Fix typos on "Best Practices for Phys Data Collection"

docs/bestpractice.rst

@@ -12,7 +12,7 @@ Why collect physiological data?
 ----------------------------------
 Physiological monitoring is a key component of understanding physiological sources of signal variance in fMRI data. Monitoring physiology during scanning is critical to enable the characterization of a given subject's physiologic state at the time of the scan, and to track variations in physiology throughout the scan. With these data, we can more accurately model how these factors manifest in the fMRI signal time series.
 
-Physiological fluctuations can be identified as "noise" or as "signals of interest", depending on the research question of the imaging experiment. For most fMRI experiments, the goal is to isolate signal fluctuations that are associated with a neural stimulus and the resulting hemodynamic response (Caballero-Gaudes et. al 2016). In these data, it is important to model and remove signals with a non-neural origin, such as breathing or cardiac related signal variance. Removing these confounds will improve the sensitivity and confidence of the fMRI analysis. In some fMRI experiments, the goal is to characterize a physiologic effect (for example, studies that map cerebrovascular reactivity aim to quantify the dilation of blood vessels during certain non-neural stimuli) (Caballero-Gaudes et. al 2016). In these studies it is essential that the relevant physiologic parameters are recorded so that the analysis produces robust, quantitative physiological parameter maps.
+Physiological fluctuations can be identified as "noise" or as "signals of interest", depending on the research question of the imaging experiment. For most fMRI experiments, the goal is to isolate signal fluctuations that are associated with a neural stimulus and the resulting hemodynamic response (Caballero-Gaudes et al. 2016). In these data, it is important to model and remove signals with a non-neural origin, such as breathing or cardiac related signal variance. Removing these confounds will improve the sensitivity and confidence of the fMRI analysis. In some fMRI experiments, the goal is to characterize a physiologic effect (for example, studies that map cerebrovascular reactivity aim to quantify the dilation of blood vessels during certain non-neural stimuli) (Caballero-Gaudes et al. 2016). In these studies it is essential that the relevant physiologic parameters are recorded so that the analysis produces robust, quantitative physiological parameter maps.
 
 Another benefit of collecting physiological data is that it provides a method to monitor the subject and/or patient during the scan in real-time. Any sudden changes in the different aspects being monitored can help those in the control room identify if the person is under duress or complying with the scan protocol. Looking out for these changes is particularly helpful during an individual's first MRI scan, when they may react poorly to the scan environment. In some protocols, tracking physiology in real-time can ensure that values stay within safe, IRB-approved limits.
 
@@ -28,7 +28,7 @@ The most common types of physiological data acquired in fMRI analysis are cardia
 
 **Breathing** is typically monitored using a "respiratory belt" around the participant's chest/diaphragm. The belt may be rigid or elastic, using MR compatible force or pressure transducers to generate a signal proportional to the chest diameter. The optimal positioning of the belt depends on the device being used, however it is best to be fairly consistent in how the belt is worn throughout a study. In some labs, multiple belts are used to better capture different types of breathing styles (e.g., "chest breathing" versus "belly breathing"). Often a belt is incorporated into the MRI scanner infrastructure, and these data can be collected by the scanner or recorded by a separate device. The peaks and troughs of the breathing trace are identified, which can provide information about breathing rate as well as breathing depth. There are three primary ways by which breathing can influence the fMRI signals. First, breathing often leads to bulk motion of the body and head (Brosch et al. 2002). These effects are typically modeled using volume registration and motion correction algorithms. Second, breathing changes the chest position which can influence the success of the shim, continuously changing B0 homogeneity throughout the scan and in turn affecting signal amplitude (Brosch et al. 2002, Raj et al. 2001). These effects are also modeled using techniques like RETROICOR. Thirdly, changing breathing rate and depth can influence blood gases, which can drive vasodilation or vasoconstriction, and thus substantially influence the fMRI signal amplitude (Chang and Glover 2009). RVT correction (Birn et al. 2008) estimates the change in breathing rate/depth to model these effects.
 
-**Blood gases** It is also possible (and recommended!) to directly record changes in blood gas levels, rather than infer them from a chest position measurement. Most commonly we measure carbon dioxide levels (CO2), which is a known vasodilator and can drive large variability in blood flow and the BOLD signal (Birn et al. 2006, Wise et al. 2004). We can also measure oxygen (O2) levels; O2 only has a mild vasoconstrictive effect on the cerebrovasculature, but O2 levels can directly influence BOLD signal contrast (Bulte et al. 2007). These two blood gases are typically strongly anti correlated with each other in most scans, but can also be manipulated independently and influence the fMRI signals through distinct mechanisms (Floyd 2003). Best practice would be to record both. Although the most accurate recordings of blood gas levels would be achieved through arterial sampling, this is not recommended for most imaging experiments. Instead, the concentrations of CO2 and O2 in arterial blood can be approximated by the partial pressure of each gas at the end of an exhalation, or the end-tidal partial pressure (commonly abbreviated as PETCO2 and PETO2) (Bengtsson et al. 2001, McSwain et al. 2010?). The person being scanned wears a nasal cannula (soft plastic tube that rests just below the nostrils) or face mask that is connected to a gas analyzer in the control room. The resulting data shows the fluctuations in CO2 and O2 across every breath; an algorithm must extract the "end-tidal" values.
+**Blood gases** It is also possible (and recommended!) to directly record changes in blood gas levels, rather than infer them from a chest position measurement. Most commonly we measure carbon dioxide levels (CO2), which is a known vasodilator and can drive large variability in blood flow and the BOLD signal (Birn et al. 2006, Wise et al. 2004). We can also measure oxygen (O2) levels; O2 only has a mild vasoconstrictive effect on the cerebrovasculature, but O2 levels can directly influence BOLD signal contrast (Bulte et al. 2007). These two blood gases are typically strongly anticorrelated with each other in most scans, but can also be manipulated independently and influence the fMRI signals through distinct mechanisms (Floyd et al. 2003). Best practice would be to record both. Although the most accurate recordings of blood gas levels would be achieved through arterial sampling, this is not recommended for most imaging experiments. Instead, the concentrations of CO2 and O2 in arterial blood can be approximated by the partial pressure of each gas at the end of an exhalation, or the end-tidal partial pressure (commonly abbreviated as PETCO2 and PETO2) (Bengtsson et al. 2001, McSwain et al. 2010). The person being scanned wears a nasal cannula (soft plastic tube that rests just below the nostrils) or face mask that is connected to a gas analyzer in the control room. The resulting data shows the fluctuations in CO2 and O2 across every breath; an algorithm must extract the "end-tidal" values.
 
 .. _howtocollectphysdata:
 
@@ -50,7 +50,7 @@ Recording devices:
 - associated signal recording/analysis software
 
 For example, ADInstruments sells the Powerlab and uses LabChart software; Biopac sells the MP160 and uses AcqKnowledge software.
-It is also important to sync the physiological recordings with the fMRI scan triggers. To do this, it will be necessary to extract the trigger pulses from your MRI scanner, typically inputting these analog signals via BNC into the same ADC recording all of the physiological information.
+It is also important to sync the physiological recordings with the fMRI scan triggers. To do this, it will be necessary to extract the trigger pulses from your MRI scanner, typically inputting these analog signals via BNC into the same ADC that is recording the physiological information.
 
 .. _whattodowithphysdata: