Integrated analysis of water quality in a mesoscale lowland basin

This article describes a modelling study on nitrogen transport from diffuse sources in the Nuthe catchment, representing a typical lowland region in the north-eastern Germany. Building on a hydrological validation performed in advance using the ecohydrological model SWIM, the nitrogen flows were simulated over a 20-year period (1981-2000). The relatively good quality of the input data, particularly for the years from 1993 to 2000, enabled the nitrogen flows to be reproduced sufficiently well, although modelling nutrient flows is always associated with a great deal of uncertainty. Subsequently, scenario calculations were carried out in order to investigate how nitrogen transport from the catchment could be further reduced. The selected scenario results with the greatest reduction of nitrogen washoff will briefly be presented in the paper

Integrated analysis of water quality in a mesoscale lowland basin

This article describes a modelling study on nitrogen transport from diffuse sources in the Nuthe catchment, representing a typical lowland region in the north-eastern Germany. Building on a hydrological validation performed in advance using the ecohydrological model SWIM, the nitrogen flows were simulated over a 20-year period (1981-2000). The relatively good quality of the input data, particularly for the years from 1993 to 2000, enabled the nitrogen flows to be reproduced sufficiently well, although modelling nutrient flows is always associated with a great deal of uncertainty. Subsequently, scenario calculations were carried out in order to investigate how nitrogen transport from the catchment could be further reduced. The selected scenario results with the greatest reduction of nitrogen washoff will briefly be presented in the paper

Neural Correlates of People's Hypercorrection of Their False Beliefs

Despite the intuition that strongly held beliefs are particularly difficult to change, the data on error correction indicate that general information errors that people commit with a high degree of belief are especially easy to correct. This finding is called the hypercorrection effect. The hypothesis was tested that the reason for hypercorrection stems from enhanced attention and encoding that results from a metacognitive mismatch between the person's confidence in their responses and the true answer. This experiment, which is the first to use imaging to investigate the hypercorrection effect, provided support for this hypothesis, showing that both metacognitive mismatch conditions—that in which high confidence accompanies a wrong answer and that in which low confidence accompanies a correct answer—revealed anterior cingulate and medial frontal gyrus activations. Only in the high confidence error condition, however, was an error that conflicted with the true answer mentally present. And only the high confidence error condition yielded activations in the right TPJ and the right dorsolateral pFC. These activations suggested that, during the correction process after error commission, people (1) were entertaining both the false belief as well as the true belief (as in theory of mind tasks, which also manifest the right TPJ activation) and (2) may have been suppressing the unwanted, incorrect information that they had, themselves, produced (as in think/no-think tasks, which also manifest dorsolateral pFC activation). These error-specific processes as well as enhanced attention because of metacognitive mismatch appear to be implicated

A unifying probabilistic framework for analyzing residual dipolar couplings

Residual dipolar couplings provide complementary information to the nuclear Overhauser effect measurements that are traditionally used in biomolecular structure determination by NMR. In a de novo structure determination, however, lack of knowledge about the degree and orientation of molecular alignment complicates the analysis of dipolar coupling data. We present a probabilistic framework for analyzing residual dipolar couplings and demonstrate that it is possible to estimate the atomic coordinates, the complete molecular alignment tensor, and the error of the couplings simultaneously. As a by-product, we also obtain estimates of the uncertainty in the coordinates and the alignment tensor. We show that our approach encompasses existing methods for determining the alignment tensor as special cases, including least squares estimation, histogram fitting, and elimination of an explicit alignment tensor in the restraint energy

A Forward Application of Age Associated Gray and White Matter Networks

To capture patterns of normal age-associated atrophy, we previously used a multivariate statistical approach applied to voxel based morphometry that identified age-associated gray and white matter covariance networks (Brickman et al. [2007]: Neurobiol Aging 28:284-295). The current study sought to examine the stability of these patterns by forward applying the identified networks to an independent sample of neurologically healthy younger and older adults. Forty-two younger and 35 older adults were imaged with standard high-resolution structural magnetic resonance imaging. Individual images were spatially normalized and segmented into gray and white matter. Covariance patterns that were previously identified with scaled subprofile model analyses were prospectively applied to the current sample to identify to what degree the age-associated patterns were manifested. Older individuals were also assessed with a modified version of the Mini Mental State Examination (mMMSE). Gray matter covariance pattern expression discriminated between younger and older participants with high optimal sensitivity (100%) and specificity (90.5%). While the two groups differed in the degree of white matter pattern expression (t (75) = 5.26, P < 0.001), classification based on white matter expression was relatively low (sensitivity = 80% and specificity = 61.9%). Among older adults, chronological age was significantly associated with increased gray matter pattern expression (r (32) = 0.591, P < 0.001) but not with performance on the mMMSE (r (31) = -0.314, P = 0.085). However, gray matter pattern expression was significantly associated with performance on the mMMSE (r (31) = -0.405, P = 0.024). The findings suggest that the previously derived age-associated covariance pattern for gray matter is reliable and may provide information that is more functionally meaningful than chronological age

Unilateral Disruptions in the Default Network with Aging in Native Space

BACKGROUND: Disruption of the default-mode network (DMN) in healthy elders has been reported in many studies. METHODS: In a group of 51 participants (25 young, 26 elder) we examined DMN connectivity in subjects' native space. In the native space method, subject-specific regional masks (obtained independently for each subject) are used to extract regional fMRI times series. This approach substitutes the spatial normalization and subsequent smoothing used in prevailing methods, affords more accurate spatial localization, and provides the power to examine connectivity separately in the two hemispheres instead of averaging regions across hemispheres. RESULTS: The native space method yielded new findings which were not detectable by the prevailing methods. The most reliable and robust disruption in elders' DMN connectivity were found between supramarginal gyrus and superior-frontal cortex in the right hemisphere only. The mean correlation between these two regions in young participants was about 0.5, and dropped significantly to 0.04 in elders (P = 2.1 x 10(-5)). In addition, the magnitude of functional connectivity between these regions in the right hemisphere correlated with memory (P = 0.05) and general fluid ability (P = 0.01) in elder participants and with speed of processing in young participants (P = 0.008). These relationships were not observed in the left hemisphere. CONCLUSION: These findings suggest that analysis of DMN connectivity in subjects' native space can improve localization and power and that it is important to examine connectivity separately in each hemisphere

Tracing brain amyloid-β in asymptomatic older adults relying on a memory marker for Alzheimer's disease

Recent approaches to the early diagnosis of Alzheimer’s disease (AD) are aimed at detecting neuropathological signatures of this type of dementia in still healthy older adults. Should these efforts prove fruitful, strategies then focus on identifying the cognitive and functional decline that ensue. These approaches have proved both little effective and costly. In the present study, we investigated the hypothesis that effective cognitive markers for AD could help detect among still healthy older adults who would have likely started to accumulate the neuropathological changes pursued by costly neuroimaging procedures. A sample of 39 healthy older adults was recruited and assessed with an extensive neuropsychological and neuroimaging protocol. As the memory marker, we used the Visual Short-Term Memory Binding Task. Using existing data, participants were divided in two groups depending on whether or not they displayed the typical binding profile seen in AD subjects (i.e., strong binders – SB and weak binders - WB). The results show that in addition to the increased binding cost seen in WB, SB and WB could only be differentiated by the amount of Amyloid- accumulated in brain regions known to be involved in this cognitive function. No other neuropsychological tests proved informative, and neither volumetric nor cortical thickness metrics provided meaningful neuropathological signals. Our findings have significant implications for our understanding of the transition from normal ageing to preclinical AD and methodological approaches currently used to ascertain it. These are discussed at length

Multivariate and Univariate Neuroimaging Biomarkers of Alzheimer's Disease

We performed univariate and multivariate discriminant analysis of FDG-PET scans to evaluate their ability to identify Alzheimer's disease (AD). FDG-PET scans came from two sources: 17 AD patients and 33 healthy elderly controls were scanned at the University of Michigan; 102 early AD patients and 20 healthy elderly controls were scanned at the Technical University of Munich, Germany. We selected a derivation sample of 20 AD patients and 20 healthy controls matched on age with the remainder divided into 5 replication samples. The sensitivity and specificity of diagnostic AD-markers and threshold criteria from the derivation sample were determined in the replication samples. Although both univariate and multivariate analyses produced markers with high classification accuracy in the derivation sample, the multivariate marker's diagnostic performance in the replication samples was superior. Further, supplementary analysis showed its performance to be unaffected by the loss of key regions. Multivariate measures of AD utilize the covariance structure of imaging data and provide complementary, clinically relevant information that may be superior to univariate measures

A New Approach to Spatial Covariance Modeling of Functional Brain Imaging Data: Ordinal Trend Analysis

In neuroimaging studies of human cognitive abilities, brain activation patterns that include regions that are strongly interactive in response to experimental task demands are of particular interest. Among the existing network analyses, partial least squares (PLS; McIntosh, 1999; McIntosh, Bookstein, Haxby, & Grady, 1996) has been highly successful, particularly in identifying group differences in regional functional connectivity, including differences as diverse as those associated with states of awareness and normal aging. However, we address the need for a within-group model that identifies patterns of regional functional connectivity that exhibit sustained activity across graduated changes in task parameters. For example, predictions of sustained connectivity are commonplace in studies of cognition that involve a series of tasks over which task difficulty increases (Baddeley, 2003). We designed ordinal trend analysis (OrT) to identify activation patterns that increase monotonically in their expression as the experimental task parameter increases, while the correlative relationships between brain regions remain constant. Of specific interest are patterns that express positive ordinal trends on a subject-by-subject basis. A unique feature of OrT is that it recovers information about functional connectivity based solely on experimental design variables. In particular, there is no requirement by OrT to provide either a quantitative model of the uncertain relationship between functional brain circuitry and subject variables (e.g., task performance and IQ) or partial information about the regions that are functionally connected. In this letter, we provide a step-by-step recipe of the computations performed in the new OrT analysis, including a description of the inferential statistical methods applied. Second, we describe applications of OrT to an event-related fMRI study of verbal working memory and H2 15 O-PET study of visuomotor learning. In sum, OrT has potential applications to not only studies of young adults and their cognitive abilities, but also studies of normal aging and neurological and psychiatric disease