Real-Time In Vivo Control of Neural Membrane Potential by Electro-Ionic Modulation

Soybaş, Zafer; Şimşek, Sefa; Erol, F.M. Betül; Erdoğan, Uğraş; Şimşek, Esra N.; Şahin, Büşra; Marçalı, Merve; Aydogdu, Bahattin; Elbüken, Çağlar; Melik, Rohat

doi:10.1016/j.isci.2019.06.038

Cited by 3 publications

(1 citation statement)

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“…The variability and noise in neural dynamics is well known to challenge the determination of neural phenomenon, e.g., the detection of onset of epileptic seizures (Biswas et al, 2014 ; Vidyaratne and Iftekharuddin, 2017 ). So, when we say “spike in activity of neuron A is a cause of the spike in activity of neuron B,” the cause makes the effect more likely and not certain due to other factors like incoming inhibitory projections from other neurons and extracellular ion concentration (Kroener et al, 2009 ; Soybaş et al, 2019 ). Moreover, additional arbitrary functional relationships between each cause and effect could exist such that these introduce arbitrary disturbances following some undisclosed probability distribution.…”

Section: From Association To Causationmentioning

confidence: 99%

Statistical Perspective on Functional and Causal Neural Connectomics: A Comparative Study

Biswas

Shlizerman

2022

Front. Syst. Neurosci.

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Representation of brain network interactions is fundamental to the translation of neural structure to brain function. As such, methodologies for mapping neural interactions into structural models, i.e., inference of functional connectome from neural recordings, are key for the study of brain networks. While multiple approaches have been proposed for functional connectomics based on statistical associations between neural activity, association does not necessarily incorporate causation. Additional approaches have been proposed to incorporate aspects of causality to turn functional connectomes into causal functional connectomes, however, these methodologies typically focus on specific aspects of causality. This warrants a systematic statistical framework for causal functional connectomics that defines the foundations of common aspects of causality. Such a framework can assist in contrasting existing approaches and to guide development of further causal methodologies. In this work, we develop such a statistical guide. In particular, we consolidate the notions of associations and representations of neural interaction, i.e., types of neural connectomics, and then describe causal modeling in the statistics literature. We particularly focus on the introduction of directed Markov graphical models as a framework through which we define the Directed Markov Property—an essential criterion for examining the causality of proposed functional connectomes. We demonstrate how based on these notions, a comparative study of several existing approaches for finding causal functional connectivity from neural activity can be conducted. We proceed by providing an outlook ahead regarding the additional properties that future approaches could include to thoroughly address causality.

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