Diffusion-weighted imaging uncovers likely sources of processing-speed deficits in schizophrenia

Abstract

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.

Keywords: cognitive deficits; diffusion-weighted imaging; endophenotypes; processing speed; schizophrenia.

Fig. S1.

(Upper Left) Corpus callosum WM region of interest was identified by thresholding the FA image at FA = 0.20. (Upper Right) The diffusion decay fit for DKI and PD models. The DKI model was limited to the range of b values between 0 and 2,500 s/mm². The biexponential PD model was fit over the entire range of b values. (Lower Left) Schematic comparison of the DKI that assumes that the signal is produced by a single pool of anisotropically diffusing water and characterizes the Gaussian and non-Gaussian properties of diffusion by measuring diffusion and kurtosis tensors and FA and KA of diffusion. (Lower Right) The biexponential PD model, proposed by Sukstanskii et al. (22), assumes that the signal is produced by two quasipools of isotropically diffusing water. Unrestricted pool (M_u) is produced by water molecules that are sufficiently away from the cellular membranes to be unaffected by them. The water near the membrane forms the restricted compartment (1 − M_u), with diffusivity that depends on both the passive diffusivity of water through the cellular/myelin membrane and the active (thick arrow) permeability via the ionic channels and water pores that use water as a substrate for compartment exchange.

Fig. 1.

Average and SD values for FA, radial diffusivity (L_┴), KA, axial kurtosis (K_║), PDI, and processing-speed measures are shown for controls, patients, and siblings.

Fig. 2.

Linear correlation analysis between processing speed and KA and FA diffusion factors showed significant (P < 0.05) linear correlations in patients and control combined (row 1), patients and controls separately (rows 2 and 3), and siblings of patients (row 4).

Fig. S2.

β Coefficients from a linear regression analysis predicting processing speed using DWI and DTI factors.