Search

Cannabis legalization and use are outpacing our understanding of its long-term effects on brain and behavior, which is fundamental for effective policy and health practices. Existing studies are limited by small samples, cross-sectional measures, failure to separate long-term from recreational use, and inadequate control for other substance use. Here, we address these limitations by determining the structural brain integrity of long-term cannabis users in the Dunedin Study, a longitudinal investigation of a population-representative birth cohort followed to midlife.We leveraged prospective measures of cannabis, alcohol, tobacco, and other illicit drug use in addition to structural neuroimaging in 875 study members at age 45 to test for differences in both global and regional gray and white matter integrity between long-term cannabis users and lifelong nonusers. We additionally tested for dose-response associations between continuous measures of cannabis use and brain structure, including careful adjustments for use of other substances.Long-term cannabis users had a thinner cortex, smaller subcortical gray matter volumes, and higher machine learning-predicted brain age than nonusers. However, these differences in structural brain integrity were explained by the propensity of long-term cannabis users to engage in polysubstance use, especially with alcohol and tobacco.These findings suggest that diminished midlife structural brain integrity in long-term cannabis users reflects a broader pattern of polysubstance use, underlining the importance of understanding comorbid substance use in efforts to curb the negative effects of cannabis on brain and behavior as well as establish more effective policy and health practices.

Although higher-order cognitive and lower-order sensorimotor abilities are generally regarded as distinct and studied separately, there is evidence that they not only covary but also that this covariation increases across the lifespan. This pattern has been leveraged in clinical settings where a simple assessment of sensory or motor ability (e.g. hearing, gait speed) can forecast age-related cognitive decline and risk for dementia. However, the brain mechanisms underlying cognitive, sensory, and motor covariation are largely unknown. Here, we examined whether such covariation in midlife reflects variability in common versus distinct neocortical networks using individualized maps of functional topography derived from BOLD fMRI data collected in 769 45-year-old members of a population-representative cohort. Analyses revealed that variability in basic motor but not hearing ability reflected individual differences in the functional topography of neocortical networks typically supporting cognitive ability. These patterns suggest that covariation in motor and cognitive abilities in midlife reflects convergence of function in higher-order neocortical networks and that gait speed may not be simply a measure of physical function but rather an integrative index of nervous system health.

BACKGROUND The delivery of education about pain neuroscience and the evidence for different treatment approaches has become a key component of contemporary persistent pain management. Chatbots, or more formally conversation agents, are increasingly being used in health care settings due to their versatility to provide interactive and individualized approaches to both capture and deliver information. Research focused on the acceptability of diverse chatbot formats can assist in developing a better understanding of the education needs of target populations and maximize the impact of these software applications within the health field. OBJECTIVE This study aimed to 1) detail the development and initial pilot testing of a multi-modality pain education chatbot (Dolores) that can be utilized across different age groups, and 2) investigate if acceptability and feedback was comparable across age groups following pilot testing. METHODS Following an initial design phase involving software engineers (n=2) and expert clinicians (n=6), 60 individuals with chronic pain who attended an outpatient clinic at one of two pain centers in Australia were recruited for pilot testing. Twenty adolescents (10-18 years), 20 young adults (19-35 years), and 20 adults (over 35 years of age) with persistent pain were recruited. Participations were given an iPad with the Dolores app installed and spent 20-30 minutes completing interactive chatbot activities to: i) gather a pain history and ii) provide education about pain and pain treatments. After the chatbot activities, participants completed a custom-made feedback questionnaire measuring acceptability constructs pertaining to health education chatbots. To determine the effect of age group on acceptability ratings and feedback provided, a series of binomial logistic regression models and cumulative odds ordinal logistic regression models with proportional odds were then generated. RESULTS Overall, acceptability was high for the following constructs: engagement, perceived value, usability, accuracy, responsiveness, adoption intention, aesthetics and overall quality. The effect of age group on all acceptability ratings was small and not statistically significant. Analysis of open-ended question responses revealed that major frustrations with the app were related to Dolores’ speech which was explored further through a comparative analysis. For providing negative feedback about Dolores’ speech, a logistic regression model showed that the effect of age group was statistically significant (χ2(2) = 11.68, P = .003) and explained 27.1% of the variance (Nagelkerke R2). Adults and young adults were less likely to comment on Dolores’ speech compared to adolescent participants (OR=0.20, 95% CI [0.05, 0.84]; OR=0.05, 95% CI [0.01, 0.43] respectively). Comments related to both speech rate (too slow) and quality (unpleasant and robotic). CONCLUSIONS This study provided support for the acceptability of pain history and education chatbots across different age groups. Chatbot acceptability for adolescent cohorts may be improved by enabling self-selection of speech characteristics such as rate and personable tone.

Ashleigh Barrett-Young,1 Wickliffe C Abraham,1 Carol Y Cheung,2 Jesse Gale,3 Sean Hogan,1 David Ireland,1 Ross Keenan,4– 6 Annchen R Knodt,7 Tracy R Melzer,6,8,9 Terrie E Moffitt,7,10 Sandhya Ramrakha,1 Yih Chung Tham,11,12 Graham A Wilson,13 Tien Yin Wong,12,14 Ahmad R Hariri,7 Richie Poulton1 1Department of Psychology, University of Otago, Dunedin, New Zealand; 2Department of Ophthalmology and Visual Sciences, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong; 3Department of Surgery & Anaesthesia, University of Otago, Wellington, New Zealand; 4Department of Radiology, Christchurch Hospital, Christchurch, New Zealand; 5Pacific Radiology Group, Christchurch, New Zealand; 6New Zealand Brain Research Institute, Christchurch, New Zealand; 7Department of Psychology and Neuroscience, Duke University, Durham, NC, USA; 8Department of Medicine, University of Otago, Christchurch, New Zealand; 9School of Psychology, Speech and Hearing, University of Canterbury, Christchurch, New Zealand; 10Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK; 11Singapore Eye Research Institute, Singapore National Eye Centre, Duke-NUS Medical School, Singapore; 12Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; 13Department of Medicine, University of Otago, Dunedin, New Zealand; 14Tsinghua Medicine, Tsinghua University, Beijing, People’s Republic of ChinaCorrespondence: Ashleigh Barrett-Young, Department of Psychology, University of Otago, PO Box 56, Dunedin, 9054, New Zealand, Email Ashleigh.barrett-young@otago.ac.nzPurpose: The retina has potential as a biomarker of brain health and Alzheimer’s disease (AD) because it is the only part of the central nervous system which can be easily imaged and has advantages over brain imaging technologies. Few studies have compared retinal and brain measurements in a middle-aged sample. The objective of our study was to investigate whether retinal neuronal measurements were associated with structural brain measurements in a middle-aged population-based cohort.Participants and Methods: Participants were members of the Dunedin Multidisciplinary Health and Development Study (n=1037; a longitudinal cohort followed from birth and at ages 3, 5, 7, 9, 11, 13, 15, 18, 21, 26, 32, 38, and most recently at age 45, when 94% of the living Study members participated). Retinal nerve fibre layer (RNFL) and ganglion cell-inner plexiform layer (GC-IPL) thickness were measured by optical coherence tomography (OCT). Brain age gap estimate (brainAGE), cortical surface area, cortical thickness, subcortical grey matter volumes, white matter hyperintensities, were measured by magnetic resonance imaging (MRI).Results: Participants with both MRI and OCT data were included in the analysis (RNFL n=828, female n=413 [49.9%], male n=415 [50.1%]; GC-IPL n=825, female n=413 [50.1%], male n=412 [49.9%]). Thinner retinal neuronal layers were associated with older brain age, smaller cortical surface area, thinner average cortex, smaller subcortical grey matter volumes, and increased volume of white matter hyperintensities.Conclusion: These findings provide evidence that the retinal neuronal layers reflect differences in midlife structural brain integrity consistent with increased risk for later AD, supporting the proposition that the retina may be an early biomarker of brain health.Keywords: optical coherence tomography, retina, Alzheimer’s, magnetic resonance imaging, brain aging

IMPORTANCE: Biological aging is a distinct construct from health; however, people who age quickly are more likely to experience poor health. Identifying pediatric health conditions associated with accelerated aging could help develop treatment approaches to slow midlife aging and prevent poor health in later life. OBJECTIVE: To examine the association between 4 treatable health conditions in adolescence and accelerated aging at midlife. DESIGN, SETTING, AND PARTICIPANTS: This cohort study analyzed data from participants in the Dunedin Study, a longitudinal investigation of health and behavior among a birth cohort born between April 1, 1972, and March 31, 1973, in Dunedin, New Zealand, and followed up until age 45 years. Participants underwent an assessment at age 45 years and had data for at least 1 adolescent health condition (asthma, smoking, obesity, and psychological disorders) and outcome measure (pace of aging, gait speed, brain age, and facial age). Data analysis was performed from February 11 to September 27, 2021. EXPOSURES: Asthma, cigarette smoking, obesity, and psychological disorders were assessed at age 11, 13, and 15 years. MAIN OUTCOMES AND MEASURES: The outcome was a midlife aging factor composite score comprising 4 measures of biological aging: pace of aging, gait speed, brain age (specifically, BrainAGE score), and facial age. RESULTS: A total of 910 participants (459 men [50.4%]) met the inclusion criteria, including an assessment at age 45 years. Participants who had smoked daily (0.61 [95% CI, 0.43-0.79] SD units), had obesity (0.82 [95% CI, 0.59-1.06] SD units), or had a psychological disorder diagnosis (0.43 [95% CI, 0.29-0.56] SD units) during adolescence were biologically older at midlife compared with participants without these conditions. Participants with asthma were not biologically older at midlife (0.02 [95% CI, −0.14 to 0.19] SD units) compared with those without asthma. These results remained unchanged after adjusting for childhood risk factors such as poor health, socioeconomic disadvantage, and adverse experiences. CONCLUSIONS AND RELEVANCE: This study found that adolescent smoking, obesity, and psychological disorder diagnoses were associated with older biological age at midlife. These health conditions could be treated during adolescence to reduce the risk of accelerated biological aging later in life.

We investigated the extent to which measures of retinal neuronal thickness capture variability in the structural integrity of the brain in a large population-based cohort followed from birth to midlife. Using data from the Dunedin Multidisciplinary Health and Development Study (n=1037; analyticn=828, aged 45 years), we specifically tested for associations between optical coherence tomography-measured retinal neuronal layers and MRI-measured structural brain integrity. We found that Study members who had thinner retinal neuronal layers had thinner average cortex, smaller total cortical surface area, smaller subcortical grey matter volumes, larger volume of white matter hyperintensities as well as older looking brains. This suggests that retinal neuronal thickness reflects differences in midlife structural brain integrity consistent with accelerated cognitive decline and increased risk for later dementia, further supporting the proposition that the retina may be a biomarker of brain aging as early as midlife.

Knowledge of a person’s risk for Alzheimer’s disease and related dementias (ADRDs) is required to triage candidates for preventive interventions, surveillance, and treatment trials. ADRD risk indexes exist for this purpose, but each includes only a subset of known risk factors. Information missing from published indexes could improve risk prediction. In the Dunedin Study of a population-representative New Zealand-based birth cohort followed to midlife (N = 938, 49.5% female), we compared associations of four leading risk indexes with midlife antecedents of ADRD against a novel benchmark index comprised of nearly all known ADRD risk factors, the Dunedin ADRD Risk Benchmark (DunedinARB). Existing indexes included the Cardiovascular Risk Factors, Aging, and Dementia index (CAIDE), LIfestyle for BRAin health index (LIBRA), Australian National University Alzheimer’s Disease Risk Index (ANU-ADRI), and risks selected by the Lancet Commission on Dementia. The Dunedin benchmark was comprised of 48 separate indicators of risk organized into 10 conceptually distinct risk domains. Midlife antecedents of ADRD treated as outcome measures included age-45 measures of brain structural integrity [magnetic resonance imaging-assessed: (i) machine-learning-algorithm-estimated brain age, (ii) log-transformed volume of white matter hyperintensities, and (iii) mean grey matter volume of the hippocampus] and measures of brain functional integrity [(i) objective cognitive function assessed via the Wechsler Adult Intelligence Scale-IV, (ii) subjective problems in everyday cognitive function, and (iii) objective cognitive decline measured as residualized change in cognitive scores from childhood to midlife on matched Weschler Intelligence scales]. All indexes were quantitatively distributed and proved informative about midlife antecedents of ADRD, including algorithm-estimated brain age (β's from 0.16 to 0.22), white matter hyperintensities volume (β's from 0.16 to 0.19), hippocampal volume (β's from −0.08 to −0.11), tested cognitive deficits (β's from −0.36 to −0.49), everyday cognitive problems (β's from 0.14 to 0.38), and longitudinal cognitive decline (β's from −0.18 to −0.26). Existing indexes compared favourably to the comprehensive benchmark in their association with the brain structural integrity measures but were outperformed in their association with the functional integrity measures, particularly subjective cognitive problems and tested cognitive decline. Results indicated that existing indexes could be improved with targeted additions, particularly of measures assessing socioeconomic status, physical and sensory function, epigenetic aging, and subjective overall health. Existing premorbid ADRD risk indexes perform well in identifying linear gradients of risk among members of the general population at midlife, even when they include only a small subset of potential risk factors. They could be improved, however, with targeted additions to more holistically capture the different facets of risk for this multiply determined, age-related disease.