Merge remote-tracking branch 'poldracklab/master'

Author: tsalo

.zenodo.json

@@ -73,6 +73,11 @@
       "affiliation": "State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University",
       "orcid": "0000-0003-0489-2619"
     },
+    {
+      "name": "Salo, Taylor",
+      "affiliation": "Department of Psychology, Florida International University",
+      "orcid": "0000-0001-9813-3167"
+    },
     {
       "name": "Poldrack, Russell A.",
       "affiliation": "Department of Psychology, Stanford University",

fmriprep/data/boilerplate.bib

@@ -292,3 +292,14 @@ @article{afni
     volume = 10,
     year = 1997
 }
+
+@article{posse_t2s,
+    author = {Posse, Stefan and Wiese, Stefan and Gembris, Daniel and Mathiak, Klaus and Kessler, Christoph and Grosse-Ruyken, Maria-Liisa and Elghahwagi, Barbara and Richards, Todd and Dager, Stephen R. and Kiselev, Valerij G.},
+    doi = {10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O},
+    journal = {Magnetic Resonance in Medicine},
+    number = 1,
+    pages = {87-97},
+    title = {Enhancement of {BOLD}-contrast sensitivity by single-shot multi-echo functional {MR} imaging},
+    volume = 42,
+    year = 1999
+}

fmriprep/workflows/bold/t2s.py

@@ -76,6 +76,16 @@ def init_bold_t2s_wf(bold_echos, echo_times, mem_gb, omp_nthreads,
             the skull-stripped optimal combination mask
     """
     workflow = Workflow(name=name)
+    workflow.__desc__ = """\
+A T2* map was estimated from preprocessed BOLD by fitting to a
+monoexponential signal decay model with log-linear regression.
+For each voxel, the maximal number of echoes with high signal in that voxel was
+used to fit the model.
+The T2* map was used to optimally combine preprocessed BOLD across
+echoes following the method described in @posse_t2s and was also retained as
+the BOLD reference.
+"""
+
     inputnode = pe.Node(niu.IdentityInterface(
         fields=['bold_echos', 'name_source', 'hmc_xforms']),
         name='inputnode')