Setting up a psychedelic study can be a long, arduous, and Kafkaesque process. Researchers are faced with a host of challenges in this rapidly evolving field, necessitating a range of considerations that remain largely unstandardised. Many of the complexities inherent to psychedelic research also challenge existing assumptions around, for example, approaches to psychiatric prescribing, the conceptual framing of the placebo effect, and definitions of selfhood. This review paper aims to formalise these unique considerations by addressing the sociocultural, political, legal, pharmacological, safety, study design and experiential facets inherent to a psychedelic study. We bring together several of the major psychedelic research teams across the United Kingdom, identify continuing areas of debate, and provide a practical, comprehensive, experience-based guide, with recommendations for policymakers and future researchers intending to set up a psychedelic research study or clinical trial.

How does the brain support the complex processes that allow us to read? Using predictive modeling we establish that visual and association cortex are closer together in individuals with stronger oral reading ability. These findings indicate that large-scale cortical geometry provides a scaffold that supports the coordinated processing required to read.

Magnetic resonance imaging (MRI) is critical for neurodevelopmental research, however access to high-field (HF) systems in low- and middle-income countries is severely hindered by their cost. Ultra-low-field (ULF) systems mitigate such issues of access inequality, however their diminished signal-to-noise ratio limits their applicability for research and clinical use. Deep-learning approaches can enhance the quality of scans acquired at lower field strengths at no additional cost. For example, Convolutional neural networks (CNNs) fused with transformer modules have demonstrated a remarkable ability to capture both local information and long-range context. Unfortunately, the quadratic complexity of transformers leads to an undesirable trade-off between long-range sensitivity and local precision. We propose a hybrid CNN and state-space model (SSM) architecture featuring a novel 3D to 1D serialisation (GAMBAS), which learns long-range context without sacrificing spatial precision. We exhibit improved performance compared to other state-of-the-art medical image-to-image translation models.

IntroductionIt has become increasingly common to record brain activity simultaneously at more than one spatiotemporal scale. Here, we address a central question raised by such cross-scale datasets: do they reflect the same underlying dynamics observed in different ways, or different dynamics observed in the same way? In other words, to what extent can variation between modalities be attributed to system-level versus observer-level effects? System-level effects reflect genuine differences in neural dynamics at the resolution sampled by each device. Observer-level effects, by contrast, reflect artefactual differences introduced by the nonlinear transformations each device imposes on the signal. We demonstrate that noise, when incorporated into generative models, can help disentangle these two sources of variation.MethodsWe apply this noise-based approach to simultaneously recorded high-frequency broadband signals from macroelectrodes and microwires in the human hippocampus.ResultsMost subjects show a complex mixture of system- and observer-level contributions to their time series. However, in one subject, the cross-scale difference is statistically attributable to an observer-level effect—i.e., consistent with the same dynamics at both microwire and macroelectrode scales.DiscussionThis study shows that noise can be used in empirical datasets to determine whether cross-scale variation arises from differences in neural dynamics or differences in observer functions.

The thoughts we experience in daily life have implications for our mental health and well-being. However, it is often difficult to measure thought patterns outside of laboratory conditions due to concerns about the voracity of measurements taken in daily life. To address this gap in the literature, our study set out to measure patterns of thought as they occur in daily life and assess the robustness of these measures and their associations with trait measurements of mental health and well-being. A sample of undergraduate participants completed multi-dimensional experience sampling surveys eight times per day for five days as they went around their normal lives. Principal Component Analysis reduced these data to identify the dimensions that explained the patterns of thought reported by our participants. We used linear modelling to map how these thought patterns related to the activities taking place at the time of the probe, highlighting good consistency within the sample, as well as substantial overlap with prior work. Multiple regression was used to examine associations between patterns of ongoing thought and aspects of mental health and well-being, highlighting a pattern of 'Intrusive Distraction' that had a positive association with anxiety, and a negative association with social well-being. Notably, this thought pattern tended to be most prevalent in solo activities and was relatively suppressed when interacting with other people (either in person or virtually). Our study, therefore, highlights the use of multi-dimensional experience sampling as a tool to understand how ongoing thought in daily life impacts on our mental health and well-being and establishes the important role social connectedness plays in the etiology of intrusive thinking.

Movie-watching is a central aspect of our lives and an important paradigm for understanding the brain mechanisms behind cognition as it occurs in daily life. Contemporary views of ongoing thought argue that the ability to make sense of events in the ‘here and now’ depend on the neural processing of incoming sensory information by auditory and visual cortex, which are kept in check by systems in association cortex. However, we currently lack an understanding of how patterns of ongoing thoughts map onto the different brain systems when we watch a film, partly because methods of sampling experience disrupt the dynamics of brain activity and the experience of movie-watching. Our study established a novel method for mapping thought patterns onto the brain activity that occurs at different moments of a film, which does not disrupt the time course of brain activity or the movie-watching experience. We found moments when experience sampling highlighted engagement with multi-sensory features of the film or highlighted thoughts with episodic features, regions of sensory cortex were more active and subsequent memory for events in the movie was better—on the other hand, periods of intrusive distraction emerged when activity in regions of association cortex within the frontoparietal system was reduced. These results highlight the critical role sensory systems play in the multi-modal experience of movie-watching and provide evidence for the role of association cortex in reducing distraction when we watch films.

We present two novel self-ordered switching (SOS) fMRI paradigms designed to investigate how brain networks facilitate the establishment of structured human behaviour during the learning of complex tasks with multiple goals. In study 1, SOS was performed with minimal pretraining and detailed feedback to capture the learning process, while in study 2 substantial pretraining and minimal feedback were used as a control where the potential for ongoing optimisation of behaviour is reduced. Study 1 revealed changes in the learning process characterised by a decrease in task-switching frequency, resulting in superior task performance for individuals who minimised switch frequency and ordered their behaviour in simple structured routines. Additionally, with practice, multiple-demand cortex activation became less responsive, and the default mode network became more responsive when performing discrimination trials. Strikingly, the opposite pattern was observed for SOS events, with multiple-demand cortex activation becoming more responsive and default mode network activation becoming less responsive with practice. These neural changes correlated with the degree of structure of behavioural routines. The neural signatures of learning were less evident in study 2, where the task was practiced prior to entering the scanner. Our studies demonstrate that the default mode network and multiple demand cortex complement each other when people learn to perform complex tasks by becoming differentially fine-tuned to routine trial demands vs. executive-switching demands.

The brain is a physically embedded and heavily interconnected system that expresses neural rhythms across multiple time scales. While these dynamics result from the complex interplay of local and inter-regional factors, the relative contribution of such mechanisms across the cortex remains unclear. Our study explores geometric, microstructural, and connectome-level constraints on cortex-wide neural activity. We leverage intracranial electroencephalography recordings to derive a coordinate system of human cortical dynamics. Using multimodal neuroimaging, we could then demonstrate that these patterns are largely explainable by geometric properties indexed by inter-regional distance. However, dynamics in transmodal association regions are additionally explainable by incorporation of inter-regional microstructural similarity and connectivity information. Our findings are generally consistent when cross-referencing electroencephalography and imaging data from large-scale atlases and when using data obtained in the same individuals, suggesting subject-specificity and population-level generalizability. Together, our results suggest that the relative contribution of local and macroscale constraints on cortical dynamics varies systematically across the cortical sheet, specifically highlighting the role of transmodal networks in inter-regional cortical coordination. Royer and colleagues find a principal dimension of neural dynamic similarity dissociating unimodal and association cortices. Systematic variations in local and macroscale constraints across the cortex also followed this pattern.

Current AI frameworks for brain decoding and encoding, typically train and test models within the same datasets. This limits their utility for cognitive training (neurofeedback) for which it would be useful to pool experiences across individuals to better simulate stimuli not sampled during training. A key obstacle to model generalisation is the degree of variability of inter-subject cortical organisation, which makes it difficult to align or compare cortical signals across participants. In this paper we address this through use of surface vision transformers, which build a generalisable model of cortical functional dynamics, through encoding the topography of cortical networks and their interactions as a moving image across a surface. This is then combined with tri-modal self-supervised contrastive (CLIP) alignment of audio, video, and fMRI modalities to enable the retrieval of visual and auditory stimuli from patterns of cortical activity (and vice-versa). We validate our approach on 7T task-fMRI data from 174 healthy participants engaged in the movie-watching experiment from the Human Connectome Project (HCP). Results show that it is possible to detect which movie clips an individual is watching purely from their brain activity, even for individuals and movies *not seen during training*. Further analysis of attention maps reveals that our model captures individual patterns of brain activity that reflect semantic and visual systems. This opens the door to future personalised simulations of brain function. Code \& pre-trained models will be made available at https://github.com/metrics-lab/sim.

Looking at caregivers' faces is important for early social development, and there is a concomitant increase in neural correlates of attention to familiar versus novel faces in the first 6 months. However, by 12 months of age brain responses may not differentiate between familiar and unfamiliar faces. Traditional group-based analyses do not examine whether these 'null' findings stem from a true lack of preference within individual infants, or whether groups of infants show individually strong but heterogeneous preferences for familiar versus unfamiliar faces. In a preregistered proof-of-principle study, we applied Neuroadaptive Bayesian Optimisation (NBO) to test how individual infants' neural responses vary across faces differing in familiarity. Sixty-one 5-12-month-olds viewed faces resulting from gradually morphing a familiar (primary caregiver) into an unfamiliar face. Electroencephalography (EEG) data from fronto-central channels were analysed in real-time. After the presentation of each face, the Negative central (Nc) event-related potential (ERP) amplitude was calculated. A Bayesian Optimisation algorithm iteratively selected the next stimulus until it identified the stimulus eliciting the strongest Nc for that infant. Attrition (15%) was lower than in traditional studies (22%). Although there was no group-level Nc-difference between familiar versus unfamiliar faces, an optimum was predicted in 85% of the children, indicating individual-level attentional preferences. Traditional analyses based on infants' predicted optimum confirmed NBO can identify subgroups based on brain activation. Optima were not related to age and social behaviour. NBO suggests the lack of overall familiar/unfamiliar-face attentional preference in middle infancy is explained by heterogeneous preferences, rather than a lack of preference within individual infants.

Mapping the flow of information through the networks of the brain remains one of the most important challenges in computational neuroscience. In certain cases, this flow can be approximated by considering just two contributing factors—a predictable drift and a randomized diffusion. We show here that the uncertainty associated with such a drift-diffusion process can be calculated in terms of the entropy associated with the Fokker–Planck equation. This entropic evolution comprises two components: an irreversible entropic spread that always increases over time and a reversible entropic current that can increase or decrease locally within the system. We apply this dynamic entropy decomposition to two-photon imaging data collected in the murine visual cortex. Our analysis reveals maps of conserved entropic flow emanating from lateromedial, anterolateral, and rostrolateral regions toward the primary visual cortex (V1). These results highlight the role of V1 as an entropic sink, facilitating the redistribution of information throughout the visual cortex. These findings offer new insights into the hierarchical organization of cortical processing and provide a framework for exploring information dynamics in complex dynamical systems.

Magnetic resonance imaging (MRI) enables non-invasive monitoring of healthy brain development and disease. Widely used higher field (>1.5 T) MRI systems are associated with high energy and infrastructure requirements, and high costs. Recent ultra-low-field (<0.1 T) systems provide a more accessible and cost-effective alternative. However, it remains uncertain whether anatomical ultra-low-field neuroimaging can be used to reliably extract quantitative measures of brain morphometry, and to what extent such measures correspond to high-field MRI. Here we scanned 23 healthy adults aged 20-69 years on two identical 64 mT systems and a 3 T system, using T1w and T2w scans across a range of (64 mT) resolutions. We segmented brain images into 4 global tissue types and 98 local structures, and systematically evaluated between-scanner reliability of 64 mT morphometry and correspondence to 3 T measurements, using correlations of tissue volume and Dice spatial overlap of segmentations. We report high 64 mT reliability and correspondence to 3 T across 64 mT scan contrasts and resolutions, with highest performance shown by combining three T2w scans with low through-plane resolution into a single higher-resolution scan using multi-resolution registration. Larger structures show higher 64 mT reliability and correspondence to 3 T. Finally, we showcase the potential of ultra-low-field MRI for mapping neuroanatomical changes across the lifespan, and monitoring brain structures relevant to neurological disorders. Raw images are publicly available, enabling systematic validation of pre-processing and analysis approaches for ultra-low-field neuroimaging.

A crucial challenge in neuroscience involves characterising brain dynamics from high-dimensional brain recordings. Dynamic Functional Connectivity (dFC) is an analysis paradigm that aims to address this challenge. dFC consists of a time-varying matrix (dFC matrix) expressing how pairwise interactions across brain areas change over time. However, the main dFC approaches have been developed and applied mostly empirically, lacking a common theoretical framework and a clear view on the interpretation of the results derived from the dFC matrices. Moreover, the dFC community has not been using the most efficient algorithms to compute and process the matrices efficiently, which has prevented dFC from showing its full potential with high-dimensional datasets and/or real-time applications. In this paper, we introduce the Dynamic Symmetric Connectivity Matrix analysis framework (DySCo), with its associated repository. DySCo is a framework that presents the most commonly used dFC measures in a common language and implements them in a computationally efficient way. This allows the study of brain activity at different spatio-temporal scales, down to the voxel level. DySCo provides a single framework that allows to: (1) Use dFC as a tool to capture the spatio-temporal interaction patterns of data in a form that is easily translatable across different imaging modalities. (2) Provide a comprehensive set of measures to quantify the properties and evolution of dFC over time: the amount of connectivity, the similarity between matrices, and their informational complexity. By using and combining the DySCo measures it is possible to perform a full dFC analysis. (3) Leverage the Temporal Covariance EVD algorithm (TCEVD) to compute and store the eigenvectors and values of the dFC matrices, and then also compute the DySCo measures from the EVD. Developing the framework in the eigenvector space is orders of magnitude faster and more memory efficient than naïve algorithms in the matrix space, without loss of information. The methodology developed here is validated on both a synthetic dataset and a rest/N-back task experimental paradigm from the fMRI Human Connectome Project dataset. We show that all the proposed measures are sensitive to changes in brain configurations and consistent across time and subjects. To illustrate the computational efficiency of the DySCo toolbox, we performed the analysis at the voxel level, a task which is computationally demanding but easily afforded by the TCEVD.

Background:Normal pressure hydrocephalus (NPH) is a potentially treatable condition causing dementia. Treatment is through insertion of a 'shunt', which improves symptoms and prolongs independence, but NPH may be under-treated. Automated brain imaging measures might have potential to identify NPH. Little is known about NPH presentation in memory clinics. This study investigates the period prevalence of NPH and potential use of imaging biomarkers for NPH, especially callosal angle (CA), in detecting NPH in UK memory services. Methods:This cohort study will use retrospective data from South London and Maudsley Clinical Records Interactive Search and linked datasets. The study population will comprise individuals aged ≥60 years with at least one referral to memory services from 2007-2024 (estimated n>20,000). Automated tools will be used to measure imaging biomarkers using routinely collected brain magnetic resonance imaging scans that are electronically linked to health records in a subset of the population (estimated n>5,000). Results:We will estimate the period prevalence of NPH in this population. We will evaluate automated measurement tools for imaging features of NPH, describe their distributions, and their variation by covariates (eg. demographics). We hypothesise that people with a diagnosis of NPH will have smaller CA compared to people with a diagnosis of dementia, and that imaging features of NPH will predict future diagnosis of NPH. Depending on our findings, we may undertake more detailed analyses, such as a nested case-control or survival analysis. Conclusions:This study will give a first understanding of NPH in UK memory services. It will inform future work to improve identification of NPH, for example through a clinical decision support tool. With rising global dementia rates, research into this potentially treatable condition causing dementia is a priority.

Adults born very preterm (i.e. at <33 weeks' gestation) are more susceptible to long-lasting structural and functional brain alterations and cognitive and socio-emotional difficulties, compared with full-term controls. However, behavioural heterogeneity within very preterm and full-term individuals makes it challenging to find biomarkers of specific outcomes. To address these questions, we parsed brain-behaviour heterogeneity in participants subdivided according to their clinical birth status (very preterm versus full term) and/or data-driven behavioural phenotype (regardless of birth status). Participants were followed-up in adulthood (median age 30 years) as part of a wider longitudinal case-control cohort study. The Network Based Statistic approach was used to identify topological components of resting state functional connectivity differentiating between (i) 116 very preterm and 83 full-term adults (43% and 57% female, respectively) and (ii) data-driven behavioural subgroups identified using consensus clustering (n = 156, 46% female). Age, sex, socio-economic status and in-scanner head motion were used as confounders in all analyses. Post hoc two-way group interactions between clinical birth status and behavioural data-driven subgrouping classification labels explored whether functional connectivity differences between very preterm and full-term adults varied according to distinct behavioural outcomes. Very preterm compared with full-term adults had poorer scores in selective measures of cognitive and socio-emotional processing and displayed complex patterns of hyper- and hypo-connectivity in sub-sections of the default mode, visual and ventral attention networks. Stratifying the study participants in terms of their behavioural profiles (irrespective of birth status) identified two data-driven subgroups: an 'At-Risk' subgroup, characterized by increased cognitive, mental health and socio-emotional difficulties, displaying hypo-connectivity anchored in frontal opercular and insular regions, relative to a 'Resilient' subgroup with more favourable outcomes. No significant interaction was noted between clinical birth status and behavioural data-driven subgrouping classification labels in terms of functional connectivity. Functional connectivity differentiating between very preterm and full-term adults was dissimilar to functional connectivity differentiating between the data-driven behavioural subgroups. We speculate that functional connectivity alterations observed in very preterm relative to full-term adults may confer both risk and resilience to developing behavioural sequelae associated with very preterm birth, while the localized functional connectivity alterations seen in the 'At-Risk' subgroup relative to the 'Resilient' subgroup may underlie less favourable behavioural outcomes in adulthood, irrespective of birth status.

Brain activity emerges in a dynamic landscape of regional increases and decreases that span the cortex. Increases in activity during a cognitive task are often assumed to reflect the processing of task-relevant information, while reductions can be interpreted as suppression of irrelevant activity to facilitate task goals. Here, we explore the relationship between task-induced increases and decreases in activity from a geometric perspective. Using a technique known as kriging, developed in earth sciences, we examined whether the spatial organisation of brain regions showing positive activity could be predicted based on the spatial layout of regions showing activity decreases (and vice versa). Consistent with this hypothesis we established the spatial distribution of regions showing reductions in activity could predict (i) regions showing task-relevant increases in activity in both groups of humans and single individuals; (ii) patterns of neural activity captured by calcium imaging in mice; and, (iii) showed a high degree of generalisability across task contexts. Our analysis, therefore, establishes that antagonistic relationships between brain regions are topographically determined, a spatial analog for the well documented anti-correlation between brain systems over time.

Psychological states influence our happiness and productivity; however, estimates of their impact have historically been assumed to be limited by the accuracy with which introspection can quantify them. Over the last two decades, studies have shown that introspective descriptions of psychological states correlate with objective indicators of cognition, including task performance and metrics of brain function, using techniques like functional magnetic resonance imaging (fMRI). Such evidence suggests it may be possible to quantify the mapping between self-reports of experience and objective representations of those states (e.g., those inferred from measures of brain activity). Here, we used machine learning to show that self-reported descriptions of experiences across tasks can reliably map the objective landscape of task states derived from brain activity. In our study, 194 participants provided descriptions of their psychological states while performing tasks for which the contribution of different brain systems was available from prior fMRI studies. We used machine learning to combine these reports with descriptions of brain function to form a ‘state-space’ that reliably predicted patterns of brain activity based solely on unseen descriptions of experience (N = 101). Our study demonstrates that introspective reports can share information with the objective task landscape inferred from brain activity. Using machine learning, this study shows that self-reported experience of task states maps onto ‘brain space’, i.e., features of different task states identified in fMRI.

Electroencephalography (EEG) microstates are “quasi-stable” periods of electrical potential distribution in multichannel EEG derived from peaks in Global Field Power. Transitions between microstates form a temporal sequence that may reflect underlying neural dynamics. Mounting evidence indicates that EEG microstate sequences have long-range, non-Markovian dependencies, suggesting a complex underlying process that drives EEG microstate syntax (i.e., the transitional dynamics between microstates). Despite growing interest in EEG microstate syntax, the field remains fragmented, with inconsistent terminologies used between studies and a lack of defined methodological categories. To advance the understanding of functional significance of microstates and to facilitate methodological comparability and finding replicability across studies, we: i) derive categories of syntax analysis methods, reviewing how each may be utilised most readily; ii) define three “time-modes” for EEG microstate sequence construction; and iii) outline general issues concerning current microstate syntax analysis methods, suggesting that the microstate models derived using these methods are cross-referenced against models of continuous EEG. We advocate for these continuous approaches as they do not assume a winner-takes-all model inherent in the microstate derivation methods and contextualise the relationship between microstate models and EEG data. They may also allow for the development of more robust associative models between microstates and functional Magnetic Resonance Imaging data.

The goal of psychological research is to understand behaviour in daily life. Although lab studies provide the control necessary to identify cognitive mechanisms behind behaviour, how these controlled situations generalise to activities in daily life remains unclear. Experience-sampling provides useful descriptions of cognition in the lab and real world and the current study examined how thought patterns generated by multidimensional experience-sampling (mDES) generalise across both contexts. We combined data from five published studies to generate a common 'thought-space' using data from the lab and daily life. This space represented data from both lab and daily life in an unbiased manner and grouped lab tasks and daily life activities with similar features (e.g., working in daily life was similar to working memory in the lab). Our study establishes mDES can map cognition from lab and daily life within a common space, allowing for more ecologically valid descriptions of cognition and behaviour.