Volume estimation of prefrontal cortical subfields using MRI and stereology

Brain Research Protocols 10 (2003) 125–138 www.elsevier.com / locate / brainresprot

Research report

Volume estimation of prefrontal cortical subfields using MRI and stereology a a a,b c, ˜ ´ Matthew A. Howard , Neil Roberts , Marta Garcıa-Finana , Patricia E. Cowell * a

Magnetic Resonance and Image Analysis Research Centre, University of Liverpool, P.O. Box 147, Liverpool L69 3 BX, UK b Department of Mathematics, Statistics and Computation, Faculty of Sciences, University of Cantabria, Santander, Spain c Department of Human Communication Sciences, University of Sheffield, 31 Claremont Crescent, Sheffield S10 2 TA, UK Accepted 16 September 2002

Abstract The objective of this protocol was to provide a rapid, neurofunctionally relevant alternative to region-drawing or automated gyral / sulcal-based techniques. The Cavalieri method and point counting [e.g. Br. J. Radiol. 73 (2000) 679] were used in conjunction with a previously established parcellation methodology [Arch. Gen. Psychiatry 57 (2000) 761] to estimate the volumes of anatomically defined subfields of the prefrontal cortex (PFC) based on landmarks visible on T 1 -weighted magnetic resonance (MR) images. Ten participants (n55 healthy adults; n55 patients) were studied. Regional PFC volume estimates derived from point counting methods were reproducible between raters (Intraclass Correlations (ICC)50.92–0.95) and repeatable within rater (ICC50.93–0.99). Predicted coefficients of error for individual volume estimates were less than 5%. This protocol provides an efficient means of calculating unbiased volume estimates of the PFC with predictable precision for use in both cognitive and clinical studies.  2002 Elsevier Science B.V. All rights reserved. Theme: Other systems of the CNS Topic: Association cortex and thalamocortical relations Keywords: Magnetic resonance imaging; Anatomical parcellation; Prefrontal cortex; Stereology; Cavalieri volume estimator; Point counting

1. Type of research The extensive nature of the afferent and efferent connections of the prefrontal cortex (PFC), in addition to varied and complex behavioural changes resulting from anatomical lesions, indicate that this brain area is functionally heterogeneous in humans [31,48,51,55] and nonhuman species [3,28,31]. Research examining the role of the prefrontal cortex in mental illness is consistent with this view [20,35]. Particular controversy has arisen in recent years over the exact nature of the PFC’s functional subdivisions as studied by functional neuroimaging in healthy adults (see Ref. [19] for review). Nevertheless, evidence overwhelmingly supports the view that there is neurocognitive specialisation within the prefrontal cortex, *Corresponding author. Tel.: 144-114-222-2426; fax: 144-114-2730547. E-mail address: [email protected] (P.E. Cowell).

a phenomenon which necessitates the development of measurement tools sensitive to these functional subdivisions. The objective of the current protocol was to provide an efficient means of calculating unbiased regional anatomical volume estimates, with predictable precision, that could be used in a variety of cognitive and clinical studies of the PFC. To achieve this objective, the Cavalieri method was integrated with a system of neuroanatomical parcellation that paralleled functional subdivisions of PFC supported by the neuropsychological, neuropsychiatric and neuroimaging literature. The current protocol divides the right and left PFC into dorsolateral (DL), dorsomedial (DM), orbitolateral (OL) and orbitomedial (OM) regions. Depending on their disciplines and cognitive domains of interest, researchers are concerned with different functional subdivisions of the PFC. Therefore, the protocol is designed to enable anatomical research that can flexibly parallel these functional subdivisions. For example, by comparing the DL to the

1385-299X / 02 / $ – see front matter  2002 Elsevier Science B.V. All rights reserved. PII: S1385-299X( 02 )00202-7

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combined orbital PFC regions (OL plus OM), the method could be used to study the prefrontal cortex along the traditionally recognised functional divisions of dorsolateral and orbital-basal frontal systems [31,42,p.94]. Alternatively, the protocol also allows subdivisions of both lateral and medial PFC into dorsal and orbital subfields, functional demarcations that are substantiated by recent cognitive neuroimaging studies [34,55]. These features of the protocol are shared with the method developed by Gur et al. [35]. The novel contribution of this protocol is its application of the Cavalieri method with stereological point counting [33] to the neuropsychiatrically validated anatomical landmarks described above [35]. The method provides unbiased volume estimates with predictable precision [58,60] and is more efficient than traditional region drawing. The methodology is applicable to landmarks clearly visible on a T 1 -weighted magnetic resonance (MR) image. It was used in the current protocol to compute brain tissue volumes that include both grey and white matter, but may also be used in conjunction with segmented images. Analysis is relatively rapid in the hands of a skilled rater because it eliminates the need to outline anatomical regions and evaluate every slice of the MR image. We have shown that the methodology is repeatable, and reproducible in the examination of healthy adults [10] and cases involving neuropathology [11].

2. Time required Total time required per subject is less than 2 h. (a) MR image acquisition: 20 min each subject. (b) Image analysis: approximately 90 min each subject. This process consists of image alignment and orientation to standard sagittal plane (20 min), point counting (50 min), combined data entry, volume and coefficient of error (CE) computation (20 min).

3. Materials (a) A 1.5 T Whole Body MR Imaging System (SIGNA

5x, General Electric, Milwaukee, USA) and proprietary quadrature head-coil to acquire MR images. (b) Sun Ultra 10 Workstation (Sun Microsystems, CA, USA) to operate image analysis software. (c) Specialist software packages for image analysis, volumetric computation and statistical analysis. These were NRIA (Brain Behaviour Laboratory, University of Pennsylvania, USA) for standardised alignment of images in three dimensions; ANALYZE (Mayo Foundation, MN, USA) for parcellation and demarcation of the PFC subfields, plus stereological point counting, and; S-PLUS (Statsci Europe) for computation of volume estimates and intra- and inter-rater reliability using Pearson correlations and intraclass correlations (ICC).

4. Detailed procedure

4.1. Image acquisition MR images of the brain, obtained for 10 human volunteers, were used in this protocol. Five cases were from a database of healthy adults (women, n53; men, n52) ranging in ages from 18 to 25 years [9]. Five cases were from clinical databases (women, n52; men, n53) ranging in ages from 24 to 53 years. Of the five clinical cases, two were diagnosed with mesial temporal lobe epilepsy, one with focal aphasia, and two with organic amnesia. Lesions of the prefrontal cortex were not a primary clinical feature of the five patient cases. However, some atrophy of the frontal cortex may have been present, as noted in other anatomical studies of these patient groups [39,64]. MR images were acquired with the informed written consent of each participant. For each participant, coronal T 1 -weighted MR images, encompassing the whole brain, were acquired using a three-dimensional spoiled gradient echo sequence (3D SPGR) with the following scanning parameters: TE 9 ms, TR 34 ms, flip angle 308, field of view 20 cm, acquisition matrix 2563192 pixels, 124 slices of thickness 1.6 mm. Coronal acquisition was used to minimise blood flow artefacts.

Fig. 1. (a–c) Sagittal, coronal and transaxial planes from an MR image prior alignment to the standardised sagittal orientation; (d) A plane is taken through the AC–PC line to correct for anterior-to-posterior tilt and a transaxial view from the resulting plane is shown in (e). In (e) the orbital cavities are more pronounced on one side. To adjust for side-to-side pitch, also visible from (b), the image was rotated around the midline axis until the resulting image appeared with orbital cavities evenly on both sides as in (f). A sagittal plane was then taken along midline (as shown in (g)) from a more superior view of the transaxial plane shown in (f). The resulting image (h) was then rotated until the AC–PC line was at zero degrees (i.e. horizontal to the window frame in NRIA) to give the final image shown in (i) which is saved and reformatted. (j–k) Sagittal, coronal and transaxial planes from the reformatted image. At sagittal midline, the following landmarks should be visible: cerebellum and brain stem clearly separated by the 4th ventricle, fluid space separating the tectum and tegmentum, maximal area of the cerebellar vermis, minimal area of cortical gray matter, fluid space separating the cingulate cortex from the corpus callosum. From the coronal view, the tops of the orbital cavities at maximum diameter should be level. From the transaxial view, the anatomical midline should be oriented parallel to the vertical axis of the window frame in NRIA.

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4.2. Image pre-processing PFC subfield volume estimation was dependent on parcellation of the 3D dataset according to macroanatomical landmarks. Parcellation required resizing of the dataset to isotropic voxels, reformatting the image, and orienting it to a standardised sagittal plane orthogonal to the bicommissural plane. Therefore, after acquisition, MR volume datasets were transferred to a SUN workstation running the software package NRIA (Brain Behavior Laboratory, University of Pennsylvania, USA), which enabled the operator to view, reslice, rotate, and resize the volume dataset using multiple planes. The standardised sagittal plane was derived to correct for variation in head position within the scanner. Fig. 1 depicts the original (Fig. 1a–c) and realigned (Fig. 1j–l) 3D MR images viewed from sagittal, coronal and axial planes. Re-alignment was carried out using the 3D tool in the IMAGE menu of NRIA software. The 3D tool allows the operator to view, create new planes, rotate images and view the results in sagittal, coronal and axial planes. For this protocol, each MR image was aligned according to the criteria below, where X was the right–left axis, Y the dorsal–ventral axis, and Z the anterior–posterior axis (axes were defined with respect to cranial anatomy dimensions). An overview of the procedure and its objectives is provided below. A step-by-step description is provided in Fig. 1.

4.2.1. YZ plane correction To correct for variation in anterior to posterior head tilt, a transaxial plane along the bi-commissural axis was taken from the uncorrected sagittal plane (Fig. 1d). The resulting axial plane can be viewed in Fig. 1e. 4.2.2. XY plane correction A further rotation of the transaxial plane, around the midsagittal axis, was taken along the superior-most aspect of the orbital cavities to correct for side-to-side tilt (Fig. 1e,f). A coronal plane, orthogonal to the longitudinal fissure, was identified at the point where the orbital cavities were at maximum cross-sectional area to confirm this correction of side-to-side tilt. The orbital cavities are extrabrain landmarks, but since brain anatomy is asymmetrical with respect to structure of the right and left hemispheres, we opted to use a system that would be reproducible across raters and would not add systematic bias. 4.2.3. XZ plane correction To correct for deviations from sagittal midline, a plane taken through the longitudinal fissure of the corrected transaxial plane (Fig. 1g) resulted in the standardised sagittal plane (Fig. 1h). 4.2.4. YZ image rotation The standardised sagittal image was rotated so that the

bi-commissural axis was positioned at zero degrees (i.e. parallel to the image window frame in NRIA) (Fig. 1i). This correction in positioning ensured that vertical and horizontal lines used in the parcellation process (see Section 4.3) would transect similar anatomical landmarks across all subjects. Transformed images were saved such that the reformatted, resized volumes had a voxel size of 0.781 cubic mm. Sagittal, coronal and axial planes from the transformed image are shown in Fig. 1j–l. Images were subsequently imported into the software package ANALYZE for further processing that entailed parcellation of the subfields and stereological point counting.

4.3. Parcellation of PFC anatomical subfields Within each hemisphere, PFC was parcellated into four subfields: (i) dorsolateral (DL), (ii) dorsomedial (DM), (iii) orbitolateral (OL), (iv) orbitomedial (OM) as illustrated in Fig. 2. Landmarks demarcating the individual subfields are defined below using anatomy consistent with Damasio’s brain atlas [16]. Where possible, subcortical and midline structures were referenced as landmarks. This was done to minimise use of landmarks that would incorporate high interindividual and interhemispheric variation, such as those related to cortical gyral patterns [5] or discontinuities in gyral and sulcal landmarks within the frontal cortex [16, p. 8]. In cases where suitable midline or subcortical structures were not available for the demarcation of cortical zones, readily identifiable cortical structures were used. Note that the aim was not to map regions directly to specific gyral or Brodmann delineations (although these were used as anatomical guidelines), but rather to create subfields that mapped broadly to neurofunctionally defined zones. A brief rationale for each regional division is provided in this section (below), and more detailed discussion is provided in Section 6.2. In practical terms, the landmarks below served as the basis for two types of operation. Some landmarks provided fixed boundaries that were marked with editing tools in ANALYZE software. Other landmarks were used by the rater to visualise boundaries that shifted from one slice to the next. Together, these processes allowed the rater to determine which points should be selected during the point counting process which is detailed in Section 4.5 below.

4.3.1. Orbital /dorsal demarcation The division between the orbital and dorsal subfields was delineated by the bicommisural plane (Fig. 2, upper left panel). Images were edited in ANALYZE so that this demarcation was visible as a horizontal line on all slices. Delineation of dorsal and orbital subfields using the bicommissural plane enabled separation of BAs 46 and 9 on the lateral surface from functionally distinct regions associated with BA 47 [53,55]. On the medial surface, dorsal / orbital demarcations enabled separation of regions

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Fig. 2. Landmarks used to demarcate the PFC subfields. The dorsal-orbital and medial-lateral landmarks are shown in the upper and lower left frames, respectively. The posterior boundaries are depicted in the upper center and upper right frames for the dorsal subfields, and in the lower center and lower right frames for the orbital subfields. Stereological grids appear as red crosses on the four frames, with counted points removed on the DM (upper center), DL (upper right), OM (lower center) and OL (lower right) subfields. The sections shown were selected by virtue of their ability to clearly depict application of anatomical landmarks described in Section 4.3.

regulating higher order aspects of emotion and cognition [20,34,52] from those involved in more basic olfactory and affective processes [6,7,21,69].

4.3.2. Medial /lateral demarcation This border was demarcated at the first axial slice superior to the olfactory sulcus. There, the medial-most aspect of grey matter of the arcuate [22, p. 26] or transverse orbital sulcus [16, p. 114] divides medial from lateral (Fig. 2, lower left panel). Slice numbers for the location of the medial / lateral border in the sagittal plane were noted within the right and left hemispheres so that distinctions could be made during point counting. The grey matter of the arcuate sulcus was a readily identifiable landmark for all 10 cases in the reliability study and numerous other cases assessed with the method in subsequent research [10,11]. In the dorsal subfields, medial / lateral demarcation enabled separation of lateral cortical areas involved in goal directed cognition [51,53,55] from cortical areas involved in integrating affective cognition and self referencing behaviours [20,34,52]. In the orbital

subfields, the medial / lateral boundary created subfields characterised medially by sensory and affect processing regions [7,21,34,69] and laterally by structures involved in higher order affect and cognition [17,51,55]. Furthermore, the medial / lateral demarcation allowed cingulate regions to be separated from other cortical structures in both dorsal and orbital subfields.

4.3.3. Posterior boundary for dorsal regions The genu of the corpus callosum, viewed at sagittal midline, formed the posterior boundary of the DL and DM regions (Fig. 2, upper three panels). Images were edited using the ‘Image Edit’ function within ANALYZE so that the demarcation was visible as a vertical line on all slices. Gyral and cytoarchitectural boundaries between precentral and prefrontal cortices on the lateral brain surface are not readily observable from MR images, particularly in planar section. Thus, a conservatively anterior border (i.e. one that would exclude all premotor cortex, but may also possibly exclude some posterior prefrontal cortex) that could be reproduced across individual cases was needed.

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The anterior-most aspect of the callosal genu served this purpose. Examination of this boundary’s projection from medial to lateral dorsal frontal cortex was carried out using images from Damasio’s [16] atlas and confirmed that some prefrontal tissue may have been excluded in the majority of cases (e.g. posterior portions of BAs 8 and 45 on the lateral surface). However, on the medial surface, all prefrontal regions were included (Fig. 4).

4.3.4. Posterior boundary for orbital regions The posterior boundaries of the orbital regions were demarcated using a combination of anatomical features. Natural anatomical borders were easily identifiable for orbital regions. Therefore, it was not necessary to rely on a single landmark, such as the corpus callosum, as was used for the dorsal regions. At midline, in most cases, a boundary between medial prefrontal brain tissue and CSF was clearly visible. In cases where the cortical border with the CSF was obscured in the midline slices of the MR image, the anteroventral-most tip of the corpus callosum guided the posterior cortical boundary (Fig. 2, lower center panel). Laterally, the boundary followed the anterior-most portion of the caudate nucleus. More laterally, the boundary was demarcated by the anterior branch of the sylvian fissure (Fig. 2, lower right panel). These anatomical features were visualised by the rater during point counting. This methodology enabled inclusion of orbital prefrontal regions in their entirety (e.g. full posterior extent of BA 47 and 11, laterally, and BAs 12 and 25, medially). 4.4. Application of the Cavalieri method: volume estimation and error prediction Volume measurements of the four PFC subfields were obtained by applying the Cavalieri method in combination with point counting, a method which provides efficient and unbiased volume estimation [13,41,44,58]. The structure of interest is exhaustively sectioned end to end by a series of parallel planes a distance T apart. Provided that the first section has uniform random position within T, the volume of the structure is estimated without bias as the sum of the areas of the sections multiplied by the sampling period, T, i.e.

OA

i

(1)

i 51

(3)

i

i 51

The notation est 2V indicates that the volume estimator is subject to two sampling processes, namely sectioning and point counting. Predicting the precision of the volume estimator est 2V is not a trivial problem since the section areas are not independent. Therefore, the known formula s /Œ]n (where s is the S.D. of the volume estimator from one observation and n is the total number of observations) cannot be applied. Instead, the error prediction formula is based on complex approximations of the variance that take into account geometrical properties of the structure of interest [14,29,30,32,41]. The coefficient of error of the volume estimator can be expressed as follows: 2 CE 2S (est 2V ) 5 CE Sec (est 2V ) 1 CE 2PC (est 2V )

(4)

where CE 2Sec (est 2V ) represents the component of error due to the variability in area between sections, and CE 2PC (est 2V ) represents the component of error due to point counting within sections. CE 2Sec (est 2V ) depends on the smoothness properties of the area function for the structure of interest. One might expect the prefrontal lobule in the human brain to be roughly ellipsoid in shape, and to yield a continuous (i.e. smooth) area function. However, given the planar nature of the anatomical boundaries in this protocol (e.g. medial / lateral demarcation), the subfields are more arbitrary than ellipsoid in shape, and abrupt changes can be detected in the area functions (Fig. 3). The estimator of CE 2Sec (est 2V ) when the area function shows points of significant discontinuity (as in the current study) is:

O

s P 22 i d 2 CE Sec (est 2V ) 5 ]]] 12

SS SO n

?

3

] P 2i 2 0.0724 ? (B¯ /ŒA¯ )

i 51

S O D DD O

? n

1/2

n 21

24

Pi

i51

Pi Pi 11

i51

OP P D

n 22

where n is the number of sections, and A i is the area of the ith section. Section areas may be estimated by point counting. Specifically, a regular grid of test points is overlain on the images with uniform random position, and points falling within the anatomical boundary of the subfield of interest are counted. The unbiased estimator of section area, A i , is then Aˆ i 5 a p ? Pi

OP n

est 2V 5 T ? a p ?

n

n

est 1V 5 T

where a p is the unit area per point of the grid and Pi is the number of points counted for the ith section. The volume estimator using point counting is, thus,

(2)

1

i

(5)

i 12

i51

An estimator of the point counting error is: ] 22 1/2 CE 2PC (est 2V ) 5 0.0724 ?s Pid ?sB¯ /ŒA¯ d ?sn ? Pid (6) ] ] ] [33,46]. In Eqs. (5) and (6), B /ŒA is a dimensionless ¯ shape coefficient, where A is the mean area per section, ] and B is the mean boundary length per section. A¯ is ] estimated by point counting, and B by counting intersec-

O

O

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Fig. 3. Empirical area function of the PFC regions (right and left hemispheres), A(x), which represents the area of the intersection between the region of interest and the MR image at x. Note that the area function shows discontinuities, and consequently, the coefficient of error of the volume estimate is predicted by Eqs. (5)–(7). For each of the regions, the volume estimate is the area under the corresponding empirical area function.

tions between transect boundaries and an isotropic and uniform random positioned square grid of test lines [33]. Finally, by substituting Eqs. (5) and (6) into Eq. (4), the coefficient of error of the volume estimate derived from the stereological method can be predicted by

SO D n

CE S (est 2V ) 5

4.5. Stereology parameters

21

Pi

i51

F SO

O O ] 1 0.0543 ? B¯ ŒA¯ ?Sn O P D G n

isotropic, and independent between sections. The relevance of Eq. (7) in relation to its application to volume estimates of the prefrontal cortex is discussed further in Section 5.2.

n21

n22

1 ? ] 3 P 2i 2 4 Pi Pi 11 1 Pi Pi 12 12 i51 i 51 i 51 n

1/2

i

D

1/2

(7)

i 51

This formula has been derived assuming that the position of the square grid is not only uniform random, but also

Point counting was carried out in ANALYZE using stereology menus. The test system consisted of a computer-generated isotropic grid that was randomly superimposed upon the structures of interest. In the current protocol (FOV520 cm, 2563192 acquisition matrix, interpolated to form a 2563256 pixel matrix), a coefficient of error below 5% was maintained by point counting with a grid size of 737 pixels for OL and OM subfields (a p 529.9 mm 2 ), and 10310 pixels for the DL and DM

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Fig. 4. Lateral (left), frontal (center) and medial (right) views of the PFC subfields mapped onto a 3-D brain reconstruction. In all frames, subfields are coloured as follows: DL, red; DM, gold; OL, green; OM, blue.

subfields (a p 561.0 mm 2 ). Points were counted, with a random start point, on every third slice for the DL subfield (T52.343 mm) and every second slice for the DM, OL and OM subfields (T51.562 mm). Approximately 150–200 points were counted per structure, across 10–20 sections. ] ] ] The shape coefficients, B /ŒA, estimated for the DL, DM, OL and OM subfields were 5.65, 5.99, 5.48 and 5.19, respectively. The number of points counted for each slice was recorded and entered into S-PLUS. Subfield volumes and their associated coefficients of error were computed using Eqs. (3) and (7).

4.6. Reproducibility and repeatability assessment Rater 1 (PEC) and Rater 2 (MAH) independently measured the volumes of the subfields on the 3D MR

Table 1 Gyral neuroanatomy and Brodmann Areas (BAs) for the four PFC subfields Subfield

Gyri

Brodmann Areas

DM

Superior frontal gyrus, frontal pole, cingulate gyrus Middle and inferior frontal gyri, superior frontal gyrus, frontal pole Gyrus rectus, medial orbital gyrus, ]]] ]]]]] anterior and posterior orbital gyri, cingulate gyrus, frontal pole Lateral, anterior and posterior orbital ]] gyri, inferior and middle frontal gyri, frontal pole.

8, 9, 10, 24, 32, 6

DL OM

OL

8, 9, 10, 44, 45, 46 ] 10, 11, 12, 24, 25, 32 ] ] 10, 11, 47 ]

Gyri and BAs that fall completely within one subfield for the majority of cases are underlined; gyri and BAs that are only partially included within a subfield are in plain text (some regions span across more than one subfield and / or lie partly outside the demarcations for the PFC in this protocol); small segments of BAs included only in a minority of cases are in italics (note that BA44 and most of BA6 are premotor regions). Both gyral and Brodmann classifications are consistent with Damasio [16].

images acquired for the 10 participants. Rater 2 measured the volumes of the subfields on the same 10 images 4 weeks apart, using identical stereological parameters. Pearson’s product moment correlation coefficients and intraclass correlation coefficients (ICC) [4] were computed in S-PLUS to investigate the inter- and intra-rater reliability of volume estimates for each PFC subfield.

5. Results

5.1. Anatomical composition of the subfields The 3-D rendering in Fig. 4 illustrates the anatomical layout of the four subfields on an MR image. According to Damasio [16], who comprehensively mapped regions of the human cortical surface onto MR images, our PFC subfields traverse both sulcal and Brodmann-based definitions as outlined in Table 1. As indicated in Sections 1 and 4.3, the subfields were neurofunctionallybased; further rationale for the anatomical subdivision is provided in the discussion in Section 6.2.

5.2. Coefficient of variation and coefficient of error It is important to emphasise, as discussed in Section 4.4, that the Cavalieri method allows one to predict the measurement error of each volume estimate. This property makes it useful for analysing the contribution of biological and stereological factors to volume measures derived using this methodology. An application of this principle is described below. The squared coefficient of variation for the volume estimator given in Eq. (3) can be decomposed as follows: 2

2

2

CV (est 2V ) 5 CV b (V ) 1 Mean b [CE S (est 2V )]

(8)

where CVb (V ) is the coefficient of variation across in-

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Table 2 Mean volumes (column 2) and total coefficients of variation (column 3) for each prefrontal subfield shown with components of variance related to stereological (column 4) and biological variation (column 5) ]]]]] 2 b S (est 2V )] (%)

Structure

Mean b [est 2V ]

CV(est 2V ) (%)

œMean [CE

CVb (V ) (%)

RDM LDM RDL LDL ROM LOM ROL LOL

22.6 21.8 31.8 28.5 12.6 12.0 10.8 11.1

24.3 23.0 41.9 42.7 20.8 22.6 35.1 33.8

3.7 3.7 4.0 4.5 3.4 3.6 3.6 3.5

24.0 22.7 41.7 42.5 20.5 22.3 34.9 33.6

5.3. Reproducibility and repeatability

dividuals’ ‘true’ anatomical volumes (biological variation), and Mean b [CE S2 (est 2V )] is the average of the square coefficient of error across individuals that is associated with the stereological estimation procedure (see Section 4.4). Table 2 shows the total variation of the volume estimate calculated from Rater 1’s data. The square root of the mean CE 2S (est 2V ) for the 10 participants (previously estimated by Eq. (7)) is also listed for each subfield. Application of Eq. (8) enables estimation of biological volume variation for each subfield. Note that the volume variation for each structure is essentially due to the biological variation (e.g. for RDM, these values are total 24.3% and biological 24.0%). Thus, the error from the stereological method will increase the volume variation by approximately 1–2% across all PFC subfields (e.g. for RDM, the total CV is 1.25% higher than the biological variation). There is an additional source of error produced by the observer when interpreting landmarks and boundaries between tissues. This error, produced by the observer in the point counting process, cannot be separated from that of the stereological procedure when only one observer is implicated in the study. The contribution to the variability of volume estimation coming from the observer is investigated in the next section.

Table 3 shows the two raters’ mean volume estimates (cc or ml) for each of the four subfields within each hemisphere. Coefficients of variation (CV), relating to the total variation, and mean coefficients of error (CE), relating to the variation predicted from the stereological measurement procedure, are also listed. Results of the inter-rater reliability study are also shown in Table 3. Pearson’s r produced correlation coefficients ranging from 0.93 to 0.98. ICCs ranged from 0.92 to 0.95. Scatter plots in Fig. 5 show all data points lying close to the identity line. This confirmed that, as well as having a high degree of covariation, the two raters’ measurements were similar in absolute value. Table 4 shows the results of the repeatability study. The resulting correlations indicated that volume estimates of a single rater were highly repeatable (ICCs: 0.93–0.99; Pearson’s r: 0.95–0.99).

6. Discussion The key objectives of the protocol, as outlined in Section 1, are discussed below.

Table 3 Means, coefficients of variation (CV) and average coefficients of error (CE) for the right (R) and left (L) PFC regions measured for 10 cases by two independent raters Structure

RDM LDM RDL LDL ROM LOM ROL LOL

Rater 1

Rater 2

Reliability

Mean

CV

CE

Mean

CV

CE

ICC

Pearson’s r

22.6 21.8 31.8 28.5 12.6 12.0 10.8 11.1

24.3 23.0 41.9 42.7 20.8 22.6 35.1 33.8

3.7 3.7 4.0 4.5 3.4 3.6 3.6 3.5

21.9 21.6 29.2 26.5 12.8 12.1 9.9 10.2

24.9 21.2 38.1 39.6 25.1 27.4 34.9 36.7

3.6 3.5 3.7 4.2 3.5 3.4 3.9 3.9

0.93 0.92 0.94 0.94 0.95 0.92 0.95 0.95

0.94 0.93 0.97 0.97 0.97 0.94 0.98 0.98

Inter-rater reliability of the measurements was calculated using ICCs and Pearson r values. CV5(S.D. / mean)3100; the formula for CE can be found in Section 4.4 of the text in Eq. (7).

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Fig. 5. Inter-rater reliability plotted for the PFC subfields. Values represent total volume (right plus left hemisphere) estimates (cc) and are graphed against the identity line. In all scatterplots, data from the five healthy adults are shown as filled circles and the five clinical cases are shown as open squares. Most data points fell on or close to the identity line. Note that cases which were not close to the identity line for OM, DM and DL regions were patients.

6.1. Alternative and support protocols: parcellation and volume estimation Table 4 Means and S.D.s (in parentheses) for the right (R) and left (L) PFC regions measured for 10 cases by one rater on two occasions Structure

Time 1

Time 2

RDM LDM RDL LDL ROM LOM ROL LOL

21.93 (5.47) 21.55 (4.58) 29.21 (11.13) 26.45 (10.48) 12.80 (3.21) 12.14 (3.33) 9.90 (3.46) 10.16 (3.73)

22.74 (6.00) 22.37 (5.61) 29.07 (11.84) 26.90 (11.32) 12.87 (3.24) 12.34 (3.42) 10.31 (3.82) 10.76 (3.94)

Reliability ICC

Pearson’s r

0.93 0.98 0.98 0.98 0.97 0.99 0.93 0.96

0.96 0.98 0.98 0.98 0.97 0.99 0.95 0.97

Intra-rater reliability of the measurements was calculated using ICCs and Pearson r values.

Region drawing techniques were originally used to measure the PFC subfields upon which our methodology is based [35]. That method [35] was highly reliable and reproducible, but it relied on gray / white matter segmentation to establish some landmarks, and was more time consuming than the stereological approach which eliminated the need to draw regions on each MR image section. In order to establish the stability of the measurement procedure across laboratories, we compared the control participants’ mean for each prefrontal subfield as published by Gur et al. [35] (total gray and white matter volume, averaged across control men and women’s values) to those from the current dataset (mean regional PFC volumes for the 10 reliability cases, averaged across raters). The values are listed in Table 5.

M. A. Howard et al. / Brain Research Protocols 10 (2003) 125–138 Table 5 Mean PFC subfield volumes (ml) for the 10 cases from the current reliability data set (regional brain volumes, averaged across the two raters) and for the healthy control group published by Gur et al. [35] (regional gray and white matter volumes combined, and averaged across control men and women’s values). The data adapted from Gur et al. [35] have been reproduced with permission from the American Medical Association, Copyright 2000 Structure RDM LDM RDL LDL ROM LOM ROL LOL TOTAL

Current protocol

Gur et al. [35]

22.3 21.7 30.5 27.5 12.7 12.1 10.3 10.6

22.8 23.4 28.2 24.5 13.0 13.2 12.3 11.9

147.7

149.3

Total PFC volume is the sum of the values of regional PFC volumes in each column.

The data from Table 5 show a high degree of consistency across the two studies despite differences in scanning parameters, subjects studied and volumetric methods. The main methodological similarity was the use of comparable anatomical landmark systems. In both studies, the total prefrontal volume was approximately 150 ml, and dorsal regions were larger than orbital regions. The largest and most asymmetrical volume was the dorsolateral subfield, where right DL was larger than left in both studies. Also, the rank order in relative size of the four subfield volumes, when averaged across the hemisphere, was identical. These findings suggest that both methods may be used to derive comparable regional PFC volume measurements. In a similar comparison, Sheline et al. [60] examined planimetric and stereological point counting methods for assessing volumetry of the whole frontal lobe. These authors concluded that while both methods yielded high repeatability and precision, the point counting method was less time consuming, free from mathematical bias, and offered a means for predicting methodological error. The results from our current analysis are consistent with the conclusions of Sheline et al. [60]. Numerous research groups have developed methodologies for anatomically parcellating the frontal lobe, and more specifically, the PFC. Some have used techniques where MR images are imported into software packages that allow manual outlines of anatomical features to be traced across a number of slices. The slice areas were then summed to give volume estimates [35,59]. Others have developed automated algorithms that identify anatomical landmarks based on gyral morphology to subdivide the frontal cortex [8,12]. Techniques that use a combination of manual and automated methods are also available. For example, Schlaepfer et al. [61] geometrically divided a segmented annulus of cortical gray matter to estimate dorsolateral PFC volume. Using a methodology similar to those developed for parcellating the whole cortex [8,57],

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Wible et al. [68] divided prefrontal gray matter into seven regions by manually delineating sulci as the dividing points for parcellation units, extending these with connection algorithms, and confirming the final outcome by inspection of 3D renderings. The Cavalieri method is a well-documented technique used extensively in optical dissection and micro-anatomical studies of post mortem tissue [47,54,65]. The two main advantages of the Cavalieri method are its easy implementation and high efficiency. In the present reliability study, efficiency was indicated by CEs that were below 5% for all PFC subfields in the 10 cases measured. Approximations of the Cavalieri estimator’s precision in calculating CEs can be expressed in terms of sampling period and square grid size. This property makes CEs useful in optimising the sampling design. It should be noted, however, that prediction of Cavalieri sampling precision (i.e. estimation of the CE) is a complex issue because of the dependency between the data involved. Moreover, the prediction of the precision of the Cavalieri estimator enables one to construct confidence intervals for significance tests if one assumes that the Cavalieri estimator is normally distributed. However, currently, little is known about the statistical distribution of Cavalieri estimators. With respect to studies of human frontal cortex, the Cavalieri method has been used, in conjunction with cytoarchitectural confirmation, to estimate the volume of Broca’s area (i.e. BA 44,45,47) in post mortem brains [36,37]. However, this methodology has not been frequently used in neuroimaging studies of human cerebral cortex. As mentioned above, Sheline et al. [60] used the Cavalieri method to estimate right and left frontal lobe volume in a sample of MR images, but we are not aware of any methods to date that apply stereological techniques to estimate the volumes of PFC subfields with MR images.

6.2. Neurofunctional validity of the four PFC subfields There are three lines of evidence that support the division of PFC into subfields on the basis of dorsal / orbital, medial / lateral and right / left dimensions. These lines of evidence derive from the neuropsychological, neuropsychiatric and functional neuroimaging literatures, and are reviewed below. The neuropsychological literature supports a distinction between dorsolateral regions (equivalent to DL in the current protocol) that mediate higher order cognitive functions, and orbital regions (equivalent to OM1OL in the current protocol), medial-orbital regions in particular, that mediate mood, affective behaviour and social aspects of cognition. Studies of individuals with dorsolateral PFC lesions reported lower performance on subtasks of standardised intelligence tests, as well as deficits in memory, Wisconsin Card Sorting Task (WCST), abstract concept formation and modification [38,40,49]. In contrast, damage to orbital PFC has been shown to lead to increased impulsive aggressive behaviour [17], as well as impair-

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ments in social cognition [1] and risk judgment [6,66]. Neuropsychiatric investigations support the use of dorsal / orbital, medial / lateral and left / right distinctions in the study of PFC using both structural [35] and functional neuroimaging measures [15,62]. In general, findings parallel the distinctions in functional anatomical organisation laid out by neuropsychologists. For example, the role of the dorsolateral PFC in higher order cognitive abilities was shown for the WCST [67], abstraction and attention [35] in both healthy controls and patients with schizophrenia. In contrast, structural or physiological anomalies of the orbital prefrontal cortex have been associated with poor premorbid adjustment, negative symptoms and depression in women with schizophrenia [35], and with suicide and clinical symptomatology in depression [20,43,45]. In concordance with clinical data, functional neuroimaging in healthy adults has also provided compelling evidence for the existence of functionally discrete regions of PFC on the basis of dorsal / orbital, medial / lateral and left / right dimensions. In particular, dorsolateral PFC has been associated with cognitive domains ranging from initiation and selection of movement [27,63] to maintenance and integration of information during delays [23,56] in humans, as well as categorical representation in monkeys [26]. However, there is considerable controversy as to which psychological dimensions are isomorphic with the dorsal / orbital subdivisions of lateral PFC. Models explored include aspects of memory involved in encoding and retrieval [24,25], simple versus complex monitoring [51], and stimulus manipulation versus maintenance during working memory [19,55]. Other studies have developed functional neuroimaging paradigms to investigate the role of medial PFC regions, sometimes including anterior cingulate cortex. Dorsal medial regions have been implicated in motor programming [18] with the dorsal anterior cingulate cortex being involved in cognitive aspects of motor control by virtue of connections with dorsolateral PFC and subcortical systems [52]. The orbital medial PFC is involved in a range of functions including affect [21] and olfaction [69]. As with lateral PFC, there have been multiple efforts to understand medial PFC organisation (including the cingulate cortex) on the basis of the dorsal / orbital anatomical dimension and related axes of cognitive function [7,20]. Gusnard et al. [34] suggested that medial PFC provides the basis for a default state of neurocognitive function in contrast to goal-directed cognition, for which dorsal regions are important in introspective self-referencing, and orbital regions in the regulation of emotion.

6.3. Troubleshooting The inception of high resolution 3D MR imaging sequences has enabled estimation of structure volume through visualisation of anatomical features. The T 1 -

weighted 3D SPGR sequence used in the current protocol has good spatial resolution, which enables reformatting into specific planes for optimal visualisation. Repeated sampling of datasets may result in image degradation, thus, multi-stage processes of reformatting and resizing should be minimised whenever possible. When data are reformatted, the primary concern should be maintenance of the image’s visual integrity. The current protocol, using NRIA software, minimises such resampling effects by transforming the volume to the required orientation in a single operation using rapid linear interpolation algorithms. When using anatomically-based measurement techniques, a researcher must evaluate whether individual differences or pathological characteristics inherent to a particular patient population will affect measurement. In the cases used for reliability testing of the current protocol, we found that the spatial relationship of our landmarks did not change as a function of clinical status (Fig. 5). In subsequent research using the current protocol [10,11] only one participant out of 130 (a comparison subject in the autism study) had neuromorphology that did not enable demarcation of the medial–lateral boundary. Aylward et al. [2] noted the inherent difficulties of using the corpus callosum (CC) as a landmark for the posterior PFC demarcation in individuals with callosal agenesis. Given that the CC is used as a landmark in the current protocol, it is possible that size or shape differences in this structure could affect measurement of dorsal PFC volumes. Researchers concerned about the impact of callosal anatomy on PFC parcellation could measure and statistically covary callosal size (e.g. area or length) from the dorsal PFC measures. In target populations where caudate morphology differed between clinical control groups, a similar covariance procedure could be used when analysing orbital PFC volumes. An additional limitation of protocols for parcellating the cerebral cortex, such as we have employed, is the lack of consensus as to how regions defined by gross structural landmarks such as gyral configuration [8,12] or other anatomical features [35] relate to anatomy at the level of cytoarchitectural or behavioural organisation [12,68]. Functional and structural mapping of gyri to Brodmann Areas is under way (e.g. Morosan et al. [50]) and is yielding results which address some of these concerns. The current protocol does not entirely eliminate the difficulties associated with assessing individual cases with highly atypical or idiosyncratic neuromorphological features. However, in the hand of a skilled rater, it provides a measurement tool that combines efficiency with high reproducibility. In addition, it offers an alternative to methodologies that parcellate the PFC on sulcal and gyral configurations alone. The current protocol may be used to compute regional volumes for the brain (as in the current protocol) or in conjunction with MR images segmented for tissue type so that regional gray and white matter volumes can be estimated.

M. A. Howard et al. / Brain Research Protocols 10 (2003) 125–138

7. Quick procedure (i) Acquire 3D SPGR sequence of the whole brain for each participant. (ii) Transfer images to a SUN workstation for processing. (iii) Using the 3D tool in the IMAGE menu of the software package NRIA, align the brain volumes to a standardised sagittal plane orthogonal to the bicommisural plane and correct for head tilt in 3D. Save the reoriented images. (iv) Import reoriented images into ANALYZE software. Demarcate the dorsal-orbital (bicommissural plane) and dorsal posterior (corpus callosum genu at midline) boundaries. Locate medial-lateral (arcuate sulcus) boundary for each hemisphere. (v) Using stereology menus in ANALYZE, apply appropriate stereological parameters and anatomical landmarks to count the number of points within each subfield. (vi) Calculate volume estimates and CEs for the eight PFC subfields in S-PLUS using formulae specified by the Cavalieri method. (vii)Compare inter- and intra-rater reliability for the eight subfields using the data from 10 participants.

8. Essential literature references [3,31,34,35,55,58,60]

Acknowledgements The authors thank Simon Keller, Jocasta Webb and Vanessa Sluming for their help with the figures and manuscript revisions.

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