Magnetic resonance imaging of peripheral vascular disease and muscle atrophy in diabetes

Knowledge of the state of tissue hydration in patients suffering from peripheral vascular disease and neuropathy as a result of diabetes is important in their treatment. Further, because magnetic resonance imaging (MRI) is uniquely able to generate information about soft tissues and their water content, it is ideal for studying disorders of this kind. The feet and hands, often affected in diabetes, are ideal for studying fundamental aspects of the disease state and the response of patients to treatment. In this preliminary study, two related areas are reported: the measurement of diffusion coefficients in the finger and the visualization of the distribution of edema and muscle atrophy in the feet of people suffering from diabetes. Diffusion coefficients of water have been measured in the normal finger as a baseline study for a current patient study. It was found that the measured diffusion coefficient increased with subject age; this is not consistent with a direct-hydration model and it is conjectured that this could be linked to structural changes in proteins. Linked to this study, we have also imaged the feet of patients suffering from diabetes. Magnetization transfer has clearly demonstrated changes in muscle tissue with atrophy caused by motor neuropathy-in general, the amount of tissue water is increased as muscle volume decreases. Further, it is evident that these changes can be related to changes in cross-linking of protein and collagen molecules as muscle fibers become thinned, thus relating these studies to the diffusion coefficient measurements. The studies of the feet have also revealed artifacts in the images, consistent with the deposition of ferrous material in tissues. It is surmised that this is caused by hemosiderin deposits at ulcer sites associated with progress of the disease. MRI could be a useful tool for monitoring the distribution of ulcers below the skin surface and provide a means of determining the response of patients to treatment. © 1994 Chapman & Hall

Applications of multi-nuclear magnetic resonance spectroscopy at 7T

AIM: To discuss the advantages of ultra-high field (7T) for 1H and 13C magnetic resonance spectroscopy (MRS) studies of metabolism

Quantifying the test-retest reliability of cerebral blood flow measurements in a clinical model of on-going post-surgical pain:A study using pseudo-continuous arterial spin labelling

AbstractArterial spin labelling (ASL) is increasingly being applied to study the cerebral response to pain in both experimental human models and patients with persistent pain. Despite its advantages, scanning time and reliability remain important issues in the clinical applicability of ASL. Here we present the test–retest analysis of concurrent pseudo-continuous ASL (pCASL) and visual analogue scale (VAS), in a clinical model of on-going pain following third molar extraction (TME). Using ICC performance measures, we were able to quantify the reliability of the post-surgical pain state and ΔCBF (change in CBF), both at the group and individual case level. Within-subject, the inter- and intra-session reliability of the post-surgical pain state was ranked good-to-excellent (ICC>0.6) across both pCASL and VAS modalities. The parameter ΔCBF (change in CBF between pre- and post-surgical states) performed reliably (ICC>0.4), provided that a single baseline condition (or the mean of more than one baseline) was used for subtraction. Between-subjects, the pCASL measurements in the post-surgical pain state and ΔCBF were both characterised as reliable (ICC>0.4). However, the subjective VAS pain ratings demonstrated a significant contribution of pain state variability, which suggests diminished utility for interindividual comparisons. These analyses indicate that the pCASL imaging technique has considerable potential for the comparison of within- and between-subjects differences associated with pain-induced state changes and baseline differences in regional CBF. They also suggest that differences in baseline perfusion and functional lateralisation characteristics may play an important role in the overall reliability of the estimated changes in CBF. Repeated measures designs have the important advantage that they provide good reliability for comparing condition effects because all sources of variability between subjects are excluded from the experimental error. The ability to elicit reliable neural correlates of on-going pain using quantitative perfusion imaging may help support the conclusions derived from subjective self-report

Learning to identify CNS drug action and efficacy using multistudy fMRI data

The therapeutic effects of centrally acting pharmaceuticals can manifest gradually and unreliably in patients, making the drug discovery process slow and expensive. Biological markers providing early evidence for clinical efficacy could help prioritize development of the more promising drug candidates. A potential source of such markers is functional magnetic resonance imaging (fMRI), a noninvasive imaging technique that can complement molecular imaging. fMRI has been used to characterize how drugs cause changes in brain activity. However, variation in study protocols and analysis techniques has made it difficult to identify consistent associations between subtle modulations of brain activity and clinical efficacy. We present and validate a general protocol for functional imaging–based assessment of drug activity in the central nervous system. The protocol uses machine learning methods and data from multiple published studies to identify reliable associations between drug-related activity modulations and drug efficacy, which can then be used to assess new data. A proof-of-concept version of this approach was developed and is shown here for analgesics (pain medication), and validated with eight separate studies of analgesic compounds. Our results show that the systematic integration of multistudy data permits the generalized inferences required for drug discovery. Multistudy integrative strategies of this type could help optimize the drug discovery and validation pipeline