Gender Differences in Emotional Connotative Meaning of Words Measured by Osgood's Semantic Differential Techniques in Young Adults.

Semantic differential techniques are a useful, well-validated tool to assess affective processing of stimuli and determine how that processing is impacted by various demographic factors, such as gender. In this paper, we explore differences in connotative word processing between men and women as measured by Osgood's semantic differential and what those differences imply about affective processing in the two genders. We recruited 94 young participants (47 men, 47 women, ages 18-39) using an online survey and collected their affective ratings of 120 words on three rating tasks: Evaluation (E), Potency (P), and Activity (A). With these data, we explored the theoretical and mathematical overlap between Osgood's affective meaning factor structure and other models of emotional processing commonly used in gender analyses. We then used Osgood's three-dimensional structure to assess gender-related differences in three affective classes of words (words with connotation that is Positive, Neutral, or Negative for each task) and found that there was no significant difference between the genders when rating Positive words and Neutral words on each of the three rating tasks. However, young women consistently rated Negative words more negatively than young men did on all three of the independent dimensions. This confirms the importance of taking gender effects into account when measuring emotional processing. Our results further indicate there may be differences between Osgood's structure and other models of affective processing that should be further explored.

Women have farther to fall: gender differences between normal elderly and Alzheimer's disease in verbal memory engender better detection of Alzheimer's disease in women.

We analyzed verbal episodic memory learning and recall using the Logical Memory (LM) subtest of the Wechsler Memory Scale-III to determine how gender differences in AD compare to those seen in normal elderly and whether or not these differences impact assessment of AD. We administered the LM to both an AD and a Control group, each comprised of 21 men and 21 women, and found a large drop in performance from normal elders to AD. Of interest was a gender interaction whereby the women's scores dropped 1.6 times more than the men's did. Control women on average outperformed Control men on every aspect of the test, including immediate recall, delayed recall, and learning. Conversely, AD women tended to perform worse than AD men. Additionally, the LM achieved perfect diagnostic accuracy in discriminant analysis of AD versus Control women, a statistically significantly higher result than for men. The results indicate the LM is a more powerful and reliable tool in detecting AD in women than in men.

Temporospatial components of brain ERPs as biomarkers for Alzheimer's disease.

INTRODUCTION: Developing biomarkers that distinguish individuals with Alzheimer's disease (AD) from those with normal cognition remains a crucial goal for improving the health of older adults. We investigated adding brain spatial information to temporal event-related potentials (ERPs) to increase AD identification accuracy over temporal ERPs alone. METHODS: With two-step principal components analysis, we applied multivariate analyses that incorporated temporal and spatial ERP information from a cognitive task. Discriminant analysis used temporospatial ERP scores to classify participants as belonging to either the AD or healthy control group. RESULTS: Temporospatial ERPs produced a cross-validated area under the curve of 0.84. Adding spatial information through a formal procedure significantly improves classification accuracy. DISCUSSION: A weighted combination of temporospatial ERP markers performs well in detecting AD. Because ERPs are noninvasive and inexpensive, they may be promising biomarkers for AD that can add functional information to other biomarker systems while providing the individual's probability of correct classification.

ERP C250 shows the elderly (cognitively normal, Alzheimer's disease) store more stimuli in short-term memory than Young Adults do.

OBJECTIVE: To determine how aging and dementia affect the brain's initial storing of task-relevant and irrelevant information in short-term memory. METHODS: We used brain Event-Related Potentials (ERPs) to measure short-term memory storage (ERP component C250) in 36 Young Adults, 36 Normal Elderly, and 36 early-stage AD subjects. Participants performed the Number-Letter task, a cognitive paradigm requiring memory storage of a first relevant stimulus to compare it with a second stimulus. RESULTS: In Young Adults, C250 was more positive for the first task-relevant stimulus compared to all other stimuli. C250 in Normal Elderly and AD subjects was roughly the same to relevant and irrelevant stimuli in Intratrial Parts 1-3 but not 4. The AD group had lower C250 to relevant stimuli in part 1. CONCLUSIONS: Both normal aging and dementia cause less differentiation of relevant from irrelevant information in initial storage. There was a large aging effect involving differences in the pattern of C250 responses of the Young Adult versus the Normal Elderly/AD groups. Also, a potential dementia effect was obtained. SIGNIFICANCE: C250 is a candidate tool for measuring short-term memory performance on a biological level, as well as a potential marker for memory changes due to normal aging and dementia.

Memory timeline: Brain ERP C250 (not P300) is an early biomarker of short-term storage.

Brain event-related potentials (ERPs) offer a quantitative link between neurophysiological activity and cognitive performance. ERPs were measured while young adults performed a task that required storing a relevant stimulus in short-term memory. Using principal components analysis, ERP component C250 (maximum at 250 ms post-stimulus) was extracted from a set of ERPs that were separately averaged for various task conditions, including stimulus relevancy and stimulus sequence within a trial. C250 was more positive in response to task-specific stimuli that were successfully stored in short-term memory. This relationship between C250 and short-term memory storage of a stimulus was confirmed by a memory probe recall test where the behavioral recall of a stimulus was highly correlated with its C250 amplitude. ERP component P300 (and its subcomponents of P3a and P3b, which are commonly thought to represent memory operations) did not show a pattern of activation reflective of storing task-relevant stimuli. C250 precedes the P300, indicating that initial short-term memory storage may occur earlier than previously believed. Additionally, because C250 is so strongly predictive of a stimulus being stored in short-term memory, C250 may provide a strong index of early memory operations.

C145 as a short-latency electrophysiological index of cognitive compensation in Alzheimer's disease.

Brain plasticity and cognitive compensation in the elderly are of increasing interest, and Alzheimer's disease (AD) offers an opportunity to elucidate how the brain may overcome damage. We provide neurophysiological evidence of a short-latency event-related potential (ERP) component (C145) linked to stimulus relevancy that may reflect cognitive compensation in early-stage AD. Thirty-six subjects with early-stage, mild AD and 36 like-aged normal elderly (controls) had their EEG recorded while performing our Number-Letter task, a cognitive/perceptual paradigm that manipulates stimulus relevancies. ERP components, including C145, were extracted from ERPs using principal components analysis. C145 amplitudes and spatial distributions were compared among controls, AD subjects with high performance on the Number-Letter task, and AD subjects with low performance. Compared to AD subjects, control subjects showed enhanced C145 processing of visual stimuli in the occipital region where differential processing of relevant stimuli occurred. AD high performers recruited central brain areas in processing task relevancy. Controls and AD low performers did not show a significant task relevancy effect in these areas. We conclude that short-latency ERP components can detect electrophysiological differences in early-stage AD that reflect altered cognition. Differences in C145 amplitudes between AD and normal elderly groups regarding brain locations and types of task effects suggest compensatory mechanisms can occur in the AD brain to overcome loss of normal functionality, and this early compensation may have a profound effect on the cognitive efficiency of AD individuals.

The impact of AD drug treatments on event-related potentials as markers of disease conversion.

This paper investigates how commonly prescribed pharmacologic treatments for Alzheimer's disease (AD) affect Event-Related Potential (ERP) biomarkers as tools for predicting AD conversion in individuals with Mild Cognitive Impairment (MCI). We gathered baseline ERP data from two MCI groups (those taking AD medications and those not) and later determined which subjects developed AD (Convert->AD) and which subjects remained cognitively stable (Stable). We utilized a previously developed and validated multivariate system of ERP components to measure medication effects among these four subgroups. Discriminant analysis produced classification scores for each individual as a measure of similarity to each clinical group (Convert->AD, Stable), and we found a large significant main Group effect but no main AD Medications effect and no Group by Medications interaction. This suggested AD medications have negligible influence on this set of ERP components as weighted markers of disease progression. These results provide practical information to those using ERP measures as a biomarker to identify and track AD in individuals in a clinical or research setting.

Brain ERP components predict which individuals progress to Alzheimer's disease and which do not.

Predicting which individuals will progress to Alzheimer's disease (AD) is important in both clinical and research settings. We used brain Event-Related Potentials (ERPs) obtained in a perceptual/cognitive paradigm with various processing demands to predict which individual Mild Cognitive Impairment (MCI) subjects will develop AD versus which will not. ERP components, including P3, memory "storage" component, and other earlier and later components, were identified and measured by Principal Components Analysis. When measured for particular task conditions, a weighted set of eight ERP component_conditions performed well in discriminant analysis at predicting later AD progression with good accuracy, sensitivity, and specificity. The predictions for most individuals (79%) had high posterior probabilities and were accurate (88%). This method, supported by a cross-validation where the prediction accuracy was 70-78%, features the posterior probability for each individual as a method of determining the likelihood of progression to AD. Empirically obtained prediction accuracies rose to 94% when the computed posterior probabilities for individuals were 0.90 or higher (which was found for 40% of our MCI sample).

Predicting conversion from mild cognitive impairment to Alzheimer's disease using neuropsychological tests and multivariate methods.

Behavioral markers measured through neuropsychological testing in mild cognitive impairment (MCI) were analyzed and combined in multivariate ways to predict conversion to Alzheimer's disease (AD) in a longitudinal study of 43 MCI patients. The test measures taken at a baseline evaluation were first reduced to underlying components (principal component analysis, PCA), and then the component scores were used in discriminant analysis to classify MCI individuals as likely to convert or not. When empirically weighted and combined, episodic memory, speeded executive functioning, recognition memory (false and true positives), visuospatial memory processing speed, and visuospatial episodic memory were together strong predictors of conversion to AD. These multivariate combinations of the test measures achieved through the PCA were good, statistically significant predictors of MCI conversion to AD (84% accuracy, 86% sensitivity, and 83% specificity). Importantly, the posterior probabilities of group membership that accompanied the binary prediction for each participant indicated the confidence of the prediction. Most of the participants (81%) were in the highly confident probability bins (.70-1.00), where the obtained prediction accuracy was more than 90%. The strength and reliability of this multivariate prediction method were tested by cross-validation and randomized resampling.

Cognitive Dimensions in Alzheimer's Disease, Mild Cognitive Impairment, and Normal Elderly: Developing a Common Metric.

The aim of this research was to assess similarity in cognitive factor structures underlying neuropsychological test performance of elders belonging to three clinical groups: Alzheimer's disease (AD), Mild Cognitive Impairment (MCI), and normal elderly. We administered a battery of neuropsychological tests to 214 elderly participants in the groups. First, the underlying cognitive structure of a Combined-Set of AD, MCI, and Control subjects was determined by Principal Components Analysis (PCA), including quantitative relationships (loadings) between the test measures and the factors. The PCA resolved 17 neuropsychological test measures into 6 interpretable factors, accounting for 78% of the variance. This cognitive structure was compared with separate cognitive structures from an AD-Set, an MCI-Set, and a Control-Set (different individuals in each set) in additional PCA using Procrustes factor rotation. Analysis of congruence coefficients between each set and the Combined-Set by a bootstrapping statistical procedure supported the factor invariance hypothesis. These close similarities across groups in their underlying neuropsychological dimensions support the use of a common metric system (the factor structure of a Combined-Set) for measuring neuropsychological factors in all these elderly individuals.

Diagnosis of Alzheimer's disease using neuropsychological testing improved by multivariate analyses.

Neuropsychological assessment aids in the diagnosis of Alzheimer's disease (AD) by objectively establishing cognitive impairment from standardized tests. We present new criteria for diagnosis that use weighted combined scores from multiple tests. Our method employs two multivariate analyses: principal components analysis (PCA) and discriminant analysis. PCA (N = 216 participants) created more interpretable cognitive dimensions by resolving 49 test measures in our neuropsychological battery to 13 component scores for each participant. The component scores were used to build discriminant functions that classified each participant as either an early-stage AD (N = 55) or normal elderly (N = 78). Our discriminant function performed with high accuracy, sensitivity, and specificity (nearly all >90%) in the development, a cross-validation, and a new-subjects validation. When contrasted to two different traditional empirical methods for diagnosis (using cutscores and defining AD as falling below 5% on two or more test domains), our results suggested that the multivariate method was superior in classification (approximately 20% more accurate).

Brain event-related potentials: diagnosing early-stage Alzheimer's disease.

A pattern of components from brain event-related potentials (ERPs) (cognitive non-invasive electrical brain measures) performed well in separating early-stage Alzheimer's disease (AD) subjects from normal-aging control subjects and shows promise for developing a clinical diagnostic for probable AD. A Number-Letter task elicited brain activity related to cognitive processes. In response to the task stimuli, brain activity was recorded as ERPs, whose components were measured by principal components analysis (PCA). The ERP component scores to relevant and irrelevant stimuli were used in discriminant analyses to develop functions that successfully classified individuals as belonging to an early-stage Alzheimer's disease group or a like-aged Control group, with probabilities of an individual belonging to each group. Applying the discriminant function to the developmental half of the data showed 92% of the subjects were correctly classified into either the AD group or the Control group with a sensitivity of 1.00. The two crossvalidation results were good with sensitivities of 0.83 and classification accuracies of 0.75-0.79. P3 and CNV components, as well as other, earlier ERP components, e.g. C145 and the memory "Storage" component, were useful in the discriminant functions.