A full-brain, bootstrapped analysis of diffusion tensor imaging robustly differentiates Parkinson disease from healthy controls.

There is a compelling need for early, accurate diagnosis of Parkinson's disease (PD). Various magnetic resonance imaging modalities are being explored as an adjunct to diagnosis. A significant challenge in using MR imaging for diagnosis is developing appropriate algorithms for extracting diagnostically relevant information from brain images. In previous work, we have demonstrated that individual subject variability can have a substantial effect on identifying and determining the borders of regions of analysis, and that this variability may impact on prediction accuracy. In this paper we evaluate a new statistical algorithm to determine if we can improve accuracy of prediction using a subjects left-out validation of a DTI analysis. Twenty subjects with PD and 22 healthy controls were imaged to evaluate if a full brain diffusion tensor imaging-fractional anisotropy (DTI-FA) map might be capable of segregating PD from controls. In this paper, we present a new statistical algorithm based on bootstrapping. We compare the capacity of this algorithm to classify the identity of subjects left out of the analysis with the accuracy of other statistical techniques, including standard cluster-thresholding. The bootstrapped analysis approach was able to correctly discriminate the 20 subjects with PD from the 22 healthy controls (area under the receiver operator curve or AUROC 0.90); however the sensitivity and specificity of standard cluster-thresholding techniques at various voxel-specific thresholds were less effective (AUROC 0.72-0.75). Based on these results sufficient information to generate diagnostically relevant statistical maps may already be collected by current MRI scanners. We present one statistical technique that might be used to extract diagnostically relevant information from a full brain analysis.

Parametric mapping of [18F]FPCIT binding in early stage Parkinson's disease: a PET study.

We have shown that fluorinated N-3-fluoropropyl-2-beta-carboxymethoxy-3-beta-(4-iodophenyl) nortropane ([(18)F]FPCIT) and PET offer a valuable means of quantifying regional abnormality in dopamine transporter (DAT) imaging associated with Parkinson's disease (PD). The objective of this study was to delineate the topographic distribution of DAT binding in early stage idiopathic PD using statistical parametric analysis of [(18)F]FPCIT PET data. We performed dynamic PET studies in 15 hemi-parkinsonian (Hoehn & Yahr I) patients and 10 age-matched normal volunteers over 100 min and calculated images of [(18)F]FPCIT binding ratios on a pixel-by-pixel basis. Statistical parametric mapping (SPM) was then used to localize binding reductions in PD and to compute the absolute change relative to normal. [(18)F]FPCIT binding decreased significantly in the contralateral posterior putamen of the PD group (P < 0.001, corrected). A significant reduction was also seen in the ipsilateral putamen, which was smaller in extent but localized more posteriorly. A quantitative comparison of DAT binding in the two clusters showed that the onset of motor symptoms in PD was associated with an approximate 70% loss relative to the normal mean in the contralateral posterior putamen. These results suggest that SPM analysis of [(18)F]FPCIT PET data can be used to quantify and map abnormalities in DAT activity within the human striatum. This method provides a useful tool to track the onset and progression of PD at its earliest stages.

Visualizing the evolution of abnormal metabolic networks in the brain using PET.

By applying novel statistical methods and visualization techniques to PET data obtained from combined groups of patients and normals, we are able to illustrate topographic covariance profiles unique to neurodegenerative disorders such as Parkinson's Disease at various stages of progression. Each profile represents a neuroanatomical network of metabolically covarying regions. The expression of the profile in each patient is characterized by a subject score which can correlate with independent clinical disease severity measures. To visualize these profiles, a semi-automatic routine is used (3D) animation of the metabolic topography as it evolves from initial to final stages of the disease.