MRI texture-based radiomics analysis for the identification of altered functional networks in alcoholic patients and animal models

Alcohol-Induced Changes in Brain Microstructure: Uncovering Novel Pathophysiological Mechanisms of AUD Using Translational DTI in Humans and Rodents

Alcohol use disorder (AUD) induces significant structural alterations in both gray and white matter, contributing to cognitive and functional impairments. This chapter presents a translational neuroimaging approach using diffusion-weighted MRI in humans and rodents to uncover novel pathophysiological mechanisms underlying AUD. Our studies demonstrate that increased mean diffusivity (MD) in gray matter reflects microglial reactivity and reduced extracellular space tortuosity, leading to enhanced volume neurotransmission. In white matter, fractional anisotropy (FA) reductions indicate progressive deterioration of key tracts, particularly the fimbria/fornix, linked to impaired cognitive flexibility. Importantly, longitudinal analyses reveal that white matter degeneration continues during early abstinence, suggesting that neuroinflammation and demyelination persist beyond alcohol cessation. Finally, we discuss how neuromodulatory interventions, such as transcranial magnetic stimulation (TMS), may promote recovery by enhancing myelin plasticity. These findings provide crucial insights into AUD's neurobiological underpinnings and highlight potential therapeutic targets for improving treatment outcomes.

Radiomic texture analysis based on neurite orientation dispersion and density imaging to differentiate glioblastoma from solitary brain metastasis

BACKGROUND

We created discriminative models of different regions of interest (ROIs) using radiomic texture features of neurite orientation dispersion and density imaging (NODDI) and evaluated the feasibility of each model in differentiating glioblastoma multiforme (GBM) from solitary brain metastasis (SBM).

METHODS

We conducted a retrospective study of 204 patients with GBM (n = 146) or SBM (n = 58). Radiomic texture features were extracted from five ROIs based on three metric maps (intracellular volume fraction, orientation dispersion index, and isotropic volume fraction of NODDI), including necrosis, solid tumors, peritumoral edema, tumor bulk volume (TBV), and abnormal bulk volume. Four feature selection methods and eight classifiers were used for the radiomic texture feature selection and model construction. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of the models. Routine magnetic resonance imaging (MRI) radiomic texture feature models generated in the same manner were used for the horizontal comparison.

RESULTS

NODDI-radiomic texture analysis based on TBV subregions exhibited the highest accuracy (although nonsignificant) in differentiating GBM from SBM, with area under the ROC curve (AUC) values of 0.918 and 0.882 in the training and test datasets, respectively, compared to necrosis (AUCtraining:0.845, AUCtest:0.714), solid tumor (AUCtraining:0.852, AUCtest:0.821), peritumoral edema (AUCtraining:0.817, AUCtest:0.762), and ABV (AUCtraining:0.834, AUCtest:0.779). The performance of the five ROI radiomic texture models in routine MRI was inferior to that of the NODDI-radiomic texture model.

CONCLUSION

Preoperative NODDI-radiomic texture analysis based on TBV subregions shows great potential for distinguishing GBM from SBM.