# The Electromechanical Connectome: Integrating Voltage, Mechanical Nano-Forces, and Subcellular Fluid Phase Dynamics in Human Neural Computation

## TL;DR

This paper proposes a new framework called the electromechanical connectome that integrates electrical, mechanical, and fluid dynamics in neurons to explain brain function and disease.

## Contribution

The paper introduces the novel concept of the electromechanical connectome, linking membrane potentials, mechanical forces, and phase-separated droplets in neural computation.

## Key findings

- Electrostatic fields influence lipid arrangement and organelle interactions.
- Mechanical dynamics in synapses affect channel energy and vesicle transport.
- Phase-separated micro-reactors regulate signaling and metabolism in neurons.

## Abstract

Electrophysiology, mechanobiology, and the study of soft matter within cells demonstrate increasing amounts of evidence that neuronal signaling arises from interactions between membrane potential, force, and phase. Herein, we have attempted to collect and organize the evidence for each of these areas of study into an approximate structure called the electromechanical connectome: a three-way state–space (membrane potentials, nanoscale mechanical forces, and cytoplasmic rheology, including phase-separated liquid–liquid droplets) where membrane potentials, nanoscale mechanical forces, and cytoplasmic rheology, and phase-separated liquid–liquid droplets are likely to influence one another, influencing synaptic processing, plasticity and network stability. We will also attempt to illustrate the following: how changes in electrostatic fields can be used to alter the arrangement of lipids, hydration, and dielectric microdomains, and the contact geometry between organelles and activity dependent transcription; how mechanical dynamics associated with spines, axons, and the active zone of synapses may be used to modify the energy landscape of channels, the docking and priming of vesicles, and the transport of cytoskeletons; and how viscosity corridors, along with phase-separated micro-reactors, can be used to regulate the kinetics of signaling, molecular trafficking and metabolic processes in local environments. With these connections in mind, we will propose a multiphysical attractor model in which cognition is the result of navigating through metastable manifolds, while neurodegenerative disease may be a result of the progressive loss of electromechanical coherence, phase boundary control and energetic flexibility. Finally, we will present testable hypotheses and use AI-enabled digital twin methods to potentially quantify the early deformation of manifolds and provide precision biomarkers and therapeutic options.