Authors:

HUYS, Q.J.M., ORBEN, A.

Reference:

Year of publication:

In Press

CBU number:

9263

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:

Mione, V., Kristensen, F.H., ASSEM, M., Schuffelgen, U., Kyllingsbæk, S., Buckley, M., MITCHELL, D.J., DUNCAN, J.

Reference:

Year of publication:

In Press

CBU number:

9261

Abstract:

A much-replicated finding in human brain imaging is a distributed “multiple-demand” or MD system, increasing in activity for many kinds of cognitive demand, and centrally involved in cognitive control. MD regions are proposed to encode a distributed mental model of critical task events, bound together in the roles and relationships needed to direct action selection. Though previous data hint at a corresponding network in the macaque, there has been no direct comparison to human data. Here we used functional magnetic resonance imaging to measure whole brain activation in a multi-step saccadic maze task, compared to a control requiring similar moves but without goal-based decisions. Human data were a close match to the canonical MD network, extended to include adjacent regions and in particular much of the canonical dorsal attention network. Monkey data suggested correspondences in dorsomedial frontal, lateral and medial parietal, insula/orbitofrontal and posterior temporal cortex. In lateral frontal cortex there was just a single, largely dorsal activation patch, in contrast to multiple distinct human patches. In macaque as in human, together with previous data, our findings suggest an extended and strongly interconnected brain network recruited by increased cognitive challenge.

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:

Song, Y., Prisciandaro, J.J., APSVALKA, D., Bernard, M., Berrington, A., Castelo-Branco, M., Britton, M/K., CORREIA, M.A., Cuypers, K., Domagalik, A., Dydak, U., Duncan, N.W., Dwyer, G.E., Gong, t., Greenhouse, I., Hat, K., Hehl, M., Honda, S., Horton. C., Hui, S.C.N., Jackson, S.R., Jones, D.L., Klan, M.S., Lyoo, I.K., Mada, M.O., McNamara, B.V., Mullins, P.G., Muska, E., Nakajima, S., Nishio, H., Pereira, A.P., Porges, E.C., Rowsell,M., Ruopp, R., Shortell, D.D., Smith, C.M. Swinnen, S., Šušnjar, A., Tseng, L-Y., Violante, I.R., Yoon, S., Edden, R.A.E

Reference:

Year of publication:

2025

CBU number:

9258

Abstract:

BACKGROUND Gamma-aminobutyric acid (GABA) and glutathione (GSH) play a significant role in the functioning of a healthy brain and can both be quantified using magnetic resonance spectroscopy (MRS). Several small-scale studies have suggested MRS measured GABA may fluctuate with the menstrual cycle, but the effects on GSH are unknown. Utilising recent developments in MRS acquisition, this multi-lab study explores this issue across 4 distinctive brain regions. NEW METHODS Data were analysed from 12 independent sites from which a total of 30 women were scanned during three phases of their menstrual cycle corresponding to early follicular, ovulation and mid luteal phases. HERMES and HERCULES sequences were used to measure GABA and GSH in voxels located in the left motor cortex, left posterior insular, medial parietal and medial frontal. Linear mixed models were used to assess the variability contributed by site, participant and menstrual cycle phase. RESULTS Similar variance was attributed to site and menstrual cycle phase for both GABA and GSH data. No systematic changes in GABA or GSH were revealed for any voxel as a consequence of menstrual cycle phase. COMPARISON WITH EXISTING METHODS Despite our larger sample size and inclusion of more brain regions we fail to replicate previous findings of GABA change as a consequence of menstrual cycle phase. We also show for the first time that MRS measures of GSH so not significantly alter with cycle. CONCLUSIONS Our findings suggest that the menstrual cycle has minimal impact on MRS measures of GABA and GSH. The presence of a menstrual cycle should not be used as justification for exclusion of women in MRS studies.

URL:

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:

Karimi-Rouzbahani, H., Rich, A.N., WOOLGAR, A.

Reference:

Year of publication:

2026

CBU number:

9256

Abstract:

The multiple-demand network (MDN), a set of highly interconnected, domain-general regions, which is active across a wide variety of cognitively demanding tasks, is thought to support cognitive functions by integrating distinct types of information depending on the task. However, the spatiotemporal characteristics with which each node in the MDN encodes information remains unclear. We collected fMRI and MEG data from separate participants performing a complex visual stimulus-response mapping task. We used multivariate pattern analysis (MVPA) to decode various task-related types of information—stimulus details, motor responses, and mapping rules—in both the MDN and visual areas. We used model-based MEG-fMRI fusion to compare the high temporal resolution data from MEG with high spatial resolution data from fMRI, extracting commonalities that reflect both the timecourse and location with which these different task features were represented. Early on, visual regions encoded information about the visual hemifield of the stimulus, while later, the MDN encoded the fine-grained details of the stimuli within the same hemifield and the task rules. We observed distinct temporal profiles of information coding for the cingulo-opercular vs. frontoparietal sub-networks of the MDN. This study offers insights into the dynamic information processing of the MDN and provides information-coding-based support for at least two sub-networks within the multiple-demand network.

URL:

Authors:

Mel’nikov, M.Y., Shakhzadayev, R., Baiturlin, Z., Batyrkhanov, D., Arman, D., Berdibayeva, D., Zholdassova, M., Kalmagambetov, D., Solodovnikov, M., Doskaliyev, A., MITCHELL, D., Akshulakov, S., Kustubayeva, A.

Reference:

Year of publication:

2026

CBU number:

9255

Abstract:

Purpose: Resting-state functional MRI (rs-fMRI) indices may reflect altered physiology of brain tumours. While neural activity-related rs-fMRI features have been investigated, frequencies typically associated with non-neural vascular activity have received less attention. The current study therefore investigated effects of tumour grade and frequency band on rs-fMRI indices of interhemispheric differences in activity and connectivity. Methods: Forty-six brain tumour patients (grades I-IV) underwent a single 10 min session of rs-fMRI. Interhemispheric indices (differences between the tumour mask and a matched contralateral non-tumour region) were calculated based on medians and ranges of functional parameters, including amplitude of low-frequency fluctuations (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), and degree centrality (DC) across the slow-5 (0.01–0.027 Hz), slow-4 (0.027–0.073 Hz), and slow-3 (0.073–0.166 Hz) frequency bands. The indices were residualized for age, sex, tumour relative volume, laterality, and localization. Then repeated measures ANOVA was used to assess effects of tumour grade and frequency band on the indices of tumour-specific activity. Results: Tumour-specific indices of median ALFF, median fALFF, ReHo variability, and median DC were significantly greater in the slow-3 band (typically considered to reflect vascular signals of non-neural origin) compared to slower bands (typically considered to be coupled to neural activity). Trends towards larger ALFF variability and median ReHo indices in the slow-3 band, relative to slow-4, were also observed, along with a trend for low-grade gliomas (I & II) to express larger ALFF (median and variability indices), compared to grade III gliomas. Conclusion: Our findings highlight the potential of rs-fMRI frequency-specific analysis in glioma research. The frequency band-dependent differences in spontaneous activity within tumour-infiltrated cortex might be indicative of vascular changes.

URL:

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