Evaluating network brain connectivity in alcohol postdependent state using Network-Based Statistic.

Area of Science:

• Neuroscience
• Systems Neuroscience
• Neuroimaging

Background:

• Functional magnetic resonance imaging (fMRI) using blood oxygen level dependent (BOLD) signals is crucial for studying brain network interactions.
• Statistical dependency measures from BOLD signals offer insights into brain functional states and have translational potential for clinical and animal studies.
• Network-Based Statistic (NBS) enhances statistical power for detecting interconnected network alterations compared to mass-univariate tests.

Purpose of the Study:

• To investigate alterations in functional connectivity (FC) of the rat brain in a post-dependent (PD) state, an animal model relevant to alcoholism.
• To apply NBS to resting-state fMRI data to identify network-level changes in functional brain connectivity.

Main Methods:

• Resting-state fMRI data were acquired from 14 control and 13 PD rats.
• Pearson correlations were computed between 47 brain regions of interest (ROIs) to assess functional connectivity.
• NBS was employed to analyze network-based statistical differences in FC between groups.

Main Results:

• NBS identified a statistically significant network of altered FC in PD rats, including the hippocampus, amygdala, lateral hypothalamus, and raphe nucleus.
• Univariate comparisons with false discovery rate (FDR) correction did not reveal any significant individual connections.
• Correlations within the identified subnetwork decreased or became negative (anti-correlated) in PD rats compared to controls.

Conclusions:

• The PD state is characterized by a disconnected subnetwork involving brain regions critical for addictive behaviors.
• NBS is a powerful tool for detecting subtle, network-level alterations in brain connectivity that may be missed by univariate analyses.
• These findings provide evidence for network-level disruptions in the brain associated with alcohol dependence in an animal model.