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The assessment of cognitive functions such as prospective memory, episodic memory, attention, and executive functions benefits from an ecologically valid approach to better understand how performance outcomes generalize to everyday life. Immersive virtual reality (VR) is considered capable of simulating real-life situations to enhance ecological validity. The present study attempted to validate the Virtual Reality Everyday Assessment Lab (VR-EAL), an immersive VR neuropsychological battery, against an extensive paper-and-pencil neuropsychological battery. Forty-one participants (21 females) were recruited: 18 gamers and 23 non-gamers who attended both an immersive VR and a paper-and-pencil testing session. Bayesian Pearson's correlation analyses were conducted to assess construct and convergent validity of the VR-EAL. Bayesian t-tests were performed to compare VR and paper-and-pencil testing in terms of administration time, similarity to real-life tasks (i.e., ecological validity), and pleasantness. VR-EAL scores were significantly correlated with their equivalent scores on the paper-and-pencil tests. The participants' reports indicated that the VR-EAL tasks were significantly more ecologically valid and pleasant than the paper-and-pencil neuropsychological battery. The VR-EAL battery also had a shorter administration time. The VR-EAL appears as an effective neuropsychological tool for the assessment of everyday cognitive functions, which has enhanced ecological validity, a highly pleasant testing experience, and does not induce cybersickness.

Although recent studies provide insight into the molecular mechanisms of the effects of ketamine, the antidepressant mechanism of ketamine enantiomers and their metabolites is not fully understood. In view of the involvement of mechanisms other than the N-methyl-D-aspartate receptor in ketamine's action, we investigated the effects of (R)-ketamine, (S)-ketamine, (R)-norketamine [(R)-NK], (S)-NK, (2R,6R)-hydroxynorketamine [(2R,6R)-HNK], and (2S,6S)-HNK on monoaminergic neurotransmission in the prefrontal cortex of mice. The extracellular monoamine levels in the prefrontal cortex were measured by in vivo microdialysis. (R)-Ketamine and (S)-ketamine acutely increased serotonin release in a dose-dependent manner, and the effect of (R)-ketamine was greater than that of (S)-ketamine. In contrast, (S)-ketamine caused a robust increase in dopamine release compared with (R)-ketamine. Both ketamine enantiomers increased noradrenaline release, but these effects did not differ. (2R,6R)-HNK caused a slight but significant increase in serotonin and noradrenaline but not dopamine release. (S)-NK increased dopamine and noradrenaline but not serotonin release. Differential effects between (R)-ketamine and (S)-ketamine were also observed in a lipopolysaccharide-induced model of depression. An α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid (AMPA) receptor antagonist, 2,3-dioxo-6-nitro-1,2,3,4- tetrahydrobenzo[f]quinoxaline-7-sulfonamide (NBQX), attenuated (S)-ketamine-induced, but not (R)-ketamine-induced serotonin release, whereas NBQX blocked dopamine release induced by both enantiomers. Local application of (R)-ketamine into the prefrontal cortex caused a greater increase in prefrontal serotonin release than that of (S)-ketamine. (R)-Ketamine strongly activates the prefrontal serotonergic system through an AMPA receptor-independent mechanism. (S)-Ketamine-induced serotonin and dopamine release was AMPA receptor-dependent. These findings provide a neurochemical basis for the underlying pharmacological differences between ketamine enantiomers and their metabolites.

Few independent studies have examined the psychometric properties of the NIH Toolbox Cognition Battery (NIHTB-CB) in older adults, despite growing interest in its use for clinical purposes. In this paper we report the test-retest reliability and construct validity of the NIHTB-CB, as well as its agreement or concordance with traditional neuropsychological tests of the same construct to determine whether tests could be used interchangeably. Sixty-one cognitively healthy adults ages 60-80 completed \"gold standard\" (GS) neuropsychological tests, NIHTB-CB, and brain MRI. Test-retest reliability, convergent/discriminant validity, and agreement statistics were calculated using Pearson's correlations, concordance correlation coefficients (CCC), and root mean square deviations. Test-retest reliability was acceptable (CCC = .73 Fluid; CCC = .85 Crystallized). The NIHTB-CB Fluid Composite correlated significantly with cerebral volumes (r's = |.35-.41|), and both composites correlated highly with their respective GS composites (r's = .58-.84), although this was more variable for individual tests. Absolute agreement was generally lower (CCC = .55 Fluid; CCC = .70 Crystallized) due to lower precision in fluid scores and systematic overestimation of crystallized composite scores on the NIHTB-CB. These results support the reliability and validity of the NIHTB-CB in healthy older adults and suggest that the fluid composite tests are at least as sensitive as standard neuropsychological tests to medial temporal atrophy and ventricular expansion. However, the NIHTB-CB may generate different estimates of performance and should not be treated as interchangeable with established neuropsychological tests.

Rey's Auditory Verbal Learning Test (AVLT) is a widely used word list memory test. We update normative data to include adjustment for verbal memory performance differences between men and women and illustrate the effect of this sex adjustment and the importance of excluding participants with mild cognitive impairment (MCI) from normative samples. This study advances the Mayo's Older Americans Normative Studies (MOANS) by using a new population-based sample through the Mayo Clinic Study of Aging, which randomly samples residents of Olmsted County, Minnesota, from age- and sex-stratified groups. Regression-based normative T-score formulas were derived from 4428 cognitively unimpaired adults aged 30-91 years. Fully adjusted T-scores correct for age, sex, and education. We also derived T-scores that correct for (1) age or (2) age and sex. Test-retest reliability data are provided. From raw score analyses, sex explained a significant amount of variance in performance above and beyond age (8-10%). Applying original age-adjusted MOANS norms to the current sample resulted in significantly fewer-than-expected participants with low delayed recall performance, particularly in women. After application of new T-scores adjusted only for age, even in normative data derived from this sample, these age-adjusted T-scores showed scores <40 T occurred more frequently among men and less frequently among women relative to T-scores that also adjusted for sex. Our findings highlight the importance of using normative data that adjust for sex with measures of verbal memory and provide new normative data that allow for this adjustment for the AVLT.

To determine how well machine learning algorithms can classify mild cognitive impairment (MCI) subtypes and Alzheimer's disease (AD) using features obtained from the digital Clock Drawing Test (dCDT). dCDT protocols were administered to 163 patients diagnosed with AD(n = 59), amnestic MCI (aMCI; n = 26), combined mixed/dysexecutive MCI (mixed/dys MCI; n = 43), and patients without MCI (non-MCI; n = 35) using standard clock drawing command and copy procedures, that is, draw the face of the clock, put in all of the numbers, and set the hands for \"10 after 11.\" A digital pen and custom software recorded patient's drawings. Three hundred and fifty features were evaluated for maximum information/minimum redundancy. The best subset of features was used to train classification models to determine diagnostic accuracy. Neural network employing information theoretic feature selection approaches achieved the best 2-group classification results with 10-fold cross validation accuracies at or above 83%, that is, AD versus non-MCI = 91.42%; AD versus aMCI = 91.49%; AD versus mixed/dys MCI = 84.05%; aMCI versus mixed/dys MCI = 84.11%; aMCI versus non-MCI = 83.44%; and mixed/dys MCI versus non-MCI = 85.42%. A follow-up two-group non-MCI versus all MCI patients analysis yielded comparable results (83.69%). Two-group classification analyses were achieved with 25-125 dCDT features depending on group classification. Three- and four-group analyses yielded lower but still promising levels of classification accuracy. Early identification of emergent neurodegenerative illness is criterial for better disease management. Applying machine learning to standard neuropsychological tests promises to be an effective first line screening method for classification of non-MCI and MCI subtypes.

Objectives: A substantial body of research has documented age-related declines in cognitive abilities among adults over 60, yet there is much less known about changes in cognitive abilities during midlife. The goal was to examine longitudinal changes in multiple cognitive domains from early midlife through old age in a large national sample, the Midlife in the United States (MIDUS) study. Methods: The Brief Test of Adult Cognition by Telephone (BTACT) was administered on two occasions (MIDUS 2, MIDUS 3), an average of 9 years apart. At MIDUS 3, those with the cognitive assessment (N=2518) ranged in age from 42 to 92 years (M=64.30; SD=11.20) and had a mean education of 14.68 years (SD=2.63). The BTACT includes assessment of key aging-sensitive cognitive domains: immediate and delayed free recall, number series, category fluency, backward digit span, processing speed, and reaction time for attention switching and inhibitory control, which comprise two factors: episodic memory and executive functioning. Results: As predicted, all cognitive subtests and factors showed very small but significant declines over 9 years, with differences in the timing and extent of change. Processing speed showed the earliest and steepest decrements. Those with higher educational attainment scored better on all tests except reaction time. Men had better executive functioning and women performed better on episodic memory. Conclusions: Examining cognitive changes in midlife provides opportunities for early detection of cognitive impairments and possibilities for preventative interventions. (JINS, 2018, 24, 805–820)

Cognitive dysfunction cut across diagnostic categories and is present in both schizophrenia and bipolar disorder, although with considerable heterogeneity in both disorders. This study examined if distinct cognitive subgroups could be identified across schizophrenia and bipolar disorder based on the intellectual trajectory from the premorbid phase to after illness onset. Three hundred and ninety-eight individuals with schizophrenia (n = 223) or bipolar I disorder (n = 175) underwent clinical and neuropsychological assessment. Hierarchical and k-means cluster analyses using premorbid (National Adult Reading Test) and current IQ (Wechsler Abbreviated Scale of Intelligence) estimates were performed for each diagnostic category, and the whole sample collapsed. Resulting clusters were compared on neuropsychological, functional, and clinical variables. Healthy controls (n = 476) were included for analyses of neuropsychological performance. Cluster analyses consistently yielded three clusters: a relatively intact group (36% of whole sample), an intermediate group with mild cognitive impairment (44%), and an impaired group with global deficits (20%). The clusters were validated by multinomial logistic regression and differed significantly for neuropsychological, functional, and clinical measures. The relatively intact group (32% of the schizophrenia sample and 42% of the bipolar sample) performed below healthy controls for speeded neuropsychological tests. Three cognitive clusters were identified across schizophrenia and bipolar disorder using premorbid and current IQ estimates. Groups differed for clinical, functional, and neuropsychological variables, implying their meaningfulness. One-third of the schizophrenia sample belonged to the relatively intact group, highlighting that neuropsychological assessment is needed for the precise characterization of the individual.

Alzheimer's disease (AD) studies are increasingly targeting earlier (pre)clinical populations, in which the expected degree of observable cognitive decline over a certain time interval is reduced as compared to the dementia stage. Consequently, endpoints to capture early cognitive changes require refinement. We aimed to determine the sensitivity to decline of widely applied neuropsychological tests at different clinical stages of AD as outlined in the National Institute on Aging - Alzheimer's Association (NIA-AA) research framework. Amyloid-positive individuals (as determined by positron emission tomography or cerebrospinal fluid) with longitudinal neuropsychological assessments available were included from four well-defined study cohorts and subsequently classified among the NIA-AA stages. For each stage, we investigated the sensitivity to decline of 17 individual neuropsychological tests using linear mixed models. 1103 participants (age = 70.54 ± 8.7, 47% female) were included: n = 120 Stage 1, n = 206 Stage 2, n = 467 Stage 3 and n = 309 Stage 4. Neuropsychological tests were differentially sensitive to decline across stages. For example, Category Fluency captured significant 1-year decline as early as Stage 1 (β = -.58, p < .001). Word List Delayed Recall (β = -.22, p < .05) and Trail Making Test (β = 6.2, p < .05) became sensitive to 1-year decline in Stage 2, whereas the Mini-Mental State Examination did not capture 1-year decline until Stage 3 (β = -1.13, p < .001) and 4 (β = -2.23, p < .001). We demonstrated that commonly used neuropsychological tests differ in their ability to capture decline depending on clinical stage within the AD continuum (preclinical to dementia). This implies that stage-specific cognitive endpoints are needed to accurately assess disease progression and increase the chance of successful treatment evaluation in AD.