# Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes

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## Abstract

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## 1. Introduction

^{®}toolbox, which is uploaded as Supplementary Material to this article and is freely available for download from www.lucafaes.net/msID.html and https://github.com/danielemarinazzo/multiscalePID.

## 2. Information Transfer Decomposition in Multivariate Processes

#### 2.1. Interaction Information Decomposition

#### 2.2. Partial Information Decomposition

## 3. Multiscale Information Transfer Decomposition

#### 3.1. Multiscale Representation of Multivariate Gaussian Processes

#### 3.2. State Space Processes

#### 3.2.1. Formulation of State Space Models

#### 3.2.2. State Space Models of Filtered and Downsampled Linear Processes

#### 3.3. Multiscale IID and PID

## 4. Simulation Experiment

- (a)
- isolation of ${Y}_{1}$ and ${Y}_{2}$ and unidirectional coupling ${Y}_{3}\to {Y}_{4}$, obtained setting $b=c=0$;
- (b)
- common driver effects ${Y}_{2}\leftarrow {Y}_{1}\to {Y}_{3}$ and unidirectional coupling ${Y}_{3}\to {Y}_{4}$, obtained setting $b=0$ and $c=1$;
- (c)
- isolation of ${Y}_{1}$ and unidirectional couplings ${Y}_{2}\to {Y}_{4}$ and ${Y}_{3}\to {Y}_{4}$, obtained setting $b=0.5$ and $c=0$;
- (d)
- common driver effects ${Y}_{2}\leftarrow {Y}_{1}\to {Y}_{3}$ and unidirectional couplings ${Y}_{2}\to {Y}_{4}$ and ${Y}_{3}\to {Y}_{4}$, obtained setting $b=0.5$ and $c=1$.

## 5. Application

## 6. Conclusions

## Supplementary Materials

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 1.**Venn diagram representations of the interaction information decomposition (IID) (

**a**,

**b**) and the partial information decomposition (PID) (

**c**). The IID is depicted in a way such that all areas in the diagrams are positive: the interaction information transfer ${\mathcal{I}}_{ik\to j}$ is positive in (

**a**), denoting net synergy, and is negative in (

**b**), denoting net redundancy.

**Figure 2.**Schematic representation of a linear VAR process and of its multiscale representation obtained through filtering (FLT) and downsampling (DWS) steps. The downsampled process has an innovations form state space model (ISS) representation from which submodels can be formed to compute the partial variances needed for the computation of information measures appearing in the IID and PID decompositions. This makes it possible to perform multiscale information decomposition analytically from the original VAR parameters and from the scale factor.

**Figure 3.**Graphical representation of the four-variate VAR process of Equation (20) that we use to explore the multiscale decomposition of the information transferred to ${Y}_{4}$, selected as the target process, from ${Y}_{2}$ and ${Y}_{3}$, selected as the source processes, in the presence of ${Y}_{1}$, acting as the exogenous process. To favor such exploration, we set oscillations at different time scales for ${Y}_{1}$ (${f}_{1}=0.1$) and for ${Y}_{2}$ and ${Y}_{3}$ (${f}_{2}={f}_{3}=0.025$), induce common driver effects from the exogenous process to the sources modulated by the parameter c and allow for varying strengths of the causal interactions from the sources to the target as modulated by the parameter b. The four configurations explored in this study are depicted in (

**a**–

**d**).

**Figure 4.**Multiscale information decomposition for the simulated VAR process of Equation (20). Plots depict the exact values of the entropy measures forming the interaction information decomposition (IID, upper row) and the partial information decomposition (PID, lower row) of the information transferred from the source processes ${Y}_{2}$ and ${Y}_{3}$ to the target process ${Y}_{4}$ generated according to the scheme of Figure 3 with four different configurations of the parameters. We find that linear processes may generate trivial information patterns with the absence of synergistic or redundant behaviors (

**a**); patterns with the prevalence of redundant information transfer (

**b**) or synergistic information transfer (

**c**) that persist across multiple time scales; or even complex patterns with the alternating prevalence of redundant transfer and synergistic transfer at different time scales (

**d**).

**Figure 5.**Interaction information decomposition (IID) of the intracranial EEG information flow from subcortical to cortical regions in an epileptic patient. The joint transfer entropy from depth Channels 11 and 12 to cortical electrodes (

**a**); the transfer entropy from depth Channel 11 to cortical electrodes (

**b**); the transfer entropy from depth Channel 12 to cortical electrodes (

**c**) and the interaction transfer entropy from depth Channels 11 and 12 to cortical electrodes (

**d**) are depicted as a function of the scale $\tau $, after averaging over the eight pre-ictal segments (left column) and over the eight ictal segments (right column). Compared with pre-ictal periods, during the seizure, the IID evidences marked increases of the joint and individual information transfer from depth to cortical electrodes and low and almost unvaried levels of interaction transfer.

**Figure 6.**Partial information decomposition (PID) of the intracranial EEG information flow from subcortical to cortical regions in an epileptic patient. The synergistic transfer entropy from depth Channels 11 and 12 to cortical electrodes (

**a**); the redundant transfer entropy from depth Channels 11 and 12 to cortical electrodes (

**b**); the unique transfer entropy from depth Channel 11 to cortical electrodes (

**c**) and the unique transfer entropy from depth Channel 12 to cortical electrodes (

**d**) are depicted as a function of the scale $\tau $, after averaging over the eight pre-ictal segments (left column) and over the eight ictal segments (right column). Compared with pre-ictal periods, during the seizure, the PID evidences marked increases of the information transferred synergistically and redundantly from depth to cortical electrodes and of the information transferred uniquely from one of the two depth electrodes, but not from the other.

**Figure 7.**Multiscale representation of the measures of interaction information decomposition (IID, top) and partial information decomposition (PID, bottom) computed as a function of the time scale for each of the eight seizures during the pre-ictal period (black) and the ictal period (red). Values of joint transfer entropy (TE), individual TE, interaction TE, redundant TE, synergistic TE and unique TE are obtained taking the depth Channels 11 and 12 as sources and averaging over all 64 target cortical electrodes. Increases during seizure of the joint TE, individual TEs from both depth electrodes, redundant and synergistic TE and unique TE from the depth electrode 12 are evident at low time scales for almost all considered episodes.

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**MDPI and ACS Style**

Faes, L.; Marinazzo, D.; Stramaglia, S.
Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes. *Entropy* **2017**, *19*, 408.
https://doi.org/10.3390/e19080408

**AMA Style**

Faes L, Marinazzo D, Stramaglia S.
Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes. *Entropy*. 2017; 19(8):408.
https://doi.org/10.3390/e19080408

**Chicago/Turabian Style**

Faes, Luca, Daniele Marinazzo, and Sebastiano Stramaglia.
2017. "Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes" *Entropy* 19, no. 8: 408.
https://doi.org/10.3390/e19080408