The diagnosis of idiopathic Parkinson's disease (IPD) is entirely clinical. The fact that
neuronal damage begins 5-10 years before occurrence of sub-clinical signs, underlines the
importance of preclinical diagnosis.
A new approach for in-vivo pathophysiological assessment of IPD-related neurodegeneration
was implemented based on recently developed neuroimaging methods. It is based on non-
invasive magnetic resonance data sensitive to brain tissue property changes that precede
macroscopic atrophy in the early stages of IPD.
This research aims to determine the brain tissue property changes induced by
neurodegeneration that can be linked to clinical phenotypes which will allow us to create a
predictive model for early diagnosis in IPD.
We hypothesized that the degree of disease progression in IPD patients will have a differential
and specific impact on brain tissue properties used to create a predictive model of motor and
non-motor impairment in IPD.
We studied the potential of in-vivo quantitative imaging sensitive to neurodegeneration-
related brain tissue characteristics to detect changes in patients with IPD. We carried out
methodological work within the well established SPM8 framework to estimate the sensitivity
of tissue probability maps for automated tissue classification for detection of early IPD. We
performed whole-brain multi parameter mapping at high resolution followed by voxel-based
morphometric (VBM) analysis and voxel-based quantification (VBQ) comparing healthy
subjects to IPD patients.
We found a trend demonstrating non-significant tissue property changes in the olfactory bulb
area using the MT and R1 parameter with p<0.001. Comparing to the IPD patients, the
healthy group presented a bilateral higher MT and R1 intensity in this specific functional
region. These results did not correlate with age, severity or duration of disease. We failed to
demonstrate any changes with the R2* parameter.
We interpreted our findings as demyelination of the olfactory tract, which is clinically
represented as anosmia. However, the lack of correlation with duration or severity complicates its implications in the creation of a predictive model of impairment in IPD.