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Predicting beneficial effects of atomoxetine and citalopram on response inhibition in Parkinson's disease with clinical and neuroimaging measures

Ye, Zheng; Rae, Charlotte L; Nombela, Cristina; Ham, Timothy; Rittman, Timothy; Jones, Peter Simon; Rodríguez, Patricia Vázquez; Coyle-Gilchrist, Ian; Regenthal, Ralf and Altena, Ellemarije, et al. (2016) In Human Brain Mapping 37(3). p.37-1026
Abstract

Recent studies indicate that selective noradrenergic (atomoxetine) and serotonergic (citalopram) reuptake inhibitors may improve response inhibition in selected patients with Parkinson's disease, restoring behavioral performance and brain activity. We reassessed the behavioral efficacy of these drugs in a larger cohort and developed predictive models to identify patient responders. We used a double-blind randomized three-way crossover design to investigate stopping efficiency in 34 patients with idiopathic Parkinson's disease after 40 mg atomoxetine, 30 mg citalopram, or placebo. Diffusion-weighted and functional imaging measured microstructural properties and regional brain activations, respectively. We confirmed that Parkinson's... (More)

Recent studies indicate that selective noradrenergic (atomoxetine) and serotonergic (citalopram) reuptake inhibitors may improve response inhibition in selected patients with Parkinson's disease, restoring behavioral performance and brain activity. We reassessed the behavioral efficacy of these drugs in a larger cohort and developed predictive models to identify patient responders. We used a double-blind randomized three-way crossover design to investigate stopping efficiency in 34 patients with idiopathic Parkinson's disease after 40 mg atomoxetine, 30 mg citalopram, or placebo. Diffusion-weighted and functional imaging measured microstructural properties and regional brain activations, respectively. We confirmed that Parkinson's disease impairs response inhibition. Overall, drug effects on response inhibition varied substantially across patients at both behavioral and brain activity levels. We therefore built binary classifiers with leave-one-out cross-validation (LOOCV) to predict patients' responses in terms of improved stopping efficiency. We identified two optimal models: (1) a "clinical" model that predicted the response of an individual patient with 77-79% accuracy for atomoxetine and citalopram, using clinically available information including age, cognitive status, and levodopa equivalent dose, and a simple diffusion-weighted imaging scan; and (2) a "mechanistic" model that explained the behavioral response with 85% accuracy for each drug, using drug-induced changes of brain activations in the striatum and presupplementary motor area from functional imaging. These data support growing evidence for the role of noradrenaline and serotonin in inhibitory control. Although noradrenergic and serotonergic drugs have highly variable effects in patients with Parkinson's disease, the individual patient's response to each drug can be predicted using a pattern of clinical and neuroimaging features.

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@article{77cb95b2-0822-490e-95be-1e42e6cc150a,
  abstract     = {<p>Recent studies indicate that selective noradrenergic (atomoxetine) and serotonergic (citalopram) reuptake inhibitors may improve response inhibition in selected patients with Parkinson's disease, restoring behavioral performance and brain activity. We reassessed the behavioral efficacy of these drugs in a larger cohort and developed predictive models to identify patient responders. We used a double-blind randomized three-way crossover design to investigate stopping efficiency in 34 patients with idiopathic Parkinson's disease after 40 mg atomoxetine, 30 mg citalopram, or placebo. Diffusion-weighted and functional imaging measured microstructural properties and regional brain activations, respectively. We confirmed that Parkinson's disease impairs response inhibition. Overall, drug effects on response inhibition varied substantially across patients at both behavioral and brain activity levels. We therefore built binary classifiers with leave-one-out cross-validation (LOOCV) to predict patients' responses in terms of improved stopping efficiency. We identified two optimal models: (1) a "clinical" model that predicted the response of an individual patient with 77-79% accuracy for atomoxetine and citalopram, using clinically available information including age, cognitive status, and levodopa equivalent dose, and a simple diffusion-weighted imaging scan; and (2) a "mechanistic" model that explained the behavioral response with 85% accuracy for each drug, using drug-induced changes of brain activations in the striatum and presupplementary motor area from functional imaging. These data support growing evidence for the role of noradrenaline and serotonin in inhibitory control. Although noradrenergic and serotonergic drugs have highly variable effects in patients with Parkinson's disease, the individual patient's response to each drug can be predicted using a pattern of clinical and neuroimaging features.</p>},
  author       = {Ye, Zheng and Rae, Charlotte L and Nombela, Cristina and Ham, Timothy and Rittman, Timothy and Jones, Peter Simon and Rodríguez, Patricia Vázquez and Coyle-Gilchrist, Ian and Regenthal, Ralf and Altena, Ellemarije and Housden, Charlotte R and Maxwell, Helen and Sahakian, Barbara J and Barker, Roger A and Robbins, Trevor W and Rowe, James B},
  issn         = {1065-9471},
  keyword      = {Adrenergic Uptake Inhibitors,Aged,Atomoxetine Hydrochloride,Brain,Citalopram,Cross-Over Studies,Double-Blind Method,Female,Humans,Inhibition (Psychology),Magnetic Resonance Imaging,Male,Neuropsychological Tests,Parkinson Disease,Prognosis,Psychomotor Performance,Psychotropic Drugs,Serotonin Uptake Inhibitors,Journal Article,Randomized Controlled Trial,Research Support, Non-U.S. Gov't},
  language     = {eng},
  number       = {3},
  pages        = {37--1026},
  publisher    = {Wiley-Blackwell},
  series       = {Human Brain Mapping},
  title        = {Predicting beneficial effects of atomoxetine and citalopram on response inhibition in Parkinson's disease with clinical and neuroimaging measures},
  url          = {http://dx.doi.org/10.1002/hbm.23087},
  volume       = {37},
  year         = {2016},
}