期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2016
卷号：113
期号：47
页码：13504-13509
DOI：10.1073/pnas.1608246113
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：SignificanceAdvanced, non-Gaussian diffusion-weighted imaging (DWI) measurements that probe white matter microstructure across a range of diffusion contrasts were sensitive to diagnosis-specific abnormalities in schizophrenia and independently predicted patient-control differences in processing speed. Two orthogonal statistical factors extracted from DWI measurements explain most of diagnosis-related differences in processing speed. Moreover, DWI measurements explained a similar degree of variance in processing speed in patients and controls separately and in siblings of patients. This link remains contiguous across the diagnostic boundary and was not driven by subject selection or antipsychotic medication. The non-Gaussian diffusion white matter metrics are promising surrogate imaging markers for modeling cognitive deficits and perhaps, guiding treatment development. Schizophrenia, a devastating psychiatric illness with onset in the late teens to early 20s, is thought to involve disrupted brain connectivity. Functional and structural disconnections of cortical networks may underlie various cognitive deficits, including a substantial reduction in the speed of information processing in schizophrenia patients compared with controls. Myelinated white matter supports the speed of electrical signal transmission in the brain. To examine possible neuroanatomical sources of cognitive deficits, we used a comprehensive diffusion-weighted imaging (DWI) protocol and characterized the white matter diffusion signals using diffusion kurtosis imaging (DKI) and permeability-diffusivity imaging (PDI) in patients (n = 74), their nonill siblings (n = 41), and healthy controls (n = 113). Diffusion parameters that showed significant patient-control differences also explained the patient-control differences in processing speed. This association was also found for the nonill siblings of the patients. The association was specific to processing-speed abnormality but not specific to working memory abnormality or psychiatric symptoms. Our findings show that advanced diffusion MRI in white matter may capture microstructural connectivity patterns and mechanisms that govern the association between a core neurocognitive measure--processing speed--and neurobiological deficits in schizophrenia that are detectable with in vivo brain scans. These non-Gaussian diffusion white matter metrics are promising surrogate imaging markers for modeling cognitive deficits and perhaps, guiding treatment development in schizophrenia.
