Authors:

Deng, F, HENSON, R.N., Muniz-Terrera, G., Malhotra, P., O’Brien, J.T., Ritchie, C.W., Lawlor, B. & Naci, L.

Reference:

Year of publication:

In Press

CBU number:

9269

Abstract:

Alzheimer’s disease (AD) neuropathology starts decades before clinical manifestations, but the early indicators of AD in midlife remain unclear. Functional segregation of brain networks is a key marker of brain health. It remains unknown, however, whether inherited risk of AD impacts network segregation from midlife in individuals who are cognitively healthy but carry inherited risk for late-life AD. To address this question, we investigate which brain networks show the strongest age-related segregation loss in the Cam-CAN lifespan cohort (18-88 years, N=652), and whether APOE ε4 genotype impacts segregation of age-vulnerable networks in the midlife PREVENT cohort (40-59 years, N=210), cross-sectionally and longitudinally. Higher-order networks showing the most significant age-related decline are the default mode (DMN), frontal-parietal control (FPN) and salience (SN) networks. Cognitively healthy midlife APOE ε4 carriers have higher segregation across the brain cross-sectionally, accompanied by greater longitudinal decline in the DMN over two years, relative to non-carriers. Higher DMN segregation is associated with better episodic and relational memory across the PREVENT cohort. These findings suggest that functional segregation may serve as a potential biomarker, providing insights into the mechanisms through which APOE influences brain function and cognition from healthy midlife, on average 23 years before dementia onset.

URL:

Data available, click to request

Authors:

NORRIS, D. & Kalm, K.

Reference:

Year of publication:

-

CBU number:

9268

Abstract:

Here we present a model of sequence learning and recall based on the idea that the function of memory is to maintain an up-to-date representation of the environment that can be used to guide future perception and action. The representation of the environment is considered to be a prior which is combined with information in short-term memory to construct a posterior representation. That posterior representation drives recall and, in turn, is used to update the priors. This prediction-update cycle is a form of Bayesian filter. We apply the model to data from the Hebb (1961) task in which participants learn sequences over repeated presentations in an immediate serial recall task. The model is shown to simulate a wide range of data on the Hebb effect including the ability to learn multiple lists at once, the effect of list spacing, the differential impact of variation in the beginning versus end of lists, learning from response errors, interference between similar lists, and the effects of repetition on forward and backward recall. The model’s ability to account for these phenomena follows directly from its basic computational principles.

Data for this project is available at: https://github.com/DennisNorris/Bayesian_filter_hebb

Authors:

KARBAT, G., CRESPO-GARCIA, M., Vishe, G., ANDERSON, M.C., & Laundau, A.N.

Reference:

Year of publication:

2026

CBU number:

9264

Abstract:

Understanding the organizing principles of brain activity can advance neurotechnology and medical diagnosis. Traditionally, neural activity is viewed as consisting oscillations in distinct frequency bands. However, emerging evidence suggests these oscillations often manifest as transient bursts rather than sustained rhythms. We examine the hypothesis that rhythmicity (sustained vs bursty) adds a further dimension to brain organization. Using a rhythmicity measure, we segment neurophysiological spectra from 859 participants across datasets, species, recording techniques, ages 18–88, sexes, brain regions, and cognitive states in health and disease. Our results reveal a universal rhythmicity-resolved spectral architecture with two categories: high-rhythmicity bands exhibiting sustained oscillations and new low-rhythmicity bands dominated by brief bursts. This architecture reflects two modes of operation: sustained bands suitable for maintaining ongoing activity, and transient bands which can signal responses to change. The rhythmicity-resolved architecture provides a unifying framework that bridges human and non-human findings, enables individualized spectral definitions, and offers a principled basis for understanding brain activity.

URL:

Data for this project is available at: https://github.com/laaanchic/LAVI 11

Authors:

Pickavance, J., O'Nions, E., Hammad, M., Jackson, L., Lightfoot, K., McEachan, ORBEN, A., Ryan. D., Shire, K., Wood, M.L., Wright, J. Lewer, D.

Reference:

Year of publication:

2026

CBU number:

9262

Abstract:

Background Social media is a central part of the lives of adolescents in 2025. The recent rise of short-form content and gamification features has coincided with an increasing prevalence of mental health problems among this age group. Many policy makers are considering restrictions to the amount of time under-16s spend on social media. Despite this, there is limited contemporary evidence about the extent of their social media use, nor meaningful estimates of the effect a reduction may have on their mental health. Here, we estimate daily social media usage for adolescents in the culturally and ethnically diverse city of Bradford, England, plus its association with their mental health. Method We did a cross-sectional analysis of data from Born in Bradford: Age of Wonder 2023-24, a school-based survey of students aged 12–15 (n = 8,466). We weighted the sample to be representative of the city-wide population of 12–15-year-olds and report the median daily screen time spent on social media apps by age, sex, and ethnicity. We used a log-linear model to estimate the effect of daily social media screen time on anxiety and depression symptoms (RCADS-25), adjusting for age, sex, ethnicity, free school meal eligibility, special educational needs, deprivation, and season of survey completion. Predictions from this model were used to estimate the change in prevalence of clinical threshold symptomatology associated with a range of daily screen time limits. Results The median time spent using social media apps was 3.36 h per day (IQR 1.88–5.44). Longer durations of social media use were associated with greater mental health symptoms after adjustment for potential confounders. In a scenario where this association is causal, capping social media use at a maximum of 3 h per day would lead to a 1.25ppt (95% CI 0.74ppt – 1.76ppt) decrease in the prevalence of clinical threshold symptomatology (a reduction from 10.7% to 9.5%), equivalent to 13 fewer cases in a typical school of 1000 pupils. Conclusions All groups of adolescents spend a large of amount of time using social media apps each day. We observed a significant association between social media use and symptoms of anxiety and depression. Assuming a causal relationship, daily time limits placed on social media may yield meaningful reductions in anxiety and depression symptomatology. Nevertheless, we cannot demonstrate strong evidence of a causal relationship, and robust methods such as controlled trials or natural experiments are needed to precisely determine the benefits and harms of policies restricting access for under-16s. Key practitioner message What is known? Meta-analytic evidence links more time spent on social media usage to poorer mental health, but there is limited contemporary data, existing studies underrepresent less advantaged adolescents, and effect sizes are poorly contextualised. What is new? Data from >8,000 adolescents aged 12-15 in a diverse urban setting in England indicate a median usage of 3.36 hours per day (IQR 1.88-5.44) on a normal school week. Although some subgroups report lower use of social media, all subgroups have a median use of 2.61 hours or more. Capping the daily limit to 3-hours would reduce usage for more than half of 12-15-year-olds and was equivalent to a 1.25 percentage point reduction in the absolute prevalence of clinical threshold anxiety and depression symptomatology. What is significant for clinical practice? Daily usage limits could be associated with meaningful reductions in anxiety and depression for young people. Nevertheless, estimation of causal effects of social media use on mental health is difficult for methodological reasons. Experimental trials are required to determine the benefits and harms of policies restricting social media access for under-16s.

URL:

Data for this project is available at: https://doi.org/10.17605/OSF.IO/ZP3GN

Authors:

Grødem, E.O.S., Vidal-Pineiro, D., Sørensen, O., Bartr´es-Faz, D., Brandmaier, A.M., Cattaneo, G., Garrido, P.F., HENSON, R.N., Kuhn, S., Lindenberger, U., MacIntosh, B.J., Nyberg, L., Pascual-Leone, A., Smith, S.M., Sol´e-Padull´es, C., Solana-S´anchez, J., Otto Watne, L., Walhovd, K.B., Bjørnerud, A., Fjell1, A.M.

Reference:

Year of publication:

2026

CBU number:

9260

Abstract:

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. 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 earlyadulthood levels from systematic differences in change when studying adult brain aging.

URL:

Data for this project is available at: https://github.com/EdvardGrodem/brain-trajectories

Authors:

FRISBY, S.L., HALAI, A.D., Cox, C.R., Clark, A., Shimotake, A., Kikuchie, T., Kuneida, T., Arakawa, Y., Takahashi, R., Ikeda, A., Matsumoto, R., Rogers, T.T. & LAMBON RALPH, M.

Reference:

Year of publication:

2026

CBU number:

9259

Abstract:

Intracranial electrophysiology offers a unique insight into the nature of information representation in the brain – it can be used to disentangle information encoded in gamma and high gamma frequencies from information encoded in lower frequencies. We used regularised logistic regression to decode animacy from time-frequency power and phase extracted from electrocorticography (ECoG) grid electrode data recorded on the surface of human vATL. Power in gamma (30 – 60 Hz) and high gamma (60 – 200 Hz) produced reliable decoding, indicating that semantic information is indeed expressed by local populations in vATL. However, power from a wide range of frequencies (4 – 200 Hz) produced significantly higher decoding accuracy and also exhibited the same rapidly-changing dynamic code previously observed when decoding voltage. These findings support the theory that semantic information is encoded by a local vATL “hub” that interacts with distributed cortical “spokes”. We are unable to share raw data for this study because patients did not provide informed consent for us to do so. However, matrices containing power, phase, and voltage features (columns) for each stimulus (rows) are available at https://osf.io/m5v42/ . Code is available at https://github.com/slfrisby/ECoG_LASSO .

URL:

Data for this project is available at: https://osf.io/m5v42/

Authors:

GURUNANDAN, K., GREVE, A., Wilmot, E. & HENSON, R.N.

Reference:

Year of publication:

In Press

CBU number:

9257

Abstract:

Prediction error (PE) is the discrepancy between predictions and new information. For a binary reward outcome, PE may be signed (positive if the outcome was better than predicted, and negative if the outcome was worse than predicted), or unsigned (absolute value of “surprise”). Using a “variable choice” paradigm, De Loof et al. (2018) examined the role of PE in one-shot learning of unknown translations of known words, and showed that associative memory for the translation was greater when (financial) reward was more unexpected, and lesser when an expected reward was not received (i.e., signed PE); an effect that they replicated in several subsequent studies. However, other work on PE in declarative memory has assumed that memory is greater when an outcome is more unexpected, without any explicit reward (i.e., unsigned PE). We replicated De Loof et al.’s paradigm with and without financial reward, and found that memory was explained slightly better by unsigned PE (Experiments 1A-1B). However, we also identified a potential confound in the paradigm that could explain the results without any role of PE, as confirmed by simulations. We therefore designed a modified version of the paradigm that circumvents this confound (Experiment 2). Results were inconsistent with the PE account. We conclude that variable choice paradigms may not be well-suited to investigate the role of PE in one-shot declarative learning, and that the purported role of signed PE in declarative memory requires further investigation.

Data for this project is available at: https://osf.io/b48ga

Authors:

FLEMING, H., WHINES, A., WHELAN, P., LAU, I., GALLACHER, K., MEHRHOF, S., NORD, C.

Reference:

Year of publication:

2026

CBU number:

9254

Abstract:

Central to survival is our ability to learn that the sensory properties of food are associated with metabolic interoceptive signals (e.g., changing blood glucose). These signals influence cognition and brain activity, shaping the hedonic evaluation of flavours, and activating reward-related brain regions such as the ventral striatum. However, there remains a substantial gap in understanding how metabolic rewards shape behaviour, particularly in humans. We hypothesised that metabolic interoceptive rewards may function as Pavlovian stimuli, eliciting a Pavlovian approach bias which modulates everyday instrumental decision-making via Pavlovian-instrumental transfer. To test this, in a double-blind design, over one week, participants (N = 53) consumed two novel, flavoured drinks: one containing the tasteless carbohydrate maltodextrin, and one a calorie-free control. In a subsequent lab session, participants completed a Pavlovian-instrumental transfer task, performing a points-based instrumental decision-making task while tasting calorie-free versions of each flavour. Participants also rated liking and wanting of the flavours. As predicted, flavours previously experienced with calories were rated as significantly more liked after conditioning. However, counter to our hypothesis, the calorie-associated flavours did not enhance instrumental responding; computational modelling instead indicated a suppression of action. These findings reveal a dissociation between hedonic preferences and action: while metabolic rewards shaped liking, they did not invigorate behaviour. This highlights the complexity of interoceptive reinforcement learning and points to the need for further work to understand how and when internal metabolic expectations shape action.

URL:

Data for this project is available at: https://doi.org/10.17605/OSF.IO/RCUSG

Authors:

YANG, C. Parish, O., Klimovich-Gray, A., Wingfield, Cai., Marslen-Wilson, W.D., ZHANG, C., WOOLGAR, A., Thwaites, A.

Reference:

Year of publication:

2026

CBU number:

9253

Abstract:

The Kymata Soto Language Dataset comprises raw electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings from 15 native Russian speakers and 20 native English speakers as they listened to approximately seven minutes of conversational speech in their respective native languages. Each participant heard the same conversational speech stimulus multiple times (four repetitions for Russian speakers and eight for English speakers). The dataset includes transcriptions of the recordings, along with timestamp annotations for each phoneme and word. Organized according to the Brain Imaging Data Structure (BIDS), this dataset facilitates in-depth research into brain responses to naturalistic speech. To validate the dataset and our preprocessing pipeline, we employed Python-based analyses, revealing consistent low-level loudness perception trends across both language groups. All EEG and MEG data, audio recordings, transcriptions with timestamp annotations, and validation codes are open source, promoting transparency and reproducibility.

URL:

Data for this project is held by an external institution. Please contact the authors to request a copy.

Authors:

JeYoung, J., Atkinson-Clement, C., Kaiser, M.& LAMBON RALPH, M.A.

Reference:

Year of publication:

In Press

CBU number:

9250

Abstract:

The ventromedial anterior temporal lobe (ATL) is a core transmodal hub for semantic memory, yet non-invasive modulation of this region has remained challenging. Transcranial ultrasound stimulation (TUS) offers high spatial precision suitable for deep brain targets. In this study, we investigated whether theta-burst TUS (tbTUS) to the ventromedial ATL enhances semantic memory, using a multimodal neuroimaging approach—magnetic resonance spectroscopy (MRS), functional MRI (fMRI), and voxel-based morphometry (VBM). Compared to control stimulation, tbTUS improved semantic task performance. MRS showed decreased GABA and increased Glx, reflecting shifts in excitation-inhibition balance, alongside increases in NAA, creatine and choline, suggesting enhanced neuronal metabolism. fMRI demonstrated reduced ATL activity during semantic processing and strengthened effective connectivity across the semantic network. VBM revealed increased ATL grey matter volume. These findings provide convergent evidence that tbTUS modulates neurochemistry, functional dynamics, and brain morphology to enhance semantic memory, highlighting its neurorehabilitation potential.

URL:

Data for this project is available at: https://doi.org/10.17605/OSF.IO/FVK7C