# Kernel-Transformed Functional Connectivity Entropy Reveals Network Dedifferentiation in Bipolar Disorder

## TL;DR

A new method using brain connectivity patterns reveals disrupted network organization in bipolar disorder, independent of movement effects.

## Contribution

Introduced kernel-transformed functional connectivity entropy as a novel framework to detect network dedifferentiation in bipolar disorder.

## Key findings

- Bipolar disorder shows increased entropy in global and modular brain networks compared to controls.
- Manic symptom severity correlates negatively with global entropy in bipolar disorder patients.
- Network dedifferentiation remains significant even after controlling for head motion effects.

## Abstract

What are the main findings?
Developed a kernel-transformed functional connectivity (FC) entropy framework that acts as a reweighting filter to enhance connectivity contrast and suppress noise, facilitating sensitive quantification of weight distributions.Bipolar Disorder (BD) exhibited widespread network dedifferentiation (increased entropy) at global, modular (DMN, VAN, DAN), and nodal levels, independent of head motion effects.

Developed a kernel-transformed functional connectivity (FC) entropy framework that acts as a reweighting filter to enhance connectivity contrast and suppress noise, facilitating sensitive quantification of weight distributions.

Bipolar Disorder (BD) exhibited widespread network dedifferentiation (increased entropy) at global, modular (DMN, VAN, DAN), and nodal levels, independent of head motion effects.

What are the implications of the main findings?
Kernel-transformed FC entropy provides a distribution-sensitive complement to conventional linear FC metrics for quantifying network dysregulation in BD.Multiscale entropy abnormalities support kernel-transformed entropy as a promising candidate metric for characterizing symptom-related networks and potential stratification in BD.

Kernel-transformed FC entropy provides a distribution-sensitive complement to conventional linear FC metrics for quantifying network dysregulation in BD.

Multiscale entropy abnormalities support kernel-transformed entropy as a promising candidate metric for characterizing symptom-related networks and potential stratification in BD.

Background: Resting-state functional MRI (rs-fMRI) studies typically rely on linear Pearson correlation to characterize brain connectivity, potentially overlooking the distributional characteristics of functional networks. This study introduces a kernel-transformed functional connectivity (FC) entropy framework to quantify network dedifferentiation in bipolar disorder (BD). Methods: We utilized a Gaussian kernel function to execute a nonlinear similarity transformation (referred to as reweighting) on standard linear correlation matrices. This approach acts as a functional filter to amplify the contrast between strong and weak connections. Multiscale entropy (global, modular, and nodal) was subsequently calculated to characterize the uniformity of connectivity weight distributions. Results: Compared to Normal Controls (NCs), patients with BD exhibited significantly higher entropy at the global level and within the Default Mode, Salience, and Somatosensory-Motor networks, indicating widespread network dedifferentiation (distributional flattening). These alterations were robust across different kernel widths and remained significant after rigorously controlling for head motion (Mean FD). Furthermore, manic symptom severity (YMRS) was negatively correlated with global entropy, suggesting a pathological “locking-in” or rigidity of specific neural circuits during manic states. Conclusions: The kernel-transformed FC entropy serves as a distribution-sensitive complement to conventional linear metrics. Our findings highlight network dedifferentiation as a key pathophysiological feature of BD and suggest this framework as a promising candidate metric for characterizing network dysregulation.