Table 1. Definitions of variables.

$\mathrm{\Phi }(x,y,t)$	Raw images of light intensity over time as captured by the camera.
$\mathrm{\Phi }\prime (x,y,t)$	Detrended images of light intensity over time.
$M(x,y)$	Mean value of each pixel’s time course.
$S(x,y)$	Standard deviation of each pixel’s time course (after detrending).
$B_{SAT}(x,y)$	Boolean pixel mask for saturation,
	where included pixels have a value of 1 and saturated pixels 0.
$B_{SNR}(x,y)$	Boolean pixel mask for signal-to-noise,
	where included pixels have a value of 1 and low SNR pixels 0.
$B_{LC}(x,y)$	Boolean pixel mask for local correlation,
	where included pixels have a value of 1 and low local correlation pixels 0.
$B_0(x,y)$	Combination Boolean mask using the three masks:
	$B_{SAT}(x,y)$, $B_{SNR}(x,y)$, and $B_{LC}(x,y)$.
$B_M(x,y)$	Manual segmentation.
$B_G(x,y)$	Guided segmentation.
${\lambda }_1$	Masking parameter for SNR mask.
${\lambda }_2$	Masking parameter for correlation mask.
$\mathrm{\Delta }{\mu }_a(x,y,t)$	Images of changes in the absorption coefficient over time.
$L$	Optical pathlength.
$g$	Spatial Gaussian smoothing kernel.
$\mathrm{\Delta }{\mu }_{a,i}^{\prime }(x,y,t)$	Spatially-smoothed images of changes in the absorption coefficient over time.a
$P(x,y)$	Number of pixels included in each pixel’s interpolation after smoothing.
$B_{G2}(x,y)$	Guided segmentation interpolated by smoothing.
$S_i(t)$	Global signal averaged over the segmented brain.a
${\beta }_i(x,y)$	Regression coefficient at each pixel for the global signal.a
$\mathrm{\Delta }{\mu }_{a,i}^{″}(x,y,t)$	Images of changes in the absorption coefficient over time
	after spatial smoothing and global signal regression.a
$R(n,m)$	Matrix of correlation coefficients.
$M(n,m)$	Boolean mask for correlation coefficient matrix.
$F(n,m)$	Matrix of Fisher-transformed correlation coefficients.

^a The subscript i denotes the segmentation method.