Table 1.

The following checklist is provided as a means to summarize the guidelines in this article to help the reader cross-check whether s/he can further improve the manuscript before submission. Each question refers to a numbered section in the main text that can be consulted again for more detail.

Topic	Checklist
2.1.1 Choosing a good title	Is the title short, specific, and informative about the results?
2.1.2 Structured abstract: Clarity and consistency	Is the most relevant information described in a motivating way? Can you reduce the abstract further to improve clarity? Is the abstract structured similarly to the structure of the main body of the paper? Is the data in the abstract and main manuscript consistent and complete?
2.2.1 Scope, context, significance, and aim of the work.	Is the scope, context, and significance of the work established? Has the previous work been described and cited properly? Are the aim and hypothesis clearly defined?
3.1.1 Human participants	Are all relevant demographic, clinical, and other relevant characteristics described? Are all participant and data inclusion/exclusion criteria clearly defined? Are all ethical issues and procedures discussed? Is approval from the local ethics committee clearly addressed? Are excluded participants disclosed and well justified?
3.1.2 Sample size and statistical power analysis	In cases where no effect is observed: Was a power analysis performed? Was the selection of sample size, power, alpha levels, and effect size reported and justified? A posthoc power analysis may state the sample size needed to achieve statistical significance in case the study was underpowered.
3.2.1 Experimental design (or “study design”)	Is the following information provided for the study design? All studies: The duration of recording; the environment in which the participant is placed (e.g., lighting conditions, auditory conditions, objects or displays in their visual field, etc.). Specific to block- and event-related designs: The number of conditions; the number of blocks or trials per condition; the order in which the blocks or trials are presented; the duration of each block or trial; and the duration of interblock or intertrial intervals. A diagram that provides details of the timings of stimulus and images of the stimuli themselves.
3.2.2 Participant instructions, training, and interactions	Were incentives, instructions, and feedback to the participants clearly outlined? What experimental conditions could have influenced the participant’s performance?
3.3.1 fNIRS device and acquisition parameters description	Is the acquisition set up and instrumentation sufficiently described? (system, wavelengths, sample rate, number of channels, and other parameters)
3.3.2 Optode array design, cap, and targeted brain regions	Is the description of optode array design, cap, and targeted brain regions complete?
3.3.3 For publications on instrumentation/hardware development	Are all crucial hardware and software performance characteristics and validation steps reported? Are the architecture and all crucial components (light source, detector, and multiplexing strategies) sufficiently described? What standards/norms were followed and what safety regulations were considered (i.e., maximum permissible skin exposure)? For instrumentation or methods development papers: Is phantom-based performance characterization reported? For application studies: Are regular system quality checks reported?
3.4.1 fNIRS signal quality metrics and channel rejection	How was signal quality of fNIRS channels checked and were bad channels rejected?
3.4.2 Motion artifacts	How were motion artifacts identified and removed?
3.4.3 Modified Beer–Lambert law, parameters and corrections	What were the assumptions, parameters, and models selected to derive concentrations from the raw fNIRS signals using the mBLL? How were estimation errors corrected/what are the signals’ units?
3.4.4 Impact of confounding systemic signals on fNIRS	How did your study distinguish between the variety of physiological processes that comprise fNIRS signal changes? Have you considered all factors of possible physiological confounds?
3.4.5 Strategy for statistical tests and removal of confounding signals	Have the overall preprocessing and statistical testing strategies clearly been identified and outlined?
3.4.6 Filtering and drift regression	How were confounding signals outside of the main fNIRS band of interest tackled? (High/low-pass filtering/GLM drift regression)
3.5.1 Strategies for enhancing the reliability of brain activity measurements	What strategies were pursued to correct for physiological confounds and changes in the extracerebral tissue compartment? How were confounding signals identified and separated and what was done to reduce the likelihood of false positives/negatives?
3.5.2 Strategy 1: Enhance depth sensitivity through instrumentation and signal processing	How was depth sensitivity achieved? If multidistance measurements were performed, what are the source–detector separations used? What signal processing methods were applied to remove confounding physiological components in the fNIRS signals? How are the limitations discussed?
3.5.3 Strategy 2: Signal processing without intrinsic depth-sensitive measurements	If no depth-sensitivity/multidistance measurements are available: What signal processing methods were applied to minimize confounding physiological components? How are the limitations discussed?
3.5.4 Strategy 3: Incorporating measurements of changes in systemic physiology in the fNIRS signal processing	If other physiological signals were used for the removal of confounding signals in the fNIRS signals, which ones? Are all relevant parameters and steps sufficiently described?
3.6.1 Hemodynamic response function estimation: Block averaging versus general linear model	What is the effective number of trials used for HRF estimation? In GLM approaches: What confounding signal regressors were used and how were they modeled? What method was used to estimate regressor weights?
3.6.2 HRF estimation: Selection of the HRF regressor in GLM approaches	In GLM approaches: How was the HRF modeled? What shape/function was used for the HRF regression? What are the parameters? If a fixed shape was used, what is the justification?
3.6.3 Statistical analysis: General remarks	What statistical tests were performed and are all corresponding parameters (e.g., assumed distribution, degrees of freedom, $p$-values, etc.) reported? Is the effect size stated?
3.6.4 Statistical analysis of GLM results	What regressors were included in GLM to explain effects of interest and confounds for fNIRS data? What statistical model and methods have been used for testing the hypothesis at the first and second levels?
3.6.5 Statistical analysis: Multiple comparisons problem	If statistical analysis was performed on multiple regions/voxels/network components, were family-wise errors corrected? What correction method was applied?
3.6.6 Specific guidelines for data processing in clinical populations	Are clinical variability and expected alterations of behavioral, neuronal, and vascular responses considered when interpreting the results?
3.6.7 Specific guidelines for data processing in neurodevelopmental studies	How were the increased noise, artifacts, and analysis handled specifically for the developmental populations? Is the artifact rejection procedure well documented in the manuscript?
3.6.8 Connectivity analysis	What correlation indices have been used? How were the statistical thresholds determined?
3.6.9 Image reconstruction	What head anatomy was used and how was coregistration between optical elements and head geometry performed? How was the head anatomy segmented and into what tissue types? How was the head mesh generated? What optical properties were used for each tissue type? What model/approach was used for the generation of sensitivity profiles and image reconstruction?
3.6.10 Single trial analysis and machine learning	What efforts were undertaken to understand and interpret the classifier weights and outputs? What was the training and test size, how were (hyper-) parameters selected? Was training and test data strictly separated, especially in approaches that use learned filters, regressors, or the GLM? Was cross validation performed and if yes, what kind?
3.6.11 Multimodal fNIRS integration	Was the sensor coplacement/localization/registration sufficiently described? What were the methods used for data fusion and multimodal analysis?
4.1 Figures and visualization	Was the measurement set up, optode array configuration and placement, and experimental protocol visualized? Is a sensitivity analysis included? If the processing pipeline is complex, is it depicted in a simplified block diagram? Are both brain maps and time courses available and provided? Are results linked to anatomical locations? Are both ${\mathrm{HbO}}_2$ and Hb reported? Are higher order statistics of the data visualized as well?
4.2 Concise text and rigor	Are the results presented in a concise and well-organized manner? What efforts were undertaken to minimize confirmation bias? Are negative results reported, if present?
5.1 Discussion of the results in light of existing studies: Strengths, limitations, and future work	Are all relevant results discussed? Is any part of the discussion based on results that were not presented? Were caveats from confounding physiology sufficiently addressed? Is the presented work sufficiently compared and contextualized with existing studies? Are strengths and weaknesses clearly outlined and discussed? Are potential next steps discussed?
5.2 Conclusion	Are 3 to 5 conclusions drawn that summarize the main findings of the study in a concise way? Do they include the significance of the result? Are the conclusions based on the results of the study?
6.1 Proper citations	Are all the statements that reference to an original work agree with the information provided therein?
7.1 Preregistration, data, and code sharing	Is data/code made available to other researchers to reproduce the results? Is data shared in a common data format that the community supports (e.g., snirf)?