Investigating the Impact of Local Manipulations on Spontaneous and Evoked Brain Complexity Indices: A Large-Scale Computational Model

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The study employs a whole-brain in-silico model, which means a computer simulation model of the brain, to investigate the large-scale effects of local node alterations. It contributes to the understanding of how local alterations in specific brain regions can have far-reaching consequences on the overall complexity of brain networks, and it highlights the importance of investigating these mechanisms for potential interventions or treatments.

 

While the summary highlights the intriguing aspects of the study, it doesn't explicitly mention any drawbacks or limitations. Here are some potential drawbacks or limitations that might be associated with the described study:

 

1.     Simplifications in the In-Silico Model: In-silico models necessarily simplify the complexity of real biological systems. The accuracy and reliability of the study's findings depend on the accuracy of the model and the assumptions made during its creation. If the model does not fully capture the intricacies of real brain networks, the results may have limited generalizability.

 

2.     Assumptions and Parameters: The study likely involves making certain assumptions and choosing specific parameters for the in-silico model. The accuracy of the results depends on the validity of these assumptions and the appropriateness of the chosen parameters. Deviations from real-world conditions may affect the relevance of the findings.

 

3.     Complexity Reduction: The study focuses on network complexity metrics, but complexity reduction itself might be a simplification of the real-world scenario. Real brain alterations are likely to involve multiple factors, and reducing them to a single metric might overlook some aspects of the complexity dynamics.

 

4.     Interpretation of Results: The interpretation of results, especially in complex systems like the brain, can be challenging. While the study identifies a drop in perturbational complexity following local node silencing, the functional implications and real-world significance of this drop need to be carefully considered.

 

5.     Applicability to Clinical Conditions: If the study aims to contribute to our understanding of brain alterations in clinical conditions, it's essential to consider how well the findings from the in-silico model can be extrapolated to real-world clinical scenarios.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The authors integrate a perturbational approach with local node manipulations in a whole-brain computational model. The proposed method has promising results, but it should be improved in the description and clarification of some statements.

Minor comments:

Abstract (Ln 23): Avoid the term ‘best captured’, give numerical values. In scientific papers, we should use numerical values.

Ln 66: References should be listed by order.

Ln 120-127: All equations should be details described. For example, it is not clear what notes C, a_ee, m_k, etc.

Ln 160, 190: Figure 1 doesn’t have notation A,B, C and D…In the manuscript,  Fig. 1D, 1B are mentioned.  

Ln 303-307:  In Eq. 14 and Eq. 15 subscripts should be used (example log2, p1)

 

Ln 685: In scientific papers avoid beginning sentences by Overall.

Author Response

Please see the attachment

Author Response File: Author Response.pdf