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Contains fulltext : 293093.pdf (Publisher’s version ) (Open Access), Introduction: Lesion-symptom mapping is a key tool in understanding the relationship between brain structures and behavior. However, the behavioral consequences of lesions from different etiologies may vary because of how they affect brain tissue and how they are distributed. The inclusion of different etiologies would increase the statistical power but has been critically debated. Meanwhile, findings from lesion studies are a valuable resource for clinicians and used across different etiologies. Therefore, the main objective of the present study was to directly compare lesion-symptom maps for memory and language functions from two populations, a tumor versus a stroke population. Methods: Data from two different studies were combined. Both the brain tumor (N = 196) and stroke (N = 147) patient populations underwent neuropsychological testing and an MRI, pre-operatively for the tumor population and within three months after stroke. For this study, we selected two internationally widely used standardized cognitive tasks, the Rey Auditory Verbal Learning Test and the Verbal Fluency Test. We used a state-of-the-art machine learning-based, multivariate voxel-wise approach to produce lesion-symptom maps for these cognitive tasks for both populations separately and combined. Results: Our lesion-symptom mapping results for the separate patient populations largely followed the expected neuroanatomical pattern based on previous literature. Substantial differences in lesion distribution hindered direct comparison. Still, in brain areas with adequate coverage in both groups, considerable LSM differences between the two populations were present for both memory and fluency tasks. Post-hoc analyses of these locations confirmed that the cognitive consequences of focal brain damage varied between etiologies. Conclusion: The differences in the lesion-symptom maps between the stroke and tumor population could partly be explained by differences in lesion volume and topography. Despite

In northern peatlands, near-saturated surface conditions promote valuable ecosystem services such as carbon storage and drinking water provision. Peat saturated hydraulic conductivity (Ksat) plays an important role in maintaining wet surface conditions by moderating drainage and evapotranspiration. Peat Ksat can exhibit intense spatial variability in three dimensions, and can change rapidly in response to disturbance. The development of skilful predictive equations for peat Ksat and other hydraulic properties, akin to mineral soil pedotransfer functions, remains a subject of ongoing research. We report a meta-analysis of 2,507 northern peat samples, from which we developed linear models that predict peat Ksat from other variables, including depth, dry bulk density, von Post score (degree of humification), and categorical information such as surface microform type and peatland trophic type (e.g., bog, fen). Peat Ksat decreases strongly with increasing depth, dry bulk density and humification; and increases along the trophic gradient from bog to fen peat. Dry bulk density and humification are particularly important predictors, and increase model skill greatly; our best model, which includes these variables, has a cross-validated r2 of 0.75, and little bias. A second model that includes humification but omits dry bulk density, intended for rapid field estimations of Ksat, also performs well (cross-validated r2 = 0.64). Two additional models that omit several predictors perform less well (cross-validated r2 ∼ 0.5), and exhibit greater bias, but allow Ksat to be estimated from less comprehensive data. Our models allow improved estimation of peat Ksat from simpler, cheaper measurements.

In northern peatlands, near-saturated surface conditions promote valuable ecosystem services such as carbon storage and drinking water provision. Peat saturated hydraulic conductivity (Ksat) plays an important role in maintaining wet surface conditions by moderating drainage and evapotranspiration. Peat Ksat can exhibit intense spatial variability in three dimensions and can change rapidly in response to disturbance. The development of skillful predictive equations for peat Ksat and other hydraulic properties, akin to mineral soil pedotransfer functions, remains a subject of ongoing research. We report a meta-analysis of 2,507 northern peat samples, from which we developed linear models that predict peat Ksat from other variables, including depth, dry bulk density, von Post score (degree of humification), and categorical information such as surface microform type and peatland trophic type (e.g., bog and fen). Peat Ksat decreases strongly with increasing depth, dry bulk density, and humification; and increases along the trophic gradient from bog to fen peat. Dry bulk density and humification are particularly important predictors and increase model skill greatly; our best model, which includes these variables, has a cross-validated r² of 0.75 and little bias. A second model that includes humification but omits dry bulk density, intended for rapid field estimations of Ksat, also performs well (cross-validated r² = 0.64). Two additional models that omit several predictors perform less well (cross-validated r² ∼ 0.5), and exhibit greater bias, but allow Ksat to be estimated from less comprehensive data. Our models allow improved estimation of peat Ksat from simpler, cheaper measurements.

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