Table 1.

Outstanding Questions.

1. What is the neurobiological correlate of a functional connection? Does this differ by brain region or brain state? Does this differ as a function of development?

2. If regions may be too coarse and connections too noisy, what is the optimal feature for predictive model building, and how does that feature map onto the brain? How might deep learning approaches best integrate across measures at multiple levels of analysis, and what will we learn about the brain from resulting models?

3. How different are individualized parcellations from each other, and how do these differences relate to various individual differences in behavior, cognition, and clinical symptoms? How do these relationships change over the course of development? How do individualized parcellations change connection-based measures of FC, relative to connection-based measures derived from group-level parcellations? And how do these differences in regions and connections relate to differences at the circuit, synaptic, neuronal, and molecular levels?

4. What are the effects of different preprocessing pipelines on individual differences in FC data? Are regions or connections more robust to different processing approaches?

5. Given that most individualized FC approaches incorporate group-level information, what is the best way to apply these methods to idiosyncratic subjects? Similarly, given that most individualized parcellations have been developed in relatively healthy young adults, how do these methods perform in other populations?

6. The likelihood that all individuals have precisely the same number of regions (i.e., nodes) is low. When parcellating the brain for FC analyses, how do we acknowledge heterogeneity within a sample, while also permitting between-subject comparisons (i.e., correspondence)?

7. Is there a limit to the utility of individualized FC approaches? In what situations is it better to rely on group-level methods?