Rehabilitation Modulates High-Order Interactions Among Large-Scale Brain Networks in Subacute Stroke

The recovery of motor functions after stroke is fostered by the functional integration of large-scale brain networks, including the motor network (MN) and high-order cognitive controls networks, such as the default mode (DMN) and executive control (ECN) networks. In this paper, electroencephalography signals are used to investigate interactions among these three resting state networks (RSNs) in subacute stroke patients after motor rehabilitation. A novel metric, the O-information rate (OIR), is used to quantify the balance between redundancy and synergy in the complex high-order interactions among RSNs, as well as its causal decomposition to identify the direction of information flow. The paper also employs conditional spectral Granger causality to assess pairwise directed functional connectivity between RSNs. After rehabilitation, a synergy increase among these RSNs is found, especially driven by MN. From the pairwise description, a reduced directed functional connectivity towards MN is enhanced after treatment. Besides, inter-network connectivity changes are associated with motor recovery, for which the mediation role of ECN seems to play a relevant role, both from pairwise and high-order interactions perspective.

Comparison between directed causal flow metrics for the assessment of resting-state EEG motor network connectivity in subacute stroke patients

Isolated effective coherence (iCoh) is a measure of neural causal functional connectivity from EEG signals that was proven to overperform the Generalized Partial Directed Coherence (gPDC). However, iCoh sensitivity in the identification of reliable functional neural connections with respect to random links was not investigated. This study aims to compare the sensitivity of iCoh and gPDC with a statistical surrogates' approach. The cerebral motor network topology of a cohort of subjects in sub-acute stage after stroke was investigated. iCoh showed enhanced statistical discriminative power of the relevant connections within the motor network with respect to gPDC. This property influenced the assessment of ipsilesional intra-hemispheric topographic variations occurring in the population after a physical rehabilitation program.

A Multi-Variate framework to assess reliability and discrimination power of Bayesian estimation of Intravoxel Incoherent Motion parameters

PURPOSE

To propose a multivariate multi-step framework for a systematic assessment of the estimation reliability and discriminability of Intravoxel Incoherent Motion (IVIM) model parameters.

METHODS

Monte-Carlo simulations were generated on a range of SNRs and in different IVIM combinations considering: i) a dense discretization with 24 b-values; ii) a discretization with 9 b-values. A state-of-the-art Bayesian fitting method was adopted. The framework assessed: i) the best model between mono- and bi-exponential, through the BIC index; ii) the fitting accuracy; iii) the power in discriminating two different IVIM parameters distributions of estimated coefficients, using a multivariate test. Exemplificative oncologic cases were also presented.

RESULTS

The bi-exponential fitting was reliable for perfusion fraction higher than 5%, with high accuracy in D estimation, acceptable error for f, but high uncertainty in D*. The discrimination of two distributions is generally feasible if differences in D values (at least 0.3 x10^-3 mm²/s) are present; in the case of similar D values, a minimal difference of 5% in f can be discriminated just in case of balanced sample size and dense b-values discretization, whereas the impact of D* is quite negligible. These results were also supported by clinical examples.

CONCLUSIONS

IVIM model is generally accurate in estimating diffusion, but uncertainties related to perfusion estimation are not negligible and compromise the discrimination power when different populations should be differentiated. The proposed framework should be adopted as interpretative guidelines to better understand when IVIM model applied on real data can provide reliable findings.