Widespread intra-axonal signal fraction abnormalities in bipolar disorder from multicompartment diffusion MRI: Sensitivity to diagnosis, association with clinical features and pharmacologic treatment

Despite diffusion tensor imaging (DTI) evidence for widespread fractional anisotropy (FA) reductions in the brain white matter of patients with bipolar disorder, questions remain regarding the specificity and sensitivity of FA abnormalities as opposed to other diffusion metrics in the disorder. We conducted a whole-brain voxel-based multicompartment diffusion MRI study on 316 participants (i.e., 158 patients and 158 matched healthy controls) employing four diffusion metrics: the mean diffusivity (MD) and FA estimated from DTI, and the intra-axonal signal fraction (IASF) and microscopic axonal parallel diffusivity (Dpar) derived from the spherical mean technique. Our findings provide novel evidence about widespread abnormalities in other diffusion metrics in BD. An extensive overlap between the FA and IASF results suggests that the lower FA in patients may be caused by a reduced intra-axonal volume fraction or a higher macromolecular content in the intra-axonal water. We also found a diffuse alteration in MD involving white and grey matter tissue and more localised changes in Dpar. A Machine Learning analysis revealed that FA, followed by IASF, were the most helpful metric for the automatic diagnosis of BD patients, reaching an accuracy of 72%. Number of mood episodes, age of onset/duration of illness, psychotic symptoms, and current treatment with lithium, antipsychotics, antidepressants, and antiepileptics were all significantly associated with microstructure abnormalities. Lithium treatment was associated with less microstructure abnormality.

Automated summary

Despite evidence of widespread fractional anisotropy (FA) reductions in the brain white matter of bipolar disorder patients, there are still questions regarding the specificity and sensitivity of FA abnormalities compared to other diffusion metrics. A study using diffusion MRI was conducted on 316 participants, revealing abnormalities in other diffusion metrics in bipolar disorder, particularly in mean diffusivity (MD) and intra-axonal signal fraction (IASF). Machine Learning analysis showed that FA and IASF were the most helpful metrics for diagnosing bipolar disorder with an accuracy of 72%. Factors such as number of mood episodes, age of onset/duration of illness, psychotic symptoms, and current treatment with lithium and other medications were significantly associated with microstructure abnormalities, while lithium treatment was associated with fewer abnormal structures.