Measuring the Non-linear Directed Information Flow in Schizophrenia by Multivariate Transfer Entropy

Harmah, Dennis Joe; Li, Cunbo; Li, Fali; Liao, Yuanyuan; Wang, J.; Ayedh, Walid Mohammed Ahmed; Bore, Joyce Chelangat; Yao, Dezhong; Dong, Wei; Xu, Peng

doi:10.3389/fncom.2019.00085

Cited by 34 publications

(18 citation statements)

References 67 publications

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“…This can be attempted in future works. For instance (Harmah et al, 2019) in a recent paper, evaluated multivariate TE in people with schizophrenia, and found that multivariate TE outperformed bivariate TE and Granger causality analysis under various signal-to-noise conditions. However, it is to be stressed that this difference between our results and those obtained with multivariate TE are probably not so strong as in other works, since Trentool implements some tools for post-hoc corrections of multivariate effects, i.e., a partial correction of spurious information flow.…”

Section: Discussionmentioning

confidence: 99%

Transfer Entropy as a Measure of Brain Connectivity: A Critical Analysis With the Help of Neural Mass Models

Ursino

Ricci

Magosso

2020

Front. Comput. Neurosci.

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Assessing brain connectivity from electrophysiological signals is of great relevance in neuroscience, but results are still debated and depend crucially on how connectivity is defined and on mathematical instruments utilized. Aim of this work is to assess the capacity of bivariate Transfer Entropy (TE) to evaluate connectivity, using data generated from simple neural mass models of connected Regions of Interest (ROIs). Approach: Signals simulating mean field potentials were generated assuming two, three or four ROIs, connected via excitatory or by-synaptic inhibitory links. We investigated whether the presence of a statistically significant connection can be detected and if connection strength can be quantified. Main Results: Results suggest that TE can reliably estimate the strength of connectivity if neural populations work in their linear regions, and if the epoch lengths are longer than 10 s. In case of multivariate networks, some spurious connections can emerge (i.e., a statistically significant TE even in the absence of a true connection); however, quite a good correlation between TE and synaptic strength is still preserved. Moreover, TE appears more robust for distal regions (longer delays) compared with proximal regions (smaller delays): an approximate a priori knowledge on this delay can improve the procedure. Finally, non-linear phenomena affect the assessment of connectivity, since they may significantly reduce TE estimation: information transmission between two ROIs may be weak, due to non-linear phenomena, even if a strong causal connection is present. Significance: Changes in functional connectivity during different tasks or brain conditions, might not always reflect a true change in the connecting network, but rather a change in information transmission. A limitation of the work is the use of bivariate TE. In perspective, the use of multivariate TE can improve estimation and reduce some of the problems encountered in the present study.

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Section: Discussionmentioning

confidence: 99%

Transfer Entropy as a Measure of Brain Connectivity: A Critical Analysis With the Help of Neural Mass Models

Ursino

Ricci

Magosso

2020

Front. Comput. Neurosci.

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“…On this basis, the approach proposed here has the potential to be applicable to the inference of structural and effective connectivity in in vitro and in vivo neuronal circuits with a strong impact on the comprehension of basic mechanisms underlying information acquisition, storage, and transmission in physiological or pathological scenarios. Particular relevance in this regard concerns the investigation of network properties in neurological disorders or pathologies, such as schizophrenia, Alzheimer’s disease, strokes and epilepsy [ 60 , 61 , 62 , 63 , 64 ], where our approach can be useful not only in predicting general features of connectivity but also in identifying the unbalance between excitation and inhibition.…”

Section: Discussionmentioning

confidence: 99%

Inferring Excitatory and Inhibitory Connections in Neuronal Networks

Ghirga

Chiodo

Marrocchio

et al. 2021

Entropy

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The comprehension of neuronal network functioning, from most basic mechanisms of signal transmission to complex patterns of memory and decision making, is at the basis of the modern research in experimental and computational neurophysiology. While mechanistic knowledge of neurons and synapses structure increased, the study of functional and effective networks is more complex, involving emergent phenomena, nonlinear responses, collective waves, correlation and causal interactions. Refined data analysis may help in inferring functional/effective interactions and connectivity from neuronal activity. The Transfer Entropy (TE) technique is, among other things, well suited to predict structural interactions between neurons, and to infer both effective and structural connectivity in small- and large-scale networks. To efficiently disentangle the excitatory and inhibitory neural activities, in the article we present a revised version of TE, split in two contributions and characterized by a suited delay time. The method is tested on in silico small neuronal networks, built to simulate the calcium activity as measured via calcium imaging in two-dimensional neuronal cultures. The inhibitory connections are well characterized, still preserving a high accuracy for excitatory connections prediction. The method could be applied to study effective and structural interactions in systems of excitable cells, both in physiological and in pathological conditions.

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“…To overcome these limitations, conditional mutual information methods such as momentary TE 67 should be employed to estimate direct causality by conditioning out the effects of possible common drivers. Other alternatives to reduce the effects of common drivers exist 68 – 71 . Nevertheless, these techniques share the issue of estimation of neural interactions when the number of nodes in the analysis is large.…”

Section: Discussionmentioning

confidence: 99%

Information flow in the rat thalamo-cortical system: spontaneous vs. stimulus-evoked activities

Ishizu

Shiramatsu

Hitsuyu

et al. 2021

Sci Rep

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The interaction between the thalamus and sensory cortex plays critical roles in sensory processing. Previous studies have revealed pathway-specific synaptic properties of thalamo-cortical connections. However, few studies to date have investigated how each pathway routes moment-to-moment information. Here, we simultaneously recorded neural activity in the auditory thalamus (or ventral division of the medial geniculate body; MGv) and primary auditory cortex (A1) with a laminar resolution in anesthetized rats. Transfer entropy (TE) was used as an information theoretic measure to operationalize “information flow”. Our analyses confirmed that communication between the thalamus and cortex was strengthened during presentation of auditory stimuli. In the resting state, thalamo-cortical communications almost disappeared, whereas intracortical communications were strengthened. The predominant source of information was the MGv at the onset of stimulus presentation and layer 5 during spontaneous activity. In turn, MGv was the major recipient of information from layer 6. TE suggested that a small but significant population of MGv-to-A1 pairs was “information-bearing,” whereas A1-to-MGv pairs typically exhibiting small effects played modulatory roles. These results highlight the capability of TE analyses to unlock novel avenues for bridging the gap between well-established anatomical knowledge of canonical microcircuits and physiological correlates via the concept of dynamic information flow.

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Measuring the Non-linear Directed Information Flow in Schizophrenia by Multivariate Transfer Entropy

Cited by 34 publications

References 67 publications

Transfer Entropy as a Measure of Brain Connectivity: A Critical Analysis With the Help of Neural Mass Models

Transfer Entropy as a Measure of Brain Connectivity: A Critical Analysis With the Help of Neural Mass Models

Inferring Excitatory and Inhibitory Connections in Neuronal Networks

Information flow in the rat thalamo-cortical system: spontaneous vs. stimulus-evoked activities

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