Add QA/QC community practices document

.readthedocs.yml

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 version: 2
 
 build:
-  os: "ubuntu-20.04"
+  os: "ubuntu-lts-latest"
   tools:
     python: "3.10"
 

docs/source/index.rst

@@ -60,6 +60,7 @@ Community Practices for Collecting Physiological Data
       Setup <phys_set_up>
       Data Collection <collect_phys>
       Data Processing <process_phys>
+      Data Quality <qa_qc>
       References <references>
 
       Licence <https://github.com/physiopy/physiopy-community-guidelines?tab=CC-BY-SA-4.0-1-ov-file#readme>

docs/source/qa_qc.md

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+# 6. QA/QCing physiological data
+
+In the Data Collection section of this documentation, we have mentioned different procedures to include during data acquisition to assess and optimize the quality of the recorded signals at scan time. In this section, we discuss post-acquisition methods to evaluate the quality of physiological data, how to mitigate certain artefacts, and the overall relevance of physiological data quality for application in neuroimaging contexts. 
+
+```{important}
+The current version of this section is under active development, and will continue to be expanded. In future, more guidelines will be provided for specific physiological modalities and a specific section on ‘Tools and Resources for QA/QC’ will be added; currently this document is focused on principles and concepts only. If you are interested in helping to develop material for this section, you can submit an issue on our [Github repository](https://github.com/physiopy/physiopy-community-practices/issues), join the Physiopy Slack by emailing physiopy.community@gmail.com, and/or subscribe to [the newsletter](https://physiopy-community.beehiiv.com/) to get details on the next QA/QC community meeting, alongside other Physiopy updates.
+```
+
+## What is QA/QC?
+
+Physiological quality assessment (QA) refers to the process of evaluating and identifying unreliable signals or  signal segments, whereas physiological quality control (QC) encompasses the methods to handle/mitigate poor-quality signals. These procedures are crucial for ensuring the validity of experimental results, for instance if physiological data is used to model sources of noise in neuroimaging modalities or to characterize physiological states and processes in relation to brain function. Despite the consensus of many physiological data users within Physiopy’s community on the importance of QA/QC in physiological data acquisition and application, QA/QC methodologies for physiological data are rarely reported in detail in neuroimaging scientific literature.
+
+## When is QA/QC important?
+
+Some amount of QA/QC is always recommended. The extent to which a researcher makes this a priority, and the thresholds they set to identify unreliable or poor-quality signals will vary with context. For example, in studies developing predictive models from large physiological datasets, a higher degree of signal noise may be tolerable, particularly if the primary aim is prediction and the model is expected to be robust to noisy inputs. On the other hand, QA/QC may be more important when poor signal quality, and associated artefacts, can introduce systematic bias or confounds. A common case is when the condition/state of interest is more prevalent in a population that also happens to exhibit more movement during data collection, and therefore an algorithm could learn to associate movement-related artifacts with the condition rather than the physiological features. In such cases, QA/QC can help reduce these confounds and lead to more accurate and interpretable findings.
+
+## Assessing signal quality in context
+
+Rather than solely relying on automated signal quality indices (SQIs), it is highly advisable to visually examine the timeseries (Power et al., 2020) to better differentiate physiological events from artifacts and determine the appropriate intervention (i.e., interpolation, rejection of segments, etc.). Different types of artefacts independent of the specific modality can be encountered while QAing physiological signals: clipping, motion, drop or loss of signal. Clipping artefacts can be described as truncated segments (horizontal plateaus), and are related to signal saturation. Motion can introduce high frequency noise in the signal or other waveform irregularities. Finally, drop or loss of signal is characterized by a transient drop in the signal-to-noise ratio or a complete loss of signal.
+
+Other physiological events that look like irregularities may be observed, but can be associated with different physiological states (e.g., increased vigilance) or pathological conditions (e.g., atrial fibrillation–a type of heart arrhythmia). In such cases, irregularities in the signal are reflecting important physiological information, and should not be discarded. However, it is important to note that the identification of irregular physiological events can be used to inform subsequent analyses. For example, ectopic heartbeats should be excluded when calculating heart rate variability, while breathing irregularities have been found to correspond to characteristic changes in fMRI data (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, 1996; Power et al., 2020). 
+
+Furthermore, the interpretation of physiological variability should also take into account the experimental paradigm and the expected physiological response to the task. For example, the timing of observed irregularities should be considered in relation to task events, such as button presses or other movements that may induce changes in the physiological recordings. Similarly, tasks or experimental manipulations may unintentionally or intentionally alter respiration (e.g., through voluntary breathing changes or the use of respiratory masks), which may in turn affect cardiac activity. Thus, variability that is temporally aligned with or physiologically plausible given the task should not necessarily be considered artifactual. Physiological signals can be entrained by task events, which should be considered when distinguishing artifacts from meaningful physiological responses.
+
+Rather than manually inspecting the entire timeseries, automated flagging can be used to identify segments potentially containing poor-quality signals. Many physiological metrics are calculated from peaks and/or troughs (e.g., heart/respiratory rate, respiratory amplitude). Algorithms to automatically detect those features are often impacted by the signal quality, and can also make errors (e.g., misplaced peak, missed peak, extra peak), which can influence the accuracy of the metrics of interest. Segments to validate can be identified on the basis of the rate/inter-peak intervals: very low rate/very high inter-peak intervals can be indicative of a missed peak, whereas very high rate/very low inter-peak intervals can reflect an extra peak. However, relying only on automatically flagged segments can overlook misplaced peaks, as these may not appear as outliers in rate or inter-peak interval metrics, but still impact your metrics of interest. Despite this limitation, this approach serves as an efficient heuristic for initial screening, particularly when working with large datasets.
+
+Concurrently recording multiple physiological modalities – such as cardiac and respiratory data – during fMRI acquisition enables simultaneous multi-channel evaluation. Given the physiological coupling between the corresponding systems, this cross-modal context can offer valuable insight into the underlying nature of physiological events, guiding QCing decisions (e. g., whether or not to reject some irregularities). For example, large-amplitude fluctuations in a respiratory waveform can be cross-referenced with cardiac inter-beat intervals (IBI), where co-occurring IBI variations would indicate a likely true respiratory event (e.g., respiratory sinus arrhythmia) rather than an isolated artifact. However, this relationship should be interpreted with caution in the MRI environment, as being inside the scanner may itself alter cardiac and respiratory activity, as well as their underlying physiological coupling. In particular, MRI-related anxiety has been associated with changes in the normally expected cardiorespiratory relationship, including the occurrence of negative respiratory sinus arrhythmia (nRSA), characterized by heart-rate deceleration during inspiration (Rassler et al., 2018). MRI-related anxiety has also been associated with the emergence of a prominent respiratory oscillation around 0.32 Hz (approximately 19 breaths/min), described as an “emotional breathing” oscillation (Pfurtscheller et al., 2024). These effects may also change over the course of the acquisition as participants habituate to the scanner environment and become more relaxed.
+
+## Recovering lost signal
+
+This section gives a brief overview of the different methods that exist to correct poor-quality physiological data segments. However, regardless of the chosen strategy, it is important to verify its effectiveness since it will influence the features or regressors that would be later computed. For example, if you are interested in heart rate variability, incorrect beat placement outweighs missing values (see [HeartPy’s simulation on the matter](https://python-heart-rate-analysis-toolkit.readthedocs.io/en/latest/heartrateanalysis.html#on-the-accuracy-of-peak-position); van Gent, Farah, Nes, & Arem, 2018a; van Gent, Farah, Nes, & van Arem, 2018b). In such a case, masking the segment might be a better option than interpolating a missed/incorrectly detected beat or manually correcting the peak if in doubt. Alternative metrics that are less affected by outliers could also be considered (e.g., instantaneous heart rate). Furthermore, it is important to consider that the influence of artefacts or deviations introduced during the recovery if the physiological signal may be attenuated in magnitude or temporally extended at later stages of the processing pipeline, particularly when the recovered signal used to compute metrics within a sliding window or metrics subsequently convolved with an impulse response function. 
+
+1. Manual peak/trough correction
+
+As mentioned previously, automatic feature detection algorithms are susceptible to errors. Although it is more time-consuming and does not guarantee complete accuracy, manual feature correction leverages the data curator’s expertise and contextual understanding of the research goals to resolve complex edge cases.
+
+2. Interpolation
+
+Interpolation estimates missing values based on the surrounding data points and the chosen algorithm (e.g., linear, quadratic, cubic, nearest-neighbor). Consequently, interpolation must be applied cautiously when missing peaks occur during periods of high signal variance (irregular rhythms that could be biologically plausible) or rapid fluctuation. This method is also less reliable when applied to long segments. However, defining a ‘long’ segment remains context-dependent, as it depends on the specific metric of interest and whether it targets slow or fast dynamics (i.e., longer segments are more tolerable for lower frequency signals).
+
+3. Imputation
+
+Data imputation replaces missing or unreliable values using estimated or plausible values. These techniques can be useful to reconstruct missing signals when working with quasiperiodic signals. More and more algorithms based on deep learning have been developed in the past years, showing promising results (Xu et al., 2022). Nevertheless, much like interpolation, caution is necessary as imputed data can be speculative, particularly when applied over long gaps.
+
+4. Filtering
+
+The use of filtering strategies depends on the metrics of interest you may want to compute on your signals and the artifact type. For example, when calculating the derivative of the respiratory trace, filtering can be applied a priori if cardiac contamination is present. However, filter selection requires caution, as improper parameters can introduce signal distortion.
+
+5. Masking
+
+The process of masking consists of replacing values in segments with zeros or NaN values. As discussed previously, masking might be preferred over interpolation or imputation depending on the metrics of interest. When working with physiological regressors, slightly padding or extending the masks helps account for temporal delays.
+
+6. Recovering signals from fMRI data
+
+If the physiological recordings are unusable across an entire run, deep learning can potentially reconstruct or infer these signals directly from the fMRI data. However, the feasibility of this approach depends on the specific signal of interest and the downstream application. For example, Wang and colleagues (2025) have proposed a framework for reconstructing low-frequency respiratory volume and heart rate time series from fMRI data. Other studies have also shown the feasibility of such an approach on different populations and using different machine/deep learning techniques (Addeh et al., 2023; Bayrak et al., 2020; Bayrak et al., 2021; Salas et al., 2020).
+
+## Reporting data quality
+
+Given that a QA/QC pipeline could vary depending on the metrics of interest to extract from the signal and their specific use, we encourage transparency in reporting the criteria used to assess the quality of physiological signals. We also encourage researchers to share raw timeseries and not only derivatives when possible, and to provide clearly documented code for data preprocessing (including QA/QCing) and data analysis. Since different quality control pipelines can be applied on the same dataset, researchers should provide clear documentation regarding which data were used, and which were rejected. If this is systematically reported, it would be easier to evaluate the impact of quality control variability on different analyses, which could help provide more standardized QA/QC guidelines for physiological data.
+
+## Concluding remarks
+
+Even though this documentation aims to provide guidelines on QA/QC practices for physiological data, remaining gaps must be acknowledged. Given the current lack of transparency in the application of QA/QC for physiological data in neuroscientific literature, there is a clear need to better document these procedures across different use cases, populations and specific physiological modalities. Reporting standards à la COBIDAS would be a valuable addition to the field. Initiatives toward this goal have started to be undertaken by the Society for Psychophysiological Research for physiological signals (specifically for heart rate and heart rate variability reporting; Quigley et al., 2024) However, recording physiological signals concurrently with fMRI poses its own set of distinct challenges (e.g., different types of artifacts to consider during the preprocessing), and often serves specific applications, such as physiological noise modeling for fMRI data denoising. Thus, efforts to characterize the effects of concurrent neuroimaging on the acquisition and quality of physiological data are crucial (Schumann et al., 2021).