Abstract
Mindfulness and compassion meditation have a positive impact on
cognition, mood, behavior, and general health, based on recent studies in neuroscience.
However, the research methodology is still insufficient to determine and measure
different mental states during meditation, especially in minority populations.
Intersectional Neuroscience, which is an innovative research model, may provide
some solutions since it adapts modern research procedures to include disadvantageous
groups of participants (e.g., ethnic minorities, patients with chronic diseases, like
cancer, heart disease, or depression). Evaluating Multivariate Maps of BODY
Awareness (EMBODY) is a task designed to accommodate diverse neural structures
and functions, using the multi-voxel pattern analysis (MVPA) classifiers, with
functional magnetic resonance imaging (fMRI). The EMBODY task applies
individualized artificial intelligence algorithms to the fMRI data, in order to identify
mental states during breath-focused meditation, a basic skill that stabilizes
attention.
This chapter describes a potential application of the Intersectional Neuroscience (IN)
approach to developing useful metrics of meditation practice, including participants
from disadvantageous groups. Hopefully, these findings can be explored in-depth, and
possibly applied to patients with triple-negative breast cancer (TNBC), in the future.