Exploring Maternal Reflective Functioning and Cognitive Shifts During the Transition to Motherhood

Barel Rafaeli, Lee; Cywiak, Vanessa; Aharak, Daniel; Okon-Singer, Hadas; Enav, Yael Reflective Functioning—the ability to attribute meaning, beliefs, and intentions to one’s own and others’ behaviors—is crucial in parenthood. The transition to motherhood brings significant psychological changes, increasing vulnerability to postpartum blues, anxiety, and depression. In Israel, this transition is further complicated by ongoing war, political violence, and recent terror events, which can heighten stress and negatively impact cognitive processes, including Reflective Functioning and maladaptive Cognitive Biases.

This research evaluates a group intervention designed to enhance Reflective Functioning during pregnancy and postpartum, assessing its impact on cognitive processes. Specifically, it aims to: (1) determine whether the intervention leads to cognitive processing changes in a general population of mentally healthy women, as measured by a cognitive battery, and (2) examine whether women who develop PPD—either in the control or intervention group—exhibit cognitive shifts due to the intervention. Additionally, the study explores whether modifying maladaptive cognitive biases through intervention can serve as a preventive measure for PPD.

Using machine-learning for prediction of anxiety and depression

Anxiety and depression are distinct – albeit overlapping – psychiatric diseases, currently diagnosed by self-reported-symptoms. This research presents a new diagnostic tool, which rigorously tests for differences in cognitive biases among anxious and depressed individuals. A comprehensive behavioral test battery quantifies various cognitive biases. Advanced machine-learning tools, developed for this study, analyzed these results. These tools detect unique patterns that characterize anxiety versus depression to predict group membership. The analysis also discloses which specific behavioral measures contributed to the prediction, pointing to key cognitive mechanisms in anxiety versus depression. These results lay the ground for improved diagnostic instruments and more effective and focused individually-based treatment.

Relevant literature:

Richter, T., Fishbain, B., Richter-Levin, G., & Okon-Singer, H. (2021). Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions. Journal of Personalized Medicine, 11(10), 957.

Richter, T., Fishbain, B., Markus, A., Richter-Levin, G., & Okon-Singer, H. (2020). Using machine learning-based analysis for behavioral differentiation between anxiety and depression. Scientific reports, 10(1), 1-12.

Richter, T., Fishbain, B., Fruchter, E., Richter-Levin, G., & Okon-Singer, H. (2021). Machine learning-based diagnosis support system for differentiating between clinical anxiety and depression disorders. Journal of Psychiatric Research, 141, 199-205.

Cognitive Training Using Machine Learning

Cognitive training comprises a class of relatively new therapeutic interventions that target mechanisms implicated across different mental health conditions. Cognitive training programs are designed to enhance emotional functioning, either directly by cultivating different strategies for emotional processing and/or responses or indirectly by bolstering cognitive control processes in a non-emotional context, which in turn should improve emotional functioning.

Although many recent studies indicate that cognitive training shows merit, others fail to demonstrate its efficacy. These inconsistent findings may at least partly result from differences in individuals’ ability to benefit from cognitive training in general, and from specific training types in particular. Consistent with the move toward personalized medicine, we propose using machine learning approaches to help optimize cognitive training gains. More specifically, machine learning algorithms are incorporated to identify which individuals will be most responsive to cognitive training in general and to discern which methods may be a better fit for certain individuals.

Relevant Literature:

Shani, R., Tal, S., Zilcha-Mano, S., & Okon-Singer, H. (2019). Can machine learning approaches lead toward personalized cognitive training?. Frontiers in behavioral neuroscience, 64.

Shani, R., Tal, S., Derakshan, N., Cohen, N., Enock, P. M., McNally, R. J., ... & Okon-Singer, H. (2021). Personalized cognitive training: Protocol for individual-level meta-analysis implementing machine learning methods. Journal of psychiatric research, 138, 342-348.