DATA ACQUISITION

PET scans were performed on a Siemans/CPS Ecat HR+ (962) head scanner with a total field view of 15 cm. A bolus of radiolabelled water (H2^^0) at a concentration of 55 mBq/ml and a flow rate of 10 ml/min was administered as an intravenous cannula. For each scan, approximately 10-15 mCi of H2^^0 in 3 ml of normal saline was flushed into the subject over 20 seconds, at a rate of 10 ml/minute by an automatic pump. After a delay of a 30 second background scan, counts were detected in the head which peaked 30-40 seconds later (depending on individual circulation time). Data acquisition time lasted 90 seconds and the interval between successive H2^^0 administrations was 8 minutes. The integrated radioactivity counts accumulated over the 90 second acquisition period were corrected for background noise and were used as an index of regional cerebral blood flow (rCBF), which reliably reflects blood flow in the physiological range (Fox and Mintun, 1989; Mazziotta et a l, 1985). Correction for attenuation was made by performing a transmission scan with an exposed ^^Ge/^^Ga external source at the beginning of each study. Images were reconstructed in 3D by filtered back projection (Hanning filter, cut off frequency 0.5Hz), giving a transaxial resolution of 8.5 mm full width at half maximum (Spinks et a l, 1992). The reconstructed images contained 128 x 128 pixels, each 2.05 x 2.05 x 2.00 mm in size.

In addition to the PET scans, subjects also underwent a T1-weighted structural magnetic resonance image (MRI) scan (Siemans 2 Tesla Magnetom Vision MRI camera, Erlargen, Germany). This was obtained for three reasons: (i) to screen subjects with any brain abnormalities, (ii) for coregistration with the PET scans (see below) and (iii) to contribute to a group averaged MRI (within each experiment) with which to check anatomical locations of activations.

III. DATA TRANSFORMATION

In order to transform the images obtained from the scanner into data where statistical inferences could be made about blood flow involvement, a number of procedures had to be employed. These procedures avoid contamination of the data by variables other than the ones of interest, such as: (i) interscan movement artifacts (i.e. brain structures that vary in location from scan-to-scan) and (ii) intersubject variability artifacts (i.e. brain structures that vary in location from person-to-person). The procedures employed are discussed below.

All data were analysed with statistical parametric mapping (Frackowiak and Friston, 1994), using the 97 or the 99 version of the SPM software (Wellcome Department of Cognitive Neurology, London, UK: http//www.fil.ion.ucl.ac.uk/spm) implemented in Matlab (Mathworks Inc. Sherbom MA, USA). Four data transformations were employed prior to statistical analysis: (a) realignment, (b) coregistration of PET and MRI images, (c) spatial normalization and (d) smoothing. Figure II. 1 summarises the steps involved in the analysis. Each of these is discussed in turn, below.

Ill.a. Realignment

Realignment was performed on the data obtained to correct for movement related variance between scans. The scans from each subject were realigned using the first as a reference. The parameters of this transformation were estimated using a ‘least squares approach’ (Friston et al., 1995a). This approach assumes that there is an approximately linear relationship between the images obtained and their partial derivatives, with respect to parameters of the transformation. These estimates are used to re-position the images. This procedure produces a mean realigned image of all scans, for each subject, as well as realigned images corresponding to each scan.

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Figure II I The SPM Algorithm. Parameter Estimates are shown for an fMRI Time Series.

R ealm im ienl S in o o tlu n s —► G eneral lin ear m ode

N o n n ah gation

^ T em p late

Param eter estim ate

Ill.b. Coregistration of PET and MRI

The mean of the PET realigned scans was used as a template to place each subject’s T1-weighted structural MRI into the same space. The images of both PET and MRI scans were partitioned into grey matter, white matter, cerebro-spinal fluid and scalp. These partitions were then registered together simultaneously.

III.c. Spatial Normalisation

For each subject all realigned PET images and the MRI image were transformed into a standard space (Talairach and Toumeaux, 1988). The normalisation procedure determines the spatial transformation that minimizes the sum of square difference between an image and a linear combination of one or more template images. The transformation begins with a 12 parameters (3 translation, 3 rotations, 3 zooms and 3

shears) affine registration to match the size and position of the image. This is followed by a global non-linear warping that matches the overall brain shape. The process uses a Bayesian framework to maximize the smoothness of the warps. Affine and non-linear spatial transformations are performed in order to match each scan to a template image (Montreal Neurological Institute [MNI])(Evans, 1994) conforming to the Talairach and Toumoux (1988) standard space (Ashbumer and Friston, 1997; Cocosco et a l, 1997; Friston et a l, 1995b). When images are stereotactically normalized, one voxel in the transformed image represents 2 mm in the x, y and z dimensions, using the coordinate system of the Talairach and Toumoux Atlas (1988). However, because the MNI brain is bigger (especially in the x direction) than the brain used by Talairach and Toumoux, there is no exact anatomical correspondence between the coordinates obtained in SPM and the ones reported in the Talairach and Toumoux’s Atlas.

Spatial normalization facilitates inter-subject averaging and enables reporting of activations as co-ordinates within a known standard space. This group-based approach increases: a) the signal relative to that obtained from single subjects; and b) the degrees of freedom in the statistical model.

Ill.d. Smoothing

As a final pre-statistical processing step, the images were smoothed using an isotropic Gaussian kemel filter of 16 mm. This smoothing reduces the influence of individual variations in gyral anatomy and stmcture-function relationships, thereby improving the signal-to-noise ratio. Because this stage uses a Gaussian kemel, the data conforms more closely to a Gaussian field model, which is important in order to make statistical inferences about the data (see below).

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