This research explores the effects of drowsiness on variability in functional connectivity (FC) during resting-state functional magnetic resonance imaging. The study utilized a cohort of students (MRi-Share) and classified individuals into drowsy (N=68), alert (N=96), and mixed/undetermined states based on observed respiratory oscillations. Five different processing methods were employed, the reference method, two correction methods based on physiological and global regression approaches, and two based on Gaussian standardizations. According to the reference methodology, the results indicate that drowsy individuals exhibit higher cortico-cortical FC than alert individuals. However, the differences between drowsy and alert states were reduced when applying correction methods based on physiological and global regression approaches. The global regression-based strategy was the most effective among these correction methods, minimizing significant FC differences to only 3.3% of the total FCs. Utilizing the Gaussian-based methods, both cortico-subcortical and intra-default mode network regions demonstrated significantly greater FCs in awake than drowsy subjects. These findings align with previous studies suggesting that, in the descent to sleep, the cortex isolates itself to facilitate the transition into deeper sleep stages while also disconnecting the default mode network. The Gaussian standardization methods and the global regression-based correction approach efficiently address the hemodynamic variations caused by the rapid alternation between the N1 stage and wakefulness. These variations contribute to the measurement of cortico-cortical pseudo connectivity observed in the reference methodology. In summary, these findings underscore the importance of considering drowsiness in rs-fMRI studies and demonstrate that there is no single optimal correction methodology for processing fMRI data