R/DATA.r
JFRC2NP.surf.RdSurface model of the Insect Brain Name Working Group neuropil segmentation in JFRC2 coordinate space
Note that the supplementary information for the Insect Brain Name
working group only includes neuropil information in the IBN
space, which can be downloaded from
ftp://flybase.org/flybase/associated_files/InsectBrainNomenclature_RawData.zip.
However Arnim Jenett, Kazunori Shinomiya and Kei Ito generated a full brain
segmentation based on the JFRC2 template (used by the Virtual
Fly Brain project and internally at Janelia Farm for several years.)
This surface model was generated in Amira from the segmentation in the file
JFRCtempate2010.mask130819.am with md5
d0a40b38d1a0045a423d947ebf1778d2. The data are available in a github
repository https://github.com/VirtualFlyBrain/DrosAdultBRAINdomains.
The original full size model was then simplified to reduce the number of
vertices resulting
Kei Ito, Kazunori Shinomiya, Masayoshi Ito, J. Douglas Armstrong, George Boyan, Volker Hartenstein, Steffen Harzsch, Martin Heisenberg, Uwe Homberg, Arnim Jenett, Haig Keshishian, Linda L. Restifo, Wolfgang Rössler, Julie H. Simpson, Nicholas J. Strausfeld, Roland Strauss, Leslie B. Vosshall, Insect Brain Name Working Group (2013). A systematic nomenclature for the insect brain. Neuron 81 (4), 755-765. doi:10.1016/j.neuron.2013.12.017
# list the materials for the different surface regions
materials(JFRC2NP.surf)
#> name id col
#> AME_R AME_R 1 #3C29CC
#> LO_R LO_R 2 #29CC99
#> NO NO 3 #47CC29
#> BU_R BU_R 4 #2960CC
#> PB PB 5 #CC8229
#> LH_R LH_R 6 #CC2855
#> LAL_R LAL_R 7 #CC6C28
#> SAD SAD 8 #9CCC27
#> CAN_R CAN_R 9 #EBBD79
#> AMMC_R AMMC_R 10 #47CC29
#> ICL_R ICL_R 11 #CCA229
#> VES_R VES_R 12 #FF7E7E
#> IB_R IB_R 13 #B529CC
#> ATL_R ATL_R 14 #7E7EFF
#> CRE_R CRE_R 15 #79EB93
#> MB_PED_R MB_PED_R 16 #CC69C0
#> MB_VL_R MB_VL_R 17 #29CCB4
#> MB_ML_R MB_ML_R 18 #A529CC
#> FLA_R FLA_R 19 #282DCC
#> LOP_R LOP_R 20 #7E7EFF
#> EB EB 21 #29CC6C
#> AL_R AL_R 22 #FF23DA
#> ME_R ME_R 23 #DC1717
#> FB FB 24 #A429CC
#> SLP_R SLP_R 25 #8BCC29
#> SIP_R SIP_R 26 #28CC4D
#> SMP_R SMP_R 27 #2987CC
#> AVLP_R AVLP_R 28 #27CCCC
#> PVLP_R PVLP_R 29 #6728CC
#> WED_R WED_R 30 #CC2763
#> PLP_R PLP_R 31 #00A48D
#> AOTU_R AOTU_R 32 #28CC60
#> GOR_R GOR_R 33 #CC2859
#> MB_CA_R MB_CA_R 34 #5E5ECC
#> SPS_R SPS_R 35 #EB9FD7
#> IPS_R IPS_R 36 #6565F7
#> SCL_R SCL_R 37 #C927CC
#> EPA_R EPA_R 38 #29CC3F
#> GNG GNG 39 #CC28A7
#> PRW PRW 40 #29CC85
#> GA_R GA_R 41 #141766
#> AME_L AME_L 42 #3B28CC
#> LO_L LO_L 43 #28CC99
#> BU_L BU_L 44 #2860CC
#> LH_L LH_L 45 #CC2855
#> LAL_L LAL_L 46 #CC6C28
#> CAN_L CAN_L 47 #EBBD79
#> AMMC_L AMMC_L 48 #46CC28
#> ICL_L ICL_L 49 #CCA228
#> VES_L VES_L 50 #FF7E7E
#> IB_L IB_L 51 #B528CC
#> ATL_L ATL_L 52 #7E7EFF
#> CRE_L CRE_L 53 #79EB93
#> MB_PED_L MB_PED_L 54 #CC69BF
#> MB_VL_L MB_VL_L 55 #28CCB4
#> MB_ML_L MB_ML_L 56 #A528CC
#> FLA_L FLA_L 57 #282DCC
#> LOP_L LOP_L 58 #0100FF
#> AL_L AL_L 59 #FF23D9
#> ME_L ME_L 60 #DB1717
#> SLP_L SLP_L 61 #8BCC28
#> SIP_L SIP_L 62 #28CC4D
#> SMP_L SMP_L 63 #2887CC
#> AVLP_L AVLP_L 64 #26CCCC
#> PVLP_L PVLP_L 65 #6728CC
#> WED_L WED_L 66 #CC2663
#> PLP_L PLP_L 67 #00A38D
#> AOTU_L AOTU_L 68 #28CC5F
#> GOR_L GOR_L 69 #CC2828
#> MB_CA_L MB_CA_L 70 #5D5ECC
#> SPS_L SPS_L 71 #EB9ED7
#> IPS_L IPS_L 72 #6464F7
#> SCL_L SCL_L 73 #C926CC
#> EPA_L EPA_L 74 #28CC3E
#> GA_L GA_L 75 #141666
if (FALSE) {
# plot the surface
plot3d(JFRC2NP.surf)
# calculate volumes ond surface areas for all regions
if(require("RvtkStatismo") && require("tidyr") && require("ggplot2") && require("stringr")){
# convert each region to a mesh3d object, note that we use simplify=FALSE
# to stop sapply mangling the result
meshes=sapply(JFRC2NP.surf$RegionList,
function(r) as.mesh3d(subset(JFRC2NP.surf, r)), simplify=FALSE)
# now calculate information for each mesh
# NB vtkMeshInfo returns a list so we need to convert to a vector
# to end up with a nice matrix output
vs=sapply(meshes, function(x) unlist(vtkMeshInfo(x)))
# now make that into a tidy data.frame and plot
df=data.frame(key=colnames(vs), t(vs),
region=sub("_[LR]", "", colnames(vs)),
side=str_match(colnames(vs), "_([LR])")[,2])
df2=gather(df, measure, value, volume:surfaceArea)
library(ggplot2)
qplot( value, region, data=df2, col=side, facets = . ~ measure) + scale_x_log10()
}
}