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[ENH] Changes related to Issue #81 #84

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[ENH] Changes related to Issue #81 #84

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0ad2116
[ENH] Changes related to Issue #81
justinehansen Dec 1, 2022
cab05ae
[STY] Fix style
justinehansen Dec 1, 2022
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6 changes: 4 additions & 2 deletions docs/user_guide/annotations.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@ Brain maps and annotations
Beyond standard atlases, the ``neuromaps`` toolbox offers access to a
repository of brain maps (which we frequently refer to as brain annotations).
These annotations are spatial maps representing some feature of interest and
are available in one of the four standard coordinate systems.
are available in at least one of the four standard coordinate systems.

We have curated a set of brain annotations that we make available through a
standard interface. You can search for available annotations using the
Expand Down Expand Up @@ -39,7 +39,9 @@ second entry in the tuple provides a brief description of what the map
represents. (Because these descriptors are encoded directly in the filenames of
the relevant maps we are somewhat limited in terms of space.) The last two
entries in the tuple correspond to the coordinate system and density/resolution
in which the annotations are provided.
in which the annotations are provided. More details on each annotation, as well
as papers to cite if you use the data, exist on our `Wiki
<https://github.com/netneurolab/neuromaps/wiki/Annotation-information>`_.

This function also accepts keyword arguments in case we want to narrow down the
list of returned annotations:
Expand Down
7 changes: 7 additions & 0 deletions docs/user_guide/transformations.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -54,6 +54,13 @@ It's just as easy to transform the data to a different space:
>>> print(fsavg_lh.agg_data().shape)
(163842,)

.. important::

Partial volume effects (PVE) in PET images are increased when volumetric
data is transformed to the surface, resulting in maps that are heavily
biased by the underlying curvature of the surface. For this reason, it is
generally not recommended to transform PET volumes to the surface.

Note that you can also transform between different resolutions within the
MNI152 coordinate system:

Expand Down
12 changes: 12 additions & 0 deletions neuromaps/datasets/annotations.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,8 @@
from pathlib import Path
import re
import shutil
import numpy as np
import warnings

from nilearn.datasets.utils import _fetch_file

Expand Down Expand Up @@ -228,4 +230,14 @@ def fetch_annotation(*, source=None, desc=None, space=None, den=None, res=None,
shutil.move(dl_file, fn)
data.append(str(fn))

# warning for specific maps
warn = [np.logical_and(np.logical_or(dset['source'] == 'beliveau2017',
dset['source'] == 'norgaard2021'),
dset['space'] == 'MNI152') for dset in info]
if any(warn):
warnings.warn('Data from beliveau2017 and norgaard2021 is best used in'
' the provided fsaverage space '
'(e.g. source=\'beliveau2017\', space=\'fsaverage\', '
'den=\'164k\'). MNI152 maps should only be used for '
'subcortical data.')
return _groupby_match(data, return_single=return_single)
260 changes: 256 additions & 4 deletions neuromaps/datasets/data/osf.json
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Original file line number Diff line number Diff line change
Expand Up @@ -226,6 +226,48 @@
"6196cb28bbe37803c059ee6d"
]
},
{
"source": "beliveau2017",
"desc": "az10419369",
"space": "fsaverage",
"den": "164k",
"hemi": "R",
"format": "surface",
"fname": "source-beliveau2017_desc-az10419369_space-fsaverage_den-164k_hemi-R_feature.func.gii",
"rel_path": "beliveau2017/az10419369/fsaverage/",
"checksum": "df01756cd50af0e9ebcc2c6df0405135",
"title": null,
"tags": [
"receptors",
"PET"
],
"redir": null,
"url": [
"4mw3a",
"638543a2c86e2818da88f993"
]
},
{
"source": "beliveau2017",
"desc": "az10419369",
"space": "fsaverage",
"den": "164k",
"hemi": "L",
"format": "surface",
"fname": "source-beliveau2017_desc-az10419369_space-fsaverage_den-164k_hemi-L_feature.func.gii",
"rel_path": "beliveau2017/az10419369/fsaverage/",
"checksum": "23501cf62b1e7c2881d830cdf4d0ebff",
"title": null,
"tags": [
"receptors",
"PET"
],
"redir": null,
"url": [
"4mw3a",
"638543b0723a11190518c8a6"
]
},
{
"source": "beliveau2017",
"desc": "cimbi36",
Expand All @@ -246,6 +288,48 @@
"6196cb34bbe37803bd59f194"
]
},
{
"source": "beliveau2017",
"desc": "cimbi36",
"space": "fsaverage",
"den": "164k",
"hemi": "R",
"format": "surface",
"fname": "source-beliveau2017_desc-cimbi36_space-fsaverage_den-164k_hemi-R_feature.func.gii",
"rel_path": "beliveau2017/cimbi36/fsaverage/",
"checksum": "d31200bc45b31953f6d8c3632b5d7044",
"title": null,
"tags": [
"receptors",
"PET"
],
"redir": null,
"url": [
"4mw3a",
"638543cdc86e2818da88f9e7"
]
},
{
"source": "beliveau2017",
"desc": "cimbi36",
"space": "fsaverage",
"den": "164k",
"hemi": "L",
"format": "surface",
"fname": "source-beliveau2017_desc-cimbi36_space-fsaverage_den-164k_hemi-L_feature.func.gii",
"rel_path": "beliveau2017/cimbi36/fsaverage/",
"checksum": "2dcf2c82b5fb96ac6921d560a3416e64",
"title": null,
"tags": [
"receptors",
"PET"
],
"redir": null,
"url": [
"4mw3a",
"638543bf228a5a18987bc2c1"
]
},
{
"source": "beliveau2017",
"desc": "cumi101",
Expand All @@ -266,6 +350,48 @@
"6196cb3f0b0c1e03d87fbf65"
]
},
{
"source": "beliveau2017",
"desc": "cumi101",
"space": "fsaverage",
"den": "164k",
"hemi": "R",
"format": "surface",
"fname": "source-beliveau2017_desc-cumi101_space-fsaverage_den-164k_hemi-R_feature.func.gii",
"rel_path": "beliveau2017/cumi101/fsaverage/",
"checksum": "7b5bab02f7edbdc255b5c72c93189d4e",
"title": null,
"tags": [
"receptors",
"PET"
],
"redir": null,
"url": [
"4mw3a",
"638543dbc86e2818d988f7ea"
]
},
{
"source": "beliveau2017",
"desc": "cumi101",
"space": "fsaverage",
"den": "164k",
"hemi": "L",
"format": "surface",
"fname": "source-beliveau2017_desc-cumi101_space-fsaverage_den-164k_hemi-L_feature.func.gii",
"rel_path": "beliveau2017/cumi101/fsaverage/",
"checksum": "e76c623cb51a28f2105d26dab0f2ebe0",
"title": null,
"tags": [
"receptors",
"PET"
],
"redir": null,
"url": [
"4mw3a",
"638543ea1daa6b183975935a"
]
},
{
"source": "beliveau2017",
"desc": "dasb",
Expand All @@ -286,6 +412,48 @@
"6196cb1c0b0c1e03ca7fd263"
]
},
{
"source": "beliveau2017",
"desc": "dasb",
"space": "fsaverage",
"den": "164k",
"hemi": "R",
"format": "surface",
"fname": "source-beliveau2017_desc-dasb_space-fsaverage_den-164k_hemi-R_feature.func.gii",
"rel_path": "beliveau2017/dasb/fsaverage/",
"checksum": "e4921dcc6da907318ef47d7b15250b84",
"title": null,
"tags": [
"receptors",
"PET"
],
"redir": null,
"url": [
"4mw3a",
"63854385c86e2818da88f94b"
]
},
{
"source": "beliveau2017",
"desc": "dasb",
"space": "fsaverage",
"den": "164k",
"hemi": "L",
"format": "surface",
"fname": "source-beliveau2017_desc-dasb_space-fsaverage_den-164k_hemi-L_feature.func.gii",
"rel_path": "beliveau2017/dasb/fsaverage/",
"checksum": "21fb41e524fbb5cdf2d60a3ffdd49712",
"title": null,
"tags": [
"receptors",
"PET"
],
"redir": null,
"url": [
"4mw3a",
"63854394228a5a18987bc26e"
]
},
{
"source": "beliveau2017",
"desc": "sb207145",
Expand All @@ -306,6 +474,48 @@
"6196cb10bbe37803b559fe58"
]
},
{
"source": "beliveau2017",
"desc": "sb207145",
"space": "fsaverage",
"den": "164k",
"hemi": "R",
"format": "space",
"fname": "source-beliveau2017_desc-sb207145_space-fsaverage_den-164k_hemi-R_feature.func.gii",
"rel_path": "beliveau2017/sb207145/fsaverage/",
"checksum": "4bb6b0f54f2cdf3b3dba9e63cf3daa8d",
"title": null,
"tags": [
"receptors",
"PET"
],
"redir": null,
"url": [
"4mw3a",
"638542b2a98e5f1ac2103866"
]
},
{
"source": "beliveau2017",
"desc": "sb207145",
"space": "fsaverage",
"den": "164k",
"hemi": "L",
"format": "space",
"fname": "source-beliveau2017_desc-sb207145_space-fsaverage_den-164k_hemi-L_feature.func.gii",
"rel_path": "beliveau2017/sb207145/fsaverage/",
"checksum": "5520fb0ed11a4037b22de7b63fa9704b",
"title": null,
"tags": [
"receptors",
"PET"
],
"redir": null,
"url": [
"4mw3a",
"638542c2c86e2818d588f461"
]
},
{
"source": "ding2010",
"desc": "mrb",
Expand Down Expand Up @@ -1533,13 +1743,13 @@
]
},
{
"source": "norgaard2020",
"source": "norgaard2021",
"desc": "flumazenil",
"space": "MNI152",
"res": "1mm",
"format": "volume",
"fname": "source-norgaard2020_desc-flumazenil_space-MNI152_res-1mm_feature.nii.gz",
"rel_path": "norgaard2020/flumazenil/MNI152/",
"fname": "source-norgaard2021_desc-flumazenil_space-MNI152_res-1mm_feature.nii.gz",
"rel_path": "norgaard2021/flumazenil/MNI152/",
"checksum": "e4eab79ed371d7362741f66bb44388d8",
"title": null,
"tags": [
Expand All @@ -1549,7 +1759,49 @@
"redir": null,
"url": [
"4mw3a",
"6196caeb0b0c1e03bf7fdd23"
"638553fe228a5a18d67bcac8"
]
},
{
"source": "norgaard2021",
"desc": "flumazenil",
"space": "fsaverage",
"den": "164k",
"hemi": "R",
"format": "surface",
"fname": "source-norgaard2021_desc-flumazenil_space-fsaverage_den-164k_hemi-R_feature.func.gii",
"rel_path": "norgaard2021/flumazenil/fsaverage/",
"checksum": "4f711daa3327499295fb7e9b13552197",
"title": null,
"tags": [
"receptors",
"PET"
],
"redir": null,
"url": [
"4mw3a",
"6385540d228a5a18d67bcadd"
]
},
{
"source": "norgaard2021",
"desc": "flumazenil",
"space": "fsaverage",
"den": "164k",
"hemi": "L",
"format": "surface",
"fname": "source-norgaard2021_desc-flumazenil_space-fsaverage_den-164k_hemi-L_feature.func.gii",
"rel_path": "norgaard2021/flumazenil/fsaverage/",
"checksum": "3735d34649c932a9eecce57ff688030c",
"title": null,
"tags": [
"receptors",
"PET"
],
"redir": null,
"url": [
"4mw3a",
"638553fe228a5a18d67bcac8"
]
},
{
Expand Down