Cortical Network Structure Mediates Brain Stimulation Response
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Solution Overview
Problem
Current methods for neural stimulation, informed by Spike-Timing Dependent Plasticity (STDP), struggle to accurately predict network-wide functional connectivity changes in vivo, leading to inconsistent results and off-target effects in treating neural disorders.
Innovation Solution
A neural interface technology that employs optogenetics and micro-electrocorticography (ECoG) to record neural activity, combined with nonparametric hierarchical additive modeling, which considers both stimulation protocol features and underlying network-level functional connectivity to predict network-wide stimulation-induced functional connectivity changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pairwise stimulation informed by STDP is used to induce targeted functional connectivity change, then connectivity modification between stimulation targets is achieved, but prediction accuracy of network-wide functional connectivity changes deteriorates
Solution Approach 1:
The patent implements a feedback mechanism by using recorded neural activity to update the predictive model of functional connectivity changes. The system continuously refines its predictions by comparing expected connectivity changes with actual recorded changes, allowing it to adapt to individual network characteristics and improve prediction accuracy over time while maintaining reliable connectivity modification.
Solution Approach 2:
The patent applies preliminary action by measuring baseline functional connectivity before stimulation and using this pre-stimulation state to predict post-stimulation connectivity changes. This preliminary measurement of the network's initial state allows the system to account for individual variability and provide more accurate predictions of stimulation-induced functional connectivity changes.
2Device complexity
If stimulation protocol features alone are considered for predicting functional connectivity changes, then simplicity of prediction model is maintained, but prediction accuracy deteriorates
Solution Approach 1:
The patent merges multiple sources of information including stimulation protocol features, baseline functional connectivity metrics, and network topology characteristics into a unified predictive model. This combination allows the system to maintain reasonable model complexity while significantly improving prediction accuracy by integrating both stimulation parameters and individual network properties.
Solution Approach 2:
The patent develops a universal predictive framework that can accommodate different stimulation protocols and network types. The model uses a standardized set of functional connectivity metrics and network features that can be applied across various stimulation conditions and brain regions, providing both simplicity through standardization and accuracy through comprehensive feature integration.
3Adaptability or versatility
If network-level functional connectivity is not considered in stimulation design, then stimulation protocol can be applied universally, but therapeutic outcome consistency deteriorates
Solution Approach 1:
The patent applies local quality by tailoring stimulation parameters to the specific functional connectivity characteristics of each individual's neural network. Rather than applying a universal protocol, the system adjusts stimulation based on locally measured network properties such as baseline connectivity strength, network topology, and functional modules, thereby improving therapeutic outcome consistency for each individual.
Solution Approach 2:
The patent implements dynamics by making the stimulation protocol adaptive rather than static. The system uses measured functional connectivity to dynamically adjust stimulation parameters, and the predictive model is continuously refined based on recorded responses. This dynamic adaptation allows the protocol to be universally applicable while maintaining consistency through individualized optimization.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach provides a more accurate prediction of stimulation-induced functional connectivity changes, highlighting the dominant role of network structure over stimulation protocol, and offers a promising framework for developing targeted neural stimulation therapies.
Implementation Method 1
The inventive technology employs optogenetics, a stimulation technology in which neurons are rendered light-sensitive by viral-mediated expression of opsins and thus able to be activated by incident light
Implementation Method 2
FCC is measured at the network level by recording neural activity via a micro-electrocorticography (ECoG) array covering ~1 cm2 of the primary sensorimotor cortex
Data Source
AI summary
Cortical network structure that mediates response to brain stimulation, and associated systems and methods are disclosed herein. In one embodiment, a method for brain stimulation includes: delivering an input stimulus to an area of the brain, via a cortical implant; in response to delivering the input stimulus, generating neural signals in the brain; and generating a predicted outcome of the input stimulus. The predicted outcome is based on a set of data derived from a model that combines: protocol features that are brain agnostic, and network features that are based on interactions between neural nodes of the brain.