Cortex

from mcot.surface.cortex import Cortex

class mcot.surface.cortex.Cortex(surfaces)

A cortex defined by 2 or more cortical meshes (sorted from inner to outer).

The first one will generally be the white/gray matter boundary and the last one the pial surface with arbitrarily many cortical meshes defined in between.

A ValueError will be raised if the cortical meshes describe different structures

__init__(surfaces)

Creates a new cortex from the provided cortical meshes.

Parameters

surfaces (Iterable[CorticalMesh]) – 2 or more cortical meshes describing the same brain structure (in the same hemisphere if appropriate)

Raises

ValueError

Inheritance diagram

Methods

distance(mask[, affine])	Computes for each non-zero voxel in mask the closest vertex.
gyral_bias(curvature[, use_cortex, …])	Computes the theoretical gyral bias based on a script originally derived by David van Essen.
segment(shape[, affine])	Segments a volume based on the cortical meshes.
volume([use_wb])	Computes the total volume of the cortex.
wedge_volume([use_wb, atpoint])	Computes the total wedge volume associated with every mesh triangle.

Attributes

hemisphere	Hemisphere containing the structure described by the cortical meshes.
nfaces	Number of faces in the cortical meshes.
nvertices	Number of vertices in the cortical meshes.
primary	Name of the structure described by the cortical meshes.

distance

Cortex.distance(mask, affine=None)

Computes for each non-zero voxel in mask the closest vertex.

Each vertex is represented by the line elements connecting that surface between the different cortical layers. The distance to each voxel center is calculated based on the distance to these line element.

Parameters

• mask – (Nx, Ny, Nz) array, which is non-zero for all the voxels that will be processed

• affine – transformation from the voxel space of mask to the mm-space in which the cortex has been defined

Returns

labeled array with ‘distance’, ‘closest’, ‘valid’:

• valid: boolean, which is True wherever the input mask is non-zero

• distance: contains the distance from the closest vertex (np.inf wherever the input mask is zero)

• closest: contains the index of the closest vertex (-1 wherever the input mask is zero)

gyral_bias

Cortex.gyral_bias(curvature, use_cortex=None, zero_curv_threshold=0.05)

Computes the theoretical gyral bias based on a script originally derived by David van Essen.

Converted from a matlab script from Stamatios Sotiropoulos.

Parameters

• curvature – (N, ) array describing the curvature of the surface at every point

• use_cortex – which part of the cortex to consider (e.g. remove the medial wall), default: not masked

• zero_curv_threshold – threshold below which the surface is considered to be flat

Returns

(Nfaces, )-array with the relative fiber density expected to cross each surface element

segment

Cortex.segment(shape, affine=None)

Segments a volume based on the cortical meshes.

Parameters

• shape – (Nx, Ny, Nz) tuple giving the shape of the resulting array

• affine – (4 x 4) array giving the transformation from voxel to mm coordinates

Returns

(Nx x Ny x Nz) array, that is 0 outside the brain, and increases by 1 for every surface crossed into the brain (so WM will be len(self.surfaces)).

volume

Cortex.volume(use_wb=False)

Computes the total volume of the cortex.

Parameters

use_wb – use workbench to compute the wedge volume if True (about 1.5 times larger than my implementation)

Returns

float with the total volume

wedge_volume

Cortex.wedge_volume(use_wb=False, atpoint=False)

Computes the total wedge volume associated with every mesh triangle.

Uses the algorithm from http://dx.doi.org/10.1101/074666 (Winkler et al, 2016)

Parameters

• use_wb – use workbench to compute the wedge volume if True (about 1.5 times larger than my implementation)

• atpoint – if True assigns the wedge volume to the vertices rather than the faces

Returns

(Nfaces, )-array with the wedge volume for every surface element