Exploring White Matter Microstructural Abnormalities Using MRI in Women With Premenstrual Dysphoric Disorder via...

Area of Science:

• Neuroimaging
• Psychiatry
• Brain Network Analysis

Background:

• Premenstrual dysphoric disorder (PMDD) neurobiology and white matter network alterations remain unclear.
• Understanding these changes is crucial for identifying diagnostic and therapeutic targets.

Purpose of the Study:

• To identify a core structural network in PMDD using integrated diffusion tensor imaging (DTI) metrics.
• To develop a predictive model for PMDD based on neurostructural findings.

Main Methods:

• A prospective case-control study involving 42 PMDD patients and 42 healthy controls.
• Diffusion tensor imaging (DTI) sequences were acquired on a 3.0 T scanner.
• Tract-based spatial statistics (TBSS), network-based statistics (NBS), and graph theory analyses were employed, with a combined predictive model built using LASSO regression.

Main Results:

• PMDD patients showed widespread microstructural and connectivity alterations, including changes in the internal capsule, enhanced edge connectivity, and altered network topology.
• The integrated DTI-based predictive model achieved a high predictive performance (AUC = 0.855).
• Key predictive features involved altered connectivity in the left inferior frontal and inferior occipital regions.

Conclusions:

• A DTI-based predictive model effectively identified neurostructural alterations in PMDD.
• Significant changes in brain connections and network properties were observed in the left inferior frontal and inferior occipital regions of PMDD patients.