Differences between revisions 8 and 9
Deletions are marked like this. Additions are marked like this.
Line 5: Line 5:
||We applied a hierarchical Bayesian model (figure on the right) on 10,449 Brainmap experiments across 83 behavioral task categories. By formalizing the notion that performing a given task engages multiple cognitive components, each supported by overlapping brain regions.

we identified cognitive components that are shared across tasks. The estimated components enabled the derivation of quantitative maps of functional specialization, revealing complex zones of frontal and parietal regions ranging from being highly specialized to highly flexible. An independent resting-state fMRI dataset (N=1000) was used to explore the network organization of the specialized and flexible regions. Cortical regions specialized for the same components were strongly coupled, suggesting that components function as partially isolated networks. Although functionally flexible regions participated in multiple cognitive components, they exhibited heterogeneous selectivity across components. This heterogeneous selectivity was strongly predicted by the connectivity between functionally flexible and specialized regions. Functionally flexible regions might support the core of the brain’s information processing capacity, binding or integrating the processing power of segregated, specialized brain networks. Our results suggest that heterogeneous networks of functionally specialized and flexible association regions may contribute to the ability to execute multiple and varied tasks. || {{attachment:BrainmapCognitiveOntology_Yeo2014_model.png|test | width=400 height=233}}||
||We applied a hierarchical Bayesian model (figure on right) on 10,449 Brainmap experiments across 83 behavioral tasks. By formalizing the notion that performing a given task engages multiple cognitive components, each supported by overlapping brain regions, we identified cognitive components that are shared across tasks. The estimated components enabled the derivation of quantitative maps of functional specialization, revealing complex zones of frontal and parietal regions ranging from being highly specialized to highly flexible. An independent resting-state fMRI dataset (N=1000) was used to explore the network organization of the specialized and flexible regions. Cortical regions specialized for the same components were strongly coupled, suggesting that components function as partially isolated networks. Functionally flexible regions participate in multiple components to different degrees. This heterogeneous selectivity was strongly predicted by the connectivity between functionally flexible and specialized regions. Functionally flexible regions might support the core of the brain’s information processing capacity, binding or integrating the processing power of segregated, specialized brain networks. The estimated components in FreeSurfer space and MNI152 space, as well as specificity and flexibility estimates are available for download. || {{attachment:BrainmapCognitiveOntology_Yeo2014_model.png|test | width=400 height=233}}||

top

Specialization and Flexibility in Human Association Cortex

We applied a hierarchical Bayesian model (figure on right) on 10,449 Brainmap experiments across 83 behavioral tasks. By formalizing the notion that performing a given task engages multiple cognitive components, each supported by overlapping brain regions, we identified cognitive components that are shared across tasks. The estimated components enabled the derivation of quantitative maps of functional specialization, revealing complex zones of frontal and parietal regions ranging from being highly specialized to highly flexible. An independent resting-state fMRI dataset (N=1000) was used to explore the network organization of the specialized and flexible regions. Cortical regions specialized for the same components were strongly coupled, suggesting that components function as partially isolated networks. Functionally flexible regions participate in multiple components to different degrees. This heterogeneous selectivity was strongly predicted by the connectivity between functionally flexible and specialized regions. Functionally flexible regions might support the core of the brain’s information processing capacity, binding or integrating the processing power of segregated, specialized brain networks. The estimated components in FreeSurfer space and MNI152 space, as well as specificity and flexibility estimates are available for download.

test

References

Yeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, Roffman JL, Smoller JW, Zollei L., Polimeni JR, Fischl B, Liu H, Buckner RL. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol 106(3):1125-65, 2011.

Parcellations in FreeSurfer Surface Space

7 Network Estimate

7 Network Confidence

17 Network Estimate

17 Network Confidence

Downloads

Resting State Cortical Parcellation in fsaverage, fsaverage6 and fsaverage5 Space. These subjects are also currently in the developmental version of FreeSurfer which can be accessed from the Martinos center network (/autofs/cluster/freesurfer/centos4.0_x86_64/dev/subjects/). These subjects will be officially included in some future FreeSurfer release. Note however, the zip file linked here corresponds exactly to that from the reference, while functional parcellations in the developmental and official release versions of FreeSurfer are subjected to modifications/improvements.

Information about Downloads

There are three folders in "Yeo_JNeurophysiol11_FreeSurfer.zip", corresponding to the "fsaverage", "fsaverage5" and "fsaverage6" surface space. "fsaverage" contains the high resolution version of the parcellation, while "fsaverage6" and "fsaverage5" contain lower resolution versions of the parcellation. The parcellations were computed in "fsaverage5" space and upsampled to "fsaverage6" and "fsaverage".

The structure of each folder follows that of a preprocessed freesurfer subject. In particular, "fsaverage/label/", "fsaverage5/label/", "fsaverage6/label/" contain all the parcellation and confidence files. For example, "fsaverage/label/rh.Yeo2011_7Networks_N1000.annot" is the 7-network parcellation for 1000 subjects on the right hemisphere and "fsaverage/label/lh.Yeo2011_17NetworksConfidence_N1000.mgz" is the confidence map for the 17-network parcellation for 1000 subjects on the left hemisphere.

Example Usage

See README in unzipped folder

Parcellations in Nonlinear MNI152 Volume Space

7 Network Tight Mask

7 Network Liberal Mask

17 Network Tight Mask

17 Network Liberal Mask

Downloads

Resting State Cortical Parcellation in nonlinear MNI152 space. We are working to put these subjects in FreeSurfer. Note however, the zip file linked here corresponds exactly to that from the reference, while functional parcellations in the developmental and official release versions of FreeSurfer are subjected to modifications/improvements.

Information about Downloads

1. FSL_MNI152_FreeSurferConformed_1mm.nii.gz is the FSL MNI152 1mm template interpolated and intensity normalized into a 256 x 256 x 256 1mm-isotropic volume (obtained by putting the FSL MNI152 1mm template through recon-all using FreeSurfer 4.5.0)

2. Yeo2011_7Networks_MNI152_FreeSurferConformed1mm.nii.gz is a volume consisting of 7 cortical networks projected into MNI152 space. The cortical ribbon is defined by putting the FSL MNI152 1mm template through recon-all using FreeSurfer 4.5.0. The slices of this volume is shown in Yeo et al., 2011.

3. Yeo2011_7Networks_MNI152_FreeSurferConformed1mm.nii.gz is a volume consisting of 7 cortical networks projected into MNI152 space. The cortical ribbon is defined in a more liberal fashion than in (2). More specifically, the cortical mask is obtained by nonlinear warping 1000 subjects (from Yeo et al. 2011, Buckner et al. 2011) into MNI152 space via the FreeSurfer recon-all pipeline. An initial mask is first obtained where a vox l is decided to be a cortical voxel if the cortex of more than 150 subjects (out of 1000 subjects) were mapped to the voxel or if the voxel is labeled as part of the cortical ribbon from (2). This cortical mask is th n smoothed, and small holes and islands in the masks are removed by simple morphological operations.

4. Yeo2011_7Networks_ColorLUT.txt is a FreeSurfer readable text file specifying how the 7 networks are named, numbered and colored in Yeo et al. 2011:

  • Index

    Network Name

    R

    G

    B

    0

    NONE

    0

    0

    0

    0

    1

    7Networks_1

    120

    18

    134

    0

    2

    7Networks_2

    70

    130

    180

    0

    3

    7Networks_3

    0

    118

    14

    0

    4

    7Networks_4

    196

    58

    250

    0

    5

    7Networks_5

    220

    248

    164

    0

    6

    7Networks_6

    230

    148

    34

    0

    7

    7Networks_7

    205

    62

    78

    0

In particular the networks are numbered from 7Networks_1 to 7Networks_7. The first column of the text file specifies the value of voxels in the nifty values corresponding to the particular network. The second column f the text file specifies the named of the networks. For example, from the text file, voxels whose values = 3 corresponds to the network 7Networks_3.Columns 3 to 5 corresponds to the R, G, B values (ranges from 0 to 55) of the networks. Last column is all zeros (FreeSurfer's default).

5. Yeo2011_17Networks_MNI152_FreeSurferConformed1mm.nii.gz is a volume consisting of 17 cortical networks projected into MNI152 space. The cortical ribbon is defined in the same fashion as (2), i.e., by putting the F L MNI152 1mm template through recon-all using FreeSurfer 4.5.0.

6. Yeo2011_17Networks_MNI152_FreeSurferConformed1mm.nii.gz is a volume consisting of 17 cortical networks projected into MNI152 space. The cortical ribbon is defined in a more liberal fashion than in (2) and in the s me way as (3).

7. Yeo2011_17Networks_ColorLUT.txt is a FreeSurfer readable text file specifying how the 17 networks are named, numbered and colored in Yeo et al. 2011:

  • Index

    Network Name

    R

    G

    B

    0

    NONE

    0

    0

    0

    0

    1

    17Networks_1

    120

    18

    134

    0

    2

    17Networks_2

    255

    0

    0

    0

    3

    17Networks_3

    70

    130

    180

    0

    4

    17Networks_4

    42

    204

    164

    0

    5

    17Networks_5

    74

    155

    60

    0

    6

    17Networks_6

    0

    118

    14

    0

    7

    17Networks_7

    196

    58

    250

    0

    8

    17Networks_8

    255

    152

    213

    0

    9

    17Networks_9

    200

    248

    164

    0

    10

    17Networks_10

    122

    135

    50

    0

    11

    17Networks_11

    119

    140

    176

    0

    12

    17Networks_12

    230

    148

    34

    0

    13

    17Networks_13

    135

    50

    74

    0

    14

    17Networks_14

    12

    48

    255

    0

    15

    17Networks_15

    0

    0

    130

    0

    16

    17Networks_16

    255

    255

    0

    0

    17

    17Networks_17

    205

    62

    78

    0

Example Usage

See README in unzipped folder

Other downloads

Cerebral Surface Parcellations in Caret Space

Movies of Cortical Seed-based Functional Connectivity

Cerebellar Parcellation in MNI Space

Movies of Cerebellar Seed-based Functional Connectivity

Striatum Parcellation in MNI Space

Code for the von Mises-Fisher Mixture Model Clustering

BrainmapOntology_Yeo2015 (last edited 2015-10-15 20:35:10 by ThomasYeo)