The clinical practice of selective serotonin reuptake inhibitor (SSRI) augmentation relies heavily on clinical judgment and trial-and-error. Unfortunately, the drug combinations prescribed today fail to provide relief for all treatment-resistant depressed patients. In order to identify potentially more effective treatments, we developed a computational model of the monoaminergic neurotransmitter and stress-steroid systems that neuroadapts to chronic administration of combinations of antidepressant drugs and hormones by adjusting the strengths of its transmittersystem components (TSCs). We used the model to screen 60 chronically administered drug/hormone pairs and triples, and identified as potentially therapeutic those combinations that raised the monoamines (serotonin, norepinephrine, and dopamine) but lowered cortisol following neuroadaptation in the model. We also evaluated the contributions of individual and pairs of TSCs to therapeutic neuroadaptation with chronic SSRI using sensitivity, correlation, and linear temporal-logic analyses. All three approaches found that therapeutic neuroadaptation to chronic SSRI is an overdetermined process that depends on multiple TSCs, providing a potential explanation for the clinical finding that no single antidepressant regimen alleviates depressive symptoms in all patients. (Dinan, 1996;Ma and Morilak, 2005;Ziegler et al., 1999), while cortisol can alter expression of proteins involved in monoaminergic synaptic transmission, including serotonergic receptors, monoamine synthesis enzymes, and monoamine oxidase (MAO) (Hucklebridge et al., 1998;McAllister-Williams et al., 2007;Nexon et al., 2011).Here we represent the major interactions between the monoaminergic neurotransmitter systems and the HPA axis in a computational model that we refer to as the Monoamine-Stress model (MS-model). It extends our previously published computational model of the monoaminergic neurotransmitter system but differs in its structure, training procedure, and analysis (Camacho and Anastasio, 2017). Specifically, the MS-model takes the form of a recurrent network in order to use a more efficient learning procedure to train its more extensive representations of neurobiological interactions, and to conform to a larger set of experimental observations. Acute administration of substances (such as drugs or hormones) has been observed to alter neuronal activity levels, and chronic (days to weeks) substance exposure can lead to adaptive changes in neurons that move their activity levels back toward their original levels (Blier and De Montigny, 1987;Turrigiano, 2008Turrigiano, , 1999. We simulated neuroadaptive changes by allowing a subset of transmitter system components (TSCs, mainly proteins such as neurotransmitter or neurohormone receptors or transporters) to adjust their strengths (corresponding to factors such as expression levels, sensitivities, and synaptic locations) incrementally up or down. TSC-strength configurations that restored the activities of DR, LC, VTA, and PVN back toward normative b...