dyconnmap.chronnectomics package

Submodules

dyconnmap.chronnectomics.dwell_time module

Dwell Time

Dwell time measures the time (when used in the context of functional connectivity microstates) a which a state is active consecutive temporal segments (Dimitriadis2019_).


Dimitriadis2019

Dimitriadis, S. I., López, M. E., Maestu, F., & Pereda, E. (2019). Modeling the Switching behavior of Functional Connectivity Microstates (FCμstates) as a Novel Biomarker for Mild Cognitive Impairment. Frontiers in Neuroscience, 13.

dyconnmap.chronnectomics.dwell_time.dwell_time(x)[source]

Dwell Time

Compute the dwell time for the given symbolic, 1d time series.

Parameters

x (array-like, shape(N)) – Input symbolic time series.

Returns

  • dwell (dictionary) – KVP, where K=symbol id and V=array of dwell time.

  • mean (dictionary) – KVP, where K=symbol id and V=mean dwell time.

  • std (dictionary) – KVP, where K=symbol id and V=std dwell time.

dyconnmap.chronnectomics.flexibility_index module

Flexibility Index

In the context of graph clustering it was defined in (Basset2011), flexbility is the frequency of a nodea change module allegiance; the transition of brain states between consecutive temporal segments. The higher the number of changes, the larger the FI will be.

\[FI = \frac{\text{number of transitions}} {\text{total symbols - 1}}\]

Basset2011

Bassett, D. S., Wymbs, N. F., Porter, M. A., Mucha, P. J., Carlson, J. M., & Grafton, S. T. (2011). Dynamic reconfiguration of human brain networks during learning. Proceedings of the National Academy of Sciences, 108(18), 7641-7646.

dyconnmap.chronnectomics.flexibility_index.flexibility_index(x)[source]

Flexibility Index

Compute the flexibility index for the given symbolic, 1d time series.

Parameters

x (array-like, shape(N)) – Input symbolic time series.

Returns

fi – The flexibility index.

Return type

float

dyconnmap.chronnectomics.occupancy_time module

Occupancy Time

The fraction of number of distinct symbols occuring in the symbolic time series (Dimitriadis2019_).


Dimitriadis2019

Dimitriadis, S. I., López, M. E., Maestu, F., & Pereda, E. (2019). Modeling the Switching behavior of Functional Connectivity Microstates (FCμstates) as a Novel Biomarker for Mild Cognitive Impairment. Frontiers in Neuroscience, 13.

dyconnmap.chronnectomics.occupancy_time.occupancy_time(x)[source]

Occupancy Time

Compute the occupancy time for the given symbolic, 1d time series.

Parameters

x (array-like, shape(N)) – Input symbolic time series.

Returns

ot – KVP, where K=symbol id and V=occupancy time.

Return type

dictionary

Module contents

dyconnmap.chronnectomics.dwell_time(x)[source]

Dwell Time

Compute the dwell time for the given symbolic, 1d time series.

Parameters

x (array-like, shape(N)) – Input symbolic time series.

Returns

  • dwell (dictionary) – KVP, where K=symbol id and V=array of dwell time.

  • mean (dictionary) – KVP, where K=symbol id and V=mean dwell time.

  • std (dictionary) – KVP, where K=symbol id and V=std dwell time.

dyconnmap.chronnectomics.flexibility_index(x)[source]

Flexibility Index

Compute the flexibility index for the given symbolic, 1d time series.

Parameters

x (array-like, shape(N)) – Input symbolic time series.

Returns

fi – The flexibility index.

Return type

float

dyconnmap.chronnectomics.occupancy_time(x)[source]

Occupancy Time

Compute the occupancy time for the given symbolic, 1d time series.

Parameters

x (array-like, shape(N)) – Input symbolic time series.

Returns

ot – KVP, where K=symbol id and V=occupancy time.

Return type

dictionary