Modulating human brain responses via optimal natural image selection and synthetic image generation

Understanding how human brains interpret and process information is important. Here, we investigated the selectivity and inter-individual differences in human brain responses to images via functional MRI. In our first experiment, we found that images predicted to achieve maximal activations using a group level encoding model evoke higher responses than images predicted to achieve average activations, and the activation gain is positively associated with the encoding model accuracy. Furthermore, aTLfaces and FBA1 had higher activation in response to maximal synthetic images compared to maximal natural images. In our second experiment, we found that synthetic images derived using a personalized encoding model elicited higher responses compared to synthetic images from group-level or other subjects' encoding models. The finding of aTLfaces favoring synthetic images than natural images was also replicated. Our results indicate the possibility of using data-driven and generative approaches to modulate macro-scale brain region responses and probe inter-individual differences in and functional specialization of the human visual system

No. 01 November, 2020

1. Approximately one-half of the students enrolled in a rural dropout-recovery school had experienced child maltreatment, which is generally higher than comparable national estimates. 2. Nearly 90% of these students had experienced household challenges (e.g., parental separation, incarceration, mental illness, and family violence), a percentage that is several times higher than national estimates. 3. Over 70% of students were exposed to three or more Adverse Childhood Events (ACEs), a percentage that far exceeds national estimates. 4. The COVID-19 pandemic and measures to contain it present serious mental and behavioral health challenges for at-risk youths such as those in the current sample