\r\n\tThe biological activities of the bioactive compounds are based on the lead or the privileged scaffold present in the structure. The different scaffolds present in natural bioactive compounds are indole, purine, chromone, coumarin, benzothiphene, lactone, etc. These privileged scaffolds modify into multiple molecules for having different bioactivity. Some of the bioactive compounds in large quantity have an adverse effect on health. Recently, bioactive compounds are widely used in green chemistry, nanotechnology, and metal chelation. \r\n\tThe book provides a reference for a wide range including chemistry, analytical techniques, medicinal chemistry, pharmacology, nanotechnology, etc.
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1. Introduction
Detection of a change is defined as the process of demonstrating that climate or a system affected by climate has changed in some defined statistical sense, without providing a reason for that change. Attribution is defined as the process of evaluation of the relative contribution of multiple causal factors to a change or event with an assignment of statistical confidence. However, the observed changes must be able to be detected (IPCC 2010).
Attribution to a change in climatic conditions includes the assessments that attribute an observed change in a variable of interest to a specific observed change in climate conditions based on the process knowledge and relative importance of a change in climate condition in determining the observed impacts (Hao et al. 2008; Liu and Xia 2011). The associated confidence levels should be evaluated for the data, model, methods, and the factors used in the study (IPCC 2010).
Seibert et al. (2010) used the three different approaches for change detection modeling employing a modified version of the HBV (Hydrologiska Byråns Vattenbalansavdelning) model (Bergstrom 1976, 1992) to conclude that catchment-scale runoff increases following severe wildfire. The application of the HBV model as a change detection tool indicated the increases in peak flows following severe wildfire and the related road building and harvesting of the dead and damaged forest vegetations.
The parameter uncertainty of various parameter sets is commonly known in hydrologic and climatologic modeling. It is an issue seldom addressed in modeling approaches for detecting changes (Pappenberger and Beven 2006; Seibert and McDonnell 2010). Employing a large number of parameter sets rather than a single set of parameter values facilitates the assessment of the associated uncertainty.
The detection of climate change impacts on the observed climate and elements of the hydrological cycle have made a great progress, recently (Amiri and Eslamian, 2010). Based on the climate model simulation, the optimal methods have been used to detect the responses of observed change to Green House Gas emissions from the other external forcing at large spatial scales. Presently, the detection of anthropogenic influence is not yet possible for all of the climate variables. It is still difficult to attribute the observed changes in climate or variables of interest on a spatial scale lower than five thousands kilometers and temporal scales of less than fifty years. For the basin aquifers recharged by precipitation or surplus irrigation and influenced by artificial and strong human activities, the detection studies mainly focused on analytical approaches to link physical impacts to changes in temperature or precipitation as a tool. For the basins with better observational data and more sensitivity towards climate change, the use of formal detection methods to identify the pattern responses of the hydrological cycle to external forcing is a valuable and promising area of further research (Liu and Xia 2011).
Xoplaki et al. (2008) investigated data requirement for climate change detection and modeling research in the Mediterranean. They indicated that data availability allows the validation of scientific results on climate change detection and attribution.
The need for long measurements of climate and hydrologic data for studies of climate variability and change is very important. New et al. (1999, 2000) have developed the fields for many climate variables and it is essential to develop these further and extend them to some hydrological variables such as discharge and runoff, for both climate variability and change studies and also climate model validation.
Most of the investigations in climate variability and change detection have focused on only temperature, due to well representation by the available network. The temperature measurement exhibits the relatively high correlation decay lengths. Both upcoming impacts and those of previous events are, however, much more dependent upon the changes in precipitation. The changes are not only vital for hydrology, but are also much more important than temperature for many other sectors, such as agriculture and range management. The studies of large-scale changes in precipitation are hampered by the requirement to obtain access to considerably more precipitation data than is conventionally available. A similar case can also be met for runoff data. Climate change detection studies need to be undertaken on a global scale, and both the available networks of runoff and precipitation data are inadequate. Presently, the best that can be achieved are the investigations on the regional and catchment scales (Cihlar et al. 2000).
A method for detecting the impacts of disturbance on catchment-scale hydrology is the paired catchment approach. The method combines rainfall-runoff modeling to account for natural fluctuations in daily streamflow, uncertainty analyses using the generalized likelihood uncertainty estimation method to identify and separate hydrologic model uncertainty from unexplained variation, and GLS regression change detection models to provide a formal experimental framework for detecting changes in daily streamflow relative to variations in daily hydrologic and climatic data (Zégre et al. 2010).
Precipitation data indicate both increasing and decreasing trends for different regions of the world. Zhang et al. (2007) detected the human influence on twentieth century precipitation trends.
The main objective of this study is to describe the statistical techniques for detecting changes in hydrological events. The flood records are selected for this purpose. Statistical tests and distributions, significance levels and confidence intervals, risk and uncertainty and nonstationarity are discussed in detail for the flood series.
2. Detection of change in flood records
Graphical analysis is generally the first attempt at detecting change in a flood record. Unfortunately, the natural variation of year-to-year flooding greatly exceeds the variation expected due to climate change of recent years. Thus, the latter would likely not be visually evident from a graphical portrayal of a flood record. In comparison, it takes a considerable level of urban development before the hydrologic effects of urbanization can be graphically detected, especially if the trend is temporally gradual rather than abrupt. Whether change is due to global warming or urbanization, we can not be certain whether the nonstationarity factor will cause a change in the probability distribution of floods or just its moments. Thus, more sophisticated methods of detection are needed.
Commonly, the next step in detection of change is with statistical methods. Some of the problems with statistical detection of the effects of climate change include uncertainty in the distribution from which the sample was drawn, outliers, poorly measured values, no knowledge as to when the climate change began to significantly influence flooding, and the compounding effects of land cover change such as deforestation. In addition to these factors, identifying a statistically significant change requires the specification of a statistical level of significance. The value selected is a central factor in statistical decision making, yet a systematic way of identifying the optimum level of significance is not known. The selection of a level of significance is not a trivial decision as the power of the test will depend on the level selected.
Eslamian et al. (2009) investigated to detect an existing trend in wind speed and to evaluate the effect of climate change on frequency analysis of wind speed in Iran. The purpose of this study was to present the recent trends and variations in measured wind speed at twenty-two gauging stations along the whole country of Iran. In addition, the effect of climate change was evaluated in frequency analysis and heterogeneity. For understanding wind behavior in time periods, the trend test and frequency analysis were performed for evaluating wind magnitude and duration.
3. Selection of statistical method to detect trend
Statistical methods are generally designed to be most sensitive to one type of change, such as the change in central tendency, the change in dispersion, or the change in the statistical distribution. Change can be gradual or abrupt and different statistical methods should be applied to such data. A change in a data set that is characterized by gradually varying flows may not be detected if the statistical method applied is more sensitive to abrupt change. Evidence does not currently exist as to the distributional effect that climate change introduces to a flood record. For example, will prolonged climate change cause annual maximum floods to follow a Generalized Extreme Value (GEV) distribution rather than the commonly accepted log-Pearson type III distribution (LP3)? Bulletin 17B, which was developed to estimate flood frequencies and, therefore, flood risk, assumes that hydrologic data follows a LP3 (Interagency 1982). However, many recent studies in regards to precipitation data are based on other distributions. For example, Koutsoyiannis (2004), Stedinger (2000), Gellens (2002), and Karin and Zwiers (2005) selected the GEV distribution to model extreme events while Wilby and Wigley (2002) and Semenov and Bengtsson (2002) chose the gamma distribution to model daily precipitation events. Therefore, if agreement does not currently exist on the appropriate distribution to represent hydrologic data, it will be difficult to determine the appropriate distribution as the concept of nonstationarity is introduced. To compound the problem, climate change is expected to be gradual and thus, the distribution may be subject to continual change.
In addition to the appropriate distribution, the effects of climate change on the moments of a distribution are unknown. The studies have suggested that climate change will increase the more intense rainfalls but have little effect on total annual rainfalls (Hennessy et al. 1997; Karl and Knight 1997; Wilby and Wigley 2002). Kharin and Zwiers (2005) found a significant change in the location and scale parameters for the GEV distribution in a global analysis of precipitation extremes; however, the effects of climate change are expected to vary regionally. Therefore, global analyses may not be applicable at the regional level. Since the expected changes in the statistical distribution as well as the moments are not known and, therefore, must be assumed, this reduces the ability of statistical tests to effectively decide whether or not change has occurred. Without this knowledge, the best statistical method to detect climate change can not be selected without recognizing the importance of this type of uncertainty.
4. The assumption of a start time
Before the problem of modeling can be solved, the first issue that must be addressed is detection of change. The most obvious question is: when did the effect of climate change begin to significantly influence the hydrologic variable of interest, e.g., annual maximum peaks? For example, Olsen et al. (1999) varied the start and end dates of flood records analyzed for gauges in the Missouri and Mississippi River basin. Based on the linear regression results, they found that different record lengths within the same flood record influenced the significance of the trend detected. Therefore, knowledge of the time at which nonstationarity began is necessary in order to correctly identify trends.
Identifying the start time of nonstationarity is important because the time that climate change is assumed to have become influential will influence the model used to represent the hydrologic change. If incorrectly selected, the model type can greatly affect projected changes in hydrologic data. For example, let’s assume that we know that climate change will introduce a linear trend in the hydrologic variable. If the start date is quite uncertain, then the slope of the linear trend will be biased depending on the assumed start time. An incorrectly assumed early start time will lead to an underpredicted slope. Likewise, assuming a late start time would result in a relatively steep slope and long-term overprediction. Figure 1 shows the mean annual discharge (cfs) for the USGS gauge 05464500 at Cedar Rapids, Iowa. A linear trend was fit to the data with two start times: 1903 and 1960, represented by the solid and dashed regression lines, respectively. If extrapolated to the year 2050, the model based on a 1960 start time projects a mean discharge that is 12% greater than the model based on a later start time. Therefore, the uncertainty of predictions due to inaccurate start times can be significant. A statistical test that is sensitive to the start date needs to have high statistical power. Otherwise, incorrect start times will result, with the subsequent impacts on models, future projections, and risk estimation.
The Anacostia River at Hyattsville, MD, can be used to illustrate the effect of start time on the trend of peak discharge rates. Figure 2 shows the annual maximum peak discharge from 1939 to 1988. In the late 1950’s, urban development influenced flood flows, with the effect apparent in Figure 2. Linear models were used to model the trend in flood peaks as a function of time:
1955−1988:qp=344+54.11*tt=1in1955E1
1960−1988:qp=4178+29.57*tt=1in1960E2
Figure 1.
Variation in Trend Modeled based on Different Start Times for the Mean Annual Discharge (cfs) for the USGS Cedar Rapids Gauge (05464500) with the Dashed and Solid Line Representing a 1960 and 1903 Start Time, Respectively
While the record lengths are similar (34 and 29 years, respectively), the equations are quite different. The estimated floods for the year 2011 would be 6528 cfs (185 m3/s) and 5716 cfs (162 m3/s) for Eqs. 1 and 2, respectively. This represents a difference of 13% based solely on the start time. This example illustrates the sensitivity of estimated flood magnitudes to the start time for modeling time trends.
Figure 2.
Peak Discharge Data for Annacostia River at Hyattsville, Maryland.
Assuming that the start time can be reasonably estimated, the next question of interest is: What affect will climate change have on the physical processes that determine the nature or characteristics of the climate change? Will the climatic change influence the statistics of the hydrologic variable, e.g., increase the mean or the variance, or will it change the distribution, e.g., from a LP3 to a GEV? The accuracy of projected discharges will greatly depend on the change assumed, which will subsequently influence the accuracy of risk estimates.
5. Selection of statistical distributions
As mentioned in the discussion of statistical method selection, the appropriate distribution for hydrologic data is unknown. While this leads to difficulties in trend detection, it also influences the projection of hydrologic events, such as flooding. Bulletin 17B currently recommends the LP3 distribution; however, the GEV distribution is recommended by many studies as well (Martins and Stedinger 2000). Both distributions represent extreme data; however, the extreme events projected by each distribution can vary. For example, Figure 3 compares the frequency curve fit to the Cedar Rapids annual maximum peak discharge at the USGS gauge 05464500 with both the LP3 and GEV distributions. The GEV distribution projects a 100-yr flood that is 12.6% greater than that of the LP3 distribution. Therefore, depending on the distribution selected, engineers may over or underestimate the 100-yr storm designing flood management structures. Therefore, it is important that the correct probability distributed is selected for hydrologic data for design and policy development.
Figure 3.
Frequency Curve for LP3 and GEV Distribution of Cedar Rapids Annual Maximum Peak Discharge Data.
6. Selection of statistical models
In addition to the start time and distribution selection, uncertainties in future predictions will result from the model form selected to represent the change being analyzed. For example, evidence points to a nonlinear trend in hydrologic data as the result of climate change, but some global models suggest an increasing function while other models suggest a decreasing function because of policies that control CO2 emissions. Likewise, even when urbanization is known to influence measured flood magnitudes, it has been difficult to identify the model structure that approximates the temporal effects of changes in the physical processes associated with urban land cover change. Linear trends are often assumed as other more complex functions do not lead to greater accuracy. Yet, the assumed model structure will dictate the magnitude of floods projected for future land cover conditions. Assuming an incorrect function form to represent the trend of increasing flood discharge rates will influence peak discharge estimated for the future. This is another source of uncertainty as an incorrect model structure can lead to overprediction or underprediction of design floods and their associated risks.
To illustrate the potential effect of model structure on floods estimated for future times, the flood series for the Anacostia River (see Figure 2) was fitted for the 1955-1988 period using a linear model (Eq. 1) and the following power or log-linear model:
1955−1988:qp=2384*t0.153E3
Based on this power model form, the 2011 estimated discharge would be 4425 cfs (125 m3/s), which differs from the discharge estimated using Eq. 1 by 2103 cfs (59.5 m3/s), or 38.4%. The effect of model structure is significant and this issue is must be considered in an attempt to model the effects of climate change on hydrologic data.
This same problem will influence the accuracy of modeling the effects of hydrologic nonstationarity due to a changing climate. It is difficult to even detect whether or not climate change has introduced systematic variation into a flood record let alone identifying the structural form of the temporal change induced by the climate change. Much effort will need to be expended on the detection of change and to identify the best model structure will be a central modeling issue. The model structure finally adopted will greatly influence assessments of future flood risk and the design of hydrologic and hydraulic infrastructure with design lives that will cover the period of climate change.
7. Confidence intervals under changing conditions
The third issue important to the modeler and to policy makers is: How can confidence intervals be computed on projected discharges when the distribution and parameters of future discharges are unknown? Given the lack of certainty in the distribution of climate-affected discharges, the most obvious choice of methods for computing confidence intervals would be those used for linear regression analysis. This approach requires a minimum of inputs, such as the standard error of estimate, the sample size of the existing record, and the standard deviation of the time variable. One problem with this approach is the lack of stationarity. Confidence intervals computed using traditional methods assume stationarity. A new approach will be needed. Uncertainty associated with the nonstationarity will likely lead to much wider confidence intervals on hydrologic variables such as peak discharge rates. An approach based on Monte Carlo simulation for different levels of nonstationarity may be necessary to produce more accurate assessments of the confidence of projected discharges.
With the current state of the art, nonparametric methods are the generally accepted approach to detection of change. Numerous tests are available, but many of these lack statistical power. For example, the Runs Test, which was designed to assess the presence or lack of randomness, i.e., independence, could be applied over portions of a flood record to identify the portions of the record that were not homogeneous. If the other causative factors, such as urbanization can be ruled out, then the test may detect an approximate time at which nonstationarity began. Given the low statistical power of the test, the best estimate of the start date will likely be very imprecise.
The Kendall Tau Test is one of the more commonly used tests for detecting nonrandomness. This test is often preferred because it is designed for data with a monotonically increasing trend, as opposed to an episodic change. It can be applied to either long or short flood records, although the accuracy of the decision will depend on the record length.
Tests for serial independence, such as the Pearson Test and the nonparametric Spearman Test, can be effective for identifying the existence of trends. However, when the Spearman Test is applied to hydrologic data where the data are ordered by year of occurrence, the critical values generally available do not apply. Some analyses have correlated the hydrologic variable with the integer of time, i.e., 1 to n, used as the second variable. The Spearman Test statistic has a different distribution function when the integer of time is applied as one of the variables rather than the adjacent value of the discharge value (Conley and McCuen 1997). Both of these tests should use only the peak discharge sequence rather than correlating discharge and time.
8. Level of significance for detection decisions
The above are all important issues to those involved in assessing the effects of climate change, yet they may not be the most important issue. Regardless of the distribution assumed or the statistical test selected, the significance of an effect will depend on the level of significance adopted for decision making. Karl and Knight (1997) used the 5% level of significance to determine whether increases in precipitation within the United States were significant in the 20th century. Burns and Elnur (2002) reported hydrologic trends detected based on a 10% level of significance. Olsen et al. (1999) identified trends detected in flood records with both a 1% and 5% levels of significance. Traditionally, a 5% level is used, but evidence that this is really appropriate for hydrologic variables has not been addressed. It is unlikely that a 5% level of significance would lead to detection of hydrologic change due to a changing climate, as the sampling variation is generally quite dominant and would overwhelm the effect of climate change. Additionally, use of a 5% level will likely lead to a test having low statistical power. For example, Figures 3a and b display two time series simulated based on normally distributed errors and the same intercept and slope coefficients; however, the standard error for was increased by 100% from the Figure 3a to the Figure 3b time series. The Kendall Tau Test was applied to each data set and Z-values equal to 2.85 and 1.31 were calculated for the data in Figure 3a and 3b, respectively. Therefore, at the 5% level of significance, the null hypothesis was rejected for Figure 3a and accepted for Figure 3b. The null hypothesis was rejected at the 10% level of significance for Figure 3a. Therefore, the variation within the data influences the level of significance at which a trend will be detected, which is a concern when dealing with variables that contain high variation, such as hydrologic data. Before any statistical test is adopted, the issue of statistical power and the level of significance needs to be studied.
Figure 3a,b.
a and b. Simulated Time Series with Intercept = 1000, Slope = 1.25, and Se = 100 and 200., respectively, with Resulting Kendall Tau Statistics equal to 1.31 and 2.85, respectively.
The null hypothesis of interest to this issue is: climate change has not introduced nonstationarity into the flood series. The alternative hypothesis would be that the flood series is nonstationary. Using a small level of significance of 5% or 1% gives some assurance that a true null hypothesis will not be falsely rejected, but it also increases the chance of not identifying an effect and accepting the null hypothesis, when, if fact, it is false. A 5% level of significance will then likely lead to not making an adjustment of the flood series for nonstationarity, whereas use of a higher level of significance would dictate making such an adjustment. It seems that adjusting a series with a minimal effect of climate change would be preferable to failing to make a needed adjustment, even if the adjustment is small. Thus, the proper level of significance to be used in climate change analyses need to be investigated.
9. Nonstationarity and flood risk
Engineering designs are commonly based on an estimate of the 100-yr discharge, where the discharge is based on an analysis of the historic flood record or on a regression model fitted using regional flood records. Assuming that global climate change models are correct and that extreme rainfalls are expected to increase over time, then it is reasonable to assume that the time series of annual maximum discharges will increase over the next century. Under these conditions, storms of the size on which a design was based will occur more frequently. Thus, the 100-yr discharge of future times will be larger than the current 100-yr discharge. Likewise, under nonstationary conditions the current 100-yr flood event will occur more frequently (Olsen et al. 1998). The likelihood of the bridge opening passing runoff magnitudes will decrease, which means that the risk of failure will continuously increase with time.
Engineering design and risk assessments need to consider this nonstationarity of the T-yr discharge, where T is the return period used in design, e.g., T = 100yrs. A design made in 2010 based on the 100-yr discharge assessed using current information and knowledge will not have the same risk of failure as the climate changes. This should be considered in the design. If the design life for the 2010 project is 50 years, it may not be appropriate to design for the estimated 100-yr event for 2010 meteorological and hydrological conditions as then the project would be underdesigned. Similarly, designing for 2060 climate conditions would assume overdesign for each of the 49 years between 2010 and 2059. The optimal design discharge under these nonstationary global climate conditions would need to account for the rate of change of discharge over time. As many climate change scenarios show an increasing trend with time, the nonlinearity of the nonstationarity would require a temporally adjusted risk analysis.
Analyses have shown that the location and scale parameters of annual maximum flood series are expected to increase with increasing global climate change. These would raise the frequency curve and increase the exceedence probability of a flood magnitude. This is easily shown using a binomial risk analysis. Consider the case of a site where the 2010 conditions indicate a flood skew of 0.3 with the log moments shown in Table 1 for the decades that define the design life of the project. Assume that a project is designed to control the 100-yr flood magnitude of 1411 cms (49821 cfs). Assuming climate change will cause the log moments to increase as shown, then the return period of the design discharge increases over the 30 year period from the current 100 years to a 41-year event in 30 years. The binomial risk for each decade would change from 9.56% in the first decade, to 14% in the second decade and 19.1% in the third decade. Therefore, over the design life of the project, the project as designed has an increased likelihood of being exceeded. This change in the expected exceedence probability would provide a benefit-cost ratio of the project that was less than the ration on which a design based on stationary conditions would provide. Failure to account for the effect of climate change in the design would lead to long-term under design.
This conclusion is not intended to suggest that the project should be designed to the 100-yr flood condition at the end of the design life, as this would reflect a long-term design that would exceed the 100-yr protection. For example, if the project were designed for the 2040 flood moments, with a discharge of 2733 cms (96521 cfs), then the annual exceedence probability for current conditions would suggest a design exceedence probability of 0.00383, which reflects a return period of 261 years. Except in the last year of the design life of the project, the facility would have a protection that exceeds the required value. Using this value in a project benefit-cost ratio would provide a value that would exceed the long-term ratio that could be expected over the design life.
Decade
Log mean
Log sd
Flow (cfs)
Flow (cms)
K
p
T (yrs)
Prob.
2010
3.12
0.62
49821
1411
2.544
0.0100
100
0.0956
2020
3.15
0.63
56605
1603
2.456
0.0126
79
0.1074
2030
3.21
0.65
73070
2068
2.288
0.0177
57
0.1416
2040
3.28
0.67
96521
2733
2.116
0.0242
41
0.1908
Table 1.
Binomial Risk over Time
If the intent is to provide, on average, 100-yr protection, then an integrated procedure is needed. Such a method would need to consider the temporally changing flood potential at the site, as indicated by the changing moments. The continually changing flood risk would need to be estimated. A method developed to integrate the effect of the changing flood risk would be expected to account for these changes in flood potential.
10. Conclusions and recommendations
An important benefit of the modeling approach is that, in addition to quantification of change resulting from a disturbance, comparison of model parameters between pre- and post-event periods provides an indication of hydrological processes alteration by a severe event.
Detecting the effect of climate change in measured hydrologic data is a difficult, but important, task (Eslamian 2006). It has ramifications to assessing flood risk, the design of water resource infrastructure, and the avoidance of assigning too much weight to other nonstationary factors, such as urbanization, that contribute to hydrologic change. If the effect of climate change is even marginally significant, but not accounted for when attempting to assess the effects of urbanization in hydrologic data, then the effects of urbanization will likely be overstated. The results of such analyses will lead to biased designs. Infrastructure design that fails to account for climate change can be inadequate to meet the safety needs of a community, as the likelihood of severe flooding will increase because of climate change. Thus, floods that occur over the design life will likely be larger and more frequent than designed for. These situations are central to the issue of assessing flood risk, which has obvious implications to public safety, resource allocation, and the disruption of facility use.
The implications of global warming are significant, as policies will be made to address the issue and climate change may have significant economic effects. Therefore, uncertainties in projections of the effects of climate change must be considered in designing infrastructure, establishing public policies, and in economic decisions. A few of the uncertainties have been discussed in this paper, with an emphasis on uncertainties related to climate change modeling. Projecting to the future represents extrapolation, and given the uncertainties in modeling procedures, data, and our theoretical knowledge of the underlying processes, decision makers must consider these uncertainties. Record lengths of data used to calibrate climate models are short and contain very significant levels of nonsystematic variation. Such uncertainty will be carried over to projections made to the year 2100. The nonsystematic variation often reflects our lack of a full understanding of causal factors. Efforts through research need to be made to reduce these uncertainties in order to increase the accuracy of projections into the future.
Given the uncertainties of and interactions between the different model parameters, such explanations need to be approached with caution. Nevertheless, these suggestions of altered processes can direct further investigation and hypothesis formulation.
The following observations are especially important for the further climate variability investigations:
Daily runoff series for a few hundred smaller natural catchments, about 1000 km2 catchment area, employed for research purpose distributed over the globe.
Monthly runoff series for the top few hundred catchments around the world, possibly having natural flows.
Long time series of hydrological records
Impacts of climate change on water quality are also largely determined by hydrological changes and by the nature of pollutants as flushing or dilution-controlled. The most significant impact of urban development on water resources is an increase in overall surface runoff and the flashiness of the associated storm hydrograph. The increase in impervious surface area associated with urban development also contributes to degradation of water quality as a result of non-point source pollution. The modelling studies on the combined impacts of climate change and urban development have found that either change may be more significant, depending on scenario assumptions and basin characteristics, and that each type of change may amplify or ameliorate the effects of the other (Praskievicz and Chang 2009).
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McCuen",authors:[{id:"58107",title:"Prof.",name:"Saeid",middleName:null,surname:"Eslamian",fullName:"Saeid Eslamian",slug:"saeid-eslamian",email:"prof.eslamian@gmail.com",position:null,institution:{name:"Isfahan University of Technology",institutionURL:null,country:{name:"Iran"}}},{id:"60787",title:"Ms",name:"Kristin Leigh",middleName:null,surname:"Gilroy",fullName:"Kristin Leigh Gilroy",slug:"kristin-leigh-gilroy",email:"kgilroy@umd.edu",position:null,institution:null},{id:"60788",title:"Prof.",name:"Richard",middleName:null,surname:"McCuen",fullName:"Richard McCuen",slug:"richard-mccuen",email:"rhmccuen@eng.umd.edu",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Detection of change in flood records",level:"1"},{id:"sec_3",title:"3. Selection of statistical method to detect trend",level:"1"},{id:"sec_4",title:"4. The assumption of a start time",level:"1"},{id:"sec_5",title:"5. Selection of statistical distributions",level:"1"},{id:"sec_6",title:"6. Selection of statistical models",level:"1"},{id:"sec_7",title:"7. Confidence intervals under changing conditions",level:"1"},{id:"sec_8",title:"8. Level of significance for detection decisions",level:"1"},{id:"sec_9",title:"9. Nonstationarity and flood risk ",level:"1"},{id:"sec_10",title:"10. Conclusions and recommendations",level:"1"}],chapterReferences:[{id:"B1",body:'AmiriM. J.EslamianS. S.2010 Investigation of climate change in Iran, Journal of Environmental Science and Technology, 34208216'},{id:"B2",body:'BergstromS.1976 Development and application of a conceptual runoff model for Scandinavian catchments”. SMHI Reports RHO, 7Norrko ping, Sweden, 134.'},{id:"B3",body:'BergstromS.1992 The HBV model-its structure and applications”. SMHI Reports RH, 4Norrko ping, Sweden.'},{id:"B4",body:'BurnD. H.ElnurM. A. H.2002 Detection of Hydrologic Trends and Variability”. 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Isfahan University of Technology, Isfahan, Iran
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\n
1. Introduction
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Neurofibromatosis is a collection of three distinct autosomal dominant genetic disorders including neurofibromatosis type 1 (NF1), neurofibromatosis type 2 (NF2), and schwannomatosis. Each of these disorders has their own genetic variant, symptoms, and disease course [1]. These disorders are neurocutaneous syndromes, which represent a group of central nervous system (CNS) disorders with simultaneous lesions of other organs such as the skin or eye. One core common symptom among these conditions is that they cause tumors of nerve sheath [2].
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In this chapter, we discuss cognitive, academic, and adaptive effects of neurofibromatosis over the course of the lifespan. Through review and synthesis of the extant literature, we summarize what is currently known regarding cognitive sequelae associated with neurofibromatosis and discuss the functional impact with regard to learning, academics, and overall quality of life (QoL). Neurofibromatosis is a multisystem disorder that can cause multiple nerve sheath tumors throughout the body [1]. Each of the three conditions present with their own distinct as well as overlapping symptoms that can have a negative impact on QoL (e.g., chronic pain, bone abnormalities, skin disorders, hearing problems, and learning disabilities) [3, 4]. The presence of benign and malignant tumors, depending on their presentation and treatment regimen, can impact cognitive and developmental functioning [1]. Understanding the functional impact of this disorder is especially crucial in order to improve quality of life throughout the lifespan, as there is no known cure for neurofibromatosis [3]. NF1 is one of the most prevalent and researched genetic disorders. In contrast, prevalence rates of NF2 and Schwannomatosis are much lower, and related research is much more limited. As such, this chapter will focus on the most common of the genetic conditions, NF1.
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NF1 is characterized by cutaneous symptoms, including café-au-lait spots, skin neurofibromas, bone abnormalities (e.g., scoliosis), and glial cell tumors (gliomas) [2]. It is associated with a range of developmental and cognitive issues that are present throughout the lifespan. Cognitive and learning problems are the most common complications associated with NF1 [5, 6, 7]. In contrast, we did not find any studies that directly investigate the cognitive impact and learning issues of NF2 or Schwannomatosis. This is likely in part because these conditions are less prevalent and believed not to be directly associated with learning issues or academic struggles. That said, these are multisystem conditions that can impact vision and hearing, which can have indirect impact on cognitive skills and learning. Thus, we will discuss the cognitive effects of NF2 and Schwannomatosis indirectly by looking at associated common symptoms of the disorders that can impact cognition. NF2 is defined by bilateral vestibular schwannomas (i.e., benign Schwann cell tumors on the vestibulocochlear nerve), which can cause hearing loss and balance issues [1]. Schwannomatosis is the newest recognized form of neurofibromatosis and is characterized by multiple schwannomas that typically occur in adulthood [1]. The degree of physical/medical phenotypical symptom presentation of each of these conditions is highly variable [1, 3]. Not surprisingly, the cognitive impact of these disorders has been found to be just as variable, which will be discussed more in detail below. At this time, the current literature does not demonstrate to what extent specific cognitive skills are related to each NF phenotype, and it is not yet known whether the presence of predisposing genetic factors for each variant of NF explain this heterogeneity of cognitive outcomes.
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2. Neurofibromatosis type 1 (NF1)
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Because the phenotypic expression of NF1 is so variable, some individuals living with NF1 are unaware they have the disorder while others are significantly impacted. Additionally, symptoms and signs of NF1 can be fluid and can change in presentation throughout a person’s life [8]. In more severe presentations, NF1 can cause physical disfigurement and can be accompanied by significant neurological problems, such as brain tumor and seizures [2]. As noted above, NF1 is a disorder that affects multiple systems in the body, including the brain.
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There have been many studies that have investigated the cognitive and learning issues associated with NF1 across age groups throughout the lifespan. One reason that the cognitive and learning struggles associated with NF1 have been well-researched is that NF1 is a single gene disorder (i.e., a mutation of the tumor suppressor gene on chromosome 17), and as such it presents an opportunity to investigate cognitive dysfunction at the molecular and cellular level [9]. The NF1 gene encodes the neurofibromin protein, which serves a vital role in regulating the development of the brain [10]. Brain abnormalities have been detected in magnetic resonance imaging (MRI) studies of those with NF1, such as increased white matter volume, increased subcortical gray matter volume in the thalamus right caudate, decreased cortical gray matter density, T2 hyperintensities (T2H), macrocephaly, and reduced integrity of white matter microstructure [11, 12, 13]. Research has also indicated that thalamic T2H as well as volume abnormalities in the corpus callosum, putamen, and amygdala are specifically associated with cognitive deficits in NF1 [11, 14]. Of note, studies looking into the number of T2 spots and how this relates to cognitive impairment have been inconsistently documented [15].
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Medical complications that can co-occur with NF1 may lead to or compound cognitive deficits. For example, children with oncological complications of NF1 (e.g., brain tumors) are at risk for long-term cognitive issues as a result of treatment with chemotherapy and/or cranial irradiation [16]. Optic gliomas, tumors that arise from the nerve sheath of the optic nerve, are fairly common in children with NF1 and are sometimes associated with visual impairment, which can impact cognitive skills. The presence of a brain tumor also increases the risk of seizures or additional tumors arising in other areas of the brain [17], which can lead to specific cognitive deficits dependent on the area of the brain it is impacting. NF1 has also been associated with increased rates of other rarer neurological conditions that have known cognitive effects, including cortical dysplasia and hemimegalencephaly, as well as cerebrovascular diseases such as Moyamoya syndrome [17, 18, 19].
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Just as the severity of phenotypic expression and incidences of medical symptoms are quite variable within those with NF1, the impact on the CNS and subsequent cognitive and academic functioning are significantly heterogeneous. Cognitive and academic weaknesses are some of the most common symptoms in NF1 [5, 6, 7]. Cognitive weaknesses can present challenges for the individual, and this has been shown to occur across the lifespan [7]. Findings from studies with very young children have noted that developmental delays and subsequent academic struggles and learning disabilities are pervasive [12, 20]. With regard to investigations with adults and elderly adults, cognitive weaknesses have been noted to be fairly stable over time from childhood [6, 21, 22]. Overall, the level and type of functional impairment may vary depending on what period in life an individual is in (e.g., preschool, school aged, college, working adult, elderly). Across age groups, cognitive issues associated with NF1 have significant associated morbidities, including weaker adaptive skills [15]. Additional consequences of cognitive difficulties associated with NF1 include poorer academic achievement and overall reduced QoL [3].
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Below we will provide an in-depth discussion on the cognitive morbidities associated with NF1 as indicated by current research. Table 1 summarizes specific cognitive domains and findings related to the NF1 population, including overall intellectual ability as well as underlying cognitive functions including language, nonverbal skills, memory, attention, executive functions, academic skills, and adaptive skills.
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Domain
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Definition
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Common findings
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Intellectual ability (IQ)
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Summary score of overall cognitive/reasoning ability
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Multiple studies suggest IQ to be mildly reduced (IQ ~ 90)
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Language
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How well a person expresses (including speech) and understands language
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Studies have varied. Weaknesses with expressive language and speech are more common than receptive language issues Limited studies in adults.
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Nonverbal skills
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Visual spatial and fluid reasoning skills
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Weaknesses are very common; however, recent studies suggest that findings are confounded by executive function demands inherent in nonverbal measures
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Memory
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Learning and retention of information
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Studies on explicit memory have been variable. Weakness with working memory (short term memory) are common
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Attention
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Ability to focus, maintain focus on a task
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Multiple studies have noted attention problems to be very common. Up to 70% of children demonstrate deficit(s) in one or more aspect of attention
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Executive functioning
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A collection of higher order skills that assist with complex goal directed behavior
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Weaknesses are common. Specific weaknesses with planning/organization and working memory
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Academic skills
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Skills learned in school that include reading, writing, and mathematics
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Weaknesses are very common. Studies vary in prevalence from 20 to 75%, which appears in part to how learning problems are defined
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Adaptive skills
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Basic skills needed for independent living
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Mildly reduced, similar to IQ above
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Table 1.
Cognitive domains affected in NF1.
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Studies investigating specific cognitive domains as they relate to NF1 have been wide ranging in their outcomes. Early on, it was believed that in childhood, NF1 was associated with a “nonverbal learning disability” (NLD) profile, a former term for what encompasses deficits in visual–spatial, fine motor, and handwriting abilities in the context of preserved verbal functioning [23]; however, later research challenged this notion with findings indicating that features of NLD are inconsistent among NF1 populations [24, 25]. Additionally, the comorbidity of learning difficulties with these deficits has been found to significantly vary [6, 24]. This is likely in part due to the heterogeneity of the clinical presentation of the condition as well as methodological issues used in research studies, including differences in approaches to cognitive measurement and how learning problems are operationally defined.
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Additional studies examining the cognitive outcomes associated with NF1 have led to mixed findings and indicate varying degrees of prevalence of cognitive and academic problems. Hyman et al. [6] noted that these issues were likely due to research design factors, such low sample sizes, lack of controls, subject and control selection, as well as how learning problems are operationally defined. Individual cognitive test sensitivity and measures with overlapping cognitive domains have also been identified as leading to variability [26]. For example, performance on a commonly used visuospatial task in the assessment of nonverbal skills in children, the Rey-Osterrieth Complex Figure Test [27], can be undermined by weaknesses in attention and executive functions, as well as motor demands on the measure. Studies have varied in findings related to the prevalence of cognitive issues associated with NF1, though most note that cognitive issues are quite prevalent. Hyman et al. [6] noted that 81% of their sample had moderate to severe cognitive issues in one or more cognitive domains.
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2.1 Intellectual ability
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Intellectual ability is a cognitive construct that is commonly measured by an Intelligence Quotient (IQ), which represents an individual’s performance on an intelligence test relative to similar-aged individuals and culminates performance across verbal and nonverbal problem-solving skills. [28] IQ represents what Charles Spearman (1904) proposed in the early 20th century as the g factor, which is thought to contribute to successful performance across various cognitive skills. As such, IQ tests utilize a collection of cognitive tasks to determine a person’s overall intellectual functioning. Some of the most commonly used IQ tests are the Wechsler Intelligence Tests, which include various versions of assessments for individuals in preschool through adulthood. Most intelligence tests, like the Wechsler tests, are comprised of verbal and nonverbal reasoning tasks as well as cognitive efficiency tests, including working memory and processing speed. This is particularly the case with the older versions of the Wechsler tests, on which most of the published literature on NF1 is based. IQ scores are typically standard scores with a mean of 100 and a standard deviation of 15.
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Numerous studies have investigated IQ in NF1 populations. One of the more consistent cognitive findings in NF1 in children is that overall IQ is slightly lower than the normal population. That is, studies investigating IQ have placed the mean overall IQ approximately 10 points lower than normative sample [6, 29, 30]. This finding has been documented when compared to siblings controlling for environmental influences [30]. Hyman et al. [6] compared cognitive performance of 81 children with NF1 to 49 sibling controls. They found that the NF1 group demonstrated mildly reduced FSIQ with a mean of 90.6 compared to sibling mean of 102.6. Interestingly, this study found no associations between IQ and clinical severity, familial history of NF1, gender or age. Socioeconomic status was the only significant predictor of IQ in NF1 in their sample. Mild delays in IQ have also been noted with very young children, and given that difficulties have been found to be stable across the lifetime, this pattern has been noted in adults as well [14, 31]. In a combined adult and pediatric sample of 103 patients with NF1, Ferner et al. [32] noted an overall mean IQ score of 88.6 [32]. This finding appears to be consistent across cultures. Descheemaeker et al. found the overall IQ to be 89.96 in a Dutch-speaking sample [21]. With regard to elderly adults, there is very limited research investigating NF1; however, one small study noted mild delays in overall intellectual ability [22]. Taken together, these studies provide further support for the lack of progressive decline in IQ over time in NF1. In summary, it appears that NF1 is associated with average but mildly reduced overall IQ , which appears stable over the course of a lifespan.
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Despite overall average intelligence, NF1 is associated with greater prevalence of intellectual disability. Intellectual Disability (ID), formerly known as Mental Retardation, is defined in the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-V) as an IQ approximately two standard deviations below the population mean with associated deficits in adaptive functioning [33]. Studies have varied on findings related to the actual prevalence of ID in NF1 populations. Early studies were believed to have significantly overestimated the prevalence of ID due to methodological issues as well as how ID was defined [5]. The rate of ID in NF1 is believed to be 6–7%, which is much less than what was previously believed, though still two to three times the normative expectation [6]. The prevalence of ID increases if neurological complications (brain tumor, seizures) are not excluded [34].
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The overall composite score of the Full-Scale IQ likely masks the underlying subtle cognitive profile of NF1. Nearly 80% of people with NF1 have some cognitive deficit [14]. Thus, recent studies have focused on more discrete cognitive domains which we will discuss more below.
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2.2 Language
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Verbal skills are a collection of cognitive processes that involves language. Language is commonly divided into expressive and receptive language, which is how well a person uses language to relay their thoughts and ideas (including use of speech) and how a person understands language, respectively.
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Weaknesses with aspects of language have been found in populations with NF1. Delays in early language development have been noted children as young as 10 months, which appear to persist [20, 35, 36]. NF1 has been associated with weaknesses with nearly all aspects of language; however, studies have not been consistent [37, 38]. Expressive language problems, especially with speech/articulation, have been more consistently found than deficits with receptive language. Additional speech issues include problems with prosody, overall voice qualify, and aspects of speech sounds [37]. Hyman et al. [6] found that 44% of children with NF1 in their sample received speech-language therapy. Batista et al. [36] assessed central auditory temporal function in children with NF1 and correlated it with the results of language testing. They compared 25 NF1 patients to 22 healthy controls on audiometric and language tasks. They found no problems with peripheral acoustic hearing; however, the NF1 group performed more poorly on the temporal auditory processing task. Weaknesses with phonological skills in children have also been documented in several studies [38, 39, 40]. Phonological skills are not only associated with language delays but are also a core component of reading disability, which will be discussed further below. Studies on children have documented further weaknesses with verbal concept formation and comparisons as measured by the Similarities subtest from the Wechsler Intelligence Scales for Children (WISC). These findings were consistent compared to normal population and sibling control group [37, 41]. However, studies with childhood populations have not been consistent, as some studies noted that the differences in language disappear when IQ is controlled for [6, 38]. Verbal fluency has been found to be a relatively preserved cognitive function in children [42, 43]. Available literature on language in adult and elderly adult populations is relatively scarce as most studies in NF1 have been with pediatric populations.
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2.3 Nonverbal skills
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Nonverbal skills are a collection of visual perceptual, visual spatial, or visual-motor skills. They include visual perception, understanding spatial relations, and ability to integrate information from visual stimulus. Visuospatial (also referred to as visuoperceptual) skills have been found to be impaired in most studies involving children [5, 6, 12, 24, 26, 29, 34]. These studies have noted specific deficits in angulation, visual organization, and object recognition. The findings have been consistent when comparing children to normative sample or sibling control.
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Early studies on children with NF1 noted a significant discrepancy between verbal and nonverbal reasoning abilities. Weaknesses were noted with nonverbal reasoning skills, while verbal skills were believed to be preserved [25, 44]. However, several follow-up studies did not find the same discrepancy between verbal IQ and perceptual (nonverbal) IQ [6, 25]. Hyman et al. [6] actually noted a pattern opposite of what was expected, with males with NF1 having weaker verbal than nonverbal reasoning compared to females with NF1. It is now clear that NF1 is condition that can impact a range of cognitive functions not limited to nonverbal reasoning.
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In addition to nonverbal reasoning, studies with children and adults suggest weaknesses with many aspects of nonverbal skills including visual perception, visual-motor integration, form discrimination, visual organization [6, 21, 41, 42]. Indeed, weaknesses with aspects of visual spatial skills are common in NF1. However, several studies have not found significant differences between NF1 and controls regarding aspects of nonverbal skills [38, 45, 46]. Van Eylen et al. reviewed studies that directly assessed visuoperceptual and visual spatial functioning of children with NF1 [26]. They argue that the measures used to assess nonverbal skills are likely confounding findings. That is, many tasks that are purported to assess nonverbal skills also require other cognitive domains, most notably executive functions. In their sample, they found that when controlling for executive functions and IQ , performance on nonverbal tasks was not impaired. A similar pattern of weaknesses on nonverbal tasks has been documented in adults [21]. Overall, it appears that NF1 is associated with weaker visual/nonverbal skills; however, there are many confounds to previous studies which temper this conclusion.
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2.4 Memory
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Memory is our ability to encode, store, and retrieve previously learned information. Neuroscientists have identified many forms of memory, which at a basic level is divided into explicit and implicit memory. Cognitive tests of memory often only assess a small portion of memory functions. Cognitive tests typically focus on working memory and explicit memory. Working memory is our ability to actively hold information in mind for a short duration. It is commonly conceptualized as part of a collection of higher order executive functions.
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Cognitive tests assess explicit memory with verbal and visual tests. Studies in both children and adults identifying memory weaknesses in NF1 have been variable, and several studies have not found a significant difference in memory performance than controls [6, 21, 37, 41]. Hyman et al. [6] did not find a significant difference in performance on verbal and visual explicit memory tests in children with NF1 compared to sibling controls. Similarly, Krab et al. [41] did not find a significant difference in NF1 children’s performance on verbal or visual memory tasks when compared to children with no learning disabilities, children with specific learning disabilities, and children with general learning disabilities. In contrast, several studies have documented explicit memory weakness in children with NF1 [10, 14, 44, 47]. Bulgheroni et al. [47] assessed visual memory with the Rey Complex Figure Test (RCFT) [48]. They compared 18 children with NF1 to 17 siblings and 18 typically developing children. They found that the children with NF1 performed worse on recall memory, with no difference found regarding recognition memory. This pattern suggests that the NF1 had more difficulty with efficient retrieval rather coding and storing of the information, which is often due how the information was initially organized (an executive function). Overall, studies on explicit memory are mixed.
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2.5 Attention
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Attention involves of collection of processes that allows a person to engage in certain cognitive processing while ignoring others [51]. Attention is a complex system that has many subcomponents that includes focused attention, sustained attention, divided attention, and selective attention.
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Cognitive weakness with attention is very common to children, adolescents, and adults with NF1 [7, 37]. Children with NF1 have frequently been reported to exhibit impaired performance on tasks measuring the ability to sustain and switch attention [6, 52]. These findings appear to be consistent across measures of both visual and auditory sustained attention, as well as divided auditory attention and response inhibition [53]. In a large cohort study of 199 children with NF1, approximately 54% were at risk for inattentive behavior based on parent and teacher ratings [43].
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Up to 50% of individuals with NF1 meet diagnostic criteria for attention-deficit hyperactivity disorder (ADHD), [37, 54] and research has indicated that incidence rates of ADHD are much more common in children with NF1 than in immediate family members [55]. Neurocognitive deficits associated with NF1 have been found to be more severe in individuals with comorbid ADHD. While both groups have been found to demonstrate deficits in sustained attention, individuals with NF1 and comorbid ADHD have been indicated to be at higher risk [10]. Reduced attention skills in children with NF1 and ADHD have also been found to negatively impact the ability to process and respond to verbal instructions of increasing complexity, suggesting that receptive language skill development may also be vulnerable in this group as a result of attentional difficulties [10].
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The behavioral phenotype of ADHD in NF1 also appears to differ from ADHD in the general population. In a large cohort study by Hyman et al. [6], ADHD co-occurrence in children with NF1 occurred equally in frequency among males and females, which differs from the 3:1 ratio of males to females in the general population [43]. Research suggests that ADHD in NF1 also differs from typical ADHD in that the combined subtype appears to occur at the highest frequency followed by the inattentive subtype, while the hyperactive/impulsive subtype is typically found at the highest rates in children with ADHD alone [56]. Additionally, while clinical symptoms of ADHD in children with NF1 and those diagnosed with ADHD are comparable, differences lie in performance deficits specific to each group such that response inhibition processes have been found to be compromised in ADHD, but not in NF1 when compared to healthy controls, suggesting that response inhibition deficits may be less strong compared to those occurring in ADHD [57]. It has also been suggested that NF1/ADHD is not associated with increased frequency of executive deficits related to behavioral inhibition as it is in the general ADHD population [6]. A study comparing individuals with NF1/ADHD with a group of participants with ADHD and no NF1 found that ADHD symptomatology in NF1 did not exacerbate attention deficits and suggested that ADHD cannot account for all attention impairments in NF1 [57].
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Various brain-based characteristics associated with NF1 have been presumed to contribute to the neurocognitive deficits in NF1. For example, increased brain volume due to increased white matter and an enlarged corpus callosum appear to be characteristic of children with NF1 and may interfere with integration and processing of information [56]. Regarding attentional processes specifically, an fMRI study investigating ventral attention networks in the brain found that children with NF1 demonstrated hypoactivation in the temporoparietal junction and the anterior cingulate cortex when compared to typically developing children, which was associated with poorer selective attention and attentional control [58].
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The presence of attentional deficits in children with NF1 is associated with even greater risk for poorer performance in other cognitive functions, learning, social skills, and academic achievement [12, 56]. Social outcomes in particular appear to be worse in this group than in children with NF1 only [56]. A study examining face perception in children with NF1 found that sustained attention to faces in a social context is reduced in this population, which may inhibit the processing of socially relevant information needed for successful reciprocal social interactions [59]. Research also suggests that the risk of developing a specific learning disorder is higher in children with NF1 who have a diagnosis of ADHD [6]. As with other domains, it is suggested that while the literature on attentional problems primarily investigates these issues in childhood, these difficulties likely persist into adulthood without treatment.
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2.6 Executive functions
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Executive functions include a wide range of higher-order cognitive processes that serve goal-directed behaviors, including working memory, planning, organization, inhibition, flexibility. Because executive functioning encompasses a wide range of processes, studies investigating executive functioning in individuals with NF1 vary greatly in terms of the areas of focus and measures used [37]. Of note, many neuropsychological measures of executive function have been found to lack correlation with functional/behavioral ratings of the same constructs when evaluating individuals with NF1, and it has been suggested that Behavior Rating Inventory of Executive Function (BRIEF) rating scale items are more predictive of performance in real-world tasks outside of the structured testing environment [43]. Children with NF1 demonstrate significant impairments across all composite scores on the BRIEF [43, 52]. Differences remained even after controlling for VIQ [43].
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Despite this, executive dysfunction has been noted on performance measures as well. Beaussart et al. [50] conducted a meta-analysis of executive functioning in children with NF1. They included 19 studies in their analysis, with a total of 805 children with NF1 and 667 control subjects. They found a moderate effect with executive functions, indicating that children with NF1 had greater overall executive functioning impairments than controls. However, they noted variability in sub-domains of executive functions including cognitive flexibility, planning and problem solving, inhibitory control, and working memory. They found significant effect sizes for each sub-domain, with moderate effect sizes for working memory and planning/organization and small effect sizes for cognitive flexibility and inhibition. Significance remained even after controlling for moderating variables of executive functioning measures, control group composition, IQ , and ADHD.
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Weaknesses with working memory in NF1 populations are much more consistent and prevalent than with explicit memory. Several studies have documented verbal and visual working memory weaknesses [10, 14, 49]. Beaussart et al. [50] found a moderate to large effect size for working memory problems. The effect size for verbal working memory was larger than nonverbal working memory. They note that differences in effect size may be due to the psychometric properties of the working memory tasks [50].
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Executive function deficits have also been distinguished in NF1 adult populations. In particular, weaknesses in working memory and cognitive flexibility have been noted [21]. Very limited information is known with regard to executive functions in the elderly. Costa de et al. [22] noted working memory weaknesses in this population; however, this study was limited by a very small NF1 group.
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2.7 Academic learning
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Academic learning entails the use of basic educational skills to be successful in the classroom. This includes reading, writing, and mathematics. Academic learning struggles are one of the most common concerns of parents of children with NF1 [54]. Estimates of learning disabilities have significantly varied between studies. Research has found prevalence rates of learning difficulties to be 20–70% [5, 6, 41]. The variability is in part due to how each study operationalized the definition of “learning disability,” as the definition of learning disability has changed over the years. Previously an IQ-academic discrepancy model in which an individual performing much more poorly in an academic skill as compared to his or her overall intelligence level was used to define learning disability; however, this limited definition of a specific learning disability has received increased scrutiny and is rarely used today [60]. Hyman et al. [6] found that 20% of their child sample met the strict definition (discrepancy model) of specific learning disability (SLD), which is double the rate found in the normal population. In contrast, Krab et al. [41] used a different definition that examined “learning efficacy” and found that 75% percent of their sample had learning difficulties based on this definition. This study also noted a connection between disease severity and increase in learning struggles; however, this pattern has not been consistent in other studies. They argue that this is due to the fact that other studies do not systematically measure severity and other methodological issues.
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Despite the disagreement in overall prevalence rates of learning struggles, studies have been consistent in that NF1 is associated with significantly higher rate of learning disabilities in children when compared to normative sample and sibling controls [5, 6, 38, 41].
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Specific learning disability in the area of reading has been noted in childhood NF1 populations. Weaknesses have been found regarding phonological awareness, word decoding, fluency, and reading comprehension [5, 38, 39, 54]. Cutting and Levine [38] compared four groups that included children without reading difficulties, a reading disability group, an NF1 group without reading disability, and an NF1 group with reading disability. They found that children with NF1 with reading struggles performed similarly as the reading disability group.
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Learning struggles in mathematics and written expression in children with NF1 have also been noted. Math difficulties have been noted with computation and application of math concepts [6, 41, 49, 54]. Krab et al. [41] found that 23% of their NF1 sample met the criteria for a specific learning disability in mathematics and 77% demonstrated learning efficiency struggles with mathematics. However, another study did not find learning disability in mathematics [46]. Problems with writing including graphomotor control (penmanship) and spelling have noted as well [49, 61].
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2.8 Adaptive skills
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Adaptive skills are a collection of functional behaviors needed to effectively meet the demands of our environment. Adaptive functions are often divided into Conceptual skills, Practical skills, and Social skills. The DSM-V notes that adaptive deficits result in the failure to meet developmental and social standard for independent living without support.
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Several studies have noted adaptive deficits in children with NF1 [15, 62]. In a cross sectional study of 104 children with NF1, Eby et al. [15] found that 46.5 percent of their sample demonstrated adaptive functioning impairment. They found mild reductions across Conceptual, Social and Practical skill domains. Less is known about the specific adaptive domains that are impacted in adults and elderly adults. While adaptive demands change as individuals develop across the lifespan, it is likely that because cognitive difficulties remain stable with age, adaptive deficits are associated with adults with NF1 as well.
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3. NF2 and schwannomatosis
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We were unable to locate any studies that directly investigate cognitive weaknesses in NF2 or Schwannomatosis populations. It is likely that this has not been investigated, as these disorders are rarer and typically have less brain involvement. As such, we will discuss the cognitive effects of NF2 and Schwannomatosis with regard to common symptoms associated with these disorders and how these symptoms may impact cognitive functions.
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NF2 is defined in part by bilateral vestibular schwannomas [63]. Vestibular schwannomas are nonmalignant tumors that arise from eighth cranial nerve. The vestibular schwannomas can impact hearing, balance, at times vision, and facial weakness [65]. Hearing loss is progressive due to the presence of schwannomas and treatment, and it has been found in 60% of adults and 30% of children with NF2 [65]. Hearing loss can lead to decreased QoL [66] and can impact language development. Hearing loss has also been associated with decreased performance on intellectual and academic skills [67, 68]. Olivier et al. [69] investigated sensorineural hearing loss associated with intellectual and learning struggles in children with brain tumors. They found that children with severe hearing loss demonstrated greater difficulty with reading with weaker phonological skills, processing speed, and reading [69].
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Schwannomatosis is clinically distinguished from NF2 by the lack of bilateral vestibular schwannomas and ependymomas [64]. All neurofibromatoses, including NF1, NF2, and schwannomatosis, have schwannomas. Depending on the size and location, schwannomas can also be associated with pain [3]. Chronic pain has been associated with cognitive weaknesses with memory, attention, processing speed, and executive functions [70]. More research is needed to determine the possible cognitive sequalae associated with NF2 and Schwannomatosis. Further research is also warranted to distinguish whether differences in these sequelae exist depending on the age of the individual.
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4. Conclusions
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Neurofibromatosis is associated with effects on cognitive domains that impact learning, adaptive functioning, and quality of life across the lifespan of individuals affected by these disorders. The three distinct genetic disorders that encompass neurofibromatosis have their own genetic variant, symptoms, and disease course that result in differences in phenotypic expression as well as impact on the brain. While patterns of neurocognitive outcomes vary among and within each disorder, relatively less research has been conducted on those with NF2 and Schwannomatosis as compared to NF1. In particular, more research is needed investigating cognitive sequalae associated with NF2 and Schwannomatosis as these conditions at least indirectly are associated with cognitive weaknesses which can impact overall quality of life, likely from diagnosis through late adulthood.
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Within NF1, cognitive deficits are much more common yet highly variable within and between individuals. The heterogeneity of the cognitive outcomes is likely due to a combination of reasons, including genetic factors that have not been adequately elucidated yet, as well as methodological issues. Current research does not yet indicate to what extent differences among each NF phenotype are related to differences in typical cognitive deficits associated with each genetic variant. Common methodological issues in the literature include composition of control groups, evolving/varying definitions of cognitive domains and learning disorders, and limitations inherent in specific cognitive tests. Nonetheless, the current literature indicates that IQ , expressive language, visual spatial and fluid reasoning, and working memory are commonly impacted to some extent. Attention and executive functions appear to also be compromised in individuals with NF1, which are a factor in difficulties in receptive language, memory, academic skills, and adaptive skills. Most studies are focused on children, though existing adult studies suggest that cognitive deficits are present and similar to child studies, likely due to the stability of difficulties over time. Overall, evaluation of cognitive skills in those with neurofibromatosis is important in order to determine the functional impact that potential deficits may have on an individual, especially with regard to academic performance and adaptive functioning. This is especially significant due to the fact that neurofibromatosis is not a curable condition, which necessitates treatment that directly targets cognitive, academic, and adaptive problems directly. Regular monitoring of these individuals with respect to cognitive skills can aid in necessary intervention planning and should occur as early as possible to detect and treat issues that can arise early in development.
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Conflict of interest
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The authors have no conflict of interest.
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\n',keywords:"neurofibromatosis, schwannomatosis, cognitive, neurocognitive, learning, lifespan",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/69961.pdf",chapterXML:"https://mts.intechopen.com/source/xml/69961.xml",downloadPdfUrl:"/chapter/pdf-download/69961",previewPdfUrl:"/chapter/pdf-preview/69961",totalDownloads:11,totalViews:0,totalCrossrefCites:0,dateSubmitted:"March 27th 2019",dateReviewed:"October 4th 2019",datePrePublished:"November 22nd 2019",datePublished:null,readingETA:"0",abstract:"In this chapter, we will review cognitive issues faced by individuals living with neurofibromatosis. The chapter will discuss the complicated and sometimes inconsistent cognitive issues and adaptive functioning struggles associated with NF1, NF2, and schwannomatosis. We will review neurocognitive outcomes associated with each of these conditions across the lifespan while focusing on NF1. Specific neurocognitive domains we will review include: intellect, memory, language, nonverbal skills, attention, and executive functions. We will discuss the heterogeneity of the cognitive phenotype for each of these conditions. We will include how associated medical complications such as brain tumor, seizures, and hearing loss can impact neurocognitive outcomes. The chapter will also review the functional consequence of cognitive difficulties including academic struggles, learning disabilities, and decreased quality of life that are sometimes seen in this population.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/69961",risUrl:"/chapter/ris/69961",signatures:"Brian S. Potter and Leanne Mendoza",book:{id:"8729",title:"Neurofibromatosis - Current Trends and Future Directions",subtitle:null,fullTitle:"Neurofibromatosis - Current Trends and Future Directions",slug:null,publishedDate:null,bookSignature:"Dr. Francesco Signorelli and Dr. Raffaella Messina",coverURL:"https://cdn.intechopen.com/books/images_new/8729.jpg",licenceType:"CC BY 3.0",editedByType:null,editors:[{id:"74494",title:"Dr.",name:"Francesco",middleName:null,surname:"Signorelli",slug:"francesco-signorelli",fullName:"Francesco Signorelli"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Neurofibromatosis type 1 (NF1)",level:"1"},{id:"sec_2_2",title:"2.1 Intellectual ability",level:"2"},{id:"sec_3_2",title:"2.2 Language",level:"2"},{id:"sec_4_2",title:"2.3 Nonverbal skills",level:"2"},{id:"sec_5_2",title:"2.4 Memory",level:"2"},{id:"sec_6_2",title:"2.5 Attention",level:"2"},{id:"sec_7_2",title:"2.6 Executive functions",level:"2"},{id:"sec_8_2",title:"2.7 Academic learning",level:"2"},{id:"sec_9_2",title:"2.8 Adaptive skills",level:"2"},{id:"sec_11",title:"3. NF2 and schwannomatosis",level:"1"},{id:"sec_12",title:"4. Conclusions",level:"1"},{id:"sec_13",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Plotkin SR, Wick A. Neurofibromatosis and Schwannomatosis. Seminars in Neurology. 2018;38(1):73-85'},{id:"B2",body:'Lu-Emerson C, Plotkin SR. The Neurofibromatoses. Part 1: NF1. Reviews in Neurological Diseases. 2009;6(2):E47-E53'},{id:"B3",body:'Vranceanu AM et al. 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Assessing children\'s copy productions of the Rey-Osterrieth complex figure. Journal of Clinical and Experimental Neuropsychology. 1985;7(3):264-280'},{id:"B28",body:'Braaten EB, Norman D. Intelligence (IQ) testing. Pediatrics in Review. 2006;27(11):403-408'},{id:"B29",body:'North K et al. Cognitive function and academic performance in children with neurofibromatosis type 1. Developmental Medicine and Child Neurology. 1995;37(5):427-436'},{id:"B30",body:'Stewart DR et al. Care of adults with neurofibromatosis type 1: A clinical practice resource of the American College of Medical Genetics and Genomics (ACMG). Genetics in Medicine. 2018;20(7):671-682'},{id:"B31",body:'Lorenzo J et al. Developmental trajectories of young children with neurofibromatosis type 1: A longitudinal study from 21 to 40 months of age. The Journal of Pediatrics. 2015;166(4):1006-1012.e1001'},{id:"B32",body:'Ferner RE et al. Intellectual impairment in neurofibromatosis 1. 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Developmental Medicine and Child Neurology. 2013;55(2):111-125'},{id:"B38",body:'Cutting LE, Levine TM. Cognitive profile of children with neurofibromatosis and reading disabilities. Child Neuropsychology. 2010;16(5):417-432'},{id:"B39",body:'Chaix Y et al. Deficit in phonological processes: A characteristic of the neuropsychological profile of children with NF1. Child Neuropsychology. 2018;24(4):558-574'},{id:"B40",body:'Thompson HL. Evaluation of phonological processing skills of young children with neurofibromatosis type 1. Developmental Medicine and Child Neurology. 2018;60(7):642'},{id:"B41",body:'Krab LC et al. Impact of neurofibromatosis type 1 on school performance. Journal of Child Neurology. 2008;23(9):1002-1010'},{id:"B42",body:'Watt SE et al. An examination of lexical and sublexical reading skills in children with neurofibromatosis type 1. Child Neuropsychology. 2008;14(5):401-418'},{id:"B43",body:'Payne JM et al. Assessment of executive function and attention in children with neurofibromatosis type 1: Relationships between cognitive measures and real-world behavior. Child Neuropsychology. 2011;17(4):313-329'},{id:"B44",body:'Bawden H et al. MRI and nonverbal cognitive deficits in children with neurofibromatosis 1. Journal of Clinical and Experimental Neuropsychology. 1996;18(6):784-792'},{id:"B45",body:'Moore BD 3rd et al. Brain volume in children with neurofibromatosis type 1: Relation to neuropsychological status. Neurology. 2000;54(4):914-920'},{id:"B46",body:'Mazzocco MM. Math learning disability and math LD subtypes: Evidence from studies of Turner syndrome, fragile X syndrome, and neurofibromatosis type 1. Journal of Learning Disabilities. 2001;34(6):520-533'},{id:"B47",body:'Bulgheroni S et al. Visuoperceptual impairment in children with NF1: From early visual processing to procedural strategies. Behavioural Neurology. 2019;2019:7146168'},{id:"B48",body:'Meyers JE, Meyers KR. Rey Complex Figure Test and Recognition Trial. Odessa, Flor: Psychological Assessment Resources; 1995'},{id:"B49",body:'Billingsley RL et al. Cortical morphology associated with language function in neurofibromatosis, type I. Brain and Language. 2003;85(1):125-139'},{id:"B50",body:'Beaussart M-L et al. Systematic review and meta-analysis of executive functions in preschool and school-age children with neurofibromatosis type 1. Journal of the International Neuropsychological Society. 2018;24(9):977-994'},{id:"B51",body:'Loring DW. INS Dictionary of Neuropsychology. New York; Oxford: Oxford University Press; 1999'},{id:"B52",body:'Pride N et al. Corpus callosum morphology and its relationship to cognitive function in neurofibromatosis type 1. Journal of Child Neurology. 2010;25(7):834-841'},{id:"B53",body:'Isenberg JC et al. Attention skills in children with neurofibromatosis type 1. Journal of Child Neurology. 2013;28(1):45-49'},{id:"B54",body:'Levine TM et al. Cognitive profile of neurofibromatosis type 1. Seminars in Pediatric Neurology. 2006;13(1):8-20'},{id:"B55",body:'Koth CW et al. The association of neurofibromatosis type 1 and attention deficit hyperactivity disorder. Child Neuropsychology. 2000;6(3):185-194'},{id:"B56",body:'Pride NA et al. The impact of ADHD on the cognitive and academic functioning of children with NF1. Developmental Neuropsychology. 2012;37(7):590-600'},{id:"B57",body:'Lion-Francois L et al. Attention and executive disorders in Neurofibromatosis 1: Comparison between NF1 with ADHD symptomatology (NF1 + ADHD) and ADHD per Se. Journal of Attention Disorders. 6 Jun 2017. doi: 10.1177/1087054717707579'},{id:"B58",body:'Pride NA et al. Impaired engagement of the ventral attention system in neurofibromatosis type 1. Brain Imaging and Behavior. 2018;12(2):499-508'},{id:"B59",body:'Lewis AK et al. Attention to faces in social context in children with neurofibromatosis type 1. Developmental Medicine and Child Neurology. 2019;61(2):174-180'},{id:"B60",body:'Fletcher JM, Vaughn S. Response to intervention: Preventing and remediating academic difficulties. Child Development Perspectives. 2009;3(1):30-37'},{id:"B61",body:'Gilboa Y et al. The handwriting performance of children with NF1. Research in Developmental Disabilities. 2010;31(4):929-935'},{id:"B62",body:'Klein-Tasman BP et al. Adaptive behavior in young children with neurofibromatosis type 1. International Journal Of Pediatrics. 2013;2013:690432'},{id:"B63",body:'Kresak JL, Walsh M. Neurofibromatosis: A review of NF1, NF2, and Schwannomatosis. Journal of Pediatric Genetics. 2016;5(2):98-104'},{id:"B64",body:'Kehrer-Sawatzki H et al. Phenotypic and genotypic overlap between mosaic NF2 and schwannomatosis in patients with multiple non-intradermal schwannomas. Human Genetics. 2018;137(6-7):543-552'},{id:"B65",body:'North HJD, Lloyd SKW. Hearing rehabilitation in Neurofibromatosis type 2. Advances in Oto-Rhino-Laryngology. 2018;81:93-104'},{id:"B66",body:'Chen J et al. A cerebellopontine angle mouse model for the investigation of tumor biology, hearing, and neurological function in NF2-related vestibular schwannoma. Nature Protocols. 2019;14(2):541-555'},{id:"B67",body:'Farinetti A et al. International consensus (ICON) on audiological assessment of hearing loss in children. European Annals of Otorhinolaryngology, Head and Neck Diseases. 2018;135(1s):S41-s48'},{id:"B68",body:'Grasty MA et al. Hearing loss after cardiac surgery in infancy: An unintended consequence of life-saving care. The Journal of Pediatrics. 2018;192:144-151.e141'},{id:"B69",body:'Olivier TW et al. Cognitive implications of ototoxicity in pediatric patients with Embryonal brain tumors. Journal of Clinical Oncology. 2019;37:1566-1575'},{id:"B70",body:'Nadar MS et al. The cognitive functions in adults with chronic pain: A comparative study. Pain Research & Management. 2016;2016:5719380'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Brian S. Potter",address:"brian.potter@stjude.org",affiliation:'
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