Possible to change FD "threshold" in .html for QA purposes?

[ryanw412]

When opening the .html file showing visual reports of the output, the FD shows a seemingly arbitrary bar that doesn't correspond to the default .5mm threshold. For example, one subject's .html shows a FD bar at .48mm, and another shows at .41mm. It would be convenient to have this bar be constant for all subjects for quality assessment across subjects. I know that there are commands to censor volumes that are FD>.5mm, but I'm more concerned about the visual inspection aspect. Is there any way to set this to be the same (preferably .5mm) for all subjects?

Thanks!

[julfou81]

Hi,
Good question! I don't know how is calculated this line in FMRIPREP report.
For QA purposes, you may use MRIQC where you can see stats of number of volumes above a certain FD threshold (number and percentage of volumes above a certain threshold). But there as well, there is not bar with the visual threshold visible on the reports (as far as I know, I didn't try the latest version of MRIQC). Also, there is still one unresolved issue regarding this visual inspection in MRIQC: https://github.com/poldracklab/mriqc/issues/790

I agree, it would be nice to have a visual display similar to what is done with denoiser :

[oesteban]

Thanks @ryanw412 and @julfou81 for this suggestion. I think this would be very useful.

I'm transferring the issue to nipreps/niworkflows, which is where the code to generate that plot lives:

niworkflows/niworkflows/viz/plots.py

Lines 574 to 755 in 5792f74

	def confoundplot(
	tseries,
	gs_ts,
	gs_dist=None,
	name=None,
	units=None,
	tr=None,
	hide_x=True,
	color="b",
	nskip=0,
	cutoff=None,
	ylims=None,
	):
	import seaborn as sns
	# Define TR and number of frames
	notr = False
	if tr is None:
	notr = True
	tr = 1.0
	ntsteps = len(tseries)
	tseries = np.array(tseries)
	# Define nested GridSpec
	gs = mgs.GridSpecFromSubplotSpec(
	1, 2, subplot_spec=gs_ts, width_ratios=[1, 100], wspace=0.0
	)
	ax_ts = plt.subplot(gs[1])
	ax_ts.grid(False)
	# Set 10 frame markers in X axis
	interval = max((ntsteps // 10, ntsteps // 5, 1))
	xticks = list(range(0, ntsteps)[::interval])
	ax_ts.set_xticks(xticks)
	if not hide_x:
	if notr:
	ax_ts.set_xlabel("time (frame #)")
	else:
	ax_ts.set_xlabel("time (s)")
	labels = tr * np.array(xticks)
	ax_ts.set_xticklabels(["%.02f" % t for t in labels.tolist()])
	else:
	ax_ts.set_xticklabels([])
	if name is not None:
	if units is not None:
	name += " [%s]" % units
	ax_ts.annotate(
	name,
	xy=(0.0, 0.7),
	xytext=(0, 0),
	xycoords="axes fraction",
	textcoords="offset points",
	va="center",
	ha="left",
	color=color,
	size=8,
	bbox={
	"boxstyle": "round",
	"fc": "w",
	"ec": "none",
	"color": "none",
	"lw": 0,
	"alpha": 0.8,
	},
	)
	for side in ["top", "right"]:
	ax_ts.spines[side].set_color("none")
	ax_ts.spines[side].set_visible(False)
	if not hide_x:
	ax_ts.spines["bottom"].set_position(("outward", 20))
	ax_ts.xaxis.set_ticks_position("bottom")
	else:
	ax_ts.spines["bottom"].set_color("none")
	ax_ts.spines["bottom"].set_visible(False)
	# ax_ts.spines["left"].set_position(('outward', 30))
	ax_ts.spines["left"].set_color("none")
	ax_ts.spines["left"].set_visible(False)
	# ax_ts.yaxis.set_ticks_position('left')
	ax_ts.set_yticks([])
	ax_ts.set_yticklabels([])
	nonnan = tseries[~np.isnan(tseries)]
	if nonnan.size > 0:
	# Calculate Y limits
	valrange = nonnan.max() - nonnan.min()
	def_ylims = [nonnan.min() - 0.1 * valrange, nonnan.max() + 0.1 * valrange]
	if ylims is not None:
	if ylims[0] is not None:
	def_ylims[0] = min([def_ylims[0], ylims[0]])
	if ylims[1] is not None:
	def_ylims[1] = max([def_ylims[1], ylims[1]])
	# Add space for plot title and mean/SD annotation
	def_ylims[0] -= 0.1 * (def_ylims[1] - def_ylims[0])
	ax_ts.set_ylim(def_ylims)
	# Annotate stats
	maxv = nonnan.max()
	mean = nonnan.mean()
	stdv = nonnan.std()
	p95 = np.percentile(nonnan, 95.0)
	else:
	maxv = 0
	mean = 0
	stdv = 0
	p95 = 0
	stats_label = (
	r"max: {max:.3f}{units} $\bullet$ mean: {mean:.3f}{units} "
	r"$\bullet$ $\sigma$: {sigma:.3f}"
	).format(max=maxv, mean=mean, units=units or "", sigma=stdv)
	ax_ts.annotate(
	stats_label,
	xy=(0.98, 0.7),
	xycoords="axes fraction",
	xytext=(0, 0),
	textcoords="offset points",
	va="center",
	ha="right",
	color=color,
	size=4,
	bbox={
	"boxstyle": "round",
	"fc": "w",
	"ec": "none",
	"color": "none",
	"lw": 0,
	"alpha": 0.8,
	},
	)
	# Annotate percentile 95
	ax_ts.plot((0, ntsteps - 1), [p95] * 2, linewidth=0.1, color="lightgray")
	ax_ts.annotate(
	"%.2f" % p95,
	xy=(0, p95),
	xytext=(-1, 0),
	textcoords="offset points",
	va="center",
	ha="right",
	color="lightgray",
	size=3,
	)
	if cutoff is None:
	cutoff = []
	for i, thr in enumerate(cutoff):
	ax_ts.plot((0, ntsteps - 1), [thr] * 2, linewidth=0.2, color="dimgray")
	ax_ts.annotate(
	"%.2f" % thr,
	xy=(0, thr),
	xytext=(-1, 0),
	textcoords="offset points",
	va="center",
	ha="right",
	color="dimgray",
	size=3,
	)
	ax_ts.plot(tseries, color=color, linewidth=0.8)
	ax_ts.set_xlim((0, ntsteps - 1))
	if gs_dist is not None:
	ax_dist = plt.subplot(gs_dist)
	sns.displot(tseries, vertical=True, ax=ax_dist)
	ax_dist.set_xlabel("Timesteps")
	ax_dist.set_ylim(ax_ts.get_ylim())
	ax_dist.set_yticklabels([])
	return [ax_ts, ax_dist], gs
	return ax_ts, gs

Any suggestions in the form of PR (or patches, or anything) will be very welcome. Otherwise, we cannot really give any timeframe for the implementation of this feature. Please join our fMRIPrep bi-monthly meetings for more info.