Apply suggestions from code review

Co-Authored-By: Chris Markiewicz <[email protected]>

Author: oesteban

docs/usage.rst

@@ -106,28 +106,29 @@ Reusing precomputed derivatives
 Reusing a previous, partial execution of *fMRIPrep*
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 *fMRIPrep* will pick up where it left off a previous execution, so long the work directory
-points to the same location, and such directory has not been changed/manipulated.
+points to the same location, and this directory has not been changed/manipulated.
 
 Using a previous run of *FreeSurfer*
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-*fMRIPrep* will automatically reuse a previous run of *FreeSurfer* if the ``$SUBJECTS_DIR``
-points to a subfolder called ``freesurfer/`` under the folder selected as output directory
-(``<output_dir>/freesurfer``).
-Please note that derivatives for all participants, as generated by FreeSurfer_ must be found
-under that directory.
-Alternatively, you can use the ``--fs-subjects-dir`` flag to specify a different location
-for the existing FreeSurfer outputs.
+*fMRIPrep* will automatically reuse previous runs of *FreeSurfer* if a subject directory
+named ``freesurfer/`` is found in the output directory (``<output_dir>/freesurfer``).
+Reconstructions for each participant will be checked for completeness, and any missing
+components will be recomputed.
+You can use the ``--fs-subjects-dir`` flag to specify a different location to save
+FreeSurfer outputs.
+If precomputed results are found, they will be reused.
 
 The *anatomical fast-track*
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~
 Starting with version 20.1.0, *fMRIPrep* has a command-line argument (``--anat-derivatives <PATH>``)
-to prescribe a path from which the preprocessed information derived from the T1w, T2w (if present) and
+to indicate a path from which the preprocessed information derived from the T1w, T2w (if present) and
 FLAIR (if present) images.
 This feature was envisioned to help process very large multi-session datasets where the anatomical
 images can be averaged (i.e., anatomy is not expected to vary substantially across sessions).
-An example of this kind of processing is `My Connectome <https://openneuro.org/datasets/ds000031/>`__.
-The dataset contains 107 sessions for a single-subject.
-Most of them containing anatomical information which, given the design of the dataset, can be averaged
+An example where this kind of processing would be useful is
+`My Connectome <https://openneuro.org/datasets/ds000031/>`__, a dataset that contains
+107 sessions for a single-subject.
+Most of these sessions contain anatomical information which, given the design of the dataset, can be averaged
 across sessions as no substantial changes should happen.
 In other words, the anatomical information of the dataset can be considered as *cross-sectional*.
 Before version 20.1.0, preprocessing this dataset would be hard for two limitations: