One objective of modern neuroimaging is to identify markers that can aid in diagnosis, disease progression monitoring and long-term drug impact analysis. In this study, Parkinson-associated physiopathological modifications were characterized in six subcortical structures by simultaneously measuring quantitative magnetic resonance parameters sensitive to complementary tissue characteristics (i.e. volume atrophy, iron deposition and microstructural damage). Thirty patients with Parkinson's disease and 22 control subjects underwent 3-T magnetic resonance imaging with T₂*-weighted, whole-brain T₁-weighted and diffusion tensor imaging scans. The mean R₂* value, mean diffusivity and fractional anisotropy in the pallidum, putamen, caudate nucleus, thalamus, substantia nigra and red nucleus were compared between patients with Parkinson's disease and control subjects. Comparisons were also performed using voxel-based analysis of R₂*, mean diffusivity and fractional anisotropy maps to determine which subregion of the basal ganglia showed the greater difference for each parameter. Averages of each subregion were then used in a logistic regression analysis. Compared with control subjects, patients with Parkinson's disease displayed significantly higher R₂* values in the substantia nigra, lower fractional anisotropy values in the substantia nigra and thalamus, and higher mean diffusivity values in the thalamus. Voxel-based analyses confirmed these results and, in addition, showed a significant difference in the mean diffusivity in the striatum. The combination of three markers was sufficient to obtain a 95% global accuracy (area under the receiver operating characteristic curve) for discriminating patients with Parkinson's disease from controls. The markers comprising discriminating combinations were R₂* in the substantia nigra, fractional anisotropy in the substantia nigra and mean diffusivity in the putamen or caudate nucleus. Remarkably, the predictive markers involved the nigrostriatal structures that characterize Parkinson's physiopathology. Furthermore, highly discriminating combinations included markers from three different magnetic resonance parameters (R₂*, mean diffusivity and fractional anisotropy). These findings demonstrate that multimodal magnetic resonance imaging of subcortical grey matter structures is useful for the evaluation of Parkinson's disease and, possibly, of other subcortical pathologies.

Brain atrophy measured by magnetic resonance structural imaging has been proposed as a surrogate marker for the early diagnosis of Alzheimer's disease. Studies on large samples are still required to determine its practical interest at the individual level, especially with regards to the capacity of anatomical magnetic resonance imaging to disentangle the confounding role of the cognitive reserve in the early diagnosis of Alzheimer's disease. One hundred and thirty healthy controls, 122 subjects with mild cognitive impairment of the amnestic type and 130 Alzheimer's disease patients were included from the ADNI database and followed up for 24 months. After 24 months, 72 amnestic mild cognitive impairment had converted to Alzheimer's disease (referred to as progressive mild cognitive impairment, as opposed to stable mild cognitive impairment). For each subject, cortical thickness was measured on the baseline magnetic resonance imaging volume. The resulting cortical thickness map was parcellated into 22 regions and a normalized thickness index was computed using the subset of regions (right medial temporal, left lateral temporal, right posterior cingulate) that optimally distinguished stable mild cognitive impairment from progressive mild cognitive impairment. We tested the ability of baseline normalized thickness index to predict evolution from amnestic mild cognitive impairment to Alzheimer's disease and compared it to the predictive values of the main cognitive scores at baseline. In addition, we studied the relationship between the normalized thickness index, the education level and the timeline of conversion to Alzheimer's disease. Normalized thickness index at baseline differed significantly among all the four diagnosis groups (P < 0.001) and correctly distinguished Alzheimer's disease patients from healthy controls with an 85% cross-validated accuracy. Normalized thickness index also correctly predicted evolution to Alzheimer's disease for 76% of amnestic mild cognitive impairment subjects after cross-validation, thus showing an advantage over cognitive scores (range 63–72%). Moreover, progressive mild cognitive impairment subjects, who converted later than 1 year after baseline, showed a significantly higher education level than those who converted earlier than 1 year after baseline. Using a normalized thickness index-based criterion may help with early diagnosis of Alzheimer's disease at the individual level, especially for highly educated subjects, up to 24 months before clinical criteria for Alzheimer's disease diagnosis are met.