Fiber Tracking & DTI Flashcards
What does structural connectivity refer to?
It is physical white matter fibre connections between remote brain areas
What is functional connectivity?
It is statistical dependencies in neural activation time series of remote brain areas
What is the difference between structural and functional connectivity in a nutshell?
How are physically connected vs. How these connections are put into use
What technique is used to measure structure connectivity?
Diffusion Tensor Imaging (DTI), a specific MRI sequence
Who coined a term connectome?
Sporns and Hagman did it independently in 2005
A connectome (connection + -ome) is a network of structurally or functionally connected brain areas
How is connectivity matrix of the brain interpreted?
Columns and rows correspond to brain areas, the cells tell us whether the elements are connected
What directions do colours in tractography maps code?
Dorsal-ventral - blue
Posterior-anterior - green
Left-right - red
What brain tissue is in focus of DTI?
White matter (inside fibre tracks, axonal connections) — connectome is about the connections between these matters
What is diffusion?
It is a property of molecules to distribute evenly in the space (based on Brownian motion)
Why can diffusion of water molecules in brain be informative about fibre tracks?
Fat and water don’t go well together — diffusion is not happening through the fat, it is happening along the fat
Fat in the brain = myelin
Diffusion of water molecules in the brain depends on local myelin content
What is a tensor?
A diffusion tensor is a mathematical representation of the diffusion properties of water molecules, an item used to describe diffusion
How are isotropic and anisotropic diffusion and, hence, tensors different?
Isotropic
Diffusion: no external restriction => happening in all directions similarly, e.g. ventricles
Tensor: ball-like, with similar eigenvectors
Anisotropic
Diffusion:only in parallel to the axonal fibres, never in perpendicular, e.g. corpus calossum
Tensor: cigar-shaped, one eigenvector is more stretched into a certain direction
What is fractional anisotropy? How is it measured?
Fractional anisotropy (FA) is a scalar value derived from diffusion tensor imaging (DTI) that measures the degree of anisotropy or directionality of water diffusion.
FA values range from 0 to 1, where 0 is isotropic diffusion (equal diffusion in all directions) and 1 is highly anisotropic diffusion (diffusion predominantly along a single direction).
How is the starting voxel called?
A seed-voxel
What is a streamline?
The term streamline is used to designate the contiguous set of 3D points produced by tractography algorithms
We cannot claim that we are actually tracking fibres, that is why the term streamline is used
What anatomical contraints are there for the streamlines?
- Start: in grey matter — seed voxel at GM/WM
- End: in grey matter — crop at GM/WM
- Should never go through CSF — exclude implausible fibre tracts
Describe the connectome workflow
- High-resolution structural MRI (to delineate GM and WM) + DWIs (for WM)
- Different brain areas are defined on the basis of cortical thickness maps
- Superimposing brain areas on DTI and looking at the results of fibre tracking — whether there are streamlines that touch our ROIs
- Subtract the streamlines that are not touched
- Connectivity matrices: when we don’t have directional information, the matrix is symmetrical: e.g. there is more connectivity within the hemispheres than between the hemispheres, mostly connected via cortical matter
- An alternative to a matrix is a circular plot: two hemispheres
How much individual variations are there in brain connections?
A lot of individual variation:
* only 7% of possible connections are present in all participants;
* 20% of possible connections are not present in any participants
What are two approaches to representing connections in a network?
Binary vs. Weighted connections = Connection present/absent vs. Scalar
How to weigh connectivity between two areas?
- Number of streamlines
- Length of fibres
- (Average) fractional anisotropy: 0-1
- Generalised fractional anisotropy: also takes into account additional diffusion properties, such as radial diffusivity
- Streamline volume density: the density or concentration of streamlines (fiber tracts) within a specific volume of brain tissue, dissimilates between strong connections and connections between big areas
- Streamline surface density: the density of streamlines on the surface of brain structures or ROIs
How is a DTI acquired?
- Diffusion MTI is structural MRI (anatomy information, not functional connections => the data is no affected by any cognitive processes)
- Works on all modern MRI scanners (even though there are scanners specifically optimised for the connectome)
- At least 6 (= 3 directions multiplied by 2) diffusion directions needed (more are beneficial, e.g. 98)
- Time to acquire one full scan ~15 minutes
- Procedure: RF with 90 flip angle -> diffusion gradient -> RF with 180 flip angle -> diffusion gradient; signal is acquired in 90ms between FID and echo
What is a graph?
A graph is mathematical representation of a network G=(V,E), it is a collection if vertices (nodes) and their relationships (edges)
What are the characteristics of brain networks?
- Most networks have low network degree — exponential degree/strength distribution => high resilience of the whole system, low chance of destroying the network by randomly lesioning a random network (heavy-tailed distribution)
- Brain paths (a number of edges you need to pass through to get to a node — short paths in the brain): the brain is rather sparse, there are not so good connections between clusters
- A high degree of local clustering: 3 nodes, if 2 are connected, is the 3rd also connected — short paths and very efficient connectivity within a cluster
- Modularity: a lot of connections inside a module, a few connections outside a module
What are two defining features a small world network? What is common and what is different from regular and random networks?
A small-world network is a type of network characterized by two key properties: high clustering coefficient and short average path length.
Regular networks: strong connections between immediate neighbors, but long paths
Random networks: low clustering, but short paths
