Functional connectome fingerprinting identifies individuals based on brain connectivity patterns. This study demonstrates that individual variability in brain connectivity is both robust and reliable, using data from the Human Connectome Project (HCP) to show that functional connectivity profiles can uniquely identify individuals across different scan sessions and conditions. The frontoparietal network was found to be the most distinctive, and connectivity profiles were shown to predict fluid intelligence, indicating the potential to infer individual characteristics from functional connectivity. The study used 126 subjects, each scanned in six sessions, and found that connectivity profiles could identify individuals with high accuracy. Network-based identification using the frontoparietal network outperformed whole-brain and other networks. The study also explored factors affecting identification accuracy, including edge contributions, timecourse length, and parcellation schemes. Results showed that connectivity profiles are more reliable than motion or anatomical differences. The study further demonstrated that connectivity profiles can predict cognitive traits like fluid intelligence, highlighting the potential for using functional connectivity to understand individual differences in behavior. The findings suggest that individual variability in brain connectivity is significant and can be used to infer individual characteristics, providing a foundation for future research on individual differences in brain function and behavior.Functional connectome fingerprinting identifies individuals based on brain connectivity patterns. This study demonstrates that individual variability in brain connectivity is both robust and reliable, using data from the Human Connectome Project (HCP) to show that functional connectivity profiles can uniquely identify individuals across different scan sessions and conditions. The frontoparietal network was found to be the most distinctive, and connectivity profiles were shown to predict fluid intelligence, indicating the potential to infer individual characteristics from functional connectivity. The study used 126 subjects, each scanned in six sessions, and found that connectivity profiles could identify individuals with high accuracy. Network-based identification using the frontoparietal network outperformed whole-brain and other networks. The study also explored factors affecting identification accuracy, including edge contributions, timecourse length, and parcellation schemes. Results showed that connectivity profiles are more reliable than motion or anatomical differences. The study further demonstrated that connectivity profiles can predict cognitive traits like fluid intelligence, highlighting the potential for using functional connectivity to understand individual differences in behavior. The findings suggest that individual variability in brain connectivity is significant and can be used to infer individual characteristics, providing a foundation for future research on individual differences in brain function and behavior.