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. 2021 Oct 27;32(13):2762–2772. doi: 10.1093/cercor/bhab379

Elevated Dopamine Synthesis as a Mechanism of Cognitive Resilience in Aging

Claire J Ciampa 1, Jourdan H Parent 2, Molly R Lapoint 3, Kaitlin N Swinnerton 4, Morgan M Taylor 5, Victoria R Tennant 6, A J Whitman 7, William J Jagust 8,9, Anne S Berry 10,✉
PMCID: PMC9247426  PMID: 34718454

Abstract

Aging is associated with declines in multiple components of the dopamine system including loss of dopamine-producing neurons, atrophy of the dopamine system’s cortical targets, and reductions in the density of dopamine receptors. Countering these patterns, dopamine synthesis appears to be stable or elevated in older age. We tested the hypothesis that elevation in dopamine synthesis in aging reflects a compensatory response to neuronal loss rather than a nonspecific monotonic shift in older age. We measured individual differences in striatal dopamine synthesis capacity in cognitively normal older adults using [18F]Fluoro-l-m-tyrosine positron emission tomography cross-sectionally and tested relationships with longitudinal reductions in cortical thickness and working memory decline beginning up to 13 years earlier. Consistent with a compensation account, older adults with the highest dopamine synthesis capacity were those with greatest atrophy in posterior parietal cortex. Elevated dopamine synthesis capacity was not associated with successful maintenance of working memory performance overall, but had a moderating effect such that higher levels of dopamine synthesis capacity reduced the impact of atrophy on cognitive decline. Together, these findings support a model by which upregulation of dopamine synthesis represents a mechanism of cognitive resilience in aging.

Keywords: atrophy, compensation, older adults, PET, working memory

Although aging is accompanied by decline in cognition including executive function and episodic memory, there is substantial variation in individual aging trajectories. The mechanisms underlying resistance to decline in brain structure and function in aging is an area of active research. Further, there is increasing focus on resilience—the factors underlying successful maintenance of cognitive performance despite neurobiological losses (Stern et al. 2019; Arenaza-Urquijo and Vemuri 2020). Here we examined a candidate neural mechanism of resilience in aging: compensatory upregulation of dopamine synthesis.

Aging is associated with losses in the structure and function of frontoparietal cortex. Patterns of cortical atrophy in normal aging reveal prominent thinning in frontal and parietal lobes (Salat 2004; Thambisetty et al. 2010). Accompanying cortical atrophy, there is consistent evidence for age-related declines in the neurotransmitter systems that modulate frontoparietal activity. Postmortem analyses reveal reductions of midbrain dopamine-producing neurons (Fearnley and Lees 1991), reduced density of both D1 (Cortés et al. 1989; Rinne et al. 1990) and D2 receptors (Severson et al. 1982; Seeman et al. 1987), and reduction in dopamine transporter density in older adults relative to young (Allard and Marcusson 1989; Bannon et al. 1992; Van Dyck et al. 1995; Rinne et al. 1998). Positron emission tomography (PET) imaging has replicated these findings in the living human brain to reveal lower dopamine receptor and transporter density in older adults (for meta-analysis see Karrer et al. 2017), even after accounting for differences in gray matter volume (Seaman et al. 2019; Smith et al. 2019).

Both frontoparietal atrophy and alterations in dopamine function may contribute to age-related declines in executive function. Cortical thickness has been linked to cognition both cross-sectionally and longitudinally, with thin and thinning cortex associated with worse executive function (Burzynska et al. 2012; Vaqué-Alcázar et al. 2020). Similarly, the dopamine system plays a central role in maintaining optimal cognitive function (Cools and Robbins 2004; Cools and D’Esposito 2011) and is particularly implicated in executive processes such as working memory (Bäckman and Nyberg 2013; Roffman et al. 2016). Alterations in brain dopamine likely contribute to reduced working memory performance in aging (Bäckman et al. 2011; Juarez et al. 2019) and in disorders characterized by dopaminergic disruption (Abi-Dargham et al. 2002; Grogan et al. 2018).

Although many components of the dopamine system are reduced in older age, it appears dopamine synthesis is stable or upregulated (Karrer et al. 2017). Elevated dopamine synthesis capacity in older adults has been reported in [18F]Fluoro-l-m-tyrosine (FMT) PET studies in humans and nonhuman primates (DeJesus et al. 1997; Braskie et al. 2008; Berry et al. 2016), in human postmortem analyses (Wolf et al. 1991; Kish et al. 1995), and in rodent models (Greenwood et al. 1991). Evidence that dopamine synthesis is elevated in older age has been interpreted to reflect a neurochemical mechanism of compensation (Wolf et al. 1991; Kish et al. 1995; Braskie et al. 2008; Berry et al. 2016; Berry et al. 2018a; Berry et al. 2018b), but this proposal has yet to be tested empirically in human aging.

To investigate the possibility that upregulation of dopamine synthesis reflects a mechanism of compensation, we measured [18F]FMT net tracer influx (Ki) in cognitively normal older adults and examined relationships with longitudinal declines in cortical thickness (i.e., cortical atrophy). Longitudinal magnetic resonance imaging (MRI) structural timepoints ranged from up to 12 years prior to the [18F]FMT scan to up to 3 years following the [18F]FMT scan (Fig. 1). We next probed the extent to which this putative mechanism of compensation was associated with successful maintenance of cognition despite atrophy. Together, this study aimed to establish, for the first time, elevated dopamine synthesis as a mechanism of compensation and cognitive resilience in aging.

Figure 1.

Figure 1

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Schematic showing time in years between PET scans, MR scans, and Listening Span sessions for each participant.

Methods

Participants

Analyses included 38 older adults (mean age = 77.15, standard deviation (SD) = 4.68, age range = 61–85; 21 female; mean years of education = 16.66, SD = 1.99, years of education range = 12–20) and 15 young adults (mean age = 24.60, SD = 3.92, age range = 20–32; 4 female; mean years of education = 15.73, SD = 1.87, years of education range = 13–20) who were part of a larger study examining the role of dopamine in reward-related memory and received a [18F]FMT PET scan. Of the 38 older adults, 33 identified as White or Caucasian, 3 identified as Black or African American, and 2 identified as Asian. Of the 15 young adults, 8 identified as White or Caucasian, 1 identified as Black or African American, 4 identified as Asian, and 2 identified as more than one race. Older adults had 2 or more 1.5 T MRI structural scans, and a subset of older adults (n = 33) had 2 or more neuropsychological testing sessions. Ten additional older adults participated in the reward-related memory study but did not have 2 or more 1.5 T MRI structural scans and are not included in these analyses. Young adults’ [18F]FMT Ki data were used to replicate previous reports demonstrating striatal [18F]FMT Ki is elevated in older adults relative to young (Braskie et al. 2008; Berry et al. 2016).

Older adults were recruited from the Berkeley Aging Cohort Study (BACS), an ongoing longitudinal study of healthy aging. Participants were characterized as cognitively normal as previously described (Berry et al. 2016), did not take medications affecting cognition and had no history of neurological, psychiatric, or major systemic illness. Older adults had a Mini-Mental State Examination score of 25 or greater (mean = 28.87; SD = 1.21; range = 25–30), and a Geriatric Depression Scale score of no more than 10 (mean = 3.76; SD = 3.01; range = 0–10). Young adults were recruited via advertisements on University of California, Berkeley campus. The institutional review boards at the University of California, Berkeley and Lawrence Berkeley National Laboratory approved the study, and participants provided written informed consent.

3 T Structural MRI Acquisition and Processing

3 T T1-weighted volumetric magnetization prepared rapid gradient echo image (MPRAGE) were used for analyses of PET data. Whole-brain MPRAGE scans were collected on a 3 T TIM/Trio scanner (Siemens Medical Systems; software version B17A) with a 32-channel head coil (voxel size = 1 × 1 × 1 mm3; TR = 2300 ms; matrix = 256 × 240 × 160; FPV = 256 × 240 × 160 mm3; sagittal plan; 160 slices; 5 min acquisition time). MPRAGE scans were processed using FreeSurfer version 5.3 (https://surfer.nmr.mgh.harvard.edu/). For PET processing, cerebellar gray was used as the reference region. To limit possible contamination from signal in midbrain and brainstem, the most anterior 25% of cerebellar gray was removed using Mango software (http://ric.uthscsa.edu/mango/index.html). Striatal regions of interest (ROIs) were manually drawn on each participant’s scan as previously described (Mawlawi et al. 2001) using Mango software. Striatal ROIs included dorsal caudate (DCA), dorsal putamen (DPUT), ventral striatum (VST), and caudate body. Primary [18F]FMT analyses combined ROIs into a single whole striatum ROI to optimize detection of dopamine–atrophy relationships. Secondary analyses report results for DCA [18F]FMT Ki, which has been a focus of previous reports demonstrating age group differences in dopamine synthesis capacity and associations with cognition (Berry et al. 2016; Berry et al. 2018c). Averaging across all subjects, there were 0.70 years (SD = 0.87) between the 3 T MRI and [18F]FMT PET scan.

PET Data Acquisition and Processing

The irreversible PET tracer [18F]FMT was used to measure dopamine synthesis capacity. [18F]FMT was synthesized at Lawrence Berkeley National Laboratory, as described in VanBrocklin et al. (2004). Participants were injected with ~2.5 mCi of [18F]FMT as a bolus in an antecubital vein and scanned using a Biograph Truepoint 6 PET/CT (Siemens Medical Systems). Dynamic acquisition frames were collected over a 90-minute period in 3D mode (25 frames total: 5 × 1 min, 3 × 2 min, 3 × 3 min, 14 × 5 min). Reconstruction of the scans involved an ordered subset expectation maximization algorithm with weighted attenuation, corrected for scatter, and smoothed with a 4 mm full-width-at-half-maximum (FWHM) kernel. [18F]FMT data were processed using SPM12 software (http://www.fil.ion.ucl.ac.uk/spm). Images were realigned to the middle (12th) frame in order to correct for motion between frames, and the first 5 images were summed before realignment. PET images were coregistered to the 3 T T1-weighted structural scan using the mean of the frames corresponding to the first 20 min of data acquisition as a target. Patlak plotting was used to perform graphical analysis for irreversible tracer binding (Patlak and Blasberg 1985) with posterior gray cerebellum as the reference region. Ki maps were generated from PET frames corresponding to 25 min and 90 min (Ito et al. 2006; Ito et al. 2007), which represent the amount of tracer accumulated in the brain relative to the reference region. Ki can be expressed as Ki = k2k3/(k2 + k3), where k2 is the rate constant for the return of free [18F]FMT from brain back to plasma and k3 is the rate constant for the trapping of brain [18F]FMT by aromatic amino acid decarboxylase. These images are comparable to Ki images obtained using a blood input function but are scaled to the volume of tracer distribution in the reference region. Partial volume correction (PVC) was applied to limit contributions of surrounding gray matter, white matter and CSF to the measurement of striatal [18F]FMT Ki. PVC was applied to Ki images using the Geometric Transfer Matrix approach (Rousset et al. 1998). To apply the PVC in native space, we used FreeSurfer-generated ROIs for gray matter cortical and subcortical regions, white matter, and CSF with manually drawn striatal ROIs substituting for the automated striatal segmentation (Berry et al. 2016).

1.5 T Structural MRI Acquisition and Processing

1.5 T T1-weighted MPRAGE scans were used for analyses of longitudinal changes in cortical thickness. Participants had at least two 1.5 T T1-weighted scans (mean total scans = 3.26; SD = 1.20; range = 2–6). Whole-brain MPRAGE scans were collected on a 1.5 T Magnetom Avanto System (Siemens Medical Systems) with a 12-channel head coil (TR = 2110 ms; TE = 3.58 ms; FA = 15°; matrix = 256 × 256; FOV = 256; sagittal plane; voxel size = 1 × 1 × 1 mm; 160 slices). MPRAGE scans were processed using FreeSurfer version 5.3. The FreeSurfer pipeline for measuring cortical thickness has been described in previous publications (Fischl and Dale 2000). For analyses of cortical thickness, FreeSurfer uses a vertex-based surface processing stream in which cortical thickness is calculated as the average distance between each point on the white matter surface to the closest point on the pial surface. We used FreeSurfer’s longitudinal pipeline to create an unbiased within-subject template from all time points (Reuter and Fischl 2011) using robust, inverse consistent registration (Reuter et al. 2010). The template was used to increase reliability and statistical power during longitudinal processing steps, which included motion correction, atlas registration, skull stripping, and parcellations (Reuter et al. 2012). For analyses of longitudinal changes in cortical thickness, vertex-wise statistical surface maps were created using a general linear model (GLM) framework in FreeSurfer’s graphical user interface, QDEC. Before using the GLM, each participant’s data were smoothed using a Gaussian kernel with a FWHM of 10 mm.

Listening Span Task

We used the Listening Span task (Daneman and Carpenter 1980) to examine relationships between dopamine, atrophy and executive function. Previous research has established relationships between striatal [18F]FMT Ki and performance on the Listening Span task such that higher striatal [18F]FMT Ki is associated with better performance (Cools et al. 2008; Landau et al. 2009). Further, Listening Span performance has been used as a proxy for striatal dopamine function (van der Schaaf et al. 2013). The Listening Span task is a measure of working memory in which participants listen to sets of 2–7 sentences and must recall the final word of each sentence. Performance was measured as total recall, the sum of the number of words participants correctly recalled for each set. Longitudinal change in Listening Span total recall performance was examined in a subset of 32 out of 38 participants who performed the Listening Span task at multiple timepoints (mean total sessions = 5.88; SD = 2.62; range = 2–11). One participant was excluded as an outlier due to performance less than 5 SD from the group mean.

Statistical Analyses

To replicate previous findings that [18F]FMT Ki is elevated in older adults relative to young (Braskie et al. 2008; Berry et al. 2016), we performed repeated measures ANOVA with factors age (young versus older) and striatal region (DCA, DPUT, VST, caudate body). For this study to serve as an independent replication, primary age group analyses were performed on a subset of participants representing a completely independent sample from our previous reports (n = 15 young; n = 29 older adults).

Subsequent analyses included the entire older adult sample. We tested relationships between longitudinal change in cortical thickness using whole-brain exploratory vertex-wise analyses, [18F]FMT Ki, and longitudinal change in Listening Span performance. Vertex-wise regression analyses were performed using a simple GLM in QDEC and a two-stage model. In the first stage, the annual atrophy rate was calculated for each participant, and in the second stage, either [18F]FMT Ki or longitudinal Listening Span slope was regressed against the atrophy rate. Analyses adjusted for age, sex, and years of education and were submitted to vertex-wise Monte Carlo correction for multiple comparisons (P < 0.05).

Next, we tested relationships between [18F]FMT Ki and longitudinal cognition using multiple linear regression models with Listening Span slope as the dependent variable. The first model included variables [18F]FMT Ki, age, sex, and years of education. The second model tested a possible moderating effect of [18F]FMT Ki on the relationship between Listening Span slope and cortical atrophy rate consistent with the current framework of cognitive resilience (Arenaza-Urquijo and Vemuri 2020). This model included variables [18F]FMT Ki, cortical atrophy rate, an interaction term [18F]FMT Ki * cortical atrophy rate, age, sex, and years of education. For interaction models, [18F]FMT Ki and atrophy rate were mean centered. Cortical atrophy rate was measured within the region of overlap in parietal cortex that was identified in both vertex-wise regression analyses of [18F]FMT Ki and Listening Span performance. We acknowledge that the relatively small sample size limits our ability to detect small or medium interaction effects. Power analyses based on effect sizes from our previous research reporting a significant moderating effect of striatal [18F]FMT Ki (change in R2 = 0.29 following addition of the interaction term; Berry et al. 2018a) indicated n = 31 participants are required to detect this effect size with 0.80 power and alpha 0.05. We performed power analyses using G*Power 3.1 (Faul et al. 2009) for linear multiple regression (fixed model, R2 deviation from 0). All other statistical analyses were performed using SPSS, version 27 (IBM, Armonk, New York, USA).

Results

Age Group Differences in [ 18F]FMT Ki

Striatal [18F]FMT Ki was higher in older adults (n = 29) relative to young (n = 15), representing an independent replication of previous reports (Braskie et al. 2008; Berry et al. 2016). Repeated measures ANOVA with factors age group (young, older) and region (DCA, DPUT, VST, caudate body) revealed a main effect of age (F(1,42) = 19.15, P < 0.001, ηp2 = 0.31, 95% unstandardized CI of the difference in means [0.002, 0.006]), and no region by age interaction (F(1,42) = 0.06, P = 0.81). Age-group differences were not a product of PVC, as a main effect of age group was also apparent for non-PVC [18F]FMT Ki (F(1,42) = 13.64, P < 0.001, ηp2 = 0.25, unstandardized CI [0.001, 0.003]). Figure 2 illustrates age group differences in whole striatum (t(42) = 4.19, P < 0.001, Cohen’s d = 1.33, unstandardized CI [0.002, 0.006]) and DCA [18F]FMT Ki PVC data (t(42) = 4.45, P < 0.001, Cohen’s d = 1.42, unstandardized CI [0.002, 0.006]), which are the focus of our analyses.

Figure 2.

Figure 2

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Age group differences in [18F]FMT Ki. Older adults showed significantly higher [18F]FMT Ki in the striatum (A) and the DCA (B) compared with young adults. *P < 0.001.

Cortical Atrophy Associated with [ 18F]FMT Ki and Longitudinal Working Memory

Exploratory vertex-wise longitudinal cortical thickness analyses revealed greater posterior parietal atrophy was associated with higher [18F]FMT Ki measured in whole striatum. Analyses adjusted for age, sex, and years of education and were submitted to Monte Carlo correction for multiple comparisons (P < 0.05; Fig. 3a; Table 1a). Individuals with higher [18F]FMT Ki showed greater atrophy in left supramarginal gyrus and right postcentral gyrus. Relationships between atrophy and [18F]FMT Ki measured in DCA were not significant after Monte Carlo correction. There were no regions for which greater atrophy was associated with lower [18F]FMT Ki.

Figure 3.

Figure 3

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Cortical regions showing a relationship between change in thickness, striatal [18F]FMT Ki, and Listening Span. (A) Higher [18F]FMT Ki was significantly related to reductions in cortical thickness. Cool colors represent regions for which participants with higher [18F]FMT Ki values in whole striatum had higher rates of cortical thinning. (B) Reductions in Listening Span performance was associated with reductions in cortical thickness. Warm colors represent regions for which participants with declines in working memory function had declines in cortical thickness. (C) Regions of overlap between [18F]FMT Ki analyses (blue) and Listening Span analyses (red) are displayed in purple. Each analysis adjusted for age, sex, and years of education and underwent Monte Carlo correction for multiple comparisons (P = 0.05). Color bars display significance using -log(10) P value.

Table 1.

Clusters on the cortical surface where whole striatum [18F]FMT Ki and change in Listening Span performance were associated with change in cortical thickness

Region Hemisphere Association with thickness Size (mm2) Peak vertex* CWP**
X Y Z
(A) Whole striatum [18F]FMT Ki associated with change in thickness
Supramarginal Left Negative 610.07 --45.8 --28.0 33.0 0.018
Postcentral Right Negative 628.01 31.7 --31.1 58.9 0.018
(B) Listening Span associated with change in thickness
Supramarginal Left Positive 804.72 --48.1 --46.7 43.0 0.0024
Cuneus Left Positive 822.89 --13.3 --67.6 11.78 0.0014
Supramarginal Left Positive 558.30 --45.3 --35.2 24.9 0.034
Pars triangularis Left Positive 520.13 --39.8 26.9 5.8 0.05
Postcentral Right Positive 2739.22 32.5 --36.0 51.4 0.0001
Lingual Right Positive 967.07 7.4 --65.8 6.3 0.0003
Rostral middle frontal Right Positive 949.37 37.3 20.4 25.3 0.0004
Fusiform Right Positive 724.59 29.8 --65.9 --6.4 0.0065
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*Montreal Neurological Institute (MNI) coordinates of vertex with peak CWP value.

**Cluster-wise probability (CWP) resulting from cluster-wise Monte Carlo correction for multiple comparisons. Only clusters with CWP < 0.05 survived Monte Carlo correction.

Exploratory vertex-wise longitudinal cortical thickness analyses revealed several frontoparietal regions for which greater atrophy was associated with decline in Listening Span performance (Fig. 3b; Table 1b; Monte Carlo P < 0.05, adjusting for age, sex, and years of education). Individuals with longitudinal decline in Listening Span performance showed greater atrophy in left supramarginal gyrus, left pars triangularis (inferior frontal gyrus), left cuneus, right postcentral gyrus, right rostral middle frontal gyrus, right lingual gyrus, and right fusiform gyrus.

Vertex-wise longitudinal thickness analyses identified regions of posterior parietal cortex for which greater atrophy was associated with both decline in working memory performance and higher [18F]FMT Ki (Fig. 3c). Regions of overlap were right postcentral and left supramarginal gyri.

[ 18F]FMT Ki and Longitudinal Working Memory

We tested the relationship between striatal [18F]FMT Ki and longitudinal change in Listening Span performance in a regression model that included variables [18F]FMT Ki, age, sex, and years of education. Higher [18F]FMT Ki was associated with declines in Listening Span performance (whole striatum: t(27) = −2.21, P = 0.04, unstandardized CI [−368.15, −14.27]; DCA: t(27) = −2.05, P = 0.05, unstandardized CI [−281.55, 0.41]; Fig. 4a, Supplementary Table 1). These results were not affected by adjusting for the number of Listening Span testing sessions (whole striatum: t(26) = −2.28, P = 0.03, unstandardized CI [−388.15, −20.16]; DCA: t(26) = −1.98, P = 0.06, unstandardized CI [−277.12, 5.01]).

Figure 4.

Figure 4

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Associations among [18F]FMT Ki, Listening Span, and change in cortical thickness. Negative numbers on the x-axis indicate reductions in thickness over time (atrophy). (A) Striatal [18F]FMT Ki was negatively related to change in Listening Span (P = 0.04). (B) There was a positive relationship between change in thickness and change in Listening Span (P < 0.0001). (C) There was a significant interaction between [18F]FMT Ki and change in thickness (P = 0.009) such that the impact of change in thickness on Listening Span was reduced for participants with the highest levels of striatal [18F]FMT Ki.

We next tested relationships between [18F]FMT Ki and longitudinal change in Listening Span performance with a regression model that included cortical atrophy, [18F]FMT Ki, cortical atrophy * [18F]FMT Ki interaction, age, sex, and years of education as independent variables. Cortical atrophy was measured within the region in parietal cortex for which atrophy rate was identified to be related to both [18F]FMT Ki and longitudinal change in Listening Span performance in exploratory analyses (vertex-wise regression analyses purple overlap; Fig 3c). Thickness declined −0.01 mm/year on average in this parietal overlap region. As expected, cortical atrophy was significantly related to change in Listening Span performance, with greater atrophy associated with working memory decline (Fig. 4b, Table 2a). Critically, there was a significant atrophy by [18F]FMT Ki interaction (Table 2a). The interaction between cortical atrophy and [18F]FMT Ki revealed that for low and moderate levels of [18F]FMT Ki, reductions in cortical thickness were associated with reductions in Listening Span performance. For high levels of [18F]FMT Ki, this relationship was not observed (Fig. 4c). These findings are in line with the current framework of cognitive resilience (Arenaza-Urquijo et al. 2019) by which higher levels of [18F]FMT Ki are associated with better-than-expected cognition given an individual’s level of atrophy. Results were consistent for models using DCA measures of [18F]FMT Ki (Table 2b), and did not change following adjustment for the number of MRI and behavioral timepoints (t(23) = −4.04, P = 0.001, unstandardized CI [−7888.88, −2547.05]). Additional analyses accounting for time differences between PET scans and MR scans and between PET scans and Listening Span sessions demonstrate that these time differences do not change the results (see Supplementary Methods and Supplementary Table 2). Further, interaction results did not change for analyses using non-PVC [18F]FMT Ki data (whole striatum: t(25) = −3.09, P = 0.005, unstandardized CI [−10094.12, −2021.10]; DCA: t(25) = −2.17, P = 0.04, unstandardized CI [−9073.96, −229.71]).

Table 2.

Results of the moderation analyses examining the effect of [18F]FMT Ki on the relationship between cortical atrophy and change in Listening Span performance

Variable Estimate (B) SE 95% CI β t P
(A) Listening Span, F(6, 25) = 7.56, P < 0.001, R2 = 0.65, adjusted R2 = 0.56
Cortical atrophy 24.44 6.00 12.09, 36.80 0.81 4.07 <0.001***
Striatal [18F]FMT Ki −18.65 94.10 −212.46, 175.15 −0.03 −0.20 0.84
Atrophy*[18F]FMT −3469.91 1216.72 −5975.78, −964.03 −0.38 −2.85 0.009**
Age at [18F]FMT 0.001 0.06 −0.12, 0.12 0.003 0.02 0.99
Sex 0.26 0.44 −0.65, 1.17 0.08 0.58 0.57
Years of education −0.11 0.11 −0.34, 0.11 −0.13 −1.03 0.31
(B) Listening Span, F(6, 25) = 7.28, P < 0.001, R2 = 0.64, adjusted R2 = 0.55
Cortical atrophy 27.43 6.06 14.95, 39.91 6.06 4.53 <0.001***
DCA [18F]FMT Ki 57.75 68.20 −82.70, 198.21 0.13 0.85 0.41
Atrophy*[18F]FMT −3263.96 1201.81 −5739.13, −788.80 −0.39 −2.72 0.01*
Age at [18F]FMT 0.03 0.06 −0.10, 0.15 0.07 0.41 0.69
Sex 0.29 0.43 −0.60, 1.18 0.51 0.68 0.51
Years of education −0.09 0.11 −0.32, 0.14 −0.10 −0.80 0.43
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Table 2A reports results for whole striatum [18F]FMT Ki. Table 2B reports results for dorsal caudate (DCA) [18F]FMT Ki. *P < 0.05, **P < 0.01, ***P < 0.001. Estimates (B) and 95% Confidence Intervals (CI) are unstandardized, and β values are standardized.

Discussion

We determined dopamine synthesis capacity is elevated in older adults in the context of reductions in cortical thickness. These data are consistent with a model by which age-related losses are met with upregulation in dopamine synthesis enzymes. Moderation analyses revealed that higher levels of dopamine synthesis capacity were associated with relative stability of cognitive performance when accounting for rates of cortical atrophy. Together, we propose elevated dopamine synthesis capacity represents a neurobiological mechanism of cognitive resilience in aging.

Evidence that elevated dopamine synthesis capacity is a compensatory mechanism in aging aligns with previous research in rodent models and in Parkinson’s disease. In rodent models, both age-related and experimentally induced denervation of dopamine-producing neurons are met with upregulation of the dopamine synthesis enzyme tyrosine hydroxylase (Greenwood et al. 1991; reviewed in Zigmond et al. 1990). In human Parkinson’s disease patients, multitracer PET studies reveal reduced density of transporters in patients relative to controls, but stable dopamine synthesis capacity in early stages of the disease (Lee et al. 2000; Nandhagopal et al. 2011). Although these studies in Parkinson’s disease do not directly associate measures of dopamine synthesis capacity with disease symptoms, the authors propose that the eventual failure of compensatory strategies likely contributes to the escalation of motor symptoms (Nandhagopal et al. 2011). In the absence of disease, our findings in normal aging suggest that the dopamine system is sensitive to subtle alterations to neurobiological health. Through evaluation of longitudinal cognitive performance, we extend previous work in Parkinson’s disease to directly implicate elevated dopamine synthesis in stabilizing cognitive performance.

Relationships between greater cortical atrophy and higher striatal dopamine synthesis capacity were significant in posterior parietal regions. Surprisingly, the current dataset did not reveal associations between prefrontal cortex thinning and [18F]FMT Ki, though we do not interpret this null effect strongly given the high-functioning characteristics of the BACS sample (Fjell et al. 2006) and our limited sample size. Like prefrontal cortex, parietal cortex is highly implicated in working memory performance (Wager and Smith 2003; Riddle et al. 2020), and has substantial direct projections to striatum, as shown by studies in nonhuman primates (Selemon and Goldman-Rakic 1985; Yeterian and Pandya 1993) and recapitulated by human diffusion tensor imaging (Jarbo and Verstynen 2015). Although defining the mechanisms by which losses within parietal cortex may stimulate upregulation of dopamine synthesis are beyond the scope of this study, it is possible that ablation of direct parietal-striatal inputs triggers dopamine upregulation. Additionally, the striatum and posterior parietal cortex share multisynaptic connections that are bidirectional. These regions commonly show correlated functional network activity in human functional MRI studies (Di Martino et al. 2008; Zhang and Li 2012; Jarbo and Verstynen 2015). It is possible atrophy-related disruptions in broader frontoparietal network activity, or an unknown third variable may cause elevated dopamine synthesis capacity. Studies in animal models will be necessary to demonstrate the causal nature of these relationships and resolve the underlying neural mechanisms. These studies may also test the extent to which losses in prefrontal cortex are associated with upregulation of dopamine synthesis.

This study replicates 2 previous human [18F]FMT PET studies that reported higher dopamine synthesis capacity in older adults relative to young adults (Braskie et al. 2008; Berry et al. 2016), and clarifies the relationships between higher synthesis capacity and cognition. Each demonstration of age effects on [18F]FMT Ki relied on independent samples, and produced similarly large effect sizes in age group differences (Cohen’s d = 1.42–1.68). Our previous research investigating the functional impact of elevated dopamine synthesis has shown mixed results with some studies suggesting an overall positive impact of higher dopamine synthesis capacity on executive function in older adults (Landau et al. 2009) and others suggesting a negative impact (Klostermann et al. 2012; Berry et al. 2016, also discussed in Berry et al. 2018c). Although relatively low sample sizes (n = 15–23) may contribute to these inconsistent effects, the current findings clearly reveal that higher levels of dopamine synthesis capacity may simultaneously signal declines in neurobiological health and cognition but may also act to partially rescue cognitive performance. Specifically, we found higher levels of dopamine synthesis capacity in older adults whose Listening Span working memory performance was declining. Critically, accounting for reductions in cortical thickness eliminated this relationship, and a significant interaction between dopamine synthesis capacity and atrophy revealed that the negative effect of atrophy on cognition was mitigated by higher levels of dopamine synthesis capacity. Thus, higher dopamine synthesis capacity appears to bestow better-than-expected cognitive trajectories given neural losses.

Although these findings shed light on the functional impact of elevated dopamine synthesis capacity in aging, it is important to acknowledge limitations of the available dataset, which includes the limited sample size. Despite relatively low sample size, we were able to detect interaction effects between [18F]FMT and gray matter thinning (R2 = 0.65 for the whole model; interaction term estimate = −3469.91). Notably, the large effect size of the [18F]FMT Ki interaction models reported here are consistent with our previous research, which demonstrated a moderating effect of higher levels of [18F]FMT on relationships between fMRI functional connectivity and executive function (R2 = 0.63 for the whole model; interaction term estimate = −2052.38). Future research will benefit from larger samples, which will allow for the testing of possible effects of sex, race, and lifestyle factors on elevated dopamine synthesis capacity in aging. Additionally, it is worth noting that many participants showed longitudinal improvement in performance, which is common in healthy samples (Landau et al. 2018) and which we interpret to reflect practice-related learning. Given these learning effects, the relationship between elevated dopamine synthesis and cognitive trajectories should be interpreted with some nuance, as successful maintenance of Listening Span performance reflects retention of both working memory processes and those cognitive processes underlying practice-related improvement such as learning and memory.

Dopamine’s moderating effect on atrophy’s relationship with cognitive decline is consistent with the current framework describing cognitive resilience—the ability to cope with age or disease-related changes in the brain (Arenaza-Urquijo and Vemuri 2020). The shift from behavioral to biological definitions of neurological disease, particularly in Alzheimer’s disease (AD) research, has intensified efforts to both define the terminology around cognitive resilience and reserve (Cabeza et al. 2018; Walhovd et al. 2019; Burke et al. 2019; Stern et al. 2019; Pettigrew and Soldan 2019; Stern et al. 2020) and characterize the underlying neural mechanisms within a common framework. Although the behavioral correlates of cognitive resilience have been studied for decades and include greater education (Stern et al. 1994; Rentz et al. 2010), IQ (Rentz et al. 2010; Rentz et al. 2017), exercise (Soto et al. 2015; Ritchie et al. 2016), and sex (female; Ossenkoppele et al. 2020), how these resilience effects manifest neuronally is less clear. The current evidence implicating elevated activity of neuromodulator systems as a mechanism of resilience is compelling given the role of neuromodulators in cognitive function and broad inputs to most cortical and subcortical brain structures. Additional research is needed to establish relationships between elevated dopamine synthesis capacity in aging and behavioral and lifestyle factors associated with resilience, though there is some previous dopamine PET research demonstrating positive associations between striatal dopamine transporter density and IQ in older adults (Li et al. 2020) and greater density of striatal dopamine D2/3 receptors in older adults who are physically active (Dang et al. 2017).

Previous brain imaging studies have identified metabolic signatures of resilience in anterior cingulate and lateral prefrontal cortex (Arenaza-Urquijo et al. 2019) and functional connectivity signatures in left prefrontal cortex in the context of AD-related pathology (Franzmeier et al. 2018). An intriguing possibility is that these resilience mechanisms are partially mediated by increases in dopamine activity. Relevant to the findings of Arenza-Urquijo et al., lateral prefrontal and anterior cingulate cortex receive direct dopaminergic input (Ott and Nieder 2019) and are also highly connected to the striatum (Alexander et al. 1986). Dopamine’s modulation of anterior cingulate is a proposed mechanism of motivation-related increases in attentional effort and performance (Schweimer and Hauber 2006; Cowen et al. 2012; Yee and Braver 2018) and may account for successful maintenance of cognitive performance in some older adults. Relevant to the findings of Franzmeier et al., our previous research indicated relationships between dopamine synthesis capacity and left prefrontal cortex functional connectivity (Berry et al. 2018c). Elevated dopamine synthesis in older adults restored youth-like relationships between left prefrontal cortex functional connectivity and executive function (Berry et al. 2018c). Although the AD-pathological and neuromodulatory mechanisms of age-related changes in brain function and cognition are largely studied separately in humans, there are clear points of interaction (Jacobs et al. 2021) that require programmatic investigation to further establish the role of neuromodulator systems in compensation and resilience.

Longitudinal measures of cortical thickness and cognition allowed novel investigation of the role of higher dopamine synthesis capacity in compensation and cognitive resilience. This study is limited by the heterogeneity in the relative timing of our measures of interest, and by having only a single measurement of dopamine synthesis capacity. Longitudinal studies are needed to demonstrate that there is, indeed, an increase in [18F]FMT Ki over time, consistent with upregulation accounts. Longitudinal [18F]FMT Ki measurement would also rule out the possibility that those older adults with greatest atrophy had higher levels of dopamine synthesis initially. Further, it will be valuable to collect both measures of dopamine synthesis capacity and measures of dopamine receptor density or transporter density to test the possibility that synthesis is upregulated in response to specific losses in other components of the dopamine system. Multitracer studies may also be critical in establishing to what extent excessive dopamine synthesis can itself produce declines in cognitive performance—perhaps generating inverted-u-shaped or “overdose” effects by overwhelming existing receptors or transporters. Finally, the current study was underpowered to probe differences in aging trajectories associated with genetic polymorphisms affecting the dopamine system. Emerging cross-sectional MR research has established relationships between dopamine COMT and DRD2 polymorphisms, cortical thickness and cognitive function (Miranda et al. 2019; Miranda et al. 2021). Future research could leverage large, publicly available datasets like the Alzheimer’s Disease Neuroimaging Initiative to test hypotheses about the role of “optimal” dopamine polymorphisms in the successful maintenance of cognitive function or cortical thickness despite AD-related pathology.

Funding

National Institutes of Health grants AG058748, AG034570, AG062542, AG044292, and Alzheimer’s Association Award AARF-17-530186. 3 T MR data were collected at the Henry H. Wheeler, Jr Brain Imaging Center, which receives support from the National Science Foundation through their Major Research Instrumentation Program, award number BCS-0821855.

Notes

Conflict of Interest: Dr Jagust has served as a consultant to Biogen, Bioclinica, Roche/Genentech, Grifols and holds an equity interest in Optoceutics.

Supplementary Material

Supplemental_bhab379
Click here for additional data file. (19.5KB, docx)

Contributor Information

Claire J Ciampa, Department of Psychology, Brandeis University, Waltham, MA 02453, USA.

Jourdan H Parent, Department of Psychology, Brandeis University, Waltham, MA 02453, USA.

Molly R Lapoint, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA.

Kaitlin N Swinnerton, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA.

Morgan M Taylor, Department of Psychology, Brandeis University, Waltham, MA 02453, USA.

Victoria R Tennant, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA.

A J Whitman, Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.

William J Jagust, Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA; Molecular Biophysics and Integrated Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.

Anne S Berry, Department of Psychology, Brandeis University, Waltham, MA 02453, USA.

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