MR spectroscopy in translational neuroscience (2010)

The rapid development of magnetic resonance imaging over the last 30 years has overshadowed that of its parent field, nuclear magnetic resonance (MR) spectroscopy. The physical chemical basis for the two is the same: nuclei that possess a nuclear spin (like hydrogen atoms in water and fat molecules) are placed in an external field and excited by irradiation at the appropriate radio-frequency (RF) to create a detectable RF signal. This signal can be acquired using spatial encoding techniques to provide an image of the distribution of nuclei within tissue, which provides the well-established, clinically useful, MR image. Alternatively, a different approach can be utilized to detect the chemical metabolites present in the tissue at MR detectable concentrations, since in principle, each of these metabolites has an “MR fingerprint” related to its unique chemical structure. Because MR imaging is commonly used clinically, it has become part of the public consciousness, while the use of MR spectroscopy, which has had more limited and specialized clinical application, has been much less well known. The paper in this issue of JCN by Nikas and colleagues presents a new approach of using MR spectroscopy and metabolomics in neuroscience that permits non-destructive assay of specific sites in the brain for complex molecules that may be involved in specific neurodegenerative disorders. The first clinical applications of MR spectroscopy (Bottomley, 1989) utilized P-31 to study alterations in cellular bioenergetics in a variety of diseases. The landmark papers of Frahm and colleagues (1987, 1989a, b; Bruhn et al. 1987), who demonstrated that it was possible to obtain high quality MRS proton spectra of human brain, shifted the emphasis of clinical MRS to proton spectroscopic studies. The applications of proton MR spectroscopy have traditionally focused on a small number of target molecules that are concentrated in neurons such as N-acetyl-acetate (NAA) or glia (such as myo-inositol), or in cellular components such as membranes (choline), or are related to energy metabolism (creatine or lactate). Change in these markers provides a means for differentiating whether an abnormal site identified on MR imaging is, for example, a tumor (low NAA/creatinine and NAA/choline, high choline creatine ratios) or infarct (high lactate and choline, low NAA and creatine) or brain abscess (no NAA). Even early in the development of the analysis and interpretation of in vivo MR spectra, several groups began to employ relatively sophisticated statistical methods to extract the maximum amount of information present (see for example Kuesel et al., 1996; Stoyana et al., 2001). The more recent emphasis on NMR and MR metabolimics has led to a more intense effort in applying a variety of statistical methods as well as pattern recognition algorithms to analyze in vivo spectra (see for example Linden et al., 2001; Ebbels et al., 2009). The approach used by Nikas and colleagues looks at a wider array of metabolic markers (17 in this case), and assesses their levels in mice with the R6/2 model of Huntington's disease vs. wildtype mice. They used a mathematical approach to identify a pattern of changes that exemplify the Huntington mice, and then tested 31 unknown mice, classifying 100% of them correctly. It is not difficult to imagine the translational implications of such an approach. By combining anatomical specificity with what is essentially a metabolomics approach (identifying the signatures of different disorders as expressed by patterns of changes in metabolites that are measurable with MR spectroscopy), it may be possible to spot disease processes that are highly localized within the brain, even at a stage in the illness when there are few clinical signs. The ability to screen patients for neurodegenerative and neurogenetic disorders, at key sites in the brain that these disorders target, can potentially identify those patients at risk for developing a neurological disorder, and provide a way to mark and follow the progress of patients during interventions that prevent damage before neuronal death makes the neurological impairment irreversible. The implications of this approach for neuroscience are equally compelling. Most neurodegenerative disorders have exquisite anatomical specificity in the brain. We do not understand the reasons for this specificity in most cases, but in Parkinson's disease, Alzheimer's disease, multiple systems atrophy, spinocerebellar ataxia, and amyotrophic lateral sclerosis, to name just a few, the damage extends through the nervous system involving synaptically connected chains of neurons, while leaving other nearby, but not synaptically related systems untouched. As we develop mouse models for these disorders, it is important to determine whether the mouse version of the disease moves through neural systems in a pattern that mimicks the original disorder. MR spectroscopy may provide a way to do that non-destructively, so that one can determine the precise timing and pattern of involvement of different structures in a single living individual. This will be a great improvement over the current methods, which require assaying multiple time points in different individuals, who may have variations in the patterns and pace of progression that make clear reconstruction of the timeline more difficult when it must be acquired by piecing together data from many individuals. These methods hold the promise not only of validating disease models, but of understanding the basis of the system selectivity of these disorders in the first place, by allowing us to study pre-symptomatic phases of the neuro-degenerative process. Because of these implications, we have asked Nikas and colleagues to prepare their article as a JCN Toolbox contribution. Like other papers in this series, we hope that the new information will be of value to the field of systems neuroscience, and change the way that many of us do our work. Adding MR spectroscopy as a routine tool, with precise anatomical-chemical determinations non-destructively in living animals may allow a new dimension in system neuroscience investigation.