This capsule processes input NWB files containing raw fiber photometry data by generating baseline-corrected (ΔF/F) and motion-corrected traces, which are then appended back to the NWB file.
Three baseline estimation methods are implemented:
- "poly": Fits a 4th-order polynomial using Ordinary Least Squares (OLS).
- "exp": Fits a biphasic exponential using OLS:
$a\cdot e^{-b\cdot t} + c\cdot e^{-d\cdot t}$ - "bright": Robust fit with [Biphasic exponential decay (bleaching)] x [Increasing saturating exponential (brightening)] using Iteratively Reweighted Least Squares (IRLS):
$b_{\infty} \cdot (1 + b_{slow} e^{-t/\tau_{slow}} + b_{fast} e^{-t/\tau_{fast}}) \cdot (1-b_{bright} e^{-t/\tau_{bright}}))$
Motion correction is carried out in two steps:
- Estimating motion attenuation: A second-order Butterworth filter is applied to smooth the isosbestic trace.
- Removing the motion component: Robust regression is used to subtract the motion signal from all channels.
All parameters are passed to run_capule.py using python run_capule.py [parameters].
Parameters are defined in __main__ using argparse.
Key parameters include:
- '--source_pattern': Specifies the regular expression used to locate input NWB files containing raw fiber data.
- '--dff_methods': Defines the method(s) to be used for calculating ΔF/F.
The primary output is the updated NWB file, which includes the newly processed data:
- Baseline-corrected traces (ΔF/F): Stored as
[Channel]_[Fiber]_dff-[method](e.g.,G_1_dff-poly). - Fully preprocessed traces (ΔF/F with motion correction): Stored as
[Channel]_[Fiber]_preprocessed-[method](e.g.,G_1_preprocessed-poly).
For quality control (QC), the qc subdirectory contains visualizations for each fiber and ΔF/F method. These figures display raw, ΔF/F, and preprocessed (ΔF/F with motion correction) traces of all channels. For example, Fiber1_bright.png. Within the qc subdirectory is also the quality_control.json for the aind-qc-portal.