disparity between function and structure; areas such as the accumbens with signal intensity values intermediate between gray and white matter for both functional and structural images may be especially susceptible to this problem. Since the position of the caudate is not tightly linked to the cortical rim, which is used to scale the image linearly in methods of standardization such as those of Talairach and Tournoux Ž1988. ŽFig. 1., statistical maps of the caudate may lose statistical power if all individuals’ brains are not aligned correctly. Some considerable anatomical variation may result in methods based only on one person’s prototypic brain space we.g. the atlas of Talairach and Tournoux Ž1988.x or even ‘average’ brains derived from population samples. Furthermore, since all caudate nuclei are not the same shape, there is considerable variation in the proportion of individuals for whom a given x᎐y coordinate
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will fall either inside or outside the caudate nucleus. In an alternative to the bounding box approach, our recent studies of the striatum traced outlines of the caudate and putamen on the MRI and used the nuclear edge to morph the structures ŽShihabuddin et al., 1998.. This approach allowed visualization of change within the striatum and exploratory statistical probability mapping within the caudate outline with edges aligned by radial adjustment. However, this approach, too, is largely limited to the dorsal striatum where clear structure boundaries and a simple convex shape make caudate edge alignment feasible. The complex structures of the ventral striatum and other
basal forebrain structures, which lack such clear ovals but yet contain specific and clearly defined white matter and surface landmarks, make the application of another spatial standardizing technique that uses thin-plate splines ŽBookstein, 1991. especially suitable. In this method, we map anchor points located in each person’s MR image to the mean location for the group. This produces an MR and PET image in which sets of anatomical structures are all located at the same x and y coordinates and are thus suitable for statistical imaging. Combining different individuals’ brains is analogous to averaging together photographs of dif-
Fig. 2. Comparison of bounding box and landmark methods of subject standardization. Top Row: Three faces with bounding box are averaged to standard coordinates. Note that while chin and top of head have been aligned, the eyes and nose produce a recognizable but blurred image due to non-linear variation in their x- and y-coordinate position. The eyes and their positioning relative to the chin and top of the head are analogous to the caudate and its positioning relative to the front and back of the head in the bounding box method. Bottom row. Three faces with landmarks Žlight gray. applied. The average position of the landmarks is calculated, and each image is standardized to this position and redrawn Žrightmost face image.. Note much sharper appearance of features in the center of the face due to non-linear landmark method.
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ferent people’s faces; every one has a different shape. To achieve interpretable differences in pupil color, lip size or nose length, each individual’s eyes, mouth and nose must be placed at the same x and y location in every image ᎏ a process that requires stretching every face to fit some common mold. Fig. 2 shows two major approaches applied to human faces as analogs to the PET functional image: the linear bounding box method, which is based on linear stereotaxic assumptions not requiring a coregistered anatomical image, and warping using thin-plate spline methods which takes advantage of having individual anatomical landmarks ŽBookstein, 1991; DeQuardo et al., 1996.. The thin-plate spline method has been applied to the MRI for an entire brain sagittal section ŽBookstein, 1991; Arndt et al., 1996; DeQuardo et al., 1996., but its use in the basal forebrain or with coregistered PET images has not been reported. We believe that the thinplate spline method is very well suited to the basal forebrain, where some structures Že.g. the anterior commissure, the ventral tip of the internal capsule, and the fornix. are easily identified on MRI and thus allow reliable landmarking, but some other structures Že.g. the substantia innominata, nucleus accumbens, and diagonal band. have somewhat indistinct margins that are difficult to trace reliably. Because these areas are potentially of great interest in schizophrenia and other affective, cognitive and neurodegenerative disorders, the development of reliable and reproducible standardized methods for analysis is a key need for functional studies of patient᎐control differences, effects of new atypical neuroleptic drugs, and changes with illness duration and age. Availability of a group of 25 rigorously screened healthy normal volunteers with coregistered PET and MRI scans provided us an opportunity to test the reliability of various landmark points and the characteristics of variance images.
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ments. Their mean age was 36.4 years old Žmedian 30 years, range 21᎐52.. Subjects with head trauma, history of treatment with psychoactive medication, psychiatric illness in self or a first-degree relative, or substance abuse were excluded. Inclusion criteria for the study were: Ž1. normal medical history and physical examination including blood chemistry and urinalysis within normal limits and a negative urine toxicology; Ž2. English as the native language; Ž3. minimum of high school education; Ž4. right-handed based on the Edinburgh Handedness Test. Subjects that were found on the basis of a semi-structured psychiatric interview ŽAndreasen et al., 1992. to have a psychiatric illness in self or a first-degree relative, or a history of substance abuse or dependence, were excluded. To rule out neurological or systemic medical disorders, we examined the subject for: Ž1. evidence of specific brain disorders on history, physical examination, complete blood cell count with differential Žto rule out infection, anemia or blood dyscrasia., serum chemistries, including electrolytes, liver enzymes, vitamin B 12 and folate Žto rule out electrolyte imbalance, renal failure, hepatic failure, or severe nutritional deficiency., RPM and MA-TO Žto rule out syphilis.; Ž2. evidence of specific diagnosable pathology on MRI of the brain Žincluding lacunar infarcts ) 5 mm, brain malformations, cysts; patients only showing ventricular enlargement or sulcal atrophy are included.; Ž3. history of a seizure disorder; Ž4. history of a significant loss of consciousness Ž) 30 min. or hypoxic event; Ž5. history of substance dependence during their lifetime, substance abuse within the last 6 months that would meet DSM-IV criteria, or positive urine screen for substances of abuse on the day of the scan; and Ž7. evidence of clinically significant uncorrectable visual acuity or hearing impairment. The Mount Sinai School of Medicine Institutional Review Board reviewed the project, and all subjects provided written informed consent.
2. Methods 2.2. PET scan acquisition 2.1. Subjects Normal subjects Ž n s 25; 15 males, 10 females. were recruited through newspaper advertise-
PET scans were obtained using our GE 2048 head-dedicated scanner with measured resolution of no more than 4.6 mm full-width half-maximum
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Žin any of the 15 planes, range 4.2᎐4.6. in plane Žsee Evans et al., 1991. at 0᎐5 cm from slice center, which is the region of the basal forebrain. Subjects received 5 mCi 18 F-deoxyglucose and we obtained 15 slices at 6.5-mm intervals. Total slice counts of 3 000 000 were typical. Scans were reconstructed with a blank and a transmission scan using the Hanning filter, width 3.15. A thermosetting plastic face mask was used to hold the subject stationary during the 20᎐30 min of image acquisition. 2.3. MRI acquisition and reslicing We used the General Electric system Ž1.5 tesla. and SPGR sequence Žrepetition time s 24 ms, echo time s 5 ms, flip angle s 40⬚. for contiguous 1.2-mm-thick axial slices, with a 256 = 256 pixel matrix in a 23-cm field of view; this sequence was chosen for minimum differences in signal intensity for gray matter areas in frontal, temporal and occipital cortex Žto facilitate uniform segmentation. in combination with the maximal difference in grayrwhite matter signal intensity values. Radiofrequency inhomogeneities were measured with a cylindrical, CuSO4-water phantom. The phantom values were adjusted to match the mean human brain values because of slight differences in signal intensity. The images were scaled to 0᎐255. A profile of the phantom was extracted and had a slope of y0.02 unitsrpixel from front to back; since the usual anteroposterior distance in the brain is 160 pixels, the maximum difference in flatness from front to back was 3.2 units or approx. 1.6% of typical white matter values. Within the 50᎐60-pixel basal forebrain window described here, this would be less than one unit. We maintained all details of image acquisition uniform throughout the normal series, and phantoms were imaged at regular intervals to insure consistency. Lastly, the brain was positioned in standard anatomical space; the anterior commissure ŽAC., posterior commissure ŽPC. and the midline of the brain in a coronal section with the anterior commissure were located, and the MR and coregistered PET images were resectioned so that the AC᎐PC line was horizontal and the midline sagittal plane was at exactly 90⬚ from this
line. A recent rigorous examination of reslicing effects using similar image acquisition and slice thickness concluded that the distortion that results as a result of realignment, reslicing and interpolation is minimal ŽDeshmukh et al., 1997.. 2.4. PETr MRI coregistration For accurate anatomical analysis, we have coregistered every PET with MRI using our own version of the surface-fitting method of Pelizzari et al. Ž1989.. Brain edges were visually traced on an MRI axial slice at a midstriatal level and at an approximately matching PET slice using a semiautomated thresholding algorithm. Inter-tracer reliability on 27 individuals was 0.99 for area. The PET volume is then translated Ž x, y . and rotated on the center of mass to minimize root-meansquare difference in the edges. The PET was then resectioned, and a sagittal PET and MRI edge was similarly produced. The PET volume was translated and rotated in sagittal plane using the center of the atlas bone as the rotation point to avoid mismatch in some other coregistration systems due to non-anatomical rotation. The coronal plane was treated similarly and the axial plane redone as the fourth step. Coregistration was tested by scanning a subject with capillary tubes fastened to the face filled with a mixture of copper sulfate Ž0.5 M. and 18 F-fluorodeoxyglucose ŽFDG.. The resulting MR and PET images were coregistered using the surface-fitting method, and the distance between the marker center Žoutside and independent of the traced edges. was found to be 1.79 mm, which is quite comparable to the 1.83 mm reported by Pelizzari et al. Ž1989, their Table 1.. Our accuracy is similar to whole image matching methods such as that of Mallison et al. Ž1993., who achieved 2.4-mm accuracy. 2.5. Anatomical considerations of the basal forebrain The forebrain or prosencephalon includes the cerebral cortex, basal ganglia, thalamus, hypothalamus and associated limbic structures. The basal forebrain includes the basal ganglia, hypothalamus, and ventral cortical structures. In this report, we focus on that part of the basal forebrain
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that includes the striatum, pallidum and substantia innominata ŽFig. 3.. The striatum is divided into the dorsal striatum Žcaudate and putamen. and ventral striatum Žnucleus accumbens, striatal bridges in the substantia innominata and olfactory tubercle . ŽFig. 3, bottom row, light stippling.. The pallidum is, likewise, divided into dorsal pallidum Žglobus pallidus., ventral pallidum Žventral pallidum and pallidal-like cell clusters in the substantia innominata. ŽFig. 3, bottom heavy stippling., and substantia nigra, pars reticulata Žnot studied here.. The substantia innominata includes the heterogeneous subcortical zone ventral to the caudate, putamen, nucleus accumbens and globus pallidus. The substantia innominata contains the ventral pallidum Žespecially the area just ventral to the anterior commissure at the level of the crossing of the anterior commissure.; the corticopetal system, which includes the clusters of cholinergic neurons known as basal nucleus of Meynert and adjacent non-cholinergic neurons, some of which also project to cortex; the nucleus of the diagonal band ŽFig. 3, black spots.; and the clusters of other ventral striatal and ventral pallidal cells in the olfactory tubercle, and the extended amygdala. The extended amygdala is a strip of medial nucleus-like and central nucleus-
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like amygdaloid cells and axons extending from medial and central nuclei of the amygdala proper and coursing through the substantia innominata, abutting the shell of the nucleus accumbens, and continuing into the bed nucleus of the stria terminalis, which is fundamentally an amygdaloid structure ŽFig. 3, cross-hatching .. These forebrain structures are a constant feature of mammalian brains, including the human ŽHaber, 1987; Alheid et al., 1990; Martin et al., 1991; Voorn et al., 1994.. Although the ventral structures have only recently been delineated in the human brain, they are amenable to PET imaging analysis and investigation using MR images. 2.6. Striatal di¨ isions The striatum can be subdivided not only by nuclear definitions Žcaudate, putamen, nucleus accumbens. but also according to a system of connectional schemata with some regions sufficiently large for functional imaging consideration. Fig. 4 illustrates some of these schemata, including a prefrontal cortexrlimbic associated zone in the ventral caudate, putamen and nucleus accumbens, and a dorsolateral sensory-motor zone dominated by cortical input from the sensory,
Fig. 3. Anterior to posterior series of coronal sections of basal forebrain in schematic form.
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motor and association cortices. These zones are similarly divided into ventral, central and dorsolateral striatal sections ŽHaber et al., 1995.. Note that the dimensions of these zones, each in the range of 10 mm high and more than 10 mm wide, are amenable to functional imaging if structural image coregistration and individual alignment are adequate. There are other important subdivisions of the striatum. The first is the patchŽstriosome. ᎐matrix organization ŽFig. 4., based on differential neurochemical, neurotransmitter, and connectional grounds. The patch᎐ matrix dichotomy has been recently studied in the human brain ŽHaber and Watson, 1985; Johnston et al., 1990; Manley et al., 1994; Hurd and Herkenham, 1995.. Approximately 85% of the striatum is matrix and 15% is patch, which is conserved across the rat, rhesus monkey and human ŽJohnston et al., 1990.. The number of patches in a cross-sectional area of striatum is also constant among species, but surprisingly the size of the patches increases 23 = from the rat to the human, where the cross-sectional area averages 1.4 mm2 ŽJohnston et al., 1990.. This crosssectional area of the patches is still too small to be resolved by present PET technology but is at the threshold of resolution of anatomical MRI and functional MRI. 2.7. Landmark placement Landmarks were chosen to correspond to reliably identified grayrwhite or grayrCSF boundaries instead of being based entirely on tracing regions. We anticipated that clearly identifiable landmarks Že.g. 6a at the ventricular tip or 10a at the putamen in Fig. 5. would proportionately adjust less clearly identifiable structures lying between them into alignment. Regions bounded by curved surfaces were aligned using short sections of spline curves, a method we termed ‘landmark by spline’. For example, points 6a᎐6e were placed by tracing the lateral edge of the ventricle by depositing points every 3᎐6 pixels on an enlarged image. A spline curve was then fit to the set of user-defined points. Next, the two endpoints and landmark points 25%, 50% and 75% of the distance along
Fig. 4. Major connectivity regions of the striatum.
the curve were calculated and placed automatically. This procedure allowed proportional alignment of a smoothly curving edge with equal numbers of final landmarks, a requirement of the thin-plate spline method. 3. Results 3.1. Two-dimensional (2D) landmark images and ¨ ariance images Twenty-five normal subjects have been landmarked Ž35 points, see Fig. 5. for the coronal slice passing through the anterior limit of the midline crossing of the anterior commissure. The average landmark positions were computed, and each individual’s MRI was morphed to the average landmark coordinates ŽFigs. 6 and 7.. To assess improvement in standardization, we computed the mean MRI and pixel-by-pixel vari-
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Fig. 5. Landmarks and size measurements for basal forebrain.
Fig. 6. Coronal MRI in two individuals and formation of average image. Alignment of caudate and ventricle of different shapes across the two individuals Žleft and center. to provide a merged and aligned image Žright.. The aligned and average image is as clearly demarcated as either of the two individuals. Only the left hemisphere has had landmarks, and the method is most accurate between the greatest x and y extent of the landmark points.
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2.03, d.f.s 24,24, P- 0.05.. This reduction in MRI signal-intensity variance, which needs to be considered with respect to the scale of gray Žtypically 120 units. and white Žtypically 165 units., reflects an improvement of approximately 20% of the distance between gray and white intensity values. This improvement can be seen in the sharper image in the landmark image in Fig. 7. 3.2. Reliability
Fig. 7. Anatomical outline of areas drawn on average MRI image Ž n s 25.. Average MRI of 25 normal subjects after thin-plate spline warping to compare with Fig. 6, which indicates anchor points and example of warped image with n s 2. Note alignment of major gray regions with less improvement in areas peripheral to spline landmarks.
ance images for bounding box and landmark methods ŽFig. 8.. If all caudates are well aligned across subjects, the variance in the center should be relatively low and should remain low as one examines pixels on the edge of the caudate; if the caudates are poorly aligned and sized, then the pixels at the caudate’s edge should have internal capsule pixels Žwhite matter with relatively high values. in some subjects and caudate pixels Žgray matter with relatively low values. in other subjects, resulting in high variation. We also computed the variance of the MRI signal-intensity values for each of the 840 pixels within an area bounded by the medial edge of the caudate, lateral edges of the caudate and putamen, and ventrally by the ventral substantia innominata. With the bounding box method of alignment, the average MRI variance was 761 ŽS.D.s 27.6.; but with the landmark method, it was 374 ŽS.D.s 19.3 units.; a conservative F-ratio test based only on the mean variance values showed a significantly smaller variance for the landmark method Ž F s
Interrater reliability was assessed in eight subjects independently landmarked by JHF and SG. For landmarks 5 and 6 ŽFig. 5., the mean absolute distance between the x, y locations for these points was 1.03 ŽS.D.s 1.33. and 1.45 ŽS.D.s 1.62. mm, indicating excellent agreement between a highly trained neuroanatomist ŽJHF. and a trained non-neuroanatomist ŽSG.. We next expressed the x and y coordinates of these points as a difference from the midpoints of landmarks 1 and 4 ŽFig. 5., to remove variation associated with the arbitrary position of the head within the field of view. The intraclass correlations ranged from 0.96 to 0.99 for the x and y coordinates of these structures. 4. Discussion Our application of the thin-plate spline method ŽBookstein, 1991. to coronal sections partly addresses problems associated with the axial orientation Že.g. risk of partial-volume effects at the brain’s lowest margin, variation in the contours and anteroposterior positioning of the caudate, and variation in size of the ventricle.. The method yields x, y, z data sets for analysis of the position and greatest extent of structures. However, there are important limitations. First, no method is better than the functional image resolution; diminished values due to adjacency of ventricular cavities will still occur. These errors will be smaller than those associated with placing the center of a stereotaxic ROI inside the ventricle on a functional image, but larger ventricles will still influence functional values. Methods of local correction, such as those proposed by Meltzer et al. Ž1996., are needed to more fully adjust for local structural influences.
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Fig. 8. Mean PET, PET variance, mean MRI, and MRI variance for bounding box and landmark methods. Mean and variance of PET Žleft two columns. and mean and variance of MRI Žright two columns. for 25 individuals when slices are aligned with two methods. Ž1. Top row: bounding-box method aligns images on lateral and superior cortical margins and has linear proportional adjustment. Mean images show caudate and putamen Žcompare to Fig. 5.. Variance images show pixel by pixel variance. Color bar numeric legend is for standard deviation for comparison with published values. PET data is in units of relative metabolic rate Žpixel value divided by mean whole brain value.. Relative metabolic rate gray values for an ROI in the striatum are typically 1.2᎐1.4 with S.D. 0.08᎐0.20 Žsee Shihabuddin et al., 1998.. MRI data is scaled from 0᎐255 for each person to provide a comparable gray᎐white data range across all subjects Žsee text.. Ž2. Bottom row: landmark method with thin-plate spline adjustment. Note sharper image with landmark adjustment method. Variance images are presented with color scale adjusted so that largest variance in both images is pink and smallest variance is purple to allow direct comparison of the two methods. Note that a ring boundary for the caudate is prominent in the PET variance image for the bounding box method Žtop row., but nearly missing in the landmark method. An ideal method might produce an entirely uniform image, but variable placement produces an image with the appearance of spatial differentiation Že.g. enhances caudate edges. since areas of rapid spatial variation are on structure perimeters. High variance in key structures reduces the possibility of having statistically significant probability mapping results. This error results since variances are spuriously high, primarily due to inclusion of caudate and non-caudate pixels at the same x᎐y coordinate locations. In the peripheral regions of the image outside the landmarked areas, variance rises as standardization transformation is extrapolated beyond set landmark points.
Second, the spline method does not provide the same level of volumetric estimation that actual tracing can yield. The landmark method aligns pixels across subjects but does not provide an edge for small structures that are difficult to outline. Thus, pixels in the ventral striatum will be placed in the same relative position in areas such as the substantia innominata or accumbens, but the exact number of these pixels, and hence the contour and volume, will not be assessed. Furthermore, in this example, because the substantia innominata contains a mix of cell clusters, the measured metabolic rate in that structure will
reflect this mixture. Despite the fact that the nucleus accumbens is approximately the same size as the caudate, it is difficult to achieve equally high inter-tracer reliabilities in MRI analyses due to its lower contrast edges Žtechnically lower values of the 2D spatial first derivative.. Thus, the location of structural abnormalities may be detected by carrying out statistical probability mapping on the anatomical images Žcf. Andreasen, 1995., and PET ligand or metabolic measures subjected to statistical probability mapping, but unequivocal size determinations cannot result. Third, we present only 2D warping landmarks
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for a single coronal slice. Since the anteroposterior position of structures differs across subjects, 3D warping is desirable. Although such structures as the center of the anterior commissure provide unique x᎐y᎐z sets of coordinates, the surface of the caudate will require a 3D generalization of the ‘landmark-by-spline’ method we discuss for 2D analysis. However, thin-plate spline standardized single slices will still yield anatomically more accurate and statistically more powerful striatal significance probability mapping. The dorsal-most portions of the caudate and putamen have the advantage of being easily traced on axial brain sections. In contrast, tracing of the ventral striatum is highly problematic. Notwithstanding these difficulties, the high dopamine receptor density, and the rich efferent and afferent connections to thalamic and limbic areas, make areas of the ventral striatum ᎏ the nucleus accumbens, the striatal bridges, and striatal sections of the substantia innominata, the ventral pallidum, the bed nucleus of the stria terminalis, and the extended amygdala ᎏ key candidates for disturbed function in schizophrenia and other brain diseases. With the advent of high-resolution, resectionable MRI images, this terra incognita is a prime area for new morphometric and functional exploration in man. Acknowledgements This project was supported by the Charles A. Dana Foundation and by grants from the National Institute of Mental Health, MH40071 and MH56489. Thanks are due to Drs. Lina Shihabuddin, M. Mehmet Haznedar, and Erin A. Hazlett, who screened the normal volunteers for the presence of psychiatric illness, and to Richard Azueta, who was responsible for subject recruitment. References Alheid, G.F., Heimer, L., Switzer, R.C. III, 1990. Basal ganglia. In: Paxinos, G. ŽEd.., The Human Nervous System. Academic Press, Sydney, pp. 483᎐582. Andreasen, N.C., 1995. PET and the w 15 OxH 2 O technique, Part 1: Statistical analysis of images. American Journal of Psychiatry 152, 1704.
Andreasen, N.C., Flaum, M., Arndt, S., 1992. The Comprehensive Assessment of Symptoms and History ŽCASH.: an instrument for assessing diagnosis and psychopathology. Archives of General Psychiatry 49, 615᎐623. Arndt, S., Rajarethinam, R., Cizadlo, T., O’Leary, D., Downhill, J., Andreasen, N.C., 1996. Landmark-based registration and measurement of magnetic resonance images: a reliability study. Psychiatry Research: Neuroimaging 67, 145᎐154. Bartels, P.H., Subach, J.A., 1976. Significance probability mapping and automated interpretation of complex pictorial scenes. In: Preston, E., Onoe M. ŽEds.., Digital Processing of Biomedical Imagery. Academic Press, New York, pp. 101᎐114. Bartlett, E.J., Brodie, J.D., Simkowitz, P., Dewey, S.L., Rusinek, H., Volkow, N.D., Wolf, A.P., Smith, G., Wolkin, A., Cancro, R., 1996. Time-dependent effects of a haloperidol challenge on energy metabolism in the normal human brain. Psychiatry Research: Neuroimaging 60, 91᎐99. Bookstein, F.L., 1991. Morphometric Tools for Landmark Data: Geometry and Biology. Cambridge University Press, New York. Bremner, J.D., Innis, R.B., Ng, C.K., Staib, L.H., Salomon, R.M., Bronen, R.A., Duncan, J., Southwick, S.M., Krystal, J.H., Rich, D., Zubal, G., Dey, H., Soufer, R., Charney, D.S., 1997. Positron emission tomography measurement of cerebral metabolic correlates of yohimbine administration in combat-related post-traumatic stress disorder. Archives of General Psychiatry 54, 246᎐254. Buchsbaum, M.S., Cappelletti, J., Ball, R., Hazlett, E., King, A.C., Johnson, J., Wu, J., DeLisi, L.E., Holcomb, H., Dowling-Zimmerman, S., Kessler, R., 1984a. PET image measurement in schizophrenia and affective disorders. Annals of Neurology 15, 157᎐165. Buchsbaum, M.S., Coppola, R., Cappelletti, J., 1982a. Positron emission tomography, EEG and evoked potential topography: new approaches to local function in pharmaco-electroencephalography. In: Herrmann, W.M. ŽEd.., EEG in Drug Research. Gustav Fischer Verlag, Heidelberg, pp. 193᎐207. Buchsbaum, M.S., Ingvar, D.H., Kessler, R., Waters, R.N., Cappelletti, J., van Kammen, D.P., King, A.C., Johnson, J.L., Manning, R.G., Flynn, R.W., Mann, L.S., Bunney, W.E., Jr., Sokoloff, L., 1982b. Cerebral glucography with positron emission tomography. Archives of General Psychiatry 39, 251᎐259. Buchsbaum, M.S., Kessler, R., King, A.C., Johnson, J., Cappelletti, J., 1984b. Simultaneous cerebral glucography with positron emission tomography and topographic electroencephalography. In: Pfurtscheller, G., Jonkman, E.J., Lopes da Silva, F.H. ŽEds.., Brain Ischemia: Quantitative EEG and Imaging Techniques. Elsevier Science Publishers, Amsterdam, pp. 263᎐269. Buchsbaum, M.S., Potkin, S., Marshall, J., Lottenberg, S., Teng, C.Y., Heh, C.W., Tafalla, R., Reynolds, C., Abel, L., Plon, L., Bunney, W.E., 1992. Effects of clozapine and thiothixene on glucose metabolic rate in schizophrenia. Neuropsychopharmacology 6, 155᎐163.
M.S. Buchsbaum et al. r Psychiatry Research: Neuroimaging Section 84 (1998) 113᎐125 Chugani, H.T., Phelps, M.E., Mazziotta, J.C., 1987. Positron emission tomography study of human brain functional development. Annals of Neurology 22, 487᎐497. DeQuardo, J.R., Bookstein, F.L., Green, W.D.K., Brunberg, J.A., Tandon, R., 1996. Spatial relationships of neuroanatomic landmarks in schizophrenia. Psychiatry Research: Neuroimaging 67, 81᎐95. Deshmukh, A.R., Desmond, J.E., Sullivan, E.V., Lane, B.F., Matsumoto, B., Marsh, L., Lim, K.O., Pfefferbaum, A., 1997. Quantification of cerebellar structures with MRI. Psychiatry Research: Neuroimaging 75, 159᎐171. Desmond, J.E., Lim, K.O., 1997. On- and offline Talairach registration for structural and functional MRI studies. Human Brain Mapping 5, 58᎐73. Duffy, F.H., Bartels, P.B., Burchfiel, J.L., 1981. Significance probability mapping: an aid in the topographic analysis of brain electrical activity. Electroencephalography and Clinical Neurophysiology 51, 455᎐462. Early, T.S., Posner, M.I., Reiman, E., Raichle, M.E., 1989. Hyperactivity of the left striato-pallidal projection. Psychiatric Developments 2, 85᎐108. Evans, A.C., Thompson, C.J., Marrett, E., Meyer, E., Mazza, M., 1991. Performance evaluation of the PC-2048: a new 15-slice encoded-crystal PET scanner for neurological studies. IEEE Transactions of Medical Imaging 10, 90᎐98. Friston, K.J., 1995. Commentary and opinion. II. Statistical parametric mapping: ontology and current issues. Journal of Cerebral Blood Flow and Metabolism 15, 361᎐370. Friston, K.J., Frith, C.D., Liddle, P.F., Frackowiak, S.J.R., 1991. Plastic transformation of PET images. Journal of Computer Assisted Tomography 15, 634᎐639. Gur, R.E., Mozley, D., Resnick, S.M., Mozley, L.H., Shtasel, D.L., Gallacher, F., Arnold, S.E., Karp, J.S., Alavi, A., Reivich, M., Gur, R.C., 1995. Resting cerebral glucose metabolism in first-episode and previously treated patients with schizophrenia relates to clinical features. Archives of General Psychiatry 52, 657᎐667. Haber, S.N., 1987. Anatomical relationships between the basal ganglia and the basal nucleus of Meynert in human and monkey forebrain. Proceedings of the National Academy of Sciences of the United States of America 84, 1408᎐1412. Haber, S.N., Kunishio, K., Mizoobuchi, M., Lynd-Balta, E., 1995. The orbital and medial prefrontal circuit through the primate basal ganglia. Journal of the Neurosciences 15, 4851᎐4867. Haber, S.N., Watson, S.J., 1985. The comparative distribution of enkephalin, dynorphin and substance P in the human globus pallidus and basal forebrain. Neuroscience 14, 1011᎐1024. Harris, G.J., Links, J.M., Pearlson, G.D., Camargo, E.E., 1991. Cortical circumferential profile of SPECT cerebral perfusion in Alzheimer’s disease. Psychiatry Research: Neuroimaging 40, 167᎐180.
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Holcomb, H.H., Cascella, N.G., Thaker, G.K., Medoff, D.R., Dannals, R.F., Tamminga, C.A., 1996. Functional sites of neuroleptic drug action in the human brain: PETrFDG studies with and without haloperidol. American Journal of Psychiatry 153, 41᎐49. Hurd, Y.L., Herkenham, M., 1995. The human neostriatum shows compartmentalization of neuropeptide gene expression in dorsal and ventral regions: in situ hybridization histochemical analysis. Neuroscience 64, 571᎐586. Jernigan, T.L., Zatz, L.M., Naeser, A., 1979. Semiautomated methods for quantitating CSF volume on cranial computed tomography. Radiology 132, 463᎐466. Johnston, J.G., Gerfen, C.R., Haber, S.N., van der Kooy, D., 1990. Mechanisms of striatal pattern formation: conservation of mammalian compartmentalization. Developmental Brain Research 57, 93᎐102. Mallison, R.T., Miller, E.G., Greene, R., McCarthy, G., Charney, D.S., Innis, R.B., 1993. Computer-assisted coregistration of multislice SPECT and MR brain images by fixed external fiducials. Journal of Computer Assisted Tomography 17, 952᎐960. Manley, M.S., Young, S.J., Grover, P.M., 1994. Compartmental organization of the peptide network in the human caudate nucleus. Journal of Chemical Neuroanatomy 7, 191᎐201. Martin, L.J., Powers, R.E., Dellovade, T.L., Price, D.L., 1991. The bed nucleus᎐amygdala continuum in human and monkey. Journal of Comparative Neurology 309, 444᎐485. Meltzer, C.C., Zubieta, J.K., Links, J.M., Brakeman, P., Stumf, M.J., Frost, J., 1996. MR based correction of brain PET measurements for heterogeneous gray matter radioactivity distribution. Journal of Cerebral Blood Flow and Metabolism 16, 650᎐658. Pelizzari, C.A., Chen, G.T.Y., Spelbring, D.R., Weichselbaum, R.R., Chen, C.T., 1989. Accurate three-dimensional registration of CT, PET, andror MR images of the brain. Journal of Computer Assisted Tomography 13, 20᎐26. Shihabuddin, L., Buchsbaum, M.S., Hazlett, E.A., Haznedar, M.M., Harvey, P.D., Newman, A., Schnur, D.B., SpiegelCohen, J., Wei, T., Machac, J., Knesaurek, K., Vallabhojosula, S., Singer, M.B., Ciaravolo, T.M., Luu-Hsia, C., 1998. Dorsal striatal size, shape, and metabolic rate in neuroleptic-naive and previously medicated schizophrenic patients performing a verbal learning task. Archives of General Psychiatry 55, 235᎐243. Talairach, J., Tournoux, P., 1988. Co-planar Stereotaxic Atlas of the Human Brain. Thieme Medical Publishers, New York. Voorn, P., Brady, L.S., Schotte, A., Bernendse, H.W., Richfield, E.K., 1994. Short communication: evidence for two neurochemical divisions in the human nucleus accumbens. European Journal of Neuroscience 6, 1913᎐1916.
A method of basal forebrain anatomical standardization for functional image analysis Monte S. Buchsbaum a,U , James H. Fallonb , Tse-Chung Wei a , Steven Guich b , Jacqueline Spiegel-Cohena , Matthew Hamiltonb , Cheuk Tang a a
Department of Psychiatry, Mount Sinai School of Medicine, Box 1505, One Gusta¨ e L. Le¨ y Place, New York, NY 10029-6574, USA b Department of Anatomy and Neurobiology, Uni¨ ersity of California, Ir¨ ine, College of Medicine, 364 Med. Surg. II, Ir¨ ine, CA 92697-1275, USA Received 6 June 1998; received in revised form 28 September 1998; accepted 12 October 1998
Abstract Functional as well as structural assessment of the basal forebrain has mostly focused on the dorsal caudate and putamen in axial slices where they are easily outlined or their centers located with stereotaxic methods. The more ventral extent of the basal forebrain, where the irregular form and indistinct boundaries of the nucleus accumbens and substantia innominata are difficult to trace and where the brain’s ventral surface may contribute partial volume artifacts to measurement, has been less studied. We present a method based on coronal sections, landmarks placed on clearly visible anchor points, and the computational technique of thin-plate spline warping which allows the alignment of groups of individuals to common coordinates for pixel-by-pixel statistical mapping. The reliability of the landmarks across independent raters yields a median absolute difference of 1.3᎐1.6 mm. The validity of the method is confirmed by variance maps which reveal significant decreases in variance over spindle and bounding box alignment. 䊚 1998 Elsevier Science Ireland Ltd. All rights reserved. Keywords: Positron emission tomography; Magnetic resonance imaging; Striatum; Pallidum; Caudate; Putamen; Nucleus accumbens; Substantia innominata
U
Corresponding author. Tel.: q1 212 2415294; fax: q1 212 4230819; e-mail: [email protected]
0925-4927r98r$ - see front matter 䊚 1998 Elsevier Science Ireland Ltd. All rights reserved. P I I: S 0 9 2 5 - 4 9 2 7 Ž 9 8 . 0 0 0 5 2 - 3
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1. Introduction 1.1. Anatomical standardization for functional image analysis
al., 1991; Friston, 1995.. Recent comparisons of these two variants have revealed good agreement ŽDesmond and Lim, 1997.. 1.2. Assessment of the striatum
During the first decade of functional imaging studies, anatomical regions were assessed primarily by the generation of geometric regions or circumferential strips along the brain edge ŽBuchsbaum et al., 1982a; Harris et al., 1991., stereotaxic placement of regions of interest Že.g. Buchsbaum et al., 1982b, 1984a., or drawing of regions on the functional image Že.g. Chugani et al., 1987.. These methods were limited by the need to select individual brain areas in advance and their inability to reveal unanticipated functional units of the cortex. The evaluation of every pixel in an image with a statistical map, as suggested by Bartels and Subach Ž1976., offered an alternative approach, which was used for electroencephalographic topography Že.g. Duffy et al., 1981. and computed tomographic brain images ŽJernigan et al., 1979.. Variants of significance probability mapping have been applied for group map contrasts and topographic display of statistical parameters such as test᎐retest reliability Že.g. Buchsbaum et al., 1982a, 1984a,b; Friston et al., 1991; Friston, 1995. over the last two decades. In order for the statistical test, most commonly a t-test comparing groups or a paired t-test comparing two conditions studied in a group, to be interpretable, all of the pixels containing the same anatomical structure must be aligned across subjects. Otherwise, each pixel location would contain information from a mixture of different anatomical structures and no real functional map could be created. It should be noted that all standard stereotaxic approaches to locating electrode positions or regions of interest require similar adjustment to describe coordinates of action or assessment Že.g. Buchsbaum et al., 1984b.. Approaches to standardization of brain slice images have included the use of proportional brain bounding boxes based on brain length and width ŽAndreasen, 1995. and linear adjustments of the brain in different regions based on the atlas of Talairach and Tournoux Ž1988. developed further by Friston and his associates ŽFriston et
The size, shape, and location of the striatum provide localization problems different from those associated with major cortical surface areas. When linear proportional adjustment based on brain edges is used to locate the center of the dorsal caudate, errors in the 2᎐3-mm range are typical as estimated by applying the method to actual magnetic resonance images ŽMRI. ŽBuchsbaum et al., 1992.. This degree of error is small compared with the 8᎐10-mm resolution of older positron emission tomography ŽPET. scanners but sizable compared with the 4.5-mm resolution of current scanners and the 6᎐10-mm width of the caudate, for example. While axial slices provide clear boundaries of the dorsal caudate and putamen, the more ventral outlines at or below the level of the internal capsule are often ambiguous. Because of these difficulties, we Že.g. Shihabuddin et al., 1998. and other investigators have tended to leave more ventral areas of the basal forebrain not fully evaluated. Various approaches to the evaluation of the striatum have been used. Holcomb et al. Ž1996. used an MRI-based technique that combined circular regions of interest ŽROIs. across dorsoventral levels. Gur et al. Ž1995., applying a geometric flexible template method, similarly obtained averages across all slices in which the striatum was present. Bartlett et al. Ž1996. used a square 5 = 5-mm box placed in the center of the MRI templates coregistered to the PET image of the striatum. Bremner et al. Ž1997. traced the dorsal caudate on MRI wtheir Fig. 1 approx. corresponding to Talairach and Tournoux Ž1988. level 18᎐20 mm Ždorsal to the anterior commissure to posterior commissure line.x and transferred the caudate outline to the coregistered PET. Thus, both stereotaxic atlas and MRI coregistration methods have been used, but data analysis has been largely confined to dorsal striatum, with emphasis on the dorsal-most caudate. In an important theoretical article, Early et al.
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Fig. 1. Non-linear change in edges of the striatum. Left: Average MRI from 25 individuals based on thin-plate spline warping of edge, midline, and striatal landmarks. Middle: Matching atlas illustration ŽTalairach and Tournoux, 1988. with landmarks applied. Initial square grid superimposed shows original rectilinear coordinates. Right: Atlas standardized to average coordinates of normal subjects with deformation grid from thin-plate spline method indicating non-linear change. Such non-linearity would not be corrected for in the proportional standardization in a purely atlas-based method as opposed to an MRI-based non-linear method of anatomical standardization for statistical probability mapping. Atlas was digitized at resolution matching the MRI field of view Že.g. 256 = 256 pixels. and, for this reason, small scale numbers at margin are blurred; this has no effect on the transformation.
Ž1989. highlighted the ventral striatum and ventral pallidum in their model of thought disorder in schizophrenia. In analyzing their own data, however, these investigators carried out only a limited assessment of the basal forebrain Žno lower than the dorsoventral level of the anterior commissure ŽTalairach and Tournoux, zs 0 mm, a level approximately halfway from the dorsalmost aspect of the caudate nucleus at zs q24 mm to the ventral-most tip of the nucleus accumbens at zs y20 mm.. Obstacles to a more thorough examination of the basal forebrain in imaging studies include partial volume effects and the variations in the exact angle of brain orientation, which may especially affect assessment of inferior areas on MRI. To attempt to overcome these problems, a method based both on aligning coronal sections and individual landmarks appears desirable. With the advent of high-resolution PET and thin-slice MRI instruments, the possibility of creating accurate and detailed statistical contrast images based on an individual’s own brain as an atlas has broadened our scientific tools for exploration. Accurate anatomical localization can
be provided by coregistering MRIs to the functional scan. Because brain activity does not necessarily coincide with brain structure, the plastic transformation method ŽFriston et al., 1991., which relies upon the functional image intensity profile, may create local positional biases based on disparity between function and structure; areas such as the accumbens with signal intensity values intermediate between gray and white matter for both functional and structural images may be especially susceptible to this problem. Since the position of the caudate is not tightly linked to the cortical rim, which is used to scale the image linearly in methods of standardization such as those of Talairach and Tournoux Ž1988. ŽFig. 1., statistical maps of the caudate may lose statistical power if all individuals’ brains are not aligned correctly. Some considerable anatomical variation may result in methods based only on one person’s prototypic brain space we.g. the atlas of Talairach and Tournoux Ž1988.x or even ‘average’ brains derived from population samples. Furthermore, since all caudate nuclei are not the same shape, there is considerable variation in the proportion of individuals for whom a given x᎐y coordinate
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will fall either inside or outside the caudate nucleus. In an alternative to the bounding box approach, our recent studies of the striatum traced outlines of the caudate and putamen on the MRI and used the nuclear edge to morph the structures ŽShihabuddin et al., 1998.. This approach allowed visualization of change within the striatum and exploratory statistical probability mapping within the caudate outline with edges aligned by radial adjustment. However, this approach, too, is largely limited to the dorsal striatum where clear structure boundaries and a simple convex shape make caudate edge alignment feasible. The complex structures of the ventral striatum and other
basal forebrain structures, which lack such clear ovals but yet contain specific and clearly defined white matter and surface landmarks, make the application of another spatial standardizing technique that uses thin-plate splines ŽBookstein, 1991. especially suitable. In this method, we map anchor points located in each person’s MR image to the mean location for the group. This produces an MR and PET image in which sets of anatomical structures are all located at the same x and y coordinates and are thus suitable for statistical imaging. Combining different individuals’ brains is analogous to averaging together photographs of dif-
Fig. 2. Comparison of bounding box and landmark methods of subject standardization. Top Row: Three faces with bounding box are averaged to standard coordinates. Note that while chin and top of head have been aligned, the eyes and nose produce a recognizable but blurred image due to non-linear variation in their x- and y-coordinate position. The eyes and their positioning relative to the chin and top of the head are analogous to the caudate and its positioning relative to the front and back of the head in the bounding box method. Bottom row. Three faces with landmarks Žlight gray. applied. The average position of the landmarks is calculated, and each image is standardized to this position and redrawn Žrightmost face image.. Note much sharper appearance of features in the center of the face due to non-linear landmark method.
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ferent people’s faces; every one has a different shape. To achieve interpretable differences in pupil color, lip size or nose length, each individual’s eyes, mouth and nose must be placed at the same x and y location in every image ᎏ a process that requires stretching every face to fit some common mold. Fig. 2 shows two major approaches applied to human faces as analogs to the PET functional image: the linear bounding box method, which is based on linear stereotaxic assumptions not requiring a coregistered anatomical image, and warping using thin-plate spline methods which takes advantage of having individual anatomical landmarks ŽBookstein, 1991; DeQuardo et al., 1996.. The thin-plate spline method has been applied to the MRI for an entire brain sagittal section ŽBookstein, 1991; Arndt et al., 1996; DeQuardo et al., 1996., but its use in the basal forebrain or with coregistered PET images has not been reported. We believe that the thinplate spline method is very well suited to the basal forebrain, where some structures Že.g. the anterior commissure, the ventral tip of the internal capsule, and the fornix. are easily identified on MRI and thus allow reliable landmarking, but some other structures Že.g. the substantia innominata, nucleus accumbens, and diagonal band. have somewhat indistinct margins that are difficult to trace reliably. Because these areas are potentially of great interest in schizophrenia and other affective, cognitive and neurodegenerative disorders, the development of reliable and reproducible standardized methods for analysis is a key need for functional studies of patient᎐control differences, effects of new atypical neuroleptic drugs, and changes with illness duration and age. Availability of a group of 25 rigorously screened healthy normal volunteers with coregistered PET and MRI scans provided us an opportunity to test the reliability of various landmark points and the characteristics of variance images.
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ments. Their mean age was 36.4 years old Žmedian 30 years, range 21᎐52.. Subjects with head trauma, history of treatment with psychoactive medication, psychiatric illness in self or a first-degree relative, or substance abuse were excluded. Inclusion criteria for the study were: Ž1. normal medical history and physical examination including blood chemistry and urinalysis within normal limits and a negative urine toxicology; Ž2. English as the native language; Ž3. minimum of high school education; Ž4. right-handed based on the Edinburgh Handedness Test. Subjects that were found on the basis of a semi-structured psychiatric interview ŽAndreasen et al., 1992. to have a psychiatric illness in self or a first-degree relative, or a history of substance abuse or dependence, were excluded. To rule out neurological or systemic medical disorders, we examined the subject for: Ž1. evidence of specific brain disorders on history, physical examination, complete blood cell count with differential Žto rule out infection, anemia or blood dyscrasia., serum chemistries, including electrolytes, liver enzymes, vitamin B 12 and folate Žto rule out electrolyte imbalance, renal failure, hepatic failure, or severe nutritional deficiency., RPM and MA-TO Žto rule out syphilis.; Ž2. evidence of specific diagnosable pathology on MRI of the brain Žincluding lacunar infarcts ) 5 mm, brain malformations, cysts; patients only showing ventricular enlargement or sulcal atrophy are included.; Ž3. history of a seizure disorder; Ž4. history of a significant loss of consciousness Ž) 30 min. or hypoxic event; Ž5. history of substance dependence during their lifetime, substance abuse within the last 6 months that would meet DSM-IV criteria, or positive urine screen for substances of abuse on the day of the scan; and Ž7. evidence of clinically significant uncorrectable visual acuity or hearing impairment. The Mount Sinai School of Medicine Institutional Review Board reviewed the project, and all subjects provided written informed consent.
2. Methods 2.2. PET scan acquisition 2.1. Subjects Normal subjects Ž n s 25; 15 males, 10 females. were recruited through newspaper advertise-
PET scans were obtained using our GE 2048 head-dedicated scanner with measured resolution of no more than 4.6 mm full-width half-maximum
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Žin any of the 15 planes, range 4.2᎐4.6. in plane Žsee Evans et al., 1991. at 0᎐5 cm from slice center, which is the region of the basal forebrain. Subjects received 5 mCi 18 F-deoxyglucose and we obtained 15 slices at 6.5-mm intervals. Total slice counts of 3 000 000 were typical. Scans were reconstructed with a blank and a transmission scan using the Hanning filter, width 3.15. A thermosetting plastic face mask was used to hold the subject stationary during the 20᎐30 min of image acquisition. 2.3. MRI acquisition and reslicing We used the General Electric system Ž1.5 tesla. and SPGR sequence Žrepetition time s 24 ms, echo time s 5 ms, flip angle s 40⬚. for contiguous 1.2-mm-thick axial slices, with a 256 = 256 pixel matrix in a 23-cm field of view; this sequence was chosen for minimum differences in signal intensity for gray matter areas in frontal, temporal and occipital cortex Žto facilitate uniform segmentation. in combination with the maximal difference in grayrwhite matter signal intensity values. Radiofrequency inhomogeneities were measured with a cylindrical, CuSO4-water phantom. The phantom values were adjusted to match the mean human brain values because of slight differences in signal intensity. The images were scaled to 0᎐255. A profile of the phantom was extracted and had a slope of y0.02 unitsrpixel from front to back; since the usual anteroposterior distance in the brain is 160 pixels, the maximum difference in flatness from front to back was 3.2 units or approx. 1.6% of typical white matter values. Within the 50᎐60-pixel basal forebrain window described here, this would be less than one unit. We maintained all details of image acquisition uniform throughout the normal series, and phantoms were imaged at regular intervals to insure consistency. Lastly, the brain was positioned in standard anatomical space; the anterior commissure ŽAC., posterior commissure ŽPC. and the midline of the brain in a coronal section with the anterior commissure were located, and the MR and coregistered PET images were resectioned so that the AC᎐PC line was horizontal and the midline sagittal plane was at exactly 90⬚ from this
line. A recent rigorous examination of reslicing effects using similar image acquisition and slice thickness concluded that the distortion that results as a result of realignment, reslicing and interpolation is minimal ŽDeshmukh et al., 1997.. 2.4. PETr MRI coregistration For accurate anatomical analysis, we have coregistered every PET with MRI using our own version of the surface-fitting method of Pelizzari et al. Ž1989.. Brain edges were visually traced on an MRI axial slice at a midstriatal level and at an approximately matching PET slice using a semiautomated thresholding algorithm. Inter-tracer reliability on 27 individuals was 0.99 for area. The PET volume is then translated Ž x, y . and rotated on the center of mass to minimize root-meansquare difference in the edges. The PET was then resectioned, and a sagittal PET and MRI edge was similarly produced. The PET volume was translated and rotated in sagittal plane using the center of the atlas bone as the rotation point to avoid mismatch in some other coregistration systems due to non-anatomical rotation. The coronal plane was treated similarly and the axial plane redone as the fourth step. Coregistration was tested by scanning a subject with capillary tubes fastened to the face filled with a mixture of copper sulfate Ž0.5 M. and 18 F-fluorodeoxyglucose ŽFDG.. The resulting MR and PET images were coregistered using the surface-fitting method, and the distance between the marker center Žoutside and independent of the traced edges. was found to be 1.79 mm, which is quite comparable to the 1.83 mm reported by Pelizzari et al. Ž1989, their Table 1.. Our accuracy is similar to whole image matching methods such as that of Mallison et al. Ž1993., who achieved 2.4-mm accuracy. 2.5. Anatomical considerations of the basal forebrain The forebrain or prosencephalon includes the cerebral cortex, basal ganglia, thalamus, hypothalamus and associated limbic structures. The basal forebrain includes the basal ganglia, hypothalamus, and ventral cortical structures. In this report, we focus on that part of the basal forebrain
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that includes the striatum, pallidum and substantia innominata ŽFig. 3.. The striatum is divided into the dorsal striatum Žcaudate and putamen. and ventral striatum Žnucleus accumbens, striatal bridges in the substantia innominata and olfactory tubercle . ŽFig. 3, bottom row, light stippling.. The pallidum is, likewise, divided into dorsal pallidum Žglobus pallidus., ventral pallidum Žventral pallidum and pallidal-like cell clusters in the substantia innominata. ŽFig. 3, bottom heavy stippling., and substantia nigra, pars reticulata Žnot studied here.. The substantia innominata includes the heterogeneous subcortical zone ventral to the caudate, putamen, nucleus accumbens and globus pallidus. The substantia innominata contains the ventral pallidum Žespecially the area just ventral to the anterior commissure at the level of the crossing of the anterior commissure.; the corticopetal system, which includes the clusters of cholinergic neurons known as basal nucleus of Meynert and adjacent non-cholinergic neurons, some of which also project to cortex; the nucleus of the diagonal band ŽFig. 3, black spots.; and the clusters of other ventral striatal and ventral pallidal cells in the olfactory tubercle, and the extended amygdala. The extended amygdala is a strip of medial nucleus-like and central nucleus-
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like amygdaloid cells and axons extending from medial and central nuclei of the amygdala proper and coursing through the substantia innominata, abutting the shell of the nucleus accumbens, and continuing into the bed nucleus of the stria terminalis, which is fundamentally an amygdaloid structure ŽFig. 3, cross-hatching .. These forebrain structures are a constant feature of mammalian brains, including the human ŽHaber, 1987; Alheid et al., 1990; Martin et al., 1991; Voorn et al., 1994.. Although the ventral structures have only recently been delineated in the human brain, they are amenable to PET imaging analysis and investigation using MR images. 2.6. Striatal di¨ isions The striatum can be subdivided not only by nuclear definitions Žcaudate, putamen, nucleus accumbens. but also according to a system of connectional schemata with some regions sufficiently large for functional imaging consideration. Fig. 4 illustrates some of these schemata, including a prefrontal cortexrlimbic associated zone in the ventral caudate, putamen and nucleus accumbens, and a dorsolateral sensory-motor zone dominated by cortical input from the sensory,
Fig. 3. Anterior to posterior series of coronal sections of basal forebrain in schematic form.
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motor and association cortices. These zones are similarly divided into ventral, central and dorsolateral striatal sections ŽHaber et al., 1995.. Note that the dimensions of these zones, each in the range of 10 mm high and more than 10 mm wide, are amenable to functional imaging if structural image coregistration and individual alignment are adequate. There are other important subdivisions of the striatum. The first is the patchŽstriosome. ᎐matrix organization ŽFig. 4., based on differential neurochemical, neurotransmitter, and connectional grounds. The patch᎐ matrix dichotomy has been recently studied in the human brain ŽHaber and Watson, 1985; Johnston et al., 1990; Manley et al., 1994; Hurd and Herkenham, 1995.. Approximately 85% of the striatum is matrix and 15% is patch, which is conserved across the rat, rhesus monkey and human ŽJohnston et al., 1990.. The number of patches in a cross-sectional area of striatum is also constant among species, but surprisingly the size of the patches increases 23 = from the rat to the human, where the cross-sectional area averages 1.4 mm2 ŽJohnston et al., 1990.. This crosssectional area of the patches is still too small to be resolved by present PET technology but is at the threshold of resolution of anatomical MRI and functional MRI. 2.7. Landmark placement Landmarks were chosen to correspond to reliably identified grayrwhite or grayrCSF boundaries instead of being based entirely on tracing regions. We anticipated that clearly identifiable landmarks Že.g. 6a at the ventricular tip or 10a at the putamen in Fig. 5. would proportionately adjust less clearly identifiable structures lying between them into alignment. Regions bounded by curved surfaces were aligned using short sections of spline curves, a method we termed ‘landmark by spline’. For example, points 6a᎐6e were placed by tracing the lateral edge of the ventricle by depositing points every 3᎐6 pixels on an enlarged image. A spline curve was then fit to the set of user-defined points. Next, the two endpoints and landmark points 25%, 50% and 75% of the distance along
Fig. 4. Major connectivity regions of the striatum.
the curve were calculated and placed automatically. This procedure allowed proportional alignment of a smoothly curving edge with equal numbers of final landmarks, a requirement of the thin-plate spline method. 3. Results 3.1. Two-dimensional (2D) landmark images and ¨ ariance images Twenty-five normal subjects have been landmarked Ž35 points, see Fig. 5. for the coronal slice passing through the anterior limit of the midline crossing of the anterior commissure. The average landmark positions were computed, and each individual’s MRI was morphed to the average landmark coordinates ŽFigs. 6 and 7.. To assess improvement in standardization, we computed the mean MRI and pixel-by-pixel vari-
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Fig. 5. Landmarks and size measurements for basal forebrain.
Fig. 6. Coronal MRI in two individuals and formation of average image. Alignment of caudate and ventricle of different shapes across the two individuals Žleft and center. to provide a merged and aligned image Žright.. The aligned and average image is as clearly demarcated as either of the two individuals. Only the left hemisphere has had landmarks, and the method is most accurate between the greatest x and y extent of the landmark points.
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2.03, d.f.s 24,24, P- 0.05.. This reduction in MRI signal-intensity variance, which needs to be considered with respect to the scale of gray Žtypically 120 units. and white Žtypically 165 units., reflects an improvement of approximately 20% of the distance between gray and white intensity values. This improvement can be seen in the sharper image in the landmark image in Fig. 7. 3.2. Reliability
Fig. 7. Anatomical outline of areas drawn on average MRI image Ž n s 25.. Average MRI of 25 normal subjects after thin-plate spline warping to compare with Fig. 6, which indicates anchor points and example of warped image with n s 2. Note alignment of major gray regions with less improvement in areas peripheral to spline landmarks.
ance images for bounding box and landmark methods ŽFig. 8.. If all caudates are well aligned across subjects, the variance in the center should be relatively low and should remain low as one examines pixels on the edge of the caudate; if the caudates are poorly aligned and sized, then the pixels at the caudate’s edge should have internal capsule pixels Žwhite matter with relatively high values. in some subjects and caudate pixels Žgray matter with relatively low values. in other subjects, resulting in high variation. We also computed the variance of the MRI signal-intensity values for each of the 840 pixels within an area bounded by the medial edge of the caudate, lateral edges of the caudate and putamen, and ventrally by the ventral substantia innominata. With the bounding box method of alignment, the average MRI variance was 761 ŽS.D.s 27.6.; but with the landmark method, it was 374 ŽS.D.s 19.3 units.; a conservative F-ratio test based only on the mean variance values showed a significantly smaller variance for the landmark method Ž F s
Interrater reliability was assessed in eight subjects independently landmarked by JHF and SG. For landmarks 5 and 6 ŽFig. 5., the mean absolute distance between the x, y locations for these points was 1.03 ŽS.D.s 1.33. and 1.45 ŽS.D.s 1.62. mm, indicating excellent agreement between a highly trained neuroanatomist ŽJHF. and a trained non-neuroanatomist ŽSG.. We next expressed the x and y coordinates of these points as a difference from the midpoints of landmarks 1 and 4 ŽFig. 5., to remove variation associated with the arbitrary position of the head within the field of view. The intraclass correlations ranged from 0.96 to 0.99 for the x and y coordinates of these structures. 4. Discussion Our application of the thin-plate spline method ŽBookstein, 1991. to coronal sections partly addresses problems associated with the axial orientation Že.g. risk of partial-volume effects at the brain’s lowest margin, variation in the contours and anteroposterior positioning of the caudate, and variation in size of the ventricle.. The method yields x, y, z data sets for analysis of the position and greatest extent of structures. However, there are important limitations. First, no method is better than the functional image resolution; diminished values due to adjacency of ventricular cavities will still occur. These errors will be smaller than those associated with placing the center of a stereotaxic ROI inside the ventricle on a functional image, but larger ventricles will still influence functional values. Methods of local correction, such as those proposed by Meltzer et al. Ž1996., are needed to more fully adjust for local structural influences.
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Fig. 8. Mean PET, PET variance, mean MRI, and MRI variance for bounding box and landmark methods. Mean and variance of PET Žleft two columns. and mean and variance of MRI Žright two columns. for 25 individuals when slices are aligned with two methods. Ž1. Top row: bounding-box method aligns images on lateral and superior cortical margins and has linear proportional adjustment. Mean images show caudate and putamen Žcompare to Fig. 5.. Variance images show pixel by pixel variance. Color bar numeric legend is for standard deviation for comparison with published values. PET data is in units of relative metabolic rate Žpixel value divided by mean whole brain value.. Relative metabolic rate gray values for an ROI in the striatum are typically 1.2᎐1.4 with S.D. 0.08᎐0.20 Žsee Shihabuddin et al., 1998.. MRI data is scaled from 0᎐255 for each person to provide a comparable gray᎐white data range across all subjects Žsee text.. Ž2. Bottom row: landmark method with thin-plate spline adjustment. Note sharper image with landmark adjustment method. Variance images are presented with color scale adjusted so that largest variance in both images is pink and smallest variance is purple to allow direct comparison of the two methods. Note that a ring boundary for the caudate is prominent in the PET variance image for the bounding box method Žtop row., but nearly missing in the landmark method. An ideal method might produce an entirely uniform image, but variable placement produces an image with the appearance of spatial differentiation Že.g. enhances caudate edges. since areas of rapid spatial variation are on structure perimeters. High variance in key structures reduces the possibility of having statistically significant probability mapping results. This error results since variances are spuriously high, primarily due to inclusion of caudate and non-caudate pixels at the same x᎐y coordinate locations. In the peripheral regions of the image outside the landmarked areas, variance rises as standardization transformation is extrapolated beyond set landmark points.
Second, the spline method does not provide the same level of volumetric estimation that actual tracing can yield. The landmark method aligns pixels across subjects but does not provide an edge for small structures that are difficult to outline. Thus, pixels in the ventral striatum will be placed in the same relative position in areas such as the substantia innominata or accumbens, but the exact number of these pixels, and hence the contour and volume, will not be assessed. Furthermore, in this example, because the substantia innominata contains a mix of cell clusters, the measured metabolic rate in that structure will
reflect this mixture. Despite the fact that the nucleus accumbens is approximately the same size as the caudate, it is difficult to achieve equally high inter-tracer reliabilities in MRI analyses due to its lower contrast edges Žtechnically lower values of the 2D spatial first derivative.. Thus, the location of structural abnormalities may be detected by carrying out statistical probability mapping on the anatomical images Žcf. Andreasen, 1995., and PET ligand or metabolic measures subjected to statistical probability mapping, but unequivocal size determinations cannot result. Third, we present only 2D warping landmarks
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for a single coronal slice. Since the anteroposterior position of structures differs across subjects, 3D warping is desirable. Although such structures as the center of the anterior commissure provide unique x᎐y᎐z sets of coordinates, the surface of the caudate will require a 3D generalization of the ‘landmark-by-spline’ method we discuss for 2D analysis. However, thin-plate spline standardized single slices will still yield anatomically more accurate and statistically more powerful striatal significance probability mapping. The dorsal-most portions of the caudate and putamen have the advantage of being easily traced on axial brain sections. In contrast, tracing of the ventral striatum is highly problematic. Notwithstanding these difficulties, the high dopamine receptor density, and the rich efferent and afferent connections to thalamic and limbic areas, make areas of the ventral striatum ᎏ the nucleus accumbens, the striatal bridges, and striatal sections of the substantia innominata, the ventral pallidum, the bed nucleus of the stria terminalis, and the extended amygdala ᎏ key candidates for disturbed function in schizophrenia and other brain diseases. With the advent of high-resolution, resectionable MRI images, this terra incognita is a prime area for new morphometric and functional exploration in man. Acknowledgements This project was supported by the Charles A. Dana Foundation and by grants from the National Institute of Mental Health, MH40071 and MH56489. Thanks are due to Drs. Lina Shihabuddin, M. Mehmet Haznedar, and Erin A. Hazlett, who screened the normal volunteers for the presence of psychiatric illness, and to Richard Azueta, who was responsible for subject recruitment. References Alheid, G.F., Heimer, L., Switzer, R.C. III, 1990. Basal ganglia. In: Paxinos, G. ŽEd.., The Human Nervous System. Academic Press, Sydney, pp. 483᎐582. Andreasen, N.C., 1995. PET and the w 15 OxH 2 O technique, Part 1: Statistical analysis of images. American Journal of Psychiatry 152, 1704.
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