Symptomatic and Functional Outcomes and Early Prediction of Response to Escitalopram Monotherapy and Sequential Adjunctive Aripiprazole Therapy in Patients With Major Depressive Disorder

Kennedy, Sidney H.; Lam, Raymond W.; Rotzinger, Susan; Milev, Roumen; Blier, Pierre; Downar, Jonathan; Evans, Kenneth R.; Farzan, Faranak; Foster, Jane A.; Frey, Benício N.; Giacobbe, Peter; Hall, Geoffrey B.; Harkness, Kate L.; Hassel, Stefanie; Ismail, Zahinoor; Leri, Francesco; McInerney, Shane; MacQueen, Glenda; Minuzzi, Luciano; Müller, Daniel J.; Parikh, Sagar V.; Placenza, Franca; Quilty, Lena C.; Ravindran, Arun; Sassi, Roberto; Soares, Cláudio N.; Strother, Stephen C.; Turecki, Gustavo; Vaccarino, Anthony L.; Vila-Rodriguez, Fidel; Yu, Jing; Uher, Rudolf

doi:10.4088/jcp.18m12202

Cited by 74 publications

(94 citation statements)

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“…Fifty‐nine healthy control participants were recruited at academic healthcare centers across Canada as a subset of participants in the first Canadian Biomarker Integration Network in Depression study (CAN‐BIND‐1; (Kennedy et al, ; Lam et al, )). Participants were aged between 18–60 years, had no psychiatric or unstable medical diagnoses and sufficient fluency in English to complete all study procedures.…”

Section: Methodsmentioning

confidence: 99%

“…In this study, we assessed test–retest reliability of an emotional conflict task in healthy comparison participants collected within a Canadian Biomarker Integration Network in Depression (CAN‐BIND) protocol (Kennedy et al, ; Lam et al, ). The Canadian Biomarker Integration Network in Depression (CAN‐BIND‐1) Program aims to identify biomarkers of antidepressant treatment response in patients with major depressive disorder (MDD).…”

Section: Introductionmentioning

confidence: 99%

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Reliability of a functional magnetic resonance imaging task of emotional conflict in healthy participants

Hassel

Sharma

Alders

et al. 2019

Human Brain Mapping

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Task‐based functional neuroimaging methods are increasingly being used to identify biomarkers of treatment response in psychiatric disorders. To facilitate meaningful interpretation of neural correlates of tasks and their potential changes with treatment over time, understanding the reliability of the blood‐oxygen‐level dependent (BOLD) signal of such tasks is essential. We assessed test–retest reliability of an emotional conflict task in healthy participants collected as part of the Canadian Biomarker Integration Network in Depression. Data for 36 participants, scanned at three time points (weeks 0, 2, and 8) were analyzed, and intra‐class correlation coefficients (ICC) were used to quantify reliability. We observed moderate reliability (median ICC values between 0.5 and 0.6), within occipital, parietal, and temporal regions, specifically for conditions of lower cognitive complexity, that is, face, congruent or incongruent trials. For these conditions, activation was also observed within frontal and sub‐cortical regions, however, their reliability was poor (median ICC < 0.2). Clinically relevant prognostic markers based on task‐based fMRI require high predictive accuracy at an individual level. For this to be achieved, reliability of BOLD responses needs to be high. We have shown that reliability of the BOLD response to an emotional conflict task in healthy individuals is moderate. Implications of these findings to further inform studies of treatment effects and biomarker discovery are discussed.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reliability of a functional magnetic resonance imaging task of emotional conflict in healthy participants

Hassel

Sharma

Alders

et al. 2019

Human Brain Mapping

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“…We did not assess for the presence of cerebrovascular disease in our sample, although there is an association between cardiovascular disease and MDD, 87 but also with MDD and other medical conditions. 88 Given the relatively young age of our samples (e.g., Lam and colleagues, 19 Addington and colleagues, 24 Santesteban-Echarri and colleages 89 and Kennedy and colleagues 90 ), this is unlikely to be a driving factor in neuroimaging results, but medical comorbidity is an important consideration in studies of psychiatric disease. No routine screening for substance use was performed, potentially affecting our findings.…”

Section: Limitationsmentioning

confidence: 98%

The Canadian Biomarker Integration Network in Depression (CAN-BIND): magnetic resonance imaging protocols

MacQueen

Hassel

Arnott

et al. 2019

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Site/scanner identification Toronto General Hospital (TGH)-GE 3.0 Tesla Signa HDxt CAMH (CAM)-GE 3.0 Tesla Discovery MR750 McMaster University (MCU)-GE 3.0 Tesla Discovery MR750 University of Calgary (UCA)-GE 3.0 Tesla Discovery MR750 University of British Columbia (UBC)-Philips 3.0 Tesla Intera Queens University (QNS)-Siemens 3.0 Tesla TrioTim JPN's top viewed articles* 1. N-Acetylcysteine in psychiatry: current therapeutic evidence and potential mechanisms of action Dean et al.

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“…We used EEG data from the CAN-BIND-1 study. 30 score decreased by least 50% in the first 8 weeks of the treatment and as a nonresponder if the score decrease was less than 50% (more details are given in the eMethods in the Supplement).…”

Section: Data Acquisition and Preprocessing Participantsmentioning

confidence: 99%

“…The shortcomings of the previous studies 7, [15][16][17]19 stem, to a considerable degree, from relatively small sample sizes available to their authors, typically ranging from 12 to 50 participants. The research described in this publication uses data from a large, Canada-wide, multicenter study, the first Canadian Biomarker Integration Network in Depression (CAN-BIND-1) study, 30 to overcome this limitation and address some of the questions remaining from the previous research. In particular, the present study attempts to compare the predictive power of a wide range of diverse features and maximally reduce the bias of the estimate of the prediction accuracy by using cross-validation.…”

Section: Introductionmentioning

confidence: 99%

Use of Machine Learning for Predicting Escitalopram Treatment Outcome From Electroencephalography Recordings in Adult Patients With Depression

et al. 2020

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IMPORTANCE Social and economic costs of depression are exacerbated by prolonged periods spent identifying treatments that would be effective for a particular patient. Thus, a tool that reliably predicts an individual patient's response to treatment could significantly reduce the burden of depression. OBJECTIVE To estimate how accurately an outcome of escitalopram treatment can be predicted from electroencephalographic (EEG) data on patients with depression. DESIGN, SETTING, AND PARTICIPANTS This prognostic study used a support vector machine classifier to predict treatment outcome using data from the first Canadian Biomarker Integration Network in Depression (CAN-BIND-1) study. The CAN-BIND-1 study comprised 180 patients (aged 18-60 years) diagnosed with major depressive disorder who had completed 8 weeks of treatment. Of this group, 122 patients had EEG data recorded before the treatment; 115 also had EEG data recorded after the first 2 weeks of treatment. INTERVENTIONS All participants completed 8 weeks of open-label escitalopram (10-20 mg) treatment. MAIN OUTCOMES AND MEASURES The ability of EEG data to predict treatment outcome, measured as accuracy, specificity, and sensitivity of the classifier at baseline and after the first 2 weeks of treatment. The treatment outcome was defined in terms of change in symptom severity, measured by the Montgomery-Åsberg Depression Rating Scale, before and after 8 weeks of treatment. A patient was designated as a responder if the Montgomery-Åsberg Depression Rating Scale score decreased by at least 50% during the 8 weeks and as a nonresponder if the score decrease was less than 50%. RESULTS Of the 122 participants who completed a baseline EEG recording (mean [SD] age, 36.3 [12.7] years; 76 [62.3%] female), the classifier was able to identify responders with an estimated accuracy of 79.2% (sensitivity, 67.3%; specificity, 91.0%) when using only the baseline EEG data. For a subset of 115 participants who had additional EEG data recorded after the first 2 weeks of treatment, use of these data increased the accuracy to 82.4% (sensitivity, 79.2%; specificity, 85.5%). CONCLUSIONS AND RELEVANCE These findings demonstrate the potential utility of EEG as a treatment planning tool for escitalopram therapy. Further development of the classification tools presented in this study holds the promise of expediting the search for optimal treatment for each patient.

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Symptomatic and Functional Outcomes and Early Prediction of Response to Escitalopram Monotherapy and Sequential Adjunctive Aripiprazole Therapy in Patients With Major Depressive Disorder

Cited by 74 publications

References 0 publications

Reliability of a functional magnetic resonance imaging task of emotional conflict in healthy participants

Reliability of a functional magnetic resonance imaging task of emotional conflict in healthy participants

The Canadian Biomarker Integration Network in Depression (CAN-BIND): magnetic resonance imaging protocols

Use of Machine Learning for Predicting Escitalopram Treatment Outcome From Electroencephalography Recordings in Adult Patients With Depression

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