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This study assesses the reliability of resting-state dynamic causal modelling (DCM) of magnetoencephalography (MEG) under conductance-based canonical microcircuit models, in terms of both posterior parameter estimates and model evidence. We use resting-state MEG data from two sessions, acquired 2 weeks apart, from a cohort with high between-subject variance arising from Alzheimer's disease. Our focus is not on the effect of disease, but on the reliability of the methods (as within-subject between-session agreement), which is crucial for future studies of disease progression and drug intervention. To assess the reliability of first-level DCMs, we compare model evidence associated with the covariance among subject-specific free energies (i.e., the 'quality' of the models) with versus without interclass correlations. We then used parametric empirical Bayes (PEB) to investigate the differences between the inferred DCM parameter probability distributions at the between subject level. Specifically, we examined the evidence for or against parameter differences (i) within-subject, within-session, and between-epochs; (ii) within-subject between-session; and (iii) within-site between-subjects, accommodating the conditional dependency among parameter estimates. We show that for data acquired close in time, and under similar circumstances, more than 95% of inferred DCM parameters are unlikely to differ, speaking to mutual predictability over sessions. Using PEB, we show a reciprocal relationship between a conventional definition of 'reliability' and the conditional dependency among inferred model parameters. Our analyses confirm the reliability and reproducibility of the conductance-based DCMs for resting-state neurophysiological data. In this respect, the implicit generative modelling is suitable for interventional and longitudinal studies of neurological and psychiatric disorders.

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The binding of information from different sensory or neural sources is critical for associative memory. Previous research in animals suggested that the timing of theta oscillations in the hippocampus is critical for long-term potentiation, which underlies associative and episodic memory. Studies with human participants showed correlations between theta oscillations in medial temporal lobe and episodic memory. Clouter et al. directly investigated this link by modulating the intensity of the luminance and the sound of the video clips so that they 'flickered' at certain frequencies and with varying synchronicity between the visual and auditory streams. Across several experiments, better memory was found for stimuli that flickered synchronously at theta frequency compared with no-flicker, asynchronous theta, or synchronous alpha and delta frequencies. This effect - which they called the theta-induced memory effect - is consistent with the importance of theta synchronicity for long-term potentiation. In addition, electroencephalography data showed entrainment of cortical regions to the visual and auditory flicker, and that synchronicity was achieved in neuronal oscillations (with a fixed delay between visual and auditory streams). The theoretical importance, large effect size, and potential application to enhance real-world memory mean that a replication of theta-induced memory effect would be highly valuable. The present study aimed to replicate the key differences among synchronous theta, asynchronous theta, synchronous delta, and no-flicker conditions, but within a single experiment. The results do not show evidence of improved memory for theta synchronicity in any of the comparisons. We suggest a reinterpretation of theta-induced memory effect to accommodate this non-replication.

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INTRODUCTION: Entorhinal cortex (EC) is the first cortical region to exhibit neurodegeneration in Alzheimer's disease (AD), associated with EC grid cell dysfunction. Given the role of grid cells in path integration (PI)-based spatial behaviors, we predicted that PI impairment would represent the first behavioral change in adults at risk of AD. METHODS: We compared immersive virtual reality (VR) PI ability to other cognitive domains in 100 asymptomatic midlife adults stratified by hereditary and physiological AD risk factors. In some participants, behavioral data were compared to 7T magnetic resonance imaging (MRI) measures of brain structure and function. RESULTS: Midlife PI impairments predicted both hereditary and physiological AD risk, with no corresponding multi-risk impairment in episodic memory or other spatial behaviors. Impairments associated with altered functional MRI signal in the posterior-medial EC. DISCUSSION: Altered PI may represent the transition point from at-risk state to disease manifestation in AD, prior to impairment in other cognitive domains.

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Healthy aging is typically accompanied by cognitive decline. Previous work has shown that engaging in multiple, non-work activities during midlife can have a protective effect on cognition several decades later, rendering it less dependent on brain structural health; the definition of "cognitive reserve". Other work has shown that increasing age is associated with reduced segregation of large-scale brain functional networks. Here we tested the hypothesis that functional segregation (SyS) mediates this effect of middle-aged lifestyle on late-life cognition. We used fMRI data from three tasks in the CamCAN dataset, together with cognitive data on fluid intelligence, episodic memory, and retrospective lifestyle data from the Lifetime of Experiences Questionnaire (LEQ). In all three tasks, we showed that SyS related to fluid intelligence even after adjusting for the (nonlinear) age effects. However, we found no evidence that SyS in late-life mediated the relationship between non-specific (non-occupation) midlife activities and either measure of cognition in late-life. Thus, the brain correlates of cognitive reserve arising from mid-life activities remain to be discovered.

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Fast mapping (FM) is a hypothetical, incidental learning process that allows rapid acquisition of new words. Using an implicit reaction time measure in a FM paradigm, Coutanche and Thompson-Schill (Coutanche, M. N., & Thompson-Schill, S. L. (2014). Fast mapping rapidly integrates information into existing memory networks. Journal of Experimental Psychology: General, 143(6), 2296-2303. https://doi.org/10.1037/xge0000020) showed evidence of lexical competition within 10 min of non-words being learned as names of unknown items, consistent with same-day lexicalisation. Here, Experiment 1 was a methodological replication (N = 28/group) that found no evidence of this RT competition effect. Instead, a post-hoc analysis suggested evidence of semantic priming. Experiment 2 (N = 60/group, online study, pre-registered on OSF) tested whether semantic priming remained when making the stimulus set fully counterbalanced. No evidence for either lexical competition nor semantic priming was detected. Experiment 3 (n = 64, online study, pre-registered on OSF) tested whether referent (a)typicality boosted lexical competition (Coutanche, M. N., & Koch, G. E. (2017). Variation across individuals and items determine learning outcomes from fast mapping. Neuropsychologia, 106, 187-193. https://doi.org/10.1016/j.neuropsychologia.2017.09.029), but again no evidence of lexical competition was observed, and Bayes Factors for the data combined across all three experiments supported the hypothesis that there is no effect of lexical competition under FM conditions. These results, together with our previous work, question whether fast mapping exists in healthy adults, at least using this specific FM paradigm.

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INTRODUCTION: Stakeholder engagement remains scarce in basic brain research. However, it can greatly improve the relevance of investigations and accelerate the translation of study findings to policy. The Lifebrain consortium investigated risk and protective factors influencing brain health using cognition, lifestyle and imaging data from European cohorts. Stakeholder activities of Lifebrain-organized in a separate work package-included organizing stakeholder events, investigating public perceptions of brain health and dissemination. Here, we describe the experiences of researchers and stakeholders regarding stakeholder engagement in the Lifebrain project. METHODS: Stakeholder engagement in Lifebrain was evaluated through surveys among researchers and stakeholders and stakeholders' feedback at stakeholder events through evaluation forms. Survey data were analysed using a simple content analysis approach, and results from evaluation forms were summarized after reviewing the frequency of responses. RESULTS: Consortium researchers and stakeholders experienced the engagement activities as meaningful and relevant. Researchers highlighted that it made the research and research processes more visible and contributed to new networks, optimized data collection on brain health perceptions and the production of papers and provided insights into stakeholder views. Stakeholders found research activities conducted in the stakeholder engagement work package to be within their field of interest and research results relevant to their work. Researchers identified barriers to stakeholder engagement, including lack of time, difficulties in identifying relevant stakeholders, and challenges in communicating complex scientific issues in lay language and maintaining relationships with stakeholders over time. Stakeholders identified barriers such as lack of budget, limited resources in their organization, time constraints and insufficient communication between researchers and stakeholders. CONCLUSION: Stakeholder engagement in basic brain research can greatly benefit researchers and stakeholders alike. Its success is conditional on dedicated human and financial resources, clear communication, transparent mutual expectations and clear roles and responsibilities. PUBLIC CONTRIBUTION: Patient organizations, research networks, policymakers and members of the general public were involved in engagement and research activities throughout the project duration.

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The entorhinal cortex (EC) is the first cortical region to exhibit neurodegeneration in Alzheimer's disease (AD), associated with EC grid cell dysfunction. Given the role of grid cells in path integration, we predicted that path integration impairment would represent the first behavioural change in adults at-risk of AD. Using immersive virtual reality, we found that midlife path integration impairments predicted both hereditary and physiological AD risk, with no corresponding impairment on tests of episodic memory or other spatial behaviours. Impairments related to poorer angular estimation and were associated with hexadirectional grid-like fMRI signal in the posterior-medial EC. These results indicate that altered path integration may represent the transition point from at-risk state to disease onset in AD, prior to impairment in other cognitive domains.

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Cardiovascular ageing contributes to cognitive impairment. However, the unique and synergistic contributions of multiple cardiovascular factors to cognitive function remain unclear because they are often condensed into a single composite score or examined in isolation. We hypothesized that vascular risk factors, electrocardiographic features and blood pressure indices reveal multiple latent vascular factors, with independent contributions to cognition. In a population-based deep-phenotyping study (n = 708, age 18-88), path analysis revealed three latent vascular factors dissociating the autonomic nervous system response from two components of blood pressure. These three factors made unique and additive contributions to the variability in crystallized and fluid intelligence. The discrepancy in fluid relative to crystallized intelligence, indicative of cognitive decline, was associated with a latent vascular factor predominantly expressing pulse pressure. This suggests that higher pulse pressure is associated with cognitive decline from expected performance. The effect was stronger in older adults. Controlling pulse pressure may help to preserve cognition, particularly in older adults. Our findings highlight the need to better understand the multifactorial nature of vascular aging.

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The preservation of cognitive function in old age is a public health priority. Cerebral hypoperfusion is a hallmark of dementia but its impact on maintaining cognitive ability across the lifespan is less clear. We investigated the relationship between baseline cerebral blood flow (CBF) and blood oxygenation level-dependent (BOLD) response during a fluid reasoning task in a population-based adult lifespan cohort. As age differences in CBF could lead to non-neuronal contributions to the BOLD signal, we introduced commonality analysis to neuroimaging to dissociate performance-related CBF effects from the physiological confounding effects of CBF on the BOLD response. Accounting for CBF, we confirmed that performance- and age-related differences in BOLD responses in the multiple-demand network were implicated in fluid reasoning. Age differences in CBF explained not only performance-related BOLD responses but also performance-independent BOLD responses. Our results suggest that CBF is important for maintaining cognitive function, while its non-neuronal contributions to BOLD signals reflect an age-related confound. Maintaining perfusion into old age may serve to support brain function and preserve cognitive performance.

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It is well documented that some brain regions, such as association cortices, caudate, and hippocampus, are particularly prone to age-related atrophy, but it has been hypothesized that there are individual differences in atrophy profiles. Here, we document heterogeneity in regional-atrophy patterns using latent-profile analysis of 1,482 longitudinal magnetic resonance imaging observations. The results supported a 2-group solution reflecting differences in atrophy rates in cortical regions and hippocampus along with comparable caudate atrophy. The higher-atrophy group had the most marked atrophy in hippocampus and also lower episodic memory, and their normal caudate atrophy rate was accompanied by larger baseline volumes. Our findings support and refine models of heterogeneity in brain aging and suggest distinct mechanisms of atrophy in striatal versus hippocampal-cortical systems.

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Cognitive tests sensitive to the integrity of the medial temporal lobe (MTL), such as mnemonic discrimination of perceptually similar stimuli, may be useful early markers of risk for cognitive decline in older populations. Perceptual discrimination of stimuli with overlapping features also relies on MTL but remains relatively unexplored in this context. We assessed mnemonic discrimination in two test formats (Forced Choice, Yes/No) and perceptual discrimination of objects and scenes in 111 community-dwelling older adults at different risk status for cognitive impairment based on neuropsychological screening. We also investigated associations between performance and MTL sub-region volume and thickness. The at-risk group exhibited reduced entorhinal thickness and impaired perceptual and mnemonic discrimination. Perceptual discrimination impairment partially explained group differences in mnemonic discrimination and correlated with entorhinal thickness. Executive dysfunction accounted for Yes/No deficits in at-risk adults, demonstrating the importance of test format for the interpretation of memory decline. These results suggest that perceptual discrimination tasks may be useful tools for detecting incipient cognitive impairment related to reduced MTL integrity in nonclinical populations.

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INTRODUCTION: With the pressing need to develop treatments that slow or stop the progression of Alzheimer's disease, new tools are needed to reduce clinical trial duration and validate new targets for human therapeutics. Such tools could be derived from neurophysiological measurements of disease. METHODS AND ANALYSIS: The New Therapeutics in Alzheimer's Disease study (NTAD) aims to identify a biomarker set from magneto/electroencephalography that is sensitive to disease and progression over 1 year. The study will recruit 100 people with amyloid-positive mild cognitive impairment or early-stage Alzheimer's disease and 30 healthy controls aged between 50 and 85 years. Measurements of the clinical, cognitive and imaging data (magnetoencephalography, electroencephalography and MRI) of all participants will be taken at baseline. These measurements will be repeated after approximately 1 year on participants with Alzheimer's disease or mild cognitive impairment, and clinical and cognitive assessment of these participants will be repeated again after approximately 2 years. To assess reliability of magneto/electroencephalographic changes, a subset of 30 participants with mild cognitive impairment or early-stage Alzheimer's disease will also undergo repeat magneto/electroencephalography 2 weeks after baseline. Baseline and longitudinal changes in neurophysiology are the primary analyses of interest. Additional outputs will include atrophy and cognitive change and estimated numbers needed to treat each arm of simulated clinical trials of a future disease-modifying therapy. ETHICS AND DATA STATEMENT: The study has received a favourable opinion from the East of England Cambridge Central Research Ethics Committee (REC reference 18/EE/0042). Results will be disseminated through internal reports, peer-reviewed scientific journals, conference presentations, website publication, submission to regulatory authorities and other publications. Data will be made available via the Dementias Platform UK Data Portal on completion of initial analyses by the NTAD study group.

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The schema-linked interactions between medial prefrontal and medial temporal lobe (SLIMM) model predicts that memory for object locations is a U-shaped function of the expectancy of those locations. Using immersive virtual reality, we presented participants with 20 objects in locations that varied in their congruency with a kitchen schema. Bayes factors across four experiments (137 adults in total) confirmed the (preregistered) prediction of better memory for highly expected and unexpected locations relative to neutral locations. This U shape was found in location recall and in forced-choice recognition in which the foil locations were matched for expectancy, controlling for the bias toward guessing expected locations. A second prediction was that the two ends of the U shape are associated with different expressions of memory: recollection of unexpected locations and familiarity for expected locations. BFs, propagated across experiments, provided evidence against this second prediction; recollection was associated with both ends of the U shape. These findings further constrain theories about the role of schema in episodic memory.

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Localising the sources of MEG/EEG signals often requires a structural MRI to create a head model, while ensuring reproducible scientific results requires sharing data and code. However, sharing structural MRI data often requires the face go be hidden to help protect the identity of the individuals concerned. While automated de-facing methods exist, they tend to remove the whole face, which can impair methods for coregistering the MRI data with the EEG/MEG data. We show that a new, automated de-facing method that retains the nose maintains good MRI-MEG/EEG coregistration. Importantly, behavioural data show that this "face-trimming" method does not increase levels of identification relative to a standard de-facing approach and has less effect on the automated segmentation and surface extraction sometimes used to create head models for MEG/EEG localisation. We suggest that this trimming approach could be employed for future sharing of structural MRI data, at least for those to be used in forward modelling (source reconstruction) of EEG/MEG data.

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Stimulus repetition normally causes reduced neural activity in brain regions that process that stimulus. Some theories claim that this "repetition suppression" reflects local mechanisms such as neuronal fatigue or sharpening within a region, whereas other theories claim that it results from changed connectivity between regions, following changes in synchrony or top-down predictions. In this study, we applied dynamic causal modeling (DCM) on a public fMRI dataset involving repeated presentations of faces and scrambled faces to test whether repetition affected local (self-connections) and/or between-region connectivity in left and right early visual cortex (EVC), occipital face area (OFA) and fusiform face area (FFA). Face "perception" (faces versus scrambled faces) modulated nearly all connections, within and between regions, including direct connections from EVC to FFA, supporting a non-hierarchical view of face processing. Face "recognition" (familiar versus unfamiliar faces) modulated connections between EVC and OFA/FFA, particularly in the left hemisphere. Most importantly, immediate and delayed repetition of stimuli were also best captured by modulations of connections between EVC and OFA/FFA, but not self-connections of OFA/FFA, consistent with synchronization or predictive coding theories, though also possibly reflecting local mechanisms like synaptic depression.

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Early detection of Alzheimer's Disease (AD) is vital to reduce the burden of dementia and for developing effective treatments. Neuroimaging can detect early brain changes, such as hippocampal atrophy in Mild Cognitive Impairment (MCI), a prodromal state of AD. However, selecting the most informative imaging features by machine-learning requires many cases. While large publically-available datasets of people with dementia or prodromal disease exist for Magnetic Resonance Imaging (MRI), comparable datasets are missing for Magnetoencephalography (MEG). MEG offers advantages in its millisecond resolution, revealing physiological changes in brain oscillations or connectivity before structural changes are evident with MRI. We introduce a MEG dataset with 324 individuals: patients with MCI and healthy controls. Their brain activity was recorded while resting with eyes closed, using a 306-channel MEG scanner at one of two sites (Madrid or Cambridge), enabling tests of generalization across sites. A T1-weighted MRI is provided to assist source localisation. The MEG and MRI data are formatted according to international BIDS standards and analysed freely on the DPUK platform (https://portal.dementiasplatform.uk/Apply). Here, we describe this dataset in detail, report some example (benchmark) analyses, and consider its limitations and future directions.

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Brain connectivity analyses have conventionally relied on statistical relationship between one-dimensional summaries of activation in different brain areas. However, summarizing activation patterns within each area to a single dimension ignores the potential statistical dependencies between their multi-dimensional activity patterns. Representational Connectivity Analyses (RCA) is a method that quantifies the relationship between multi-dimensional patterns of activity without reducing the dimensionality of the data. We consider two variants of RCA. In model-free RCA, the goal is to quantify the shared information for two brain regions. In model-based RCA, one tests whether two regions have shared information about a specific aspect of the stimuli/task, as defined by a model. However, this is a new approach and the potential caveats of model-free and model-based RCA are still understudied. We first explain how model-based RCA detects connectivity through the lens of models, and then present three scenarios where model-based and model-free RCA give discrepant results. These conflicting results complicate the interpretation of functional connectivity. We highlight the challenges in three scenarios: complex intermediate models, common patterns across regions, and transformation of representational structure across brain regions. The article is accompanied by scripts (https://osf.io/3nxfa/) that reproduce the results. In each case, we suggest potential ways to mitigate the difficulties caused by inconsistent results. The results of this study shed light on some understudied aspects of RCA, and allow researchers to use the method more effectively.

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Early detection of Alzheimer's disease (AD) is essential for developing effective treatments. Neuroimaging techniques like Magnetic Resonance Imaging (MRI) have the potential to detect brain changes before symptoms emerge. Structural MRI can detect atrophy related to AD, but it is possible that functional changes are observed even earlier. We therefore examined the potential of Magnetoencephalography (MEG) to detect differences in functional brain activity in people with Mild Cognitive Impairment (MCI) - a state at risk of early AD. We introduce a framework for multimodal combination to ask whether MEG data from a resting-state provides complementary information beyond structural MRI data in the classification of MCI versus controls. More specifically, we used multi-kernel learning of support vector machines to classify 163 MCI cases versus 144 healthy elderly controls from the BioFIND dataset. When using the covariance of planar gradiometer data in the low Gamma range (30-48 Hz), we found that adding a MEG kernel improved classification accuracy above kernels that captured several potential confounds (e.g., age, education, time-of-day, head motion). However, accuracy using MEG alone (68%) was worse than MRI alone (71%). When simply concatenating (normalized) features from MEG and MRI into one kernel (Early combination), there was no advantage of combining MEG with MRI versus MRI alone. When combining kernels of modality-specific features (Intermediate combination), there was an improvement in multimodal classification to 74%. The biggest multimodal improvement however occurred when we combined kernels from the predictions of modality-specific classifiers (Late combination), which achieved 77% accuracy (a reliable improvement in terms of permutation testing). We also explored other MEG features, such as the variance versus covariance of magnetometer versus planar gradiometer data within each of 6 frequency bands (delta, theta, alpha, beta, low gamma, or high gamma), and found that they generally provided complementary information for classification above MRI. We conclude that MEG can improve on the MRI-based classification of MCI.

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The experience of novelty can enhance memory for information that occurs close in time, even if not directly related to the experience-a phenomenon called "behavioural tagging." For example, an animal exposed to a novel spatial environment shows improved memory for other information presented previously. This has been linked to neurochemical modulations induced by novelty, which affect consolidation of memories for experiences that were encoded around the same time. Neurophysiological research in animals has shown that novelty benefits weakly encoded but not strongly encoded information. However, a benefit that is selective to weak memories seems difficult to reconcile with studies in humans that have reported that novelty improves recollection, but not familiarity. One possibility is that the novelty increases activity in hippocampus, which is also associated with processes that enable recollection. This is consistent with another prediction of behavioural tagging theory, namely that novelty only enhances consolidation of information that converges on the same neuronal population. However, no study has directly explored the relationship between encoding strength and retrieval quality (recollection versus familiarity). We examined the effects of exposure to a novel immersive virtual reality environment on memory for words presented immediately beforehand, under either deep or shallow encoding tasks, and by testing both recall memory immediately, and recognition memory with remember/know instructions the next day. However, Bayes factors showed no evidence to support the behavioural tagging predictions: that novelty would improve memory, particularly for shallowly encoded words, and this improvement would differentially affect familiarity versus recollection.

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