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Background: Studies investigating cognitive impairments in psychosis and depression have typically compared the average performance of the clinical group against healthy controls (HC), and do not report on the actual prevalence of cognitive impairments or strengths within these clinical groups. This information is essential so that clinical services can provide adequate resources to supporting cognitive functioning. Thus, we investigated this prevalence in individuals in the early course of psychosis or depression. Methods: A comprehensive cognitive test battery comprising 12 tests was completed by 1286 individuals aged 15–41 (mean age 25.07, s.d. 5.88) from the PRONIA study at baseline: HC (N = 454), clinical high risk for psychosis (CHR; N = 270), recent-onset depression (ROD; N = 267), and recent-onset psychosis (ROP; N = 295). Z-scores were calculated to estimate the prevalence of moderate or severe deficits or strengths (>2 s.d. or 1–2 s.d. below or above HC, respectively) for each cognitive test. Results: Impairment in at least two cognitive tests was as follows: ROP (88.3% moderately, 45.1% severely impaired), CHR (71.2% moderately, 22.4% severely impaired), ROD (61.6% moderately, 16.2% severely impaired). Across clinical groups, impairments were most prevalent in tests of working memory, processing speed, and verbal learning. Above average performance (>1 s.d.) in at least two tests was present for 40.5% ROD, 36.1% CHR, 16.1% ROP, and was >2 SDs in 1.8% ROD, 1.4% CHR, and 0% ROP. Conclusions: These findings suggest that interventions should be tailored to the individual, with working memory, processing speed, and verbal learning likely to be important transdiagnostic targets. [ABSTRACT FROM AUTHOR]

The literature on subjective memory concerns (SMC) as a predictor for future cognitive decline is varied. Furthermore, recent research has pointed to additional complexity arising from variability in the experience of SMC themselves (i.e. whether they are remitting or sustained over time). We investigated the associations between SMC and objectively measured cognition in an Australian population-based cohort. Four waves (4-year intervals between waves) of data from 1236 participants (aged 62.4 ± 1.5 years, 53% male) were used. We categorized participants as experiencing SMC, when they indicated that their memory problems might interfere with their day-to-day life and/or they had seen a doctor about their memory. SMC was categorized as "no" reported SMC, "remitting", "new-onset" or "sustained" SMC. Cognitive assessment of immediate and delayed recall, working memory, psychomotor speed, attention and processing speed were assessed using a neuropsychological battery. Eighteen percent of participants were characterised as having SMC: 6% (77) "remitting", 6% (77) "new-onset" and 6% (69) "sustained" SMC. There was no consistent evidence for an association between SMC and subsequent decline in cognition. However, SMC was associated with poorer performance on contemporaneous tasks of attention and processing speed compared to "no" SMC. Asking about SMC may indicate a current decline in cognitive function but, in this sample at least, did not indicate an increased risk of future decline. [ABSTRACT FROM AUTHOR]

Study Objective: What is the association between anticholinergic burden and specific domains of cognitive function in older adults who are initially without major cognitive impairment? Design: Post‐hoc analysis of longitudinal observational data from the ASPirin in Reducing Events in the Elderly (ASPREE) study. Patients: 19,114 participants from Australia and the United States aged 70 years and older (65 years and older for US minorities) were recruited and followed for a median of 4.7 years. At enrollment, participants were free of known cardiovascular disease, major physical disability, or dementia. Measurements: Cognitive assessments administered at baseline and biennially at follow‐up visits included the Modified Mini‐Mental State examination (3MS), Hopkins Verbal Learning Test–Revised (HVLT‐R) delayed recall, Controlled Oral Word Association Test (COWAT), and Symbol Digit Modalities Test (SDMT). Anticholinergic burden was calculated at baseline using the Anticholinergic Cognitive Burden (ACB) scale and grouped as scores of 0 (no burden), 1‐2 (low to moderate), or 3+ (high). Main Results: Linear mixed effects models were used to assess the relationship between ACB score and cognition over time. After adjusting for sex, age, education, minority status, smoking status, hypertension, diabetes, depression, chronic kidney disease, country, and frailty, participants with a high ACB score had worse performance over time for 3MS (Adjusted [Adj] B=‐0.092, P=0.034), HVLT‐R delayed recall (Adj B=‐0.104, P<0.001), COWAT (Adj B=‐0.151, P<0.001), and SDMT (Adj B=‐0.129, P=0.026), than participants with an ACB score of 0. A low to moderate ACB score was also associated with worse performance over time for HVLT‐R delayed recall (Adj B=‐0.037, P=0.007) and COWAT (Adj B=‐0.065, P=0.003), compared to those with no ACB. Conclusions: Anticholinergic burden predicts worse cognitive function over time in initially dementia‐free older adults, particularly for executive function (COWAT) and episodic memory (HVLT‐R). [ABSTRACT FROM AUTHOR]

Inflammation is repressed by interleukin 10 (IL10), a potent anti-inflammatory cytokine, and unchecked inflammation can have detrimental effects on cognition. In healthy older adults enrolled in the Australian Research Council Longevity Intervention (ARCLI) cohort we explored whether a known functional single nucleotide polymorphism (SNP) in the promoter region of IL10, -1082 G/A (rs1800896), was associated with reaction times on computerized cognitive testing that included elements of processing speed (i.e., reaction time). Participants were aged 60-75 years (240 females, 158 males), free of dementia and psychiatric disorders, and provide a blood sample. Processing speed was measured using the Swinburne University Computerized Cognitive Assessment Battery (SUCCAB), which includes measures of reaction time (in milliseconds, ms) on six tasks. Blood-derived DNA was genotyped for the IL10 rs1800896 SNP and presence of the APOE E4 allele. General linear models for each SUCCAB subtest were fitted, with age, sex, education (years), APOE E4 carrier status, and IL10 genotype as independent variables. Carriers of the IL10 AA genotype had significantly slower reaction times on multiple tests compared to carriers of the minor allele (AG, GG) and lower IL10 serum levels. Although IL10 SNPs have not been detected in Alzheimer's disease genome-wide associated studies, these results support further exploration of IL10 mechanisms as a possible resilience factor., Competing Interests: Declaration of Competing Interest The authors have no actual or potential conflict of interests to declare with respect to the research, authorship, and/or publication of this article., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Objective The study aimed to generate preliminary Australian normative data for the performance of individuals aged 8-15 years on the Rey Tangled Lines Test (Rey TLT), and to chronicle age-related trends in the acquisition of processing speed and accuracy. The Rey TLT is a neuropsychometric test that queries processing speed and was intended to assist in the differentiation of problems in fine eye tracking from wider cognitive disorders. This instrument does not require verbal or motor responses and may be administered in less than 10 min. To date, it has been unavailable for inclusion in a child or adolescent cognitive assessment battery due to the absence of normative data for this population. Methods N = 120 children aged 8-15 years (68 females and 52 males) were recruited to establish four, 2-year chronological age groups (i.e., 8-9, 10-11, 12-13, and 14-15 years) with n = 30 participants per group. The Rey TLT was administered to each participant in a single session. Response time and accuracy were documented. Results Sets of normative data for Australian children's performance on the Rey TLT were collected. Completion time decreased and task accuracy increased with age; younger children generally performed more slowly and less accurately than older children. Conclusions The Rey TLT appears to be sensitive to changes in age of children and is quick and simple to administer. It holds promise for inclusion in a battery of psychological tests to examine processing speed and fine eye tracking. [ABSTRACT FROM AUTHOR]

Characteristics of the neighbourhood environment, including the built and natural environment, area-level socio-economic status (SES) and air pollution, have been linked to cognitive health. However, most studies have focused on single neighbourhood characteristics and have not considered the extent to which the effects of environmental factors may interact. We examined the associations of measures of the neighbourhood built and natural environment, area-level SES and traffic-related air pollution (TRAP) with two cognitive function domains (memory and processing speed), and the extent to which area-level SES and TRAP moderated the associations. We used cross-sectional data from the AusDiab3 study, an Australian cohort study of adults (mean age: 61 years) in 2011-12 (N = 4141) for which geocoded residential addresses were available. Spatial data were used to create composite indices of built environment complexity (population density, intersection density, non-commercial land use mix, commercial land use) and natural environment (parkland and blue spaces). Area-level SES was obtained from national census indices and TRAP was based on estimates of annual average levels of nitrogen dioxide (NO 2 ). Confounder-adjusted generalised additive mixed models were used to estimate the independent associations of the environmental measures with cognitive function and the moderating effects of area-level SES and TRAP. The positive associations between built environment complexity and memory were stronger in those living in areas with higher SES and lower NO 2 concentrations. A positive association between the natural environment and memory was found only in those living in areas with lower NO 2 concentrations and average or below-average SES. Built environment complexity and the natural environment were positively related to processing speed. Complex urban environments and access to nature may benefit cognitive health in ageing populations. For higher-order cognitive abilities, such as memory, these positive effects may be stronger in areas with lower levels of TRAP., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey were used to calculate weighted norms for the written version of the Symbol Digits Modalities Test (SDMT) by gender, 5-year age groups and four levels of educational attainment. The sample comprised 14,456 Australians (47% male; age range 15–100), of whom 25% reported a tertiary qualification, 30% reported a technical qualification (diploma or trade certificate), 16% reported completing Year 12 (final year of high school), and 29% reported their highest level of educational attainment to be Year 11 or below. Participants were excluded if they reported physical or neurological conditions that limited performance. Age, gender, and education were all significantly associated with SDMT performance, as was poor health, and cultural background. The reported norms are of greater scope and precision than previously available and have utility in a range of clinical and research settings. Indeed, normative data for the SDMT that are representative of a national population have not previously been published. [ABSTRACT FROM AUTHOR]

The environment we live in, and our lifestyle within this environment, can shape our cognitive health. We investigated whether sociodemographic, neighbourhood environment, and lifestyle variables can be used to predict cognitive health status in adults. Cross-sectional data from the AusDiab3 study, an Australian cohort study of adults (34-97 years) ( n = 4141) was used. Cognitive function was measured using processing speed and memory tests, which were categorized into distinct classes using latent profile analysis. Sociodemographic variables, measures of the built and natural environment estimated using geographic information system data, and physical activity and sedentary behaviours were used as predictors. Machine learning was performed using gradient boosting machine, support vector machine, artificial neural network, and linear models. Sociodemographic variables predicted processing speed ( r 2 = 0.20) with good accuracy. Lifestyle factors also accurately predicted processing speed ( r 2 = 0.20) with good accuracy. Lifestyle factors also accurately predicted processing speed ( r 2 = 0.29) but weakly predicted memory ( r 2 = 0.10). Neighbourhood and built environment factors were weak predictors of cognitive function. Sociodemographic (AUC = 0.84) and lifestyle (AUC = 0.78) factors also accurately classified cognitive classes. Sociodemographic and lifestyle variables can predict cognitive function in adults. Machine learning tools are useful for population-level assessment of cognitive health status via readily available and easy-to-collect data.

Although previous research has identified contemporaneous associations between cognitive deficits and symptom phenotypes in Attention Deficit/Hyperactivity Disorder, no studies have as yet attempted to identify direction of effect. The present study used cross-lagged path modeling to examine competing hypotheses about longitudinal associations between rapid naming speed and symptoms of inattention in children. 1,506 school-age twins from Australia and the U.S. were tested for inattention, hyperactivity/impulsivity, and rapid naming speed at three and four time points, respectively. Symptom severity of inattention from Kindergarten to fourth grade is consistently predicted by previous rapid naming, over and above auto-regressive and correlational associations in the model. Likewise, inattention symptoms have a small but significant predictive effect on subsequent rapid naming. The findings support a reciprocal relationship between naming speed and ADHD inattentive symptoms. [ABSTRACT FROM AUTHOR]

Age-related decrease in testosterone levels is a potential risk factor for cognitive decline in older men. However, observational studies and clinical trials have reported inconsistent results on the effects of testosterone on individual cognitive domains. Null findings may be attributed to factors that studies have yet to consider. In particular, individual variations in polyglutamine (CAG) length in the androgen receptor (AR) gene could alter androgenic activity in brain regions associated with cognitive processes including memory and executive functions. However, the role of AR CAG repeat length as a moderator of the relationship between testosterone levels and cognition has not been investigated. Therefore, we aimed to examine the relationship between baseline calculated free testosterone (cFT) levels, change in cFT levels over 18 months and CAG repeat length on cognitive performance in memory, executive function, language, attention and processing speed domains. These relationships were examined in 304 cognitively normal older male participants of the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study of Ageing. In the attention and processing speed domain, a short CAG repeat length appears to exacerbate the effects of low baseline cFT levels that are also lower than expected at follow-up. These results highlight that individual variations in AR CAG repeat length should be considered in future studies and clinical trials that examine the complex relationship between testosterone and cognition., (Crown Copyright © 2021. Published by Elsevier Inc. All rights reserved.)

The present study aimed to investigate whether Australian adolescents with Delayed Sleep-Wake Phase Disorder have impaired cognitive performance and whether chronobiological treatment for Delayed Sleep-Wake Phase Disorder improves adolescents' sleep, daytime functioning and cognitive performance. Adolescents with Delayed Sleep-Wake Phase Disorder (mean = 15.68 ± 2.1 y, 62% f) reported significantly later sleep timing (d = 1.03-1.45), less total sleep time (d = 0.82) and greater daytime sleepiness (d = 2.66), fatigue (d = 0.63) and impairment (d = 2.41), compared to good sleeping adolescents (mean = 15.9 ± 2.4 y, 75% f). However, there were no significant between-group differences (all p > 0.05) in performance on the Operation Span (ηp 2  = 0.043), Digit Span (forwards: ηp 2  = 0.002, backwards: ηp 2  = 0.003), Letter Number Sequencing (ηp 2  < 0.001) (working memory) and Digit-Symbol Substitution Tasks (ηp 2  = 0.010) (processing speed). Adolescents with Delayed Sleep-Wake Phase Disorder went on to receive 3 weeks of light therapy. At 3 months post-treatment, adolescents with Delayed Sleep-Wake Phase Disorder reported significantly advanced sleep timing (d = 0.56-0.65), greater total sleep time (d = 0.52) and improved daytime sleepiness (d = 1.33), fatigue (d = 0.84) and impairment (d = 0.78). Performance on the Operation Span (d = 0.46), Letter Number Sequencing (d = 0.45) and Digit-Symbol Substitution tasks (d = 0.57) also significantly improved., (Copyright © 2018. Published by Elsevier Ltd.)

Objective: This study aimed to investigate the association between white matter tracts and multiple aspects of attention and working memory deficits in a relatively acute traumatic brain injury (TBI) sample., Method: Neuropsychological measures of attention and working memory were administered to 20 participants with complicated mild-to-very severe TBI and 20 control participants. Tract-based spatial statistics was used to assess fractional anisotropy (FA) and mean diffusivity (MD) of white matter tracts for 15 TBI participants and 20 controls., Results: When compared to controls, participants with TBI were found to have lower FA (p < 0.001) and higher MD (p < 0.001) values in the majority of white matter tracts. TBI participants were also slower to complete tasks including Trail Making Test, Hayling, computerized Selective Attention Task, n-back and Symbol Digit Modalities Test (p < 0.001), when compared to controls. When controlling for age and estimated premorbid intelligence, slowed information processing speed following TBI was found to be associated with FA values in the corpus callosum, superior longitudinal fasciculus, cingulum, inferior fronto-occipital fasciculi, corona radiata and cerebral white matter., Conclusion: The results highlight the widespread damage associated with TBI, as well as the impact of these alterations on information processing speed.