The study examined four dimensions of sleep variability calculated from within-person standard deviations: sleep-onset time, waking time, sleep duration and the number of movement-defined sleep interruptions. These measures were combined into an equally weighted Composite Variability Score, abbreviated CVS. The investigators also evaluated SRI1, an existing measure of sleep regularity. Because the analysis was observational, the results demonstrate associations rather than showing that irregular sleep caused lower cognitive performance.

At the first subsequent cognitive assessment, all four individual variability measures and the composite score were associated with lower fluid-intelligence scores. For CVS, the estimated association was −0.1444 points per exposure-standard-deviation increase, with a 95% confidence interval from −0.1970 to −0.0918. After correction for six exposure tests across two outcomes using the Benjamini–Hochberg procedure, the result remained statistically significant at q < 0.0001. The researchers used type 3 heteroscedasticity-consistent, or HC3, inference to account for heteroscedasticity in the statistical models.

The models accounted for estimated age at accelerometry, sex, within-country regional socioeconomic scores, country, body mass index, smoking, history of alcohol consumption, average sleep duration and assessment timing. In separate longitudinal analyses, the researchers additionally adjusted for earlier performance in the same cognitive domain. Under this stricter specification, waking-time variability, sleep-duration variability and CVS continued to show small negative associations with later fluid intelligence, with estimates of −0.0591, −0.0483 and −0.0506, respectively. However, their 12-test false-discovery-rate q-values ranged from 0.0610 to 0.0704 and did not cross the prespecified corrected significance threshold.

Sleep variability also added little information beyond SRI1: the increase in explained variance was no greater than 0.00079, and that incremental contribution did not meet the study’s multiplicity-corrected threshold. Estimates became weaker after selection weighting, an indication that the findings were sensitive to factors affecting which participants were retained in the analysis. Together, the first-assessment results consistently linked greater sleep variability with lower fluid intelligence, while the longitudinal findings were smaller, directionally similar and less robust. The study therefore supports variability as a possible marker of cognitive performance, but it does not establish that sleep irregularity predicts future cognitive decline or that measuring it offers a clinical advantage over the Sleep Regularity Index.

Subject of Research: Associations between night-to-night sleep variability and subsequent fluid-intelligence performance in a UK Biobank cohort

Article Title: Night-to-night sleep variability and subsequent cognitive performance: a UK Biobank cohort study

Article References: Cai, W.-M., Rui, D., Yang, Y.-Y., Zhang, M.-R., Xue, X., Ma, Y., Gao, Y.-H., & Liu, L. (2026). Night-to-night sleep variability and subsequent cognitive performance: a UK Biobank cohort study. BMC Public Health. https://doi.org/10.1186/s12889-026-29996-1

Image Credits: AI Generated

DOI: 10.1186/s12889-026-29996-1

Keywords: sleep variability, fluid intelligence, UK Biobank, cognitive performance, wrist accelerometry, sleep regularity, cohort study, sleep duration, Night-to-night, sleep, variability, subsequent

The finding offers a cell-level clue about age-related changes in the ability to solve new problems. Fluid intelligence was assessed as part of a broader series of cognitive tests administered before surgery, covering reasoning, memory, attention, language and fine motor coordination. After surgery, researchers used microscopy to identify and count immature astroglia in selected areas of the hippocampus, a brain region involved in learning and memory. Older patients generally had more of the abnormal cells, while greater cell numbers were also associated with lower fluid-intelligence scores. The investigators described the work as one of the first studies to associate astroglial abundance with human cognition.

Astroglia have traditionally been viewed primarily as support cells that help maintain a healthy environment around neurons and support their communication. Their role in cognition has received less attention than that of neurons and has often been investigated through animal models or indirect measures such as gene activity. The new research differs by examining human tissue directly and quantifying the presence of immature astroglia. “We found that older patients tended to have more abnormal immature astroglia—and that higher numbers of these cells were also associated with poorer performance on tests of fluid intelligence,” said first author Aswathy Ammothumkandy, PhD, of the USC Stem Cell lab.

The research was made possible by the unusual availability of tissue removed during surgery to treat severe, drug-resistant seizures caused by mesial temporal lobe epilepsy. The operation yielded hippocampal tissue from 29 patients, allowing the team to pair cellular observations with cognitive testing conducted beforehand. This design provided a rare opportunity to study individual cells in a human brain and compare them with measured reasoning performance. The approach also illustrates why the findings should be interpreted as an association: the small, selected surgical population had epilepsy, and the investigators did not show that removing or changing the cells would alter cognition.

Non-invasive tools commonly used to study brain function, including electroencephalography and magnetic resonance imaging, generally do not resolve individual cells well enough for this type of cell-count analysis. Microscopic examination can reveal cellular features in detail, but tissue from living people is rarely available for research outside specialized clinical circumstances. Here, the integration of clinical care, cognitive assessment, surgery and laboratory analysis gave researchers access to matched biological and behavioral information. The participants’ epilepsy and the surgical context remain central limitations on how broadly the results can be generalized, even though the study was supported in part by the National Institutes of Health and published in Frontiers in Cellular Neuroscience.

Mesial temporal lobe epilepsy can involve seizures and cognitive decline, making the population clinically relevant but also distinctive. The researchers said the findings could have implications beyond epilepsy, and they emphasized that aging affects everyone. “Aging is something we’re all familiar with and all impacted by—it’s a matter of when, not if,” said co-corresponding author Michael Bonaguidi, PhD, who leads the USC Stem Cell lab. “What this study provides is a new therapeutic target that could someday help us slow down or potentially even reverse age-related declines in our ability to solve new problems.” The statement identifies a proposed research direction rather than a demonstrated treatment.

No drug, procedure or clinical intervention was tested in the study, and the researchers emphasized that further work is needed to determine whether abnormal immature astroglia contribute directly to cognitive decline. The data also do not establish that the cells are a cause of the age-related pattern; they could be a marker, a consequence, or part of a broader process involving epilepsy, aging and brain function. The authors’ suggestion that astroglia could represent a target for preserving cognition should therefore be understood as a hypothesis for future investigation, not an available treatment or an established clinical strategy. The scope of the evidence is especially limited because all participants were people undergoing surgery for severe, drug-resistant seizures.

Ammothumkandy is now investigating molecular differences among immature astroglia that might reveal possible drug targets. By comparing cells from people with epilepsy with control samples, she aims to identify features that distinguish abnormal cells and explore ways to restore them to a healthier state. Another possible avenue for future investigation is neuromodulation, which uses electrical or magnetic stimulation to alter nervous-system activity. The broader goal is to understand whether healthy or abnormal astroglial states might become accessible therapeutic targets for cognitive conditions, including accelerated age-related decline in people with epilepsy. These proposed approaches are described in the release as directions for research rather than interventions validated in the present experiment.

Charles Liu, MD, PhD, director of the USC Neurorestoration Center and a co-author of the study, said the work could benefit patients while informing wider questions about brain function. “Understanding processes like cognitive decline, which can be accelerated in people with epilepsy, could help us improve these patients’ lives while also shedding light on broader questions about brain function that affect us all,” Liu said. The authors also proposed that astroglia could be a shared target for cognitive decline and seizure control, based partly on previous work by the team. The present experiment, however, directly evaluated the relationship between immature astroglial abundance, age and fluid intelligence; it did not test seizure outcomes or show that targeting astroglia would improve either cognition or seizures.

For now, the most defensible conclusion is that abnormal immature astroglia become more prevalent with age in the hippocampus of this surgical epilepsy cohort and are associated with weaker performance on tests of fluid intelligence. The study supplies direct human-tissue evidence that warrants further investigation into why these cells accumulate, what molecular changes define their abnormal state and whether they actively impair reasoning. It does not establish a general-population trend, a direct causal mechanism or an available therapy. Instead, it identifies a specific cellular feature for additional investigation, with the eventual aim of determining whether modifying astroglial biology could protect cognitive function or support better control of epilepsy. The findings are strongest as an association in the 29-person surgical sample and should not yet be read as proof that abnormal immature astroglia drive age-related cognitive decline in people without epilepsy.

Subject of Research: Associations between immature hippocampal astroglia, age and fluid intelligence in adults with epilepsy

Article Title: Immature brain support cells may be linked to human age-related cognitive decline

Article References: Immature brain support cells may be linked to human age-related cognitive decline. Original publication (Bibliographic details unavailable.)

Image Credits: AI Generated

DOI: Not provided

Keywords: astroglia, fluid intelligence, cognitive decline, hippocampus, epilepsy, brain tissue, aging, USC, human brain, cellular neuroscience, Immature, brain

The central finding is that brain-based models perform best when they predict broad, composite measures of general cognition rather than narrow abilities such as working memory, processing speed or cognitive flexibility. Among young people aged 8 to 21, models explained 11% to 39% of variance in general cognitive ability. In adults aged 22 to 37, estimates ranged from less than 1% to 36%, while older cohorts aged 55 to 89 yielded cross-sectional values of approximately 5% to 40%. The strongest within-age results clustered around 36% to 40%, suggesting that high predictive accuracy is possible at different life stages when models are trained and tested within the same developmental period. This is a review of published prediction studies, not a new experiment or a single population-wide test of one model.

Longitudinal results show that measured brain characteristics can carry information about later cognition. In youth, structural and functional models explained up to 17.5% of variance in general cognitive performance two years later. Studies of older adults found models accounting for about 11% to 36% of variance at one year, 19% to 28% at two years and 20% to 25% at three years. These intervals do not establish a consistent decline in explained variance as follow-up lengthened. The results instead describe associations between measured brain features and subsequent cognitive scores; they do not demonstrate that the brain features cause later differences or that one scan can reliably forecast an individual’s future cognitive decline.

The cognitive target is a major source of variation. General intelligence was more predictable than specific functions in both young and adult samples. In youth, one set of models explained 39% of variance in general cognition, compared with 38% for crystallized ability and 26% for fluid intelligence. In adults, crystallized abilities were generally predicted more accurately than fluid abilities, with reading-related outcomes reaching 16% to 22% explained variance. Working memory and processing speed were often minimally predictable, at around 2% to 4%. An exception appeared in older adults, where processing speed reached 17% to 19% explained variance, compared with 7% to 8% for semantic memory or executive function. The review therefore identifies predictability as dependent on both the cognitive domain and the population in which it is measured.

Imaging modality produced another clear pattern. Functional connectivity generally outperformed diffusion measures, while anatomical features offered moderate predictive value. Resting-state functional connectivity alone typically explained 4% to 10% of variance in general cognition, although longer scans raised the estimate to about 20%. Task-based connectivity performed better: working-memory connectivity predicted roughly 28% of general cognitive variance, compared with 8% for resting-state connectivity, and one study obtained 36% from a combination of working-memory, language and relational-processing tasks. In youth, task-based connectivity also outperformed resting-state approaches, but the reverse pattern emerged in older adults. There, structural features generally predicted cognition better than resting-state connectivity, and models based on volumes of the hippocampus, entorhinal cortex and lateral ventricles explained 20% to 35% of variance at baseline and up to three years ahead.

Combining data did not always guarantee an improvement. Some multimodal models performed as well as their strongest functional component alone, and adding grey-matter volume, white-matter integrity and resting-state fluctuation to a structural model did not beat the structural model by itself. Yet one model combining task activation, resting-state connectivity and several structural MRI measures explained 32% of variance in general cognition, compared with 7% for a single modality. The review favors hierarchical stacking, in which separate models are built for each modality and their outputs are combined in a final model, because this can improve performance, reduce dimensionality and clarify each modality’s contribution. Task selection also matters: language-derived connectivity was the strongest predictor of oral reading, whereas working-memory connectivity was better for picture vocabulary. Simply including more tasks did not guarantee higher accuracy, making strategic selection important.

Algorithm choice affected performance, but the most complex method was not consistently necessary. Deep-learning systems sometimes outperformed traditional approaches when applied to structural connectome data, yet elastic net and ridge regression often achieved comparable accuracy with lower computational costs and greater interpretability. Elastic net was particularly consistent across populations and outcomes. In older cohorts, random forests performed well for processing speed and some longitudinal predictions, while kernel ridge regression was superior for quantitative reasoning in one comparison. These differences favor matching the algorithm to the target, population and study design rather than assuming that a more elaborate artificial-intelligence system is inherently superior. Cross-validation remains standard for estimating accuracy, but external testing on an independent dataset is essential for demonstrating that performance transfers beyond the sample in which a model was developed.

Confounding variables pose a major challenge to interpretation. Sex, age, education and socioeconomic circumstances are associated with both brain characteristics and cognitive performance, so a model may exploit information that is not specific to brain-cognition relationships. The review describes large differences in the optimal task used to predict fluid intelligence for males and females, underscoring the need to test performance within demographic groups. More fundamentally, adding baseline cognitive scores and grey-matter volume can raise explained variance to 70% to 81%, while demographic information alone explains 31% to 38% of cognitive variation in some older-adult datasets and may add little beyond what simpler clinical assessments provide. Brain models can remain valuable for identifying neural markers, but their added value must be demonstrated beyond demographic and cognitive information alone.

Several changes could make future models more useful. Repeated cognitive testing across days or weeks could capture the natural fluctuation of executive functions, which vary with fatigue, stress and environmental demands and are often represented in datasets by a single assessment. Standardized, domain-based assessments such as the Research Domain Criteria framework could provide measures that are consistent across diagnostic and developmental boundaries. Models should also use age-appropriate brain parcellations, because adult-derived divisions of the cortex may fail in developing brains. Clinical deployment remains premature: although imaging models could eventually support estimates of post-stroke cognitive recovery or trajectories of decline, the field must first show that they add meaningful and reproducible information beyond less expensive measures in independent populations.

The review itself did not analyze new data, and its comparisons are constrained by differences in datasets, cognitive tasks, inclusion criteria, quality control and analysis pipelines. Many studies used overlapping cohorts, including the Human Connectome Project, the Adolescent Brain Cognitive Development Study and UK Biobank, and most relied on internal cross-validation rather than external validation. The few cross-dataset studies produced lower effect sizes, meaning the headline range may represent upper-bound estimates. Large resources also disproportionately represent Western, educated, industrialized, rich and democratic populations, narrowing the conclusions that can be drawn about global applicability. Finally, successful prediction is not the same as explanation: a model can forecast a cognitive score without revealing which neural processes cause it. The review therefore positions brain-based prediction as a promising measurement tool whose scientific and clinical value will depend on rigorous testing, transparent confounding control, harmonized measures and longitudinal evidence.

Subject of Research: Predictive accuracy of brain-based models for cognition across youth, adulthood and older age

Article Title: Effect sizes for brain-based predictive models of cognition across the lifespan

Article References: Christensen, E., Wiersch, L., Wang, M., McPherson, B., Shearer, H., Noble, S., Lam, K., Poline, J. B., & Dhamala, E. (2026). Effect sizes for brain-based predictive models of cognition across the lifespan. Communications Psychology, 4(1), Article 143. https://doi.org/10.1038/s44271-026-00528-2

Image Credits: AI Generated

DOI: 10.1038/s44271-026-00528-2

Keywords: brain imaging, cognitive prediction, neuroimaging, machine learning, functional connectivity, crystallized ability, fluid intelligence, aging, predictive modeling, brain–cognition relationship, Effect, sizes

The study, published as a brief research report in Frontiers in Cellular Neuroscience, combined preoperative neuropsychological testing with analysis of surgically removed hippocampal tissue. Mesial temporal lobe epilepsy is the most common form of adult epilepsy, and people whose seizures are not controlled with medication may undergo removal of the epileptic hippocampus. That clinical setting creates a rare opportunity to connect cognitive performance measured before surgery with cellular abnormalities in the same individuals’ brain tissue. The investigators used it to test whether an unusual astroglial population previously identified in this patient group was associated with particular cognitive deficits.

Epilepsy is associated with accelerated brain aging and progressive cognitive impairment, with cognitive decline especially pronounced in older patients. The researchers examined whether the Dcx-positive, star-shaped cells in epileptic hippocampus tracked specific domains of cognition rather than serving as a nonspecific indicator of poor overall brain health. The targets included fluid intelligence and reasoning, visuospatial reconstruction, visuospatial memory, verbal learning and other functions assessed in the preoperative evaluation. The approach was made possible because these patients often have detailed cognitive testing and, when surgery is required, provide the hippocampal tissue needed for direct cellular examination.

The cognitive cohort included 40 Spanish-speaking adults with pharmacoresistant mesial temporal lobe epilepsy. Their average age was 39.88 years, their average disease duration was 26.74 years, and they had completed an average of 7.9 years of education. The researchers retrospectively analyzed the Neuropsychological Screening Battery for Hispanics together with a grooved-pegboard fine-motor test. Raw scores were converted to education-adjusted z-scores using education-based normative data, while the pegboard measure was normalized for age and education. The 15 tests or domains covered intelligence, attention, processing speed, language, visuospatial ability, visual and verbal memory, executive functioning and fine-motor dexterity.

Among the 15 measures, the clearest age-associated declines occurred in fluid intelligence and reasoning, visuospatial reconstruction and visuospatial memory. Verbal delayed recall, visual attention, processing speed and divided attention also showed significant correlations with age in the initial analysis, but those findings did not withstand correction for multiple comparisons using the Benjamini–Hochberg false-discovery-rate procedure. Regression models then examined age alongside disease duration. The combined model was significant for fluid intelligence, although neither variable reached conventional significance independently. Age had the stronger inverse association and lower p-value, accounting for 8.3% of residual variance after adjustment for disease duration, compared with 2.7% for disease duration. Neither age nor disease duration independently predicted the two visuospatial outcomes after adjustment.

For the cellular analysis, investigators examined 29 surgically resected hippocampi. Seventeen cases came from the team’s earlier work and 12 additional cases were processed through the same staining and quantification pipeline. Dcx-positive cells with a star-like shape were counted in three regions of the dentate gyrus: the granule cell layer, the hilus and the molecular layer. Prior work by the group established that all of these cells also expressed the astroglial markers GFAP and S100β, while they lacked co-expression with markers of granule neurons, mossy cells, interneurons, microglia and PSA-NCAM-positive immature neurons. The cells were therefore treated as a practical histological index of pathological immature astroglia, and the earlier study had not detected this population in control postmortem hippocampus.

Among the 29 patients, the number of Dcx-positive immature astroglial cells increased significantly with age in the molecular layer, with p = 0.0187. Counts in the granule cell layer and hilus showed no significant age-related change. A regression model supported this regional pattern: age at surgery was a positive predictor of molecular-layer cell counts and uniquely explained 17.3% of residual variance, while disease duration was not significant and accounted for 2.7%. Age and disease duration themselves were positively correlated, but disease duration was not associated with immature-astroglia counts in any of the three regions. The result therefore points to an age-associated accumulation of the abnormal cells in one hippocampal compartment rather than a uniform increase throughout the dentate gyrus.

The direct link with cognition was tested in 19 patients for whom both Spanish-language neuropsychological data and histology were available. Greater numbers of immature astroglia in the hilus and molecular layer were associated with lower Raven’s fluid-intelligence z-scores, with p values of 0.04 and 0.02, respectively. The association in the granule cell layer approached significance at p = 0.052. These correlations did not survive correction for multiple testing, and immature-astroglia counts were not significantly associated with visuospatial reconstruction, visuospatial memory or the other tested cognitive domains. The pattern was therefore specific to abstract, nonverbal reasoning in the available data, rather than a general relationship between cell abundance and every measure of cognition.

More complex regressions produced an important qualification. When age, disease duration and immature-astroglia counts were modeled together, the overall models were significant, but none of the individual predictors remained independently significant. Age contributed the largest unique share of residual variance in fluid intelligence, from 12.6% to 25.4%, while astroglial measures contributed 8.3% to 15.6% and disease duration only 3.3% to 4.1%. Models containing age plus astroglial counts retained a negative age–intelligence relationship, whereas the astroglial counts lost significance. In models with disease duration plus astroglial counts, both were negatively associated with fluid intelligence. The authors interpret these results as evidence that age and immature astroglia share some variance in relation to cognitive performance, while also cautioning against claiming an independent effect from such a small sample.

Several limitations constrain how far the findings can be generalized. The histological sample was small, the subset with paired cognitive data included only 19 patients, and the investigators did not conduct sex-based analyses because statistical power was insufficient. Seizure frequency and other potentially relevant clinical factors could not be examined because complete seizure records were unavailable. Surgery occurred within one year of cognitive testing for 10 of the 19 paired cases, but the remaining nine underwent surgery between one and five years afterward. In addition, Dcx positivity and star-like morphology provided a practical proxy rather than a comprehensive molecular definition. The authors recommend additional astroglial markers, single-cell sequencing and spatial transcriptomics for future work to characterize these cells and the signaling pathways associated with them.

Even with those caveats, the study offers a specific new lead for studying cognitive decline in epilepsy. It links an age-related cellular change in the hippocampus with fluid intelligence, a domain that also depends heavily on frontal brain networks. Whether hippocampal immature astroglia affect reasoning through disrupted hippocampal–frontal communication, or instead accumulate as a consequence of an already deteriorating brain, remains unresolved. The work also does not show that the same process occurs in healthy aging or in other neurological disorders. Future mechanistic and larger clinical studies will be needed to determine whether the cells are a cause, a consequence or part of a broader network of changes involving seizures, neuroinflammation, oxidative stress, blood–brain barrier leakage and neuronal hyperactivity. For now, the evidence identifies immature astroglia as a candidate cellular correlate—and possible research target—rather than a proven cause of declining intelligence in people with epilepsy.

Subject of Research: Association of hippocampal immature astroglia with age-related fluid-intelligence decline in people with mesial temporal lobe epilepsy

Article Title: An age-related decline in human fluid intelligence corresponds with immature astroglia prevalence

Article References: Ammothumkandy, A., Corona, L., Atai, N. A., Abedi, A., Jimenez, N., Armacost, M., D’Orazio, L. M., Zuverza-Chavarria, V., Cayce, A., Shariq, M., McGinn, R., Lee, D., Lee, B., Heck, C., Russin, J. J., Liu, C. Y., Smith, J. A. D., & Bonaguidi, M. A. (2026). An age-related decline in human fluid intelligence corresponds with immature astroglia prevalence. Frontiers in Cellular Neuroscience, 20, Article 1926804. https://doi.org/10.3389/fncel.2026.1926804

Image Credits: AI Generated

DOI: 10.3389/fncel.2026.1926804

Keywords: astroglia, fluid intelligence, mesial temporal lobe epilepsy, cognitive decline, aging, hippocampus, doublecortin, neuropsychology, dentate gyrus, epilepsy, age-related, decline

The research team recruited 148 adults from the general population, ranging in age from 18 to 60, and recorded their brain activity with electroencephalography across three separate laboratory sessions spaced three months apart. Participants completed three distinct cued task-switching paradigms. In the parity/magnitude task, they classified single digits as odd or even, or as smaller or larger than five, depending on the color of a fixation cross. In the global/local task, they judged either the overall shape of a compound geometric figure or the small elements composing it. In the number/letter task, they evaluated either the numeric or the alphabetic member of a character pair. Because the cue announcing which rule to apply appeared 800 to 1200 milliseconds before the target stimulus, the design allowed the researchers to cleanly separate endogenous preparation, the internal act of getting ready to switch, from exogenous implementation, the actual execution of the new rule once the target arrives.

Rather than preselecting frequency bands and electrode sites as most previous studies have done, the team employed a data-driven mass-univariate approach spanning the entire scalp, frequencies from 3 to 60 Hertz, and the full time course of each trial. They contrasted switch trials, which required a change of rule set, with repeat trials, which did not, and used cluster-based permutation testing to identify statistically robust differences. Two distinct oscillatory signatures emerged. The first was a transient increase in theta power, roughly 4 to 8 Hertz, over frontal regions beginning a few hundred milliseconds after the cue. The second was a sustained suppression of alpha and beta power, roughly 8 to 30 Hertz, centered over left lateral parietal cortex, which appeared within 100 to 200 milliseconds of cue onset and persisted through target processing.

Both effects were stronger on switch trials than on repeat trials, confirming that they track the additional cognitive demands imposed by rule changes. But the two signatures told very different stories when the researchers probed their individual-difference properties. Using latent change structural equation modeling, a statistical technique that extracts the difference between switch and repeat conditions at the latent level while correcting for measurement error, the team tested whether each oscillatory effect reflected a common flexibility factor generalizing across all three tasks. The frontal theta effect, despite appearing in every task, showed no significant shared variance across tasks. Its variance was statistically indistinguishable from zero, and a constrained model with the flexibility factor fixed to zero fit the data nearly five times better according to Akaike weights. In other words, theta enhancement appears to index task-specific control processes, tailored to the particular demands of each switching paradigm, rather than a general capacity for flexibility.

The alpha/beta suppression, by contrast, behaved like a true trait marker. Cue-related and target-related alpha/beta effects each loaded significantly onto a latent flexibility factor spanning all three tasks, with excellent reliabilities of 0.95 or higher. When the two models were combined, the cue-related and target-related flexibility factors correlated at r = 0.98, sharing 96 percent of their variance, suggesting they tap the same underlying construct. This sustained suppression, the authors argue, reflects domain-general attentional reconfiguration: the reallocation of attentional resources that begins the moment a cue signals a rule change and must be maintained until the target has been processed. The pattern lends strong support to two-stage models of task switching, which propose that cues trigger goal-shifting and rule retrieval while targets require stimulus-response mapping, and that the attentional processes initiated during the first stage must remain active through the second.

Perhaps the most striking finding concerns what the neural flexibility measures did not correlate with. Neither the cue-related nor the target-related alpha/beta flexibility factor showed a significant association with fluid intelligence, measured with the Berlin Intelligence Structure Test, or with working memory capacity, assessed with a battery of span and updating tasks. This dissociation is theoretically consequential. Influential frameworks such as process overlap theory posit that domain-general executive functions act as bottlenecks underlying the positive correlations observed across diverse cognitive tasks, and resource-based accounts predict that individuals with greater executive resources should perform better across the board. The present results complicate that picture: flexibility, at least as indexed by alpha/beta suppression, appears to be a neurally coherent dimension that is largely independent of general cognitive ability. The authors caution that their confidence intervals do not exclude small to medium correlations, but the pattern is consistent with prior evidence of a dissociation between flexibility and intelligence.

The study also connected the neural data to behavior in an unexpected way. Using the drift-diffusion model, a computational framework that decomposes reaction time distributions into parameters such as drift rate, the speed of evidence accumulation, and non-decision time, the duration of perceptual and motor processes outside the decision itself, the researchers derived process-based behavioral measures of flexibility. Stronger cue-elicited alpha/beta suppression was associated with smaller switch costs in non-decision time, suggesting that individuals whose brains suppress alpha/beta power more vigorously upon seeing a switch cue prepare their task sets more effectively. Drift rate, by contrast, did not correlate with the neural flexibility measures, indicating that the oscillatory signature captures preparatory attentional processes rather than the efficiency of evidence accumulation itself. This dissociation underscores that neural and behavioral measures of flexibility are not interchangeable operationalizations of the same construct but reflect partially distinct facets of a multi-component ability.

Methodologically, the study sets a new standard for individual-differences research in cognitive neuroscience. The authors note that the reliability of neural flexibility measures has received insufficient attention in the field, despite reliability being a prerequisite for valid individual-differences research. Raw difference scores between switch and repeat conditions proved poorly reliable, ranging from 0.00 to 0.10 for theta and 0.35 to 0.53 for alpha/beta, which would have doomed any correlational analysis. The latent change score framework circumvented this problem by modeling change as a latent variable, partitioning true score variance from measurement error. Combined with the large sample, the three-month test-retest design, and the use of three distinct tasks to eliminate task-specific confounds such as processing speed and stimulus familiarity, the approach provides a template for how oscillatory brain measures can be validated as meaningful psychological constructs.

The findings also carry clinical relevance. Reduced cognitive flexibility characterizes numerous conditions, including schizophrenia, depression, and autism spectrum disorder, and individual differences in flexibility predict academic achievement, problem-solving ability, and mental health outcomes. If alpha/beta suppression proves to be a reliable neural marker of domain-general flexibility, it could eventually inform the assessment of cognitive control capacities, the development of targeted interventions, and the understanding of why some individuals adapt readily to changing demands while others struggle. The authors emphasize, however, that their operationalization captures goal-directed set shifting within the task-cueing paradigm and may not encompass broader, more ecologically valid forms of flexibility such as those assessed by the Wisconsin Card Sorting Test.

Limitations remain. The 32-electrode montage lacks the spatial resolution to precisely localize effects, the 3 to 60 Hertz analysis window excluded higher gamma activity, and the central stimulus presentation precluded examination of lateralized attentional patterns. Cluster-based permutation testing, while controlling false positives, can reduce sensitivity to smaller effects and does not permit exact localization of cluster extent. Future work should test whether alpha/beta suppression generalizes beyond cued task switching using broader cognitive batteries and higher-density recordings. Nevertheless, the core conclusion stands: the brain’s oscillatory dynamics offer a measurable window into one of cognition’s most essential capacities, and the sustained dampening of alpha and beta rhythms over parietal cortex may be the most faithful neural echo of the mind changing gears.

Subject of Research: Oscillatory neural signatures of cognitive flexibility in cued task switching

Article Title: Task-specific theta enhancement and domain-general alpha/beta suppression as oscillatory signatures of cognitive flexibility

Article References: Hülsemann, M., Löffler, C., & Schubert, A.-L. (2026). Task-specific theta enhancement and domain-general alpha/beta suppression as oscillatory signatures of cognitive flexibility. Communications Psychology, 4(1), Article 140. https://doi.org/10.1038/s44271-026-00521-9

Image Credits: AI Generated

DOI: 10.1038/s44271-026-00521-9

Keywords: cognitive flexibility, task switching, EEG, theta oscillations, alpha suppression, beta suppression, executive functions, attentional reconfiguration, individual differences, working memory, fluid intelligence, drift-diffusion model

A team led by researchers at Amsterdam UMC and the Wellcome Sanger Institute set out to squeeze more value from the fluid intelligence measures collected in the UK Biobank, a resource of roughly half a million British volunteers. Fluid intelligence, the capacity to solve novel problems independent of acquired knowledge, was assessed through short verbal-numerical reasoning tests administered at the baseline in-person visit and in later online questionnaires. Only about 62 percent of participants ever completed one of these tests, and earlier studies typically analyzed just a subset of those. By integrating scores from five different testing occasions and then statistically imputing scores for participants who never took any test, the team expanded the effective sample from roughly 270,000 measured individuals to more than 455,000.

The imputation itself relied on a matrix-completion algorithm called SoftImpute, which fills in missing entries by exploiting low-dimensional structure in the data. The researchers began with 154 biobank variables correlated with intelligence, then deliberately pruned that set down to 82 variables whose genetic correlation profile aligned more closely with intelligence than with a residual noncognitive component of educational attainment. This refinement step proved crucial: it modestly reduced raw prediction accuracy, from a correlation of about 0.52 to 0.46, but reduced the importation of socioeconomic and other noncognitive signal into the imputed phenotype. Notably, an accuracy of 0.46 falls below the test-retest reliability of the fluid intelligence test itself, which the team measured at roughly 0.6, meaning the imputed values add noise but, according to the authors’ theoretical framework, need not add systematic bias.

That theoretical framework is one of the study’s most distinctive contributions. The authors formally derived the conditions under which pooling measured and imputed phenotypes recovers consistent population genetic effects. Two assumptions matter most: measurement error in the observed scores must be uncorrelated with genotype, and the imputed values must be calibrated to the intended cognitive construct in the unmeasured group, both in their mean and in their covariance with each genetic variant. Although these assumptions cannot be proven directly, they yield a testable consequence: the genetic correlation profile of the combined analysis should match that of an independent GWAS targeting the same construct. The team checked this against the COGENT consortium GWAS of cognitive function and found close correspondence.

The validation also exploited within-family GWAS, a technique that separates direct genetic effects from confounding sources such as genetic nurture, assortative mating and population stratification. In population-based analyses, a negative correlation between direct effects and the so-called nontransmitted coefficient has previously been interpreted as a signature of ascertainment bias. For the baseline fluid intelligence measure alone, that correlation was strongly negative at about minus 0.54. Integrating scores across testing occasions moved it closer to zero, and adding imputed values pushed it to a point estimate that was no longer significantly different from zero, exactly what the ascertainment-bias hypothesis predicted.

The payoff for common-variant discovery was substantial. Combining measured and imputed scores raised the number of independent genome-wide significant lead SNPs from 390 to 550, and meta-analyzing the result with COGENT pushed the total to 620 lead SNPs in a GWAS of 490,700 individuals. Larger sample sizes also improved gene prioritization, strengthened enrichment of associated genes in brain tissue, and monotonically improved polygenic index prediction of measured cognitive ability in three independent cohorts: the ALSPAC birth cohort, the Millennium Cohort Study and the INTERVAL study. Crucially, direct genetic effect estimates within families also increased with sample size, indicating that the gains were not an artifact of population-level confounding.

The most striking results came from the rare variant side. Using whole-exome sequencing data, the researchers tested whether ultra-rare protein-truncating variants and damaging missense variants in individual genes were associated with fluid intelligence. With measured scores alone, only five genes passed the false discovery rate threshold of 1 percent. With the combined measured-and-imputed phenotype, that number jumped to 26, a 5.2-fold increase in gene-level discovery, while effect size estimates remained essentially unchanged, suggesting the imputation introduced power rather than distortion. Fourteen of the 26 genes are already cataloged in the DDG2P database of developmental disorder genes, a significant enrichment given that only about 13 percent of tested protein-coding genes belong to that list.

Eight of the significant genes had no strong prior evidence of involvement in intelligence or neurodevelopmental conditions, yet the evidence that they matter is compelling. Across a cohort of more than 31,000 exome-sequenced probands with neurodevelopmental conditions, damaging de novo mutations in the 12 newly implicated non-DDG2P genes occurred 2.59 times more often than expected by chance. And in aggregate, protein-truncating variants in the newly discovered genes were significantly associated with lower measured cognitive ability in both ALSPAC and the Millennium Cohort Study, providing external replication that does not depend on the imputed phenotype at all. Comparisons with educational attainment showed the imputed intelligence measure outperformed that proxy, yielding 26 significant genes versus nine for educational attainment.

The findings carry a broader message about the genetics of cognition: rare variants with moderate to large effects on intelligence, estimated at roughly 0.25 to 1 standard deviations per carrier, are more pervasive than previously appreciated. The associations largely persisted even after excluding individuals with recorded diagnoses of intellectual disability, autism or epilepsy, implying that damaging variants in these genes can shape cognitive ability across the population without necessarily pushing carriers over a clinical diagnostic threshold. Polygenic background and environmental factors likely modify how such variants manifest clinically, blurring the traditional dominant-versus-recessive dichotomy into more of a gradient.

The authors are careful to frame the work as a pragmatic response to the limitations of existing biobank data rather than an endorsement of prediction as a substitute for measurement. Imputed phenotypes blur the boundary between a construct of interest and its correlates, and some residual noncognitive signal almost certainly remains in the combined measure, as reflected in slightly elevated genetic correlations with educational attainment and household income. The team argues that the most robust path forward is better phenotyping at the design stage, with routine collection of high-quality cognitive measures and deliberate strategies to minimize selection bias. Still, for researchers working with the biobanks that exist today, the study offers both a powerful tool and a principled framework for deciding when imputed traits can be trusted, and it opens a route to finding new neurodevelopmental risk genes hiding in the genomes of people who never took the test.

Subject of Research: Statistical imputation of fluid intelligence scores in the UK Biobank to reduce ascertainment bias and boost genetic discovery for cognition and neurodevelopmental conditions

Article Title: Imputation of fluid intelligence scores reduces ascertainment bias and increases power for analyses of common and rare variants

Article References: van den Berg, D. M., Malawsky, D. S., Huang, W., Danecek, P., Walter, K., Kalantzis, G., Wade, I., Palmer, D., Birney, E., Verweij, K. J. H., Smit, D. J. A., Lindsay, S. J., Hurles, M. E., Abdellaoui, A., & Martin, H. C. (2026). Imputation of fluid intelligence scores reduces ascertainment bias and increases power for analyses of common and rare variants. Nature Genetics. https://doi.org/10.1038/s41588-026-02787-5

Image Credits: AI Generated

DOI: 10.1038/s41588-026-02787-5

Keywords: fluid intelligence, UK Biobank, genome-wide association study, ascertainment bias, phenotype imputation, rare variants, polygenic scores, neurodevelopmental disorders, genetic correlations, SoftImpute, developmental disorder genes, cognitive ability

The study drew on the Spanish Environment and Childhood (INMA) project, a population-based birth cohort established between 2003 and 2008 across four regions of Spain: Asturias, Gipuzkoa in the Basque Country, Sabadell in Catalonia, and Valencia. From 2,644 eligible women recruited during first-trimester prenatal visits, the researchers measured omega-3 fatty acid proportions in the cord plasma of 953 newborns, ultimately analysing data from 716 mother-child pairs who had at least one valid cognitive assessment during follow-up. Cord blood is a powerful biomarker because it reflects maternal diet and fatty acid transfer to the foetus during the third trimester, a critical window characterised by the rapid foetal brain growth spurt and the highest rate of polyunsaturated fatty acid accretion in development.

The team focused on three omega-3 fatty acids with distinct biological roles. Docosahexaenoic acid, or DHA, is the most abundant omega-3 in the brain and serves as the primary structural component of neuronal membranes, supporting synaptogenesis, membrane fluidity, signal transduction, and myelination, particularly in the prefrontal cortex. Eicosapentaenoic acid, or EPA, protects brain health mainly by reducing neuroinflammation and facilitating placental DHA transport. Alpha-linolenic acid, or ALA, is an essential fatty acid that mammals cannot synthesise de novo and which provides direct neuroprotection while supporting neuronal growth and plasticity. Because the human conversion of ALA to DHA is marginal and to EPA inefficient, direct dietary intake of these compounds during pregnancy is necessary.

Cognitive function was assessed repeatedly using internationally validated computer-based neuropsychological tests. Inattentiveness was measured at approximately ages 5, 7, and 11 using the Conners’ Kiddie Continuous Performance Test and the Attention Network Test, with higher values of hit reaction time standard error indicating greater inattentiveness. Working memory was evaluated at ages 7, 11, and 15 with the N-back task, using detectability in the demanding 3-back condition as the primary outcome. Fluid intelligence was assessed at around ages 9 and 15 with Raven’s Progressive Matrices and a Spanish adaptation of the Test of Primary Mental Abilities. All scores were converted to age-specific z-scores to allow comparison across different instruments and follow-up waves.

The statistical approach was what set this study apart from most previous nutritional epidemiology. Rather than comparing cognitive test results at isolated time points, the researchers used linear mixed-effects models with interaction terms between child age and fatty acid exposure, allowing them to model entire developmental trajectories from age 5 to 15. Models included cohort-level and individual-level random effects, and were adjusted for child sex, maternal age, maternal education, preconception body mass index, Mediterranean diet adherence, and alcohol consumption during pregnancy. Confounders were selected using a directed acyclic graph, and missing covariate data were handled through multiple imputation.

The results revealed a striking divergence between ALA and DHA. In overall models averaging across the whole follow-up period, higher cord plasma proportions of both ALA and DHA were associated with lower inattentiveness, and higher DHA was linked to better working memory. But the trajectory models told a more nuanced story. Children with higher prenatal ALA exposure showed significant early advantages in attention and fluid intelligence, with predicted differences between high and low exposure groups reaching 0.76 standard deviations for fluid intelligence at age 5. Yet these advantages attenuated and even reversed over time: by age 15, children with higher ALA showed slightly worse fluid intelligence scores than their lower-exposure peers, as children with lower prenatal ALA appeared to catch up and overtake them.

DHA displayed the opposite temporal pattern. Children with higher cord plasma DHA began with markedly better working memory in early childhood, with a predicted advantage of 0.54 standard deviations at age 5, but this gap gradually converged as lower-exposure children improved. Meanwhile, the benefits of DHA for attention and fluid intelligence emerged later and grew steadily stronger: children with higher DHA showed progressively less inattentiveness from age 7 onward, with the predicted difference widening to 0.67 standard deviations by age 15, and better fluid intelligence emerging by adolescence. EPA, present at very low relative abundance in cord plasma, showed no significant associations with any cognitive outcome.

The authors propose biological explanations for these divergent patterns. ALA’s early boost may reflect its influence on neuronal membrane fluidity, gene expression, and synaptic plasticity during the period when neural networks are rapidly expanding, with its effects later swamped by the growing influence of children’s own diets and postnatal environments. DHA, by contrast, is directly incorporated into neuronal membranes of the prefrontal cortex, a region that begins functioning in early childhood but continues structural refinement through adolescence. A stable structural foundation laid down during gestation could therefore support the progressive maturation of attentional control and executive networks, producing advantages that become increasingly evident as higher-order cognitive functions consolidate. The early working memory advantage of DHA is consistent with the heavy reliance of that function on the dorsolateral prefrontal cortex, a region characterised by rapid DHA accretion during late gestation.

The study’s robustness was tested extensively. Results remained virtually identical across sensitivity analyses adjusting for postnatal fish and nut intake, breastfeeding duration, maternal verbal IQ, mercury exposure, and smoking during pregnancy. Applying inverse probability weighting to account for attrition bias produced consistent trajectories and even strengthened some associations, including a progressive longitudinal gain in working memory for the highest ALA group that reached statistical significance after additional adjustment. Findings also survived false discovery rate correction. The researchers acknowledge limitations, however: cord plasma fatty acids were expressed as relative percentages rather than absolute concentrations, cord levels reflect exposure near delivery rather than cumulative intake across pregnancy, and the observational design cannot establish strict causality, even though the use of an objective prenatal biomarker preceding outcomes by more than a decade establishes clear temporality.

The implications reach beyond academic interest. The findings underscore the importance of distinguishing between individual omega-3 fatty acids when evaluating their role in neurodevelopment, rather than treating omega-3 as a single nutritional category, and they highlight the need for long-term follow-up to capture cognitive associations that may be delayed by years. For pregnant women and clinicians, the study adds to existing evidence that maternal nutrition during the third trimester, when the foetal brain undergoes its most intense period of fatty acid accumulation, may have consequences that unfold across an entire decade of cognitive development. The authors call for replication in diverse populations, integration of postnatal biomarker data to disentangle prenatal from postnatal influences, and neuroimaging studies to identify the brain structural and functional mediators underlying these distinct temporal trajectories.

Subject of Research: Associations between prenatal omega-3 fatty acid exposure measured in cord plasma and longitudinal cognitive development in children

Article Title: Omega-3 cord plasma proportions and cognitive function trajectories from 5 to 15 years of age: a population-based cohort study

Article References: Pinar-Martí, A., Ayala-Aldana, N., Bernardo-Castro, S., Manidis, A., Cadman, T., Sala-Vila, A., Lázaro, I., Lertxundi, N., Subiza-Pérez, M., González-Safont, L., Vioque, J., Riaño-Galán, I., Rodríguez-Dehli, C., López-Sabater, M. C., Vrijheid, M., Guxens, M., Foraster, M., & Julvez, J. (2026). Omega-3 cord plasma proportions and cognitive function trajectories from 5 to 15 years of age: a population-based cohort study. European Journal of Epidemiology. https://doi.org/10.1007/s10654-026-01461-8

Image Credits: AI Generated

DOI: 10.1007/s10654-026-01461-8

Keywords: omega-3 fatty acids, DHA, ALA, prenatal nutrition, cognitive development, neurodevelopment, working memory, attention, fluid intelligence, birth cohort, cord blood, childhood adolescence

The variant in question, known as rs10191329, sits in the stretch of DNA between two genes called DYSF and ZNF638. It does not change any protein sequence, yet a large genome-wide association study of more than 12,000 people with multiple sclerosis previously linked carrying its A allele to greater cross-sectional physical disability. Subsequent work connected the same allele to brain atrophy, thinning of the retinal nerve fibre layers, and elevated levels of serum neurofilament light chain, a blood biomarker of neuroaxonal damage. Together, these findings pointed toward a mechanism centred on the central nervous system itself rather than on the immune attack that defines relapsing disease.

That pattern intrigued the study’s authors. If the A allele erodes the nervous system’s resilience, they reasoned, its influence might not stop at physical disability. Cognitive impairment is one of the most common and disabling features of multiple sclerosis, and the concept of cognitive reserve, the brain’s capacity to tolerate damage while preserving function, is well established across neurodegenerative conditions including Alzheimer’s and Parkinson’s disease. The team therefore hypothesised that rs10191329 might influence cognition not only in multiple sclerosis but in healthy people and in other neurological disorders, and turned to the UK Biobank to test the idea at population scale.

The UK Biobank recruited roughly half a million participants aged 40 to 69 between 2006 and 2010, collecting genetic data alongside questionnaires, physical assessments, imaging and biological samples. From this resource the researchers constructed five mutually exclusive cohorts of European ancestry: people with a recorded multiple sclerosis diagnosis, people with Parkinson’s disease, people with all-cause dementia, people with migraine as a negative disease control, and healthy controls. After excluding participants with missing genotype data, the final analytical sample comprised 399,031 individuals, including 373,530 controls, 2,026 people with multiple sclerosis, 2,337 with dementia, 1,466 with Parkinson’s disease and 19,672 with migraine.

The cognitive outcomes came from three tests administered at recruitment. Reaction time was measured in a computerised card-matching task, with higher scores indicating slower responses. Fluid intelligence was scored as the number of correct answers out of 13 on a reasoning test, with higher scores indicating better performance. Prospective memory, the ability to remember to carry out an intended action, was assessed as a binary outcome based on whether participants correctly recalled a task instruction on their first attempt. The researchers confirmed that the multiple sclerosis, Parkinson’s and dementia cohorts showed the expected pattern of poorer performance compared with controls and the migraine group, validating the measures.

Statistical models adjusted for age, sex, the first four genetic principal components and the Townsend deprivation index, a measure of socioeconomic status. Linear outcomes were rank-inverse normal transformed to satisfy model assumptions, and false discovery rate correction was applied across all tests simultaneously. Sensitivity analyses added educational leaving age as a covariate and repeated the models under dominant and recessive genetic frameworks. As a positive control, the team also examined the relationship between the variant and self-reported disability claims among the multiple sclerosis cohort.

The results were striking in their direction, if modest in their magnitude. Among healthy controls, each copy of the A allele was associated with slower reaction time, a beta of 0.01 standard deviations per allele; lower fluid intelligence, a beta of minus 0.02 standard deviations; and higher error rates on the prospective memory task, an odds ratio of 1.06. All three associations reached study-wide significance. In the multiple sclerosis group, carriers of two A alleles were 5.9 percent more likely to claim disability allowance than common-allele homozygotes, 52.4 percent versus 46.5 percent, although the regression estimate did not reach statistical significance. In the other disease cohorts, associations were directionally concordant but not significant, with slower reaction time in multiple sclerosis, Parkinson’s and dementia, lower fluid intelligence across all cohorts, and impaired prospective memory in dementia.

Power calculations help explain the pattern of significance. Using reaction time as an example, the study had 98 percent power to detect a per-allele effect larger than 0.15 standard deviations in the multiple sclerosis cohort of roughly 2,000 people, but only 63 percent power for a 0.1 standard deviation effect. In other words, the analysis was well equipped to detect moderate genetic effects within disease groups but underpowered for the small effects observed in the enormous control sample. The authors are careful to note that the cognitive tests were designed as pragmatic population measures, not as validated outcomes for multiple sclerosis, and that prevalent cases in the Biobank are biased toward milder disease, both of which would attenuate detectable associations.

One obvious alternative explanation is education. A previous Mendelian randomisation analysis suggested genetic overlap between the severity locus and educational attainment, and cognitive reserve is strongly shaped by education. Yet adjusting for educational leaving age, a proxy for educational attainment, did not meaningfully attenuate the associations, making it unlikely that schooling alone accounts for the signal. The variant’s effects, whatever their mechanism, appear to operate at least partly independently of educational background, consistent with a direct influence on brain health rather than a purely sociodemographic confound.

The broader implication is that a variant discovered as a modifier of multiple sclerosis severity may act through general properties of central nervous system resilience, influencing how the brain performs even in the absence of disease. The effect sizes are small, and the authors emphasise that large disease-specific cohorts are needed to determine whether the variant shapes cognition within multiple sclerosis and other disorders, and whether its influence extends to populations of non-European ancestry, which this study did not examine. Still, the convergence of evidence, spanning disability, brain atrophy, neurofilament light chain, retinal thinning and now cognition, sketches a coherent picture of a genetic locus that quietly sets the baseline health of the nervous system, and in doing so helps determine how hard neurological disease hits when it arrives.

Subject of Research: Association between the multiple sclerosis severity allele rs10191329A and cognitive function in the UK Biobank

Article Title: The Multiple Sclerosis Severity Allele rs10191329A and Cognitive Function: A UK Biobank Study

Article References: Zimianiti, I., Waters, S., Harroud, A., Stridh, P., Dobson, R., & Jacobs, B. M. (2026). The Multiple Sclerosis Severity Allele rs10191329 A and Cognitive Function: A UK Biobank Study. Annals of Clinical and Translational Neurology, 13(10), 2143-2147. https://doi.org/10.1002/acn3.70458

Image Credits: AI Generated

DOI: 10.1002/acn3.70458

Keywords: multiple sclerosis, rs10191329, UK Biobank, cognitive function, genetics, neurodegeneration, DYSF-ZNF638 locus, neurofilament light chain, cognitive reserve, fluid intelligence, reaction time, prospective memory