Cognitive deficits, clinical variables, and white matter microstructure in schizophrenia: a multisite harmonization study.

Cognitive deficits are among the best predictors of real-world functioning in schizophrenia. However, our understanding of how cognitive deficits relate to neuropathology and clinical presentation over the disease lifespan is limited. Here, we combine multi-site, harmonized cognitive, imaging, demographic, and clinical data from over 900 individuals to characterize a) cognitive deficits across the schizophrenia lifespan and b) the association between cognitive deficits, clinical presentation, and white matter (WM) microstructure. Multimodal harmonization was accomplished using T-scores for cognitive data, previously reported standardization methods for demographic and clinical data, and an established harmonization method for imaging data. We applied t-tests and correlation analysis to describe cognitive deficits in individuals with schizophrenia. We then calculated whole-brain WM fractional anisotropy (FA) and utilized regression-mediation analyses to model the association between diagnosis, FA, and cognitive deficits. We observed pronounced cognitive deficits in individuals with schizophrenia (p < 0.006), associated with more positive symptoms and medication dosage. Regression-mediation analyses showed that WM microstructure mediated the association between schizophrenia and language/processing speed/working memory/non-verbal memory. In addition, processing speed mediated the influence of diagnosis and WM microstructure on the other cognitive domains. Our study highlights the critical role of cognitive deficits in schizophrenia. We further show that WM is crucial when trying to understand the role of cognitive deficits, given that it explains the association between schizophrenia and cognitive deficits (directly and via processing speed).

Elucidating the relationship between white matter structure, demographic, and clinical variables in schizophrenia-a multicenter harmonized diffusion tensor imaging study.

White matter (WM) abnormalities are repeatedly demonstrated across the schizophrenia time-course. However, our understanding of how demographic and clinical variables interact, influence, or are dependent on WM pathologies is limited. The most well-known barriers to progress are heterogeneous findings due to small sample sizes and the confounding influence of age on WM. The present study leverages access to the harmonized diffusion magnetic-resonance-imaging data and standardized clinical data from 13 international sites (597 schizophrenia patients (SCZ)). Fractional anisotropy (FA) values for all major WM structures in patients were predicted based on FA models estimated from a healthy population (n = 492). We utilized the deviations between predicted and real FA values to answer three essential questions. (1) "Which clinical variables explain WM abnormalities?". (2) "Does the degree of WM abnormalities predict symptom severity?". (3) "Does sex influence any of those relationships?". Regression and mediator analyses revealed that a longer duration-of-illness is associated with more severe WM abnormalities in several tracts. In addition, they demonstrated that a higher antipsychotic medication dose is related to more severe corpus callosum abnormalities. A structural equation model revealed that patients with more WM abnormalities display higher symptom severity. Last, the results exhibited sex-specificity. Males showed a stronger association between duration-of-illness and WM abnormalities. Females presented a stronger association between WM abnormalities and symptom severity, with IQ impacting this relationship. Our findings provide clear evidence for the interaction of demographic, clinical, and behavioral variables with WM pathology in SCZ. Our results also point to the need for longitudinal studies, directly investigating the casualty and sex-specificity of these relationships, as well as the impact of cognitive resiliency on structure-function relationships.

Investigating Sexual Dimorphism of Human White Matter in a Harmonized, Multisite Diffusion Magnetic Resonance Imaging Study.

Axonal myelination and repair, critical processes for brain development, maturation, and aging, remain controlled by sexual hormones. Whether this influence is reflected in structural brain differences between sexes, and whether it can be quantified by neuroimaging, remains controversial. Diffusion-weighted magnetic resonance imaging (dMRI) is an in vivo method that can track myelination changes throughout the lifespan. We utilize a large, multisite sample of harmonized dMRI data (n = 551, age = 9-65 years, 46% females/54% males) to investigate the influence of sex on white matter (WM) structure. We model lifespan trajectories of WM using the most common dMRI measure fractional anisotropy (FA). Next, we examine the influence of both age and sex on FA variability. We estimate the overlap between male and female FA and test whether it is possible to label individual brains as male or female. Our results demonstrate regionally and spatially specific effects of sex. Sex differences are limited to limbic structures and young ages. Additionally, not only do sex differences diminish with age, but tracts within each subject become more similar to one another. Last, we show the high overlap in FA between sexes, which implies that determining sex based on WM remains open.

Improving the predictive potential of diffusion MRI in schizophrenia using normative models-Towards subject-level classification.

Diffusion MRI studies consistently report group differences in white matter between individuals diagnosed with schizophrenia and healthy controls. Nevertheless, the abnormalities found at the group-level are often not observed at the individual level. Among the different approaches aiming to study white matter abnormalities at the subject level, normative modeling analysis takes a step towards subject-level predictions by identifying affected brain locations in individual subjects based on extreme deviations from a normative range. Here, we leveraged a large harmonized diffusion MRI dataset from 512 healthy controls and 601 individuals diagnosed with schizophrenia, to study whether normative modeling can improve subject-level predictions from a binary classifier. To this aim, individual deviations from a normative model of standard (fractional anisotropy) and advanced (free-water) dMRI measures, were calculated by means of age and sex-adjusted z-scores relative to control data, in 18 white matter regions. Even though larger effect sizes are found when testing for group differences in z-scores than are found with raw values (p < .001), predictions based on summary z-score measures achieved low predictive power (AUC < 0.63). Instead, we find that combining information from the different white matter tracts, while using multiple imaging measures simultaneously, improves prediction performance (the best predictor achieved AUC = 0.726). Our findings suggest that extreme deviations from a normative model are not optimal features for prediction. However, including the complete distribution of deviations across multiple imaging measures improves prediction, and could aid in subject-level classification.

White matter abnormalities across the lifespan of schizophrenia: a harmonized multi-site diffusion MRI study.

Several prominent theories of schizophrenia suggest that structural white matter pathologies may follow a developmental, maturational, and/or degenerative process. However, a lack of lifespan studies has precluded verification of these theories. Here, we analyze the largest sample of carefully harmonized diffusion MRI data to comprehensively characterize age-related white matter trajectories, as measured by fractional anisotropy (FA), across the course of schizophrenia. Our analysis comprises diffusion scans of 600 schizophrenia patients and 492 healthy controls at different illness stages and ages (14-65 years), which were gathered from 13 sites. We determined the pattern of age-related FA changes by cross-sectionally assessing the timing of the structural neuropathology associated with schizophrenia. Quadratic curves were used to model between-group FA differences across whole-brain white matter and fiber tracts at each age; fiber tracts were then clustered according to both the effect-sizes and pattern of lifespan white matter FA differences. In whole-brain white matter, FA was significantly lower across the lifespan (up to 7%; p < 0.0033) and reached peak maturation younger in patients (27 years) compared to controls (33 years). Additionally, three distinct patterns of neuropathology emerged when investigating white matter fiber tracts in patients: (1) developmental abnormalities in limbic fibers, (2) accelerated aging and abnormal maturation in long-range association fibers, (3) severe developmental abnormalities and accelerated aging in callosal fibers. Our findings strongly suggest that white matter in schizophrenia is affected across entire stages of the disease. Perhaps most strikingly, we show that white matter changes in schizophrenia involve dynamic interactions between neuropathological processes in a tract-specific manner.

Neurological Soft Signs Are Associated With Altered White Matter in Patients With Schizophrenia.

Neurological soft signs (NSS) are related to grey matter and functional brain abnormalities in schizophrenia. Studies in healthy subjects suggest, that NSS are also linked to white matter. However, the association between NSS and white matter abnormalities in schizophrenia remains to be elucidated. The present study investigated, if NSS are related to white matter alterations in patients with schizophrenia. The total sample included 42 healthy controls and 41 patients with schizophrenia. We used the Neurological Evaluation Scale (NES), and we acquired diffusion weighted magnetic resonance imaging to assess white matter on a voxel-wise between subject statistic. In patients with schizophrenia, linear associations between NES with fractional anisotropy (FA), radial, axial, and mean diffusivity were analyzed with tract-based spatial statistics while controlling for age, medication dose, the severity of the disease, and motion. The main pattern of results in patients showed a positive association of NES with all diffusion measures except FA in important motor pathways: the corticospinal tract, internal capsule, superior longitudinal fascicle, thalamocortical radiations and corpus callosum. In addition, exploratory tractography analysis revealed an association of the right aslant with NES in patients. These results suggest that specific white matter alterations, that is, increased diffusivity might contribute to NSS in patients with schizophrenia.

The cortical signature of impaired gesturing: Findings from schizophrenia.

Schizophrenia is characterized by deficits in gesturing that is important for nonverbal communication. Research in healthy participants and brain-damaged patients revealed a left-lateralized fronto-parieto-temporal network underlying gesture performance. First evidence from structural imaging studies in schizophrenia corroborates these results. However, as of yet, it is unclear if cortical thickness abnormalities contribute to impairments in gesture performance. We hypothesized that patients with deficits in gesture production show cortical thinning in 12 regions of interest (ROIs) of a gesture network relevant for gesture performance and recognition. Forty patients with schizophrenia and 41 healthy controls performed hand and finger gestures as either imitation or pantomime. Group differences in cortical thickness between patients with deficits, patients without deficits, and controls were explored using a multivariate analysis of covariance. In addition, the relationship between gesture recognition and cortical thickness was investigated. Patients with deficits in gesture production had reduced cortical thickness in eight ROIs, including the pars opercularis of the inferior frontal gyrus, the superior and inferior parietal lobes, and the superior and middle temporal gyri. Gesture recognition correlated with cortical thickness in fewer, but mainly the same, ROIs within the patient sample. In conclusion, our results show that impaired gesture production and recognition in schizophrenia is associated with cortical thinning in distinct areas of the gesture network.