Dataset Information

Combined white matter imaging suggests myelination defects in visual processing regions in schizophrenia.

ABSTRACT: Diverse pathological changes occur in the white matter (WM) of patients with schizophrenia. Various microstructural alterations including a reduction in axonal number or diameter, reduced myelination, or poor coherence of fibers could account for these changes. Abnormal integrity of macromolecules such as myelin ('dysmyelination') can be studied by applying multiple modalities of WM imaging such as diffusion tensor imaging (DTI) and magnetization transfer imaging (MTI) in parallel. Using ultra-high field (7 Tesla) MTI in 17 clinically stable patients with schizophrenia and 20 controls, we evaluated the voxelwise distribution of macromolecular WM abnormalities. Patients had a significant reduction in magnetization transfer ratio (MTR) in WM adjacent to visual processing regions and inferior temporal cortex (Cohen's d=1.54). Among the regions showing MTR reduction, a concurrent reduction in fractional anisotropy (FA) occurs proximal to the lingual gyrus. Multiple regression analysis revealed that the degree of FA reduction in the putatively 'dysmyelinated' regions in patients predicted impaired processing speed (PS; β=0.74; P=0.003), a core cognitive dysfunction in schizophrenia. In controls, MTR/FA in the occipito-temporal regions were not associated with PS. Our findings suggest that dysmyelination in visual processing regions is present in patients with schizophrenia with greatest cognitive and functional impairment. Combined DTI/MTI deficits in the occipito-temporal region may be an important variable when considering potential treatment targets for improving cognitive function in schizophrenia.

SUBMITTER: Palaniyappan L

PROVIDER: S-EPMC3712891 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:ObjectiveFailure of antipsychotic medications to resolve symptoms in patients with schizophrenia creates a clinical challenge that is known as treatment resistance. The causes of treatment resistance are unknown, but it is associated with earlier age at onset and more severe cognitive deficits. The authors tested the hypothesis that white matter deficits that are involved in both neurodevelopment and severity of cognitive deficits in schizophrenia are associated with a higher risk of treatment resistance.MethodsThe study sample (N=122; mean age, 38.2 years) included schizophrenia patients at treatment initiation (N=45), patients whose symptoms were treatment responsive (N=40), and patients whose symptoms were treatment resistant (N=37), as well as healthy control subjects (N=78; mean age, 39.2 years). White matter regional vulnerability index (RVI) was tested as a predictor of treatment resistance and cognitive deficits. Higher RVI is indicative of better agreement between diffusion tensor imaging fractional anisotropy across the brain in an individual and the pattern identified by the largest-to-date meta-analysis of white matter deficits in schizophrenia.ResultsPatients with treatment-resistant symptoms showed the highest white matter RVI (mean=0.38 [SD=0.2]), which was significantly higher than the RVI among patients with treatment-responsive symptoms (mean=0.30 [SD=0.02]). At the onset of treatment, schizophrenia patients showed significantly higher RVI than healthy control subjects (mean=0.18 [SD=0.03] and mean=0.13 [SD=0.02], respectively). RVIs were significantly correlated with performance on processing speed and negative symptoms.ConclusionsSchizophrenia affects white matter microstructure in specific regional patterns. Susceptibility to white matter regional deficits is associated with an increased likelihood of treatment resistance. Developments to overcome schizophrenia treatment resistance should consider white matter as an important target.

Dataset Information

Combined white matter imaging suggests myelination defects in visual processing regions in schizophrenia.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets