Joint pattern analysis applied to PET DAT and VMAT2 imaging reveals new insights into Parkinson's disease induced presynaptic alterations

Abstract

Most neurodegenerative diseases are known to affect several aspects of brain function, including neurotransmitter systems, metabolic and functional connectivity. Diseases are generally characterized by common clinical characteristics across subjects, but there are also significant inter-subject variations. It is thus reasonable to expect that in terms of brain function, such clinical behaviors will be related to a general overall multi-system pattern of disease-induced alterations and additional brain system-specific abnormalities; these additional abnormalities would be indicative of a possible unique system response to disease or subject-specific propensity to a specific clinical progression. Based on the above considerations we introduce and validate the use of a joint pattern analysis approach, canonical correlation analysis and orthogonal signal correction, to analyze multi-tracer PET data to identify common (reflecting functional similarities) and unique (reflecting functional differences) information provided by each tracer/target. We apply the method to [¹¹C]-DTBZ (VMAT2 marker) and [¹¹C]-MP (DAT marker) data from 15 early Parkinson's disease (PD) subjects; the behavior of these two tracers/targets is well characterized providing robust reference information for the method's outcome. Highly significant common subject profiles were identified that decomposed the characteristic dopaminergic changes into three distinct orthogonal spatial patterns: 1) disease-induced asymmetry between the less and more affected dorsal striatum; 2) disease-induced gradient with caudate and ventral striatum being relatively spared compared to putamen; 3) progressive loss in the less affected striatum, which correlated significantly with disease duration (p < 0.01 for DTBZ, p < 0.05 for MP). These common spatial patterns reproduce all known aspects of these two targets/tracers. In addition, orthogonality of the patterns may indicate different mechanisms underlying disease initiation or progression. Information unique to each tracer revealed a residual striatal asymmetry when targeting VMAT2, consistent with the notion that VMAT2 density is highly related to terminal degeneration; and a residual DAT disease-induced gradient in the striatum with relative DAT preservation in the substantia nigra. This finding may be indicative either of a possible DAT specific early disease compensation and/or related to disease origin. These results demonstrate the applicability and relevance of the joint pattern analysis approach to datasets obtained with two PET tracers; this data driven method, while recapitulating known aspects of the PD-induced tracer/target behaviour, was found to be statistically more robust and provided additional information on (i) correlated behaviors of the two systems, identified as orthogonal patterns, possibly reflecting different disease-induced alterations and (ii) system specific effects of disease. It is thus expected that this approach will be very well suited to the analysis of multi-tracer and/or multi-modality data and to relating the outcomes to different aspects of disease.

Keywords: Data fusion; Dopamine; Parkinson's disease; Pattern analysis; Positron emission tomography.

Fig. 1

[¹¹C]-dihydrotetrabenazine (DTBZ) PET image (left) and [¹¹C]d-threo-methylphenidate (MP) PET image (right) for a Parkinson's disease (PD) subject. PD subject showed characteristic asymmetric tracer uptake in the less and more affected hemispheres. PD subject also showed spatio-temporal pattern of dopaminergic loss with the posterior putamen (putamen 3) affected before the anterior putamen (putamen 1) and caudate. PET = Positron Emission Tomography.

Fig. 2

Illustration of the decomposition and regression step. X and Y are the whitened input matrices (feature by subject) of non-displaceable binding potential (BP_ND) values obtained from step 2. The transformed data (canonical variates) U and V are calculated using CCA in step 3, which contains the most highly correlated subject scores along each component (in this case 5). The CCA weights matrices (A and B) are the regression coefficients from least absolute shrinkage and selection operator (LASSO) in step 4. X_residual and Y_residual are the regression residuals. CCA = canonical correlation analysis.

Fig. 3

Scatter plots for average DTBZ and MP BP_ND values in the less affected putamen versus disease duration (estimated from the time of symptoms onset) in months. Both DTBZ (left) and MP (right) BP_ND values correlated significantly with disease duration. S15 fell outside the 95% confidence interval. BP_ND = non-displaceable binding potential. DTBZ = dihydrotetrabenazine. MP = methylphenidate.

Fig. 4

Common spatial patterns along the first three pairs of canonical variates for DTBZ and MP. Stars indicate the ROIs with significant CCA loadings. ROI = region of interest; CCA = canonical correlation analysis; GP = globus pallidus; VS = ventral striatum; SN = substantia nigra; VTA = ventral tegmental area. DTBZ = dihydrotetrabenazine. MP = methylphenidate.

Fig. 5

Correlation between subject scores and disease duration as estimated from the time of symptoms onset (months) for DTBZ and MP along the third pair of canonical variates. DTBZ = dihydrotetrabenazine. MP = methylphenidate.

Fig. 6

Unique spatial patterns along the first three pairs of canonical variates for DTBZ (top) and MP (bottom). Stars indicate the ROIs with significant CCA loadings. ROI = region of interest; CCA = canonical correlation analysis; GP = globus pallidus; SN = substantia nigra; VS = ventral striatum; VTA = ventral tegmental area. DTBZ = dihydrotetrabenazine. MP = methylphenidate.