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

Abstract

Background: The neurostructural underpinnings of premenstrual dysphoric disorder (PMDD), particularly integrated white matter and network alteration, remain unclear.

Purpose: To identify a core structural network in PMDD by integrating multiple diffusion tensor imaging (DTI)-derived metrics and to develop a predictive model.

Study type: Prospective case-control study.

Subjects: Forty-two PMDD patients (age: 23.86 ± 1.32 years), diagnosed according to the American Psychiatric Association DSM-5, and 42 healthy controls (age: 23.79 ± 1.72 years).

Field strength/sequence: 3.0 T, T1-weighted three-dimensional gradient-echo and echo planar imaging DTI sequences.

Assessment: Microstructural and connectivity features were extracted from DTI using tract-based spatial statistics (TBSS), network-based statistics (NBS), and graph theory analyses. A combined predictive model was constructed by integrating the most stable features from the three single-modality models via least absolute shrinkage and selection operator (LASSO) regression.

Statistical tests: Group comparisons were performed using two-sample t-tests or Mann-Whitney U tests, with false discovery rate correction. Features were selected using LASSO and integrated to construct a combined model. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC) using leave-one-out cross-validation. p < 0.05 was considered significant.

Results: PMDD patients exhibited widespread microstructural and connectivity alterations, including elevated axial diffusivity in the right posterior limb of the internal capsule, enhanced edge connectivity, and altered network topology. The combined model achieved significantly superior predictive performance (AUC = 0.855) compared with the TBSS-based model (AUC = 0.699) and the network-based model (AUC = 0.727), and a higher AUC than the graph-based model (AUC = 0.790). Key predictive features included two enhanced edges originating from the left inferior frontal gyrus and reduced degree centrality of the left inferior occipital gyrus and sulcus.

Data conclusion: Our DTI-based predictive model showed alterations in brain connections and network properties in the left inferior frontal and inferior occipital regions of PMDD patients.

Level of evidence: 2:

Technical efficacy: Stage 2.