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. 2019 Mar 12;116(11):5108-5117.
doi: 10.1073/pnas.1807983116. Epub 2019 Feb 22.

Neuromelanin-sensitive MRI as a Noninvasive Proxy Measure of Dopamine Function in the Human Brain

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Free PMC article

Neuromelanin-sensitive MRI as a Noninvasive Proxy Measure of Dopamine Function in the Human Brain

Clifford M Cassidy et al. Proc Natl Acad Sci U S A. .
Free PMC article

Abstract

Neuromelanin-sensitive MRI (NM-MRI) purports to detect the content of neuromelanin (NM), a product of dopamine metabolism that accumulates with age in dopamine neurons of the substantia nigra (SN). Interindividual variability in dopamine function may result in varying levels of NM accumulation in the SN; however, the ability of NM-MRI to measure dopamine function in nonneurodegenerative conditions has not been established. Here, we validated that NM-MRI signal intensity in postmortem midbrain specimens correlated with regional NM concentration even in the absence of neurodegeneration, a prerequisite for its use as a proxy for dopamine function. We then validated a voxelwise NM-MRI approach with sufficient anatomical sensitivity to resolve SN subregions. Using this approach and a multimodal dataset of molecular PET and fMRI data, we further showed the NM-MRI signal was related to both dopamine release in the dorsal striatum and resting blood flow within the SN. These results suggest that NM-MRI signal in the SN is a proxy for function of dopamine neurons in the nigrostriatal pathway. As a proof of concept for its clinical utility, we show that the NM-MRI signal correlated to severity of psychosis in schizophrenia and individuals at risk for schizophrenia, consistent with the well-established dysfunction of the nigrostriatal pathway in psychosis. Our results indicate that noninvasive NM-MRI is a promising tool that could have diverse research and clinical applications to investigate in vivo the role of dopamine in neuropsychiatric illness.

Keywords: Parkinson’s disease; dopamine; magnetic resonance imaging; neuromelanin; schizophrenia.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
NM-MRI CNR correlates with NM concentration across sections of postmortem midbrain tissue from individuals without PD pathology. An axial view of a postmortem specimen of the right hemimidbrain is shown in photographic images (A and C) and NM-MRI images (B and D). The specimen is immersed in MRI-invisible lubricant (Fomblin perfluoropolyether Y25), contained within a custom dish. Arrows in A and B highlight the location of the SN, which appears as a dark band in A and as a hyperintense band in B. Dye gridlines were applied to the specimen (C) using the grid insert (shown in the dish in C, Inset) as a stamp. These gridlines were used to dissect the specimen into grid sections. NM-MRI CNR measurements were averaged in the same grid sections after image processing (D). Well markers shown at the four cardinal points in B were used in the registration of the grid, shown in blue in D superimposed on the preprocessed NM-MRI image averaged across sections. Grid sections containing the periaqueductal gray (PAG) are indicated by tan asterisks. (E) Scatterplot displaying the correlation between NM concentration and NM-MRI CNR for a single specimen. Grid-map Insets of this specimen displayed beside the axes indicate NM concentration (Top Left by y axis) and NM-MRI CNR (Bottom Right by x-axis) according to a normalized grayscale for each grid section. (F) Scatterplot displaying the correlation between NM concentration and NM-MRI CNR for all seven specimens. Tan data points represent sections including the PAG, where CNR was less strongly correlated with NM concentration. The discontinuous line represents the linear fit of all data points across all grid sections and specimens, including PAG+ sections. The continuous line represents the linear fit excluding the PAG+ sections. A, anterior; M, medial; P, posterior.
Fig. 2.
Fig. 2.
Spatial normalization and anatomical masks for voxelwise analysis on NM-MRI images. (A) Template NM-MRI image created by averaging the spatially normalized NM-MRI images from 40 individuals in MNI space. Note the high signal intensity in the SN and moderate intensity in the PAG area. (B) Masks for the SN (yellow voxels) and the crus cerebri (CC) (pink voxels) reference region (used in the calculation of CNR) are overlaid onto the template in A. These anatomical masks were made by manual tracing on the NM-MRI template. (C) Quality checks for spatial normalization of NM-MRI images. Overlap 3D images on the Left indicate the percentage of subjects with spatially overlapping signal in SN and outside the midbrain for a superior (z = −15) and an inferior (z = −18) slice. These images were made by creating binary maps of each subject’s preprocessed NM-MRI image (thresholded at CNR = 10%) and calculating the percentage of overlap for each voxel across all subjects without neurodegenerative disease (PD subjects were excluded given the manifest signal decreases, which can be seen in overlap images for this group in SI Appendix, Fig. S1). Lines plotted on the Right show a consistent CNR landscape over the x coordinate [from left (x = −15) to the midline (x = 0) and to the right (x = +15)] for normalized NM-MRI images in all study subjects; two plots [one for an anterior (y = −19) and one for a posterior (y = −23) line] are shown for each of two slices, as indicated by axial midbrain images in the Center. The location of the left SN (first peak from the left) and right CC (last trough from the left) is indicated by arrows and plotted lines are color-coded by group (gray: healthy controls; yellow: PD patients; red: schizophrenia patients; blue: CHR individuals; black: average of all subjects), which highlight the consistency of these landmarks in normalized images across subjects. A, anterior; L, left; P, posterior; R, right.
Fig. 3.
Fig. 3.
Voxelwise NM-MRI analysis are sensitive to topographic patterns of NM variation within the SN. (A) Raw NM-MRI images of the midbrain (intensity-normalized to the respective CC reference regions and with identical contrast adjustments for visualization purposes). The SN, indicated by arrows, has markedly higher signal in a healthy control (Left) compared with a patient with PD (Right). (B) t-statistic map of the SN showing the size of the signal decrease in NM-MRI CNR in PD compared with matched controls (with darker blue values indicating more negative t-statistic values and greater decreases in PD and lighter yellow values indicating more positive t-statistic values and greater increases in PD, relative to controls) overlaid onto the NM-MRI template. This decrease was more pronounced in more lateral, posterior, and ventral SN voxels, as denoted in the map by the location of darker blue voxels and as shown in the scatterplots on the Right (each data point is one voxel in the SN mask). Continuous lines in the scatterplots indicate the linear fit of the relationship between the anatomical location of each voxel, in the x (absolute x coordinates with respect to the midline) (Top Right), y (Middle Right), and z (Bottom Right) directions and the t-statistic of the group difference between PD and controls. For the y direction, a discontinuous curve shows the quadratic fit, which describes well the relationship between y coordinate and t-statistic values. These topographic relationships were not driven by voxels at the edges of the SN mask since eroding the boundaries of the mask did not impact the results.
Fig. 4.
Fig. 4.
NM-MRI CNR correlates with measures of dopamine function across individuals without neurodegenerative illness. (A) Map of SN voxels where NM-MRI CNR positively correlated (thresholded at P < 0.05, voxel level) with a PET measure of dopamine-release capacity in the associative striatum (green voxels) overlaid on the NM-MRI template image. The scatterplot on the Right shows NM-MRI CNR extracted from the significant voxels in A (with values ranked, consistent with a nonparametric analysis), plotted against dopamine-release capacity in the associative striatum ([11C]raclopride displacement after amphetamine administration; ranked values). These plotted data showed a (biased) effect size of ρ = 0.69; the unbiased effect size, calculated via a leave-one-out procedure (SI Appendix), was ρ = 0.62 (95% CI = 0.14–0.86). Coronal view of anatomical image by the y axis of the scatterplot shows the location of the associative striatum (yellow outline), manually traced on a representative subject’s T1 anatomical image. (B) Mean resting CBF map derived from 31 individuals (axial view). B, Inset shows detail of the midbrain with a green outline indicating the location of the SN voxels where NM-MRI CNR was related to dopamine-release capacity (the voxels shown in solid green in A). The scatterplot on the Right shows the correlation of resting CBF extracted from these voxels to NM-MRI CNR extracted from these same voxels (note that voxel selection is unbiased here).
Fig. 5.
Fig. 5.
NM-MRI CNR correlates with the severity of psychotic symptoms. The map on the Left shows SN voxels where NM-MRI CNR was positively correlated with the severity of psychotic symptoms (thresholded at P < 0.05, voxel level) in patients with schizophrenia (red voxels) or with the severity of attenuated psychotic symptoms in individuals at CHR for psychosis (blue voxels). The “psychosis-overlap” voxels where both effects are present are displayed in pink. Scatterplots on the Right show the correlation of mean NM-MRI CNR extracted from psychosis-overlap voxels with severity of psychotic symptoms in patients with schizophrenia (r = 0.52) (Top Right) and attenuated psychotic symptoms in CHR individuals (r = 0.48) (Bottom Right). (Note that our approach here of extracting signal from a conjunction mask derived from two separate cohorts of individuals should reduce but may not eliminate the bias in these effect-size estimates.) Analysis of this relationship in CHR revealed an influential outlier data point [shown encircled by a dotted line; Cook’s distance = 0.61, cutoff (4/n) = 0.16] that counteracted the overall group trend. When eliminating this data point, the strength of the correlation increased from r = 0.48 to r = 0.64.

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