Assessment of motor symptoms and functional impact in prodromal and early Huntington disease.

The Functional Rating Scale Taskforce for pre-Huntington Disease (FuRST-pHD) is a multinational, multidisciplinary initiative with the goal of developing a data-driven, comprehensive, psychometrically sound, rating scale for assessing symptoms and functional ability in prodromal and early Huntington disease (HD) gene expansion carriers. The process involves input from numerous sources to identify relevant symptom domains, including HD individuals, caregivers, and experts from a variety of fields, as well as knowledge gained from the analysis of data from ongoing large-scale studies in HD using existing clinical scales. This is an iterative process in which an ongoing series of field tests in prodromal (prHD) and early HD individuals provides the team with data on which to make decisions regarding which questions should undergo further development or testing and which should be excluded. We report here the development and assessment of the first iteration of interview questions aimed to assess functional impact of motor manifestations in prHD and early HD individuals.

A functional coefficient model view of the Feldstein–Horioka puzzle

What does the saving–investment (SI) relation really measure and how should the SI relation be measured? These are two of the most discussed issues triggered by the so-called Feldstein–Horioka puzzle. Based on panel data we introduce a new variant of functional coefficient models that allows to separate long and short to medium run parameter dependence. The new modeling framework is applied to uncover the determinants of the SI relation. Macroeconomic state variables such as openness, the age dependency ratio, government current and consumption expenditures are found to affect the SI relation significantly in the long run.

Spatio-temporal dependencies in functional connectivity in rodent cortical cultures

Models of functional connectivity in cortical cultures on multi-electrodes arrays may aid in understanding how cognitive pathways form and improve techniques that aim to interface with neuronal systems. To enable research on such models, this study uses both data- and model-driven approaches to determine what dependencies are present in and between functional connectivity networks derived from bursts of extracellularly recorded activity. Properties of excitation in bursts were analysed using correlative techniques to assess the degree of linear dependence and then two parallel techniques were used to assess functional connectivity. Three models presenting increasing levels of spatio-temporal dependency were used to capture the dynamics of individual functional connections and their consistencies were verified using surrogate data. By comparing network-wide properties between model generated networks and functional networks from data, complex interdependencies were revealed. This indicates the persistent co-activation of neuronal pathways in spontaneous bursts, as can be found in whole brain structures.

Unraveling the electronic structures of low-valent naphthalene and anthracene iron complexes: x-ray, spectroscopic, and density functional theory studies

Naphthalene and anthracene transition metalates are potent reagents, but their electronic structures have remained poorly explored. A study of four Cp*-substituted iron complexes (Cp* = pentamethylcyclopentadienyl) now gives rare insight into the bonding features of such species. The highly oxygen- and water-sensitive compounds [K(18-crown- 6){Cp*Fe(η4-C10H8)}] (K1), [K(18-crown-6){Cp*Fe(η4-C14H10)}] (K2), [Cp*Fe(η4-C10H8)] (1), and [Cp*Fe(η4-C14H10)] (2) were synthesized and characterized by NMR, UV−vis, and 57Fe Mössbauer spectroscopy. The paramagnetic complexes 1 and 2 were additionally characterized by electron paramagnetic resonance (EPR) spectroscopy and magnetic susceptibility measurements. The molecular structures of complexes K1, K2, and 2 were determined by single-crystal X-ray crystallography. Cyclic voltammetry of 1 and 2 and spectroelectrochemical experiments revealed the redox properties of these complexes, which are reversibly reduced to the monoanions [Cp*Fe(η4-C10H8)]− (1−) and [Cp*Fe(η4-C14H10)]− (2−) and reversibly oxidized to the cations [Cp*Fe(η6-C10H8)]+ (1+) and [Cp*Fe(η6-C14H10)]+ (2+). Reduced orbital charges and spin densities of the naphthalene complexes 1−/0/+ and the anthracene derivatives 2−/0/+ were obtained by density functional theory (DFT) methods. Analysis of these data suggests that the electronic structures of the anions 1− and 2− are best represented by low-spin FeII ions coordinated by anionic Cp* and dianionic naphthalene and anthracene ligands. The electronic structures of the neutral complexes 1 and 2 may be described by a superposition of two resonance configurations which, on the one hand, involve a low-spin FeI ion coordinated by the neutral naphthalene or anthracene ligand L, and, on the other hand, a low-spin FeII ion coordinated to a ligand radical L•−. Our study thus reveals the redox noninnocent character of the naphthalene and anthracene ligands, which effectively stabilize the iron atoms in a low formal, but significantly higher spectroscopic oxidation state.

Epoch-specific functional networks involved in working memory

Working memory (WM) is not a unitary construct. There are distinct processes involved in encoding information, maintaining it on-line, and using it to guide responses. The anatomical configurations of these processes are more accurately analyzed as functionally connected networks than collections of individual regions. In the current study we analyzed event-related functional magnetic resonance imaging (fMRI) data from a Sternberg Item Recognition Paradigm WM task using a multivariate analysis method that allowed the linking of functional networks to temporally-separated WM epochs. The length of the delay epochs was varied to optimize isolation of the hemodynamic response (HDR) for each task epoch. All extracted functional networks displayed statistically significant sensitivity to delay length. Novel information extracted from these networks that was not apparent in the univariate analysis of these data included involvement of the hippocampus in encoding/probe, and decreases in BOLD signal in the superior temporal gyrus (STG), along with default-mode regions, during encoding/delay. The bilateral hippocampal activity during encoding/delay fits with theoretical models of WM in which memoranda held across the short term are activated long-term memory representations. The BOLD signal decreases in the STG were unexpected, and may reflect repetition suppression effects invoked by internal repetition of letter stimuli. Thus, analysis methods focusing on how network dynamics relate to experimental conditions allowed extraction of novel information not apparent in univariate analyses, and are particularly recommended for WM experiments for which task epochs cannot be randomized.

Connecting mean field models of neural activity to EEG and fMRI data

Progress in functional neuroimaging of the brain increasingly relies on the integration of data from complementary imaging modalities in order to improve spatiotemporal resolution and interpretability. However, the usefulness of merely statistical combinations is limited, since neural signal sources differ between modalities and are related non-trivially. We demonstrate here that a mean field model of brain activity can simultaneously predict EEG and fMRI BOLD with proper signal generation and expression. Simulations are shown using a realistic head model based on structural MRI, which includes both dense short-range background connectivity and long-range specific connectivity between brain regions. The distribution of modeled neural masses is comparable to the spatial resolution of fMRI BOLD, and the temporal resolution of the modeled dynamics, importantly including activity conduction, matches the fastest known EEG phenomena. The creation of a cortical mean field model with anatomically sound geometry, extensive connectivity, and proper signal expression is an important first step towards the model-based integration of multimodal neuroimages.

Model estimation of cerebral hemodynamics between blood flow and volume changes: A data-based modeling approach

It is well known that there is a dynamic relationship between cerebral blood flow (CBF) and cerebral blood volume (CBV). With increasing applications of functional MRI, where the blood oxygen-level-dependent signals are recorded, the understanding and accurate modeling of the hemodynamic relationship between CBF and CBV becomes increasingly important. This study presents an empirical and data-based modeling framework for model identification from CBF and CBV experimental data. It is shown that the relationship between the changes in CBF and CBV can be described using a parsimonious autoregressive with exogenous input model structure. It is observed that neither the ordinary least-squares (LS) method nor the classical total least-squares (TLS) method can produce accurate estimates from the original noisy CBF and CBV data. A regularized total least-squares (RTLS) method is thus introduced and extended to solve such an error-in-the-variables problem. Quantitative results show that the RTLS method works very well on the noisy CBF and CBV data. Finally, a combination of RTLS with a filtering method can lead to a parsimonious but very effective model that can characterize the relationship between the changes in CBF and CBV.

Analysis of diffusion tensor magnetic resonance imaging data using principal component analysis

An analysis method for diffusion tensor (DT) magnetic resonance imaging data is described, which, contrary to the standard method (multivariate fitting), does not require a specific functional model for diffusion-weighted (DW) signals. The method uses principal component analysis (PCA) under the assumption of a single fibre per pixel. PCA and the standard method were compared using simulations and human brain data. The two methods were equivalent in determining fibre orientation. PCA-derived fractional anisotropy and DT relative anisotropy had similar signal-to-noise ratio (SNR) and dependence on fibre shape. PCA-derived mean diffusivity had similar SNR to the respective DT scalar, and it depended on fibre anisotropy. Appropriate scaling of the PCA measures resulted in very good agreement between PCA and DT maps. In conclusion, the assumption of a specific functional model for DW signals is not necessary for characterization of anisotropic diffusion in a single fibre.

The global distribution of phytoplankton size spectrum and size classes from their light-absorption spectra derived from satellite data

The absorption spectra of phytoplankton in the visible domain hold implicit information on the phytoplankton community structure. Here we use this information to retrieve quantitative information on phytoplankton size structure by developing a novel method to compute the exponent of an assumed power-law for their particle-size spectrum. This quantity, in combination with total chlorophyll-a concentration, can be used to estimate the fractional concentration of chlorophyll in any arbitrarily-defined size class of phytoplankton. We further define and derive expressions for two distinct measures of cell size of mixed populations, namely, the average spherical diameter of a bio-optically equivalent homogeneous population of cells of equal size, and the average equivalent spherical diameter of a population of cells that follow a power-law particle-size distribution. The method relies on measurements of two quantities of a phytoplankton sample: the concentration of chlorophyll-a, which is an operational index of phytoplankton biomass, and the total absorption coefficient of phytoplankton in the red peak of visible spectrum at 676 nm. A sensitivity analysis confirms that the relative errors in the estimates of the exponent of particle size spectra are reasonably low. The exponents of phytoplankton size spectra, estimated for a large set of in situ data from a variety of oceanic environments (~ 2400 samples), are within a reasonable range; and the estimated fractions of chlorophyll in pico-, nano- and micro-phytoplankton are generally consistent with those obtained by an independent, indirect method based on diagnostic pigments determined using high-performance liquid chromatography. The estimates of cell size for in situ samples dominated by different phytoplankton types (diatoms, prymnesiophytes, Prochlorococcus, other cyanobacteria and green algae) yield nominal sizes consistent with the taxonomic classification. To estimate the same quantities from satellite-derived ocean-colour data, we combine our method with algorithms for obtaining inherent optical properties from remote sensing. The spatial distribution of the size-spectrum exponent and the chlorophyll fractions of pico-, nano- and micro-phytoplankton estimated from satellite remote sensing are in agreement with the current understanding of the biogeography of phytoplankton functional types in the global oceans. This study contributes to our understanding of the distribution and time evolution of phytoplankton size structure in the global oceans.

Ecophysiological and bioclimatic foundations for a global plant functional classification

Question: What plant properties might define plant functional types (PFTs) for the analysis of global vegetation responses to climate change, and what aspects of the physical environment might be expected to predict the distributions of PFTs? Methods: We review principles to explain the distribution of key plant traits as a function of bioclimatic variables. We focus on those whole-plant and leaf traits that are commonly used to define biomes and PFTs in global maps and models. Results: Raunkiær's plant life forms (underlying most later classifications) describe different adaptive strategies for surviving low temperature or drought, while satisfying requirements for reproduction and growth. Simple conceptual models and published observations are used to quantify the adaptive significance of leaf size for temperature regulation, leaf consistency for maintaining transpiration under drought, and phenology for the optimization of annual carbon balance. A new compilation of experimental data supports the functional definition of tropical, warm-temperate, temperate and boreal phanerophytes based on mechanisms for withstanding low temperature extremes. Chilling requirements are less well quantified, but are a necessary adjunct to cold tolerance. Functional traits generally confer both advantages and restrictions; the existence of trade-offs contributes to the diversity of plants along bioclimatic gradients. Conclusions: Quantitative analysis of plant trait distributions against bioclimatic variables is becoming possible; this opens up new opportunities for PFT classification. A PFT classification based on bioclimatic responses will need to be enhanced by information on traits related to competition, successional dynamics and disturbance.

Palaeovegetation of China: a pollen data-based synthesis for the mid-Holocene and last glacial maximum.

Pollen data from China for 6000 and 18,000 14C yr bp were compiled and used to reconstruct palaeovegetation patterns, using complete taxon lists where possible and a biomization procedure that entailed the assignment of 645 pollen taxa to plant functional types. A set of 658 modern pollen samples spanning all biomes and regions provided a comprehensive test for this procedure and showed convincing agreement between reconstructed biomes and present natural vegetation types, both geographically and in terms of the elevation gradients in mountain regions of north-eastern and south-western China. The 6000 14C yr bp map confirms earlier studies in showing that the forest biomes in eastern China were systematically shifted northwards and extended westwards during the mid-Holocene. Tropical rain forest occurred on mainland China at sites characterized today by either tropical seasonal or broadleaved evergreen/warm mixed forest. Broadleaved evergreen/warm mixed forest occurred further north than today, and at higher elevation sites within the modern latitudinal range of this biome. The northern limit of temperate deciduous forest was shifted c. 800 km north relative to today. The 18,000 14C yr bp map shows that steppe and even desert vegetation extended to the modern coast of eastern China at the last glacial maximum, replacing today’s temperate deciduous forest. Tropical forests were excluded from China and broadleaved evergreen/warm mixed forest had retreated to tropical latitudes, while taiga extended southwards to c. 43°N.

Present-day and mid-Holocene biomes reconstructed from pollen and plant macrofossil data from the former Soviet Union and Mongolia

Fossil pollen data supplemented by tree macrofossil records were used to reconstruct the vegetation of the Former Soviet Union and Mongolia at 6000 years. Pollen spectra were assigned to biomes using the plant-functional-type method developed by Prentice et al. (1996). Surface pollen data and a modern vegetation map provided a test of the method. This is the first time such a broad-scale vegetation reconstruction for the greater part of northern Eurasia has been attempted with objective techniques. The new results confirm previous regional palaeoenvironmental studies of the mid-Holocene while providing a comprehensive synopsis and firmer conclusions. West of the Ural Mountains temperate deciduous forest extended both northward and southward from its modern range. The northern limits of cool mixed and cool conifer forests were also further north than present. Taiga was reduced in European Russia, but was extended into Yakutia where now there is cold deciduous forest. The northern limit of taiga was extended (as shown by increased Picea pollen percentages, and by tree macrofossil records north of the present-day forest limit) but tundra was still present in north-eastern Siberia. The boundary between forest and steppe in the continental interior did not shift substantially, and dry conditions similar to present existed in western Mongolia and north of the Aral Sea.

Antidepressant medications reduce subcortical-cortical resting-state functional connectivity in healthy volunteers

Studies have revealed abnormalities in resting-state functional connectivity in those with major depressive disorder specifically in areas such as the dorsal anterior cingulate, thalamus, amygdala, the pallidostriatum and subgenual cingulate. However, the effect of antidepressant medications on human brain function is less clear and the effect of these drugs on resting-state functional connectivity is unknown. Forty volunteers matched for age and gender with no previous psychiatric history received either citalopram (SSRI; selective serotonergic reuptake inhibitor), reboxetine (SNRI; selective noradrenergic reuptake inhibitor) or placebo for 7 days in a double-blind design. Using resting-state functional magnetic resonance imaging and seed based connectivity analysis we selected the right nucleus accumbens, the right amygdala, the subgenual cingulate and the dorsal medial prefrontal cortex as seed regions. Mood and subjective experience were also measured before and after drug administration using self-report scales. Despite no differences in mood across the three groups, we found reduced connectivity between the amygdala and the ventral medial prefrontal cortex in the citalopram group and the amygdala and the orbitofrontal cortex for the reboxetine group. We also found reduced striatal-orbitofrontal cortex connectivity in the reboxetine group. These data suggest that antidepressant medications can decrease resting-state functional connectivity independent of mood change and in areas known to mediate reward and emotional processing in the brain. We conclude that hypothesis-driven seed based analysis of resting-state fMRI supports the proposition that antidepressant medications might work by normalising the elevated resting-state functional connectivity seen in depressed patients.