There is limited geriatrics‐oriented clinical pharmacological information available to guide pharmacotherapy in late‐life psychiatric disorders. In this paper, we review available data on interindividual differences in drug exposure and central nervous system functioning, amplified by drug–drug interactions in the elderly, that may contribute to variable responses to treatment and significant adverse drug effects. The inclusion of greater numbers of elderly persons in clinical trials and the vigorous application of clinical pharmacologic methodology (i.e., pharmacoepidemiology, population pharmacokinetic modeling, and pharmacogenetics) will be critical for improving safety and personalization of drug and dose selection for elderly patients. Clinical Pharmacology & Therapeutics (2008); 85, 1, 89–93 doi:

MR diffusion tensor imaging (DTI) can measure and visualize organization of white matter fibre tracts in vivo. DTI is a relatively new imaging technique, and new tools developed for quantifying fibre tracts require evaluation. The purpose of this study was to compare the reliability of a novel clustering approach with a multiple region of interest (MROI) approach in both healthy and disease (schizophrenia) populations. DTI images were acquired in 20 participants (n=10 patients with schizophrenia: 56 ± 15 years; n=10 controls: 51 ± 20 years) (1.5 Tesla GE system) with diffusion gradients applied in 23 non-collinear directions, repeated three times. Whole brain seeding and creation of fibre tracts were then performed. Interrater reliability of the clustering approach, and the MROI approach, were each evaluated and the methods compared. There was high spatial (voxelbased) agreement within and between the clustering and MROI methods. Fractional anisotropy, trace, and radial and axial diffusivity values showed high intraclass correlation (p<0.001 for all tracts) for each approach. Differences in scalar indices of diffusion between the clustering and MROI approach were minimal. The excellent interrater reliability of the clustering method and high agreement with the MROI method, quantitatively and spatially, indicates that the clustering method can be used with confidence. The clustering method avoids biases of ROI drawing and placement, and, not limited by a priori predictions, may be a more robust and efficient way to identify and measure white matter tracts of interest.

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