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Antidepressant side effects and their impact on treatment outcome in people with major depressive disorder : an iSPOT-D report

- Braund, Taylor, Tillman, Gabriel, Palmer, Donna, Gordon, Evian, Rush, A., Harris, Anthony

  • Authors: Braund, Taylor , Tillman, Gabriel , Palmer, Donna , Gordon, Evian , Rush, A. , Harris, Anthony
  • Date: 2021
  • Type: Text , Journal article
  • Relation: Translational Psychiatry Vol. 11, no. 1 (2021), p.
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  • Description: Side effects to antidepressant medications are common and can impact the prognosis of successful treatment outcome in people with major depressive disorder (MDD). However, few studies have investigated the severity of side effects over the course of treatment and their association with treatment outcome. Here we assessed the severity of side effects and the impact of treatment type and anxiety symptoms over the course of treatment, as well as whether side effects were associated with treatment outcome. Participants were N = 1008 adults with a current diagnosis of single-episode or recurrent, nonpsychotic MDD. Participants were randomised to receive escitalopram, sertraline, or venlafaxine-extended release with equal probability and reassessed at 8 weeks regarding Hamilton Rating Scale Depression (HRSD17) and Quick Inventory of Depressive Symptomatology (QIDS-SR16) remission and response. Severity of side effects were assessed using the Frequency, Intensity, and Burden of Side Effects Rating (FIBSER) scale and assessed at day 4 and weeks 2, 4, 6, and 8. Frequency, intensity, and burden of side effects were greatest at week 2, then only frequency and intensity of side effects gradually decreased up to week 6. Treatment type and anxiety symptoms did not impact the severity of side effects. A greater burden—but not frequency or intensity—of side effects was associated with poorer treatment outcome and as early as 4 days post-treatment. Together, this work provides an informative mapping of the progression of side effects throughout the treatment course and their association with treatment outcome. Importantly, the burden of side effects that are present as early as 4 days post-treatment predicts poorer treatment outcome and should be monitored closely. iSPOT-D: Registry name: ClinicalTrials.gov. Registration number: NCT00693849. © 2021, The Author(s).

Intrinsic functional connectomes characterize neuroticism in major depressive disorder and predict antidepressant treatment outcomes

- Braund, Taylor, Breukelaar, Isabella, Griffiths, Kristi, Tillman, Gabriel, Palmer, Donna, Bryant, Richard, Phillips, Mary, Harris, Anthony, Korgaonkar, Mayuresh

  • Authors: Braund, Taylor , Breukelaar, Isabella , Griffiths, Kristi , Tillman, Gabriel , Palmer, Donna , Bryant, Richard , Phillips, Mary , Harris, Anthony , Korgaonkar, Mayuresh
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Biological Psychiatry: Cognitive Neuroscience and Neuroimaging Vol. 7, no. 3 (2022), p. 276-284
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  • Description: Background: Antidepressant efficacy in people with major depressive disorder remains modest, yet identifying treatment-predictive neurobiological markers may improve outcomes. While disruptions in functional connectivity within and between large-scale brain networks predict poorer treatment outcome, it is unclear whether higher trait neuroticism, which has been associated with generally poorer outcomes, contributes to these disruptions and to antidepressant-specific treatment outcomes. Here, we used whole-brain functional connectivity analysis to identify a neural connectomic signature of neuroticism and tested whether this signature predicted antidepressant treatment outcome. Methods: Participants were 226 adults with major depressive disorder and 68 healthy control subjects who underwent functional magnetic resonance imaging and were assessed on clinical features at baseline. Participants with major depressive disorder were then randomized to 1 of 3 commonly prescribed antidepressants and after 8 weeks completed a second functional magnetic resonance imaging and were reassessed for depressive symptom remission/response. Baseline intrinsic functional connectivity between each pair of 436 brain regions was analyzed using network-based statistics to identify connectomic features associated with neuroticism. Features were then assessed on their ability to predict treatment outcome and whether they changed after 8 weeks of treatment. Results: Higher baseline neuroticism was associated with greater connectivity within and between the salience, executive control, and somatomotor brain networks. Greater connectivity across these networks predicted poorer treatment outcome that was not mediated by baseline neuroticism, and connectivity strength decreased after antidepressant treatment. Conclusions: Our findings demonstrate that neuroticism is associated with organization of intrinsic neural networks that predict treatment outcome, elucidating its biological underpinnings and opportunity for better treatment personalization. © 2021 Society of Biological Psychiatry

Sequential sampling models without random between-trial variability : the racing diffusion model of speeded decision making

- Tillman, Gabriel, Van Zandt, Trish, Logan, Gordon

  • Authors: Tillman, Gabriel , Van Zandt, Trish , Logan, Gordon
  • Date: 2020
  • Type: Text , Journal article , Review
  • Relation: Psychonomic Bulletin and Review Vol. 27, no. 5 (2020), p. 911-936
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  • Description: Most current sequential sampling models have random between-trial variability in their parameters. These sources of variability make the models more complex in order to fit response time data, do not provide any further explanation to how the data were generated, and have recently been criticised for allowing infinite flexibility in the models. To explore and test the need of between-trial variability parameters we develop a simple sequential sampling model of N-choice speeded decision making: the racing diffusion model. The model makes speeded decisions from a race of evidence accumulators that integrate information in a noisy fashion within a trial. The racing diffusion does not assume that any evidence accumulation process varies between trial, and so, the model provides alternative explanations of key response time phenomena, such as fast and slow error response times relative to correct response times. Overall, our paper gives good reason to rethink including between-trial variability parameters in sequential sampling models. © 2020, The Psychonomic Society, Inc.
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