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Whole-Brain 3D Activation and Functional Connectivity Mapping in Mice using Transcranial Functional Ultrasound Imaging

Whole-Brain 3D Activation and Functional Connectivity Mapping in Mice using Transcranial Functional Ultrasound Imaging

Published on: February 24, 2021

Multiple Kernel Learning Model for Relating Structural and Functional Connectivity in the Brain.

Sriniwas Govinda Surampudi¹, Shruti Naik², Raju Bapi Surampudi^2,3

¹CVIT, IIIT-Hyderabad, Hyderabad, 500032, India.

Scientific Reports

|February 21, 2018

Summary

This summary is machine-generated.

This study introduces a multi-scale diffusion model to link brain structure and function, improving predictions of functional connectivity. The new model accurately captures brain dynamics, outperforming previous methods.

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Area of Science:

Cognitive Neuroscience
Computational Neuroscience
Neuroimaging

Background:

Relating structural connectivity (SC) to functional connectivity (FC) is crucial for understanding brain network dynamics.
Existing models, like the single-diffusion-kernel (SDK) model, have limitations in accurately predicting FC from SC.

Purpose of the Study:

To propose and validate a novel multi-scale diffusion model for predicting functional connectivity from structural connectivity.
To investigate the role of inter-regional co-activations (latent parameters) in bridging SC and FC.

Main Methods:

Developed a multi-scale diffusion model formulated as a reaction-diffusion system.
Employed a multiple kernel learning (MKL) scheme to estimate latent parameters from training data.
Validated the model's predictive accuracy on subject-specific functional connectivity datasets.

Main Results:

The multi-scale diffusion model accurately predicts subject-specific functional connectivity (FC) from structural connectivity (SC) with 71% accuracy.
The proposed model surpasses the performance of existing linear and non-linear prediction models.
Learned latent parameters offer insights into age-specific brain reorganization.

Conclusions:

The multi-scale diffusion model provides a more accurate and biologically plausible framework for understanding the SC-FC relationship.
This approach enhances our ability to model brain network dynamics and characterize neuroplasticity.