Anders Martin Fjell

How environmental variation shapes the human cerebral cortex remains incompletely understood. Here we compare cortical brainprints – individual profiles of regional cortical morphology – in 210 twins (age 16-78 years) to distinguish prenatal, adult naturalistic and experimentally induced environmental variation from genetic contributions. Monozygotic twins showed higher brainprint similarity than dizygotic twins, consistent with genetic effects. Lasting influences of early environment were evident in lower brainprint similarity in monozygotic twin-pairs with greater birthweight discordances, driven by surface area across the cortical ribbon. In contrast, within-pair differences in adult weight and lifestyle had minimal influence. Still, a 10-week virtual-reality navigation intervention revealed training-induced changes in the gray-white interface, with curvature and area changes supported by microstructural reconfigurations. Relative brainprint similarity increased in monozygotic but diverged in dizygotic pairs following training. These findings show that distinct environmental influences acting at prenatal and adult stages are reflected in different features of the human cerebral cortex.

Episodic memory and cognitive control declines in aging. The default-executive coupling hypothesis of aging (DECHA) suggests that a neural correlate of cognitive decline in aging is increased functional connectivity (FC) between lateral prefrontal areas and the default mode network. Here, in a lifespan sample (n=552, 6-81years), we tested FC between the left dorsolateral prefrontal cortex (dLPFC) and the default mode network (DMN) during the encoding and retrieval phases of an episodic memory fMRI task. We created two age groups based on evidence for episodic memory decline after 30 years: a younger group encompassing childhood, adolescence, and young adulthood (6-29 years) and an aging group (30-81 years). To test if the dLPFC-DMN FC was associated with changes in cognitive control, we used longitudinal change (up to 10 years) in a Stroop inhibition/switching task linked to the prefrontal cortex. Results showed (i) lower dLPFC and DMN connectivity with age in the younger group and higher connectivity in the aging group, and (ii) this FC was associated with an age-related increase in the inhibition task completion time, particularly in the aging group. However, dLPFC showed similar relationships with other networks, particularly salient attentional and control subnetworks, and despite decline in cognitive control associated with memory performance, memory-related FC between dLPFC and DMN did not.

In recent decades, the United States and several European countries have seen a plateau and reversal of the population-level gains in cognitive test scores that accumulated over the 20th century-the Flynn effect. We examined whether the Flynn effect and its reversal-declining mean scores across cohorts-differ by socioeconomic status, and whether changes in education can account for this. Using compulsory military conscription cognitive tests linked to administrative records with near-complete population coverage, we studied 579,379 Norwegian men from 25 birth cohorts (1967-1991). Trend reversals appeared first among sons of high-income fathers, with low- and middle-income groups peaking later and showing smaller postpeak declines. These socioeconomic differences were consistent with convergence in educational attainment at conscription: across paternal-income ranks, cohort changes in test scores aligned with cohort gains in education at conscription age, with estimated schooling effect sizes in line with quasi-experimental evidence. Results were robust to adjustment for parental education, and birth weight and height at testing as proxies for prenatal conditions and childhood nutrition. Together, the findings suggest an underlying population-level decline in cognitive scores that began earlier than previously assumed but was temporarily offset in lower-income strata by expanding educational attainment. After educational expansion plateaued, scores declined across all groups, most steeply among higher-income strata. Upper-secondary education completion thus emerges as a potential lever for improving cognitive performance and reducing income-related disparities in cognitive ability scores, while highlighting that broader societal pressures on cognitive test performance may have started earlier than recognized.

Prevailing models typically treat cognitive aging as gradual, with rates of change evolving slowly over time and individual differences expressed mainly as variation in level, onset, and rate of decline. We test this view against an alternative in which individual trajectories are organised as extended plateaus of stability punctuated by brief episodes of accelerated loss. Harmonising 728,000 memory tests from 80,000 participants across three population cohorts, we introduce a simulation-calibrated framework to identify genuine memory stability. 10% of adults ≥ 70 showed stable performance over a decade, and in an MRI subgroup (n ≈ 2,000), these individuals exhibited lower rates of brain atrophy, linking cognitive stability to brain maintenance. However, analyses projecting trajectories backward in time showed that many late-life stable trajectories would imply implausibly low mid-life performance, indicating prior unobserved decline. Across individuals, 54% showed at least one stability period lasting 10 years, but only 0.4% maintained stability over 24 years under our criterion. Models based solely on gradual decline are insufficient to account for the observed temporal structure of memory change, and smooth population-level decline can emerge from punctuated individual dynamics. Together, the findings align with a complex-systems view where stability is often a transient state rather than a lifelong trait.

Brain age models - machine-learning predictions of chronological age from brain imaging - are widely interpreted as markers of accelerated brain aging. Here we show that this interpretation cannot be supported. Because these models are trained to predict chronological age, they prioritize features that change similarly across people and actively downweight features that capture differences in individual trajectories, precisely the property an aging-rate biomarker must have. In effect, brain age models are optimized to ignore the very signal they are used to study, thereby risking converting stable between-person differences into apparent accelerated aging. Using theoretical analysis, simulations, and longitudinal MRI, we confirm both predicted failure modes: brain age models indicated "accelerated aging" in participants with low birth weight despite no longitudinal evidence, while a single hippocampal volume measurement was more sensitive than the brain age gap to tau-related neurodegeneration. Across much of the brain age literature, it is therefore not possible to determine whether reported effects reflect brain aging or stable anatomical differences, and the brain age gap should not be interpreted as a marker of brain aging or brain health. We propose alternative strategies that reorient prediction targets from shared age-related patterns to individual differences in change.

Hva betyr egentlig «vellykket» kognitiv aldring? I media løftes ofte enkeltfenomener frem – som personer med høy funksjon eller tidlig demens, og det gis spesifikke livsstilsråd som er knyttet til å beholde høy funksjon. I denne artikkelen diskuterer vi hva forskning faktisk viser om kognitiv aldring, og hva som i større grad er myter enn realiteter. Vi oppsummerer kunnskapen om hvordan hjernen og kognitive funksjoner endrer seg gjennom livet, og viser at de store forskjellene man ser i hvordan eldre fungerer, ofte reflekterer stabile forskjeller som etableres tidlig, allerede i fosterliv og i løpet av barndom og ungdomstid, like mye som forskjeller i aldring i seg selv. Vi går deretter gjennom tre seiglivede påstander: at demensrisiko nesten kan halveres gjennom livsstilsendringer, at mye søvn er nødvendig for effektiv «hjernevask», og at høy utdanning bygger kognitiv reserve som beskytter mot aldersrelatert kognitiv svikt og demens. Til slutt diskuterer vi nye samfunnstrender som tilsier at risiko for kognitiv svikt og demens er avtagende, men advarer om at denne utviklingen er et resultat av gunstige samfunnsforhold som må vedlikeholdes. Skal den positive trenden fortsette, må vi investere i faktorer som bygger kognitiv kapasitet og motstandskraft gjennom hele livsløpet, særlig i de tidlige leveårene. Nøkkelord: livsløpsperspektiv, utvikling, aldring, kognisjon, hjernehelse

Individual differences in the volumes of brain structures are often linked to various conditions, including Alzheimer’s disease, schizophrenia, and overall brain health. However, it remains unclear to what extent these differences reflect individual levels present from young adulthood or diverging aging trajectories from later ages. In this study, we analyze the aging dynamics of the volumes of six brain structures based on magnetic resonance imaging (MRI) scans from a large cross-cohort longitudinal sample of cognitively healthy adults (n = 8,311 with 18,520 MRIs, ages from 18 to 97 years). From general assumptions about structural brain dynamics and measurement noise, a stochastic dynamical model was fitted to the data to estimate both the variability and persistence of structural changes across adulthood. Using this model, we calculated how much of the variance of volumetric differences between individuals can be attributed to stable levels from young adulthood versus systematic changes at older ages, as well as the theoretical sensitivity of longitudinal studies to detect individual differences in change. The findings were as follows: (1) Before age 60 years, inter-individual differences in neuroanatomical volumes almost exclusively reflect stable differences between individuals, while the influence from systematic differences in rate-of-change increases thereafter: up to 50% of the variation being due to differences in change at 80 years. In contrast, ventricular volume reflects differences in change from early adulthood. (2) Current brain-age models are unlikely to be sensitive to detect differences in aging trajectories. (3) Imaging studies have low reliability in detecting inter-individual brain changes before age 60 years. After 60 years, the study reliability increases sharply with longer intervals between scans and more modestly with additional intermediate observations. In conclusion, our results reinforce the view that it is critical to distinguish stable early adulthood levels from systematic differences in change when studying adult brain aging.

Brain age models - machine-learning predictions of chronological age from brain imaging - are widely interpreted as markers of accelerated brain aging. Here we show that this interpretation cannot be supported. Because these models are trained to predict chronological age, they prioritize features that change similarly across people and actively downweight features that capture differences in individual trajectories, precisely the property an aging-rate biomarker must have. In effect, brain age models are optimized to ignore the very signal they are used to study, thereby risking converting stable between-person differences into apparent accelerated aging. Using theoretical analysis, simulations, and longitudinal MRI, we confirm both predicted failure modes: brain age models indicated “accelerated aging” in participants with low birth weight despite no longitudinal evidence, while a single hippocampal volume measurement was more sensitive than the brain age gap to tau-related neurodegeneration. Across much of the brain age literature, it is therefore not possible to determine whether reported effects reflect brain aging or stable anatomical differences, and the brain age gap should not be interpreted as a marker of brain aging or brain health. We propose alternative strategies that reorient prediction targets from shared age-related patterns to individual differences in change.

Dementia incidence has been declining in Western societies for decades, but whether this reflects higher cognitive capacity entering old age, slower cognitive decline, or both remains unresolved. Analysing ~783,000 episodic memory assessments from ~219,000 individuals across five longitudinal cohorts, we find that later-born cohorts benefit from a double dividend: higher memory levels entering old age and slower rates of decline. The projected 20-year cohort advantage at age 80 is of sufficient magnitude to plausibly account for the observed 13% per-decade decline in dementia incidence reported in meta-analyses. Generational gains are disproportionately concentrated among the fastest-declining individuals, and are reflected in lower hippocampal atrophy rates in an independent sample. A formal bounding analysis shows that the double dividend is robust across a range of plausible period assumptions, consistent with environmental conditions operating across the lifespan having reshaped the architecture of human cognitive aging.