Nonlinear approach to difference imaging in diffuse optical tomography

Difference imaging aims at recovery of the change in the optical properties of a body based on measurements before and after the change. Conventionally, the image reconstruction is based on using difference of the measurements and a linear approximation of the observation model. One of the main benefits of the linearized difference reconstruction is that the approach has a good tolerance to modeling errors, which cancel out partially in the subtraction of the measurements. However, a drawback of the approach is that the difference images are usually only qualitative in nature and their spatial resolution can be weak because they rely on the global linearization of the nonlinear observation model. To overcome the limitations of the linear approach, we investigate a nonlinear approach for difference imaging where the images of the optical parameters before and after the change are reconstructed simultaneously based on the two datasets. We tested the feasibility of the method with simulations and experimental data from a phantom and studied how the approach tolerates modeling errors like domain truncation, optode coupling errors, and domain shape errors

Moderate and heavy metabolic stress interval training improve arterial stiffness and heart rate dynamics in humans

Traditional continuous aerobic exercise training attenuates age-related increases of arterial stiffness, however, training studies have not determined whether metabolic stress impacts these favourable effects. Twenty untrained healthy participants (n = 11 heavy metabolic stress interval training, n = 9 moderate metabolic stress interval training) completed 6 weeks of moderate or heavy intensity interval training matched for total work and exercise duration. Carotid artery stiffness, blood pressure contour analysis, and linear and non-linear heart rate variability were assessed before and following training. Overall, carotid arterial stiffness was reduced (p  0.05). This study demonstrates the effectiveness of interval training at improving arterial stiffness and autonomic function, however, the metabolic stress was not a mediator of this effect. In addition, these changes were also independent of improvements in aerobic capacity, which were only induced by training that involved a high metabolic stress

A model-based iterative learning approach for diffuse optical tomography

Diffuse optical tomography (DOT) utilises near-infrared light for imaging spatially distributed optical parameters, typically the absorption and scattering coefficients. The image reconstruction problem of DOT is an ill-posed inverse problem, due to the non-linear light propagation in tissues and limited boundary measurements. The ill-posedness means that the image reconstruction is sensitive to measurement and modelling errors. The Bayesian approach for the inverse problem of DOT offers the possibility of incorporating prior information about the unknowns, rendering the problem less ill-posed. It also allows marginalisation of modelling errors utilising the so-called Bayesian approximation error method. A more recent trend in image reconstruction techniques is the use of deep learning, which has shown promising results in various applications from image processing to tomographic reconstructions. In this work, we study the non-linear DOT inverse problem of estimating the (absolute) absorption and scattering coefficients utilising a ‘model-based’ learning approach, essentially intertwining learned components with the model equations of DOT. The proposed approach was validated with 2D simulations and 3D experimental data. We demonstrated improved absorption and scattering estimates for targets with a mix of smooth and sharp image features, implying that the proposed approach could learn image features that are difficult to model using standard Gaussian priors. Furthermore, it was shown that the approach can be utilised in compensating for modelling errors due to coarse discretisation enabling computationally efficient solutions. Overall, the approach provided improved computation times compared to a standard Gauss-Newton iteration

Brain structural concomitants of resting state heart rate variability in the young and old: evidence from two independent samples

Previous research has shown associations between brain structure and resting state high-frequency heart rate variability (HF-HRV). Age affects both brain structure and HF-HRV. Therefore we sought to examine the relationship between brain structure and HF-HRV as a function of age. Data from two independent studies were used for the present analysis. Study 1 included 19 older adults (10 male, age range 62-78 years) and 19 younger adults (12 male, age range 19-37). Study 2 included 23 older adults (13 males; age range 55-75) and 27 younger adults (19 males; age range 18-34). The rootmean- square of successive R-R-interval differences (RMSSD) from ECG recordings was used as timedomain measure of HF-HRV. MRI scans were performed on a 3.0-T Siemens Magnetom Trio scanner. Cortical reconstruction and volumetric segmentation were performed with the Freesurfer image analysis suite, including 12 regions as regions-of-interests (ROI). Zero-order and partial correlations were used to assess the correlation of RMSSD with cortical thickness in selected ROIs. Lateral orbitofrontal cortex (OFC) cortical thickness was significantly associated with RMSSD. Further, both studies, in line with previous research, showed correlations between RMSSD and anterior cingulate cortex (ACC) cortical thickness. Meta-analysis on adjusted correlation coefficients from individual studies confirmed an association of RMSSD with the left rostral ACC and the left lateral OFC. Future longitudinal studies are necessary to trace individual trajectories in the association of HRV and brain structure across aging

Optimal stomatal behaviour around the world

© 2015 Macmillan Publishers Limited. All rights reserved. Stomatal conductance (g s) is a key land-surface attribute as it links transpiration, the dominant component of global land evapotranspiration, and photosynthesis, the driving force of the global carbon cycle. Despite the pivotal role of g s in predictions of global water and carbon cycle changes, a global-scale database and an associated globally applicable model of g s that allow predictions of stomatal behaviour are lacking. Here, we present a database of globally distributed g s obtained in the field for a wide range of plant functional types (PFTs) and biomes. We find that stomatal behaviour differs among PFTs according to their marginal carbon cost of water use, as predicted by the theory underpinning the optimal stomatal model and the leaf and wood economics spectrum. We also demonstrate a global relationship with climate. These findings provide a robust theoretical framework for understanding and predicting the behaviour of g s across biomes and across PFTs that can be applied to regional, continental and global-scale modelling of ecosystem productivity, energy balance and ecohydrological processes in a future changing climate