Studying neuroanatomy using MRI

The study of neuroanatomy using imaging enables key insights into how our brains function, are shaped by genes and environment, and change with development, aging, and disease. Developments in MRI acquisition, image processing, and data modelling have been key to these advances. However, MRI provides an indirect measurement of the biological signals we aim to investigate. Thus, artifacts and key questions of correct interpretation can confound the readouts provided by anatomical MRI. In this review we provide an overview of the methods for measuring macro- and mesoscopic structure and inferring microstructural properties; we also describe key artefacts and confounds that can lead to incorrect conclusions. Ultimately, we believe that, though methods need to improve and caution is required in its interpretation, structural MRI continues to have great promise in furthering our understanding of how the brain works

Brain aging differs with cognitive ability regardless of education

Higher general cognitive ability (GCA) is associated with lower risk of neurodegenerative disorders, but neural mechanisms are unknown. GCA could be associated with more cortical tissue, from young age, i.e. brain reserve, or less cortical atrophy in adulthood, i.e. brain maintenance. Controlling for education, we investigated the relative association of GCA with reserve and maintenance of cortical volume, -area and -thickness through the adult lifespan, using multiple longitudinal cognitively healthy brain imaging cohorts (n = 3327, 7002 MRI scans, baseline age 20-88 years, followed-up up to 11 years). There were widespread positive relationships between GCA and cortical characteristics (level-level associations). In select regions, higher baseline GCA was associated with less atrophy over time (level-change associations). Relationships remained when controlling for polygenic scores for both GCA and education. Our findings suggest that higher GCA is associated with cortical volumes by both brain reserve and -maintenance mechanisms through the adult lifespan

Brain aging differs with cognitive ability regardless of education

Higher general cognitive ability (GCA) is associated with lower risk of neurodegenerative disorders, but neural mechanisms are unknown. GCA could be associated with more cortical tissue, from young age, i.e. brain reserve, or less cortical atrophy in adulthood, i.e. brain maintenance. Controlling for education, we investigated the relative association of GCA with reserve and maintenance of cortical volume, -area and -thickness through the adult lifespan, using multiple longitudinal cognitively healthy brain imaging cohorts (n = 3327, 7002 MRI scans, baseline age 20–88 years, followed-up for up to 11 years). There were widespread positive relationships between GCA and cortical characteristics (level-level associations). In select regions, higher baseline GCA was associated with less atrophy over time (level-change associations). Relationships remained when controlling for polygenic scores for both GCA and education. Our findings suggest that higher GCA is associated with cortical volumes by both brain reserve and -maintenance mechanisms through the adult lifespan.</p

Brain aging differs with cognitive ability regardless of education

Higher general cognitive ability (GCA) is associated with lower risk of neurodegenerative disorders, but neural mechanisms are unknown. GCA could be associated with more cortical tissue, from young age, i.e. brain reserve, or less cortical atrophy in adulthood, i.e. brain maintenance. Controlling for education, we investigated the relative association of GCA with reserve and maintenance of cortical volume, -area and -thickness through the adult lifespan, using multiple longitudinal brain imaging cohorts (n = 3327, 7002 MRI scans, baseline age 20-88 years, followed-up up to 11 years). There were widespread positive relationships between GCA and cortical characteristics (level-level associations). In select regions, higher baseline GCA was associated with less atrophy over time (level-change associations). Relationships remained when controlling for polygenic scores for both GCA and education. Our findings suggest that higher GCA is associated with cortical volumes by both brain reserve and -maintenance mechanisms through the adult lifespan

Sleep duration and brain structure - phenotypic associations and genotypic covariance

The question of how much sleep is best for the brain attracts scientific and public interest, and there is concern that insufficient sleep leads to poorer brain health. However, it is unknown how much sleep is sufficient and how much is too much. We analyzed 51,295 brain magnetic resonnance images from 47,039 participants, and calculated the self-reported sleep duration associated with the largest regional volumes and smallest ventricles relative to intracranial volume (ICV) and thickest cortex. 6.8 hours of sleep was associated with the most favorable brain outcome overall. Critical values, defined by 95% confidence intervals, were 5.7 and 7.9 hours. There was regional variation, with for instance the hippocampus showing largest volume at 6.3 hours. Moderately long sleep (&gt; 8 hours) was more strongly associated with smaller relative volumes, thinner cortex and larger ventricles than even very short sleep (&lt; 5 hours), but effect sizes were modest. People with larger ICV reported longer sleep (7.5 hours), so not correcting for ICV yielded longer durations associated with maximal volume. Controlling for socioeconomic status, body mass index and depression symptoms did not alter the associations. Genetic analyses showed that genes related to longer sleep in short sleepers were related to shorter sleep in long sleepers. This may indicate a genetically controlled homeostatic regulation of sleep duration. Mendelian randomization analyses did not suggest sleep duration to have a causal impact on brain structure in the analyzed datasets. The findings challenge the notion that habitual short sleep is negatively related to brain structure

Individual variations in “brain age” relate to early life factors more than to longitudinal brain change

Brain age is a widely used index for quantifying individuals’ brain health as deviation from a normative brain aging trajectory. Higher than expected brain age is thought partially to reflect above-average rate of brain aging. Here, we explicitly tested this assumption in two independent large test datasets (UK Biobank [main] and Lifebrain [replication]; longitudinal observations ≈ 2,750 and 4,200), by assessing the relationship between cross-sectional and longitudinal estimates of brain age. Brain age models were estimated in two different training datasets (n ≈ 38,000 [main] and 1,800 individuals [replication]) based on brain structural features. The results showed no association between crosssectional brain age and the rate of brain change measured longitudinally. Rather, brain age in adulthood was associated with the congenital factors of birth weight and polygenic scores of brain age, assumed to reflect a constant, lifelong influence on brain structure from early life. The results call for nuanced interpretations of cross-sectional indices of the aging brain and question their validity as markers of ongoing within-person changes of the aging brain. Longitudinal imaging data should be preferred whenever the goal is to understand individual change trajectories of brain and cognition in aging

Self-reported sleep relates to hippocampal atrophy over the adult lifespan – results from the Lifebrain consortium

Objectives Poor sleep is associated with multiple age-related neurodegenerative and neuropsychiatric conditions. The hippocampus plays a special role in sleep and sleep-dependent cognition, and accelerated hippocampal atrophy is typically seen with higher age. Hence, it is critical to establish how the relationship between sleep and hippocampal volume loss unfolds across the adult lifespan. Methods Self-reported sleep measures and MRI-derived hippocampal volumes were obtained from 3105 cognitively normal participants (18–90 years) from major European brain studies in the Lifebrain consortium. Hippocampal volume change was estimated from 5116 MRIs from 1299 participants for whom longitudinal MRIs were available, followed up to 11 years with a mean interval of 3.3 years. Cross-sectional analyses were repeated in a sample of 21,390 participants from the UK Biobank. Results No cross-sectional sleep—hippocampal volume relationships were found. However, worse sleep quality, efficiency, problems, and daytime tiredness were related to greater hippocampal volume loss over time, with high scorers showing 0.22% greater annual loss than low scorers. The relationship between sleep and hippocampal atrophy did not vary across age. Simulations showed that the observed longitudinal effects were too small to be detected as age-interactions in the cross-sectional analyses. Conclusions Worse self-reported sleep is associated with higher rates of hippocampal volume decline across the adult lifespan. This suggests that sleep is relevant to understand individual differences in hippocampal atrophy, but limited effect sizes call for cautious interpretation