Lifetime history of major depressive disorder is associated with decreased reward learning: Evidence from a novel online version of the probabilistic reward task.

BACKGROUND: The Probabilistic Reward Task (PRT) is a signal detection task that assesses reward learning. In laboratory versions of the task, individuals with current or past major depressive disorder (MDD) were characterized by reduced response bias towards a more frequently rewarded stimuli, compared to controls. Our main goal was to develop and validate a novel online version of the PRT, and, in exploratory analyses, evaluate whether lifetime history of depression was associated with blunted reward learning. METHODS: 429 participants recruited via CloudResearch completed questionnaires assessing psychiatric history and an online PRT featuring visually appealing stimuli. 108 participants reported either current or past diagnosis of MDD (lifetime MDD group), and were compared to 321 without lifetime MDD. RESULTS: Participants showed overall increase in response bias, validating the online PRT. Females with lifetime MDD (N = 43), compared to females without prior history of MDD (N = 173), exhibited blunted response bias towards the more frequently rewarded stimulus (i.e., reduced reward learning). LIMITATIONS: Participants did not undergo a structured clinical interview, thus we cannot confirm whether they met full diagnostic criteria for depression. CONCLUSIONS: The online PRT yielded similar psychometric properties as laboratory versions of the task. In exploratory analyses, females with lifetime MDD showed a lower propensity to modulate behavior as a function of rewards, which might contribute to heightened vulnerability for developing MDD in females. Future studies should consider social, cultural, and neurobiological factors contributing to sex differences in reward responsiveness and how factors may relate to disease prognosis and treatment outcomes.

Testing biomimetic structures in bioinspired robots: how vertebrae control the stiffness of the body and the behavior of fish-like swimmers.

Our goal is to describe a specific case of a general process gaining traction amongst biologists: testing biological hypotheses with biomimetic structures that operate in bioinspired robots. As an example, we present MARMT (mobile autonomous robot for mechanical testing), a surface-swimmer that undulates a submerged biomimetic tail to power cruising and accelerations. Our goal was to test the hypothesis that stiffness of the body controls swimming behavior and that both stiffness and behavior can be altered by changes in the morphology of the vertebral column. To test this hypothesis, we built biomimetic vertebral columns (BVC) outfitted with variable numbers of rigid ring centra; as the number of centra increased the axial length of the intervertebral joints decreased. Each kind of BVC was tested in dynamic bending to measure the structure's apparent stiffness as the storage and loss moduli. In addition, each kind of BVC was used as the axial skeleton in a tail that propelled MARMT. We varied MARMT's tail-beat frequency, lateral amplitude of the tail, and swimming behavior. MARMT's locomotor performance was measured using an on-board accelerometer and external video. As the number of vertebrae in the BVC of fixed length increased, so, too, did the BVC's storage modulus, the BVC's loss modulus, MARMT's mean speed during cruising, and MARMT's peak acceleration during a startle response. These results support the hypothesis that stiffness of the body controls swimming behavior and that both stiffness and behavior can be altered by changes in the morphology of the vertebral column.