navis.tortuosity

navis.tortuosity(x, seg_length=10)

Calculate tortuosity of a neuron.

See Stepanyants et al., Neuron (2004) for detailed explanation. Briefly, tortuosity index T is defined as the ratio of the branch segment length L (seg_length) to the Euclidian distance R between its ends.

The way this is implemented in navis:

1. Each linear stretch (i.e. between branch points or branch points to a leaf node) is divided into segments of exactly seg_length geodesic length. Any remainder is skipped.

2. For each of these segments we divide its geodesic length L (i.e. seg_length) by the Euclidian distance R between its start and its end.

3. The final tortuosity is the mean of L / R across all segments.

Note

If you want to make sure that segments are as close to length L as possible, consider resampling the neuron using navis.resample_skeleton().

Parameters:

• x (TreeNeuron | MeshNeuron | NeuronList) – Neuron to analyze. If MeshNeuron, will generate and use a skeleton representation.

• seg_length (int | float | str | list thereof, optional) – Target segment length(s) L. If neuron(s) have their .units set, you can also pass a string such as “1 micron”. seg_length must be larger than the current sampling resolution of the neuron.

Returns:

tortuosity – If x is NeuronList, will return DataFrame. If x is single TreeNeuron, will return either a single float (if single seg_length is queried) or a DataFrame (if multiple seg_lengths are queried).

Return type:

float | np.array | pandas.DataFrame

See also

navis.segment_analysis()

This function provides by-segment morphometrics, including tortuosity.

Examples

>>> import navis
>>> n = navis.example_neurons(1)
>>> # Calculate tortuosity with 1 micron seg lengths
>>> T = navis.tortuosity(n, seg_length='1 micron')
>>> round(T, 3)
1.054