The Antidepressant Treatment Response Index as a Predictor of Reboxetine Treatment Outcome in Major Depressive Disorder

Caudill, Marissa M.; Hunter, Aimee M.; Cook, Ian A.; Leuchter, Andrew F.

doi:10.1177/1550059414532443

Cited by 15 publications

(5 citation statements)

References 46 publications

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“…Cordance and cingulate theta power are closest to proof of concept, with studies reporting successful treatment prediction across different medication classes and study designs (14,29,31,47–49,51,105). ATR has been successful across medication classes, but only when tested by its original developers (58,59). A direct and identical replication of at least some of those findings is still necessary.…”

Section: Discussionmentioning

confidence: 99%

Electroencephalographic Biomarkers for Treatment Response Prediction in Major Depressive Illness: A Meta-Analysis

Widge

Bilge

Montana

et al. 2019

AJP

143

129

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Objective: Reducing unsuccessful treatment trials could improve depression treatment. Quantitative analysis of the electro-encephalogram (QEEG) might predict treatment response, and is being commercially marketed for this purpose. The authors sought to (1) quantify the reliability of QEEG for response prediction in depressive illness and (2) identify methodological limitations of the available evidence. Method: The authors performed a meta-analysis of diagnostic accuracy for QEEG in depressive illness, based on articles published between January 2000 and November 2017. The review included all articles that used QEEG to predict response during a major depressive episode, regardless of patient population, treatment, or QEEG marker. The primary meta-analytic outcome was the accuracy for predicting response to depression treatment, expressed as sensitivity, specificity, and the logarithm of the diagnostic odds ratio (DOR). Raters also judged each article on indicators of good research practice. Results: In 76 articles reporting 81 biomarkers, the meta-analytic estimates showed sensitivity 0.72 (0.67–0.76), specificity 0.68 (0.63–0.73), log(DOR) 1.89 (1.56–2.21), and area under the receiver-operator curve 0.76 (0.71–0.80). No specific QEEG biomarker or specific treatment showed greater predictive power than the all-studies estimate in a meta-regression. Funnel plot analysis suggested substantial publication bias (arcsine asymmetry test, t=6.33, p=2.64e-8). Most studies did not use ideal practices. Conclusions: QEEG does not appear clinically reliable for predicting depression treatment response due to under-reporting of negative results, a lack of out-of-sample validation, and insufficient direct replication of prior findings. Until these limitations are remedied, QEEG is not recommended for guiding psychiatric treatment selection.

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Section: Discussionmentioning

confidence: 99%

Electroencephalographic Biomarkers for Treatment Response Prediction in Major Depressive Illness: A Meta-Analysis

Widge

Bilge

Montana

et al. 2019

AJP

143

129

View full text Add to dashboard Cite

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“…Norepinephrine (NE) is a neurotransmitter essential for stress reactivity, resilience, and arousal, and NE signaling is thought to be dysregulated in several psychiatric disorders [1][2][3]. Medications targeting the noradrenergic system are used to treat anxiety and depression [4], and recent studies have suggested that guanfacine, an α2A adrenergic receptor agonist (α2AR), could be effective for treating pediatric anxiety disorders [5], as well as stress-induced relapse in cocaine-users [6][7][8][9] and in smokers [10]. Animal models have also identified effects of guanfacine on behaviors related to stress response, anxiety, and depression [11,12].…”

Section: Introductionmentioning

confidence: 99%

Interaction between noradrenergic and cholinergic signaling in amygdala regulates anxiety- and depression-related behaviors in mice

Mineur

Cahuzac

Mose

et al. 2018

Neuropsychopharmacol

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Medications that target the noradrenergic system are important therapeutics for depression and anxiety disorders. More recently, clinical studies have shown that the α2-noradrenergic receptor (α2AR) agonist guanfacine can decrease stress-induced smoking relapse during acute abstinence, suggesting that targeting the noradrenergic system may aid in smoking cessation through effects on stress pathways in the brain. Acetylcholine (ACh), like the nicotine in tobacco, acts at nicotinic acetylcholine receptors (nAChRs) to regulate behaviors related to anxiety and depression. We therefore investigated interactions between guanfacine and ACh signaling in tests of anxiolytic and antidepressant efficacy in female and male C57BL/6J mice, focusing on the amygdala as a potential site of noradrenergic/cholinergic interaction. The antidepressant-like effects of guanfacine were blocked by shRNA-mediated knockdown of α2AR in amygdala. Knockdown of the high-affinity β2 nAChR subunit in amygdala also prevented antidepressant-like effects of guanfacine, suggesting that these behavioral effects require ACh signaling through β2-containing nAChRs in this brain area. Ablation of NE terminals prevented the anxiolytic- and antidepressant-like effects of the nicotinic partial agonist cytisine, whereas administration of the cholinesterase antagonist physostigmine induced a depression-like phenotype that was not altered by knocking down α2AR in the amygdala. These studies suggest that ACh and NE have opposing actions on behaviors related to anxiety and depression and that cholinergic signaling through β2-containing nAChRs and noradrenergic signaling through α2a receptors in neurons of the amygdala are critical for regulation of these behaviors.

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“…A qEEG biomarker, the Antidepressant Treatment Response (ATR) index, has been associated with outcomes of treatment with SSRIs in patients with MDD. In an open study of norepinephrine reuptake inhibitor reboxetine, the ATR index predicted response with 70.6 % sensitivity and 87.5 % specifi city, and remission with 87.5 % sensitivity and 64.7 % specifi city (Caudill et al 2014 ). These results suggest that the ATR index may be a useful biomarker of clinical response during NRI treatment of adults with MDD.…”

Section: Eeg To Predict Adverse Effects and Evaluate Antidepressant Ementioning

confidence: 98%

Textbook of Personalized Medicine

Jain¹

2015

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The Antidepressant Treatment Response Index as a Predictor of Reboxetine Treatment Outcome in Major Depressive Disorder

Cited by 15 publications

References 46 publications

Electroencephalographic Biomarkers for Treatment Response Prediction in Major Depressive Illness: A Meta-Analysis

Electroencephalographic Biomarkers for Treatment Response Prediction in Major Depressive Illness: A Meta-Analysis

Interaction between noradrenergic and cholinergic signaling in amygdala regulates anxiety- and depression-related behaviors in mice

Textbook of Personalized Medicine

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