Many studies have analyzed what organizational features distinguish the left and right hemispheres of the human brain, with most differences typically being found in language areas. In this analysis, we test whether supervised learning can categorize an unseen hemisphere as right or left based on functional connectivity. Using data from the Human Connectome Project, we find success to be extremely high (accuracies > .90) in right-handed participants (Edinburgh Handedness Inventory [EHI] > 0). Accuracies are still high, but slightly lower when trained on left-handed participants (EHI <= 0). In a third analysis, we test whether the same can be done to identify handedness along with hemisphere chirality. This does not succeed, however, we show that individuals’ hemispheres are less distinct the more left-handed they are. Our findings can inform developmental and post-injury work on hemispheric organization.
Generative AI Mental Health Chatbots as Therapeutic Tools: Systematic Review and Meta-Analysis of Their Role in Reducing Mental Health Issues
Background: To date, there is no comprehensive paper that systematically synthesizes the effect of generative AI chatbot’s impact on mental health. Can generative AI chatbots