Siamak K. Sorooshyari

This study uses machine learning to distinguish dopamine modulation features between neuroanatomical regions and stimulation strengths in adult animals, finding unique predictive features not detectable by conventional analysis.

This study identifies transcriptomic differences in the amygdala and hippocampus of stress-susceptible and resilient rats, revealing gene expression patterns associated with vulnerability and resilience to anxiety-like behavior.

The paper presents a matrix-based method to infer arousal state by integrating neural, metabolic, and immune system data.