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Alireza Atri - One of the best experts on this subject based on the ideXlab platform.
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a web based normative calculator for the uniform data set uds Neuropsychological Test battery
Alzheimer's Research & Therapy, 2011Co-Authors: Meghan B. Mitchell, Lynn W. Shaughnessy, Janet C. Sherman, Alireza Atri, Joseph J. Locascio, Steven D. Shirk, Sandra WeintraubAbstract:Introduction: With the recent publication of new criteria for the diagnosis of preclinical Alzheimer’s disease (AD), there is a need for Neuropsychological tools that take premorbid functioning into account in order to detect subtle cognitive decline. Using demographic adjustments is one method for increasing the sensitivity of commonly used measures. We sought to provide a useful online z-score calculator that yields estimates of percentile ranges and adjusts individual performance based on sex, age and/or education for each of the Neuropsychological Tests of the National Alzheimer’s Coordinating Center Uniform Data Set (NACC, UDS). In addition, we aimed to provide an easily accessible method of creating norms for other clinical researchers for their own, unique data sets. Methods: Data from 3,268 clinically cognitively-normal older UDS subjects from a cohort reported by Weintraub and colleagues (2009) were included. For all Neuropsychological Tests, z-scores were estimated by subtracting the raw score from the predicted mean and then dividing this difference score by the root mean squared error term (RMSE) for a given linear regression model. Results: For each Neuropsychological Test, an estimated z-score was calculated for any raw score based on five different models that adjust for the demographic predictors of SEX, AGE and EDUCATION, either concurrently, individually or without covariates. The interactive online calculator allows the entry of a raw score and provides five corresponding estimated z-scores based on predictions from each corresponding linear regression model. The calculator produces percentile ranks and graphical output. Conclusions: An interactive, regression-based, normative score online calculator was created to serve as an additional resource for UDS clinical researchers, especially in guiding interpretation of individual performances that appear to fall in borderline realms and may be of particular utility for operationalizing subtle cognitive impairment present according to the newly proposed criteria for Stage 3 preclinical Alzheimer’s disease.
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A web-based normative calculator for the uniform data set (UDS) Neuropsychological Test battery
Alzheimer's Research and Therapy, 2011Co-Authors: Steven D. Shirk, Meghan B. Mitchell, Lynn W. Shaughnessy, Janet C. Sherman, Joseph J. Locascio, Sandra Weintraub, Alireza AtriAbstract:INTRODUCTION: With the recent publication of new criteria for the diagnosis of preclinical Alzheimer's disease (AD), there is a need for Neuropsychological tools that take premorbid functioning into account in order to detect subtle cognitive decline. Using demographic adjustments is one method for increasing the sensitivity of commonly used measures. We sought to provide a useful online z-score calculator that yields estimates of percentile ranges and adjusts individual performance based on sex, age and/or education for each of the Neuropsychological Tests of the National Alzheimer's Coordinating Center Uniform Data Set (NACC, UDS). In addition, we aimed to provide an easily accessible method of creating norms for other clinical researchers for their own, unique data sets.\n\nMETHODS: Data from 3,268 clinically cognitively-normal older UDS subjects from a cohort reported by Weintraub and colleagues (2009) were included. For all Neuropsychological Tests, z-scores were estimated by subtracting the raw score from the predicted mean and then dividing this difference score by the root mean squared error term (RMSE) for a given linear regression model.\n\nRESULTS: For each Neuropsychological Test, an estimated z-score was calculated for any raw score based on five different models that adjust for the demographic predictors of SEX, AGE and EDUCATION, either concurrently, individually or without covariates. The interactive online calculator allows the entry of a raw score and provides five corresponding estimated z-scores based on predictions from each corresponding linear regression model. The calculator produces percentile ranks and graphical output.\n\nCONCLUSIONS: An interactive, regression-based, normative score online calculator was created to serve as an additional resource for UDS clinical researchers, especially in guiding interpretation of individual performances that appear to fall in borderline realms and may be of particular utility for operationalizing subtle cognitive impairment present according to the newly proposed criteria for Stage 3 preclinical Alzheimer's disease.
Steven D. Shirk - One of the best experts on this subject based on the ideXlab platform.
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a web based normative calculator for the uniform data set uds Neuropsychological Test battery
Alzheimer's Research & Therapy, 2011Co-Authors: Meghan B. Mitchell, Lynn W. Shaughnessy, Janet C. Sherman, Alireza Atri, Joseph J. Locascio, Steven D. Shirk, Sandra WeintraubAbstract:Introduction: With the recent publication of new criteria for the diagnosis of preclinical Alzheimer’s disease (AD), there is a need for Neuropsychological tools that take premorbid functioning into account in order to detect subtle cognitive decline. Using demographic adjustments is one method for increasing the sensitivity of commonly used measures. We sought to provide a useful online z-score calculator that yields estimates of percentile ranges and adjusts individual performance based on sex, age and/or education for each of the Neuropsychological Tests of the National Alzheimer’s Coordinating Center Uniform Data Set (NACC, UDS). In addition, we aimed to provide an easily accessible method of creating norms for other clinical researchers for their own, unique data sets. Methods: Data from 3,268 clinically cognitively-normal older UDS subjects from a cohort reported by Weintraub and colleagues (2009) were included. For all Neuropsychological Tests, z-scores were estimated by subtracting the raw score from the predicted mean and then dividing this difference score by the root mean squared error term (RMSE) for a given linear regression model. Results: For each Neuropsychological Test, an estimated z-score was calculated for any raw score based on five different models that adjust for the demographic predictors of SEX, AGE and EDUCATION, either concurrently, individually or without covariates. The interactive online calculator allows the entry of a raw score and provides five corresponding estimated z-scores based on predictions from each corresponding linear regression model. The calculator produces percentile ranks and graphical output. Conclusions: An interactive, regression-based, normative score online calculator was created to serve as an additional resource for UDS clinical researchers, especially in guiding interpretation of individual performances that appear to fall in borderline realms and may be of particular utility for operationalizing subtle cognitive impairment present according to the newly proposed criteria for Stage 3 preclinical Alzheimer’s disease.
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A web-based normative calculator for the uniform data set (UDS) Neuropsychological Test battery
Alzheimer's Research and Therapy, 2011Co-Authors: Steven D. Shirk, Meghan B. Mitchell, Lynn W. Shaughnessy, Janet C. Sherman, Joseph J. Locascio, Sandra Weintraub, Alireza AtriAbstract:INTRODUCTION: With the recent publication of new criteria for the diagnosis of preclinical Alzheimer's disease (AD), there is a need for Neuropsychological tools that take premorbid functioning into account in order to detect subtle cognitive decline. Using demographic adjustments is one method for increasing the sensitivity of commonly used measures. We sought to provide a useful online z-score calculator that yields estimates of percentile ranges and adjusts individual performance based on sex, age and/or education for each of the Neuropsychological Tests of the National Alzheimer's Coordinating Center Uniform Data Set (NACC, UDS). In addition, we aimed to provide an easily accessible method of creating norms for other clinical researchers for their own, unique data sets.\n\nMETHODS: Data from 3,268 clinically cognitively-normal older UDS subjects from a cohort reported by Weintraub and colleagues (2009) were included. For all Neuropsychological Tests, z-scores were estimated by subtracting the raw score from the predicted mean and then dividing this difference score by the root mean squared error term (RMSE) for a given linear regression model.\n\nRESULTS: For each Neuropsychological Test, an estimated z-score was calculated for any raw score based on five different models that adjust for the demographic predictors of SEX, AGE and EDUCATION, either concurrently, individually or without covariates. The interactive online calculator allows the entry of a raw score and provides five corresponding estimated z-scores based on predictions from each corresponding linear regression model. The calculator produces percentile ranks and graphical output.\n\nCONCLUSIONS: An interactive, regression-based, normative score online calculator was created to serve as an additional resource for UDS clinical researchers, especially in guiding interpretation of individual performances that appear to fall in borderline realms and may be of particular utility for operationalizing subtle cognitive impairment present according to the newly proposed criteria for Stage 3 preclinical Alzheimer's disease.
Grant L Iverson - One of the best experts on this subject based on the ideXlab platform.
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developing cognition endpoints for the center tbi Neuropsychological Test battery
Frontiers in Neurology, 2020Co-Authors: Jonas Stenberg, Justin E Karr, Douglas P Terry, Simen B Saksvik, Toril Skandsen, Noah D Silverberg, Grant L IversonAbstract:Background: Measuring cognitive functioning is common in traumatic brain injury (TBI) research, but no universally accepted method for combining several Neuropsychological Test scores into composite, or summary, scores exists. This study examined several possible composite scores for the Test battery used in the large-scale study Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI). Methods: Participants with mild traumatic brain injury (MTBI; n = 140), orthopedic trauma (n = 72), and healthy community controls (n = 70) from the Trondheim MTBI follow-up study completed the CENTER-TBI Test battery at 2 weeks after injury, which includes both traditional paper-and-pencil Tests and Tests from the Cambridge Neuropsychological Test Automated Battery (CANTAB). Seven composite scores were calculated for the paper and pencil Tests, the CANTAB Tests, and all Tests combined (i.e., 21 composites): the overall Test battery mean (OTBM); global deficit score (GDS); Neuropsychological deficit score-weighted (NDS-W); low score composite (LSC); and the number of scores ≤5th percentile, ≤16th percentile, or <50th percentile. Results: The OTBM and the number of scores <50th percentile composites had distributional characteristics approaching a normal distribution. The other composites were in general highly skewed and zero-inflated. When the MTBI group, the trauma control group, and the community control group were compared, effect sizes were negligible to small for all composites. Subgroups with vs. without loss of consciousness at the time of injury did not differ on the composite scores and neither did subgroups with complicated vs. uncomplicated MTBIs. Intercorrelations were high within the paper-and-pencil composites, the CANTAB composites, and the combined composites and lower between the paper-and-pencil composites and the CANTAB composites. Conclusion: None of the composites revealed significant differences between participants with MTBI and the two control groups. Some of the composite scores were highly correlated and may be redundant. Additional research on patients with moderate to severe TBIs is needed to determine which scores are most appropriate for TBI clinical trials.
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examining 3 month Test reTest reliability and reliable change using the cambridge Neuropsychological Test automated battery
Applied Neuropsychology, 2020Co-Authors: Rune Hatlestad Karlsen, Justin E Karr, Simen B Saksvik, Grant L Iverson, Astri J Lundervold, Odin Hjemdal, Alexander Olsen, Toril SkandsenAbstract:: The Cambridge Neuropsychological Test Automated Battery (CANTAB) is a battery of computerized Neuropsychological Tests commonly used in Europe in neurology and psychiatry studies, including clinical trials. The purpose of this study was to investigate Test-reTest reliability and to develop reliable change indices and regression-based change formulas for using the CANTAB in research and practice involving repeated measurement. A sample of 75 healthy adults completed nine CANTAB Tests, assessing three domains (i.e., visual learning and memory, executive function, and visual attention) twice over a 3-month period. Wilcoxon signed-rank Tests showed significant practice effects for 6 of 14 outcome measures with effect sizes ranging from negligible to medium (Hedge's g: .15-.40; Cliff's delta: .09-.39). The Spatial Working Memory Test, Attention Switching Task, and Rapid Visual Processing Test were the only Tests with scores of adequate Test-reTest reliability. For all outcome measures, Pearson's and Spearman's correlation coefficients ranged from .39 to .79. The measurement error surrounding difference scores was large, thus requiring large changes in performance (i.e., 1-2 SDs) in order to interpret a change score as reliable. In the regression equations, Test scores from initial Testing significantly predicted reTest scores for all outcome measures. Age was a significant predictor in several of the equations, while education was a significant predictor in only two of the equations. The adjusted R2 values ranged between .19 and .67. The present study provides results enabling clinicians to make probabilistic statements about change in cognitive functions based on CANTAB Test performances.
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comparing the Neuropsychological Test performance of operation enduring freedom operation iraqi freedom oef oif veterans with and without blast exposure mild traumatic brain injury and posttraumatic stress symptoms
Journal of The International Neuropsychological Society, 2015Co-Authors: Daniel Storzbach, Grant L Iverson, Maya E Oneil, Saw Myo Roost, Halina M Kowalski, Laurence M Binder, Jesse R Fann, Marilyn HuckansAbstract:To compare Neuropsychological Test performance of Veterans with and without mild traumatic brain injury (MTBI), blast exposure, and posttraumatic stress disorder (PTSD) symptoms. We compared the Neuropsychological Test performance of 49 Operation Enduring Freedom/Operation Iraqi Freedom (OEF/OIF) Veterans diagnosed with MTBI resulting from combat blast-exposure to that of 20 blast-exposed OEF/OIF Veterans without history of MTBI, 23 OEF/OIF Veterans with no blast exposure or MTBI history, and 40 matched civilian controls. Comparison of Neuropsychological Test performance across all four participant groups showed a complex pattern of mixed significant and mostly nonsignificant results, with omnibus Tests significant for measures of attention, spatial abilities, and executive function. The most consistent pattern was the absence of significant differences between blast-exposed Veterans with MTBI history and blast-exposed Veterans without MTBI history. When blast-exposed Veteran groups with and without MTBI history were aggregated and compared to non-blast-exposed Veterans, there were significant differences for some measures of learning and memory, spatial abilities, and executive function. However, covariation for severity of PTSD symptoms eliminated all significant omnibus Neuropsychological differences between Veteran groups. Our results suggest that, although some mild neurocognitive effects were associated with blast exposure, these neurocognitive effects might be better explained by PTSD symptom severity rather than blast exposure or MTBI history alone. (JINS, 2015, 21, 1-11). Language: en
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does familiarity with computers affect computerized Neuropsychological Test performance
Journal of Clinical and Experimental Neuropsychology, 2009Co-Authors: Grant L Iverson, Brian L Brooks, Lynn V Ashton, Lynda G Johnson, Thomas C GualtieriAbstract:The purpose of this study was to determine whether self-reported computer familiarity is related to performance on computerized neurocognitive Testing. Participants were 130 healthy adults who self-reported whether their computer use was “some” (n = 65) or “frequent” (n = 65). The two groups were individually matched on age, education, sex, and race. All completed the CNS Vital Signs (Gualtieri & Johnson, 2006b) computerized neurocognitive battery. There were significant differences on 6 of the 23 scores, including scores derived from the Symbol–Digit Coding Test, Stroop Test, and the Shifting Attention Test. The two groups were also significantly different on the Psychomotor Speed (Cohen's d = 0.37), Reaction Time (d = 0.68), Complex Attention (d = 0.40), and Cognitive Flexibility (d = 0.64) domain scores. People with “frequent” computer use performed better than people with “some” computer use on some Tests requiring rapid visual scanning and keyboard work.
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relationship between postconcussion headache and Neuropsychological Test performance in high school athletes
American Journal of Sports Medicine, 2003Co-Authors: Michael W Collins, Grant L Iverson, Melvin Field, Mark R Lovell, Karen M Johnston, Joseph C Maroon, Freddie H FuAbstract:Background: The relevance of headache to outcome after sports-related concussion is poorly understood.Hypotheses: High school athletes reporting headache approximately 1 week after injury will have significantly more other concussion symptoms and will perform more poorly on Neuropsychological Tests than athletes not experiencing headache.Study Design: Prospective cohort study.Methods: Study participants included 109 high school athletes who had sustained concussion and who were divided into two groups: those reporting headache 7 days after injury and those reporting no headaches. The two groups were compared regarding on-field markers of concussion severity at the time of injury and symptoms and neurocognitive Test results collected via ImPACT, a computerized Neuropsychological Test battery and postconcussion symptom scale, at a mean of 6.8 days after injury.Results: Athletes reporting posttraumatic headache demonstrated significantly worse performance on reaction time and memory ImPACT neurocognitive com...
Meghan B. Mitchell - One of the best experts on this subject based on the ideXlab platform.
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a web based normative calculator for the uniform data set uds Neuropsychological Test battery
Alzheimer's Research & Therapy, 2011Co-Authors: Meghan B. Mitchell, Lynn W. Shaughnessy, Janet C. Sherman, Alireza Atri, Joseph J. Locascio, Steven D. Shirk, Sandra WeintraubAbstract:Introduction: With the recent publication of new criteria for the diagnosis of preclinical Alzheimer’s disease (AD), there is a need for Neuropsychological tools that take premorbid functioning into account in order to detect subtle cognitive decline. Using demographic adjustments is one method for increasing the sensitivity of commonly used measures. We sought to provide a useful online z-score calculator that yields estimates of percentile ranges and adjusts individual performance based on sex, age and/or education for each of the Neuropsychological Tests of the National Alzheimer’s Coordinating Center Uniform Data Set (NACC, UDS). In addition, we aimed to provide an easily accessible method of creating norms for other clinical researchers for their own, unique data sets. Methods: Data from 3,268 clinically cognitively-normal older UDS subjects from a cohort reported by Weintraub and colleagues (2009) were included. For all Neuropsychological Tests, z-scores were estimated by subtracting the raw score from the predicted mean and then dividing this difference score by the root mean squared error term (RMSE) for a given linear regression model. Results: For each Neuropsychological Test, an estimated z-score was calculated for any raw score based on five different models that adjust for the demographic predictors of SEX, AGE and EDUCATION, either concurrently, individually or without covariates. The interactive online calculator allows the entry of a raw score and provides five corresponding estimated z-scores based on predictions from each corresponding linear regression model. The calculator produces percentile ranks and graphical output. Conclusions: An interactive, regression-based, normative score online calculator was created to serve as an additional resource for UDS clinical researchers, especially in guiding interpretation of individual performances that appear to fall in borderline realms and may be of particular utility for operationalizing subtle cognitive impairment present according to the newly proposed criteria for Stage 3 preclinical Alzheimer’s disease.
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A web-based normative calculator for the uniform data set (UDS) Neuropsychological Test battery
Alzheimer's Research and Therapy, 2011Co-Authors: Steven D. Shirk, Meghan B. Mitchell, Lynn W. Shaughnessy, Janet C. Sherman, Joseph J. Locascio, Sandra Weintraub, Alireza AtriAbstract:INTRODUCTION: With the recent publication of new criteria for the diagnosis of preclinical Alzheimer's disease (AD), there is a need for Neuropsychological tools that take premorbid functioning into account in order to detect subtle cognitive decline. Using demographic adjustments is one method for increasing the sensitivity of commonly used measures. We sought to provide a useful online z-score calculator that yields estimates of percentile ranges and adjusts individual performance based on sex, age and/or education for each of the Neuropsychological Tests of the National Alzheimer's Coordinating Center Uniform Data Set (NACC, UDS). In addition, we aimed to provide an easily accessible method of creating norms for other clinical researchers for their own, unique data sets.\n\nMETHODS: Data from 3,268 clinically cognitively-normal older UDS subjects from a cohort reported by Weintraub and colleagues (2009) were included. For all Neuropsychological Tests, z-scores were estimated by subtracting the raw score from the predicted mean and then dividing this difference score by the root mean squared error term (RMSE) for a given linear regression model.\n\nRESULTS: For each Neuropsychological Test, an estimated z-score was calculated for any raw score based on five different models that adjust for the demographic predictors of SEX, AGE and EDUCATION, either concurrently, individually or without covariates. The interactive online calculator allows the entry of a raw score and provides five corresponding estimated z-scores based on predictions from each corresponding linear regression model. The calculator produces percentile ranks and graphical output.\n\nCONCLUSIONS: An interactive, regression-based, normative score online calculator was created to serve as an additional resource for UDS clinical researchers, especially in guiding interpretation of individual performances that appear to fall in borderline realms and may be of particular utility for operationalizing subtle cognitive impairment present according to the newly proposed criteria for Stage 3 preclinical Alzheimer's disease.
Joseph J. Locascio - One of the best experts on this subject based on the ideXlab platform.
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a web based normative calculator for the uniform data set uds Neuropsychological Test battery
Alzheimer's Research & Therapy, 2011Co-Authors: Meghan B. Mitchell, Lynn W. Shaughnessy, Janet C. Sherman, Alireza Atri, Joseph J. Locascio, Steven D. Shirk, Sandra WeintraubAbstract:Introduction: With the recent publication of new criteria for the diagnosis of preclinical Alzheimer’s disease (AD), there is a need for Neuropsychological tools that take premorbid functioning into account in order to detect subtle cognitive decline. Using demographic adjustments is one method for increasing the sensitivity of commonly used measures. We sought to provide a useful online z-score calculator that yields estimates of percentile ranges and adjusts individual performance based on sex, age and/or education for each of the Neuropsychological Tests of the National Alzheimer’s Coordinating Center Uniform Data Set (NACC, UDS). In addition, we aimed to provide an easily accessible method of creating norms for other clinical researchers for their own, unique data sets. Methods: Data from 3,268 clinically cognitively-normal older UDS subjects from a cohort reported by Weintraub and colleagues (2009) were included. For all Neuropsychological Tests, z-scores were estimated by subtracting the raw score from the predicted mean and then dividing this difference score by the root mean squared error term (RMSE) for a given linear regression model. Results: For each Neuropsychological Test, an estimated z-score was calculated for any raw score based on five different models that adjust for the demographic predictors of SEX, AGE and EDUCATION, either concurrently, individually or without covariates. The interactive online calculator allows the entry of a raw score and provides five corresponding estimated z-scores based on predictions from each corresponding linear regression model. The calculator produces percentile ranks and graphical output. Conclusions: An interactive, regression-based, normative score online calculator was created to serve as an additional resource for UDS clinical researchers, especially in guiding interpretation of individual performances that appear to fall in borderline realms and may be of particular utility for operationalizing subtle cognitive impairment present according to the newly proposed criteria for Stage 3 preclinical Alzheimer’s disease.
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A web-based normative calculator for the uniform data set (UDS) Neuropsychological Test battery
Alzheimer's Research and Therapy, 2011Co-Authors: Steven D. Shirk, Meghan B. Mitchell, Lynn W. Shaughnessy, Janet C. Sherman, Joseph J. Locascio, Sandra Weintraub, Alireza AtriAbstract:INTRODUCTION: With the recent publication of new criteria for the diagnosis of preclinical Alzheimer's disease (AD), there is a need for Neuropsychological tools that take premorbid functioning into account in order to detect subtle cognitive decline. Using demographic adjustments is one method for increasing the sensitivity of commonly used measures. We sought to provide a useful online z-score calculator that yields estimates of percentile ranges and adjusts individual performance based on sex, age and/or education for each of the Neuropsychological Tests of the National Alzheimer's Coordinating Center Uniform Data Set (NACC, UDS). In addition, we aimed to provide an easily accessible method of creating norms for other clinical researchers for their own, unique data sets.\n\nMETHODS: Data from 3,268 clinically cognitively-normal older UDS subjects from a cohort reported by Weintraub and colleagues (2009) were included. For all Neuropsychological Tests, z-scores were estimated by subtracting the raw score from the predicted mean and then dividing this difference score by the root mean squared error term (RMSE) for a given linear regression model.\n\nRESULTS: For each Neuropsychological Test, an estimated z-score was calculated for any raw score based on five different models that adjust for the demographic predictors of SEX, AGE and EDUCATION, either concurrently, individually or without covariates. The interactive online calculator allows the entry of a raw score and provides five corresponding estimated z-scores based on predictions from each corresponding linear regression model. The calculator produces percentile ranks and graphical output.\n\nCONCLUSIONS: An interactive, regression-based, normative score online calculator was created to serve as an additional resource for UDS clinical researchers, especially in guiding interpretation of individual performances that appear to fall in borderline realms and may be of particular utility for operationalizing subtle cognitive impairment present according to the newly proposed criteria for Stage 3 preclinical Alzheimer's disease.
