  With the rise of Large Language Models (LLMs), the novel metric "Brainscore"
emerged as a means to evaluate the functional similarity between LLMs and human
brain/neural systems. Our efforts were dedicated to mining the meaning of the
novel score by constructing topological features derived from both human fMRI
data involving 190 subjects, and 39 LLMs plus their untrained counterparts.
Subsequently, we trained 36 Linear Regression Models and conducted thorough
statistical analyses to discern reliable and valid features from our
constructed ones. Our findings reveal distinctive feature combinations
conducive to interpreting existing brainscores across various brain regions of
interest (ROIs) and hemispheres, thereby significantly contributing to
advancing interpretable machine learning (iML) studies. The study is enriched
by our further discussions and analyses concerning existing brainscores. To our
knowledge, this study represents the first attempt to comprehend the novel
metric brainscore within this interdisciplinary domain.
