78 research outputs found
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TDP-43 Proteinopathy and Motor Neuron Disease in Chronic Traumatic Encephalopathy
Epidemiological evidence suggests that the incidence of amyotrophic lateral sclerosis is increased in association with head injury. Repetitive head injury is also associated with the development of chronic traumatic encephalopathy (CTE), a tauopathy characterized by neurofibrillary tangles throughout the brain in the relative absence of β-amyloid deposits. We examined 12 cases of CTE and, in 10, found a widespread TAR DNA-binding protein of approximately 43 kd (TDP-43) proteinopathy affecting the frontal and temporal cortices, medial temporal lobe, basal ganglia, diencephalon, and brainstem. Three athletes with CTE also developed a progressive motor neuron disease with profound weakness, atrophy, spasticity, and fasciculations several years before death. In these 3 cases, there were abundant TDP-43–positive inclusions and neurites in the spinal cord in addition to tau neurofibrillary changes, motor neuron loss, and corticospinal tract degeneration. The TDP-43 proteinopathy associated with CTE is similar to that found in frontotemporal lobar degeneration with TDP-43 inclusions, in that widespread regions of the brain are affected. Akin to frontotemporal lobar degeneration with TDP-43 inclusions, in some individuals with CTE, the TDP-43 proteinopathy extends to involve the spinal cord and is associated with motor neuron disease. This is the first pathological evidence that repetitive head trauma experienced in collision sports might be associated with the development of a motor neuron disease
The latent factor structure underlying regional brain volume change and its relation to cognitive change in older adults.
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Reliable Change on Neuropsychological Tests in the Uniform Data Set.
Longitudinal normative data obtained from a robust elderly sample (i.e., believed to be free from neurodegenerative disease) are sparse. The purpose of the present study was to develop reliable change indices (RCIs) that can assist with interpretation of test score changes relative to a healthy sample of older adults (ages 50+). Participants were 4217 individuals who completed at least three annual evaluations at one of 34 past and present Alzheimer's Disease Centers throughout the United States. All participants were diagnosed as cognitively normal at every study visit, which ranged from three to nine approximately annual evaluations. One-year RCIs were calculated for 11 neuropsychological variables in the Uniform Data Set by regressing follow-up test scores onto baseline test scores, age, education, visit number, post-baseline assessment interval, race, and sex in a linear mixed effects regression framework. In addition, the cumulative frequency distributions of raw score changes were examined to describe the base rates of test score changes. Baseline test score, age, education, and race were robust predictors of follow-up test scores across most tests. The effects of maturation (aging) were more pronounced on tests related to attention and executive functioning, whereas practice effects were more pronounced on tests of episodic and semantic memory. Interpretation of longitudinal changes on 11 cognitive test variables can be facilitated through the use of reliable change intervals and base rates of score changes in this robust sample of older adults. A Web-based calculator is provided to assist neuropsychologists with interpretation of longitudinal change
Regression-Based Norms for a Bi-factor Model for Scoring the Brief Test of Adult Cognition by Telephone (BTACT)
Abstract The current study developed regression-based normative adjustments for a bi-factor model of the The Brief Test of Adult Cognition by Telephone (BTACT). Archival data from the Midlife Development in the United States-II Cognitive Project were used to develop eight separate linear regression models that predicted bi-factor BTACT scores, accounting for age, education, gender, and occupation-alone and in various combinations. All regression models provided statistically significant fit to the data. A three-predictor regression model fit best and accounted for 32.8% of the variance in the global bi-factor BTACT score. The fit of the regression models was not improved by gender. Eight different regression models are presented to allow the user flexibility in applying demographic corrections to the bi-factor BTACT scores. Occupation corrections, while not widely used, may provide useful demographic adjustments for adult populations or for those individuals who have attained an occupational status not commensurate with expected educational attainment
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