Item Details

title

QUANTIFYING FEELINGS IN PATIENTS WITH TREATMENT-RESISTANT DEPRESSION BEFORE AND AFTER ELECTROCONVULSIVE THERAPY

author

Jones, Rachel

abstract

How patients report feeling directly informs diagnoses and treatment evaluations for depression. Standard clinical practice involves trial-and-error methods to match patients with optimal treatments; yet this approach results in prolonged suffering for the one-third of patients who do not respond to multiple antidepressants. Electroconvulsive therapy (ECT) is an effective medical procedure for these patients with treatment-resistant depression, though little is known about what brain or behavioral measures characterize this depressive phenotype or how ECT alters brain function in responsive patients. Computational methods may help bridge these gaps by linking neurobiological activity with measures of clinically relevant behaviors, pinpointing mechanisms that differentiate treatment-resistant depression and those that change from successful ECT treatment. In the following chapters, we deployed computational models of learning and affective processing to characterize patients before and after ECT treatment. In Chapter 1, we measured how positive and negative outcomes differentially influenced subjective feelings in patients with treatment-resistant depression. In Chapter 2, we investigated how ECT treatment altered the same affective mechanisms in responsive patients. In this study, ECT, non-ECT depression, and control cohorts completed two research visits according to the ECT treatment timeline. All participants completed a probabilistic reward and punishment learning task while receiving functional magnetic resonance imaging scanning, and we administered clinical assessments afterward. Overall, we found that the influence of distinct reward and punishment learning computations on feelings differentiated and predicted ECT patients from the other cohorts and that patients’ subjective feelings changed to become like those of controls following ECT treatment. We demonstrated that increased emotional reactivity to adverse rewards and punishments after treatment was predictive of patients who reported positive ECT response, and we pinpointed brain correlates of these mechanisms. We further showed that ECT treatment and antidepressants may affect different neurocomputational mechanisms to engender depression improvement. Our results suggest that measures of behavioral and emotional dynamics can discern between individuals who need ECT from those who do not, as well as ECT treatment response. In turn, these objective measures of feeling states may be clinically leveraged to optimize both diagnostic assessments and treatment decisions for individuals with depression.

subject

computational psychiatry

depression

ECT

fMRI

subjective feelings

Treatment-resistant Depression

contributor

Kishida, Kenneth T. (advisor)

Mayberg, Helen S. (committee member)

Chiu, Pearl H. (committee member)

Gligorovic, Predrag V. (committee member)

date

2024-02-13T09:36:09Z (accessioned)

2023 (issued)

degree

Neuroscience (discipline)

embargo

2025-02-12 (terms)

2025-02-12 (liftdate)

identifier

http://hdl.handle.net/10339/102916 (uri)

language

en (iso)

publisher

Wake Forest University

type

Dissertation