Encoding of sensory prediction errors in the human cerebellum.

A central tenet of motor neuroscience is that the cerebellum learns from sensory prediction errors. Surprisingly, neuroimaging studies have not revealed definitive signatures of error processing in the cerebellum. Furthermore, neurophysiologic studies suggest an asymmetry, such that the cerebellum may encode errors arising from unexpected sensory events, but not errors reflecting the omission of expected stimuli. We conducted an imaging study to compare the cerebellar response to these two types of errors. Participants made fast out-and-back reaching movements, aiming either for an object that delivered a force pulse if intersected or for a gap between two objects, either of which delivered a force pulse if intersected. Errors (missing the target) could therefore be signaled either through the presence or absence of a force pulse. In an initial analysis, the cerebellar BOLD response was smaller on trials with errors compared with trials without errors. However, we also observed an error-related decrease in heart rate. After correcting for variation in heart rate, increased activation during error trials was observed in the hand area of lobules V and VI. This effect was similar for the two error types. The results provide evidence for the encoding of errors resulting from either the unexpected presence or unexpected absence of sensory stimulation in the human cerebellum

Retrieval of Vegetation Biochemicals Using a Radiative Transfer Model and Hyperspectral data

Accurate quantitative estimation of vegetation biochemical characteristics is necessary for a large variety of agricultural and ecological applications. The advent of hyperspectral remote sensing has offered possibilities for measuring specific vegetation variables that were difficult to measure using conventional multi-spectral sensors. In this study, the potential of biophysical modelling to predict leaf and canopy chlorophyll contents in a heterogeneous grassland is investigated. The well-known PROSAIL model was inverted with HyMap measurements by means of a look-up table (LUT). HyMap images along with simultaneous in situ measurements of chlorophyll content were acquired over a National Park. We tested the impact of using multiple solutions and spectral sub-setting on parameter retrieval. To assess the performance of the model inversion, the RMSE and R2 between independent in situ measurements and estimated parameters were used. The results of the study demonstrated that inversion of the PROSAIL model yield higher accuracies for Canopy chlorophyll content, in comparison to Leaf chlorophyll content (R2=0.84, RMSE=0.24). Further a careful selection of spectral subset, which comprised the development of a new method to subset the spectral data, proved to contain sufficient information for a successful model inversion. Consequently, it increased the estimation accuracy of investigated parameters (R2=0.87, RMSE=0.22). Our results confirm the potential of model inversion for estimating vegetation biochemical parameters using hyperspectral measurements.JRC.DG.G.3-Monitoring agricultural resource

Laterality Differences in Cerebellar-Motor Cortex Connectivity

Lateralization of function is an important organizational feature of the motor system. Each effector is predominantly controlled by the contralateral cerebral cortex and the ipsilateral cerebellum. Transcranial magnetic stimulation studies have revealed hemispheric differences in the stimulation strength required to evoke a muscle response from the primary motor cortex (M1), with the dominant hemisphere typically requiring less stimulation than the nondominant. The current study assessed whether the strength of the connection between the cerebellum and M1 (CB-M1), known to change in association with motor learning, have hemispheric differences and whether these differences have any behavioral correlate. We observed, in right-handed individuals, that the connection between the right cerebellum and left M1 is typically stronger than the contralateral network. Behaviorally, we detected no lateralized learning processes, though we did find a significant effect on the amplitude of reaching movements across hands. Furthermore, we observed that the strength of the CB-M1 connection is correlated with the amplitude variability of reaching movements, a measure of movement precision, where stronger connectivity was associated with better precision. These findings indicate that lateralization in the motor system is present beyond the primary motor cortex, and points to an association between cerebellar M1 connectivity and movement execution

Does the Normalized Difference Vegetation Index explain spatial and temporal variability in sap velocity in temperate forest ecosystems?

Understanding the link between vegetation characteristics and tree transpiration is a critical need to facilitate satellite-based transpiration estimation. Many studies use the Normalized Difference Vegetation Index (NDVI), a proxy for tree biophysical characteristics, to estimate evapotranspiration. In this study, we investigated the link between sap velocity and 30 m resolution Landsat-derived NDVI for 20 days during 2 contrasting precipitation years in a temperate deciduous forest catchment. Sap velocity was measured in the Attert catchment in Luxembourg in 25 plots of 20×20 m covering three geologies with sensors installed in two to four trees per plot. The results show that, spatially, sap velocity and NDVI were significantly positively correlated in April, i.e. NDVI successfully captured the pattern of sap velocity during the phase of green-up. After green-up, a significant negative correlation was found during half of the studied days. During a dry period, sap velocity was uncorrelated with NDVI but influenced by geology and aspect. In summary, in our study area, the correlation between sap velocity and NDVI was not constant, but varied with phenology and water availability. The same behaviour was found for the Enhanced Vegetation Index (EVI). This suggests that methods using NDVI or EVI to predict small-scale variability in (evapo)transpiration should be carefully applied, and that NDVI and EVI cannot be used to scale sap velocity to stand-level transpiration in temperate forest ecosystems

Dynamic modulation of cerebellar excitability for abrupt, but not gradual, visuomotor adaptation

The cerebellum is critically important for error driven adaptive motor learning, as evidenced by the fact that cerebellar patients do not adapt well to sudden predictable perturbations. However, recent work has shown that cerebellar patients adapt much better if the perturbation is gradually introduced. Here we explore physiological mechanisms that underlie this distinction between abrupt and gradual motor adaptation in humans. We used Transcranial Magnetic Stimulation (TMS) to evaluate whether neural mechanisms within the cerebellum contribute to either process during a visuomotor reach adaptation. When a visuomotor rotation was introduced abruptly, cerebellar excitability changed early in learning, and approached baseline levels near the end of the adaptation block. However, we observed no modulation of cerebellar excitability when we presented the visuomotor rotation gradually during learning. Similarly, we did not observe cerebellar modulation during trial-by-trial adaptation to random visuomotor displacements or during reaches without perturbations. This suggests that the cerebellum is most active during the early-phases of adaptation when large perturbations are successfully compensated

Canopy-scale biophysical controls of transpiration and evaporation in the Amazon Basin.

Canopy and aerodynamic conductances (gC and gA) are two of the key land surface biophysical variables that control the land surface response of land surface schemes in climate models. Their representation is crucial for predicting transpiration (λET) and evaporation (λEE) flux components of the terrestrial latent heat flux (λE), which has important implications for global climate change and water resource management. By physical integration of radiometric surface temperature (TR) into an integrated framework of the Penman?Monteith and Shuttleworth?Wallace models, we present a novel approach to directly quantify the canopy-scale biophysical controls on λET and λEE over multiple plant functional types (PFTs) in the Amazon Basin. Combining data from six LBA (Large-scale Biosphere-Atmosphere Experiment in Amazonia) eddy covariance tower sites and a TR-driven physically based modeling approach, we identified the canopy-scale feedback-response mechanism between gC, λET, and atmospheric vapor pressure deficit (DA), without using any leaf-scale empirical parameterizations for the modeling. The TR-based model shows minor biophysical control on λET during the wet (rainy) seasons where λET becomes predominantly radiation driven and net radiation (RN) determines 75 to 80 % of the variances of λET. However, biophysical control on λET is dramatically increased during the dry seasons, and particularly the 2005 drought year, explaining 50 to 65 % of the variances of λET, and indicates λET to be substantially soil moisture driven during the rainfall deficit phase. Despite substantial differences in gA between forests and pastures, very similar canopy?atmosphere "coupling" was found in these two biomes due to soil moisture-induced decrease in gC in the pasture. This revealed the pragmatic aspect of the TR-driven model behavior that exhibits a high sensitivity of gC to per unit change in wetness as opposed to gA that is marginally sensitive to surface wetness variability. Our results reveal the occurrence of a significant hysteresis between λET and gC during the dry season for the pasture sites, which is attributed to relatively low soil water availability as compared to the rainforests, likely due to differences in rooting depth between the two systems. Evaporation was significantly influenced by gA for all the PFTs and across all wetness conditions. Our analytical framework logically captures the responses of gC and gA to changes in atmospheric radiation, DA, and surface radiometric temperature, and thus appears to be promising for the improvement of existing land?surface?atmosphere exchange parameterizations across a range of spatial scales

Canopy-scale biophysical controls on transpiration and evaporation in the Amazon Basin

Canopy and aerodynamic conductances (gC and gA) are two of the key land surface biophysical variables that control the land surface response of land surface schemes in climate models. Their representation is crucial for predicting transpiration (?ET) and evaporation (?EE) flux components of the terrestrial latent heat flux (?E), which has important implications for global climate change and water resource management. By physical integration of radiometric surface temperature (TR) into an integrated framework of the Penman?Monteith and Shuttleworth?Wallace models, we present a novel approach to directly quantify the canopy-scale biophysical controls on ?ET and ?EE over multiple plant functional types (PFTs) in the Amazon Basin. Combining data from six LBA (Large-scale Biosphere-Atmosphere Experiment in Amazonia) eddy covariance tower sites and a TR-driven physically based modeling approach, we identified the canopy-scale feedback-response mechanism between gC, ?ET, and atmospheric vapor pressure deficit (DA), without using any leaf-scale empirical parameterizations for the modeling. The TR-based model shows minor biophysical control on ?ET during the wet (rainy) seasons where ?ET becomes predominantly radiation driven and net radiation (RN) determines 75 to 80?% of the variances of ?ET. However, biophysical control on ?ET is dramatically increased during the dry seasons, and particularly the 2005 drought year, explaining 50 to 65?% of the variances of ?ET, and indicates ?ET to be substantially soil moisture driven during the rainfall deficit phase. Despite substantial differences in gA between forests and pastures, very similar canopy?atmosphere "coupling" was found in these two biomes due to soil moisture-induced decrease in gC in the pasture. This revealed the pragmatic aspect of the TR-driven model behavior that exhibits a high sensitivity of gC to per unit change in wetness as opposed to gA that is marginally sensitive to surface wetness variability. Our results reveal the occurrence of a significant hysteresis between ?ET and gC during the dry season for the pasture sites, which is attributed to relatively low soil water availability as compared to the rainforests, likely due to differences in rooting depth between the two systems. Evaporation was significantly influenced by gA for all the PFTs and across all wetness conditions. Our analytical framework logically captures the responses of gC and gA to changes in atmospheric radiation, DA, and surface radiometric temperature, and thus appears to be promising for the improvement of existing land?surface?atmosphere exchange parameterizations across a range of spatial scales