Rebecca R. Saxe

This study finds scene-selective cortical regions in human infants as young as 2-5 months old, before independent navigation experience, suggesting early development driven by visual exposure.

The paper uses fMRI to show that language network lateralization is adult-like by age 4, despite continued development of other language network features.

The paper identifies convergent and selective functional zones in the insula using fMRI data, revealing a hierarchical convergence architecture across pain, appetitive, aversive, and cognitive control domains.

This study uses fMRI to show that toddlers as young as 19-36 months have left frontal and temporal cortex responses to comprehensible language, similar to adults, suggesting early language system development.

This study analyzes 766 awake infant fMRI sessions to identify factors affecting data collection and retention, offering insights to improve future research.

This study develops a computational model to predict visual habituation and dishabituation in infants and adults using looking time data.

The paper investigates when face-selective cortical responses emerge in infants, finding early selectivity in FFA, STS, and MPFC but not OFA in young infants.

This study investigates how the occipital place area (OPA) represents navigational affordances and ego-motion using dynamic movie stimuli, finding that OPA responds to both cues and their conflicts, unlike PPA and MPA.

The paper investigates neural responses to dialogue and monologue in left-hemisphere language regions, finding no difference in response to dialogue versus monologue.

This study uses child-friendly movies to identify age-related changes in brain regions selective for faces, scenes, and objects from age 3 to 12 years.

The paper shows that acute social isolation leads to midbrain activation similar to hunger, correlating with social craving.

This paper evaluates fMRI denoising methods using real data by comparing standard and intersubject multivariate pattern dependence metrics to assess their effectiveness.

This paper introduces and applies multivariate pattern connectivity (MVPC) to study brain region interactions by analyzing fine-grained response patterns, revealing new connections not detected by univariate methods.

This study introduces a framework using neural networks to model nonlinear interactions between brain regions in fMRI data, showing improved accuracy and revealing task-dependent functional subdivisions.