Evaluation and statistical inference for living connectomes.
Standard tractography can use diffusion measurements from a living brain to generate a large collection of candidate white-matter fascicles; the connectome. Linear Fascicle Evaluation (LiFE) takes any connectome and uses a forward modelling approach to predict diffusion measurements in the same brain. LiFE predicts the measured diffusion signal using the orientation of the fascicles present in a connectome. LiFE uses the difference between the measured and predicted diffusion signals to measure prediction error. The connectome model prediction error is used to compute two metrics to evaluate the evidence supporting properties of the connectome. One metric -the strength of evidence - compares the mean prediction error between alternative hypotheses. The second metric - the earth movers distance - compares full distributions of prediction error. These metrics can be used for: 1. Comparing tractography algorithms 2. Evaluating the quality of tractography solutions for individual brains or group of brains and 3. Testing hypotheses about white-matter tracts and connections.
Perform statistical inference on white-matter connectomes: Compare white-matter connectomes, show the evidence for white-matter tracts and connections between brain areas.
This work was supported by grants by US National Science Fundation (NSF BCS-1228397; NSF IIS-1636893; NSF BCS-1734853) and National Institute for Health (NEI EY015000).
Read the description of the calculations in the documentation inside the file, life_demo.m by typing the following in the matlab prompt:
>> edit life_demo
This final step will run the life_demo code. The code will perform the operations described here.
life_demo.m runs in about 30 minutes on a modern Intel processor with 8GB of RAM. This code has been tested with MatLab 2012b on Ubuntu 12.10 and Mac OSX 10.9.