Review for "Predicting functional networks from region connectivity profiles in task-based versus resting-state fMRI data"

Completed on 20 Feb 2018 by Krzysztof Jacek Gorgolewski .

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Functional connectivity as measured by resting state fMRI could one day be an important clinical biomarker. This paper attempts to push forward our understanding of intrinsic brain connectivity during rest and while performing a cognitive task.

Comments to author

"Predicting functional networks from region connectivity profiles in task-based versus resting-state fMRI data" by Rosaro et al. has the potential to contribute to our understanding of resting state connectivity meaningfully, but is held back by a confusing and unclear presentation.

After reading the abstract, introduction and the methods section, I was not clear what the authors attempted to predict from connectivity measures. My best guess is that the task was to predict which brain network a brain region belongs to given a vector of its connectivity measures with all other brain regions. A task formulated this way is, however, straightforward if we assume correspondence of connectivity measures across all the input samples. This assumption means that the first value of the vector always corresponds to connectivity with region A, second with region B, etc. for all input samples. The consequence of such encoding is that the connectivity vector for region A will have a correlation value of 1 at first value of the connectivity vector. In other words, the identity of brain regions is represented as noisy one-hot-encoding. All the network or the classifier has to do is to figure out which brain regions correspond to which networks - something that could be done without any knowledge of brain connectivity.

This is just speculation since I was not able to grasp the details of the analysis to confirm what was being predicted and how connectivity was encoded.

To improve clarity in the future revision of the manuscript, I recommend adding a conceptual figure presenting the prediction task in terms of dependent and independent variables (features and labels).

More specific comments:

- The abstract is confusing. "In this study we use a large cohort of publicly available data to test to which extent one can associate a brain region to one of these Intrinsic Connectivity Networks looking only at its connectivity pattern, and examine at how the correspondence between resting and task-based patterns can be mapped in this context." This sentence too long and convoluted.

- Page 3: "we will explore..." -> "We will explore..."

- Page 4: missing citation for the HCP project

- Page 4: "has been proved to increase the quality of the original data" citation needed.

- Page 4: "connectivity map" might be a better term than "correlation image"

- Page 4: How was the assignment of each brain region to a brain network performed? Shen and Yeo's parcellations differ in region definitions.

- Page 5: "Finally, the 282 resulting individual FC matrices were concatenated together" it's unclear if this was done separately for task and rest or the data was combined first. What dimension were the matrices concatenated along?

- Page 5: was the cross-validation performed across participants or nodes? Or both? If so why?

- Page 6: Table 1 is missing the MLP results

- Prediction accuracy on another dataset (with different acquisition parameters) would be good evidence of the robustness of your findings.