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Systemic lupus erythematosus (SLE) is a chronic autoimmune disease, in which cognitive dysfunction is common, but poorly understood. This study aims to characterize the prevalence and patterns of cognitive dysfunction in SLE.
Method:
SLE patients (n = 95) and demographically matched healthy controls (n = 48) underwent cross-sectional cognitive testing using the 1-hr conventional neuropsychological test battery recommended by the American College of Rheumatology for use in SLE. We used standard deviations (SD) from the healthy control group to define impairment. For each cognitive test we compared SLE and control groups using independent samples t-tests (or alternatives when needed). We performed cluster analysis using a machine learning algorithm to look for patterns of cognitive dysfunction.
Results:
The SLE group performed significantly worse than healthy controls on every cognitive test. The largest differences were in the domains of verbal fluency, working memory and attention, while fine motor and psychomotor speed were the least affected domains. As expected, the prevalence of cognitive dysfunction varied depending on the SD cut-off used, with 49% of participants being >1.5 SD below the healthy control mean in at least two cognitive domains. Heat mapping showed variability in the pattern of dysfunction between individual patients and cluster analysis confirmed the presence of two clusters of patients, which were those significantly impaired versus those having preserved cognition.
Conclusions:
Cognitive dysfunction is common in SLE but markedly heterogeneous across both cognitive domains and across the SLE group. Cluster analysis supports the use of a binary definition of cognitive dysfunction in SLE.
Neuropsychiatric symptoms are related to disease progression and cognitive decline over time in cerebral small vessel disease (SVD) but their significance is poorly understood in covert SVD. We investigated neuropsychiatric symptoms and their relationships between cognitive and functional abilities in subjects with varying degrees of white matter hyperintensities (WMH), but without clinical diagnosis of stroke, dementia or significant disability.
Methods:
The Helsinki Small Vessel Disease Study consisted of 152 subjects, who underwent brain magnetic resonance imaging (MRI) and comprehensive neuropsychological evaluation of global cognition, processing speed, executive functions, and memory. Neuropsychiatric symptoms were evaluated with the Neuropsychiatric Inventory Questionnaire (NPI-Q, n = 134) and functional abilities with the Amsterdam Instrumental Activities of Daily Living questionnaire (A-IADL, n = 132), both filled in by a close informant.
Results:
NPI-Q total score correlated significantly with WMH volume (rs = 0.20, p = 0.019) and inversely with A-IADL score (rs = −0.41, p < 0.001). In total, 38% of the subjects had one or more informant-evaluated neuropsychiatric symptom. Linear regressions adjusted for age, sex, and education revealed no direct associations between neuropsychiatric symptoms and cognitive performance. However, there were significant synergistic interactions between neuropsychiatric symptoms and WMH volume on cognitive outcomes. Neuropsychiatric symptoms were also associated with A-IADL score irrespective of WMH volume.
Conclusions:
Neuropsychiatric symptoms are associated with an accelerated relationship between WMH and cognitive impairment. Furthermore, the presence of neuropsychiatric symptoms is related to worse functional abilities. Neuropsychiatric symptoms should be routinely assessed in covert SVD as they are related to worse cognitive and functional outcomes.
On continuous recognition tasks, changing the context objects are embedded in impairs memory. Older adults are worse on pattern separation tasks requiring identification of similar objects compared to younger adults. However, how contexts impact pattern separation in aging is unclear. The apolipoprotein (APOE) ϵ4 allele may exacerbate possible age-related changes due to early, elevated neuropathology. The goal of this study is to determine how context and APOE status affect pattern separation among younger and older adults.
Method:
Older and younger ϵ4 carriers and noncarriers were given a continuous object recognition task. Participants indicated if objects on a Repeated White background, Repeated Scene, or a Novel Scene were old, similar, or new. The proportions of correct responses and the types of errors made were calculated.
Results:
Novel scenes lowered recognition scores compared to all other contexts for everyone. Younger adults outperformed older adults on identifying similar objects. Older adults misidentified similar objects as old more than new, and the repeated scene exacerbated this error. APOE status interacted with scene and age such that in repeated scenes, younger carriers produced less false alarms, and this trend switched for older adults where carriers made more false alarms.
Conclusions:
Context impacted recognition memory in the same way for both age groups. Older adults underutilized details and over relied on holistic information during pattern separation compared to younger adults. The triple interaction in false alarms may indicate an even greater reliance on holistic information among older adults with increased risk for Alzheimer’s disease.
There is limited research on the prognostic value of language tasks regarding mild cognitive impairment (MCI) and Alzheimer’s clinical syndrome (ACS) development in the cognitively normal (CN) elderly, as well as MCI to ACS conversion.
Methods:
Participants were drawn from the population-based Hellenic Longitudinal Investigation of Aging and Diet (HELIAD) cohort. Language performance was evaluated via verbal fluency [semantic (SVF) and phonemic (PVF)], confrontation naming [Boston Naming Test short form (BNTsf)], verbal comprehension, and repetition tasks. An additional language index was estimated using both verbal fluency tasks: SVF-PVF discrepancy. Cox proportional hazards analyses adjusted for important sociodemographic parameters (age, sex, education, main occupation, and socioeconomic status) and global cognitive status [Mini Mental State Examination score (MMSE)] were performed.
Results:
A total of 959 CN and 118 MCI older (>64 years) individuals had follow-up investigations after a mean of ∼3 years. Regarding the CN group, each standard deviation increase in the composite language score reduced the risk of ACS and MCI by 49% (8–72%) and 32% (8–50%), respectively; better SVF and BNTsf performance were also independently associated with reduced risk of ACS and MCI. On the other hand, using the smaller MCI participant set, no language measurement was related to the risk of MCI to ACS conversion.
Conclusions:
Impaired language performance is associated with elevated risk of ACS and MCI development. Better SVF and BNTsf performance are associated with reduced risk of ACS and MCI in CN individuals, independent of age, sex, education, main occupation, socioeconomic status, and MMSE scores at baseline.
Smartphones have the potential for capturing subtle changes in cognition that characterize preclinical Alzheimer’s disease (AD) in older adults. The Ambulatory Research in Cognition (ARC) smartphone application is based on principles from ecological momentary assessment (EMA) and administers brief tests of associative memory, processing speed, and working memory up to 4 times per day over 7 consecutive days. ARC was designed to be administered unsupervised using participants’ personal devices in their everyday environments.
Methods:
We evaluated the reliability and validity of ARC in a sample of 268 cognitively normal older adults (ages 65–97 years) and 22 individuals with very mild dementia (ages 61–88 years). Participants completed at least one 7-day cycle of ARC testing and conventional cognitive assessments; most also completed cerebrospinal fluid, amyloid and tau positron emission tomography, and structural magnetic resonance imaging studies.
Results:
First, ARC tasks were reliable as between-person reliability across the 7-day cycle and test-retest reliabilities at 6-month and 1-year follow-ups all exceeded 0.85. Second, ARC demonstrated construct validity as evidenced by correlations with conventional cognitive measures (r = 0.53 between composite scores). Third, ARC measures correlated with AD biomarker burden at baseline to a similar degree as conventional cognitive measures. Finally, the intensive 7-day cycle indicated that ARC was feasible (86.50% approached chose to enroll), well tolerated (80.42% adherence, 4.83% dropout), and was rated favorably by older adult participants.
Conclusions:
Overall, the results suggest that ARC is reliable and valid and represents a feasible tool for assessing cognitive changes associated with the earliest stages of AD.
The purpose of this exploratory study was to describe associations between NIH Toolbox-Cognition Battery subtests and legacy measures of neurocognitive function in two samples with neurological conditions (stroke and sickle cell disease (SCD)).
Method:
This exploratory secondary analysis uses data from two studies that assessed cognition at one time point using the NIH Toolbox-Cognition Battery, the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), and subtests from the Delis-Kaplan Executive Functions System (DKEFS). People with stroke (n = 26) and SCD (n = 64) were included. Associations between the NIH Toolbox-Cognition Battery subtests and corresponding legacy measures were examined using linear correlations, Bland–Altman analysis, and Lin’s Concordance Correlation Coefficient.
Results:
Linear correlations and Lin’s Concordance Correlation Coefficient were poor to strong in both samples on NIH Toolbox-CB subtests: Flanker Inhibitory Control and Attention (r = .35 to .48, Lin CCC = .27 to .37), Pattern Comparison Processing Speed (r = .40 to .65, Lin CCC = .37 to .62), Picture Sequence Memory (r = .19 to .55, Lin CCC = .18 to .48), Dimensional Change Card Sort (r = .39 to .77, Lin CCC = .38 to .63), Fluid Cognition Composite (r = .88 to .90, Lin CCC = .60 to .79), and Total Cognition Composite (r = .64 to .83, Lin CCC = .60 to .78). Bland–Altman analyses demonstrated wide limits of agreement across all subtests (–3.17 to 3.78).
Conclusions:
The NIH Toolbox-Cognition Battery subtests may behave similarly to legacy measures as an overall assessment of cognition across samples at risk for neurological impairment. Findings should be replicated across additional clinical samples.
Cognitive impairment affects older adults’ capacity to live independently and make lifestyle decisions (lifestyle decision-making capacity; LS-DMC). Cognitive screens and clinical interviews are often used to assess people’s need for living-supports prior to conducting comprehensive LS-DMC assessments in busy clinical settings. This study investigated whether the QuickSort – a brief new cognitive screen – provides efficient and accurate information regarding patients’ LS-DMC when initially interviewed.
Methods:
This is an observational and diagnostic accuracy study of older inpatients (≥60 years) consecutively referred for neuropsychological assessment of LS-DMC (n = 124). The resources required by inpatients with questionable LS-DMC were quantified (length of hospital stay, living-supports). QuickSort scores, patient background information, and two common cognitive screens were used to differentiate between older inpatients (n = 124) who lacked (64%)/did not-lack (36%) LS-DMC.
Results:
Hospitalizations averaged 49 days, with 62% of inpatients being readmitted within one year. The QuickSort differentiated between those lacking/not-lacking LS-DMC better than two common cognitive screens and patient information. The likelihood that inpatients lacked LS-DMC increased by a factor of 65.26 for QuickSort scores <2 and reduced by a factor of 0.32 for scores ≥13. Modeling revealed that the post-test likelihood of lacking LS-DMC increased to 99% (scores <2) and reduced to 30% (scores ≥ 13) in settings where many inpatients lack LS-DMC.
Conclusions:
Older adult inpatients with questionable LS-DMC have a high risk of extended hospitalization and readmission. The QuickSort provides time-efficient and sensitive information regarding patients’ LS-DMC, making it a viable alternative to longer cognitive screens that are used at the initial interview stage.
Reaction time variability (RTV) has been estimated using Gaussian, ex-Gaussian, and diffusion model (DM) indices. Rarely have studies examined interrelationships among these performance indices in childhood, and the use of reaction time (RT) computational models has been slow to take hold in the developmental psychopathology literature. Here, we extend prior work in adults by examining the interrelationships among different model parameters in the ABCD sample and demonstrate how computational models of RT can clarify mechanisms of time-on-task effects and sex differences in RTs.
Method:
This study utilized trial-level data from the stop signal task from 8916 children (9–10 years old) to examine Gaussian, ex-Gaussian, and DM indicators of RTV. In addition to describing RTV patterns, we examined interrelations among these indicators, temporal patterns, and sex differences.
Results:
There was no one-to-one correspondence between DM and ex-Gaussian parameters. Nonetheless, drift rate was most strongly associated with standard deviation of RT and tau, while nondecisional processes were most strongly associated with RT, mu, and sigma. Performance worsened across time with changes driven primarily by decreasing drift rate. Boys were faster and less variable than girls, likely attributable to girls’ wide boundary separation.
Conclusions:
Intercorrelations among model parameters are similar in children as has been observed in adults. Computational approaches play a crucial role in understanding performance changes over time and can also clarify mechanisms of group differences. For example, standard RT models may incorrectly suggest slowed processing speed in girls that is actually attributable to other factors.
Using the African Neuropsychology Battery (ANB), we seek to develop normative data by examining the demographic effects for two learning process scores: initial learning (Trial One) and learning ratio (LR, the percentage of items learned relative of to-be-learned material following Trial 1).
Methods:
Healthy participants from the Democratic Republic of Congo completed the four memory tests of the ANB: the African Story Memory Test (ASMT), African List Memory Test (ALMT), African Visuospatial Memory Test (AVMT), and African Contextual Visuospatial Memory Test (ACVMT). We developed indices of learning for each subtest, as well as aggregate learning indices for Trial 1 and LR, and composite indices examining verbal, visual, contextual, and noncontextual learning, and grand indices comprising all four subtests.
Results:
Trial 1 and LR scores each demonstrated acceptable intercorrelations across memory tests. We present normative data for Trial 1 and LR by age and education.
Conclusion:
These data provide normative standards for evaluating learning in Sub-Saharan Africa.
Wiedemann-Steiner syndrome (WSS) is a rare Mendelian disorder of the epigenetic machinery caused by heterozygous pathogenic variants in KMT2A. Currently, the specific neurocognitive profile of this syndrome remains unknown. This case series provides insight into the cognitive phenotype of WSS.
Methods:
This study involves a retrospective medical chart review of 10 pediatric patients, each with a molecularly confirmed diagnosis of WSS who underwent clinical neuropsychological evaluation at an academic medical center.
Results:
The majority of patients performed in the below average to very low ranges in Nonverbal Reasoning, Visual/Spatial Perception, Visuoconstruction, Visual Memory, Attention, Working Memory and Math Computation skills. In contrast, over half the sample performed within normal limits on Receptive Vocabulary, Verbal Memory, and Word Reading. Wilcoxon signed rank test showed weaker Nonverbal versus Verbal Reasoning skills (p = .005). Most caregivers reported deficits in executive functioning, most notably in emotion regulation.
Conclusions:
Nonverbal reasoning/memory, visuospatial/construction, attention, working memory, executive functioning, and math computation skills are areas of weakness among those with WSS. These findings overlap with research on Kabuki syndrome, which is caused by variants in KMT2D, and suggest disruption in the neurogenesis of the hippocampal formation may drive shared pathogenesis of the two syndromes.