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

Hum Brain Mapp. 2016 Mar;37(3):1026-37. doi: 10.1002/hbm.23087. Epub 2016 Jan 12.

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 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.

Keywords: Parkinson's disease; impulsivity; machine learning; noradrenaline; response inhibition; serotonin; stratification.

Publication types

  • Randomized Controlled Trial
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adrenergic Uptake Inhibitors / therapeutic use
  • Aged
  • Atomoxetine Hydrochloride / therapeutic use*
  • Brain / drug effects
  • Brain / pathology
  • Brain / physiopathology
  • Citalopram / therapeutic use*
  • Cross-Over Studies
  • Double-Blind Method
  • Female
  • Humans
  • Inhibition, Psychological*
  • Magnetic Resonance Imaging / methods*
  • Male
  • Neuropsychological Tests
  • Parkinson Disease / diagnosis
  • Parkinson Disease / drug therapy*
  • Parkinson Disease / pathology
  • Parkinson Disease / physiopathology
  • Prognosis
  • Psychomotor Performance / drug effects
  • Psychomotor Performance / physiology
  • Psychotropic Drugs / therapeutic use*
  • Serotonin Uptake Inhibitors / therapeutic use

Substances

  • Adrenergic Uptake Inhibitors
  • Psychotropic Drugs
  • Serotonin Uptake Inhibitors
  • Citalopram
  • Atomoxetine Hydrochloride