Abstract Background 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, covering up to 11 years. Cross-sectional analyses were repeated in a sample of 21390 participants from the UK Biobank. Results The relationship between self-reported sleep and age differed across sleep items. Sleep duration, efficiency, problems, and use of medication worsened monotonously with age, whereas subjective sleep quality, sleep latency, and daytime tiredness improved. Women reported worse sleep in general than men, but the relationship to age was similar. No cross-sectional sleep – hippocampal volume relationships was found. However, worse sleep quality, efficiency, problems, and daytime tiredness were related to greater hippocampal volume loss over time, with high scorers showing on average 0.22% greater annual loss than low scorers. Simulations showed that longitudinal effects were too small to be detected as age-interactions in cross-sectional analyses. Conclusions Worse self-reported sleep is associated with higher rates of hippocampal 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.
Why education is linked to higher cognitive function in aging is fiercely debated. Leading theories propose that education reduces brain decline in aging, enhances tolerance to brain pathology, or that it does not affect cognitive decline but rather reflects higher early-life cognitive function. To test these theories, we analyzed 407.356 episodic memory scores from 170.795 participants >50 years, alongside 15.157 brain MRIs from 6.472 participants across 33 Western countries. More education was associated with better memory, larger intracranial volume and slightly larger volume of memory-sensitive brain regions. However, education did not protect against age-related decline or weakened effects of brain decline on cognition. The most parsimonious explanation for the results is that the associations reflect factors present early in life, including propensity of individuals with certain traits to pursue more education. While education has numerous benefits, the notion that it provides protection against cognitive or brain decline is not supported.
Abstract Short sleep is held to cause poorer brain health, but is short sleep associated with higher rates of brain structural decline? Analysing 8,153 longitudinal MRIs from 3,893 healthy adults, we found no evidence for an association between sleep duration and brain atrophy. In contrast, cross-sectional analyses (51,295 observations) showed inverse U-shaped relationships, where a duration of 6.5 (95% confidence interval, (5.7, 7.3)) hours was associated with the thickest cortex and largest volumes relative to intracranial volume. This fits converging evidence from research on mortality, health and cognition that points to roughly seven hours being associated with good health. Genome-wide association analyses suggested that genes associated with longer sleep for below-average sleepers were linked to shorter sleep for above-average sleepers. Mendelian randomization did not yield evidence for causal impacts of sleep on brain structure. The combined results challenge the notion that habitual short sleep causes brain atrophy, suggesting that normal brains promote adequate sleep duration—which is shorter than current recommendations.
Abstract & Learning Objectives: Brain and cognition vary and change markedly across the lifespan. I use magnetic resonance imaging and cognitive data to show that while general age trends can be identified in brain and cognition, there are great individual differences through life, and these are influenced by several factors, including at early life stages. Hence, in some respects, aging starts in the womb. Recognizing and understanding the impact of early relative to later stage factors on neurocognitive lifespan differences, changes and aging is a major challenge. Adequately meeting this challenge is crucial both to understand the mechanisms at work early in life, and to identify what and how residual variance may be affected by later life factors. Thus, knowledge of the timing of influences on brain and cognition along the lifespan is needed to develop realistic plans for prevention and intervention to optimize brain and cognition at different ages. I discuss how example factors such as prenatal drug exposure, birth weight, genetics, education, income, and “baseline” general cognitive ability, as well as cognitive training interventions relate to differences and/or changes in the human brain along the lifespan. Example findings are drawn from the studies of the Center for Lifespan Changes in Brain and Cognition (LCBC), where we follow individuals ranging in age from 0 to 100 years. Our studies are in part linked to Norwegian registry data, including the Mother, Father and Child Cohort study (Moba), the Norwegian Twin Registry and the Medical Birth Registry. Linkage to registry data on normal variation of pre-and perinatal characteristics, as well as studies of groups with known early biomedical risk, such a prenatal drug exposure, enable investigation of the possible impact of neurodevelopmental factors on brain and cognitive function through the entire life course. I also discuss how genetically informed studies of brain and cognition sampling broader age spans may contribute to our understanding of the timing of influences. Selectivity of samples constitute a challenge to generalizability in all human research. I discuss how research across international databases can, beyond boosting power and detect consistency of effects, help us appreciate there are diverse associations of possible factors of influence on different groups. This is crucial, as we need to understand to what extent various factors’ association with brain and cognition are universal or cohort-specific, prior to mechanistic understanding. Thus, in this presentation, I will discuss how transdisciplinary, longitudinal, multi-method, and multi-cohort research can illuminate factors that may influence brain and cognition, and their potential timing, in a lifespan perspective. Upon conclusion of this course, learners will be able to: 1. Recognize that differences in brain and cognition even at advanced age may reflect early life factors, rather than, or in addition to, differences in brain and cognitive change with age 2. Describe consistency as well as diversity of factors’ (such as SES) associations with brain and cognition across cohorts of different age and origin. 3. Evaluate differences in factors present early in life, including at birth (“different offset”) before attributing variance in brain and cognitive function to changes with age (“different slope”)
Cognitive control enables goal-oriented adaptation to a fast-changing environment and has a protracted development spanning into young adulthood. The neurocognitive processes underlying this development are poorly understood. In a cross-sectional sample of participants 8-19 years old (n = 108), we used blind source separation of EEG data recorded in a Flanker task to derive electrophysiological measures of attention and conflict processing, including a N2-like frontal negative component and a P3-like parietal positive component. Outside the recording session, we examined multiple behavioral measures of interference control derived from the Flanker, Stroop, and Anti-saccade tasks. We found a positive association between age and P3 amplitude, but no relationship between age and N2 amplitude. A stronger N2 was age-independently related to better performance on Stroop and Anti-saccade measures of interference control. A Gratton effect was found on the Flanker task, with slower reaction times on current congruent and better accuracy on current incongruent trials when preceded by incongruent as opposed to congruent trials. The Gratton effect on accuracy was positively associated with age. Together, the findings suggest a multifaceted developmental pattern of the neurocognitive processes involved in conflict processing across adolescence, with a more protracted development of the P3 compared to the N2.
To investigate public perspectives on brain health.Cross-sectional multilanguage online survey.Lifebrain posted the survey on its website and social media and shared it with stakeholders. The survey was open from 4 June 2019 to 31 August 2020.n=27 590 aged ≥18 years from 81 countries in five continents completed the survey. The respondents were predominantly women (71%), middle aged (41-60 years; 37%) or above (>60 years; 46%), highly educated (69%) and resided in Europe (98%).Respondents' views were assessed regarding factors that may influence brain health, life periods considered important to look after the brain and diseases and disorders associated with the brain. We run exploratory linear models at a 99% level of significance to assess correlates of the outcome variables, adjusting for likely confounders in a targeted fashion.Of all significant effects, the respondents recognised the impact of lifestyle factors on brain health but had relatively less awareness of the role socioeconomic factors might play. Most respondents rated all life periods as important for the brain (95%-96%), although the prenatal period was ranked significantly lower (84%). Equally, women and highly educated respondents more often rated factors and life periods to be important for brain health. Ninety-nine per cent of respondents associated Alzheimer's disease and dementia with the brain. The respondents made a connection between mental health and the brain, and mental disorders such as schizophrenia and depression were significantly more often considered to be associated with the brain than neurological disorders such as stroke and Parkinson's disease. Few respondents (<32%) associated cancer, hypertension, diabetes and arthritis with the brain.Differences in perceptions of brain health were noted among specific segments of the population. Policies providing information about brain-friendly health behaviours and targeting people less likely to have relevant experience may be needed.
Memory encoding and retrieval are critical sub-processes of episodic memory. While the hippocampus is involved in both, less is known about its connectivity with the neocortex during memory processing in humans. This is partially due to variations in demands in common memory tasks, which inevitably recruit cognitive processes other than episodic memory. Conjunctive analysis of data from different tasks with the same core elements of encoding and retrieval can reduce the intrusion of patterns related to subsidiary perceptual and cognitive processing. Leveraging data from two large-scale functional resonance imaging studies with different episodic memory tasks (514 and 237 participants), we identified hippocampal-cortical networks active during memory tasks. Whole-brain functional connectivity maps were similar during resting state, encoding, and retrieval. Anterior and posterior hippocampus had distinct connectivity profiles, which were also stable across resting state and memory tasks. When contrasting encoding and retrieval connectivity, conjunctive encoding-related connectivity was sparse. During retrieval hippocampal connectivity was increased with areas known to be active during recollection, including medial prefrontal, inferior parietal, and parahippocampal cortices. This indicates that the stable functional connectivity of the hippocampus along its longitudinal axis is superposed by increased functional connectivity with the recollection network during retrieval, while auxiliary encoding connectivity likely reflects contextual factors.
Abstract Background Grid cells are spatially modulated cells in the entorhinal cortex (EC) that fire in a hexagonally patterned grid which tiles the environment. These cells are assumed important in human spatial navigation. The EC is vulnerable to neurodegenerative processes in both normal aging and Alzheimer’s disease and decline in grid cell function may be a key factor in understanding age‐related navigational decline. Recent work suggests that conjunctive grid and head direction cells can allow for the detection of grid‐like activity based on movement direction. If moving in alignment with a hexagonal grid, EC activation should be higher than movement not aligned with the grid. The present study attempts replicate findings from previous studies of grid‐like signals detected through fMRI. Methods The sample included 64 (40 female, 24 male) adults, ages ranging from 18 to 78 (M = 37.67). Participants were subject to a fMRI grid cell paradigm, tasked to passively navigate through a room and remember the location of objects. The movement directions in the virtual environment were used to find the mean grid orientation for each participant set through a general linear model with the sixfold pattern as a regressor, and this was used as a regressor in a second GLM to calculate the grid magnitude, a measure of the stability of the grid codes. Results A one‐sample t‐test showed no significant grid code magnitudes (M = 0.002, SD = 0.091) compared to zero for a sixfold symmetry t(63) = 0.19, p = .85. For age‐related analysis, the sample was divided into a younger and older sample through a median split using the median of 30.5 years. A Two Sample t‐test did not show any difference between the younger (M = 0.021, SD = 0.089) and older (M = ‐0.017, SD = 0.090) groups, t(61.99) = ‐1.68, p = .097. Figure 1 shows the mean grid code magnitudes for both age groups. Conclusion Preliminary results suggest no evidence for stable grid‐like representations in the present sample. The present study is reasonably powered relative to the previous study, and possible reasons and limitations hindering identification of grid‐like representations should be discussed and further researched.
Abstract 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.
'%3e%3cpath fill='%23012169' d='M0 0v30h60V0z'/%3e%3cpath stroke='%23fff' stroke-width='6' d='m0 0 60 30m0-30L0 30'/%3e%3cpath stroke='%23C8102E' stroke-width='4' d='m0 0 60 30m0-30L0 30' clip-path='url(%23b)'/%3e%3cpath stroke='%23fff' stroke-width='10' d='M30 0v30M0 15h60'/%3e%3cpath stroke='%23C8102E' stroke-width='6' d='M30 0v30M0 15h60'/%3e%3c/g%3e%3c/svg%3e)
'/%3e%3c/defs%3e%3cpath fill='%23012169' d='M0 0h10080v5040H0z'/%3e%3cpath stroke='%23fff' stroke-width='.6' d='m0 0 6 3m0-3L0 3' clip-path='url(%23b)' transform='scale(840)'/%3e%3cpath stroke='%23e4002b' stroke-width='.4' d='m0 0 6 3m0-3L0 3' clip-path='url(%23c)' transform='scale(840)'/%3e%3cpath stroke='%23fff' stroke-width='840' d='M2520 0v2520M0 1260h5040'/%3e%3cpath stroke='%23e4002b' stroke-width='504' d='M2520 0v2520M0 1260h5040'/%3e%3cg fill='%23fff'%3e%3cuse xlink:href='%23d' x='2520' y='3780'/%3e%3cuse xlink:href='%23a' x='7560' y='4200'/%3e%3cuse xlink:href='%23a' x='6300' y='2205'/%3e%3cuse xlink:href='%23a' x='7560' y='840'/%3e%3cuse xlink:href='%23a' x='8680' y='1869'/%3e%3cuse xlink:href='%23e' x='8064' y='2730'/%3e%3c/g%3e%3c/svg%3e)