Abstract Located in the Adirondack Mountains of northern New York State, Huntington Wildlife Forest (HWF) is a 6000‐ha research and education facility operated by SUNY ESF (State University of New York, College of Environmental Science and Forestry) with continuous long‐term monitoring (LTM) programs spanning over six decades. One of the ‘cradles’ of acid rain research in North America, HWF was in the first cohort of National Atmospheric Deposition Program (NADP) sites beginning in 1978. HWF is currently the only location (NY‐20) in New York with the full suite of NADP programs in operation, including atmospheric mercury speciation (AMNet), along with EPA CASTNET. Nearby to NY‐20 at HWF, Arbutus Lake and its forested watershed have been the focus of intensive LTM since installation of v‐notch weirs at the lake outlet and inlet in 1991 and 1994, respectively. Discharge at these locations has been monitored continuously at 15‐min intervals since 1999. Lake outlet water chemistry samples were collected starting in 1983. Weekly sampling of water chemistry at both weirs began in 1995 and was expanded to include two headwater streams and groundwater wells in 2007. More recently, LTM programs at HWF have been augmented by participation in the PhenoCam Network since 2008, collection of high‐resolution LiDAR in 2009, and installation of a precision NY Mesonet weather station in 2016. In 2018, we installed sensor networks that continuously monitor soil microclimate and snow depth. Lastly, we improved data access via a new website ( www.adk-ltm.org ) where users can create custom queries and visualize outputs.
Huntington disease (HD) is a neurodegenerative condition with prominent motor (including oculomotor), cognitive, and psychiatric effects. While neuropsychological deficits are present in HD, motor impairments may impact performance on neuropsychological measures, especially those requiring a speeded response, as has been demonstrated in multiple sclerosis and schizophrenia. The current study is the first to explore associations between oculomotor functions and neuropsychological performance in HD. Participants with impaired oculomotor functioning performed worse than those with normal oculomotor functioning on cognitive tasks requiring oculomotor involvement, particularly on psychomotor speed tasks, controlling for covariates. Consideration of oculomotor dysfunction on neuropsychological performance is critical, particularly for populations with motor deficits.
We lack a mechanistic explanation for the stereotyped pattern of white matter loss seen in Huntington's disease (HD). While the earliest white matter changes are seen around the striatum, within the corpus callosum, and in the posterior white matter tracts, the order in which these changes occur and why these white matter connections are specifically vulnerable is unclear. Here, we use diffusion tractography in a longitudinal cohort of individuals yet to develop clinical symptoms of HD to identify a hierarchy of vulnerability, where the topological length of white matter connections between a brain area and its neighbors predicts the rate of atrophy over 24 months. This demonstrates a new principle underlying neurodegeneration in HD, whereby brain connections with the greatest topological length are the first to suffer damage that can account for the stereotyped pattern of white matter loss observed in premanifest HD.
Objective: To serially quantify the blood coagulation within hematoma of patients with hyper-acute intracerebral hemorrhagic (ICH) stroke using non-invasive quantitative susceptibility mapping (QSM) MRI. Introduction: A blood clot is a combination of aggregated red blood cells, fibrin, platelets, hemosiderin, and other cell debris. An accurate evaluation of clot formation within hematoma could advance the clinical management of hematoma expansion, blood pressure management, and reversal of anticoagulants. Post-ICH hemolysis changes the heme iron oxidation state from oxy to deoxyhemoglobin (deoxy-Hb) resulting in unpaired iron electrons on aggregated RBC’s deoxy-Hb inducing magnetic susceptibility (χ). Therefore, a region with a higher number of aggregated RBC deoxy-Hb molecules, the dominant component of clots, will exhibit a higher positive χ susceptibility. We hypothesized that coagulated blood within hematoma will exhibit a higher positive χ in comparison to the non-clotted which can be quantified using quantitative susceptibility mapping (QSM), which is an advanced MRI image-processing algorithm. Methods: For proof of concept, we measured susceptibilities of 5 human blood phantoms with various percentages of the clot. Twenty-four patients with acute spontaneous ICH were enrolled and serially imaged 3 times within 12-24 (T1), 36-48 (T2), and 60-72(T3) hours of last known well (LNW). A 3D anatomical and multi-echo gradient echo images were obtained using a 3T MRI system. Hematoma and edema volumes were segmented and used as a region of interest (ROI). The rate of coagulation was assessed by measuring the change in susceptibilities within the hematoma. Results: The blood phantom exhibited a linear relationship between the percent coagulation and χ (R 2 =0.91). The QSM maps showed a significant increased in hematoma susceptibility over time (T1=0.29 ± 0.04, T2=0.36 ± 0.04, T3= 0.45 ± 0.04 ppm, p<0.0001). The overall average rate of coagulation was 0.00290 ± 0.0029 ppm per hour. No significant change in hematoma volume (18.9 ±3.1 cc) over time. A significant edema growth over time (T1=25.3 ± 3.6, T2= 28.1 ± 3, T3= 32.37 cc, p<0.05). Conclusion: In conclusion, we present novel surrogate imaging markers of coagulation within the hematoma of ICH.
Background: Huntington disease (HD) is a genetic neurodegenerative disease leading to progressive motor, cognitive, and behavioral decline.Subtle changes in these domains are detectable up to 15 years before a definitive motor diagnosis is made.This period, called prodromal HD, provides an opportunity to examine lifestyle Tweet
Neuronal compensation is widely assumed to account for the dissociation between brain pathology and (absence of) behavioural change during the prodromal and early stages of neurodegenerative conditions such as Huntington’s disease and Alzheimer’s disease (Barulli and Stern, 2013; Dennis and Cabeza, 2013; Scheller et al., 2014). Despite varying degrees of structural loss, patients demonstrate a level of performance during many tasks that is indistinguishable from their earlier performance, and is often similar to that of a normal population (Obeso et al., 2004; Malejko et al., 2014; Papoutsi et al., 2014; Kloppel and Gregory, 2015). Performance is maintained until pathological factors progress and performance levels begin to deteriorate. However, neuronal mechanisms that underlie such postulated compensation in neurodegeneration are poorly understood due to the complexity in defining what compensation actually is and how it can be measured.
The characterization of compensation in neurodegeneration that we present here is derived from theoretical models of compensation in healthy ageing and Alzheimer’s disease (Lovden et al., 2010; Barulli and Stern, 2013; Reuter-Lorenz and Park, 2014). The complementary processes that may account for improved performance in the presence of structural degeneration include utilization of brain reserve and/or cognitive reserve, brain maintenance, and compensation (Barulli and Stern, 2013). Brain reserve describes the differences in brain size and structure that may support maintenance of function during ageing (or pathology). Cognitive reserve conversely is the preservation of functional efficiency and capacity despite neuronal degeneration until a critical point is reached. It is associated with lifestyle factors, including education and socio-economic status, which modulate the cognitive effects of ageing (Stern, 2006; Barulli and Stern, 2013). It is suggested that cognitive reserve comprises neuronal reserve, which accounts for the increased efficiency; and neural compensation where task-unrelated regions are recruited to perform a function (Stern, 2006). This is consistent with the concept of flexibility that, as a proxy for functional capacity and intelligence, describes the brain’s ability to optimize performance to cope with existing demands; these changes eventually leading to more permanent changes in the brain (Lovden et al., 2010).
Compensation may also represent processes where activation within existing network regions increases. This is compatible with brain maintenance, whereby susceptibility to ageing (or pathology) can impact onset of cognitive decline, and other models of compensation, which promote the concept of augmented activation in existing networks (Barulli and Stern, 2013). The Scaffolding Theory of Aging and Cognition (STAC) in particular, proposes that both brain structure and function deteriorate with age, but that compensatory scaffolding counteracts adverse effects of neuronal and functional decline (Reuter-Lorenz and Park, 2014). This is congruent with changes that occur in neurodegenerative disease where structure degenerates, but performance is maintained due to compensatory changes in brain activity. Furthermore, STAC suggests that once deterioration becomes suitably severe, compensatory effects dissipate; just as functional compensation declines as neurodegenerative pathology progresses and structural degeneration becomes too severe.
In characterizing compensation, we suppose that in a subset of prodromal patients with pathological loss of brain tissue there is reorganization within the brain that enables them to function at the same level as those without disease-related neuronal loss. As mentioned above, compensation may present as increased activation in a task-relevant brain region or recruitment of a brain region not typically associated with the function or network being tested. The latter is difficult to assess as there may be reasons for increased activity other than compensation. Furthermore, compensation may simply represent a situation whereby the rate of disease-related neuronal dysfunction is slowed over time, supporting the idea of preserved cognitive function. Here, we will focus on the notion that evidence of compensation in neurodegenerative disease is present when behaviour in patients is more similar to that of the normal population due to changes in brain activity and in the presence of structural degeneration (Barulli and Stern, 2013; Scheller et al., 2014).
If compensation is defined as a lack of change in behaviour despite progressive brain pathology, then it is the absence or decreased severity of a behavioural deficit that needs to be measured as an outcome; this is challenging. In standard experimental paradigms, task-related changes in behaviour are used to explain changes in brain activity. Behavioural changes can be accounted for by concomitant changes in brain activity that ultimately differentiate the group(s) under investigation. When ‘absence’ of behavioural changes is the outcome variable, interpretation of alterations in brain activity is difficult (and sometimes impossible); we can only surmise that these changes may facilitate maintenance of normal performance. Furthermore, when investigating populations with neurodegenerative disease one might postulate an additional indeterminate effect of disease pathology on brain activity. Disease pathology may not only directly affect brain activity in terms of compensation, but may also exert subtle effects unrelated to maintenance of behaviour. Thus, it is important when attempting to operationalize compensation to try and account for pathological burden and be aware of its potential impact on the measurement of variables.
A recent review identified three components necessary to characterize compensation in ageing: extent of pathology, behavioural performance, and a measure of brain activity, such as signals derived from functional MRI measurements (Dennis and Cabeza, 2013). ‘Successful compensation’ was identified as a positive relationship between task performance and functional MRI signals, modified by age-related neuronal alterations. This model could be extended to characterize compensation in neurodegeneration. However, it does not directly account for concomitant changes in pathology across individuals during the course of neurodegenerative disease. To quantify compensatory behaviour effectively in neurodegeneration, not only should the functional MRI signal as a marker of brain activation and network-relevant task performance be explored, but it should be examined across a spectrum of pathology. We hypothesize that compensation occurs in cases where increased brain activation is needed to maintain normal levels of behaviour in the presence of structural loss. Eventually pathology becomes too severe resulting in behaviour as well as brain activation decreasing with structure over time.
An illustration of our hypothesized underlying model is shown in Fig. 1. The crucial components of compensation are a performance outcome (Y), an activation signal compensator (C) (e.g. functional MRI signal), and brain volume (X) (as a proxy for disease load). The horizontal axis represents time (or age of the participants), and the vertical axis represents scores on measures standardized to have equal means at the first observation. Curves indicate change over time for brain activity, performance, and brain volume. Three phases are depicted by the vertical dashed lines: Phase 1 spans (T0,T1), Phase 2 spans (T1,T2) and Phase 3 spans (T2,T3). In neurodegenerative disease, disease load is expected to steadily increase over time regardless of phase. Phase 1 is compensation, as brain activation (C) increases in reaction to brain deterioration (X), and performance (Y) is maintained. In Phase 2, disease effects start to overwhelm compensation, activation plateaus, and performance starts to deteriorate. Phase 3 shows relentless disease effects, brain activation decreases and there is acceleration in the deterioration rate of performance. The curves are idealized; there might be several stages where phasic change is monotonic rather than linear, and turns at the thresholds may be gradual rather than sharp. The important point to appreciate from Fig. 1 is that compensation leads to specific long-term patterns of change over time for three key variables.
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Figure 1
Underlying compensation model showing change in key variables over time (activation, performance, brain volume). Measures are assumed to be standardized to have the same mean value at the first time point. Three phases are defined by the thresholds at T1 and T2 (dashed vertical lines). Phase 1 spans (T0, T1), Phase 2 spans (T1 to T2), and Phase 3 spans (T2 to T3). Phase 1 illustrates compensation in which brain volume decreases, activation increases, and performance is maintained. Phase 2 indicates that disease effects are beginning to overwhelm compensation, as activation flattens and performance begins to decrease. Phase 3 shows the complete swamping of compensation by disease effects with all three variables decreasing.
Currently, the most commonly used biomarker of alcohol consumption patterns is carbohydrate-deficient transferrin (CDT). However, the CDT has limited sensitivity and requires the use of blood. Recently, we have shown that digital DNA methylation techniques can both sensitively and specifically detect heavy alcohol consumption (HAC) using DNA from blood or saliva. In order to better understand the relative performance characteristics of these two tests, we compared an Alcohol T-Score (ATS) derived from our prior study and serum CDT levels in 313 (182 controls and 131 HAC cases) subjects discordant for HAC. Overall, the Receiver Operating Characteristic (ROC) area under the curve (AUC) analyses showed that DNA methylation predicted HAC status better than CDT with AUCs of 0.96 and 0.87, respectively (p < 0.0001). The performance of the CDT was affected by gender while the ATS was not, while both were affected by age. We conclude that DNA methylation is a promising method for quantifying HAC and that further studies to better refine its strengths and limitations are in order.
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