biopsykit.protocols.mist module¶

Module representing the Montreal Imaging Stress Task (MIST) protocol.

class biopsykit.protocols.mist.MIST(name=None, structure=None, **kwargs)[source]¶

Bases: biopsykit.protocols.base.BaseProtocol

Class representing the Montreal Imaging Stress Task (MIST) protocol and data collected within a MIST study.

The general structure of the MIST can be specified by passing a structure dict to the constructor.

Up to three nested structure levels are supported:

1st level: study part: Different parts of the study where the MIST was conducted, such as: “Pre”, “MIST”, and “Post”
2nd level: phase: Different MIST phases that belong to the same study part, such as: “MIST1”, “MIST2”, “MIST3” (for study part “MIST”) or “Questionnaires”, “Rest”, “Training” (for study part “Pre”)
3rd level: subphase: Different MIST subphases that belong to the same phase, such as: “Baseline”, “Arithmetic Task”, “Feedback”

Parameters

name (str, optional) – name of protocol or None to use “MIST” as default name. Default: None
structure (dict, optional) –
nested dictionary specifying the structure of the MIST study.

Up to three nested structure levels are supported:
- 1st level: study part: Different parts of the study where the MIST was conducted, such as: “Pre”, MIST”, and “Post”
- 2nd level: phase: Different MIST phases that belong to the same study part, such as: “MIST1”, “MIST2”, “MIST3” (for study part “MIST”) or “Questionnaires”, “Rest”, “Training” (for study part “Pre”)
- 3rd level: subphase: Different MIST subphases that belong to the same phase, such as: “Baseline”, “Arithmetic Task”, “Feedback”
If a study part has no division into finer phases (or a phase has no division into finer subphases) the dictionary value can be set to None.
**kwargs –
additional parameters to be passed to MIST and its superclass, BaseProtocol, such as:
- saliva_plot_params: dictionary with parameters to style saliva_plot()
- hr_mean_plot_params: dictionary with parameters to style hr_mean_plot()
- hr_ensemble_plot_params: dictionary with parameters to style hr_ensemble_plot()

Examples

>>> from biopsykit.protocols import MIST
>>> # Example: MIST study consisting of three parts. Only the MIST part consists of different
>>> # phases and subphases
>>> structure = {
>>>     "Before": None,
>>>     "MIST": {
>>>         "MIST1": {"BL": 60, "AT": 240, "FB": 120},
>>>         "MIST2": {"BL": 60, "AT": 240, "FB": 120},
>>>         "MIST3": {"BL": 60, "AT": 240, "FB": 120}
>>>     },
>>>     "After": None
>>> }
>>> MIST(name="MIST", structure=structure)

References

Dedovic, K., Renwick, R., Mahani, N. K., Engert, V., Lupien, S. J., & Pruessner, J. C. (2005). The Montreal Imaging Stress Task: Using functional imaging to investigate the effects of perceiving and processing psychosocial stress in the human brain. Journal of Psychiatry and Neuroscience, 30(5), 319-325.

hr_ensemble_plot(ensemble_id, subphases=None, **kwargs)[source]¶

Draw heart rate ensemble plot.

Parameters

ensemble_id (str) – identifier of the ensemble data to be plotted. Ensemble data needs to be computed using compute_hr_ensemble() first.
subphases (dict, optional) – dictionary with phases (keys) and subphases (values - dict with subphase names and subphase durations) or None to retrieve MIST information from structure dict. When passing None, it is assumed that the study part containing the MIST is named MIST.
**kwargs (dict, optional) – optional arguments for plot configuration to be passed to hr_ensemble_plot().

Returns

fig (matplotlib.figure.Figure) – figure object
ax (matplotlib.axes.Axes) – axes object

Return type

Tuple[matplotlib.figure.Figure, matplotlib.axes._axes.Axes]

See also

compute_hr_ensemble(): compute heart rate ensemble data
hr_ensemble_plot(): Heart rate ensemble plot

name: str¶: Study or protocol name

structure: Dict[str, Any]¶

Structure of protocol, i.e., whether protocol is divided into different parts, phases, or subphases.

If protocol is not divided into different parts protocol_structure is set to None.

saliva_types: Sequence[str]¶: List of saliva data types present in the study.

test_times: Sequence[int]¶

Start and end time of psychological test (in minutes).

If no psychological test was performed in the protocol test_times is set to [0, 0].

sample_times: Dict[str, Sequence[int]]¶

Dictionary with sample times of saliva samples (in minutes).

Sample times are either provided explicitly using the sample_times parameter in add_saliva_data() or by extracting it from the saliva data (if a time column is present).

saliva_data: Dict[str, SalivaRawDataFrame]¶

Dictionary with saliva data collected during the study.

Data in SalivaRawDataFrame format can be added using add_saliva_data().

hr_data: Dict[str, HeartRateSubjectDataDict]¶

Dictionary with heart rate data collected during the study. If the study consists of multiple study parts each part has its own HeartRateSubjectDataDict. If the study has no individual study parts (only different phases), the name of the one and only study part defaults to Study (to ensure consistent dictionary structure).

Data in HeartRateSubjectDataDict format can be added using add_hr_data().

rpeak_data: Dict[str, SubjectDataDict]¶

Dictionary with R peak data collected during the study. If the study consists of multiple study parts each part has its own SubjectDataDict. If the study has no individual study parts (only different phases), the name of the one and only study part defaults to Study (to ensure consistent dictionary structure).

Data in SubjectDataDict format can be added using add_hr_data().

hr_results: Dict[str, pd.DataFrame]¶

Dictionary with heart rate results.

Dict keys are the identifiers that are specified when computing results from hr_data using compute_hr_results().

hr_above_baseline_results: Dict[str, pd.DataFrame]¶

Dictionary with heart rate above baseline results.

Dict keys are the identifiers that are specified when computing results from hr_data using compute_hr_above_baseline().

hrv_results: Dict[str, pd.DataFrame]¶

Dictionary with heart rate variability ensemble.

Dict keys are the identifiers that are specified when computing ensemble from rpeak_data using compute_hrv_results().

hrv_above_baseline_results: Dict[str, pd.DataFrame]¶

Dictionary with heart rate variability above baseline results.

Dict keys are the identifiers that are specified when computing results from rpeak_data using compute_hrv_above_baseline().

hr_ensemble: Dict[str, Dict[str, pd.DataFrame]]¶

Dictionary with merged heart rate data for heart rate ensemble plot.

Dict keys are the identifiers that are specified when computing ensemble HR data from hr_data using compute_hr_ensemble().

See also

hr_ensemble_plot(): heart rate ensemble plot

saliva_plot_params: Dict[str, Any]¶

Plot parameters for customizing the general saliva plot for a specific psychological protocol.

See also

saliva_plot(): saliva plot

hr_mean_plot_params: Dict[str, Any]¶

Plot parameters for customizing the general HR mean plot for a specific psychological protocol.

See also

hr_mean_plot(): HR mean plot

hr_ensemble_plot_params: Dict[str, Any]¶

Plot parameters for customizing the general HR ensemble plot for a specific psychological protocol.

See also

hr_ensemble_plot(): HR ensemble plot

biopsykit.protocols.cft module biopsykit.protocols.plotting module