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Accurate Predictive Models of Atomoxetine’s and Citalopram’s
Effect on Motor Inhibition in Parkinson’s Disease
Z Ye1,6, CL...
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Predicting effects of atomoxetine and citalopram in Parkinson's disease


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Presented at OHBM2014 (Hamburg)

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Predicting effects of atomoxetine and citalopram in Parkinson's disease

  1. 1. Accurate Predictive Models of Atomoxetine’s and Citalopram’s Effect on Motor Inhibition in Parkinson’s Disease Z Ye1,6, CL Rae1,2, C Nombela1, T Ham1, T Rittman1, PS Jones1, P Vázquez Rodríguez1, Ian Coyle-Gilchrist1, R Regenthal3, E Altena1, CR Housden1, H Maxwell1, BJ Sahakian4,5, RA Barker1, Trevor Robbins1,4, JB Rowe1,2,4 1University of Cambridge Department of Clinical Neurosciences 2MRC Cognition and Brain Sciences Unit 3University of Leipzig 4Behavioural and Clinical Neuroscience Institute 5University of Cambridge Department of Psychiatry 6Donders Institute for Brain, Cognition and Behaviour Contact Dr Rowe Measures PD Control Diff. Sex (male:female) 21:13 23:19 n.s. Age (years) 67 (7) 67 (7) n.s. Cognitive impairment (MMSE) 29 (2) 29 (1) n.s. Duration of symptoms (years) 9 (6) -- -- Hoehn & Yahr 2.0 (0.6) -- -- Disease severity (UPDRS-III) 23 (8) -- -- L-dopa equivalent dose (mg/day) 547 (304) -- -- Atomoxetine level (ng/ml) 402 (179) -- -- Citalopram level (ng/ml) 40 (16) -- -- Background Motor inhibition deficits occur in Parkinson’s disease (PD) even in the absence of impulse control disorders. Their aetiology is multifactorial, including the loss of noradrenergic and seroton- ergic projects to the forebrain and structural change in fronto- striatal circuits1-3. Our recent studies have showed that selective noradrenaline (atomoxetine) and serotonin (citalopram) reuptake inhibitors improve motor inhibition in a subgroup of PD patients, in terms of both performance and brain activity.4,5. However, the impact of treatment depends on individual differences in clinical characteristics and brain imaging. Go Stop Variable delay max. 1000 ms Control (stop>Go) 0 5 Primarily funded by References 1. Goldstein et al. 2011. Eur J Neurol 2. Politis et al. 2010. Neurob Dis 3. Rae et al. 2014. Neuroimage 4. Ye et al. 2014. Biol Psyhiat 5. Ye et al. 2014. Brain Methods & Results • Double-blind randomized placebo-controlled crossover study with 34 PD patients (three sessions: 40mg atomoxetine, 30mg citalopram or placebo) and 42 controls (1 session, no drug). • Stop task: 360 Go trials, 80 stop trials (tracking algorithm to 50% inhibition, figure); SSRT estimated by integration method; confirmed poor motor inhibition in PD (figure). Large individual variability led to no main effect of either drug. • DTI: 3-T, 63 directions, analysed with FSL; fractional anisotropy and mean diffusivity extracted from the anterior internal capsule (frontostriatal connection). • fMRI during the stop task: 3-T, whole-brain, 3mm resolution, analyzed with SPM. See figure for main effects of task and PD; parameter estimates extracted from the right inferior frontal gyrus, bilateral pre-supplementary motor areas and striatum. • Benchmark for behavioural efficacy: 30% reduction in the impact of PD on the SSRT. • Support vector machine with a radial function kernel; leave- one-out cross validation; inferences from 5000 permutations; 79-85% accuracy in predicting the drug response. This Study We developed two predictive models to identify patients who are most likely to benefit from these new candidate therapies. Clinical model which used demographic (sex, age) and clinical measures (cognitive impairment, disease severity, levodopa equivalent dose, plasma concentration of each drug), in combination with structural brain imaging data. Mechanistic model which used the demographic and clinical data, plus functional brain imaging measures. 400 500 600 Ctrl PD-PLA PD-ATO PD-CIT GoRT(ms) * Disease effects PD-PLA<Control (stop>Go) Optimal models for 30% improvement Accuracy Perm. Features for optimal Atomoxetine prediction Clinical: R fractional anisotropy, drug concentration, levodopa dose 79.4% <0.005 Mechanistic: activation in R caudate nucleus, L caudate nucleus, R pre-SMA 85.3% <0.001 Features for optimal Citalopram prediction Clinical: R fractional anisotropy, age, R mean diffusivity, cognition 79.4% <0.005 Mechanistic: activation in L caudate nucleus, R putamen, R pre-SMA 85.3% <0.005 Conclusion • Increasing noradrenaline or serotonin improves stopping efficiency of a subgroup of patients with Parkinson’s disease. • The clinical model is of relevance to prospective trialists and clinicians, to enable patient stratification in future clinical trials and for potential treatment decision for individual patients. • The mechanistic model provides the link to preclinical and comparative studies noradrenergic and serotonergic systems for cognitive control. 110 160 210 Ctrl PD-PLA PD-ATO PD-CIT SSRT(ms) * 90 80 70 60 50 10 20 30 40 50 Benchmark (30%) 10 20 30 40 50 Cross-validation accuracy(%) RobustnessBest model performance ATO-clinical ATO-mechanistic CIT-clinical CIT-mechanistic Stop-signal task Predictive models