Studies applying Free Water Imaging have consistently reported significant global increases in extracellular FW in populations of individuals with early psychosis. However, these published studies focused on homogenous clinical participant groups (e.g., only first episode or chronic), thereby limiting our understanding of the time course of free water (FW) elevations across illness stages. Moreover, the relationship between FW and duration of illness has yet to be directly tested. Leveraging our multi-site diffusion magnetic resonance imaging(dMRI) harmonization approach, we analyzed dMRI scans collected by 12 international sites from 441 healthy controls and 434 individuals diagnosed with schizophrenia-spectrum disorders who represent different illness stages and ages (15-58 years). We characterized the pattern of age-related FW changes by assessing whole brain white matter in individuals with schizophrenia and healthy controls.Quadratic and non-parametric curves were used to model between-group FW differences in averaged whole brain white matter. In individuals with schizophrenia, whole brain FW was higher than in controls across all ages, with the greatest FW values observed from 15 to 23 years of age (effect size range=[0.70-0.87]). Following this peak, FW exhibited a monotonic decrease until reaching a minima at the age of 39 years. After 39 years of age, an attenuated monotonic increase in FW was observed, but with markedly lower effect sizes when compared to younger patients (effect size range =[0.32-0.43]). Importantly, FW was found to be negatively associated with duration of illness in schizophrenia (p=0.006), independent of the effects of age. In summary, our study finds in a large, age-diverse sample that participants with schizophrenia with a shorter duration of illness showed higher FW values compared to participants with more prolonged illness. Our findings indicate that FW might be a reliable imaging marker of acute, extracellular processes which appear to occur predominantly in the early stages of schizophrenia.
White matter hyperintensities (WMH) are a typical feature of cerebral small vessel disease (CSVD). This condition contributes to about 50% of dementias worldwide, a massive health burden in aging. Microstructural alterations in the deep white matter (DWM) have been widely examined in CSVD. However, little is known about abnormalities in the superficial white matter (SWM) and their relevance for processing speed, the main cognitive deficit in CSVD. In this paper, 141 patients with CSVD were studied. Processing speed was assessed by the completion time of the Trail Making Test Part A. White matter abnormalities were assessed by WMH burden (lesion volume on T2-FLAIR) and diffusion MRI, including DTI and free-water (FW) imaging microstructure measures. The results of our study indicate that the superficial white matter may play a particularly important role in cognitive decline in CSVD. SWM imaging measures resulted in a large contribution to processing speed, despite a relatively small WMH burden in the SWM. SWM FW had the strongest association with processing speed among all imaging markers and, unlike the other diffusion MRI measures, significantly increased between two patient subgroups with the lowest WMH burdens (possibly representing early stages of disease). When comparing two patient subgroups with the highest WMH burdens, the involvement of WMH in the SWM was accompanied by significant differences in processing speed and white matter microstructure. Given significant effects of WMH volume and regional FW on processing speed, we performed a mediation analysis. SWM FW was found to fully mediate the association between WMH volume and processing speed, while no mediation effect of DWM FW was observed. Overall, our findings identify SWM abnormalities in CSVD and suggest that the SWM has an important contribution to processing speed. Results indicate that FW in the SWM is a sensitive marker of microstructural changes associated with cognition in CSVD. This study extends the current understanding of CSVD-related dysfunction and suggests that the SWM, as an understudied region, can be a potential target for monitoring pathophysiological processes in future research.
Fiber tract segmentation is a prerequisite for tract-based statistical analysis. Brain fiber streamlines obtained by diffusion magnetic resonance imaging and tractography technology are usually difficult to be leveraged directly, thus need to be segmented into fiber tracts. Previous research mainly consists of two steps: defining and computing the similarity features of fiber streamlines, then adopting machine learning algorithms for fiber clustering or classification. Defining the similarity feature is the basic premise and determines its potential reliability and application. In this study, we adopt geometric features for fiber tract segmentation and develop a novel descriptor (FiberGeoMap) for the corresponding representation, which can effectively depict fiber streamlines’ shapes and positions. FiberGeoMap can differentiate fiber tracts within the same subject, meanwhile preserving the shape and position consistency across subjects, thus can identify common fiber tracts across brains. We also proposed a Transformer-based encoder network called FiberGeoMap Learner, to perform segmentation based on the geometric features. Experimental results showed that the proposed method can differentiate the 103 various fiber tracts, which outperformed the existing methods in both the number of categories and segmentation accuracy. Furthermore, the proposed method identified some fiber tracts that were statistically different on fractional anisotropy (FA), mean diffusion (MD), and fiber number ration in autism.
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