File structure
We first provide some further details to help explain the files names in BigMac. Second we provide a detailed file tree. As the full dataset is several TB of data, users are welcome to request any subset of the data as required. Expected file sizes for each modality are indicated below.
File naming
In vivo MRI
The in vivo MRI data were acquired from two scanning sessions. The folder last-scan contains data that was acquiried in the last in vivo scanning session. Here the brain is in its most similar state to the ex vivo MRI. The folder prime-de provides data where the functional and structural MRI have previously been released on the PRIME-DE database, where similar data (though without the dwi) is available for another 19 animals for cross subject comparisons.
This data was preprocessed using FSL tools and pipelines specifically designed for macaque data (MrCat: https://github.com/neuroecology/MrCat).
Postmortem MRI processing
Both raw and minimally processed MRI data are provided. The scripts used to process the data can be found in BigMac/MRI/Postmortem/scripts/preprocessing Briefly, the pre-processing includes some combination of Gibbs correction, signal drift correction, data normalisation and registration.
We also provide the output of various model fits including:
the diffusion tensor (Basser 1994)
the ball and stick model (Behrens 2007, extracting fibre orientations)
xtract (Warrington 2020, reconstructing wm tracts)
Not all of the postmortem MRI lies in the same native space as data were acquired over three different scanning sessions The different sessions include:
structural MRI, T1maps, b04k @ 0.6mm
b10k and b07k @ 1.0mm, 1000 grad dir
b04k @ 1.0mm, b04k/b07/b10k data with linear + spherical tensor encoding
Consequently, we provide flirt/fnirt warp fields between different domains as well as warp fields to the F99 standard space.
In vivo data: the minimally processed with/without the data is ~4GB/8GB. Postmortem data: the minimally processed data with/without the raw data, bedpostx or xtract outputs is ~7GB/29GB.
Microscopy labels
Note the microscopy slides are labelled in the following way:
The brain was sectioned and stained in two blocks (anterior/posterior). e.g. Anterior/P001a or Posterior/H020x
P/H indicates PLI or Gallyas histology
001 or 020 is the number of the slide on which the tissue section was mounted.
In the anterior block, slide 001a is the most anterior section and slide 100 is towards the middle of the AP axis.
In the posterior block, slide 001a is the most posterior section and slide 100 is towards the middle of the AP axis.
For the most anterior/posterior sections, multiple tissue sections were mounted on the same glass slide. These sections are therefore named 001a 001b 001c etc.
The ‘x’ is used to indicate that the slide had only a single tissue section such as in H020x
Some of the histology slides were too large to be digitised in a single image. Consequently we acquired two images, of the left and right hand side of the slide respectively. These are labelled ‘a’ and ‘b’. e.g. Anterior/H080x/H080a_structureTensor & Anterior/H080x/H080b_structureTensor
PLI data
Polarised light imaging utilises the birefringence of tissue to estimate the optic axis orientation per pixel. We typically assume that tissue birefringence is dominated by myelin and that the optic axis aligns with the fibre. The PLI resolution is approx 4 microns/pix.
PLI outputs several maps:
HSV_FOM.tif - colour coded orientations weighted by retardance values.
Inclination.tif - very approximate estimate of out-of-plane angle, used solely for MRI-microscopy coregistration.
In_plane.tif - the orientation within the microscopy plane.
Retardance.tif -describes the ellipticity of the polarisation, determined by the quantity, birefringence and inclination of the birefringent media.
Transmittance.tif - related to the amount of light extinguished by the sample.
Each PLI folder is <1.2GB depending on the size of the slide.
Myelin stained histology
The digitised histology images are provided at a resolution of 0.28 microns/pix in svs (Leica) format. These images can be viewed using the openly available QuPath viewer https://qupath.github.io/
Each digitised slide (svs) is up to < 10 GB. Larger slides were imaged in two parts, named H???a.svs and H???b.svs
Structure tensor analysis
The myelin stained histology slides were processed for structure tensor analysis to estimate the primary fibre orientation per pixel. Here we use a Gaussian kernel of 10 microns and estimate summary metrics over a local neighbourhood of 150x150 pixels, which is approx 40x40 microns.
The summary metrics include:
HSV.tif - the colour coded orientations weighted by the staining density
Orientation.tif - the mean in-plane fibre orientation across neighbourhood
RBG_thumb.tif - mean RGB value for neighbourhood
struc_tensor.mat - a matlab sturcture with 7 variables:
thumb - as above
FOD - the 2D fibre orientation distribution i.e. a frequency histogram of all orientations within the neighbourhood
k1/k2 - dispersion parameters from fitting a Bingham distribution to the FOD. Only k2 is meaningful for the 2D distribution.
O - the orientation
ODI1/ODI2 - the orientation dispersion index calculated from k1/k2
Each Gallyas slide (with the structure tensor and registration output, but without the svs files) is < 500MB.
MRI-microscopy registration
The PLI and structure tensor data were co-registered to the structural MRI using TIRL (Huszar 2019). TIRL is initialised using its slice_to_volume function and the provided configuration files.
volume.timg contains transforms to take the MRI data from voxel to real-world coordindates.
slice.timg contains transforms to take the 2D microscopy image to real-world coordinates that are coregistered to the MRI.
slice.tif is an image of the microscopy data downsampled to the resolution of the MRI.
volume.png is an image of the coregistered MRI data.
slice.tif and volume.png can be overlaid to assess the quality of the coregistration.
File tree
The organisation of BigMac files. You are welcome to ask for all or a subset of the data
BigMac-clean
MRI
Invivo
last-scan
dwi
data
bvals
bvecs
data.nii.gz
nodif_brain_mask.nii.gz
S0.nii.gz
data.dtifit
dti_FA.nii.gz
dti_MD.nii.gz
dti_V1.nii.gz
...
masks
nodif_brain_mask.nii.gz
raw
...
func
data
func.nii.gz
func_cleaned.nii.gz
masks
brain_mask.nii.gz
raw
...
raw
...
struct
data
struct.nii.gz
struct_brain.nii.gz
masks
brain_mask.nii.gz
raw
...
prime-de
dwi
data
bvals
bvecs
data.nii.gz
nodif_brain_mask.nii.gz
S0.nii.gz
data.dtifit
dti_FA.nii.gz
dti_MD.nii.gz
dti_V1.nii.gz
...
masks
nodif_brain_mask.nii.gz
raw
...
func
data
func.nii.gz
func_cleaned.nii.gz
masks
brain_mask.nii.gz
raw
...
raw
...
struct
data
struct.nii.gz
struct_brain.nii.gz
masks
brain_mask.nii.gz
raw
...
Postmortem
dwi
b04k
0.6mm
data
bvals
bvecs
data.nii.gz
nodif_brain_mask.nii.gz
S0.nii.gz
data.bedpostX
dyads1.nii.gz
dyads2_thr0.05.nii.gz
dyads3_thr0.05.nii.gz
mean_f1samples.nii.gz
mean_f2samples.nii.gz
mean_f3samples.nii.gz
...
xfms
xtract
native
...
standard
...
data.dtifit
dti_FA.nii.gz
dti_MD.nii.gz
dti_V1.nii.gz
...
masks
mask_gm.nii.gz
mask_wm.nii.gz
nodif_brain_mask.nii.gz
nodif_brain_mask_unfilled.nii.gz
raw
...
reg
diff2standard.nii.gz
dwi_0.6mm_2_dwi_1.0mm_b10k_fnirt
...
dwi_0.6mm_2_dwi_1.0mm_b04k_fnirt
...
dwi_0.6mm_2_F99_fnirt
...
dwi_0.6mm_2_dwi_1.0mm_b10k_flirt
...
dwi_0.6mm_2_struct_flirt
...
standard2diff.nii.gz
1.0mm
data
bvals
bvecs
data.nii.gz
nodif_brain_mask.nii.gz
S0.nii.gz
data.bedpostX
dyads1.nii.gz
dyads2_thr0.05.nii.gz
dyads3_thr0.05.nii.gz
mean_f1samples.nii.gz
mean_f2samples.nii.gz
mean_f3samples.nii.gz
...
xfms
xtract
native
...
standard
...
data.dtifit
dti_FA.nii.gz
dti_MD.nii.gz
dti_V1.nii.gz
...
masks
mask_gm.nii.gz
mask_wm.nii.gz
nodif_brain_mask.nii.gz
nodif_brain_mask_unfilled.nii.gz
raw
...
reg
diff2standard.nii.gz
dwi_1.0mm_b4k_2_dwi_1.mm_b10k_flirt
...
dwi_1.0mm_b4k_2_struct_fnirt
...
dwi_1.0mm_2_dwi_0.6mm_flirt
...
dwi_1.0mm_b4k_2_dwi_1.mm_b10k_fnirt
...
dwi_1.0mm_2_dwi_0.6mm_fnirt
...
dwi_1.0mm_b4k_2_F99_fnirt
...
standard2diff.nii.gz
b07k
1.0mm
data
bvals
bvecs
data.nii.gz
nodif_brain_mask.nii.gz
S0.nii.gz
data.bedpostX
dyads1.nii.gz
dyads2_thr0.05.nii.gz
dyads3_thr0.05.nii.gz
mean_f1samples.nii.gz
mean_f2samples.nii.gz
mean_f3samples.nii.gz
...
xfms
xtract
native
...
standard
...
data.dtifit
dti_FA.nii.gz
dti_MD.nii.gz
dti_V1.nii.gz
...
masks
mask_gm.nii.gz
mask_wm.nii.gz
nodif_brain_mask.nii.gz
nodif_brain_mask_unfilled.nii.gz
raw
...
reg
diff2standard.nii.gz
README
standard2diff.nii.gz
b10k
1.0mm
data
bvals
bvecs
data.nii.gz
nodif_brain_mask.nii.gz
S0.nii.gz
data.bedpostX
dyads1.nii.gz
dyads2_thr0.05.nii.gz
dyads3_thr0.05.nii.gz
mean_f1samples.nii.gz
mean_f2samples.nii.gz
mean_f3samples.nii.gz
...
xfms
xtract
native
...
standard
...
data.dtifit
dti_FA.nii.gz
dti_MD.nii.gz
dti_V1.nii.gz
...
masks
mask_gm.nii.gz
mask_wm.nii.gz
nodif_brain_mask.nii.gz
nodif_brain_mask_unfilled.nii.gz
raw
...
reg
diff2standard.nii.gz
dwi_1.0mm_2_dwi_0.6mm_fnirt
...
dwi_1.0mm_2_struct_fnirt
...
dwi_1.0mm_2_dwi_0.6mm_flirt
...
dwi_1.0mm_2_F99
...
standard2diff.nii.gz
TensorEncoding
{b04k/b07k/b10k}
1.0mm
data.LTE (linear tensor encoding)
bvals
bvecs
data.nii.gz
nodif_brain_mask.nii.gz
data.LTE.dtifit
dti_FA.nii.gz
dti_MD.nii.gz
dti_V1.nii.gz
...
data.STE (spherical tensor encoding)
bvals
bvecs
data.nii.gz
nodif_brain_mask.nii.gz
masks
nodif_brain_mask.nii.gz
raw
README
b04k+b07k+b10k.merged (data which is merged and fitted with dtd_gamma model)
data.nii.gz
dtd_gamma_MD.nii.gz
dtd_gamma_MKa.nii.gz
dtd_gamma_MKi.nii.gz
dtd_gamma_MKt.nii.gz
dtd_gamma_MKa.nii.gz
...
b04k+b07k.merged (as above)
b04k+b07k.nii.gz
dtd_gamma_MD.nii.gz
dtd_gamma_MKa.nii.gz
dtd_gamma_MKi.nii.gz
dtd_gamma_MKt.nii.gz
dtd_gamma_MKa.nii.gz
...
raw
...
scripts
bedpostx_single_shell.sh
dtifit.sh
FLIRTconfig
...
FNIRTconfig
...
preprocessing
README
...
xtract.sh
struct
bSSFP
data
struct.nii.gz
struct_brain.nii.gz
masks
brain_mask.nii.gz
raw
...
reg
bSSFP_2_MGE_flirt
...
MGE
data
struct.nii.gz
struct_brain.nii.gz
T1-like
T1-like_2.nii.gz
T1-like_2_with-background.nii.gz
T1-like.nii.gz
masks
brain_mask.nii.gz
brain_mask_unfilled.nii.gz
mask_csf.nii.gz
mask_gm.nii.gz
mask_wm.nii.gz
raw
...
reg
README
struct_2_dwi_0.6mm_flirt
...
struct_2_dwi_1.0mm_b10k_fnirt
...
struct_2_dwi_1.0mm_b4k_fnirt
...
struct_2_F99_flirt
...
struct_2_F99_fnirt
...
T1maps
data
T1.nii.gz
masks
brain_mask.nii.gz
raw
...
reg
T1maps_2_struct_flirt
...
Standard
F99
data
mri
...
surf
...
McLaren
...
reg
F99_2_dwi_0.6mm_fnirt
...
F99_2_dwi_1.0mm_b10k_fnirt
...
F99_exvivo_2_struct_fnirt
...
F99_2_dwi_1.0mm_b04k_fnirt
...
F99_2_struct_fnirt
...
Microscopy
PLI
{Anterior/Posterior}
P001a ... P101x (anterior)
P001a ... P067x (posterior)
FOM_HSV.tif
Inclination.tif
In_plane.tif
Masks
P????_mask.tif
OR
P????_L_mask.tif
P????_R_mask.tif
Reg2MRI
{Full/Left/Right} (register full image or left/right hemisphere separately)
configuration.json
slice.tif
slice.timg
volume.png
volume.timg
Retardance.tif
Transmittance.tif
Gallyas
{Anterior/Posterior}
H001a ... H103x (anterior)
H001a ... H067x (posterior)
H???{a/b/x}_structureTensor (here x (a/b) indicates the slide was digitised in one (two) image(s))
HSV.tif
Orientation.tif
RGB_thumb.tif
struc_tensor.mat
Masks
H????_mask.tif
OR
H????_L_mask.tif
H????_R_mask.tif
Reg2MRI
{Full/Left/Right} (register full image or left/right hemisphere separately)
configuration.json
slice.tif
slice.timg
volume.png
volume.timg
svs
H???x.svs
OR
H???a.svs
H???b.svs