Sarah Lidstone

Effects of Expectation on Placebo-Induced Dopamine Release in Parkinson Disease

CONTEXT Expectations play a central role in the mechanism of the placebo effect. In Parkinson disease (PD), the placebo effect is associated with release of endogenous dopamine in both nigrostriatal and mesoaccumbens projections, yet the factors that control this dopamine release are undetermined. OBJECTIVE To determine how the strength of expectation of clinical improvement influences the degree of striatal dopamine release in response to placebo in patients with moderate PD. DESIGN Randomized, repeated-measures study with perceived expectation as the independent between-subjects variable. SETTING University of British Columbia Hospital, Vancouver, British Columbia, Canada. Patients Thirty-five patients with mild to moderate PD undergoing levodopa treatment. Intervention Verbal manipulation was used to modulate the expectations of patients, who were told that they had a particular probability (25%, 50%, 75%, or 100%) of receiving active medication when they in fact received placebo. MAIN OUTCOME MEASURES The dopaminergic response to placebo was measured using [11C]raclopride positron emission tomography. The clinical response was also measured (Unified Parkinson Disease Rating Scale) and subjective responses were ascertained using patient self-report. RESULTS Significant dopamine release occurred when the declared probability of receiving active medication was 75%, but not at other probabilities. Placebo-induced dopamine release in all regions of the striatum was also highly correlated with the dopaminergic response to open administration of active medication. Whereas response to prior medication was the major determinant of placebo-induced dopamine release in the motor striatum, expectation of clinical improvement was additionally required to drive dopamine release in the ventral striatum. CONCLUSIONS The strength of belief of improvement can directly modulate dopamine release in patients with PD. Our findings demonstrate the importance of uncertainty and/or salience over and above a patients prior treatment response in regulating the placebo effect and have important implications for the interpretation and design of clinical trials.

Accurate Event-Driven Motion Compensation in High-Resolution PET Incorporating Scattered and Random Events

With continuing improvements in spatial resolution of positron emission tomography (PET) scanners, small patient movements during PET imaging become a significant source of resolution degradation. This work develops and investigates a comprehensive formalism for accurate motion-compensated reconstruction which at the same time is very feasible in the context of high-resolution PET. In particular, this paper proposes an effective method to incorporate presence of scattered and random coincidences in the context of motion (which is similarly applicable to various other motion correction schemes). The overall reconstruction framework takes into consideration missing projection data which are not detected due to motion, and additionally, incorporates information from all detected events, including those which fall outside the field-of-view following motion correction. The proposed approach has been extensively validated using phantom experiments as well as realistic simulations of a new mathematical brain phantom developed in this work, and the results for a dynamic patient study are also presented.

Understanding the Placebo Effect: Contributions from Neuroimaging

Neuroimaging studies have provided a major contribution to our understanding of the mechanisms of the placebo effect in neurological and psychiatric disorders. Expectation of symptom improvement has long been believed to play a critical role in the placebo effect, and is associated with increased endogenous striatal dopamine release in Parkinson’s disease and increased endogenous opioid transmission in placebo analgesia. Evidence from positron emission tomography and functional magnetic resonance imaging studies suggests that expectations of symptom improvement are driven by frontal cortical areas, particularly the dorsolateral prefrontal, orbitofrontal, and anterior cingulate cortices. The ventral striatum is involved in the expectation of rewarding stimuli and, together with the prefrontal cortex, has also been shown to play an important role in the placebo-induced expectation of therapeutic benefit. Understanding the mechanisms of the placebo effect has important implications for treatment of several medical conditions, including depression, pain, and Parkinson’s disease.

Placebo effect and dopamine release.

The placebo effect can be encountered in a great variety of medical conditions, but is particularly prominent in pain, depression and Parkinsons disease. It has been shown that placebo responses play a part in the effect of any type of treatment for Parkinsons disease, including drug therapy, deep brain stimulation and dopamine tissue transplantation. Recent studies have demonstrated that the placebo effect in Parkinsons disease is related to the release of substantial amounts of endogenous dopamine in both the dorsal and ventral striatum. As the ventral striatum is involved in reward processing, these observations suggest that the placebo effect may be linked to reward mechanisms. In keeping with this placebo-reward model, most recent experiments have shown activation of the reward circuitry in association with placebo responses in other disorders. In addition, as dopamine is the major neurotransmitter in the reward circuitry, the model predicts that the release of dopamine in the ventral striatum could be involved in mediating placebo responses not only in Parkinsons but also in other medical conditions.

Investigation of Subject Motion Encountered During a Typical Positron Emission Tomography Scan

Subject motion has a known detrimental effect on brain Positron Emission Tomography image resolution. Numerous motion compensation techniques exist to address this issue, however prior to their application every effort should be made to limit subject motion. Using a Polaris motion tracking system subject motion was observed under typical scanning conditions for both healthy and Parkinsons disease (PD) volunteers. Motions in the range of 0 to 5 mm were observed for healthy subjects, and 0 to 20 mm for PD subjects. The most common source of motion was due to interaction between the subject and the attending nurse/scanning staff, especially during examination of the subjects symptoms (motions up to 8 mm). Less common activities resulting in significant motions were the use of a bedpan (20 mm), the removal of a cushion from under the subjects legs (5 mm) and leg readjustments (3 mm). Awareness of the effect each of these activities had on head motion can be used to motivate further limitations on these motions. Measured motions were also extrapolated to various regions in the brain, specifically the cerebellum, occipital cortex, and striatum. Subject head rotation about the vertical and horizontal axes resulted in the greatest displacement of regions in the cerebellum, while rotations about the subjects long axis primarily impacted the displacement of the occipital cortex region. This measurement provides motion related information about the expected accuracy of time activity curves for different brain regions.

Effect of data processing on the accuracy of biologically relevant measures in high resolution pet brain imaging using the HRRT

High resolution positron esmission tomotgraphy (PET) imaging using depth of interaction decoding (DOI) schemes, such as available on the high resolution research tomography (HRRT), is particularly susceptible to (i) potential instrumentation instability and (ii) statistical noise in the data and (iii) patient movement during scanning. These potential sources of uncertainty are exacerbated in dynamic imaging where a large range of count rate and number of acquired counts is observed and scan duration extends over 60 min or more. Here we evaluated the impact of scanner stability and patient motion on the feasibility of obtaining rather sophisticated biological measures such as intervention derived changes in synaptic dopamine levels Δ(DA). It was found that (i) observed variations in an empirical parameter accounting for the different energy response of the two scintillator layers (LSO/LYSO) and influencing the accuracy of the scatter correction may lead to up to 10% variations in binding potentials (BPND) estimates; frequent scanner calibrations are thus required; (ii) coregistration is essential when estimating Δ(DA) in small structures given typical patient motion. Up to ∼25% change in the BPND values was observed after accurate coregistration; this is larger than an 8–10% variation expected from the statistical quality of the data. With these corrections in place we found that data from the HRRT can robustly distinguish topological variation in Δ(DA) occurring as a consequence of different stimuli.

Effect of measurement uncertainty on region of interest based and parametric binding potential estimates for the high resolution research tomograph (HRRT)

High resolution Positron Emission Tomography (PET) imaging leads to very small pixel sizes. Generally the increase in resolution is not paralleled by a corresponding increase in sensitivity which may cause the count density per voxel to be low. Here we are exploring how the statistical quality of the data acquired with the high resolution research tomograph (HRRT) influences the accuracy of the determination of the binding potential (BP) for typical human studies performed with 11middotC-raclopride. Susceptibility to noise was tested for 3 modelling approaches: the Logan graphical model, the simplified reference tissue method (RTM) and the delayed ratio method (DRM). For each approach BP was calculated on a region of interest (ROI) and voxel basis (parametric maps). Using a method based on experimentally defined replicas of time activity curves (TACs) representative of those obtained in human scans we found that for this tracer the contribution of the statistical noise to the BP determination is ~ 5-8 % when the TACs are evaluated on an ROI basis (either ROI TACs used as input, or ROI placed on the BP parametric image) and 9-12 % when calculated on a single pixel basis. The Logan approach was found to suffer from a considerable bias due to statistical noise when the BP was calculated on a single pixel basis, while RTM and DRM showed no such bias. Overall, for this tracer and these scanning conditions the RTM proved to be the least sensitive to statistical noise in the data.

Comparison between the ROI based and pixel based analysis for neuroreceptor studies performed on the high resolution research tomograph (HRRT)

Parametric imaging refers to evaluating kinetic model parameters from time activity curves (TACs) estimated from every image pixel in contrast to region of interest (ROI) based analysis where the parameters are evaluated from TACs derived from predefined ROIs. Parametric imaging is thus more sensitive to statistical and motion induced noise. Here we evaluate the feasibility of parametric imaging for two modeling approaches, the simplified reference tissue model (RTM) and the tissue input Logan graphical method (L), for data acquired on the high resolution research tomograph (HRRT). The small image pixel size of this tomograph makes the image pixel values particularly sensitive to statistical noise and to artifacts due to subject motion. Comparing parametric BP estimates to those obtained with the ROI based approach a large downward bias (up to 28%) was observed for the Logan approach, while no bias was observed for the RTM method. The correlation between the BP values obtained with the ROI and parametric approach was better and less affected by motion for RTM (r2 > 0.9) compared to L (r2 > 0.45). The correlation between the BP obtained with the two methods was found to be significantly affected by patient motion and in general better for the ROI based approach. We conclude that parametric imaging on the HRRT is feasible for selected modeling approaches.